Growth, Inequality and Poverty in Madagascar, Africa Region Working Paper Series No. 111 April 2008

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1 Growth, Inequality and Poverty in Madagascar, Africa Region Working Paper Series No. 111 April 2008 Abstract. T he paper examines changes in poverty and inequality in Madagascar between the years 2001 and During this period Madagascar s economic progress has been notable. Yet the record for poverty and living standards is mixed. Inequality has declined considerably, the depth of poverty has fallen by almost 25 percent, and income grew faster for the poor than the average. But poverty remains pervasive in Madagascar, with more than two thirds of the population below the poverty line. And though the incidence of poverty has barely changed, the number of the poor has increased by some two million individuals. Large disparities persist between urban and rural areas, as well as across provinces. Regression analysis shows that these disparities persist even after controlling for a wide range of socio-economic and demographic household characteristics. By matching household-level survey data from the Enquête Périodique auprès des Ménages to community-level census data we identify three factors that largely explain the provincial variation in poverty rates: (i) infrastructure, (ii) land tenure and cropping patterns, and (iii) climate shocks. As for the future, simulations for benchmark years 2007 and 2010 project incremental reductions in poverty rates on the order of percent per yearas estimates of earnings functions, provide supporting evidence of these barriers. JEL Codes: I31, I32, O1, O55. Key Words: Madagascar; poverty; inequality; growth incidence analysis. Authors Affiliation and Sponsorship Nicola Amendola University of Rome Tor Vergata Giovanni Vecchi University of Rome Tor Vergata The Africa Region Working Paper Series expedites dissemination of applied research and policy studies with potential for improving economic performance and social conditions in Sub-Saharan Africa. The Series publishes papers at preliminary stages to stimulate timely discussion within the Region and among client countries, donors, and the policy research community. The editorial board for the Series consists of representatives from professional families appointed by the Region s Sector Directors. For additional information, please contact Paula White, managing editor of the series, (81131), pwhite2@worldbank.org or visit the Web site: The findings, interpretations, and conclusions expressed in this paper are entirely those of the author(s), they do not necessarily represent the views of the World Bank Group, its Executive Directors, or the countries they represent and should not be attributed to them.

2 Growth, Inequality and Poverty in Madagascar, Nicola Amendola University of Rome Tor Vergata Giovanni Vecchi (*) University of Rome Tor Vergata April 2008 (*) This paper is the product of a joint AFTH3 and AFTP1 collaboration. Corresponding author: giovanni.vecchi@uniroma2.it. We would like to thank Benu Bidani, Stefano Paternostro, Ken Simler and David Stifel whose comments, advice and support have been invaluable. We are grateful to Tiaray Razafimanantena for useful comments, and to Elena Celada and Laza Razafiarison for help at various stages of the project. The usual disclaimer applies. Funding from the Japan PHRD preparation grant for Madagascar PRSC V is gratefully acknowledged.

3 Contents 1 Introduction Economic Growth Household Surveys in Madagascar Poverty and Inequality Dynamics The Incidence of Poverty The Depth and Severity of Poverty Inequality Changes in the Poverty Profile Growth, Inequality and Poverty Growth Incidence Analysis Growth Elasticities of Poverty Growth-Inequality Decomposition Sectoral Decomposition of Poverty A Model of Household Consumption Poverty and Growth Projections Summary and Final Remarks List of References Appendix 1 Regression Analysis of Household Consumption Appendix 2 Sensitivity Analysis of Poverty Estimates to the Choice of Different Deflators.. 33 Appendix 3 Sectoral Value Added And Population Growth Rates, List of Figures Figure 1 Real GDP (billions of Ariary at 2000 constant prices) and GDP per capita,... 6 Figure 2 Sectoral growth rates, Figure 3 GDP shares (1984 prices), Figure 4 First-order stochastic dominance test, Madagascar Figure 5 First-order stochastic dominance by urban/rural area, Figure 6 Second-order stochastic dominance test, Madagascar, Figure 7 Second-order stochastic dominance by urban/rural area Figure 8 Lorenz Curves for Madagascar, 2001 and Figure 9 Changes in the poverty gap index, Figure 10 The growth incidence curve for Madagascar, Figure 11 Growth incidence curves for urban and rural areas in Madagascar, List of Tables 3

4 Table 1 EPM 2001 versus EPM 2005: A Comparison... 8 Table 2 Poverty and Inequality Trends, Madagascar (%) Table 3 Inequality and mean consumption by province, 2001 and Table 4 Inequality Decompositions, Madagascar 2001 and Table 5 Decompositions of the changes in aggregate inequality, 2001 and Table 6 Poverty estimates by province in 2001 and Table 7 Comparison of poverty profiles, 2001 and Table 8 Growth rates (%) among the poor, Madagascar Table 9 Growth elasticities of poverty for Madagascar, 2001 and Table 10 Growth-inequality decompositions, Madagascar Table 11 Sectoral decomposition of poverty, Table 12 Impact of growth on poverty in Madagascar Table 13 Regression Estimates of Consumption Models for Rural Households, Table 14 Regression Estimates of Consumption Models for Urban Households, Appendix Table 15 Provincial Deflators, Madagascar 2001 and Appendix Table 16 Regional vs. provincial deflators: Sensitivity of poverty estimates

