Vol. 17 No. 2
The Europiean Journal of Applied Economics has been supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia.
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Vol. 17 No. 2 Publisher: Singidunum University E d it o r ia l B o a r d
Professor Milovan Stanišić, Singidunum University, Serbia mstanisic@singidunum.ac.rs Professor Francesco Frangialli, Hong Kong Polytechnic University, Hong Kong frangialli@gmail.com Professor Gunther Friedl, Technische Universität München, Germany gunther.friedl@wi.tu-muenchen.de Professor Karl Ennsfellner, IMC University of Applied Sciences, Krems, Austria (karl.ennsfellner@fh-krems.ac.at Professor Gyorgy Komaromi, International Business School, Budapest, Hungary gyorgy@komaromi.net Professor Vasile Dinu, University of Economic Studies, Bucharest, Romania dinu_cbz@yahoo.com Professor Ada Mirela Tomescu, University of Oradea, Oradea, Romania ada.mirela.tomescu@gmail.com Professor Radojko Lukić, University of Belgrade, Serbia rlukic@ekof.bg.ac.rs Professor Alexandar Angelus, Lincoln University, USA angelus@lincolnuca.edu Professor Milan Milosavljević, Singidunum University, Serbia mmilosavljevic@singidunum.ac.rs Professor Olivera Nikolić, Singidunum University, Serbia onikolic@singidunum.ac.rs Professor Goranka Knežević, Singidunum University, Serbia gknezevic@singidunum.ac.rs Professor Mladen Veinović, Singidunum University, Serbia mveinovic@singidunum.ac.rs Professor Jovan Popesku, Singidunum University, Serbia jpopesku@singidunum.ac.rs Professor Zoran Jeremić, Singidunum University, Serbia zjeremic@singidunum.ac.rs Professor Vesselin Blagoev, Varna University of Management, Bulgaria blagoev@vum.bg Professor Michael Minkov, Varna University of Management, Bulgaria minkov@iuc.bg Professor Ionel Bostan, Department of Economics, Al. I. Cuza University, Romania ionel_bostan@yahoo.com Associate Professor Christine Juen, Austrian Agency for International Mobility and Cooperation in Education, Science and Research, Wien, Austria chrisine.juen@oead.at Associate Professor Anders Steene, Södertörn University, Stockholm/Hudinge, Sweden anders.steene@sh.se Associate Professor Ing. Miriam Jankalová, University of Zilina, Prague, Czech Republic miriam.jankalova@fpedas.uniza.sk Associate Professor Bálint Molnár, Corvinus University of Budapest, Budapest, Hungary molnarba@inf.elte.hu Associate Professor Vesna Spasić, Singidunum University, Serbia vspasic@singidunum.ac.rs Associate Professor Michael Bukohwo Esiefarienrhe, University of Agriculture, Dept. of Maths/Statistics, Makurdi, Nigeria esiefabukohwo@gmail.com Associate Professor Goh Yen Nee, Graduate School of Business, Universiti Sains Malaysia, Malaysia yngoh@usm.my Associate Professor Blaženka Hadrović Zekić, Faculty of Economics in Osijek, Croatia hadrovic@efos.hr Research Associate Professor Aleksandar Lebl, Research and Development Institute for Telecommunications and Electronics, Belgrade, Serbia lebl@iritel.com Roberto Micera, PhD, Researcher, National Research Council (CNR), Italy roberto.micera@ismed.cnr.it Assistant Professor Patrick Ulrich, University of Bamberg, Germany patrick.ulrich@uni-bamberg.de Assistant Professor Jerzy Ładysz, Wrocław University of Economics, Poland jerzy.ladysz@ue.wroc.pl Assistant Professor Konstadinos Kutsikos, University of the Aegean, Chios, Greece kutsikos@aegean.gr Assistant Professor Theodoros Stavrinoudis, University of Aegean, Chios, Greece tsta@aegean.gr Assistant Professor Marcin Staniewski, University of Finance and Management, Warsaw, Poland staniewski@vizja.pl Assistant Professor Gresi Sanje, İstanbul Bilgi Üniversitesi, Istanbul, Turkey gresi.sanje@bilgi.edu.tr Assistant Professor Michaeł Biernacki, Wrocław University of Economics, Poland michal.biernacki@ue.wroc.pl Assistant Professor Piotr Luty, Wrocław University of Economics, Poland piotr.luty@ue.wroc.pl Assistant Professor Vânia Costa, Polytechnic Institute of Cávado and Ave, Barcelos, Portugal vcosta@ipca.pt Assistant Professor Tihana Škrinjarić, University of Zagreb, Croatia tskrinjar@net.efzg.hr Luu Tien Dung, PhD, Lecturer - Researcher, Lac Hong University, Dong Nai, Vietnam dunglt@lhu.edu.vn Assistant Professor Dharmendra Singh, Modern College of Business and Science, Oman dharmendra@mcbs.edu.om Associate Professor Slađana Čabrilo, I-Shou University, Kaohsiung City, Taiwan (R.O.C.) sladjana@isu.edu.tw Ed it o r ia l O f f ice
Editor in Chief: Managing Editor: Technical Editor: English Language Editor:
Professor Žaklina Spalević, Singidunum University Gordana Dobrijević, Associate Professor, Singidunum University Jovana Maričić, Singidunum University Marijana Prodanović, Assistant Professor, Singidunum University
zspalevic@singidunum.ac.rs gdobrijevic@singidunum.ac.rs jmaricic@singidunum.ac.rs mprodanovic@singidunum.ac.rs
Prepress: Miloš Višnjić Design: Aleksandar Mihajlović ISSN: 2406-2588 The European Journal of Applied Economics is published twice a year. Contact us: The European Journal of Applied Economics 32 Danijelova Street, 11010 Belgrade, Serbia Phone No. +381 11 3094046, +381 11 3093284 Fax. +381 11 3093294 E-mail: journal@singidunum.ac.rs Web: www.journal.singidunum.ac.rs Printed by: Caligraph, Belgrade
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CONTENTS 1 - 18 19 - 33 34 - 53 54 - 66
67 - 88 89 - 103 104 - 118 119 - 135
136 - 146
An Analysis of Factors Influencing the Development of Social Enterprises in the Republic of Serbia Maja Ivanović Djukić, Marija Petrović Randjelović, Miljana Talić
Tourist Satisfaction with Quality of Service, Food, Atmosphere, and Value for Money in Restaurants of Major Cities of the Western Balkans
Bojan Živadinović
An Exploratory Analysis of Financial Inclusion in Chad
Mahamat Ibrahim Ahmat Tidjani
Conceptualizing Integrated Policymaking: Does the Diversification of Environmental Policy Instruments Contribute to Increased Sustainability?
Vlastimir Vučić, Miljana Radović Vučić
Volatility Spillover and Contagion Effects Between Eurodollar Future and Zero Coupons Markets: Evidence from Italy
Konstantinos Tsiaras
The Influence of Sociodemographic Characteristics of Residents on the Perception of Tourism Development Impacts Ilinka Stojković, Jelena Tepavčević, Ivana Blešić, Milan Ivkov, Viktorija Šimon
Infrastructure Development, Institutions, and Intra-Regional Trade: The Case of East Africa James Ochieng, Daniel Abala, Mary Mbithi
The Explanatory Factors of Sovereign Credit Default Swaps Spreads: A Quantile Regression Approach Radhia Zemirl, Mohand Chitti
The Influence of Human Resources on the Development of Leading Tourism Destinations in Serbia Pambayun Kinasih Yekti Nastiti, Apriani Dorkas Rambu Atahau, Supramono Supramono
III
147 - 160 161 - 177
IV
FDI Inflow Effects on Western Balkan Area's Labour Markets Milica Perić, Nemanja Stanišić
Does Urbanization Intensify Carbon Emissions in Nigeria? Muhammad Shehu
EJAE 2020, 17(2): 1 - 18 ISSN 2406-2588 UDK: 005.961:005.914.3]:364-3(497.11) DOI: 10.5937/EJAE17-27375 Original paper/Originalni naučni rad
AN ANALYSIS OF FACTORS INFLUENCING THE DEVELOPMENT OF SOCIAL ENTERPRISES IN THE REPUBLIC OF SERBIA Maja Ivanović Djukić*, Marija Petrović Randjelović, Miljana Talić Faculty of Economics, University of Niš, Serbia
Abstract: The paper investigates the impact of different factors on the establishment and development of social enterprises (SPs). The aim of this paper is to identify the key stimulants and restrictions of development of SPs in the Republic of Serbia, and to propose measures, the application of which can increase their long-term sustainability. The paper is based on exploratory research using data covering 27 interviews with founders of social enterprises in the Republic of Serbia. By applying the methods of regression analysis, it was found that favorable financial resources (especially grants) are the key stimulants for the establishment of social enterprises, while the enthusiasm of managers of social enterprises has the greatest impact on their development. It was found also that legal regulations and the macroeconomic environment, as well as difficulties in accessing the market, are serious restrictions on the establishment of social enterprises, while lack of knowledge in the field of marketing and management limit the development of social enterprises in the Republic of Serbia.
Article info: Received: July 4, 2020 Correction: September 4, 2020 Accepted: September 10, 2020
Keywords: Social enterprises, stimulants, restrictions, long-term sustainability.
INTRODUCTION Modern society is characterized by the presence of numerous social problems, such as: unemployment, poverty, social exclusion, crime, economic inequality, etc. To solve these problems, policymakers in many countries around the world are trying to find innovative solutions. One of the ways to solve the problem of long-term unemployment and exclusion of certain social groups is the establishment and development of economically sustainable companies that have a social mission and for which the term social enterprises has been adopted (Bacq & Janssen, 2011). Social enterprise is any private activity conducted in the public interest, organized within an entrepreneurial strategy whose main goal is not to maximize profits, but to achieve certain economic and *E-mail: majaidj@gmail.com
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social goals, and can bring innovative solutions to the problem of social exclusion and unemployment (OECD/European Commission, 2013). EMES network researchers list the criteria that an organization needs to meet in order to be considered a social enterprise. On the one hand, four criteria are listed that reflect the economic and entrepreneurial dimensions of the organization: (1) continuous activity of production and sale of goods and/or services; (2) a high degree of autonomy; (3) a significant level of economic risk; and (4) a minimum amount of paid work. On the other hand, five criteria are listed that connect the social character of the entrepreneurial initiative: (1) an explicit goal for the benefit of the community; (2) an initiative launched by a group of citizens; (3) decision-making power that is not based on capital and ownership; (4) participatory nature including all actors of the activity; and (5) limited profit distribution (Defourni & Nissens, 2006, p. 6). Social enterprises play a very important role in society. They help to solve the problem of social inclusion and poverty reduction, provide support to vulnerable social groups (helping the elderly, taking care of children, etc.), participate in solving environmental problems, contribute to the preservation of old crafts, contribute to the development of science and culture, etc. (Hjorth, 2013). For that reason, most countries in the world encourage the development of this group of economic entities with a social mission (Rosandić, 2018). For example, the European Commission adopted new specific legislation in the field of social entrepreneurship, and EU Member States have created formal strategies or policies for supporting social enterprise development. In 2015, the Council adopted conclusions on promoting Social Economy. In 2020, the Commission developed a European Action Plan for Social Economy to enhance social innovation (Borzaga et al, 2020). Moreover, various forms of support for the development of social enterprises are provided at the national and local levels (networks and mutual support mechanisms; research, education and skills development, resources available to social enterprises etc.). Additionally, state bodies and nongovernmental organizations promote social entrepreneurship in order to raise the awareness of the population about the role and importance of these economic entities. Thanks to all these activities, the number of social enterprises and social entrepreneurs has been increasing worldwide (Borzaga, et al, 2020, p. 104). However, a large number of social enterprises face a number of problems that have a disincentive effect on the intentions of social entrepreneurs, and which limit the development of already-established social enterprises. These problems can be very different, starting from difficulties in providing financial sources and the socialization of workers, to profit distribution, business expansion, understanding of the local community, etc. In order to stimulate the establishment of social enterprises and encourage their development, it is necessary to identify the factors that have the greatest impact (positive and negative) on their business. The subject of this paper is to identify the key success factors of social enterprises, focusing on the Republic of Serbia. The aim of the paper is to propose measures whose application can lead to the development of social entrepreneurship in the Republic of Serbia. The paper will first analyze the success factors of social enterprises based on a review of the literature. Then, the situation in this field in the Republic of Serbia will be analyzed, and hypotheses will be formulated. The second part of the paper will examine the impact of various factors on the development of social enterprises using statistical methods on data collected by primary research in 27 social enterprises in the Republic of Serbia. In the conclusion, recommendations for social entrepreneurs and economic policy makers will be given.
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LITERATURE REVIEW
Entrepreneurs face a large number of problems, especially in the initial period after establishment. For that reason, many of them leave the business (Stefanovic, et al, 2013). Results from many empirical studies show that up to 20% of entrepreneurial ventures do not survive the first year of business (Fritsch et al., 2006). The situation is even more difficult regarding social enterprises. The number of factors which have an impact on the survival and success of the social enterprises is much higher compared to commercial enterprises, given that they aspire to create economic and social value (Moizer & Tracey, 2010). In order to increase the survival rate of entrepreneurial ventures in the social sphere, it is very useful to identify factors that have a positive impact (stimulate the establishment and development of social enterprises), as well as constraints (factors that discourage their establishment and hinder the development of social enterprises). Empirical studies in this area are very rare, because there no adequate databases on the number and achieved performances of social enterprises exist yet. An even more serious problem is the lack of a unified methodology for monitoring the success of social enterprises. Given the hybrid model of these enterprises, it is necessary to monitor the financial performances and the degree of success in achieving the social mission, which is not easy at all. There are dilemmas as to which social criterion is the most acceptable for monitoring the success of social enterprises, given that they can have very different social goals. The next dilemma is which indicator should be given priority in concluding (economic or social). Despite numerous problems and dilemmas, certain empirical studies are present. For example, Yitshaki et al. (2008) examined the impact of external and internal factors on the long-term sustainability of social enterprises in Israel on a sample of 33 social enterprises. Using the descriptive statistical method, they concluded that the development of social enterprises was greatly influenced by the following factors: the ability to obtain financial resources, creating legitimacy, support from the local authorities, and creating entrepreneurial and personal networks of the founders with key stakeholders. A very serious empirical study on a sample of 26,000 respondents from 36 countries was conducted by Hoogendoorn, Thurik & Van der Zwan (2019). They examined the influence of a large number of factors in the decision to establish a social enterprise, and made a comparison with the decision to establish a commercial enterprise. By applying the regression analysis method, they proved that the following factors have a statistically significant impact on the development of social entrepreneurship: availability of financial resources, characteristics of social entrepreneurs, and complexity of administrative procedures. Taking into account the results of numerous theoretical papers and empirical studies, we have grouped all factors into the following groups: ◆ Characteristics of the social entrepreneur, ◆ Characteristics of social business, and ◆ Characteristics of the macroeconomic environment in which the social business operates. Characteristics of the Social Entrepreneur The first group of factors is related to the demographic characteristics of the social entrepreneur, his/her entrepreneurial orientation, skills, and knowledge that he/she possesses etc. Previous studies show that age and gender have a certain impact on the decision to start a social business and perseverance in its development (Hoogendoorn et al., 2019). Global Entrepreneurship Monitor (GEM) shows that members of the younger generation are more willing to start their own business than the older 3
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generation (Bosma et al, 2015, p. 21). However, Parker's neoclassical life cycle theory has a completely different view. This theory predicts that the two dominant groups of social entrepreneurs are: idealistic entrepreneurs, those that create a business at a younger age, and wealthy individuals, who deal with social entrepreneurship at an older age, as evidenced by empirical study conducted by Hoogendoorn et al. (2019). Regarding gender, most previous studies proved that women are more inclined to start entrepreneurial ventures with a social mission (Hoogendoorn et al., 2019). The impact of education has not been fully examined, so it is not possible to say with certainty whether level of education increases the likelihood of dealing with social entrepreneurship, and how it affects its success (Nga & Shamuganathan, 2010). The entrepreneurial orientation has a much greater impact than demographic characteristics on starting and developing a new business in the social sphere, especially the ability to create a vision, risk propensity, perseverance, commitment, and the like. First of all, the decision to start a social enterprise requires an extremely clear vision, great enthusiasm, and emphasized moral responsibility that encourages engagement in the social sphere (Zahra et al., 2009). The social entrepreneur must have a clear vision of what society would look like if the problem that he or she identified was solved (Best, 2018). This image inspires him/her, and encourages enthusiasm to find solutions in the direction of its realization. It also shows to the entrepreneur the direction of action, and clear steps to achieve it. In order for a social entrepreneur to succeed and remain persistent during the crisis, it is necessary to develop extremely high moral responsibility, empathy for other people's problems, etc. (Martin & Osberg, 2015, p. 34). The next important characteristic of an entrepreneur is risk propensity. It is important for all entrepreneurs, because it plays an important role in decision-making as for whether to deal with the entrepreneurship or not (Zahra et al., 2008; Shaw & Carter, 2007; Tan et al., 2005; Peredo, 2006). However, this does not mean that all groups of entrepreneurs face the same types of risks. In social entrepreneurship, reputation and pervasiveness are important, and personal and family resources are rarely used as sources of funding (Shaw & Carter, 2007). In this regard, social entrepreneurs face a lower degree of personal financial risk. Instead, they face a personal risk of a non-financial nature (loss of reputation in society). Empirical study conducted by Hoogendoorn et al. (2019) confirms that social entrepreneurs are more willing to face risks of a non-financial type, such as the risk of losing their reputation in society. Appraisal of entrepreneurship knowledge, skills, and experience might have a direct influence on the intentions of all entrepreneurs. Entrepreneurial activity depends largely on how people perceive the feasibility of the new business. Entrepreneurial skills and knowledge make it easier for an individual to recognize market opportunities and start a new business. Entrepreneurial experience affects the efficiency of current and future decision-making (Genty et al., 2015). In addition to the knowledge, skills, and experience in the field of entrepreneurship, social entrepreneurs need certain knowledge that is not necessary for commercial entrepreneurs. Given that social entrepreneurs are very often financed by various funds, knowledge in the field of project management, ability to negotiate with local authorities, etc., is needed (Haugh, 2007; Sharir & Lerner, 2006). Related to this is specific knowledge of accounting and financial management, such as: the calculation of grants and overheads; ad hoc or emergency projects.
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Characteristics of the Social Enterprises
The next group of factors is related to the characteristics of social enterprises. The need to create economic and social value makes the business of social enterprises much more complex than commercial enterprises, and their success can be greatly influenced by the ability to obtain and use financial and human resources. The management of human resources can also have an impact on the survival and development of social enterprises. Social enterprises mainly employ members of marginalized social groups. Finding these employees is generally not a problem, but their socialization and education can very often be a problem which limits the development of a social enterprise. Members of marginalized social groups are mostly people who have been unemployed or completely excluded from society for many years, so they face difficulties to adapt to the rules imposed by a social entrepreneur, and very often do not have an appropriate attitude towards the authorities, many of them finding it difficult to adopt new knowledge. All these problems can place a serious barrier to the development of business of social entrepreneurs (Austin et al., 2006; Haugh, 2007; Parker, 2009). Very important elements that affect the success of the social business are the company characteristics, such as: location, size, activity, etc. Many social entrepreneurs locate their business in areas where the market is not sufficiently developed, which can create problems in generating income, because the low standard of living of the population does not promise growth in demand and an increase in the volume of work. In addition, the products and services of social enterprises are very often intended for vulnerable social groups, consumers who are often unable to pay a real economic price for them, so many of them give products to the most vulnerable groups for free or at a price determined by consumers. This can create problems in regular revenue generation and ensuring economic sustainability (DiDomenico et al., 2010; Mair & Marti, 2006). The size of social enterprises can affect costs because the advantage of economies of scale cannot be taken. The increase in costs is additionally influenced by the large share of manual labor (due to the tendency to employ as many members of marginalized social groups as possible, a lower degree of automation and dominance of manual labor, and many of these companies use traditional technology, seek to maintain the old crafts, which also imply manual work). Social enterprises very often cover specific market corners in order to be competitive. These can be: organic production, traditional crafts, food preparation, production of products to order or services on request, etc. (Ivanovic-Djukic & Seldenbah, 2019). In countries where social enterprises are more developed, this problem is partially solved by grouping enterprises with the aim of exchanging information and innovation, and using economies of scale in certain sectors (Nicholls, 2009; Borzaga, et al, 2020, p. 19). Characteristics of the Macroeconomic Environment Macroeconomic environment includes several segments, such as: institutional framework (legislative framework, level of corruption, market demand, access to raw materials, etc.), financial support (availability and cost of capital and opportunities of finance) and social ambience (related to establishing relationships with key stakeholders, local authorities, establishing support for local and national authorities, etc.) (Dorado, 2006; Sharir & Lerner, 2006; Ivanović-Đukić, et al. 2019). The institutional framework can have a major impact on business of social enterprises and their achieved results (Zahra et al., 2008). Various financial incentives, tax incentives, incentives to employ marginalized social groups, can act as an incentive to establish social enterprises (Corner & Ho, 2010). Also, the presence of support, understanding, and assistance from national and local authorities can 5
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have a great impact on the development and improvement of the business of social enterprises, which in turn creates great benefits for that same community (Mair & Martí, 2006). For example, launching various projects to solve current social problems of the social community can stimulate the establishment of social enterprises (Haugh, 2007). Likewise, funding and providing institutional assistance and support to the social entrepreneurs’ initiatives can lead to the improvement of social enterprise operations and their development (Hoogendoorn, et al., 2010). In contrast, an unfavorable tax system and various discriminatory legal regulations can pose a serious burden to social enterprises (Mair & Martí,, 2009). Likewise, complicated administrative procedures and a lack of legislation in the field of social economy can pose serious barriers to the establishment of social enterprises (Van der Zwan et al., 2010). However, the presence of corruption forces social entrepreneurs to invest and spend part of their resources on bribery, which reduces their limited resources and limits its development (Hoogendoorn et al., 2019). This can further increase the uncertainty and costs of doing business for social enterprises, and reduce their growth opportunities. The ability to provide financial resources is a factor which has an extremely large impact on all entrepreneurs. Several studies have shown that social entrepreneurs face more difficulties in obtaining financial resources compared to commercial entrepreneurs (Sharir & Lerner, 2006; Zahra et al., 2009). First of all, the return on investment in social enterprises is difficult to estimate (because profit is not the primary goal, as it is in commercial enterprises), which makes it difficult or impossible for social entrepreneurs to access the capital market (Weerawardena & Mort, 2006). Thus, social entrepreneurs often use funds intended to create social value of the public sector, or from philanthropists and from commercial investments and lending from the private sector (Lyons & Kickul, 2013). In addition, social enterprises can gain access to finance from various donor groups for projects that result in a certain social contribution (Best, 2018; Austin et al. 2006; Nicholls, 2009; Zahra et al., 2009). In recent years, social entrepreneurs have increasingly used crowdsourcing and crowdfunding, especially in the start-up phase. Crowdfunding financing is considered the fastest way to find interested investors, using available sites dedicated to this goal (Best, 2018). These sources of financing can have a major impact on the social enterprise operations. Meanwhile, access to these resources is largely conditioned by the business characteristics, the activities of social enterprises, the legal form, as well as the information of its founders and experience in project management. Social capital (trust, networks of social entrepreneurs, their connections with government representatives, etc.) can play a very important role in the development of social enterprises (Vidal, 2005). However, lack of social capital can be a serious limitation to the development of social enterprises. Without trust in local authorities and business partners, social entrepreneurs will always be careful when investing additional capital and hiring new employees, which will reduce the use of their maximum effects for the social community (Hoogendoorn et al., 2019).
METHODOLOGY The Context of the Research and the Hypotheses Development The development of social entrepreneurship, as well as the establishment of social enterprises in the Republic of Serbia, began in 2000. However, there are no exact data on how many social enterprises exist, nor what their characteristics are. The only research on the number and nature of social enterprises was conducted within the study Mapping of Social Enterprises (2011), according to which there were 1,160 social enterprises. However, later studies have shown that many of the companies mapped 6
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as social enterprises (SEs) do not meet the criteria set for social enterprises. The real number of social enterprises that meet the conditions defined by the EU are much smaller (Vukmirović et al., 2014). According to the latest EU report, the estimated number of SEs in Serbia is 411, the number of SEs per million inhabitants is 59 (Borzaga, et al, 2020, p. 106). Various barriers are the cause of the limited number of social enterprises in the Republic of Serbia. Previous studies show that obtaining financial resources is one of the most serious barriers of all entrepreneurs in Serbia (Culkin & Simmons, 2019, p. 50; Rakic, 2018, p. 12). The challenges of social entrepreneurs are even higher compared to other entrepreneurs (Kusinkova & Rosandić, 2017, pp. 24-28; Borzaga, et al, 2020, p. 141). Unlike most developed countries, where the registration of a social enterprise receives a grant from the state, in the Republic of Serbia there are still almost no incentives set aside for the establishment of social enterprises (Rosandić, 2018). Moreover, the Republic of Serbia is characterized by investors' lack of interest in investing in this type of business, primarily due to: insufficient attractiveness (high return on investment cannot be expected, and the state does not stimulate investment in this sector with tax relief or any other measures) and possible problems (wrong or late decisions) due to democratic decision-making (where decisions are not made on the basis of ownership). Even when social entrepreneurs provide a modest initial capital, they very often have problems with finding and equipping business space, procuring equipment, providing raw materials, etc. (Borzaga, et al, 2020, p. 145). Non-governmental organizations (NGOs) have played significant role in overcoming this problem (Vukmirović et al., 2014). NGOs found various financial support programs for the development of social enterprises, and encouraged individuals with a strong moral responsibility to launch projects with a social mission. The largest number of new social enterprises in Serbia has evolved from these social projects (financed by various international and national funds) funds (Vukmirović et al., 2014; Ivanović-Djukic & Seldenbah, 2019). In other words, financial incentives were a significant incentive for the establishment of social enterprises in Serbia. Our first hypothesis is: H1: Favorable sources of financing (especially grants) are the factors that have the greatest positive impact on the establishment of social enterprises in the Republic of Serbia. The results of recent studies conducted in Serbia have shown that, among new social enterprises, there are those that have entrepreneurial capacity and follow a clear social vision and, thus, achieve a significant social effect (Kusinikova & Rosandić, 2017). One of the reasons are the characteristics of their founders. The founders of social enterprises in the Republic of Serbia are characterized by a high degree of self-initiative and commitment to solving various social problems, and a high degree of ethical responsibility. Among the founders of social enterprises, great enthusiasm is especially visible when forming a company: the best intention exists to do something good for a specific vulnerable group and, eventually, for themselves (Borzaga, et al, 2020, p. 141). This enthusiasm often knows how to overcome obstacles that seem insurmountable and to help those who would not find help on the other side. Due to their enthusiasm and persistence, social enterprises in the Republic of Serbia survive, despite all the problems. Accordingly, our second hypothesis is: H2: The enthusiasm of the social entrepreneur and their commitment to solving their chosen social problem are factors that have the greatest positive impact on the survival and development of social enterprises in the Republic of Serbia.
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ĐUKIĆ. I. M., RANDJELOVIĆ. P. M., TALIĆ. M. AN ANALYSIS OF FACTORS INFLUENCING THE DEVELOPMENT OF SOCIAL ENTERPRISES IN THE REPUBLIC OF SERBIA
A very serious limitation on the establishment and development of social enterprises in the Republic of Serbia is the unfavorable institutional framework due to which problems arise when entering the market. The Law on Social Entrepreneurship has not yet been adopted in the Republic of Serbia. Its adoption has been waited for many years, as well as the establishment of a budget fund to encourage the development of social enterprises. In order to register and legally run social enterprises, several other laws must be consulted, which can have a disincentive effect on potential entrepreneurs when making a decision to start a business (Ivanović-Đukić & Seldenbah, 2018). Access to the market can be a much more serious problem. If a social enterprise is established in industries related to social protection, or in service activities, barriers to market entry are generally not large, as capital investment is usually modest and often does not require a highly skilled workforce. Therefore, social enterprises can attract consumers relatively easily, and access the market. However, if a social enterprise intends to operate in other industries, market access is extremely difficult. For example, it is necessary to provide significant capital for the establishment of a social enterprise in the field of industrial production. Since grants and favorable funds are limited, the social entrepreneur must invest his or her own capital, or give up on their job. The offer of products in branches where there is a large number of large companies, which produce machines, prevents access to social enterprises, because labor costs have a large share in their work (many jobs are done manually, hiring as many members of marginalized social groups as possible). Since there are no financial incentives for social enterprises in the Republic of Serbia, and consumers' awareness of their role in society is low (so they do not choose their products), their competitive position in these branches is very unfavorable. Many studies show that trust of the population (potential consumers) in the services of social enterprises in the initial period is very low, which also decreases the likelihood of their success in the initial period (Vukmirović et al., 2014; Kusinkova & Rosandić, 2017). Overall, there are a number of challenges for social entrepreneurs in Serbia's macroeconomic environment. For these reasons, they perceive it as unfavorable. We assume that such an unfavorable environment has a disincentive effect on the establishment and development of social enterprises in Serbia. Therefore, our next hypothesis is: H3: The macroeconomic environment is factor which has the greatest negative impact on the establishment of social enterprises in the Republic of Serbia. The next factor that has a great impact on the development of social enterprises is the knowledge and experience of their founders in managing a business. The founders of most social enterprises in the Republic of Serbia are people from non-economic educational profiles (journalists, sociologists, artists, etc.), who work on achieving social missions, but do not have enough experience in marketing, sales, financial management, business performance monitoring, business negotiations, etc. (Ivanovic-Djukic & Seldenbah, 2018). Due to the lack of knowledge and experience in the field of management, the following problems often occur: poor organization and division of labor (one person works everything, which reduces productivity and increases costs). Lack of knowledge in the field of marketing leads to the fact that they do not have clearly defined products, do not perform market analysis, and do not know to whom they can sell their products, do not examine the behavior of competitors and set the prices of their products very high, do not know the customer communication procedure and promotional activities (they don't have developed promotional materials, they don't have a website or a Facebook page), so they don't have a brand built. All this, along with the lack of sales skills, limits the income of social workers, so they are often 8
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ĐUKIĆ. I. M., RANDJELOVIĆ. P. M., TALIĆ. M. AN ANALYSIS OF FACTORS INFLUENCING THE DEVELOPMENT OF SOCIAL ENTERPRISES IN THE REPUBLIC OF SERBIA
financially dependent on donors or the state. A particular problem that emerges is that most social enterprises do not have the resources to hire partners (designers, web designers, marketing agencies, etc.) who could help overcome these problems, which significantly slows down the development of social enterprises. Our last hypothesis is: H4: The lack of knowledge and experience in management and marketing are factors that significantly limit the development of social enterprises in the Republic of Serbia. The Sampling Method and Sample Characteristics To analyze the success factors of social enterprises in the Republic of Serbia, we conducted a primary survey. Research was conducted in the second half of 2019, by surveying the founders of social enterprises in the Republic of Serbia. The research covered 27 social enterprises. The questionnaire included two parts. The first part of the questionnaire consisted of questions related to general information about the founder/s and the characteristics of social enterprises. The second part of the questionnaire was related to the attitudes of the founders of social enterprises about the factors that act as an incentive and restrictive on the establishment of social enterprises in the Republic of Serbia. The following characteristics of the founders of social enterprises were examined: age, education, gender, previous entrepreneurial experience, and previous managerial experience. In terms of age and education, the structure is as follows: the largest share is founders under the age of 45, and people with a higher level of education. The age and educational structure are shown in Table 1. Table 1. The Structure of a Sample in % Age
Primary education
Secondary education
Post-secondary education
Total
25 and under
1.0
10.7
13.0
24.7
26-30
1.1
18.0
9.7
28.8
31-45
1.0
16.6
13.0
30.6
46-60
0.5
7.3
6.6
14.4
over 60
0.1
0.6
0.8
1.5
Total
3.7
53.2
43.1
100.0
Source: Author's own calculation
Regarding the gender structure, 78% of companies are founded by women, while men are founders of 22% of social enterprises. In the total sample, only 15% of founders had previous managerial experience, and 12% of respondents had previous entrepreneurial experience. The next segment consisted of the characteristics of social enterprises: age, size, legal form. Regarding the age of enterprises, the structure is as follows: enterprises under 1 year of age accounted for 16% of the sample, enterprises between 1 and 3 years of age accounted for 36%, enterprises between 3 and 5 years of age accounted for 28% and enterprises operating for more than 5 years accounted for 20% of the sample. In terms of size, 80% of enterprises belong to the group of micro-enterprises, while the rest (20%) belongs to small enterprises. 20% were social enterprises registered as citizens' associations companies (craft cooperatives), public-civil partnerships were 4%, and associations for professional rehabilitation of the disabled person were 12%. 9
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ĐUKIĆ. I. M., RANDJELOVIĆ. P. M., TALIĆ. M. AN ANALYSIS OF FACTORS INFLUENCING THE DEVELOPMENT OF SOCIAL ENTERPRISES IN THE REPUBLIC OF SERBIA
Regarding the sources of financing, 50% of the funds for the establishment of the largest number of social enterprises in the Republic of Serbia were provided from international funds, the companies for which establishment were provided funds by account slightly smaller share (30%), and 20% of social enterprises were financed by the personal savings of their founders. The second part of the questionnaire was related to the attitudes of the social enterprises founders concerning the factors that influence the establishment and development of social enterprises in the Republic of Serbia, according to their perception. A five-point Likert scale was used to evaluate the answers, starting from 1 - I do not agree at all, up to 5 - I completely agree. Model and Variables To check the validity of the hypotheses, a regression analysis was applied. Two regression models were formed. The first examines the impact of certain groups of factors on the establishment of a social enterprise. The second examines the impact of factors on the social enterprise development. The dependent variable was successfully measured on the basis of a long-term sustainability rate, following an example of research conducted by Yitshaki (2008). The long-term sustainability rate was measured based on the average income growth rate and the average growth rate of success in achieving social goals. The average success rate in achieving social goals is measured by the average growth rate of the number of employees, members of marginalized social groups or the average growth rate of members of vulnerable social groups who were provided assistance. The independent variables in the first model were factors that had the greatest impact on the establishment of social enterprises according to the perception of entrepreneurs. Meanwhile, independent variables in the second model were factors influencing development of social enterprises (according to the perception of social entrepreneurs).
RESULTS AND DISCUSSION The average values of the attitudes of the founders of social enterprises concerning the influence of certain factors on the operations of social enterprises are given in Table 2. As can be seen, the majority of the founders of social enterprises believe that available financial resources, the possibility of obtaining other resources, and the recognized market opportunities (unmet need of a vulnerable group or a social problem that must be solved) stimulate the establishment of social enterprises. Meanwhile, the majority of the respondents disagree with the claim that legal regulations, local government support, and market access had a stimulating effect on starting a social business. Regarding to the factors influencing the development of social enterprises, the opinion of most of the surveyed founders is that very important factors that contribute to the development of their enterprises are their enthusiasm, commitment, and persistence in solving social problems, and knowledge in project management that allows them to provide additional funding. Moreover, the majority of respondents do not agree with the statement that their knowledge in the field of management and marketing contributes to the development of their business. The attitude towards knowledge in the field of human resource management is approximately neutral.
10
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ĐUKIĆ. I. M., RANDJELOVIĆ. P. M., TALIĆ. M. AN ANALYSIS OF FACTORS INFLUENCING THE DEVELOPMENT OF SOCIAL ENTERPRISES IN THE REPUBLIC OF SERBIA
Table 2. Descriptive Statistics
Mean
Standard Deviation
Sustainability rate
1.83
1.03
Available financial resources had an incentive for the establishment of the SP
4.89
0.53
The possibility of obtaining resources had an incentive to establish the SP
4.78
.950
Legal regulations and state support had an incentive to establish the SP
1.25
0.79
Support of local authorities had an incentive to establish the SP
1.32
1.23
Easy market access had an incentive to establish the SP
2.28
1.04
A recognized market need had an incentive for the establishment of the SP
3.73
1.42
My enthusiasm has a positive effect on the development of the SP
4.12
0.99
My commitment (persistence) to solving social problems has a positive effect on the SP development
4.68
1.14
My knowledge and experience in management affect the SP development
2.76
1.17
My knowledge and experience in marketing affect the SP development
2.98
1.45
My knowledge of project management affect the SP development
3.93
1.04
My knowledges related to human management affect the SP development
3.11
0.75
Variable
Valid N
27
Source: Author's own calculation
In order to check whether the influence of these factors was statistically significant in the long-term sustainability of social enterprises, a regression analysis was performed. The results are shown in Table 3: Table 3 Regression Analysis Variable Constant
Model 1
Model 2
2.099
3.838*
Available financial resources had an incentive for the establishment of the SP
0.98**
The possibility of obtaining resources had an incentive to establish the SP
0.65
Legal regulations and state support had an incentive to establish the SP
-0.67*
Support of local authorities had an incentive to establish the SP
0.02
Easy market access had an incentive to establish the SP
-0.17**
A recognized market need had an incentive for the establishment of the SP
0.34
0.49**
11
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ĐUKIĆ. I. M., RANDJELOVIĆ. P. M., TALIĆ. M. AN ANALYSIS OF FACTORS INFLUENCING THE DEVELOPMENT OF SOCIAL ENTERPRISES IN THE REPUBLIC OF SERBIA
Variable
Model 1
Model 2
My enthusiasm has a positive effect on the development of the SP
0.49**
My commitment (persistence) to solving social problems has a positive effect on the SP development
0.84*
My knowledge and experience in management affect the SP development
-0.38*
My knowledge and experience in marketing affect the SP development
-0.64**
My knowledge of project management affect the SP development
0.43*
My knowledges related to human management affect the SP development
0.05
R
0. 3216
0.3458
R2
0.2913
0.3154
Dependent variable: Long-term survivability rat; Significance:** at 5%, * at 10%. Source: Author's own calculation
The available financial resources (grants, subsidies, etc.) have the greatest positive impact on the establishment of social enterprises. The impact of this factor was positive and statistically significant at a significance level of 10%. In this way, the first hypothesis was proved. The possibility of obtaining other resources (business space, equipment, materials...), the recognized need from the market, and the support of local authorities are also factors which have a positive impact on the establishment of social enterprises, but the impact of these factors was not statistically significant. However, elements of the institutional environment, legal regulations, state support, and market access negatively impact the establishment of social business. The impact of these factors is statistically significant, which proves the second hypothesis. The second model shows that the greatest positive impact on the development of social enterprises is the enthusiasm and the commitment of founders to solve social problems, to a somewhat smaller degree, but knowledge in the field of project management also has a positive impact. The impact of all of these factors is statistically significant, which proves the third hypothesis. A limitation of the social enterprises’ development is the lack of knowledge and experience in the field of marketing and management. These factors negatively affect the social enterprises’ long-term sustainability, and their impact is statistically significant. This proves the last hypothesis. The knowledge of the founders related to human resource management has a positive, but statistically insignificant, impact on the development of social enterprises. Discussion and Recommendations for the Economic Policymakers The results of the research have shown that a very significant incentive for the establishment of social enterprises is the availability of resources, primarily financial. This is in line with all previous studies conducted globally, (Sharir & Lerner, 2006; Zahra et al., 2009) as well as in the Republic of Serbia (Rosandić, 2018; Rakic, 2018; Culkin & Simmons, 2019). This can be explained by the fact that favorable financial resources are very limited in the Republic of Serbia, while the capital market is almost underdeveloped, banks have very unfavorable lending conditions for beginners (and commercial entrepreneurs); and social enterprises are forced to find their own sources of financing. This problem can be solved by offering much more incentive funds for social entrepreneurs from state funds or local community funds. 12
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ĐUKIĆ. I. M., RANDJELOVIĆ. P. M., TALIĆ. M. AN ANALYSIS OF FACTORS INFLUENCING THE DEVELOPMENT OF SOCIAL ENTERPRISES IN THE REPUBLIC OF SERBIA
Furthermore, it can be useful to organize trainings for the founders of social enterprises in the field of project management, and provide the information on favorable sources of financing, in order to use funds from international funds intended for the development of social entrepreneurship. The next group of factors identified by our research, with significant but negative impact on the development of social entrepreneurship, is legal regulations and the institutional framework. This is expected, given that the Republic of Serbia has not yet adopted the Law on Social Entrepreneurship, and there are no appropriate bodies in public administration responsible for development, coordination and monitoring in the field of social entrepreneurship, which creates problems for the development of this sector. This is in line with previous studies (Vukmirović et al., 2014; Kusinikova, & Rosandić, 2017; Borzaga, et al., 2020). It is necessary to create laws and institutions for regulating social-entrepreneurial activities, and to facilitate the establishment and operation of social enterprises. A legal framework is necessary in order to strictly define which activities are included in social entrepreneurship, and what their rights and responsibilities are. Moreover, it is necessary to include all institutions that have legal, financial and political power for the faster development of social entrepreneurship, and an increase in the number of social enterprises. Their task is to enable the creation of an institutional framework that encourages the establishment and development of social enterprises, by providing various financial incentives and tax relief. It is also necessary to consider opportunities for reducing taxes, contributions, and fees in order to provide greater incentives to social entrepreneurs. It is also necessary to analyze the benefits of social entrepreneurship for socioeconomic development, and to better and more clearly develop the legal framework related to social entrepreneurship. In order to prove the importance of social entrepreneurship for the economy and employment, it is necessary to develop mechanisms for the documenting and statistical monitoring of social entrepreneurship. There is a need to encourage the development of social and business support institutions, such as social business incubators, development agencies, social business centers, business parks, etc. Market access had a slightly less negative impact on the development of social enterprises, but statistically significant nonetheless. The main reason is primarily high costs, due to the large share of labor costs (most jobs are done by hand, because one of the goals of social enterprises is to employ as many members of marginalized social groups as possible, and very often to preserve old crafts and traditions). Due to high costs, the prices of their products are relatively high. Given that these companies often offer unique products and that these products are made by people from marginalized social groups, it is desirable to provide assistance from local authorities at the national level as well in the field of promotion of their products, subsidizing costs (especially labor costs of marginalized social groups). In addition, the activities of policymakers should be routed towards raising public awareness of the role of social enterprises, which will in turn affect the growth of demand for their products. To raise awareness of the importance and role of social enterprises, it is important to transfer best practices and experiences from developed to less developed countries and from developed to less developed parts of the country. In addition to create an information system on the general concept of social entrepreneurship, it is desirable to promote products and services offered by existing social enterprises. Among the characteristics of entrepreneurs, the factors that have a significant but negative impact on the development of social enterprises are the knowledge of social entrepreneurs in the field of management and marketing, which is similar to the previous research (Ivanović-Đukić, Seldenbach, 2019). It is necessary to organize a much larger number of trainings and workshops in the field of digital marketing, business planning, work organization, and other areas of management. It is also desirable to organize educational programs in the field of social entrepreneurship within the formal education program. 13
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ĐUKIĆ. I. M., RANDJELOVIĆ. P. M., TALIĆ. M. AN ANALYSIS OF FACTORS INFLUENCING THE DEVELOPMENT OF SOCIAL ENTERPRISES IN THE REPUBLIC OF SERBIA
There are currently very few such programs in the Republic of Serbia. Education in this area is organized mainly through informal educational events, such as conferences, symposiums, and workshops. In order to change the situation and improve the business of social enterprises, it is necessary to offer many more educational programs through a formal and informal education system. The factors that have a significant and positive impact, which initiate (encourage) the establishment and development of social enterprises, are enthusiasm and emphasizing the moral responsibility of individuals, who become social entrepreneurs to help a vulnerable social group or help solve specific social problems. The importance of the personal characteristics of social entrepreneurs has been explained in many previous papers (Martin & Osberg, 2015, p. 34 Best, 2018). In addition to various forms of help and support for social entrepreneurs, promotion and rewarding are needed. It is possible to introduce various forms of awards for the most successful social entrepreneur in a particular year. The award programs would be accompanied by certain promotional activities. In that way, enthusiasm and even greater engagement of these socially responsible people would be encouraged. At the same time, social entrepreneurship in the Republic of Serbia would be promoted and awareness of the population about the role and importance of social entrepreneurship would be raised.
CONCLUSION This paper analyzes the factors that influence the establishment and development of social enterprises in the Republic of Serbia. Based on literature reviews and previous empirical studies, all factors are classified into the following groups: characteristics of the entrepreneur (demographic characteristics of the entrepreneur, his entrepreneurial orientation, possessed skills and knowledge); characteristics of the social enterprise (size, activity, legal form, management); and characteristics of the macroeconomic environment in which the business takes place (legislative framework, corruption, market demand, access to raw materials, difficulties in exports, delays in collection of claims, availability and price of capital, financial opportunities, the possibility of establishing relationships with key stakeholders, local authorities, establishing support from local and national authorities, etc.). The primary research was conducted on the sample of 27 SE in order to identify the factors that have the greatest impact on the establishment of social enterprises in the Republic of Serbia. By applying the methods of regression analysis, it was found that favorable financial resources (especially grants) are key stimulants for the establishment of social enterprises (the first hypothesis of the paper is proven). Results show that enthusiasm of managers of social enterprises has the greatest impact on their development (the second hypothesis of the paper is proven). It was found also that legal regulations and the macroeconomic environment, as well as difficulties in accessing the market, are serious restrictions to the establishment of social enterprises (the third hypothesis of the paper is proven), while lack of knowledge in the field of marketing and management limit the development of social enterprises in the Republic of Serbia (the last hypothesis of the paper is proven). Measures have been proposed to economic policymakers in the direction of mitigating the negative effects of disincentive factors and strengthening the effects of factors that positively affect the development of social entrepreneurship.
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ĐUKIĆ. I. M., RANDJELOVIĆ. P. M., TALIĆ. M. AN ANALYSIS OF FACTORS INFLUENCING THE DEVELOPMENT OF SOCIAL ENTERPRISES IN THE REPUBLIC OF SERBIA
Limitations to the Study
The study we conducted has several limitations. Firstly, the limitation of the study refers to the measurement of the development (success) of SE indicated by a long-term sustainability rate, following an example of research conducted by Yitshaki (2008). Secondly, the importance of different factors influencing the establishment and development of SE was measured on the basis of the perception of the surveyed entrepreneurs (subjective criteria). Some of the reasons for using unreliable and subjective measures of SE performance and the factors influencing them are a lack of accepted methodology and generally accepted indicators for monitoring the performance of social enterprises, as well as a very limited number of empirical research in this area. Our future research will be focused on the development of a universally accepted methodology, and the development of objective criteria for monitoring the success of social enterprises. The next limitation of our paper is the sample size of 27 SEs. Such a sample is small compared to the similar research papers conducted worldwide. Consequently, the results we obtained may not be comparable enough with the results of the studies that have already been conducted in this area. However, if we take into account the fact that there are only 411 SEs in Serbia, (it is 6.5%) our sample is representative (6.5%).
