Towards Equilibrium 2021

THE ECONOMICS SOCIETY

TOWARDS EQUILIBRIUM ST. STEPHEN'S COLLEGE

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OUR SINCERE THANKS TO Mr Benston John, Staff Advisor, The Economics Society Mr Sanjeev Grewal, Head of Department, Department of Economics

THE EDITORIAL TEAM Hritic Gautam Jasjeev Singh Sahni Shagun Tyagi Siddharth Vikas Arora

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LIST OF CONTENTS Perceived Returns to Education and its Impact on Schooling Decisions Anvi Agarwal and Shagnik Chakravarty, St. Xavier’s College, Mumbai (Autonomous) The Covid-19, Economic Crisis and Policy Retaliation: A Dynamic General Equilibrium Analysis Anusil Das, Dipanjan Paul, Shouryo Basu and Sreeja Khan, St. Xavier’s University, Kolkata Feminist Economics and Policy: A Post-COVID Perspective Bhavya Pandey, Radhika Khandelwal and Treesha Lall, Daulat Ram College, Delhi University Consumption Response to Liquidity Shocks: Evidence from the Indian Demonetization Nirbhay Vadera Varma, Revant Aggarwal and Ritika Susarla, Ashoka University Gold as an Obsession: A Comparative Analysis of India and China during Economic Crisis Jatin Bhatnagar, Delhi Technological University Employment Situation in India Post-COVID-19: An Analysis Through the Paradox of Toil Namit Mahajan, Sudipta P Kashyap, Ramjas College, Delhi University, CHRIST (DTB University), BGR Campus From ‘Dragon’ to the land of ‘Ascending Dragon’: Can India be Atma-nirbhar by replacing China with Vietnam in its supply chain?

Kashish Gupta and Kanika Gupta, Shri Ram College of Commerce, Lady Shri Ram College for Women, Delhi University Bridging the Gap: Continued Relevance of Development Banks Aneesha Chandra and Vaidehi Maheshwari, Ashoka University What Will The Common Man Say? Nisha Gokhale and Shreya Singh, St. Xavier’s College, Mumbai A Behavioural Framework for Intra- Household Bargaining Deep Mehta, St. Xavier’s College, Kolkata (Autonomous) A Financial Adversity of the 21st Century: The U.S-China Trade War Anushka Saxena, Lady Shri Ram College for Women, Delhi University Economics of food: An Analysis of the Indian Food Market during the Coronavirus Lockdown Astha Mukherjee, Gopika Venugopal, Mahak Kohli and Medhawi Rajgaria, Maitreyi College, Delhi University COVID-19, Child Labour Incidence and Policy Responses: A Dynamic Trade Theoretic Analysis Monishankar Dutta and Rahul Sinha, St. Xavier’s University, Kolkata

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FROM THE EDITORS Dear Reader, We, The Economics Society of St. Stephen’s College, Delhi, are elated to present to you our annual journal, Towards Equilibrium.

Curating the best of what Indian undergraduate economics has to offer, Towards Equilibrium is the oil lamp of ingenuity in the dark woods of intellectual disarray. This year’s edition brims with a motley of vital topic areas: a noteworthy amount of research of the impact of the elephant in the room, the Covid-19 pandemic; the topical dynamics in the financial infrastructure and the attendant role of the government; the intricacies of household behaviour and consumption pattern models; the novel policy perspectives from a Feminist Economics vantage point; and, the effects of the tango of trade in international markets. With a veritable rainbow of perspectives, methodologies, research paradigms, and directions for budding future researchers, the journal is a testament to the potential of these future economists and their relentless drive to cultivate research that helps us better understand the world that we live in.

Our first article probes into the factors leading to a difference in the perceived returns to education, and their concomitant implications on schooling decision. We thereafter see an intriguing analysis of the economic nosedive and the policy patches in the wake of the Covid19 pandemic, through the lens of a dynamic general equilibrium model. A captivating research project then sheds light on the status of women during the pandemic and presents a feminist economic account of a post-Covid world. Then, our next article evaluates the response of the financial infrastructure to the liquidity shock induced by the Indian demonetization process, juxtaposing it with household consumption patterns. Our succeeding research paper dives into the impact of Covid-19 on the price of gold in India and China. Subsequently, we see an assessment into the mindset of the pandemic-affected citizens from the yardstick of employment dynamics, through the tint of the Keynesian Paradox of Toil. Then, an article appraises the prospect of substituting Chinese imports with those from Vietnam as a part of India’s revamped, self-sufficient trade outlook by constituting a Vector Error Correction Model. Thereafter, a cerebrate piece argues the relevance of Development Banks in financial infrastructural development, assesses the role of the government in this respect, and provides supplementary policy recommendations.

The succeeding research scrutinizes the fault lines that exist in the usage of current econometric models to predict economic crises, vis-à-vis the relative efficacy in gaping these faults by assessing narrative and core issues. Next, an absorbing article beholds a behavioural framework to chart out intra-household bargaining dynamics. The article that follows considers the wrinkle in the fabric of international comity due to the United States-China economic skirmishes, and its overarching impact on worldwide trade policies. Another research undertaking assesses the food purchasing and consumption behaviour patterns of the different income segments and food groups in India during the period of the Covid-19 pestilence. The surging incidence of child labour as another blow from the pandemic -along with its labour policy response- is discerned and dissected in the penultimate article.

The Editorial Team of Towards Equilibrium would like to express our earnest gratitude to the Department of Economics at St. Stephen’s College, Delhi, for its unwavering support and guidance. We would also like to acknowledge the strenuous effort put forth by the contributors, and recognize their herculean strive to enrich the corpus of the literature of their respective

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topic areas. Additionally, we would like to recognize the work put in by everyone else involved in the creation of this journal.

Lastly, we would be remiss if we do not offer our heartfelt thanks to you, the reader, for picking up this humble result of our collective endeavour and undying curiosity about the world of Economics.

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PERCEIVED RETURNS TO EDUCATION AND ITS IMPACT ON SCHOOLING DECISIONS -Anvi Agarwal, Shagnik Chakravarty 1 ABSTRACT In this paper, we empirically calculate perceived returns to education by surveying students from low and high-income families in Mumbai and Kolkata. These results are interpreted using insights from theoretical models that link education with wages. This allows us to interpret how differences between real and perceived returns may affect schooling decisions. Following that, we discuss the reasons behind formation of perceived returns. Risk perceptions towards education are compared using an f-test. We also decompose our data by characteristic variables like family income, parental educational level, age, and gender, and analyse how they affect one’s perceptions toward education. For this, t-tests are conducted along with logistic regression analysis. Here, we find that perceived returns are universally low for early education.

Lower-income students underestimate returns to tertiary schooling. Variability in future expected wages is higher among high-income students. Parental education has a statistically significant positive impact on perceived returns. Age is negatively correlated with perceived returns for low-income students but positively correlated for high-income ones. Females significantly underestimate returns to education. Finally, we conduct an appraisal of existing techniques to improve perceptions towards education and delineate scenarios under which one technique might be more effective than another.

Keywords: Returns to education, Perceived returns to education, S-shaped model, Returns to education in India, Schooling decisions JEL classification: I260, I220 I. INTRODUCTION For any individual, whether to continue education beyond a certain point is an important investment decision. Under Becker’s (1993) human capital theory, an individual would only continue schooling if expected future benefits(i.e., higher wages) exceed costs. Expected future benefits, or the returns to education, is therefore, an important variable which influences schooling decisions. Calculating returns to education is complicated, and while estimates of real returns exist, these results are confined to academic papers that use advanced econometric techniques, thereby making it inaccessible to typical families. Thus, it is the returns perceived by students and their parents that end up influencing actual educational decisions, and these are usually formed under uncertainty and limited information. Inaccurate perceptions about returns to education can distort the demand for schooling, affect future labour market outcomes, and keep average educational attainments low (Sequeira et al., 2016).

In India, the need to consider this possibility is especially high as educational levels are low even when measured real returns are high. As we see, the gross enrolment ratio (GER) at primary level is 99.2%, but it drops to 80% by 8th grade, and further to 56.2% and 23% for senior-secondary (10-12th grade) and tertiary education (MHRD, 2018). In comparison, tertiary education enrolment in the US and China are 87% and 39% respectively. Not only that, drop-out rates are above 20% at senior-secondary level even though the mean salary of Indian graduates is 66% higher than high-school pass-outs (NSSO, 2014). Of course, there are several plausible explanations for this such as poverty, credit constraints, accessibility, health, and 1

St. Xavier’s College, Mumbai

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discrimination. Yet, if underestimating the returns at any level is also a limiting factor for individuals, improving awareness about real returns may be the most cost-effective strategy in improving their educational attainment, and more generally, in raising the demand for schooling.

II. REVIEW OF LITERATURE As a developing nation, India is one of the youngest countries in the world. With 38.1 million children out of school (UNESCO, 2016) and dropout rates of 15% and 11% for girls and boys respectively, the need to consider various reasons for dropping out of school is essential. Here, we find that physical accessibility is not a deterrent: Close to 95% of all children have a school within half a mile of their residence (PROBE Team & Centre for Development Economics, 1999).

Studies in developed countries like Slovenia (Stanovnik, 1997) and Ireland (Barrett et. al, 2002) have shown that increased demand for skilled labour results in a higher rate of return to schooling and education. Further, several studies show that students of first world countries with good education systems have a better perception of the increase in income with the attainment of different levels of education. A handful of studies for the United States including Smith and Powell (1990); Betts (1996); Dominitz and Manski (1996); Avery and Kane (2004) and Rouse (2004) have proven this.

In India, there is extensive literature on returns to education, but hardly any on the perceived returns and its impact. Chaudhri and Rao (1970) concluded that when the investment into education is taken into account, rates of return reduce with consecutive higher levels of education, thereby forming a concave returns graph. Although, when just the increase in income is considered for each year of schooling, the graph is more or less linear. For the sake of this paper, the most recent educational return numbers have been extracted from Singhari & Madheswaran (2016).

Evidence from field experiments suggests that perceived returns to education are typically lower than real returns in developing countries. Nguyen (2008) conducted a randomised controlled trial (RCT) in Madagascar where she observed that providing statistics about real returns to education raised the average attendance of students by 3.5%. In Jensen (2010), students at randomly selected schools in the urban Dominican Republic who were given information on higher returns completed on average 0.20–0.35 more years of school than students who were not. In India, the NSSO (71st round, 2014) data on drop-outs shows that the main reason cited for dropping out is “economic and domestic activities”. “Financial constraints” is ranked third in importance. Here, prioritising work over schooling may partly be a consequence of underestimating the returns to education.

This literature survey indicates that there exists a difference between perceived and real returns to education. This difference may explain why dropping out of school seems rational to many Indians, though in reality, their decision creates an education-based-poverty trap. Although estimates of the real returns to education exist, the results are confined to academic papers that use advanced econometric techniques, thereby making it inaccessible to typical students and parents. Thus, it is the returns perceived by students and/or their parents that end up influencing actual schooling decisions, and these are likely to be based on imperfect and incomplete information. Thereby, having little reason to believe that the level of education chosen by parents is either individually or socially efficient. Underestimating the returns to education may therefore be lowering the demand for schooling even when measured real returns are high.

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III. RESEARCH QUESTIONS 1. What are the differences between real and perceived returns to education, and how might they impact schooling decisions?

2. What are the factors influencing the formation of perceived returns to education?

3. How do characteristics such as family income, age, parental education and gender influence one’s perceptions of education?

IV. HYPOTHESIS H0 (1): There is no significant difference between real and perceived returns H0 (2): Family income does not affect perceived returns.

H0 (3): Gender does not affect perceived returns.

H0 (4): Age does not affect perceived returns.

H0 (5): Parental education does not affect perceived returns.

V. OBJECTIVES 1) To find empirically the perceived returns to education among lower and higher income households in Mumbai and Kolkata 2) To understand the implications of the difference between real and perceived returns on schooling decisions 3) To discuss the factors behind the formation of (distorted) perceived returns 4) To analyse how perceived returns may vary along with variables such as age, gender, parental education, family income, etc.

VI. METHODOLOGY The following are some conceptual standardizations across the paper: Levels of Educational attainment – In order to divide the various years of education into classes, we’ve used the brackets followed by the Indian education system and the variable ‘n’ and ‘t’ are used efficiently display the levels and the time period (in years) to complete the specific levels of education respectively. The values of the same are given as follows● Illiterate (𝑛 = 0) – Haven’t attended any educational institute, zero years of education. ● Primary school (𝑛 = 1, 𝑡 = 5) – Till 5th grade, with 7 years of schooling (2 years of kindergarten + 5 years from grade 1 to 5). ● Middle/Upper primary school (𝑛 = 2, 𝑡 = 3) - Till 8th grade, with 10 years of schooling ● Secondary school (𝑛 = 3, 𝑡 = 2) - Till 10th grade, with 12 years of schooling. ● Senior/Higher secondary school (𝑛 = 4, 𝑡 = 2) - Till 12th grade, with 14 years of schooling. ● College (𝑛 = 5, 𝑡 = 4) – Till bachelors’ degree graduation. Since the duration of courses varies from 3-5 years (with 3- and 4-year courses more prevalent and common), the study has taken the duration to achieve this level of education as 4 years. Another

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important point here is that for ease of regarding the year of the degree, the first year is frequently regarded as the 13th grade, second year as 14th and so on.

Defining low- and high-income families: The specific ranges for low- and high-income groups were defined retrospectively from the survey data (see Table 1).

Table 1: Defining low and high-income categories

Source: Survey Data Since it is difficult for respondents to think in terms of educational returns, our survey recorded answers in rupees for future expected income. Using this data, we calculate perceived returns for various educational levels and annualize them using the Compound Annual Growth Rate method.

• Returns to education in grade brackets – One unique value for each level, which is interpreted as the percentage increase in income when a person passes an additional educational level.

•

Returns annualized (CAGR) – The rate of return to education per year, compounded annually, where t= time period (in years) of education for level ‘n’

•

Returns at level n would be interpreted as each year of additional schooling after level ‘n-1’ and before level ‘n’ would increase a person’s income by provided the individual completes level ‘n’ of education.

VII. PRIMARY DATA SAMPLING Target population: Students from low- and high-income families, currently studying in 8th to 16th grade.

Target Area: Urban and suburban parts of Mumbai and Kolkata.

Survey instrument: Questionnaire Survey Methods: Telephonic interview with participants from low-income families. Online survey via Google forms for participants from higher-income families.

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Collection of samples: Randomisation method was used to recruit participants from lowincome families. Convenience and snowballing methods were used to get participants from higher-income backgrounds.

Conducting the survey: The telephonic interviews, as well as the online questionnaire survey, was conducted between 1st to 10th October 2020. One of the researchers volunteered at an NGO named ‘Youth Empowerment Foundation’ that helps the underprivileged communities around the country. The contact details of participants from low-income backgrounds were extracted from the NGO’s database after securing the required permissions. The participants from well-off backgrounds were sampled with the researchers’ contacts.

The questionnaire is attached as Appendix 1. The participants were told not to take inflation into account when thinking of future expected wages. Hence, all income figures are in the current rupee value.

VIII. ANALYSIS LINKING EDUCATION WITH WAGES

There are three main models for linking educational level with future expected wages: (a) Diminishing returns; (b) S-shaped curve; and (c) Exponential curve

Figure 1.2: Exponential Returns to Education Source: Author’s construction These models represent different conceptions of returns to education. In Fig 1.1 is true, then education exhibits diminishing returns i.e., returns are highest at earliest levels and reduce with an increase in qualification. There are good reasons behind this: the cost of primary schooling is low compared to higher levels; it is often a requirement for access to higher education; and the productivity differential between illiterate persons and primary graduates is significant (Psacharopoulos, 1994). Evidence from field experiments supports this view (Psacharopoulo and Patrinos, 2004).

However, if Fig 1.2 is true, then returns to early education are the lowest and wages rise rapidly with an increase in educational attainment. This model is premised on research that shows that tertiary/higher-education wage premiums have been increasing over time and are highest in comparison to other educational levels (Azam, 2009; Montenegro and Patrinos, 2014).

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Figure 1.3: S-shaped returns to Education Source: Author’s construction

Source: Author’s construction Finally, as in Fig 1.3, education may also yield S-shaped returns. Under this model, returns for early education are low; they rise sharply at higher levels before reducing again. Notably, it differs from the exponential model since the highest increase in wages is experienced after secondary or senior-secondary schooling, and not after tertiary education. This model is likely true in regions where there is a shortage of high-skilled jobs (Banerjee and Duflo, 2011). Here, a lack of demand for high-skilled labour reduces the value of tertiary education. This role of demand in determining the returns to education has been documented extensively in the literature (Foster and Rosenzweig, 1996; World Bank, 2000).

IX. COMPARING REAL AND PERCEIVED RETURNS In Figure 2, we find that the real returns to education in India (urban) are increasing with every level of education (except middle-school, but only marginally). This is indicative of the exponential model (Fig 1.2) since wages are rising at an increasing rate with respect to educational qualification. Between middle and secondary level returns, there is a difference of 6.5% which suggests a sharp rise in returns after middle school. Here, as in our empirical results, the rate of return is annualised. The interpretation is: Each year of additional schooling

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after middle-school and before secondary completion would increase wages by 12.7% for an individual who completes secondary education.

Figure 2: Real Returns to Education in India (Urban) Source: Author’s construction based on data from Agarwal (2011)

Figure 3.1: Perceived Returns of Low-Income Students Source: Survey results

Figure 3.2: Perceived Returns of High-Income Students Source: Survey results

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Fig 3.1 and 3.2 represent the perceived returns for students of low-income and high-income households respectively. Our results in Fig 3.1 show that for students of low-income households, returns for higher-secondary level are perceived to be highest (17.4%). Following that, there is a sharp decline in perceived returns for college education (10.6%). This is suggestive of the S-shaped model since returns are initially low, then rise rapidly at higher levels before falling again. For high-income students in Fig 3.2, perceived returns for highersecondary and college level are highest and roughly the same (20.9% and 20.1% respectively).

Thus, their expectations resemble the exponential model since returns are greatest at the highest levels of education.

Implications of Differences between Real and Perceived Returns Upon comparing our empirical results with real returns, some differences stand out. First, as seen in Table 10 of Appendix 2, both samples have significantly underestimated returns to early education. At primary level, estimated real returns are 5.6 percent whereas our lowincome and high-income samples have reported 1.1 and 1.7 percent respectively. In fact, these figures are likely an overestimation owing to the effect of outliers on our small sample size (n=211). 75 percent of low-income and 74 percent of high-income respondents reported the same future expected wages for illiterate and primary levels i.e., their perceived returns to primary education were zero (negative if foregone work experience is considered). This is in stark contrast to the diminishing returns model.

One factor behind this could be the low quality of primary education in India. Duraisamy (2002) argues that qualitative factors such as high pupil-teacher ratios, poorly qualified teachers, low financing of public schools, high teacher-absentee rates, etc. may be driving down returns to primary education. In fact, Singhari and Madheswaran (2016) show that returns to primary schooling have been declining in India since the 1990s. A report by Pratham (2012) found that only 46.5% students in Class V could read Class I textbooks in Indian government schools. This infrastructural lack of focus on the quality of early education may be why perceived returns were found to be so low viz-a-viz higher education. Here, knowledge about the increasing automation of low-skilled jobs may also be a factor.

Second, the samples peak at different qualifications. This is most evident at college level where the difference between perceived returns for the two samples is 9.5 percent. High-income students thus value tertiary education much more highly than low-income students. In part, this reflects the changing attitudes between job security and specialisation. Many of our lowincome respondents suggested that they wanted to apply for government jobs after 12th graduation. In India, completing higher secondary (10+2) makes one eligible for most government jobs (railways, defence, admin staff, etc.). Given the payment standards and wage security, government jobs are extremely lucrative when compared to private-sector jobs, especially for lower-income classes. This is supported by a recent study conducted across 10 Indian cities which showed that demand for government jobs among 18- to 30-year-olds had increased during the Covid-19 pandemic, with 82.3 percent of respondents preparing to apply for government jobs (Mint, 2020). This drives up the perceived returns for higher-secondary as a host of jobs become available upon graduation.

Aside from job security, the nature of employment matters too. Low-income groups are overrepresented in the casual workforce while high-income individuals are more likely to be regular workers (ILO, 2018). As we see in Figure 4, differences in educational qualification matter more for regular workers. For both genders, the wage premium for casual workers is flat i.e., educational returns are very low. Among regular workers, however, the wage premium

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for tertiary education is large. The ratio of the wage-rate of the highest-educated to illiterates is only 1.3 for casual workers compared to 3.5 for regular workers.

Figure 4: Wages for Casual and Regular workers by Educational Level (2011-12 INR) Source: Author’s construction based on NSSO estimates

Given this data, we see that there is no incentive for a student to opt for higher education if s/he expects to join the casual labour force. On the other hand, individuals who expect to join the regular workforce have a strong incentive to acquire higher education. This may be why perceived returns for tertiary education fall for the low-income sample while remaining high for the high-income group. Interestingly, when asked to estimate wages for current thirty-yearolds, the perceived returns for college-level were calculated to be 12.7 and 15.8 percent for low-income and high-income samples respectively (see Table 11 of Appendix 2), compared to 10.6 and 20.1 percent for themselves. The low-income students thus believed that others would benefit more from tertiary education than themselves, while high-income students believed the opposite.

Finally, belief in the S-shaped or exponential model of returns to education implies that it makes sense for families to choose ‘winners’ and ‘losers’ among their children. In our sample, both income-groups undervalue early education while generally overvaluing higher levels of schooling. Under these beliefs, poor families who can only afford to educate their children till primary or middle-school levels will receive low returns while richer families who educate their children beyond secondary levels will face significantly higher returns. As a result, poor families are motivated to ‘choose’ a child who is most likely to reach higher levels of education and invest all their resources on him/her. Over time, this can lead to an increase in drop-out rates and greater inequality both within and among families (Schultz, 2004). Akresh et. al (2010) found that in Burkina Faso, teenagers were more likely to be enrolled in school if they scored high on a test, but less likely to be enrolled if their sibling had scored high. This kind of ‘winner-picking’ in education indicates that perceived returns play an important role in determining real schooling decisions.

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X. FAMILY INCOME AND PERCEIVED RETURNS

Table 2: t-Test results for low-income versus high-income perceived returns (for oneself) Source: Author’s analysis of survey data Since t-stat lies outside the region of acceptance, we reject H0(2): Family income does not affect perceived returns. At α=0.05, perceived returns of the high-income sample are significantly greater than the lower-income sample. Upon disaggregating the data, we find that for primary and middle levels, we are unable to reject H0(2), but for secondary, highersecondary, and college levels, perceived returns for both samples are significantly different.

Thus, family income seems to impact perceptions toward higher-level education more distinctly, while perceived returns for early education appear to be universally low.

Interestingly, looking at the t-Test results in Appendix 2, Tables 12 and 13, we find that the lower-income sample believes that they will lower educational returns than the population average, while the higher-income sample believes that they will face better educational returns than the population average2 . These results are consistent across every educational level. This may be due to self-perceptions about one’s ability to ‘convert’ education into higher wages, and is discussed in detail soon.

1

Parental Educational Level and Perceived Returns In Table 3, we find that in general, children of parents with higher levels of education reported higher perceived returns. The only outlier is middle-school (M) which shows the opposite trend. Similarly, as seen in Figure 9 of Appendix 2, we find that for students of parents with lower levels of education (illiterate, primary and middle), expected wages for ‘population average’ is higher than ‘oneself’, while this trend is reversed for students of parents with higher levels of educational attainment (secondary, higher secondary, college and above). Simply put, students of parents with high educational attainment believe that will fare better than the population average, and vice versa. Some potential reasons for this are discussed below.

2

Although we fail to reject H0 for the higher-income sample (Appendix 2, Table 10), the mean of perceived returns for ‘own’ is greater at every educational level compared to ‘population average’. Also, when a t-Test is conducted on expected wages rather than perceived returns of the high-income sample, we find that wages For ‘own’ are significantly greater than ‘population average’ at α=0.05. These suggest that the high-income sample believes that they will face higher educational returns than the population average.

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Table 3: t-Test results summarised for parental educational levels Source: Author’s analysis of survey data XI. HOW MAY FAMILY INCOME AND PARENTAL EDUCATION BE AFFECTING PERCEIVED RETURNS? In this section, we consider the influence of family income and parental education together since they are highly correlated both in our sample and in the population (ILO Wage Report, 2014).

One reason why perceived returns are positively correlated with high-family income/greaterparental-education may be the complementarity between ‘ability’ and education (Harmon et al., 2003). Here, ability refers to the efficiency with which one can ‘convert’ education into higher wages. Ability depends on non-quantifiable factors such as soft-skills, and here, students of high family income/greater parental education have a distinct advantage. For instance, consider the English language fluency. Chakraborty and Bakshi (2016) found that in India, a 10% lower probability of learning English in primary schools leads to a decline in weekly wages by 8%.

Students from higher-family-income/greater-parental- education are more likely to be fluent in English (among other soft-skills), and may thus be more confident about getting higher-paid jobs that require competency in soft-skills. In fact, a major reason why students of the population average (and vice-versa) may be their self-perceptions about their ‘ability’ i.e., their soft-skills (Appendix 2: Figure 7, Tables 11 and 12). In other words, their belief that they don’t possess the adequate tools to ‘convert’ education into higher wages may be lowering their perceived returns to education.

Other reasons may be that students from high-family-income/greater-parental-education grow up in a better learning environment and have access to higher quality education. The difference between the quality of private and public education in India is well documented (Pratham, 2012), and its impact on perceived returns have been discussed above. Brand and Xie (2010) have noted the effect of neighbourhood and peer influences on risk perceptions on education.

The extent to which one’s neighbourhood (including one’s immediate friend-circle) values education may, in fact, affect perceived returns. A student from a privileged background is also likely to have better contacts and information about employment, and therefore has a better chance of securing a high-paying job in the formal sector (Krishnan, 1996; Siphambe, 2000).

Knowledge of these qualitative differences may be affecting our sample’s perceived returns, especially for tertiary education.

Finally, under financial market imperfections, differences in family backgrounds entail different marginal costs in attaining education. This affects children of poorer families adversely as they face higher costs, especially at the college level (Checchi, 2006). This may be either due to debt financing or due to nepotism in labour markets which reduce educational

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returns for the less privileged. Agnarsson and Carlin (2002) find that 13% of the marginal return to additional schooling in Sweden is due to family background. Lam and Schoeni (1993) show that returns to schooling drop by about one-third when parental schooling is controlled for in Brazil. Thus, beliefs about higher costs to education and nepotism in the labour market (both of which may be justified) may be driving down the perceived returns to education for students from less privileged families.

XII. Analysis of Variance Most papers in the literature have neglected the role of risk perceptions in schooling decisions.

For poor families, risk perception may deter investment in education even when expected returns are high. Thus, a poor student may decide to not go to college if s/he is extremely unsure of their future earnings. Since we find that perceived returns for the lower-income sample falls sharply for tertiary education, it is imperative to ask whether risk perceptions toward education are significantly different among both sampled groups. Here, we use a right-tailed F-test to compare the variances of both samples.

Variable 1: Higher-income sample’s perceived returns for college Variable 2: Lower-income sample’s perceived returns for college α = 0.05 H0: σ21 = σ22 Ha: σ21 > σ22 F > Fcritical, so we reject H0.

Table 4: f-test between the lower and higher income sample’s perceived returns Source: Author’s analysis of survey data Thus, our evidence suggests that the variance for the high-income sample’s perceived returns for college is higher. This is in contradiction to studies (Nguyen, 2008; Attanassio and Kaufmann, 2008) which show that risk perceptions (as understood through variance in future expected earnings) toward education are much higher among low-income groups. According to our data, variability in future earnings does not explain low perceived returns for college among lower-income students. Here, financial constraints, lack of high-skilled jobs, quality of education, etc. may play a greater role in determining perceived returns.

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Table 5: t-test for the expected future income - males v/s females Source: Author’s analysis of survey data The t-test in Table 5 shows that there is a significant difference between the genders’ perception of future wages. Tables 14 and 15 in Appendix 2 show that this difference holds at both income levels. Across both income groups, males also reported a consistently higher future expected wage than females at all levels of education (See Appendix 2, Figure 8&9).

Putting this into perspective, Figure 5 plots this gap in their expectation as a ratio of the expectation of males. It is calculated as follows:

Figure 5: Difference in perception of one’s future wages (males v/s females, as a % of males’ perception) along with the real wage gap Source: Author’s Construction based on survey results; Source*: IGIDR The gap in expectation of future wages follows a decreasing linear trend. Here, a variety of social factors such as a belief in patriarchy (rather, belief that employers in the labour market are patriarchal) to women having an ingrained sense of lower self-worth may explain why women expect lower wages than men across the educational spectrum. Since we asked respondents to report expected wages at 30, women likely took motherhood into consideration.

Other factors constant, ‘motherhood penalty’ accounts for up to 50% of the wage gap between genders (Waldfogel, 1998). Belief in the ‘motherhood penalty’ may be why women have lower expectations about future wages. Interestingly, the variables follow a similar pattern till the secondary level. Following that, the real wage-gap falls steeply for higher-secondary and

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college while perceived differences remain roughly the same (Figure 5). Thus, in our sample, the difference in perception of future wages between males and females is greater than in reality for higher educational levels. This may be one of the factors behind low tertiary educational attainment among females.

Table 6: T-test results between the expected income of oneself v/s the population at all levels of education Source: Author’s analysis of survey data

Figure 6: Predicted monthly wages – oneself v/s others Source: Author’s Construction based on survey results Table 6 shows that the LI female sample’s perceived future wages were significantly lower than the population average at every level of education. However, all other groups’ perceived wages are higher than the population average. This indicates that the positive effect of belonging to higher-income families on perceived wages is more significant than the negative effect of gender. This may be because jobs relating to domestic help, largely occupied by women, are paid lesser than traditional jobs occupied by men with the same academic attainment (Nandal, 2005). For LI females with high levels of educational qualification, the supply of secure and well-paid jobs remains low, which either makes them opt out of the labour force or settle as underemployed and underpaid workers (Das & Mehta, 2012). Formal sector jobs are very competitive and require connections, and here, low-income families (especially women) are likely to be at a disadvantage.

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XIII. AGE AND PERCEIVED RETURNS

Table 7: Optimism level of the sample by t-tests Source: Author’s analysis of survey data

Table 8: Gross Enrolment Rates (GER) in India for 2015-16 Source: MHRD, 2018 Table 7 indicates that the students currently pursuing senior secondary education and their first year in degree college have less incentive to complete their education compared to the ones in their final 3 years of a degree college and secondary school. There is a sense of uncertainty about the future in senior secondary school since the shift in educational institutes depends on entrances and board exam scores, which might make an individual pessimistic about the future.

Additionally, infamously tough entrances and the coaching ‘shadow education’ industry built around it puts students under considerable pressure (Bray, 2003). A study by Smith & Wertlieb (2005) shows that the academic and social expectation of first-year college students do not align with their expectations, thereby making it a little tough for them to adjust and cope.

The Gross Enrolment Rates (Table 8) support the findings from Table 7. As students transition from senior secondary to college, there is a steep fall of 35% in enrolment rates. There are various reasons that students prefer to not continue their education. ‘Education Quality Upgradation and Inclusion Programme’ report by the HRD ministry states “empirical evidence points towards the persistence of economic, social, locational, and regional disparities in access to higher education. The higher education system and institutions have to recognise and adapt to meet the demand from diverse communities of students”. The sample data collected also shows the additional factor of optimism of income that may make them lose interest in pursuing their further education.

XIV. POLICY REVIEW Having analysed how perceived and real returns vary along with characteristic variables, it may now be instructive to discuss various methods of intervention that can make perceptions about education more attuned to reality. Here are three possible methods: 1. Role Models (Nguyen, 2008) – Role models serve as a direct ‘witness’ of educational success to students and parents. By appealing to their common background, this method can raise perceived returns through a direct emotional connection with the target group.

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2. Financial recognition (Sequeira et.al, 2014) – Rewarding educational achievement with fellowships/scholarships makes schooling profitable in the short run. It reduces uncertainty about the future, thereby incentivising educational investment.

3. Flyers and awareness campaigns – Providing information about real schooling outcomes is a cost-effective way to change perceptions. Likely effective in situations with great information uncertainty.

Role models can be effective among lower-income families. Since the models share similar backgrounds with the students, it is easier to relate to them and build a level of trust. Financial recognition will likely be more effective in raising perceptions of students pursuing higher education since it is difficult to discern talent during early education. This would especially be effective for low-income students since apart from the reassurance of education being a good investment, it would also support poorer families that require immediate financial gains. Flyers and campaigns are the most cost-effective and can be used with any target group.

XV. CONCLUSION In this paper, we empirically calculate the perceived returns to education in India and discuss how perceptions vary along with characteristic variables. Perceived returns to primary education are found to be low universally. Low-income families underestimate returns at the college level. There is low optimism about future expected wages in the 11th-13th grade age bracket, and there is a significant difference in educational perceptions between males and females. Low-income females perceive their income to be lower than the population average.

Volatility in perceived returns to college education is higher for students hailing from well-todo families, which is contradictory to most findings in other developing nations. Based on our results, we also recommend various interventions to attune perceptions about education with reality.

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Attanasio, O., & Kaufmann, K. (2008). School Choices, Subjective Expectations and Credit Constraints. Bocconi University and University College London Working Paper.

Mehtabul, A. (2009). India's Increasing Skill Premium: Role of Demand and Supply. IZA Discussion Paper No. 3968, Available at SSRN: https://ssrn.com/abstract=1336085 Banerjee, A. V., & Duflo, E. (2011). Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty. PublicAffairs. https://doi.org/10.1002/jid.2936 Barrett, A., FitzGerald, J., & Nolan, B. (2002). Earnings inequality, returns to education and immigration into Ireland.

Labour Economics, 9(5), 665–680. https://doi.org/10.1016/S0927-5371(02)00073-8 Chaudhri, D., & Rao, P. (1970). Private and Social Returns to Higher Education: A Case Study of Delhi Graduates. Economic and Political Weekly, 5(14), 605-608. Retrieved November 7, 2020, from http://www.jstor.org/stable/4359824 Duraisamy, P. 2002. "Changes in Returns to Education in India, 1983–94: By Gender, AgeCohort and Location." Economics of Education Review, 21:6, pp. 609-22.

Gouda, S., & Sekhar, T. V. (2014). Factors Leading to School Dropouts in India: An Analysis of National Family Health Survey-3 Data. IOSR Journal of Research & Method in Education.

4(6): https://www.researchgate.net/publication/269932850_Factors_Leading_to_School_Dropout s_in_India_An_Analysis_of_National_Family_Health_Survey-3_Data Gouda, S., & Sekhar, T. (2014). Factors Leading to School Dropouts in India: An Analysis of National Family Health Survey-3 Data. International Journal of Research & Method in Education. DOI: 10.9790/7388-04637583 Halvorsen, T. (2016). Higher Education in Developing Countries: Peril and Promise, a Decade and a Half Later: Development Lost?. International Journal of African Higher Education. 3. DOI: 10.6017/ijahe.v3i1.9638 International Labour Organisation. (2020). Retrieved 25 December 2020, from https://www.ilo.org/wcmsp5/groups/public/---asia/---ro-bangkok/---sronew_delhi/documents/publication/wcms_638305.pdf Jensen, R. (2010). The (Perceived) Returns To Education And The Demand For Schooling.

The Quarterly Journal of Economics. https://cega.berkeley.edu/assets/cega_research_projects/58/The_Perceived_Returns_to_Ed ucat n_and_the_Demand_for_Schooling.pdf Montenegro, Claudio E. & Patrinos, Harry Anthony, (2014). "Comparable estimates of returns to schooling around the world," Policy Research Working Paper Series 7020, The World Bank.

National Sample Survey Office (NSSO). (2014). Employment and Unemployment Situation in India, NSS Report No. 554(68/10/1), NSS 68th Round, July 2011–June 2012 (Ministry of Statistics & Programme Implementation, Government of India).

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Nguyen, T. (2008). Information, Role Models and Perceived Returns to Education: Experimental Evidence from Madagascar. Massachusetts Institute of Technology. https://www.povertyactionlab.org/sites/default/files/documents/Nguyen%202008.pdf Pratham (2012). Annual Status of Education Report (Rural) 2011. Pratham, New Delhi.

PROBE Team, Centre for Development Economics (1999). Public Report on Basic Education in India.

Oxford University Press. https://www.undp.org/content/dam/india/docs/public_report_basic_education_india.pdf Psacharopoulos, G., & Patrions, H. (2004). "Returns to Investment in Education: A Further Update." Education Economics, 12:2, pp. 111-34.

Psacharopoulos, G. (1994). "Returns to Investment in Education: A Global Update." World Development, 22:9, pp. 1325-43.

Radhika (2020). Dropout rates increasing in Classes 9 and 10 in some states: MHRD. Career 360. https://news.careers360.com/dropout-rates-increasing-in-classes-9-and-10in-some-states-mhrd Akresh, R., Bagby, E., Walque, D., & Kazianga, H,. (2012). "Child Ability and Household Human Capital Investment Decisions in Burkina Faso," Economic Development and Cultural Change, University of Chicago Press, vol. 61(1), pages 157-186.

Schultz, T. (2004). "Evidence of Returns to Schooling in Africa from Household Surveys: Monitoring and Restructuring the Market for Education." Journal of African Economies, 13, pp. ii95-ii148.

Sequeira, Sandra & Spinnewijn, Johannes & Xu, Guo. (2016). "Rewarding schooling success and perceived returns to education: Evidence from India," Journal of Economic Behavior & Organization, Elsevier, vol. 131(PA), pages 373-392.

The sharp increase was seen in preference for government jobs. (2020). Retrieved 25 December 2020, from https://www.livemint.com/news/india/sharp-increase-seen-inpreference-for-government-jobs-11594526931270.html Singhari, S., & Madheswaran, S. (2016). The Changing Rates of Return to Education in India: Evidence from NSS Data. The Institute for Social and Economic Change. http://www.isec.ac.in/WP%20358%2020Smritirekha%20Singhari%20and%20S%20Mad hesw aran%20-%20Final.pdf Smith, H., & Powell, B. (1990). Great Expectations: Variations in Income Expectations Among College Seniors. Sociology of Education, 63(3), 194-207. doi:10.2307/2112837 Stanovnik, T. (1997). The returns to education in Slovenia. Economics of Education Review, 16(4), 443–449. https://doi.org/10.1016/S0272-7757(97)00006-X World Bank, 2011, ‘Gender Differences in Employment and Why They Matter’, in World Development Report 2012: Gender Equality and Development, World Bank, Washington DC, Ch. 5.

Das, M. & Mehta, S. (2012). Issue Brief: Poverty and social exclusion in India. World Bank https://openknowledge.worldbank.org/bitstream/handle/10986/26338/114156-BRI-IndiaPSE-women-Brief-PUBLIC.pdf?sequence=1&isAllowed=y

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Tejani, S. (2016). Jobless growth in India: an investigation. Cambridge Journal of Economics, 843–870. DOI:10.1093/cje/bev025 Nandal, S. (2005). Extent and Causes of Gender and Poverty in India: A Case Study of Rural Haryana.

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7(2), 182-190 https://vc.bridgew.edu/cgi/viewcontent.cgi?referer=https://www.google.com/&httpsredir=1& article=1442&context=jiws Nichols, C. (2016) Time Ni Hota Hai: time poverty and food security in the Kumaon hills, India, Gender, Place & Culture, 23:10, 1404-1419, DOI: 10.1080/0966369X.2016.1160871 Dommaraju, P. (2009). Timing of First Birth in India. Genus, 65(1), 81–101.

DOI:10.4402/genus-53 Bray, M. (2003). Adverse effects of private supplementary tutoring: Dimensions, implications and government responses.

Paris: UNESCO IIEP. https://www.researchgate.net/publication/308412747_Adverse_Effects_of_Supplementary_P rivate_Tutoring_Dimensions_Implications_and_Government_Responses Smith, J. & Wertlieb, E. (2005) Do First-Year College Students' Expectations Align with their First-Year Experiences?, NASPA Journal, 42:2, 153-174, DOI: 10.2202/1949-6605.1470 APPENDIX 1

The Questionnaire SECTION1: DEMOGRAPHIC QUESTIONS 1 (a). Age 1 (b). Sex 1 (c). What standard are you studying in? (Note for college students: if you're in the First year, enter '13'. Second-year students enter '14' and so on) 1 (d). Family income (monthly) ___________ 1 (e). What is the educational level of your parents?

SECTION 2: EXPECTED INCOME WHEN YOU WERE 30 The next 5 questions all relate to the same hypothetical scenario mentioned below.

Question: Suppose you were to complete x-level of education, stop attending school, and immediately start working afterwards. Think about what kinds of jobs you might get. How much do you think you will earn when you are 30 years old? Enter answer as monthly salary.

In the questions below, different values for the ‘x level of education' are mentioned.

Answer in today's money value. Do not take inflation into account.

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2 (a). x = No educational qualification i.e. never attended school (illiterate) 2 (b). x = primary school education (5th grade) 2 (c). x=middle school education (8th grade) 2 (d). x=secondary school education (10th grade) 2 (e). x=senior secondary school education (12th grade) 2 (f). x= college education (bachelor’s degree) SECTION 3: GUESSING INCOME OF CURRENT 30-YEAR-OLDS In this section, we are asking you to guess the average monthly salary of people who are currently 30 years old who have completed x level of education. This is different from the previous section in that we are asking you to guess the average salary of others (not just about the people you know personally, but people across the country), not yourself. Once again, think about the kind of jobs they will be qualified for in each scenario, and then write down the average monthly salary that a current 30-year-old with that level of education receives.

There are no wrong answers. Just write down what you think an average 30-year-old receives.

Please enter only numeric values. The answer should be the monthly salary.

3 (a). x = No educational qualification i.e. never attended school (illiterate) 3 (b). x = primary school education (5th grade) 3 (c). x=middle school education (8th grade) 3 (d). x=secondary school education (10th grade) 3 (e). x=senior secondary school education (12th grade) 3 (f). x= college education (bachelor’s degree) APPENDIX 2 Educational Level Primary Middle Secondary Higher Secondary College

Real Returns 5.6 6.2 11.4 12.2 15.9

Low-Income Sample 1.1 3.3 7.0 17.4 10.6

High-Income Sample 1.7 3.6 14.0 20.9 20.1

Table 9: Comparing Real and Perceived Returns

Educational Level Primary Middle

Low-Income Sample 3.0 3.1

High-Income Sample 2.0 5.2

Towards Equilibrium 2021 | 26 Secondary Higher Sec College

4.8 11.3 12.7

10.1 13.7 15.8

Table 10: Perceived Returns for Current 30-year-olds

Mean Variance t Stat t Critical two-tail

LI own 20.80857105 524.5951972 -6.93563796 1.962515333

HI own 33.96469226 1408.807486

Table 11: t-Test results for low-income versus high-income perceived returns (for oneself)

Mean Variance t Stat t Critical two-tail

LI own 20.80857105 524.5951972 -2.161765286 1.96477774

LI popln.

22.96670436 624.9863933

Table 12: t-Test results for perceived returns of low-income (oneself) versus low-income (population average)

HI popln

HI own

Mean

31.65503121

33.96469226

Variance

1067.913776

1408.807486

t Stat

-1.819749854

t Critical two-tail

1.964255274

Table 13: t-Test results for perceived returns of high-income (oneself) versus high-income (population average)

Figure 7: Average Future Expected Wages by Parent’s Education Level Mean

HI Male 52495.09804

HI Female 34682.91782

Towards Equilibrium 2021 | 27 t Stat t Critical two-tail

7.724186413 1.964424727

Table 14: t-test for the expected future income – HI males v/s HI females Mean t Stat t Critical two-tail

LI Male 28211.02151 8.698949335 1.964475628

LI Female 18968.46847

Table 15: t-test for the expected future income – LI males v/s LI females

Figure 8: Lower-income sample’s perceived incomes Source: Author’s Construction based on survey results

Figure 9: Higher-income sample’s perceived incomes Source: Author’s Construction based on survey results

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High income

Low Income

Illiterate

Primary

middle

secondary

higher sec

college

Oneself

25532

26797

29083

36159

50892

88383

Others

17806

18671

20730

26623

38113

59036

Illiterate

Primary

middle

secondary

higher sec

college

Oneself

17859

18540

20015

22606

30162

39359

Others

17828

19222

20722

23768

32207

45157

Table 16: Average Future Expected Wages by Family Income

Figure 10: Average Future Expected Wages by Parental Educational Level Source: Author’s Construction based on survey results

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THE COVID-19, ECONOMIC CRISIS AND POLICY RETALIATION: A DYNAMIC GENERAL EQUILIBRIUM ANALYSIS -Anusil Das, Dipanjan Paul, Shouryo Basu and Sreeja Khan1 ABSTRACT This paper attempts to analyse the ceteris paribus effects of ‘supply chain disruptions’, ‘strict government regulations’, and ‘change in saving rate’ on urban unemployment thereby identifying a pattern of labour migration, in the unprecedented ‘COVID’ scenario. In doing this we use the Heckscher-Ohlin structure in a Harris-Todaro type economy, which has three sectors- a manufacturing sector, an agricultural sector and a skilled sector. We extend our representation to the long run by portraying a Solowian economy in which the accumulation of domestic capital stock plays a decisive role as a meter for unemployment and wage-disparity.

Finally, we say that both in the short run as well as long run we see an unambiguous effect on inter-sectoral migration amongst the agricultural and the manufacturing sectors, thus also an unambiguous effect on unemployment which is a counterproductive outcome of the government's policy response.

JEL CLASSIFICATION: D50, E24, F2, F61, J6, O10.

Keywords: Covid-19, General Equilibrium, Migration, Unemployment I. INTRODUCTION Intramural migration is an indispensable and inevitable integrant of the economic and social life of a country. Migrants account for floating and invisible citizens alternating between the origin and the destination areas and remaining on the fringe of the society. In today’s world, extensive migration is a usual practice as the global acumen of individuals expands. The manoeuvre has been beneficial to both immigrants’ importation and exportation nations.

Internal migration customarily comes about from a rural area in the country to an urbanized area such as the country’s capital. These migrants are often wondering for a better standard of living or employment opportunities. Empirical analysis reveals that the asymmetrical shift of people from one place to another results in an adverse outcome.

Migration within a nation positively contributes to the destination’s overall economy and development. Internal migration mainly occurs due to economic purposes. Numerous individuals from the countryside proceed to their country’s cities anticipating to earn more and bring their family up to a soaring standard of living. In large parts of our nation, there has been a humongous migration due to the COVID-19 pandemic. Thousands and thousands of labourers marched back to their villages for the sake of finding some warmth and empathy.

Census 2001 revealed that in India internal migrants account for as large as 309 million which was about 28% of the population. The data published by NSSO (2007-08) manifested a rise in internal migrants by 17 million in six years. The benefaction of migrants to the GDP of the Indian economy often goes unnoticed. The estimated data shows that migrants contribute 1

St. Xavier’s University, Kolkata

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around 10% of the country’s GDP where almost 70% of the migrants are women. Census 2011 showed that the total number of internal migrants is 450 million which is approximately 35% higher than in 2001 excluding child labour. West Bengal, Uttar Pradesh and Bihar have the highest number of migrants followed by Punjab, Rajasthan and Madhya Pradesh.

The sharp increase in migration over the years is due to the fact of the rapid urbanization of our cities and towns. 93% of India’s total migrants get involved in the informal sector which includes seasonal or periodic migrant labourers in the Green Revolution.

Large scale migration persuaded by greater and greener pastures of economic progressiveness is a folk tale as most of the migration is for the subsistence and living and falls under the citatory of distress migration. Low threshold incomes perpetually force labourers for migration.

In the general context of the ongoing urbanization and rural industrialization, exploitation and utilization of labours take new conformation that is a combination and an ingenious adaptation of the older forms of control and bondage contextualized to new conditions of capitalism. The foisting of the lockdown as a measure to contain the exponential growth thumped the unskilled and the semi-skilled migrant labourers the most. A rough estimate indicates that around 30 million migrants walked or were stranded in camps on their way back.

ILO estimates that around 400 million workers in the informal economy both unskilled and semi-skilled will be falling deeper into poverty during this crisis. The procedure for recuperation is going to be long-drawn-out and throbbing for those migrants who would be looking for urban spaces.

II. LITERATURE REVIEW A few theoretical models of rural-urban migration and the informal sector are available in the literature. Harris-Todaro (1970) exhibits rural-urban migration in a dual economy framework.

The informal and formal sectors are the subdivisions of the urban sector where migrants not getting employed in the formal sector spontaneously get a job in the informal sector. In his model, there subsists open urban unemployment despite the existence of the informal sector.

There are four distinct different income groups within the working class which result in a positive degree of inequality in the dispersal of income of the labourers, which has been firmly discerned by Harris-Todaro (1970). Sen (1974) proposed several politics taking into context the welfare of society. Contemplating the urban sector in small segments and their basic models have been extended in several directions by Stiglitz (1982) and Grinols (1991). Shadow wage rate in the urban formal sector was shown by Stiglitz (1982) whereas Grinols (1991) was engrossed in modelling the General Equilibrium effects of foreign capital inflow in less developed economies.

Datta Chaudhari (1989), Sarkar and Ghosh (1989) came to a conclusion where the policy which reduces the rate of interest paid by the informal sector also tends to reduce the aggregate output and employment in the industrial sector. Studies shown by Joshi & Joshi (1976) on India have noted that average informal sector earnings are roughly equal to if not higher than rural earnings. By taking into account the Heckscher-Ohlin framework and considering the manufacturing sector as a capital-intensive sector and the agricultural sector as labour intensive, we have viewed the manufacturing sector as an import-competing sector and the agricultural sector and skilled sector as export-competing sectors.