5 1 Introduction Madagascar is one of the world s poorest countries today, ranked 143 out of 177 according to UNDP (2006). Yet, the situation has not always been so dire. A number of studies examining the economic performance of Madagascar in the early 1960s, soon after the country had gained full independence, show that the Malagasy Republic was once among the richest countries in Africa see World Bank (2007). Several decades later, living standards have plummeted to subsistence level for most of Malagasy population, and other social indicators are far below the Millennium Development Goal targets. An account of Madagascar s more recent economic performance is provided by a study conducted jointly by the Malagasy National Statistical Institute (INSTAT), Cornell University and the World Bank see World Bank (2002). The main report contains a comprehensive assessment of the evolution of poverty and other welfare indicators during the 1990s. Among the main findings of the report is the fact that national poverty rates remained relatively steady, while significant swings both in urban and rural areas and across provinces were observed. This point is further highlighted in the conclusion of the report: further work should be devoted to understanding what causes geographic variations in poverty; among others, differences in land quality, infrastructure, and climate should be explored as potential differentiating factors. (p. 35). This paper attempts to perform this task, as part of a broader inquiry into the trends of living standards, poverty and inequality in Madagascar during the first half of the 2000s. The paper uses household survey data from the Enquête Périodique auprès des Ménages (EPM) fielded in 2001 and 2005, with the aims to (i) update the poverty and inequality profiles, (ii) to identify the changes in the distribution of income and in absolute poverty, and (iii) to project poverty rates to the present day. Our findings show that while the trend in the headcount ratio is statistically fragile, and fails to identify the trend in poverty, the use of the poverty gap index leads to clear-cut results. Let us note incidentally, that the task of identifying the poverty trend is further complicated by the occurrence of a severe political crisis which started in December 2001 and resulted in a dramatic recession in the year 2002 (GDP per capita dropped by 15 percent). We argue, that the recession s negative impact on the living standards potentially affects the interpretation of many of the empirical results. The second aim of this paper is to investigate the determinants of poverty. The availability of census data from the 2001 Recensement des Communes has allowed us to estimate a household consumption model in which we control not only for demographic and socio-economic household characteristics, but also for features of the communities in which households live. In particular, the paper focuses on the role of infrastructure, structure of the agricultural sector, and climatic events. The third and last aim of the paper is to update poverty rates. We use population and sectoral GDP growth rate projections to forecast poverty in the year We also venture into longerterm poverty forecasts for the year The paper is organized as follows. Section 2 provides a description of Madagascar s recent performance in terms of economic growth. Section 3 discusses the comparability of EPM 2001 and 2005 data. Section 4 and 5 contain the bulk of the descriptive statistics concerning poverty and inequality changes; while section 4 focuses on the main trends, section 5 concentrates on changes in the structure of poverty and inequality. In Section 6 we investigate the mechanics of the changes in poverty by carrying out standard decomposition techniques, growth incidence analysis, and estimating poverty elasticities to growth. In section 7 we match survey data with census data and use regression analysis to explore the determinants of poverty. Section 8 projects poverty rates in the years 2007 and Section 9 concludes.

6 2 Economic Growth The trend in real gross per capita domestic product (GDP) is often a useful starting point element for the analysis of poverty dynamics, inequality and other social indicators. This section provides an overview of the recent economic development of the Malagasy GDP, based on national account data. 1 Figure 1 plots the time series of the real gross domestic product (GDP) from 1980 to In this period, total GDP increased, on average, by 1.4 percent per year, while the population grew 2.9 percent per year. As a result, per capita GDP decreased, on average, by 1.5 percent per year. The positive swing in the GDP per capita first occurred in the mid 1990s, but was abruptly interrupted by the political crisis of The effects of the crisis on GDP per capita are clearly visible in Figure 1: during 2002 the economy fell into a deep recession with per capita GDP shrinking by 15 percent. After the crisis the economy rebounded quickly, with GDP growth averaging 5 percent per year thereafter. By 2005 the GDP per capita had (almost) returned to its 2001 level. Figure 1 Real GDP (billions of Ariary at 2000 constant prices) and GDP per capita, GDP (left y-scale) GDP per head (right y-scale) Source: World Bank database year Figure 2 shows the pattern of sectoral GDP growth rates between 1996 and During this decade economic growth was driven by the secondary and the tertiary sectors. The graph shows that the crisis heavily hit those sectors, while the impact on the agricultural sector was only modest (minus 1.3 percent). The recovery after the crisis was fast and sustained, largely based on the performance of the secondary and tertiary sectors. It is worth noting however, that the growth rates in the secondary sector have declined in the recent years. 1 For more see IMF (2007) and OECD (2007). 6