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ĐUKIĆ. I. M., RANDJELOVIĆ. P. M., TALIĆ. M. AN ANALYSIS OF FACTORS INFLUENCING THE DEVELOPMENT OF SOCIAL ENTERPRISES IN THE REPUBLIC OF SERBIA
Sharir, M. & Lerner, M. (2006). Gauging the success of social ventures initiated by individual social entrepreneurs. Journal of World Business, 41(1), 6-20. DOI: 10.1016/j.jwb.2005.09.004. Shaw, E. & Carter, S. (2007). Social entrepreneurship: Theoretical antecedents and empirical analysis of entrepreneurial processes and outcomes. Journal of Small Business and Enterprise Development, 14(3), 418-434. DOI: 10.1108/14626000710773529. Stefаnović, S., Ivanović-Djukić, M. & Janković-Milić, V. (2013). The analysis of key challenges and constraints to the stability and growth of an entrepreneurial sector in Serbia. Journal of Balkan and Near Eastern Studies, 15(3), 364-365. DOI: 10.1080/19448953.2013.789330 Tan, W.L., Williams, J. & Tan, T.M. (2005). Defining the ‘social’ in ‘social entrepreneurship’: Altruism and entrepreneurship. The International Entrepreneurship and Management Journal. 1(3), 353-365. DOI: 10.1007/s11365-005-2600-x. Van der Zwan, P.V.D., Thurik, A R. & Grilo, I. (2010). The entrepreneurial ladder and its determinants. Applied Economics, 42(17), 2183-2191. DOI: 10.1080/00036840701765437. Vidal, I. (2005). Social enterprise and social inclusion: Social enterprises in the sphere of work integration. International Journal of Public Administration, 28(9), 807–825. DOI: 10.1081/PAD-200067347. Vukmirović, D. et al. (2014). Economic impact of social enterprises in the Republic of Serbia. Begrade: National Statistical Office. Retrieved April 30, 2020, from http://socijalnoukljucivanje.gov.rs/wp-content/ uploads/2014/06/economic_impact_of_social_enterprises_in_the_republic_of_serbia_RZS.pdf. Weerawardena, J. & Sullivan Mort, G. (2006). Investigating social entrepreneurship: A multidimensional model. Journal of World Business, 41(1), 21-35. DOI: 10.1016/j.jwb.2005.09.001 Yitshaki, M., Lerner, M. & Sharir, M. (2008). What are social ventures? Toward a theoretical framework and empirical examination of successful social ventures. In G.E. Shockley, P.M. Frank & R.R. Stough (Eds.), Non-market Entrepreneurship: Interdisciplinary Approaches (pp. 217-241). Cheltenham, UK: Edgar Elgar. Zahra, S.A., Gedajlovic, E., Neubaum, D.O. & Shulman, J. M. (2009). A typology of social entrepreneurs: Motives, search processes and ethical challenges. Journal of Business Venturing, 24(5), 519-532. DOI: 10.1016/j.jbusvent.2008.04.007. Zahra, S.A., Rawhouser, H.N., Bhawe, N., Neubaum, D.O. & Hayton, J.C. (2008). Globalization of social entrepreneurship opportunities. Strategic entrepreneurship Journal, 2(2), 117-131. DOI: 10.1002/sej.43.
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ANALIZA FAKTORA KOJI UTIČU NA RAZVOJ SOCIJALNIH PREDUZEĆA U REPUBLICI SRBIJI Rezime: U radu je ispitivan uticaj različitih faktora na osnivanje i razvoj socijalnih preduzeća. Cilj rada je bio da se identifikuju ključni podsticaji i ograničenja razvoja socijalnih preduzeća u Srbiji i predlože mere čija primena može povećati njihovu dugoročnu održivost. Rad je zasnovan na empirijskom istraživanju u kome su korišćeni podaci dobijeni anketiranjem 27 osnivača socijalnih preduzeća u Srbiji. Primenom metoda regresione analize je pokazano da su najveći stimulansi za osnivanje socijalnih preduzeća povoljni izvori finansiranja (naročito bespovratna sredstva), dok najveći uticaj na razvoj socijalnih preduzeća ima entuzijazam i posvećenost njihovih menadžera. Rezultati su takođe pokazali, da pravni propisi i makroekonomski ambijent destimulišu osnivanje socijalnih preduzeća, dok nedostatak znanja u oblasti marketinga i menadžmenta ograničavaju njihov razvoj.
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Ključne reči: Socijalna preduzeća, podsticaji, ograničenja, dugoročna održivost.
EJAE 2020, 17(2): 19 - 33 ISSN 2406-2588 UDK: 005.346:640.4(497-15) DOI: 10.5937/EJAE17-27360 Original paper/Originalni naučni rad
TOURIST SATISFACTION WITH QUALITY OF SERVICE, FOOD, ATMOSPHERE, AND VALUE FOR MONEY IN RESTAURANTS OF MAJOR CITIES OF THE WESTERN BALKANS Bojan Živadinović* Singidunum University, Belgrade, Serbia
Abstract: An important factor in modern-day tourism is restaurant guests’ feeling of satisfaction. Motivated by the desire to quantify this feeling and make it an operational element of recommendations for further development of one segment of restaurant tourism, this paper examines the expressed satisfaction of foreign tourists in restaurants of selected cities in the Western Balkans. The second goal of this paper is to show the extent to which local visitors and domestic tourists are satisfied with the service provided in the analysed catering facilities. Comments that are an integral part of the research part of this paper’s analysis were collected from one of the most popular websites in the field of restaurant industry ‒ Trip Advisor.
Article info: Received: July 3, 2020 Correction: July 28, 2020 Accepted: September 8, 2020 Keywords: restaurants, foreign tourists, local visitors, Trip Advisor, tourist satisfaction.
INTRODUCTION In the digital era of the fourth industrial revolution, tourism is stretching beyond all boundaries and is becoming a global phenomenon. The restaurant industry, being a significant part of the tourism industry, actively follows the newest hospitality trends. The requirements of the increasingly-demanding restaurant customers are being met by listening to the market and continuous introduction of innovations. Mass tourism is losing its dominant position in the overall tourism scope, and the educated tourist of the information age seeks an active vacation. Tourism in cities is recording growth rates, while city restaurants are in growing demand. Restaurants form an integral and significant share of the city’s hospitality offer. The topic of this paper is the assessment of the expressed degree of satisfaction of restaurant customers, both local and foreign, in the Western Balkans region, i.e., in the cities that used to be capitals of former socialist Federal Republic of Yugoslavia (SFRY) republics, and are now the capital cities of the newly founded states in the region. As such, they still relate to one another in every sense, including tourism – of which restaurants are a part. *E-mail: restoranizvor.zivadinovic2@gmail.com
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After arriving to their target destination, tourists satisfy their sustenance needs using the services of these hospitality establishments. Moreover, some guests wish to learn about different cultures by sampling local food specialities (Alderighi, Bianchi, & Lorenzini, 2016). Restaurants’ food, service, and ambience might become some of the more significant magnets for tourists. Whatever the reason for visiting restaurants in the region’s major cities, which are subject of this paper’s analysis, restaurateurs have an important task ahead of them: to leave as good an impression on their visitors as possible – not only by fulfilling their expectations, but also by striving to surpass them. Noticeably, better-educated modern era tourists do not shy away from sharing their comments about restaurants on social media networks, websites that specialize in specific commentary, etc. The already well-proven importance of oral publicity, which has been the subject of numerous scientific papers, has transformed, and became quite significant as electronic oral publicity (eWOM), too. Social networks have transformed interpersonal relations (Yang, 2020). In the contemporary world, online commentary has reached such a magnitude of popularity that the restaurant choice, its financial state, and the development of new products are decisively dependent on it (Li, Lee, Lee, & Yang, 2020). This has, however, led to some negative effects too, such as the posting of fake comments that often lead consumers astray (Li, Lee, Lee, & Yang, 2020). Guests from different countries appraise the same service differently (Radojević, Stanišić, & Stanić, 2019). Nevertheless, the advantages brought on by the social networks far outweigh their negative effects (Yang, 2020). Posted comments contain information on the level of satisfaction with service processes. Based on the comments written by restaurant customers on the Trip Advisor website, the future restaurant customer assembles as realistic an image as possible of the restaurant that they would potentially visit, deliberates its characteristics and offers, and finally reaches a decision on whether to actually visit it or not. The analysis of these comments is the foundation of this paper, and the conclusions drawn here were based on said analysis. For managers and other interested parties, they may serve as a significant recommendation in undertaking future activities on enhancing restaurant operations.
LITERATURE OVERVIEW eWOM and TripAdvisor Researchers within the hospitality sector have studied oral publicity for a number of years, and have consistently emphasised its significance. Word-of-mouth (WoM) is a powerful marketing tool, capable of shaping guests’ opinions and behaviours. WoM has a tremendous influence on the guest’s perception (Nam, Baker, Ahmad, & Goo, 2020). Oral communication is informal; the direction of its influence is towards other guests, and it describes the characteristics of a particular service or a product (Yen & Tang, 2019). Digital technologies have led to multiplications of content posted on social media in various forms by hospitality industry customers. These comments, available to everyone, enable potential consumers to read and analyse them. Oral publicity, which is one of a restaurant business’s strongest tools, is being replaced in the modern era with electronic oral publicity (eWOM). Electronic oral publicity denotes every negative or positive comment of the current or past customer, which is published publicly on social media and specialised websites and, as such, is accessible by all (Mariani & Visani, Embedding eWOM into efficiency DEA modelling: An application to the hospitality sector, 2019). Guests have become more sophisticated, and a high level of interactivity is being achieved through new social media (Čačić & Mašić, 2013). It is of great importance for restaurants that potential customers, by reading reviews regarding their services, believe in the trustworthiness of what is written. 20
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Tourism businesses should constantly indicate the need for honesty and trustworthiness on social media (Zainal, Harun, & Lily, 2017). Modern customers use information obtained from social media prior to purchasing, during the service delivery, and after the service delivery itself (Colladon, Guardabascio, & Rosy, 2019). The so called eWOM is non-commercial information based on customer experience. Electronic publicity has a permanent status, unlike oral publicity where, after the live communication ceases, the communication itself vanishes. Motives related to positive comments are linked to enjoyment and desire to help others, while they can also be influenced by economic benefits (receiving premiums and points for posting positive comments). Motives related to negative comments are based on negative feelings caused by poor hospitality, and the desire to help others not to experience these issues (Hu & Kim, 2018). The eWOM system helps managers find out what is the maximum price guests are willing to pay for services and products in a restaurant (Nieto-Garcia, Munoz-Gallego, & Gonzalez-Benito, Tourists’ willingness to pay for an accommodation: The effect of eWOM and internal reference price, 2017). The modern consumer finds information through internet web portals. This information is of great value for both restaurant guests and managers. TripAdvisor is a web portal that chooses the best from the great number of hospitality establishments in its network – by categories, marks and ranks them, and in doing so enables customers to have an overview of a broad spectrum of hospitality establishments sorted by categories (Knežević, Barjaktarević, & Obradović, 2014). TripAdvisor is the biggest website in the world of tourism, and the number of posted comments is growing each year. TripAdvisor is also becoming more popular by the year (Khorsand, Rafiee, & Kayvanfar, 2020). The platform has more than 455 million visitors – on average monthly – and 630 million restaurant and hotel views (Giglio, Pantano, Bilotta, & Melewar, 2019). Essentially, TripAdvisor should help guests utilise the experiences of previous guests who have already used that service, prior to using the service themselves (Nilashi, et al., 2018). Comments posted on TripAdvisor are exceptionally important in the restaurant industry; regular review follow-up, through timely reactions, may improve the restaurant’s turnover (Tepavčević, Blešić, & Bradić, 2018). The credibility of the comments on the network might influence their quality, while in the future the customers will need to separate fake comments from the credible ones (Filieri, Alguezaui, & McLeay, 2015).
Satisfaction and Attributes of the Restaurant Service The quality of restaurant service is the guest’s estimation of the service offered in a particular restaurant compared to competitive hospitality establishment. Quality in a restaurant is seen through the tangible and intangible aspects of the service (Han & Hyun, 2017). Quality in a restaurant is crucial for profitability, and can be both objective and subjective. The objective quality is that which the restaurant owns in the technical sense, while the subjective quality is the way in which a guest perceives that quality (Konuk, 2019). The quality of service, food quality, atmosphere, and value for money are just a few attributes of the restaurant service that impact customer satisfaction. Since the Trip Advisor website, serving as a database for this paper, contains only four of the listed attributes used by restaurant guests to express their satisfaction – they are the subject of the paper research; it would be useful, however, for future papers to research other attributes influencing customer satisfaction, too. Satisfaction is a guest’s subjective feeling regarding current and previous experiences in the restaurant (Jeong & Jang, 2011).
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Restaurant’s Atmosphere Quality
The atmosphere is defined as continued work on the restaurant’s design, which should invoke certain emotions in guests that will influence the probability of increased spending. Guests receive these signals through their senses (sight, sound, smell, touch). Employees and management improve the atmosphere through agreeable music, adequate lighting, smells, and colours. The atmosphere is a combination of visual elements with what is audible. Aside from these elements, the atmosphere is particularly impacted by hygiene and odours, as well as the furniture layout and temperature. Research have shown that the tempo of music in a restaurant may affect consumption and length of stay. Psychological research has shown that guest behaviour is linked to the environment: when it comes to restaurants, good food and good service alone are not a guarantee of guest satisfaction. Success in achieving guest satisfaction to a large degree depends on the quality of the environment (Ariffin, Bibon, & Abdulah, 2012). Colours stimulate restaurant guests and pose a strong visual attraction, thus influencing their perception and behaviour. Guest behaviour may also be influenced by colour shade, its tone and light. For example, romantic dining would require the use of a single colour and dim lighting, while fast-food restaurants have bright lights associated with speed (Tantanetewin & Inkarojit, 2018). The atmosphere is a factor of satisfaction that influences guest behaviour and impacts consumption. Ambience consists of six elements, namely: aesthetics, illumination, restaurant ambience, restaurant appearance, tableware and employees’ appearance (Jeong & Jang, 2011). Restaurant appearance influences choices and the atmosphere may at times be essential in creating the guests’ experience that surpasses their expectations (Basri, Ahmad, Anaur, & Ismail, 2016). One should point out that the destination allure plays a substantial role in choosing a restaurant. Destination differentiation in times of similar offers is crucial for attracting tourists (Popesku, 2016).
Food Quality Food quality can be perceived through a variety of elements, such as: food presentation, diverse menus, the presence of healthy ingredients in meals, the taste of food, freshness, and temperature (Jeong & Jang, 2011). Cooks are very important for food quality in restaurants. Cooks are leaders; they possess creative abilities, administrative, and technical skills (Peng, Annie, & Hung, 2017). On the other hand, the appeal of the dish name on the restaurant menu affects its sale. Creatively thought-out names of certain food items can increase their sale by 27% (Kim, Youn, & Rao, 2017). Food is linked to a nation’s culture, and food quality is central for gastro-tourism. Food represents the culture of the destination, and is a part of the tourism offer (Ellis, Park, Kim, & Yeoman, 2018). For foreign tourists, food is not just a means for satisfying their essential physiological needs, but a medium for experiencing other cultures. Through food, the hosts convey to the tourists the identity of the destination and bring its lifestyle closer to the guests (Lai, Khoo-Lattimore, & Ying, 2018). Foreign tourists are only interested in authentic food, made from local ingredients. In addition to home-made food, which is made mostly from local ingredients, there are also national home-made dishes – such as, for example, Leskovac barbecue, and international dishes – such as Viennese steak, Parisian steak, etc. The optimal ratio of meals offered in restaurants is 30% of home-made food, 30% of national food while the rest is international food (Kalenjuk, Tešanović, Gagić, Erdelji, & Banjac, 2015). On the other hand, managers should pay attention to what the restaurant offers, and should constantly introduce innovations in food supply such as, for example, meals containing whole grains, meals with a lower fat content, vegetarian meal options, etc. (Kim, Park, Kim, & Ryu, 2013). 22
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Service Quality
The positioning of hospitality establishments on the market largely depends on the quality of service. In order for tourism companies to be consistently highly positioned, it is necessary to constantly work on raising the quality of services, aiming to satisfy guests (Čerović, 2019). The quality of service is directly related to the satisfaction of employees, who should be motivated to work, since the employee-guest interaction greatly contributes to customer satisfaction (Alhelalat, Habiballah, & Twaissi, 2017). Professional staff is imperative for every restaurant. A single sommelier in a restaurant greatly multiplies the demand for wine (15-25%). His/her knowledge of combining food and wine also influences the sale of meals in a restaurant (Gagić, 2016). As mentioned before, an experienced and educated staff is necessary to provide quality service and achieve guest satisfaction. Predicated on quality staff, which will provide high quality service and achieve guest satisfaction, restaurants maintain their competitive position (Kim & Jang, 2020).
Value for Money A lot of attention needs to be paid to pricing in restaurants, because price is a powerful weapon in attracting customers and increasing sales (Yim, Lee, & Kim, 2014). The period of the Internet development brings changes in the price formation in the hospitality industry; the method differs significantly from the time when information technologies were not represented to this extent. Internet sales are also emerging in the restaurant industry, and new sales channels are being created (Lee, Hallak, & Sardeshmukh, 2019). The price formation in restaurants is influenced by several factors, one of which is the season (Čerović, Spasić, & Radović, 2020). During the high season, restaurant prices are higher, while off-season prices are more affordable. Lowering the prices leads to increased revenue, and vice versa (Ubavić, 2012). Price is very often an important indicator of quality and expected satisfaction, because many studies have shown that price can be used to assess quality, mainly since a higher price for a particular product or service indicates higher quality and vice versa (Kiatkawsin & Han, 2019).
METHODOLOGY The research section of this paper is based on the comments of domestic and foreign tourists on the Trip Advisor website, written in regard to service satisfaction in the following cities of the Western Balkans: Belgrade, Zagreb, Sarajevo, Podgorica, Ljubljana and Skopje – capital cities of the former constituent republics of the common state of SFRY which, for a long time, was a common framework for all forms of development, including tourism. There was a total of 3,467 restaurants on the website; out of that number, 614 restaurants had no ratings, i.e., they didn’t garner a single comment. Thus, the remaining 2,853 restaurants that garnered reviews by domestic or foreign guests were analysed. A total of 123,298 reviews/comments were gathered from said restaurants. The main goal of this research is to examine and compare the levels of expressed satisfaction of local and foreign guests in the cities of the region and to investigate the impact of time on this relationship. In order to obtain somewhat more general conclusions about the influence of different examined variables on guest satisfaction, grades (1‒5) were divided into two categories: poorly satisfied guests (grades 1, 2 and 3) and satisfied guests (grades 4 and 5). These two categories were used as a dependent variable, which was modelled by the logistic regression method through the influence of a city, type of guest, and the year when the review was written, as independent variables. R statistical programming 23
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ŽIVADINOVIĆ. B. TOURIST SATISFACTION WITH QUALITY OF SERVICE, FOOD, ATMOSPHERE, AND VALUE FOR MONEY IN RESTAURANTS OF MAJOR CITIES OF THE WESTERN BALKANS
language was used to perform logistic regression (Core Team, R., 2018). Initially, a model was defined that included all mentioned independent variables and their interactions, so elements were gradually excluded from that model, but only if the models obtained in this way were not significantly different from the initial one based on the likelihood-ratio test. When the simplest model was obtained, which according to the likelihood-ratio test does not differ significantly from the initial one, it was used to make multiple comparisons between different categories in order to show which combinations of independent variables significantly differently affect the expressed guest satisfaction.
RESEARCH RESULTS In order to perform the category analysis of the written comments according to the user’s place of residence, all comments not associated with a particular location were removed before analysis. This resulted in 85,983 unique ratings, which were associated with location and date of the comment. Based on users’ place of residence, the comments were divided into two groups: comments by foreign and comments by domestic restaurant visitors.
Number of restaurants included in the study
Graph 1. Number of Restaurants Included in the Study, Per City
Belgrade Ljubljana Podgorica
Sarajevo
city
Skopje
Zagreb
Based on Graph 1, one can notice that Belgrade, with over 1,000 restaurants, is far ahead of other cities in terms of representation on Trip Advisor website, while Podgorica with 125 restaurants, is the least represented.
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Number of comments
Graph 2. Total Number of Restaurant Reviews
Type of guest local visitors foreign visitors
Belgrade
Ljubljana
Podgorica
Sarajevo
city
Skopje
Zagreb
The data from Graph 2 show the total number of ratings on the Trip Advisor website, depending on the city and the type of guest who rated the establishment. Only the ratings with a defined location were summarized. Although Belgrade is the most represented on the Trip Advisor website in terms of the number of restaurants, the number of unique reviews that guests left on the site is fairly equal to Zagreb and Ljubljana (Graph 2). The reason for this is the far greater number of comments per restaurant for these two cities. Thus, e.g., Ljubljana is the only city that has a restaurant with over 1,000 reviews from foreign tourists, while the most rated restaurants in Belgrade have only 500-600 reviews by foreign tourists. Ljubljana and Zagreb are also in the lead in terms of the number of comments from local visitors. The following graph shows the change in the number of guests’ comments by city from year to year: Graph 3. The Structure of Restaurant Evaluation Frequency in the Region
Number of comments
Belgrade
Type of guest
local visitors foreign visitors
year
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ŽIVADINOVIĆ. B. TOURIST SATISFACTION WITH QUALITY OF SERVICE, FOOD, ATMOSPHERE, AND VALUE FOR MONEY IN RESTAURANTS OF MAJOR CITIES OF THE WESTERN BALKANS
Graph 3 indicates the increase in the number of reviews written by Trip Advisor users over the years, depending on the city and the guest type. Only the ratings with defined location were summarized. The upward trend in the number of comments on the Trip Advisor website (Graph 3) over the years is most likely caused by the growing popularity of the site. One can notice that, prior to 2013 there is a relatively small number of comments, and that the number of written reviews has an exponential growth up to 2016, when it reaches a plateau. Later on, 2018 shows a certain decline, which is probably due to the fact that the collected data did not cover the entire calendar year (data were collected in mid-November 2018). Since guest satisfaction was surveyed on an annual basis, for statistical analysis we considered a five-year period – from 2013 to 2017. The years prior to 2013, were omitted due to a relatively small sample, i.e., a small number of comments for most of the surveyed cities. The year 2018 was omitted because the collected data did not cover the whole year. Therefore, the total number of reviews in the analysis was reduced to 65,989 unique ratings. Based on the data summary (Graph 4), one can conclude that Sarajevo and Belgrade are in the lead in terms of number of the most favourable ratings (5), assigned by both foreign and domestic tourists, while, e.g., Skopje, shows the biggest discrepancy between domestic and foreign tourists’ ratings. Local visitors are the most dissatisfied in Ljubljana, while foreign tourists are the most dissatisfied in Skopje. Graph 4. Percentage Share of Ratings (1‒5) by City and Guest Type local visitors local visitors
Zagreb Skopje Sarajevo Podgorica Ljubljana Rating
city
Belgrade foreign localvisitors visitors
Zagreb Skopje Sarajevo Podgorica Ljubljana Belgrade
The numbers next to each row indicate the total number of ratings making up the given category combination. Only the ratings for the period 2013-2017 are summarized.
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Graph 5. Percentage Share of Ratings (1‒5) by City, Year and Guest Type local Local visitors visitors
local Foreignvisitors visitors
year
Rating
The numbers next to each row indicate the total number of ratings making up the given category combination. The impact of the year on the ratio of ratings by cities is shown in Graph 5. The analysis of this graph clearly indicates a high number of negative comments from domestic visitors in 2013 in Podgorica, but this observation should be taken incredulously since it is based on a very small sample (only 19 comments). When you look at the trends of ratings for other cities over time, you can see that the share of the most favourable ratings by foreign visitors increases slightly over the years for all cities except Skopje, which also has the lowest share of the most favourable ratings compared to other analysed ratings. The share of the least favourable ratings (1 and 2) given by foreign visitors is quite steady over time, and only the example of Podgorica shows a slight downward trend in the share of these ratings over time. The situation is similar with the ratings of local visitors, i.e., these ratings are quite stable over time; only in the case of Podgorica is it possible to notice an extremely large variability over the years, which should, above all, be attributed to the small sample of comments written about restaurants in this city. In order to examine the influence of various factors on the trends in achieving restaurant guests’ satisfaction, logistic regression was used. Since this method models relationships between two classes, guest ratings are summarized into two categories: poorly satisfied (rating 1, 2, and 3) and satisfied (rating 4 and 5). A comparison of different models of logistic regression with the likelihood-ratio test showed that the year has no statistically significant impact on expressed guest satisfaction, and that the simplest model, which includes all significant variables, contains city, guest type and city-guest interaction. Thus, the ratio of satisfied and poorly satisfied visitors depends on the city, the type of visitor, and their interaction. In order to gain better insight into the significant variables according to the logistic regression model, the data are summarized in Graph 6, similarly t 27
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Graph 6. Percentage Share of Poorly Satisfied (ratings 1, 2 and 3) and Satisfied (ratings 4 and 5) Guests visitors localLocalvisitors
Zagreb Skopje Sarajevo Podgorica Ljubljana
city
Belgrade
satisfaction satisfied
Foreign visitors local visitors
poorly satisfied
Zagreb Skopje Sarajevo Podgorica Ljubljana Belgrade
This representation summarizes the data according to significant variables in the logistic regression model. In order to determine which categories statistically differ significantly, multiple comparisons were conducted based on the logistic regression model between different types of visitors within each city. In addition, the satisfaction of domestic and foreign visitors in all cities was compared with the satisfaction expressed in Belgrade. Table 1. Multiple Comparisons Between Different Categories Based on the Model Obtained by Logistic Regression Model estimate (Log odds)
95% degree of reliability (lower)
95% degree of reliability (higher)
p-value
Significance
Ljubljana, locals – Belgrade, locals
-0.655
-0.797
-0.512
< 0.001
***
Podgorica, locals – Belgrade, locals
-0.487
-0.898
-0.077
0.008
***
Sarajevo, locals – Belgrade, locals
-0.038
-0.286
0.211
1
Skopje, locals – Belgrade, locals
-0.145
-0.410
0.120
0.75
Zagreb, locals – Belgrade, locals
-0.430
-0.564
-0.296
< 0.001
***
Ljubljana, foreigners – Belgrade, foreigners
-0.154
-0.258
-0.049
< 0.001
***
Podgorica, foreigners – Belgrade, foreigners
-0.391
-0.653
-0.130
< 0.001
***
Sarajevo, foreigners – Belgrade, foreigners
0.012
-0.130
0.155
1
Skopje, foreigners – Belgrade, foreigners
-0.563
-0.726
-0.400
< 0.001
***
Zagreb, foreigners – Belgrade, foreigners
-0.205
-0.311
-0.098
< 0.001
***
Belgrade, foreigners – Belgrade, locals
0.034
-0.096
0.165
0.998
Ljubljana, foreigners – Ljubljana, locals
0.535
0.415
0.656
< 0.001
Podgorica, foreigners – Podgorica, locals
0.130
-0.338
0.599
0.998
Sarajevo, foreigners – Sarajevo, locals
0.084
-0.171
0.340
0.99
Skopje, foreigners – Skopje, locals
-0.383
-0.666
-0.101
0.001
**
Zagreb, foreigners – Zagreb, locals
0.259
0.148
0.370
< 0.001
***
Comparison
28
***
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Based on the statistical processing of data using logistic regression, several conclusions can be drawn from the table. In terms of local visitors’ (residents of Serbia) satisfaction, Belgrade ranks first, relative to other capitals of former Yugoslavia. Local visitors of Belgrade restaurants are significantly more satisfied than local visitors of restaurants in Ljubljana, Podgorica, and Zagreb. Local visitors of restaurants in Skopje and Sarajevo are also satisfied in a similar percentage as local visitors of Belgrade restaurants, and there is no significant difference in the degree of satisfaction expressed. In terms of both local visitors and foreign tourists, Belgrade is in the lead when it comes to guest satisfaction. The only city where the satisfaction of foreign tourists is similar to that of Belgrade is Sarajevo, while in Belgrade foreign tourists are far more satisfied than in Ljubljana, Podgorica, Skopje, and Zagreb. In Belgrade, there is no significant difference between domestic and foreign restaurant visitors when it comes to the satisfaction expressed. Regarding the satisfaction of domestic and foreign visitors, there is no significant difference between the two in restaurants of Sarajevo and Podgorica, unlike Ljubljana and Zagreb – where foreign visitors are far more satisfied than domestic visitors, and Skopje – where domestic visitors are far more satisfied than the foreign. The complete analysis, based on the comments written by Trip Advisor website visitors, showed that Belgrade and Sarajevo are in the lead in terms of satisfaction when it comes to both domestic and foreign restaurant visitors, compared to other capitals of former Yugoslavia.
CONCLUSION Relevant literature and presented research have shown that the Trip Advisor website is often visited and used in the restaurant industry, both for reading and for writing reviews (comments) about restaurants around the world. By reading the comments, domestic and foreign visitors create an image and assess the level of satisfaction that a visit to a selected hospitality establishment will potentially provide. By writing personal impressions about the visited restaurants, the guests spread negative or positive publicity/electronic "oral" publicity (eWOM). The data analysis (reviews from the Trip Advisor website) showed that restaurants in Belgrade and Sarajevo are in the lead in terms of the expressed satisfaction of both domestic and foreign visitors when competing with restaurants in the remaining selected cities. This state of affairs certainly goes to the advantage of Belgrade and Sarajevo and is a positive stimulus to these cities, unlike in Ljubljana, Zagreb, Skopje, and Podgorica, where foreign and domestic restaurant visitors showed less satisfaction. Such results are of great importance for restaurant managers in cities that were subject to analysis, and often such research and written comments from specialised websites and social networks are guidelines for taking corrective or more aggressive measures in the restaurant business. This one, and similar pieces of research, may be viewed as guidelines to the highest social instances in determining the steps within the government tourism strategies, of which restaurants are an integral part. Future users of restaurant services, assessing the comments for good or bad aspects of them, will be able to see all aspects of satisfaction offered by a potential specific chosen hospitality establishment more correctly. The current state of affairs is in favour of Belgrade (when it comes to comments from the Trip Advisor website), and that is an indicator to the employees in the restaurant industry in the Serbian capital that, according to the results of this research, they are doing a fine job.
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ZADOVOLJSTVO TURISTA KVALITETOM USLUGE, HRANOM, ATMOSFEROM I ODNOSOM CENA-KVALITET U RESTORANIMA VELIKIH GRADOVA NA ZAPADNOM BALKANU
Rezime: Važan faktor u modernom turizmu je osećaj zadovoljstva gostiju restorana. Motivisan željom da se taj osećaj kvantifikuje i učini operativnim elementom preporuka za dalji razvoj jednog segmenta restoranskog turizma, ovaj rad proučava izraženo zadovoljstvo stranih turista u restoranima odabranih gradova na Zapadnom Balkanu. Drugi cilj je da se pokaže do koje mere su lokalni posetioci i domaći turisti zadovoljni uslugom koja se pruža u analiziranim ugostiteljskim objektima. Komentari koji su sastavni deo istraživačkog dela analize ovog rada prikupljeni su sa jednog od najpopularnijih sajtova iz oblasti restoranske industrije ‒ Trip Advisor.
Ključne reči: restorani, strani turisti, lokalni posetioci, Trip Advisor, zadovoljstvo turista.
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EJAE 2020, 17(2): 34 - 53 ISSN 2406-2588 UDK: 336.02(674.3) DOI: 10.5937/EJAE17-27027 Original paper/Originalni naučni rad
AN EXPLORATORY ANALYSIS OF FINANCIAL INCLUSION IN CHAD Mahamat Ibrahim Ahmat Tidjani* Unit of Economics and Management, Félix Houphouët-Boigny University, Abidjan/Côted’Ivoire, Ivory Coast
Abstract: This paper aims to explore the state of financial inclusion in Chad. Adopting a Multiple Correspondence Analysis (MCA) on a sample of 1000 individuals from the Global Findex (2017), the study measured the inclusiveness of financial systems in Chad through a Financial Inclusion Index (FII). Furthermore, it assessed the distribution of the FII using the factor decomposition of the Gini coefficient. The findings showed that the average FII was low, 24.89%, and it varied between 7.43% and 60.35%. Financial institution account, deposit, withdrawal, and debit card ownership were the most influential indicators of financial inclusion in Chad. Moreover, the paper revealed that, despite its low level, financial inclusion was not smoothly distributed among the Chadian population (Gini coefficient of 0.196). The analysis of the financial inclusion inequality profile showed that there was a persistent financial inclusion gender gap in Chad, exacerbated by discriminations in education and income. Thus, policy interventions should target the provision of formal accounts, a reduction of costs of financial services (withdrawal and debit cards), and promoting formal savings by developing adequate savings products, to foster financial inclusion in Chad. Furthermore, these policies should be gender-responsive while considering its interaction with education and income.
Article info: Received: Jun 15, 2020 Correction: July 31, 2020 Accepted: September 14, 2020
Keywords: Financial inclusion index, multiple correspondence analysis, inequality decomposition, Chad.
INTRODUCTION 1.1 Background Several years of politico-military conflicts and structural problems, such as endemic corruption and weak institutions, have impeded efforts to pave the road for development in Chad. The growth prospect of Chad is characterized by two episodes during the last two decades prior to 2015. An average growth of about 3% occurred before 2003 and 9% occurred after the oil exploitation in 2003 (MEPD, 2017) . However, that prosperity has not impacted the livelihood of poor people that much. 34
*E-mail: ahmattidja@yahoo.com
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As a result, the income gap between the rich and the poor, and the interregional inequality has widened (Gadom et al., 2018; Gadom et al., 2019). Although poverty declined by 8 percentage points between 2003 and 2011, the latest data on poverty from 2011 indicate that 47% of the population still lives below the national poverty line (MEPD, 2017)1. The recent oil price shocks, security threats to the region, and the Covid-19 outbreak add further vulnerabilities, and might increase the incidence and severity of poverty. The gender-disaggregated human development index (HDI) shows that women fall behind men in human development in Chad and that their deprivations are more pronounced in education and standard of living (UNDP, 2018). Access to finance allows the poor to invest in their education, health, start-up small businesses, or sustain existing ones, and manage their financial risks, thus boosting shared prosperity (Abor et al., 2018; Coulibaly & Yogo, 2020; Dixit & Ghosh, 2013; Kuada, 2019). Financial inclusion has become a global development agenda, the World Bank’s Universal Financial Access by 2020 is an example. In this respect, Chad has implemented three national strategies to promote access and usage of formal finance by the poor and women specifically in remote areas. These are the SNMF (Stratégie Nationale de la Microfinance) in 2009, the PAFIT (Programme d’Appui à la Finance Inclusive au Tchad) for the period 2010-2014, and the PADLFIT (Programme National d’Appui au Développement Local et à la Finance Inclusive au Tchad) for the period 2017-2021. However, the financial system in Chad remains one of the least inclusive in the region. The Global Findex report indicates that only 22% of Chadians had access to formal financial services in 2017. Access to formal financial services is limited or simply inexistent in some remote areas. Only about 22% of adults reported owning an account in 2017 (figure 1, panel a). Formal account ownership was driven by mobile money account (15.23%) against 8.73% for the financial institutions account (Demirguc-Kunt et al., 2018). The account penetration rate in Chad is low compared to the average of peer sub-Saharan African (SSA) low-income countries (32%). Panel (b) of Figure 1 reveals that Chad falls short by far from SSA in other dimensions of financial inclusion, including savings and credit. The propensity to save in Chad was low, only 3% of adults saved at a formal financial institution in 2017, against 15% in SSA. Similarly, only 3% of Chadians borrowed from a formal financial institution whereas, on average, 8% of adults did so in SSA. The extant literature provides evidence that gender, education, income, age, residence area, work status, and trust in financial institutions are some of the individual level determinants of financial inclusion (Fungáčová & Weill, 2015; Soumaré et al., 2016; Zins & Weill, 2016), whereas population density, per capita GDP, employment, age dependency ratio, and internet usage are their macro level counterparts (Allen et al., 2014; Park & Mercado Jr, 2018; Sha'ban et al., 2019; Uddin et al., 2017). Figure (1): Financial Inclusion Indicators in SSA in 2017
Source: author, using the Global Findex (2017) data. 1 Ministère de l’Economie et de la Planification du Développement (MEPD).
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1.2 Statement of the Problem Financial inclusion plays a vital role in economic development. It affects the income distribution, and helps close the gender gap in economic opportunities, leading to the empowerment of women (De Haan & Sturm, 2017; Weber & Ahmad, 2014). Despite its vital role, access to finance is lacking in Chad, like in many other developing countries. Account ownership in Chad (22%) is also relatively low, compared to 43% and 35%, respectively, in SSA and low-income countries (Demirguc-Kunt et al., 2018). Furthermore, the global Findex (2017) dataset reveals double-digit gaps in account ownership by gender, income, and education in Chad. These gaps stand, respectively, at 18, 20, and 40 percentage points for gender, income, and education level. In addition, formal financial services are concentrated in urban areas, and quasi-inexistent in remote areas. By relaxing the liquidity constraints of the previously excluded, financial inclusion promotes economic activities (Inoue & Hamori, 2016), and is a vital tool for the sustainability of development (Kuada, 2019). However, financial inclusion in Chad has attracted less attention from academic researchers. Few crosscountry studies include Chad in their analysis. Therefore, what is the level of financial inclusion in Chad? And how is financial inclusion distributed among the Chadian population? To the best of my knowledge, no study has investigated the financial inclusion in the specific context of Chad in-depth, nor provided answers to such questions. This paper aims to explore the state of financial inclusion in Chad. More specifically, it proposes to measure the level of financial inclusion in Chad by constructing a multidimensional financial inclusion index and assess its distribution among the Chadian population according to their characteristics, gender, education, and income level. The remainder of the paper is structured as follows. Section II reviews the relevant literature. Section III describes the data and the method of the analysis. Section IV presents the empirical results. Section V concludes, and provides some policy implications.
LITERATURE REVIEW The classical literature of finance has mainly focused on financial development in the development of the functioning of financial markets and intermediaries in terms of size, efficiency, and stability. The literature has established that such development affects a country’s economic growth, its level of poverty, and income inequality (Aka, 2010; Ayyagari et al., 2020; Demetriades & James, 2011; Ibrahim & Alagidede, 2018; Jauch & Watzka, 2016; Kaidi & Mensi, 2019). However, following the recent observation that even a well-developed financial system can still be exclusive, the focus is shifted to financial inclusion, which has become at the focus of international debates. Financial inclusion is defined as the process of securing access to all segments of society to formal financial services, which must be useful, affordable, and adequate to their needs. This paper develops the consumer choice theory and the new Keynesian theory as theoretical frameworks for financial inclusion. The classical assumptions of the consumer choice theory apply. Individuals are rational, self-interested, and interact in a competitive market. Financial services are considered as other normal goods that consumers can purchase. Therefore, at equilibrium, for a given price, the demand for financial services is equal to the supply. At this point, financial exclusion that may arise is a voluntary one. However, in practice, such an equilibrium may not necessarily exist because of several reasons including, i) the absence of supply tough demand exists in the market, which is a common situation in most rural areas in developing countries; ii) the presence of price barriers that prevent the intersection between the supply and the demand; and iii) and the absence of demand for financial services, reflecting voluntary exclusion. 36
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Under the New-Keynesian theory, principal-agent problems, moral hazard, and adverse selection distort the well-functioning of financial markets, resulting in financial exclusion, even in a competitive market. High-interest rates tend to attract riskier borrowers (adverse selection), and affect their incentive for repayment (moral hazard). Thus, banks ration credits, because they cannot unambiguously distinguish riskier borrowers from creditworthy ones. Above the optimal level of interest rate, banks deny credit, even if the potential borrower is willing to pay a high-interest rate. Thus, creditworthy borrowers can be denied credit, leading to financial exclusion in a competitive market. Empirically, Honohan (2008) measured households’ access to finance using separate indicators. Sarma and Pais (2011), on the other hand, constructed a financial inclusion index, following closely the methodology of the UNDP to overcome the problems of cross-country comparability that arise when using separate indicators of financial inclusion. However, their index assumes perfect substitutability between the dimensions, and subjectively assigns weights to financial inclusion dimensions. Cámara and Tuesta (2014) employed a multivariate technique, a two-stage Principal component Analysis (PCA), to endogenously generate the weights of their index, thus overcoming the problems of perfect substitutability and subjectivity in Sarma and Pais’ index. Other studies investigate the impact and determinants of financial inclusion. In a naturel experiment in Mexico, Bruhn and Love (2014) explored the impact of improved access to finance through branch expansion on the poor. They concluded that financial inclusion reduced poverty in Mexico, and the impact was manifested through labour market participation. A study by Swamy (2014) assessed the gendered impact of access to finance on households poverty in India. The study revealed that finance inclusion boosted the income of the poor in India, and the impact was more pronounced for women than men. Churchill and Marisetty (2020) confirmed the poverty reduction effects of financial inclusion on a sample of 45,000 Indian households. Soumaré et al. (2016) used demand-side data to assess the determinants of financial inclusion in Central and West African countries. Their study identified income, gender, education, residence area, age, employment status, household size, marital status, and trust in the financial system as determinants of financial inclusion. On household data from Nigeria, Dimova and Adebowale (2018) investigated the impact improved access to finance on households’ welfare and inequality. The findings indicated that, though financial inclusion improved households’ welfare, it increased inter-household inequalities. Recent findings by Adegbite and Machethe (2020) have pointed to the persistence of the gender gap in financial inclusion among small farmers in Nigeria, with negative effects on several sustainable development outcomes. This brief literature shows that there is no country-specific study that has investigated in-depth the financial inclusion in Chad. Thus, the present paper seeks to fill this gap by exploring the state of financial inclusion in Chad.
METHODOLOGY 3.1 Data Source Data used in this paper were from the World Bank Global Findex (2017). The target population was civilian, non-institutionalized population above the age of 15. The survey was conducted by Gallup INC, in association with the Gallup World Pool on 144 economies around the globe. For each economy, a national representative sample of at least 1,000 individuals was drawn using a simple or double stratification technique. The survey was conducted using telephone interviewing or face to face in countries where telephone coverage was less than 80%. The dataset contains financial inclusion indicators and individual characteristics. 37
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3.2 Construction of the Financial Inclusion Index This paper used a multivariate technique, the Multiple Correspondence Analysis (MCA), to construct the financial inclusion index. The MCA was preferred to PCA because of its ability to accommodate both categorical and quantitative data, unlike PCA (Kassambara, 2017). The MCA identifies similarities/ dissimilarities between individuals (in rows) as well as the relationships between variable categories (in columns) by using a contingency table. It locates the n individuals in the dataset as a cloud point in a space of dimension m (the number of variable categories). Each individual has a Coordinate (Profile) and a Mass representing their weights. The space, for which an average weight can be computed, uses chi-squared metrics to measure the distance between individuals. It can be represented by several Axes (dimensions), each associated with a relative Inertia (eigenvalues). The Total Inertia, which is the total variance explained by the axes, is computed as a weighted sum of the distances between the average cloud weight and the points located in that cloud. Thus, the MCA assigns endogenous weights to the variable categories and produces row scores (individuals scores). These individual scores are used in the construction of the Financial Inclusion Index (FII) following Minvielle and BRY (2003). The aggregation formulas are given by the following equations (1) and (2):
1 FII it = K
FII i
K
Jk
∑∑W k =1 jk
∑ =
p t
k k jk i , jk
I
λt * FII it
∑
p t
λt
(1)
(2)
Equation (1) computes the sub-indices for the (t) dimensions retained. Equation (2) aggregates the sub-indices from the equation (1) to form the FII. K is the total number of variables in the analysis;
W jkk is the normalized score of the jth category on each of the retained axes; I ik, jk an indicator variable
taking 1 if individual i chooses category jk and 0 otherwise. In equation (2), p is the total number of the axes retained for the analysis and λt their respective eigenvalues.
3.3 Inequality in Financial Inclusion The Gini and concentration coefficients are used to compute the financial inclusion inequality in Chad. Following Yitzhaki (1983), the covariance-based definition of the generalized Gini and concentration coefficients can, respectively, be expressed as follows:
Z GINI ( Z ) = −a cov( , (1 − H ( Z )) a −1 ) µ (Z )
(3)
Z −a cov( ,(1 − G ( Z ))a −1 ) CONC ( Z , Y , a) = µ (Z )
(4)
where Z and Y are two random variables; µ ( Z ) , the mean of Z, H(Z), and G(Y) are the cumulative distribution functions, respectively. The parameter a is an inequality aversion coefficient. 38
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The standard Gini and concentration coefficients are obtained for a=2. The standard Gini coefficient is a measure of inequality of a distribution, whereas the concentration coefficient measures how a random variable Z is concentrated on observations with high ranks in a random variable Y. Following Lerman and Yitzhaki (1985), the natural decomposition of the generalized Gini coefficient can be expressed as follows:
µ (Z j ) CONC ( Z j , Z, a) j =1 µ ( Z ) J
GINI ( Z , a) = ∑
(5)
where j=1…. J represents the categories of individual characteristics, and µ ( Z ) , the mean of the jth category of the random variable Z, the remaining parameters are as defined above. The decomposition of the financial inclusion inequality helps to identify the contribution of each characteristic to the total inequality, as well as the between and within-group inequalities. The within-group inequality captures inequality due to the variability of financial inclusion within each group, whereas the between-group inequality measures inequality in financial inclusion across the groups. j
EMPIRICAL RESULTS 4.1 Descriptive Statistics To construct the FII, 23 indicators (binary) were chosen based on their relevance to measuring financial inclusion, and data availability. To analyze the financial inclusion inequality profile, three categorical variables (gender, education, and income) were used for the decomposition. Variables description and summary statistics are provided in Tables (A1) and (A2), respectively, in the appendix. In general, access to and usage of formal financial services was low in Chad in 2017. Financial inclusion was driven by mobile money (15%) penetration, which exceeded by 6 percentage points formal account ownership (9%) in Chad. The use of digital financial instruments, such as debit cards, credit cards, and online transactions, was very low in Chad. Only about 3% of adults owned a debit/credit card and fewer than 2% of adults made online transactions in the year prior to 2017. Furthermore, savings and borrowing were low, with fewer than 3% of adults reported having saved or borrowed in or from formal financial institutions. However, in terms of financial resilience, 37% answered yes when asked whether they could come up with 1/20 of the GNI within the next month. With respect to socio-economic characteristics, there was a near gender balance in the sampled population, and the population was nearly equally distributed in the income quintiles. However, the majority of the population had a lower level of education, 87% of which having completed primary education, or not having finished it at all.
4.2 Computation of the Financial Inclusion Index (FII) Table (1) below displays the financial inclusion dimensions and the relative inertias from the MCA estimation. A total of 23 dimensions were extracted, where the first ones summarized the largest amount of variability in the data. The extracted dimensions represented a total inertia of 0.23, and is useful in observing the inertia ratio. The amount of variation in the data accounted for by each dimension is quantified usin the corresponding principal inertia. Thus, the first four dimensions summarized, respectively, 63%, 7%, 5%, and 5%, meaning that these dimensions jointly explained 81% of the total available information in the data. The other 19 dimensions jointly explained the remaining 19%. 39
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Principal inertias presented in Table (1) were adjusted for the poor fit of MCA that inflates the total inertia and underestimates the principal inertias. A proposition is, therefore, made to consider only the axes that have principal inertias greater than the inverse of the number of active variables. Based on this principal, in Table (1), only the first dimension with a principal inertia of 0.15 satisfied this criteria, given that the inverse of the number of variables is 1/23=0.04. Greenacre (2007) proposes another adjustment method based on the average inertia of the off-diagonal elements of the Burt matrix, the Joint Correspondence Analysis (JCA). In this paper, the poor fit of the MCA was corrected by using the JCA that iteratively adjusts the average inertia of the off-diagonal elements of the Burt matrix. The scree plot test is generally used to assess the number of dimensions to retain for further analysis. The test is based on a graphical representation of the principal inertias against the different dimensions obtained from the MCA estimation. According to this test, only the dimensions that are visually located before the shift in the slope of the graphical representation are retained. However, because the purpose of this study was not to reduce the dimensionality of the data but rather to construct a financial inclusion index, all extracted dimensions were used to capture the total available information in the data. Table (1): Dimensions and Inertias from MCA Estimation Dimension
Principal inertia
Percent
dim 1
.1455146
63.44
63.44
dim 2
.0159005
6.93
70.37
dim 3
.0122316
5.33
75.70
dim 4
.0111704
4.87
80.57
dim 5
.0077102
3.36
83.93
dim 6
.0069601
3.03
86.96
dim 7
.0053047
2.31
89.28
dim 8
.0045549
1.99
91.26
dim 9
.0030409
1.33
92.59
dim 10
.0029307
1.28
93.87
dim 11
.0021699
0.95
94.81
dim 12
.001999
0.87
95.68
dim 13
.0019088
0.83
96.52
dim 14
.0015527
0.68
97.19
dim 15
.0013449
0.59
97.78
dim 16
.0012113
0.53
98.31
dim 17
.0010987
0.48
98.79
dim 18
.0007588
0.33
99.12
dim 19
.0005674
0.25
99.36
dim 20
.000519
0.23
99.59
dim 21
.0003368
0.15
99.74
dim 22
.0002154
0.09
99.83
dim 23
.0001568
0.07
99.90
Total
.2293895
100.00
Source: Author’s computation using the Global Findex (2017) dataset. 40
Cumul percent
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The financial inclusion index (FII) constructed following the procedure described in subsection (3.2) is a measure of the intensity of the use of formal financial services in Chad. Thus, a higher/lower score was attributed to individuals who used more/less formal financial services. For ease of interpretation and presentation, the index was normalized using the min-max scaling and multiplied by 100, making the computed index vary between 0 and 100. To reduce the influence of outliers in the data, the index was winsorized at 1% of the extreme values. Table (2) below presents the summary statistics of the computed FII. Financial inclusion level in Chad in 2017 was low, with an average score of 24.89%. Furthermore, access to and use of formal financial services in Chad varied widely between the population, as shown by the disparity between the minimum (7.43%) and maximum (60.35%) values of the FII. Table (2): Summary Statistics of the Computed Financial Inclusion Index (FII) Variable
Obs
Mean
Std. Dev.
