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Examining recent trends in poverty, inequality, and vulnerability (an executive summary)

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overty reduction has always been a top priority in the public policy agenda of various political administrations in the Philippines. The new Aquino administration is no exception as it actively pushes for programs and policies that would fight poverty and reduce its rate to half of what it was in 1990 by 2015. To be able to pursue this more earnestly, it is important to craft a roadmap for poverty reduction where the strategic interventions to be made are based on a careful examination of data on poverty, vulnerability, and inequality. In a recent paper titled “Examining recent trends in poverty, inequality, and vulnerability” written by Dr. Jose Ramon Albert and Mr. Andre Philippe Ramos, Senior Research Fellow and Research Specialist, respectively, at the Philippine Institute for Development Studies (PIDS), poverty, as based on statistics released by the National Statistical Coordination Board (NSCB) for 2000, 2003, and 2006, is seen not to have substantially changed since the start of the millennium. Although the proportion of the population who were considered poor decreased from 33.6 percent in 2000 to 30 percent in 2003, the poverty rate in 2006 (32.9%) practically went back to what it was in 2000.

Thus, poverty has remained mostly unchanged and has also continued to be a predominantly rural phenomenon, with three out of every four poor persons found in the rural areas. The picture becomes more daunting when one considers these figures—based on simulation exercises done on the 2006 FIES (Family Income and Expenditure Survey) income data, it will take more than 17 years for half of the poor to exit poverty even if the per capita incomes of all persons in the country were to increase uniformly by 2 percent annually (adjusted for inflation). And it will take an average time of 40 years for the poor to exit poverty if annual growth per capita is at 1 percent. A look at the inequality situation Analyzing how growth has been divided among the poor and nonpoor, Albert and Ramos, also examined the growth rate of (real) per capita income across corresponding quintiles of the population using the socalled Growth Incidence Curve (GIC). Their analysis shows that growth in the Philippines for the period 2000–2006 may be considered to be relatively propoor because in comparison with the population in the upper income percentile, those in the bottom 30 percent benefited more in terms of the average growth rate. The same trend was observed in the urban areas for the same period. PN 2010-03 (Summary)

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Unfortunately, however, this propoor growth was quite modest since in the rural areas, those at the lower and middle portions of the income distribution benefited less from growth during said period than those at the upper end of the distribution. This trend is validated by inequality statistics such as the gini coefficient and generalized entropy measures analyzed by the authors. Their study notes that while inequality went down as a whole for the country and urban areas for the period 2000–2006, the rural areas, however, suffered from increased inequality largely brought about by differences in the top of the income distribution ladder. In view of these changes in income distribution, headcount poverty in the country decreased only by 0.7 percent during this period. Had there been no increase in the inequality seen in the rural areas where the upper-income groups were the ones who benefited more from growth, headcount poverty would have fallen from 33.6 percent to 22.6 percent. Implications: need to reorient propoor policy and program thrusts, and to have sustained economic growth As shown by the statistics, despite the many propoor policies, programs, and projects instituted, gains in the fight against poverty

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Policy Notes

have still been modest at best. Such modest gains can be attributed to improper targeting mechanisms for propoor projects, and the absence of monitoring and evaluation systems for program implementation. Propoor public interventions that do not seem to have an impact should therefore be reoriented, especially those with implementation and targeting issues. Policies and programs geared toward preventing the transmission of poverty from one generation to the next, especially by way of human resource investments and population management must thus be essential components of any sustainable reduction strategy of poverty and vulnerability. The government’s conditional cash transfer program, for instance, if well executed and monitored, shows promise. Improving nonfarm income in rural areas must also be a policy thrust. Sustained economic growth is important for dramatically reducing poverty, but this entails a serious management of resources, including population management. A comprehensive roadmap for economic development and poverty reduction based on analysis of historical trends and a specific identification of goals and targets has to be drawn up, especially if the country aims to meet its commitments to the Millennium Development Goals. 