7 Figure 2 Sectoral growth rates, Primary Secondary Tertiary 10 Annual growth rate (%) Source: World Bank data. Figure 3 shows the trends of sectoral GDP shares between the years While the evolution of the share of the secondary sector is stable over the whole period, the primary and tertiary sectors move along different trajectories. Between the mid 1980s and early 1990s, the share of the primary sector fluctuates mildly around 35 percent. Then, starting in 1996, the primary share decreases while the share of services begins to increase. This pattern, interrupted only and temporary reversed by the effects of the 2001 political crisis, resumes after Overall, Figure 3 depicts a process of a slowly changing economic structure. Only in recent years, signs that are typically associated with modern economic growth, such as a significant decline of the share of agriculture and a rise in the share of secondary and tertiary sectors, have become visible. Figure 3 GDP shares (1984 prices), Share of GDP (%) year share primary sect share tertiary sect share secondary sect Source: World Bank database. 7

8 From the evidence examined in this section, two main conclusions emerge. First, the impact of economic growth on living standards is likely to be greatly reduced by the high rates of population growth. While the total GDP growth rate between has been relatively high (6.5 percent), it translated only into a 3.5 percent increase in per capita terms. Second, the sectoral composition of growth may prove to be ineffective in combating poverty. According to Stifel (2007), 80.1 percent of the population live in households headed by agricultural workers; yet, productivity growth in agriculture during was low and slowly improving. The highest gains accrued to workers employed in services, accounting for 17.4 percent of the labour force. 3 Household Surveys in Madagascar The data used for this paper are drawn from the Enquête Périodique auprès des Ménages (EPM), a nationally representative household-level survey carried out by the Direction des Statistiques des Menages (DSM) of the national statistical institute (INSTAT). The EPM started off in 1993 with the aim to assess the living standards of the population. Since its debut, EPM was repeated in 1997, 1999, 2001, 2002, 2004 and Given our focus on poverty comparisons over time, it is important to discuss the comparability of these surveys. For the early surveys of 1993 to 1999, the World Bank (2002) provides an exhaustive analysis of the methodological problems arising from different survey designs and different choices underlying the welfare indicator. Unfortunately, we lack a similar (systematic) assessment for the surveys subsequent to the World Bank report. Instead, we have to rely on piecemeal information available in a variety of documents, as well as on a number of personal communications with INSTAT staff. This section focuses on the EPM for the years 2001 and Table 1 compares the 2001 and 2005 EPM survey designs, showing that no major differences exist between the two surveys. Sample design Table 1 EPM 2001 versus EPM 2005: A Comparison Two-stage stratified: 12 strata, (1 st ) 303 ZD (clusters), (2 nd ) 5,080 household (16 per urban cluster, and 18 per rural cluster). Two-stage stratified: 44 strata, (1 st ) 561 ZD (clusters), (2 nd ) 11,781 household (21 per cluster). Rounds One: Oct to Nov One: Sept 05 to Nov 10, 2005 Actual sample size Representativeness Time reference, recall period Consumption 23,170 individuals corresponding to 5,080 households (3,040 urban, 2,040 rural) provincial level (6 faritany), and urban/rural within each province Expenditures on food and beverages: last week, year. Non-food commodities: last month, year. Expenditures on both food and non-food include in-kind consumption items. 54,966 individuals corresponding to 11,781 households (5,859 urban, 5,922 rural) regional level (22 faritra), and urban/rural within each region Idem Idem Of special concern to us is the consistency of the welfare indicator s definition. This is crucial to guaranteeing the consistency in the sense of Ravallion and Bidani (1994) of poverty profiles in different periods. The 2001 and 2005 consumption aggregates were constructed using almost 2 Data from the 2002 survey are reported to be lacking in reliability by most analysts we consulted with, but the matter is not discussed in any publication we are aware of. 8

9 identical methodologies. This is the result of a strategy pursued in 2005, when inter-temporal poverty comparisons were wisely included in the agenda. 3 Three more issues are worth mentioning. First, there is a discrepancy between the EPM-based estimates of the total population estimates and similar estimates by the IMF. Even though the discrepancy does not affect the estimates of the class of poverty measures used in the paper, it matters when comparing the absolute numbers of the poor. Second, while the 2005 EPM allowed the use of regional deflators, the 2001 EPM was based on provincial deflators. The (potential) nuisance that arises from the use of different deflators plays a negligible role in the analysis pursued in the rest of the paper. Appendix 2 illustrates the robustness of the poverty profile to the choice of different deflators. Third, the procedure used to update the poverty line between 2001 and 2005 fails to account for substitution effects that may occur in response to changes in relative prices of basic goods (particularly, in response to the dramatic changes associated with the dramatic 2002 crisis). Further research is needed to address this issue. 4 The evidence presented in this section leads to the conclusion that there are neither substantial differences in the designs underlying the two EPM surveys, nor are there inconsistencies in the construction of the consumption aggregate. 4 Poverty and Inequality Dynamics In this section we describe the trends in poverty and inequality measures between the years 2001 and Some caution may be appropriate when interpreting the results in light of the political crisis of The 2001 poverty profile describes the living standards immediately before the crisis; during 2002, the year of the crisis, poverty increased substantially, from 69.7 percent to 75 percent according to INSTAT (2006). It follows that the poverty profile in 2005, far from being the result of a relatively flat pattern of growth, is the outcome of a buoyant recovery process towards the pre-crisis level. Poverty comparisons based on 2001 (as opposed to 2002) are therefore likely to under-estimate the role of economic growth in affecting the dynamics of poverty. While all this is important in interpreting the estimates in Table 2, we will revisit this issue in section 6.3. The main findings summarized in Table 2 are discussed in the remainder of this section. We first highlight the main trends in a selection of poverty and inequality indicators. Next, we identify a number of facts that constitute the explicandum for the rest of the paper. 3 In contrast, the comparability of household consumption aggregates for the 1993, 1997 and 1999 EPM household surveys is not straightforward. See World Bank (2002), Appendix A1. 4 See Ravallion and Lokshin (2006) and Arndt and Simler (2005). 9