Min
Max
FII
1000
24.888
9.305
7.427
60.346
Source: Author’s computation using the Global Findex (2017) dataset.
Table (A3) in the appendix reports the details of the MCA estimation. The first three columns of the table display, respectively, the mass (weights), overall quality, and the relative inertia of each variable category. The mass represents the weights endogenously generated by the MCA, the overall quality indicates the quality of the fit, and the relative inertia measures how much of the variation in the data is accounted for by the corresponding variable category. The other two blocs of columns display the coordinates (profile or position), squared correlation, and contributions of variable categories to the construction of dimensions (1) and (2), respectively. It is for the sake of brevity that only the two-first dimensions were presented. Since the first dimension explains as much variance as possible in the data, I comment on results only pertaining to this dimension, and specifically column (7) of the table. Column (7) shows the contribution, which is the share of the corresponding variable category to the construction of the first dimension. It is found that account ownership at financial institutions “Account_fin” (7%), deposit “Fin9” (6%), withdrawal “Fin10” (6%), and debit card “Fin2” (5%) were the most important indicators of financial inclusion in Chad in terms of their relative contribution to the construction of the first dimension. These are the basic functions performed by the traditional banking system, representing the access (Account_fin) and usage (Fin9; Fin10; Fin2) dimensions. Account ownership serves as an entry point to the formal financial system, thus justifying its relatively high contribution over the other indicators. Deposit and withdrawal are the classical transactions performed by the banking system. The importance of digital financial instrument, debit card ownership, in the context of Chad can be explained by the bancarization policy of salaries of civil servants in 2009 by the government. However, a striking observation is that mobile money accounted only for 0.8% to the construction of dimension (1), even though mobile money account (15%) exceeded financial institutions account ownership (9%) in Chad in 2017. However, this result can be explained by the institutional settings of mobile money services in Chad. Mobile money services providers operate through financial institutions (banks), and their activities are restricted to only domestic remittances (transfers) of a limited amount of money, unlike in other countries like Kenya, where MPESA provides additional services savings and credits.
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4.3 Inequality in Access to Finance in Chad Table (3) displays inequality in financial inclusion and its decomposition by individual characteristics. Despite its low level (24.89%), access to and use of financial services in Chad were not smoothly distributed among the population, as indicated by the Gini of financial inclusion (0.196). The Gini coefficient was broken down according to individual characteristics, including gender, education, and income, to draw the profile of financial inclusion inequality in Chad. This exercise helped identifying the contribution of each characteristic to the total inequality, as well as the within and between-group inequalities. Table (3) indicates the presence of inequality in financial inclusion inside and across the groups, since the within/between inequalities were different zero. For the gender dimension, financial inclusion distribution was more highly skewed amongst women than amongst men, as indicated by their respective Gini coefficients, 0.233 and 0.164. This result was supported by the non-zero value of the within-group gender inequality (0.064), which accounted for 33% of the overall financial inclusion inequality. There was also evidence of a gender gap in financial inclusion in Chad, indicated by the between-group inequality different from zero (0.005). This means that men were more likely to gain access to and use formal financial services in Chad than women. Such between-group inequality accounted for 3% of the total financial inclusion inequality in Chad. Table (3): Financial Inclusion Inequality Profile in Chad Group decomposition Gender
Gini
Within
Between
Within (%Gini)
Between (%Gini)
0.196
0.064
0.005
32.653
2.551
0.060
0.009
30.612
4.592
0.064
0.005
32.653
2.551
Female
0.233
Male
0.164
Education level
0.196
Primary
0.221
Secondary
0.102
Tertiary
0.132
Income level
0.196
Poorest 20%
0.219
Second 20%
0.180
Middle 20%
0.197
Fourth 20%
0.200
Richest 20%
0.168
Source: Author’s computation using the Global Findex (2017) dataset.
There was also evidence of a within-group education inequality of 0.060 amongst those who completed primary or less, secondary, and tertiary or more education. Moving from primary to secondary education drastically reduced within-group inequalities (from 0.22 to 0.10). However, within-group inequality increased amongst individuals who completed tertiary or more education. This result might be explained by other relevant specific characteristics. The within-education inequality contributed up 42
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to 31% to the construction of the total inequality. There was also evidence of a gap between the three sub-groups of education. The between inequality accounted for 5% of the total financial inclusion inequality in Chad. That was the highest value among the three values of between inequalities. Thus, education had the most un-equalizing effect (the most discriminating factor) on financial inclusion, consequently exacerbating financial inclusion inequality in Chad. This finding points to the importance of improving access to education in expanding financial inclusion in Chad. The analysis of the income level showed that the richest 20% and the poorest 20% had, respectively, the lowest and highest within-group inequalities. Financial inclusion distribution was not smooth in the other quintiles as well. It raised in some cases and decreased in others. The within-group income inequality accounted for 33% of the total financial inclusion inequality. Likewise, there was financial inclusion inequality between the income quintiles, which accounted for 3% of the total inequality. Both contributions are comparable to the contributions of the between and within-group gender inequality, respectively. However, the financial inclusion inequality profile presented in Table (3) may be incomplete because of the possible influence of the interactions between individual characteristics. Tables (4), and (5) below complement the analysis by considering the following interactions: gender-education, and gender-income, respectively. In Table (4), the within-group inequality showed that inequality in financial inclusion is higher among women with primary education or less than among those who completed secondary education. However, there were no women who completed tertiary or more education in the sample, corroborating the gender gap in education in Chad reported by (UNDP, 2018). Within the group of men, financial inclusion inequality was higher among those who had completed primary education or less and those who had completed tertiary education or more than among those with secondary education. The between-group inequality indicated that women who had completed primary education or less and those who had completed secondary education had less access to financial services than men within the corresponding categories of education. This result corroborates the presence of a gender gap in financial inclusion in Chad found in Table (3), and the gap is further exacerbated by gender discrimination in education. Table (4): Financial Inclusion Inequality Profile in Chad: Gender and Education Group (gender & education)
Gini
Female & completed primary or less
0.24259
Female & secondary
0.12258
Male & completed primary or less
0.19446
Male & secondary
0.09535
Male & completed tertiary or more
0.13159
Within
Between
0.057
0.012
Source: Author’s computation using the Global Findex (2017) dataset. Note: the group ‘female & completed tertiary’ is missing because there was no female individual with a tertiary education in the sample.
Table (5) presents the financial inclusion inequality profile considering the interaction between gender and income level. Once again, there were between and within-group inequalities, as indicated by their non-zero values, 0.009 and 0.06, respectively. In the within-group inequality, inequality was higher/lower among women belonging to the poorest 20%/richest 20%, respectively, evidencing that income exacerbates the financial inclusion gap among women in Chad. 43
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For the group of men, inequality was higher/lower for men belonging to the poorest 20%/second poorest 20%, indicating that the discriminating power of income in financial inclusion is lower among men. Based on the between-group inequality (0.009), the study shows that financial inclusion inequality is higher for women than men, and for a given level of income, women have a lower likelihood to access finance than men do in Chad. Thus, Tables (4) and (5) jointly show that there was a persistent gender gap in access to formal financial services in Chad and gender discrimination in education and income (UNDP, 2018), exacerbating inequality in access to and use of formal financial services. Overall, the findings of this study corroborate from another angle the importance of gender, education, and income in expanding financial inclusion found in studies by (Fungáčová & Weill, 2015; Soumaré et al., 2016; Zins & Weill, 2016). However, these studies have not investigated the interrelations among individual characteristics in explaining financial inclusion. Thus, the present study has the merit of drawing the profile of the specific target in each group and the possibility of interaction between the groups that can be used to expand financial inclusion in Chad. Table (5): Financial Inclusion Inequality Profile in Chad: Gender and Income Group (gender& income)
Gini
Female & Poorest 20%
0.26970
Female & Second 20%
0.21455
Female & Middle 20%
0.24356
Female & Fourth 20%
0.25421
Female & Richest 20%
0.17416
Male & Poorest 20%
0.17913
Male & Second 20%
0.14172
Male & Middle 20%
0.14504
Male & Fourth 20%
0.15870
Male & Richest 20%
0.15543
Within
Between
0.060
0.009
Source: Author’s computation using the Global Findex (2017) dataset.
CONCLUDING REMARKS AND POLICY IMPLICATIONS This paper explores the state of financial inclusion in Chad. Specifically, it measures the level of access to and use of formal financial services in Chad, and assesses its distribution amongst the Chadian population. To achieve these objectives, the paper mobilizes cross-sectional data from the World Bank, the Global Findex (2017), on a sample of 1,000 individuals. The paper employs a multivariate technique, the Multiple Correspondence Analysis (MCA) to construct a Financial Inclusion Index (FII). The findings indicate that the average level of financial inclusion (FII) in Chad is low, 24.89% and varies between 7.43% and 60.35%. Furthermore, financial institutions account ownership, deposit, withdrawal, and debit card ownership, respectively, are found to be the most important indicators of financial inclusion in Chad in terms of their contribution to the construction of the FII.
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The study further assesses the financial inclusion inequality profile, adopting factor decomposition of the Gini coefficient. The findings show that, in addition to its low level, financial inclusion is not equally distributed among the Chadian population. Although the Gini coefficient is low (0.196), there exists room for smooth financial inclusion distribution in Chad. The decomposition analysis provides evidence of both within and between-group financial inclusion inequality in gender, education, and income. Gender and income have the most un-equalizing effects on financial inclusion in Chad in terms of within-group contribution, whereas education has the most discriminating power in terms of between-group inequality. The within-group inequality shows that inequality is higher amongst women than amongst men, and amongst the poor (poorest 20%) than amongst the rich (richest 20%). The between-group inequality shows that inequality is higher for individuals with lower educational levels (primary or less) than for those with a higher one (tertiary or more). In further assessing the inequality profile, interaction terms between gender, education, and income are included. It has found a persistent gender gap in access to financial services in Chad, exacerbated by discriminations in education and income. Firstly, the paper reveals that policy interventions should target the provision of formal accounts, a reduction of costs of financial services, such as withdrawal and debit cards, and channelling savings to the formal financial system, in order to foster financial inclusion in Chad. This can be achieved with the provision of no-frills bank accounts, incentivizing banks to reach the poor by fuelling competition, and sensitizing people to save formally by developing appropriates mechanisms, such as the experiments of locked boxes for savings in the Philippines. Secondly, policy interventions that are responsive to gender, education, and income, which are found to exacerbate inequality in financial inclusion in Chad, need to be designed. Thus, policies aimed at closing the gender gap in education and promoting incomegenerating activities for women may foster and facilitate the distribution of financial inclusion in Chad. Improving women’s education increases their financial literacy, and boosts their income so that they can afford formal financial services. Future research needs to identify the specific constraints for women’s access to education and economic participation in order to adequately address these issues in Chad.
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Soumaré, I., Tchana Tchana, F., & Kengne, T. M. (2016). Analysis of the determinants of financial inclusion in Central and West Africa. Transnational Corporations Review, 8(4), 231-249. Swamy, V. (2014). Financial Inclusion, Gender Dimension, and Economic Impact on Poor Households. World development, 56, 1-15. Uddin, A., Chowdhury, M. A. F., & Islam, M. N. (2017). Determinants of financial inclusion in Bangladesh: Dynamic GMM & quantile regression approach. The Journal of Developing Areas, 51(2), 221-237. UNDP, H. (2018). Human development indices and indicators: Briefing note for countries on the 2018 Statistical Update. Weber, O., & Ahmad, A. (2014). Empowerment through microfinance: The relation between loan cycle and level of empowerment. World development, 62, 75-87. Yitzhaki, S. (1983). On an extension of the Gini inequality index. International economic review, 24(3), 617-628. Zins, A., & Weill, L. (2016). The determinants of financial inclusion in Africa. Review of Development Finance, 6(1), 46-57.
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APPENDIXES Table (A1): Description of variables used in the study Indicators
Code
Description
Financial institution account
accnt_fin
Do you have an account at a formal financial institution?
Account mobile money
accnt_mob Do you have an account in a mobile money services provider?
Debit Card
fin2
Do you, personally, have a debit card?
Credit Card
fin7
Do you, personally, have a credit card?
Loan for home,
fin19
Do you currently have a loan to purchase a home, apartment, or land?
Borrowing
fin22a
Have you borrowed money from a bank or another type of formal financial institution?
Saving
fin17a
Have you saved money using an account at a bank or another type of formal financial institution?
Online_bill_pay
fin14a
Have you used the Internet, whether on a mobile phone, a computer, or some other device, to make bill payments?
Bought online
fin14b
Have you used the Internet, whether on a mobile phone, a computer, or some other device, to buy something online?
Financial resilience
fin24
Is it possible to come up with [1/20 of GNI per capita in local currency] within the next month?
Own debit card use
fin4
have you used your OWN debit card DIRECTLY to make a purchase?
Mobile phone use for payments
fin5
did you ever use a MOBILE PHONE or the Internet to make a payment, to buy something, or to send money FROM your account at a bank or another type of financial institution?
Mobile phone use to check account
fin6
have you checked your account balance using a mobile phone or the Internet?
Credit card use
fin8
have you, personally, used your credit card?
Deposit
fin9
has money ever been DEPOSITED into your personal account(s)?
Withdrawal
fin10
has money ever been TAKEN OUT of your personal account(s)?
Remittance received: account
fin29a
Have you received money through a bank or another type of formal financial institution?
Remittance: mobile
fin29b
Have you received money through a mobile phone?
Sent money: account
fin27a
Have you sent money through a bank or another type of formal financial institution?
Sent money: mobile
fin27b
Have you sent money through a mobile phone?
Payment: account
fin31a
Have you used an account at a bank or another type of formal financial institution to directly make a payment?
Payment: mobile
fin31b
Have you used a mobile phone to make a payment?
Gender
gender
Is the gender of the respondent. It is a dummy variable that takes 1 if the respondent is male and 0 if female.
Education
educ
Is the educational level attained by the respondent. It is a categorical variable taking 1 if completed primary or less, 2 if completed secondary and 3 if tertiary or more.
Income quintile
Inc_quint
It is a categorical variable taking 1 if the respondent belongs to the poorest quintile; 2, if the second poorest; 3, if the middle; 4 if the fourth and 5 if the richest quintile.
Source: The Global Findex (2017). 48
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Table (A2): Descriptive statistics Variables Account fin no yes Account mob no yes Debit card no yes credit card no yes Bill payment online no yes Bought online no yes Saving no yes Loan no yes Borrowed no yes Fin resilience not possible possible Gender female Male Education complete secondary complete Income level poorest second 2 middle 2 fourth 2 richest
percentage
observation
91.25 8.753
853 147
84.77 15.23
824 176
96.67 3.332
881 56
96.79 3.215
899 39
98.74 1.261
956 24
98.26 1.742
947 23
97.36 2.638
926 47
96.76 3.235
941 49
97.13 2.872
939 40
62.88 37.12
549 429
51.28 48.72
363 637
87.29 12.16 .5444
662 323 15
19.84 20.16 19.85 20.15 20
161 185 172 191 291
Source: Author’s calculation on the Global Findex (2017).
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Table (A3): Detailed results of the MCA dimension_1 Categories
mass
overall quality
%inert
coord
sqcorr
dimension_2 contrib
coord
sqcorr
contrib
account no
0.031
0.924
0.013
0.258
0.881
0.015
-0.057
0.042
0.009
yes
0.012
0.924
0.034
-0.666
0.881
0.040
0.146
0.042
0.023
no
0.037
0.983
0.010
0.215
0.969
0.013
0.027
0.015
0.002
yes
0.006
0.983
0.056
-1.248
0.969
0.073
-0.154
0.015
0.014
no
0.036
0.816
0.003
0.083
0.419
0.002
-0.081
0.397
0.021
yes
0.008
0.816
0.015
-0.388
0.419
0.008
0.378
0.397
0.098
no
0.038
0.734
0.002
0.088
0.649
0.002
0.032
0.084
0.003
yes
0.002
0.931
0.042
-1.706
0.919
0.052
-0.196
0.012
0.008
sysmiss
0.003
0.260
0.009
0.289
0.139
0.002
-0.269
0.121
0.018
no
0.039
0.402
0.001
0.047
0.354
0.001
0.017
0.048
0.001
yes
0.002
0.770
0.029
-1.555
0.769
0.030
0.056
0.001
0.000
sysmiss
0.003
0.273
0.009
0.293
0.140
0.002
-0.286
0.134
0.020
no
0.041
0.835
0.001
0.057
0.810
0.001
0.010
0.025
0.000
yes
0.002
0.920
0.017
-1.157
0.917
0.021
-0.067
0.003
0.001
sysmiss
0.000
0.184
0.006
0.345
0.045
0.000
-0.603
0.138
0.014
account_fin
account_mob
fin2
fin7
fin19
fin22a no
0.041
0.782
0.001
0.049
0.755
0.001
0.009
0.027
0.000
yes
0.002
0.909
0.017
-1.271
0.909
0.021
-0.001
0.000
0.000
sysmiss
0.001
0.226
0.005
0.240
0.058
0.000
-0.409
0.168
0.014
no
0.040
0.801
0.001
0.066
0.732
0.001
0.020
0.070
0.001
yes
0.002
0.932
0.027
-1.487
0.929
0.033
-0.080
0.003
0.001
sysmiss
0.001
0.310
0.009
0.330
0.080
0.001
-0.557
0.230
0.033
no
0.042
0.428
0.000
0.030
0.421
0.000
0.004
0.006
0.000
yes
0.001
0.751
0.016
-1.405
0.720
0.015
0.290
0.031
0.008
sysmiss
0.001
0.184
0.009
0.251
0.034
0.000
-0.525
0.150
0.022
no
0.041
0.368
0.000
0.024
0.354
0.000
0.005
0.014
0.000
yes
0.001
0.738
0.011
-1.190
0.716
0.010
0.210
0.022
0.004
sysmiss
0.001
0.186
0.005
0.154
0.036
0.000
-0.313
0.150
0.011
0.024
0.870
0.005
0.182
0.810
0.006
-0.049
0.060
0.005
fin17a
fin14a
fin14b
fin24 Not possible 50
EJAE 2020 17(2) 34 - 53
IBRAHIM. M. TIDJANI. A. AN EXPLORATORY ANALYSIS OF FINANCIAL INCLUSION IN CHAD
Possible
0.019
0.912
0.007
-0.246
0.841
0.008
0.071
0.070
0.008
sysmiss
0.001
0.238
0.002
0.257
0.177
0.000
-0.150
0.060
0.002
no
0.001
0.910
0.029
-1.820
0.895
0.035
-0.233
0.015
0.007
yes
0.001
0.838
0.018
-1.982
0.830
0.020
-0.204
0.009
0.003
sysmiss
0.041
0.933
0.002
0.096
0.920
0.003
0.012
0.013
0.000
no
0.004
0.939
0.049
-1.422
0.887
0.059
-0.344
0.052
0.042
yes
0.001
0.839
0.025
-2.058
0.839
0.028
-0.032
0.000
0.000
sysmiss
0.039
0.987
0.008
0.194
0.954
0.011
0.036
0.033
0.005
no
0.004
0.927
0.045
-1.415
0.876
0.053
-0.340
0.051
0.038
yes
0.001
0.905
0.028
-1.900
0.901
0.033
-0.134
0.004
0.002
sysmiss
0.039
0.986
0.008
0.194
0.953
0.011
0.036
0.033
0.005
fin4
fin5
fin6
fin8 no
0.001
0.651
0.009
-1.352
0.641
0.008
-0.167
0.010
0.001
yes
0.001
0.746
0.023
-1.654
0.740
0.023
0.159
0.007
0.003
sysmiss
0.042
0.769
0.001
0.063
0.769
0.001
-0.002
0.001
0.000
no
0.002
0.825
0.028
-1.299
0.771
0.029
-0.346
0.055
0.025
yes
0.003
0.948
0.046
-1.721
0.933
0.058
-0.222
0.015
0.012
sysmiss
0.038
0.985
0.009
0.197
0.949
0.011
0.038
0.036
0.005
no
0.002
0.821
0.027
-1.277
0.771
0.028
-0.327
0.051
0.023
yes
0.003
0.952
0.047
-1.749
0.935
0.059
-0.238
0.017
0.013
sysmiss
0.038
0.985
0.009
0.197
0.950
0.011
0.038
0.036
0.005
no
0.008
0.577
0.010
-0.185
0.155
0.002
0.305
0.422
0.071
yes
0.002
0.912
0.019
-1.102
0.875
0.022
0.226
0.037
0.011
sysmiss
0.032
0.777
0.006
0.130
0.506
0.004
-0.095
0.271
0.027
no
0.004
0.510
0.007
-0.378
0.488
0.004
0.080
0.022
0.002
yes
0.007
0.834
0.015
-0.389
0.387
0.008
0.419
0.447
0.107
sysmiss
0.032
0.778
0.006
0.129
0.495
0.004
-0.098
0.283
0.028
no
0.008
0.609
0.011
-0.285
0.336
0.005
0.257
0.273
0.049
yes
0.003
0.925
0.029
-1.306
0.920
0.036
0.102
0.006
0.003
sysmiss
0.032
0.850
0.009
0.190
0.733
0.009
-0.076
0.117
0.017
no
0.004
0.660
0.010
-0.541
0.660
0.008
0.012
0.000
0.000
yes
0.007
0.869
0.018
-0.534
0.619
0.015
0.340
0.250
0.076
sysmiss
0.032
0.844
0.009
0.188
0.718
0.008
-0.078
0.126
0.018
fin9
fin10
fin29a
fin29b
fin27a
fin27b
51
EJAE 2020 17 (2) 34 - 53
IBRAHIM. M. TIDJANI. A. AN EXPLORATORY ANALYSIS OF FINANCIAL INCLUSION IN CHAD
fin31a no
0.004
0.452
0.013
-0.492
0.431
0.007
0.107
0.020
0.004
yes
0.001
0.659
0.010
-1.158
0.623
0.009
0.280
0.036
0.006
sysmiss
0.038
0.578
0.002
0.078
0.551
0.002
-0.017
0.027
0.001
no
0.004
0.351
0.011
-0.418
0.346
0.005
0.055
0.006
0.001
yes
0.001
0.746
0.013
-1.145
0.671
0.012
0.383
0.075
0.016
sysmiss
0.038
0.580
0.002
0.078
0.552
0.002
-0.018
0.028
0.001
fin31b
Source: Author’s calculation on the Global Findex (2017).
52
EJAE 2020 17(2) 34 - 53
IBRAHIM. M. TIDJANI. A. AN EXPLORATORY ANALYSIS OF FINANCIAL INCLUSION IN CHAD
ISTRAŽIVAČKA ANALIZA FINANSIJSKE INKLUZIJE U ČADU
Rezime: Ovaj rad ima za cilj da istraži stanje finansijske inkluzije u Čadu. Usvajanjem višestruke analize korespondencije (MCA) na uzorku od 1000 pojedinaca iz Global Findex-a (2017), studija je merila inkluzivnost finansijskih sistema u Čadu kroz Indeks finansijske inkluzije (FII). Pored toga, procenjena je raspodela FII koristeći faktor dekompozicije Džini koeficijenta. Zaključci su pokazali da je prosečna FII bila niska, 24,89% i da je varirala između 7,43% i 60,35 %. Računi finansijskih institucija, polaganje i podizanje novca i vlasništvo nad debitnim karticama bili su najuticajniji pokazatelji finansijske inkluzije u Čadu. Pored toga, u radu je pokazano da, uprkos njenom niskom stepenu, finansijska inkluzija nije ravnomerno raspoređena među populacijom u toj zemlji (Džini koeficijent 0,196). Analiza profila nejednakosti finansijske inkluzije pokazala je da u Čadu postoji stalni jaz između polova u vezi sa finansijskom inkluzijom, a tu situaciju pogoršava diskriminacija u obrazovanju i prihodima. Stoga bi intervencije politikama trebalo da budu usmerene na obezbeđivanje formalnih računa, smanjenje troškova finansijskih usluga (podizanje novca i debitne kartice) i podsticanje formalne štednje kreiranjem adekvatnih štednih proizvoda, kako bi se podstakla finansijska inkluzija u Čadu. Pored toga, te politike bi trebalo responzivne na pol, uz istovremeno vođenje računa o njihovoj interakciji sa obrazovanjem i prihodima.
Ključne reči: Indeks finansijske inkluzije, višestruka analiza korespondencije, dekompozicija nejednakosti, Čad.
53
EJAE 2020, 17(2): 54 - 66 ISSN 2406-2588 UDK: 338.21:620.91 502.171:620.9 DOI: 10.5937/EJAE17-26902 Original paper/Originalni naučni rad
CONCEPTUALIZING INTEGRATED POLICYMAKING: DOES THE DIVERSIFICATION OF ENVIRONMENTAL POLICY INSTRUMENTS CONTRIBUTE TO INCREASED SUSTAINABILITY? Vlastimir Vučić*, Miljana Radović Vučić SMC Swiss Management Center AG, Zug, Switzerland
Abstract: Most urgent societal issues are crosscutting the boundaries of established jurisdiction. The conventional environmental policy domain is unable to achieve environmental objectives by itself, and each policy sector must integrate environmental objectives. For instance, the lack of clarity of how the integration of environmental objectives into energy policy has transformed and modified energy policy is the reason behind the low levels of integrated policy-making achieved. The present research attempts to clarify how the diversification of environmental policy instruments contributes to integrated policy-making. The present research explicitly confirms that that an increase in the extent of inclusion of environmental policy instruments within relevant policy domains increases to the extent of diversification of environmental policy instruments; that an increase in the extent of inclusion of environmental policy instruments within relevant policy domains and structures that coordinate and monitor efforts within relevant policy domains increases to the extent of diversification of environmental policy instruments; and, that an increase in the extent of inclusion of environmental policy instruments within relevant policy domains ultimately resulting in a cross-sectoral instrument blend results in the increase in the extent of diversification of environmental policy instruments.
Article info: Received: Jun 8, 2020 Correction: August 3, 2020 Accepted: September 12, 2020
Keywords: Sustainable development (SD), environmental policy integration (EPI), renewable electricity (RES-E), policy instruments, integrated policymaking.
INTRODUCTION Most severe societal issues over health, food, energy, transport, climate, innovations, freedom, etc., are crosscutting the boundaries of established jurisdiction, namely through policy domains and governance levels. Increasing requirements for integrated policymaking over these issues are apparent; however, obstacles for integrated policymaking, such as inappropriate instrument mixes, competing attention over issues, competing and incoherent objectives, fragmentation, compartmentation, etc., are making it harder to achieve. 54
*E-mail: vv@smceducation.eu
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VUČIĆ. V., VUČIĆ. R. M. CONCEPTUALIZING INTEGRATED POLICYMAKING: DOES THE DIVERSIFICATION OF ENVIRONMENTAL POLICY INSTRUMENTS CONTRIBUTE TO INCREASED SUSTAINABILITY?
These obstacles gain in relevance when these societal issues are confronted with hierarchical governance and its traditional forms of subsystem involvement. In hierarchical governance and its traditional forms of subsystem involvement, policymaking takes place with a relatively stable set of actors, each of whom displays a set of beliefs, interests, and perceptions, and which genuinely do not allow for an integrated policymaking approach towards these issues. To further add to the complexity of integrated policymaking, the number of actors taking part in the policymaking has increased, namely due to increasing emphasis on public participation of private industry, public administration, interest groups, civil society, the general public, etc. Most severe societal issues that are crosscutting the boundaries of established jurisdiction link the incompatible objectives of economic competitiveness, social development, and environmental protections with the concept of sustainability or sustainable development (SD). Furthermore, these severe issues thus link the incompatible objectives of integrating concerns of environmental protection with economic competitiveness and social development or Environmental policy integration (EPI). Therefore, one of the most important illustrations of SD can be observed as its emphasis on the inclusion or integration of environmental concerns or objectives into policy domains that are not related to the domain of conventional environmental policy. The claims for the inclusion or integration of environmental concerns or objectives into policy domains that are not related to the domain of conventional environmental policy are found in the thorough literature review (see, for instance, Adelle and Russel, 2013; Jordan and Lenschow, 2010; Mullally and Dunphy, 2015; Runhaar et al., 2014; Runhaar et al., 2017; Solorio, 2011; Söderberg, 2011; Uittenbroek et al., 2013; Wamsler, 2015; Wejs, 2014). Accordingly, it has been revealed explicitly that the conventional environmental policy domain is not able to achieve environmental objectives by itself, and that each policy sector must take into consideration and integrate environmental objectives if these objectives are to be achieved in any way. The literature, thus, explicitly views EPI as a necessary modification of policymaking to guide society as a whole in a more sustainable manner. Similarly, societal issues over the energy sector are, thus, crosscutting the boundaries of established jurisdiction, namely through policy domains and governance levels. Apart from the more general literature which, for instance, illustrates that the integration of environmental concerns or objectives into energy policy is a stance upon which the European Union (EU) governs its energy-related issues (see, for instance, Solorio, 2011), the more explicit literature reveals reasons for studying the energy sector in the context of the present research: CO2 emission reductions, economic impacts, and energy security (see, for instance, IEA, 2019). Another important reason for studying the energy sector in the context of the present research is certainly the potential of renewable electricity (RES-E) (Knudsen 2010, 2012). Another important matter illustrated by the literature in general is that there is a lack of clarity on how the integration of environmental concerns or objectives into energy policy has transformed and modified energy policy. For this reason, modest levels of integrated policy-making in the energy sector, as well as in sectors policy domains that are not related to the domain of conventional environmental policy, have so far been achieved. The present research thus aims to clarify how the integrated policymaking adds value to the diversification of environmental policy instruments and inclusion or integration of environmental concerns or objectives into the policy domain of energy.
55
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VUČIĆ. V., VUČIĆ. R. M. CONCEPTUALIZING INTEGRATED POLICYMAKING: DOES THE DIVERSIFICATION OF ENVIRONMENTAL POLICY INSTRUMENTS CONTRIBUTE TO INCREASED SUSTAINABILITY?
LITERATURE REVIEW
Conceptualizing Integrated Policymaking EPI became the most important concept in environmental governance with the publication of the Brundtland report in 1987, which defined the SD as a “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” (WCED, 1987, p. 2), illustrating that the integration of economic competitiveness, social development, and environmental protection is central to the concept of SD. Following the publication of the Brundtland Report, the integration was officially recognized as a principle of international law. For instance, EPI was legally accepted by the Treaty on European Union (EU) in 1993, and incorporated into the Treaty establishing the European Community (TEC), prescribing integration of environmental protection into the EU’s relevant policies and activities, most explicitly about the promotion of sustainability. Similarly, to properly understand European integration development, it is necessary to understand the role energy has played in this process. It is rather difficult to explain the creation of the EU without taking into consideration the creation of the European Coal Organization (ECO) in 1946 and the Organization for European Economic Co-operation (OEEC) in 1948, obviously showing that energy was a foundation of European integration. A similar motivation is found behind the creation of the European Coal and Steel Community (ECSC) in 1952 and the European Atomic Energy Community (EURATOM) in 1958, constituting the basic pillars of the European Economic Community (EEC). Moreover, a similar motivation is found behind the Élysée Treaty signed in 1963 between France and Germany, aimed at reconciliation of relationship, which was later anchored in the EU. However, despite efforts, the integration process has not developed sufficiently enough to form a basis for a common energy policy. It was not until the Lisbon reform in 2009 and the Treaty on the Functioning of the European Union (TFEU) that brought the necessary changes to this sector and set the policy goals, otherwise known as energy trinity, i.e., security of supply, affordable energy, and environmental sustainability. The commencement of the “Cardiff Process” in 1998 illustrates a step forward towards the practical application of EPI with Renewable Energy Sources (RES) and energy efficiency forming the basis of a sustainable energy system, and with the 2020 Climate and Energy Package adopted in 2009 is largely viewed as the flagship instrument of the EU’s forward-looking perspective on this sustainable energy model (Oberthür and Pallaemarts, 2010). The package involves a 20 percent reduction in greenhouse gas (GHG) emissions (from 1990 levels), 20 percent of energy from RES, and a 20 percent improvement in energy efficiency. Similarly, a new science-policy-society interface for the 2030 Agenda emphasizes the role of European Advisory Councils on the Environment and SD. Accordingly, the 2030 Climate and Energy Framework adopted in 2014 involves a 40 percent reduction in GHG emissions (from 1990 levels), 32 percent of energy from RES, and a 32.5 percent improvement in energy efficiency. In addition, the implementation of the 2030 Agenda at the national, sub-national, and local level necessitates a strong association between the most important actors, namely governments, the scientific community, and a broad variety of actors. The European Advisory Councils on the Environment and SD play an important role in terms of knowledge dissemination and agenda setting.
Framework for the Analysis of Integrated Policymaking EPI is defined as “the incorporation of environmental objectives into all stages of policymaking in non-governmental policy sectors, with specific recognition of this goal as a guiding principle for the planning and execution of policy” (Lafferty and Hovden, 2003, p. 9). Furthermore, this principle should 56
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VUČIĆ. V., VUČIĆ. R. M. CONCEPTUALIZING INTEGRATED POLICYMAKING: DOES THE DIVERSIFICATION OF ENVIRONMENTAL POLICY INSTRUMENTS CONTRIBUTE TO INCREASED SUSTAINABILITY?
be “accompanied by an attempt to aggregate presumed environmental consequences into an overall evaluation of policy, and a commitment to minimize contradictions between environmental and sectoral policies by giving principled priority to the former over the latter” (Lafferty and Hovden, 2003, p. 9). A similar term to EPI is climate policy integration (CPI) (Jordan and Lenschow, 2010). Even though there are different meanings linked to CPI, the notion is the same as that of EPI. EPI strategies found in the literature are relatively diverse, and range from regulatory and financial to institutional and organizational. Likely due to such a diverse nature of these strategies, an overview of EPI is virtually non-existent. Some of the most frequent examples include Strategic Environmental Assessment (SEA), Environmental Impact Assessment (EIA), Green budgeting, Green taxes, Biodiversity conservation markets, Environmental units within sectoral departments, Green departments, Combination of departments, and Environmental reporting obligations, Sustainable development strategies (SDS), National environmental plans, etc. EPI strategies also include partnerships between public and private actors, voluntary sector-wide agreements between public and private actors, voluntary sectoral self-governance, municipal voluntarism, self-organized and self-governed management. The literature also illustrates different frameworks on of how to measure levels of EPI achieved (see for instance Weber and Driessen, 2010; Dupont and Oberthür, 2012), generally distinguishing the following levels of integrated policymaking: coordination, harmonization, and prioritization, or more recent studies (see, for instance, Uittenbroek et al., 2013; Brouwer et al., 2013), generally distinguishing the following levels of integrated policymaking: inclusion, consistency, weighting, and reporting. Broadly speaking, the performance of EPI strategies could be evaluated in terms of physical indicators, such as a reduction of climate risks, CO2 emissions, environmental quality, etc. (see Adelle and Russel, 2013). However, given that such evaluation would be rather difficult to take place, the reported levels of EPI found in the mentioned literature relate to EPI strategies that are influential in decision-making or policymaking, as well as in the implementation of these decisions or policies. Evaluative EPI strategies are found in the literature focusing on particular policy sectors and in different countries: noise and spatial planning (Weber and Driessen, 2010), bioenergy policy (Söderberg, 2011), water policy (Brouwer et al., 2013), urban planning (Uittenbroek et al., 2013), etc. Evaluative EPI strategies are also found focusing on the performance of particular strategies: sustainable development strategies (Steurer and Hametner, 2013), green budgeting (Russel and Benson, 2013), market-based mechanisms, such as cap and trade systems, green taxes and biodiversity conservation markets (Ward and Cao, 2012; Lu et al., 2012; Alvarado – Quesada et al., 2014), etc. Furthermore, the literature points to the governance of EPI as the most important obstacle in decisionmaking or policymaking. In trade-offs, sectoral and environmental issues and concerns necessarily come into conflict and there is a lack of political will to prioritize environmental issues and concerns (see Dupont and Oberthür, 2012). The literature points to the implementation of EPI as another obstacle, because relevant institutions and bodies, such as governmental ministries, lack the resources, power, and authority to enforce EPI (see Dupont and Oberthür, 2012). Lists of factors that ease or constrain EPI are also found in more general literature: cultural, instrumental, economic, organizational, and political factors, as well as in empirical research: contextual, procedural and organizational factors (Weber and Driessen, 2010), political factors, network, skills, personal motivation, relationships between policy sectors and organizational factors, decision-making, the characteristics of the actors, the outcomes of the environmental assessment, the legal basis (Arts et al., 2012), entry and exit barriers, transaction costs, complete information, appropriate actors, and property rights (Alvarado-Quesada et al., 2014). Similarly, lists of factors that ease or constrain CPI are also identified: institutional, socio-cultural, cognitive, knowledge and informational, and financial factors (Biesbroek et al., 2013). 57
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VUČIĆ. V., VUČIĆ. R. M. CONCEPTUALIZING INTEGRATED POLICYMAKING: DOES THE DIVERSIFICATION OF ENVIRONMENTAL POLICY INSTRUMENTS CONTRIBUTE TO INCREASED SUSTAINABILITY?
Policy Framing as a Method of Learning in Integrated Policymaking
EPI strategies found in the literature can also be observed from an institutional perspective (see, for instance, Wejs, 2014; Wejs and Cashmore, 2014; Wejs et al., 2014), in which it is emphasized that finding the appropriate discourse is necessary to gain legitimacy for EPI. According to the particular research, framed as a mechanism of socio-economic development, climate change is less difficult to integrate than framed as an environmental problem. The literature has already widely endorsed a policy-learning approach to analyzing integrated policymaking, illustrating the increasing focus on the role of a new science-policy-society interface, necessitating a strong association between the most important actors, namely governments, the scientific community, and a broad variety of actors. The implication of learning in policymaking contributes to a change in the policy process and policymaking, which can be viewed as a precondition for sustainability. Broadly speaking, learning is possible by adapting to changing conditions and acquiring new knowledge based on experience gained through the policy process. Thus, in contrast to hierarchical governance and its traditional forms of subsystem involvement, the policy-learning approach to analyzing integrated policymaking implies that policy is created in networking processes with both public and private actors that have different ideas and interests. Policy networks can, therefore, subsequently be described as informal structures, enabling communication and interaction between actors. To avoid any potential ambiguity involved with the policy-learning approach to analyzing integrated policymaking, a unifying concept of policy framing is introduced. According to Rein and Schön (1993, p. 146), “framing is a way of selecting, organizing, interpreting, and making sense of a complex reality to provide guideposts for knowing, analyzing, persuading and acting”. Moreover, “a frame is a perspective from which an amorphous, ill-defined, problematic situation can be made sense of and acted on” (Rein and Schön, 1993, p. 146). Policy frames can thus help interpret environmental requirements as a mechanism toward the achievement of environmental protection and market harmonization in general. Lenschow and Zito (1998) analyze how policy frames have influenced the manner the European Community (EC) actors understand the integration of environmental and economic policy objectives. They argue that there are three EC policy frames: conditional, classic, and sustainability, illustrating how the integration of environmental and economic policy objectives takes place. The conditional environmental policy frame conceptualizes the EC environmental regulation to prevent trade distortions produced by the diverging national environmental standards rather than to create a European environmental policy regime. The conditional environmental policy frame thus assumes that environmental and economic policy function independently from one another, and that policymaking must be necessarily viewed as a choice between incompatible objectives of environmental and economic policy. Several regulatory features follow such an assumption, and are observable in an explicitly hierarchical structure of administration, according to which EC is distinguished as the primary actor in the economy to create harmonized market conditions. According to this frame, environmental regulations and standards are applied uniformly within EU member states, which implies the use of command-and-control policy instruments. The classic environmental policy frame conceptualizes the EU environmental regulation to increase environmental awareness in terms of limiting safety, health, and environmental risks, requiring policy compromise. Environmental and economic policies continue to function independently from one another, however. The EU is still viewed as the primary actor in the market aiming to create harmonized market conditions with command-and-control policy instruments remaining uniformly applied within EU member states. The sustainability policy frame no longer assumes that environmental and economic policy function independently from one another, and acknowledges 58
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policy compromises as a necessary condition for long-term economic development. Decision-making or policymaking thus applies the principle of partnership and integrated policymaking, which prescribe the internalization of environmental costs in market transactions, i.e., economic policy instruments, self-regulation, learning tools, etc.
Policy Instruments and Integrated Policymaking The principle of integrated policymaking thus suggests the internalization of environmental costs in market transactions, i.e., economic policy instruments. Economic policy instruments or market-based policy instruments are mechanisms that recommend conduct through explicit market indications. As such, market-based policy instruments prescribe both firms and individuals undertaking pollution control efforts that are in their interest, and also to fulfill policy requirements. Market-based policy instruments of the sustainability policy frame, therefore, contrast other environmental regulations and standards of the conditional and classic policy frames, which are applied uniformly within EU member states. Environmental regulations and standards with a uniform application are inclined to force firms to assume similar shares of the pollution control burden, irrespective of the cost, which can be expensive and counterproductive. In contrast, by providing incentives for the reductions in pollution by those firms which can achieve those reductions in the cheapest possible manner, market-based policy instruments allow any desired level of pollution control effort to be achieved at the lowest possible cost for society. Market-based policy instruments thus have the potential to provide powerful incentives for firms to obtain cheaper and improved pollution-control technologies. Market-based policy instruments can be considered within four broad categories: government subsidy reductions, market friction reductions, tradable permits, and pollution charges. Within the context of market-based policy instruments, the literature illustrates an increasing interest in the design of subsidy regimes to accommodate the adequate development of RES-E. Accordingly, subsidy regimes can be considered within two broad approaches: direct and indirect (Batlle et al., 2011). Direct approaches refer to investment support, such as support mechanisms, tax reductions capital grants, etc. A feed-in tariff (FIT) is an example of a direct approach and support mechanism. Indirect approaches refer to institutional support mechanisms and implicit payments, such as positive discriminatory rules, below-cost provision of infrastructure, funding of research and development, etc.
METHODOLOGY The Development of the Research Hypotheses The present research has obtained the research hypothesis based on the literature review. The main research hypothesis can be summarized as follows: H1:
The higher the extent of inclusion of environmental policy instruments within relevant policy domains, the higher the extent of diversification of environmental policy instruments.
H2:
The higher the extent of inclusion of environmental policy instruments within relevant policy domains and structures that coordinate and monitor efforts within relevant policy domains, the higher the extent of diversification of environmental policy instruments.
H3:
The higher the extent of inclusion of environmental policy instruments within relevant policy domains ultimately resulting in cross-sectoral instrument blend, the higher the extent of diversification of environmental policy instruments. 59
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Research Designs
Sekaran and Bouqie (2009, p.3) define business research as “organized, systematic, data-based, critical objective, specific inquiry or investigation into a specific problem, undertaken to find answers or solutions to it.” The present research can thus be understood as a process of finding a solution to an issue after a thorough study and analysis of the various actors. Given that there are no earlier studies or analyses to refer to the subject, the present research will, therefore, look for patterns and ideas to create hypotheses. The hypotheses will be tested and confirmed. It is expected that the hypotheses will not only provide conclusive answers to the subject, but also provide guidance on what future research should be conducted. The present research will use a survey as a research technique. The present research will use e-post, on-line, face-to-face, group distribution, and individual distribution as data collection methods. The survey was taken from January 15th, 2020 to February 15th, 2020. A sample for the data collection includes EU institutions and bodies (namely, the European Council and the European Commission), academic institutions (public and private universities), and private investors (investment funds and venture capitalists). Approximately 700 questionnaires were distributed in order to obtain the desired number of 100 confirmed respondents. The present research will also use descriptive statistics to summarize the data in a more compact and graphical form. Furthermore, the present research is designed to apply its findings to solve a specific problem. It represents the application of existing knowledge to the improvement of public policies and managerial practices. Finally, the present research is research in which a conceptual and theoretical structure is developed, which is then tested by empirical observation. Particular instances are, thus, deducted from general inferences.
ANALYSIS H1:
The higher the extent of inclusion of environmental policy instruments within relevant policy domains, the higher the extent of diversification of environmental policy instruments.
Figure 1. Hypothesis 1. 45 40 35 30 25 20 15 10 5 0
Category 1 Strongly agree
Agree
Source: Compiled by the author (2020) 60
Neither agree nor disagree
Disagree
Strongly disagree
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Approximately 700 questionnaires were distributed in order to obtain the desired number of 100 confirmed respondents. The statistics show that 27 percent of the respondents strongly agree that an increase in the extent of inclusion of environmental policy instruments within relevant policy domains increases to the extent of diversification of environmental policy instruments. Accordingly, another 41 percent of the respondents agree with said statement, followed by 17 percent, who neither agree nor disagree, 7 percent who disagree, and 8 percent who strongly disagree. H2:
The higher the extent of inclusion of environmental policy instruments within relevant policy domains and structures that coordinate and monitor efforts within relevant policy domains, the higher the extent of diversification of environmental policy instruments.
Figure 2. Hypothesis 2. 45 40 35 30 25 20 15 10 5 0
Category 1 Strongly agree
Agree
Neither agree nor disagree
Disagree
Strongly disagree
Source: Compiled by the author (2020)
Approximately 700 questionnaires were distributed in order to obtain the desired number of 100 confirmed respondents. The statistics illustrate that 31 percent of the respondents strongly agree that an increase in the extent of inclusion of environmental policy instruments within relevant policy domains and structures that coordinate and monitor efforts within relevant policy domains increases to the extent of diversification of environmental policy instruments. Another 39 percent of the respondents agree with said claim, followed by 10 percent who neither agree nor disagree, 12 percent who disagree, and 8 percent who strongly disagree.
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H3:
The higher the extent of inclusion of environmental policy instruments within relevant policy domains ultimately resulting in cross-sectoral instrument blend, the higher the extent of diversification of environmental policy instruments.
Figure 3. Hypothesis 3. 50 45 40 35 30 25 20 15 10 5 0
Category 1 Strongly agree
Agree
Neither agree nor disagree
Disagree
Strongly disagree
Source: Compiled by the author (2020)
Approximately 700 questionnaires were distributed in order to obtain the desired number of 100 confirmed respondents. The statistics display that 25 percent of the respondents strongly agree that an increase in the extent of inclusion of environmental policy instruments within relevant policy domains ultimately resulting in a cross-sectoral instrument blend results in the increase in the extent of diversification of environmental policy instruments. Accordingly, 44 percent of the respondents agree with said claim, followed by another 14 percent who neither agree nor disagree, 9 percent who disagree, and 8 percent who strongly disagree.