We have upshot the effects of government’s regulation, supply chain disruptions and an

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increase in the precautionary savings on the manufacturing and the agricultural sector. By taking help from Solow (1956) from the technique adapted from Gupta (1993) we have discussed the effects of an increase in saving rate in the long run. We have considered the wage in the agricultural sector as flexible as opposed to the fixed-wage rate in the manufacturing sector. Harris-Todaro framework has been used to explain the migration and the reverse migration between the manufacturing and the agriculture sector. Dasgupta and Rajeev (2020) have made a theoretical analysis of the pandemic in a Keynesian macroeconomic framework in the short run which differs from our paper which reveals the effects of Covid-19 in the informal sector primarily the agricultural sector using a trade-theoretic microeconomic model.

III. EMPIRICAL EVIDENCE AND MOTIVATION

Figure 1: *Third Week of June Source: Centre for Monitoring Indian Economy Figure 1- shows a steep rise in the average unemployment rate in India during the pre-Covid19 and during the COVID-19 crisis. The data provided by CMIE states that unemployment levels have risen to 27% from 9%. It stood at 29.22% in the urban areas, as against 26.69% in the rural areas. However, the average rate of unemployment had fallen when the Government announced the NREGP expansion programme in the rural sector and reverse migration was at a peak level.

Figure 2- shows the contraction in India’s GDP growth rate as a result of the Covid-19 virus.

The World Bank reports a 5.2 percent contraction in global GDP in 2020 and a Historic contraction of per capita income. India’s real GDP for the April-June 2021 quarter fell by 23.9% in comparison to the same quarter a year ago.

The table (Table 1- Statistical Appendix) had been depicted in Figure 1 above which shows the line charts of the rate of unemployment from May 2017 to April 2019, i.e., almost for two years from 2017-19. Throughout we see an upward trend in the data, with a slight fall in both Urban and Rural unemployment. Rural unemployment falling from 4% to 3% (approx.) in July 2017.

Urban unemployment falling from 4% to a little over 3% (approx.). We see a notable fall in rural unemployment in the month of May 2018 from 6% in March 2018 to a little over 4% (approx.). We also see a fall in total unemployment in this month of 2018. There is however a notable rise in urban unemployment between January and February of 2019 and eventually, it falls in April 2019. Another notable fact is that there is a fall in rural unemployment in the

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months of November, January and March 2019. However, total unemployment rises from close to 6% to close to 8% (approx.).

Figure 2: GDP Growth Rate in India Source: Ministry of Statistics and Programme Implementation (MOSPI), Government of India

Figure 3: Labour Force Participation and Unemployment Rate in India during the COVID19 Crisis Source: Centre for Monitoring Indian Economy

The movement observed in the unemployment rate corresponds to the level of reverse migration among unskilled workers in the economy. (Table 2- Statistical Appendix) summarizes the rate and level of reverse migration during the COVID-19 crisis.

Given these empirical facts, two questions that arise are the following. First, what were the channels through which the COVID-19 crisis penetrated into the Indian economy and particularly in the labour market? Second, what caused the Government policy response to COVID-19 to produce counterproductive outcomes and lead to policy failure. We attempt to provide possible explanations to these questions by building an analytically tractable theoretical model in section 5. Few comparative statics and policy analysis have been carried out in section 6. The basic short-run model is extended to long-run dynamics in section 7.

Finally, section 8 concludes the paper.

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IV. THE MODEL Given the backdrop, the paper attempts to analyze the effect of COVID-19 on rural-urban migration of unskilled labour, urban unemployment and informalization of labour in terms of supply chain disruption, investment uncertainty, the imposition of government regulation and precautionary savings.

IV.1 Assumptions and Description of the Economy We consider a short-run three-sector, three-factor Harris-Todaro type economy with fixed factor coefficient technology. The stylized economy consists of the following sectors. There is a rural agricultural sector (A) and an urban manufacturing sector (M) both of which uses unskilled labour (L) and capital (K) as factors of production. There is a service sector (S) that uses capital (K) and a specific factor – skilled labour (S). The commodity produced by the agricultural sector is given by XA , the commodity produced by the manufacturing sector is given by XM and the commodity produced by the skilled sector is given by XS. Labour market distortions exist in terms of rigid wage and unskilled labour in the urban manufacturing sector.

There is involuntarily urban employment due to the rigid wage rate that leads to Harris-Todaro (1970) type rural-urban migration.

The unionized urban manufacturing wage (𝑊 M ) is exogenously given and is greater than the flexible rural wage ( WA ). This rural-urban wage inequality leads to the migration of unskilled labour from the rural sector to the urban sector. Migration equilibrium is attained when the expected urban wage equals the rural wage. In this migration equilibrium, unemployment continues to persist. Therefore, the Harris Todaro equilibrium is suboptimal.

We assume the rural agricultural sector is relatively labour intensive as compared to the urban manufacturing sector which is relatively capital intensive. In other words, this means that the capital-labour ratio(K/L) is lower for the commodity produced in the agricultural sector( XA ) than the commodity produced in the manufacturing sector (XA), that is, where ___ is the unskilled labour-output ratio in the i-th sector, i = A, M

There are constant returns to scale in the production of both commodities which means that the number of output increases in the same proportion as the increase in the number of inputs.

Perfect competition exists in both commodities and factor markets. There is perfect mobility of capital and unskilled labour within the agricultural and manufacturing sectors but skilled labour is specific to the skilled sector.

Total capital endowment is assumed to be the sum of domestic capital denoted by KD and foreign capital which is fixed and is denoted by 𝐾 F . Tastes and preferences are the same across the nations. The model assumes there is transportation cost (T) and government regulation (ρ) in the urban manufacturing sector. International trade is always balanced between nations. This is an extension of the Heckscher-Ohlin (H-O) model based on Jones (1965, 1971) framework and Chaudhuri and Mukhopadhyay (2010).

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IV.2 Equational Structure (Numeraire)

(1) (2) (3) (4) (5) (6) (7) (8) (9)

IV.3 Working of the Model We have 7 equations and 7 unknowns – WA, r, Ws, Xa, XM, Xs, U Eq. (8) shows the trade balance condition, that is, the value of exports = value of imports It determines the National Income Y. Since , 𝑊 M is fixed,

is obtained from eq. (2)

Putting the value of

in eq. (1), we obtain

Putting the value of

in eq. (3), we obtain

From eq. (6), we obtain From eq. (5) and eq. (7), we obtain

and

as a function of U

From eq. (4), we obtain It is a decomposable structure because the factor prices are determined by the commodity prices alone and do not depend on the factor endowments.

V. COMPARATIVE STATICS In the short run, we obtained the following three propositions: V.1. Supply-Chain Disruption

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An increase in the transportation cost (T) leads to a fall in the rental rate of capital (r) and a subsequent increase in the unskilled rural wage (WA ) and skilled wage (WS). This causes the rural wage to exceed the expected urban wage resulting in a reverse migration of unskilled labour from the manufacturing sector to the rural sector and ultimately leading to a fall in urban unemployment.

Proposition 1: A Supply-Chain Disruption in the manufacturing sector in terms of an increase in the transportation cost ( T ) leads to reverse migration of unskilled labour from the urban sector to the rural sector which augments informalization of labour and lowers urban unemployment. Skilled labour gains at a cost to capital owners in terms of factor prices.

V.2. Investment Uncertainty A fall in the total capital endowment (𝐾 ) leads to a fall in the total quantity produced by the manufacturing sector ( XM ) and a rise in the total quantity produced by the agricultural sector (XA) this follows directly from the Rybczynski Theorem.

The level of output produced by the service sector (XS) remains unchanged. Following the fall in XM, the probability of getting an urban job decreases. This causes the expected urban wage to fall short of the rural wage resulting in a reverse migration of unskilled labour from the manufacturing sector to the rural sector and ultimately leading to a fall in urban unemployment.

Proposition 2: An Investment Uncertainty in terms of fall in the total capital endowment (𝐾 ) would lead to reverse migration of unskilled labour from the urban sector to the rural sector which augments informalization of labour and lowers urban unemployment.

V.3. Government Regulation As the government imposes strict regulations in factories (increase in ρ), it would lead to a fall in the labour-output ratio (aLM) in the manufacturing sector. The capital used also falls causing an increase in the rental rate of capital (r) and a subsequent fall in the skilled (WS) and unskilled (WA) wage rates. This causes the expected urban wage to exceed the rural wage resulting in the migration of unskilled labour from the rural sector to the urban sector and ultimately leading to a rise in urban unemployment.

Proposition 3: Government Regulation in terms of increase in (ρ) would lead to migration of unskilled labour from the rural sector to the urban sector resulting in a rise in the level of urban unemployment and a fall in the informalization of labour.

VI. AN EXTENSION: A DYNAMIC LONG RUN EQUILIBRIUM MODEL We develop a dynamic long-run equilibrium model which is an extension of Solow (1956) in Jones type general equilibrium. The model assumes that skilled labour and capital owners save a constant fraction ‘s’ of their income and that capital depreciates at a constant rate ‘ ’. In our

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model, we assume that unemployment continues to persist even in the long run. This is proved in Gupta (1998).

Equation of motion for capital accumulation and its time path:

For Particular Integral, For Complementary Function,

Let the trial solution be,

For stability of equilibrium

P.I + C.F

In the Long Run,

Equational Structure: (Numeraire)

(1) (2) (3)

(4)

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(5) (6) (7) (8)

In the long run, we have the following three propositions: VI.1. Supply Chain Disruption A rise in transportation costs (ceteris paribus), reduces the net revenue from the manufacturing (capital-intensive) sector. Thus we see a fall in the rate of return (r), hence a rise in agricultural wage and also a rise in the skilled wage. a) Now, if the rise in skilled wage is dominated by a fall in the rate of return, capital stock (domestic) falls. Hence, we see a contraction of the manufacturing sector and an expansion in the agricultural sector. Thus, the agricultural wage is more than the expected manufacturing wage, thus reverse-migration occurs and we see a fall in unemployment.

Proposition 4: Capital decumulation, given that the fall in the rental rate (r) dominates the rise in skilled wage rate (Ws), owing to which the level of unemployment, migration and informalization of labour in the short run is reinforced in the long run. b) Now suppose in the previous scenario, if they fall in rate of return is dominated by the rise in the skilled wage, capital stock (domestic) rises. Hence, the manufacturing sector expands and the agricultural sector contracts. This initiates migration and thus a rise in unemployment.

Proposition 5: Capital accumulation, given that the rise in skilled wage rate (Ws) dominates the fall in the rental rate (r), owing to which the level of unemployment, migration and informalization of labour remains ambiguous in the long run.

VI.2. Government Regulation A rise in the regulations (ceteris paribus) leads to a fall in the labour per unit of output in the manufacturing sector. Now, we see a fall in skilled wage as well as a rise in agricultural wage but we see a fall in the rate of return. a) If the rise in the rate of return is dominated by the fall in the skilled wage, we see a fall in the domestic stock of capital. Thus, the manufacturing sector contracts and the agricultural sector expands. This leads to a higher agricultural wage, and thus reverse migration, hence a fall in unemployment.

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Proposition 6: Capital decumulation, given that the fall in skilled wage rate (WS) dominates the rise in the rental rate (r), owing to which the level of unemployment, migration and informalization of labour in the short run is reinforced in the long run. b) If the rise in the rate of return dominates a fall in the skilled wage, the domestic capital stock increases. This leads to an expansion in the manufacturing sector and the agricultural sector contracts. This leads to the rise of the fixed, expected manufacturing wage over the flexible agricultural wage. Thus, migration and a rise in unemployment.

Proposition 7: Capital accumulation, given that the rise in the rental rate (r) dominates the fall in skilled wage rate (WS), owing to which the level of unemployment, migration and informalization of labour remains ambiguous in the long run.

VI.3. Saving Rate A rise in saving rate, that is, precautionary saving, (ceteris paribus) causes a rise in the domestic capital stock. Hence, an expansion in the manufacturing sector and a contraction in the agricultural sector occurs. And this results in a fixed manufacturing wage, which is higher than the flexible agricultural wage. Thus, leads to migration and a rise in unemployment.

Proposition 8: An increase in the precautionary saving rate (s) would lead to the migration of unskilled labour from the rural sector to the urban sector resulting in a rise in the level of urban unemployment.

VII. CONCLUSION We show in our paper that in the short run, an increase in transportation cost leads to reversemigration from the urban manufacturing sector to the rural agricultural sector resulting in a fall in unemployment in this stylized, small developing country. An increase in the regulatory measures of the government leads to a fall in the rural wage rate and hence their migration from the rural to the urban sector. This aggravates the problem of unemployment in the urban manufacturing sector. An investment uncertainty in terms of a fall in total capital endowment in the manufacturing sector leads to contraction of the manufacturing sector resulting in a reduction of the probability to find a job in the urban sector and a subsequent fall in the expected urban wage. Thus, reverse migration takes place from the urban sector to the rural sector.

However, in the long run, we show that the capital stock is directly proportional with the skilled wage rate, the rate of precautionary savings, and the rental rate of capital and inversely proportional to the depreciation rate of capital. In the long run, an increase in transportation cost or the increase in the regulatory measures of the government, we observe that two situations arise. Firstly, a situation of capital decumulation results in a reverse migration of unskilled labour from the urban sector to the rural sector and a subsequent fall in urban unemployment. Secondly, a situation of capital accumulation results in the migration of labour from the rural sector to the urban sector and the subsequent rise in urban unemployment. Next, we consider an increase in the savings rate (precautionary), which would lead to capital accumulation which in turn encourages rural-urban migration and urban unemployment. In our model, we show that unemployment continues to persist both in the short run as well as in the long run.

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However, in the short run, the informal sector acts as a shock-absorbing sector. The fact that the rural unskilled wage is flexible, it follows that whenever there is reverse migration of unskilled labour from the urban sector to the rural sector, the agricultural (informal) sector readily absorbs this pool of incoming unskilled labour without resulting in any unemployment.

This in turn effectively reduces urban unemployment. Thus, the informal sector plays a crucial role in a small developing economy as it can reduce urban unemployment.

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REFERENCES Acharyya, R. (2013). International Economics: An Introduction to Theory and Policy. Oxford University Press.

Chaudhuri, S., & Mukhopadhyay, U. (2010). Revisiting the Informal Sector: A General Equilibrium Approach (1 ed.). New York: Springer.

Dasgupta, D., & Rajeev, M. (2020, May). The paradox of a Supply Constrained Keynesian Equilibrium The COVID-19 Case. Economic & Political Weekly, 55, pp. 22-27.

Datta Chaudhuri, T. (1989). A theoretical analysis of the informal sector. World Development, 17 (3), 351-355.

Grinols, E. L. (1991). Unemployment and Foreign Capital: The Relative Opportunity Costs of Domestic Labor and Welfare. Economica, 58, 107-121.

Gupta, M. R. (1993). Rural-urban migration, informal sector and development policies A theoretical analysis. Journal of Development Economics, 41 (1), 137-151.

Gupta, M. R. (1998). Foreign Capital and Technology Transfer in a Dynamic Model. Journal of Economics, 67, 75-92.

Harris, J. R., & Todaro, M. P. (1970). Migration, Unemployment and Development: A TwoSector Analysis. The American Economic Review, 60, 126-142.

Jones, R. W. (1965). The Structure of Simple General Equilibrium Models. Journal of Political Economy, 73 (6), 557-572.

Jones, R. W. (1971). A three-factor model, in theory, trade, and history. In J. N. Bhagwati, Trade, Balance of Payments and Growth (pp. 3-21). Amsterdam: North-Holland Publishing.

Joshi, H., & Joshi, V. (1976). Surplus Labour and the City: A Study of Bombay. Oxford University Press.

Krugman, P. R., Obstfeld, M., & Melitz, M. J. (2018). International Trade: Theory and Policy (11 ed.). Boston: Pearson.

Salvatore, D. (2013). International Economics (11 ed.). Wiley.

Sarkar, A., & Ghosh, M. (1989). A model of the informal sector. Indian Statistical Institute Working Paper.

Sen, A. K. (1974). Informational bases of alternative welfare approach: Aggregation and income distribution. Journal of Public Economics, 3 (4), 387-403.

Solow, R. M. (1956). A Contribution to the Theory of Economic Growth. The Quarterly Journal of Economics, 70, 65-94.

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Stiglitz, J. E. (1982). The structure of labour markets and shadow prices in LDC’s, in Sabot, ed., Migration and the labour market in developing countries. Westview Press, Boulder, CO. 13-64.

MATHEMATICAL APPENDIX (1) (2) (3) Eq. (2) can be rewritten as: (2.1) Taking the total differential we get, (Since

and

)

Similarly, differentiating eq.(1) and eq.(3) we get, (1.1) (3.1) Plugging the value of

in eq. (1.1) and eq. (3.1) we get, &

(5) (6) (7) Taking the total differential of eq. (6) we get, (Since

)

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Similarly totally differentiating eq. (5) and eq. (7) we get, , where

(5.1) (7.1)

Using Cramer’s Rule we get,

Here, By assumption,

(4) Eq.(4) can be rewritten as (4.1)

Totally differentiating eq. (4.1) we get,

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STATISTICAL APPENDIX Month

Unemployment Rate (%) India

Urban

Rural

Nov 2020

6.51

7.07

6.26

Oct 2020

6.98

7.15

6.90

Sep 2020

6.67

8.45

5.86

Aug 2020

8.35

9.83

7.65

Jul 2020

7.40

9.37

6.51

Jun 2020

10.18

11.68

9.49

May 2020

21.73

23.14

21.11

Apr 2020

23.52

24.95

22.89

Mar 2020

8.75

9.41

8.44

Feb 2020

7.76

8.65

7.34

Jan 2020

7.22

9.70

6.06

Dec 2019

7.60

9.02

6.93

Table 1: The unemployment rate in India-Rural and Urban States

Total out migrants, (based on place of the last residence, 0-9 years)

Number of out-migrants by major destination states

Maharashtra

Delhi

Gujara t

Punjab

Others

Number of return migrants Due to COVID 19 and lockdowns

Number of districts for which return migration

Bihar

31.88

3.27

5.22

2.52

1.78

19.08

23.6

32

UP

50.46

12.11

9.69

5.37

3.02

20.28

17.48

31

Rajasthan

14.1

2.36

0.98

3.30

0.70

6.75

12.09

22

MP

12.19

3.48

0.57

1.50

0.16

6.49

10.72

24

Odisha

6.26

0.71

0.22

1.18

0.06

4.09

2.19

4

Jharkhand

6.92

0.64

0.38

0.38

0.11

5.41

1.1

3

Table 2: Volume and rate of regional migration in India

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FEMINIST ECONOMICS AND POLICY: A POST-COVID PERSPECTIVE - Bhavya Pandey, Radhika Khandelwal and Treesha Lall1

I. INTRODUCTION Feminist economics pushes for an alternate economic framework centred on the concept of care. It focuses on equity and social justice as opposed to the neo-classical, free-market principles that form the basis of the current economy. The modelling of the theory on these lines finds motive in the fact that women are consistently pushed to the bottom rungs in the current economic set-up. This happens because; Firstly, the economy, interlinked with societal structures, assigns gender roles to different fields of occupation. Women, in this case, are generally assigned the jobs of ‘care-givers’, secondary players in the economy while the men are ‘providers’, the primary players, model the ‘Homo Oeconomicus’, on whom basic economic theoretical concepts such as rational choice, resource allocation and efficiency are built. This model seldom accommodates women’s roles in the economy and so; The economy rewards tangible productivity, linking labour with output. Therefore, supporters of the capitalist economy such as healthcare, education and unpaid are generally underpaid and undervalued. Women engaged in these fields are neither financially reimbursed for their roles in the economy and nor are adequate policy measures being taken to induce them into the formal job market.

While feminist economics is not a comprehensive field of study as much as it is an integration of different perspectives, their guiding principles reflect a need for gender equality in economics. This principle, its manifestations in several different economic theories and its implications and needs in the Indian economy post-COVID in particular, will be scaled through the length of this paper.

This paper aims to establish the theories and rationale of feminist economic models in light of the COVID-19 pandemic and the deepening of existing inequalities that ensued as a result of it. By way of examining the (albeit unconventional and broadly defined) feminist school of economic thought, the paper draws parallels with the background of traditional schools of economic thought and critiques them. It aspires to identify the contribution and potential of a gender lens towards analysing responses to the pandemic and further corroborates the need for gender-sensitive economic, development, and public policy in order to emerge from the pandemic to a better aligned socio-economic social structure. Lastly, the paper sets out to illuminate the potential of feminist economic thought for the re-development and fundamental revisiting of the concept and connotation of “the economy” and further de-patriarchalizing economic activities, growth, and decisions.

The findings of this paper are based on secondary data analysis and are a result of a blended approach of both qualitative and quantitative research methods. The methods undertaken involve an exploratory, evidence-based literature review, examination of current trends, and 1

Daulat Ram College, Delhi University

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inference from data-based observations. This in-depth study employs a contextual theoretical approach grounded by the deconstruction of models, in order to identify and respond to the intersections of gender and other micro, meso, and macro-level economic issues with special emphasis to the COVID-19 pandemic.

Our paper offers a critical review of the theoretical literature on the relationship between the production of scientific knowledge and its use in policy formulation and implementation -extensive academic output, using a diversity of approaches and analytical frameworks. The purpose of this article is to offer a literature review on the subject of feminist economics and policy, based on selected authors, and to discuss their arguments and some of the main theoretical approaches to explain or back the relationship between the production of scientific knowledge and its use in policy formulation and implementation -- with the specific backdrop of the needs of a post-pandemic world in mind.

On the one hand, economic development alone is insufficient to ensure significant progress in important dimensions of women’s empowerment, in particular, significant progress in decision-making ability in the face of pervasive stereotypes against women’s ability. On the other hand, women’s empowerment leads to improvement in some aspects of children’s welfare (health and nutrition, in particular), but at the expense of some others (education). This suggests that neither economic development nor women’s empowerment is the magic bullet it is sometimes made out to be. In order to bring about equality between men and women, in the view asserted by our conclusions, a very desirable goal in and of itself, it will be necessary to continue to take policy actions that favour women at the expense of men, and it may be necessary to continue doing so for a very long time. And especially in the context of the pandemic, the paper does not limit itself to calling for gender-sensitive policies and initiatives but also provides for a checklist for such policy-formation in terms of the main pillars backing the philosophy and rationale behind policy-making, going forward.

II. BACKGROUND AND MOTIVATION That the participation of women in the labour force is much less than that of men, is no unknown fact. Labour force participation (LFP rate) data released by the National Sample Survey Organisation of India (NSSO) shows that the rate has been in fact on a downward trend from 30.28% in 1990 to 20.53% in 2019, except for the few years around 2005 when the rate hovered around 31%, as a result of which only nine countries of the world, including Syria and Iraq, currently have a lower proportion of working women than India. In India, among urban women who do work, domestic cleaning work is the second most common profession after textile-related jobs, the periodic labour force survey (PLFS) data published by the NSSO shows.

While some of the falls in women’s workforce participation are explained by higher rates of higher education enrolment of women - as more than 50% representation was of women in Undergrad, M.Phil and PhD degrees in 2018-19, indicating that more young women are in higher education rather than working or looking for jobs, the data also points to a fall in working rates for older women. Among women in the prime working ages of 30-50, more than two in three women are not in the workforce, with the majority of them reporting that they are “attending to domestic duties only".

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Figure 1: Country-wise data of Labour Force Participation Rate (in %) as of 2019 Source: World Bank Development Indicators

The final facet to the current problem has been posed by the COVID-19 pandemic and its economic toll on women. As UN Women (2020)22 puts it,” For countless women in economies of every size, along with losing income, unpaid care and domestic work burden have exploded.” Globally, 70% of the frontline workers and healthcare workers, who are battling it out in the face of the pandemic, are women. And yet, they are nowhere close to being at par with their male counterparts. This calls for an immediate, urgent, targeted response to the socioeconomic crisis in the wake of the pandemic, that is rooted in the principles of gender budgeting, female workforce participation, and feminist economic approaches to policymaking and implementation.

This paper stems from an observation about the skewed narrative of relatively ‘mainstream’ discourse in the domain of Economics -- one that does not take into account the gendered aspect of theory and schools of thought. As aptly summarized by Dandekar (1982)33, “Economic development is generally conceived and defined to mean growth in per capita Gross Domestic Product. It follows that to contribute to economic development, women must engage themselves in what is called 'economic’ or 'gainful' activity as distinct from 'household' or 'non-market' activity. In other words, for full integration of women in economic development, women must enter the 'labour force' on an equal footing with men.”

Hence, deriving from all of the above motivations and observations, the objectives of this study are manyfold: -

Providing some broad parameters to identify the status of women in the Indian economy, Analysing the female labour force participation in India, and juxtaposing it with the gender-budgeting initiatives in order to evaluate the performance of the same, Relooking at the parameters delineating the status of women in the Indian economy, by taking into account the effects of the COVID-19 pandemic, Establishing the theories and rationale of feminist economic models in light of the COVID-19 pandemic to provide for a checklist for such policy-formation in terms of

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the main pillars backing the philosophy and rationale behind policy-making, going forward.

Figure 2: Women and men living in poverty in 2030 Source: UN Women

III. DIMENSIONS OF THE ISSUE This study aims to establish a relationship between the theories and rationale of feminist economics models in the light of the pandemic, COVID -19 and the critical analysis of the existing inequalities that it has created. We understand how the pandemic is based on the deterioration of gender gap rates and worsen the parallels amongst the traditional school of thoughts.

To get into the thorough scrutiny of our model, we have emphasized our study on the threefold levels, considering the paradigm shifts in the economy.

III.1. Household Level This level of analysis deeply encourages the study of gender budgeting and its impact at the household level. It focuses on microeconomic analyses at the grass-root level by defining the problems and the factors involved in the exploitation of women, in particular. The study aims to be covering societal issues - from caste or gender-based discrimination to the negligence of women participation in the workforce.

III.2. State Level Under this understanding, a scrutiny of the austerity politics of the nation, proposed public policy frameworks, and the performance of the gender-driven policies is undertaken in order to delineate the status of decision making that can positively impact women. Further, the scope and extent of intervention in light of the COVID-19 crisis is explored.

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III.3. Macroeconomic Level This level analyses gender disparities and biases in workplaces, industries and other sociomacroeconomic dimensions. Here, factors such as collective gender wage-gaps, occupational segregation and the shadowed care economy are discussed.

IV. METHODOLOGY The explorations of this paper depend on secondary data analysis and are a consequence of a mixed methodology of both subjective and quantitative examination strategies - utilizing a descriptive framework. The strategies embraced include an exploratory assessment of current economic indicators and derivation from contemporary policy decisions. This in-depth study employs a contextual theoretical approach grounded in the deconstruction of models, to recognize and react to the convergence of gender and development issues, and finally contextualise an ideal approach for India to address the same. The data analysed has been sourced from multiple United Nations entities, the World Bank Development Indicators and that released by the Ministries of Women and Child Development, and Statistics and Programme Implementation, Government of India.

V. LITERATURE REVIEW AND ANALYSIS V.1. Household Level Women in India represent 29 per cent of the labour force, down from 35 per cent in 2004. More than half of the work done by women in India is unpaid, and almost all of it is informal and unprotected. Women are not well represented in most sectors, including business leaders.

Though they comprise almost 40 per cent of agricultural labour, they control only 9 per cent of land in India. Women are also shut out of the formal financial system. Nearly half of India’s women do not have bank or savings accounts for their own use, and 60 per cent of women have no valuable assets to their names.

Women participation in the labour force has always been underestimated. Due to the indicators that affect Feminist economics are immeasurable, it becomes really difficult for economists to understand and analyse the criticalities of the same.

The disparities at the macroeconomic level have only grown. The economic opportunity gap has worsened, widening to 257 years, compared to 202 years last year. The greatest challenges to closing this gap are women's under-representation in emerging roles, such as cloud computing, engineering and data and Artificial Intelligence.

"Violence, forced marriage and discrimination in access to health remain pervasive. The situation and the trend are more positive in terms of gender gaps in education... But a large difference persists for literacy rate; only two-thirds of women are literate compared with 82 per cent of men," the WEF has said.

India ranks high on the political empowerment sub-index, largely because the country was headed by a woman for 20 of the past 50 years. But, female political representation today is low as women make up only 14.4 per cent of Parliament (122nd rank globally) and 23 per cent of the cabinet (69th), the report said.

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The factors that have led to the deterioration of women participation in the labour force is ranging from domestic violence, social exclusion and other economic indicators which are immeasurable.

Despite the progress, women and girls continue to face multiple barriers based on gender and its intersections with other factors, such as age, ethnicity, poverty, and disability, in the equal enjoyment of the right to quality education. This includes barriers, at all levels, to access quality education and within education systems, institutions, and classrooms.

The other factors that decentralize the gender inequality in the availability of resources and education include: ● ● ● ●

harmful gender stereotypes and wrongful gender stereotyping child marriage and early and unintended pregnancy gender-based violence against women and girls lack of inclusive and quality learning environments and inadequate and unsafe education infrastructure, including sanitation ● poverty

Figure 3: For the Sixth Year in the row, the underrepresentation of women and women of colour in senior management cannot be explained by attrition alone Source: Women in the Workplace 2020, LeanIn.Org and McKinsey, 2020 The above figure shows that at the beginning of 2020, the representation of women in the corporate world was trending in the right direction. This was most pronounced in senior management: between January 2015 and January 2020, representation of women in seniorvice-president positions grew from 23 to 28 per cent, and representation in the C-suite grew from 17 to 21 per cent. This highlights the fact that the progress towards gender parity still remains slow.

Furthermore, especially in the context of India, the COVID-19 pandemic has further augmented the issues faced by women in households. Another recent analysis by McKinsey Global Institute noted that women are more vulnerable to COVID-19-related economic effects because of the already existing gender inequalities -- it estimates that female job loss rates owing to COVID-19 are about 1.8 times higher than the male job loss rates globally, at 5.7% versus 3.1%, respectively. Earlier this year as well, UN Secretary-General António Guterres noted that “COVID-19 could reverse the limited progress that has been made on gender

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equality and women's rights”. According to UN Women44, an estimated 740 million women are employed in the informal economy. In developing nations, such work constitutes more than two-thirds of female employment - such as in India.

V.2. Macroeconomic Level It will be prudent to analyse gender biases in the workplace as a mirror and consequence of the hegemonic patriarchal narrative that Indian society largely supports. While progress in the development of the status of women has been exponential, especially in terms of higher education (ERIC, 2016) unacceptable gender disparities and stereotypes continue to largely prevail in India. The most obvious of this is the labour-force participation of women in India, which dropped to 21.36% in 2016 from an already low 25.68% in 2010 (World Bank data).

Resulting in significant wage gaps, a lack of promotions and high-level jobs for women, many sources identify gender bias in the workplace as spills from structural gender roles. For example, in leadership roles in the workplace, suitable characteristics are often assumed to be aggressiveness, confidence and decisiveness, or characteristics people associate with ‘masculinity’. Women, in contrast, are assumed to be too emotional and weak to assume leadership responsibilities. Women are also expected to primarily devote their talents to ‘caregiving duties in the household. The findings of research conducted amongst construction workers in Tamil Nadu (Barnabas, Anbarasu D, Pauls S, 2009) finds that men earn within the range of Rs. 71 to 250 while women earn in the range of Rs. 51 to 160 per day. Amongst the workers, it was found that both men and women subscribed to seemingly untrue and baseless stereotypes about women being ‘afraid of heights’ and physically incapable of handling construction work.

Another aspect of gender bias in the economy is occupational segregation. This refers to an inherent differentiation between the occupations of men and women in the economy, leading to differentiation in opportunities and rewards available to them (Prasad, Pratap, 2017). This occurs as horizontal and vertical occupational segregation (World Bank), where horizontal refers to the differentiation of sectors of occupation between the two sexes in the economy and vertical refers to the differentiation in positions held by men and women in the same sector.

The ORF summarises reasons for the ‘skills gap’, which ultimately leads to disparate occupational segregation. ● Sociocultural - Factors that shape aspirations and ambitions based on biased expectations and stereotyping of a woman’s ability and responsibility. ● Informational - There is a considerable gap in the accessibility of information and opportunities provided to men and women in education and jobs. For example, there is evidence of a systematic lack of investment in the education and skill développement of female children in India (Barcellos, Leandro S. Carvalho, Lleras-Muney, 2014). ● Institutional - Institutional factors such as the safety, mobility and time restraints on women hamper their abilities to perform at par with men based on the market scale of efficiency.

There is also evidence that points to negotiation failures and a lack of bargaining power as a prevailing reason for a significant wage gap between men and women. Menzel and Woodruff (2019) allude to the ‘monopsony power’ of employers in the labour markets in Bangladesh’s garment sector. Hirsch provides an overview of this argument, explaining that a lack of women’s mobility and a general limitation in geographical job searches give women a significant disadvantage in bargaining, making female labour supply ‘less elastic’ to wage cuts.

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This lack of mobility pertains to women’s domestic responsibilities towards the dependant members of the households such as children and the elderly. On the other hand, in ‘Women Don’t Ask’, Linda Babock draws attention to the internalization of socio-cultural oppression among women that prevent women from attaining top spots in the formal workspace.

Many experts claim that the selective traits required to advance in the corporate set-up of the economy have been designed to reward the dominant of the two sexes, as the primary ‘resource holder’ (B. Coric, 2018) (Cabeza, Johnson and Tyner). The Global Human Capital Gender Advisory Council report (2008) refers to this as a ‘mini-me’ syndrome amongst the men who occupy high-level positions in the economy creating structural segregation through succession planning. For example, according to Manning, there is a bias in the workplace against women as they often remove themselves from the workforce at the peak of their careers to provide for their families. Moreover, a majority of these women later have difficulties getting inducted back into the workplace (ILO,2018). “Our labour market severely punishes those who at any point in their lives sacrifice career for family”, Manning adds. The economy, chained to modern society continues to be, as Coric puts it, designed ‘for men by men’, skewed in performance appraisal and expectations.

V.3. The Care Economy A common thread that runs through the analysis of gender inequality in the formal economy is a systemic lack of representation of women in these sectors. Whilst analysing widespread issues such as occupational segregation and ‘the pipeline problem’, labour markets were found to be un-accommodative towards women, mainly owing to implicit instabilities from unpaid housework, care work and reproductive responsibilities, leading to a lack of mobility, societal resistance and occupational segregation.

A separate lens, however, must compel us to argue against the consistent overlooking of female-centred economic sectors instead of pushing for female representation in male designed and dominated areas. Here, paid and unpaid care work emerges as a major point of contention.

Folbre (2011) defines Care work by its functionality: activities that are personal or activities that benefit those “lacking political voice” such as children, the elderly and the disabled. Care work thus encompasses activities that are domestic and unpaid as well as those that are underpaid extensions of the formal economy.

In 2018, 8.9 billion hours were spent by individuals in unpaid care work (ILO, 2018) in Asia and the Pacific. About 80% of this unpaid care work is done by women. Reports by ILO (2018) show that women in India, specifically spend 297 minutes per day engaged in care work, as opposed to the 31 minutes spent on such activities by men. The same report shows that the nature of the care work that engages women is more time consuming and physically strenuous as compared to those performed by men.

The statistics on unpaid care work explain some of the extensive gender-based disparities in the Indian economy. For example, there is a negative causal relationship between the amount of unpaid care work performed by the female population of a region and the female labour participation rates there.

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Figure 4: The global dimension of unpaid work: regional snippets Source: ILO Calculations, based on a study by Charmes, 2018

Figure 5 Note: Left graph presents the predicted value of female activity rates as a percentage of total women in the working-age population by time devoted to unpaid care work by women, controlling for GDP per capita, fertility rate, female unemployment rate, female years of schooling, urbanisation rate, maternity leave and regional dummies. The right graph presents the estimated female share of the labour force by the female-to-male ratio of time devoted to unpaid care activities, controlling for GDP per capita, fertility rate, urbanisation rate, maternity leave and gender inequality in unemployment and education Source: World Bank (2014), World Development Indicators and OECD(2014), Gender, Institutions and Development Database This explains several of the most severe problems with women in the economy in India. The expectation for women to take up care work and domestic work hampers their ability to participate in the labour force because of the ‘double burden’, in which they must engage in

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domestic work as well as participate in the market. This takes a toll on women’s mental and physical health and well-being (Sengupta and Sachdeva, 2017). Moreover, this limits women to engage in primarily vulnerable and unstable jobs that are usually undervalued and underpaid.

This section of women includes daily wage workers, rural agricultural labourers and urban domestic workers. Domestic workers in India may be a prime example of a lack of safety mechanisms available to women in an ‘unskilled’ occupation (Chandramouli, Kodandarama).

Domestic workers, who are predominantly women, are usually uneducated and migrants from rural agricultural sectors relocated to urban labour markets. These women are often uneducated and poor. These women do not have access to formal mechanisms such as labour unions and legislations or complete market information, as a result, they are generally underpaid, denied safe workspaces and made to work long hours. Domestic workers also often face violence, harassment and sexual abuse (WEIGO).

Owing to occupational segregation and over-arching patriarchal conditioning, paid care work is also predominantly constituted by women. Paid care work consists of paid services such as teaching and healthcare services. Even though with India’s fast predicted ageing population, care work is essential, it is poorly remunerated and formalised in the economy. India care workers grapple with poor infrastructure, pay and regulation in their jobs.

Feminist economists push for more recognition of the shadowed care economy. They believe care work produces one of the most essential resource bases of the formal capitalist economy: social reproduction. Social reproduction refers to the act of caring for oneself and for others. It refers to the activities that enable persons to contribute to the workplace such as education, nursing, emotional support and nutrition. However, current economic hierarchies push care work and social reproduction to the bottom rungs, rewarding these activities the least. In India, owing to strong patriarchal traditions, care work is almost fully unpaid and unrecognised. This is a primary reason for inequality in the economy, with special reference to gender inequality.

In India, however, this may prove to be more of a challenge than with other nations of similar demographics. Patriarchal norms in India continue to be strongly held by households and society, showing minimal demand for care workers to be given monetary acknowledgement.

(Choudhary, Tripathy and George, ESAF, 2009) However, it must be noted that care work and social reproduction is intrinsically linked to the formal economy. For example, ‘The crisis of social reproduction’, essentially the lack of proper remuneration and infrastructure in care sectors, shows direct links to the ‘crisis of work’ and the rise of automotive industries that may extensively cut back employment. India is a preeminent target of the work crisis (Hester, 2018). With automotive industries pushing people out of employment, care work is one of the largest employing sectors in India (ORF, 2020). To raise the standards of living of the people within the country, it may be essential to re-centre economic structures around the care economy. According to estimates, the amount of unpaid care work undertaken by women in India could measure up to the creation of 11 million potential jobs. This could be leveraged by policymakers to make more care-centred transitions in the Indian economy (ORF, 2020).

V.4. The State Level Gender equality is a basic, albeit imperative sociological indicator for any society. Its standard is additionally found in the SDG 2030 Agenda where the signatories, including India, reaffirmed their responsibility to mainstreaming gender improvement and guaranteeing equivalent portrayal of women in political and economic dynamics. Strengthening, expanding

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and encouraging the gender budgeting standards for asset distributions as a basic essential for India to accomplish progress towards the SDG-5 on gender equality. In view of a survey of the principles of financial devolution and central government budget reports, it is obvious that gender-sensitive concerns remain a dismissed part of India's financial strategy. Gender planning ought to get sufficient concentration and be concurred high need by the fifteenth Finance Commission of India, in order for better indicators and results to be realised at the grassroots level.

According to Rudra (2018)55, “gender budgeting has both intrinsic and instrumental relevance.

GRB is critical for eliminating gender inequalities with significant improvements in social, educational, health and economic indicators of a country.” Furthermore, it translates into a “systematic gender-differentiated impact of fiscal provisions, programmes and policies”.

However, the implementation of gender budgeting is yet to be made ‘mainstream’ in the context of India - no more than seventeen Indian states have gender budgets, the horizontal and vertical devolution of the states’ share of fiscal transfers (by the Centre, as mandated by the Finance Commission) has no mention or factoring in of the gender lens, as well as, gender inequalities are yet to be tackled by the intergovernmental fiscal mechanisms. These issues have been highlighted in the recent past by researchers such as Lahiri (2019)66 and Rudra (2018).

Figure 6: Sequential Phases of Gender Budgeting Source: Gender Budgeting Handbook, Ministry of Women and Child Development, Government of India, 2015 It is not unknown that Indian society has been grappling with ‘magnitudinal’ disadvantages to women in all walks of life for many years now. With more than 60 per cent of women getting married before 25 years of age in India, the UNDP Gender Development Index ranks India among the lowest. In the Gender Inequality Index, meanwhile, India is ranked at a dismal 125th place out of 159 countries in 2018-19.

The GII is an inequality index released by UNDP that measures gender inequalities in three important aspects of human development: ● Reproductive Health, measured by maternal mortality ratio and adolescent birth rates; ● Empowerment, measured by the proportion of parliamentary seats occupied by females and proportion of adult females and males aged 25 years and older with at least some secondary education; and ● Economic status expressed as labour market participation and measured by the labour force participation rate of female and male populations aged 15 years and older.

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Table 1: Gender Differentials in India’s Key Development Indicators Source: Rudra (2018) In the Gender Inequality Index (GII), India is at 122 out of 162 countries. The report of 2019 forecasts that it may take 202 years to close the gender gap in economic opportunity. These observations highlight not only the dire issues related to state-level policies in the address of gender issues through the lenses of development and economic policy but also the exclusionary nature of the measurement of our ‘welfare’ indicators - the most prominent example being that of GDP. According to the World Economic Forum (2016), “Women do a majority of this unpaid work (on average in the OECD countries women do about twice as much, 150 minutes a day more, as men in the home). Because it is not measured, it is generally overlooked by economic policy. The omission of housework and caring (part of “home production” in the technical language) is so familiar now that few people realise there was a vigorous debate about whether or not to include it in GDP when the modern system of national income accounts was created in the 1940s.” In a 2011 study, the OECD concluded that the inclusion of women’s contribution in terms of “home production would add between 20% and 50% to the GDP of its member countries.”

At the state level, it is also essential to bear in mind workplace policies, especially at a time marked by crisis and uncertainty, the choices companies and institutions make today in terms of their policies, will have consequences on gender equality for decades to come. According to McKinsey’s Women in Workplace report of 2020, “if companies make significant investments in building a more flexible and empathetic workplace—and there are signs that this is starting to happen—they can retain the employees most affected by today’s crises and nurture a culture in which women have equal opportunity to achieve their potential over the long term.” However, due to the challenges created by the COVID-19 crisis, as many as two million women are considering leaving the workforce, only in the United States. This response is expected to be further augmented in informal and unregulated workplaces, especially in the Indian context where women’s participation is determined by societal and familial constructs.

In order to create better and more inclusive workplaces, both men and women should be offered flexi-time, job-sharing opportunities, and work-from-home options as much as possible, in

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order to be able to take on the ‘new normal’ better and share workloads in homes and communities as well as workspaces. Organizations should additionally try to incorporate onsite childcare facilities wherever possible. Lastly and most importantly, organizations need to deploy systems and policies to facilitate women’s navigation through a system designed for men by encouraging the need to raise concerns and ensure they are being addressed throughout organisational frameworks and networks. Maternity leaves and other government workplace policies have done little to correct these biases and hence, have been unable to successfully raise the FLP (Pande, 2017). Moreover, rather than empowering women, many gendersensitive workplace policies firmly place the exclusive responsibility of shouldering the ‘double burden’ onto women.

According to research by Pande (2017), policymaking has not been successful in bringing about the improvement in gender indicators as expected, especially in public institutions. Most of all, policy changes despite decades of interventions and policy catalysis have been unable to bring about changes in the deep-rooted regressive behavioural and social norms. Hence, for the effective implementation of a robust gender budgeting or gender lens expenditure ideal, India needs broader support along with the constitutional principles and institutional mechanisms to steer policy-making and coordination at all levels of federal governance.

Lastly, a paper by Griffin (2015), “ -- adopting a discursive approach to gender and governance that situates gender centrally in understanding governance discourses and their reproduction of common sense (about what people do, how they labour, where they invest and so on), -- argues that the governance of crisis in the contemporary era, in particular the various actors, institutions, policies and ideas that have sought to describe and ‘contain’ the global financial crisis, are gendered,” further reinforcing the rationale for looking at crises and austerity through a gendered lens.

VI. INTERLUDE VI.1. COVID-19 Perspective Emphasizing the issues ranging from healthcare, domestic violence to female education, we can draw conclusions about how the pandemic has affected the female population within these domains.

The COVID-19 pandemic has put some health systems under immense pressure and stretched others beyond their capacity. As such, responding to this public health emergency and successfully minimizing its impact requires every health resource to be leveraged. Failure to protect health care in this rapidly changing context exposes health systems to critical gaps in services when they are most needed, and can have a long-lasting impact on the health and wellbeing of populations. Care sectors such as nursing, healthcare and old-age care need greater investment now that the magnitude of epidemic and disease spread is projected to rise.

There is a need for extra efforts to strengthen the public health system and increase health budgets to optimise service delivery and health facilities ensuring women safety and security.

According to the FICCI-EY study on the economic impact of the pandemic on the healthcare sector, virus outbreak had resulted in a 70-80 per cent drop in footfall, test volumes, and a 5070 per cent drop in revenues at the end of March. The study also revealed that the sector is expected to witness short-term operating losses. The maximum impact of the pandemic has been felt by the women. Therefore, it is said that we still have a long way to go.

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There are clear links between COVID-19 and domestic violence, which also impact the economic and social crisis. Multiple reports, however, suggest that such measures are increasing the incidence of domestic violence and not only in number but also in severity. We find that layoffs, loss of income, extended domestic stays, and exposure to habits due to stayat-home orders are driving up the incidence of domestic violence. Moreover, these domestic violence increases are driving economic and social crises due to the form and severity of the violence, the burden placed on government, a crisis of resources, and decreases in the productivity of workforces. Domestic violence increase resulting from COVID-19 is an indirect driver of economic and social crisis. Social isolation has led the abusers to take more advantage of it.

Poverty is one of the most important factors for determining whether a girl can access and complete her education. Studies consistently reinforce that girls who face multiple disadvantages — such as low family income, living in remote or underserved locations or who have a disability or belong to a minority ethnolinguistic group — are farthest behind in terms of access to and completion of education.

Violence also prevents girls from accessing and completing education – often girls are forced to walk long distances to school placing them at an increased risk of violence and many experience violence while at school. Most recent data estimates that approximately 60 million girls are sexually assaulted on their way to or at school every year. This often has serious consequences for their mental and physical health and overall well-being while also leading to lower attendance and higher dropout rates. An estimated 246 million children experience violence in and around school every year, ending school-related gender-based violence is critical. Adolescent pregnancies can be a result of sexual violence or sexual exploitation. Girls who become pregnant often face strong stigma, and even discrimination, from their communities.

VI.2. Feminist Economic Theories and Models Feminist economics provides a consolidation of various theories and proposals with a central motive: to provide an economic framework that centres on equality, sustainability and care.

Some pivotal transformational theories suggested in feminist economics are as follows: VI.2.1. The Care Diamond According to the UNDP, women’s labour force participation rate is “M-shaped”: it drops when they are in their late 20s and early 30s and are typically caring for preschool children. The reason for the M-shaped curve is quite clear. Women tend to quit work first at marriage, and then after the birth of their first child. Nearly half of the women who have had full-time permanent jobs leave work after marriage, and again, nearly half of those who retained fulltime work quit the labour force after the birth of their first child. Thus, the percentage of nonworking women increases after the birth of their first child. At the same time, many also raised the difficulty of balancing work and family obligations.

This suggests that even if childcare arrangements are available, the job makes it impossible for women to choose to raise children while working. These expectations include long working hours, long commuting time, and uninterrupted work (no maternity leave). There are also concerns about the quality of work. However, small corporations are exempt from these regulations. Also, there is considerable pressure on women, and especially men, not to take the full year off. The take-up rate of paternity leave is abysmally low at 1.56 per cent, while that

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of maternity leave is 89.7 per cent of those eligible. Some suggest that this is because enforcement of these family-friendly policies is weak and that the government uses “administrative guidance”, rather than sanctions and punishments, to encourage employers. n, the tax and social security systems reinforce women’s secondary role in the labour market by setting a limit on how much they can earn before losing their status as a dependent spouse and therewith their exemption from paying tax and social security contributions. As a result, the forces that bind women to the caregiver role are multiple and reinforce each other. They are further reinforced by the state’s expectations that a woman will be a housewife, a caregiver and a low-wage worker.

VI.2.2. Multi-Sector Model A multisector model is used in principle, “to study the allocation of resources across different economic activities”. As any country and its economy grows, its production, economic activities and employment structure transition from agriculture to industry and services, with services eventually claiming the vast majority of the country’s employment engagement. “One class of institution-based multi-sector models is motivated by the fact that official statistics only report formal market activities”. However, there are plenty of studies that show that a large fraction of resources in any economy is allocated to activities in the informal sector and activities at home. These studies are based on the time-use surveys conducted by individual countries, such as the United Kingdom and the United States, and on a cross-country survey conducted by the World Bank in 2000. Home production is substantial in most of the time-use surveys. The production activities at home and in the informal sector can all potentially be accounted for so that they can enter the official statistics and formally contribute to the value of economic development, which becomes even more important for developing countries. The multisector model by Stephen Parente, Richard Rogerson, and Randall Wright (2000), is one that fits the bill aptly and says that goods can be produced in the nonmarket sector or the market sector. They show that distortionary policy that affects capital accumulation has a larger impact on measured GDP in their multisector model than in the usual one-sector model. Hence, these models add to the understanding of the dynamics of women’s participation in the economy and their true contribution to economic development.

VII. RECOMMENDATIONS 1. Integration of gender lens in the Local Self Government Bodies of the nation, under the Panchayati Raj system, in order to call for greater independence and involvement of women in financial and political decision-making. Further, put in place unions for domestic workers and other paid care workers in all areas.

2. Put in place an independent central and state-level mechanism for increased investment in the care economy.

3. Exploring gender-sensitive resource transfers by way of integrating gender in the horizontal and vertical devolution of the states’ share of fiscal transfers.