10 Table 2 Poverty and Inequality Trends, Madagascar (%) Urban Rural National Urban Rural National Headcount % c. i. [39.9, 48.6] [72.6, 82.0] [65.9, 73.5] [48.0, 55.9] [71.2, 75.7] [66.7, 70.8] Poverty Gap % c. i. [15.7, 21.0] [36.1, 43.5] [32.0, 38.0] [17.4, 21.2] [27.1, 30.6] [25.3, 28.2] Poverty Gap Squared % c. i. [8.1, 11.7] [21.3, 27.1] [18.6, 23.2] [8.3, 10.6] [13.3, 15.7] [12.4, 14.4] Gini Index % c. i. [.419,.459] [.402,.488] [.445,.492] [.382,.429] [.313,.357] [.347,.383] Theil Index % c. i. [.305,.376] [.297,.441] [.362,.441] [.266,.362] [.179,.289] [.232,.314] Background Statistics Total population 3,588 12,079 15,667 4,146 14,701 18,847 Population share Mean PCE 475, , , , , ,644 Mean PCE among poor 178, , , , , ,376 Notes: confidence intervals are based on linearized standard errors. The poverty line used for 2001 is 197,720 Ariary/head/year (988,600 FMG/head/year); for 2005 the poverty line is 305,300 Ariary/head/year. The latter value is obtained by updating the poverty line for 2001 (duly converted into Ariary) using the 2005 Consumer Price Index. Source: Authors calculation based on EPM data. 4.1 The Incidence of Poverty At the national level, the incidence of poverty has not changed. While point estimates in Table 2 show that the headcount ratio decreased from 69.7 percent in 2001 to 68.7 percent in 2005, the change is not statistically significant. In contrast, the number of poor people has increased by some 2 million units during the same period. As recently noted by Chakravarti, Kanbur and Mukherjee (2006), while the economist s instinct is probably to conclude that poverty in Madagascar has decreased this goes against the instinct of those who work directly with the poor, for whom the absolute numbers notion makes more sense as they cope with more poor on the streets or in soup kitchens (p. 471). In short, the question of whether poverty in Madagascar has increased or decreased is a non trivial one. The result of a first-order stochastic dominance (FOD) analysis adds to the difficulty of identifying the trend in poverty incidence. We carried out the FOD test, where the null hypothesis is that the incidence of poverty in 2005 is lower than in 2001, regardless of the poverty line chosen. Figure 4 shows that this hypothesis is unambiguously rejected by the data. Not only do the cumulative density functions intersect, but they do so almost exactly at the poverty line, which suggests that the ranking of poverty would change if the poverty line changed slightly. 10

11 Figure 4 First-order stochastic dominance test, Madagascar per capita expenditure (2005 ariary) Source: Authors estimates on EPM data. Poverty changes were not uniformly distributed across the national territory. Table 1 shows an asymmetric pattern of the incidence of poverty between rural and urban areas. This is a major issue in a country where poverty in rural areas accounts for 83 percent of total poverty. During period, the headcount ratio increased from 44.3 percent to 52.0 percent in urban area, while in rural areas it decreased from 77.3 percent to Once again, the analysis of the numbers of individuals classified as poor leads to opposite conclusions: approximately half a million people were added to the stock of the poor in urban areas, while the number of poor people in rural areas increased by 1.5 million. The population growth in rural areas during was about 22 percent, compared to 15.5 percent in urban areas: whether this pattern is due to differences in fertility rates or, instead, to urban-to-rural migratory flows requires further investigation. Figure 5 shows the results of a FOD test carried out separately for rural and urban areas. The cumulative density functions cross in both areas, suggesting that the ordering of poverty as measured by the headcount ratio is not robust to the choice of the poverty line. 5 Note that while the change in the headcount ratio in urban areas is statistically significant, this does not hold true for the change in rural areas. 11