Policy Notes

Philippine Institute for Development Studies Surian sa mga Pag-aaral Pangkaunlaran ng Pilipinas

ISSN 1656-5266

No. 2010-03 (July 2010)

Examining recent trends in poverty, inequality, and vulnerability Jose Ramon G. Albert and Andre Philippe E. Ramos

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hile the most recent economic growth figures of the Philippines are much better than expected, it is important to recognize that even when economic growth occurs, members of society do not benefit equally from this growth. Government, therefore, has to develop social protection mechanisms that would reduce the deprivation suffered by the marginalized sectors of society such as the poor. Poverty reduction has often been claimed to be the top priority in the public policy agenda. Proof of this is the commitment made by almost all countries in the world, including the Philippines, to meeting the UN Millennium Development Goals (MDGs), one of which is to reduce poverty rate by 2015 to half of what it was in 1990. For the Philippines specifically, the 2004–2010

Medium-Term Philippine Development Plan (MTPDP) explicitly identifies its goal “to fight poverty by building prosperity for the greatest number of the Filipino people.” Toward achieving this goal, the government has implemented a multitude of propoor social protection tools such as the Kapit-Bisig Laban sa Kahirapan-Comprehensive and Integrated Delivery of Social Services (KALAHI-CIDSS) program. In the face of rising food and fuel prices in 2008 and the global financial crisis of 2009, the Philippine government also established an Accelerated Hunger Mitigation Plan that included a school-feeding program and an Economic Resiliency Plan. These plans were meant to shield poor people from possible adverse effects of these crises. PIDS Policy Notes are observations/analyses written by PIDS researchers on certain policy issues. The treatise is holistic in approach and aims to provide useful inputs for decisionmaking. The authors are Senior Research Fellow and Research Specialist, respectively, at the Institute. The views expressed are those of the authors and do not necessarily reflect those of PIDS or any of the study’s sponsors.


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As a new administration takes the helm of government in the country, it will have to craft a roadmap for economic development and poverty reduction. A concrete poverty reduction strategy, however, entails not only a set of interventions but also a careful examination of data on poverty, vulnerability, and inequality. In this regard, this Policy Note examines recent trends in the data and points out areas where a possible reorientation of programs and thrusts is called for on the basis of such trends and linkages. Looking at the data that matter Management of any poverty reduction strategy requires credible poverty data. In the Philippines, official poverty statistics are based on per capita income data sourced from the triennial Family Income and Expenditure Survey (FIES) conducted by the National Statistics Office (NSO), and poverty thresholds1 determined by the National Statistical Coordination Board (NSCB). Other household surveys conducted by the NSO during non-FIES years such as the Annual Poverty Indicator Survey (APIS) also provide useful portraits for national poverty assessments (Reyes 2004). These data sources also form the basis of studies that identify ______________ 1

The poverty thresholds represent the minimum level of per capita income required by an individual to fulfill his or her basic food and nonfood needs. A household is considered as poor if its per capita income is less than the poverty threshold. When a household is poor, all members of that household are considered poor.

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factors associated with and contributing to poverty (Albert and Collado 2004). To address the growing demands by local government units for statistics, the NSCB released poverty data at the municipal level that involved a combination of information from the FIES and the census of population. Beyond these income poverty data used in describing poverty conditions, however, there are also other nonmonetary indicators such as education and health outcomes, and access to basic services and utilities that have to be analyzed to truly understand the poverty situation. Poverty, after all, is a multifaceted and multidimensional phenomenon; thus, it is not enough to learn about poverty conditions from monetary indicators such as per capita income. In addition, one must also take into account the prospects of future well-being of individuals; thus, the level of their vulnerability to poverty must also be measured (Albert et al. 2008). Poverty and vulnerability, in turn, are intertwined with issues about distribution. Greater inequality in income distribution leads to a smaller share of resources being obtained by the poor and the vulnerable. When growth occurs, much of the share of such growth goes to the nonpoor, thereby preventing the country from achieving improved and sustained economic growth, which is necessary for poverty reduction.