4. Calling for multi-stakeholder involvement towards gender lens fiscal decisions budgeting and investing in particular, on both government policy and private engagement ends.

5. Applying gender mainstreaming across national and local policies that rely on the creation of capital infrastructure. Ensure government welfare schemes such as safety, public transportation, old age care and healthcare that reduces women’s unpaid care burdens and furthers women’s skill development and career prospects.

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6. Offering gender-sensitive training to key city stakeholders (police, transport operatives, urban planners, and national and local government officials).

7. Conducting gender-lens data collection and analysis across relevant campaigns, policies, projects, and organisations.

Figure 7: Gender Sensitive Policy Pillars Post- COVID Checklist: Authors (2020) VIII. CONCLUSION As the ongoing pandemic rages on globally and brings economies, cultures, and governments to their knees - the changes being discerned during this historic time are bound to impact the threads of the social fabric of the world as we know it. The decision of the government of India of extending assistance to women Jan Dhan account holders with direct cash benefit transfers, encouraging credit-lending to self-help groups in rural areas, and some state governments leading the distribution of period products in their districts through community collectives are a few of the many ways in which our country is pooling in its resources to help the women and girls of the last mile.

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Historically, crises have been instrumental in bringing about societal changes - be it women taking up jobs in factories when men had to fight during the Second World War or the increasing number of women breadwinners post the Great Recession. However, during the current pandemic, the shift towards more equitable caregiving wouldn’t come as a systemic or natural change, albeit it will need an intentional push to come about.

IX. LIMITATIONS OF THIS PAPER 1. Since the models we suggested are extracted from the other countries, it is extremely difficult to forecast their application and implementation in the long run. It is quite unfeasible to implement them in such a manner that it leads to gender equality providing women with better access to resources and more power.

2. The contribution by the women involved in the unpaid care work also gets negligent in the measurement of the economic indicators. This makes the analysis models proposed quite ambiguous and makes the policymakers rethink the gender budgeting plans.

3. The figures derived are from secondary sources. This doesn’t validate the fact that the analysis is perfectly accurate and reliable. Also, it makes the makeup of the gender budgeting plans irrelevant for the indicators whose figures that stand unknown to us.

4. Since not all the indicators are measurable and known to us for the computation of the gender budgeting, we still can’t rely completely on the models proposed by us.

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CONSUMPTION RESPONSE TO LIQUIDITY SHOCKS: EVIDENCE FROM THE INDIAN DEMONETISATION - Nirbhay Vadera Varma, Revant Aggarwal, Ritika Susarla1 ABSTRACT One of the most important dates in the timeline of India’s fight against black money was 8th November 2016, when the Indian government declared that 86% of the currency in circulation would cease to be legal tender. While the success of demonetisation in the fight against corruption is debatable, the exercise led to an acute shortage of cash in the economy, which posed a severe liquidity shock in the entire economy, especially for households. This affected households’ consumption in the immediate short run with lasting effects in the long run. Our paper uses the Consumer Pyramids data from CMIE to understand the impact of the demonetisation-induced liquidity shock on household consumption patterns and to evaluate the differential impact of demonetization on the poorest households of the country. The paper aims to evaluate the extent to which mechanisms such as financial infrastructure in the form of bank branches helped households deal with the effects of the liquidity shock. The paper also examines the role played by borrowing and dissaving, and exchange of new notes in mitigating the impact of the shock. The question is analysed using a fixed-effects model on the consumer pyramids data at the household level. We find that demonetisation did indeed reduce consumption for all households, however, the reduction in consumption was lower for the poorest households. We suggest that the lower reduction for the poorest households is due to these households already consuming very little and thus being able to deal with the impacts of the shock using the limited, over-the-counter exchange mechanism and ATM withdrawals.

Along with this, we also empirically verify that the presence of greater financial infrastructure helped households mitigate the impact of the shock on their consumption, this was especially true for the poorest households.

ACKNOWLEDGEMENT We are grateful for the continuous support of Professor SK Ritadhi of the Department of Economics at Ashoka University. His extensive feedback and guidance throughout the process of ideation, analysis and final presentation has been crucial in putting together this paper. He has kindly devoted hours of his time to us and his advice has been invaluable. We also thank the Centre for Economic Data and Analysis (CEDA) at Ashoka University for providing us with access to data from the Centre for Monitoring Indian Economy (CMIE).

I. INTRODUCTION & MOTIVATION Events that pose a shock to liquidity are common in developing countries. Natural disasters like floods or famines, unpredictable changes in monetary policy, devaluation of the currency ,and fiscal policy shocks are common occurrences in developing countries. While such macroeconomic shocks also induce liquidity shocks, it is often difficult to disentangle the liquidity-related impacts of such shocks from the other impacts they have on the economy.

However, the Indian demonetization, by virtue of being an unanticipated, exogenous shock, gave rise to a pure liquidity crisis whose impacts on the economy can be clearly measured and isolated.

1

Ashoka University

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The Indian government declared that high-value currency notes of 500 and 1000 rupees would cease to be legal tender from the 9th of November 2016, and would be replaced by new 500 and 2000 notes. This mammoth exercise aimed to combat corruption by bringing back undeclared wealth held as cash, and unaccounted money into the formal tax system, and to eliminate the scourge of counterfeit currency that was circulating in the economy. As a consequence of the resultant cash crunch, this exercise later aimed to catalyse digitization and make the Indian economy cashless (Gopinath et al. 2019).

Although demonetization predominantly targeted relatively wealthy individuals with substantial amounts of undeclared wealth as cash, it had significant impacts on the poor. In the short run, the demonetization policy acted as a negative macroeconomic shock to the economy seen by decreased output and employment, especially in the more cash-dependent informal sector2 (Lahiri 2020). Consumption and expenditure decisions of households, especially those dependent on cash for income, were impacted due to the short-term shortage of cash (Karmankar 2019). A shortage in supply of new notes only worsened these impacts, as people had to stand in long lines outside banks, possibly losing daily wage income. This manifested as a massive shock to consumption and liquidity for households, especially in the short run.

1

This paper aims to study the differential impact of the liquidity shock inflicted by demonetization on household consumption with respect to initial consumption levels and financial infrastructure. We further try to evaluate how borrowing and dissaving-related decisions impacted how different households dealt with this liquidity shock. Through our baseline regression on various kinds of consumption, we find that consumption is reduced for all households in the immediate short run. This impact, however, was less pronounced on poor households who have a higher cost of reducing consumption and were already close to their subsistence levels.

We examine the extent to which different households smoothened their consumption to deal with this shock by testing for borrowing and dis-saving related decisions pre- and postdemonetization. We find that borrowing does not increase post demonetization, but falls for the poor. This indicates that households were not able to borrow to smoothen their consumption after the shock; however, these results can also be attributed to the extensive margins on which borrowing data is measured in the given dataset. To further evaluate the mechanisms by which households dealt with this shock, we use a triple interaction regression model between a measure of the shock, the initial poverty level of a household, and their access to financial infrastructure in the form of banks. We find that the decline in household consumption was lower for districts with higher bank branch density, and this impact was more pronounced for the poor. We further analyse the mechanism through which bank branch density helped mitigate this shock.

The rest of the paper is divided into four sections, section (2) covers a brief review of previous literature on household consumption response to such liquidity shocks, section (3) describes the dataset and the variables we use for our analysis. Section (4) covers our empirical strategy and main findings. Section (5) concludes.

2

The undesirable increase in demand for MGNREGA jobs# points towards a sign of rural distress which is mainly encompassed by an informal cash dependent sector which was the worst hit by this negative economic shock (Lahiri 2020). This rise in demand for employment can be seen as a direct consequence of the economic shock on income of rural and informal workers

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II. THEORETICAL UNDERPINNINGS: DEMONETISATION AND THE ECONOMY Demonetization was a massive unanticipated shock to the economy. It had little success in achieving its stated goals while having imposed significant costs on the public3 (Lahiri, 2019).

This suggests that the liquidity constraint had the largest impact on private consumption expenditure, which is the largest component of GDP. Thus, this liquidity shock should have led to a drastic decline in economic growth, even though the official GDP statistics do not reflect this (Subramanian, 2019).

1

Several studies have shown the impact of income and liquidity shocks on household consumption. The most recent widespread economic shock, the COVID-19 pandemic, spending on household necessities increased by around 50%, grocery spending increased by 7.5% as compared to the previous year, and there were sharp reductions in expenditure on restaurants, retail, and all forms of travel, in America (Baker et.al, 2020). In developing countries, economic shocks at the individual level4 lead to a liquidity crunch for households as a result of the loss of household income. In Indonesia, Kim and Prskawetz (2006) find that an unemployment shock led to a 17% fall in educational expenditure5 . The wealth gap in an economy also plays an important role in determining household consumption levels. Amromin et.al (2018) found that the wealth distribution in the US shifted sharply leftward between 2006 and 2010, in the aftermath of the Global Financial Crisis - the share of households holding negative net worth increased from 16% to 24%6 . The 2013 US Government shutdown, which caused a large, unexpected drop in pay for two weeks for government employees, was a shock to liquidity for these employees. Analysis by Gelman et.al (2018) revealed that, at the margin, affected government employees spent about $0.58 less in response to each dollar of lost liquidity. Agarwal and Qian (2014) show that an unanticipated exogenous positive income shock in Singapore due to the Growth Dividend Program, which constituted a large one-time cash payout, increased average consumer spending by $0.80 for every $1 received during the ten subsequent months. Baker (2014) finds that liquid assets play a more important role than debt in the spending response to a shock.

2

3

4

Although liquidity and income shocks have negative consequences on households across the economy, the degree to which each household is affected, and the extent to which they are able to cope with the monetary effects of the shock depend largely on the mechanisms available to mitigate the effects of such shocks. In India, there are only around 14 commercial bank branches per 100,000 adults. Moreover, while around 70% of the Indian population lives in rural areas, only 37% of all commercial bank branches are in these areas (Wadhwa, 2018).

3

Although bank deposits grew by almost 6 million rupees, bank credit did not pick up in any significant way, but fell marginally on impact. There was a steep 15 million decline in the size of the labour force from November 2016-February 2017 (Lahiri, 2019).

4

Natural disasters and household specific risk factors such as mortality and illness.

5

However, they also find that death of any household member and a natural disaster have a positive and significant impact on the consumption of goods, while unemployment decreases food and nonfood consumption significantly. They attribute their results to the fact that coping mechanisms like assets, increasing labour supply and transfers, overcompensate the actual consumption loss due to an economic hardship.

6

They find that reductions in net worth due to a shock are associated with negative consumption growth rates, and conclude that differential access to financial liquidity plays a major role in consumption during an economic downturn.

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Narayan and Karmankar7 (2019) show that households that did not have a bank account prior to demonetization suffered more in terms of consumption, income and expenditure than households that had at least one bank account. Thus, the repercussions of the liquidity shock on each household differs based on access to mechanisms that help mitigate its effects.

Consequently, we are interested in analysing the differential impacts of the liquidity shock posed by demonetization, based on the availability of mechanisms such as financial infrastructure, to cope with the effects of the shock.

1

The degree of access to mechanisms that help mitigate the effects of economic shocks also affect the extent to which households can smooth their consumption in the face of liquidity shocks. Liquidity fluctuations cause income changes which should affect consumption.

Permanent income models hypothesize that consumers use savings to smooth income fluctuations, and should respond little to changes in income. However, it is not always likely that households have access to savings, especially in developing countries. Gelman et.al (2018) found that, during the liquidity shock of the 2013 US government shutdown, even workers with low liquid assets smoothened their consumption using low-cost methods8 to shift the timing of payments for committed expenditures. Hence, precautionary savings and insurance, in the form of government welfare programs, family labour supply and family networks play an important role in consumption smoothing (Jappelli and Pistaferri, 2010).

2

III. DATA We empirically analyse our research question using the Consumer Pyramids dataset from the Centre for Monitoring Indian Economy (CMIE). This dataset provides detailed estimates of data on the size and composition of Indian consumer markets, including data on household income, expenditure, assets and wide-ranging demographic data for a large sample of about 158,624 households. Data is collected thrice every year, in ‘waves’ or periods of four months, through a household survey that surveys a large panel of sample households repeatedly over time. The dataset gives us estimates for 315 towns, and state-wise data for 99 urban regions and 97 rural regions.

Consumer Pyramids is well-suited for the empirical analysis of our question for several reasons. The demographic data in this dataset are far more recent and up-to-date than Census data. Cross-tabulation of the data by household income groups, occupation, age and education composition helps in the analysis of the differential impacts of demonetization on different socio-economic groups. Most importantly, the dataset provides observations on a monthly basis. Since demonetization posed short term shocks to liquidity, this feature is key in analysing the impacts of the shock on liquidity. The sample contains more observations on urban households than rural households and hence is not equally representative of both. In order to account for this, observations are weighted to be representative of the population as a whole population, these weights are used in our empirical model for accuracy of results.

Another drawback of this dataset is that some important variables are defined on the extensive margin only, thereby hindering us from making inferences about the degree of change in these variables.

7

Narayanan and Karmakar (2019) run a regression studying the impact of demonetization on households in rural areas. Their results, described here, are reported with a high degree of confidence.

8

While some workers who were affected by the shutdown delayed mortgage payments, the others shifted credit card balance payments.

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To measure the differential impact of demonetization based on financial infrastructure, we require district-wise data on bank branches and bank branches per capita. Since the Consumer Pyramids dataset lacks data on this, we merged this dataset with the Basic Statistics Return (BSR) data from the Reserve Bank of India to obtain data on bank branches. The BSR does not have data for all districts, so we drop observations for these districts while analyzing the differential impact of bank branch density.

SUMMARY STATISTICS & KEY VARIABLES OF INTEREST Since we are measuring changes in household consumption due to changes in liquidity induced by demonetization, our key variable of interest is the natural log of household average monthly per capita expenditure, which helps us measure percentage changes in household consumption.

We also measure the changes in log of household average monthly luxury expenditure and log of household average monthly essential food expenditure, to see how the different aspects of household consumption were affected by the liquidity shock. The variable measuring average essential food expenditure includes data on expenditure on pulses, vegetables, fruit, meat, fish and milk. The variable measuring average luxury expenditure includes data on expenditure on snacks, noodles, cakes, packaged food, tea, coffee, sugar, sweets, beverages, alcohol/tobacco, restaurants, recreation expenses, ghee, pickles, dry fruit. We do not use income data due to possible mis-reporting (Deaton, 1997), and hence focus our results mostly on consumption. In our analysis, poor households are defined as households who belong to the bottom decile of consumption. Bank branch density is a measure of financial infrastructure, which gives us a measure of the number of bank branches per capita. The summary statistics describing all the above variables are in Tables (1) and (2).

Table 1 : Summary Statistics of key variables, Pre Demonetization

Table 2 : Summary statistics of key variables, Post Demonetization

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IV. MEASURING THE DIFFERENTIAL IMPACT ON HOUSEHOLDS IV.1. Empirical Strategy We study the impact of demonetisation in the period immediately after it was implemented, i.e. November and December 2016. We chose this period because this is when the liquidityrelated effects of the demonetisation were most severe, and the demonetized notes could only be exchanged/deposited till 31st December 20169 . To check the robustness of these results in a longer time frame, we run our specifications for a longer time period of November 2016 February 2017 and present the output in appendix 2, In all specifications, we get similar results as the baseline.

To measure the differential impact of demonetization on consumption for the poor and nonpoor households, we analyse the following model1.

ln_consumption𝑖𝑡 = 𝛽1Demonetisation𝑡 + 𝛽2Demonetisation𝑡 * Poor𝑖 + 𝛽3After_demonetisation𝑖 + 𝛽4After_demonetisation𝑡 *Poor𝑖 + Month𝑡 + Time𝑡 + 𝛼𝑖 +u𝑖𝑡 The regressand ‘ln_consumption’ refers to the natural log of consumption expenditure. Taking logs allows us to deal with outliers and also gives easily interpretable results in terms of percentages. ‘Demonetisation’ is a dummy variable that takes the value one in the months of November and December 2016 and 0 otherwise. ‘Demonetisation*Poor’ is the interaction between the above defined Demonetisation dummy and Poor, the dummy for poor households, which takes the value 1 if the household belongs to the lowest decile of consumption in the dataset10 and 0 otherwise. ‘After_demonetisation’ takes the value 1 for all periods starting from January 2017, and 0 otherwise. ‘After_demonetisation*Poor’ is the interaction between this variable and the previously defined dummy for poor households. Including these two controls in our regression allows us to use observations for the period after demonetisation while accounting for the possibility that the economy might have changed in the periods post demonetisation.

2

We use monthly fixed effects to control for month-wise seasonality in consumption, wherein certain months may see greater consumption by households on average, as compared to other months. We also control for a linear time trend in consumption, to account for any overall trends in consumption. Finally we add household fixed effects to account for unobserved heterogeneity on the household level. Since we are studying an aggregate level shock and we expect the impact of the shock to vary at the district level, we cluster standard errors by district.

We estimate the above model for the household’s log of monthly per capita total consumption expenditure, log of monthly luxury consumption expenditure, and log of monthly essential foods consumption expenditure. The results are presented in table 3.

Column (1) of table 3 shows the impact of the demonetisation shock on total consumption expenditure. Demonetisation led to a decline in the total consumption expenditure for nonpoor households by around 20%11 . For poor households, this decline in total consumption was by 9.5%. Column (2) shows that the - fall in average luxury expenditure for the non-poor was even larger (27.6%) than their decline in total consumption, while for the poor households we observe the decline in luxury consumption (9.2%) to be similar to the decline in total 3

9

Source: https://m.rbi.org.in/Scripts/FAQView.aspx?Id=122 Appendix 1 presets results when ‘Poor’ is defined as the lowest quartile of consumption 11 As the y variable is in log terms, the percentage change in consumption for non-poor households is thus given by exp(𝛽1) -1 and exp(𝛽1+𝛽2) -1 10

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consumption. Column (3) shows the impact on essential food consumption. We observe a lesser decline in essential food consumption among the non-poor (13.2%) compared to the decline in total expenditure, and an even lower decline (4.7%) for the poor households, compared to their reduction in total consumption.

Table 3 : Baseline regression results The lower decline in consumption of essential food items as compared to total expenditure and luxury expenditure can be explained by the greater costs of reducing consumption of essential foods as compared to the costs of reducing luxury expenditure. While making household optimization decisions, both poor and non-poor households chose to keep their essential foods consumption smooth in the demonetisation period and rely on reducing luxury expenditure.

Consumption fell by more for richer households as compared to poorer households in all three categories of consumption, contradictory to our expectation that the poorest households would be worse hit by shock as they are more likely to be employed in the cash-intensive and lowpaying informal sector (LaPorta and Shleifer, 2014).

There could be three possible reasons for the same- Firstly, already low levels of consumption among the poorest households implies that they were closer to their subsistence levels and thus had a greater cost of reducing consumption from those levels. Secondly, poorest households consumed very little, thus they were better able to meet their consumption requirements through the limited over- the-counter exchange and withdrawals from ATMs. Thirdly, poorest households held mostly lower denomination notes, protecting them from the ban on higher denomination notes.

In the next section we will discuss the test for the first two channels which could explain the lower reduction in consumption for the poorest households. We cannot check for the third channel as we do not have data on currency denomination held by households.

IV.2. Mechanisms for Consumption Smoothing The funds to support consumption could be acquired by exchanging savings in old demonetised notes for new notes, or withdrawing new notes from ATMs, liquidating non-cash savings such as gold, or borrowing in new currency. Of these, exchanging currency or withdrawing from

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the ATMs would have been the least costly, and in most cases it can be safely assumed to be less costly than reducing consumption. However, this channel was constrained by the large shortages of cash due to printing press constraints.

We use the following framework to test whether the lower decline in consumption for poor households was driven by greater costs of reducing consumption for the poor households, or due to the poor households being able to deal better with the liquidity shock using just the exchange mechanism.

The branch on the left deals with the possibility that the poorest households were not able to meet their consumption requirements after the demonetisation shock through just the exchange mechanism and non-poor households did not borrow/dissave. In this case, we would observe a significant increase in borrowings/dis-saving for the poorest households and no significant increase in the borrowing/dissaving of the non-poor if their cost of reducing consumption was significantly greater for the poor than the non-poor households. On the other hand, we would observe no significant increase in borrowings/dissaving for any household if the cost of reducing consumption for the poorest was not significantly different from the same cost for the non-poor.

The branch on the right deals with the possibility that the poorest households were able to meet their consumption requirements after the demonetisation shock through just the exchange mechanism and the non-poor not increasing borrowing/dissaving. In this case, regardless of the cost of reducing consumption, poorer households would not display any significant increase in borrowing/dissaving.

Theory suggests that the poorest households do, in fact, have a higher cost of reducing consumption. We expect the cost of reducing consumption to be greater for the poorest households as compared to the richer households because the poorest households are already consuming very little. At such levels of consumption, reducing consumption by even a small

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amount would be nearly impossible12 . Thus, if we do not observe a significant increase in borrowing/dissaving for the poorest, it would likely be due to either the exchange mechanism being enough to mitigate the liquidity shock on the poorest households or due to the poorest households not having access to the channels of borrowing or dissaving.

1

It is important to note that even if the exchange mechanism was not enough for the poor, the poor might not be borrowing/dissaving simply due to their lack of access to credit or being able to dissave, however, even in that case the results would still suggest that the lower reduction in consumption for the poor was driven by the exchange mechanisms. A limitation of this test arises due to the data being at an extensive margin, thus we are not able to observe the exact value increase/decrease in borrowing/dissaving and this biases the results towards showing no significant changes. The next subsection on borrowing discusses this problem in greater detail.

We will test whether poor households significantly increased their borrowings/dissaving due to demonetisation. If we find that poor households did increase their borrowings/dissaving, while non-poor households did not, this would imply that the cost of borrowings/dissaving was less than the cost of reducing consumption for the poor and greater than the cost of reducing consumption for the non-poor. This result would suggest that the exchange mechanism was not-enough for even the poorest households, and these households had to rely on borrowing/dissaving to meet consumption requirements. Further, the lower reduction in consumption for the poorest households was driven largely by the greater cost of reducing consumption for the poor households and facilitated by borrowings/dissaving.

If we instead find that poor households did not significantly increase their borrowings/dissaving, it would suggest that the poorest households dealt with the shock mainly through the exchange mechanism, and did not/could not use borrowings/dissaving to mitigate the shock. This result would suggest that the lower decline in consumption for the poorest households was driven mainly by the exchange mechanism.

H0: there was no significant increase in the borrowings/dissaving of the poor households.

IV.2.1. Borrowing We first look at the borrowing behaviour of households in the months immediately after demonetisation. As we have data on borrowing only at the extensive margin, i.e. whether a household an outstanding borrowing from a particular source or not, we estimate the impact of demonetisation on borrowing using the following linear probability modelPr(Borrowing𝑖𝑡) = 𝛽1Demonetisation𝑡 + 𝛽2Demonetisation𝑡* Poor𝑖 + 𝛽3After_demonetisationt + 𝛽4After_demonetisation𝑡 * Poor𝑖 + Month𝑡 + Time𝑡 + 𝛼𝑖 +u𝑖𝑡 Here the right-hand side is the same as the model (1), the only difference is in the choice of regressand. We estimate the model three times, with the regressand as- any outstanding borrowing, any outstanding informal borrowing, and any outstanding bank borrowing. The results are presented in Table 4.

12

The poor already consume close to their autonomous consumption level.

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Table 4 : Regression Results on Borrowing The results for informal borrowing are in column (2) of table 4. These suggest that there was a decrease in the probability of any household having any outstanding informal borrowing during the demonetisation period, however, this result is not significant. We can use an alternate arrangement13 of the above regression model to show that the decrease in borrowing for poorer households was statistically significant. This is possible only if on average, poor households paid back more of their informal borrowings during this period than they increased their informal borrowings. This phenomenon could be driven by informal lenders not having enough new currency to give out new loans, leading to higher costs of new borrowing, or it could be a result of poor households not requiring additional borrowings. Or it could even be due to poor households not being seen as worthy of credit. There is also the possibility of this decline being driven by an increase in repayments during demonetisation, this could be the case if the creditors were accepting repayments in old currency, and households found it easier to dispose of their old currency in this way than to exchange it. The repayments channel would be more reasonable for the nonpoor households that were struggling to get their unaccounted currency exchanged. Poor households that were already suffering from a negative shock on their income and consumption would not have enough money to pay back their outstanding borrowings while struggling to sustain their already low levels of consumption.

1

We observe similar results for any outstanding borrowing in column (1) of table 4. These results are not surprising as a large portion of those with any outstanding borrowings had informal borrowings, suggesting a large chunk of all borrowings were informal borrowings.

Column (3) of table 4, which captured the impact of demonetisation on bank borrowings shows an insignificant change. This suggests that bank borrowings or bank loan repayment did not change by much during the months immediately after demonetisation for all households. These results however must be interpreted with caution as we are dealing with data for borrowing on an extensive margin. The results of these estimations are thus biased toward showing no effect.

It might be the case that households that already have borrowings are increasing their value of borrowings during demonetisation, while those that did not have borrowings initially are not increasing borrowings possibly due to constraints. In such a case we would get a result suggesting no change in borrowings, whereas households that could increase borrowings actually did borrow to deal with the shock of demonetisation. . Thus the results of table (4) are not enough to comment on how borrowing played a role in mitigating the impact of 13

Alternate arrangement results presented in appendix 3

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demonetisation. However, the fact that we get a significant negative coefficient on the interaction suggests that the probability of having outstanding borrowing decreased for the average poor household under the demonetisation shock. This provides evidence that the results we obtain are not simply due to the data being at an extensive margin and so we fail to reject the null for borrowings IV.2.2. Dis-saving In this section, we discuss the possibility of households liquidating their non-cash savings to deal with the demonetisation shock. We primarily focus on gold and real estate as saving instruments due to their prevalence as saving instruments across income groups in India, as evident from our dataset and confirmed by Prina, 2015. We do not conduct the analysis for savings in the form of formal financial assets or risky assets as the Indian poor do not have access to these saving instruments (Dupas et.al, 2016). For our analysis, we estimate the same model as before, with the regressand being whether the household has any savings in gold and whether the household has any savings in real estate. We present the results of this estimation in Table 5.

Column (1) of Table 5 shows the results for savings in gold. The results suggest that there was no significant change in whether households held gold or not due to demonetisation. However, these results might not completely capture the use of gold in mitigating the demonetisation shock. First- we would not expect households that held gold to sell all their holdings of gold to deal with the shock, however, our data only captures gold holdings on an extensive margin and thus does not tell us if households sold off some gold to deal with the shock. SecondImmediately after demonetisation, there was a rush to convert holdings of old currency into gold and other precious metals. This increased purchase of gold would hide the effect of households selling gold to deal with the shock, however, we would not expect this rush to buy gold to be a reason for increased gold purchase among the poorest of households. Thus the results of Table (5) are not enough to comment on how gold dis-savings played a role in mitigating the impact of demonetisation. Kaur (2017) shows that demonetisation reduced gold prices, this suggests that the possible returns to households by selling gold were lesser.

Column (2) shows the results for savings in real estate, we again see no significant change in real estate holdings. The Indian real estate market is largely cash-based. Along with this, real estate is largely indivisible beyond a point, and thus selling real estate to smooth consumption might not be a viable option for most households. Moreover, there is a possible problem of the extensive margin and the coefficients, in any case, are insignificant.

These results show that for both poor and nonpoor households, we do not observe a significant change in savings, this suggests that poor households did not dissave at the extensive margin to deal with the impact of demonetisation. Thus we fail to reject the null for dissaving.

Considering the results from sections IV.2.1. and IV.2.2, we fail to reject the null that poorest households did not use borrowings or dissaving to deal with the shock of demonetisation. This is only possible if the poorest households dealt with the demonetisation shock through just the exchange mechanism. These results suggest that the lower reduction in consumption for the poorest households was driven largely by the exchange mechanism.

We now provide further suggestive evidence of the importance of the exchange mechanism in driving the lower reduction in consumption for the poorest households. The mean monthly per capita consumption in the poorest households in our sample is 1117.341, this figure is around

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1/20th of the weekly cash exchange limit of ₹20,000. Also, this amount is about half of the daily limit for ATM withdrawals (initially ₹2000, later revised to ₹2500 and then ₹4000). Even under severe shortages in the availability of cash, banks and ATMs would be able to provide some fraction of the exchange/withdrawal limits to the customers, if this fraction was even 1/20th, members of the poorest households would easily be able to deal with the liquidity shock solely through the exchange mechanism. If this is true, it further suggests that the decline in consumption for the poorest households was not due to the shortages of cash in the liquidity shock, rather the declines were driven by the income shock. An income shock was felt by the poor households due to them being largely employed in the cash-intensive informal sector, which would have suffered greatly under demonetisation.

Table 5 : Regression Results on Dis-Saving In the next sections, we will explore the role of financial infrastructure in mitigating the impacts of the demonetisation shock.

IV.3. Financial infrastructure In this section, we measure the presence of financial infrastructure as the branch density in a district, i.e. the number of bank branches per capita (per million population) in a district. We would expect a greater presence of financial infrastructure to help mitigate the liquidity shock by improving access to these facilities.

1) Allowing for better functioning of the exchange mechanism to deal with the liquidity shock: Greater branch density would imply lesser people in the district have to rely on each branch to exchange their old currency over the counter and assuming higher branch density to be correlated with higher ATM density, lesser people per every ATM. As lesser people rely on each branch, we can expect the queues for exchanging/withdrawing currency to be shorter, thus reducing the time cost of the exchange mechanism. Along with this, greater branch density might imply lesser distances between people and branches, reducing costs of travelling to branches for exchange, however, this might not hold in all cases. Further, districts with lesser branch density might experience shortages due to logistical constraints even if there is not a shortage of currency at the bank level.

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2) Bank account holders would benefit from higher bank branch density as it would be easier for them to process payments without cash, using bank supported payment instruments such as cheques and demand drafts. These payment instruments would allow households with sufficient bank balance to use their cash in the bank in place of cash in hand for consumption expenditure, however, this might not work for supporting all forms of consumption as these payment instruments might not be accepted by shopkeepers with lower financial literacy, especially for small transactions.

3) There would possibly be greater access to bank borrowing in areas with greater bank branch density, however, this channel might not be in play, as we observed in section 3.2.1 that there was not a significant increase in bank borrowings.

We measure the impact of financial infrastructure on helping mitigate the consumption shock and the differential impact of financial infrastructure in mitigating the shock for poor households under the following modelLn_consumption𝑖𝑡d = 𝛽1Demonetisation𝑡 + 𝛽2Demonetisation𝑡 * Poor𝑖 + 𝛽3Demonetisation𝑡 * Branch_densityd + 𝛽4Demonetisation𝑡 * Branch_densityd * Poor𝑖+ 𝛽5After_demonetisation𝑖 + 𝛽6After_demonetisation𝑡 * Poor𝑖 + 𝛽7After_Demonetisation𝑡 * Branch_densityd + 𝛽8After_Demonetisation𝑡 * Poor𝑖 * Branch_densityd + month𝑡 + time𝑡+ 𝛼𝑖 +u𝑖𝑡 - (2) The above model is an extension of model (1), we have two additional variables of interest and two additional controls. ‘Demonetisation*Branch_density’ is the interaction between the short demonetisation period dummy, and branch density captures the bank branches per million in the district. ‘Demonetisation*BranchDensity*Poor’ is the interaction between the ‘Demonetisation* Branch_density’ and the dummy for poor households. The two additional controls ‘AfterDemonetisation*Branch_density’ and ‘AfterDemonetisation*Poor*BranchDensity’ are defined similarly, with ‘After_Demonetisation’ taking the value 1 for all periods from January 2017 onwards and 0 otherwise.

We estimate this model for household per capita total expenditure, luxury expenditure, and essential food expenditure. The results of this estimation are presented in table 6.

Column (1) shows the impact of demonetisation and the role of financial infrastructure in mitigating the shock on total expenditure. In the first row, we observe a significant negative coefficient, pointing towards the negative shock of demonetisation on consumption. In the second row, we observe a positive significant coefficient for ‘Demon*Density’, suggesting that bank branch density did, in fact, play an important role in mitigating the shock to consumption.

The third row displays a positive coefficient, significant at the 10% level for ‘Demon*Poor’ suggesting poorer households did not reduce consumption by as much as non-poor households.

The fourth row shows the coefficient on the triple interaction, this coefficient is positive and significant suggesting that higher branch density helped mitigate the impacts of the shock on consumption for the poorest households by more than it did for the other households.

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Table 6 - Regression Results on Bank Branch Density We observe similar signs and significance for the first two rows of column (2); however, magnitudes are greater, suggesting that the percentage reduction in luxury consumption due to demonetisation was greater than the percentage reduction in total consumption on average, and higher branch density helped households deal with the shock on luxury expenditure by greater than it did for total expenditure (in percentage terms). ‘Demon*Poor’ has a highly insignificant positive coefficient. The triple interaction has a positive and significant coefficient, the magnitude is more than twice that of the coefficient on the triple interaction in column (1).

This indicates that higher branch density helped mitigate the impacts of the shock on luxury consumption for the poorest households by more than it did for overall consumption as compared to the other households.

We again observe similar sign and significance for the first two rows of column (3); however, magnitude is smaller in the first row, and greater in the second row, suggesting that the percentage reduction in essential food consumption due to demonetisation was lesser than the percentage reduction in total consumption on average, and higher branch density helped households deal with the shock on essential food expenditure by more than it did for total expenditure (in percentage terms). Again ‘Demon*Poor’ has a highly insignificant positive coefficient. The coefficient on the triple interaction is positive and significant at the 10% level, the magnitude is almost the same as the coefficient on the triple interaction for total expenditure (column (1)).

The insignificant coefficient on ‘Demon*Poor’ and significant positive coefficient on the triple interaction in column (2) and column (3), along with the positive coefficient on ‘Demon*Poor’ in Table 6. Section 4.2 suggests that a large part of the mitigation of the shock for poor households’ luxury, and essential foods consumption happened through greater financial infrastructure. As we have discussed earlier in this section, districts with greater bank branch density would have a better functioning exchange mechanism with lower costs of exchange, lesser shortages of new currency and better access to financial infrastructure for the households. Thus, this result is consistent with our finding that poorer households dealt with the liquidity shock mostly through the exchange mechanism through improved access to credit facilities via these additional bank branches in a given district.

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V. CONCLUSION & POLICY IMPLICATIONS Demonetization affected all households differently based on their income levels, geographic location , and access to mechanisms to mitigate the effects. However, all households reduced consumption, with the households at the lowest decile of consumption reducing their expenditure by a lot lesser than non-poor households. All households reduced consumption of luxury goods than they did on essential foods. Poor households reduced consumption of essential foods by lesser, probably due to already low levels of consumption among these households. Mechanisms like borrowing or dissaving were used minimally to smoothen consumption- we find no significant increase in borrowing or dissaving in the initial months after the shock. Contrarily, outstanding borrowing went down at an extensive margin.

On the other hand, mechanisms like bank branches helped households smoothen their consumption. Higher bank branch density at the district level is associated with smaller declines in consumption. This effect was more pronounced for the poorest households.

Our results also have a range of policy implications. The most apparent policy intervention would be to improve financial infrastructure, especially in underserved rural areas, and improve access to formal banking. In addition to improving access to banks and incentivizing the use of bank accounts and other mechanisms of formal saving, it is key to undertake financial literacy programs to help households benefit from these mechanisms. Not only will improved financial infrastructure and access to formal banking help mitigate the effect of income and liquidity shocks, but it also encourages savings, thereby facilitating greater consumption smoothing. Improved financial infrastructure will also lead to overall economic development.

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Baker, Scott R. 2014. “Debt and the Consumption Response to Household Income Shocks.”

Stanford University. https://web.stanford.edu/~srbaker/Papers/Baker_DebtConsumption.pdf.

Baker, Scott R., RA Farrokhnia, Stephen Meyer, Michaela Pagel, and Constantine Yannelis.

2020. “HOW DOES HOUSEHOLD SPENDING RESPOND TO AN EPIDEMIC? CONSUMPTION DURING THE 2020 COVID-19 PANDEMIC.” National Bureau of Economic Research, (April).

Chodorow-Reich, Gabriel, Gita Gopinath, Prachi Mishra, and Abhinav Narayanan. 2019.

“Cash and the Economy: Evidence from India's Demonetization.” Quarterly Journal of Economics 135, no. 1 (December): 57-103.

“Consumer Pyramids.” n.d. https://consumerpyramidsdx.cmie.com/.

Dimopoulou, Angeliki. 2019. “Essays on Indian household savings, poverty and inequality within the context of economic growth.”

Dupas, Pascaline, Dean Karlan, Jonathan Robinson, and Diego Ubfal. 2016. “Banking the Unbanked? Evidence from three countries.” Economic Growth Centre, Yale University. https://elischolar.library.yale.edu/cgi/viewcontent.cgi?article=2062&context=egcenterdiscussion-paper-series.

Gelman, Michael, Shachar Kariv, Matthew D. Shapiro, Dan Silverman, and Steven Tadelis.

2018. “How individuals respond to a liquidity shock: Evidence from the 2013 government shutdown.”

Journal of Public Economics. https://www.sciencedirect.com/science/article/abs/pii/S004727271830118X. Jappelli, Tullio, and Luigi Pistaferri. 2010. “THE CONSUMPTION RESPONSE TO INCOME CHANGES.” National Bureau of Economic Research. https://www.nber.org/system/files/working_papers/w15739/w15739.pdf.

Karmakar, Sudipto, and Abhinav Narayanan. 2019. “Do Households Care about Cash?

Exploring the Heterogeneous Effects of India’s Demonetization.” Research in Economics and Mathematics(REM). em.rc.iseg.ulisboa.pt/wps/pdf/REM_WP_073_2019.pdf.

Kaur, Manpreet. November 2016. “DEMONETISATION AND GOLD: A STUDY OF INDIA.” Scholarly Research Journal for Humanity Science & English Language Online ISSN

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2348-3083, SJ IMPACT FACTOR 2016 = 4.44, www.srjis.com UGC Approved Sr. No.48612, DEC-JAN 2018, VOL-5/25. http://www.srjis.com/pages/pdfFiles/15176592353.%20Dr.%20Manpreet%20Kaur.pdf.

Khwaja, Asim I., and Atif Mian. 2008. “Tracing the Impact of Bank Liquidity Shocks: Evidence from an Emerging Market.” American Economic Review 98 (4): 1413-1442.

Kim, Jungho, and Alexia Prskawetz. 2006. “External shocks, household consumption and fertility in Indonesia.” Vienna Institute of Demography Working Papers 04. https://www.econstor.eu/bitstream/10419/96998/1/515406961.pdf.

Lahiri, Amartya. 2020. “The Great Indian Demonetization.” Journal of Economic Perspectives 34 (1): 55-74.

LaPorta, Rafael, and Andrei Shleifer. 2014. “Informality and Development.” American Economic Review 28 (3). https://www.aeaweb.org/articles?id=10.1257/jep.28.3.109.

Nair, Shalini. 2017. “As Rural Hands Return, NREGA Demand Spikes over 60 per Cent.”

Indian Express, January 9, 2017. https://indianexpress.com/article/india/as-rural-hands-returnnrega-demand-spikes-over-60-per-cent-4465577/.

Prina, Silvia. 2015. “Banking the poor via savings accounts: Evidence from a field experiment.”

Journal of Development Economics 115 (2015) 16–31. https://punarjitroyc.weebly.com/uploads/4/6/3/3/46337267/prina_2015.pdf.

Subramanian, Arvind. 2019. “India’s GDP Mis-estimation: Likelihood, Magnitudes, Mechanisms, and Implications.” Centre for International Development at Harvard University.

Wadhwa, Sagar. 2019. “Impact of Demonetization on Household Consumption in India.”

Brown University, (November). https://sagarwadhwa.com/Files/sagar_demonetization_latest.pdf.

APPENDIX Appendix 1 Poor defined as lowest quartile of consumption

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Appendix 2 Demonetisation period as of November 2016 - February 2017

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Appendix 3 Interacting the base demonetisation variables with * NonPoor y𝑖𝑡 = 𝛽1Demonetisation𝑡 * NonPoor + 𝛽2Demonetisation𝑡 * Poor𝑖 + 𝛽3After_demonetisationt* NonPoor + 𝛽4After_demonetisation𝑡 *Poor𝑖 + Month𝑡 + Time𝑡 + 𝛼𝑖 +u𝑖𝑡

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y𝑖𝑡d = 𝛽1Demonetisation𝑡 * NonPoor + 𝛽2Demonetisation𝑡 * Poor𝑖 + 𝛽3Demonetisation𝑡 * Branch_densityd* NonPoor + 𝛽4Demonetisation𝑡 * Branch_densityd * Poor𝑖+ 𝛽5After_demonetisation𝑖 * NonPoor + 𝛽6After_demonetisation𝑡 * Poor𝑖 + 𝛽7After_Demonetisation𝑡 * Branch_densityd * NonPoor+ 𝛽8After_Demonetisation𝑡 * Poor𝑖 * Branch_densityd + month𝑡 + time𝑡+ 𝛼𝑖 +u𝑖𝑡

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GOLD AS AN OBSESSION: A COMPARATIVE ANALYSIS OF INDIA AND CHINA DURING ECONOMIC CRISIS -Jatin Bhatnagar, Satvik Rastogi1 ABSTRACT The objective of this research paper is to analyse the impact of Covid-19 on gold prices in India as compared to China.

The main inspiration for this research came from the common notion about India’s obsession with gold as an asset (especially during times of crisis). Moreover, the prices of gold in the Indian market have been rising consistently since the global financial crisis of 2008, we wanted to analyse the nature of the impact of gold in the current economic climate facilitated by the coronavirus pandemic.

For the purpose of this research, we have taken the monthly gold prices and monthly closing stock data of SENSEX and SHANGHAI Stock Exchange from Bloomberg. After that, we have calculated the rate of return of all the four indices and used E-Views to calculate correlation between the rate of return of stock indices with respect to the rate of return of gold of their respective countries.

Through our research, we found out that the correlation between gold and SHANGHAI Stock Exchange was much higher as compared to gold and SENSEX in terms of rate of return.

I. LITERATURE REVIEW In 2010, Ho, Wang and Liou conducted research to understand the relationship between gold prices and the dollar index. Through their research, they concluded that dollar indices do affect the price of gold.

Another research conducted by Prerana Baber, Ruturaj Baber and Dr George Thomas aims to understand the factors that affect the price of gold with special reference to India during the time period 2002-12. With the help of correlation and trend analysis, they concluded that factors such as inflation, international business environment, political environment, market conditions, its induction in the commodity market and buying behaviour of consumers are responsible for driving gold prices higher. In addition to this, they also found out that factors like its production, demand and dollar price fluctuation also have a positive correlation with the price of gold.

In 2016, Muhammad Shaique, Abdul Aziz and Gobind M. Herani conducted a case study to examine the impact of gold prices on the stock exchange market in the long run. In order to do this, they examined the relationship between the Karachi Stock Exchange 100 index (KSE100) and gold prices from October 1993 to May 2014. Through their research, they concluded that there is no relationship between gold and the stock market in the long run.

In 2011, Le and Chang studied the relationship between gold and the exchange rate. This was done to analyse whether or not gold prices are susceptible to exchange rate fluctuations.

Through their research, they concluded that gold has the ability to resist changes in the internal and external purchasing power of domestic currency.

1

Delhi Technological University

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In 2017, Kamola Bayram, Adam Abdullah and Ahamed Kameel Mydin Meera conducted research to identify the opportunity cost of holding gold, in relation to the stock market for the case of Turkey. The main focus of their research was to analyse whether or not gold acts as a safe haven or a hedge asset for investors in times of distress. For the purpose of this research, they analysed daily data for the time period 2005-14. They concluded that gold exhibits safe haven by outperforming the average portfolio in times of distress. Moreover, they also found that there was no significant correlation between gold and the stock market.

In 2017, Dr A. Jasmine of Tbak College for Women conducted research to analyse the impact of GST on gold among consumers, the import market, the functioning of the World Gold Council, the accounting treatment on gold and its impact before and after implementation of GST on gold. Through her research, she concluded that GST would marginally impact the price of gold. However, because gold is the most favoured asset among Indians, it does not lead to a very significant impact on gold prices. In 2015, Adam Abdullah & Mohd Jaffri Abu Bakar analysed the impact of interest rate and inflation on gold prices. For the purpose of this research, they analysed the data from 1971 to 2013. Through their research, they concluded that real gold is inversely related to the real rate of interest and real inflation rate, hence nominal rates are positively related to commodity prices. In the historical period, whenever the stock market collapsed or when the dollar rate fell, gold prices were considered as the safe haven, and its prices also remained the same or moved upwards for a period (Bhunia, and Mukhuti, 2013).

The supply of money via the supply of debt is the tool by which the market interest rate is adjusted in accordance to a central bank interest rate target” (Abdullah, 2013).

Baur and Lucey (2009) presented evidence of the potential for gold to act as a safe haven asset.

According to their findings, gold tends to hold its value when stock markets experience extreme negative returns.

Gold consumption observed a sharp acceleration during the 1990s amidst liberalization of gold import policy, strong economic growth and favourable movements in gold prices. Gold is now being used as an alternative for the dollar since its collapse (Turk and Rubino, 2008).

Kanan (2010) observed that for the higher income level group‘s platinum is a substitute for gold but for the majority, gold is “a way of life”.

I.1 INTRODUCTION Gold is a highly valuable metal, a luxury good that has many practical uses in our day to day life, one of its useful properties, malleability allows us to beat it into sheets, turn it into wires or any other shape that we desire. Traditionally gold was only used for making jewellery but today it is used for manufacturing by nearly all industries. Gold is a very good conductor for electricity, making it ideal for making electronics, hence nearly all electronic equipment today use small quantities of gold. Gold also has other applications in areas such as the medical industry, aeronautics, and the glassmaking industry.

I.2 HISTORY OF GOLD Gold has been respected throughout history all over the world for its unique colour and properties, it doesn’t corrode, can be melted over a common flame, making it very easy to work with and stamp as coins. The reason behind the unique colour of gold is the heavier atoms in gold, making the electrons move faster, creating the absorption of some light; this process was explained by Einstein's theory of relativity. It has been interwoven into our cultures due to its significance. The first gold coin dates back to 800 BC, three hundred years later the first pure gold coin was minted under the reign of King Croesus of Lydia. Kingdoms and empires were built and destroyed over gold, later it became a universally accepted form of payment. Unlike

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other currencies gold always holds its value, it is a great way to pass on wealth to other generations.

I.3. THE KING OF BAD TIMES Gold has always proven to be a great hedge against inflation. This is because its price rises with an increase in the cost of living. During the last 50 years, investors have observed the trend that the cost of gold rises while stock markets fall during high inflation years. This is due to the fact that when fiat currency loses its purchasing power to inflation, gold tends to be priced in those currency units. Moreover, people view gold as a good store of value, so they start investing in gold when they start believing that their local currency is losing its value. Gold also performs well during times of deflation. Deflation is the time period when prices decrease, businesses slow down and debt in the economy increases. Deflation was last seen during the great depression in the 1930s, during the great depression, the relative purchasing power of gold rose, while other prices dropped. This happened because during such times people wanted to hoard cash and the safest way to do that, at the time, was to purchase gold. Gold outperforms other assets not only during financial crises but also when world tensions are at their peak. This is why gold is also known as the “crisis commodity”. Gold prices rise the highest during times of geopolitical uncertainty and when trust in the government is low. One recent example can be of BREXIT, gold prices saw a lot of fluctuations when the United Kingdom decided to withdraw itself from the European Union. A lot of it boils down to human psychology, gold has always been the go-to investment during times of uncertainty and fear, which arises out of recessions and depressions.

There exists an inverse relationship between gold prices and the stock market. Take the 2008 financial crises for example, in 2008 Lehman brothers, the fourth largest investment bank in the US at that time collapsed. This collapse was caused by the subprime mortgage crisis, which led to the crash of most major world economies. Tensions grew and investors started panic buying gold to mitigate their losses which led to a sharp rise in prices. The prices rose from 700$ per ounce to a 1900$ record high. Around the same time, the BSE index in India fell by 50% causing gold prices to increase massively.

I.4. GOLD AS A STRATEGIC ASSET Gold is scarce but highly liquid. It has gone mainstream and has become a common part of an investor’s portfolio. It is viewed as a source of long-term returns. It’s a great way to mitigate losses when the market is in turmoil, it has consistently outperformed fiat currencies. It is highly liquid and can be used when money is required urgently. It is considered a good strategy to allocate 10% to 15% of a portfolio towards gold.

I.5. INDIA VS. CHINA: HOW DO BOTH COUNTRIES VIEW GOLD IN COMPARISON TO OTHER ASSETS The purpose behind taking India and China for this paper was simple, they are the largest consumers of gold worldwide. In 2010, India’s demand for gold was 1001 tons, while China's was 676 tons. Fast forward to 2018, India’s demand fell to 760 tons while China's grew to 1058 tons. China is also the world’s largest gold producer. There are several reasons behind this reversal. Firstly, the lack of an Indian-indexed gold price, while in 2014, China set up the Shanghai gold exchange, giving it a global ecosystem. Secondly, jewellers in India have failed to impress millennials since there hasn’t been much innovation in the designs for the current generation while Chinese jewellers are doing very well in this aspect. More and more millennials in china are buying gold due to the innovations made by Chinese jewellers.

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In 2019, the world was hit by the SARS-CoV-2 pandemic, which led to lockdowns in most parts of the world. Businesses closed down, supply chains were disrupted, and demand took a big hit. Financial markets came crashing next, the figures reminded the world of the 2008 financial crisis. Against the above backdrop, this study was thus organized. The study dwells on the impact of COVID 19 on the stock markets and in turn the impact of the stock markets on gold prices for two of the world’s largest economic powerhouses, India and China.