12 Figure 5 First-order stochastic dominance by urban/rural area, URBAN.8 empirical cdf RURAL.8 empirical cdf Source: Authors estimates on EPM data per capita expenditure (2005 ariary) When poverty is so pervasive throughout the country, it becomes desirable to consider measures other than the headcount poverty. The poverty gap index (PG) and poverty gap squared index (PG2) are obvious choices, and their utilization is investigated in the following section. 4.2 The Depth and Severity of Poverty According to the estimates in Table 2, in the period between the poverty gap index has decreased by one fourth, from 34.9 percent to 26.8 percent, suggesting that the living standards of the poor have improved significantly during this period. Figure 6 shows the result of a second-order stochastic dominance (SOD) test. Here, the hypothesis tested is that poverty in 2005, as measured by the PG index, is lower than in We find that the poverty deficit curve for 2005 is always below the poverty deficit curve for 2001, that is the average distance from the poverty line in 2005 is lower than in 2001, regardless the choice of the poverty line. The reduction in the depth of poverty is substantial and not affected by the choice of the poverty line. 12

13 Figure 6 Second-order stochastic dominance test, Madagascar, Area under the poverty incidence curve per capita expenditure (2005 ariary) Source: Authors estimation on EPM 2001 and 2005 data. The pro-rural bias in the reduction of poverty incidence also characterizes the decline in the depth of poverty. The poverty gap decreased in rural areas (-27.4 percent), while it increased though only slightly and not significantly in urban areas (+5.5 percent). Figure 7 shows that this result is robust to the choice of poverty line. Figure 7 Second-order stochastic dominance by urban/rural area URBAN Area under the poverty incidence curve RURAL Area under the poverty incidence curve Source: Authors estimation on EPM 2001 and 2005 data per capita expenditure (2005 ariary) The poverty gap squared (PG2) decreased even more dramatically than the PG, from 20.9 percent in 2001 to 13.4 in 2005 (minus 35.9 percent). This indicates that the poorest among the 13

14 poor benefited even more than the average poor during the period 2001 and 2005, and this improvement was, once again, more pronounced in rural areas. 4.3 Inequality The evidence in Table 2 suggests that between 2001 and 2005 a substantial income redistribution occurred in Madagascar. In this subsection, we investigate the trends in inequality by focusing on its geographical dynamics. Table 3 shows the estimates of inequality at the provincial level, separately by urban and rural areas. This represents the maximum level of disaggregation allowed by the 2001 survey. Three popular inequality indices are considered, the Gini index, the Theil index and the Mean Logarithmic Deviation. According to Table 3 inequality decreased nationally. This finding is robust to the choice of the inequality index. Similarly, the provincial pattern of inequality indicates that inequality decreased throughout the country (with the exceptions of Toamasina, Antsiranana and Mahajanga, when using the Theil index). The provincial trends show, however, a significant dispersion in inequality reduction rates, with Antananarivo, Fianarantsoa and Toliara standing out as the provinces in which inequality decreased at the fastest pace. Table 3 Inequality and mean consumption by province, 2001 and 2005 Province % Change GINI urban rural total urban rural total urban rural total Antananarivo Fianarantsoa Toamasina Mahajanga Toliara Antsiranana Madagascar THEIL urban rural total urban rural total urban rural total Antananarivo Fianarantsoa Toamasina Mahajanga Toliara Antsiranana Madagascar MEAN LOGARITHMIC DEVIATION urban rural total urban rural total urban rural total Antananarivo Fianarantsoa Toamasina Mahajanga Toliara Antsiranana Madagascar MEAN CONSUMPTION urban rural total urban rural total urban rural total Antananarivo Fianarantsoa Toamasina Mahajanga Toliara Antsiranana Madagascar Note: bootstrapped standard errors available from the Authors upon request. Source: Authors calculation based on EPM data. 14

15 Figure 8 illustrates the Lorenz curves for 2001 and 2005 at the national level (top-left graph) and by urban and rural areas (bottom graphs). In all graphs, the 2005 curve unambiguously dominates the 2001 curve, which makes the general reduction in inequality measures a well grounded finding. Inequality reduction is more pronounced in rural areas than in urban areas. Figure 8 Lorenz Curves for Madagascar, 2001 and Per capita expenditure share of poorest p100% Legend: Cumulative population share, p 0 1 URBAN 1 1 RURAL 1 Per capita expenditure share of poorest p100% Per capita expenditure share of poorest p100% Cumulative population share, p Cumulative population share, p 0 Source: Authors calculation on EPM 2001 and 2005 data. In order to investigate the structure of inequality and its dynamics, we have decomposed inequality levels and changes using the methods described in Shorrocks (1980) and Mookherjee and Shorrocks (1982). Table 4 shows the results of the decomposition of the levels of inequality by urban and rural groups (top panel), and by province (bottom panel). The point of this decomposition is to separate total inequality (I TOT ) in the distribution into two components, often referred to as the within- and the between-components. The within component can be described as the level of inequality (I W ) that would be observed if there were no differences in mean levels of expenditures across population subgroups. Likewise, the between component (I B ) is the level of inequality that would be observed in the absence of differences in expenditures within groups. Shorrocks (1980) derived a class of inequality indices (the so-called Generalized Entropy Indices) that are additively decomposable, i.e. such that I W + I B = I TOT. With regard to the decomposition by urban and rural area, Table 4 shows that in 2001 the within component explains 89 percent of total inequality, which increases to 93 percent for the year Similarly, the decomposition of inequality by province, shows that the largest contribution to total inequality is due to the within component (83 percent in 2001, 95 percent in 2005). This pattern suggests that inequality reduction policies in Madagascar should focus on reducing inequality within population sub-groups (provinces and urban/rural areas), rather than on narrowing the gap in mean expenditures between the groups. 15