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Table 1. Poverty incidence, poverty gap, and poverty squared gap, Philippines, Trends in poverty, by urban and rural areas (in percent): 2000, 2003, and 2006 growth, and Urban Rural National inequality 2000 2003 2006 2000 2003 2006 2000 2003 2006 Official poverty statistics2 released by the NSCB for Poverty incidence Rate 18.47 16.29 19.17 48.35 43.31 46.23 33.59 30.03 32.89 2000, 2003, and 2006 Share (to national) 27.15 26.66 28.73 72.85 73.34 71.27 100.0 100.0 100.0 indicate that poverty has Poverty gap Rate 4.96 4.32 5.28 15.7 13.72 14.6 10.4 9.1 10.01 not substantially changed Share (to national) 23.58 23.33 26.02 76.43 76.67 73.99 100.0 100.0 100.0 since the start of the Data source: Family Income and Expenditure Survey (FIES) conducted by the National Statistics Office (NSO) and the millennium (Table 1). National Statistical Coordination Board (NSCB) official poverty lines. While the proportion of the population who were Figure 1. Poverty rates and poverty gap ratios across selected Southeast Asian countries, 1980–2005 considered poor decreased from 33.6 percent in 2000 to 30 percent in 2003, the poverty rate in 2006 (32.9%) went back to practically what it was in 2000.

Poverty has remained mostly unchanged and has continued to be a predominantly rural phenomenon, with about three out of every four poor persons shown to have been residing in rural areas. This profile practically remains the same as in the 1990s. Compared with other neighboring countries in Southeast Asia such as Cambodia, Indonesia, Malaysia, and Thailand, the Philippines has had a fairly modest poverty reduction (Figure 1).3 Thailand, for instance, was at the same level of development as the Philippines half a century ago; but now, it is far ahead in terms of improvements in the poverty rates, poverty gap ratios, and other socioeconomic indicators. Indonesia displayed a phenomenal reduction in poverty in the period 1980 to 2005. In 1985, it had a poverty rate that exceeded that of the Philippines by more than 20 percent but in a

Source: Povcalnet, World Bank

span of two decades, both countries became the same with regard to the incidence of poverty. Moreover, for the Philippines, the downward ______________ 2 The poverty rate is the ratio of the number of poor people to the total population. The poverty gap, which describes the depth of poverty, is the average, over all people, of the proportionate gaps between poor people’s per capita income and the poverty line (as a proportion of the poverty line). 3 International poverty monitoring is undertaken by the World Bank with its estimates of poverty rates (generated using consumption data and 1.25 US dollar per person per day poverty lines in purchasing power parity terms).

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Figure 2. Average (and median) time to exit poverty in the Philippines

Note: Authors’ calculations using data from the 2006 Family Income and Expenditure Survey (FIES) and official poverty lines.

trend prior to the Asian financial crisis has halted, with the poverty measures remaining at roughly the same levels for a decade after the crisis. In order to dramatically reduce poverty, sustained economic growth is necessary. A simulation on the income data from the 2006 FIES (Figure 2) suggests, however, that even if the per capita incomes of all persons in the country were to increase uniformly by 2 percent annually (in real terms, i.e., adjusted for inflation), it will take more than 17 years for half of the poor to exit poverty. And at 1 percent growth in incomes, the average time for the poor to exit poverty would be 40 years. ______________ 4 The GIC is obtained by first dividing the (real per capita) income distribution into percentiles for two periods of study. Then the growth rate of the real per capita income of a particular percentile from one period to the next would be computed to generate the GIC. In particular, at the 50th percentile, the GIC provides the growth rate of the median (real) per capita income.

