Growth, Inequality and Poverty in Madagascar, 2001-2005 Africa Region Working Paper Series No. 111 April 2008 Abstract

he paper examines changes in poverty and inequality in Madagascar between the years 2001 and 2005. 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 0.5-2.0 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), Email: pwhite2@worldbank.org or visit the Web site: http://www.worldbank.org/afr/wps/index.htm. 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.

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Growth, Inequality and Poverty in Madagascar, 2001-2005

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.

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Contents

1 Introduction .......................................................................................................................... 5

2 Economic Growth ................................................................................................................. 6

3 Household Surveys in Madagascar ....................................................................................... 8

4 Poverty and Inequality Dynamics ......................................................................................... 9

4.1 The Incidence of Poverty ........................................................................................... 10

4.2 The Depth and Severity of Poverty ............................................................................ 12

4.3 Inequality ................................................................................................................... 14

5 Changes in the Poverty Profile ........................................................................................... 17

6 Growth, Inequality and Poverty .......................................................................................... 20

6.1 Growth Incidence Analysis ........................................................................................ 20

6.2 Growth Elasticities of Poverty ................................................................................... 21

6.3 Growth-Inequality Decomposition ............................................................................ 22

6.4 Sectoral Decomposition of Poverty ........................................................................... 23

7 A Model of Household Consumption ................................................................................. 24

8 Poverty and Growth Projections ......................................................................................... 25

9 Summary and Final Remarks ............................................................................................. 26

List of References........................................................................................................................ 28

Appendix 1 – Regression Analysis of Household Consumption ................................................ 30

Appendix 2 – Sensitivity Analysis of Poverty Estimates to the Choice of Different Deflators .. 33

Appendix 3 – Sectoral Value Added And Population Growth Rates, ......................................... 35

List of Figures Figure 1 – Real GDP (billions of Ariary at 2000 constant prices) and GDP per capita, ............... 6 Figure 2 – Sectoral growth rates, 1996-2006 ................................................................................ 7 Figure 3 – GDP shares (1984 prices), 1980-2006 ......................................................................... 7 Figure 4 – First-order stochastic dominance test, Madagascar 2001-2005 ................................. 11 Figure 5 – First-order stochastic dominance by urban/rural area, 2001-2005 ............................. 12 Figure 6 – Second-order stochastic dominance test, Madagascar, 2001-05. ............................... 13 Figure 7 – Second-order stochastic dominance by urban/rural area ........................................... 13 Figure 8 – Lorenz Curves for Madagascar, 2001 and 2005 ........................................................ 15 Figure 9 – Changes in the poverty gap index, 2001-2005 ........................................................... 18 Figure 10 – The growth incidence curve for Madagascar, 2001-2005 ........................................ 20 Figure 11 – Growth incidence curves for urban and rural areas in Madagascar, 2001-05 .......... 21 List of Tables

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Table 1 – EPM 2001 versus EPM 2005: A Comparison ............................................................... 8 Table 2 – Poverty and Inequality Trends, Madagascar 2001-2005 (%) ...................................... 10 Table 3 – Inequality and mean consumption by province, 2001 and 2005 ................................. 14 Table 4 – Inequality Decompositions, Madagascar 2001 and 2005 ............................................ 16 Table 5 – Decompositions of the changes in aggregate inequality, 2001 and 2005 .................... 16 Table 6 – Poverty estimates by province in 2001 and 2005 ........................................................ 17 Table 7 – Comparison of poverty profiles, 2001 and 2005 ......................................................... 19 Table 8 – Growth rates (%) among the poor, Madagascar 2001-2005 ....................................... 21 Table 9 – Growth elasticities of poverty for Madagascar, 2001 and 2005 .................................. 22 Table 10 – Growth-inequality decompositions, Madagascar 2001-2005 .................................... 23 Table 11 – Sectoral decomposition of poverty, 2001-2005 ........................................................ 24 Table 12 – Impact of growth on poverty in Madagascar............................................................. 26 Table 13 – Regression Estimates of Consumption Models for Rural Households, .................... 30 Table 14 – Regression Estimates of Consumption Models for Urban Households, ................... 32 Appendix Table 15 – Provincial Deflators, Madagascar 2001 and 2005 .................................... 34 Appendix Table 16 – Regional vs. provincial deflators: Sensitivity of poverty estimates ...... 34

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 2007. We also venture into longer-term poverty forecasts for the year 2010.

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 2010. Section 9 concludes.

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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 2005. 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 2001-2002. 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, 1980-2006

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Source: World Bank database.

Figure 2 shows the pattern of sectoral GDP growth rates between 1996 and 2006. 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).