16 MLD (%) Theil (%) MLD (%) Theil (%) Table 4 Inequality Decompositions, Madagascar 2001 and Index Within Between Index Within Between by urban/rural area (100) (100) (100) (100) Source: Authors calculation on EPM data. (89) (89) (83) (83) (11) (11) by province (17) (17) (100) (100) (100) (100) (93) (95) (95) (96) (7) (5) (5) (4) With regard to inequality changes, Table 5 shows the results of a popular decomposition technique, first introduced by Mookherjee and Shorrocks (1982). As with the static decomposition, we start by partitioning the population into subgroups, say provinces. Next, we apply the decomposition to the mean logarithmic deviation, thereby following common practice. The formula used, here omitted, decompose the total change in inequality into three components: (A) pure inequality effect arising from changes in inequality within groups, (B) population-share effect (or allocational effect) arising from changes in the number of people within different groups, and (C) income effect arising from changes in relative expenditures between groups. Following Jenkins (1995), we decomposed the percentage change of the mean logarithmic variation. Table 5 shows the results. For both sub-group partitions, the changes in within-group-inequality (columns A) accounts for most of the changes in aggregate inequality. The population-share effect (column B) is negligible. Changes in mean expenditures (column C) across provinces and between urban and rural areas are significantly equalizing. Overall, Table 6 suggests that what dominated inequality changes between 2001 and 2005 was the contribution from changes in inequality within provinces and within urban/rural areas. sub-group partition Table 5 Decompositions of the changes in aggregate inequality, 2001 and 2005 change in MLD (%) % change in MLD accounted for by changes in population shares within-group inequalities sub-group mean incomes (A) (B) (C) urban/rural province Source: Authors calculation on EPM data. The main results from the analysis carried out in this section can be summarized as follows. First, inequality at the national level unambiguously decreased in the period between 2001 and Second, even if inequality decreased in both rural and urban areas, rural areas experienced the largest reduction in inequality. Third, a provincial breakdown of inequality reveals large and persistent differences across the national territory, with the provinces of Antananarivo, Fianarantsoa and Toliara faring best. Fourth, the decomposition analysis shows that the observed decline in inequality is largely driven by the decline in inequality within provinces (urban/rural areas) rather than by the convergence of average consumption incomes between provinces (urban/rural areas). 16

17 5 Changes in the Poverty Profile In this section we provide the poverty profiles for 2001 and 2005 based on the main geographic, demographic and socio-economic characteristics of the households. The purpose is to examine the changes in the poverty profiles in order to provide a more in-depth analysis of the structure of poverty changes during the observed period. Table 6 provides the geographic profile based on the three Foster-Greer-Thorbecke indices of poverty. Focussing on the headcount ratio, estimated poverty changes between 2001 and 2005 are found to vary widely across provinces. The incidence of poverty increased in Antananarivo (+18.7 percent), it decreased in Toamasina, Antsiranana and Fianarantsoa ( 12.6 percent, 7.4 and 6.7 percent, respectively), while remained relatively stable in the other provinces. The provincial variation in poverty changes is less pronounced when we consider the depth of poverty. According to Table 6, the poverty gap index decreased in all provinces during the observed period, but at different rates. The relative performance in terms of poverty reduction can be appreciated by looking at the map in Figure 9. The map uses green colours to identify the best performing provinces (the darker the better) and turns into reds (denoting slowest provinces) passing through the orange. The map shows that the fastest decline in poverty took place in the western provinces (Fianarantsoa and Toamasina), and in Mahajanga. Antananarivo is the laggard province. Table 6 Poverty estimates by province in 2001 and % change in index urban rural total urban rural total urban rural total Headcount ratio Antananarivo Fianarantsoa Toamasina Mahajanga Toliara Antsiranana Madagascar Poverty Gap Antananarivo Fianarantsoa Toamasina Mahajanga Toliara Antsiranana Madagascar Poverty Gap Squared Antananarivo Fianarantsoa Toamasina Mahajanga Toliara Antsiranana Madagascar Note: poverty lines here. Source: Authors calculation based on EPM data. Table 7 describes the poverty profiles for 2001 and 2005 according to a number of demographic and socio-economic characteristics of the head of household and the household. The trends in poverty levels mirror the trends identified above in the paper: headcount ratios vary little, while the poverty gap and the poverty gap squared indices decline substantially. The striking feature of Table 7, however, is the substantial immobility that emerges from the comparison of the 17