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Comparing income growth rates across income quintiles Actual data can also provide clues on how growth has been divided among the poor and nonpoor. For instance, Ravallion and Chen (2003) suggest an examination of the Growth Incidence Curve (GIC),4 which compares the growth rate of (real) per capita income across corresponding quintiles of the population. If the average growth rate for the poor is greater than zero, then growth is considered as absolutely propoor. It is considered to be relatively propoor if the average growth rate for the poor vis-Ă -vis other income groups is at least as large as the overall average. For the period 2000 to 2006, Figure 3 (a) shows that growth in the Philippines was absolutely propoor because the GIC lies entirely above zero for the bottom 30 in the per capita income percentile. At the same time, growth for this period may also be considered as relatively propoor because in comparison with those in the upper percentile, the bottom 30 percent benefited more in terms of the average growth rate. Unfortunately, however, this propoor growth was quite modest. As seen in Figure 3 (b), while in the urban areas, growth was relatively propoor for this period, the case was not so in the rural areas where growth, albeit being absolutely propoor, was, however, not relatively propoor. This means that in the rural areas, those at the lower and middle portions of the income distribution/percentile benefited less


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from the growth than those at the upper end of the distribution. Thus, the rural areas continue to suffer from income poverty. The above trend is validated by inequality statistics such as the gini coefficient and the generalized entropy (GE) measures.5 Table 2 shows that for the same period, 2000 to 2006, inequality went down for the country as a whole, as well as for urban areas. However, inequality went up in rural areas during the same period. And the GE measures show that a larger portion of this increase in inequality in the rural areas resulted from the upper end of the income distribution. Meanwhile, using the Datt and Ravallion (1991) decomposition (Table 3), the extent to which changes in poverty rates can be attributed to a rise in average income or to changes in income inequality may be shown. The residual of the decomposition may be viewed as the interaction

between the two growth and redistribution effects. For the period 2000 to 2006, the growth in (real) incomes across the country, coupled with changes in distribution (and interaction factors), resulted in a decrease in the head count poverty of only 0.7 percent. Had the income distribution not changed (which led to greater inequality in the rural areas), the reduction in poverty rate would have been much larger. Headcount poverty would have fallen ______________ 5

The Gini coefficient, the most common measure of inequality, takes a range of values from zero to one, with an increasing value suggesting higher inequality. A value of 0 indicates perfect equality, that is, all individuals have exactly the same income while a value of 1 indicates perfect inequality, where only one person has all of the income while the rest have none. The GE measures also describe inequality but allows for different sensitivities to income differences at various parts of the distribution: GE(0) is more sensitive to income differences at the lower tail of the distribution; GE(1) is uniformly sensitive to income differences across the distribution; and GE(2) is particularly sensitive to income differences at the top of the distribution. As is the case in the gini coefficient, a value equal to 0 for the GE measures indicates perfect equality; on the other hand, a higher value indicates more inequality.

Figure 3. Growth incidence curve based on 2000–2006 real per capita income: (a) Philippines, and (b) across urban and rural areas Philippines

Rural

20

40

60

80

Percentiles Upper/Lower 95% confidence bound Growth Incidence Curve (GIC) Growth rate in mean

100

-2 -4

0

-4

-2

-3

-3

-2

-1

-1

-1

0

0

0

1

1

1

Urban

0

20

40 60 Percentiles

80

100

0

20

40 60 Percentiles

80

100

Upper/Lower 95% confidence bound

Upper/Lower 95% confidence bound

Growth Incidence Curve (GIC)

Growth Incidence Curve (GIC)

Growth rate in mean

Growth rate in mean

(a)

(b)

Note: Authors’ calculations using data from the Family Income and Expenditure Survey (FIES).

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Table 2. Per capita income* inequality statistics in 2000 and 2006 by urban and rural areas

Urban Gini GE (0) GE (1) GE (2)

Rural

2000

2006

2000

2006

0.46462 0.36589 0.44055 1.03343

0.44132 0.32888 0.36444 0.64924

0.41577 0.28296 0.33442 0.62249

0.42425 0.29546 0.35837 0.71443

Philippines 2000 2006 0.47422 0.37833 0.45655 1.09568

0.45811 0.35052 0.39989 0.75268

* adjusted for cost of living differences and inflation using the official poverty lines as a deflator with the city of Manila in 2000 as the base area and base period, respectively.