CONCLUSION Most severe societal issues are crosscutting the boundaries of established jurisdiction through policy domains and governance levels. Even though there are increasing requirements for integrated policymaking of these issues, obstacles for integrated policymaking are making it increasingly difficult to achieve it, particularly when these societal issues are confronted with hierarchical governance and its traditional forms of subsystem involvement. To add to the complexity of integrated policymaking, the number of actors involved in the policymaking has increased. Most severe societal issues that are crosscutting the boundaries of established jurisdiction connect the incompatible objectives of economic competitiveness, social development, and environmental protections with the concept of SD, and link the incompatible objectives of integrating concerns of environmental protection with economic competitiveness and social development or EPI. 62
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Therefore, one of the most important illustrations of SD is the inclusion or integration of environmental concerns or objectives into policy domains that are not related to the domain of conventional environmental policy, i.e., EPI. The necessity for the inclusion or integration of environmental concerns or objectives into policy domains that are not related to the domain of conventional environmental policy are found in the thorough literature review, which reveals the inability of the conventional environmental policy domain to achieve environmental objectives by itself, and that each policy sector must take into consideration and integrate environmental objectives if they are anyhow to be achieved. The literature, therefore, views EPI as a necessary modification of policymaking to guide society as a whole in a more sustainable manner. In a similar manner, societal issues over the energy sector are crosscutting the boundaries of established jurisdiction, namely through policy domains and governance levels. The literature reveals that, within the context of the present research, the energy sector is very important due to how the EU governs its energy-related issues, as well as due to more explicit reasons, such as the CO2 emission reductions, economic impacts, and energy security, and to the potential of RES-E. The lack of clarity on how the integration of environmental concerns or objectives into energy policy has transformed and modified energy policy is the main reason behind the modest levels of integrated policy-making in the energy sector achieved. The present research thus attempts to clarify how the integrated policymaking adds value to the diversification of environmental policy instruments and inclusion or integration of environmental concerns or objectives into the policy domain of energy. Accordingly, in H1, 68 percent of the respondents either strongly agree or agree that an increase in the extent of inclusion of environmental policy instruments within relevant policy domains increases to the extent of diversification of environmental policy instruments. In H2, 70 percent of the respondents either strongly agree or agree that an increase in the extent of inclusion of environmental policy instruments within relevant policy domains and structures that coordinate and monitor efforts within relevant policy domains increases to the extent of diversification of environmental policy instruments. In H3, 69 percent of the respondents strongly agree that an increase in the extent of inclusion of environmental policy instruments within relevant policy domains ultimately resulting in a cross-sectoral instrument blend results in the increase in the extent of diversification of environmental policy instruments. Within the context of the present research, any attempt to diversify environmental policy instruments can be seen from the perspective of integrated policymaking as a deliberate and structured effort by policymakers to deal with the policy issue by modifying actions of the integrated policymaking.
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KONCEPIRANJE INTEGRISANOG KREIRANJA POLITIKE: DA LI DIVERSIFIKACIJA INSTRUMENATA ZAŠTITE ŽIVOTNE SREDINE MOŽE DA DOVEDE DO POVEĆANJA ODRŽIVOSTI SAME POLITIKE?
Rezime: Najteža društvena pitanja prelaze granice uspostavljene nadležnosti. Konvencionalni domen politike zaštite životne sredine ne može sam da ostvari ciljeve zaštite životne sredine i zato svaki domen politike individualnih sektora mora da uzme u obzir i integriše ciljeve zaštite životne sredine. Na primer, nedostatak jasnoće kako je integracija odredbi ili ciljeva zaštite životne sredine transformisala i modifikovala energetsku politiku glavni je razlog za postignute skromne nivoe integrisanog kreiranja politika. Sadašnje istraživanje pokušava da razjasni kako diversifikacija instrumenata politike zaštite životne sredine doprinosi integrisanom kreiranju politika. Sadašnje istraživanje izričito potvrdjuje da se povećava stepen uključivanja instrumenata politike zaštite životne sredine u relevantne domene politika do stepena diversifikacije instrumenata politike zaštite životne sredine; da se povećava stepen uključivanja instrumenata politike zaštite životne sredine u relevantne domene politika i struktura koje koordiniraju i prate napore unutar relevantnih domena politike povecava do stepena diversifikacije instrumenata politike zastite zivotne sredine; i da se povećava stepen uključivanja instrumenata politike zaštite životne sredine u relevantne domene politika i medjusektorskom mešavinom instrumenata do stepena diversifikacije instrumenata politike zaštite životne sredine.
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Ključne reči: Održivi razvoj (OR), Integracija politike zaštite životne sredine, Energija iz obnovljivih izvora, Instrumenti politike, Integrisano kreiranje politike.
EJAE 2020, 17(2): 67 - 88 ISSN 2406-2588 UDK: 336.76(450)"2005/'2017" DOI: 10.5937/EJAE17-26893 Original paper/Originalni nauÄ?ni rad
VOLATILITY SPILLOVER AND CONTAGION EFFECTS BETWEEN EURODOLLAR FUTURE AND ZERO COUPONS MARKETS: EVIDENCE FROM ITALY Konstantinos Tsiaras* University of Ioannina, Greece
Abstract: This paper examines the time-varying conditional correlations between the Eurodollar futures market and the zero coupons of Banca Fideuram. We apply a bivariate dynamic conditional correlation (DCC) GARCH model in order to capture potential contagion effects between the markets for the period 2005-2017. Empirical results reveal contagion during the under-investigation period regarding the twenty-one bivariate models, showing that the Eurodollar futures market has a major impact on the zero coupons of Banca Fideuram. Findings have crucial implications for policymakers who provide regulations for the above-mentioned derivative markets.
Article info: Received: Jun 3, 2020 Correction: July 31, 2020 Accepted: September 10, 2020
Keywords: DCC-GARCH model, EURODOLLAR future market, zero coupons, financial contagion, dynamic conditional correlations. Jel Classification: C58, C61, G11, G15.
INTRODUCTION Τhis paper investigates the potential volatility spillover and contagion effects (Dimitriou, Kenourgios & Simos 2013) of the Eurodollar futures market and the zero coupons of Banca Fideuram. We consider the zero coupons of Banca Fideuram ending from 2018 to 2033. By employing a bivariate DCC-GARCH model, we show significant volatility spillover effects (Sehgal, Ahmad & Deisting 2015; Li & Giles 2015; Aboura & Chevallier 2015; Antonakakis, Floros & Kizys 2016). Moreover, we use the definition of contagion as suggested by Forbes and Rigobon (2002). They defined contagion as a significant increase in cross-market linkages after a shock. Dynamic conditional correlations reveal contagion effects (Dimitriou & Kenourgios 2015; Sensoy & Hacihasanoglu 2015) in sub-periods between the Eurodollar futures market and all the zero coupons of Banca Fideuram. *E-mail: konstantinos.tsiaras1988@gmail.com
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TSIARAS. K. VOLATILITY SPILLOVER AND CONTAGION EFFECTS BETWEEN EURODOLLAR FUTURE AND ZERO COUPONS MARKETS: EVIDENCE FROM ITALY
The motivation for this paper is analyzed as follows. Firstly, there is no other empirical research investigating the conditional second moments of the distribution between the Eurodollar futures market and the zero coupons of Banca Fideuram. Secondly, the potential existence of contagion between the Eurodollar futures market and the zero coupons of Banca Fideuram provides new evidence for financial theory. Thirdly, the under-investigation period is of great importance, since it entails major economic crises i.e., the financial crisis of 2008. The paper is organized as follows. Section 2 presents the literature review and Section 3 provides the data characteristics. Section 4 provides the methodology. Section 5 shows the empirical results. The last section provides the conclusion.
LITERATURE REVIEW There are numerous empirical studies investigating the spillovers among different future and financial markets (Mensi et al 2013; Kavussanos et al 2014; Li et al 2014; Antonakakis and Kizys 2015; Du and He 2015; Ewing and Malik 2016; Bagchi 2017; Roy and Roy 2017; Ma et al 2019; Tsiaras and Simos 2020; Tsiaras 2020, Tsiaras 2020). Mensi et al (2013) find evidence of spillovers between the S&P 500 and commodity price indices for energy, food, gold, and beverages over the turbulent period from 2000 to 2011. Kavussanos et al (2014) examine the existence of spillover effects between commodity and freight markets for the period 2006-2009. By using different GARCH models, they show the existence of spillovers effects. Li et al (2014) show potential spillovers and dynamic conditional correlations between spot and forward tanker freight markets. By using a multivariate GARCH model, they examine the period from 2006 to 2011. Antonakakis and Kizys (2015) find evidence of volatility spillover effects between commodity and FOREX markets: crude oil, gold, silver, platinum, CHF/USD, GBP/USD, EUR.USD. They investigate the period 1987 to 2014. Du and He (2015) found evidence of significant spillover between crude oil and stock markets using daily data of the S&P 500 stock index and West Texas Intermediate (WTI). Based on their results, they supported the existence of positive risk spillovers from stock to crude oil markets and negative spillovers from crude oil to stock markets. Ewing and Malik (2016) examine the volatility of oil and US stock market prices incorporating structural breaks using daily data from 1996 to 2013. By employing univariate and bivariate GARCH models, they find no volatility spillovers between the two markets. Bagchi (2017) investigates the dynamic relationship between crude oil price volatility and stock markets in the emerging economies like BRIC (Brazil, Russia, India and China) countries. By using a AR-APARCH model, he finds evidence of positive and negative relationships between the underinvestigation markets. Roy and Roy (2017) show the financial contagion in Indian commodity derivative markets vis-à-vis bond, FOREX, gold, and stock markets. They applied a multivariate DCC-GARCH model for the period 2006-2016. Ma et al (2019) examine the inter-connectedness between WTI oil price returns and the returns of listed firms in the US energy sector for the period 2008-2018. 68
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TSIARAS. K. VOLATILITY SPILLOVER AND CONTAGION EFFECTS BETWEEN EURODOLLAR FUTURE AND ZERO COUPONS MARKETS: EVIDENCE FROM ITALY
They show that, although idiosyncratic information is mostly independent of oil shocks, individual energy stock returns do respond to WTI price movements. Tsiaras and Simos (2020) prove the spillover effects among S&P 500, four national equity markets and the respective FOREX markets for the period from 2010 to 2018. Tsiaras (2020) investigates and proves the spillovers between JPY/USD, KRW/USD, EUR/USD and INR/USD futures markets for the period 2014-2019. In our paper, we provide empirical evidence of spillover effects between major future FOREX market and Zero Coupons derivative markets. To the best of our knowledge, there is no previous empirical evidence providing evidence of spillover effects between the under-investigation market.
DATA CHARACTERISTICS We use daily data for Eurodollar futures market (DGCX-EUR/USD CONTINUOUS AVG.- SETT. PRICE) and sixteen zero coupons of Banca Fideuram (BANCA FIDEURAM ZERO CPN. 2018, BANCA FIDEURAM ZERO CPN. 2019, BANCA FIDEURAM ZERO CPN. 2020, BANCA FIDEURAM ZERO CPN. 2021, BANCA FIDEURAM ZERO CPN. 2022, BANCA FIDEURAM ZERO CPN. 2023, BANCA FIDEURAM ZERO CPN. 2024, BANCA FIDEURAM ZERO CPN. 2025, BANCA FIDEURAM ZERO CPN. 2026, BANCA FIDEURAM ZERO CPN. 2027, BANCA FIDEURAM ZERO CPN. 2028, BANCA FIDEURAM ZERO CPN. 2029, BANCA FIDEURAM ZERO CPN. 2030, BANCA FIDEURAM ZERO CPN. 2031, BANCA FIDEURAM ZERO CPN. 2032 and BANCA FIDEURAM ZERO CPN. 2033). We downloaded data from the Datastream database. We set the period from January 4, 2005 to December 11, 2017 (3375 observations). We use the market returns generated by the equation rt = log(pt) - log(pt-1) , where pt is the price of future market on day t and pt-1 is the price of future market on day t-1. In tables 1, 2, 3 and 4 we see the summary statistics for the markets returns. BANCA FIDEURAM ZERO CPN. 2032 exhibits the highest mean value (0,00023071). Based on the highest maximum (0,077701), the second minimum (-0,066133) and the second highest std. deviation (0,0095707) values, BANCA FIDEURAM ZERO CPN. 2032 presents the largest fluctuations among all the markets. Additionally, all market returns are negatively skewed, except the cases of BANCA FIDEURAM ZERO CPN. 2018, BANCA FIDEURAM ZERO CPN. 2019, BANCA FIDEURAM ZERO CPN. 2020 and BANCA FIDEURAM ZERO CPN. 2021. Furthermore, we observe that all market returns show excess kurtosis. In addition, Jarque-Bera statistic results indicate the rejection of the null hypothesis of normality for all market returns. ADF (Dickey and Fuller 1979) test results reject the null hypotheses of unit root at 1% level, showing that the daily market returns appropriate for further testing.
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TSIARAS. K. VOLATILITY SPILLOVER AND CONTAGION EFFECTS BETWEEN EURODOLLAR FUTURE AND ZERO COUPONS MARKETS: EVIDENCE FROM ITALY
Table 1 - Summary Statistics of the Daily Market Logarithmic Returns DGCXEUR/USD CONTINUOUS AVG.- SETT. PRICE
BANCA FIDEURAM ZERO CPN. 2018
BANCA FIDEURAM ZERO CPN. 2019
BANCA FIDEURAM ZERO CPN. 2020
BANCA FIDEURAM ZERO CPN. 2021
Mean
-3.62e-005
0.00014653
0.00016334
0.00017082
0.00017633
Minimum
-0.034722
-0.034328
-0.037916
-0.032092
-0.036549
Maximum
0.032842
0.052911
0.057069
0.057384
0.059144
Std. Deviation
0.0058591
0.0040629
0.0042757
0.0050665
0.0054361
Skewness
-0.0067747
0.56365***
0.59049***
0.66735***
0.23680***
t-Statistic
0.16075
13.374
14.011
15.835
5.6187
p-Value
0.87229
8.5689e-041
1.3378e-044
1.7961e-056
1.9243e-008
Excess Kyrtosis
2.4752***
16.889***
16.837***
13.692***
9.9221***
t-Statistic
29.374
200.43
199.81
162.49
117.75
p-Value
1.1804e-189
0.00000
0.00000
0.00000
0.00000
Jarque-Bera
861.58***
40293***
40062***
26614***
13876***
p-Value
8.1273e-188
0.00000
0.00000
0.00000
0.00000
ADF Test
-34.0035***
-36.1749***
-35.1774***
-35.273***
-35.4105***
Table 2 - Summary Statistics of the Daily Market Logarithmic Returns
70
BANCA FIDEURAM ZERO CPN. 2022
BANCA FIDEURAM ZERO CPN. 2023
BANCA FIDEURAM ZERO CPN. 2024
BANCA FIDEURAM ZERO CPN. 2025
Mean
0.0001646
0.00018798
0.0002024
0.00020046
Minimum
-0.036124
-0.051395
-0.048579
-0.045075
Maximum
0.059525
0.047033
0.049644
0.052734
Std. Deviation
0.0058674
0.0062433
0.0066364
0.0066885
Skewness
-0.14823***
-0.10383***
-0.23445***
-0.17699***
t-Statistic
3.5171
2.4636
5.5629
4.1996
p-Value
0.00043625
0.013754
2.6528e-008
2.6742e-005
Excess Kyrtosis
11.316***
8.4188***
6.8660***
6.0796***
t-Statistic
134.29
99.909
81.481
72.149
p-Value
0.00000
0.00000
0.00000
0.00000
Jarque-Bera
18021***
9973.1***
6660.3***
5215.4***
p-Value
0.00000
0.00000
0.00000
0.00000
ADF Test
-35.3086***
-34.3199***
-35.7359***
-34.922***
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Table 3 - Summary Statistics of the Daily Market Logarithmic Returns BANCA FIDEURAM ZERO CPN. 2026
BANCA FIDEURAM ZERO CPN. 2027
BANCA FIDEURAM ZERO CPN. 2028
BANCA FIDEURAM ZERO CPN. 2029
Mean
0.00020225
0.00020596
0.00020595
0.00021448
Minimum
-0.056538
-0.057316
-0.056162
-0.06922
Maximum
0.058081
0.046397
0.049962
0.051293
Std. Deviation
0.0071898
0.0076207
0.0081711
0.0084838
Skewness
-0.18968***
-0.28312***
-0.31747***
-0.40512***
t-Statistic
4.2871
6.7177
7.5328
9.6126
p-Value
1.8099e-005
1.8466e-011
4.9673e-014
7.0759e-022
Excess Kyrtosis
6.4695***
5.4861***
5.1496***
5.6284***
t-Statistic
76.775
65.105
61.111
66.794
p-Value
0.00000
0.00000
0.00000
0.00000
Jarque-Bera
5904.1***
4277.5***
3785.8***
4547.1***
p-Value
0.00000
0.00000
0.00000
0.00000
ADF Test
-34.5052***
-35.2119***
-35.0801***
-35.9567***
Table 4 - Summary Statistics of the Daily Market Logarithmic Returns BANCA FIDEURAM ZERO CPN. 2030
BANCA FIDEURAM ZERO CPN. 2031
BANCA FIDEURAM ZERO CPN. 2032
BANCA FIDEURAM ZERO CPN. 2033
Mean
0.00021384
0.00021572
0.00023071
0.00022541
Minimum
-0.062678
-0.055032
-0.066133
-0.064688
Maximum
0.059321
0.076138
0.077701
0.074629
Std. Deviation
0.0087642
0.0092783
0.0095707
0.0097384
Skewness
-0.20305***
-0.069956**
-0.15964***
-0.14954***
t-Statistic
4.8178
1.6599
3.7878
3.5482
p-Value
1.4516e-006
0.096938
0.00015197
0.00038786
Excess Kyrtosis
5.6736***
5.0302***
5.8516***
5.4135***
t-Statistic
67.330
59.694
69.443
64.244
p-Value
0.00000
0.00000
0.00000
0.00000
Jarque-Bera
4549.9***
3560.9***
4829.5***
4133.8***
p-Value
0.00000
0.00000
0.00000
0.00000
ADF Test
-34.9155***
-34.9721***
-35.1153***
-34.9907*** 71
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TSIARAS. K. VOLATILITY SPILLOVER AND CONTAGION EFFECTS BETWEEN EURODOLLAR FUTURE AND ZERO COUPONS MARKETS: EVIDENCE FROM ITALY
Figure 1 graphs the logarithmic returns for DGCX-EUR/USD CONTINUOUS AVG.- SETT. PRICE, BANCA FIDEURAM ZERO CPN. 2018, BANCA FIDEURAM ZERO CPN. 2019, BANCA FIDEURAM ZERO CPN. 2020, BANCA FIDEURAM ZERO CPN. 2021, BANCA FIDEURAM ZERO CPN. 2022, BANCA FIDEURAM ZERO CPN. 2023, BANCA FIDEURAM ZERO CPN. 2024, BANCA FIDEURAM ZERO CPN. 2025, BANCA FIDEURAM ZERO CPN. 2026, BANCA FIDEURAM ZERO CPN. 2027, BANCA FIDEURAM ZERO CPN. 2028, BANCA FIDEURAM ZERO CPN. 2029, BANCA FIDEURAM ZERO CPN. 2030, BANCA FIDEURAM ZERO CPN. 2031, BANCA FIDEURAM ZERO CPN. 2032 and BANCA FIDEURAM ZERO CPN. 2033. Based on the virtual observation of the graph, we see time varying levels of fluctuations, indicating the presence of heteroskedasticity and appropriate the use of the DCC-GARCH model. Figure 1 - Actual Series of the Logarithmic Returns of the Markets.
METHODOLODY In the first stage, we generate the daily logarithmic returns: (1)
, with t = 1,…,T where
μ is constant
and
, where where
is standardized errors and
is standardized residuals, defined as follows: and
are i.i.d.
is conditional variance depending on
(2) and
for each market lagged one period, generated by the univariate GARCH(1,1) model (Bollerslev 1986): 72
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TSIARAS. K. VOLATILITY SPILLOVER AND CONTAGION EFFECTS BETWEEN EURODOLLAR FUTURE AND ZERO COUPONS MARKETS: EVIDENCE FROM ITALY
(3) where ω is constant, a and b are ARCH and GARCH effects. In the second stage, we employ the Engle (2002) representation of the bivariate GARCH model inorder to estimate the bivariate conditional variance matrix ( Ht is N x N matrix, with N the number of markets, i = 1,…,N) as follows: (4) is the conditional variance matrix given by: (5)
Rt is the condition correlation matrix of N x N dimension, and is defined, as follows: (6) where the N x N symmetric positive definite matrix
is given by: (7)
i s the N x N unconditional variance matrix of parameters, satisfying α + β < 1.
ut , and α and β are nonnegative scalar
EMPIRICAL RESULTS In this section, we present the empirical results generated by the multivariate DCC-GARCH model. Sub-section 5.1 shows the results of the univariate GARCH model, while in sub-section 5.2 we analyze the results of the multivariate DCC-GARCH model. In sub-section 5.3, we report an analysis of the generated Dynamic Conditional Correlations (DCCs). RESULTS OF THE UNIVARIATE GARCH (1,1) MODEL Tables 5, 6, 7 and 8 show the estimated values for mean equation and univariate GARCH (1,1) model. We observe statistically significant μ for all the market returns, except the case of DGCX-EUR/USD CONTINUOUS AVG.- SETT. PRICE. Additionally, empirical results report statistically significant ω for all the market returns. Moreover, ARCH (a) and GARCH (b) terms are highly significant for all the markets returns.
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Table 5 - Estimates of Univariate GARCH (1,1) Model DGCXEUR/USD CONTINUOUS AVG.- SETT. PRICE
BANCA FIDEURAM ZERO CPN. 2018
BANCA FIDEURAM ZERO CPN. 2019
BANCA FIDEURAM ZERO CPN. 2020
BANCA FIDEURAM ZERO CPN. 2021
constant (μ)
0.0000377
0.0000221*
0.0000702***
0.0001333***
0.0001804***
t-Statistic
0.4768
1.517
3.051
3.603
3.624
p-Value
0.6335
0.1294
0.0023
0.0003
0.0003
constant (ω)
0.050450*
0.001737*
0.005068*
0.016843*
0.040020*
t-Statistic
1.107
1.288
1.370
1.464
1.478
p-Value
0.2683
0.1979
0.1708
0.1433
0.1394
ARCH ( a )
0.037845***
0.114787***
0.099745***
0.081683***
0.070081***
t-Statistic
7.898
4.879
3.732
3.699
3.345
p-Value
0.0000
0.0000
0.0002
0.0002
0.0008
GARCH (b)
0.964238***
0.896551***
0.908039***
0.922360***
0.931229***
t-Statistic
227.3
49.48
42.07
48.76
48.81
p-Value
0.0000
0.0000
0.0000
0.0000
0.0000
Table 6 - Estimates of Univariate GARCH (1,1) Model
74
BANCA FIDEURAM ZERO CPN. 2022
BANCA FIDEURAM ZERO CPN. 2023
BANCA FIDEURAM ZERO CPN. 2024
BANCA FIDEURAM ZERO CPN. 2025
constant (μ)
0.0002175***
0.0002566***
0.0002897***
0.0003358***
t-Statistic
3.763
3.874
3.815
4.154
p-Value
0.0002
0.0001
0.0001
0.0000
constant (ω)
0.062064*
0.117230*
0.181788*
0.373052**
t-Statistic
1.628
1.878
1.656
1.990
p-Value
0.1036
0.0604
0.0978
0.0466
ARCH ( a)
0.064147***
0.066864***
0.060533***
0.074506***
t-Statistic
3.509
4.691
3.863
3.808
p-Value
0.0005
0.0000
0.0001
0.0001
GARCH (b)
0.935513***
0.931312***
0.935657***
0.917570***
t-Statistic
54.52
65.63
55.49
41.82
p-Value
0.0000
0.00000
0.0000
0.0000
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Table 7 - Estimates of Univariate GARCH (1,1) Model BANCA FIDEURAM ZERO CPN. 2026
BANCA FIDEURAM ZERO CPN. 2027
BANCA FIDEURAM ZERO CPN. 2028
BANCA FIDEURAM ZERO CPN. 2029
constant (μ)
0.0003728***
0.0003899***
0.0003379***
0.000410***
t-Statistic
4.199
4.145
3.702
3.874
p-Value
0.0000
0,0000
0.0002
0.0001
constant (ω)
0.585090***
0.865811***
0.803986***
1.664445***
t-Statistic
2.282
2.165
2.104
2.049
p-Value
0.0225
0.0304
0.0354
0.0406
ARCH ( a)
0.078349***
0.086326***
0.075854***
0.101611***
t-Statistic
4.625
4.194
4.193
3.700
p-Value
0.0000
0.0000
0.0000
0.0002
GARCH (b)
0.910445***
0.898462***
0.911873***
0.874166***
t-Statistic
44.88
34.42
40.40
23.27
p-Value
0.0000
0.0000
0.0000
0.0000
Table 8 - Estimates of Univariate GARCH (1,1) Model BANCA FIDEURAM ZERO CPN. 2030
BANCA FIDEURAM ZERO CPN. 2031
BANCA FIDEURAM ZERO CPN. 2032
BANCA FIDEURAM ZERO CPN. 2033
constant (μ)
0,000423***
0.000457***
0.000462***
0.000448***
t-Statistic
3.785
3.884
3.786
3.596
p-Value
0.0002
0.0001
0.0002
0.0003
constant (ω)
1.354293**
1.555353***
1.497339***
1.648639***
t-Statistic
1.903
2.129
2.453
2.399
p-Value
0.0571
0.0334
0.0142
0.0165
ARCH ( a)
0.083461***
0.088127***
0.080689***
0.082399***
t-Statistic
3.603
4.198
4.700
4.553
p-Value
0.0003
0.0000
0.0000
0.0000
GARCH (b)
0.898062***
0.893645***
0.902314***
0.899750***
t-Statistic
28.59
31.76
40.47
37.88
p-Value
0.0000
0.0000
0.0000
0.0000
75
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TSIARAS. K. VOLATILITY SPILLOVER AND CONTAGION EFFECTS BETWEEN EURODOLLAR FUTURE AND ZERO COUPONS MARKETS: EVIDENCE FROM ITALY
In figure 2, we observe the behavior of conditional variances for DGCX-EUR/USD CONTINUOUS AVG.- SETT. PRICE, BANCA FIDEURAM ZERO CPN. 2018, BANCA FIDEURAM ZERO CPN. 2019, BANCA FIDEURAM ZERO CPN. 2020, BANCA FIDEURAM ZERO CPN. 2021, BANCA FIDEURAM ZERO CPN. 2022, BANCA FIDEURAM ZERO CPN. 2023, BANCA FIDEURAM ZERO CPN. 2024, BANCA FIDEURAM ZERO CPN. 2025, BANCA FIDEURAM ZERO CPN. 2026, BANCA FIDEURAM ZERO CPN. 2027, BANCA FIDEURAM ZERO CPN. 2028, BANCA FIDEURAM ZERO CPN. 2029, BANCA FIDEURAM ZERO CPN. 2030, BANCA FIDEURAM ZERO CPN. 2031, BANCA FIDEURAM ZERO CPN. 2032, and BANCA FIDEURAM ZERO CPN. 2033. We see strongly volatile conditional variances for all the market returns over time. Additionally, results indicate a common movement of conditional volatilities. Figure 2 - Conditional Variances of the Univariate GARCH (1,1) Model.
RESULTS OF THE BIVARIATE DCC-GARCH (1,1) MODEL, DIAGNOSTIC TESTS AND SELECTED INFORMATION CRITERIA Tables 9, 10, 11 and 12 present the results of the bivariate DCC model estimations. We observe that the average CORij is statistically significant for the pairs of markets: DGCX-EUR/USD CONTINUOUS AVG.- SETT. PRICE - BANCA FIDEURAM ZERO CPN. 2027, DGCX-EUR/USD CONTINUOUS AVG.- SETT. PRICE - BANCA FIDEURAM ZERO CPN. 2029 and DGCX-EUR/USD CONTINUOUS AVG.- SETT. PRICE - BANCA FIDEURAM ZERO CPN. 2032. Furthermore, we see statistically significant α and β parameters, indicating strong ARCH and GARCH effects for all the pairs of market returns (Efimova and Serletis 2014; Li and Giles 2014; Sehgal and Ghosh 2016; Chang et al 2018; Sun et al 2019; Sukhonpitumart et al 2020; Yu et al 2020; Belhassine 2020). Additionally, we provide the estimates of the degrees of freedom (v) and of the log-likelihood. 76
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Table 9 - Estimates of the Bivariate DCC-GARCH (1,1) Model, Degrees of Freedom, Log-likelihood DGCXEUR/USD CONTINUOUS AVG.- SETT. PRICE - BANCA FIDEURAM ZERO CPN. 2018
DGCXDGCXDGCXEUR/USD EUR/USD EUR/USD CONTINUOUS CONTINUOUS CONTINUOUS AVG.- SETT. AVG.- SETT. AVG.- SETT. PRICE - BANCA PRICE - BANCA PRICE - BANCA FIDEURAM FIDEURAM FIDEURAM ZERO CPN. ZERO CPN. ZERO CPN. 2019 2020 2021
Average CORij
-0.028369
-0.029169
-0.26245
-0.028106
t-Statistic
-0.7921
-0.8427
-0.7534
-0.7739
p-Value
0.4284
0.3995
0.4513
0.4390
alpha (α)
0.008466***
0.010216***
0.008200***
0.008050***
t-Statistic
3.041
3.571
3.473
3.449
p-Value
0.0024
0.0004
0.0005
0.0006
beta (β)
0.983542***
0.979266***
0.983939***
0.984555***
t-Statistic
179.9
185.2
226.1
240.0
p-Value
0.0000
0.0000
0.0000
0.0000
degrees of freedom (df)
5.730267***
5.875022***
6.500954***
6.450702***
t-Statistic
12.31
12.45
12.39
12.46
p-Value
0.0000
0.0000
0.0000
0.0000
log-likelihood
28728.993
28046.932
27161.298
26627.008
Table 10 - Estimates of the Bivariate DCC-GARCH (1,1) Model, Degrees of Freedom, Log-likelihood DGCXDGCXDGCXDGCXEUR/USD EUR/USD EUR/USD EUR/USD CONTINUOUS CONTINUOUS CONTINUOUS CONTINUOUS AVG.- SETT. AVG.- SETT. AVG.- SETT. AVG.- SETT. PRICE - BANCA PRICE - BANCA PRICE - BANCA PRICE - BANCA FIDEURAM FIDEURAM FIDEURAM FIDEURAM ZERO CPN. ZERO CPN. ZERO CPN. ZERO CPN. 2022 2023 2024 2025 Average CORij
-0.032175
-0.029485
-0.026158
-0.030716
t-Statistic
-0.9473
-0.8569
-0.7185
-0.8789
p-Value
0.3435
0.3915
0.4725
0.3795
alpha (α)
0.007305***
0.008021***
0.008913***
0.007824***
t-Statistic
3.280
3.461
3.563
3.340
p-Value
0.0010
0.0005
0.0004
0.0008
beta (β)
0.984699***
0.983735***
0.982894***
0.984395***
t-Statistic
233.1
245.0
215.6
216.9
p-Value
0.0000
0.0000
0.0000
0.0000
degrees of freedom (df)
6.448894***
6.700370***
6.856377***
7.074205***
t-Statistic
12.41
11.73
11.79
12.19
p-Value
0.0000
0.0000
0.0000
0.0000
log-likelihood
26326.015
26048.641
25727.520
25603.802
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Table 11 - Estimates of the Bivariate DCC-GARCH (1,1) Model, Degrees of Freedom, Log-likelihood DGCXDGCXDGCXDGCXEUR/USD EUR/USD EUR/USD EUR/USD CONTINUOUS CONTINUOUS CONTINUOUS CONTINUOUS AVG.- SETT. AVG.- SETT. AVG.- SETT. AVG.- SETT. PRICE - BANCA PRICE - BANCA PRICE - BANCA PRICE - BANCA FIDEURAM FIDEURAM FIDEURAM FIDEURAM ZERO CPN. ZERO CPN. ZERO CPN. ZERO CPN. 2026 2027 2028 2029 Average CORij
-0.021714
-0.036694*
-0.020921
-0.034145*
t-Statistic
-0.6194
-1.080
-0.6453
-1.064
p-Value
0.5357
0.2803
0.5188
0.2872
alpha (α)
0.007685***
0.007194***
0.006878***
0.007204***
t-Statistic
3.413
3.301
3.168
3.132
p-Value
0.0006
0.0010
0.0015
0.0018
beta (β)
0.984614***
0.985149***
0.985076***
0.984160***
t-Statistic
243.9
253.2
243.3
213.3
p-Value
0.0000
0.0000
0.0000
0.0000
degrees of freedom (df)
6.853648***
6.925275***
7.264400***
7.044709***
t-Statistic
12.27
11.90
11.70
11.62
p-Value
0.0000
0.0000
0.0000
0.0000
log-likelihood
25334.343
25132.457
24870.512
24754.147
Table 12 - Estimates of the Bivariate DCC-GARCH (1,1) Model, Degrees of Freedom, Log-likelihood DGCXDGCXDGCXDGCXEUR/USD EUR/USD EUR/USD EUR/USD CONTINUOUS CONTINUOUS CONTINUOUS CONTINUOUS AVG.- SETT. AVG.- SETT. AVG.- SETT. AVG.- SETT. PRICE - BANCA PRICE - BANCA PRICE - BANCA PRICE - BANCA FIDEURAM FIDEURAM FIDEURAM FIDEURAM ZERO CPN. ZERO CPN. ZERO CPN. ZERO CPN. 2026 2027 2028 2029
78
Average CORij
-0.030906
-0.029990
-0.036048*
-0.029070
t-Statistic
-0.9881
-0.9409
-1.147
-0.8904
p-Value
0.3232
0.3468
0.2513
0.3733
alpha (α)
0.007099***
0.007192***
0.006640***
0.007193***
t-Statistic
3.039
2.999
3.031
3.168
p-Value
0.0024
0.0027
0.0025
0.0015
beta (β)
0.983747***
0.984130***
0.984981***
0.984612***
t-Statistic
209.6
196.7
222.6
214.6
p-Value
0.0000
0.0000
0.0000
0.0000
degrees of freedom (df)
7.029137***
7.042781***
6.986029***
7.010423***
t-Statistic
11.65
11.62
11.84
12.00
p-Value
0.0000
0.0000
0.0000
0.0000
log-likelihood
24602.282
24394.485
24298.304
24222.677
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In tables 13, 14, 15, and 16, we report the results of the diagnostic tests and information criteria. statistic results suggest that the null hypothesis of no spillovers is rejected at 1% significance level. Ljuing-Box test results (Hosking, 1980; Li-McLeod, 1983) provide evidence of no serial autocorrelation, suggesting the absence of misspecification errors of the estimated multivariate GARCH model. Moreover, the estimated AIC and SIC information criteria are presented.
x2(4)
Table 13 - Diagnostic Tests and Information Criteria DGCXDGCXDGCXDGCXEUR/USD EUR/USD EUR/USD EUR/USD CONTINUOUS CONTINUOUS CONTINUOUS CONTINUOUS AVG.- SETT. AVG.- SETT. AVG.- SETT. AVG.- SETT. PRICE - BANCA PRICE - BANCA PRICE - BANCA PRICE - BANCA FIDEURAM FIDEURAM FIDEURAM FIDEURAM ZERO CPN. ZERO CPN. ZERO CPN. ZERO CPN. 2018 2019 2020 2021 x2 (4)
8610.8**
7362.6**
1550.4**
1156.0**
p-Value
0.0000
0.0000
0.0000
0.0000
Hosking (50)
202.361
221.370
211.544
219.100
p-Value
0.4400240
0.1432761
0.2743670
0.1687156
Hosking (50)
171.515
166.338
226.453
230.384
2
p-Value
0.9133156
0.9506138
0.0808038
0.0571307
Li-McLeod (50)
202.575
221.452
211.587
219.146
p-Value
0.4358555
0.1424173
0.2736908
0.1681628
Li-McLeod (50)
172.026
166.843
226.539
230.334
p-Value
0.9087771
0.9476324
0.0802170
0.0573922
Akaike
0.002067
0.002187
0.002342
0.002436
Schwarz
0.023842
0.023961
0.024117
0.024211
2
Table 14 - Diagnostic Tests and Information Criteria DGCXDGCXDGCXDGCXEUR/USD EUR/USD EUR/USD EUR/USD CONTINUOUS CONTINUOUS CONTINUOUS CONTINUOUS AVG.- SETT. AVG.- SETT. AVG.- SETT. AVG.- SETT. PRICE - BANCA PRICE - BANCA PRICE - BANCA PRICE - BANCA FIDEURAM FIDEURAM FIDEURAM FIDEURAM ZERO CPN. ZERO CPN. ZERO CPN. ZERO CPN. 2022 2023 2024 2025 x2 (4)
1970.3**
2510,7**
1463.2**
688.23**
p-Value
0.0000
0.0000
0.0000
0.0000
Hosking (50)
221.416
202.490
204.207
195.549
p-Value
0.1427906
0.4375132
0.4043224
0.5756642
Hosking (50)
201.280
181.741
194.714
191.925
p-Value
0.4217686
0.7901022
0.5527005
0.6082992
Li-McLeod (50)
221.507
202.618
204.279
195.733
p-Value
0.1418376
0.4350033
0.4029490
0.5720145
2
Li-McLeod (50)
201.571
182.022
194.924
192.107
p-Value
0.4161116
0.7857841
0.5484897
0.6046953
Akaike
0.002489
0.002537
0.002594
0.002616
Schwarz
0.024263
0.024312
0.024369
0.024390
2
79
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Table 15 - Diagnostic Tests and Information Criteria DGCXDGCXDGCXDGCXEUR/USD EUR/USD EUR/USD EUR/USD CONTINUOUS CONTINUOUS CONTINUOUS CONTINUOUS AVG.- SETT. AVG.- SETT. AVG.- SETT. AVG.- SETT. PRICE - BANCA PRICE - BANCA PRICE - BANCA PRICE - BANCA FIDEURAM FIDEURAM FIDEURAM FIDEURAM ZERO CPN. ZERO CPN. ZERO CPN. ZERO CPN. 2027 2028 2029 2026 x2 (4)
850.54**
1487.7**
688.45**
11490144.8**
p-Value
0.0000
0.0000
0.0000
0.0000
Hosking (50)
189.425
189.649
198.376
191.851
p-Value
0.6931695
0.6890983
0.5191595
0.6479014
Hosking (50)
210.468
205.508
211.294
218.450
p-Value
0.2587621
0.3423628
0.2460402
0.1520976
Li-McLeod (50)
189.609
189.939
198.546
192.042
p-Value
0.6898251
0.6856063
0.5157553
0.6442619
2
Li-McLeod (50)
210.356
205.489
211.277
218.314
p-Value
0.2605135
0.3427115
0.2463003
0.1536092
Akaike
0.002663
0.002698
0.002744
0.002765
Schwarz
0.024438
0.024473
0.024519
0.024539
2
Table 16 - Diagnostic Tests and Information Criteria DGCXDGCXDGCXDGCXEUR/USD EUR/USD EUR/USD EUR/USD CONTINUOUS CONTINUOUS CONTINUOUS CONTINUOUS AVG.- SETT. AVG.- SETT. AVG.- SETT. AVG.- SETT. PRICE - BANCA PRICE - BANCA PRICE - BANCA PRICE - BANCA FIDEURAM FIDEURAM FIDEURAM FIDEURAM ZERO CPN. ZERO CPN. ZERO CPN. ZERO CPN. 2030 2031 2032 2033 x2 (4)
1615.6**
1320.5**
1265,8**
898,61**
p-Value
0.0000
0.0000
0.0000
0.0000
Hosking (50)
188.043
187.970
205.352
203.574
p-Value
0.7179321
0.7192047
0.3826575
0.4164732
Hosking2 (50)
207.557
202.431
192.019
197.642
p-Value
0.3063950
0.3995570
0.6064352
0.4938025
Li-McLeod (50)
188.272
188.175
205.438
203.,681
p-Value
0.7138843
0.7155944
0.3810407
0.4144023
Li-McLeod2 (50)
207.483
202.369
192.069
197.676
p-Value
0.3076582
0.4007574
0.6054549
0.4931197
Akaike
0.002791
0.002828
0.002845
0.002858
Schwarz
0.024566
0.024603
0.024619
0.024633
Figures 3 and 4 plot the conditional covariances for all the pairs of market returns during the whole period. We observe a tremble trend for all the conditional covariances. Additionally, conditional covariances seem to be extremely volatile. 80
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Figure 3 - Conditional Covariances of the Bivariate DCC-GARCH (1,1) Model.
Figure 4 - Conditional Covariances of the Bivariate DCC-GARCH (1,1) Model.
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ANALYSIS OF THE DYNAMIC CONDITIONAL CORRELATIONS (DCCS) Tables 17, 18, 19 and 20 show the descriptive statistics of the dynamic conditional correlations (DCCs) of the twenty-one pairs of markets generated by Equation 5. We observe the highest mean value (0,29828) for the pair of markets DGCX-EUR/USD CONTINUOUS AVG.- SETT. PRICE - BANCA FIDEURAM ZERO CPN. 2028. The highest std. deviation value for the pair of markets DGCX-EUR/USD CONTINUOUS AVG.- SETT. PRICE - BANCA FIDEURAM ZERO CPN. 2019 indicates that the specific DCC experiences larger fluctuations. The statistically significant Skewness, Excess Kyrtosis and the Jarque-Bera test statistics indicate that the DCCs for all the pairs of markets are not normally distributed. Table 17 - Statistical Properties of the Multivariate GARCH-DCC’s DGCXDGCXDGCXDGCXEUR/USD EUR/USD EUR/USD EUR/USD CONTINUOUS CONTINUOUS CONTINUOUS CONTINUOUS AVG.- SETT. AVG.- SETT. AVG.- SETT. AVG.- SETT. PRICE - BANCA PRICE - BANCA PRICE - BANCA PRICE - BANCA FIDEURAM FIDEURAM FIDEURAM FIDEURAM ZERO CPN. ZERO CPN. ZERO CPN. ZERO CPN. 2018 2019 2020 2021
82
Mean
-0.039373
-0.042172
-0.03832
-0.043446
Minimum
-0.35803
-0.36773
-0.33772
-0.33122
Maximum
0.16949
0.19866
0.15557
0.13102
Std. Deviation
0.086969
0.088029
0.083352
0.081366
Skewness
-0.66274***
-0.59605***
-0.77505***
-0.86371***
p-Value
1.0141e-055
2.0647e-045
1.5787e-075
2.4504e-093
Excess Kyrtosis
0.77644***
0.67404***
0.74112***
0.71865***
p-Value
3.1330e-020
1.2544e-015
1.4292e-018
1.4835e-017
Jarque-Bera
331.84***
263.73***
415.13***
492.25***
p-Value
8.7272e-073
5.3801e-058
7.1627e-091
1.2891e-107
Akaike
0.002791
0.002828
0.002845
0.002858
Schwarz
0.024566
0.024603
0.024619
0.024633
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Table 18 - Statistical Properties of the Multivariate GARCH-DCC’s DGCXDGCXDGCXDGCXEUR/USD EUR/USD EUR/USD EUR/USD CONTINUOUS CONTINUOUS CONTINUOUS CONTINUOUS AVG.- SETT. AVG.- SETT. AVG.- SETT. AVG.- SETT. PRICE - BANCA PRICE - BANCA PRICE - BANCA PRICE - BANCA FIDEURAM FIDEURAM FIDEURAM FIDEURAM ZERO CPN. ZERO CPN. ZERO CPN. ZERO CPN. 2022 2023 2024 2025 Mean
-0.044479
-0.036367
-0.03233
-0.043351
Minimum
-0.29975
-0.32593
-0.32002
-0.31119
Maximum
0.13255
0.15368
0.16649
0.1375
Std. Deviation
0.075008
0.078369
0.086434
0.079562
Skewness
-0.59792***
-0.61477***
-0.61657***
-0.70375***
p-Value
1.0982e-045
3.3949e-048
1.8166e-048
1.3480e-062
Excess Kyrtosis
0.46461***
0.67357***
0.40503***
0.70271***
p-Value
3.5133e-008
1.3114e-015
1.5354e-006
7.4775e-017
Jarque-Bera
231.46***
276.40***
236.91***
348.03***
p-Value
5.4935e-051
9.5830e-061
3.6013e-052
2.6732e-076
Table 19 - Statistical Properties of the Multivariate GARCH-DCC’s DGCXDGCXDGCXDGCXEUR/USD EUR/USD EUR/USD EUR/USD CONTINUOUS CONTINUOUS CONTINUOUS CONTINUOUS AVG.- SETT. AVG.- SETT. AVG.- SETT. AVG.- SETT. PRICE - BANCA PRICE - BANCA PRICE - BANCA PRICE - BANCA FIDEURAM FIDEURAM FIDEURAM FIDEURAM ZERO CPN. ZERO CPN. ZERO CPN. ZERO CPN. 2026 2027 2028 2029 Mean
-0.033146
-0.042371
-0.032106
-0.042044
Minimum
-0.29929
-0.28349
-0.25206
-0.2736
Maximum
0.11153
0.092663
0.10489
0.096035
Std. Deviation
0.077377
0.071889
0.0673
0.069413
Skewness
-0.96329***
-0.95533***
-0.87138***
-0.93736***
p-Value
1.2552e-115
9.3446e-114
5.7226e-095
1.3692e-109
Excess Kyrtosis
0.93473***
0.91733***
0.87606***
1.0724***
p-Value
1.3611e-028
1.3410e-027
2.5739e-025
4.2329e-037
Jarque-Bera
644.83***
631.70***
535.03***
655.95***
p-Value
9.4906e-141
6.7291e-138
6.5873e-117
3.6472e-143
83
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Table 20 - Statistical Properties of the Multivariate GARCH-DCC’s DGCXDGCXDGCXDGCXEUR/USD EUR/USD EUR/USD EUR/USD CONTINUOUS CONTINUOUS CONTINUOUS CONTINUOUS AVG.- SETT. AVG.- SETT. AVG.- SETT. AVG.- SETT. PRICE - BANCA PRICE - BANCA PRICE - BANCA PRICE - BANCA FIDEURAM FIDEURAM FIDEURAM FIDEURAM ZERO CPN. ZERO CPN. ZERO CPN. ZERO CPN. 2030 2031 2032 2033 Mean
-0.038035
-0.037904
-0.043152
-0.037287
Minimum
-0.25894
-0.26307
-0.25104
-0.25792
Maximum
0.095731
0.10924
0.097284
0.10538
Std. Deviation
0.065429
0.06739
0.064469
0.068924
Skewness
-0.87481***
-0.79454***
-0.86477***
-0.92264***
p-Value
1.0575e-095
2.7998e-079
1.4595e-093
3.0921e-106
Excess Kyrtosis
0.87804***
0.90575***
1.0857***
1.0885***
p-Value
2.0112e-025
6.0021e-027
5.4860e-038
3.5911e-038
Jarque-Bera
538.89***
470.47***
586.42***
645.45***
p-Value
9.5881e-118
6.8992e-103
4.5696e-128
6.9584e-141
Figures 5 and 6 present the pair-wise Dynamic Conditional Correlations (DCCs). We observe strong co-movements for all DCCs. DCCs have positive values in sub-periods, indicating the existence of contagion, implying the specific correlations risky for any investor. Furthermore, we can notice the effects of major economic events on the DCC graphs as we see that the lines are bouncing above and beyond, i.e. (a) the bankruptcy of Lehman Brothers (14/09/2008), (b) the European Central Bank announcement of an aggressive money-creation program, printing more than one trillion new euros (22/01/2015), (c) Black Monday (24/08/2015), and (d) the United Kingdom referendum (23/06/2016), among others. Figure 5 - Dynamic Conditional Correlations for All the Pairs of Markets Generated by the Bivariate DCC-GARCH (1,1) Model.
84
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Figure 6 - Dynamic Conditional Correlations for All the Pairs of Markets Generated by the Bivariate DCC-GARCH (1,1) Model.
CONCLUSIONS This paper investigates the potential volatility spillovers effects and the existence of contagion effects of the Eurodollar futures market and sixteen zero coupons of Banca Fideuram by employing a bivariate DCC-GARCH model. We set the under-investigation period from 2005 until 2017. To the best of our knowledge, this is the first empirical study investigating volatility spillovers between the Eurodollar futures market and the zero coupons of Banca Fideuram. The main empirical results are summarized as follows. Based on the descriptive statistics, BANCA FIDEURAM ZERO CPN. 2032 returns present the largest fluctuations compared to the rest markets. Furthermore, results of the bivariate DCC-GARCH model indicate strong evidence of volatility spillover effects. DCCs analysis shows evidence of strong co-movements for all the pairs of markets. Additionally, DCCs reveal contagion for all the pairs of markets in sub-periods. The empirical results are of interest to policymakers, who provide regulations for the under-investigation derivative markets, as well as to market-makers.