18 structure of poverty between 2001 and 2005 (column poverty share ). For most of the poverty covariates considered in the table, poverty shares changed very little. 6 There are three notable exceptions to the general lack of action in Table 7, which we will comment on briefly. Figure 9 Changes in the poverty gap index, Source: Authors calculations based on EPM data. First, the poverty profiles based on the employment status of the head of household change significantly between 2001 and The poverty incidence among households headed by selfemployed workers is substantially higher than among wage-earners (and even higher than when headed by an unemployed person). This is consistent with the higher concentration of selfemployed workers in the agricultural sector (and in rural areas) where productivity is low and poverty rates are high. Note also, that according to Table 7 the living standards of households headed by wage-earners decreased markedly between 2001 and 2005, while increasing for households headed by self-employed workers. Second, the structure of the poverty risks by economic sector changes in favour of households headed by individuals with employment in agriculture. As before, this is consistent with the improvements in poverty measures in rural areas. Third, Table 7 provides support to an argument made by Minten and Stifel (2004): remoteness and poverty are positively correlated. By comparing 2001 and 2005 we note, however, a flattening in the structure of poverty risks. This is, perhaps, a sign that, at least to some extent, infrastructures in rural Madagascar have improved, but other factors may also be playing a role. It is impossible to distinguish on the basis of simple correlation analysis, but we will revisit this issue in section 7. Finally, in contrast with previous findings see World Bank (2002) Table 7 provides no evidence in support of the argument that gender affects the risk of poverty in Madagascar; poverty rates for households headed by females are not significantly higher than the average. 6 In this situation, it was deemed needless to carry out formal tests for statistical significance of the observed differences. 18

19 Table 7 Comparison of poverty profiles, 2001 and Pop. Poverty Pop. Poverty H PG PG2 share share Share share H PG PG2 Gender Female Male Age Status Married Divorced Widower Single Education None Primary Lower secondary Upper secondary Post secondary Employment status Not employed Wage earner Self-employed Economic sector Agriculture Industry Services Size Remoteness Most remote Least remote Second. city Large city Note: H stands for headcount ratio, PG is the poverty gap index, PG2 is the poverty gap squared index. Source: Authors calculation based on EPM data. 19

20 6 Growth, Inequality and Poverty After examining the changes in growth (section 2), and the trends in poverty and inequality (section 4), this section examines their interplay. In section 6.1 we examine growth incidence curves to assess the extent to which, during , economic growth was pro-poor. In section 6.2 we estimate the elasticity of poverty measures to economic growth. In sections 6.3 and 6.4 we implement both growth-inequality and sectoral decomposition techniques to identify the mechanics of the observed poverty decline. Why did poverty decline? Was it caused by the buoyant economic growth which followed the 2002 crisis? Or was it a consequence of the income redistribution documented in section 4? Or was it a combination of both growth and distribution shifts? 6.1 Growth Incidence Analysis Growth incidence curves (GIC), proposed by Ravallion and Chen (2003), plot per capita expenditure growth rates against quintiles ranked by per capita expenditure. The GIC provides an intuitive measure of how much the observed growth has favored the poor relative to the non poor. Figure 10 shows the GIC for Madagascar, based on household per capita expenditures from the 2001 and 2005 EPM. Note that during this period the national average growth rate of PCE has been negative (-1.4 percent). According to the GIC estimated in Figure 10, the poorest 70 percent of the population experienced larger than the average growth. This indicates that process of economic growth has been unambiguously and strongly pro-poor. Figure 10 The growth incidence curve for Madagascar, Growth rate of per capita expenditure 2001 to 2005 (%) Average growth rate: -1.4% Percentiles Source: Authors calculations based on EPM data. Figure 11 shows GICs estimated separately for the urban and rural areas. The shape of the curves indicates that growth during the period was clearly pro-poor in both rural and urban areas. However, in urban areas the average growth rate of expenditures was strongly negative. Table 8 shows the growth rates for selected expenditure percentiles; the comparison between the rural and urban patterns in growth rates is almost self-explanatory. While in rural areas the poor experience large and positive growth rates, in urban areas the poor are less penalized by the decline in average income. 20

21 Figure 11 Growth incidence curves for urban and rural areas in Madagascar, Growth rate of per capita expenditure 2001 to 2005 (%) Average growth rate: -15.5% URBAN Growth rate of per capita expenditure 2001 to 2005 (%) Average growth rate: +7.1% RURAL Percentiles Percentiles Source: Authors calculations based on EPM data. Table 8 Growth rates (%) among the poor, Madagascar percentile Madagascar Urban Rural mean Source: Authors calculations based on EPM data. 6.2 Growth Elasticities of Poverty In this section we estimate growth elasticities of poverty based on the (estimated) per capita expenditure density function. Following Kakwani (1993), we use the following formula: Pt, Pt, 1 Pt, (1) G t zf t, z Ft, z if if 0 0 where P(t,α) is the Foster-Greer-Thorbecke poverty measure with parameter α in period t, f(t,z) and F(t,z) denote, respectively, the probability density function and the cumulative density function of per capita expenditure in period t, and z is the absolute poverty line. 7 Table 9 shows the non-parametric estimates of the elasticities defined in equation (1), calculated for t = 2001 and t = If compared to estimates by other studies for other countries, the elasticities in Table 9 are low. For instance, Ravallion and Chen (1997) estimated the growth elasticity of the incidence of poverty to be between -2.0 and While it is hard to comment on the absolute magnitude of the elasticities, it is worth noting that in Madagascar between 2001 and 2005 elasticities have almost doubled, regardless of the poverty measure considered. Kakwani and Son (2004) showed that growth elasticity of poverty decreases with the initial level of economic development and increases with the initial level of inequality. 8 This implies that economic growth is more effective in reducing poverty in rich 7 See also Duclos and Araar (2006). 8 The result does not hold true for the headcount ratio, according to proposition 1. 21