Table 3. Growth in income and changes in inequality effects on poverty rate (from 2000 to 2006)

Urban Headcount Poverty Rate Initial (Baseline) Year Succeeding Year Change in Headcount Poverty Rate Growth Component Redistribution Component Residual

18.45 19.17 0.72 -6.91 9.23 -1.60

2000–2006 Rural National 48.41 46.23 -2.19 -19.24 15.47 1.58

33.62 32.89 -0.73 -11.43 11.42 -0.71

Note: Authors’ calculations using Family Income Expenditure Survey (FIES) per capita income data and official poverty lines.

from 33.6 percent to 22.6 percent, with poverty rates going down from 18.5 percent to 11.5 percent in the urban areas and from 48.4 percent to 29.2 percent in the rural areas. Vulnerability assessment While descriptions of poverty and inequality conditions, as discussed above, are useful as inputs to policy and program formulation, it is equally useful and important to examine future welfare of households, which is often dependent on the risks that households face. Households often experience shocks, either as part of their specific conditions or of the areas where they reside. Such conditions often result in volatility, PN 2010-03

Policy Notes

particularly downward movements, in their incomes. Without sufficient assets or mechanisms to help households mitigate the impact of income shocks, there may be irreversible losses to their welfare that may possibly lead them into a state of perpetual poverty. Some households may even engage in risk-mitigating strategies to reduce the chance of substantial income losses, but these strategies typically yield low average returns, further locking them into poverty. Government would therefore be well advised to understand vulnerability and to design interventions meant at reducing vulnerability. Albert et al. (2008) adapted a methodology attributable to Chaudhuri (2000), with the use of per capita income data in order to measure vulnerability in the Philippines. Vulnerability estimation was carried out on the FIES for the years 2000, 2003, and 2006, using the following household characteristics as variables: z number of dependents (aged less than 15); z number of adults (aged 15 and older); z sex of the household head; z educational attainment of the household head; z age of the household head; z major sector of employment of head (together with whether or not the employment is self-employment); z ownership of land; z use of electricity; z whether or not the household can be classified as agricultural; and z region where household resides.


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Using the resulting vulnerability estimates, households are classified as vulnerable if the probability of their becoming poor is greater than the national poverty rate and as nonvulnerable if the probability is less. The vulnerable are further categorized into highly vulnerable if the probability of their being poor is greater than 50 percent and relatively vulnerable otherwise. The incidence of vulnerability across the population for the years 2000, 2003, and 2006 by poverty status is shown in Table 4. The drop in poverty rates from 2000 to 2003 was accompanied by a drop in the proportion of people belonging to vulnerable households. Corollarily, the rise in headcount poverty rates from 2003 to 2006 was accompanied by a rise in the percentage of the population belonging to households vulnerable to income poverty. It must be noted that the returns to education, especially basic education, on vulnerable households are rather high. This can be seen in Table 5 which shows that the proportion of households that are not vulnerable rises considerably as the educational attainments of the household heads improve. Poverty and vulnerability: similar stories Vulnerability to income poverty, just like poverty itself, is more of a rural phenomenon. Likewise, there are also significant disparities in vulnerability levels, just like poverty rates, across the regions. For instance, for the period 2000 to 2006, between one to three in twenty households residing in Metro Manila

were highly vulnerable; in the Autonomous Region of Muslim Mindanao (ARMM), between three to four out of five households are highly vulnerable. These parallel the proportions in terms of poverty. Breaking down households by employment of their heads in the major sectors, the most vulnerable households are also those with heads who work in agriculture while the least vulnerable are those with heads employed in the services sector. But this is not to equate poverty with vulnerability. In 2000 and 2003,

Table 4. Percentage of the population belonging to highly vulnerable, relatively vulnerable, and not vulnerable households: 2000, 2003, and 2006