Figure 1 Source: World Gold Council

II. DATA AND METHODOLOGY For the purpose of this research, we have collected the data of monthly closing stock of both the indices and gold prices of the respective economies in the past seven quarters.

From this data, we are going to calculate the rate of return from gold indices of both economies and then calculate the average rate of return. We are then going to compare the average rate of return in both the indices to find out which economy offers a better rate of return to the investor.

We are also going to plot a regression line of the monthly rate of return offered by stock indices on the monthly rate of return offered by the gold of respective economies to find out which market has a better correlation with respect to its gold index.

II.1. OBJECTIVES OF THE RESEARCH The following are the objectives of the research1) To understand which country has a higher correlation between gold and stock index in terms of rate of return 2) To understand that in which country, gold offers a higher average rate of return

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Null and Alternative Hypothesis PRIMARY HYPOTHESIS H0: There is no correlation between the rate of return of gold and stock market in India H1: There is correlation between the rate of return of gold and stock market in India Independent Variable: Rate of Return of BSE index Dependent Variable: Rate of return of Gold SECONDARY HYPOTHESIS H0: There is no correlation between the rate of return of gold and stock market in China H1: There is correlation between the rate of return of gold and stock market in China Independent Variable: Rate of Return of SHANGHAI index Dependent Variable: Rate of return of Gold NOTE: For the purpose of this research, whenever we use the term “during Covid-19”, we mean to say from the time period when the rate of return offered by the stock exchanges fell drastically and remained below the rate of return offered by gold till the time till revived back to offering the rate of return higher than gold.

II.2. TIME SERIES ANALYSIS For the purpose of this research, we have considered the monthly gold prices and monthly closing stock data of SENSEX and SHANGHAI Stock Exchange in the past 7 quarters from Bloomberg.com.

Thereafter, we have calculated the rate of return from both indices using the following formula: Return (%) = (P1 – P0 / P0) X 100 Where, P0 = closing stock price for the previous month P1= closing stock price for the current month

II.3. IDENTIFICATION OF TREND OF RATE OF RETURN

Chart 1.1: Trend of Rate of return of SENSEX(Blue) and Gold in India(orange)

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Chart 1.2: Trend of Rate of Return of SHANGHAI(Blue) and Gold in China(orange)

NOTE: We did not find any trend in the case of the rate of return offered by the stock exchange or gold in either of the countries. This is particularly because of (i)volatility in terms of rate of return offered by both the indices and (ii)this trend is more visible in the case of monthly closing stock data. Hence, we are not going to calculate the regression equations for each of these indices.

III. DATA ANALYSIS AND RESULTS We have obtained the data from E-Views by estimating the regression equation of the rate of return offered by the stock index on gold in both economies. We have estimated separate regression equations for: (a) The past seven quarters (b) Since the time the Covid-19 pandemic affected the rate of return offered by the stock indices of the two economies The results from our calculations are as follows:

Table 1.1: Sensex on Gold (For past seven quarters)

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Table 2.1: Shanghai on Gold (For past seven quarters)

III.1. COMPARATIVE ANALYSIS OF RATE OF RETURN OFFERED BY BOTH INDICES SINCE THE IMPACT OF COVID-19

Table 1.2: Sensex on Gold (During Covid-19)

Table 2.2: Shanghai on Gold (During Covid-19)

III.2. INTERPRETATION OF VALUE OF PARAMETERS IN BOTH THE INDICES In the case of the past seven quarters, When the rate of return on gold increases by 1 unit, on average, it leads to a decline of 0.008652 units in terms of rate of return on SENSEX. Moreover, the t-value of the rate of return on gold is less than t-critical. Therefore, we reject the null. However, the value of R2 is very low. Therefore, we conclude that this is not a good regression model and there are factors other than gold affecting the rate of return on BSE. Hence, there is no correlation between rate of return of gold and stock market in India When the rate of return on gold increases by 1 unit, on average, it leads to a decline of 0.3879 units in terms of rate of return on the SHANGHAI Stock exchange.

Moreover, the t-value is more than t-critical. This means that there is a significant influence of gold at 95% confidence interval. Therefore, we reject the null. However, the value of R2 is very low. Therefore, we would conclude that this is not a good regression model and there are factors other than gold affecting the value of the rate of return on the SHANGHAI stock exchange.

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During the Covid-19 pandemic when the rate of return on gold increases by 1 unit, on average, it leads to a decline of 0.4941 units in terms of rate of return on SENSEX. Moreover, the t-value of rate of return on gold is less than t-critical. This means that there is a significant influence of gold at 95% confidence interval. Therefore, we reject the null.

Therefore, we reject the null. However, the value of R2 is very low. Therefore, we conclude that this is not a good regression model and there are factors other than gold affecting the rate of return on BSE. When the rate of return on gold increases by 1 unit, on average, it leads to a decline of 0.979 units in terms of rate of return on SHANGHAI Stock exchange.

Moreover, the t-value is more than t-critical. This means that there is a significant influence of gold at 95% confidence interval. Therefore, we reject the null. Moreover, the value of R2 is very high. Therefore, we would conclude that this is a good regression model and there is a high degree of correlation between the rate of return on gold and rate of return on the SHANGHAI stock exchange.

IV. SUMMARY OF FINDINGS AND CONCLUSION The main objective of this research paper was to analyse whether or not Indians pull out their money from riskier assets such as stocks and invest into gold during the time of economic crisis. However, contrary to common belief, our regression model points out that this negative relationship between stocks and gold expressed in terms of rate of return is not at all strong.

In comparison to China, one of the fastest growing economies in the world for the past few decades, this relationship is much tighter.

This suggests that the common notion of Indians being obsessed with gold during such times is not true, at least with respect to assets such as stocks.

In fact, as our study points out, the rate of return from stocks is not even one of the leading factors contributing to a rise in the rate of return from gold. This suggests other factors such as inflation rate, currency value, dollar index and GST among others that are responsible for change in gold prices.

This means that in case of China, investors are way more likely to pull out their money from riskier assets such as stocks and invest into safe assets such as gold as compared to investors in Indian stock markets during the time of economic crisis.

ACKNOWLEDGEMENT We would like to thank our supervisor, Prof. (Assistant) Dr Ratnam Mishra, under whose guidance we were able to complete this research. Her expertise was crucial in formulating our research questions and methodology. She also helped us to make our research more insightful through her invaluable and constant feedback throughout our research.

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REFERENCES Baber, Prerana. Baber, Ruturaj. & Dr George. “Factors Affecting Gold Prices: A Case Study of India,” March 2013.

“The Optimum Price of Gold,” The Wall Street Journal, January 7, 1998.

Shaique, Muhammad. Aziz, Abdul. and M. Herani, Gobind. “Impact of gold prices on stock exchange market: a case of Karachi stock exchange market of Pakistan,” April 2016.

Le, Thai-Ha. & Chang, Youngho. “Dynamic Relationships between the Price of Oil, Gold and Financial Variables in Japan: A Bounds Testing Approach,” 2011.

Bayram, Kamola. Abdullah, Adam. & Kameel Mydin Meera, Ahamed. “Financial Market Risk and Gold Investment in an Emerging Market: The Case of Turkey,” December 2017.

Dr. A, Jasmine. “Impact of GST on the gold market,” Global Journal for Research Analysis, Vol. 6, Issue-12, December 2017.

Abdullah, Adam. & Jafri Abu Bakar, Mohd. “The Application of Gold Price, Interest Rates and Inflation Expectations in Capital Markets,” International Journal of Economics and Finance, January 2015.

Bhunia, Amalendu. & Mukhuti, Somnath. “Impact of domestic gold price on stock price indices-An empirical study of Indian stock exchanges,” The Universal Journal of Marketing and Business Research. 2013.

Abdullah. “The Gibson Paradox: Real Gold, Interest Rates and Prices,” International Business Research, 2013.

Baur, D. & Lucey, B.M. “Flights and contagion – An empirical analysis of stock-bond correlations,” Journal of Financial Stability, 2009.

Turk & Rubino. “The Collapse of the Dollar and How to Profit from It,” Crown Publishing Group, Business & Economics, 2008.

Kannan, R. “An econometric analysis,” World Gold Council, 2010.

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APPENDIX Year 2019

2020

Month January February March April May June July August September October November December January February March April May June July August September October November December

Quarter Q1

Q2

Q3

Q4

Q1

Q2

Q3

Rate of Return (SENSEX)

Rate of Return (GOLD)

1.07359497 -7.467162418 -1.260399339 -1.748969744 0.804649219 4.857310538 0.395745912 -3.574712739 -3.780245494 -1.656555538 -1.127450464 1.285338008 5.957740852 23.05332832 -14.4192322 3.836332458 -7.684715998 -7.707370302 -2.715991671 1.695829663

-0.791086687 -3.862190705 0.050652146 1.718136945 5.070457585 2.199721704 11.58748552 -4.000519211 4.813542822 -1.339043835 1.935146444 5.820933812 0.155155276 3.335511824 4.712937153 5.127172673 3.796148526 9.709096127 -2.606745772 -3.493860322

Q4

Table 3.1: Rate of Return of Sensex and Gold in India in the past 7 quarters

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Year

Month

Quarter

2019

January February March April May June July August September October November December January February March April May June July August September October November December

Q1

2020

Q2

Q3

Q4

Q1

Q2

Q3

Rate of (SHANGHAI)

Return Rate of Return (GOLD)

-13.78875824 -5.093936236 0.141047614 6.081539503 -2.766081428 1.556638326 1.577808749 -0.656560371 -0.821630159 1.948763079 -6.202706064 2.412685471 3.23296505 4.513430596 -3.991569602 0.270280952 -4.638997936 -10.90035123 -2.58821705 5.231056283

-3.438966097 -2.584376098 -0.177228491 3.455806682 6.679980561 1.605107873 3.95042874 0.760194901 0.301650046 -2.230565076 5.640546458 0.38867045 1.933898861 -0.853649303 5.645813049 2.173899949 5.600033014 10.38169303 -0.621083457 -4.46432661

Q4

Table 3.2: Rate of Return of Shanghai Stock Exchange and Gold in China in the past seven quarters

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EMPLOYMENT SITUATION IN INDIA POST-COVID-19: AN ANALYSIS THROUGH THE PARADOX OF TOIL -Namit Mahajan & Sudipta P Kashyap1 ABSTRACT The COVID-19 pandemic has driven the world into an unprecedented emergency and vulnerability. The continued sharp decrease in working hours all around the world because of the COVID-19 episode implies that 1.6 billion labourers in the casual economy – that is almost 50% of the worldwide labour force – remain in impending peril of having their vocations destroyed.

In India, COVID-19 has additionally uncovered the weakness of metropolitan easy-going citizens, a large number of whom are migrants. They were among the first to be shocked by the lockdown measures as financial activities ended, threatening the existence of numerous little metropolitan manufacturing units and occupations of these labourers. In most metropolitan units, the locations are connected with convenience at working environments and the joblessness may have constrained these labourers to clear their shelters also. With the minimal decision, they are compelled to return to their town in urgency. With restricted information accessible on inter-state relocation and work in casual areas, it is hard to calculate the quantities of travellers who lost positions and convenience during the pandemic and returned home.

This helps us to draw an easy conclusion regarding the high levels of unemployment that has led to an increase in the desire to work more. But what shall happen to the aggregate employment of the country with this impulse of the citizens?

This paper describes a prediction of a probable decrease in the rates of aggregate employment by the application of the paradox of Toil. The paradox of toil is closely related to the Keynesian ideology and forms a part of the fallacy of composition. Based on this analysis, as the desire to work more increases in the mindsets of the pandemic affected citizens, the country is said to witness a fall in its aggregate employment.

Keywords: Paradox of toil, Keynesian ideology, aggregate employment, pandemic, the fallacy of composition, desire to work.

I. INTRODUCTION The COVID-19 pandemic has driven the world into a phenomenal emergency and vulnerability, calling to speed up the usage of the Centenary Declaration. It called upon constituents to seek after 'with unwavering force its [ILO] sacred order for social equity by additional building up of its human-focused way to deal with the fate of work'. It called for putting labourers' privileges and the necessities and yearnings surprisingly at the core of monetary, social and natural approaches. The worldwide local area and ILO's constituents have occupied with an aggregate undertaking to handle the overwhelming human impact of the pandemic; however, more is required.

1

Ramjas College, Delhi University, Christ (DTB University), BGR Campus

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The paradox of toil expresses that under specific conditions if individuals wish to work longer hours, this can cause falling real income and rising joblessness. The mystery is that people have a motivation to work longer, however, if everyone needs to work longer hours, it can cause joblessness.

The paradox of toil, however, just works under specific conditions: ● Zero nominal interest rates (zero bound rate) ● Deflationary pressures ● Falling output The COVID-19 seems to satisfy almost all the assumptions for the application of the paradox of toil. The scene after the pandemic will involve workers willing to work more than usual due to the state that the pandemic has pushed them to. With low income, high layoffs and low standard of living, people shall come out of the pandemic to earn more. But, according to our paper, India shall be home to the paradox of toil, crushing the hopes of workers by the reduction in aggregate employment even though the entire labour population will be looking forward to working harder.

II. RESEARCH PROBLEM The central problem of our research is to explain the concept of the paradox of toil, its implications with relation to the employment rates in the country, and give an overall analysis as to how the country is expected to face a decrease in its total employment levels.

III. RESEARCH GAPS AND NEED FOR STUDY We believe that the study shall help the government in understanding the occurrence of a backlash on investing highly in employment and also help all the citizens who believe that their aim to secure a job and wake up at dawn deciding to work more, may not be the right mode of inspiration to walk out of the pandemic.

IV. RESEARCH METHODOLOGY Our paper is primarily dependent on qualitative analysis and therefore uses secondary data to justify the hypothesis. The data used in the paper is spread across different periods from 20152020. The statistics shall cover the total unemployment rates from 2015-2020, the unemployment rates particularly in the year 2020 between May and September, the workforce participation rates, level of output between May and September and the interest rates and GDP between the same period etc.

Due to the use of secondary data, the study adopts descriptive statistics and majorly includes variables such as the total levels of employment, GDP Growth, employment levels in terms of months and output levels to name a few.

The secondary data was collected for two variables, the first being the employment rates between 2015-2020. Graphical analysis was used in an attempt to see the levels of employment change over five years. The second variable taken is the employment rates between May and September in the year 2020. This data was collected to see the difference in employment levels

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in the last 5 years and over the last five months. The source for both the variables is from the World Bank database.

V. RESEARCH OBJECTIVES 1. To explain and prove the existence and concept of the paradox of toil 2. To examine its association with periods during and once after the covid pandemic 3. To interpret the current, future and probable effect of the paradox in the rates of employment.

4. To conclude and predict a decrease in aggregate employment due to the role of the paradox and state its limitations.

VI. SCHEME OF STUDY The research paper shall be organized as follows: The first section gives a brief introduction to the topic. It covers the research problem, the significance of the study, the research objectives, the methodology and the scope of the study.

The second section contains a detailed review of literature in the chosen field. The third section of the paper gives a brief explanation of the Paradox of toil. The fourth section gives a brief analysis of the current employment rates and following trends to establish the probable employment rates shortly which is by the end of the pandemic. The fifth section establishes the relationship between the pandemic and the paradox, interpretations and conclusions and the final section will include the limitations of the study.

VII. LITERATURE REVIEW 1. The paradox of Toil by G B Eggertsson: This paper proposes a paradox: the paradox of toil. Assume everybody awakens one day and chooses they need to work more. What happens to the aggregate income? This paper shows that, under specific conditions, total business falls; that is, there is less work in the aggregate since everybody needs to work more. The conditions for the paradox to apply are that the momentary nominal interest rate is zero and there are deflationary pressing factors and output contraction, much as during the Great Depression in the United States and, maybe, the 2008 monetary emergency in enormous pieces of the world. The oddity of work is firmly associated with the Keynesian thought of the paradox of thrift. Both are instances of the fallacy of composition.

2. A comment on Casey Mulligan’s test of the paradox of Toil by G Eggertsson: This note contemplates Casey Mulligan's experimental trial of the paradox of toil. Mulligan recommends that one can test it by checking if an expansion in labor supply, because of occasional components, diminished yield in 2009. This note shows that in the setting of the standard New Keynesian model this test is invalid. The note moreover examines two different tests proposed by Mulligan and recommends that they don't resolve the contention.

3. Pegging the interest rate on bank reserves: a resolution of New Keynesian puzzles and paradoxes by B Diba, O Loisel: Introducing bank stores (or cash) into the fundamental New Keynesian (NK) model offers a goal of NK puzzles and paradoxes. The subsequent models nearby balance determinacy under an exogenous financing cost on

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stores and an exogenous nominal load of banks, in any event, for a discretionarily little money related rubbing. The financial contact turns out to be vanishingly little, the models join to the fundamental NK model and serve to choose a specific balance of that model - which additionally offers a goal of the paradox of toil.

4. Unemployment in the time of COVID-19: A research agenda: This paper addresses the aggregate vision of a gathering of researchers in professional psychology who have tried to build up an examination plan because of the enormous worldwide joblessness emergency that has been evoked by the COVID-19 pandemic. The exploration plan incorporates investigating how this joblessness emergency may vary from past joblessness periods; analyzing the idea of the misery evoked by the equal loss of work and death toll; perceiving and tending to the advantage of researchers; looking at the imbalance that underlies the unbalanced effect of the emergency on poor and average communities; building up a structure for proof-based mediations for jobless people, and inspecting the work-family interface and joblessness among youth.

VIII. THE PARADOX OF TOIL Envision that everybody awakens that very day with the very same thought: to search for some more work. From the outset, the pedestrian rationale may appear to apply. Everybody would simply somewhat work more overall, apparently, in the process they would make more total work and yield.

This paper is about a mystery: Under specific suspicions, and in a specific climate, on the off chance that everybody attempts to work more, the truth be told, diminishes total work in balance. This is the paradox of toil. It comes under the fallacy of composition. Simply taking a gander at what one individual does, holding everything constant, can be deceiving whenever everything is accumulated or when we look at its aggregate. This is genuine even in a delegate specialist economy like the one concentrated in this paper, where all laborers look and act precisely equivalent.

The purpose behind this is general equilibrium: When everyone does something, nothing is steady. In models of general equilibrium, it very well may be profoundly deceptive to fabricate instinct and draw derivations taking a gander at just a single individual in incomplete balance.

What exceptional environment and what odd conditions lead to the paradox of toil? The necessary conditions are a standard New Keynesian dynamic stochastic general equilibrium (DSGE) model that is broadly utilized in policy making foundations and is the foundation of articles in numerous acclaimed journals. The basic assumptions or conditions for the model to work are- an intertemporal unsettling influence which prompts zero interest rates and descending tension on inflation and output. In the next part we analyse the three basic assumptions of the model: VIII.1 ZERO INTEREST RATE Zero-bound interest rate is a reference to the lowest limit being 0% for momentary financing costs, past which a money related approach isn't accepted to be powerful in enhancing monetary development. A zero-interest rate strategy (ZIRP) is the point at which a national bank sets its objective transient financing cost at or near 0%. The objective is to increase financial movement by empowering minimal cost borrowing and more prominent access to lower interest rate credit by firms and people. Since nominal interest rates are limited by zero, a few

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market analysts warn that a Zero interest rate strategy can have negative results, for example, making a liquidity trap.

VIII. 2 DOWNWARD PRESSURE ON INFLATION Demand for products rises, prices follow a similar trend. During times of high joblessness or high levels of unemployment, consumers buy fewer products, which squeezes prices and decreases inflation. Low inflation can be a sign of economic issues since it could be related to shortcomings in the economy. At the point when unemployment is high or consumer confidence is low, individuals and organizations might be less ready to make investments and spend on consumption expenses, and this lower request shields them from bidding higher costs.

VIII. 3 DOWNWARD PRESSURE ON OUTPUT Output refers to the total quantity of goods and services produced. Negative growth in output is a constriction in business deals or income. It is additionally used to indicate a contraction in a nation's economy, which is reflected in a diminishing number in its gross domestic product or GDP(which is the total output in a country in a particular year) during any quarter of a given year. Negative growth is ordinarily communicated as a negative growth percentage rate.

IX. WORKING OF THE PARADOX OF TOIL IN AN ECONOMY Everyone attempting to work more because of the intertemporal disturbance causes an unsettling influence that squeezes current and future wages. What then? Firms cut their costs today and later on and stand prepared to supply whatever is requested at those costs. Then what? This prompts assumptions for deflation, which increases the real interest rates - the difference between the nominal interest rates and expected inflation - and the national bank can't balance this by cutting the nominal rate in light of the fact that the rate can't be under zero.

Higher real interest rates lead to lower demand since individuals want to spend in the future as opposed to the present time, since costs are expected to be lower in the future(and along these lines the profit on savings or interest on savings is higher). On account of lower spending today, firms request less work. Along these lines, more work supply prompts lower wages, more deflation and also increases the interest rates, which results in lower spending, which prompts less recruiting of labourers. Therefore, here lies the paradox.

More work supply => lower wages => higher deflation => increased interest rates => lower spending => less hiring of workers.

Working of the paradox of thrift: The paradox of thrift is closely related to another paradox, the paradox of toil. John Maynard Keynes (1933) posed the accompanying inquiry: What occurs if an individual attempts to save more? Holding all the other things constant, that individual accumulates more savings. In any case, consider the possibility that everyone attempts this simultaneously. Their higher savings lead to less aggregate demand.

The lower aggregate demand, therefore, prompts lower total output and income, consequently diminishing the capacity of individuals to save. Eventually, at that point, aggregate savings reduce. Consequently, while the paradox of toil is that if everyone attempts to work more there will be less total business, the mystery of frugality is that if everyone attempts to save more

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there will be less aggregate savings. In the model of this paper, the paradox of toil applies with equivalent power as the paradox of thrift.

Figure 1 Source: Economics Help2

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Assume that because of falling expectations for living standards (for example falling real wages) everybody needs to work more hours. This will cause an expansion in labour supply and a fall in the wage rate. Falling wages lead to deflation assumptions and falling prices as firms can reduce the cost in light of lower wages. Because nominal interest rates can't fall under zero, deflation results in higher real interest rates.

Figure 2 Source: Economics Help3

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Greater real interest rates in an economy in recession decrease total interest further, prompting lower real GDP. With lower real GDP, firms see less demand for their products and in this way demand for labourers decreases too.

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https://www.economicshelp.org/wp-content/uploads/2016/05/effect-increased-supply-

wages.png 3

https://www.economicshelp.org/wp-content/uploads/2013/09/fall-in-ad-arrow-ad-as.png

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X. LIMITATIONS ● The hypothesis accepts flexible labour markets, yet wages are frequently sticky downwards. Indeed, even in the great recession of 2008-12, we frequently saw nominal wages resistant and sticky to falling. Subsequently, even in a liquidity trap, at the zerobound rate, an expansion in the labour supply doesn't necessarily have to reduce the real wages. ● National banks have proposed negative loan interest rates. In principle, this can conquer the issue of increasing real interest rates at the zero bound interest rate.

XI. COVID-19 AND THE EMPLOYMENT SITUATION IN INDIA The COVID-19 pandemic has impacted billions of lives and has undoubtedly shaken the world economy. Hundreds of millions of people lost their jobs or were laid off. The desire to work more has increased, and through the study, we try and analyse the impact of the situation on the aggregate employment situation in India. India’s employment situation between 2015-2020 has been dynamic. Numerous factors are responsible for shaping Indian employment.

XII. HISTORY OF UNEMPLOYMENT IN INDIA Unemployment is characterized as a circumstance where labourers who are fit for working and ready to work, don't look for some kind of employment. It is communicated as a proportion of the all-out number of jobless people to the absolute workforce. In India, business and joblessness are determined by the National Sample Survey Organization (NSSO) and the Labour Bureau. The NSSO gives evaluations of joblessness utilizing various methodologies and reference periods that group the action status of a person, which are utilized in the assessment cycle (Thind, 2013). Note: The Usual status approach utilizes a reference time of 365 days going before the date of review. The Current week by week status approach utilizes a reference time of 7 days going before the date of the study, while the Current day by day status approach utilizes every day of the first week as the reference time frame.

The issue of joblessness has been developing since Independence, and particularly so for the adolescent. In 2015, more than 30% of India's childhood was neither utilized nor in schooling nor preparing, probably the most elevated rate on the planet.

A few elements have added to the unemployment issue in India, including high population increase, the slow pace of monetary advancement (and occupation creation), joint family framework that causes a few people to rely upon a couple, just as the beginning of innovation.

Mehta (2019) additionally credits the caste system, the predominance of agriculture, fall of small industries and cottage industries (making craftsmen jobless), the moderate development of industrialization, fewer reserve funds and subsequently less speculation (henceforth, less work openings) and the deficiency of power, coal and crude materials in India.

XIII. LABOUR FORCE AND LFPR According to World Bank estimates, India had 476 million people in the workforce in 2015.

The Labour Force Participation Ratio (LFPR) was around 52 per cent. In 2016, The workforce had 480 million people. In 2018, the number of people had increased to 487 million. The LFPR was 47.98 per cent. In 2019, India had 494 people in its labour force. In 2020, it was expected

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to grow to around 500 million, with India adding around 6.6 million each year to its labour force.

Figure 3 Source: World Bank4

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XIV. UNEMPLOYMENT BETWEEN 2015-20 Data from the World Bank and CMIE (Centre for Monitoring Indian Economy) presents a vivid picture of the ever-changing unemployment situation in India. In 2015, All India Unemployment Rate was around 5.57 per cent. The Unemployment Rate increased to 6.1% in 2017 and fell to 5.8 per cent in 2018. The unemployment rate reached a high of 7.6 per cent in April of 2019.

Figure 4: India – Unemployment Rate ( 2015-20 ) Source: Author

XV. UNEMPLOYMENT DURING THE COVID-19 CRISIS https://data.worldbank.org/indicator/SL.TLF.TOTL.IN?end=2020&locations=IN&start=20 14&view=chart 4

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In 2020, the monthly unemployment rates varied greatly. In April 2020, India hit a recordbreaking low of 23.52 per cent. In May 2020, it dropped to 21.73 per cent and almost halved to 10.18 per cent in June of 2020. In December 2020, the unemployment rate was 9.06 per cent.

Table 1 Source: CMIE (Centre for Monitoring Indian Economy)5

1

The COVID-19 pandemic, and the ensuing lockdown estimates initiated, yielded an exceptional spike in India's joblessness rates, especially during the long stretches of April and May. The CMIE (Centre for Monitoring Indian Economy) ascertains its general joblessness rate by directing a review that incorporates over 174,000 family units over four months.

XVI. FORMAL SECTOR DURING THE COVID 19 PANDEMIC Information from the Centre for Monitoring Indian Economy (CMIE), a free body that measures and tracks various economic indicators, has said that salaried positions endured a very big hit during the COVID-19 lockdown, with the complete misfortune assessed to be at 18.9 million during April 2020-July 2020.

The situation of salaried representatives has deteriorated since the lockdown, with a deficiency of 17.7 million salaried positions in April, extra 0.1 million positions in May, trailed by an addition of 3.9 million positions in June and then again, a deficiency of 5 million positions in July.

XVII. INFORMAL SECTOR DURING THE COVID 19 PANDEMIC As organizations and industrial facilities stayed shut, more than 80% labourers in the informal sector lost work, as per a new overview by a non-profit organisation, Action Aid India.

5

https://unemploymentinindia.cmie.com/

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The thorough report was delivered on August 13, 2020. It depends on a review led during the third period of lockdown, between May 14 and May 22, under which more than 11,530 labourers across 21 states were met.

Around 89 per cent of the all-out labourers studied were working preceding the lockdown, four per cent were jobless and roughly seven per cent were in the work market and during the time spent searching for work, the report noted. Nonetheless, before the finish of the third period of lockdown, the level of the working population tumbled to eight per cent and inside a range of 60 days of lockdown, the joblessness rate had arrived at 78 per cent.

XVIII. UNEMPLOYMENT IN INDIA AND THE PARADOX OF TOIL Analysis Of The Assumptions XVIII.1. ZERO INTEREST RATE We are in a major philanthropic, clinical, and monetary emergency at present because of Covid19. These are for sure unprecedented occasions. Furthermore, extraordinary occasions require unprecedented measures. Notwithstanding, there can be a decision inside the class of extraordinary measures. Furthermore, that decision should be made cautiously. This choice is tied in with settling on a particular decision. Given the approaching downturn because of Covid-19, the RBI has diminished loan fees to empower financial action in India. RBI's repo rate is presently at 4% and the opposite repo rate is 3.35%. With impact from 31 May 2020, the loan fees for fixed stores at the State Bank of India (SBI) fluctuate somewhere in the range of 3.3% and 5.7%. Given the swelling rates (as seen over), the current genuine loan costs are extremely low, likely negative. This is, obviously, uplifting news for the borrowers, and this is without a doubt what the public specialists have expected. Nonetheless, RBI's financing cost strategy antagonistically influences the individuals who rely upon revenue pay – the retired folks, beneficiaries, widows, working-class individuals with hard brought in cash, altruistic trusts, inhabitant government assistance affiliations (RWAs), neighbourhood public bodies, non-administrative associations (NGOs), and the enrichment assets in instructive/research establishments. Considering the same rate of decrease in the interest rates of interest, there is a very huge chance that the government, once after the pandemic shall start charging zero-bound interest rates and may also touch down to negative rates if necessary.

XVIII.2. DOWNWARD PRESSURE ON INFLATION The Covid-19 emergency and lockdowns forced to control the spread of the infection, introduced a one-of-a-kind test for economies, including India. It was a synchronous demand and supply shock. Limitations on development affected the inventory, while the fall in positions affected pay and customary business exercises affected interest. Business analysts have been isolated on whether the COVID-19 emergency will end up being inflationary or deflationary.

Early signs ⁠—from inflation speculation surveys and now rising information ⁠—recommend a blend of powers prompting higher expansion feature. Financial analysts, notwithstanding, still accept that a breakdown sought after will, in the long run, lead to deflationary pressing factors overpowering any supply-driven swelling.

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XVIII.3 DOWNWARDS PRESSURE ON OUTPUT India's industrial output dropped steeply in May 2020, in what is the most recent indication of the serious monetary effect of the lockdown forced to stop the spread of the novel COVID-19.

As per government information, the Index of Industrial Production (IIP) shrunk by almost 35% for May, when contrasted with the very month a year back. While the figures show that the COVID-19-initiated lockdown significantly affected processing factory production, the May numbers are superior to the 57.6% compression announced for April 2020.

As indicated by the NSO's information, during May, production shrunk by 39.3%, contrasted with a much keener fall of 67.1% in April. Mining and power shrunk by 21% and 15.4% separately in May. The International Monetary Fund (IMF) in June said that India's economy would recoil 4.5% in the financial year 2021, in what was an inversion of its past conjecture of 1.9% development for the financial year.

XIX. INTERPRETATION The present employment situation in India fits the prerequisites for the paradox of toil to play in India. The unprecedented COVID-19 pandemic has added to the downward pressures on output and inflation. Further, the interest rates have also lowered. There is a possibility that we see zero-bound interest rates in the future.

With the layoffs, the unemployment rates in the country also increased, implying that people do not have work. Consequently, the aggregate unemployment levels have increased. It has ultimately led to them demanding more and more work. So, millions of people who lost their jobs are demanding work at the same time. With all conditions considered, we expect the total employment levels to drop in the future.

Thus, our assumptions hold, and the employment situation in India seems to fit within the framework of the paradox of toil.

XX. CONCLUSION The paradox of toil communicates that under explicit conditions if people wish to work longer hours, this can cause falling real wages and rising joblessness. The secret is that individuals have an inspiration to work longer, anyway if everybody needs to work longer hours, it can truly cause joblessness. It works under the assumptions of Zero nominal interest rates (zero bound rate), deflationary pressures and falling output. India's pace of jobless development had arisen as a top worry among business analysts who had over and again addressed how occupations were not developing as quick as the nation's GDP. They cautioned that the pace of jobless development could seriously affect India's economy which relies vigorously upon the working class populace, connected principally in salaried positions and business ventures.

While GDP development in India had been falling since the start of a year ago, the Covid stun in 2020 overwhelmingly affected India's economy and occupations. The COVID-19 appears to fulfil practically all the suspicions for the utilization of the paradox of toil. The COVID-19 pandemic, and the ensuing lockdown estimates initiated, yielded an exceptional spike in India's joblessness rates, especially during the long stretches of April and May. The scene after the pandemic will include laborers ready to work more than expected because of the express that the pandemic has pushed them to. With low pay, high layoffs and low expectation for everyday comforts, individuals will emerge from the pandemic to acquire more. In any case, as indicated

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by our paper, India will be home to the paradox of toil, squashing the expectations of laborers by decrease in total employment although the whole work populace will be anticipating work more diligently.

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Chetty, Raj, John N. Friedman, Nathaniel Hendren, and Michael Stepner. How did covid-19 and stabilization policies affect spending and employment? a new real-time economic tracker based on private sector data. No. w27431. National Bureau of Economic Research, 2020.

Cho, Seung Jin, and John V. Winters. "The distributional impacts of early employment losses from COVID-19." Available at SSRN 3602755 (2020) Coibion, Olivier, Yuriy Gorodnichenko, and Michael Weber. Labor markets during the COVID-19 crisis: A preliminary view. No. w27017. National Bureau of Economic Research, 2020.

Desai, Sonalde B., Amaresh Dubey, Brij Lal Joshi, Mitali Sen, Abusaleh Shariff, and Reeve Vanneman. "Human development in India." New York: Oxford University (2010).

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Manuscript. https://sites. google. com/site/gautieggertsson/research. Eggertsson, Gauti. The paradox of toil. No. 433. Staff Report, 2010 Kiley, Michael T. "Policy paradoxes in the New Keynesian model." Review of Economic Dynamics 21 (2016): 1-15.

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FROM ‘DRAGON’ TO THE LAND OF ‘ASCENDING DRAGON’: CAN INDIA BE ATMA-NIRBHAR BY REPLACING CHINA WITH VIETNAM IN ITS SUPPLY CHAIN - Kanika Gupta, Kashish Gupta 1 ABSTRACT The policy of Atma-nirbhar Bharat talks about making India a self-sufficient and selfgenerating economy. This self-reliance sentiment was bolstered by anti-China protests due to the Galwan valley standoff. In this context, many Indian nationalists have vouched for the boycott of “Made in China” products. This has polarised the whole idea of Atma-nirbhar Bharat as solely restricting the imports from China due to which the idea of substituting it with Vietnam has been hailed as a viable solution. This thus gives us enough impetus to empirically analyse the bilateral trade between India - China and India - Vietnam. Additionally, it is important for India to be selective and strategic while taking steps to discourage imports. Since electronics and chemicals constitute 70% share of total Chinese imports, this study primarily focuses on these two industries. In this context, this paper uses the tools of comparative data analytics and constitutes a Vector Error Correction Model (VECM) to analyse the efficacy of substituting Chinese imports with those from Vietnam from a short-term and long-term perspective.

I. INTRODUCTION India, China, and Vietnam initiated economic reforms around the same time and all have had a fair degree of success in making their respective economies more broad-based and integrated into the world economy. The following graph indicates the ‘Openness Index’ or trade to GDP ratio for these three nations.

Figure 1: Trade Dependence Ratio Source: World Bank Open Database

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Lady Shri Ram College for Women, Shri Ram College of Commerce, Delhi University

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From 1960 to 2008, there are continued instances of higher inclusiveness of trade in gross domestic product and relatively lower susceptibility to external shocks and volatility, ceteris paribus. The year 2009-10 has been identified as the year of structural break which is an implication of the global financial crisis. The global economy was in a slowdown mode due to which the contribution of international trade in the GDP for each nation has fallen by a great amount. In recent years, a majority of income for Vietnam comes from international trade whereas Indian and Chinese net exports contribute relatively less than consumption and investment to the national output.

The above diagram maps the economic growth effects of trade whereas the following chart depicts the economic welfare effect of greater market integration by juxtaposing per capita income with the Balance of trade for each nation:

Figure 2: Balance of Payment- China, India & Vietnam (1990-2019) Source: World Bank Open Database

Figure 3: GPD per capita China, India and Vietnam ( 1990-2019 ) Source: World Bank Open Database

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The graphs highlight that all three countries per head income have continuously risen throughout the years, but Chinese growth has been exponential. On the other hand, Chinese BOP showcases a W-shaped trend, which reached its maximum in 2007-08 and minimum in 2018-19. Where China and Vietnam have witnessed a surplus on international trade, India has imported more than what it exports, and this trend was further accentuated after 2008. A clear picture emerges here. The country that has built its manufacturing capacity to sustain the domestic demands and satisfy global needs is the country that has seen an exponential rise in income per head. The country that depends on foreign goods and services in the country with the lowest per capita GDP. Hence, it can be alleged that self-reliance through lower imports and greater exports will bring economic welfare for a nation.

The rate of import penetration can also measure such an advent of self-sufficiency. It is the ratio of import to domestic demand (GDP – net exports). The greater the extent of penetration, the lower the competitiveness of domestic suppliers and so poorer the scope of self-sufficiency.

Figure 4: Rate of Import Penetration Source: WITS The above-stacked area chart highlights that China is the most self-sufficient, whereas Vietnam satisfies most of its domestic demand from imports. India is also endeavouring towards selfsufficiency as China is.

This idea of self-sustenance has recently found new vigour in India. The coronavirus pandemic has proved to be a catalyst in rejuvenating the spirit of self-reliance and self-sufficiency in India. In this context, the Indian prime minister Mr. Narendra Modi launched Atma-nirbhar Bharat's scheme on 12 May 2020 amidst the lockdown to make India a self-dependent and selfgenerating economy.

"When the world is in crisis, we must pledge - a pledge which is bigger than the crisis itself.

We must strive to make the 21st century India's century. And the path to do that is selfreliance," PM Narendra Modi said.

To add to this idea, during COVID-19, global sentiments have accelerated against China.

Galway issue further strained the 'Hindi Chini bhai bhai' relationship. Since India and China share integrated bilateral trade, one of the natural perceptions of this was banning the Chinese import. Of late, the national sentiment has hence polarized the whole idea of Atma-nirbhar Bharat as solely restricting imports from China. Taking a step back, we need to analyse our dependence on China more rationally:

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Figure 5: % Share of China in India’s total import Source: WITS From less than 1% in 1990 to more than 14% in 2018, the bar diagram shows that China has gradually increased its share in the total imports of India to become the second-largest trading partner after the USA (The Economics Times 2020). On the contrary, India accounts for only 2.1% (Bureau 2018) of Chinese trade. Using simple maths, such a boycott will clearly hurt India more in the terms of both livelihoods and lives. Therefore, it’s worthwhile to access the feasibility of such a ‘wallet response’ which comes in the form of boycotts of ‘Made in China’ products. Undoubtedly, in the short term, this move might appear to be highly idealistic and not practical. But in the long run, the same can become a reality if India either finds alternatives to China or develops domestic supply chains by increasing production, or both in conjunction.

When we talk about diversifying imports, Vietnam seems to be one of the potential destinations. Low labour cost is one of the attractions for investment in Vietnam which is almost 50% lower than in China (Statista 2020). Vietnam’s biggest specialisations are in the post-2000production of electronics, textiles and furniture. However, Vietnam seems to lack the required expertise, competence, and capital-intensive machinery to take China’s position as of now. So it’s also worthwhile to examine Vietnamese dominance in India’s overall imports.

Figure 6: % Share of Vietnam in India’s total import Source: WITS

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India is now seeing Vietnam as a prospective import destination which is clearly highlighted by the bar diagram above. The exponential growth in import of Vietnam’s goods and services is bound to be witnessed in future also. When compared with China, the dependence of India on Vietnam’s commodities has been rising drastically whereas the same for Chinese products has become stagnant in the past 5 years. If this trend continues, India and Vietnam can witness greater economic integration in the coming 10 years.

Further dissecting the trade relations between India and China, the following graph speaks that electronics and chemicals form a major chunk of total imports from China. We import more than 50% of electronics and 20% of chemicals from China as of 2018. Before the 2000s, India majorly imported chemicals from China but post 2000 trends signify an increasing dependence on Chinese electronics to satisfy domestic demand for the same and muted import of Chemicals.

Figure 7: % Share of chemicals and electronics as imports from China Source: WITS Thus, when we talk about substituting Chinese imports, the limelight falls on the above two industries. Before jumping to conclusions, it’s worthwhile to understand the performance of Indian electronics and chemical industries in relation with China and Vietnam.

I.1 INDIAN CHEMICAL INDUSTRY India’s chemical industry ranks sixth (Gupta 2020) in the world in chemicals sales, contributing 3% (CII 2015) to the global chemical value chain. A consistent value creator, the industry has always remained an attractive hub of opportunities for investors and global businesses. The table below ranks the top region in terms of chemical manufacturing. No doubt, the relative market size of India is smaller than most of the other regions, but its CAGR is significantly higher.

Region

CAGR

Europe North America Latin America

2.2 2.8 4.4

Market Size billions) 176 176 26

2010

($ Market Size 2020 ($ billions) 218 231 40

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Middle east and 4.6 Asia Greater China 7.0 India 17.0 Southeast Asia 5.1 Japan 1.3

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113 22 41 78 Table 1 Source: McKinsey analysis (McKinsey n.d.)

20 221 100 68 89

A diversified manufacturing base has been a major growth driver of this industry. With a highly trained and scientific human resource base, it produces world-class products. Fig 7 shows a declining trend of reliance on Chinese imports for chemicals and so a greater impetus to domestic production. Even the Indian government provides a conducive environment like 100% FDI (Times 2019) for the growth of the chemical industry. During the COVID-19 pandemic, though some of the biggest health and economic challenges in decades came to the forefront, it has opened a myriad of opportunities for this sector. Plastics being part of the overall chemical industry, played a vital role in manufacturing ventilators, COVID-19 test kits, personal protective equipment (PPE), packaging material and so on. Such fortuitous moments have been bolstered by geopolitical tensions between China and the rest of the world. Stricter environmental norms, tighter financing and consolidation in China may lead the countries importing from China to shift their supply-chain base. This seems like a budding opportunity for India.

I.2 INDIAN ELECTRONICS INDUSTRY Over the years, rising digital penetration at the last mile in India has resulted in greater domestic demand for electronics. The country has struggled with ramping up the production on the same scale due to which it has to increase its dependence on imports for other nations, China being the front runner. The following graph shows the global standing of Indian industry relative to other nations:

Figure 8: Market Size of Electronics Industry in USD Billions ( 2016 ) Source: Statista analysis ( Statista, Statista 2016 ) As of 2020, India’s electrical industry was valued at $70 billion (Gurnaney 2020) and accounts for 3.3% of the global electronics market (Srinivasa 2020). Continuing along the same path,

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the industry is expected to grow at around 30% (Statista, Statista 2020) for the next five years.

This growth can be attributed to initiatives such as Make in India and the availability of internet at reduced costs.

The economic scenario is leading the countries to shift the supply-chain from China. This opens up a hub of opportunities for the Indian electronics industry. To achieve and facilitate this growth, the industry needs remedy from high taxation, a dearth of finance and power resources.

The table gives an overview of the total imports of electronics and chemicals and the respective share of China and Vietnam in these commodities for the past 10 years. Undoubtedly, China captures a majority of share in the import of both commodities. As of 2018, India imported approximately 26% of its chemicals and 34% of electronics from China whereas the respective share of Vietnam is 0.7% and 3.4%.

Years

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Total import of Chemi cals 3E+07 3E+07 3E+07 4E+07 4E+07 4E+07 5E+07 5E+07 4E+07 5E+07 7E+07

% of chemicals imported from China

% of chemicals imported from Vietnam

Total import of Electroni cs

16.06364 16.79263 20.65787 22.78415 22.90048 22.98011 25.99065 27.63891 24.53196 24.22365 25.66598

0.149307637 0.224617312 0.274538586 0.297425756 0.345686518 0.384586652 0.470216972 0.619989773 0.706304756 0.665079499 0.675984899

6E+07 6E+07 6E+07 8E+07 8E+07 8E+07 7E+07 8E+07 8E+07 1E+08 1E+08

% of electronic s imported from China 18.95582 28.11598 29.00781 30.00263 29.5201 31.87096 34.38765 37.23201 38.29511 41.26324 33.90456

% of electronics imported from Vietnam 0.059097 0.279362 0.667052 0.870502 1.113719 2.225545 1.722952 1.603207 1.502165 1.665131 3.361423

Table 2 Now, with regards to these 2 industries, it might be rewarding to study the interplay of both Indian exports and imports. On visualizing the intra industry trade of India for these two industries with respect of China and Vietnam using the Grubel Lloyd Index the following picture emerges: 𝐺𝐿𝑖 =

(𝑥𝑖 + 𝑀𝑖 ) − |𝑥𝑖 − 𝑀𝑖 | 𝑥𝑖 + 𝑀𝑖

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Figure 9: Grubel Lloyd Index for Intra Industry Trade Source: WITS When 𝐺𝐿𝑖 =1, the country in consideration imports as much it exports to a given region. When 𝐺𝐿𝑖 =0, there is no intra industry trade and either the country in consideration is solely importing or exporting to a given region. First, let us consider China. For electronics, this index has been close to 1 over the year which implies that India imports from and exports to China, though the magnitude of imports is undoubtedly greater than exports. When it comes to chemicals, only in the past decade the value of 𝐺𝐿𝑖 = 0.6 which again signifies a high incidence of a two-way flow of goods and services. Secondly, let’s consider Vietnam. India majorly imports chemicals from Vietnam (𝐺𝐿𝑖 = 0.2) but engages in both imports and exports for electronics (𝐺𝐿𝑖 = 0.8).

After analysing the status quo of intra industry trade, it might be valuable to look at the future potential and the relatively long term advantage India enjoys between the electronics and chemicals industry, for which the tool of revealed comparative advantage has been used:

Figure 10: Revealed Comparative Advantage Source: WTS This concept is based on the Ricardian comparative advantage concept, A value of less than one means than the country has a revealed comparative disadvantage. Here, India has this potential for chemicals but not for electronics clearly for both countries. So, it can be said India

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can walk the path of self-reliance for chemicals but developing trade resilience in the electronics industry needs structural policy reforms.

In this backdrop, the only way to reduce India’s dependency on Chinese products, in the long run, is by increasing its own domestic manufacturing base and satisfying the rest of the demand by diversifying imports, one of the potential destinations being Vietnam. With continued government support, it is worthwhile to see whether India can take over China as a manufacturing hub in the segments of electronics and chemicals.

II. LITERATURE REVIEW In the paper ‘Vietnam-India Economic Ties: Challenges and Opportunities since 2007’, Ngo Xuan Binh attempts to analyse the strategic partnership of India and Vietnam since 2007. Over the years, both countries have developed strategic economic ties, however issues such as similar export-import structure, geographical distance, cultural difference, transportation difficulties make the task at hand more complicated (Binh 2016).

A report “Chinese Blessing for the Chemical Industry” by CRISIL studies the trends of the chemical industry in China. In the decade 2009-2018, the industry witnessed a growth rate of 8-10%. This was a result of huge investments, research and development spread, easy access to low-cost capital and labour, relaxed environmental norms, and government subsidies.

However, over the next couple of years, the projected growth rate of the industry settles at around 6.25%. Change in global trade dynamics, stricter environmental regulations and the rising cost of labours are the major factors. This provides a golden opportunity for the Indian chemical industry to make its mark. However, in order to completely absorb such benefits, it becomes imperative for India to accelerate its investments in R&D, development of downstream speciality chemical and fiscal incentives (CRISIL n.d.).

A paper by NITI Aayog, “Make in India Strategy for Electronic Products”, weighs the two alternatives India has: export orientation strategy versus import substitution. The per capita income only doubled across a span of forty years when India practised import substitution. The growth since 1991 has enlarged the domestic electronics market in India, while at the same time, the global market remains relatively smaller. Thus, if we aim at the world markets, the scope for expansion is humungous. With China’s share in the global trade of electronics declining, this is a one-time opportunity for India to capture the global market (Aayog 2016).

Dipika Sahu in her paper “Impact of Bilateral Trade between India and China on Economic Growth of India” concluded that imports from China contributed around 11.5% to the Indian GDP while total exports to China during this period of consideration contributed about 14% to the GDP (Sahu, Impact of bilateral trade between India and China on the economic growth of India 2018).

A paper by Dr. Smita Dubey and Dr Harish K Dubey aims to study the preparedness of textile, automobile and pharmaceutical sectors to achieve self-reliance, problems on the way to Atmanirbhar Bharat and possible remedies. It concludes that Atma-nirbhar Bharat is a long-term concept that needs proper implementation of laws and proper allocation of funds (Dubey 2020).

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III. HYPOTHESIS Chemicals: In the long run India can practice a complete boycott of Chinese imports.

Electronics: It will be difficult for India to resist its vulnerability to Chinese imports in the short run. In the long run, dependence on Chinese imports can be reduced drastically and Vietnam can be seen as a potential alternative.

IV. RESEARCH METHODOLOGY AND RESULTS Chemicals: Tools of comparative data analytics have been deployed to study this sector, Data of production, consumption and imports has been taken from myriad sources to forecast these variables in the long run.

Electronics: The data analysis in the present study uses secondary data for three variables: Gross Domestic Product per capita and Import of electronics from China and Vietnam for 31 years from 1988 to 2018 in India which was sourced from the World Integrated Trade Solutions (WITS) database. A classical multivariable linear regression model suffers from high degree of multicollinearity, autocorrelation and non-normality of error terms which leads to spurious regression results. The presence of such specious regression ensures the absence of the linear relationship between the time series variables GDPPC and Import of electronics from the given countries. Therefore, this study motivates to investigate the existence of both short term and long-run association between the sample time series variables adopting Johansen cointegration and short-run causality of import of electronics from China and Vietnam on GDP and GDP on Import of electronics employing Vector Error Correction Model (VECM) and appropriate coefficient diagnostic test called wald statistics. Before testing the cointegration and causality the sample time series data was involved for stationarity testing using the popular Augmented Dickey Fuller (ADF). To determine the optimal lag length, the Akaike information criterion (AIC) has been used. Finally, the model is then checked for no autocorrelation, stability and normality of error terms. The entire data analysis has been performed in STATA econometrics software in this study.