22 countries (as opposed to poor countries) and in countries with low levels of inequality. Madagascar qualifies as a poor country, that has experienced both low economic growth and decreasing inequality. Hence, the observed increase in the growth elasticities of poverty is likely to stem from changes in inequality. Thanks to the reduction in inequality which occurred between 2001 and 2005, poverty in 2005 is more sensitive to economic growth than it was Table 9 Growth elasticities of poverty for Madagascar, 2001 and 2005 Elasticity to growth Headcount ratio Poverty Gap Poverty Gap Squared Source: Authors estimates based on EPM data. 6.3 Growth-Inequality Decomposition A recurrent theme on poverty reduction debates is the relative contribution of economic growth and inequality to poverty reduction. In this section we decompose the observed changes in poverty indices between 2001 and 2005 into two components: (i) the growth component (GC), which identifies the poverty change due to the growth of mean per capita expenditure, and (ii) the inequality component (IC), which identifies the poverty change due to a more equal distribution of income. Let P(t) be a poverty measure of the Foster, Green and Thorbeke (1984) class in period t. Following Muller (2006), the ideal decomposition of the variation of P over the time interval (T 0, T 1 ) can be written as follows: (2) P PT PT 1 0 T1 T1 t d t P t dl t P dt dt dt L dt T T 0 0 GC where μ(t) is the mean per capita expenditure and L(t) is the Lorenz curve in period t. We lack information on the partial derivatives in equation (2) over the entire time interval T 0,T 1, and therefore rely on the following approximation of equation (2): T1 d dt T1 (3) P P T dt P T dt R P T P T L R where r T0 L r T0 dl dt PT1, LTr P T0, LTr T1T0 PTr, LT1 PTr, LT0 L LT LT Pt Pt 1 0 P P L T T r r IC r GCˆ L r ICˆ approximate the partial derivatives in equation (2), Δμ = μ(t 1 ) - μ(t 0 ), ΔL = L(T 1 ) - L(T 0 ), and R is a residual term. Note that the decomposition depends on the arbitrary reference period T r. Datt and Ravallion (1992) recommend the use of the initial period (T r =T 0 ), but other choices are 22

23 available. One is the ending period (T r =T 1 ), another is the so-called Shapley decomposition where the growth and inequality components are assumed to be an average of the approximated decompositions with T r =T 0 and T r =T 1. Table 10 shows the results for the three decompositions described above, using the main poverty measures of the FGT class. Poverty decompositions are found to be robust to the reference period chosen; residual terms are negligible in size, with no exceptions. Table 10 Growth-inequality decompositions, Madagascar reference period Shapley Headcount (H) Change in H Growth component Inequality component Residual Poverty Gap (PG) Change in PG Growth component Inequality component Residual Poverty Gap Squared (PG2) Change in PG Growth component Inequality component Residual Source: Authors estimates based on EPM data. Overall the inequality effect is dominant. The contribution of the growth component is low, a result largely expected because of the substantial stability of mean per capita expenditure during the period considered. As argued above, however, one has to take into account the timing of the surveys. The fact that we use 2001, a year immediately preceding a major crisis, and compare it to 2005, a time by which the recovery from the crisis was just completed, makes the results in Table 9 difficult to interpret, if not misleading. In particular, the role of the growth component is likely to be severely under-estimated. 6.4 Sectoral Decomposition of Poverty Changes in the national poverty level can be decomposed into the relative contributions of changes in poverty within population sub-groups, and changes in population shares across sectors. In this section we estimate the relative contributions of these two components by exploiting the additive decomposability of the FGT class of poverty indices. Following Ravallion and Huppi (1991) we use the following formula: K (3) P P T P T n T n T n T P T R k 1 k 0 k 0 k 1 k 0 k 0 k1 k1 WITHIN GROUP INTERGROUP where R denotes a residual term. K Table 11 shows the results of the decomposition (3) for selected groups. The main result is that the within-group effects dominate, regardless of the choice of the poverty measure and the definition of population sub-groups. For instance, taking the PG decomposition by urban-rural (top panel in Table 11) we find that the change in PG within rural and urban areas (-8.2) would have caused a larger reduction in the aggregate PG index than the observed change (-8.1), were 23