Vulnerability Level

2000

2003

2006

Highly vulnerable Relatively vulnerable Not vulnerable

41.76 28.64 29.60

36.21 32.36 31.44

50.70 30.31 18.99

Table 5. Incidence of household vulnerability (in percent) by highest educational attainment of the head of the household (2000, 2003, and 2006)

Educational Attainment of Household Head

Vulnerability Status

2000

2003

2006

None

Highly vulnerable Relatively vulnerable Not vulnerable Highly vulnerable Relatively vulnerable Not vulnerable Highly vulnerable Relatively vulnerable Not vulnerable Highly vulnerable Relatively vulnerable Not vulnerable

62.79 26.76 10.44 49.4 31.89 18.7 25.05 37.68 37.27 2.43 11.35 86.22

51.42 33.58 15 46.11 35.78 18.11 16.92 39.69 43.39 1.42 12.77 85.81

74.45 22.26 3.3 62.16 30.27 7.57 35.72 46.03 18.25 3.26 20.91 75.83

Some elementary to elementary graduate Some high school to high school graduate Some college and beyond

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for instance, about 19 out of 20 poor persons belonged to vulnerable households and then about 60 percent of the nonpoor also belonged to vulnerable households. Meanwhile, the average family size of nonvulnerable households is much smaller than of vulnerable households, especially the highly vulnerable ones. As Figure 4 illustrates, the disparity is largely on account of the number of young members in the household. Looking at the implication: need to reorient propoor policy and program thrusts The assessment of vulnerability to income poverty in the country as well as the profile of poverty and inequality conditions summarized in this Policy Note show that despite the many propoor policies, programs, and projects Figure 4. Average number of young members and average number of adult members by highly vulnerable, relatively vulnerable, and not vulnerable households: 2000, 2003, and 2006

instituted, gains in the fight against poverty have still been modest at best. Such modest gains can be attributed to improper targeting mechanisms for propoor projects, and the absence of monitoring and evaluation systems for program implementation. In contrast to what had clearly been a downward trend in the poverty rate during the 1990s, it can be observed that the percentage of income poor persons in 2006 remained at the level of what it was in 2000. Neither have any clear gains been observed with regard to vulnerability to poverty. While there was a slight to moderate decrease in the proportion of the Philippine population from 2000 to 2003 who were likely to become poor in the near future, by 2006, the direction of this change had shifted, with the fraction of the population that was in danger of being poor becoming larger than it was at the start of the decade. In terms of inequality, while the statistics across the country have decreased slightly, there is evidence that this was driven by lowered inequalities in the urban areas. In the rural areas, inequalities have risen. This is indeed a cause for concern since efforts to considerably improve poverty conditions in the rural areas would likely be hampered by such rising inequalities. Past poverty assessments have suggested that households with heads engaged in agriculture are the poorer segments of the population. Because of this, government has implemented many propoor public actions in the

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agricultural sector such as agrarian reform, agricultural infrastructure, provision of inputs and subsidies to farmers, in order to spur agricultural productivity and rural incomes. Yet, little change in this sector has been evident, especially in the rather static profile of poverty and vulnerability in rural areas. This suggests the need to go beyond such public actions on agriculture. Government may want to look into the provision of incentives and opportunities for farmers to become entrepreneurial and to develop nonfarm livelihoods, especially during slack periods for farming. Any propoor projects should have proposals and conceptual frameworks that provide clearly identified links between activities or inputs, outputs, and projected outcomes. Such projects must also have proper targeting mechanisms, as well as an integrated monitoring and evaluation (M&E) plan for assessing outputs and progress toward outcomes. Despite the clear linkages between poverty (and vulnerability) with health, education, and family size, few public interventions have been formulated that take these linkages into account. Poverty and vulnerability, for instance, are currently divorced from population management even if average family sizes among poor and vulnerable households are shown to be higher than those of their nonpoor and nonvulnerable counterparts. The lack of clear attention to such a linkage has not only led to minimal