ACKNOWLEDGMENTS This article was carried out by me independently. The research is original, and has not been submitted to any other journal. I want to thank the anonymous referees for their valuable comments and suggestions which helped me to improve the paper. Any responsibility for remaining errors in the resulting work is my own. 85
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Ma, Y.R., Zhang, D., Ji, Q., & Pan, J. (2019). Spillovers between oil and stock returns in the US energy sector: Does idiosyncratic information matter? Energy Economics 81, 536-544. DOI: 10.1016/j.eneco.2019.05.003. Mensi, W., Beljid, M., Boubaker, A., & Managi, S. (2013). Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold. Economic Modelling 32, 15-22. DOI: 10.1016/j.econmod.2013.01.023. Roy, R.P., & Roy, S.S. (2017). Financial contagion and volatility spillover: An exploration into Indian commodity derivative market. Economic Modelling 67, 368-380. DOI: 10.1016/j.econmod.2017.02.019. Sehgal, S., Ahmad W., & Deisting, F. (2015). An investigation of price discovery and volatility spillovers in India’s foreign exchange market. Journal of Economic Studies 42(2), 261-284. ISSN: 0144-3585. Sensoy, A., Hacihasanoglu, E., & Nguyen, D.K., (2015). Dynamic convergence of commodity futures: Not all types of commodities are alike. Resources Policy 44(3). DOI: 10.1016/j.resourpol.2015.03.001. Singhal S., & Ghosh, S. (2016). Returns and volatility linkages between international crude oil price, metal and other stock indices in India: Evidence from VAR-DCC-GARCH models. Resources Policy. Vol. 50(3), 276-288 (2016) DOI: 10.1016/j.resourpol.2016.10.001. Sukhonpitumart, P., Jaroenjitrkam, A., Maneenop, S., & Padungsaksawasdi, C. (2020). RETURN AND VOLATILITY SPILLOVERS BETWEEN STOCK AND FUTURES MARKETS IN THAILAND. Academy of Accounting and Financial Studies Journal 24(2), 1-14. Sun, X., Haralambides, H., & Liu, H. (2019). Dynamic spillover effects among derivative markets in tanker shipping. Transportation Research Part E: Logistics and Transportation Review 122, 384-389. DOI: 10.1016/j.tre.2018.12.018. Tsiaras, K., & Simos, T. (2020). FOREX and equity markets spillover effects among USA, Brazil, Italy, Germany and Canada in the aftermath of the Global Financial Crisis. Journal of Finance and Accounting Research 2(1). DOI: 10.32350/JFAR/0201/03. Tsiaras, K. (2020). Dynamic relationship between future FOREX markets in the post Global Financial Crisis. Journal of Quantitative Methods 4(1), 30-52. DOI: 10.29145/2020/jqm/040102. Tsiaras, K. (2020). Contagion in crude oil future market and 3Y, 4Y and 5Y CDS markets for the post-Global Financial Crisis: A multivariate GARCH-cDCC approach. Ekonomická revue. Accepted Paper for publication in the upcoming issue. Yu, L., Zha, R., Stafylas, D., He, K., & Liu, J. (2020). Dependences and volatility spillovers between the oil and stock markets: New evidence from the copula and VAR-BEKK-GARCH models. International Review of Financial Analysis 68. DOI: 10.1016/j.irfa.2018.11.007.
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TSIARAS. K. VOLATILITY SPILLOVER AND CONTAGION EFFECTS BETWEEN EURODOLLAR FUTURE AND ZERO COUPONS MARKETS: EVIDENCE FROM ITALY
PRELIVANJE NESTABILNOSTI I EFEKAT ZARAZE IZMEĐU TRŽIŠTA EVRO DOLARSKIH FJUČERSA I BEZKUPONSKIH OBVEZNICA: DOKAZI IZ ITALIJE
Rezime: Ovaj rad ispituje vremenski različite uslovne korelacije između terminskog tržišta Eurodollar i nula kupona Banca Fideuram. Primenjujemo GARCH model bivarijantne dinamičke uslovne korelacije (DCC) kako bismo zabeležili potencijalne efekte zaraze između tržišta za period 2005-2017. Empirijski rezultati otkrivaju zarazu tokom istražnog perioda u vezi sa dvadeset i jednim bivarijantnim modelom, pokazujući da tržište futura Eurodollar ima veliki uticaj na nulte kupone Banca Fideuram. Nalazi imaju presudne implikacije za kreatore politika koji pružaju propise za gore navedena tržišta derivata.
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Ključne reči: DCC-GARCH model, buduće tržište EURODOLLAR, nula kupona, finansijska zaraza, dinamičke uslovne korelacije. Klasifikacija jela: C58, C61, G11, G15.
EJAE 2020, 17(2): 89 - 103 ISSN 2406-2588 UDK: 338.482:316.346(497.115) DOI: 10.5937/EJAE17-26800 Original paper/Originalni naučni rad
THE INFLUENCE OF SOCIODEMOGRAPHIC CHARACTERISTICS OF RESIDENTS ON THE PERCEPTION OF TOURISM DEVELOPMENT IMPACTS Ilinka Stojković, Jelena Tepavčević*, Ivana Blešić, Milan Ivkov, Viktorija Šimon Faculty of Natural Sciences, Department of Geography, Tourism and Hospitality, University of Novi Sad, Serbia
Abstract: Residents play an important role in the process of destination development, but a supportive attitude in regard to tourism is key to the success of the touristic destination. Various authors have examined residents’ attitudes towards the impacts of tourism development from different points of view. This paper aimed to investigate the impacts of sociodemographic characteristics of locals on their attitudes towards tourism development. The study was conducted on the territory of Sirinićka Župa, in the municipality of Štrpce. A total of 94 respondents were included in the research. The results indicated that gender and education level have not influenced residents’ attitudes. On the other hand, it has been found that the age and length of residents’ significantly influence residents’ attitudes towards tourism development impacts.
Article info: Received: May 30, 2020 Correction: July 31, 2020 Accepted: September 11, 2020
Keywords: Sirinićka Župa, residents, tourism development, tourism impacts, sociodemographic characteristics.
INTRODUCTION Local population is fundamental for the growth and success of touristic destinations, so it is important to follow a positive feeling towards the growth of tourism. The support for tourism development provided by residents can contribute to the tenacity and successful improvement of community in developing (Albu, 2020; Hai and Alamgir, 2017; Kihima and Musila, 2019) and developed countries (Castela, 2018; Rasoolimanesh et al., 2017). There is growing proof that residents of communities that attract visitors express heterogeneous views about their region`s growth (Mason and Cheyne, 2000) and their attitudes vary depending on the stage of development of the destination (Butler, 1980; Doxey, 1975). Throughout the years, residents have been exposed to tourism development effects (Brida et al., 2010) and these effects are clustered to show socio-cultural, economic, and environmental aspects of sustainable development of tourism (Andersson et al., 2016). Tourism development can negatively influence a local community *E-mail: jelenat91@gmail.com
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due to the environmental and socio-cultural costs (Lo et al., 2014; Naidoo and Sharpley, 2015). On the other hand, the effects of investing in the local community are reduced departure of educated and young people, a reduction of poverty, higher employment, encouragement of opening of small and medium enterprises, etc. (Lyons, 2015). An increasing number of research has dealt with different approaches of examination of the attitudes and perceptions of residents towards tourism development effects (Adongo et al., 2017; Cavus and Tanrisevdi, 2002; Eslami et al., 2019; Gursoy et al., 2018; Ouyang et al., 2017; Rasoolimanesh et al., 2017; Zhu et al., 2017). There are few studies on the impact of tourism either before any growth was not registered or when it has not yet considered to be a major economy (Hernandez et al., 1996). Researchers have also attempted to create links between specific local views and other facets of these populations, such as demographic influences, reliance on tourism, and closeness of residents to this growth. Although several studies have indicated that demographic factors are not correlated with local populations’ attitudes in regard to tourism development (Cui and Ryan, 2011; Nunkoo and Ramkissoon, 2010), many studies have found a significant impact of sociodemographic characteristics on shaping residents’ attitudes (Bagri and Kala, 2016; Cavus and Tanrisevdi, 2002; Jackson and Inbakaran, 2006; Mason and Cheyne, 2000; Snyman, 2014, Vareiro et al., 2013). Jackson and Inbakaran (2006) identified general characteristics of the destination residents, which are correlated with a positive attitude towards tourism growth. The results indicate that female residents are more supportive of tourism development. Residents who have completed higher education provide support for tourism development, they are employed and have a higher wage, they have a higher-ranking political role in society, work in the tourism sector, and live in an area which is urban (Jackson and Inbakaran, 2006). The authors also failed to determine any significant relationship between negative attitudes towards tourism and the demographics of residents. McGool and Martin (l994) examined the connection between the commitment of residents to their communities and their attitudes in regard to the effects of tourism growth. They determined that locals who have stronger attachments to the community have more hostile attitudes towards tourism growth in comparison to the local residents who have shown lower levels of attachment. Many studies suggest that, with the increase in the length of the period of living in the community, attitudes towards tourism growth become more negative (Khoshkam et al., 2016; Liu et al., l987). As a settlement in the foothill of the Šar mountain, Sirinićka Župa is just one of the inhabited places whose residents can experience the consequences of tourism development. In the last 20 years, the tourism development in this area stagnated due to the political situation in the area of Kosovo. Consequently, there were no official records of the number of tourist arrivals and overnights in this area in the Bureau of Statistics of the Republic of Serbia. According to internal data obtained from the Molika hotel (Hotel Molika, 2019), a significant increase was registered in the number of tourist arrivals in the last three years in comparison with the stagnation phase. This can be an indicator of the redevelopment of Šar mountain as a tourism destination which, according to the Butler’s life cycle of evolution (Butler, 1980), can be considered as a beginning of the involvement development stage. The development stage is a significant factor that can influence the interaction between tourists and hosts (Butler, 1980). According to Doxey (1975), the interaction between hosts and tourists can be explained through four phases: euphoria, apathy, irritation, and antagonism. When considering the characteristics of each phase, as well as the fact that this area just recently started to develop its tourism again, the interaction phase between the locals and guests can be considered as euphoric. The local population perceive tourism as an opportunity for economic prosperity, and awaits its tourists with enthusiasm. Indications that tourism in this area has started to develop again has aroused interest for examining residents’ perceptions towards the impacts of tourism development. 90
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LITERATURE REVIEW
Residents are an important part of the process of development and success of the destination. Various authors have investigated the views of locals regarding the effects of tourism growth from various viewpoints. By analyzing the studies about residents’ perception towards tourism impact, many different models of examining residents’ impact and perceptions can be identified. In many studies, there is an association between attitudes towards effects of tourism and sociodemographic characteristics such as gender (Mason and Cheyne, 2000; Nunkoo and Gursoy, 2012; Jani, 2018; Tepavčević et al., 2019; Janta and Christou, 2019; Alrwajfah et al., 2020, Wang, 2013), age (Bagri and Kala, 2016; Látková and Vogt, 2012; Sinclair-Maragh, 2017), nationality (Hernández and Mercader, 2015; Soontayatron; 2010), urban as well as rural areas (Rasoolimanesh et al., 2017), education levels (Miyakuni, 2012; Pavlić et al., 2019; Pham and Kayat, 2011), length of period of residence (Khoshkam et al., 2016; Liang and Hui, 2016; Xu et al., 2016), and family size (Brida et al., 2011).
Impacts of Gender on Locals’ Attitudes Towards Tourism Development Several studies have remarked that gender is a deciding element in the attitudes of locals towards the effects of tourism (Mason and Cheyne, 2000; Nunko and Gursoy, 2012). In the study conducted by Mason and Cheyne (2000), differences in the perceptions of gender-based tourism impacts have been identified, with male respondents being more positive than female respondents in terms of tourism development. Nunko and Gursoy (2012) have determined variations in attitudes in the sense that gender is a strong indicator of tourism support and negative impacts. They also indicated that the impact of gender on attitudes towards tourism development is not only influenced by biological differences characterized for the gender, but also by psychological differences between males and females (Nunko and Gursoy, 2012). In their work, Tepavčević et al. (2019) found notable differences among male and female residents. Jani (2018) and Alrwajfah et al. (2020) determined a significant impact of gender on residents’ perceptions toward tourism development. On the other hand, several studies have not identified any major gender effect on the understanding of the effects of tourism growth (Almeida-Garcia et al., 2016; Bagri and Kala, 2016; Mensah, 2012; Rasoolimanesh et al., 2015) In respect of existing literature, the following hypotheses can be posed: H1: Gender is an indicator of significant differences in locals’ attitudes towards tourism growth effects.
Impacts of Age on Locals’ Attitudes Towards Tourism Development Látková and Vogt (2012) identified that older residents are more inclined to accept that tourism has more positive effects than negative, while younger residents are more likely to perceive the negative effects of tourism growth. In their study, Rasoolimanesh et al. (2015) have found that older residents are more positive towards tourism due to more benefits they can gain. Bagri and Kala (2016) examined residents in India, and found that older residents expressed fewer positive perceptions of development of tourism when compared to younger residents. Cavus and Tanrisevdi (2002) also found that elderly community members showed less support for tourism growth than younger ones. Almeida-García et al. (2016) identified the important main effects of the tourism destination residents’ age on the perceptions of the environmental and economic impacts. They failed to find any significant effects of age on socio-cultural effects and overall attitudes of tourism development. Vareiro et al. (2013) reported a higher level of concern about the impacts of tourism, which are considered as negative among older residents compared to younger residents. 91
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Tepavčević et al. (2019) and Rasoolimanesh et al. (2015) also reported differences in prehension of tourism impacts towards the age of respondents, i.e., younger respondents have the lower perception of tourism impacts than older residents. Sinclair-Maragh (2017) identified that younger residents (18-25 years) tend to be more supportive towards tourism development than older residents. In respect of existing literature, the following hypothesis can be posed: H2: Age is an indicator of significant differences in local populations’ attitudes towards tourism growth effects.
Impacts of Education Level on Local Populations’ Attitudes Towards Tourism Development Earlier studies have determined that degree of education is the most notable single element influencing residents’ views on the travel industry. Education may improve the communication skills of community members, as well as their awareness levels concerning tourism-related issues (Sinclair-Maragh, 2017). Papastathopoulos et al. (2019) indicated on differences in perceptions of tourism development impacts depending on education level. They found that a higher education level of residents has a more powerful influence on the residents’ perception of cultural, social, and environmental impacts. On the other hand, residents with lower education levels have a stronger perception toward the economic impacts of tourism development. Pham and Kayat (2011) indicated differences among residents, depending on their education level. They found that residents with lower education level are more critical of the effects of tourism development compared to highly educated residents, who are enthusiastic towards tourism development. Miyakuni (2012) determined that residents who possess a lower level of education show a more critical attitude towards the growth of tourism than higher-educated residents. In their study, Vareiro et al. (2013) found that tourism destination residents with a completed high school education expressed more expectations regarding the positive effects of tourism. However, they also expressed low concerns about the eventual negative impacts. Almeida-García et al. (2016) reported an important effect of the degree of education on the acceptance of the environmental, socio-cultural, and economic impacts. They found that as the education level of residents increases, the perception of the effects of tourism increases as well. Tepavčević et al. (2019) reported that highly educated residents have a higher perception of all kinds of tourism impacts. They found that residents with the lowest education level have the lowest perception of all tourism effects, and their support for tourism growth is low. In respect of existing literature, the following hypothesis can be posed: H3: Level of education is an indicator of significant differences in locals’ attitudes towards tourism growth effects.
Impacts of the Length of Period of Residence on Local Populations’ Attitudes Towards Tourism Development Khoshkam et al. (2016) found different relationships between the length of period of residence and perceptions of tourism development effects. For example, they found that the time length of residence positively influences perceptions of economic impacts, while there is a negative relationship between the length of time of residence and perceptions of socio-cultural impacts. The authors found no significant relationship between the perceptions of impacts on the environment and the length of time of residence. Liang and Hui (2016) found that residential status significantly influences residents’ support for development of tourism. 92
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They also found that homeowners have a more positive attitude towards tourism development compared to tenants and dormitory residents. These findings supported the findings of several studies (Liu et al., 1987). Pham and Kayat (2011) found that the length of time of residence has a limited influence on residents’ perceptions and support for tourism development. On the other hand, Papasrathopoulos et al. (2019) and Bhat and Mishra (2020) failed to identify significant effects of length of period of residence on the perceptions of tourism development impacts. In addition, Sinclair-Maragh (2017) did not find any significant impact of length of time of residence towards their support for tourism development. In respect of existing literature, the following hypothesis can be posed: H4: The length of period of residence is an indicator of significant differences in locals’ attitudes towards tourism growth effects.
METHODOLOGY This research focused on the perception of the residents of Sirinićka Župa (Štrpce Municipality) of the effects of tourism growth (environmental, economic, socio-cultural, and physical impacts) according to their sociodemographic characteristics (gender, age, education level, and length of time of their residence). The survey, composed of two section,s was used for research purposes. The first segment of the survey consisted of questions related to the sociodemographic characteristics of the respondents, including their age, degree of education, the status of their employment, the level of their incomes, and the relatedness of their work with tourism. One question was related to the length of residency in Sirinićka Župa. The second segment of the survey consisted of items designed to measure the attitudes of locals about the effects of tourism development. In this study, the model of Muresan et al. (2016) was used. The model includes 22 items, disposed to 4 factors. The first factor (Environmental impact) included 8 items designed for measuring the perception of the residents toward environmental impacts. The items were related to problems of pollution, overcrowding, and other effects of tourism growth on the environment which are considered negative (e.g., “Development of tourism can damage the natural environment and landscape“). The second factor (Economic impacts) included 7 items associated with the economic advantages of tourism development (e.g., “Tourism creates new jobs for locals“). The third factor (Social and Cultural impacts) consisted of 4 items which were associated to be the positive effects of tourism growth on society, as well as on culture (e.g., “Tourism provides incentives for the restoration of traditional houses“). The last, fourth factor (Physical impacts) had 3 items, which were related to the perception of the improvement of the infrastructure on the local community (e.g., “Tourism can improve the living utility infrastructure (for example, water supply, sewage, the electric supply, etc.)“). A Likert scale ranging from 1 (definitely disagree) to 5 (definitely agree) was used for expressing the level of agreement offered. The research was conducted from August 2019 to January 2020 on the territory of Sirinićka Župa. Some of the respondents were surveyed online, while the rest were interviewed using the face-to-face technique. A total of 94 respondents were included in the research. All the collected surveys were valid.
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RESULTS
Sample Description The first segment of the survey was related to the socio-demographic characteristics of respondents. The socio-demographic characteristics of residents are presented in Table 1. Considering gender structure, more than half of the sample consists of male respondents (53.2%), while the female ones participated with 46.8%. When observing the age structure of respondents, it is visible that respondents from the age group “up to 24“ (33%) and “25-34“ (26.6%) constitute the largest part of the sample. Respondents from the age group “over 55“ participated with only 9.6%. Most respondents had completed high school (47.9%), followed by respondents who graduated (19.1%). The permanently employed (35.1%) made up a third of the sample, while the lowest percentage of respondents (3.2%) were retired. If we observe the level of income of respondents, it is visible that just over one-third of the sample (35.1%) earns up to 200€, followed by respondents with the level income between 201 and 500€ (31.9%). Only 4.3% of the respondents have reported a level of incomes over 1001€. Table 1 Socio-demographic Characteristics of Respondents Socio-demographic characteristics
Frequency
Percent
Gender Male
50
53.2
Female
44
46.8
Age Up to 24
31
33.0
25 – 34
25
26.6
35 – 46
19
20.2
47 – 54
10
10.6
Over 55
9
9.6
Education High school
45
47.9
College
15
16.0
Graduate
18
19.1
Master
16
17.0
Employment status Pupil/Student
26
27.7
Permanently employed
33
35.1
Temporarily employed
16
17.0
Retired
3
3.2
Unemployed
16
17.0
Income level Up to 200€
33
35.1
201 - 500€
30
31.9
501 - 1000€
27
28.7
More than 1001€
4
4.3
Source: Authors´ research 94
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Table 2 presents the results of descriptive statistical analysis. The results indicated that the lowest value was registered for the factor Environmental impacts (2.83), while the factor Social and Cultural Impacts has the highest value (4.13). The item valued the lowest by the residents is “Tourism development negatively affects the recreational facilities and entertainment” (2.06), while the item which was valued the highest is “Tourism plays an important role in the economic development of the area” (4.40). According to Doxey (1975), the negative impacts of tourism occur when development is not well planned or managed properly. If we look at the stage of the development of the area (involvement stage), it is quite expected that the impacts on the environment will be less perceived and not considered as negative. An increase of tourist turnover (arrivals and overnights), as well as the change of the development stage of the area, can result in changes in attitudes and perceptions of local residents towards the impacts of tourism development. Table 2 Descriptive Statistical Analysis Factors
Mean
Std. Deviation
Environmental Impacts
2.83
0.838
Development of tourism damages the natural environment and landscape.
2.91
1.389
Tourism causes overcrowding problems for residents.
2.66
1.258
Tourism increases air pollution.
2.81
1.314
Tourists use too much water.
2.73
1.099
Tourism results in more litter in the area.
3.29
1.151
Tourism development negatively affects the recreational facilities and entertainment.
2.06
1.105
The construction of tourist facilities destroys the environment.
2.49
1.034
Increase in traffic problems.
3.69
0.951
Economic Impacts
3.89
0.536
Tourism plays an important role in the economic development of the area.
4.40
0.780
Tourism improves locals’ living standard.
4.30
0.814
Tourism increases a community`s tax revenue.
4.00
1.097
Tourism creates new jobs for locals.
4.28
0.860
Tourism diversifies the rural economy.
3.18
1.037
Tourism results in an increase in living costs.
2.85
1.235
Revenue from tourism tax activity should be invested in the future development of tourism.
4.21
0.866
Social and Cultural Impacts
4.13
0.638
Tourism provides incentives for the restoration of traditional houses.
4.07
0.858
Interaction with tourists is a positive experience.
4.28
0.694
Shopping and restaurant options are better as a result of tourism.
4.00
0.892
Tourism development enhances more recreational opportunities for locals.
4.17
0.785
Physical Impacts
3.88
0.776
Improvement in traffic network.
4.06
0.878
Improvement in living utilities infrastructure (supply of water, sewage, electric, etc.)
3.76
1.002
Quality of public services is better.
3.81
0.919
Source: Authors´ research
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Results of T-test
T-test of independent samples according to gender (Table 3) indicated that there are no important differences among males and females regarding the understanding of the various effects of tourism growth. Obtained results did not provide support for H1. Table 3 T-test According to Gender Gender Factors
Male N=50
Female N=44
t
p
Environmental Impacts
3.86
3.89
-.845
.401.
Economic Impacts
2.76
2.91
.264
.792
Social and Cultural Impacts
3.90
3.87
.075
.940
Physical Impacts
4.14
4.13
-.211
.834
Source: Authors´ research
Results of one-way ANOVA One-way ANOVA by age of respondents (Table 4) was applied with the aim of confirming H2. The results indicated that statistically significant differences exist within the factors Environmental Impacts (p<0.05, F=2.395) and Physical Impacts (p<0.05, F=2.611). By using LSD post-hoc test, it has been determined among which groups do notable differences exist. It can be noticed that respondents from the age group “45 – 54” years express a significantly lower perception of tourism impact on the environment than the respondents from the age groups “15-24” and “25 – 34”. Within the factor Physical Impacts, respondents from the age group “15-24” expressed a significantly lower perception of Physical impacts of tourism development than respondents from groups “45 – 54” and “55 – 64” years. These results support H2 partially. Table 4 ANOVA by Age Age 25 – 34
35 – 44
45 - 54
55 – 64
LSD posthoc
15 – 24
p
F
Environmental Impacts
3.09
2.89
2.74
2.24
2.61
2.395
.050
4<1,2
Economic Impacts
3.88
3.86
3.78
4.09
4.06
.661
.621
-
Social and Cultural Impacts
4.17
4.05
3.97
4.45
4.19
1.068
.377
-
Physical Impacts
3.67
4.00
3.65
4.23
4.33
2.611
.041
1<4,5
Factors
*p<0.5 Source: Authors´ research
Taking the level of education as a variable for applying one-way ANOVA (Table 5), it has been determined that the degree of education of residents does not influence the perception of tourism development. Hence, H3 is not supported. 96
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Table 5 ANOVA by education level
High school
College
Graduated
Master
Education level F
Environmental Impacts
2.89
2.87
2.85
2.59
.518
.671
-
Economic Impacts
3.85
3.84
4.02
3.91
.470
.704
-
Social and Cultural Impacts
4.07
4.12
4.33
4.08
.764
.517
-
Physical Impacts
3.86
3.93
3.98
3.75
.279
.840
-
Factors
p
LSD post-hoc
Source: Authors´ research
In the case of length of time of residence, one-way ANOVA found significant differences (p<0.5, F=3.606) within the factor Environmental Impacts (Table 6). By applying a LSD post-hoc test, it has been determined that residents who have been living in Sirinićka Župa for less time (10 – 15 years) have a stronger perception of the effects of tourism on the local environment than those who have been living there longer (over 15 years), as well as those who were born in Sirinić Župa but currently have another place of residence. Based on the previously presented results, H4 is partly supported.
Table 6 ANOVA by the Length of Time of Residence Currently have another place of residence
Environmental Impacts
Over 15 years
Factors
10 – 15 years
Education level
3.51
2.79
2.70
F
p
LSD post-hoc
3.606
.031
1>2,3
Economic Impacts
3.87
3.83
4.00
.964
.385
-
Social and Cultural Impacts
3.77
4.11
4.28
2.234
.113
-
Physical Impacts
4.11
3.78
3.98
1.078
.345
-
*p<0.5 Source: Authors´ research
ACKNOWLEDGEMENTS: The represented research is part of project 176020 OI, funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia.
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CONCLUSION
Recognizing the pertinence of the role of the locals in the tourist destination growth, it is crucial to investigate their attitudes towards tourism development. Tourism development brings several impacts on the local community, positive, as well as the negative ones, depending on the stage of development of a destination. Although residents perceive the Social and Cultural impact the most, depending on the stage of development and the phase of interaction between residents and tourists, it can be considered both positive and negative. In this case, it can be considered as positive due to the fact that uncontrolled tourism development has still yet to occur, which can influence the space transformation, and quality of local population life (Fachrudin and Lubis, 2016; Andereck and Nyaupane, 2011; Elliot et al., 2011). In this study, residents expressed the lowest perception of environmental impact while perceiving the socio-cultural impacts the most. This could be explained by the phase in the life cycle of a tourist destination. Because of the political situation in the country, in the earlier years, tourism development in this area was stagnant. However, an increase of the number of ski tourists in Sirinićka Župa has been registered in the last few years. Besides locals and domestic tourists, a significant number of foreign tourists was registered during the winter season. Furthermore, besides skiers, tourists often visit only to walk and they do not use the ski slopes. Moreover, in line with the increase of ski tourists, an increase in the number of weekend cottages on the way to the ski center was registered in the last five years. Because of their location, weekend cottages are generally the first choice of ski tourists. The Organization “Serbia for Youth” greatly promotes tourism in this area by organizing multi-day stays for young people. All previously mentioned points are good indicators of the beginning of tourism development in Sirinićka Župa and on Šar Mountain. The focus of this study was to determine the effects of socio-demographic characteristics of the local population on attitudes towards tourism development. Referring to the results obtained in this study, it can be concluded that the residents` socio-demographic characteristics influence their attitudes towards tourism growth. Firstly, contrary to expectations, the gender of residents was not found as evidence of disparities in the residents` perceptions towards tourism effects. Although numerous authors indicated the impact of gender on residents’ attitudes (Mason and Cheyne, 2000; Nunko and Gursoy, 2012; Tepavčević et al., 2019), the findings of this research are consistent with the results obtained in the Almeida-García et al. (2016) study, which did not find any significant relationship between gender and residents’ attitudes. These results have not provided support for H1. Secondly, in many studies, age is considered a significant determinant of differences in the perception of tourism impacts (Cavus and Tanrisevdi, 2002). Significant effects of age were found in the residents’ attitudes towards Environmental Impacts and Physical Impacts. In the case of perception of environmental impacts, older residents expressed lower perception of tourism impacts than younger ones. Considering the differences in the perception of physical impacts, the youngest residents (age group 15-24) have lower prehension of physical effects of tourism development than older residents (age groups 45–54 and 55–64). Based on this, H2 is supported partially. Furthermore, many authors found significant effects of education level on residents’ attitudes (Pham and Kayat, 2011; Vareiro et al., 2013). Results of this study have not found a notable link between the degree of education and perceptions towards tourism effects. Thus, H3 is not supported. Finally, the length of time of residence was the last variable whose impact on attitudes was examined. Previous studies have confirmed the influence of the length of residence on local population attitudes (Cavus and Tanrisevdi, 2002; Liu et al., 1987). In this study, the notable correlation between the period of residence and the local population’s attitudes was found in the perception of tourism effects on the environment. Based on this, H4 is partially supported. 98
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Taking into consideration the significance of the human factor in achieving success in a sector such as tourism, the results of this study can enable better insight into the attitudes of the local residents for tourism development planners. It is mandatory to include the local population in all aspects of tourism development-economic, political and socio-cultural ones. Through the identification of factors which can influence the attitudes and support for tourism development, various strategies can be formed, which would include local population and through which they would stimulate them to get involved more actively in the tourism related activities. Considering that this area is in its redevelopment stage, local production could be stimulated with a goal of creating recognizable products with the help of the local population. In this way, local products could be branded, and the economic benefits for the local population would be observed. Finally, this research has several limitations, which provide opportunities for further future examination. First of all, the number of respondents included in the research is small. Although the period of data collection lasted several months, the greatest problem was the lack of interest of residents to fill out the survey. The period during which the interviewer stayed in the researched area was very brief, and a small number of residents was surveyed, due to which a combination of online and face-to-face techniques was used for gathering the data. With the use of the online survey, the response of younger residents was higher, and hence the higher share of younger population in the sample. Due to the fact that the research was conducted only in Sirinićka Župa, and that in the Štrpce Municipality there are few villages that are in the very vicinity of the ski centers (Brezovica, Vrbeštica, Sevce, Jažince, Štrpce, Berevce, Popovce, Bitinja), the acquired results cannot be generalized, but they relate just on the perception of the influence of tourism development of residents of Sirinićka Župa. Due to this reason, the recommendation for future research is to expand it on all villages in the municipality in order to obtain conclusions on the attitudes and perceptions of residents of the entire area concerning the influence of tourism development. Secondly, previous research has shown that locals who profit from tourism have more positive attitudes towards the effects of tourism (Milman and Pizam, 1988). Due to the stagnation period in development of a tourist destination caused by the political situation, it is obvious that residents were not financially dependent on tourism development, which is why financial dependence on tourism development was not observed. Therefore, depending on the future development of the destination, it is a recommendation for future research to consider financial dependence on tourism growth as a variable to discover whether there are differences in attitudes. Taking into account that residents’ attitudes change over time and vary depending on the stage of tourism development of the touristic destination, further research should be focused on repeating this study at another stage of development.
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UTICAJ SOCIODEMOGRAFSKIH KARAKTERISTIKA REZIDENATA NA PERCEPCIJU UTICAJA RAZVOJA TURIZMA
Rezime: Lokalno stanovništvo ima značajnu ulogu u procesu razvoja destinacije, a njihova podrška je ključna za uspeh destinacije. Brojni autori su istraživali stavove lokalnog stanovništa prema turističkim uticajima sa različitih stanovništa. Cilj ovog rada je bio istraživanje uticaja sociodemografskih karakteristika lokalnog stanovništva na njihove stavove prema razvoju turizma. Istraživanje je sprovedeno na teritoriji Sirinićke Župe, u okviru opštine Štrpce. U istraživanju je učestvovalo ukupno 94 rezidenata. Rezultati su pokazali da pol i nivo obrazovanja nemaju značajan uticaj na stavove lokalnog stanovništa. Sa druge strane, starosna dob i dužina borakva u Sirinićkoj Župi su pokazale značajan uticaj na stavove lokalnog stanovništva prema uticajima razvoja turizma.
Ključne reči: Sirinićka Župa, lokalno stanovništvo, razvoj turizma, uticaj turizma, sociodemografske karakteristike.
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EJAE 2020, 17(2): 104 - 118 ISSN 2406-2588 UDK: 330.322(6-11)"2000/2018" DOI: 10.5937/EJAE17-26791 Original paper/Originalni nauÄ?ni rad
INFRASTRUCTURE DEVELOPMENT, INSTITUTIONS, AND INTRA-REGIONAL TRADE: THE CASE OF EAST AFRICA James Ochieng*, Daniel Abala, Mary Mbithi School of Economics, University of Nairobi, Kenya
Abstract: This study empirically examines the relationship between infrastructure stock and bilateral trade flows using a panel of 11 countries in East Africa for the period 2000 to 2018. Infrastructure augmented gravity model was estimated using total bilateral exports for the countries in East Africa. Infrastructure was disaggregated into transport and information and communications technology (ICT) infrastructures. Two institutional variables, control of corruption index and regulatory quality, were incorporated in the model. By employing Poisson Pseudo Maximum Likelihood (PPML) estimator, the results confirm that both ICT and transport infrastructures and quality institutions positively impact on the volumes of total bilateral exports in East Africa. However, ICT infrastructure has a greater impact on trade flows compared to transport infrastructure. Therefore, more resources should be channelled towards increasing the stock of ICT infrastructure to propel trade and regional integration in East Africa.
Article info: Received: May 27, 2020 Correction: July 31, 2020 Accepted: September 7, 2020
Keywords: Infrastructure Stock, Institutional Quality, Intra-regional Trade, East Africa. JEL Classification: H54, F10, F15
INTRODUCTION The successful participation of countries in international trade is determined by a number of factors other than the level of tariffs and other quantitative trade restrictions. These other factors include the quantity and quality of the existing infrastructure. Infrastructure development promotes competitiveness by reducing trade costs and consequently enhancing regional economic integration (Limao and Venables, 2001; Behar and Venables, 2011). Therefore, infrastructure development enables an economy to exploit a comparative advantage, specifically in trade and that infrastructure deficiency limits international trade (Cosar and Demir, 2016; Danaubauer at al., 2018). 104
*E-mail: jamesbo1@live.com
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OCHIENG.J., ABALA. D., MBITHI. M., INFRASTRUCTURE DEVELOPMENT, INSTITUTIONS, AND INTRA-REGIONAL TRADE: THE CASE OF EAST AFRICA
Infrastructure development accelerates the pace of economic progress by enhancing more production activities, and leads to lower costs for conducting domestic and foreign trade. More infrastructure facilities lead to industrialization and many employment opportunities are created, which leads to poverty reduction in a country (Sahoo et al., 2010). Various kinds of infrastructure facilitate international trade; hard infrastructure is essential for trade between a country and the rest of the world. They include roads, rail lines, ports, and airports. Soft infrastructure also determines trade volumes, it relates to cost, time, and the number of documents needed during trade between the borders (UNCTAD, 2013). The quality of infrastructure1 in a country determines costs and volume of international trade. Furthermore, since various sectors consume infrastructure services differently, infrastructure quality affects opportunity cost and specialization in international trade (World Trade Organization, 2004). The cost of trade is a key concern to many developing countries, like those in the East Africa region. According to the World Trade Organization (2015), trade costs were 227 percent (of their ad-valorem tax equivalent) in less developed countries (LDCs) for the manufacturing sector, compared with 125 percent, 98 percent, and 82 percent in lower middle income, upper middle income and high-income countries respectively. These figures show that infrastructure in LDCs accounts for a high percentage of trade costs in comparison to the developed countries. The East Africa region has been implementing joint infrastructure programs in roads, railway, pipeline, ports, and in energy development. For example, some of the joint infrastructure projects in East Africa are spearheaded by the East African Community (EAC) Partner States. Under the transport sector, there are: two main transport corridors to facilitate in the region; the first is the northern corridor, which covers 1700 km from Mombasa port and serves Kenya, Rwanda, Burundi, Uganda, South Sudan, and Eastern DRC; the second is the central corridor, which covers 1,300 km from the port of Dar es Salaam, and serves Tanzania, Rwanda, Burundi, Uganda, Eastern DRC, and Zambia. Apart from the road infrastructure, railway infrastructure is also given consideration; this includes upgrading of existing railway tracks to Standard Gauge Railway. The EAC railway project has been planned to cover the northern corridor, from Mombasa to Malaba, then to Juba and Kigali, and finally to Bujumbura. In terms of communication infrastructure, under the EAC Broadband Information and Communications Technology (ICT) Infrastructure Network (EAC-BIN), the EAC has managed to increase the ICT infrastructure through four undersea cables, which covers the East African Coast: EASSY, SEACOM, TEAMS and LION 2 (EAC, 2011a). All these are intended to promote both intra-and inter regional trade by removing impediments in the movement of goods and services (EAC, 2011b). The performance of the East Africa region in international trade remains poor, notwithstanding the increased infrastructure projects in the region. For example, only 6 percent of total imports for the EAC Partner States are sourced from the region, while exports to the region account for only 20 percent of the total (World Trade Organization, 2019). The poor performance of trade in East Africa is partially attributed to technical barriers to trade, lack of product diversification, and a lack of a common currency. However, it is difficult to understand the trade performance of the East Africa region without understanding the contributions of infrastructure and quality of institutions. According to Francois and Manchin (2013), better institutions can promote trade. In general, the volume of trade and the ability of low-income countries to participate in international trade depends on institutional quality and access to a well-developed infrastructure. 1 Infrastructure quality is determined based on an index that describes the extent of infrastructure development in a country. From the World Economic Forum, a value of 1 refers to an extremely underdeveloped infrastructure, while a value of 7 refers to an infrastructure that is extensive and efficient by international standards.
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OCHIENG.J., ABALA. D., MBITHI. M., INFRASTRUCTURE DEVELOPMENT, INSTITUTIONS, AND INTRA-REGIONAL TRADE: THE CASE OF EAST AFRICA
The role of infrastructure on regional trade flows has been investigated in a number of previous studies (Ismail and Mahyideen, 2015; Shepherd, 2016; Raychaudhuri and De, 2016; Rehman et al., 2020). However, there is little understanding of how different forms of infrastructure and institutions affect intra-regional trade flows, particularly in East Africa. Therefore, this paper explores the impact of transport and ICT infrastructures on East Africa’s trade by incorporating quality of institutions as an element of the analysis using the Posisson-Pseudo Maximum Likelihood (PPML) estimator. The superiority of PPML in estimating trade flows is that it performs well in the presence of zero bilateral trade flows, and controls for different patterns of heteroscedasticity in the regression model (Silva and Tenreyro, 2011). The rest of the paper is organized as follows: Section 2 discusses the literature on infrastructure and trade, while Section 3 presents the methodology and data pertaining to this study. Section 4 discusses the study findings, while conclusions and policy implications are presented in Section 5.
LITERATURE Even though effort has been made worldwide to reduce tariffs, a number of trade barriers still prevail. Such barriers are classified as either ‘soft’ or ‘hard’ (De, 2006). Trade facilitation measures are always used to overcome the soft barriers; on the other hand, hard barriers are linked to infrastructure, and are minimized by measures related to transport facilitation. Gravity model was first augmented to include infrastructure variables by Bougheas et al. (1999), by extending the Dornbusch-Fischer-Samuelson model of 1997 in a study of infrastructure and trade from a sample of European countries. The authors linked the variations in trade volumes and competitiveness across countries to disparities that exist in the stock and quality of infrastructure across regions. Furthermore, they gave evidence that infrastructure development has the potential of increasing trade flows, as it acts by lowering trade costs. Celbis et al. (2014), in concurrence with these findings, argue that infrastructure development lowers transport costs, thereby increasing a country’s trade volume. They state that transport infrastructure is important to lowering trade related costs, especially during the transportation of goods. Market expansion is also realized with improved infrastructure, and with consumers being exposed to many competing producers, prices fall, resulting in welfare improvement (Henckel and McKibbin, 2010). Other studies that support the crucial role of infrastructure in promoting trade by lowering trade costs are Francois and Manchin (2013), Raychaudhuri and De (2016), and Bonfatti and Poelhekke (2017). They argue that developing the physical infrastructure plays an important role in lowering trade costs and, consequently, in increasing the volume of trade. The role of infrastructure in lowering trade costs and increasing international trade flows is also supported by Donaubauer et al. (2018), who employed a gravity model for a panel of 150 developed and emerging countries using data for the period 1992 and 2011. They found that improving infrastructure is important for reducing not only bilateral trade costs, but also multilateral trade costs. Moreover, a study by de Soyres et a.l (2018) established that countries located along the infrastructure corridors under Belt and Road Initiative experience the most gains in terms of time and trade costs, that decline by up to 11.9 percent and 10.2 percent, respectively. Using a sample of 10 economies from Asia, Ismail and Mahyideen (2015) investigated the effect of infrastructure on trade by applying a gravity model augmented for various infrastructure types. Using data for the period 2003 to 2013 and estimating a random effects model, they established that improved transport infrastructure increases the volume of trade. Rehman et al. (2020) obtained similar results for 106
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6 South Asian countries by employing a pooled mean group estimator between 1990 and 2017. Shepherd (2016) employed network analysis methods to examine the link between infrastructure development, global value chains, and trade facilitation performance for 44 countries in Sub-Saharan Africa (SSA). The study established that SSA is marginalized in world networks for value added trade and global value chains due to high trade costs resulting from inadequate infrastructure. Other panel studies that support the importance of infrastructure in promoting growth of trade include Hernandez and Taningco (2010), who applied gravity model and import data for 11 Asian economies for the period 2006 to 2008. They used a fixed effects model and found trade in industrial supplies, petroleum products, consumption, and investment goods in East Asia as being dependent quality of port infrastructure. Shepherd and Wilson (2009) used a gravity model using data for 14 ASEAN member countries for the period 2000 to 2005. They employed ordinary least squares estimator and found transport and ICT infrastructure as the main facilitators of trade in Southeast Asia. Studies by Bankole et al. (2013), Yushkova (2014), and Xing (2015) also support the role of ICT infrastructure in enhancing bilateral trade. By examining the effect of physical infrastructure, ICT, border and transport efficiency on performance of exports of 101 developing countries for the period 2004 to 2007, Portugal-Perez and Wilson (2012) employed two-stage a Heckman selection model and established that exports are mainly driven by physical infrastructure. However, they established a declining effect of physical infrastructure as income rises, as well as ICT and border/transport efficiency. In a natural experiment to assess the effects of domestic transport infrastructure on Chile’s trade flows, Martincus and Blyde (2013) combined firmlevel data between 2008 to 2011 and used a difference in difference estimator to establish that shocks to domestic infrastructure negatively influence firms’ exports. Similarly, to support the crucial role of transport infrastructure in trade facilitation, Francois and Manchin (2013) established that transport and communication infrastructure and quality of institutions are important determinants of both a country’s export volume and possibility of exports. Alvarez et al. (2018) support the crucial role of institutions in bilateral trade but argue that the effect of institutions on bilateral trade is somewhat lower than other determinants such as distance. Other studies by Yu et al (2015) and Lin and Fu (2016) also support the role of institutions in fostering international trade. Studies by Behar and Manners (2008), Djankov et al. (2006) and Bensassi et al. (2015) have found that the volume of trade increases through a reduction in trade costs, resulting from development of physical infrastructure and improvement in logistics. In a similar study, Fink et al. (2005) used a gravity model to investigate the effects of communication cost on bilateral trade. They found that cost of phone calls is an important determinant of bilateral trade. They found that a reduction in the cost of phone calls by 10 percent leads to an increase in the volume of bilateral trade by 8 percent. To emphasize the link between infrastructure, transport costs and trade, Limao and Venables (2001) estimated a gravity model for imports using data for 103 countries for the year 1990. By employing a tobit and fixed effects models, they established that distribution of infrastructure from the median to the top 25th percentile increases the volume of trade by 68 percent. Nordas and Piermartini (2004) built an index of infrastructure quality using road, airport, port, and telecommunications and time required for customs clearance as measures of infrastructure. They employed a gravity model for 138 countries using exports data for different sectors. Using ordinary least squares and fixed effects estimators, they investigated how infrastructure quality affects trade, and found that poor quality of infrastructure increases the entire transactions costs as goods are likely to be damaged during transportation. 107
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METHODOLOGY Gravity Model Following literature, this study employed an augmented gravity model to analyze the impact of infrastructure development and institutions on trade. Gravity model is considered to be the most robust partial equilibrium model in explaining the variations in bilateral trade flows (De, 2006). Gravity model was first introduced by Tinbergen (1962), and later modified by Anderson (1979) to include trade costs under the assumption that each country produces a unique product, which applies the concept of product differentiation. The model was further modified by Anderson and Van Wincoop (2003) to capture ‘multilateral resistance terms.’ The Newton’s law of gravity is given as: (1) Where F- is the gravitational force between the two objects, N1 and N2 are the respective masses, D-is the distance between the centres of the masses, and k - is a constant. The gravity model of trade states that the volume of trade between two countries varies directly with the product of two countries’ GDP and indirectly with distance between their capital cities. The model is formulated as: (2) where Tij - is trade flow, such as exports from country i to country j, k- is a constant term, Gi and Gj- are GDPs of country i and j (which are proxies of the size of each trading country) and Dij -is distance between capital city i and j. Equation (2) therefore states that trade between country i and j is directly proportional to the GDP of both exporting and importing countries and inversely proportional to the distance between them. Evidence suggest country size and geographical distance (which captures trade costs) as the most important determinants of bilateral exports between countries. Following Anderson and Van Wincoop (2003), gravity model was modified to include multilateral trade resistance variables. To include multilateral resistance terms, gravity model is augmented using ‘exporter and importer fixed effects’ to come up with an equation of the form: (3) Where
and
are exporter and importer dummies and
is the error term.
Equation (3) therefore, captures the multilateral trade resistance terms. The gravity model can be augmented to include other factors, such as infrastructure and trade facilitation measures, which are important determinants of trade costs and volume. Gravity model, therefore, gives the key link between trade flows and associated barriers. Equation (2) is non-linear in parameters, by linearizing equation (2), equation (4) is obtained: (4)
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Where ln is the natural logarithm Equation (4) is the simple (baseline) gravity model and can be estimated as it is. However, there are other determinants of volume of trade such as infrastructure, institutional variables, language, foreign direct investments (FDI), inflation, exchange rates, etc. Therefore, the model is augmented to include such variables to assess their impact on trade flows. The log-linear version of the augmented gravity equation is given as: (5)
Where
EXi,j,t -Total exports from ith Partner State to the jth Partner State (importing country) at a given time, t;
GDP represents gross domestic product; D stands for distance between capitals of trading partners; POP refers to population; ICT represents ICT infrastructure index; TRAN represents transport infrastructure index; ER stands for official exchange rate per dollar US dollar, FDI stands for net foreign direct investment inflows; CI stands for control of corruption index; REGQ stands for regulatory quality; EAC refers to free trade agreements dummy under the East African Community (EAC) customs union, with 1= member of EAC, 0-otherwise; WTO refers to membership of World Trade Organization (WTO), with 1= member of WTO, 0-otherwise; LANG represents official language dummy, with 1= same official language in country i and j, 0 –otherwise, ETH refers to ethnic language, with 1= ethnic language spoken by at least 9 percent of population in country i and j, CONT refers to contiguity, with 1= common border between country i and j, 0 otherwise and – is the error term. Estimation of the logarithmic transformation of the gravity model poses a challenge when there are zero trade flows since the logarithm of zero is not defined. Estimation of such a model using ordinary least squares produces inconsistent and biased estimates, which do not vanish with increase in sample size (Silva and Tenreyro, 2011). Therefore, the choice of estimation was based on PPML estimator by Silva and Tenreyro (2006). PPML is important in the estimation of the gravity equation because it accounts for zero bilateral trade flows between trading partners and performs well in the presence of heteroscedasticity. The final estimated model corresponds to taking the conditional expectations form of (5):
(6)
Equation (6) was estimated using the PPML estimation technique.
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Data and Sources This study investigates the effect of infrastructure development and institutions on bilateral trade in East Africa for the period 2000 to 2018. The countries included in the study are presented in Table A1 in the appendix. Bilateral exports are measured in current US dollars and obtained from World Bank’s World Integrated Trade Solution (WITS) database. GDP is measured at current US $ and obtained from World Development Indicators (WDI) of the World Bank; distance between the capital cities in East Africa is obtained from CEPII database. Transport and ICT infrastructure stock were obtained from African Development Bank (AfDB) Socio-Economic database2. Data on other macro variables, such as population, foreign direct investments, and exchange rate, were obtained from WDI. Data on control of corruption index and regulatory quality, used as indicators for institutional quality, was obtained from Worldwide Governance Indicators of the World Bank. Data on WTO trade agreements dummy, official and ethnic language dummies were obtained from the CEPII database, while EAC free trade agreements dummy was based on author’s elaboration.