IV.1. CHEMICALS On the basis of production capacity and consumption demand from 2014-2019, the following tables show the forecasted production capacity which will enable India’s domestic manufacturing base to meet its forecasted demand potential till 2030: Refer to Table 3 for the data.

A period of 6 years has been considered for calculating CAGR = 7.8%. The second column shows the forecasted production capacity if the industry continues to produce at the same rate.

In the best-case scenario, it can be expected to grow at a rate of 17% (Florian Budde 2020).

The consumption is taken to increase at a CAGR of 2.8%. The last column implies that by 2030 and so in the long run, by revamping its production capacity, India can satisfy its domestic demand without depending on China and also aim to be a net exporter of these products.

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Year 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Production (Billion KG) 0.96 0.96 0.98 1 1.1 1.4 1.509718 1.6280346 1.755623671 1.893211898 2.041582915 2.201581768 2.374119731 2.560179494 2.760820761 2.977186284 3.210508373

Best case scenarioProduction 0.96 0.96 0.98 1 1.1 1.4 1.61 1.8837 2.203929 2.57859693 3.016958408 3.529841337 4.129914365 4.831999807 5.653439774 6.614524536 7.738993707

Consumption (Billion KG) 4.2 4.2 4.5 4.6 4.8 4.9296 5.0626992 5.199392078 5.339775665 5.483949607 5.632016247 5.784080686 5.940250864 6.100637637 6.265354854 6.434519435 6.608251459

Shortfall (Billion KG) 3.24 3.24 3.52 3.6 3.7 3.5296 3.5529812 3.571357479 3.584151993 3.590737709 3.590433332 3.582498918 3.566131133 3.540458143 3.504534092 3.45733315 3.397743086

Best Case Scenario Shortfall (Billion KG) 3.24 3.24 3.52 3.6 3.7 3.5296 3.4526992 3.315692078 3.135846665 2.905352677 2.615057839 2.254239348 1.810336499 1.26863783 0.61191508 -0.180005101 -1.130742247

Table 3

IV.2. ELECTRONICS IV.2.1. AUGMENTED DICKEY FULLER TEST This test is used to check whether a given time series is stationary over time or not. If a series is showing a trend (upward trend in our case), this means that the series is non-stationary. To check more formally, a general multivariable double log regression has been performed with the following results: Log (GDP per capita) Log (import of electronics from Vietnam) Log (import of electronics from China) constant R-squared Adjusted R-squared F-statistic Prob.

Durbin Watson statistic No. of observations

Coefficient .0214784

Standard errors t-statistics .0288309 0.74

Prob.

0.464

.221629

.0534371

4.15

0.000

3.183076 0.9469 0.9421 196.16 0.0000 .2386804 25

.5342062

5.96

0.000

Table 4

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When R-squared > Durbin Watson statistic, as in this case, means that the outcome of regression is unstable and coefficients change sign on underfitting or overfitting and hence the model is non-stationary. It cannot be used for prediction and forecasting. So now the augmented dickey fuller test has been used to check for stability: Levels Variables Log (GDP capita)

Test statistic Critical Values per 0.096

Log (import of -1.574 electronics from Vietnam) Log (import of -1.952 electronics from China)

1% level 5% level 10% level 1% level 5% level 10% level 1% level 5% level 10% level

-2.492 -1.711 -1.318 -2.552 -1.734 -1.330 -2.479 -1.706 -1.315

First Difference Test Critical values statistic -3.318 1% level -2.500 5% level -1.714 10% level -1.319 -3.863 1% level -2.567 5% level -1.740 10% level -1.333 -3.728 1% level -2.485 5% level -1.708 10% level -1.316

H0: Variable is non stationary H1: Variables are stationary In the normal log form, for each variable test statistic < critical values at 1%, 5% and 10% significance level. Hence, we can not reject the null hypothesis.

On taking the first difference, for all the 3 variables, test statistic > respective critical values at the given level of significance, we reject the null hypothesis. Now, the model is stationary at the first difference. Hence, the first difference between these 3 variables will not be used for further analysis.

VISUAL REPRESENTATION OF STATIONARY AND NON-STATIONARY OF VARIABLES

NON-STATIONARY

b) STATIONARY

(a) All the 3 variables are mostly rising showcasing a constant trend over the years.

Especially, after the year 1995 it can be visualized from the line graph inserted here that the log of GDPPC and imports of electronics from Vietnam and China exhibits a rising trajectory. (b) Taking the first difference of these variables make them stationary around 0 as can be seen in the graph.

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IV.1.2. JOHANSEN – JUSELIUS COINTEGRATION TEST Cointegrathetion, an econometric property of time series variables, is a precondition for the existence of a long run econometric relationship between two or more variables having unit roots, integrated of order one. The Johansen approach shows that two or more random variables are cointegrated if each of the series is themselves non-stationary, and they have a long-run equilibrium relationship among the variables.

The precondition for applying Johansen Cointegration test is the variables must be non-stationary at level but when converting all the variables into the first difference then they will become stationary. The multivariate cointegration model can be expressed as: 𝑝−1

𝛥𝑦𝑡 = 𝛼0 + 𝜋𝑦𝑡−1 + ∑

𝑟𝑖 𝛥𝑦𝑡−𝑖 + 𝜀𝑡

𝑖≝1

where π and ri are coefficient matrices, p is the lag order based on AIC and 𝛥 is the symbol of the difference operator. Specifically, the maximum eigenvalue test and trace test are used to test for the number of cointegrating vectors which can be computed respectively as: 𝑛

𝑇(𝑟) = −𝑇 ∑

𝑙𝑛 (1 − 𝜆̂𝑖 )

𝑖=𝑟+1

𝜆(𝑟, 𝑟 + 1) = −𝑇 𝑙𝑛 𝑙𝑛 (1 − 𝜆̂ 𝑟+1 )

where 𝜆̂ 𝑖 is the expected eigenvalue of the characteristic roots and T is the sample size.

H0 (Trace test) = investigates the number of r cointegrating vectors against the alternative of n cointegrating vectors.

H0 (Maximum Eigenvalue test) = investigates the number of r cointegrating vectors against the alternative of r+1 cointegrating vectors.

The results are as follows: Cointegrating Null Alternative Trace Critical Max-L Critical regressors hypothesis hypothesis statistics value at 5% value at level 5% level Log of GDP r = 0 r=1 61.7619 29.86 47.528 20.97 per capita = 2 f(log of r ≤ 1 r=2 14.2337 15.41 9.8705 14.07 imports from r ≤ 2 r=3 4.3632 3.71 4.3632 3.76 Vietnam and China) H0 = There is no cointegration (r = 0) H1 = There is cointegration among variables Here ‘r’ means rank and if it is equal to 0 then the null hypothesis stands. Since the critical value is lesser than both trace statistic and Max-L statistics at r = 0, we reject the null hypothesis. At rank 0 there does exist cointegration. It is at rank 1, where both trace statistics and Max-L statistics are lower than the critical value at the 5% significance level, which means we do not reject the null and so there exists no cointegration among all variables. Since they are cointegrated, there exists both short term and long-run relationships among variables which are now further examined using the VECM test.

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IV.1.3. VECTOR ERROR CORRECTION MODEL The Vector Autoregressive (VAR) model is one of the special forms of system simultaneous equations. Model VAR can be applied only if the variables are not cointegrated. But since the variables taken here are both non-stationary and not cointegrated, VECM is used. It is a VAR model which has been designed to use white non-stationary data having a cointegrating relationship. It is one of the time series modelling which can directly estimate the level to which a variable can be brought back to equilibrium condition after a shock on other variables. VECM is very useful by which to estimate the short-term effect for both variables and the long-run effect of the time series data.

A Vector Error Correction Model (VECM), which can be derived from the long-run cointegrating vectors, can be used to determine the direction of this causality. 𝑘−1

𝑘−1

𝑘−1

𝛥𝑌𝑡 = 𝛼 + ∑

𝐵𝑖 𝛥𝑌𝑡−𝑖 + ∑

∅𝑖 𝛥𝑋𝑡−𝑗 + ∑

𝑖=1

𝑗=1

𝑚=1

𝑘−1

𝑘−1

𝑘−1

𝛥 𝑋𝑡 = µ + ∑

𝐵𝑖 𝛥𝑌𝑡−𝑖 + ∑

∅𝑖 𝛥𝑋𝑡−𝑗 + ∑

𝑖=1 𝑘−1

𝑗=1 𝑘−1

𝑚=1 𝑘−1

𝛥𝑍𝑡 = 𝜃 + ∑

𝐵𝑖 𝛥𝑌𝑡−𝑖 + ∑

∅𝑖 𝛥𝑋𝑡−𝑗 + ∑

𝑖=1

𝑗=1

𝑚=1

𝜌𝑖 𝛥𝑍𝑡−𝑚 + 𝜆1 𝐸𝐶𝑇𝑡−1 + 𝑒𝑟𝑟𝑜𝑟 𝑡𝑒𝑟𝑚

𝜌𝑖 𝛥𝑍𝑡−𝑚 + 𝜆2 𝐸𝐶𝑇𝑡−1 + 𝑒𝑟𝑟𝑜𝑟 𝑡𝑒𝑟𝑚 𝜌𝑖 𝛥𝑍𝑡−𝑚 + 𝜆3 𝐸𝐶𝑇𝑡−1 + 𝑒𝑟𝑟𝑜𝑟 𝑡𝑒𝑟𝑚

where, k-1 = optimal lag length reduced by 1 𝐵𝑖 , ∅𝑖 , 𝜌𝑖 = short-run dynamic coefficients of the model’s adjustment long-run equilibrium 𝜆𝑖 = speed of adjustment parameter with a negative sign 𝐸𝐶𝑇𝑡−1 = the error correction term is the lagged value of the residuals obtained from the cointegration regression of the dependent variable on the regressors. It contains long-run information derived from the long-run cointegrating relationship. 𝑌𝑡 = Log of GDP per capita, 𝑋𝑡 = Log of imports of electronics from Vietnam, 𝑍𝑡 = Log of imports of electronics from China The VECM results are as follows: Variables

𝑌𝑡−1 𝑌𝑡−2 𝑋𝑡−1 𝑋𝑡−2 𝑍𝑡−1 𝑍𝑡−2 ECT Constant

Log of GDP per Log of electronics Log of electronics imports from Vietnam imports from China (Z) capita (𝑌) (X) .3011909 -3.208052 1.69466 -.1269102 -1.941218 .3131986 .0537457 .1862277 -.0652684 -.1042307 .0350547 -.2430585 -.1513046 .0694316 -.5891285 .2084848 1.503766 .6636715 -.0803116 .8715176 -.724025 .0863859 .2610035 .3045909

NOTE: Here orange represents significance at 5% level and blue shows significance at 10% level.

Towards Equilibrium 2021 | 125 𝑘−1

𝑘−1

𝑘−1

𝛥𝑌𝑡 = 𝛼 + ∑

𝐵𝑖 𝛥𝑌𝑡−𝑖 + ∑

∅𝑖 𝛥𝑋𝑡−𝑗 + ∑

𝑖=1

𝑗=1

𝑚=1

𝜌𝑖 𝛥𝑍𝑡−𝑚 + 𝜆1 𝐸𝐶𝑇𝑡−1 + 𝑒𝑟𝑟𝑜𝑟 𝑡𝑒𝑟𝑚

𝛥𝑌𝑡 = 0.09 + 0.30𝛥𝑌𝑡−1 − 0.13𝛥𝑌𝑡−2 + 0.05𝛥𝑋𝑡−1 − 1.04𝛥𝑋𝑡−2

−0.15𝛥𝑍𝑡−1 + 0.21𝛥𝑍𝑡−2 − 0.08 𝐸𝐶𝑇𝑡−1 Where, 𝑌𝑡 = Log of GDP per capita 𝑋𝑡 = Log of imports of electronics from Vietnam 𝑍𝑡 = Log of imports of electronics from China The adjustment term (-0.08) is not statistically significant suggesting that deviations in GDP per capita does not converge in the long run. 𝑘−1

𝑘−1

𝑘−1

𝛥 𝑋𝑡 = µ + ∑

𝐵𝑖 𝛥𝑌𝑡−𝑖 + ∑

∅𝑖 𝛥𝑋𝑡−𝑗 + ∑

𝑖=1

𝑗=1

𝑚=1

𝜌𝑖 𝛥𝑍𝑡−𝑚 + 𝜆2 𝐸𝐶𝑇𝑡−1 + 𝑒𝑟𝑟𝑜𝑟 𝑡𝑒𝑟𝑚

𝛥𝑋𝑡 = 0.26 − 3.21𝛥𝑌𝑡−1 − 1.94𝛥𝑌𝑡−2 + 0.19𝛥𝑋𝑡−1 + 0.04𝛥𝑋𝑡−2

+ 0.07𝛥𝑍𝑡−1 + 1.50𝛥𝑍𝑡−2 − 0.20 𝐸𝐶𝑇𝑡−1

The adjustment term (-0.20) is not statistically significant suggesting that deviations in imports of electronics from Vietnam do not converge in the long run. 𝑘−1

𝑘−1

𝑘−1

𝛥𝑍𝑡 = 𝜃 + ∑

𝐵𝑖 𝛥𝑌𝑡−𝑖 + ∑

∅𝑖 𝛥𝑋𝑡−𝑗 + ∑

𝑖=1

𝑗=1

𝑚=1

𝜌𝑖 𝛥𝑍𝑡−𝑚 + 𝜆3 𝐸𝐶𝑇𝑡−1 + 𝑒𝑟𝑟𝑜𝑟 𝑡𝑒𝑟𝑚

𝑍𝑡 = 0.30 + 1.70𝛥𝑌𝑡−1 + 0.31𝛥𝑌𝑡−2 − 0.07𝛥𝑋𝑡−1 − 0.24𝛥𝑋𝑡−2 − 0.59𝛥𝑍𝑡−1 + 0.66𝛥𝑍𝑡−2 − 0.17 𝐸𝐶𝑇𝑡−1 The adjustment term (-0.17) is significant at 5% level suggesting that previous year’s errors (or deviations from long-run equilibrium) are corrected for within the current year at a convergence speed of 17%.

In addition to determining the direction of the causality, these equations may also be used to distinguish the short-run and the long-run relations between these variables. If variables are cointegrated, in the short run, deviations from this long-run equilibrium will feed back on the changes in the dependent variable in order to force the movement towards the long-run equilibrium. If this long-run equilibrium error directly drives the independent variable then it is responding to this feedback. If not, it is responding only to short-term shocks to the stochastic environment.

The short-run and long-run causality will be tested through two test procedures on the significance of the independent variables in the above mentioned 3 equations. First, the shortrun causality, in the sense that the dependent variable reacts to only short-term shocks coming from the independent variables will be tested by employing the standard Wald test. The second type of granger causality, namely the long-run causality, is represented by the coefficients on

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the ECT’s. The significance of these coefficients, 𝜆𝑖 will be tested by using a conventional ztest.

IV.1.4. WALD CAUSALITY TEST The cointegration test indicates a long-term equilibrium relationship among the given variables, but, in terms of a causal relationship, further testing is needed. Say in a 2 variables case, if variable A is helpful in predicting B, namely, the regression of B is based on past values of both A and B then, this can greatly enhance the explanatory ability of the regression.

Cointegration may detect the existence of a long-run relationship, it is incapable of indicating the direction of the causal effect between these variables. In this regard, a Wald test was performed to study short-term casualties. It computes a test statistic based on unrestricted regression and measures how close these estimates come to satisfy the restrictions under the null hypothesis. The results are as follows:

H0: Null hypothesis Imports of electronics from China does not cause GDP per capita in the short-run Imports from Vietnam do not cause GDP per capita in the short run.

GDP per capita does not cause the import of electronics from China in the short run.

GDP per capita does not cause imports from Vietnam in the short-run.

Chi-squared 4.28

Prob.

0.1178

3.76

0.1528

5.79

0.0553

0.61

0.7373

Decision Don’t reject Don’t reject Reject Don’t reject

This indicated no bidirectional short-term casualties between GDP per capita and electronics imports from Vietnam. But there exists a unidirectional near run casualty when it comes to China since GDP per capita causes imports of electronics from China and not vice versa.

Hence, imports from both countries do not cause income per head in the short run.

IV.1.5. GRANGER CAUSALITY TEST: H0: Null hypothesis

Chi squared GDP per capita does not granger cause import of 16.616 electronics from China GDP per capita does not granger cause import of 2.8742 electronics from Vietnam Imports from China does not granger cause 15.595 import of electronics from Vietnam Imports from Vietnam does not granger cause 1.305 import of electronics from China Imports of electronics from China does not 8.4853 granger cause GDP per capita Imports of electronics from Vietnam does not 5.4329 granger cause GDP per capita

Prob.

Decision

0.000

Reject

0.238

Do not reject

0.000

Reject

0.521

Do not reject

0.014

Reject

0.066

Reject

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The table signifies that in the long run causality runs in a bidirectional form for GDP per capita and import of electronics from China but runs unidirectionally for income per head and imports from Vietnam.

IV.1.6. LONG RUN MODEL (VECM) Log (GDP per Coefficient capita) Log (import of -.222322 electronics from Vietnam) Log (import of .2290325 electronics from China)

Standard error

Z statistics

Prob.

.0225907

-9.84

0.000

.0418832

5.47

0.000

𝐸𝐶𝑇𝑡−1 = 𝑌𝑡−1 − 𝜂𝑗 𝑋𝑡−1 − 𝜀𝑚 𝑍𝑡−1 𝐸𝐶𝑇𝑡−1 = 𝑌𝑡−1 − 0.22𝑋𝑡−1 + 0.23𝑍𝑡−1 There exists a long-term correlation between the log of electronics from China and Vietnam on GDP per capita. In the long run, a per cent increase in imports from China leads to a fall in income per head of India by .23% and an increase in the same by .22% when imported from Vietnam.

V. CONCLUSION Chemicals: If India were to produce at a CAGR of 17% for the next 10 years then the country will be highly self-reliant and self-sufficient in meeting its domestic demand and can end up as a major net exporter of chemicals. So, it can be said that in the short run even if India is not able to practice a complete boycott of Chinese imports, it can do it in the medium to long run, that is, in the next 8-10 years.

Electronics: The cointegration tests show that there exists a long-run equilibrium relationship among GDP per capita and import of electronics from China and Vietnam and the equation of which has been generated using the VEC model. For the short run, Wald test indicates that there exist unidirectional short-run casualties wherein income per head causes GDP to imports from China but not with Vietnam. It can be said that when disposable income in hands rises, a person would spend it on information technology and since the majority of the demand for electronics is satisfied by imports and Chinese imports dominate the import share, hence rise in spending gives rise to imports from China. As exhibited by the Granger causality test, in the long run there runs a bisectional causality between GDP per capita and imports from China wherein both reinforce each other. On the other hand, Vietnamese imports granger causes GDP per capita. This is also verified by the long run model formed by the VEC model. More specifically, since it has been established that an increase in imports from Vietnam increases income per head by 0.22% but imports from China reduces the same by 0.23% it makes economic sense to reduce dependency on China. This can be done in the long run by focusing on structural reforms like diversifying imports not only from Vietnam but also from Malaysia and Singapore, boosting FDI and hence disengaging with China.

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REFERENCES Aayog, Niti. 2016. "Make in India Strategy for Electronic Products."

Binh, Ngo Xuan. 2016. "Vietnam–India Economic Ties: Challenges and Opportunities since 2007*." SAGE Journals .

Bureau, PhD Research. 2018. India – China Trade Relationship: The Trade Giants of Past, Present and. New Delhi : PHD CHAMBER OF COMMERCE AND INDUSTRY. CII. 2015. Confederation of Indian Industries . https://www.cii.in/sectors.aspx?enc=prvePUj2bdMtgTmvPwvisYH+5EnGjyGXO9hLECvTu NskmvgGjRIRgzWycLq2dn10. CRISIL. n.d. "Chinese blessing for." CRISIL Journal.

Dubey, Smita. 2020. "ATMANIRBHAR BHARAT ABHIYAN: AN ANALYTICAL REVIEW." Dogo Rangsang Research Journal .

Florian Budde, Pinak Dattaray, Tejas Dave, Avinash Goyal, Suyog Kotecha, and Karthikeyan K S. 2020. McKinsey . 28 February https://www.mckinsey.com/industries/chemicals/ourinsights/indias-chemical-industry-unleashing-the-next-wave-of-growth.

Gupta, Deepti. 2020. Invest India . https://www.investindia.gov.in/sector/chemicals.

Gurnaney, Tina.

2020.

The Economics Times .

06 January . https://telecom.economictimes.indiatimes.com/news/indian-electronics-market-expected-toreach-400-billion-by-2020-assocham-nec-study/59107000.

McKinsey. n.d. "McKinsey of chemicals ."

Sahu, Dipika. 2018. "Impact of bilateral trade between India and China on economic growth of India." International Journal of Management, IT & Engineering.

Srinivasa, Kavitha. 2020. Engineering & Technology . 03 April. https://eandt.theiet.org/content/articles/2020/04/view-from-india-1bn-electronics-innovationfund-proposed/.

Statista. 2020. Statista . https://www.statista.com/statistics/744071/manufacturing-laborcosts-per-hour-china-vietnam-mexico/.

—. 2016. Statista . https://www.statista.com/statistics/268398/market-size-of-the-globalelectronics-industry-by-country/. 2020. Statista. https://www.statista.com/outlook/251/119/consumer-electronics/india.

The Economics Times. 2020. The Economics Times. 23 February. https://economictimes.indiatimes.com/news/economy/foreign-trade/us-surpasses-china-tobecome-indias-top-trading-partner/articleshow/74264765.cms.

Times, The Economics. 2019. The Economics Times . 27 August . https://www.financialexpress.com/economy/government-says-fdi-in-chemical-industry-verylow-asks-industry-to-introspect/1687637/.

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APPENDIX 1. The skewness of the variables in the double log regression model and log transformation: The variables considered in the VECM are in the log form to ensure normality and stationery of the same.

Variables Skewness Log skewness

Import of electronics Import of electronics from China from Vietnam 1.099963 2.623952 -.7318895 -.1691425

Indian per capita GDP .7361335 .3176243

2. Testing for autocorrelation: Serial correlation or autocorrelation occurs when the error terms in the model are related. When autocorrelation is present, the OLS procedure still produces unbiased estimates but increases the variances hence the OLS estimators ceases to be BLUE. Using the Lagrange-multiplier test: H0 = There is no autocorrelation Lags 1 2

Chi-squared 13.2851 8.8005

Prob.

0.15012 0.45589

Since the p-value is more than 0.05, we cannot reject the null hypothesis and hence the model does not suffer from the problem of autocorrelation.

3. Testing for normality of errors: This means while taking the average, positives tend to cancel the negatives of the error terms and hence mean=0. The central limit theorem ensures that errors follow such distribution for large observations. The t-test and F- test are not applicable unless the error term is normally distributed. Using the Jarque-Bera normality test on the residuals: H0: errors are normal

Equation Chi-squared 0.010 𝜟log of GDP per capita 𝜟log of electronics imports from China 0.378 𝜟log of electronics imports from 0.192 Vietnam All 0.579

Prob.

0.99522 0.82790 0.90843 0.99673

Since the p-value is more than 0.05, we cannot reject the null hypothesis and hence the residuals are normally distributed.

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4. Testing for model stability Using the eigenvalue stability condition: Eigenvalue 1 1 -.8601394 .6723458 + .4080283i .6723458 - .4080283i .7792462 -.09342306 + .723929i -.09342306 - .723929i -.6185566

Modulus 1 1 .860139 .786471 .786471 .779246 .729932 .729932 .618557

The VECM specification imposes 2-unit moduli and hence the model is stable.

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BRIDGING THE GAP: CONTINUED RELEVANCE OF DEVELOPMENT BANKS -Aneesha Chandra, Vaidehi Maheshwari1 I. INTRODUCTION Interest in development banks has seen a resurgence, particularly within policy discourse on how to bolster economic recovery and growth in the face of the COVID-19 pandemic and resulting economic contraction. Renewed support for development banks comes from a recognition of the limitations and vulnerabilities of the private financial sector. Dominated by commercial interests, private institutions are procyclical – over-lending in times of boom but rationing credit in times of need. Thus, there is a need for institutions that could provide countercyclical lending in addition to Central Banks.

Even in the absence of a crisis, private financial institutions have generally failed to translate savings into long term investments that are required to fund innovation and infrastructure development – such as in electricity and power generation, communications, roads and transport, ports, and so on. Such investments are vital as they generate economy-wide externalities – they enable business and industrial growth, provide employment and lead to the accumulation of skills, capital, and technology in the long run. As Chandrashekhar (2016) notes, the financial sector in most emerging market economies is insufficiently developed, under-diversified and bank-dominated. Typical commercial banks in such countries attract deposits from small savers with a preference for liquidity, short maturity periods, and low risk.

On the other hand, small, backward economies may be unable to fulfil the capital requirements needed to undertake such lumpy investments. Hence obtaining funds for large scale, high risk, illiquid infrastructure projects is a major challenge for industrial development in most of the third world today.

Development banks are well-suited to play a promising role in promoting sustainable economic recovery, structural transformation, innovation, and financial inclusion. In most cases, these institutions are mandated to provide credit assistance to certain key sectors and make investments viable. Not only do they provide short-term working capital, but they also finance long term investments. Apart from these functions, DBs are equipped to monitor firm activities to protect investments and take corrective actions. Many DBs are also involved in early-stage decisions such as “choice of technology, scale and location, requiring the acquisition of technical, financial and managerial expertise” (Chandrashekhar 2016).

However, the history of NDBs around the world has been marred by significant failures. Thorne and du Toit (2009) note that heavy financial losses and vulnerability to political capture led to NDBs delivering little in terms of development but deepening the fiscal crises in developing countries in the 1970s. Moreover, as the post-war era of extensive public spending gave way to financial liberalisation, many NDBs were privatised, restructured, or completely dissolved.

Public intervention to resolve these aforementioned market failures shifted to measures such as the provision of risk insurance, support for debt securitization, and market making, rather than traditional bank credit (Fitzgerald 2006).

1

Ashoka University

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Nevertheless, those banks that persisted provide an example for the successful and sustainable functioning of their kind. Moreover, market failures – including the ones that NDBs were intended to ameliorate – still exist today and hamper development. Efforts to resolve them through other government measures have proved to be insufficient and developing countries still experience a shortage of long-term finance and finance for high-risk sectors (Thorne & Du Toit 2009).

This paper argues that development banks can still play a critical role in bridging the gap left behind by traditional institutions – particularly in terms of financing infrastructure development. In the following sections, we briefly outline the various theoretical arguments that underpin the need for development banks.. We use existing surveys to explain the key features of national development banks (NDBs) and their organisation and funding practices.

Following that, we weigh the pros and cons of government intervention in infrastructure development. We present the model of an effective NDB in Germany to highlight the factors that underpin its success. Finally, we conclude with some policy recommendations and pointers for future research.

II. THE CASE FOR GOVERNMENTAL INTERVENTION Conventional wisdom in finance holds that government intervention can be justified when there are significant market failures. The first fundamental theorem of welfare economics states that under certain conditions, resource allocation attained by the free market is a Pareto optimal outcome. These conditions as summarized by Mazzucato & Penna (2016) are as follows: (1) There exists a complete set of markets, so that all supplied/demanded goods and services are traded at publicly known prices; (2) All consumers and producers behave competitively (all agents are price-takers); and (3) An equilibrium exists. (pp. 8) Thus, market failure occurs when any of the conditions are violated. In such cases, the inefficient allocation of resources provides space for public policy to regulate the market.

Despite concerns about government failures – a situation where public intervention leads to misallocation of resources and creates further inefficiencies – the idea is that their benefits will outweigh the associated costs.

Based on the work of Mussachio et al (2017), Stiglitz (1991; 1981) and Mazzucato & Penna (2016), we identify seven different market failure-based justifications for government intervention. One of the areas where market failure has been glaringly obvious is in the procyclical nature of most private financial institutions in the current system. Procyclicality of lending activities implies that they follow the upturns and downturns of the capitalist business cycle, thus threatening to increase volatility in the economy and making it prone to bouts of inflation and deflation. Coordination failure hampers collective action that is needed to restore stability. In such cases, private agents are unable to accurately and confidently identify expectations and preferences as a result of information asymmetries. There are clear positive externalities to the provision of information, however high screening costs and free-rider problems may disincentive individual firms or persons from information generating activities.

Thus, inadequate information leads to uncertainty which makes private credit markets hesitant to extend liquidity resulting in credit rationing problems.

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These problems are compounded by moral hazard and adverse selection challenges. Capital investments present another situation of market failure that arises when there are significant social benefits to undertaking such projects, but there lacks a market to facilitate the transfer of profits from such investments to private investors. These projects include the building of infrastructure, provision of resources like clean water or air, or the financing of research and development. The public good nature of such projects makes them extremely valuable to the economy, but the failure to internalise these benefits (due to reasons such as incomplete contracts or insufficient enforcement of property rights) leads to serious underinvestment in them.

Moreover, as Stiglitz (1991) points out, there could be certain industries characterised by high natural barriers to entry that could lead to competition failures. These include situations where natural monopolies, network effects, supply and demand-side economies of scale are at play and which could lead to Pareto inefficient outcomes (Mazzucato & Penna, 2016, 15). Insofar as one assumes that the state is risk-neutral, sovereign intervention in such situations may help spread risk over time and space. State-led countercyclical or infrastructure investment may ameliorate the causes of these market failures by signalling confidence and generating positive externality through increased demand (Gutierrez et al, 2011, p.8).

III. NATIONAL DEVELOPMENT BANKS An NDB, for our purposes, will be defined as a bank or financial institution with at least 30% state-owned equity that has been given an explicit legal mandate to reach socio-economic goals in a region, sector or particular market segment (De-Luna Martinez & Vicente, 2012).

III.1. Mandate Development Banks have been established with a wide range of policy or developmental mandates. They have five crucial roles to play in development: (i) counteract the pro-cyclical nature of private finance (ii) promote innovation and structural transformation (iii) enhance financial inclusion (iv) finance infrastructure investment and (v) support the provision of public goods. Public funds possess special knowledge of local financial markets and barriers to investment that uniquely positions them to provide long-term climate finance. NDBs can invest in sustainable development projects whose risks the private sector will not bear (Griffith-Jones et al, 2018, 3).

III.2. Ownership A survey of ninety development banks in sixty-one low and middle-income countries finds that 74% were entirely government owned and controlled, while in another 21% the private sector participated as a minority shareholder (De-Luna Martinez & Vicente, 2012).

III.3. Funding The sources that banks used to fund their business operations are summarized in Table 1, with most banks combining all of the options. 89% of the banks claim that they can borrow from other financial institutions or issue debt – guarantees that permit them to borrow at a lower cost. As the authors point out, “the provision of guarantees can be regarded as an effective way of funding DBs, provided they do not compete with the private banks” (p. 11).

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Table 1: Funding Features of DBs Source: “Global Survey of Development Banks”, by J. De Luna-Martínez, & C. L. Vicente, 2012, the World Bank, p.10. Copyright 2012 by the World Bank

III.4. Key Features Trabacchi et al (2015) summarize the salient features of NDBs that could unlock their potential in scaling up private funding:

Figure 1: Key Features of National Development Banks Source: “The Role of National Development Banks in Catalyzing International Climate Finance: Empirical Evidence from Latin America” by C. Trabacchi, et al, 2014, Handbook of Climate Change Adaptation, p. 27.

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III.5. Financial Instruments The financial instruments that NDBs have at their disposal include grants and loans (Trabacchi et al, 2015). Pre-investment, grants can provide technical assistance. They can be used to lower interest rates when the project is investment-ready and can be a guarantee fund in case of losses.

Direct loans, where the NDB assumes most or all of the credit risk, are known as Tier 1 loans.

If transactions are too large to be funded by a single local financial institution (LFI), NDBs can be an additional creditor. A third instrument is Tier 2 loans that are made by NDBs to financial intermediaries, where the LFI assumes the credit risk of the project. Each model of lending has distinct advantages and disadvantages (De Luna-Martinez & Vicente, 2012). Under Tier 1, the banks deal with customers directly, requiring them to have a large number of branches across the region. However, under this model, the customers can be offered lower interest rates as there are no middlemen depleting resources. Additionally, the development bank assumes all the credit risk. With the Tier 2 lending model, the banks can operate with lower transaction costs. The private financial institutions bear the burden of assessing and accepting loan applications. In this way, the banks can reach more customers and more locations. Under this model, the LFIs are given the opportunity to grow and reach under-served populations, under the umbrella of the development bank. The credit risk is also partially absorbed by the private institutions. Additionally, second-tier development banks report a lower ratio of nonperforming loans than their first-tier counterparts. On the other hand, the interest rates offered to customers are higher as the added cost of intermediation is passed on to them. In practice, 52% of the surveyed development banks claimed to lend through a mix of Tier 1 and Tier 2 loans, while 36% lend only through Tier 1, and the remaining 12% only through Tier 2.

IV. INFRASTRUCTURE DEVELOPMENT One of the commonalities among NBD mandates is an explicit goal to aid structural transformation. According to standard growth models in economics, infrastructure development influences overall economic development through several channels – these include but are not limited to facilitating production processes, stimulating economic activities, reducing transaction costs and improving competitiveness, and generating employment opportunities for the poor (Muhammad, 2019). Historically, it has been state-owned development banks that have spearheaded the process of industrialization in many countries.

As a case at hand, consider the BNDES, which directed close to 90 percent of its funding capacity towards public sector projects like the development of transportation and power generation in the first decade of its establishment in 1952. While it has expanded significantly in scope and size since then, 70 percent of long-term credit in Brazil in 2010 still came from BNDES (Chandrashekhar, 2016, 24). Similarly, in the context of China, the CDB has financed large scale infrastructure and urbanisation projects valued at more than US $2 trillion (Mazucatto et al, 2016).

V. ADVANTAGES OF STATE-LED DEVELOPMENT While a number of authors have documented the impact of the presence of public vs private banks on aggregate economic development, there are few studies that systematically break down the impact of their investments on different sectors. In this regard, the work of Muhammad Javid (2019) in the context of Pakistan is particularly noteworthy. They empirically test for the differential impact of public and private infrastructure investment on both the aggregate output and the subsectors of the economy (namely, industries, agriculture, and services) using a fully modified ordinary least squares (FMOLS) technique to account for potential endogeneity and reverse causality issues. They find the estimated elasticity of public

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infrastructure investment in each of the sectors (0.32, 0.11, and 0.06 respectively), except agriculture, to be greater in magnitude and statistically more significant than that of private investment. In particular, they note that public investment in infrastructure reduces the marginal costs of private production, with the scale effects encouraging higher private investment. This underscores the complementary nature of public investment in certain sectors.

Initial investment by the state could reduce the investing constraints faced by private lenders in the future.

Interestingly, while Pakistan provides one case where the state and bank-led infrastructure development lead to positive results, Zou (2006) finds that in the US, private investment contributed more to economic growth than public investment. However, this should not be surprising – countries with well-established financial markets gain more from market-led than government-directed investments. Despite this, de Rezende (2018) argues that “even in advanced economies where there exist deep and sophisticated financial markets, and regulatory, macroeconomic, and political risks are relatively low compared to other economies, institutional investors shied away from infrastructure development” (p. 19). Brownfield projects, characterised as having already built and operating assets, are more attractive as investment options to private lenders since they offer predictable revenue streams and less risk.

In such cases, NDBs are needed to identify and initiate new projects and offer their accumulated experience and technical expertise. Even when the process of infrastructure building is segmented and parts are contracted out to private entities, NDBs may play a useful role in the provision of guarantees by assuming some proportion of the risk that makes the investment profitable to private lenders, without taking on excessive liabilities.

Similarly, because NDBs are less prone to following macroeconomic trends in lending, they become major sources of finance during crises or times of bust. Micco and Panizza (2004) evaluate the impact of macroeconomic shocks on bank lending behaviour vis-à-vis different ownership categories. With an extensive bank level dataset consisting of observations from 1995-2002 for 119 countries, they find that bank lending decreases less during recessions and increases less during expansions for those owned by the state. This, they note, could be the result of the internalization of the benefits of credit stability by the state. Alternatively, if bank failures are more likely during recessions, then public banks may be able to secure a more stable deposit base because of the public presumption of, implicit or explicit, full deposit insurance.

More recently, the De Luna-Martinez and Vicente (2012) evaluation of development banks around the world shows that these institutions scale up their lending operations during periods of crises. Between 2007-2009, they find that the combined loan portfolio of DBs increased from US $1.16 trillion to US $1.58 trillion dollars. This represents a 36% increase in credit in nominal terms as compared to the 10% increase in private bank credit for the same sample of countries surveyed (p. 8). Specifically, in regions like Latin America, Gutierrez et al (2011) note that development banks assume an even more significant role as central banks face legal constraints preventing them from directly purchasing assets in financial markets. In such cases countercyclical interventions have to be made by DBs – for example, during the global financial crises, members of the Latin-American Financial Development Institutions Association increased their total assets by almost 30 percent (p.8).

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VI. DISADVANTAGES OF STATE INVOLVEMENT There are numerous advantages that NDBs have over commercial banks, such as obtaining finance at a low cost based on their high credit ratings, ability to cope with risk, and informational advantages. However, the picture is less optimistic when one considers just how detrimental NDB financing can be when done poorly. Government ownership of the banking sector in emerging market economies has generally been considered problematic as political patronage distorts incentives. If these banks fail, the debt burden is simply passed on to the taxpayers resulting in even lower national welfare and overall financial fragility. Corruption also diverts attention and economic resources from those projects that can deliver greater benefits and are best monitored, towards ‘prestige projects’ (Chandrashekhar, 2016).

High interest rates by these banks could also hurt lending activities – for example, the BNDES has come under fire in recent years for its increasing emphasis on private sector lending on commercial terms. NDB profitability in the face of deteriorating socio-environmental outcomes, stagnating access to credit for the vulnerable sectors and MSMEs, and declining levels of industrialization are serious causes of concern that undermine the existence of NDBs. Additionally, Ogbuabor et al (2018) note: Where investment projects financed by a development bank consistently fail to go through the cycle of project planning, identification, promotion, and supervision, then it is likely that such a development bank would sooner or later fail to deliver on its mandate of engendering economic development (p. 26) This conflict of interest, where the state is both the manager and the manage, has been the cause of the downfall of the Development Bank of Zambia in the 1990s, as well as the single biggest challenge for several development finance institutions in Africa today (Thorne & du Toit, 2016, 684). Finally, lending by such banks, if not properly managed and controlled, may crowd out private finance. This is undesirable from the point of view of capital/equity market development and self-sustaining economic growth in developing nations.

Public-private partnerships (PPP) are increasingly becoming the preferred alternative to public infrastructure financing. They avoid the conflict of interest that the public sector falls prey to if it is the sole party involved in funding. The private sector, with its higher risk aversion and profit-oriented interests, may be better able to reign in any unsustainable and inefficient political motives. Yet despite the many advantages of PPP, they constitute barely 5 to 10 percent of the overall economic infrastructure investment (de Rezende, 2018, 21). Moreover, PPP financing requires that commercial interests be aligned with social ones. Griffith-Jones et al (2018) note that when it comes to green or strategically important infrastructure, where interests may not align, NDBs may be preferred as the dominant lending source.

The pros and cons of public vs private investments in infrastructure are summarized below, clearly showing that there is a tradeoff between the costs of market failure and the detrimental impact of government failure which needs to be considered.

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Figure 2: A Framework Assessing the Dimensions of Public vs Private Investments in Infrastructure Source: “Toward A Framework for Assessing Private Vs Public Investment In Infrastructure”, by Mapila, K., Molders, F., Lauridsen, M.L. 2017. IFC Thought Leadership, The World Bank.

VII. A SUCCESSFUL NDB MODEL To understand how a national development bank can function effectively, we look to the example of KfW, one of Germany’s largest financial institutions. The federal government financially backs KfW, allowing it to borrow on capital markets cheaply and, as its mandate dictates, follow socioeconomic and sustainable development goals rather than pure profitmotives. The projects it focuses on range from clean energy, to technical assistance for SMEs, to student loans. Importantly, it actively participates in the domestic policy cycle. Moslener et al (2018) identify five steps to the cycle – agenda setting, negotiation, implementation, monitoring, and impact evaluation. This consistent and dynamic engagement allows KfW to shape the determination, design implementation, and evaluation of domestic policies. The authors detail three key features that allow the bank to effect policy.

The first is government financing. Guaranteed by the German government, KfW receives the AAA/aaa rating, allowing it to borrow at cheaper rates than commercial financial institutions.

It is also one of Europe’s largest bond issuers. Subsidized financing compensates the bank for taking on less-than-profitable projects that have been rejected by the commercial sector.

Secondly, it has privileged access to ministries and policymakers. Exerting influence as a largely independent body, it can provide expertise and coordinate policy programs. Their role is described as one half of a carrot-and-stick approach - for example, if the federal government enacts stringent environmental regulations, the KfW can introduce new subsidized financing instruments to ease the blow. The institutional interplay of this sort vitalizes the economy in delivering on policy directives. However, regulators may wield undue influence on the KfW’s choice of policies to support.

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The third is its expansive expertise in engineering and technology. Knowledge of capital markets, risk assessment, securitization, and bond issuance are of use in KfW’s investments in the domestic economy. However, what differentiates it from commercial banks is the possession of sector-specific expertise – from agriculture and natural resources to transport and civil engineering. In addition to increasing the chances of a project being successful, this knowledge enables the bank to identify market failures and predict the outcomes of welltargeted interventions. Further, it does not have to depend on external consultants, meaning it can define its own broad sets of criteria in making investment decisions.

It is important to acknowledge the external conditions that underpin KfW’s success. Its autonomy is protected by a strong legal framework, preventing political interests from imposing investment decisions. Perhaps counterintuitively, a functioning financial market is a prerequisite for a national development bank to fulfil the government’s policy objectives.

Lastly, an NDB must have the technical capacity to identify the market failure that has led to underfunding and to assess the project’s viability. The researchers emphasize coordination among NDBs, policymakers, and regulators.

VIII. POLICY RECOMMENDATIONS Based on the analysis by Thorne and du Toit (2009), we focus on three areas that have proven crucial in our analysis of the empirics of successful development banks. The first is creating an enabling environment, where a bank can make the most efficient use of the state’s scarce resources. The second is to ensure complementarity of funding, in that a development bank should restrict itself to funding those activities where it has a comparative advantage and does not compete with the private sector. Thirdly, the core principles of the Basel Committee on Banking Supervision suggest the establishment of an independent supervisory entity to ensure transparency.

In order to overcome political agendas, poor governance, and corruption, Rudolph et al (2011) suggest organizing NDBs as corporations – complete with shareholders, a board of directors, and management. The roles and responsibilities must be clearly defined and demarcated.

Additionally, a special law should specify a supervisory function that is independent of the groups making up the “corporation.”

IX. CONCLUSION Moving forward, it is important that plans for economic recovery and development be carried out in a sustainable fashion that does not jeopardise chances of their success. As in the past, poor governance, weak institutional frameworks, lack of critical managerial, financial, and operational expertise, and inflexible mandates could end up exacerbating the very crisis that these NDBs were set up to ameliorate. Despite their pitfalls, we find evidence and argue in favour of National Development Banks’ capacity to address market failures and financing gaps left behind by commercial and microfinance institutions. Building on the challenges identified, we discuss a few key policy recommendations for the efficient functioning of DBs – particularly the creation of an independent regulatory body, complementary funding, and impact evaluation of credit based on sector-specific indicators. To conclude, the economic challenges posed by the financial crises of the past, the pandemic of today, and the climate crisis of tomorrow advocate the continued relevance of NDBs in promoting sustainable growth.

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WHAT WILL THE COMMON MAN SAY? -Shreya Singh, Nisha Gokhale1 ABSTRACT This paper aims to assess the efficiency of current econometric models for predicting economic crises. Simultaneously, it looks at narratives and the potential underlying these narratives to predict economic crises with greater efficacy.

Keywords: Economic crises, Econometric Models, Narrative Economics, the Great Recession JEL Code No. E0, E3, E6, G0, N1 I. INTRODUCTION Since the 2000s, a discussion has reignited among economists on how economics is perceived as a discipline, questioning the age-old idea of the orthodox and the heterodox. The fault lies in the fact that the analyses of one school of thought, say neoclassicism, are considered as the whole truth and not the partial truth. Whereas in reality, the economic world is a very complex one, primarily because it is run by human behaviour.

In this regard, econometric models, to a great extent, fail to incorporate the human stream of consciousness. It is implicit in the error term. Some would argue that they do take into account the behaviour of a rational consumer. Yet, they fail to acknowledge the inherent difference between the rational consumer and the common man. Inadvertently, they also fail to predict economic crises.

For instance, the models failed to predict the Global Financial Crisis of 2007-08. Economists rely on models that make research more focused by disregarding the impact of other variables on the variable being studied. While this helps them to study a single variable in detail and establish relationships among two variables, variables are seldom affected by individual variables in isolation in the real world. The mathematical models that economists rely on facilitate the elimination of variables that can come in the way of conclusive research. As the Management Professor at Wharton, Sidney Winter argues, before the Great Recession, economists failed to recognize that it was not possible for house prices to continue to rise faster than household incomes.

Thus an approach that incorporates multiple perspectives becomes essential to decoding the economic reality guided by human behaviour. Through the course of this paper, we aim to look at another source, that perhaps will prove to be more accurate in predicting economic outcomes - narratives. What they lack in objectivity, they are more than able to make up for in their ability to encapsulate the nuances of human behaviour. The reason behind this conviction is that narratives consider something econometric models do not - that is - what will the common man say?

I.1 HYPOTHESIS Narratives predict economic crises to a greater extent than econometric models.

1

St. Xavier’s College, Mumbai

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I.2 OBJECTIVE ● To identify how accurately econometric models predict the behaviour of an economic variable. ● To understand how successful narratives are in predicting economic crises.

II. LITERATURE REVIEW The World Economic Outlook released by IMF for September 2006 projected that the period from 2007 to 2011 will be the strongest global expansionary period since the 1990s. Countries would enjoy a healthy growth rate, domestic demand will be better balanced among the developing countries and most importantly financial market conditions would remain stable as a result of reduced excessive evaluations in some sectors (IMF, 2006). What followed though was a recession almost as devastating as the Great Depression of 1929. To give the IMF its due credit, the report did talk about the increasing output gap in advanced Euro economies as well as inflationary expectations in the USA. However, it still projected a more favourable condition for financial markets in the coming future because of the tightening of monetary policies witnessed in some Euro countries as well as in Japan.

George Akerlof, a leading American economist claims that most of the economists failed to predict the crisis due to the compulsion of incorporating scientific or statistical evidence to support their claims (Akerlof & Shiller, 2009; Akerlof, 2018). He pointed out that economic thinking is very restricted in this manner as it does not take into account other subsections of the economics discipline. Christiano et al, 2018 also support this view. Their paper points out that after the Great Depression of the 1930s and the Keynesian Revolution, financial crises became a phenomenon that was not associated with macroeconomic literature. They further claim that DSGE models despite the ‘stochastic term’ do not take financial markets into account. In this regard, the paper points out that it is not a result of a faulty econometric model, rather the economists employing that model.

Franklin Allen, a finance professor at Wharton, University of Pennsylvania, claims that the “academic orthodoxy” that relegates banks and other financial institutions to a secondary role in the economic study was one of the major causes behind the inability to predict the 2007-08 crisis (Anon, 2009). Adding to this, Sidney Winter, the management professor at Wharton, asserts that reliance on mathematical models that disregard variables such as human psychology aggravated the problem.

Proposing alternative models or sources for the study of economic variables has been common post the failure of the econometric models to predict the 2007-08 Global Financial Crisis.

Wieland and Wolters have highlighted the failure of Dynamic Stochastic General Equilibrium (DGSE) models and have proposed comparing results from various macroeconomic models to render the system more robust. (Wieland and Wolters, 2012) However, a recent field of thinking has emerged in economics called ‘Narrative Economics’ which provides an alternative to excessive reliance on ‘scientific’ or ‘statistical’ evidence. The term ‘Narrative Economics’ was coined by Robert J Shiller. In his paper, Shiller describes the concept as “the study of the spread and dynamics of popular narratives, particularly those of human interest and emotion to understand economic fluctuations.” (Shiller, 2017) Citing the increasing reliance of other social sciences on narratives, he makes a strong case for incorporating narratives in economic analysis to capture the element of uncertainty that conventional econometric models fail to include. Modifying the Kermack-McKendrick SIR

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mathematical model for epidemics, Shiller studied the spread of narratives by using data from ProQuest News & Newspapers and Google Ngrams. He built narrative-based chronologies of the Great Depression of 1929 and the Great Recession of 2007-08 showing the peak of the crisis coinciding with the peak of the narrative. He advocates for research on textual search and better methods for quantifying narratives and collecting data.

III. METHODOLOGY The objective of this paper is to assess the accuracy of econometric models in predicting economic crises and also to evaluate how successful narratives can be in doing so. To achieve this, the paper undertakes a case study of the Financial Crisis of 2007-08 and does so by employing a qualitative and quantitative analysis. The data is collected from secondary sources.

Firstly the paper analyses the econometric models employed for forecasting economic crises as well as predicting the behaviour of economic variables. The paper limits itself to analysing two frequently used econometric models A. Dynamic Stochastic General Equilibrium (DSGE) The Dynamic Stochastic General Equilibrium models are used to predict the business cycles and help monitor the health of economic variables in the context of monetary policies adopted.

The paper studies the caveats that this model presents and tries to answer why this model failed to predict the Great Recession of 2007.

The DSGE involves three economic concepts. Firstly, the term dynamic indicates that it analyses the tendencies of households and firms in a macroeconomic setting over time.