poverty reduction but also to modest economic growth. Although the returns to education are clear, the opportunity costs for children of poor and vulnerable families to stay in (or return to) school appear to be too high, especially during a crisis period (Tabunda and Albert 2002). When poor and vulnerable households opt not to send their children to school during crisis periods, such mitigating actions result in long-term costs. Future incomeearning capacities of household members and, ultimately, the well-being of the household, are put at risk. The Department of Education (DepEd) has established a number of programs meant to improve participation in basic education, especially among the poor and vulnerable. But while the beneficiaries of these programs are more likely the youth from poor and vulnerable households, there are questions being raised, given the national trends in education statistics (Albert and Maligalig 2007), on how much impact such programs are having on improving the human resource investments made by the poor. Many of the propoor programs implemented thus far have had problems with targeting and implementation, as well as in their M&E processes. This may be a reason why poverty reduction in the country has been modest thus far. A rather promising public intervention is the Programang Pantawid Pampilyang Pilipino (4Ps) which was conceived to increase human capital investments and improve access of pre- and postnatal care among the poor. Beneficiaries PN 2010-03

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were carefully chosen by way of an evidencebased proxy means test. Intervention of the 4Ps has to be accelerated, but with care to ensure that monitoring systems are in place, financial costs to the program are studied carefully, and exit strategies for program beneficiaries are developed (Llanto 2008). Conclusion If the country is truly bent on hastening its efforts in reducing poverty, it is important to have a poverty reduction roadmap based on statistical information regarding poverty, vulnerability, and inequality. Propoor public interventions that do not seem to have impact have to be reoriented, especially those with implementation and targeting issues. Programs should have strong M&E systems to regularly assess the impact of the program. Policies and programs geared toward preventing the transmission of poverty from one generation to the next, especially by way of human resource investments, population management, and improving nonfarm income in rural areas must be essential components of any sustainable reduction strategy of poverty and vulnerability. 

For further information, please contact The Research Information Staff Philippine Institute for Development Studies NEDA sa Makati Building, 106 Amorsolo Street, Legaspi Village, 1229 Makati City Telephone Nos: (63-2) 894-2584 and 893-5705 Fax Nos: (63-2) 893-9589 and 816-1091 E-mail: jalbert@mail.pids.gov.ph; jliguton@mail.pids.gov.ph The Policy Notes series is available online at http://www.pids.gov.ph. Reentered as second class mail at the Business Mail Service Office under Permit No. PS570-04 NCR. Valid until December 31, 2010.

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References Albert, J.R. 2008. Issues on counting the poor. PIDS Policy Notes No. 2008-11. Makati City: Philippine Institute for Development Studies. ______ and M. Collado. 2004. Profile and determinants of poverty in the Philippines. Proceedings of the Ninth National Convention on Statistics, National Statistical Coordination Board. Albert, J.R., L.V. Elloso, and A.P. Ramos. 2008. Toward measuring household vulnerability to income poverty in the Philippines. Philippine Journal of Development XXXV, No. 1. Chaudhuri, S. 2000. Empirical methods for assessing household vulnerability to poverty. Preliminary Draft Technical Report, Economics Department, Columbia University. Llanto, G. 2008. Make 'deliberate' haste in rolling out the 4Ps. PIDS Policy Notes No. 2008-09. Makati City: Philippine Institute for Development Studies. Maligalig, D. and J.R. Albert. 2008. Measures for assessing basic education in the Philippines. PIDS Discussion Paper No. 2008-16. Makati City: Philippine Institute for Development Studies. Reyes, C. 2004. An initial verdict on our fight against poverty. PIDS Discussion Paper No. 2004-48. Makati City: Philippine Institute for Development Studies. Tabunda, A.M. and J.R. Albert. 2002. Philippine poverty in the wake of the Asian financial crisis and El Ni単o. In Impact of the East Asian financial crisis revisited, edited by Shahid Khandker. The World Bank Institute and the Philippine Institute for Development Studies [online]. http://www3.pids.gov.ph/ris/books/ pidbs02-impact.pdf [accessed January 2005].


Examining Recent Trends in Poverty, Inequality, and Vulnerability