RESULTS AND DISCUSSION The summary statistics for the variables used in the gravity is presented in Table C1 in Appendix C. Table 1 presents the findings of the general gravity model for East Africa. It includes both transport infrastructure, ICT infrastructure, and other control variables, such as quality of institutions. Table 1: Effect of ICT and Transport Infrastructure on East Africa’s Trade Dependent Variable: Exports Variable
Method: PPML
(1)
(2)
Log of GDPi
0.462*** (0.104)
0.199** (0.095)
Log of GDPj
0.835** (0.409)
0.735* (0.431)
Log of Distance
-0.462** (0.188)
-0.523** (0.202)
Log of Populationi
1.196*** (0.259)
1.246*** (0.263)
Log of Populationj
0.507*** (0.062)
0.531*** (0.070)
ICT Infrastructurei
0.647*** (0.225)
0.558** (0.246)
ICT Infrastructurej
0.051** (0.369)
0.065*** (0.018)
Transport Infrastructurei
-0.175 (0.144)
0.111 (0.139)
Transport Infrastructurej
-0.096*** (0.012)
0.097*** (0.014)
Exchange Ratei
-0.185*** (0.032)
-0.206*** (0.206)
2 See Appendix B for the measurements and methodology employed in constructing the infrastructure composite index.
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Log of Foreign Direct Investmenti
-0.097** (0.045)
-0.031 (0.032)
EAC Trade Agreements
0.452*** (0.144)
0.559*** (0.133)
WTO Trade Agreements
0.684*** (0.230)
0.768*** (0.281)
Official Language
-0.083 (0.195)
-0.068 (0.222)
Ethnic Language
0.662*** (0.167)
0.609 (0.172)
Contiguity
0.815*** (0.131)
0.775*** (0.133)
Corruption Indexi
0.416** (0.171)
Corruption Indexj
0.819* (0.472)
Regulatory Qualityi
0.583** (0.283)
Regulatory Qualityj
-0.039 (0.193)
Constant
-25.594*** (2.612)
-21.631*** (2.322)
Psuedo Log-likelihood
-1123412
-1142363
0.7987
0.8041
890
890
R2 Observations
*P<0.10, **P<0.05, ***P<0.01; Robust standard errors are in parentheses, i and j are exporting and importing countries respectively
The role of ICT infrastructure in globalization cannot be underestimated, as it lowers communication costs and simplifies the custom procedures, resulting in more trade. Increasing the stock of ICT infrastructure would increase bilateral exports among the countries in East Africa. Increasing the stock of exporting country’s ICT infrastructure significantly increases bilateral exports within East Africa. Similarly, the destination country’s ICT infrastructure is also crucial for trade. If the stock of exporting country’s ICT infrastructure increases by 10 percent, bilateral exports would increase by about 6.0 percent. This implies that a higher stock of ICT infrastructure is associated with more exports in the East African region. Although both exporter and importer countries infrastructure promote trade, the exporting country’s infrastructure has a somewhat larger impact. The results support the findings by Francois and Manchin (2013), Portugal-Perez and Wilson (2012), who established that ICT infrastructure is important for increasing exports. Generally, ICT infrastructure is important, as it enhances efficiency, hence increasing volume of exports and trade in general. The results also reveal that increasing the stock of transport infrastructure of the importing country has a greater potential for increasing the volume of exports in East Africa. At 5 percent level of significance, increasing the stock of the importing country’s transport infrastructure by 10 percent would increase in intra-East Africa exports by about 1.0 percent. The impact of the exporting country’s 111
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transport infrastructure has a positive, but not statistically significant, impact on exports. In general, it is expected that infrastructure development would promote trade by lowering transport costs. High transport costs hinder bilateral trade. The findings are similar to those of Limao and Venables (2001), Clark et al. (2004), Shepherd and Wilson (2009), Behar and Manners (2008), Francois and Manchin (2013) and Celbis et al., (2014). Infrastructure development leads to lower transport costs, hence an increased value of exports. The findings support the idea that the destination country’s infrastructure, and even the state of infrastructure in the transit countries, have strategic roles in bilateral trade (Limao and Venables, 2001). Quality of institutions is an important determinant of trade flows. The findings reveal that improvement in the regulatory of the exporting country has positive significant effect of bilateral exports. Similarly, the findings reveal that control of corruption of the exporting country is important for bilateral trade. An improvement in control of corruption index would promote bilateral exports among the countries in the East African region. The coefficient of EAC free trade agreements is positive and statistically significant at 1 percent level of significance. It implies that EAC Partner States are 1.6 times3 more likely to trade among themselves due to the regional trade agreements than with non-EAC members. Free trade agreements minimize the usual barriers associated with trade, resulting in more exports. Similarly, countries under WTO agreements are 2 times more likely to trade among themselves than with countries not under WTO agreements. The rest of the variables, such as GDPs of the exporting and importing country, distance, population exchange rat,e and foreign direct investments have the expected signs and are consistent with theory. Increase in GDP of the exporting country implies a greater capacity to produce domestically, hence more exports, while an increase in GDP of the importing country suggests a higher marginal propensity to import, hence more imports. Distance has the expected negative sign and is statistically significant for the countries in the East Africa region. Longer distance is associated with higher trade costs, thus discouraging trade. A high population is associated with a large market size, which promotes trade. Exchange rate appreciation implies more expensive exports, hence decreases the exports of the exporting country. The results also reveal that high FDI inflows discourage trade within the East African region. The presence of a common ethnic language between the countries in the East Africa region has a positive statistically significant impact on intra-East Africa trade. A common language between two trading partners reduces the cost of communication, and consequently increases exports. The effect of official language was negative, but not statistically significant. The results also show that countries that are neighbouring each other are likely to trade more compared to countries that are not neighbours.
CONCLUSION AND POLICY IMPLICATIONS To establish the relationship between infrastructure development and bilateral exports in the East Africa region, the study estimated a gravity model augmented for infrastructure using a panel of 11 countries from 2000 to 2018. The study obtained data on transport and ICT infrastructure index from AfDB socioeconomic database. Bilateral exports data was obtained from WITS of the World Bank, while other explanatory variables were obtained from WDI and CEPII database. The study also introduced control of corruption index and regulatory variables to establish the role of governance and business environment in East Africa’s trade. The variable was obtained from the Worldwide Governance Indicators of the World Bank. 3 The coefficient of EAC dummy is expressed as exponent i.e. exp (0.45) = 1.57 and WTO, exp (0.7) = 2.01
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Using PPML estimation technique, the study finds that both ICT and transport infrastructure stocks have statistically significant effect on bilateral exports. However, ICT infrastructure has a greater impact on intra-East Africa trade relative to transport infrastructure. Therefore, countries in East Africa can speed up the pace of economic integration through increased investment in ICT and transport infrastructures, which enhances trade. Based on the findings of this study, countries in the East Africa region would gain more by increasing the stock of ICT infrastructure in the region. Both importing and exporting countries ICT infrastructure have statistically significant effects on intra-East Africa trade. This implies that infrastructure development is important in the regional economic integration process, hence the EAC’s efforts to have joint infrastructure projects. In terms of transport infrastructure, the importing country’s infrastructure has the greatest impact on exports as compared to the exporting country’s transport infrastructure. As a result, in situations of resource scarcity or limited resources for infrastructure investment, more allocations should be given to ICT related infrastructure. In the long-term, countries in the East Africa region should diversify their production to expand more opportunities for trade. The East African countries would also encourage trade by improving the quality of their institutions. This could be done specifically by reducing corruption in the public sector, while improving on the regulatory quality would encourage more trade in the East Africa region. The presence of regional trade agreements is an important determinant of intra-regional trade. EAC-Partner States trade more amongst themselves due to regional trade agreements under EAC customs union. This signifies the crucial role regional trade agreements play in reducing trade barriers associated with bilateral trade. Similarly, countries under WTO agreements trade more amongst themselves.
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Raychaudhuri, A. & De, P. (2016). Trade, Infrastructure and Income Inequality in Selected Asian Countries: An Empirical Analysis. In: Roy, M. and Sinha Roy, S., Eds., International Trade and International Finance, Springer, New Delhi, 257-278. https://doi.org/10.1007/978-81-322-2797-7_12 Rehman, F., Noman, A. & Ding, Y. (2020). Does Infrastructure Increase Exports and Reduce Trade Deficit? Evidence from Selected South Asian Countries using a New Global Infrastructure Index. Journal of Economic Structures, 9(1),1-23. DOI: 10.1186/s40008-020-0183-x. Sahoo, P., Dash, R. K. & Nataraj, G. (2010). Infrastructure Development and Economic Growth in China, Institute of Developing Economies, Discussion Paper no. 261, Jetro. Retrieved from https://www.ide.go.jp/English/ Publish/Download/Dp/261.html Shepherd, B. & Wilson J. S. (2009). Trade Facilitation in ASEAN Member Countries: Measuring Progress and Assessing Priorities, Journal of Asian Economics, 20(4): 367-383. DOI: 10.1016/j.asieco.2009.03.001 Shepherd, B. (2016). Infrastructure, Trade Facilitation, and Network Connectivity in Sub-Saharan Africa, Journal of African Trade, 3(1): 1-22. DOI:10.1016/j.joat.2017.05.001 Silva, J. S. & S. Tenreyro (2006). The Log of Gravity, The Review of Economics and Statistics, 88(4): 641-658. DOI: 10.1162/rest.88.4.641 Silva, J. S. & S. Tenreyro (2011). Further Simulation Evidence on the Performance of the Poisson Pseudo-Maximum Likelihood Estimator, Economics Letters, 112(2): 220-222. DOI: 10.1016/j.econlet.2011.05.008 Tinbergen, J. (1962). Shaping the World Economy: Suggestions for an International Economic Policy. New York: The Twentieth Century Fund. UNCTAD (2013). Supporting Infrastructure Development to Promote Economic Integration: The Role of Public and Private Sectors, UNCTAD Secretariat, Geneva, Switzerland. Retrieved from https://unctad. org/meetings/en/SessionalDocuments/cimem6d2_en.pdf World Bank (2020). World Development Indicators. Accessed at: https://databank.worldbank.org/source/worlddevelopment-indicators. Accessed on 6th August, 2020. World Trade Organization, (2004). World Trade Report 2004: Exploring Linkage between the Domestic Policy Environment and International Trade, Geneva, Switzerland. DOI: 10.30875/0949bcf3-en World Trade Organization (2019). Trade Policy Review of the East African Community, WT/TPR/S/384. Retrieved from https://www.wto.org/english/tratop_e/tpr_e/s384_e.pdf Xing, Z. (2018). The Impacts of Information and Communications Technology and e-Commerce on Bilateral Trade Flows, International Economics and Economic Policy, 15: 565-586. DOI: 10.1007/s10368-017-0375-5 Yu, S., Beugelsdijk, S. & de Haan, J. (2015). Trade, Trust and the Rule of Law, European Journal of Political Economy, 37: 102–115. DOI:10.1016/j.ejpoleco.2014.11.003 Yushkova, E. (2014). Impact of ICT on Trade in Different Technology Groups: Analysis and Implications, International Economics and Economic Policy, 11: 165-177. DOI: 10.1007/s10368-013-0264-5 https://dataportal.opendataforafrica.org/nbyenxf/afdb-socio-economic-database-1960-2020. Accessed on 16th February 2020. http://www.cepii.fr/cepii/en/bdd_modele/bdd.asp. Accessed on 7th August 2020. https://wits.worldbank.org/. Accessed on 5th August 2020. https://data.mendeley.com/datasets . Data set for this study can be accessed using this link.
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APPENDIX Appendix A: List of Countries in the East Africa Region Table A1: Sample Countries in East Africa Burundi * Comoros Djibouti Eritrea Ethiopia Kenya*
Rwanda* Somalia Sudan Tanzania* Uganda*
*-Refers to East African Community Partner States
Appendix B: Data and Methodology for Generating Infrastructure Composite Index Data infrastructure variables were obtained from African Development Bank (AfDB) Socio Economic Database, 1960-2020. According to African Development Bank (2018), transport infrastructure composite index comprises of two indicators: (a)
Total Paved Roads – This is measured in kilometres per 10,000 inhabitants. This acts as a proxy for access to paved road network. It is given by the country’s total surface with macadam or bitumen, with concrete or cobblestones.
(b)
Total Road Network – This is measured in kilometres per kilometre squared of exploitable land area. It comprises of both paved and non-paved road networks.
The ICT infrastructure composite index comprises of four main indicators: (a)
Total Phone Subscriptions – This is measured as total phone subscriptions per 100 inhabitants in a country. It is given by both fixed telephone lines and mobile cellular telephone subscriptions per year.
(b)
Number of Internet Users ¬– This is measured as the total number of internet users per 100 inhabitants. It is given by the total number of internet users from any device (including mobile phones) in the total population per year.
(c)
Fixed Broadband Internet Subscribers – This is measured as the total number of fixed broadband internet subscribers per 100 inhabitants. It is given by subscriptions with access to data communications (including internet), exclusive of mobile cellular networks.
(d)
International Internet Bandwidth – This is measured by the total capacity of international internet bandwidth in megabits per second (Mbps). It is given by the total capacity of all internet exchanges offering international bandwidth.
In generating an infrastructure index, the first step involves normalizing observed values of each component to take values in the range of 0 and 100 in a given period. The next step is to calculate a composite index of each component. This involves generating weighted average of indicators for each component that has more than one indicator (African Development Bank, 2018). 116
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Appendix C: Summary Statistics Table C1: Summary Statistics for Intra-East Africa Trade Variable
Min
Max
Mean
Std. Dev
Exports (US$ ‘000)
0
793,984.4
42,322.5
103,414.9
GDP i (US$ Million)
785
87,900
19,100
19,700
GDP j (US$ Million)
785
87,900
19,100
19,700
Distance (weighted) Km
162.2
2,418.3
1,342
625.2
ICT Infrastructure i
0
29.94
4.89
7.21
ICT Infrastructure j
0
29.94
4.89
7.21
Transport Infrastructure i
0.48
17.57
6.90
5.50
Transport Infrastructure j
0.48
17.57
6.90
5.50
Corruption Index i
-1.86
0.76
-0.83
0.51
Corruption Index j
-1.86
0.76
-0.83
0.51
Regulatory quality i
-2.65
0.25
-1.04
0.71
Regulatory quality j
-2.65
0.25
-1.04
0.71
Exchange Rate i
67.32
2,263.78
966.86
623.95
Foreign Direct Investments i (US$ Million) 0.100
2,090
466
520
EAC FTA Agreements
0
1
0.74
0.44
WTO Trade Agreements
0
1
0.50
0.50
Official Language
0
1
0.60
0.49
Ethnic Language
0
1
0.34
0.47
Contiguity
0
1
0.36
0.48
Where: i and j refers to the importing and exporting countries respectively, FTA refers to free trade agreements
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RAZVOJ INFRASTRUKTURE, INSTITUCIJA, TRGOVINA UNUTAR REGIONA: SLUČAJ ISTOČNE AFRIKE
Rezime: Ova studija empirijski ispituje vezu između infrastrukturnog fonda i bilateralnih trgovinskih tokova koristeći panel od 11 zemalja istočne Afrike za period od 2000. do 2018. Infrastrukturni gravitacioni model procenjen je korišćenjem ukupnog bilateralnog izvoza za zemlje istočne Afrike. Infrastruktura je razdvojena na infrastrukturu transporta i informaciono-komunikacione tehnologije (IKT). U model su ugrađene dve institucionalne promenljive, kontrola indeksa korupcije i regulatorni kvalitet. Korišćenjem Poasonovog koeficijent pseudo-maksimalne verovatnoće (PPML), rezultati potvrđuju da i IKT i transportna infrastruktura i institucije za kvalitet pozitivno utiču na obim ukupnog bilateralnog izvoza u istočnoj Africi. Međutim, IKT infrastruktura ima veći uticaj na trgovinske tokove u poređenju sa transportnom infrastrukturom. Zbog toga bi trebalo usmeriti više resursa ka povećanju zaliha IKT infrastrukture za podsticanje trgovine i regionalne integracije u istočnoj Africi.
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Ključne reči: Infrastrukturni fond, institucionalni kvalitet, trgovina unutar regiona, Istočna Afrika. Klasifikacija JEL: H54, F10, F15
EJAE 2020, 17(2): 119 - 135 ISSN 2406-2588 UDK: 338.124.4 DOI: 10.5937/EJAE17-26097 Original paper/Originalni nauÄ?ni rad
THE EXPLANATORY FACTORS OF SOVEREIGN CREDIT DEFAULT SWAPS SPREADS: A QUANTILE REGRESSION APPROACH Radhia Zemirl*, Mohand Chitti Faculty of Economics, Department of Business and Management, University Mouloud Mammeri of Tizi-Ouzou , People's Democratic Republic of Algeria
Abstract: This article aims to analyze the main risk factors that explain the manifestation and the aggravation of sovereign risk, particularly through the dynamics of sovereign CDS spreads in euro area member countries. The explanatory factors that will be analyzed are related to general risk aversion, which is explained by the volatility of the stock markets, liquidity risk perceived by the flight to quality phenomenon, idiosyncratic risk, which is explained by the deterioration of the state of macroeconomic fundamentals. We will adopt an econometric approach with the quantile regression method applied to panel data developed by Canay (2011), because it allows to estimate the effect of the independent variables on the different regions of the distribution, of the dependent variable and also makes it possible to overcome the problem of the presence of extreme values. Finally, our model has made it possible to identify, over time and different countries, the factors which significantly explain a sovereign risk, and whose deteriorated situation is likely to lead to payment defaults, which is very important to know, especially in the current unfavorable macroeconomic context. These include the volatility of the stock markets, which shows investor mistrust, the drying up of liquidity in the bond markets, which explains the phenomenon of flight to quality, the budgetary factor, which is explained by the unsustainable debt, and the economic factor, perceived by the level of wealth of a country.
*E-mail: radhia_zemirli@hotmail.fr
Article info: Received: April 10, 2020 Correction: May 12, 2020 Accepted: June 29, 2020
Keywords: CDS spreads; Sovereign risk; Fundamentals; Risk aversion; Quantile regression.
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INTRODUCTION Before the international financial crisis and the crisis of the euro area, the risk of default of a state was mentioned only in the case of emerging countries, the default of a developed country is implausible. However, the international financial crisis of 2007 and the crisis of the euro area have shown that the risk of default of a sovereign borrower is well conceivable in developed economies, especially European countries. Indeed, because of the convergence criteria required by the Maastricht Treaty for members of the euro area, investors paid little attention to the economic indicators and debt of the countries of this union. Investor awareness of this risk began with the outbreak of the 2007 crisis, and has intensified as the crisis has turned into a euro area crisis. In recent years (mainly since 2010), investors’ perception of sovereign default risk in the euro area has changed from one country to another. The causes of this credit event are various, and can be trigged by the unsustainability of sovereign debt, a budget deficit, a commercial deficit, systemic crises (banking, stock market, foreign exchange or economic, etc.) leading to a phenomenon of contagion and an increase in international borrowing costs in the capital markets. Our work has its origins in the aggravation of sovereign risk in the euro area, which has resulted in the unsustainability of sovereign debt, and the risk of exclusion from the financial markets. This deterioration in credit quality was also accompanied by a rise in the sovereign bond yield, an increase in the CDS premium, and an unfavorable revision of the sovereign rating by rating agencies. These facts lead us to ask a crucial question around which this work is articulated, namely: “What are the main explanatory factors of sovereign CDS spreads in the euro area?” In this work, we will consider the sovereign CDS spread as a measure of the default risk of a public borrower. This spread plays a major role in determining the default risk, and is a pertinent measure of the estimated solvency of investors, as clarified by Afonso et al. (2012), De Santis (2012), and Aizenman et al. (2013), who argue in favor of this indicator as a better measure of sovereign risk. Thus, this article aims to determine the main risk factors that explain the manifestation of sovereign risk, particularly through the dynamics of sovereign CDS spreads in the euro area member countries. The explanatory factors that will be analyzed are related to general risk aversion, liquidity, and macroeconomic and macro-financial fundamentals idiosyncratic to countries. The originality of this article is that it measures sovereign risk across sovereign CDS spreads by examining various explanatory variables linked to the volatility of European stock markets, the phenomenon of flight to liquidity, and is linked to the state of fundamentals, over a period of crisis and lull period extending from 2007 to 2018. In addition, a comparative analysis between the member countries of the euro area characterized by economic vulnerability and resilience has been added in order to understand the factors which explain the aggravation of sovereign risk in these countries, which is important to know both for policymakers and for investors, especially in the current unfavorable macroeconomic context caused by the health crisis due to Covid-19, which may cause the surge in public debt. We also use an original approach to the analysis of panel data by quantile regressions, a rich method, that above all allows us to take into account the strong individual heterogeneity and the presence of extreme values. This is a plus for our study, especially since the previous work used only the classic panel data method. Thus, we will conduct an analysis of panel data by exploiting the two sources of variation in statistical information: temporal and individual, in order to determine the main risk factors that have an impact on sovereign CDS spreads. More particularly, we resort to the quantile regression method applied to 120
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panel data developed by Canay (2011), which allows us to estimate the effect of the independent variables on the different regions of the distribution, notably the quantiles, of the dependent variable and not on its average only. In other words, this technique describes how the conditional quantiles change as a function of these determinants, and also make it possible to overcome the problem of the presence of extreme values. To achieve the objective of this article and provide elements of answers to the problem posed, this article will be divided into two parts. In the first point, we will review the existing literature on the explanatory factors of sovereign CDS spreads to explain the correlations between the sovereign CDS spread and the common and idiosyncratic risk factors. The second point will focus on the econometric study in which we will present our sample and the period of our study, after which we will introduce the variables of the dynamics of the chosen of sovereign CDS spreads and, finally, we will examine the results of the classic regressions and quantile regressions of our model by highlighting the main determinants of the spread of CDS sovereign. Moreover, we will analyze the results of the comparison between the results of the quantile regressions of the panel of vulnerable countries versus robust countries of the euro area by highlighting the main explanatory factors of the sovereign CDS spreads of these two groups of countries.
LITERATURE REVIEW ON THE EXPLANATORY FACTORS OF SOVEREIGN DEFAULT RISK First of all, it seems important to review the existing literature on sovereign spreads by presenting a theoretical overview of the links between these spreads and their explanatory factors by referring to the previous works dealing with this topic. The choice to analyze this relationship can be justified, in particular, by the fact that there has been some uncertainty recently in advanced countries about a possible sovereign default, and this is why studies have started to develop, especially in the case of the countries of the euro area, contrary to the studies of the emerging countries, characterized by a considerable sovereign risk in the 1990s. Interest in the risk of sovereign default in euro area countries has emerged over the past decade following the outbreak of the 2007 international financial crisis in the US housing market and its transmission to European public bond markets. Overall, the number of studies on the determinants of sovereign spreads has increased substantially in recent years. In addition, the existing economic literature on explanatory factors for sovereign spreads in euro area countries, among others, has been given to attribute the volatility sovereign spreads to three major factors, namely: the overall risk factor or common risk, the specific risk factor or idiosyncratic risk, and the risk of contagion. Indeed, the analyses carried out in this direction, notably by Kalbaska (2013), Heinz and Sun (2014), Zeman (2014), Artus and Rodado (2012), Manganelli and Wolswijk (2009), Attinasi et al. (2009), Afonso, et al. (2012, 2015), Ertugrul and Ozturk (2013), Ordonez-Callamand et al. (2017), Arellano el al. (2017), Debarsy et al. (2018), Chaumont (2018), Mpapalika and Malikane (2019), used different methodologies and chose different explanatory variables to explain these spreads. However, it is important to emphasize that the majority of authors have convergent conclusions showing the importance of common and idiosyncratic risk factors in determining CDS spreads and sovereign bond yields of euro area countries. In addition to these factors, some studies have revealed the existence of 121
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a contagion effect within these countries, which may play a major role in the spread of risk factors from one country to another and, consequently, in the increase or widening of their spreads as a result of deteriorating fundamentals and increased risk aversion in the markets, for example: Afonso, et al. (2012, 2015), Lizarazo (2013), Artus and Rodado (2012). In what follows, we will attempt to clarify these factors relating to the aforementioned risks by looking at previous work. First, “the common risk factor” is related to the general perception of international risk by investors. It is generally considered a risk factor common to all countries because it described how economic agents perceive risk and react simultaneously to a risky situation. There are several methods for measuring risk perception in the financial markets. One example is a method that compares the current levels of different financial assets that are representative of how agents perceive risks (spreads on government bonds, volatility in financial markets, stock or exchange rate fluctuations, …) with their historical evolution to deduce an aggregate index of risk perception, as demonstrated especially by Afonso et al. (2012, 2015), Kilponen et al. (2015). In addition, for the measurement of this type of risk, Heinz and Sun (2014), Artus and Rodado (2012), Gerlash et al. (2010), Afonso et al. (2015) use the implied volatility of the S&P500 Index (VIX). This index, which is widely used in work related to sovereign spreads, is seen as a gauge of investors’ level of fear, as it reflects their degree of risk aversion, which tends to increase during periods of uncertainty turbulence. De Santis (2012) uses BBB-rated private bond yields, while Niehof (2014) expresses this corporate spread by the yield spread of a low-rated and a low-rated private bond. Allegret et al. (2016) analyze this risk from a VSTOXX index (measuring the volatility of European stock markets), and examine the Impact of the euro area crisis on European banks from this index. Overall, the effect of global risk on the dynamics of sovereign spreads is heterogeneous. It has been more pronounced during periods of stress in the global financial markets, and more pronounced in countries with high levels of public debt, and this result has been demonstrated through the aforementioned works. Second, “the idiosyncratic risk factor” reflects the probability of default of a sovereign borrower, which depends on the state of macroeconomic and macro-financial fundamentals, reflecting the ability of a sovereign country to honor its financial commitments. According to Beyond Ratings, the idiosyncratic risk assessment for a sovereign country is largely based on indicators taken into account in the country risk analysis, in particular the country’s economic performance, as well as the many risks that could have an impact on its growth potential in the short and long term. Indeed, investors in sovereign bonds monitor several types of risks, often grouped into four pillars: economic growth, public finances, external risks, and socio-political stability. The most-used indicators for measuring such risk in empirical studies are the variables that represent the country's public finances, including the ratio (Budget Balance/GDP and Public Debt/GDP), to assess the sustainability of public finances and its impact on sovereign bond yields, as seen through the work of Artus and Rodado (2012), Castelletti Font and Ben Salem (2017), Costantini et al. (2014), Afonso et al. (2015), Hatchondo et al. (2017). Indeed, a deterioration of the budget deficit is an indicator of the country's fiscal vulnerability. Furthermore, other macroeconomic and macro-financial fundamentals have been illustrated by several observations, namely: -the economic growth of a country is part of the macroeconomic fundamentals that would reflect the level of default risk. This may be represented by the GDP growth rate, the volume of industrial production (that provides information on the ability to repay its debt), or foreign exchange reserves (which are also considered as indicators of a country's wealth). 122
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- the unemployment rate, which reflects on the overall health of a country, as a high rate of unemployment can have a negative impact on a country's growth potential and fiscal position, since a high level of unemployment forces countries to pay unemployment benefits, which can weigh heavily on a country's budget. - the inflation rate is an essential indicator of macroeconomic stability, as a very high inflation can also be a way to fail, as demonstrated by Aizenman et al. (2016), Artus and Rodado (2012). - the real effective exchange rate, since an unfavorable change in this rate corresponds to a deterioration in the country's competitiveness that leads to low production, which will negatively affect the state’s revenue - this indicator was used by Artus and Rodado (2012), Tsoukalas and Arghyrou (2011). In addition, other indicators were used for the assessment of idiosyncratic risk, such as the rating of rating agencies used by Artus and Rodado (2012), Manganelli and Wolswijk (2009) and De Santis (2012), as a reference of the probability of default of an issuer. Third, “the contagion risk factor” is actually the result of the conjunction of the first two risks. Contagion is materialized by investors changing views about a country's solvency (its ability to repay debt), which further increases the cost of borrowing by more expensive premiums, making the debt of the most vulnerable states, with weak fundamentals, unsustainable. Caceres and Unsal (2011) used a principal component analysis (PCA) applied to risk premiums to identify a common factor in their evolution and in order to show the correlation existing between the variables. The assumption behind this approach is that the returns of different securities are correlated because they depend on one or more common factors. Other studies, like Afonso et al. (2012, 2015), Artus and Rodado (2012), Longstaff et al. (2011), also attempted to quantify the contagion effect using PCA. These authors have revealed that the recent dynamics of sovereign spreads have shown the existence of a dichotomy between countries of the euro area, the most powerful northern countries, and those in the most vulnerable south. Although the rise in sovereign spreads was in both groups, it was more pronounced for the southern countries compared to the period before the sovereign debt crisis, confirming the existence of a contagion effect between these groups of countries. Based on the literature review presented above, the objective of the next point is to highlight empirical evidence of the importance of the impact of common risk factors and idiosyncratic risk on the probability of default of a Sovereign state. However, we will omit the risk of contagion, which is supposed to be the subject of another study with other methods of analysis.
DATA AND METHODOLOGY This study will focus on the use of the panel data method to estimate the main determinants of sovereign risk in the euro area. By referring to this technique, we can exploit the two sources of variation in statistical information: temporal (or intra-individual variability) and individual (or inter-individual variability). This makes it possible to take into account the dynamics of behaviors and the heterogeneity that can exist between individuals (some member countries of the euro area in our case). It also makes it possible to have a large number of observations through the use of the temporal and individual dimension.
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Model Estimation Through the panel data method, we will study the existing relationship between sovereign CDS spreads and these different explanatory variables. The model is written as follows: (1) Where: Sovereign CDS spread with 5-year maturity (expressed in Log) is the dependent variable, and the explanatory variables are: Bond is the long term government bond yields, Vstoxx is the volatility index of the European stock markets, Balance represents the budget balance/GDP, Debt is the level of the public debt/GDP, Reer is the real effective exchange rate, Prod is the growth of industrial production. Our model takes into account a sample of 10 euro area member countries, namely: Germany, France, Belgium, Finland, the Netherlands, Austria, Ireland, Spain, Italy, and Portugal, with an annual periodicity ranging from 2007 to 2018. As a result, we will obtain a number of observations of 480 (10countries X 12years X 4quarters). This appreciable number of observations makes it possible to guarantee a better accuracy of the estimators, to reduce the risks of multicollinearity, and especially to widen the field of investigation. Description of the Variables The selected variables are those most commonly used in the literature for the explanation of sovereign spreads. We have chosen a specification consisting of six explanatory variables, including macroeconomic and macro-financial fundamentals (budget balance, public debt, industrial production, real effective exchange rate), and two market variables, including "Vstoxx" for risk aversion and government bond yields for liquidity risk. - The Dependent Variable "Sovereign CDS spreads with 5-year maturity" For the assessment of the sovereign default risk in our analysis, we selected the sovereign CDS spread of euro area member countries. The latter reflects the risk of default as anticipated by the market. Its price includes a premium of risk, liquidity, and counterpart, that tend to overreact in case of systemic shock. Thus, CDS spreads play a major role in determining the risk of default, and are a good measure of the estimated solvency of investors. The higher the spread, the less solvent the country. Several authors argue in favor of this indicator as a better measure of sovereign risk, including Barrios et al. (2009), Afonso et al. (2012), De Santis (2012) and Aizenman et al. (2013). The maturity of CDS 5-year was chosen because it is the most liquid asset on the markets compared to other maturities. In addition, we have added the logarithm option to our variable to be explained in order to linearize it, and reduce the strong individual heterogeneity concerning this variable. - The Independent Variables Vstoxx: is the market volatility index of the Eurostoxx50, an index that includes the 50 largest capitalizations on the euro area financial markets. It is calculated according to the same principles as the VIX (its American counterpart), which would allow an immediate comparison of the nervousness between the different markets. The Vstoxx reflects the feeling of fear in the market, hence the nickname "the indicator of fear in the euro area." When the Vstoxx is high, it means that the market is volatile 124
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and that investors will have to pay higher risk premiums (for the next 30 days). Analyzing a factor that reflects international risk aversion as a common factor is imperative to examine sovereign risk in the region, which is generally measured by uncertainty in the financial markets, hence, the selection of the variable "Vstoxx" for its quantification. Long-term Government Bond Yields: is representative of the liquidity risk, or the phenomenon of “flight to quality” or “flight to liquidity,” which designates a situation where investors seek to sell assets perceived as risky, and to buy non-risky assets, by seeking liquidity on these safe investments. This causes a surge in yields on the riskiest assets and the related risk premiums and, consequently, an imbalance in the credit market in reaction to an increase in volatility. This phenomenon that notably increases the sovereign or government bond yields generate a drying up of liquidity on risky assets of countries considered insolvent, and thus becomes unsustainable debt, from whence the strong manifestation of sovereign risk. The Budget Balance and the Public Debt: the budget balance/GDP report is widely used to measure the sustainability of public finances, and informs investors about the country's default risk (i.e., the risk that the sovereign state cannot meet its obligations in time and entirely because the repayment capacity of a country depends on its ability to generate financial resources to cover the debt service). Similarly, for the public debt/GDP ratio, which also assesses the country's fiscal position, a higher debt should increase the risk of sovereign default, and hence the return demanded by investors. We therefore expect a positive relationship between the public debt/GDP ratio and the CDS spread. Moreover, we expect a negative sign between the budgetary balance/GDP and the sovereign CDS spread, because an improvement in this balance (i.e., an increase in the surplus or a decrease in the deficit) will lower the CDS spread. The Growth of Industrial Production: measures the impact of the robustness of the countries of the euro area on the sovereign risk. Thus, an increase or decrease in this variable indicates an increase or decrease in the country's economic performance, which in turn has an effect on the creditworthiness of a country opposite these creditors, implying a volatility of sovereign CDS spreads more or less important from one country to another. We expect a negative correlation between this variable and CDS premiums. The Real effective Exchange Rate (Reer): refers to this rate as a measure of the level of external competitiveness. This indicator represents the evolution of the currency of a country in relation to all exchange rates of its main trading partners while taking into account the weight of each one. In this regard, it should be noted that the difference between the inflation rates of the euro area countries is taken into account, hence the choice of the real effective exchange rate, which takes into account the weighted average of the currencies of the main partners (adjusted inflation differentials). A drop in this rate corresponds to an improvement in the country's external competitiveness or price-competitiveness, which leads to high production in order to support the increase in exports, which will generate a balance of trade surplus. This will result in a sustained budget balance and debt level due to the large inflows of foreign currency and, therefore, lower interest rates on government bonds and less volatile CDS spreads. For that, we expect a positive correlation between the Reer and the sovereign CDS spreads. It should be noted, in this regard, that the explanatory variables used in this study have also been the subject of previous analyses. The expected signs of their correlation with sovereign CDS spreads are summarized in Table 1:
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Table 1- Presentation of the Variables and Expected Signs. Designation
Data source
Expected signs on CDS spreads
Dependent variable CDS Spreads
The risk premium of sovereign CDS with 5-year maturity
Datastream
Independent variables Balance
Budget balance as% of GDP
Eurostat
-
Debt
Public debt as% of GDP
Eurostat
+
Vstoxx
Volatility index on the market of Eurostoxx 50
Datastream
+
Bond
Long-term government bond yield (10 years)
Eurostat
+
Reer
Real effective exchange rate
Eurostat
+
Prod
The growth of industrial production
Oecd.org
-
Source: Designed by authors.
Analysis of Econometric Study Test Results Before analyzing the results of the regressions, it is important to respect a number of conditions, namely: the overall and partial significance of the model, and the existence of multicollinearity, heteroscedasticity and autocorrelation, which can bias the coefficients of the regressions in panel data. To verify this, the analysis of certain tests of the panel data method is needed to verify the robustness of the model. First, we begin by analyzing the correlation matrix in order to judge the existence of multicollinearity, which is a problem that occurs when the explanatory variables tend to demonstrate and/or measure the same phenomenon, i.e., indicate whether the selected variables generate a redundant effect or not. Table 2 represents the correlation matrix that makes it possible to measure the relationship between the dependent variable and the explanatory variables and between the explanatory variables themselves, as well as the intensity of the relationship (correlation): Table 2- Correlation Matrix Log CDS
Balance
Debt
Reer
Bond
Log CDS
1.0000
Balance
-0.4678
1.0000
Debt
0.4294
-0.1599
1.0000
Reer
-0.0575
0.0100
-0.1640
1.0000
Bond
0.6896
-0,3787
0.0981
-0.0136
1.0000
Prod
-0.3938
0.3903
0,0935
-0.0134
-0.2409
1.0000
Vstoxx
0.2496
-0.1221
-0.1864
0.0195
0.2948
-0.1261
Source: Constructed from database exploitation under Stata 15 software. 126
Prod
Vstoxx
1.0000
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As we can see from this table, there is a relationship between the different variables selected. The correlation between these values is acceptable, and the explanatory variables are weakly correlated in our analysis. This indicates the absence of a multicollinearity problem. We also use another approach to confirm these results and further prove the absence of multicollinearity, especially the VIF (Variance Inflation Factor) test, which estimates how much the variance of a coefficient is increased due to a linear relationship with other predictors. If all VIFs are equal to 1 and/or close to 1, there is no multicollinearity. However, some authors believe that there really is a problem of multicollinearity when VIF is higher than 2.5, while others worry only from 5. In our case, the results of the test in Table 3 show that all the coefficients are close to 1, which signifies the absence of multicollinearity, indicating the correct choice of variable. Table 3- Multicollinearity Test Results (VIF Test) Variable
VIF
1/VIF
Balance
1.33
0.751069
Debt
1.12
0.894717
Reer
1.03
0.971961
Bond
1.28
0.779607
Prod
1.20
0.832425
Vstoxx
1.16
0.860467
Mean VIF
1.19
Source: Constructed from database exploitation under Stata 15 software.
Moreover, we must determine whether the data series are stationary, as in the set of empirical analyzes that consider strictly stationary processes. We will demonstrate stationarity by means of the specified test namely the unit root test, in this case, the Levin test, Lin & Chu (LLC). Table 4- The Results of the Unit Root Test (LLC Test) Test LLC At the level
At first difference
Log CDS
0,2037
0.0412**
Balance
0,0000***
-
Debt
0,0021***
-
Bond
0,5519
0,0314**
Prod
0,0088***
-
Reer
0,0347**
-
Vstoxx
0,0000***
-
NB: If the P-values are less than 0.01; 0.05; 0.1. This means that the variables are stationary at the 1% ***, 5% **, 10% * threshold, respectively. Source: Constructed from database exploitation under Stata 15 software. 127
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Through the stationarity test of LLC, where the null hypothesis assumes that all series are non-stationary against the alternative hypothesis that the series are stationary, we notice that the variables are almost all stationary at level, except for the two variables Log CDS and Bond, which becomes stationary at first difference at the threshold of 5%. These results are good, quality indicators of our variables and/or our panel. It should be noted that after verifying stationarity, we continued the classic approach of the panel data method, namely, the estimation of the regressions with fixed and random effects models. As a reminder, the random effects panel model assumes independence between the unobservable individual effects and the explanatory variables, while the fixed effects model hypothesizes the correlation between the individual effects and the observable factors. Therefore, the fixed-effects model is more used in the empirical literature, because it applies better to economic phenomena. Subsequently, we realized the Hausman test in order to choose between these two models. This test revealed that our panel is a fixed effects model because the probability is lower than the threshold of statistical significance of 5%. In parallel, in order to overcome the problem of heteroscedasticity, which shows whether the error matrix is constant or not, we calculated the robust standard errors using the estimator called "Huber/White Sandwich." To obtain the robust errors, we just added the “robust” option to the “xtreg” routine of the Stata software, allowing to estimate the basic panel models and, therefore, this option has made it possible to settle any problem which arises following the presence of heteroskedasticity. Moreover, it should be noted in this regard that the regression results of our fixed effects model cannot be validated entirely due to the existence of extreme values and strong heterogeneity of our dependent variable "sovereign CDS spread" and, to a lesser extent, the other two variables. Thus, the estimator Ordinary Least Squares (MCO) which underlies the fixed effects model would not be suitable. The presence of strong heterogeneity between individuals could induce biased coefficients. This is why it is essential to correct this problem before any interpretation and validation of the empirical results, especially if the implications in terms of public policies are significant. One of the ways to take into account the severe heterogeneity of the dependent variable is to use a technique called quantile regression. This allows to estimate the impact of the explanatory variables on different parts (quantiles) of the distribution of the dependent variable, and not only its average. In this application, we apply the method proposed by Canay (2011). There are two reasons for this choice. First, the procedure proposed by Canay (2011) assimilates to a fixed effects model that we validated when choosing between fixed and random effects. Second, the procedure followed by Canay (2011) can be obtained simply in terms of programming by taking into account unobserved individual heterogeneity. This quantile method applied to panel data developed by Canay (2011) makes it possible to estimate the effect of the explanatory variables on different regions of the distribution of the dependent variable. In other words, quantile regressions attempt to assess how the conditional quantiles are modified when the determinants of the dependent variable vary. Moreover, this procedure provides more robust estimates of the coefficients linked to the explanatory variables, since the quantiles, based on a rank criterion, are less sensitive than the average to the presence of extreme values or outliers.
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EMPIRICAL RESULTS AND DISCUSSION As a reminder, we study the sensitivity of sovereign CDS spreads to the risk aversion factor in the financial markets and the bond markets and to the state of macroeconomic and macro-financial fundamentals of euro area member countries. For this, we regress the variables deemed significant on the panel data. Table 5 presents the results of our estimates: Table 5- Results of the Panel Estimation with Robust Fixed Effects Regressions and with Quantile Regressions (1) VARIABLES Balance
Debt
Reer
Bond
Prod
Vstoxx
Constant
Observations R-squared Number of country Hausman test
(2)
m1
m2
logCDS
yhat
-0.00421
-0.00410
(0.00319) 0.219
(0.00281) 0.146
0.00665**
0.00676***
(0.00261) 0.031
(0.000389) 0.000
0.00253
-0.00276
(0.0189) 0.897
(0.00799) 0.730
0.0987***
0.105***
(0.0127) 0.000
(0.00548) 0.000
-0.0115**
-0.0115***
(0.00376) 0.014
(0.00116) 0.000
0.00722***
0.00571***
(0.00123) 0.000
(0.00141) 0.000
1.517
2.057**
(2.373) 0.539
(0.857) 0.017
480
480
0.624 10 Fixed Effects
In parentheses: robust standard errors In italics: P-value Significance threshold: *** p <0.01, ** p <0.05, * p <0.1 Source: Constructed from database exploitation under Stata 15 software.
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We will now analyze the results of the model on our data. After analyzing all the possible tests and configurations, we are interested in the impact of the common risk, liquidity risk, and idiosyncratic risk on the dynamics of sovereign CDS spreads. First of all, the quantile regressions reveal that the common or global risk factor represented by the variable Vstoxx has a positive and significant impact, which corresponds to previous results. In other words, the volatility of the stock markets of the main European financial centers also had an impact on the sovereign CDS spreads, all the more since the quarterly frequency used detected the short-term effects and the quantiles took good account of the jump in the evolution of the variable. For example, an increase in Vstoxx index of 1% would increase the spread of 0.00571%. Therefore, this result is also in the context of previous studies, because he joined the explanation that sovereign spreads are mainly based on an exaggerated risk aversion. In addition, the liquidity risk represented by the bond yield has a linear relationship with the spread, and is significant. This observation suggests that an increase in public borrowing rates implies a phenomenon of flight to assets with low liquidity risk, in this case the German Bund. This reflects a contraction of liquidity in the bond market and, thus, the manifestation of liquidity risk, which causes a surge in sovereign CDS spreads. This means that the liquidity of sovereign bonds has been taken into consideration as a result of the sovereign debt crisis. Regarding the idiosyncratic risk of each country, the variables related to budgetary factors, especially debt/GDP, is significantly positive, unlike the budget balance/GDP, which is not significant in our analysis. This last result does not meet our expectations, which shows that this indicator is not taken into consideration by international investors and, therefore, does not play a major role in the evolution of sovereign CDS spreads, unlike the variable debt. Specifically, a 1% increase in debt would increase the spread by 0.00676%. This is entirely in line with our expectations. Indeed, when a country's debt increases, its risk of default increases, investors become more suspicious and, therefore, demand a higher return and the spread increases. It should be noted that the public imbalances of the countries deemed to be risky in the euro area have indeed played an important role in the increase in CDS spreads, especially after the international financial crisis of 2007 and during the debt crisis in the euro area. The swelling of these imbalances has indeed contributed significantly to the brutal revision of investors’ expectations. In these conditions of competitiveness, the adjustment in particular could contribute to improving the sustainability of public debt in the countries of the euro area, hence, the choice of the real effective exchange rate (Reer) as a possible determinant of variability of the sovereign CDS spread. However, in our case, the regressions reveal that there is no significant impact between the Reer and the spread and, moreover, the sign of the coefficient of this variable is negative, contrary to what we expected. Therefore, the result in our model of this variable relating to external competitiveness does not go in the direction predicted by theory. This can be explained by the strong heterogeneity of our sample, as not all individuals have the same economic structures. In fact, according to the “Lowess” test, which makes it possible to observe the distribution of individuals in a curve fitted to a cloud of point, the results revealed a quite important dispersion of countries and some of the extreme values, hence, the reason for the sign contrary to expectations. In addition, this shows that macroeconomic variables may have different influences on spreads over time and across selected countries. 130
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The industrial production variable that reflects the economic performance and/or the level of wealth of a country, and appears significant and with a negative coefficient, because the countries with a growing production see their spread decrease, and it shows that the country is able to repay its debt revenue from this growth. Therefore, this result corroborates previous studies and is according to the theory developed. Furthermore, in addition to this analysis, we deemed it useful to divide our sample into two groups of countries: on the one hand, countries deemed vulnerable in the euro area, namely: Spain, Italy, Ireland, Portugal, and on the other hand, a group of countries considered to be particularly robust: Germany, Finland, Austria, Belgium, France, The Netherlands, and this in order to distinguish between the factors, which explain the sovereign CDS spread in two groups of characterized countries by disparities in economic structures and, therefore, which present economic vulnerability and/or resilience. The results of the regressions of the two panels with the two groups of countries are presented in Table 6: Table 6- Results of the Panel Estimation with Robust Fixed Effects Regressions and with Quantile Regressions -Comparison Between the Panel of Vulnerable Countries and the Panel of Robust CountriesPanel 1 (Vulnerable Countries) Balance
Debt
Reer
Bond
Prod
Vstoxx
Constant
Observations R-squared Number of country Hausman test
Panel 2 (Robust Countries)
Log CDS
Yhat
Log CDS
Yhat
-0.00155
-0.000013
-0.00494
-0.00544
(0.00313) 0.654
(0.00320) 0.997
(0.00537) 0.400
(0.00368) 0.141
0.00644*
0.00751***
0.0219**
0.0217***
(0.00271) 0.098
(0.000494) 0.000
(0.00650) 0.020
(0.000868) 0.000
0.0251
0.0266**
-0.0261
-0.0301**
(0.0239) 0.370
(0.0113) 0.020
(0.0355) 0.495
(0.0122) 0.014
0.110***
0.106***
0.132**
0.133***
(0.00914) 0.001
(0.00692) 0.000
(0.0450) 0.033
(0.0217) 0.000
-0.00751***
-0.00835***
-0.00840
-0.00731***
(0.00687) 0.001
(0.00134) 0.000
(0.00493) 0.149
(0.00238) 0.002
0.00534***
0.00637***
0.00899***
0.00922***
(0.000805) 0.007
(0.00206) 0.002
(0.00189) 0.005
(0.00252) 0.000
-0.961
-1.123
2.733
3.022**
(3.192) 0.783
(1.236) 0.365
(4.281) 0.551
(1.199) 0.012
192
192
288
288
0.798
0.566
4 Fixed effects
6 Fixed effects
In parentheses: robust standard errors In italics: P-value Significance threshold: *** p <0.01, ** p <0.05, * p <0.1 Source: Constructed from database exploitation under Stata 15 software. 131
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Through the observation of this table we can make a comparative analysis of the explanatory factors between two distinct groups of countries. The results reveal that the factors explaining sovereign risk in the euro area are almost similar, whether for vulnerable countries with weak fundamentals or for robust countries in the euro area with economic resilience with the solidity of their fundamentals. Indeed, it appears that, unlike the budgetary balance/GDP, which is not significant for the vulnerable and robust countries, the public debt/GDP is significantly positive. This latest result is in line with our expectations, which shows that this indicator plays a key role in the evolution of sovereign CDS spreads. For example, a 1% increase in debt would increase the spread by 0.00751% for the first panel, while for the second by 0.0217%, which shows that the impact of the variable debt on sovereign CDS spreads is more important in robust countries. The result is similar for the other fundamentals, namely industrial production or the Reer, which are significant for the two groups of countries; however, the sign of the variable Reer is negative in Panel 2, which is contrary to expectations. In addition, the variables representing the common risk or the degree of risk aversion by investors, represented by the variable Vstoxx, as well as the liquidity risk or the phenomenon of flight to liquidity in our study, seem to be very determining for the calculation of sovereign CDS spreads for the two groups of countries. Investors are very attentive to the volatility of the stock and/or bond markets, that is to say that the determination of the sovereign CDS premium strongly depends on these factors, given the high significance of the variables taken into account. In general, our model panel data has mostly coefficients with the same signs as our expectations, however, the level of impact varies depending on the type of risk. Indeed, the result of the regressions with quantile regressions shows an increase in the importance of fundamentals in the calculation of the spread following the sovereign debt crisis. Investors have become more interested in country fundamentals, such as their debt. The variables representing the common risk represented by the volatility of the financial markets and the liquidity risk or the refuge effect in our study are very significant and have a considerable impact on the variation of sovereign CDS spreads, which corroborates the results of previous work. However, the regressions of some variables, notably the government budget balance and the Reer, do not correspond to expectations.