Secondly, it involves the stochastic term and takes into account other factors that may affect these cycles and economic growth courtesy of the regression analysis. Lastly, the term ‘general equilibrium’ signifies that it considers the system as a whole and does not model individual parts.

B. FRB/US model The FRB/US model is a large-scale new Keynesian general equilibrium model that the Federal Reserve bank of the US has been using to frame monetary and fiscal policies since 1996. It differs from the DSGE model in the sense that one can switch between assumptions regarding the expectations of the economic agents with relative ease in the FRB/US Model. ("The Fed FRB/US Model", n.d.)

The second part of the paper deals with the feasibility of studying narratives for predicting crises. To do so, the paper considers the chronology of narratives that led up to the Great Recession. Using Google Trends, we have tracked the frequency of occurrence of terms central to each narrative.

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DATA

Figure 1: Housing bubble; Source: Shiller, 2019 Google searches for the term “housing bubble” peaked in late 2005 indicating an increased interest in the real estate narrative of significant appreciation of value of land or house prices. Real estate investments rose sharply in response to the enthusiasm generated by this narrative. This narrative inadvertently led to the mortgaging of long term assets for short termed debts. In due time when the bubble did burst, creditors chose to sell their assets at extremely low prices instead of rolling over their debt, apprehensive of system-wide failure of banks; this becomes evident from the graph in the figure below, where a hike in the narrative of shadow banking is witnessed around 2005 - 06.

Figure 2: Shadow Banking in the United States Source: Google Trends

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Figure 3: Great Depression Source: Shiller, 2017 The above figure shows that the occurrence of the term “Great Depression” had risen steadily from 2005 and peaked in 2009. One must note that this figure is higher than the one in the 1930s as it had not been named back then. The credit boom in the early 2000s revived people’s memory of the Roaring Twenties. The stock market crash of 2008 corresponded to those in 1929. Bank runs, which were not observed since the Great Depression, returned with the Northern Rock bank run in the UK in 2007 bringing with them the narrative of an imminent economic crisis and increasing public anxiety.

IV. ANALYSIS IV.1. Why the 2007 Financial Crisis?

There is a lack of consensus in the academic circles about how similar the Global Financial Crisis was to earlier economic downturns. Some such as Krugman, 2009 are of the opinion that a global “saving glut” and a poorly regulated shadow banking system are the chief causes behind the crisis and that these are unique. Others, including Reinhart and Rogoff, 2009 assert that the unrestrained debt accumulation seen in this crisis was just a variation of that seen in earlier episodes.

Let us first examine in what way was this crisis considered to be different from the others preceding it. Due to greater integration of the major economies of the world post the wave of globalisation, the “saving glut” mentioned earlier had an unprecedented impact. The central role of the household sector is another unique feature of the crisis. While we acknowledge that this crisis differed from previous ones in the aforementioned ways, we cannot deny the fact that it was also similar in many ways. Many major economies witnessed a sharp rise in credit before the crisis. As the availability of marginal loans rose, the systemic risk increased. Lastly, poor regulation and monitoring by financial institutions, as a result of failure to keep up with the developments, made this crisis quite similar to others. (Christiano et al, 2018) As seen in

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other episodes of economic downturn, countries had to pay for the lack of coordination in terms of regulation.

The rationale behind choosing this particular crisis for the detailed study is twofold. As mentioned earlier, there is research that suggests that the underlying causes of the Global Financial Crisis did not vary greatly from the preceding crises. The severe impact of this crisis is well documented, matched perhaps only by the Great Depression. Since it took place in an era where looking up terms on the internet was a common practice, we believed that it would be better suited for tracking the frequency of its occurrence using Google Trends.

IV.2. The 2007 Financial Crisis - What went wrong?

While the debate on the reasons behind the 2007 financial crisis is ongoing, the majority of economists are of the view that a combination of an unhinged shadow banking sector and an unprecedented rollover crisis had provoked the crisis. The term ‘shadow bank’ refers to those financial institutions which do not work within the seams of banking regulations in place (Christiano et al, 2018). A rollover crisis occurs when all potential creditors choose to not roll over their debt and instead buy assets sold by banks at fire-sale prices under the assumption that other creditors would also employ similar measures. (He & Zeong, 2012).

The rollover crisis that caused the Great Recession was in itself a result of the variations in the household sector indices. Between 1990 and 2006 the Case-Shiller US national Home price index rose by 2.5 points on an average (Christiano et al, 2018). When these exorbitant prices started decreasing rapidly around mid-2006, the value of long term assets which the shadow banks had financed using short termed debt fell. As a result banks had to resort to selling these assets at fire-sale prices that eventually led to the rollover crisis. This in turn precipitated the Great Recession of 2007.

IV.3. Examining the Efficacy of Econometric Models “I'd Rather Have Bob Solow Than an Econometric Model, But …”

Stanley Fischer Two observations stand out when we examine the role played by the econometric models used by the Fed and other economists during the financial crisis. First, the economic community at large failed to realise the existence of a shadow banking sector in the underbelly of the US financial market, one that was not sheltered under the protective umbrella of the US Federal Reserve Bank. The primary reason could be that DSGE models as well as New Keynesian models such as the FRB/US do not identify fluctuations in the financial market as shock forces.

Appropriately, these models did not have a reason or rationale to include these credit market movements in their equations, primarily because all the post-war crises in the US and other European countries did not stem from the financial market frictions, save perhaps the banking and loans crisis in the late 1980s, which did not metastasize into a global crisis (Christiano et al., 2018).

Yet, not all pre-crisis DSGE models excluded the financial frictions. For example, the Bernard, Gertler, and Gilchrist (1999) model did emphasize the financial multiplier. However, this financial multiplier had minimal quantitative effects on how the economy responded to shocks (Lindé, Smets, and Wouters 2016). Meaning, that the few DSGE models that did incorporate the financial frictions, failed to gauge their weightage.

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Moreover, the FRB/US model, that guided the Fed’s monetary policy when the price of the mortgaged assets fell, ushered in the rollover crisis. This brings us to our second point, econometric models, at least pre-crisis ones, not only fail to predict crises but also guide the economists towards ineffective policies in the face of a crisis. The Fed’s willingness to provide a safety net for the shadow banking sector was at best hesitant, implicit and marginalised. The reason remains the same. Despite how much more sophisticated the FRB/US model is than the DSGE model, due to the inherent dichotomy between the product and the money market, it failed to prevent the shadow banks from selling their assets at fire-sale prices.

IV.4. Why should Narratives be considered?

There are two pertinent questions that arise while studying narratives a. How do we differentiate between causality and correlation, that is, do narratives actually cause a crisis or simply appear around the same time?; and b. If causality does exist, why do narratives impact the economic behaviour of individuals?

The inability to conduct controlled economic experiments limits the scope of the study of causation. However, the study of economic history can, to a certain extent, make up for this limitation. Studying certain exogenous shocks such as the aftermath of the implementation of absurd policies has revealed how these factors impact the economy. This has enabled us to model narratives as external shocks and study their impact. Let us consider macroeconomic self-fulfilling prophecies. The term, coined in 1948, is used to refer to prophecies stimulated by external events. (Shiller, 2019) A popular example often cited in this context is that of “sunspots”. In 1878, economist William Stanley Jevons observed a correlation between economic fluctuations and sunspots. Since it was beyond the realm of possibility that economic disruptions on the earth led to changes in spots on the sun, he concluded that economic fluctuations are caused by “periodic variation in the sun’s rays.” Research in the following years refuted his claim with substantial evidence until 1983 when David Cass and Karl Shell explained that sunspots could affect the economy if people believed that they could do so. (Shiller, 2019; Cass & Shell 1983) Today, sunspots refer to any external disturbance impacting the economy by virtue of people’s belief in its ability to do so. Narratives can be categorised as such extraneous disturbances.

Shiller, 2019 compares a narrative to an infectious disease and adopts the mathematical model of studying the spread of diseases for examining the spread of narratives. Narratives have the potential to “go viral” and certain narratives appear to recur throughout economic history. One can discern echoes of historical narratives in those present today. But what truly makes them impactful is people’s belief in them - to a layperson, the idea of accurate economic forecasting, having witnessed weather forecasting, is plausible. This causes people to look at prominent economists’ estimates as infallible and if a narrative surrounding the possibility of a recession is being widely circulated, they choose to delay economic activity as a precaution. This in turn generates pessimism in the economy and is instrumental in increasing the probability of a recession occurring.

In order to answer the second question, one needs to look at a few key psychological phenomena that are instrumental in amplifying the impact of narratives. Crowd psychology, that is, the impact of forming large groups on individual behaviour, affects the spread of panic narratives. Suggestibility, that is, the idea that individual behaviour imitates and reacts to that

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of others is closely related to crowd psychology. This notion is contradictory to the idea of the rational consumer, a common feature of most economic theories. Thus, the consumer is not an isolated, rational human and his behaviour is greatly influenced by the narratives that he is exposed to.

V. POLICY RECOMMENDATIONS The policy implications of the above analysis are quite prominent. The very first being, those present econometric models that deal with business cycles need to be reworked and deliberated upon. We suggest the following in this regard: ● Incorporating variables that reflect the crowd’s behaviour. An effective way would be to employ the game theory and Nash Equilibrium analyses that would represent the behaviour of the stakeholders. ● Econometric models can be ameliorated using epidemic models to track how narratives spread.

Secondly, efforts need to be made to bridge the gap between the product market and the financial market. Orthodox economists have strived towards making economics a science.

They consider the financial market to be an offshoot of the discipline, not nearly sophisticated, accurate and relevant enough for a scientific subject to involve oneself with. This attitude certainly did not bode well for the economic community at large during the crisis and certainly wouldn't in the future. Banking sector and shadow banks especially which still exist must be emphasized upon in the econometric models.

Most importantly, the primary reason why narratives can predict economic crises so accurately, is that to some extent they also drive an economy towards crises, or have the potential to.

Armed with this knowledge, manufacturing narratives to usher in crises would become relatively easier for those who benefit from these crises. Thus promoting and inculcating media literacy among the citizens is of paramount importance.

VI. CONCLUSION The beauty of any crisis is that it exposes the fault lines of the system underneath. The Great Depression exposed the inherent fallacies present within the classical theory of economics.

Keynes’ work was enough of a revolution at that time for the masses. But through the years, economists have clung to the false sense of pride in the discipline being scientific, empirical and accurate. What followed was a crisis of almost equal magnitude as the Great Depression, one which no empirical, accurate and scientific model could anticipate. Alas, there was no revolution this time; it was a reckoning. The narratives that spread before the recession were a clear indication of what was to come. The economic community was too blindsided by the rational man to notice this.

However, as traditional theories fail to predict crises, new avenues emerge. Narratives accomplish what empirical econometric models fail to - what will the common man say? Or more appropriately, what is the common man saying? Narratives are not a new phenomena.

Perhaps it is time to look at them not merely as accounts of what has happened but as tools of predicting what is yet to come.

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REFERENCES Akerlof, G., & Shiller, R. (2009). Animal spirits: How human psychology drives the economy, and why it matters for global capitalism. Princeton University Press.

Akerlof, G. (2018). Why Economists Failed to Predict the Financial Crisis [Video]. Retrieved from https://www.youtube.com/watch?v=_-c8XwiByY8 Anand, K., Gai, P., & Marsili, M. (2012). Rollover risk, network structure and systemic financial crises. Journal of Economic Dynamics and Control, 36(8), 1088–1100. doi:10.1016/j.jedc.2012.03.005 Retrieved 5 January 2021, from https://scihub.do/https://www.sciencedirect.com/science/article/abs/pii/S0165188912000760?via%3Di hub# Brayton, F., Laubach, T., & Reifschneider, D. (2014). FRB: FEDS Notes: The FRB/US Model: A Tool for Macroeconomic Policy Analysis. Federalreserve.gov. Retrieved 5 January 2021, from https://www.federalreserve.gov/econresdata/notes/feds-notes/2014/a-tool-formacroeconomic-policy-analysis.html.

Cass, D., & Shell, K. (1983). Do Sunspots Matter? Journal of Political Economy, 91(2), 193227. Retrieved 5 January, 2021, from http://www.jstor.org/stable/1832054 Christiano, Lawrence J., Martin S. Eichenbaum, and Mathias Trabandt. 2018. "On DSGE Models."

Journal of Economic Perspectives, 32 (3): 113-40. https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.32.3.113 Claessens, S., Kose, & Terrones. The Global Financial Crisis:How Similar? How Different?

How Costly?. Retrieved 5 October 2020, from https://ideas.repec.org/p/koc/wpaper/1011.html Colander, D., Föllmer, H., Haas, A., Goldberg, M., Juselius, K., & Kirman, A. et al. The Financial Crisis and the Systemic Failure of Academic Economics. Retrieved 20 September 2020, from https://keenomics.s3.amazonaws.com/debtdeflation_media/papers/Dahlem_Report_EconCris is021809.pdf Fernandez-Villaverde, J. (2009). The Econometrics of DGSE Models. Nber.org. Retrieved 7 November 2020, from https://www.nber.org/system/files/working_papers/w14677/w14677.pdf.

Fischer, S. (2021). FRB: Fischer, "I'd Rather Have Bob Solow Than an Econometric Model, But ...".

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Retrieved 6 October 2020, from https://www.imf.org/en/Publications/WEO/Issues/2016/12/31/Financial-Systems-andEconomic-Cycles Lindd, J., Smets, F., & Wouters, R. (2016). Challenges for Central Banks Macro Models. SSRN Electronic Journal, 2B, 2185 - 2256. https://doi.org/10.2139/ssrn.2780455 Shiller, R. (2017). Narrative Economics. Yale University. Retrieved 13 September 2020, from https://cowles.yale.edu/sites/default/files/files/pub/d20/d2069.pdf

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Tempelman, J. (2009). The Financial Crisis and the Implementation of Monetary Policy.

Business Economics, 44(4), 216-219. Retrieved 5 January 2021, from http://www.jstor.org/stable/23491071 The Fed - FRB/US Model. Board of Governors of the Federal Reserve System. Retrieved 7 January 2021, from https://www.federalreserve.gov/econres/us-modelsabout.htm#:~:text=The%20FRB%2FUS%20model%20is,of%20monetary%20and%20fiscal %20policies.

Why Economists Failed to Predict the Financial Crisis. (2009). Retrieved 20 September 2020, from https://knowledge.wharton.upenn.edu/article/why-economists-failed-to-predict-thefinancial-crisis/ Wieland, V., & Voilters, M. (2012). Failed forecasts and the financial crisis: How to resurrect economic modelling | VOX, CEPR Policy Portal. Retrieved 4 October 2020, from https://voxeu.org/article/failed-forecasts-and-financial-crisis-how-resurrect-economicmodelling

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A BEHAVIOURAL FRAMEWORK FOR INTRA-HOUSEHOLD BARGAINING -Deep Mehta1 ABSTRACT The literature on Intra-household negotiations is varied. Since (Becker, 1973), there have been different models proposed, the most notable being the cooperative and non-cooperative and collective models. However, empirical research has shown inconsistency in the verification of these models although there has been significant ratification. The further alteration of these theories to make better insights would be by the inclusion of concepts from behavioural and psychological economics, especially since the majority of the study is on households in the developing world where rational behaviour is of a lower degree.

This paper provides a 2 player game-theoretic model for the bargaining process in a household which shows how strategy outcomes determine changes to the household allocations and strategies are chosen by agents depending on expected outcomes. It then explains the determination of bargaining conflict and aggression via a Hawk-Dove game.

The next section outlines a special case of marital slavery and its applicability in the Covid-19 lockdown with a brief elucidation of the long term impacts of domestic violence. The concluding remarks explain the possible usage and empirical verification of the model and the suggestions for extensions in future research.

I. INTRODUCTION: INSIGHTS FROM THE LITERATURE AND BACKGROUND TO THE MODEL I.1. HOUSEHOLD SETTING A household in the developing world is typically defined by a heterosexual married couple in which both the male and the female are economic agents with individual propensities to provide labour services in exchange for compensation, usually in the form of monetary wages. Becker (1973) identifies a household as a production unit in which the constituent agents make their respective contributions to output that are aggregated for the household.

People enter into marriages as they provide higher utility than an individual livelihood.

(Becker, 1973) The minimum required consumption of individuals; their household participation constraints is the lowest utility level, or the lowest level of consumption given their utility functions, that they will accept in a marriage and this, being their “outside option”, is the consumption level they could attain if they were single units.

It follows, therefore, that people enter marriages when there is a mutually shared surplus over what they could otherwise achieve. The essence of bargaining involves the sharing of this surplus.

In the extreme case of a dictatorial partner, such as the earning husband in a patriarchal household, the surplus given to the other partner is zero while in the opposite extreme of a symmetric bargain the distribution is equal.

1

, St. Xavier’s College, Kolkata (Autonomous)

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While the initial literature focused on the Nash bargaining solution, Brown and Manser, (1980) extended the analysis by providing a Kalai-Smorodinsky solution for the household’s surplus distribution. They use this analysis to argue how Becker’s dictatorial case is optimal not owing to the interdependence of utilities but the satisfaction of minimum constraints. However, it cannot be inferred that interdependence between the utilities is not plausible in households; the behaviour of individuals depends on their level of satisfaction and their behaviour is an externality affecting the well-being of other household members. Moreover, as mentioned in a later section, regard for other members can make utilities interdependent.

I.2 COMMON GOODS AND PARETO EFFICIENCY Common goods in the household are those not specific to the consumption of any individual but to the institution of the household such that they have joint ownership. Typically, the surplus between individuals is distributed by means of the common goods that are relevant to both individuals. We, therefore, have the objective of bargaining as the determination of surplus distribution between the common good and private consumption goods.

Co-operative models like in Manser and Brown, (1980) have shown that common goods make a household’s bargaining solutions Pareto efficient as an investment in such goods raises utility levels of all the members collectively.

However, it may also be the case that there are differences in the common good as perceived by different household members. It may be that perspectives on what is beneficial for the household differ between different agents and the utility they derive from different aspects may be different or even conflicting. For example, the husband may want to purchase a vehicle for domestic usage while the wife may want an electronic appliance with the same money budget surplus for common goods. The husband may have reservations against the appliance being present in his house; it would have disutility for him apart from the opportunity cost of its purchase.

There is, therefore, a division of common goods into the preferences of different members which may or may not overlap which is consciously made in the case of bargaining fallout.

I.3 FALLOUT CONSEQUENCES The initial literature, starting from Becker, (1974) assumed that the consequence of bargaining breakdown is the breakdown of the married household; the couple is divorced and chooses single lives or new marriages. Separation of household units may also be plausible among differently related adults such as siblings or parents and offspring.

While this is plausible, it cannot be taken as a generalised case as separation may not be an option for several families for cultural, legal and social reasons. Even if possible, the costs of re-allocating capital and finding new infrastructure for a living may be heavy.

In this regard, Lundberg and Pollack, (1993) introduce the non-cooperative model of “separate spheres” where individuals, rather than divorcing on the fallout, live like separate entities while a part of the same household. Non-co-operative models like theirs do not require Pareto efficiency and the above-mentioned breakdown of common goods Q into qi and qj becomes apparent as a certain segment of the public goods is allocated to each member. The alternative, as they propose, is a Cournot equilibrium where each agent has a “reaction function” to a choice of qj assumed by the i-th agent and vice versa.

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While this is a fair understanding of strategy, it does away with interactions altogether and strategies regarding goods that must be shared due to indivisibility; such goods may include child welfare and common property. It can be considered that conflict persists and those with greater leverage in bargaining have better access to the common goods; the greater their leverage, the more likely they are to have their preferences for common goods asserted over that of their partner.

I.4 FACTORS DETERMINING ALLOCATIONS The bargaining power of an individual depends on their leverage, the costs of bargaining and the expected loss and risk in the case of fallout. Increased leverage is conventionally accepted to mean a higher threat point of the individual.

I.4.1 CONTRIBUTION TO WEALTH; INCOME AND OWNERSHIP OF RESOURCES The greater the leverage of the individual, the greater is their bargaining power as a greater portion of the resources accrues to them. The greater their respective individual utilities, the greater is said to be the total ability of the households to determine resource distribution.

Antman, (2014) finds a positive empirical relationship between the impact of a spouse in decision making and their employment when the spouse is not the head of the family. The results in Duflo, (2003), as mentioned above, also reflect the same.

Moreover, studies of agricultural income variations; depending on rainfall and crop performance, have shown how they impact decision making and the individual making decisions in the household, as in Duflo and Udry, (2004).

Resource allocations depend not just on income, but on initial capital contributions. The level of capital brought into the marriage determines the bargaining power of the individual. For example, Brown (2003) finds that the leisure time of women is higher when their dowry payments at the time of marriage are higher.

Collective models of intra-household bargaining have given the view that distributional allocations are fixed at the beginning of the marriage. Lifetime Pareto weights are determined on the bargaining done at the household’s initiation.

As in the model proposed by Chiappori and Mazzocco (2017), efficiency is obtained by maximising the weighted sum of each spouse’s lifetime utility using future values of relevant variables. Future changes in outside options of individuals do not lead to renegotiations of relative positions as they are correctly anticipated initially; lifetime Pareto weights are determined at the time of marriage (Baland and Ziparo, 2017). Collective rationality in this model implies that risks are spread between members to smooth future shocks.

Hence, for a total income level, the resource allocations between partners remain the same and do not change.

This model, however, is inadequate in the verification of empirical results, as shown in Duflo and Udry (2004) and Dercon and Krishnan (2000) wherein household behaviour is inefficient and economic fluctuations cause variations in household allocations.

Mazzocco (2007) reasons aptly that renegotiations take place in the lifetime of the household as and when the bargaining powers of the individuals change. The threat points of the individuals rise and fall as their ‘outside options’ change. This explains changes in distribution when there are shocks or fluctuations in economic conditions.

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Despite the obvious relation between income and resource ownership and bargaining power established by the above literature, it may not hold in the face of social norms that restrict bargaining power irrespective of economic contributions. (Calderon et al, 2011) show that despite an increment in their contributions to household earnings post displacement, women’s bargaining power remains intact and domestic violence increases.

Similarly, Luke and Munshi (2011) find that domestic violence against women in former slave castes increases when their incomes increase. This is analysed further in the next section.

Bloch and Rao, (2002) in their terrorism bargaining framework, show how a husband can continue to exploit his wife’s parents’ resources irrespective of dowry payments as her payoff from exiting the marriage or being sent home is worse due to cultural reasons. Hence, while income must have a non-trivial impact on budgetary allocations, the impact is not in isolation of social and psychological factors which are discussed in the next section.

I.4.2 EXTRA-HOUSEHOLD NORMS Legal norms in society determine the leverage of agents during negotiation via the specifications of protections and rights. If and when property rights are unfavourable to a member, typically the female in marriage, her outside option is much lower and the payoff on threat point is also lower. This is the case in states where female inheritance is not allowed.

When laws favour a woman more than a man for divorce settlements, the leverage of the woman is likely to be higher.

Adam et al., (2003) show how the suicide rate among married females in Canada falls when women’s rights on divorce are bettered by law. Deininger et al. 2010 (as cited in Doss, 2013) show that there is an elevated status of daughters in the Indian states of Maharashtra and Karnataka when inheritance laws are altered in their favour. These are clear demonstrations of the threat point alteration as payoffs are bettered when bargaining fails.

Social and religious norms that the agents are subject to play an important role not only in determining their costs and outside options but also in informing the philosophical beliefs on which their utility functions are based. It can likely be the case that such agents would not wish to maximise their material payoffs even when possible owing to the costs incurred in their allocation mechanism.

In societies deeply entrenched in patriarchy, religious and social norms dictate that occupations should be as per assigned gender roles; women should be involved in household work while men are engaged in monetized labour. Actions that deviate from norms for a single agent would provide negative signals to others in the community as they would be considered unethical and any positive economic and social relationships with the will become undesirable to them. For the agent concerned, such alterations in relationships and negative reactions of other agents would involve physical and psychological costs.

If it is so that a woman must not be formally employed as per the norms in her society but chooses to be so in defiance, she will likely face backlash from her neighbours, friends and immediate family members which would involve ostracism, breaking of economic ties and physical and mental abuse. Hence, even though her material payoffs from such activity are higher, she will choose not to carry it out if the sum of expected tangible and intangible costs exceeds the benefits.

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Further, Gender biases in societies determine allocations between household members of different sexes based on preferences. Li and Wu, (2011) show how the bargaining power of the female rises when she gives birth to a boy due to gender preferences in patriarchal societies.

I.5 PSYCHOLOGICAL CONCERNS Norms further impact the beliefs of the agent, such that their defiance would directly cause psychological damage to the agent. For instance, a woman may accept unfair bargains from her husband in the distribution of resources if she is under the belief that her moral duty is to be subservient to him. In such cases, utility maximising behaviour is not observed on the part of one of the agents and the game-theoretic models do not apply. This would be a dictatorship case in which there is a single agent making household allocations as per his, or in the uncommon case, her utility maximisation.

There are several factors that influence the bargaining process which has been given little regard in the literature so far. Since marriages and households involve emotional and other forms of intimacy, the negotiation process is cognizably different from that of a formal market with socially disconnected players. The payoffs to the players are not just a function of their material and quantifiable consumption but also of their psychological returns which depend on their attachments, beliefs and mental functioning. It is definitely true that the Homo Economicus assumption cannot stand in the analysis of bargaining, especially since it is focused mostly on lower-income households in developing countries. Hence, it is a likely case that agent bargaining strategies are irrational, especially in the absence of education and social and mental growth. For households below or near the poverty line, the uncertainty of their livelihoods and the psychological issues that arise from their destitute circumstances make their reasoning all the more impaired.

I.5.1 ALTRUISM AND OTHER-REGARD The utility of an individual may be positively dependent on the consumption or welfare of other individuals; partners or other dependent members of the household.

Coate (1995) shows how a government is made to focus on the development of the poor when it is in the interests of the rich to see the poor lifted out of destitution. Replicating it for the household, a greater share of consumption for the other member or their preference of public goods can raise the utility of a member.

Pareto efficiency is assured in a pure altruistic set-up where one or both of two players are altruistic. In the first case, the surplus division goes majorly to the non-altruistic member or to their preferred common good and in the second case, the surplus is transferred mainly to the general public good.

Bargaining can be understood to be smoother when there is altruism. Becker’s altruistic model in a dictatorial set-up shows how household behaviour is influenced by the dictator’s strategy and each member seeks to maximise family welfare to preserve their own (Becker, 1974).

Waldman and Bruce, (1991) show a similar paternalistic transfer model for intergenerational strategies that make children undertake savings behaviour to maximise payoff.

However, these models have the assumption that future consumption from within the household is the priority of the individuals and that they undertake a long-term strategy which is particularly shaky when reasoned for children who have expectedly short-term behaviour.

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I.5.2 BARGAINING COSTS The process of negotiation is subject to emotional friction which may affect the individuals’ state of mind. Transaction utility or disutility from purchases in a market affects the consumer’s satisfaction over the inherent utility of tangible consumption (Thaler, 1985). Similarly, the scope for transaction disutility in a bargaining process can reduce utility derived from final consumption. For an agent who is particularly averse to conflict and more so than their partner, the ability to bargain successfully will likely be lower and so will allocations.

Secondly, domestic violence is brought in as a part of the bargaining processes, especially in developing world households (Baland and Ziparo, 2017). The fear of aggressive behaviour on the part of the other member, typically the male, influences the extent to which one, typically the female, would be willing to bargain. Repeated bargaining processed would likely form expectations of other’s behaviour during the process and the reactions to the same can be made accordingly. To make models more efficient, these aspects of the bargaining process need to be included.

II. A PROPOSED BEHAVIOURAL MODEL I propose a game-theoretic framework with n=2 players who have risk-averse behaviour in which each bargaining action in the players’ set has utility values of its own and combined with the action of the other player, determines allocations.

II.1 RETURNS FROM BARGAINING ACTION Each individual must make an investment that is equal to the cost of bargaining; this involves the effort in protestation and verbal expression of disfavour to a proposed distribution or the assertion of the self-proposed solution.

The individual may get positive or negative returns from the investment, which implies that it may lead to the change in the allocation of the desired or different magnitude or even lead to a negative order change.

In this regard, it can be argued that the fear of incurring a loss from the bargaining will be higher than the appeal of a better bargain. (Tversky and Kahneman, 1979) show how the disutility from negative returns takes a non-linear form as shown in the diagram.

Figure 1: Loss Aversion Graph Source: Tversky and Kahneman, 1979

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This signifies the general case of an individual’s risk aversion in a household bargaining process. Investment in bargaining is made on the basis of expected returns and it is only when the expectation of success is very high that the investing individual will proceed.

From the discussion in the last section, we understand the various factors that determine an individual’s leverage in the given case. Hence, given the social norms, degree of altruism and wealth contributions, the players in the bargaining game form expectations of their returns from bargaining.

We take the action set of the individual w (signifying the wife) to be 𝑎𝑤 = {𝑎1𝑤 , 𝑎2𝑤 , … . 𝑎𝑛𝑤 } where each action ai, i→1,2,…n is a possible bargaining action the individual can take. The actions are assumed to be independent non-numerical elements. These actions could include reasoned verbal expressions, physical or verbal aggression, pleading behaviour among others.

To determine a complete set would require thorough observation and analysis of behaviour.

This model takes the assumption that a single action can be used in a single bargaining period.

An altered action would imply entry into a new period with an inconclusive outcome of the last period. ℎ The other player, the husband, is said to have the action set 𝑎ℎ = {𝑎1ℎ , 𝑎2ℎ , … . 𝑎𝑚 }. where each action aj, i→1,2,…m, is a possible bargaining action the husband can take.

In total, there are 𝑛 × 𝑚 possible bargaining outcomes.

We therefore have utility functions which are negatively dependent on the cost of effort in bargaining, such that 𝑈𝑤 = 𝑉𝑤 (𝐶𝑤 {𝑎𝑖𝑤 , 𝑎𝑗ℎ }, 𝑄𝑤 {𝑎𝑖𝑤 , 𝑎𝑗ℎ }, 𝑄ℎ {𝑎𝑖𝑤 , 𝑎𝑗ℎ }) − 𝑈𝑤 + 𝐵𝑤 (𝑎𝑖𝑤 , 𝑎𝑗ℎ ) And 𝑈ℎ = 𝑉ℎ (𝐶ℎ {𝑎𝑖ℎ , 𝑎𝑗𝑤 }, 𝑄ℎ {𝑎𝑖ℎ , 𝑎𝑗𝑤 }, 𝑄ℎ {𝑎𝑖ℎ , 𝑎𝑗𝑤 }) − 𝑈ℎ + 𝐵ℎ (𝑎𝑖ℎ , 𝑎𝑗𝑤 ) where Vw is the standard indirect utility from the allocated distribution, 𝑈𝑤 is the threat point and 𝐵(𝑎𝑖𝑤 , 𝑎𝑗ℎ ) is the utility or disutility from the bargaining costs derived. Player h faces a similar utility function. Conventionally, 𝐵𝑤/ℎ (𝑎𝑖𝑤 , 𝑎𝑗ℎ ) < 0. However, in the case that bargaining frictions are preferred and provide a degree of satisfaction to the agent, 𝐵𝑤/ℎ (𝑎𝑖𝑤 , 𝑎𝑗ℎ ) > 0. Here, 𝑈𝑤 = 𝑓(𝑆𝑜𝑐𝑖𝑎𝑙 𝑛𝑜𝑟𝑚𝑠, 𝑖𝑛𝑐𝑜𝑚𝑒 , 𝑝𝑠𝑦𝑐ℎ𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝑏𝑒𝑙𝑖𝑒𝑓𝑠, 𝑠𝑜𝑐𝑖𝑎𝑙 𝑛𝑒𝑡𝑤𝑜𝑟𝑘 𝑏𝑎𝑠𝑒) The indirect utility of each agent is a function of the resource allocation made to their individual consumption 𝐶𝑤/ℎ {𝑎𝑖𝑤 , 𝑎𝑗ℎ }, to their preferred household good expenditure 𝑄𝑤/ℎ {𝑎𝑖𝑤 , 𝑎𝑗ℎ }and to their partner’s preferred household good expenditure 𝑄ℎ/𝑤 {𝑎𝑖𝑤 , 𝑎𝑗ℎ } post the bargaining process.

Given that there are initial allocations, their post-bargaining allocations would incorporate changes made in the bargaining process. Hence, the wife’s post- bargaining consumption would be; 𝐶𝑤 {𝑎𝑖𝑤 , 𝑎𝑗ℎ } = 𝐶𝑤 0 + 𝑑𝐶𝑤 {𝑎𝑖𝑤 , 𝑎𝑗ℎ } Hers and her husband’s household good allocations would be; 𝑑𝑄𝑤 {𝑎𝑖𝑤 , 𝑎𝑗ℎ }and 𝑄ℎ {𝑎𝑖𝑤 , 𝑎𝑗ℎ } = 𝑄ℎ 0 + 𝑑𝑄ℎ {𝑎𝑖𝑤 , 𝑎𝑗ℎ }

𝑄𝑤 {𝑎𝑖𝑤 , 𝑎𝑗ℎ } = 𝑄𝑤 0 +

Here, dC, dQ <0 is possible as bargaining outcomes may be unfavourable.

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If the household has received increased income or wealth to be allocated, then it is possible for the change in all sets of individual and household good consumption to be positive.

This, however, is not possible if the household wealth is static or has reduced. In the former case, changes in any allocations must necessarily be offset by changes in other allocations. In the latter case alone, it is possible but not necessary that the change is negative for all allocations.

Given risk aversion under prospect theory, we can infer that if 𝑑𝐶𝑤/ℎ {𝑎𝑖𝑤 , 𝑎𝑗ℎ } = −𝑑𝐶𝑤/ℎ {𝑎𝑘𝑤 , 𝑎𝑗ℎ }, 𝑈𝑤 {𝑎𝑖𝑤 , 𝑎𝑗ℎ } < −𝑈𝑤 {𝑎𝑖𝑤 , 𝑎𝑗ℎ } where 𝑑𝐶𝑤/ℎ {𝑎𝑖𝑤 , 𝑎𝑗ℎ } > 0 and if 𝑤 ℎ 𝑤 𝑤 ℎ 𝑤 ℎ ℎ 𝑑𝐶𝑤/ℎ {𝑎𝑖 , 𝑎𝑗 } = −𝑑𝐶𝑤/ℎ {𝑎𝑖 , 𝑎𝑘 }, 𝑈𝑤 {𝑎𝑖 , 𝑎𝑗 } < −𝑈𝑤 {𝑎𝑖 , 𝑎𝑘 } for any action k in either agents’ set such that k ≠ i, j for the respective sets.

II.2 EQUILIBRIUM: COURNOT Each player will choose the action that will maximise his or her utility, given the action of the other player.

Taking the case of a super-game in which bargaining happens frequently, it is feasible to consider that individuals will form rational and accurate expectations of each other’s behaviour; action set, and choose the Nash equilibrium actions aw* and ah*.

The wife’s and husband’s optimisation problem is maximising 𝑈𝑤 and 𝑈ℎ respectively with respect to their bargaining actions 𝑎𝑖𝑤 𝑎𝑛𝑑 𝑎𝑗ℎ given the actions of their partner.

Over repeated games and via signal indicators, each partner forms expectations of the strategy that the other is likely to make till they both reach an optimum Cournot equilibrium where both play the actions that are best responses to each other such that 𝐸({𝑎𝑖𝑤 , 𝑎𝑗ℎ } ) = {𝑎𝑖𝑤 , 𝑎𝑗ℎ } for both 𝑤 ℎ 𝑤 ℎ agents and 𝑈𝑤 (𝑎𝑖𝑤 |𝑎𝑗ℎ ) > 𝑈𝑤 (𝑎−𝑖 |𝑎𝑗ℎ ), 𝑈ℎ (𝑎𝑖ℎ |𝑎𝑗𝑤 ) > 𝑈ℎ (𝑎−𝑖 |𝑎𝑗𝑤 ) where 𝑎−𝑖 𝑎𝑛𝑑 𝑎−𝑖 denote any action not chosen by the respective agent. EWMA parameters may be used for formulation of expectations although this would depend on empirical evidence.

II.3 EQUILIBRIUM: STACKELBERG Traditionally, we have Cournot equilibrium where strategies are made simultaneously.

However, in households where there is a dominant individual and a submissive individual, the former may be the leader in initiating dialogue and the latter the follower. Therefore, we have a Stackelberg equilibrium in which the leader chooses the optimal strategy based on the induced expectation of the follower’s action. The leader, commonly the male, chooses an action knowing the best response of the follower, commonly the wife, which will maximise his utility.

The leader, taken to be the husband, will choose 𝑎𝑗ℎ such that ℎ ℎ 𝑈{𝑎𝑗ℎ |𝐸 ℎ (𝑎𝑖𝑤 |𝑎𝑗ℎ )} > 𝑈{𝑎−𝑗 |𝐸 ℎ (𝑎𝑖𝑤 |𝑎−𝑗 )} and in Stackelberg equilibrium, 𝐸 ℎ (𝑎𝑖𝑤 |𝑎𝑗ℎ ) = 𝑎𝑖𝑤

STRATEGIES OF AGGRESSION: HAWK-DOVE GAME

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II.4 COSTS OF BARGAINING CONFLICT Two types of costs can be identified in the course of bargaining; 1. Psychological costs: The transaction disutility (utility) from the process; conflictoriented or otherwise.

2. Resource and Allocation impact: Damage to household resources owing to physical aggression, psychological damage to children, etc.

With regard to aggression, curtailment of violence depends on the expected costs of aggression on the respective household goods and personal consumption.

This is represented in the traditional Hawk-Dove game as in the payoff matrix given below: Wife/Husband Hawk Dove

Hawk 𝑉−𝑐 𝑉−𝑐 − 𝜕𝑤 , − 𝜕ℎ 2 2 −𝜕𝑤 , V−𝜕ℎ

Dove V−𝜕𝑤 , −𝜕ℎ V/2, V/2

The psychological and social cost measure ∂ lowers the payoff received from self-aggressive behaviour and aggressive action by the other agent. The cost measure is inclusive of the direct psychological impact of aggressive behaviour, retaliation to self-behaviour by social or institutional intervention and damage to preferred household goods. Agent h or w will avoid aggression when the negative impact on children is higher and they derive benefit from child protection. For simplicity, it is assumed that the degree of aggression across strategies does not vary significantly in this payoff matrix. However, in the model proposed, different levels of aggression of different strategies are viable and the outcomes and utilities are understandably different.

A Hawk strategy would be any strategy from the agent’s set that involves the use of aggression.

A Dove strategy would be passive and non-violent.

In the case that costs of conflict are symmetric or non-existent, the outcomes for different strategies are also the same for both agents. For each bargain that two individuals make, they realise disutility from being subjected to an aggressive or negative action, given that being aggressive themselves does not give them disutility. In the case that one partner chooses an aggressive strategy and the other does not, it is likely that the former’s influence on allocation will be higher and the latter’s will be lower as the latter will try to minimise disutility and yield to demands. This is consistent with the risk aversion characteristic given earlier.

The worst-case scenario for both having negative behaviour is fallout where they reach their threat points. However, for a single aggressive partner, the other will expectedly receive the threat point payoff as well.

If aggression is costly for only one agent, the outcome will be (Hawk, Dove). When it is significantly costly for both, it will be (Dove, Dove). In the proposed model, Hawk and Dove actions are those involving the presence and absence of aggression respectively.

However, it may be that if the same strategy is repeated consistently, the peaceful partner will expect negative behaviour in future transactions and choose negative behaviour themselves because the payoff is higher in the case. This is because they could mentally condition

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themselves to not be damaged by negative behaviour and secure household goods. If this is 𝑑𝜕 possible, 𝑑𝑡 < 0 and costs will reduce till the payoff from adopting a Dove strategy is less than the payoff from choosing a Hawk strategy.

II.5 BARGAINING DURING THE LOCKDOWN II.5.1 SPECIAL CASE OF THE MODEL: STRATEGY UNDER MARITAL SLAVERY There are cases of dictatorial games under which the following conditions apply to a female in a marriage: a.

There are no outside options such as divorce, separation or non-cooperative equilibrium b.

The psychology of the wife is such that it is rational to continue in an abusive subservience rather than commit suicide; the husband is accepted as a dominant “slave owner” for cultural or circumstantial reasons. Example: the belief that it is the wife’s duty to live like this in this life given that the husband’s abuse is unjustified and/or suicide is feared for theological reasons.

In such a case, there exists no threat point for the wife as there is no ‘outside option’. Hence, 𝑈𝑤 = 𝑉𝑤 (𝐶𝑤 , 𝑄𝑤 , 𝑄ℎ ) + 𝐵𝑤 (𝑎𝑖𝑤 , 𝑎𝑗ℎ ) There are two main features: 1.

Allocations are made exogenously and the wife has no say in the matter; behaviour does not alter the allocation made by the husband.

2.

The wife follows a cost minimisation strategy that focuses on choosing the reaction to the proposed allocation which causes the least cost; has the lowest possibility of abuse.

An equilibrium strategy results which can reasonably be inferred to be one in which the wife’s action is dutiful acceptance of the proposed distribution.

In this section, I show how this case may exist for a household in a Covid-19 lockdown.

II.5.2 HOUSEHOLD CIRCUMSTANCES The Covid-19 pandemic brought in the need for social distancing and home isolation for all countries of the globe and confinement in domestic spaces became unavoidable for a large number of individuals. Alongside, economic activity has been largely constrained due to constraints on mobility, leading to massive labour force layoffs and business shutdowns, especially in the developing world.

Outside options for household agents and other factors influencing bargaining have been impacted such that bargaining leverage of women has significantly fallen and domestic conflict has become greater.

Firstly, restricted mobility makes it infeasible for agents to exit the household and go to a friend or relative’s home. This makes fallout particularly problematic.

Secondly, poor income and economic circumstances may have disproportionate impacts on household members, reducing the leverage of those earning money, especially earning women.

Patriarchal norms and physical strength would likely take precedence as determinants of bargaining outcome if income allocations are significantly reduced for both members. Hence, women would likely have lesser power.

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Thirdly, psychological strains on members would increase the frequency and magnitude of the conflict. Economic distress, fear regarding the disease and frustration owing to confinement would increase the probability of aggressive strategies being adopted in negotiation processes.

Male unemployment is significantly correlated with higher domestic violence rates. (Bright et al., 2020) A survey conducted by the Manusher Jonno Foundation in 27 out of 64 districts in Bangladesh found 4,249 women, and 456 children to have been subjected to domestic violence in April 2020. (Sifat, 2020) Fourthly, communication and intervention are less feasible in the case of exploitation. Women may be unable to receive help from outside because family members, NGOs and law enforcement entities may not have the necessary means to communicate with them and intervene in the household physically. The costs of conflict or exploitation to the exploiter are lesser in such circumstances as the threat of penalisation does not exist. In June 2020, the survey about 57,704 women and children and the data show that 4,622 women were mentally tortured, 1,839 were physically abused, 203 sexually abused and 3,009 women faced financial constraints imposed by their husbands. (Sifat, 2020) The leverage a female member has in a traditional society may be expected to increase due to the change in demand for household services; being confined at home, the domestic requirements of household members would expectedly be larger, especially for dependents.

With the increased requirement of domestic work, the value of such work would increase.

Given that traditional culture makes household work largely exclusive for women, it is likely that the woman’s leverage must also increase. There are two explanations as to why this is not the case.

Firstly, the acknowledged value of these services may be lower than the utility they offer.

Moreover, under social norms, it would be considered the woman’s duty to carry out the services and value addition of increased work would not be recognised.

Secondly, the woman may be unable to realise her leverage if the expected costs of bargaining are higher than the expected benefits. This would largely be the case during the conflict. Given that dependents are in close proximity to their parents, the costs of conflict in bargaining may involve unwanted psychological and physical impacts on children or other members.

Studies like Duflo (2003) help explain that the preferences of women would be more towards the protection and healthy development of children than men. This would imply that women would lose more than men in the process of bargaining. Therefore, it is more optimal for the woman to adopt the “dove” strategy than the “hawk” strategy.

It is a feasible case that reduced income, lack of connection with the outside world, the absence of any institutional or non-institutional support and potential harm to children make the woman’s threat point virtually zero, implying that her utility level is now exogenously given by her husband. Hence, for the interim period; estimated at more than a year, 1. There are no outside options available to either agent; divorce and separation are unfeasible, non-cooperative equilibrium is not possible due to income and employment constraints.

2. Psychological beliefs and household goods; offspring, negate suicide as an option.

3. Lack of outside options of wife reduces costs of violence for husbands.

4. Confinement and child presence make costs of friction exceed potential benefits in reallocation for the wife.

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Hence, a cost minimization strategy is adopted.

Hence, we have a “marital slavery case” where 𝑈𝑤 = 𝑉𝑤 (𝐶𝑤 , 𝑄𝑤 , 𝑄ℎ ) + 𝐵𝑤 (𝑎𝑖𝑤 , 𝑎𝑗ℎ ).

However, this is not necessarily the case as it is possible that aggressive behaviour may be preferred by the woman if she feels that the children would be better protected therein or if the cost of damage is low for her. It may also be that exit options exist if connections are reestablished or if there are no reservations against suicide.

II.6 IMPACT Female submissiveness and increased domestic violence would lead to long term impacts.

Firstly, the trauma associated with incidents would hamper the future economic and social functioning of victims, reducing their labour efficiency and therefore, remuneration. Their sociability would also likely be hampered. Lower self-earned income and lesser social interactions would reduce women’s exit options and bargaining power in the future as it will be tougher for them to seek assistance in the case of fallout or conflict. (Day et al, 2005) Secondly, a generational impact will take place as the children witnessing household conflict will inculcate behavioural traits that will have negative consequences in adulthood; females would more likely be more submissive and prone to domestic violence and men would likely be more aggressive given childhood exposure to such behaviour. (Giallo et al) IV. CONCLUDING REMARKS: APPLICATION OF MODEL AND GUIDELINES FOR FUTURE RESEARCH The simplifying assumptions of models in the current literature need to discarded, especially since there is scope for more complex analysis given developments in behavioural science and information theory. The expectation of other’s strategy, being determined by past experience and observed signals must be modelled using empirical data and nuances of individual psychology.

IV.1 APPLICATIONS OF THE MODEL A determination of generalized action sets in households of a society can be made based on empirical data: results of participatory and experimental research. These sets can then be used for the application of the model to determine outcomes. This would also involve information obtainment about the negotiation process and how different models can lead to different policy programs. The distributional values pre and post negotiation can be used to find coefficients of change for the different strategy combinations. Noting the differences in costs of strategies for different values of microeconomic variables can help determine causal relationships between them which can then aid in policy making. If strategies and outcomes can be identified, household interventions can be guided more effectively towards lowering bargaining conflict and increasing equity.

An extension of this model can be made to model behaviour under intoxication and irrational behaviour that may tend to follow random walks. Moreover, while the traditional case is resigned to that of a household with a single couple, the case of joint family households is disregarded, as is the case of earning children and elderly. Here, models can be further developed to include lobbying and cartel games that involve agents colliding to increase bargaining power and lower costs. A thorough understanding of household psychology will make policy programs able to efficiently target development and education programs such that schemes alter consumption patterns and welfare as intended.

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REFERENCES Becker, G. S. (1973): A Theory of Marriage: Part I, Journal of Political Economy 81 (4), 813–846 Manser, M. and M. Brown (1980): Marriage and Household Decision-Making: A Bargaining Analysis, International Economic Review 21 (1), 31–44.

Lundberg, S. and R. A. Pollak (1993): Separate Spheres Bargaining and the Marriage Market, Journal of Political Economy 101 (6), 988–1010.

Duflo, E. (2003). ‘Grandmothers and Granddaughters: Old-Age Pensions and Intrahousehold Allocation in South Africa’. World Bank Economic Review, 17(1): 1–25.

Nancy Luke and Kaivan Munshi, (2011), Women as agents of change: Female income and mobility in India, Journal of Development Economics, 94, (1), 1-17 Duflo, E., and C. Udry (2004). ‘Intrahousehold Resource Allocation in Cote d’Ivoire: Social Norms, Separate Accounts and Consumption Choices’. NBER Working Paper 10498.

Cambridge, MA: National Bureau of Economic Research Brown, P. (2009). Dowry and Intrahousehold Bargaining: Evidence from China. The Journal of Human Resources, 44(1), 25-46. Retrieved October 13, 2020, from http://www.jstor.org/stable/20648887 Chiappori, P.-A., and M. Mazzocco (2017). ‘Static and Intertemporal Household Decisions’.

Journal of Economic Literature Jean-Marie Baland & Roberta Ziparo, 2017. "Intra-household bargaining in poor countries," WIDER Working Paper Series wp-2017-108, World Institute for Development Economics Research (UNU-WIDER).

Dercon, S., and P. Krishnan (2000). ‘In Sickness and in Health: Risk Sharing within Households in Rural Ethiopia’. Journal of Political Economy, 108(4): 688–72 11. Mazzocco, M. (2007). ‘Household Intertemporal Behaviour: A Collective Characterization and a Test of Commitment’. Review of Economic Studies, 74(3): 857–95.

Bloch, F., and V. Rao (2002). ‘Terror as a Bargaining Instrument: A Case Study of Dowry Violence in Rural India’. American Economic Review, 92: 1029–43.

Coate, S. (1995). ‘Altruism, the Samaritan’s Dilemma, and Government Transfer Policy’. The American Economic Review, 85(1): 46–57 Waldman, Michael & Bruce, Neil. (1991). Transfers in Kind: Why They Can Be Efficient and Nonpaternalistic.. American Economic Review. 81. 1345-51.

Adam, Christopher S. and Hoddinott, John and Ligon, Ethan. 2011. “Dynamic Intrahousehold Bargaining, Matrimonial Property Law, and Suicide in Canada”. CUDARE Working Papers 1113. The University of California, Berkeley Department of Agricultural and Resource Economics, Berkeley, CA. Cheryl Doss, Intrahousehold Bargaining and Resource Allocation in Developing Countries, The World Bank Research Observer, Volume 28, Issue 1, February 2013, Pages 52–78, https://doi.org/10.1093/wbro/lkt001

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Wu, Xiaoyu and Lixing Li, 2011. “Gender of Children, Bargaining Power, and Intrahousehold Resource Allocation in China.” Journal of Human Resources 46 (2): 295-316.