CONCLUSION The debt crisis in the euro area has demonstrated the reappearance of sovereign risk, which is confirmed by common movements in the evolution of volatility, notably on sovereign spreads suggesting the presence of a contagion effect. In this article, we have tried to determine the main risk factors that explain the variation of sovereign CDS spreads in the euro area countries, notably that they are vulnerable or robust. The explanatory factors examined are related to general risk aversion, liquidity, and fundamentals, through a new approach, notably quantile regressions. Our estimates show that the sovereign CDS spreads of vulnerable countries are more sensitive to risk factors than those of countries deemed to be robust. In addition, our model reveals that the independent variables are statically significant and have the expected signs with the exception of two variables. The results show, in fact, that there is a significant relationship between spreads and variables related to the budgetary factor, in particular public debt, liquidity, or risk aversion on the bond market, and the 132
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level of wealth of country. On the other hand, the impact of the government budget balance and Reer variables relating to idiosyncratic risk appear to be insignificant, contrary to expectations. This can be explained by the fact that investors are less sensitive to the variation of these two fundamentals, unlike the other selected indicators. In addition, it should be noted that sovereign CDS spreads are more sensitive to risk factors after the onset of financial market stress starting in August 2007 and following the worsening of the sovereign debt crisis in 2011, which has fueled investor mistrust. This confirms the cause and effect relationship between sovereign CDS spreads and sovereign risk. This study has, therefore, definitively raised the question of the resurgence of sovereign risk in advanced countries and identified the main explanatory factors for this risk measured by the sovereign CDS spreads, which provide relevant information. This makes it possible to present interesting implications both for policymakers and for investors, especially in the current unfavorable macroeconomic context caused in particular by a health crisis with serious consequences for the economies. Indeed, sovereign solvency becomes a problem when the level of public debt is high, especially if the states concerned do not have budgetary room to maneuver, and the growth potential is reduced. Thus, policymakers are required to take into account different determinants, the most significant, of sovereign risk allowing them to set up effective intervention policies and to act directly on these risk factors by finding the appropriate solutions to stem the effects of aggravation sovereign risk and to counter the effects of contagion and, thus, avoid an international crisis. For investors, the distinction between the determinants, which strongly explain sovereign risk, or the widening of sovereign spreads and their necessary consideration, allows them to make consistent previsions and forecasts, by carrying optimal investment strategies and, thus, maximizing the return on their portfolios. In addition, this knowledge of risk factors allows investors to reduce risk exposure and become less risk averse and less discriminating in the event of a country's sovereign insolvency and, therefore, reduce the risk of financial market instability, avoiding imminent of recurrence risk, which is inherent to vulnerable countries.
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FAKTORI KOJI OBRAZLAŽU SPREDOVE DRŽAVNIH SVOPOVA KREDITNIH NEIZVRŠENJA: KVANTILNI PRISTUP REGRESIJI
Rezime: Ovaj članak ima za cilj da analizira glavne faktore rizika koji objašnjavaju ispoljavanje i pogoršanje državnog rizika, posebno kroz dinamiku spredova državnih CDS u zemljama članicama evrozone. Faktori koji će se analizirati povezani su sa opštom averzijom prema riziku, koja se objašnjava nestabilnošću berzi, rizikom likvidnosti percipiranim fenomenom Flight to Quality, idiosinkratskim rizikom, koji se objašnjava pogoršanjem stanja makroekonomskih osnova. Usvojićemo ekonometrijski pristup metodom kvantilne regresije primenjenom na panele podataka koji je razvio Canay (2011), jer omogućava procenu uticaja nezavisnih promenljivih na različite regione distribucije, zavisne promenljive, a takođe omogućava da se prevaziđe problem prisustva ekstremnih vrednosti. Na kraju, naš model je omogućio da se vremenom i u različitim zemljama identifikuju faktori koji značajno objašnjavaju državni rizik i čije će pogoršanje verovatno dovesti do neizršavanja plaćanja, što je veoma važno znati, posebno u trenutnim nepovoljnim uslovima u makroekonomskom smislu. Tu spadaju nestablinost berzi, što ukazuje na nepoverenje investitora, smanjenje likvidnosti na tržištima obveznica, što se objašnjava fenomen Flight to Quality, budžetski faktor, koji se objašnjava neodrživim dugom i ekonomski faktor, sagledano nivoom bogatstva neke zemlje.
Ključne reči: širenje CDS; Suvereni rizik; Osnove; Averzija prema riziku; Kvantilna regresija.
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EJAE 2020, 17(2): 136 - 146 ISSN 2406-2588 UDK: 336:658.1 DOI: 10.5937/EJAE17-26056 Original paper/Originalni nauÄ?ni rad
IS WORKING CAPITAL MANAGEMENT ABLE TO INCREASE SUSTAINABLE GROWTH THROUGH ASSET UTILIZATION? Pambayun Kinasih Yekti Nastiti, Apriani Dorkas Rambu Atahau, Supramono Supramono* Management Department, Faculty of Economics and Business, University of Kristen Satya Wacana, Indonesia
Abstract:
Article info:
Working capital management has a strategic role to maintain a balance between liquidity and profitability so that firms have greater opportunities to operate sustainably. This study mainly aims to investigate the ability of working capital management to increase sustainable growth through asset utilization. We ran panel data regression on manufacturing firms listed in the Indonesian Stock Exchange for the years of 2010-2017 as our sample. By controlling for leverage, sales growth, and firm size, our empirical results demonstrate that working capital management negatively affects firms' asset utilization. Furthermore, the study also finds that asset utilization positively affects sustainable growth. Finally, we empirically show that asset utilization mediates the relationship between working capital management and sustainable growth. The findings imply that if Indonesian manufacturing firms manage to have efficient working capital management, they are more likely to utilize their assets efficiently which, in turn, will increase their growth optimally, without causing problems to their cash.
Received: April 9, 2020 Correction: May 19, 2020 Accepted: September 3, 2020
Keywords: working capital management, asset utilization, sustainability growth, cash conversion cycle.
INTRODUCTION Poor working capital management is the main cause of business failure (Smith, 1973). Consequently, numerous financial managers devote a great proportion of their time to manage working capital (Palombini & Nakamura, 2012; Botoc & Anton, 2018; Wang, Akbar, & Akbar, 2020). However, studies in working capital management so far are less developed than similar studies in the domain of long-term investing, financing, and dividend decisions (Palombini & Nakamura, 2012; Singh, Kumar, & Colombage 2017). Previous studies have analyzed the role of working capital management in terms of its effects on profitability (Deloof, 2003; Sharma, & Kumar, 2010; Marttonen, Monto, & Kärri,2013; Aregbeyen, 2017; 136
*E-mail: supramono@uksw.edu
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Gorondutse, Abubakar, Ali, & Naalah,,2017; Hien Tran, Abbott & Jin-Yap, 2017;), liquidity (Adekola, Samy & Knight, 2017), and firm value (Kieschnick, Laplante , & Moussawi, 2013; Baños-Caballero, García-Teruel, & Martínez-Solano, 2014; De Almeida., & Eid,2014; Wasiuzzaman, 2015, ). Thus, firms need to have a good ability to manage their working capital (Deloof, 2003; Adekola et al., 2017). Working capital management not only affects firms’ performance but also their asset utilization or commonly known as total asset turnover. Asset turnover informs us whether firms have utilized their assets efficiently (Rahim, 2017; Alarussi & Alhaderi, 2018; Zorn, Esteves ,Baur, & Lips, 2018). A greater asset turnover indicates that firms manage to generate higher sales with the same level of assets or to generate the same sales with fewer assets. Firms will achieve both conditions if they manage their working capital efficiently. Rahim (2017) and Manaf et al,.(2018) demonstrate that asset utilization affects firms’ ability to grow optimally based on their internal financing or commonly known as sustainable growth. Ashta (2008) underscores that sustainable growth indicates the extent of firms’ ability to grow without negatively affecting their cash flows. Consequently, firms that focus on their growth should also take sustainable growth into consideration to avoid financial distress or even bankruptcy (Fonseka et al., 2012). The decision on financing sources to capture growth opportunities is a strategic one for firm managers (Ganiyu, 2018). Meanwhile, Churcill and Mullins (2001) propose that sustainable growth is closely related to working capital management. However, they do not explain further how these two variables are related. It is then predicted that working capital management affects sustainable growth through asset utilization. This study aims to test (a) the effect of working capital management on asset utilization, (b) the effect of asset utilization on sustainable growth, and (c) the effect of working capital management on sustainable growth through asset utilization. By doing so, this study will contribute to the working capital management literature by offering a better understanding of the relationship between working capital management, asset utilization, and sustainable growth. It is worth noting that previous studies have not investigated the effect of working capital management on sustainable growth as mediated by asset utilization. Practically, it is expected that the results of this study will help managers make more specific plans to enhance their firms’ sustainable growth through working capital management and asset utilization.
LITERATURE REVIEW Working capital management is the decision that is related to the management of current assets and current liabilities and the interrelationship between these two accounts (Abuzayed, 2012). Some firms exhibit a great proportion of current assets to total assets and a substantial proportion of current liabilities in their financing activities (Shapiro & Balbirer, 2000). Working capital management also refers to net current assets investing, financing, and controlling activities through firms’ various policies (Padachi et al, 2012). Further, Dhole, Mishra, & Pal(2019) emphasize the importance of working capital management to avoid excessive working capital while at the same time to prevent firms from lacking working capital. A commonly used indicator of working capital management is the cash conversion cycle (Deloof, 2003; Abuzayed, 2012; Hien Tran et al., 2017). A short cash conversion cycle implies efficient working capital management. Working capital management will affect firms’ asset utilization (Rahim, 2017). Assets utilization is the use of firms assets to produce products or services offered to consumers (Rahayu, 2019). It is measured by the ratio between sales and total assets. Thus, the effect of working capital management on asset utilization is through more efficient utilization of short-term assets and increased sales, despite the unique feature of the relationship due to the trade-off between these two factors. On the one hand, firms utilize their current assets efficiently by reducing their investments in receivables and inventories. Consequently, such reductions will shorten receivables and inventory turnover. 137
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On the other hand, firms are likely to increase sales by loosening their credit sales policies (Martínez‐Sola, García‐Teruel, & Martínez‐Solano, 2012; Tauringana & Afrifa, 2013,) because customers have a longer time to pay their payables. Although many consider receivables to be unwanted assets so the firms try to shorten the collection period for accounts receivables (Hofmann, & Kotzab, 2010; Botoc & Anton, 2017), receivables are often unavoidable for firms because firms are likely to lose their customers if they cannot compete with their competitors in granting credits (Molina, & Preve,2009). By granting credits, firms offer opportunities for their customers to evaluate the quality of their products before paying. Thus, these offers are arguably more attractive to customers (Deloof, 2003). Moreover, firms have policies to increase inventories to increase sales (Abuzayed, 2012), because firms that have a sufficient level of inventories are more likely to avoid production problems (Garcia-Teruel & MartinezSolano, 2007) that will cause firms to lose opportunities to realize sales (Wang, et al., 2020). All in all, efforts to increase sales will need a longer cash conversion cycle. Asset utilization is likely to affect firms’ sustainable growth (Shapiro & Balbirer, 2000). Firms should focus on their sustainable growth, not just a high level of sales growth, by adjusting their growth with their existing financing ability to ensure their sustainability. Higgins (1977) introduces the concept of sustainable growth that refers to the level of annual sales growth that is consistent with firms’ financial policies. These financial policies are related to firms’ decisions not to issue new shares and to maintain the debt to equity ratio at certain levels. Consequently, sales growth largely depends on internal financing sources. The policy is also in line with the view of the pecking order theory, which states that companies prefer to use internal financing sources rather than external sources (Račić & Stanišić, 2017). Higgins (1977) explains that sustainable growth is affected by dividend policy, leverage policy, and asset utilization policy. Firms with more efficient asset utilization will manage to reduce the needs of assets that will lead to reduced costs, and eventually increased sustainable growth. In a similar vein, Kester (2002) argue that a consequence of increasing sustainable growth is that firms have to utilize their assets more efficiently. In other words, firms do not have to add assets to increase their sales (Rahim, 2017). Fonseka et. al (2012) empirically shows that total asset turnover affects firms’ sustainable growth. Thus, it can be predicted that asset utilization not only affects sustainable growth, but also mediates the effect of working capital management on sustainable growth.
METHODOLOGY Data Sample We generated our data from the annual reports of 165 manufacturing firms listed in the Indonesian Stock Exchange for the years of 2010-2017. We use manufacturing firms as our sample because these firms arguably exhibit higher investments in working capital than other firms. Firms with incomplete financial statements contain items needed to measure the research variables, negative book equity values, and outliers for certain variables in any of the years of sample period have not been included in this study, thus our final sample consists of 137 firms. Data sources are firms' formal websites, the website of the Indonesian Stock Exchange (http://www.idx.co.id), and IDN Financials (https://www. idnfinancials.com).
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Measurement
Our variables of interest include the dependent variable (sustainable growth), the independent variables (working capital management), and the mediating variable (asset utilization). We measure sustainable growth by using the sustainable growth rate (SGR), that is, the multiplication between ROE and Retention Rate (Higgins 1977). Meanwhile, working capital management as empirically measured by cash conversion cycle (CCC), which shows the period needed by firms to convert their cash outflows to cash inflows. Next, we used total asset turnover as a measure of asset utilization. Further, we referred to previous studies (Padachi et al., 2012; Hien Tran et al., 2017; Laghari & Chengang, 2019) in using firm size, sales growth, and leverage as the control variables. The following explains the operationalization of these variables: Table 1- Variable Operationalization Type of Variabl
Variable
Data description
Dependent
Sustainable Growth (SGR)
(net income/total equity) x (1 – dividend payout ratio)
Independent
Cash Conversion Cycle (CCC)
Cash Conversion Cycle = Account Receivable Period + Inventory Holding Period – Account Payable Period
Mediating
asset utilization (TATO)
Sales/(Total Assets)
Firm Size (FRSIZE)
Ln (Total Asset)
Sales Growth (SALESGR)
(Sales t – Sales t-1) / Sales t-1
Leverage (LEV)
Total liabilities/total assets
Control
Model Specification This study relies on panel data regression to test the relationship between working capital management, asset utilization, and sustainable growth in Indonesian manufacturing firms. Whereas, statistical data processing was conducted using STATA version 14. In line with the objectives of this study, we use the following two estimation models: The first model will test the relationship between working capital management and asset utilization: (1) The second model will test the relationship between asset utilization and sustainable growth, and the role of asset utilization in mediating the relationship between working capital management and sustainable growth: (2) To test the mediating effect, the Sobel test was introduced by Baron and Keny (1986). If both coefficients a (the effect of the independent variable on the mediator) and b (the effect of the mediator on the dependent variable) are significant, then there is a mediating effect (McKinnon, 2008). It also utilizes variations of other Sobel tests (Aroian and Goodman tests). 139
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RESULTS AND DISCUSSION
Table 2 demonstrates that the mean value of CCC is 138 days with a standard deviation of 68 days. The findings indicate that, on average, Indonesian manufacturing firms have quite long cash conversion cycles. Furthermore, a long cash conversion cycle indicates that firms are less efficient in managing their working capital or current assets. The inefficiency is likely due to the fact that Indonesian manufacturing firms opt for storing their inventories at a great level to ensure the continuity of their production processes. Meanwhile, the mean value of TATO is 1.08, with a low standard deviation (0.57). These results suggest that our sample firms operate with total assets that are lower than their sales. A high value of TATO implies that firms are better able to utilize their assets to generate higher sales that are eventually expected to enhance firms’ sustainability growth. The mean value of SGR is 3.50 with a standard deviation of 30.12, thus showing that manufacturing firms exhibit a positive growth. Table 2 - Descriptive Statistics Variables
Mean
CCC
SD
25th Percentile
75th Percentile
138.03
123.92
67.80
175.43
TATO
1.08
0.57
0.71
1.34
SGR
3.50
30.12
0.88
10.35
SALESGR
11.48
37.18
-0.19
18.67
FRSIZE
21.29
1.59
20.24
22.19
LEV
33.68
45.18
11.98
42.39
The correlation matrix and VIF values in Table 3 suggest that there is no serious multicollinearity problem. Specifically, the correlation between independent variables tends to be low with the VIF values approaching 1. These results are mainly due to the advantage of using panel data that alleviates the heteroscedasticity and autocorrelation problems. Next, we run the test to determine the appropriate regression model. Table 3- Matrix Correlation Between Variables SGR
CCC
TATO
SALESGR
FRSIZE
VIF
SGR CCC
-0.062
1.21
TATO
0.187
-0.379
SALESGR
0.115
-0.042
0.044
FRSIZE
0.046
-0.095
-0.181
0.008
-0.117
0.078
-0.029
-0.015
LEV
1.24 1.00 1.07 -0.022
1.01
Before testing our hypotheses, it is necessary to determine the appropriate panel data estimation. We then determine the Breusch-Pagan (Lagrange Multiplier) test to investigate whether the random and fixed effect models are more appropriate than common effect regression for our data panel regression. The results of this test demonstrate that random and fixed effect models are more appropriate for this study. 140
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Next, we run the Hausman test to select between the random and fixed-effect models. The findings indicate that the fixed effect model is more appropriate than the random effect model. Table 4 below demonstrates the results of our regression test with robust standard deviations (fixed effect robustness): Table 4- Regression Results Model I (Mediator=TATO) CCC
-0.0012 ***
TATO SALESGR
Model II (Dependent Variable=SGR) -0.0237 16.1385***
0.0014***
0.1260
FRSIZE
-0.3355***
3.4077
LEV
-0.0002***
-0.0493
F-test
42.77***
5.26***
Breush-Pagan Test/LM
1904.95***
123.76***
Hausman Test
96.12***
9.72*
The results of the Model I regression analysis above show that working capital management exhibits a direct and significant negative effect on firms’ asset utilization (coef. = -0.0012, p-value = 0.000 < 0.01). Firms that manage their working capital more efficiently by shortening their cash conversion cycles are better at utilizing their assets. The cash conversion cycle theory also explains that when raw materials are more quickly processed, sold, and converted back into cash, firms will arguably manage to generate higher sales growth that implies more efficient asset utilization. Besides testing the relationship between asset utilization and sustainable growth, Model II also tests the mediating effect of asset utilization (TATO) on the relationship between working capital management (CCC) and sustainable growth (SGR). Empirically, Model II demonstrates that asset utilization exhibits a significantly positive effect on firms’ sustainable growth (coef. = 16.1385, p-value = 0.009 < 0.01). Our results support that asset utilization (TATO) is a factor that enables firms to achieve sustainable growth. Fonseka, Ramos & Tian (2012) also find similar results. When firms aim to increase their sustainable growth, they have to be able to utilize their assets more efficiently or to operate more efficiently (Kester, 2002). When firms utilize their assets efficiently, they do not have to add assets to increase sales (Rahim, 2017), and firms will manage to reduce the needs of additional assets and investment costs and eventually enhance their sustainable growth (Shapiro and Balbirer, 2000). Next, we test the mediating effect of asset utilization on the relationship between working capital management and sustainable growth. According to Wahba and Elsayed (2015), to demonstrate the mediating effect, the significant relationship between the independent variable and the dependent variable is actually unnecessary and even potentially mislead the results because this relationship represents the total effect (direct effect and indirect effect). Consequently, the mediating effect is present when the independent variable significantly affects the mediating variable, and the mediating variable significantly affects the dependent variable. In line with this argument, our empirical results show that the effect of working capital management on asset utilization and the effect of asset utilization on sustainable growth is significant, but the direct effect of working capital management on sustainable growth is insignificant (coef. = -0.0237, p-value = 0.251 > 0,1). Thus, our results empirically demonstrate the mediating effect of asset utilization. 141
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Figure 1 – The Mediating Effect of Asset Utilization
Aset utilization
b= -0. 00 12
5 138 16. b=
Working capital management
c’= -0.0237 c= -0.04307.
Sustainability growth
Sobel =-5.082 Goodman-1 (Aroian)=-5.069 Goodman-2 =-5.096
The mediating role of asset utilization is also shown in Figure 1. Sobel tests (Z = -5,082), Aroian tests (Z = - 5.069) and Goodman-2 tests (Z = -5.096) give results greater than ± 1.96 (α = 5% ). Thus, these results support the previous analysis which has shown that asset utilization is able to mediate the effect of working capital management on sustainable growth.
CONCLUSION The present study investigated the ability of working capital management to increase sustainable growth through asset utilization in manufacturing firms listed in the Indonesian Stock Exchange from 2010-2017. The results demonstrate that working capital management exhibits a significantly negative influence on firms' asset utilization. Furthermore, we also find that asset utilization significantly affects sustainable growth. Moreover, asset utilization mediates the relationship between working capital management and sustainable growth. The results of this study extend the existing literature, considering that no previous studies have investigated the effect of working capital management on sustainable growth through the utilization of assets. This research has several implications. First, that working capital management, as measured by cash cycle conversion, has a negative effect on asset utilization implies that when Indonesian manufacturing firms are able to manage their working capital more efficiently by shortening their cash cycles, they are able to utilize their assets more effectively. Shorter cash conversion cycles show shorter receivables 142
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and inventory cycles, so firms may invest less in receivables and inventories. In addition, shorter cash cycles also imply that firms are quicker to realize their sales that eventually increase their total asset turnover. However, this study does not test the components of working capital management (such as receivable turnover, inventory turnover, or payable turnover), which need special considerations to facilitate firms to operate efficiently. Therefore, future research can be directed to address this issue. Second, the positive effect of asset utilization on sustainable growth suggests that firms that utilize their assets better will enhance their sustainable growth. When firms utilize their assets efficiently, they do not have to add either current assets or fixed assets to increase sales. Firms might be more inclined to reduce financing costs and operate on an economical scale. Thus, firms may be able to increase their profit as a source of sustainable growth. Third, working capital management can increase sustainable growth through asset utilization. This suggests firm managers must pay great attention to working capital management because indirectly affect the company's ability to achieve sustainable growth. Fourth, apart from this study empirically demonstrating that working capital management affects asset utilization and eventually sustainable growth, there are interesting findings to discuss regarding the extent to which the firm's sales growth must be in line with sustainable growth. In the case of manufacturing firms in Indonesia, the average sales growth was 11.48%, while the average sustainable growth was only 3.50%. When referring to the sustainable growth concept defined by Higgin (1997), sales growth in manufacturing firms in Indonesia has not been closing ideal conditions, and can even cause firms to experience financial distress. However, in practice, it does not have to be interpreted that way, considering that the SGR is built on the assumption that there is no change in financial policy. Firms rely solely on internal financing. In fact, it is possible for firms to add external financing to meet sales growth needs without causing firms to experience financial distress. Conversely, the firms also do not have to force their sales growth to be the same or close to sustainable growth due to unfavourable market conditions. For example, there is market saturation, low purchasing power, and product life cycle in the declining stage. Thus, the relationship of sustainable growth with sales growth is not something rigid. Sustainable growth should not be only viewed as a determinant of sales target policy, but also can be an input for managers to make financial policies related to sales targets.
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DA LI UPRAVLJANJE OBRTNIM KAPITALOM MOŽE DA POVEĆA ODRŽIVI RAST KORIŠĆENJEM IMOVINE? Rezime: Upravljanje obrtnim kapitalom ima stratešku ulogu da održi ravnotežu između likvidnosti i profitabilnosti, tako da preduzeća imaju veće mogućnosti da posluju održivo. Ova studija uglavnom ima za cilj da istraži sposobnost upravljanja obrtnim kapitalom u cilju povećanja održivosti rasta korišćenjem imovine. Izvršili smo regresiju panela podataka firmi koje se bave proizvodnjom a koje se kotiraju na indonežanskoj berzi, dok nam je vremenskom period od 2010-2017 poslužio kao uzorak . Kontrolom stepena zaduženosti, rasta prodaje i veličine preduzeća, naši empirijski rezultati pokazuju da upravljanje obrtnim kapitalom negativno utiče na korišćenje imovine preduzeća. Takođe, studija otkriva da korišćenje imovine pozitivno utiče na održivi rast. Na kraju, empirijski pokazujemo da korišćenje imovine posreduje u odnosu između upravljanja obrtnim kapitalom i održivog rasta. Rezultati takođe impliciraju da, ukoliko indonežanske firme koje se bave proizvodnjom uspeju da efikasno upravljaju obrtnim kapitalom, postoji veća verovatnoća da će efikasno koristiti svoju imovinu, što će za uzvrat optimalno povećati njihov rast, i neće ugroziti gotov novac kojim raspolažu..
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Ključne reči: upravljanje obrtnim kapitalom, korišćenje imovine, rast održivosti, ciklus konverzije gotovine.
EJAE 2020, 17(2): 147 - 160 ISSN 2406-2588 UDK: 331.5(497-15)"2003/2017" DOI: 10.5937/EJAE17-25663 Original paper/Originalni naučni rad
FDI INFLOW EFFECTS ON WESTERN BALKAN AREA'S LABOUR MARKETS Milica Perić*, Nemanja Stanišić Singidunum University, Belgrade, Serbia
Abstract: Labour market dependency on Foreign Direct Investments (FDI) inflow is very high in transition economies. This paper estimates the effects of FDI inflow on the employment rate and average net wages in Western Balkan economies in the period 2003–2017. The sample of economies (Albania, Bosnia and Herzegovina, Croatia, North Macedonia, Montenegro, and Serbia) was selected mainly because of the high legacy with FDI and unsaturated labour market. Presumably, FDI inflow has a positive impact on the employment rate and on average net wages in the Western Balkan countries. Employing linear mixed-effects models (LMM), the results indicate that FDI inflow changes have very low but positive and significant effects on both the changes in employment rate and on average net wages.
Article info: Received: March 11, 2020 Correction: April 13, 2020 Accepted: July 31, 2020
Keywords: average net wage, employment rate, foreign direct investments, linear mixed model, random effects.
INTRODUCTION During the last few decades, Foreign Direct Investments (FDI) have grown rapidly worldwide, although the global flow of FDI in developing countries decreased in 2017 (UNCTAD, 2018). As FDI is labelled as the key factor in global economic development, growth, and integration (Bitzenis & Marangos, 2007), it has also been adopted as a vital strategy for the development of economies in transition (Dabla-Norris et al., 2010). International trade acceleration and fast technological development, along with the global FDI, tend to change the aspect of national and local economies (Kekic, 2011). Besides the positive technological and knowledge spill-overs of FDI inflow by multinationals (MNEs), FDI inflow stimulates the productivity and the opening of the economy (import/export) of the host country (Peric & Filipovic, 2018). However, Krammer (2010) proved that the FDI inflow has a lower impact on productivity than the trade inflow. *E-mail: milicamip@gmail.com
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Additional capital, an improved business and investment climate, job creation, innovative infrastructure, technology transfer, improving national branding, etc., are the benefits of FDI inflow reflected in the host countries (Szondi, 2007; Nikolic & Bodroza, 2012; Melnyk, Kubatko & Pysarenko, 2014). According to Tanna, Li & De Vita (2018), however, high external debt constrains developing host countries from gaining benefits from FDI. From the other side, the debate about the impact of FDI inflow on labour markets in transition economies is still open. Perhaps the FDI inflow stimulates job creation more in developed countries than in developing and transition countries, mainly because of the availability of a skilled labour force and market composition. The Western Balkan countries are emerging democracies that have been engaged in many aspects of globalization during the last decades (Bartlett, 2007; Bukowski, 2005). According to Todorova (2018), FDI inflow in the Balkans reveals the influence of contemporary global capitalism. FDI was found to be suitable for battling the national economic dysfunction and recession, as well as to accelerate the development and economic growth (Chandler, 2007). Analysing the impact of transition and of political instability on FDI inflow to the Balkans countries, Brada et al. (2006) found that instability and conflicts in these countries reduce FDI inflow, causing the delay in transition. In the transition national economies, the inflow of FDI occurred mainly in the form of mergers and acquisitions (M&A) in order to stimulate the national markets and privatisation of public companies and bankrupted public and private enterprises (Estrin & Uvalic, 2013; UNCTAD, 2011). As concerns the countries under examination, Neto, Brandao & Cerqueria (2008) claim that greenfield investment had a positive effect on economic growth in all countries, but that M&A had a negative effect on developing countries in the period 1996–2006. Savic, Barjaktarovic & Konjikusic (2014) proved that cross-border credits per capita (CBCpc) represent the most important inflow of foreign capital, with the correlation coefficient of gross domestic product per capita (GDPpc)/CBCpc in the period 2005–2010 as follows: Serbia r=0.917, Croatia r=0.949, Montenegro=0.849, and BiH=0.733. Pitic et al. (2014) confirmed these outcomes, i.e., the positive and significant impact of CBCpc inflow on GDPpc. Even as one of the key factors of globalisation (OECD, 2008), and the crucial factor for development of the Balkans (Kekic, 2006), FDI inflow creates employment and wage discrepancies. In relation to this statement, the object of this research is to measure the effects of FDI inflow on labour market key indicators, i.e., employment rate and average net wages. To the best of the authors’ knowledge, the relationships between FDI inflow and the abovementioned labour market indicators in the representative countries in examination have not been tested as of yet, and this research is about to cover this gap in the literature. The other reason for selecting this sample of countries is because of its long legacy with FDI (Hadzic & Pavlovic, 2011). The motivation for analysing the relationship between FDI inflow and labour market indicators is the high sensitivity of labour market indicators to FDI inflow, in particular because of the non-saturation of the labour market in Western Balkan countries. Indeed, unlike in highly developed economies, there is more room for FDI in the countries under examination, mainly in those still in transition. Furthermore, FDI inflow in the Western Balkan area may have a strong impact on changes in GDP, which is not the case for highly developed economies. The aim of this paper is to measure the impact of FDI inflow percentage point changes on percentage point changes in the employment rate and on percentage point changes in average net wages in the Western Balkan area representative countries in the period 2003–2017. The expected overall results are the low positive and significant effects of FDI inflow on both the employment rate and average net wages. The subsequent part of this paper provides a review of the relevant academic literature regarding the impact of FDI on the labour market. The third part consists of the data and applied econometric models’ presentation. The fourth section shows the estimation and the interpretation of the results. The last part provides an overview of the research results and several recommendations. 148
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LITERATURE REVIEW Recent empirical research and projects have shown efforts to analyse the impact of FDI inflow on a host country’s economic arena and its implications for the labour market. Nevertheless, the FDI inflow contributes to the intensification of economic development and national competitiveness of the Western Balkan economies (Vesaite, 2014) The labour market differences are recognizable. Table 1 consists of eleven empirical studies on the impact of FDI on the labour market segments and six additional studies of the effect of FDI on wage inequality, which have been chosen as representative studies for the approaches used, and the results found. Table 1. Impact of FDI on Labour Market Indicators – Empirical Studies Author(s)
Methodology
The Effect of Interest and Geographical Scope
Results
Gopinath & Chen (2003)
OLS regression
Impact of FDI inward on wages in 26 countries
FDI inward was found to have a negative effect on wage inequality
Taylor & Driffield (2005)
Fixed-effects and GMM estimators
Impact of FDI inward on manufacturing industries shifts in demand towards higher skilled labour in UK
FDI inward was found to have a negative effect on wage inequality
Figini & Gorg (2006)
Fixed-effects and GMM estimators
Impact of FDI inward on wage inequality in 103 countries
FDI inward was found to have a negative effect on wage inequality in developing countries, and a positive effect in developed countries
Bhandari (2007)
OLS regression
Impact of FDI on wage inequality in transition countries
FDI was found to have a positive effect on average wages, and a negative effect on wage inequality
Stanisic (2008)
Correlation analysis
Impact of FDI on economic FDI was found to have a positive effect growth in South European on employment transition countries
Driffield et al. (2010)
GMM estimators
Impact of FDI inward on wage inequality in UK
FDI inward nationally tends to increase wage inequality, while the local FDI inward tends to decrease wage inequality
Halmos (2011) OLS regression
Impact of FDI inward on wage inequality in CEE
FDI inward was found to have a negative effect on wage inequality
Sabic et al. (2012)
Correlation analysis
Impact of FDI inflow on unemployment in Serbia
FDI inflow was found to have no significant effect on unemployment
Zulfiu (2014)
Simulation analysis
Impact of FDI inflow on FDI inflow was found to have a positive wage inequality in transition effect on domestic skilled wages, and countries negative effect on unskilled wages
Kurtovic, Talovic, & Dacic (2015)
OLS regression
Impact of FDI inflow on average net wages in Bosnia FDI inflow was found to have a positive and Herzegovina, effect on net average wages Montenegro, Macedonia and Serbia.
Domazet (2016)
Case study: analytical approach
Impact of FDI on economic FDI was found to have a partial positive growth in Bosnia and effect on employment rate in Bosnia and Herzegovina Herzegovina 149
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Jude & Silaghi (2016)
Fixed-effects and GMM estimators
Impact of FDI on employment in CEE
FDI was found to have a short-term negative effect and a long-term positive effect on employment
Hale & Xu (2016)
Causality test
Impact of FDI in host countries
FDI was found to have a positive effects on labour markets (labour force composition, employment, average productivity, wage levels, and wage inequality) in host countries
Zdravkovic & Martinovic (2016)
OLS regression
Impact of FDI on unemployment in transition countries
FDI was found to have no significant effect on unemployment
Grahovac & Softic (2017)
OLS regression
Impact of FDI inflow on the unemployment rate in the West Balkan countries
FDI inflow was found to have no significant effect on unemployment
Popovic & Eric Causality test (2018)
Impact of EU FDI inflow on Western Balkan economies
EU FDI inflow was found to have no significant effect on unit labour costs
Peric (2019)
Impact of FDI inflow on FDI inflow was found to have no significant employment and on average effect on increase in employment or wages in Serbia average wages
OLS regression
Source: Authors’ preparation
According to Table 1, there is evidence of both positive and negative impact of FDI on the indicators of the labour market. Hale & Xu (2016) claim that there is an overall positive effect of FDI on the host countries labour market. According to them, FDI is responsible for the increase in wage inequality, as well, mostly due to the workers’ skill levels. Whilst Sabic et al. (2012), Zdravkovic & Martinovic (2016), Grahovac & Softic (2017), and Peric (2019) found that there is no significant impact of FDI inflow on employment, Stanisic (2008), and Jude & Silaghi (2016) claim that FDI inflow has a positive impact on employment growth. Similarly, Domazet (2016) stresses that FDI inflow represents only a partial contribution to employment rate growth. As far as average wages are concerned, Bhandari (2007) and Kurtovic, Talovic & Dacic (2015) claim that there is an increase due to FDI inflow. Moreover, Kurtovic et al. (2015), by applying the Variance Decomposition Test (VDT), predicted that the growth of average net wages will lead to increased FDI inflow over the next ten years, but that the increase in FDI inflow will not have a significant effect on average net wages. As concerns the EU FDI inflow in Western Balkan economies, there is no significant effect on unit labour costs (Popovic & Eric, 2018). In general, FDI appear to have a slightly positive effect on the employment rate and on average net wages in transition countries. However, FDI inflow tends to increase wage inequality, as Gopinath & Chen (2003), Taylor & Driffield (2005), Figini & Gorg (2006), Driffield et al. (2010), Halmos (2011), and Zulfiu (2014), have proved. Whilst foreign companies and MNEs tend to employ more highly skilled labour, the unexpected pay gap between domestic and foreign companies may reflect the difference between the demand for employees’ ability and skills. The homogenous results from the empirical literature show that companies under foreign ownership pay higher wages and require a skilled labour force. On the basis of the literature background, the overall impact of FDI inflow on Western Balkan area representative countries remained unknown, despite previous research. This study covers this gap in the literature, and contributes to the knowledge about the effects of FDI inflow on the chosen labour market indicators in the countries under examination. The following section presents the data used for modelling and the econometric methodology. 150
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DATA AND MODEL SPECIFICATION This section presents the quantitative variables, a description of the data used for the analysis in the first subpart, and the specification of the chosen statistical models and the statement of the hypotheses in the second subpart.
DATA For modelling FDI inflow in relation to the employment rate and average net wages in Western Balkan area representative economies, data was derived from secondary sources. Therefore, the panel data for the period 2003–2017 is constructed with the information obtained from: a) Balance of payments (retrieved from the official site of each country’s national bank in the statistical database for FDI inflow), and b) Labour market section (retrieved from the official site of each country’s statistical office for both the employment rate and average net wages). A description of the variables used in directing this research is presented in Table 2 for all six Western Balkan countries (Albania, Bosnia and Herzegovina – BiH, Croatia, Macedonia, Montenegro, and Serbia), along with the source and the official methodology for calculating and presenting the data. Table 2. Description of Variables According to Data from Official Sources FDI Inflow
Employment Rate
Average Net Wage
Country/Unit of measure
million EUR, per year
average per year (age +15)
EUR, per year
Albania BiH
BA/BoP BPM6 CBBH/BoP BPM6
INSTAT SURVEY BHAS SURVEY
INSTAT SURVEY BHAS SURVEY
Croatia
HNB/BoP BPM6
DZS SURVEY
DZS SURVEY
North Macedonia
NBRM/BoP BPM6
MAKSTAT SURVEY
MAKSTAT SURVEY
Montenegro
CBCG/BoP BPM6
MONSTAT SURVEY
MONSTAT SURVEY
Serbia
NBS/BoP BPM6
RZS SURVEY
RZS Tax admin.
Note: the amounts in EUR are at current prices. Official values in convertible marks for BiH and Albanian Lek are converted into EUR in accordance with BiH’s CBBH and Albania’s BA official yearly base exchange rate, respectively. Source: Authors’ preparation
where: BA – Bank of Albania, CBBH – Central Bank of Bosnia and Herzegovina, HNB – Croatian National Bank, NBRM – National Bank of the Republic of Macedonia, CBCG – Central Bank of Montenegro, NBS – National Bank of Serbia, INSTAT – Institute of Statistics of Albania, BHAS – Agency for Statistics of Bosnia and Herzegovina, DZS – Croatian Bureau of Statistics, MAKSTAT – State Statistical Office of Republic of Macedonia, MONSTAT – Statistical Office of Montenegro, RZS – Statistical Office of the Republic of Serbia, BoP - Balance of Payments, BPM6 – Methodology of calculation by OECD (2008). Table 3 shows the descriptive statistics for each factor taken into consideration for the modelling.
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Table 3. Descriptive Statistics Descriptive Statistics N Statistic
Range
Minimum
Maximum
Statistic
Statistic
Statistic
Std. Deviation
Mean Statistic
Std. Error Statistic 3.77
Variance Statistic
Year
72
14.00
2003
2017
2011.31
0.45
14.24
IFDI
72
4246.26
77.21
4323.48
1069.61
115.50
∆IFDI
67
8.96
-0.92
8.04
0.35
0.15
1.26
1.58
∆IFDI_1
60
8.96
-0.92
8.04
0.34
0.17
1.30
1.70
∆IFDI_2
54
4.38
-0.92
3.47
0.23
0.12
0.86
0.74
∆IFDI_3
48
4.31
-0.85
3.47
0.25
0.13
0.89
0.79
EMPL
72
27.40
24.50
51.90
40.71
0.66
5.64
31.81
∆EMPL
66
1.26
-0.42
0.84
0.02
0.02
0.12
0.02
ANW
72
596.65
205.54
802.19
430.11
18.34
155.63
24221.40
∆ANW
66
0.49
-0.16
0.34
0.04
0.01
0.07
0.01
Valid N (listwise)
48
980.07 960527.79
Source: Authors’ calculation
where: IFDI – Foreign Direct Investment Inflow in million EUR, ∆IFDI – Foreign Direct Investment Inflow change rate, ∆IFDI_1 – Foreign Direct Investment Inflow change rate after 1 year, ∆IFDI_2 – Foreign Direct Investment Inflow change rate after 2 years, ∆IFDI_3 – Foreign Direct Investment Inflow change rate after 3 years, EMPL – Employment rate, ∆EMPL – Employment rate change rate, ANW – Average Net Wages in EUR, ∆ANW – Average Net Wages change rate. The number of observations differed due to the lack of official data in some countries. Variables such as GDP and population size are not included in the modelling for the following reasons. Unobserved heterogeneity (including that pertaining to the economy and population size) is captured by the country-specific random intercepts. In other words, due to the difference in population and GDP between countries, the percentage changes of the dependent variable were modelled, the reason why random effects at the country level were introduced. The above-listed abbreviations for the variables are used in the next subsection.
MODEL SPECIFICATION The model specification convention was based on Anderson (2013) and Heck, Thomas & Tabata (2014). Linear mixed-effects models (LMM) are used to process the estimation of the overall impact of the changes in IFDI on the changes in the employment rate (∆EMPL) and on the changes in average net wages (∆ANW) in the Western Balkan economies in the period from 2003 to 2017. The changes in variables are measured in percentage points. LMM is often the preferred model because of its asymptotic efficiency (minimum variance), whether or not the data is balanced. 152
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The reason for choosing LMM (i.e., to include random effects in the models) is that the impact of independent variables on the dependent variable may differ from country to country. Ex post allows the effects of EMPL and of ANW to vary, the models include random intercepts as well. This is because each country has its own increasing or decreasing trend of EMPL and ANW. The models, therefore, contain fixed effects and random intercepts (level of dependent variables) and random slopes (for the ∆IFDI variable) for each country in the sample. LMM estimates the effect of ∆IFDI and lagged ∆IFDI on ∆EMPL and on ∆ANW, while adjusting for correlation due to repeated observations on each variable over each year. Based on the theoretical assumption that IFDI requires time to show the real effects of performing in the country, the lagged terms were used. A typical assumption here is that the regression coefficients have normal distributions, and unstandardized coefficients (the coefficients are in their original metrics). In terms of likelihood ratio tests, the information criterion used to search for the best model fit is Akaike’s Information Criterion (AIC). The Maximum Likelihood (ML) method is adopted because it allows inferences to be made on the covariance parameters of the model. The Hausman test is used to evaluate the consistency of the random effects in comparison with the fixed effects. Data analysis was performed using IBM SPSS software (23) to in order to estimate the dependency amongst the changes in ∆IFDI and both ∆EMPL and ∆ANW in the countries studied. All the chosen independent variables (∆IFDI, lagged ∆IFDI) explain the ∆EMPL in model (1), and the ∆ANW in model (2) in t time for i countries. Therefore, it is a matter of modelling the percentage changes in variables rather than modelling the values of the same variables because the changes are most efficiently represented by percentages. Indeed, differentials allow the model to capture the effects with greater certainty, rather than relations often biased or artificial. The authors’ intention is to determine whether there is a quantitative relation between the dependent and independent variables, based on the following two models: ∆EMPLti = γ00 + γ10∆IFDIt + γ20∆IFDI_1t + γ30∆IFDI_2t + γ40∆IFDI_3t + µ1i∆IFDIti + µ2i∆IFDI_1ti + µ3i∆IFDI_2ti + µ4i∆IFDI_3ti + µ0i + εti
(1)
∆ANWti = γ00 + γ10∆IFDIt + γ20∆IFDI_1t + γ30∆IFDI_2t + γ40∆IFDI_3t + µ1i∆IFDIti + µ2i∆IFDI_1ti + µ3i∆IFDI_2ti + µ4i∆IFDI_3ti + µ0i + εti
(2)
where: γ00 – grand intercept capturing the variation amongst t in i, γn0 – fixed effects across groups, µ0i – between-country variation in intercepts – deviation from the average intercept, µni – random effects capturing variation in individual slope coefficients, εti – individual-level residual in t within i. ∆EMPLti - dependent variable in model (1), ∆ANWti - dependent variable in model (2), ∆IFDI with its time lags (∆IFDI_1, ∆IFDI_2, ∆IFDI_3) for the six countries - independent variables (predictors). The model estimations are expressed in the annual growth rate. The null hypothesis is that changes in both ∆EMPL and ∆ANW are not influenced by changes in ∆IFDI and its time lags. Instead, the alternative hypothesis is that changes in both ∆EMPL and ∆ANW are influenced by changes in ∆IFDI and its time lags. The next section presents the estimated results of the models applied.
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EMPIRICAL RESULTS This section presents the results of the analysis described in the previous part of the paper. In this section, model 1 is shown as (1), and model 2 as (2). The estimation was by LMM (between-groups and within-group estimation). Random effects, therefore, consider idiosyncratic qualifications of the units as non-observable and randomly distributed. The purpose of the models was to estimate the overall impact of the changes in ∆IFDI on ∆EMPL in (1) and on ∆ANW in (2). Adding lags one by one to ∆IFDI, to a total of three, both the models show some positive impact after performing for some time. For both models, the dependent variable changes in time t by a certain number of percentage points (not the percent rate), after taking into consideration the level of the changes in ∆IFDI and of the country in that and previous periods. The first next step in this process was to control the outcomes of the Hausman test. The Hausman test confirms that the use of random effects is justified, i.e., that the random effects are consistent with the fixed effects. The second next step in this process was to control for the outcomes of AICs. The results from the Hausman test and AIC are presented in the Table 4. Table 4. Hausman Test and AIC: Results (1) ∆EMPLti Hausman test AIC
(2) ∆ANWti
chisq= 0.018995
chisq= 0.56995
df=5
df=5
p-value=1
p-value= 0.9893
-114.301
-194.038
Source: Authors’ calculation
Reassuring the smallest possible value of AIC followed by the theoretical and analytical logic, the models are accepted and presented in Tables 5 and 6. Statistical significance was analysed using p-value as appropriate. Significance levels were set at the 5% level using the p-value. Table 5. FDI Inflow Effects on Employment Rate (%Δ) Estimates of Fixed Effects (a) Parameter
Estimate
Std. Error
t
Sig.