Richard Thaler, 1985. "Mental Accounting and Consumer Choice," Marketing Science, INFORMS, vol. 4(3), pages 199-214.

Kahneman, Daniel & Tversky, Amos, 1979. "Prospect Theory: An Analysis of Decision under Risk," Econometrica, Econometric Society, vol. 47(2), pages 263-291, March.

Sifat, Ridwan Islam. “Impact of the COVID-19 pandemic on domestic violence in Bangladesh.” Asian journal of psychiatry vol.

53 (2020): 102393. doi:10.1016/j.ajp.2020.102393 DOI: https://doi.org/10.1016/j.eclinm.2019.08.008

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A FINANCIAL ADVERSITY OF THE 21st CENTURY: THE U.S CHINA TRADE WAR -Anushka Saxena1 ABSTRACT Amidst geopolitical conflicts and economic tensions around the globe, the ‘x’ generation of the 21st century is faced with a seemingly unresolvable issue, one that is iconic to the founding values of this century – globalisation, multilateralism and international cooperation. The trade war between two global superpowers, the United States and China, has problematised the fabric of international trade and connectivity, thereby looming as an economic and financial threat over all nations of the world. Starting in March of 2018, the 16-month long trade war significantly hurt the economies of both parties involved, and of those caught in the cross-fire.

Paula Meyers has rightly said, “Business is a battlefield.” In light of this statement, while this trade war commenced with ulterior political motives, trade and taxes became the perfect weaponry.

Keywords: United States, China, Trade War, World Economy, Geopolitics, Tariffs, Pandemic, International Relations JEL Code No. F (International Economics) I. INTRODUCTION It all began with Donald Trump becoming the President of the United States. Since his election in 2016, the States developed an all-round mercantilist commercial policy, aiming to strengthen the US economy in a shut economic system, and leaving the world wanting more. Trump’s preoccupation with China, however, was limited to three barriers : 1. The threat that China’s increasing ambitions undermine the US’ position as a global superpower and its military strengths, 2. The trade imbalance of $378.6 billion in 2018 between the US and China (the former being in deficit), and 3. The Trump view that China’s trade vision and practices go against the World Trade Organisation (WTO)’s regulations, because technically under WTO, China is still a developing country and therefore, its markets should be very welcoming to imports from developed countries.

President Trump consistently emphasised that China is stealing jobs from the US, and engaging in unfair trade practices, something which even Saudi Arabia seemed to agree to. Not surprisingly in the game of geopolitics and geo-economics, the foundation of the trade war was also based on the fact that 67% of the US trade deficit in the world was caused due to imports from China. Adding on to this was China’s very aspiring “Make in China 2025” vision, one which aims to manufacture 100% of the products in the Chinese economy, and work towards making China the economic number 1 in the world in the next 30 years, by making the yuan currency value stronger than that of the US Dollar.

1

Lady Shri Ram College for Women, Delhi University

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However, this vision also relied on the fact that China will allow other countries to trade in their nation, only once they reveal all of their trade secrets, and that China is constantly coercing its corporates to align their vision with the Communist Party (CCP)’s Make in China 2025 vision. The common consensus among US government officials is that a huge trade deficit between the US and China accounts for greater indebtedness for the US, something which the Trump government certainly doesn’t want. Not to forget, China owns US $1.13 trillion in US Treasury Bonds.

Figure 1: The US-China Trade Deficit Source: US Census Now, the task of tariffs is to act as a tax; an additional amount to be paid on top of the cost of exports. It is important to note here that the main items the US imports from China are aluminium, steel, electrical machinery, furniture and of course, cheap labour, while China imports technical parts such as chips and soybeans from the US. Part of the task of a tariff is also to protect indigenous companies of a nation against market competition while also allowing this competition to enter the country at the cost of a tariff. And this is exactly what President Trump pursued in March of 2018. Realising the huge trade imbalance in US-China trade, on March 1st, 2018, he announced a 25% tariff on aluminium imports and 10% on that of steel, effective from March 15th, 2018. Moreover, Trump announced an additional import tax of $60 billion as “punishment” if China chose to retaliate, which it did, fortunately, or unfortunately. Not only this, but he also labelled China as a “currency manipulator”. Thus hitting a common weak spot, President Xi Jinping of China taxed the US imports of Soybean which is not just a major protein source for all of China, but the Soybean export from the US to China went from around 37.5 million metric tonnes in 2017 to negligible, in 2018.

For a long time, the States have benefited from the cheap labour provided by China, and have been on the gaining side of the trade imbalance. However, ironically, 60% of the exports from China to the US are products manufactured by American firms in China, who moved their production abroad to meet with high manufacturing demands and benefit from the low costs of input and labour. Walmart alone purchased the US $18 Billion in products from China, making it China’s 8th largest trading partner. It is difficult to say if US claims on China’s unfair trade practices are meritable or not, based on the CCP’s history of heavily regulating its markets and the fact that almost all nations in the world have a trade deficit with China. This scenario is

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also evident in the US ban on the Chinese company Huawei, the only tech giant in the world to have produced internationally networkable 5G. While the CCP also has a history of city surveillance, media and communication blackouts, and imposing massive regulations on its tech firms, the 5G is a revolutionary technology that drastically enhances data analytics, automation, machine-to-machine connectivity and network speed, making it a game-changer in networks and telecommunications in the world. While an average 4G network can cater to a population of 2000 per square kilometre, a 5G network provides the benefits mentioned above to a population of about 2.6 million people per square kilometre. For wide-area networks, a 5G network can capacitate a traditional base station’s network service by 20 times, while keeping it 3 times smaller than 4G. Not to forget, just like the US trade sanctions on China, the Huawei ban has affected almost all nations across the world and has caused a worldwide state of “dilemma”.

II. LITERATURE REVIEW Any scholarship on the U.S-China trade War largely tends to analyse the losses on both sides of the war, to draw inference regarding the future course of action for either party, as well as the potential impact on the world economy. Policymakers and economic experts also try to draw inferences based on testimonials and experiential reporting in either country, to determine the actual, translated impact on the populace, and why/ how the two countries involved must deal with their inconveniences on priority.

For example, upon looking at Neil Hughes’ work from 2005, titled “A Trade War with China”, we cannot help but look at his background as a Senior Operations Officer working in China, and dealing on an everyday basis, with the other side’s perspective on the future course of bilateral trade, while stating the devastating impacts a potential trade war may have on the American economy. His analysis on either countries’ obligations to the commitments set up by the World Trade Organisation, the premier trade regulation institution of the world, stem from his experiential and thorough understanding of realpolitik in trade.

While losses on both sides have aggravated (As shown in the course of this paper), scholarship from China, especially including experiential reportage from Xinhua or South China Morning Post, attempts to depict China as the victim, undeterred in competing against western authority.

The communism aspect, and authority of the state and the CCP, becomes an important aspect here. It can be inferred, for example, from the cited piece written by Robert Delaney for SCMP, that shows China’s economic decline as a result of its own government’s sovereign decisions (Which were ultimately taken for the betterment of the people), and there was no external power involved, but America sustained more economic losses than China due to its own, if I may say, “foolishness”.

A different type of assessment comes up from scholarship on “negotiations” in specific, because, as in the case of David Lawder’s and Jeff Mason’s work on “Phase 1 Trade Deal” (cited), the feeling of consensus-building and reconciliation is highlighted. While polarising narratives create a barrier in understanding based on ignorance/ deliberate censorship about the “other side”, negotiation and trade deal reportage highlights the larger aspect of bilateral trade in International Economics and regulatory mechanisms that have been in place under actions of the same superpowers involved in derogating them in this particular war.

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Finally, a third-party assessment is equally important to understand the trade war, and perhaps, scholarship from India acts most efficiently in this regard. As can be inferred from K. Giriprakash’s cited piece, titled “ The US-China trade war is a game of threats’ ”, we sense the urgency of nations dependent on the warring parties for the stability of the world economy.

More than its “third-world” perspective, it provides to the reader the loopholes in understanding across American and Chinese experts on the matter, as well as the unwillingness of leaders to work around geopolitical motivations (As highlighted across this paper).

Needless to say, while the author’s research is largely defined by her work with experts and scholars in India, the live reportage from America, the twisting of narratives from China, and the economic aspect of trade and tariffs, as well as the large trade debt between the two countries have been duly taken into account to frame the analysis and policy path.

III. RESEARCH STATEMENT The Paper shall attempt to draw analysis and conclusion, based on pre-existing scholarship and event-based understanding of the U.S-China trade War, beginning from 2016, and escalating across the period of the next four years (during Donald Trump’s Presidency). The Research, centred around the concept of bilateral trade in international economics, references to grassroots impacts of the decisions taken by the top 1%, and the aftermath of said impacts, which includes a detailed round of failed negotiations, as well as the further disruptions caused by the COVID-19 Pandemic.

III.1 OBJECTIVES ● To understand the Background of the U.S-China Trade War, including motivations of the American President (ex-President, Donald Trump), an escalation of tariffs; ● To set out the foreign policy background of U.S-China bilateral relations, including through an analysis of the China Sanctions bill of the U.S Senate, and the Huawei concern; ● To describe the details of the various Phases of negotiation between the two States parties to meet obligations set out by international trade institutions, and to address concerns of the various industries impacted socially and economically by the war; ● To discuss China’s economy in said context, emphasising its role in the Belt and Road Initiative, as well as in dealing with the COVID-19 Pandemic, amidst escalating tensions with the U.S; ● To frame certain policy solutions that may help guide each nation towards elevating their economic conditions, as well as come to a consensus around the concept of trade debt; and ● To draw a conclusion based on analyses drawn across objectives stated above.

IV. METHODOLOGY The Research has been conducted by the author based on a study of events that took place in the course of the trade war, combined with qualitative assessments of pre-existing scholarship and reportage on the theme, supplemented by the author’s professional engagements with International Relations think tanks across India, as well as her academic engagements with American and Chinese history. The overall period from research study to formulation has been a year and a half.

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V. ANALYSIS V.1 BILATERAL MOTIVATIONS The Foreign Policy and Bilateral relations between the two nations must be taken into account to set the foundation for the factors that impact the Trade War itself. Every policy debate and foreign policy vision of both nations is an attempt to counter each other. There exist contesting visions for the lucrative Indo-Pacific between the U.S and the Association of Southeast Asian Nations (ASEAN), wherein the former emphasises openness and the latter seeks to be inclusive of China. Most importantly, China’s lack of accountability over the causing of the pandemic (especially since the disease emerged from its high-risk Wuhan wet markets), and the blamegame being carried out between itself and the U.S over pandemic-related mishandling and misinformation, has been significantly worrisome for Trump. As a result, the U.S has made many attempts to politically target the Chinese state.

The China Sanctions Bill, termed the Uyghur Human Rights Policy Act of 2020, is one such attempt by the U.S to take matters into its hand. Through this bill, the ruling Republican party aims to not only explore the depth of Human Rights violations being faced by the Uyghur Muslims (and other minority) communities (such as Kazakh Muslims) in China’s Xinjiang region, but to also legally slap sanctions against all those who were engaged in bringing these violations to life – may it be an individual, a company, or the State. This move, more than enabling resolution to human rights violations in Xinjiang, creates economic motivations for the American policymaker and corporate thinker.

China’s Xinjiang province, located in North-West China, has been the site for flagrant ethnic cleansing and constant clashes among citizens for over a decade now. It all began when the Urumqi region of Xinjiang was faced with an uprising among its Urumqi and Uyghur citizens, which was launched to protest the killing of two Uyghur toy-factory workers by their HanChinese counterparts in 2009. While these protests were gorily suppressed by the state, they gave rise to the larger narrative of Uyghur suppression that encircles the Xinjiang province today. When the gradual disappearance of non-Han Chinese inhabitants of the region started to spark international curiosities, international media agencies such as Reuters began to investigate the issue, and soon it was found that the disappeared citizens were being transferred to the State’s “Reeducation camps”. The horror of these camps was initially denied by the ruling Communist Party, but by 2018, they were being described as “correctional facilities” and “vocational training centres”. It has been estimated that more than 2 million ethnic Muslims of China have been detained in these camps.

Since the U.S was fed-up with the skewed Chinese state advocacy, and the conflicts on matters of cybersecurity, economic dominance, regional hegemony and the Taiwan question nearly amounted to a “Cold War” between the two nations, the China Sanctions Bill was seen as an opportunity by the U.S to probe into both, the plight of the Uyghur Muslims, and the “Coronavirus accountability” question. Now, many sponsors are calling for the enactment of a separate bill for the latter, naming it the “COVID-19 Accountability Act”. The need for this Act, as recognised by South Carolina Senator Lindsey Graham (who also sponsors the idea), arises from China’s reluctance in letting the world investigate the origins of the disease, and that the country “will never cooperate with a serious investigation unless they are made to do so.”

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Coming back to the China Sanctions Bill that was introduced by Republican Senator Marco Rubio of Florida and Senator Bob Menendez, a Democrat on the Senate Foreign Relations Committee, it has passed unanimously in the U.S Senate on May 14, 2020, and is awaiting confirmation from the House of Representatives before it goes on to the President. Once enforced, it will give the U.S Department of State three months to investigate the human rights violations being faced by the Uyghur Muslims in Xinjiang and compile the number and composition of ethnic communities present in the Re-education Camps. Once a report is presented under the legislation, the U.S will have full right to impose sanctions in the form of asset freezes, travel/ visa bans, revoking Chinese business rights and investments in the U.S, and so on. Trump’s nationalist and mercantilist dream of a “Self-reliant, China-free U.S” would finally come true, and the international condemnation would subsequently follow.

The Coronavirus pandemic has played a critical role in dampening any signs of refurbishment in U.S-China relations. The U.S has accused China of stealing its research on immunisation and even of having birthed the virus deliberately, while China’s viral video ‘Once Upon a Virus’, which mocks U.S’ untimely, ignorant and mismanaged response to COVID-19, has sparked wide media coverage. At the same time, the U.S has cut funding to the World Health Organisation (WHO) at a critical time, with Trump and the American media leading the campaign against the Organisation and calling it a pawn of China. In such a scenario, it is difficult to say whether the China Sanctions Bill is a step in the right direction or a mistimed tool for undue escalation of tensions.

While the question over China’s increasing military belligerence and economic influence is pertinent, the COVID-19 pandemic calls for a collective regional (if not global) response, which makes organisations like the WHO, the ASEAN, the SAARC, the BRI, the EU or the United Nations (UN) important actors. Many argue that nationalistic responses to the crisis have been the most effective, which, in my opinion, is untrue – especially from the cases of European countries like Italy and Spain, which, from the lack of help from their EU counterparts, have spiralled out of control.

The bilateral relations between the U.S and China have been perplexed by the U.S’s ambition to stay and the top, and China’s ambition to come on top. The consequences have been dire for the international community, given the economic power that the two countries yield. There is an urgent need for bilateral reforms from either side so that the threat of a global economic shutdown can be mitigated to a certain extent, especially amidst the disruptions in global production chains and the rising unemployment caused by the Pandemic. There is also a need for international organisations to intervene in the conflict, for the sake of global stability. The debate on “Who is to blame” is ever-growing, and if the retaliatory measures continue at their current pace, the losses will be too high to ever conclude.

V.2 THE TARIFFS THEMSELVES The total tariffs applied by the US on Chinese companies exclusively total to approximately US $550 billion, while those applied by China on the States equal approximately US $185 billion. China redirected its state-owned firms to stop buying any agricultural products from the US. These measures have significantly disrupted the lives of American businesses and consumers. While the prices of cars, gadgets, furniture, beer and plane tickets, the agro-industry of America took a deep hit – China’s 25% tariff on Soybean and pork caused US agricultural exporters to suffer major losses, and the cost of soybean faced a ten-year low in the US markets.

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Stressed by the tensions in international agricultural trade, the US farmers even plead the government to find alternative markets and work out possible solutions for the trade feud; solutions that do not involve a tit-for-tat with tariffs. While American steel industries suffered back when China heavily exported steel to the country, the hiked up prices of steel in America now is worse for the consumers. Wall Street faced its worst day on 2nd August 2019, when the Dow plunged more than 750 points on the Stock Exchange. This was worsened by the other trade wars the US had going on with Mexico, Canada, Turkey and the European Union (EU).

While the US pork industry is losing $12 per animal due to Mexico’s retaliatory tariffs of 20% on American cheese, pork, steel, apples and potatoes, the EU threatened to heavily tax imports of Levi’s Jeans, Bourbon Whisky and Harley Davidson motorbikes from the US, when Trump declared plans to tax EU steel and aluminium imports. President Trump justified taxes on imported steel and aluminium by saying that they were a threat to the US economic security, and its two largest trading partners, China and Canada (respectively), were deeply upset.

Subsequently, Canada began imposing tariffs worth US $12.6 billion on US goods from 30th June 2019, as retaliation for the Trump administration’s new taxes on Canadian steel and aluminium imported to the United States.

On the other hand, the American tariffs have hurt Chinese manufacturing, the industry suffering losses of about US $15 billion. China owns 1/3rd of the technological equipment production in the Asian region, and ever since the US tariffs were imposed, a significant chunk of manufacturers shifted their industries to parts of Singapore and Taiwan, two major competitors against China in the field of manufacturing tech parts. Amidst the ongoing trade war, the Shanghai Composite Index, the strongest indicator of China’s Stock Market competence, plummeted by approximately 18%. Moreover, the US sanctions are expected to cause considerable unemployment, especially on the East Coast of China.

V.3 AN ANALYSIS OF THE NEGOTIATIONS Under much international and market pressure, both the US and China began considering trade talks and negotiatory deals to make a truce. During the Argentina G20 Summit held on December 1 and 2, 2018, the US and China governments discussed plans for a ceasefire on the trade war, and a possible course of trade negotiations for making peace and solving the trade imbalance. Consequently, the US halted on increasing the tariffs from 10% to 25% on Chinese imports, while President Xi Jinping lifted tariffs from US cars and Auto parts and even purchased American soybean for three months. When negotiations broke down, tariffs were imposed again and more rigorously on soybean, technical equipment, steel and other products.

China had also eased US auto parts tariffs on the sidelines of the Buenos Aires G20 Summit, which Jinping re-slapped in an announcement on September 23, 2019.

Through a large number of series of talks and negotiations, some chances of a truce are finally appearing in the news. When on 1st September 2019, the US government slapped the US $125 million in tariffs on Chinese goods, China launched its third complaint with the WTO against a total of US $300 million unfair trade taxes imposed by the States. As of then, the US was left with only 60 days to resolve the trade dispute, as per WTO guidelines. Then on 5th September this year, the two presidents held the 13th round of trade negotiations, in Washington, and agreed to work together to figure out practical solutions, following which on September 11, China released an exemption list for US products.

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Figure 2: The Many Trump Trade Wars Source: BBC Research Another round of mid-level trade talks happened in the US on September 19th and 20th, following which the US also received a list exempting 400 Chinese goods from tariffs. Under the cease-fire scenario, existing tariffs and counter-tariffs on many of each other’s goods would remain in place, but no additional import taxes would take effect. This bought time for U.S. and Chinese officials to restart talks that stalled last month in September after 11 rounds of negotiations. Next, following a two-day meeting on October 10th and 11th this year in Washington DC, President Trump announced that negotiators from the US and China had reached a “Phase 1” agreement that will take several weeks to finalize. As part of the Phase 1 agreement, China will reportedly purchase US $40-50 billion in US agricultural products annually, strengthen intellectual property provisions, and issue new guidelines on how it manages its currency. The apparent measures for the truce indicate a pattern of talks, upon which both presidents have remarked that they will be following the path of friendliness now, and avoid using protectionism in trade deals.

It is difficult to say if free trade is the best solution to the problem, given the fact that openness in the economy has caused many nations to become poor, (India being an example), but openness in trade negotiation is the need of the hour to purge some heat in the global trade environment. The US also wishes to strengthen cross-border relations with others it is upset by empowering trade contacts and treaties, one of which is the North American Free Trade Agreement (NAFTA), signed among the US, Canada and Mexico. Today, both nations are in trade talks, hinting towards what Trump calls a full-blown trade agreement that would settle long-running disputes over tariffs, currency rates, technology sharing and intellectual property laws.

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Figure 3: Trump and Jinping meeting at the side-lines of the 2019 G20 Summit Source: AP News V.4

WAY FORWARD

Analysts expect the US to continue to use non-tariff measures to push back against China.

Restrictions on Chinese investment into the US, limits on the ability of US firms to export technology to China, and further pressure on Chinese companies are all recommended tools. “Non-tariff measures don’t get the attention from markets that tariffs do, partly because their impact is harder to quantify, but they can have far-reaching impact,” says Michael Hirson, Asia director at the consultancy firm Eurasia Group.

Steps to resolve the trade war may also involve strategic communications between two countries, ministerial dialogues, and formulating ways to resolve trade imbalances. As for China, the country faces a national issue that needs to be addressed. Firstly, China should save its sliding currency, the Renminbi (RMB/ Yuan), which depreciated by almost 8% during the trade war months. The country is already facing a socially unstable economic trend of excessive household savings, and a further plummet in RMB can worsen the situation and devalue the currency greatly, creating a deflationary bubble. So as an immediate solution, the CCP can sell its foreign exchange reserves to maintain RMB value (currently, China owns US $3.1 trillion in FER). Secondly, China needs to encourage citizens to invest more, consume more and save less. The nation must also establish a social safety system that enables the citizens to invest in safe, liquid assets such as market receivables and account securities, and not just maintain household savings.

V.5 CHINA’S TRADE ALTERNATIVES China's economic resume, and the US’s critique of China, is incomplete without mention of the flagship Belt and Road Initiative. Today, under President Xi Jinping, an advanced version of the Ancient “Silk Road” concept, often termed as the 21st-Century ‘New Silk Road’, is becoming the focal point of the country’s geopolitical and economic approach to the world.

Introduced by President Xi Jinping on his visit to Kazakhstan in 2013, the New Silk Road, more popularly known as the “Belt and Road Initiative (BRI)”, aims to connect Asia, Europe, Africa, the Middle East and the Americas via road-rail-water networks, with China at the geoeconomic centre of the connectivity. Since its inception, the Xi government has had but one

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major goal – to create mutual benefit for all in the “neighbourhood;” to create a collective economic boom in the region, with the flagship BRI at its heart.

The traction of the BRI project can be best explained by looking at the African subcontinent as an example. In Africa, China has provided immense infrastructural benefits in exchange for BRI partnership. The fact that China is a provider of huge amounts of foreign aid in African countries overpowers their political concerns, especially since most African countries are in need of rapid urbanisation, upliftment, for which they need external infrastructural support. For example, China has provided $3 billion to Ethiopia, Ghana and Sudan in telecom support has helped with the construction of the tallest tower and the new Administrative Capital of Egypt, and will have lent approximately $1 trillion in all sorts of aid to Africa by 2025 (an estimate of the Chinese Exim bank). There have been persistent doubts on Chinese presence in Africa, considering the fact that the debt of Africa to China is rising tremendously, and that there remains certain uncertainty on the nature of “aid” China provides to Africa. At the same time, China provides loans in the name of “development financing” to African countries, and the collateral against it is taken in the form of Chinese control over minerals and natural resources those countries may be rich in. For example, China provided the $4 Billion in loans to Angola in 2004, in return for which it took over multiple oil blocks in the country. China’s military presence in Djibouti has also been a matter of concern, especially for the United States, which is trying to balance out China’s influence in Africa. Nonetheless, African countries have been seeing “the bright side” in their relations with China since the cold-war era, and will probably continue to do so for longer.

With the disruptions in the global manufacturing and labour supply chains, the massively labour-dependant Belt and Road are set to face unprecedented vagaries. It is widely-known that BRI projects around the world depended on funding and raw materials from China, and there is no local ingredient in the recipe for the BRI that could spice things up in the absence of the former. For the success of the BRI, the availability of a labour market was a critical tool. Now that migrant workers from China have been barred from entry in more than 100 countries, many of them being Belt and Road allies, the circumstance of the project across the region and beyond is flailing. This could mean that as long as workers are unable to return to foreign building sites, the projects are under threat of languishing, or stopping completely (as can be witnessed in the case of the China-Pakistan Economic Corridor or CPEC, Cambodia’s Sihanoukville Special Economic Zone, a high-speed rail project in Indonesia, and many more).

Hence, China has been toeing the line by attempting to become a helping hand. The many trains that are still functional along lines of communication in the BRI are being utilised by the Chinese Government to supply medical aid to Corona-ravaged nations. For example, a train from Yiwu in East China left for Spain on the 21st of March, 2020, filled with over 700 protective suits and 1,10,000 medical masks (Shepard, Forbes, 2020). Health diplomacy could certainly be a critical link for China to connect what was to what can be, and the “Health Silk Road'' can be seen as a move in the right direction. China can also consider restricting BRI projects to a few nations while saving its larger investments for countries that claim to be close allies of China (while being parasitic to its aid) but may react differently once the Pandemic halts. A changed BRI must also mean that it should transition from being China-centric to becoming a community project, with all member states investing in it. Perhaps, streamlining the definition, policies and motives of the BRI into a White paper may help China in this regard, both geo politically and financially.

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China’s success will largely depend on its ability to minimise its losses and land back on its feet in the disrupted global markets and supply chains while exercising constraints on its ambitions. It will, however, be an interesting endeavour to closely monitor China’s next moves, and how the world responds to them, over time.

VI. CONCLUSION The US preoccupation with China has continued for a long time. Now the question remains: What is it that the US wants? Is it market access, reform of intellectual property rights and forced technology transfer, improved ease of doing business in China or restructuring of the entire Chinese economy? The path and motivations of the trade war have been uncertain, and have been further perplexed by inconsistencies in the US government and China’s restless ambition to be on top. US President-Elect Joseph Biden’s administration presents some scope of relief in the tensions, but not to the point where China can be let off with a clean chit, considering the US deficit against China has reached a new low under Trump’s presidency, which, at any given day, is favourable for America. Given the economic power that the two countries yield in the international political economy, there needs to be more clarity and cooperation to finalize the terms that could lead to a gradual easing of these sanctions, so that the collapse of the global economy can be prevented, especially considering the impacts of the current Pandemic. However, the scope of this war goes beyond economic and market motives, for it clearly is a power play – a play between two giants who want to be number one and in the process, they step on many others, hurt economies, and still have a need to think outside their thick skin.

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REFERENCES Bermingham, Finbhar & Zhou, Cissy (2019). One year in, US-China trade war is the ‘first sentence of the first chapter’ of a new superpower rivalry. [online] South China Morning Pos. Available at : https://www.scmp.com/economy/china-economy/article/3017458/one-year-uschina-trade-war-first-sentence-first-chapter-new (Accessed on 12 October 2019).

Da Costa, Ana (2019). Why the US-China rivalry will not end with a trade deal. [online] BBC News. Available at : https://www.bbc.com/news/business-47848861 (Accessed on 13 October 2019).

Delaney, Robert (2019). Trade war is harming American economy more than China’s, claims officials sent to the US to defend Beijing’s position. [online] South China Morning Post.

Available at : https://www.scmp.com/news/china/diplomacy/article/3019071/trade-warharming-american-economy-more-chinas-claims-official (Accessed on 12 October 2019).

Elmer, Keegan (2019). ‘From bad to worse’: tariff-hit US farmers urge Donald Trump to settle trade war. [online] South China Morning Post. Available at : https://www.scmp.com/news/china/diplomacy/article/3010144/us-farmers-urge-dd-trumpresolve-trade-war-latest (Accessed on 12 October 2019).

Giriprakash, K. (2019). The US-China trade war is ‘a game of threats’. [online] The Hindu BusinessLine. Available at : https://www.thehindubusinessline.com/economy/the-us-chinatrade-war-is-a-game-of-threats/article29623556.ece (Accessed on 12 October 2019).

Hughes, Neil (2005). A trade war with China?. [online] HeinOnline. Available at : https://heinonline.org/HOL/LandingPage?handle=hein.journals/fora84&div=67&id=&page= (Accessed on 12 October 2019).

Lemire, Jonathan & Miller, Zeke (2019). China says Trump, Xi reach trade truce. [online] AP News. Available at : https://www.apnews.com/6d3b90edaa7444609c6629077f4b3ae2 (Accessed on 13 October 2019).

Mason, Jeff & Lawder, David (2019). U.S. outlines 'Phase 1' trade deal with China, suspends October tariff hike. [online] Reuters. Available at : https://www.reuters.com/article/us-usatrade-china/us-outlines-phase-1-trade-deal-with-china-suspends-october-tariff-hikeidUSKBN1WQ10X (Accessed on 13 October 2019).

Wong, Dorcas & Koty, Alexander (2019). The US-China trade war: A Timeline. [online] China Briefing. Available at : https://www.china-briefing.com/news/the-us-china-trade-wara-timeline/ (Accessed on 12 October 2019).

Zhang, Yuhan (2018). The US-China Trade War: A Political and Economic Analysis. [online] JStor. Available at : https://www.jstor.org/stable/26608823?readnow=1&seq=4#page_scan_tab_contents (Accessed on 12 October 2019).

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ECONOMICS OF FOOD: AN ANALYSIS OF THE INDIAN FOOD MARKET DURING THE CORONAVIRUS LOCKDOWN - Astha Mukherjee, Gopika Venugopal, Mahak Kohli, Medhawi Rajgaria1 ABSTRACT “The economy is driven by consumption, and consumption is driven by hunger. How can a study of economics then not involve the economics of food?”

Economies across the globe have endured financial meltdowns, civil wars and natural disasters.

Now countries are confronting a different kind of menace: a fast-spreading virus that has abruptly dented demand and supply across industries and continents. With households slowly adapting to life in the pandemic, there have been significant changes in our eating habits. With most Indians facing job insecurities, the proportion spent on food is bound to be affected, keeping in mind the complex income distribution structure of India. It is an area of great interest- to study the changes in food types that a rational consumer would consume as a repercussion of the pandemic. The food industry has unquestionably witnessed huge changes affecting its overall demand-supply scenario.

I. INTRODUCTION In addition to an inevitable economic crisis, Covid-19 has made severe changes to our lifestyle, thereby affecting our physical and psychological well-being, and inevitably our food preferences. Quarantine and self-isolation imposed on people have got them shifting back to local, homegrown ingredients with minimal risk involved in eating. With minimal options to eat outside, the trend of eating home-cooked food is clearly growing. Understanding food purchasing behaviour is certainly useful to understand consumers’ behaviour during the crisis period and to provide useful guidance in emergency management efforts. We have conducted a survey to understand how a pandemic like the novel coronavirus affects food purchasing and consumption behaviour of different food types and among different income groups in India.

The focus of the study is listed as follows: ● Analyzing changes in consumer food preference and the supply of various sections of the food industry due to pandemic and subsequent lockdown. ● Analyzing income-elasticity of demand of various income groups in the economy for various sections of the food industry. ● Analyzing prominence of demand variations of different income segments, inferring the direction of change in demand of the various sections of the food industry. ● Economic interpretation of such changes in demand and supply in the food industry.

II. METHODOLOGY A Cross-sectional primary data was collected by our research team through Google forms and telephonically. The survey was collected by specifically targeting respondents from various income groups on an under-researched problem to develop knowledge and form predictions.

1

Maitreyi College, Delhi University

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The questionnaire was framed in a qualitative manner so as to study the income elasticity for different food-groups of households from four income segments, namely: Low Income, LowerMiddle, Upper-Middle, High Income.

The survey received 165 responses in total, from various states in India (Bihar, Chhattisgarh, Delhi, Gujarat, Haryana, Himachal Pradesh, Jammu & Kashmir, Jharkhand, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Meghalaya, Odisha, Rajasthan, Tamil Nadu, Telangana, Uttar Pradesh, Uttarakhand, and West Bengal). By asking the number of members and income slab of the households, we estimated a per person income for each household by measuring the average income of a particular income segment as classified above. 𝐼𝑛𝑐𝑜𝑚𝑒 𝑟𝑎𝑛𝑔𝑒 𝑜𝑓 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑: 𝐴 𝑡𝑜 𝐶 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑖𝑛𝑐𝑜𝑚𝑒 𝑝𝑒𝑟 𝑝𝑒𝑟𝑠𝑜𝑛:

𝐴+𝐶 2

𝑥

1 𝛾

(1)

A: lower limit C: upper limit 𝛾:number of members in family

Information regarding the decrease in income during lockdown was collected assuming that there was no increase in income due to the economic impact of the pandemic. Further, the average percentage change in income was calculated for each income group.

To study the level of change in consumption and preferences of each food segment, a number line as illustrated below was used. Consequently, the percentage change in consumption was calculated, assuming 3 to be showing 100% change.

Figure 1: Scale for percentage change in consumption

Using the collected data in our developed model, we calculate income elasticity for the income segments.

Additional data from our survey includes information regarding government aid given out (mainly to low income groups), changes in demand for health supplements during the pandemic, and reasons for changes in consumption for food items.

III. STUDY OF VARIATIONS IN THE DEMAND-SUPPLY SYNOPSIS OF DIFFERENT SECTORS OF THE INDIAN FOOD INDUSTRY This pandemic has had an impact on both supply and demand side of the food economy, but the impact on various food-groups have been strikingly different in magnitude as well as nature.

In this section, we will analyse how and under the influence of what factors, different food groups have been impacted due to the coronavirus crisis and subsequent lockdown

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III.1 FOOD GRAINS (Refer Appendix I)

Foodgrain is defined as a variety of grains that are grown for human consumption. This includes Rice, Wheat, Maize and Soybeans. We will study the demand-supply data of each component of foodgrain and aggregate them to find the final demand and supply of foodgrains.

● Wheat Being the main cereal crop of India, wheat cultivation dominates the northern parts of the country. According to the Agricultural and Processed Food Products Export Development Authority (APEDA), India is the second largest producer of wheat. Wheat has a significant share.in consumption of food basket, with a 36% share in the total production of foodgrains in India. Production of the grain in the crop year 2019-20 that runs from July to June touched.an all-time high which ensured enough wheat supply and buffer with Government which can be distributed through rationing during the COVID-19 crisis. The production of wheat has also shown a slightly increasing trend, from 98.7 to 99.6 million tonnes from 2019-20 to 2020-21 with a magnitude of 0.9 million tonnes (0.91%). Stocks have shown a decline while the imports have increased by 50% which leads to an increase in supply by 0.52% (“World Agricultural Supply and Demand Estimates WASDE -600 Approved by the World Agricultural Outlook Board”, 2020). Domestic consumption of wheat has also shown an increasing trend by 1.6% even after being hit by COVID-19, while the exports of wheat remain the same. ● Rice Rice is one of the chief grains of India and makes up an important part of the national economy. India has the largest area under rice cultivation (Directorate of Rice Development, n.d.), justifying that it is a dominant crop of the country. The country had surplus rice production, however, increase in stock grains due to disruptions.in the supply chain has caused the increase in actual supply to be tapered in magnitude, rising by a meagre difference of 1.8 MMT from 2019-20 to 2020-21 (“World Agricultural Supply and Demand Estimates WASDE -600 Approved by the World Agricultural Outlook Board” 2020). The demand scenario has witnessed a nominal increase from 101.5 in 2019-20 to 102.8 MMTs in 2020-21(Projected), indicating stability in overall demand in the rice sector, while the pandemic manifests its effects on the food industry of the country. ● Soybeans India is the fifth largest producer of soy in the world (FAOSTAT, n.d.), and the soy oil accounts for a considerable part of the total oil consumption of the country.

According to data gathered from USDA, the production size in the soybean sector has expanded from 6.64 MMT in 2019-20 to 7.52 MMT in 2019-20, with actual supply rising from 7.17 MMT to 7.8 MMT correspondingly (“World Agricultural Supply and Demand Estimates WASDE -600 Approved by the World Agricultural Outlook Board,” 2020) maintaining constancy in the trend. The demand side in this commodity market does not show very contrasting outcomes as demand is nearly stable with a paltry rise

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of 0.25 MMT across the two periods in concern. The analysis therefore concludes that the COVID-19 lockdown has had an insignificant impact on the soybean sector. ● Maize Maize is the third most important food crop.in India after rice and wheat, majorly cultivated during the kharif season. In addition to being a staple food for humans and a quality feed for animals, maize serves as a basic raw material for thousands of industrial products including starch, protein, textile, gum, pharmaceutical, cosmetic, film, paper industries etc. (APEDA, n.d.). Maize supply has been stable in the country, with increase in stock due to possible supply chain disruptions causing trivial fluctuations, causing it to vary by a minimal extent of 0.23 MMTs across 2019.and 2020. The demand picture in the maize sector has witnessed a contrasting surge of around 45%, accounting to a rise from.28.4 MMT in 2019-20 to 41.18 MMT in 2020-21. Around 44% of the domestic maize consumption goes to the poultry sector as fodder for livestock. This increase in maize demand, in addition to growth in household demand, has therefore also been influenced by the increased demand for maize by the poultry sector (owing to increased rearing requirements due to decline in the demand for poultry by households).

CONCLUSION On studying the aggregate of the above foodgrains’ demand and supply we see an expectation of a slight increase in both demand and supply in 2020-21 as compared to 2019-20. One of the factors for this increase in demand may be a shift towards essential commodities by consumers due to fall in income or even panic buying due to the pandemic.

While the increase in supply could be attributed to better harvest season and government policies to help agriculture. Foodgrains being an absolute essential. in the diets of most households bear a lesser brunt of income cuts.

III. 2 DAIRY (Refer Appendix II) With a sustained growth in the production of milk and related products, dairy activities form an important part of the rural Indian economy, serving as a major source of employment and income.

Analysis of the performance of the Indian dairy sector informs that there has been efficient handling of the entire supply chain of Indian dairying, in spite of some inconveniences related to distribution of milk in locked down areas specially containment zones.

An overall increase has been observed.in the supply of major dairy products from 645 TMT in the early months of 2019 to 696 TMT in similar time periods of 2020, reflecting a rise of nearly 8% (United States Department of Agriculture, 2020).

The demand scenario of the Indian dairy sector has seen varying fluctuations over the period of its journey from the early months of the previous year.to the coronavirus lockdown in 2020.

The beginning months this year saw a surge in demand for dairy products, therefore matching the pace of increase in supply, however, the subsequent lockdown (implemented from 25th March 2020) had a toll upon this trend. Information from the Ministry of Finance reveals that milk demand fell 20-25% during lockdown. This can be attributed to reduction in bulk

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purchases from restaurants, events, and road-side eateries which remain shut down as a consequence of the lockdown, and also a partial decline in household consumption.

III.3 FRUITS (Refer Appendix III ) India is the world's largest producer of fruits such as bananas, papaya, mangoes and guavas.

Some of the major states involved in this industry include Andhra Pradesh, Maharashtra, Karnataka, Uttar Pradesh, West Bengal, among others (Opportunities in Fruits & Vegetables Sector in India, n.d.).

The fruits industry has witnessed considerable fluctuations.in demand and supply as a result of the pandemic and its consequent restrictions, the most impactful among them being disruptions in supply chain. Major fruits produced include bananas, apples, guava, grapes, ber, papaya and pineapple. Comparing the total fruits arrivals all over India in the month of March 2020 with that of the total arrivals in March 2019, there has been a sharp decline in supply accounting to 58%, from 202229 to 117385 metric tons (National Horticulture Board, 2020).

Analysis reveals that this has been a composite outcome of shortage of labour and unavailability of machinery, lower number of operational traders and trucks, and increased farmer costs. Farmers are struggling to harvest and trade perishable fruits as the lockdown has interrupted the whole transport and logistics system.

This drop in arrivals has not helped farmers get a better price at the market.

On the demand side, there has been an even sharper decline, as a consequence of the drop in bulk purchases of high value fruits (for example, strawberry, basil etc.) from hotels, restaurants, marriage halls, temple festivals and others. Household demand too has dropped pertaining to decrease in purchasing power. These dynamics have caused intense fluctuations in the price landscape, with farmers even being forced to sell their perishable fruit harvest at a third of the normal price, or worse, feed them to cattle for avoiding wastage.

III.4.VEGETABLES (Refer Appendix IV) India stands as the second largest producer of vegetables including potatoes, green peas, tomatoes, cabbage and cauliflower. Major producers including Uttar Pradesh, West Bengal, Madhya Pradesh, Bihar, and Gujarat, together account for around 55% of the national production, according to the report (Opportunities in Fruits & Vegetables Sector in India, n.d.).

Not being aloof from the fruit sector, the vegetables sector in India too has endured similar instabilities resulting from the country-wide lockdown. On the supply-side, there has been a decrease of around 68% in supply arrivals of major vegetables (including onion, potato, garlic, tomato, cauliflower and cabbage) across the country, from 0.722885 MMT in March 2019 to 0.492983 MMT in March 2020 (National Horticulture Board, 2020). Disruptions in movement of vegetables throughout the country due to closed borders, and mass migration leading to

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shortage of farm labourers trained in farm works have caused a broken supply chain round the country.

The demand side of the vegetables industry underwent complex inconsistencies while the lockdown duration. In the initial days of the lockdown, rumours resulting in hoarding of semiperishable items like potatoes and onions followed by a sudden spike in demand affected the rise in retail rates. However, the institutional procurement of vegetables by hotels and caterers had stopped due to the lockdown causing the overall demand in the industry to deplete.

III.5 POULTRY AND FISHERY The poultry and fishery sectors in different parts of the country have been hit hard amid rumours that the novel coronavirus can be transmitted through consumption of animal products, the prices of which have fallen considerably as a result. Data from the Ministry of Animal Husbandry, Dairying and Fisheries show that poultry industry was losing Rs 1,500-2,000 crores daily and spread of COVID-19 has had an adverse effect on the industry.

In Spite of rumours being shunned by FSSAI (“COVID19 MythBusters”, 2020), the demand for poultry and fishery fell significantly, making it difficult for the industry to survive. There have been claims that farmers are left with just 15 percent of their original livestock, having been forced to cull the rest with demand dropping to almost nothing. If problems continue and the farmers stay away from raising chicks, there are anticipations of prices shooting up when the market returns to normal. On a related note, prices of mutton are already soaring. Rates in Delhi have risen to Rs.700/kg, with supply faltering in some areas.

As per reports, India’s exports of buffalo meat, the country’s second largest farm export, have fallen 50 per cent, to 50,000 tonnes, in the first week of March. Shops are allowed to sell poultry, meat and fish, ensuring strict social distancing from April-15. But the pain remains.

III.6 PROCESSED FOODS Processed food can be defined as any food that has been altered in some way during preparation. Demand for packaged food items such as sweet based products, chocolates, chips, biscuits, frozen food, bread etc. has seen a sharp drop in the lockdown period. This can be attributed to the decreased purchasing power of buyers, increased health concerns and fears of contracting the virus.

Sale of FMCG in India plummeted by 30.6% from March'20 to April'20. Nielsen's data shows that FMCG sales value fell from 216 to 150 bn rupees (COVID-19: Tracking the Impact on FMCG, Retail and Media, 2020). This period suggests a fall in supply of fast-moving consumer commodities due to supply chain disruptions.

Overall, sales in April'20 fell by 33.9% as compared to those in April'19. This highlights the grave impact of lockdown even more, since percentage fall in a month from April'20 has been more significant than in the period of one year.

III.7 PULSES Indians are still by and large vegetarian in dietary habit and heavily depend upon vegetative sources to meet their daily protein requirement. As the government took a call to buy pulses

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from the farmers, many have started to diversify from the traditional food crops to grow more pulses leading to self-sufficiency in the area of major pulses. The imports are steadily declining.

India's pulses output for 2019-20 was hit by erratic rains, with production of only 23.58 MMT pulses and 3 MMT pulses being imported.

Amidst COVID-19 pandemic supply of pulses was unaffected to a large extent due to its categorization as an essential commodity and distribution through PDS. While the overall consumption of pulses has not seen any significant changes due to the pandemic, there has been a shift towards packaged branded packets of pulses from the loose purchase of pulses .Nielsen data showed that the traditional trade of branded pulses saw a growth of 72 percent in February to mid-March 2020 as against 16 percent in 2019

III.8 BEVERAGES The beverages sector forms a major part of the Indian food industry. The main items in the sector include tea and coffee, alcoholic beverages, soft drinks and packaged drinking water.

III.8.1. TEA AND COFFEE Tea, one of the main commercial crops of India, majorly produced in North-east India, North Bengal, Kangra and South India has faced a big blow in production in states like Assam and West Bengal due to pandemic. According to the Indian Tea Board, the present crisis has jeopardized the tea industry with a loss of 150 million kg of tea that accounts for 10 per cent of its total production. The lockdown restrictions have caused tea crops to be left unplucked, and the first flush of March being wasted due to supply side disruptions.

The domestic market, constituting 80% consumption of the total tea production, is in the clutch of the pandemic. The tea industry has been impacted worryingly by the lockdown.

Coffee predominantly produced in Southern states of India has faced obstructions in both harvesting and shipping, due to containment measures causing a decline in the supply. Out-ofhome consumption, through cafés, restaurants and offices etc, which accounted for a decent chunk of its total consumption, saw a considerable decline. As a result, the demand side in the coffee industry witnessed a fluctuating impact.

III.8.2. SOFT DRINKS The soft drinks industry in India too has experienced repercussions, due to the Covid-19 lockdown causing the major players in the market, namely PepsiCo and Coca-Cola, to temporarily shut down their manufacturing units across the country hampering the supply.

On the demand side too, downward trends had been noticed. Away-from-home purchasing makes up a major part of the total household purchases of soft drinks which has declined due to lockdown restrictions. The decrease in institutional demand by restaurants, hotels, and events, led to further shrinking of overall demand in the sector.

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III.8.3. BOTTLED DRINKING WATER With increased health concerns and unavailability of safe drinking water, the bottled water industry has seen flourishment over the years.

The Coronavirus lockdown has hit the packaged water industry adversely. With several manufacturing units temporarily shut down and others operating on limited capacity, the supply of bottled water saw a decline.

Demand in the industry saw changes in consumer behaviours. Out-of-home consumption declined due to shutting of offices, malls, and decreased demand from railway stations & airports. However, increased concern for pure drinking water has contributed to rise in household demand. Bisleri, one of the leading companies in this industry, in the month of April launched direct-to-consumer service of its mineral water to help meet increased demand during lockdown.

These dynamics exhibit the alterations in the concerned sector due to unprecedented lockdown.

III.8.4. ALCOHOLIC BEVERAGES The liquor industry of India is one of the most vibrant in the world and plays a very important role in tax revenue accumulation for the Indian government.

The Covid-19 pandemic has caused enormous hustle in the industry. Actual consumption in the initial month of the lockdown therefore went considerably low, owing to the lack of availability due to shutting down of shops and increased health concerns.

Demand saw a steep rise with the reopening of alcohol shops from 4th may, as a result of stockpiling efforts by buyers and panic buying increasing the risk of exhaustion of existing stocks against the inflated demand. Initial days of the reopening therefore saw significant spikes in liquor sales. According to a statement by the General Director, CIABC, an average increase of 20 to 30 per cent was seen in sales on the beginning day of the resumption.

In an attempt to control the soaring demand and potential threat of virus transmission due to long queues of buyers, several state governments raised the duty on alcoholic beverages citing the crisis. This brought in new dynamics in the market. Drastic price rises led to decrease in actual supply which had a larger negative impact on the industry as a counterproductive consequence wherein the businesses found it difficult to cover the losses incurred during the lockdown The alcohol industry, therefore, stands at an uncertain position, with market dynamics and government regulations exerting severe implications on its functionality.

IV. STUDYING INCOME-ELASTICITY OF FOOD CONSUMPTION DURING THE COVID-19 PANDEMIC (Refer to Appendix V, VI) As the pandemic slowly paced its way throughout the country, a nationwide lockdown was declared on 24th March 2020 leaving thousands of people worried about their next paycheck.

This clearly aggravated the economic crisis in India. Businesses went into losses as physical markets were shut down, househelps were told not to come due to social distancing practices.

Some faced a reduction in their salary, some completely lost their jobs. Even those with

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previously secure and unshakable income (like Landlords) had to bear the brunt due to clogging in the chain of income. Those who didn’t face a pay cut, still prepared themselves for the possibility of it. Even the Prime Minister in his address urged people not to cut the salary of their house-help or fire employees. The Government even extended various income-tax related deadlines to provide relief during the crisis. While the government is trying hard to push employers not to lay off employees, it is inevitable in these unforeseen circumstances and eventually most people are having to make a living on these slashed salaries.

Such drastic changes in income inescapably impact food consumption trends. We analyse the relation between the fall in income and changes in demand of the eight food segments. For a clear study, we divided the society into four income strata: Low Income, Lower-middle Income, Upper-middle Income, and High Income; on the basis of income per individual, and compare their income elasticities.

𝐼𝑛𝑐𝑜𝑚𝑒 𝐸𝑙𝑎𝑠𝑡𝑖𝑐𝑖𝑡𝑦 =

𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒 𝑐ℎ𝑎𝑛𝑔𝑒 𝑖𝑛 𝐷𝑒𝑚𝑎𝑛𝑑 𝑓𝑜𝑟 𝐹𝑜𝑜𝑑 𝑆𝑒𝑔𝑚𝑒𝑛𝑡

(2)

𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒 𝑐ℎ𝑎𝑛𝑔𝑒 𝑖𝑛 𝐼𝑛𝑐𝑜𝑚𝑒

IV.1 LOW INCOME GROUP The low income group has been classified as households whose per person income lies below Rs.6,000/month. The professions of respondents in this segment range from domestic help, daily wage worker, gardener to plumber.

The average percentage change in income calculated from the survey is 45%.

Following are average percentage changes in demand for low income households who experienced a fall in income:

Food Segment

Food Grains

Fruits

Vegetable s

Pulses

Dairy Products

Poultry & Fishery

Beverages

Average Change (%)

-18.84

-26.09

-39.13

-26.09

-65.22

-75.00

-62.32

Processed Foods -69.57

Using formula (1), we get the following income elasticities for low-income group for our food segments:

Food Segment

Food Grains

Fruits

Vegetable s

Pulses

Dairy Products

Poultry & Fishery

Beverages

Processed Foods

Income Elasticity

0.42

0.58

0.88

0.58

1.46

1.68

1.39

1.56

We observe a positive income-elasticity across all food segments, signifying a direct relation between income and demand. A 45% income decrease across the low income sector has caused them to decrease consumption of food.

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However, there is less decline in consumption of foodgrains, pulses, fruits, and vegetables (less responsive to changes in income) as compared to that of processed foods, dairy products, poultry & fishery, and beverages. This indicates that as income fell, they held back on consumption of the items considered luxury food by them, while the consumption of essential food items remained relatively stable.

The income-elasticity for overall food consumption among the low income group is 1.07, which can be classified as Highly Elastic Demand.

Figure 2: Income Elasticities of Low Income Group

IV.2 LOWER - MIDDLE INCOME GROUP The lower-middle income group has been classified as households whose per person income lies between Rs.6,000 and Rs.30,000 per month. The professions of respondents in this segment range from Teacher, government service job, store owner, boutique business, to retired employee.

The average percentage change in income as calculated from the survey is 34%.

Following are average percentage changes in demand for lower-middle income households who experienced income decline:

Food Segment

Food grains

Fruits

Vegetables

Pulses

Dairy Products

Poultry & Fishery

Beverages

Processed Foods

Average Change(%)

-6.90

14.94

-4.60

-12.64

-10.71

-60.00

-51.72

-63.22

Using formula(1), we get the following income elasticities for lower-middle income group for our food segments:

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Food Segment

Food grains

Fruits

Vegetables

Pulses

Dairy Products

Poultry & Fishery

Beverages

Processed Foods

Income Elasticity

0.20

0.44

0.14

0.37

0.32

1.76

1.52

1.86

We observe a positive income-elasticity across all food segments. A 34% income decrease across the lower-middle income sector has caused them to decrease food consumption.

Figure 3: Income Elasticities of Lower Middle Income Group

Again, we see that there is less decline in consumption of foodgrains and other essentials, as compared to that of processed foods, meat-based products and beverages. An interesting observation here is that the lower-middle income segment treats dairy products as a part of essentials and the consumption of dairy has faced a lesser brunt of fall in income.

In conclusion, we observe that demand for foodgrains, fruits, vegetables, pulses, and dairy products are less responsive to changes in income for the lower-middle income group as compared to other food segments.

The income-elasticity for food consumption among the lower-middle income group is 0.83, and can be classified as less-elastic demand.

IV.3 UPPER MIDDLE INCOME GROUP The upper-middle income group has been classified as households whose per person income lies between Rs.30,000 and Rs.60,000 per month. The professions of respondents in this segment range from architect, stylist, airline services to journalist.

The average percentage change in income calculated from the survey is 31%.

Following are average percentage changes in demand for upper-middle income households who experienced an income fall:

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Food Segment

Food grains

Fruits

Vegetables

Pulses

Dairy Products

Poultry & Fishery

Beverages

Processed Foods

Average Change(%)

6.06

-3.03

5.05

11.11

-6.45

35.71

-26.26

-29.29

Using formula (1), we get the following income elasticities for upper-middle income group for our food segments: Food Segment

Food grains

Fruits

Vegetable s

Pulses

Dairy Products

Poultry & Fishery

Beverages

Processed Foods

Income Elasticity

-0.20

0.10

-0.16

-0.36

0.21

1.16

0.86

0.95

Income-elasticity is positive across all food segments, except foodgrains, vegetables, and pulses. As a result of the pandemic, this income group decreased consumption of fruits, dairy, poultry, fishery, beverages, and processed foods. Interestingly, consumption of foodgrains, vegetables, and pulses increased among this sector, even after a decrease in income.

The degree of change in consumption varies:

Figure 4: Income Elasticities of Upper Middle Income Group

We observe that demand for fruits and dairy products is relatively inelastic (less responsive to changes in income) as compared to poultry, fishery, and processed food, for this income group.

The stark decline in their consumption of the latter can be attributed largely to the greater health consciousness owing to the pandemic.

The income-elasticity for food consumption among the Upper-middle income group is 0.32, which can be classified as less-elastic demand.

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IV.4 HIGH INCOME GROUP The high income group has been classified as households whose per person income lies above Rs.60,000/month. The professions of respondents in this segment range from businessmen, service-employees, real estate agents, fashion designers, advocates to bankers.

The average percentage change in income calculated from the survey is 38%.

Following are average percentage changes in demand for high income households who experienced income decrease:

Food Segment

Food grains

Fruits

Vegetables

Pulses

Dairy Products

Poultry & Fishery

Beverages

Processed Foods

Average Change(%)

2.78

13.89

8.33

-1.39

-8.33

-15.56

-37.50

-38.89

Using formula(1), we get the following income elasticities for high income group for our food segments: Food Segment

Food grains

Fruits

Vegetables

Pulses

Dairy Products

Poultry & Fishery

Beverages

Processed Foods

Income Elasticity

-0.07

-0.37

-0.22

0.04

0.22

0.41

0.99

1.03

We observe a positive income-elasticity across all food segments, except foodgrains, fruits, and vegetables. As a result of the pandemic, this income group decreased consumption of pulses, dairy, poultry & fishery, beverages, and processed foods. Interestingly, the consumption of the first three food groups increased even after decreased income.

The degree of change in consumption varies, as shown:

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Figure 5: Income Elasticities of High Income Group

Their consumption increase among three food groups can be attributed towards increased health consciousness and restricted movement .

The income-elasticity for food consumption among the high income group is 0.25, and can be classified as inelastic demand.

IV.5 COMPARISON OF INCOME ELASTICITIES AMONG THE FOUR INCOME SEGMENTS The above analysis of income elasticities among the four income segments indicates their distinct behavioral patterns of food consumption as a consequence of fall in income.

The average overall elasticity of the different income groups clearly indicates that higher the level of income, less elastic is the demand for food. The elasticities of low income group (1.07) and lower-middle group (0.83) revolve close to unitary elastic demand. As we go further up to upper-middle and high income groups, they have a relatively inelastic average incomeelasticity of 0.32 and 0.25 respectively.

Figure 6: Comparison among Income Groups

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Further analysing the relative elasticity of income groups, we observe the following: ● Food grains, vegetables, and pulses being essential, have relatively lower incomeelasticity, compared to other food groups. ● Beverages have significantly greater income-elasticity, indicating their vulnerability to a decline in income. ● Since milk has several uses, therefore dairy products are highly-elastic in case of low income segments, who tend to cut-down on certain uses of dairy products. ● Interestingly, the high income group preferred increasing consumption of fruits over pulses, and vice-versa for upper-middle group; clearly distinguishing their consumption behaviours. ● Even though there is a higher percentage decrease in income of high income group (38%) as compared to the lower-middle income group (34%), it still renders the former earning a higher absolute income. That is why high income groups could still increase consumption of some food items. ● Another reason why higher income groups are relatively inelastic is due to higher disposable income. The lower income groups spend most of what they earn, so a fall in income compels them to consume less.

Figure 7.1: Elasticities of Food Groups

Figure 7.2 : Components of Horizontal Axis of Figure 7.1

V. STUDYING CHANGE IN FOOD CONSUMPTION DURING THE COVID-19 PANDEMIC The effect on consumption of the food groups has been variant across the different income groups observed in the previous section. Here, we aim to analyse the variations and possible reasons behind the changing consumption patterns of different income groups.

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V.1 FOODGRAINS Fluctuations in the demand for foodgrains due to the lockdown have been relatively narrow in magnitude. A declining trend can be seen in lower-middle income groups with a decrease of 1.55%. Higher income groups having income more than 30,000 have shown an increase in consumption of foodgrains due staying at home. Increase in consumption has been 3.47% and 3.03% for upper-middle and high income groups respectively.

Contrastingly, in the low income group, a decline of 13.33% was seen, owing to a fall in purchasing power and lack of availability. In spite of government schemes regarding the distribution of free rice and wheat among the poor, this figure of decline has been observed, which represents the irregular success of such policies across different states, and their inability to establish complete food security.

Figure 8: Foodgrains (% Change in Consumption) V.2 FRUITS There has been a declining trend of change in demand for fruits among different income groups.

With an undeniably large fall in consumption among lower income by 62.22%. There has been a decrease in consumption among the middle income group with a fall of 8.53% among lowermiddle income groups and 4.86% among upper-middle income groups. Because of increasing health consciousness, an increase in consumption of fruits among high income groups of 9.09% can be observed. This pattern of change in food preferences among different income groups shows that as income increases people tend to prefer more fruits in their diet.

Figure 9: Fruits (% Change in Consumption)

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V.3 VEGETABLES The trend of change in demand for vegetables has been variant across different income groups due to the lockdown.

For the low income and lower-middle income groups, together comprising respondents with per capita income less than Rs. 40000 per month, a decreasing shift in demand for vegetables was observed. There was 26.67% and 0.78% decline for low and lower-middle income groups respectively, according to the data collected through a sample survey. Contrastingly, the uppermiddle and high income groups showcased an increase in demand of vegetables in the lockdown period. A comparatively voluminous increase in consumption was observed in the high income group, with percentage of increase amounting to 12.59%, while in the uppermiddle income group, a 2.08% increase was noticed.

This can be attributed to the heightened demand figures of onions and potatoes with intentions of stockpiling by buyers, during initial days of the lockdown, as well as increased health concerns among the richer sections of the society.

Figure 10: Vegetables (% Change in Consumption) V.4 PULSES Pulses play an important role in diet as a major source of protein. With our survey a declining trend among consumption of pulses has been observed in low and lower-middle income groups by 20% and 3.88% respectively. The fall in consumption of pulses has been less in comparison to other food items. When a low income group is considered, it can be a result of the PMGKY scheme where 1kg of pulse is distributed per household through a public distribution scheme.

Upper-middle income group has shown the highest increase in consumption of pulses by 6.94% notably because of it being a staple diet and a source of protein whereas the high income group has shown an increase of 4.55% in consumption. It is less in comparison to upper-middle income, probably due to the high income group relying more on other food supplements for protein.

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Figure 11: Pulses (% Change in Consumption)

V.5 DAIRY PRODUCTS Demand for dairy products such as milk, butter, curd, cheese, etc. has seen a downswing as a consequence of the lockdown.

A vast decline of around 55.56% has been observed in consumption of dairy products in the low income group. This is possibly due to a shift towards consumption of essential food items by the economically burdened sections of society. In the lower-middle income group, a decline of 2.44% was seen. Consumption of dairy products among upper-middle income groups has declined by 4.35% possibly due to lack of availability due to lockdown. Consumption among the high income group has increased, due to increasing health consciousness and the need to have a protein rich diet, by around 1.52%. This derivation brings out the observation that dairy products form an important part of the food diet of this income group.

Figure 12: Dairy Products ( % Change in Consumption)

V.6 POULTRY AND FISHERIES Our analysis of the variations in consumption of poultry and fishery items by different income segments in the economy gave out anticipated results. A combined effect of premature rumors and decrease in purchasing power due to lockdown had shrivelled effect across the whole

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consumer base. According to the data collected by our sample survey, consumption reduced by a major 73.81% for the low income group, while the decrease for lower-middle income group stood at 42.59%. The upper-middle income group saw a decline by 36.84% and the percentage decrease in the high income group is comparatively low at 19.75%.

Figure 13: Poultry and Fisheries (% Change in Consumption)

V.7 BEVERAGES Data from our survey revealed that consumption in the beverage industry has shrunk by significant proportions due to reduction in availability, decline in purchasing power, as well as increased health and safety concerns.

Varying by vast proportions, the percentage of decrease in consumption of beverages stood at 52.22% for the low income group, 42.64% for the lower-middle, 27.08% for upper-middle and 32.58% for the high income group.

But these observations show that the percentage decrease in consumption among upper-middle income groups have been less compared to other income groups showing the major importance of beverages in this income group.

Figure 14: Beverages (% Change in Consumption)

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V.8 PROCESSED FOOD Demand in this industry too has witnessed downturns as a result of the pandemic and consequent lockdown.

The highest proportionate decrease was seen in the low income group by 53.33%. The uppermiddle income group showed the least decline of 29.17%, indicating that processed food forms a comparatively more crucial part of their diet. In the high income group too, a significant decrease by around 30.30% was seen, followed by 44.96% decrease in the lower-middle income group.

Figure 15: Processed Foods (% Change in Consumption) V.9 CONCLUSION The radar chart below efficiently presents a picture of how distinct income groups have changed their consumption of the eight food-groups during lockdown. The preferences of different consumers have been shaped by factors such as availability, choices, income constraints, health consciousness, etc.

Through this diagram, it can be observed that among the low income group, a high decrease in consumption of all food items has been noted.

The respondents belonging to lower-middle income groups, have shown fluctuations in their demand trends for different food items; significant decline has been noted in consumption of items like beverages, poultry and fishery, and processed food, while minimal variations have been seen in consumption of foodgrains, fruits, vegetables, etc. A similar trend has been discerned in the upper-middle income group.

For the high income group, an inelastic and relatively positive approach of consumption has been observed for the majority of the food items. However, for processed food, poultry & fishery, and beverages, a declining trend had been observed across all income groups.

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Figure 16: Income-wise analysis of different food groups

V.10 HEALTH FOOD In the world stuck by a deadly pandemic, it is indisputable that people start to pay more attention towards their health. It is a common understanding that under these circumstances people try to boost their immunity with health products.

Even the Prime minister advised following immunity boosting tips provided by the Ministry of AYUSH, which includes taking products like Chyavanprash, Herbal Tea, Kadha, honey etc. leading to a further spike in their consumption (“Ayurveda’s immunity boosting measures for self care during COVID 19 crisis”, 2020).

Hence, as Covid-19 hits billions of lives across the nation, consumers’ have a renewed focus on immunity boosting and Ayurveda-based interventions. This has led to FMCG major Dabur witnessing exponential growth in the sales of its Chyawanprash range. They have seen a 400% surge in demand of flagship immunity booster Dabur Chyawanprash and an 80% growth in Dabur Honey, as confirmed by Mohit Malhotra, Chief Executive Officer, Dabur India Ltd. During the survey, we observed that 32.7% of respondents had a slight increase in consumption of these health foods, while 9.1% had a significant increase. On the other hand, due to the fall in income of a large portion of the population, about 7.3% of the respondents had to cut down on the consumption of healthy food even in the midst of a pandemic.

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Figure 17: Change in consumption of health foods

V.11 GOVERNMENT’S ROLE As the implementation of the lockdown hampered the availability of food items, the Centre and State governments came up with certain policies and schemes in order to ensure food security across the country.

V.11.1 PRADHAN MANTRI GAREEB KALYAN ANN YOJANA (PMGKAY) On 26th March 2020, day two of the nation-wide lockdown, the government declared free distribution of 1kg of pulses per household and 5kg food grains per person under PMGKAY. The aim was to ensure food security to around 80 crore underprivileged beneficiaries covered under the National Food Security Act (NFSA). In addition, the monthly quota for buying grains at subsidized rates had been increased from 5kg to 7kg.

Under the Atma-Nirbhar-Bharat package, a provision had been made to supply 2Kg of whole chana from government buffer stocks to migrant worker families who are not covered under NFSA or do not have ration card in the State/UT in which they are stranded (Public Distribution and Reforms, 2020).

According to data from the Ministry of Consumer Affairs, Food and Public Distribution, against monthly allocation of 1.951 LMT pulses, which works out to 5.86 LMT for 3 months, 4.81 LMT had been received by the States/UTs and 2.90 LMT had been distributed to final beneficiaries, as of May 2020 (“Monthly Summary May 2020”, 2020). There have been variations in the extent of success of these schemes across different states, due to differences in procurement and distribution rates.

V.11.2 INITIATIVES AT STATE-LEVELS Various state governments have come up with schemes as solutions to the prevailing food insecurities in the respective states. For instance, in Karnataka, state-run Indira Canteens had been ordered to provide free food packets to the underprivileged during lockdown; in Kerala, distribution of free food kits consisting of 17 items was announced, in the wake of the pandemic; in Delhi, schools and night shelters had been made key food distribution points, catering to the needs of several lakhs of people daily.

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Philanthropic initiatives and programs by NGOs have also played a major role in making basic food available to the underprivileged during lockdown.

VI. ANALYSIS OF SURVEY DATA In our survey, the decline in consumption of foodgrains was 13.33% and that of pulses was 20% by low income group. This change is significantly less than the decline in consumption of other food items like fruits, dairy products, etc.

This can be attributed to the relatively better availability of foodgrains and pulses for the low income group, as a result of aforementioned initiatives.

VII. RESULTS AND DISCUSSIONS As the key objective of our research, of understanding how a pandemic like the Novel Coronavirus affects food purchasing and consumption behaviour gets fulfilled, following are the prime conclusions that were drawn.

VII.1 CONSUMER BEHAVIOUR CHANGES Variations in the purchasing behaviour of people for food products, influenced by sociodemographic factors as well as other personal characteristics like profession, choices, etc. have been analysed.

VII.2 INDUSTRIAL ANALYSIS Through the research, changes in industrial conditions of different segments of the Indian food industry have been observed, wherein inferences were made about which food groups are being consumed in greater quantities, and which segments faced negative repercussions of the lockdown. This information comes useful in guiding upcoming businesses in the food industry regarding the current consumer demand scenario, which can help them in planning their product lines.

VII.3 ROLE OF INCOME DISPARITIES The analysis throws light on how disparities in income level and income changes influence the way expenditure is made on food. This has been explicated through the study of differences in income elasticities of various income groups in the economy.

VII.4 INSTITUTIONAL AND ORGANIZATIONAL INFLUENCES The research further explained how institutional changes such as schemes on the part of the government have affected the supply and demand of certain food items, and how external help in the form of aids from NGOs have also played a role in improving food security in the country.

VII.5 INCREASED HEALTH CONCERNS The research gave out a deduction regarding impact on demand for health food due to heightened concerns for immunity boosting in a pandemic period, for businesses dealing with health food. It also brought into focus the variability in change in demand for health food due to income differences.

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The research therefore scrutinizes the alterations in the Indian food industry, with the country's economic, political and demographic diversity playing a major role. Thus, the results of this study of economics of food during a pandemic have yielded substantial conclusions.

VIII. ACKNOWLEDGEMENT Our sincere gratitude goes to the Centre for Research, Maitreyi College for giving us the opportunity and right platform to conduct this research. We are extremely thankful to our mentors and teachers, Ms. Artika Vats and Ms. Antima Jain whose intellectual guidance and academic assistance enabled us to bring the dissertation to the required level. Their constant support has been a driving force for us to complete the paper. Our appreciation also goes to our adorable families and friends whose moral and material support enabled us to successfully conduct and complete this study.

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REFERENCES Ayurveda’s immunity boosting measures for self care during COVID 19 crisis. (2020).

In https://www.ayush.gov.in/ Ministry of Ayush, Government of India. https://www.mohfw.gov.in/pdf/ImmunityBoostingAYUSHAdvisory.pdf COVID-19: Tracking the Impact on FMCG, Retail and Media. (2020, March 16). Nielsen.com. https://www.nielsen.com/us/en/insights/article/2020/covid-19-tracking-the-impact-on-fmcgand-retail/ Directorate of Rice Development. (n.d.). www.Drdpat.Bih.Nic.In . Retrieved June 14, 2020, from http://drdpat.bih.nic.in/Productivity%20Analysis%20-%2001.htm FAOSTAT. (n.d.). www.Fao.org ; The Food and Agriculture Organization (FAO). Retrieved June 14, 2020, from http://www.fao.org/faostat/en/#data/QC/ International Grains Council. (n.d.). www.Igc.Int . Retrieved June 14, 2020, from https://www.igc.int/en/markets/marketinfo-sd.aspx Maize. (n.d.). www.Apeda.Gov.In ; Agricultural and Processed Food Products Export Development Authority (APEDA). Retrieved June 14, 2020, from https://apeda.gov.in/apedawebsite/SubHead_Products/Maize.htm Monthly Summary May 2020. (2020). In https://consumeraffairs.nic.in/ (p. 2). Ministry of Consumer Affairs, Food and Public Distribution (Department of Consumer Affairs). https://consumeraffairs.nic.in/sites/default/files/Monthly%20Summary%20May2020.pdf National Horticulture Board. (2020, March).http://nhb.gov.in/OnlineClient/currentandpreviousmonth.aspx?enc=3ZOO8K5CzcdC/ Yq6HdIxJ4o5jmAcGG5QGUXX3BlAP4= Opportunities in Fruits & Vegetables Sector in India. (n.d.). www.Mofpi.Nic.In ; Ministry of Food Processing Industries Govt. of India. Retrieved June 17, 2020, from https://mofpi.nic.in/sites/default/files/OpportunitiesinFruits%26VegetablesSectorinIndia.pdf Public Distribution and Reforms. (2020). https://dfpd.gov.in/ ; Ministry of Consumer Affairs, Food and Public Distribution, Government of India. https://dfpd.gov.in/public-distribution-reforms.htm Wheat. (n.d.). www.Apeda.Gov.In ; Agricultural and Processed Food Products Export Development Authority (APEDA). Retrieved June 14, 2020, from http://www.apeda.gov.in/apedawebsite/SubHead_Products/Wheat.htm World Agricultural Supply and Demand Estimates WASDE -600 Approved by the World Agricultural Outlook Board. (2020). In United States Department of Agriculture. https://downloads.usda.library.cornell.edu/usdaesmis/files/3t945q76s/w6634p60m/2v23wd923/latest.pdf

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APPENDIX I. FOODGRAINS I.1 RICE Year Production(MMT) Stocks(MMT) Exports(MMT) Demand(MMT) Supply(MMT) Imports(MMT)

2019-20 114.3 22.1 12.5 101.5 136.5 0.0

2020-21 115.8 22.5 12.8 102.8 138.3 0.0

Figure 18: Rice ( Blue represents Demand, Red represents Supply ) I.2 SOYBEANS Year Production(MMT) Stocks(MMT) Imports(MMT) Exports(MMT) Demand(MMT) Supply(MMT)

2019-20 6.64 0.48 0.05 1.45 4.75 7.17

2020-21 7.52 0.23 0.05 1.87 5.0 7.8

Figure 19: Soybeans ( Blue represents Demand, Red represents Supply )

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I.3 WHEAT Year Production(MMT) Stocks(MMT) Imports(MMT) Exports(MMT) Demand(MMT) Supply(MMT)

2019-20 98.7 16.2 0.5 0.3 99.7 115.4

2020-21 99.6 15.4 1.0 0.3 101.3 116.0

Figure 20: Wheat ( Blue represents Demand, Red represents Supply ) I.4 MAIZE Year Production(MMT) Stocks(MMT) Imports(MMT) Exports(MMT) Demand(MMT) Supply(MMT)

2019-20 28.4 1.1 0.3 0.4 28.4 29.8

2020-21 28.4 1.3 0.17 0.3 41.18 29.57

Figure 21: Maize ( Blue represents Demand, Red represents Supply )

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Total Foodgrains Year Demand(MMT) Supply(MMT)

2019-20 234.35 288.7

2020-21 250.28 291.67

Figure 22: Foodgrains ( Blue represents Demand, Red represents Supply )

II. DAIRY PRODUCTS Year Production(TMT) Stock(TMT) Imports(TMT) Exports(TMT) SUPPLY(TMT)

2019-20 630 15 0 10 645

Figure 23: Supply of Dairy Products

2020-21 660 35 1 15 696

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III. FRUITS Supply Data from National Horticulture Board, For Compilation details refer to the link of the excel file below: https://drive.google.com/file/d/1rVgg7CHLoRu5JwkWTSkGYeHxljAvJbX/view?usp=sharing

Figure 24: Supply of Fruits

IV. VEGETABLES Supply Data from National Horticulture Board, For Compilation refer to the link of the excel file below: https://drive.google.com/file/d/1sDKXFzu4F_pYtCPm5a3ZzBK15vQ3om_/view?usp=sharing

Figure 25: Supply of Vegetables V. SURVEY ON CHANGE IN FOOD PREFERENCES DUE TO COVID-19 CRISIS

For the Questionnaire: Refer to this link https://drive.google.com/file/d/1CBzEP1WjGKZ6fX1BaUa_wllLhgqgp0z/view?usp=sharing

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For the Analysis and Calculations: Refer to this link https://docs.google.com/spreadsheets/d/1Pj97wJPUW8W8kaO5hG4E47REqp2BxhbYyw9eo X1LGPs/edit?usp=sharing

VI. Scatter Plots

Figure 26: Scatter Plot for Foodgrains

Figure 27: Scatter Plot for Fruits

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Figure 28: Scatter Plot for Vegetables

Figure 29: Scatter Plot for Pulses

Figure 30: Scatter Plot for Dairy Products

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Figure 31: Scatter Plot for Poultry and Fishery

Figure 32: Scatter Plot for Beverages

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Figure 33: Scatter Plot for Processed Food

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COVID-19, CHILD LABOUR INCIDENCE AND POLICY RESPONSES: A DYNAMIC TRADE THEORETIC ANALYSIS - Monishankar Dutta and Rahul Sinha1 ABSTRACT The present paper attempts to offer an analytical structure towards an understanding of the effects of COVID-19 pandemic on schooling, education and the incidence of child labour. The paper explores the social cost of the global outbreak and its consequences on various sectors.

In doing so, we extend the Three-sector General Equilibrium model to incorporate the dynamics of a trade theoretic economy and derive a child’s optimal time allocation in schooling and unskilled wage earning in the labour market from household’s optimization of an intertemporal utility function. Three different cases pertaining to time allocation are obtained which crucially hinges on the level of education subsidy provided by the government. Our study considering the impact of the global outbreak has found that a fall in training cum recruitment cost in the skilled sector or an increase in education subsidy can lower the incidence of child labour.

JEL classification: I24, D5, C15, J78, J43, J46 Keywords: Child Labour, COVID-19 Pandemic, Public policy, Education.

I. INTRODUCTION AND MOTIVATION The multifarious impact of COVID-19 has been observed in every sector around the world. We have witnessed a lot of shifts in different trends and the pandemic has also made us realize that changes are inevitable in nature.

The education and schooling sector has been hugely wedged by the pandemic, its insinuations and the reverberations. The outbreak of the virus has affected approximately 120 crore students across the world and around 32 crore students in India. Due to various restrictions like travelling and measures limiting community spread, the educational institutions had been compelled to cancel normal classes and conduct classes through the online mode of communication.

Apart from education, the labour force has also been affected in innumerable ways. The detrimental effects of the pandemic were not distributed equally. They have mostly damaged the poor nations which were already in vulnerable conditions. Although in the last two decade there has been a noteworthy stride in the fight against child labour, the COVID-19 pandemic poses a real risk of backpedalling.

Figure 1, gives us a brief idea about the involvement of children in the labour force with respect to their age, gender and the sector in which they are involved. The COVID-19 pandemic has resulted in an economic downturn as a result of which positive trend may weaken and child labour may worsen especially in developing countries. The pandemic has increased economic insecurity and many people lost their job due to disruption in the supply chain as a result of which the manufacturing sector got halted. In India alone, COVID-19 is said to have left 120 million without employment as of May 2020.

1

St. Xavier’s University, Kolkata

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Figure 1 The economic contraction due to COVID-19 has reduced opportunities in the labour market mainly for the people in the marginalized sector like migrant labourers. As a result of this parents from these marginalized societies are forced to push children into hazardous and exploitive work so they can contribute to household incomes.

Table 1: Children engaged in child labour across the world Source: ILO From the table, we can see there are around 152 million children engaged in child labour, where the highest value of child labour engagement can be seen in the African regions and least value of child labour is evident in European and Central Asian regions. Most of the child labour engagement can be found in the agricultural sector or in a hazardous sector such as mining and construction.

In India, we can observe from the census data of 2011, that 10.1 million children are engaged in child labour. Uttar Pradesh, Bihar, Rajasthan, Maharashtra, and Madhya Pradesh witness relatively higher number of cases regarding child labour.

The root cause of child labour is poverty and lack of access to decent working prospects. On top of these factors, social marginalization and lack of formal as well as vocational education often force underaged minors to work in unwanted conditions where they are mostly exploited.

According to the World Bank, there is a chance of 12 million people slipping below the poverty line due to pandemic related job loss. Now, various researches have shown that poverty is co-

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related to child labour, it has indicated that a certain point percentage change in poverty can cause an increase in child labour around 0.7%. The return of migrant labourers in their respective village communities has also led to deterioration in the labour force supply in the cities, mainly the sectors such as agriculture, construction which heavily rely on migrant labourers are the worst affected. The parents from marginalized sections are forced to send their children to be employed in the workforce which can result in exploitation, In such cases the employer can hire them at a less cost as children have less bargaining powers and are unable to press for their rights.

A study by the International Labour Organization shows that the children who are not a part of the education system have been more vulnerable to this threat. The International Labour Organization has also formulated a special policy framework as a responsive measure for the pandemic. The policy comes with an objective to primarily stimulate the economy and employment, support enterprises, jobs and income. Protection of workers in the workplace is the key measure and it also aims to rely on social dialogues for solutions.

UNICEF’s agenda to tackle the pandemic is also praiseworthy. Some of its aims are to keep children well nourished, reach out to vulnerable children with basic amenities, stimulate the learning process of children, providing provisions to families in order fulfil their needs and take care of minors, protect them from violence and most importantly, protect refugee as well as migrant children and the ones affected by the consequences of the global outbreak.

We investigate the data collected from World Bank, ILO and various other sources to identify the relation between Poverty rate and Child Labour across various countries. It helps us to establish the background for our research by incorporating the idea of “Poverty Hypothesis” in our research paper.

The method involves, representation of the latest data for Poverty rate and Child Labour for 18 different countries in form of a X, Y Scatter Graph.

Table 2 describes the percentage of child labour and poverty for different low and middleincome countries obtained from the World Bank data, ILO and few other sources. Using the data in table 1 we obtain the average rate of child labour and poverty to be 19.9 per cent and 34.2 per cent respectively.

The following matrix is intended to show the cross-sectional variation of child labour and poverty for the countries listed in Table 2.

In Fig. (2), along the horizontal axis percentage share of child labour is measured while along the vertical axis poverty rate is measured. The solid vertical line originating from the horizontal axis shows the average share of percentage of child labour and the solid horizontal line originating from the vertical axis shows the average rate of poverty. This average level divides the box into four different zones. The fours different zones, zone 1-zone 4 represents the zone with (lower child labour, lower poverty), (higher child labour, lower poverty), (higher child labour, higher poverty) and (lower child labour, higher poverty), respectively. Zone 1 and Zone 3 follows Poverty hypothesis i.e. child labour varies positively with poverty rate while Zone 2 and Zone 4 violates the poverty hypothesis. From Fig. (1) we observe that most countries are concentrated either in Zone 1 or Zone 3 with an exception of Zimbabwe in Zone 4 and Iceland and Bolivia in Zone 2. The concentration of most countries in zone 1 and zone 3 clearly validates the poverty hypothesis for cross-sectional variation among middle- and lowincome countries.

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Figure 2: Relationship between Poverty and Child Labour Source: Self-computation using data from World Bank, ILO and various other sources

II. REVIEW OF LITERATURE Poverty as the primary cause behind the emergence of the problem of working children from poor and unskilled households was explored in the seminal work by Basu and Van (1998).

Basu, Das and Dutta (2009) had shown labour market imperfection as an additional source behind the emergence of the incidence of child labour. Other relevant literature which found poverty hypothesis or wealth effect as a cause behind child labour includes Basu (1999), Ray (2000), Basu and Tzannatos (2003), Emerson & Souza (2003), Krueger (1996), Edmonds and Pavenik (2005) etc. among many others. Dwibedi and Marjit (2017) explained the wealth paradox and provided a theoretical exposure based on two axioms of “inequality lowers welfare” and “inequality improves relative-status” which affects child labour supply decision.

Dwibedi and Chaudhuri (2014) had analysed the failure of price subsidy policy to the backward agriculture that fails to lower the incidence of child labour. Jafarey and Lahiri (2002) obtained that credit (borrowing) constraint hinders the way of trade sanction to curb the incidence of child labour. If borrowing constraint is relaxed then trade sanctions may produce favourable outcomes.

In this paper we attempt to provide a multi-sectoral dynamic trade-theoretical model to analyse the effect of COVID-19 policy responses on the incidence of child labour, school dropout rate and skill acquisition. Further, we also propose some alternative policy measure which could reduce the incidence of child labour and increase the rate of schooling. In doing so, we first derive endogenously the optimal time allocation of child’s time in schooling and in labour market activities and analyse its properties. Further, the impact of COVID-19 induced economic impacts are analysed in terms of multi-sectoral general equilibrium analysis. It was found that COVID-19 lead to an increase in the incidence of child labour owing to povertyhypothesis and Government’s policy response to COVID-19 disaster lead to counterproductive outcome in terms of further fuelling up the incidence of child labour. Finally, we suggest few

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policy measures which could effectively bring down the child labour incidence in the backdrop of such global pandemic.

III. DERIVATION OF CHILD LABOR SUPPLY FUNCTION This is an intertemporal decision-making problem where a poor unskilled family faces a tradeoff in terms of present gain from engaging the child in unskilled labour-intensive work which increases family’s present level of consumption and presents discounted value of future skilled earning from schooling which increases future period consumption level. The family while optimally choosing the time allocation for the child takes market unskilled adult wage rate, child wage rate and skilled wage rate as datum which are determined from the competitive general equilibrium system.

We have divided one day’s time into two equal halves each halve consisting of twelve hours.

Thus, time dynamic in our model is not only limited to two discrete period analysis but also a two-time period in each day. Skilled sector exploits this time zone difference to function and produce output for entire hours in each day by outsourcing its services during night hours. This outsourcing encounters a fixed technological cost.

In this two-period optimization framework the representative households are altruistic decision makers. Let there be ‘n’ number of homogenous households. Each household maximizes their utility which is a function of consumption in two periods and is represented by 𝑈 = 𝑙𝑜𝑔𝐶𝑡 + 𝛽𝑙𝑜𝑔𝐶𝑡+1

(1)

In period 1 the household consumption is denoted by 𝐶𝑡 . Let if 𝑙𝑐 be the time allocation of the child in school or work. If b is the amount which the child is losing for engagement in schooling by not going to work and s is the subsidy on the education. Given that income is entirely exhausted in consumption, thus, in period 1 consumption is given by 𝐶𝑡 = (𝑊 + 𝑙𝑐 𝑊𝑐 ) − 𝑏𝑙𝑐 + 𝑠𝑙𝑐 (2) The first period income consists of income from unskilled work(𝑊 + 𝑙𝑐 𝑊𝑐 ) , income from subsidy provided for education (𝑠𝑙𝑐 ) net of cost of education (𝑏𝑙𝑐 ). 2 The direct cost of education is b and the rate of subsidy is s.

In period 2, the consumption is given by 𝐶𝑡+1 = [𝑙𝑐 𝑊 + (1 − 𝑙𝐶 ) 𝑊𝑠 ] (3) In the second period, the child acquires skill for the time spent in schooling in period 1 and earns higher skilled wage in period 2 (1 − 𝑙𝐶 ) 𝑊𝑠 . However, for the time which was spent in doing unskilled work in period 1 earns the same unskilled wage rate in period 2. Finally, we normalize the total time of the child to unity. Thus, the time distribution is given by 𝑙𝑐𝑢 + 𝑙𝑐𝑠 = 1

(4)

Households maximize their utility (eq. 1) subject to the constraints in (eq. 2, eq. 3 and eq. 4).

The household’s problem can be stated as 𝑀𝑎𝑥 𝑈 = 𝑙𝑜𝑔𝐶𝑡 + 𝑙𝑜𝑔 𝐶𝑡+1 𝑆𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜, 𝐶𝑡 = (𝑊 + 𝑙𝑐 𝑊𝑐 ) − 𝑏𝑙𝑐 + 𝑠𝑙𝑐 𝐶𝑡+1 = [𝑙𝑐 𝑊 + (1 − 𝑙𝐶 ) 𝑊𝑠 ]

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𝑙𝑐𝑢 + 𝑙𝑐𝑠 = 1 The optimization yields child labour supply function, 𝐿𝑐 = 𝐿 𝑙𝐶 ( 𝑊, 𝑊𝑆 , 𝑠, 𝑏) (5) 𝑙𝑐𝑢 = 1 − 𝑙𝑐𝑠 The optimization yields an interior solution, however, there are three different cases where the household can allocate the child completely in education, or completely doing unskilled work, and/or both which crucially hinges on fulfilment of certain sufficient conditions corresponding to specific rate of education subsidies.

There arise three different situations that are represented by the following lemmas.

Due to COVID-19 the economic contraction in various sectors has pushed many families into severe poverty because of which they were forced to push their children into labour. Now even if the situation improves and the government provides a certain level of subsidy there is less chance the family will send their children to education.

Lemma 1: 𝑙𝑐∗ = 1 𝑤ℎ𝑒𝑛 𝑊 + 𝑏 − 𝑠1 ≥ 𝛽(𝑊𝑠 − 𝑊) Households would not at all be interested in education of children provided if the net opportunity cost of education is higher than the discounted gain from schooling. Lemma 1 holds under the above sufficient condition even without strict inequality. Thus 𝑠1 = 𝑊 + 𝑏 − 𝛽(𝑊𝑠 − 𝑊) is the critical subsidy level below which there will be no incentive for child’s education.

This is the situation in which the child attains for schools and does not participate in any wageearning activities.

Lemma 2: 𝑙𝑐∗ = 0, 𝑤ℎ𝑒𝑛

𝛽 ( 𝑊𝑠 −𝑊) 𝑊

= 𝑠2 − 𝑏 − 𝑊

Lemma 2 can be interpreted as the household would prefer the child to spend complete time in schooling if there is parity between the future discounted relative gain from schooling and present value of subsidy net of opportunity cost. In other words, the subsidy must be in excess to the opportunity cost of education by the magnitude equivalent to future discounted gain from schooling. The subsidy level 𝑠2 that is required to achieve 𝑙𝐶∗ = 0 is given by 𝑠2 = (𝑏 + 𝑊) +

𝛽(𝑊𝑠 − 𝑊) 𝑊

This subsidy (𝑠2 ) includes the cost of education that includes both direct cost of schooling (𝑏) and opportunity cost (W), and the relative discounted gain from schooling. Subsidies provided by either Government or various organizations covers only the direct cost of schooling, however, our analysis brings out that it must include the other parameters as well.

In this circumstance, a child allocates the time in skilling by attending school and also engaged in wage earning activities to supplement family consumption. This is the most observed scenario for most unskilled families in developing nations.

Lemma 3: 0 < 𝑙𝑐 < 1 𝑤ℎ𝑒𝑛 𝛽( 𝑊𝑠 − 𝑊) > (𝑊𝑠 + 𝑏 − 𝑠 ∗ ) It explains that the discounted gain from schooling is higher than the opportunity cost net of subsidy, thus acting as participatory constraint.

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The subsidy level that corresponds to the interior solution for 𝑙𝑐∗ is given by 𝑠 ∗ , 𝑤 + 𝑏 − 𝛽(𝑊𝑠 − 𝑊) < 𝑠 ∗ < (𝑏 + 𝑊) +

𝛽(𝑊𝑠 − 𝑊) 𝑊

Thus, the child education that corresponds to different subsidy levels is given by,

Figure 3: Summary of above 3 Lemmas Source: Self estimated Figure 4 summarizes all three lemmas. It plots the relationship between subsidies and child labour supply. If subsidy is below s1, this will be insufficient to generate any incentive for child schooling, thus 𝑙𝑐∗ is vertical up to s1 at the origin. Subsidies higher than s1 but lower than s2 is sufficient for child participation in education although not complete level of education is attained. A subsidy s2 leads to a corner solution where the household of the child prefers to have complete education. Under these conditions and increase in education subsidy targeted towards child schooling may be completely ineffective or partially effective accordingly 𝑠 < 𝑠1 𝑜𝑟 𝑠 ∈ (𝑠1 , 𝑠2 ) .

IV. THE GENERAL EQUILIBRIUM ANALOGUE We construct a three sector- four factor competitive model, motivated by the work of Jones (1965) and Jones (1971). This is a small, open and a developing economy with a non-traded

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informal sector. Sector X is an informal export competing agriculture sector which consists of unskilled adult labour and child who are employed at wage rate W and𝑊𝑐 . Sector Y is the high skilled export competing sector which consists of skilled labour who are employed at wage rate 𝑊𝑠 and uses capital k. Sector Z is an import competing manufacturing sector which use adult labour and capital ‘k’ and the wage rate is 𝑊 which is institutionally determined. Capital earns homogenous return ‘r’ across sectors. Unemployment of unskilled labour exists in the formal manufacturing sector; however, no open unemployment exists owing to existence of the informal sector.

The prices of the traded sectors are internationally determined and the price of non-traded sectors is determined in the domestic market, the demand for which comes from the expenditure of the skilled male and skilled female labour. All factors earn competitive return, however, the returns to capital are determined in the international capital market which this small open economy takes as datum.

The representative economy is given by the following set of equations: 𝑊𝑎𝐿𝑋 + 𝑊𝑐 𝑎𝑐𝑋 = 𝑃𝑋

(7)

𝑊𝑠 (1 + 𝜌)𝑎𝑠𝑌 + 𝑟𝑎𝑘𝑌 = 𝑃𝑌

(8)

𝑊𝑎𝐿𝑍 + 𝑟𝑎𝐾𝑍 = 𝑃𝑍

(9)

𝑎𝐿𝑋 𝑋 + 𝑎𝐿𝑍 𝑍 = 𝐿

(10)

𝑎𝑐𝑋 𝑋 = 𝐿𝑙𝑐 (𝑊, 𝑊𝑠 , 𝑊𝑐 )

(11)

𝑎𝑘𝑌 𝑌 + 𝑎𝑘𝑍 𝑍 = 𝐾

(12)

𝑎𝑠𝑌 𝑌 = 𝑆

(13)

This is a non-decomposable structure. Constant Returns to Scale and zero profit condition hold across all sectors in both commodity and factor markets. From equation 9 the value of 𝑟 is determined. Once 𝑟 is determined, from equation 8 the value of 𝑊𝑠 is determined. Solving simultaneous equation (7) and equations (10)-(13) we solve for 𝑊, 𝑊𝑐 , 𝑋, 𝑌 𝑎𝑛𝑑 𝑍. This completes the determination of general equilibrium values of variables in the model.

IV.1. INVESTMENT UNCERTAINTY DUE TO COVID-19 Investment uncertainty leads to a contraction in domestic capital formation which causes stock of capital in the economy to plummet, the outputs of High-skilled export sector (Sector Y) and Import competing manufacturing sector (Sector Z) contracts, respectively owing to fall in capital. This leads to an outflow of labour from Sector Y which gets absorbed in the Informal Sector (Sector X). Labour supply in sector X increases which causes unskilled adult wage rate to plummet. Sector X expands owing to inflow of labour from sector Y, thus, the expanding sector X demand more child labour which causes child wage rate to accentuate. The fall in unskilled adult labour wage causes child labour supply to increase owing to the povertyhypothesis, and an increase in child wage rate further escalates child labour supply owing to positive income effect. The following proposition is immediate.

Proposition 1: Effect of pandemic in terms of fall in domestic investment accentuates aggregate child labour supply due to fall in family income of unskilled poor families.

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As the government reduces the formal unskilled formal labour wage in sector Z, it follows from equation 8 that to maintain price-average cost parity condition the return-on-investment rises.

An increase in cost of capital in the high-skilled sector Y causes it to contract in terms of output.

The contracting sector Y lowers the demand for skilled workers, thus causing the skilled wage rate to fall (eq.7). The fall in skilled wage rate lowers the discounted present value of education for the child labour supplying family, thus supply of child labour increases while child schooling falls.

The above discussion leads to the following two propositions.

Proposition 2: Government's policy response to COVID-19 disaster in terms of labour market reforms may aggregate the problem of child labour.

IV.2. POLICY RECOMMENDATION Consider the effect of fall in training cost which enters into the high skilled import competing sector Y and import competing sector Z which causes expansion in both the sectors. Since W is determined in eq. 7, thus adult unskilled wage rate remains unchanged. However, demand for skilled labour increases in sector 2 which accentuates the skilled wage rate. Now an increase in subsidy for children education can also be a corrective alternate policy for promoting child education and lowering the incidence of child labour.

Proposition 3: A fall in training cum recruitment cost in the skilled sector or an increase in education subsidy could be alternative corrective policy which can lower the incidence of child labour

V. CONCLUSION The multifarious impact of COVID-19 has been observed in every sector around the world.

The pandemic has increased economic insecurity, disrupted supply chains and halted manufacturing of essential commodities. Along with various other sectors, the education and schooling sector has been hugely wedged by the pandemic, its insinuations and reverberations.

The school dropout rates have increased due to complications of the new-normal method of education and lack of availability of essential resources. This has further resulted in increased engagement of children in the informal labour force. Our research shows that the effect of the pandemic in terms of fall in domestic investment leads to a rise in aggregate child labour supply due to a fall in household income of unskilled and poor families. Our findings provide enough evidence to state that government's policy response to COVID-19 disaster in terms of labour market reforms may aggregate the problem of child labour as the prime focus has been on relaxing labour laws to increase inflow of foreign investment in these unprecedented times.

Finally, we suggest that a fall in training cum recruitment cost in the skilled sector or an increase in education subsidy could be an alternative corrective policy which can lower the incidence of child labour.

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REFERENCES Basu, Kaushik. March 2000. “The Intriguing Relation Between Adult Minimum Wage and Child Labour.” The Economic Journal Volume 110, Issue 462,Pages C50–C61.

Jayanta Kumar Dwibedi & Sarbajit Chaudhuri. July 14, 2007. “Globalization, Consumerism and Child Labour.” SSRN. Sarbajit Chaudhuri & Jayanta Kumar Dwibedi. 2016. “The economics of child labour in the era of globalization: policy issues.” The economics of child labour in the era of globalization: policy issues. Taylor & Francis.

D. Dasgupta & M. Rajeev, 2020. “Paradox of a Supply Constrained Keynesian Equilibrium.”

Economic & Political Weekly 55(22), 23.

S. Mahendra Dev & Rajeswari Sengupta. April 2020. “Covid-19: Impact on the Indian Economy.” Indira Gandhi Institute of Development Research, Mumbai.

Kaushik Basu & Pham Hoang Van, June 1998. “The Economics of Child Labor.” The American Economic Review pg. 412-427.

Martin Ravallion & Quentin Wodon, 2000. “Does Child Labour Displace Schooling?

Evidence on Behavioural Responses to an Enrollment Subsidy.” The economic journal 110(462), 158-175.

APPENDIX 𝑀𝑎𝑥 𝑈 = 𝐶𝑡 + 𝐶𝑡+1

(1)

Subject to, 𝐶𝑡 = (𝑊 + 𝑙𝐶 𝑊𝑐 ) − 𝑏𝑙𝑐 + 𝑠𝑙𝑐

(2)

𝐶𝑡+1 = [𝑙𝑐 𝑊 + (1 − 𝑙𝑐 )𝑊𝑠 ]

(3)

𝑙𝑐𝑢 + 𝑙𝑐𝑠 = 1

(4)

Using eq. (4), (2) & (3) boils down to 𝐶𝑡 = (𝑊 + 𝑙𝐶 𝑊𝑐 ) − 𝑏𝑙𝑐 + 𝑠𝑙𝑐 ∴ 𝐶𝑡 = 𝑊 + 𝑙𝑐 (𝑊𝑐 − 𝑏 + 𝑠)

(2.1)

𝐶𝑡+1 = [𝑙𝑐 𝑊 + (1 − 𝑙𝑐 )𝑊𝑠 ] ∴ 𝐶𝑡+1 = 𝑙𝑐 (𝑊 − 𝑊𝑠 ) + 𝑊𝑠

(3.1)

Substituting eq. (2.1) & eq. (3.1) in U we get 𝑈 =𝑙𝑜𝑔 𝑙𝑜𝑔 [ 𝑊 + 𝑙𝑐 (𝑊𝑐 − 𝑏 + 𝑠)] +𝑙𝑜𝑔 𝑙𝑜𝑔 [𝑙𝑐 (𝑊 − 𝑊𝑠 ) + 𝑊𝑠 ] To obtain an interior solution the F.O.C. is given by, 𝑑𝑈 =0 𝑑𝑙𝐶 ⇒

(𝑊 − 𝑊𝑠 ) + 𝑊𝑆 1 × (𝑊𝑐 − 𝑏 + 𝑠) + =0 𝑊 + 𝑙𝑐 (𝑊𝑐 − 𝑏 + 𝑠) 𝑙𝑐 (𝑊 − 𝑊𝑠 ) + 𝑊𝑠

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𝑙𝐶𝑆 =

𝑊𝑠 (𝑏+𝑊−𝑊𝐶 −𝑠)−𝑊 2 2(𝑊−𝑊𝑠 )(𝑊𝐶 −𝑏+𝑠)

(5) ∗

 𝑙𝐶𝑢 = 1 − 𝑙𝐶𝑆

Country

Afghanistan Argentina Bangladesh Bhutan Bolivia Cambodia Congo Ghana Iceland Mali Mexico Myanmar Nepal Nigeria Pakistan Vietnam Yemen Zimbabwe Mean

Child Labour (%)

21.4 19 13.4 3.5 20.2 8.1 38 21 21 56 4.9 9.3 21.7 31.5 21.5 13.1 23 13 19.97777778

∗

Poverty (%)

54.5 35 21.8 8.2 16.6 13.5 63.9 56.9 9 49 6.6 24.8 25.2 40.1 24.3 5.8 79 81.3 34.19444444

Table 2 Source: Self-computation using data from World Bank, ILO and various other sources

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THE ECONOMICS SOCIETY ST. STEPHEN'S COLLEGE