95% Confidence Interval Lower Bound
Upper Bound
Intercept
0.014
0.005
5.771
2.537
0.046
0.000
0.027
∆IFDI
-0.005
0.005
11.631
-1.178
0.263
-0.016
0.005
∆IFDI_1
-0.001
0.002
9.259
-0.449
0.663
-0.005
0.004
∆IFDI_2
0.021
0.006
5.046
3.577
0.016
0.006
0.036
∆IFDI_3
0.030
0.004
6.232
7.718
0.000
0.021
0.040
(a) Dependent Variable: ∆EMPL. Source: Authors’ calculation 154
df
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For the estimation of the impact of ∆IFDI on ∆EMPL, the (1) was used, represented in Table 4. The effect of ∆IFDI shows a small negative impact in the same year when IFDI occurs, but there is no statistical certainty of that because p=0.263 and the interval of confidence is between negative (-0.016) and positive (0.005) values. This is in line with the prevailing economic literature, which suggests that foreign investments require time to show their effects in the host country. In fact, after one year of IFDI performance (IFDI_1) there is almost null impact on ∆EMPL (-0.001, p=0.663), leading to the positive impact in the next periods. Model (1) shows 0.021% (p=0.016) increase in ∆EMPL for each additional unit of change in ∆IFDI after two (∆IFDI_2) years of IFDI performance. The strongest positive effect is represented by the ∆IFDI_3, meaning that for the double increase in IFDI in time t there would be a 0.03% (p=.000) increase in ∆EMPL after 3 years of foreign investment performance in the country i. The model suggests that the greater the foreign investment, the faster the growth in employment rate in Western Balkan countries. This statement is in accordance with the theoretical and empirical assumptions that there is more room for growth and faster employment increase (unsaturated labour market) in transition countries. Table 6. FDI Inflow Effects on Average Net Wages (%Δ) Estimates of Fixed Effects (a) Parameter
Estimate
Std. Error
df
t
Sig.
95% Confidence Interval Lower Bound
Upper Bound
Intercept
0.007
0.001
28.113
6.177
0.000
0.005
0.009
∆IFDI
0.013
0.013
4.598
0.946
0.391
-0.023
0.048
∆IFDI_1
0.010
0.001
3.407
7.138
0.004
0.006
0.015
∆IFDI_2
0.008
0.002
66.702
3.405
0.001
0.003
0.013
∆IFDI_3
0.005
0.005
4.006
0.911
0.414
-0.009
0.019
(a) Dependent Variable: ∆ANW. Source: Authors’ calculation
For the estimation of the impact of the ∆IFDI on ∆ANW, the (2) was used, represented in Table 5. Model (2) presents an increase in ∆ANW due to ∆IFDI in the observed period. The significant impact of the changes in ∆IFDI on ∆ANW is positive and after one (0.010, p=0.004) and after two (0.008, p=0.001) years of its performance. The model suggests that the foreign investment impacts, even if in a very small measure, the increase in ANW in Western Balkan countries. In terms of the greater impact, if foreign investments double in time t in country i, after one year the average growth rate of ∆ANW will be 0.01%. The effect of the changes in ∆IFDI appear to be positive in all the periods of observation, but there is no statistical significance in the period of FDI entrance and after three years of its performance in the country i. This last allows the statement that there is a constant low positive effect of IFDI on ANW in Western Balkan countries. In both the models, there is a positive growth rate in all six countries, and not for the entire period in case of the impact of the changes in IFDI on the changes in employment rate, meaning that the null hypothesis is rejected.
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DISCUSSION The empirical section of the paper adds to the results of earlier empirical studies with the finding that FDI inflow has a positive effect on the employment rate and on average net wages in Western Balkan representative countries. Foreign capital, in this case FDI inflow, slightly increases both the employment rate and average net wages in the long term. Surely, foreign capital requires time to show its effects on the labour market, which is why the application of lags is justified. The findings of this investigation complement those of some earlier studies. As concerns average net wages, the findings of this research are consistent with Bhandari (2007) and Kurtovic et al. (2015), authors who applied OLS regression estimation instead of LMM. As for the impact of FDI inflow on employment, Jude & Silaghi (2016) would provide an effective comparison with this research if the authors had used FDI inflow instead of FDI stock, because both the analyses claim the positive long-term effect on employment. The findings of this paper partially confirm the findings of Domazet (2016) as well, while disagreeing with Peric (2019), presumably because of the larger sample used in this research. FDI inflow showed a positive, if very low, impact on the examined labour market indicators. Although all six economies have experienced many changes on the economic level, they can be differentiated from one each other, in terms of their higher or lesser employment rate and average net wages. There is reason to believe that FDI inflow is not the sole reason for this, because Serbia, as the biggest importer of FDI among the six countries, has much lower average net wages than Croatia. Perhaps the developing countries in economic transition should look at neighbour’s economic strategies (such as Slovenia, Czech Republic, etc.) as well as thoroughly investigating the FDI inflow options before adopting it, for example, attracting FDI in high-tech sectors and strong HRM (human resource management). Moreover, regional collaboration should be strengthened in order to stimulate foreign companies to invest and to communicate with the region. Finally, it is recommended to study how to attract investors that pay higher wages, which involves further studying the availability and skills of host country employees and any potential brain drain. The focus on increasing the level of employment should be replaced with the focus on the structural level of employment. Lastly, it is recommended to enhance the efficiency and competitiveness of the national economy along with the companies through the implementation of strategies in order to solve employment and wage problems. Recommendations for the Governments are to re-evaluate FDI existing approach, such as decreasing subventions and investing in education. Large companies should invest in HRM, while small and micro firm should learn and promote HRM and business ethics. A further increase in FDI to the Western Balkan countries could be promising with more adequate development policies, which require deep institutional reforms and political consciousness and responsibility.
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CONCLUSIONS This paper has argued that the FDI inflow should enhance growth in the employment rate and in average net wages in the host country. This research empirically estimated the impact of the changes in FDI inflow on the changes in the employment rate and in average net wages in the Western Balkan area representative countries for the period 2003-2017. Linear mixed-effects models have revealed the existence of a very low but still positive impact of FDI inflow on both the labour market indicators used here: a) changes in IFDI effect the positive changes in employment rate after the second and the third year of IFDI performance in the country i; b) changes in IFDI effect the changes in average net wages mainly after the first and the second year of IFDI performance in the country i. The generalisability of these results is subject to certain limitations. Since there is little empirical evidence of the impact of FDI inflow on the main labour market indicators in transition countries, this paper offers findings to stimulate further research. In the first place, further data collection is required to determine exactly how FDI affects labour market. In the second place, it is recommended that more countries should be included in the model. Notwithstanding the relatively limited sample, this work offers valuable insights into the FDI inflow impact on labour market main indicators. However, in order to estimate the impact of FDI inflow in each individual country, one could conduct single multiple regressions to estimate the supposed impact of FDI inflow on the employment rate and on average net wages. Estimating the FDI spill-overs in each individual country may contribute to investigation of the FDI inflow time effects over the different sectors (service, industry, agriculture). The distinction between sectors may be relevant because there is reason to believe that the effect of FDI inflow is not equal for all six countries.
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Popovic, G., & Eric, O. (2018). Economic development of the Western Balkans and European Union investments. Economic Research, 31(1), 1539–1556. doi. 10.1080/1331677X.2018.1498009 Sabic, D., Vujadinovic, S., Milincic, M., Golic, R., Stojkovic, S., Joksimovic, M., Filipovic, D., & Secerov, V. (2012). The Impact of FDI on the Transition Economy in Serbia – Changes and Challenges. Acta Polytechnica Hungarica, 9(3), 65-84. Savic, N., Barjaktarovic, L., & Konjikusic, S. (2014). Foreign Capital Inflow and GDPpc in CEE Countries. Actual Problems of Economics, 3(153), 54-65. Stanisic, N. (2008). Do Foreign Direct Investments increase the economic growth of Southeastern European Transition Economies? South-Eastern Europe Journal of Economics, 42(1), 29-38. Szondi, G. (2007). The role and challenges of country branding in transition countries: The Central and Eastern European experience. Place Branding and Public Diplomacy, 3(1), 8–20. doi: 10.1057/palgrave.pb.6000044 Tanna, S., Li, C., & De Vita, G. (2018). The role of external debt in the foreign direct investment–growth relationship. International Journal of Finance and Economics, 23(4), 393-412. doi: 10.1002/ijfe.1628 Taylor, K., & Driffield, N. (2005). Wage Inequality and the Role of Multinationals: Evidence from UK Panel Data. Labour Economics, 12(2), 223-249. doi: 10.1016/j.labeco.2003.11.003 UNCTAD. (2011). World Investment report: Non-Equity Modes of International Production and Development. New York and Geneva: UN. UNCTAD. (2018). World Investment Report: Investment and new industrial polices. New York and Geneva: UN. Vesaite, R. (2014). FDI from European Union to Western Balkan Countires: is the economic development being intensified in the region? Institut Universitari d'Estudis Europeus. Todorova, S. M. (2018). Foreign Investment Inflows to Former Socialist Countries in the Balkans: Mapping Global Capitalism. Interventions: International Journal of Postcolonial Studies, 20(6), 814-831. DOI: 10.1080/1369801X.2018.1492953 Zdravkovic, A., & Martinovic, B. A. (2016). To which extent did the FDI contribute to employment growth in transition countries? In International Scientific Conference: Impact of Economic Policies on Attaining Resilient Growth, October 27th, Belgrade. Zulfiu, M. A. (2014). Simulation Analysis of the Effects of Increased Foreign Ownership on Wage. Academicus International Scientific Journal, 9, 140–158. doi: 10.7336/academicus.2014.09.10
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UTICAJ PRILIVA SDI NA TRŽIŠTE RADA ZAPADNOG BALKANA
Rezime: Zavisnost tržišta rada od priliva stranih direktnih investicija (SDI) veoma je visoka u ekonomijama u tranziciji. Ovaj rad se bavi procenom uticaja koji priliv SDI ima na stopu zaposlenosti i prosečne neto zarade u ekonomijama Zapadnog Balkana u periodu 2003–2017. Date ekonomije su uzete kao uzorak (Albanija, Bosna i Hercegovina, Hrvatska, Severna Makedonija, Crna Gora i Srbija) uglavnom zbog velikog nasleđa u vidu SDI i nezasićenog tržišta rada. Pretpostavlja se da priliv SDI pozitivno utiče na stopu zaposlenost kao i na prosečne neto zarade u zemljama Zapadnog Balkana. Koristeći linearne mešovite modele (LMM), rezultati pokazuju da promene priliva SDI imaju vrlo niske, ali pozitivne i značajne efekte kako na promene stope zaposlenosti, tako i na prosečne neto zarade.
160
Ključne reči: prosečna neto zarada, stopa zaposlenosti, strane direktne investicije, linearni mešoviti modeli, slučajni efekti.
EJAE 2020, 17(2): 161 - 177 ISSN 2406-2588 UDK: 316.334.56(669.1)"1974/2015" DOI: 10.5937/EJAE17-19472 Original paper/Originalni nauÄ?ni rad
DOES URBANIZATION INTENSIFY CARBON EMISSIONS IN NIGERIA? Muhammad Shehu* Department of Social Sciences, Federal Polytechnic, Bida, Niger State
Abstract: This study examines the urbanization and CO2 emissions nexus in Nigeria using the Autoregressive Distributed Lag (ARDL) method to analyze the annual time series data spanning from 1974 to 2015. Findings suggest that urbanization, GDP, energy use, and carbon emissions are strongly and positively correlated, while trade and carbon emissions exhibit a weak and negative correlation. The ARDL result shows a negatively significant short-term and long-term connection between urbanization and carbon emission in the Nigerian economy. In the short-term, GDP, trade and energy use positively affect carbon emission while in the long-term, trade and GDP negatively affect carbon emissions with energy use having a positive impact on carbon emissions. The study, therefore, concludes that urbanization does not cause carbon emission to rise in Nigeria, but energy use does. From the findings, it was recommended that there is a need for the use of energy-saving and environmentally friendly technology to reduce the amount of carbon emission in the economy.
Article info: Received: December 17, 2018 Correction: December 17, 2018 Accepted: January 25, 2019
Keywords: ARDL, CO2 emissions, STIRPAT Model, Urbanization.
INTRODUCTION In the pursuit of sustainable energy development as one of the foremost goals of every nation, several factors must be taken into consideration. Chief among these are improved energy efficiency and sustainability (Liu et al., 2017). However, it has been argued that some of the main factors contributing to increased environmental degradation are economic activities and unrestricted energy use (Abdallh & Abugamos et al 2017). Human activities undeniably contribute to an increase in greenhouse gases and depletion of the ozone layers. Furthermore, daily human activities in most cases inversely relate to the ecosystem and sometimes lead to environmental damages and, if not urgently addressed, may claim human lives and harm the significant factors of production (Ali et al., 2016a). Moreover, economies with poor environmental awareness and increased urbanization may trigger higher levels of environmental degradation. *E-mail: ibshmad@yahoo.co.uk
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Although several researchers agree that urbanization increases output, economic profits, affluence and inspiration to reshape politics, arts, science, and other human interests in an economy (see, Stewart and Lee, 1986; Glaeser, 2011; and Ali et al., 2016, among others), Bloom et al. (2008) add that urbanization also triggers the quick spreading of infections, increased crime, poverty, and may also lead to the degradation of environmental quality. United Nations (2014) projects that rural-urban migration comes 2050 may increase to 70 percent from the 50 percent noted in 2014, and much of this is most likely to occur in Africa. Findings from this projection formed the major discussion at the 2016 Habbit III Conference which took place in Ecuador (Quito), with growing concerns on how to devise the means to avert the negative impact of urban growth on the environmental quality of the continent. The relationship between urbanization and environmental quality has been a major source of controversy among notable scholars and policymakers. Remarkably, the recent literature is marked with various empirical claims about the connection between urbanization and environmental quality across different economies (see, for example, Zhu et al, 2012; Sadorsky, 2013; Wang et al., 2015; Ali et al., 2016; and Bilgili et al., 2017, among others). However, this paper investigates a sensible number of them and notices divergence in their findings. Factors, for example, the idea of nations examined, the models evaluated, the fundamental arrangement of statistical properties, estimation strategies utilized, and data coverage might be answerable for the varied discoveries. This study revisited the literature on urbanization-environmental quality nexus on the following point of view: First, this paper investigates the connection between urbanization and environmental quality in Nigeria, and from the observations in the literature, a few investigations on the urbanizationenvironmental quality nexus has been carried out in Nigeria (see e.g., Martínez-Zarzoso, 2008; Enete and Ayadiulo, 2012; and Adusah-Poku, 2016), with inconclusive findings. A study about Nigeria is important due to its massive population and rapid rate of development, all of which could negatively affect the country’s environment. Secondly, this paper adopts the STIRPAT model (i.e. Stochastic Impact Regression on Population, Affluence and Technology), which has gained much attention in environmental policy analysis in most researches. The main idea behind the model is that the standard of living in urban centers and the demographics are factors considered in determining environmental quality. This is unlike the Environmental Kuznet Curve (EKC) hypothesis, which only considers an increase in per capita income as the main determinant of environmental quality and may lead to erroneous conclusions. Thirdly, the Autoregressive Distributive Lag (ARDL) model founded by Pesaran et al (2001), which is capable of considering time series of different orders of integration was adopted, while also allowing for structural breaks using the Bai – Perron unit root test which endogenously discovers as far as five (5) likely breaks. The issue of structural breaks is vital due to the evidence of volatility and significant changes in the time series data employed, and neglecting structural breaks in the data employed when they actually exist may lead to erroneous estimates. The study also adopts the Granger causality test to validate the causality nexus among urbanization and carbon emissions. In structural analysis, the positive hypothesis regarding the causal arrangement of the investigated data is required, while the subsequent causality effect of sudden surprises or advancement to stated variables on the variables used in the model are summarized. Other parts of this paper are separated into four sections. The second part includes the literature review and the third part discusses the methodology used for the study. The fourth part presents the analytical framework of the study, while the last part concludes and proffers policies. Other parts of this study are divided into four sections. The second section includes the literature review and section three discusses the methodology used for the study. The fourth section presents the analytical framework of the study, while the last section concludes and proffers policies from the findings. 162
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SHEHU. M. ď&#x201A;˛ DOES URBANIZATION INTENSIFY CARBON EMISSIONS IN NIGERIA?
LITERATURE REVIEW The Environmental Kuznet Curve proposition has spurred the interest of many scholars over the years. According to Kuznet (1956), as the economic activities of a country increases, it attracts more investment opportunities, and bring in cheaper labor from the rural areas to urban centers to participate in industrialization, ignoring the initial mainstay (agriculture) for a better-paying job. Kuznet (1956) argues that in the process of migrating, the average wage inequality will reduce after 50 percent of population has migrated, but will get to a certain stage whereby the effect will have an inverted U-shaped curve. Going by Kuznet proposition, the theory implies that as the number of rural-urban migration increase because of improved economic activities, the environmental quality of the economy starts to degenerate gradually. However, this has been found to be subjective as the data used by Kuznet (1956) were more of middle-income countries in Latin America, thus generating a debate as to its evidence in other country groups, particularly low-income countries. Meanwhile, Dietz and Rosa (1994, 1997) argue against this assertion from the opinion that this model limits the determinant of environmental degradation to economic growth alone. They introduce the STIRPAT model which has gained much attention in environmental policy analysis in most researches. The main idea behind the model is that standard of living in the urban city and demographics are factors to be considered in determining environmental quality. Empirically, Poumanyvong and Kaneko (2010) analyzed the urbanization effect on CO2 emissions and energy consumption using a STIRPAT model and a balanced panel data analysis for a sample of 99 nations spanning from 1975 to 2005. The study verified that the impact of urbanization on CO2 emissions and energy consumption depends on the levels of the economies development. It further subscribed that, urbanization reduces energy consumption in low-income class, and causes energy consumption for middle and high-income classes to rise. Besides, the study noted that urbanization in all the income groups positively impacted carbon emissions, but more was reflected in the middle and high-income classes. Zhu, You and Zeng (2012) revisited the Environmental Kuznet Curve (EKC) by analyzing the data spanning from 1992 to 2008 for 20 emerging countries using semi-parametric panel data model with fixed effects. They noted little evidence for the inverted U-shape and could not confirm the Kuznet hypothesis in their analysis as they found a nonlinear connection among urbanization and carbon emissions. Evidence from 16 emerging countries, Sadorsky (2014) leaned on the STIRPAT model and applied panel regression technique to assess the link between urbanization and carbon emissions between 1979 and 2009. The findings showed that the effect of urbanization on CO2 emissions in those countries is positive, but not statistically significant. For the period 1983 to 2005, De Leon Barido & Marshall (2014) examined how the national level of CO2 emissions reacted to urbanization and environmental policies in 80 countries by using panel data analysis. Findings from their study suggest that, for the random and fixed effects, carbon emission increases by 0.95% for every 1% increase in urbanization, and for every economy with a strong environmental policy, urbanization has demonstrated more benefits to environmental quality. Their result showed specifically that the elasticity effect for higher-income and lower-income countries are -1.1 and 0.21 for an economy with a strong environmental policy, while it is 0.65 and 1.3 for economies with a weaker environmental policy, respectively. Incorporating the quadratic form of urbanization into the STIRPAT model, Wang et al. (2015) also adopted semi-parametric panel data regression analysis to explore the carbon emissions impact of urbanization in OECD countries. They confirmed from their findings that the Environmental Kuznets Curve hypothesis holds in the OECD countries. 163
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For 22 urbanized emerging economies, Rafiq et al. (2016) clarified the link between urbanization, trade openness, CO2 emissions and energy intensity by employing heterogeneous panel data analysis for a dataset spanning the period 1980 to 2010. The analysis showed that urbanization increases energy intensity and CO2 emissions, while trade openness reduces both energy intensity and CO2 emissions. For the developing countries, Sadorsky (2013) adopts panel heterogeneous regression analysis to evaluate the energy intensity effect of industrialization, urbanization and income in 76 developing economies. The result reveals that energy intensity reduces by -0.45% to -0.35 when income increase by 1%. The elasticities of industrialization stretch between 0.07 to 0.12 in the long-term. The study further argued that the urbanization impact on energy intensity is mixed as its coefficient is slightly greater than unity when it is statistically significant. In China, Sheng and Guo (2016) applied dynamic fixed effect technique, pooled mean group, and mean group to assess the urbanization impact on CO2 emissions in both short-term and long-term using the data between 1995 and 2011. The findings suggested that urbanization would have a long-lasting impact on CO2 emissions in the economy of China. Ali et al. (2016a) used the ARDL model to examine the impact of urbanization on carbon emission in the economy of Singapore between 1970 and 2015. Their findings revealed that urbanization had a strong adverse impact on CO2 emissions, while economic growth appeared to impact positively on carbon emission in the economy. They argued that urbanization should not be considered an obstacle to environmental quality when considering policies. Similar to the findings of Ali et al. (2016b), Pata (2017) examines the relationship between urbanization, industrialization, and carbon emission in Turkey between 1974 and 2013 using the ARDL model. The study concludes that in Turkey, urbanization and industrialization decrease the level of environmental quality captured by an increase in carbon emissions per head. In a study carried out on urbanization-carbon emission nexus in 20 MENA using semi-parametric panel fixed effects regression together with panel data between the period of 1980 and 2014, Abdallh and Abugamos (2017) discover little proof for EKC hypothesis, because the environmental quality of the region is found to be significantly supported by urbanization. They further conclude that economic growth and energy consumption are the major causes of CO2 emissions in the region. Liu, Yu and Gong (2017) investigates the effect of urbanization and ageing on energy intensity using two-way fixed effect model. Their findings reveal ageing negatively impacts energy intensity, energy intensity and GDP per capita are positively affected by urbanization, while energy prices and productivity negatively affect energy intensity. Bilgili et al. (2017) adopt the panel data analysis to examine urbanization-energy intensity nexus in 10 Asian countries between 1990 and 2014. They observe that urbanization has a significantly negative effect on energy intensity in both short-term and long-term. Using the threshold vector error correction method, Liu and Xie (2013) confirmed that the causality connection between energy intensity and urbanization in China is nonlinear. Similarly, Zi et al. (2015) examine the link between urbanization and carbon emissions in China using a threshold model. Arguments from their findings suggest the pattern of thresholds varies geographically, and emissions increase when the threshold of 0.43 is surpassed as residential income increases. Moreover, an increase in urbanization and industry percentage in overall GDP causes carbon emissions to rise and fall respectively. From a comparative study of the urbanization influence on CO2 emissions for the economy of China and Japan, Ouyang and Lin (2016) find a similar result for the economies as CO2 emissions show increasing growth during urbanization process in the economies, but a significant difference exists considering energy intensity, energy structure, and carbon dioxide emissions per capita among the two 164
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countries, which serves as a determining factor for carbon dioxide emissions growth. He et al. (2016) analyze the provincial panel data from 1995 to 2013 for china using the STIRPAT model. They divided the 29 provinces into 3 regions. Findings from the study reveal that the EKC hypothesis hold for the major regions of China. For region 2 and 3, urbanization negatively affects carbon dioxide emissions, while for region 1, only population influences emissions, and not urbanization. Zhang et al. (2017) incorporate a panel data consisting of 141 nations spanning between 1961-2011 into a STIRPAT model. Their results for urbanization and CO2 emissions nexus confirm EKC hypothesis and the turning point is around 73.8%. Yang et al. (2017) employ data from 266 prefecture-level cities between 2000-2010 to analyze the urbanization effect on energy used and economic growth in China using the Pooled Ordinary Least Squares (POLS) method. Results from their findings show urbanization positively and significantly affect energy used and economic growth within the period. Wang et al. (2015) study nexus among urbanization, energy consumption, and CO2 emissions in the ASEAN countries between 1980 and 2009 using panel fully modified ordinary least squares method. It was observed that CO2 emissions rise by 0.20% whenever urban population increases by 1%, a unidirectional causal nexus running from urbanization to CO2 emissions and energy consumption. Martínez-Zarzoso (2008) employs heterogeneous panel data regression analysis to examine the relationship between CO2 emissions and urbanization in selected developing countries. These countries are grouped into three (3); the low group, low-middle-income group and the upper-middle-income group. Findings from the study show that the impact of urbanization is higher than unity, 0.72, and negative for low-income group, low-middle-income group, and upper-middle-income group respectively. Adusah-Poku (2016) investigates the nexus between urbanization, population, and carbon emission in 45 sub-Saharan African countries using pooled mean group (PMG) to analyze the dynamic heterogeneous panels of the data spanning from 1990 to 2010. The study validates a short-term and long-term positive impact of both urbanization and population on carbon emissions and tends to grow faster in economies such as Nigeria and Ethiopia with larger populations, compared to the countries with smaller populations, such as Cape Verde and Equatorial Guinea. Ali et al., (2016b) for the economy of Nigeria adopts the ARDL method and STIRPAT model to explore the effect of energy use, economic growth, urbanization, and trade on CO2 emissions in Nigeria. Findings from the study suggest energy consumption, economic growth, and urbanization positively impact CO2 emissions in Nigeria in both short-term and long-term. In conclusion, it is evident from the review of literature that, while there have been several studies investigating carbon emissions and its relationship across different factors, little or no country-specific study exists on urbanization-carbon emissions nexus in Nigeria. Most of these studies have been subjective and qualitative in nature. The point of departure to this study lies in the adoption of the STIRPAT framework while also making use of the ARDL model, which accounts for the impact of the structural break in analyzing the nexus between urbanization and carbon emissions in Nigeria.
Data and Methodology The study adopts the STIRPAT model in line with the work of Poumanyvong and Kaneko (2010), Ali et al., (2016b), Ali et al., (2016a), and Zhang et al., (2017) to analyse the link between urbanization and CO2 emissions in Nigeria. The IPAT model accounts for urbanization as a factor that contributes to increased carbon emissions in the economy. Dietz and Rosa (1994) introduce the Stochastic type of the IPAT equation. 165
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The equation encompasses quantitative variables of population size (P), affluence per capita (A), and industrial weight in economic interaction measured as a polluting technology (T). It is a year based model, specified as; (1) Where: Ii ,Pi , Ai , and Ti indicate environmental impact (I) population (P), affluence (A), and technology (T) in an economy. i, α, β are the parameters to be estimated; and is the random error term. According to the pioneers, Dietz and Rosa (1994, 1997), STIRPAT is mainly applied to study the factors that affect the environment. The main argument behind the model is that CO2 emission is produced by demographics, but varies on the highly efficient standard of living in the urban city. Ali et al., (2016a) submitted that the economic activities in the urban cities may have two different effects: those connected to higher consumption and incomes that promote industrialisation, and attracts the use of fossil fuels. Following the views of the authors, the model for this study is formulated by adapting the model of Ali et al., (2016b) in the economy of Nigeria, which implies that urbanization, energy use, GDP, and trade are the main factors influencing carbon emissions in the country. The model is, therefore written as: (2) Energy consumption per capita (oil equivalent), Urbanization rate, and Trade (calculated as the ratio of import plus export to GDP at current LCU). respectively Equation (2) is further transformed into a logarithm function as: (3) For this study, the ARDL model, which is permitted for variables stationary at I(0), I(1), or a combination of I(0) & I(1) is used. It is also used because of its ability to estimate both the short-run and long-run magnitudes, as well as the error correction value. In order to estimate equation (3), the associated conditional standard autoregressive distributed lag ARDL (p, j1 ,j2 , j3 ,j4) long run model for CO2i can be expressed as: (4) However, accounting for structural breaks, the breaks are captured using in equation 5 where is a dummy variable accounting for every breaks termed as for or else represents the time period; are the dates of the structural break, where r =1, 2, 3,……., k and Br is the break dummy coefficient. (5) The short-term dynamic parameters of the impact of urbanization on carbon emission is obtainable by estimating the equation as;
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(6) From equations 4 to 6, serves as the variables long-term multiplier. While are the variables short-term multipliers, are the long-term and short-term intercept of the models. are the length of optimal lags for each of the variables. is the error correction term defined as; (7) The causal link between the dependent and independent variables is tested using the granger causality test. The model is expressed as; (8)
Zt is a 5x1 vector matrix of the endogenous variables (CO2, GDP, EU, U and T).
a vector, with a lag operator and
t
is
, a vector of idiosyncratic errors.
Discussion of Results Descriptive Statistics The descriptive statistics result represented in Table 1 shows that the variables' value for mean lies within their lowest and highest values. On average, within the period under study, the variables- CO2, Urbanization, GDP per capita, Energy use and Trade grow an average of 11.13%, 34.96%, 10.02%, 6.58% and 0.51% respectively. The result of the skewness reveals that carbon emissions, per capita GDP, and trade are negatively skewed, while Urbanization and energy use reveal a positive skewness. Following the kurtosis result, the study concludes that the variables are leptokurtic in nature, as they have values less than three. The Jarque-bera statistics result showed that the entire variables are normally distributed with a probability distribution value greater than 10%. Table 1: Descriptive Statistics InCO2
UR
InGDP
InEU
T
Mean
11.1296
34.9636
10.0177
6.5763
0.5120
Maximum
11.5588
47.7760
13.1718
6.6825
0.8181
Minimum
10.4123
23.3890
6.5405
6.5095
0.2112
Std. Dev.
0.3657
7.1085
2.2210
0.0486
0.1685
Skewness
-0.5181
0.1835
-0.1797
0.4250
-0.2433
Kurtosis
1.8397
1.9466
1.7330
1.9878
2.0274
Jarque-Bera
3.4282
1.7629
2.4574
2.4750
1.6754
Probability
0.1801
0.4142
0.2927
0.2901
0.4327
34
34
34
34
34
Observations
Source: Author’s Computation (2018)
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Correlation Test The correlation result showed that the correlation relationship between the entire variables and carbon emissions is strongly positive and statistically significant, except for trade, which was negative and insignificant. This means that strong nexus exists among energy use-carbon emission, GDP-carbon emission, urbanization-carbon emission, and are significant at 1%, while the weak negative relationship between trade and CO2 emission is insignificant and weak. This validates the absence of the multicollinearity problem among the variables. The result is presented in Table 2 below: Table 2 Correlation Matrix Test Results InCO2 InCO2 UR InGDP InEU T
UR
InGDP
InEU
T
1 ----0.6044
1
(0.0002)
-----
0.5536
0.9823
1
(0.0007)
(0.0000)
-----
0.7237
0.8749
0.8532
(0.0000)
(0.0000)
(0.0000)
-----
-0.1903
0.1264
0.2443
0.0919
1
(0.2811)
(0.4761)
(0.1638)
(0.6053)
-----
1
( ) in parenthesis denotes the probability values of the variables Source: Author’s Computation (2018)
Unit Root Test As a prerequisite for analyzing time series data with large T, it is common practice in the literature to test the series for non-stationarity. Hence, the study subjects all the series used in the model to unit root testing. The study adopts the Ng-Perron test for stationarity. The null-hypothesis of the nonstationary test states there is absent of unit root among the series. The unit root test is a compulsory test to show if the data used for the study are free from unit root problems. From the results in Table 3, the variables are stationary at I(0) and I(1). This suggests the presence of unit root problem in the data used as all the variables are not mean, reverting at levels as some only converge to long-term equilibrium after first differencing. To check if there is a long-term relationship among the variables, the ARDL bounds test is employed. Another innovation of this study is the adoption of the Bai-Perron (2003) structural break test, which is capable of determining five (5) possible breaks endogenously. Testing for structural breaks allows us to deal with multiple structural changes in the model, failure of which could lead to spurious conclusions. Capturing the structural breaks link between Urbanization-Carbon emission nexus is, however, the first in the context of Nigeria.
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Table 3: Ng Perron Unit Root Test Results [Trend and Intercept] MZa
MZt
MSB
MPT
Levels InCO2
U
-6.340
-1.776
0.280
14.371
-33.766***
-4.040***
0.120***
3.080***
-6.579
-1.672
0.254
13.884
-18.095***
-2.978***
0.165***
5.213***
-7.118
-1.674
0.235
13.072
InGDP InEU T
First Difference InCO2
-15.960*
-2.824*
0.177*
5.716*
-4.835
-1.555
0.322
18.847
-15.750*
-2.781*
0.177*
5.935*
U InGDP InEU T
-15.859*
-2.773*
0.175*
5.995*
-15.025***
-2.733***
0.182***
1.659***
Note: *, **, *** implies the level of significance at 10%, 5% and 1% respectively. Source: Author’s Computation (2018)
The optimal lag test was also carried out in the study to know the correct number of lags to use for the study. Schwarz Information Criteria (SIC) was used to decide on the optimal lag length. The finding shows an optimal lag selection of 2, which was used in the study. Table 4 below shows the result: Table 4: Optimal Lag test result Lag
LogL
LR
FPE
AIC
SIC
HQ
0
-21.6378
NA
3.84E-06
1.7186
1.9499
1.79396
1
176.02
318.803
5.72E-11
-9.4207
-8.0329
-8.9683
2
219.8772
56.5898***
1.94E-11
-10.6372
-8.0931***
-9.8079
3
254.5179
33.5233
1.54e-11***
-11.2592***
-7.5586
-10.0529***
Source: Author’s Computation (2018)
The result of the bounds test reveals the existence of a cointegrating link among the variables in the long-term. This is validated by F-statistic value found to exceed the maximum and the minimum bound class of the variables at a 5% significance level. The result is showed in table 5 below: Table 5: ARDL Bounds Test Result DEP/VARIABLES InCO2t=f(URt, InGDPt, InEUt, Tt)
F-Stat 4.75
Bounds (5%)
Outcome
I(0)
I(1)
3.47
4.57
Co-integration
Source: Author’s Computation (2018)
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In addition, the study examines the significance of structural breaks in the urbanization-emissions nexus. To determine the breaks, the Bai-Perron (2003) test was adopted; therefore incorporated into the model as fixed regressors in the ARDL model. The Bai-Perror break test result is reported in Table 6 below, and only one break is recorded for Nigeria. The break date identified to correspond with the sequence of OPEC cuts during that period which may have affected energy use. Table 6: Bai-Perron (2003) Structural Break Dates Country
Break Period
Break Range
Nigeria
2000
1982 - 1999 2000 - 2015
Compiled by the author
ARDL Estimation From the results of the unit root test, it was revealed that all the variables examined were not integrated of order 2, and therefore, we go on to estimate the ARDL model. As a benchmark, we first ran the ARDL model analysis without considering structural breaks. This was done to establish the existence of a long-term nexus among the variables. To carry out this, we used F-test proposed by Pesaran et al. (2001) to test the null hypothesis of no cointegration against the alternative hypothesis of co-integration. The maximum of 2 lags suggested by SIC is used as an optimal number of lags on each first-differenced variable. The results of the bounds test suggest that a long-term co-integrating nexus exists among the variables. This is validated by F-statistic value found to exceed both the upper and lower bound class of the variables at a 5% significance level (see Table 5). The long-term and short-term ARDL estimation result for both scenarios (with and without breaks) are presented in Table 7. In the short-term, findings from the result support the presence of a significantly negative connection between urbanization and carbon emissions in Nigeria. Significantly, a unit development in urbanization results in approximately 50% reduction in carbon emissions. Similarly, the result reveals an adverse significant connection between trade and CO2 emissions both with and without breaks. In the same vein, evidence supports the presence of a negatively significant connection between GDP and carbon emissions. Specifically, a percentage increase in GDP leads to approximately 33% reduction in carbon emissions in the short-term. However, taking structural breaks into account, the relationship is positive but insignificant. On the other hand, energy used exhibits a positively significant connection with carbon emissions in Nigeria. In particular, a percentage increase in energy use will cause carbon emissions to rise by 6.16% in the short-term, and a similar result was observed while accounting for breaks. This implies that irrespective of the consideration of breaks, the energy use affects carbon emissions negatively. From the short-term results, it can be deduced from the result that increases in carbon emissions in Nigeria can actually be attributed to increased energy consumption, not urbanization. Overall, the result reveals that the independent variables have the ability to correct about 76% of deviations of emissions from the expected equilibrium in the long-term back to equilibrium. This satisfies the a priori expectation of the error correction result which is negatively significant at a 1% significance level. The result can be seen in Table 7.
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The long-term results from the model estimation provide evidence on behalf of a significantly negative connection between carbon emissions and urbanization, GDP, and trade in Nigeria. In particular, a unit change in urbanization and trade and a percentage change in GDP causes emission to fall by 65%, 1.78% and 1.09% respectively. Similar to observations in the short-term, there is strong evidence of a positively significant connection between energy use and carbon emissions with and without breaks. From the ARDL findings, the results are found to be in consonance and against the submission of some existing studies. The long-term and short-term results of the urbanization-carbon emission nexus is against the submission of Sadorsky (2014) for 16 emerging countries, de Leon and Marshall (2014) for 80 countries, Ouyang and Lin (2016) for Japan and China, and Wang et al., (2016) for ASEAN countries, but confirms the findings of Ali et al. (2016a) for Singapore and Pata (2017) for Turkey, in that a negatively significant nexus exists between urbanization and CO2 emissions in Nigeria. The results deviate from the submission of these studies because they are panel analyses and not country-specific. Against the study of Ali et al., (2016b) for Nigeria, the study argued that the reaction or changes in consumer behaviour towards energy consumption due to the implementation of several environmental policies (such as the Paris Climate Change Agreement) and the introduction of new energy-efficient technologies (solar systems, among others) into the economy may have caused the deviation of the study findings from Ali et al. (2016b) in the long-term. The findings of our study conformed with that of Ali et al. (2016b) which only exist in the short-term, but are not valid in the long-term. Moreover, the result agrees with Bilgili et al. (2017) and Abdallh and Abugamos (2017) that urbanization is a significant determinant factor of reduction in carbon emissions in Nigeria. The findings also corroborate the findings of Abdallh and Abugamos (2017), which state that energy use is the major source of carbon emissions in a developing country like Nigeria. Finally, findings from the study suggest breaks are significant in urbanization-carbon emissions nexus in Nigeria in both the long-term and the short-term. Moreover, the results of the diagnostic checks suggest the absence of serial correlation and heteroscedasticity (see Table 7): Table 7: ARDL Estimation Result Variables
ARDL Without Breaks
ARDL With Breaks
Long Run Results Constant
-23.9420 (-2.5149)**
-48.0845 (-4.0273)***
Trend
0.7048 (6.9686)***
0.5117 (3.2651)***
UR
-0.6547 (-6.5520)***
-0.5528 (-4.2557)***
lnGDP
-1.0932 (-5.3958)***
-0.6462 (-2.2420)**
lnEU
8.8199 (5.7639)***
11.7661 (5.9487)***
T
-1.7795 (-4.8019)***
-2.1752 (-5.3343)***
--
0.3465 (1.9572)*
B
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Short Run Results Constant
-18.2453 (-2.6459)**
-53.9210 (-3.8023)***
∆Trend
0.5371 (5.5315)***
0.5738 (3.0594)**
--
0.3303 (2.1840)*
∆UR
-0.4989 (-5.9901)***
-0.6199 (-3.9151)**
∆lnGDP
-0.3341 (-2.2275)**
0.0048 (0.0325)
0.2355 (1.4856)
0.3839 (1.6861)
6.7213 (5.5443)***
6.1678 (4.4224)***
∆lnEUt-1
--
-0.2128 (-0.1164)
∆lnEUt-2
--
-0.3435 (-0.1901)
∆lnEUt-3
--
-3.3827 (-2.3898)**
-0.6509 (-2.8461)***
-1.0811 (-3.9002)***
∆Tt-1
0.5597 (2.2781)**
0.3862 (1.6740)
∆Tt-2
--
0.4786 (1.8959)*
∆Tt-3
--
0.7660 (2.6880)**
B
--
0.3885 (1.8897)*
ECMt-1
-0.7621 (-6.3034)***
-1.1214 (-8.1050)***
F-stat.
26.4063***
30.7841***
∆lnCO2t-1
∆lnGDPt-1 ∆lnEU
∆T
Bound F-stat.
172
4.7482**
14.1064***
Adj. R2
0.8913
0.9487
J.B stat
0.7981 [0.6710]
0.3384 [0.8443]
LM (1)
0.5795 [0.5698]
1.2491 [0.3321]
ARCH (1)
0.7556 [0.3919]
0.0161 [0.9001]
Ramsey test
7.5344 [0.0125]
2.1748 [0.1711]
Lag Selection (SIC) (1,0,2,0,2) (2,0,2,4,4) B represents dummy for the identified break date as identified in the Bai Perron test presented in Table 6. Standard errors are presented in brackets and probability values are presented in parentheses. The critical values for the Lower and Upper Bounds respectively are 3.03 and 4.06 for the symmetric models at 10% significance level. ***, **, and * indicate statistical significance at 1%, 5% and 10% respectively.
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Granger Causality Test Further testing on the causality nexus among the variables that was carried out. From the result, it was discovered that a uni-directional causality nexus between CO2 to urbanization and CO2 to trade at a 5% significance level. This implies that emissions drive urbanization and that urbanization does not explain emissions in Nigeria. Likewise, there is no causality nexus between GDP and CO2, EU and CO2. The implication of this is that emission is the reason behind the act of urbanization in Nigeria. See the result below in Table 8: Table 8: Granger Causality Result Null Hypothesis: UR does not Granger Cause LOGCO
Obs
F-Statistic
Prob.
32
1.1785
0.3231
9.9270
0.0006
1.5367
0.2333
1.3275
0.2819
1.6443
0.2119
0.0413
0.9596
0.4359
0.6511
3.8337
0.0342
0.4437
0.6462
1.0920
0.3499
0.0478
0.9534
6.7583
0.0042
1.0835
0.3527
1.4061
0.2625
2.8221
0.0771
3.6717
0.0389
0.8766
0.4277
2.5914
0.0934
0.9376
0.4039
3.2484
0.0544
LOGCO does not Granger Cause UR LOGGDP does not Granger Cause LOGCO
32
LOGCO does not Granger Cause LOGGDP LOGEU does not Granger Cause LOGCO
32
LOGCO does not Granger Cause LOGEU T does not Granger Cause LOGCO
32
LOGCO does not Granger Cause T LOGGDP does not Granger Cause UR
32
UR does not Granger Cause LOGGDP LOGEU does not Granger Cause UR
32
UR does not Granger Cause LOGEU T does not Granger Cause UR
32
UR does not Granger Cause T LOGEU does not Granger Cause LOGGDP
32
LOGGDP does not Granger Cause LOGEU T does not Granger Cause LOGGDP
32
LOGGDP does not Granger Cause T T does not Granger Cause LOGEU LOGEU does not Granger Cause T
32
Source: Author’s computation (2018).
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SHEHU. M. DOES URBANIZATION INTENSIFY CARBON EMISSIONS IN NIGERIA?
CONCLUDING REMARKS This paper investigates the link between urbanization and carbon dioxide emission in Nigeria using annual data from 1984 to 2015. The ARDL technique is used to analyse the data. Results from the study provide evidence of a positively significant correlation between CO2 emission and urbanization in Nigeria. The ARDL bounds test confirmed a cointegration nexus among the variables in the long-term. From the ARDL estimation result; urbanization, GDP, and trade negatively affect carbon emissions in the short-term and long-term in Nigeria. Energy consumption shows a positively significant nexus with carbon emissions in the short-term and long-term, taking into account breaks. This, by implication, means that, as urbanization, GDP, and trade incline towards reducing the amount of carbon emission into the atmosphere in the long run, energy use causes the environmental quality to diminish. This could be as a result of the migrants’ exposure to more efficient energy products, such as the renewable energy products, which aid against the use of inefficient energy products, such as oil equivalent energy use, which contributes more to carbon emission. From the causality test result, findings suggest carbon emissions drive urbanization in Nigeria. The study concludes from the findings that urbanization is not a significant factor in contributing to an increase in carbon emissions, but rather energy use. However, we recommend policies to reduce the amount of carbon emission through energy conservation, and that efficiencies should be adopted. This is achievable through the adoption of efficient renewable energy technologies.
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year
Carbon Emission (kt)
GDP per Capita (current LCU)
Energy Use per capita (oil equivalent)
Urban Population (ratio of total)
Trade (% of GDP)
1971
32280.6
181.0867
579.0964
18.151
0.244636
1972
41426.1
188.1045
585.4539
18.549
0.227636
1973
49577.84
203.8182
597.1382
18.952
0.312678
1974
62291.33
317.8672
600.4265
19.363
0.39747
1975
47395.98
362.0658
608.4557
19.78
0.411703
1976
55247.02
438.6487
622.2918
20.205
0.421381
1977
50567.93
499.6593
636.2368
20.636
0.473953
1978
48294.39
520.2937
645.8924
21.074
0.433148
1979
70289.06
601.0817
653.1639
21.518
0.438784
1980
68154.86
684.3113
665.1001
21.97
0.485713
1981
65958.33
685.3477
676.3869
22.671
0.482933
1982
65602.63
692.6157
691.7809
23.389
0.377485
1983
59929.78
729.4444
693.5561
24.122
0.270372
1984
69625.33
789.3021
677.7652
24.872
0.236089
1985
69893.02
879.5493
682.8194
25.635
0.259001
1986
73505.02
872.868
671.499
26.414
0.237168 0.416467
1987
59343.06
1270.271
676.8561
27.209
1988
70747.43
1635.607
678.8559
28.019
0.35312
1989
42441.86
2460.585
684.4483
28.842
0.603918
1990
38162.47
2955.288
697.1921
29.68
0.530302
1991
40014.3
3367.268
712.2482
30.176
0.648766
1992
64289.84
5542.176
721.9704
30.677
0.61031
1993
58268.63
6960.196
715.4378
31.182
0.581098
1994
44865.75
8974.894
680.7101
31.691
0.423089
1995
33267.02
18595.84
682.2696
32.205
0.597678
1996
38936.21
25277.37
693.7783
32.725
0.57691
1997
40175.65
25603.91
699.6507
33.247
0.7686
1998
40164.65
24198.89
687.1179
33.773
0.661732
1999
44774.07
27757.66
694.1713
34.304
0.558464
2000
79170.53
38555.41
703.2447
34.84
0.713805
2001
83339.91
39131.13
720.0472
35.669
0.818128
2002
98114.25
55400.52
724.6113
36.508
0.633836
2003
93130.8
66245.96
746.6122
37.356
0.752189
2004
97039.82
86219.74
748.3413
38.212
0.484481
2005
104689.2
106055.7
757.9587
39.074
0.507484
2006
98891.66
131191.7
744.5452
39.943
0.646093
2007
95055.97
143022.4
750.7831
40.819
0.644629
2008
96148.74
164055
752.8598
41.702
0.64973
2009
76735.64
163443.7
721.4534
42.588
0.618029
2010
92016.03
349791.6
755.9892
43.48
0.426514
2011
96093.74
391174.5
778.4994
44.362
0.527941
2012
99636.06
433955.8
798.3031
45.234
0.443801
2013
95650.03
471456.1
779.8515
46.094
0.310489
2014
99741.91
510805.4
763.3914
46.942
0.308852
2015
101750.1
525316.4
746.9312
47.776
0.211244
Source: World Development Indicators (2016) 176
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SHEHU. M. DOES URBANIZATION INTENSIFY CARBON EMISSIONS IN NIGERIA?
DA LI URBANIZACIJA POJAČAVA EMISIJU UGLJENIKA U NIGERIJI?
Rezime: Ova studija ispituje odnos urbanizacije i emisije CO2 u Nigeriji primenom metode autoregresivne distribucije kašnjenja (ARDL) za analizu podataka o godišnjim vremenskim serijama u rasponu od 1974. do 2015. Rezultati studije sugerišu da urbanizacija, BDP, upotreba energije i emisija ugljen dioksida snažno i pozitivno. koreliraju, dok trgovina i emisije ugljen dioksida pokazuju slabu i negativnu korelaciju. Rezultat ARDL pokazuje negativno značajnu kratkoročnu i dugoročnu vezu između urbanizacije i emisije ugljen dioksida u ekonomiji Nigerije. Kratkoročno, BDP, trgovina i upotreba energije pozitivno utiču na emisiju ugljen dioksida, dok dugoročno trgovina i BDP negativno utiču na emisiju ugljen dioksida upotrebom energije koja ima pozitivan uticaj na emisiju ugljen dioksida. Stoga, studija zaključuje da urbanizacija ne uzrokuje porast emisije ugljen dioksida u Nigeriji, ali upotreba energije uzrokuje. Na osnovu rezultata preporučena je neophodnost upotrebe štedljive energije i ekološki prihvatljive tehnologije za smanjenje količine emisije ugljenika u ekonomiji.
Ključne reči: ARDL, emisije CO2, STIRPAT model, urbanizacija.
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CIP - Каталогизација у публикацији Народна библиотека Србије, Београд 33 The EUROPEAN Journal of Applied Economics / editor-in-chief Žaklina Spalević. - Vol. 12, No. 1 (2015)- . - Belgrade : Singidunum University, 2015- (Belgrade : Caligraph). - 28 cm Polugodišnje. - Je nastavak: Singidunum Journal of Applied Sciences = ISSN 2217-8090. Drugo izdanje na drugom medijumu: The European Journal of Applied Economics (Online) = ISSN 2406-3215 ISSN 2406-2588 = The European Journal of Applied Economics COBISS.SR-ID 214758924