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Figure 2 – Sectoral growth rates, 1996-2006

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Figure 3 shows the trends of sectoral GDP shares between the years 1980-2006. 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 2002. 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), 1980-2006

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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 2003-2005 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 2003-2005 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 2005. 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 2005.2

Table 1 compares the 2001 and 2005 EPM survey designs, showing that no major differences exist between the two surveys.

Table 1 – EPM 2001 versus EPM 2005: A Comparison

2001 2005

Sample design

Two-stage stratified: 12 strata, (1st) 303 ZD (clusters), (2nd) 5,080 household (16 per urban cluster, and 18 per rural cluster).

Two-stage stratified: 44 strata, (1st) 561 ZD (clusters), (2nd) 11,781 household (21 per cluster).

Rounds One: Oct to Nov 2001. One: Sept 05 to Nov 10, 2005

Actual sample size 23,170 individuals corresponding to 5,080 households (3,040 urban, 2,040 rural)

54,966 individuals corresponding to 11,781 households (5,859 urban, 5,922 rural)

Representativeness provincial level (6 faritany), and urban/rural within each province

regional level (22 faritra), and urban/rural within each region

Time reference, recall period

Expenditures on food and beverages: last week, year. Non-food commodities: last month, year.

Idem

Consumption Expenditures on both food and non-food include in-kind consumption items.

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.

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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 2005. Some caution may be appropriate when interpreting the results in light of the political crisis of 2002. 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).

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Table 2 – Poverty and Inequality Trends, Madagascar 2001-2005 (%)

2001 2005 Urban Rural National Urban Rural NationalHeadcount 44.2 77.3 69.7 52.0 73.5 68.7 95% 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 18.3 39.8 34.9 19.3 28.9 26.8 95% 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 9.9 24.2 20.9 9.4 14.5 13.4 95% 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 0.439 0.445 0.469 0.405 0.335 0.365 95% c. i. [.419,.459] [.402, .488] [.445, .492] [.382, .429] [.313, .357] [.347, .383]

Theil Index 0.341 0.369 0.402 0.314 0.234 0.273 95% 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 22.9 77.1 100 22.0 78.0 100 Mean PCE 475,287 251,770 302,883 401,471 269,663 298,644 Mean PCE among poor 178,737 148,004 152,464 191,731 185,309 186,376

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.

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Figure 4 – First-order stochastic dominance test, Madagascar 2001-2005

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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 2001-2005 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 73.5.5 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 2001-2005 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.

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Figure 5 – First-order stochastic dominance by urban/rural area, 2001-2005

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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 2001-2005 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 2001. 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.

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Figure 6 – Second-order stochastic dominance test, Madagascar, 2001-05.

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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

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Source: Authors’ estimation on EPM 2001 and 2005 data.

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

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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 2001 2005 % Change GINI urban rural total urban rural total urban rural total Antananarivo 0.422 0.470 0.466 0.388 0.309 0.353 -8.1 -34.3 -24.2 Fianarantsoa 0.411 0.369 0.406 0.385 0.278 0.300 -6.3 -24.7 -26.1 Toamasina 0.424 0.356 0.402 0.392 0.390 0.398 -7.5 9.6 -1.0 Mahajanga 0.395 0.365 0.402 0.390 0.366 0.385 -1.3 0.3 -4.2 Toliara 0.450 0.386 0.433 0.431 0.348 0.373 -4.2 -9.8 -13.9 Antsiranana 0.343 0.342 0.389 0.379 0.361 0.384 10.5 5.6 -1.3 Madagascar 0.439 0.445 0.469 0.405 0.335 0.365 -7.7 -24.7 -22.2 THEIL urban rural total urban rural total urban rural total Antananarivo 0.311 0.382 0.374 0.290 0.199 0.255 -6.8 -47.9 -31.8 Fianarantsoa 0.301 0.244 0.304 0.318 0.140 0.178 5.6 -42.6 -41.4 Toamasina 0.315 0.216 0.290 0.268 0.317 0.314 -14.9 46.8 8.3 Mahajanga 0.277 0.227 0.290 0.296 0.356 0.353 6.9 56.8 21.7 Toliara 0.366 0.267 0.348 0.351 0.217 0.260 -4.1 -18.7 -25.3 Antsiranana 0.193 0.197 0.260 0.290 0.263 0.298 50.3 33.5 14.6 Madagascar 0.341 0.369 0.402 0.314 0.234 0.273 -7.9 -36.6 -32.1 MEAN LOGARITHMIC DEVIATION urban rural total urban rural total urban rural total Antananarivo 0.315 0.372 0.375 0.249 0.160 0.206 -21.0 -57.0 -45.1 Fianarantsoa 0.283 0.226 0.274 0.248 0.128 0.151 -12.4 -43.4 -44.9 Toamasina 0.311 0.209 0.268 0.265 0.255 0.265 -14.8 22.0 -1.1 Mahajanga 0.255 0.224 0.272 0.250 0.236 0.253 -2.0 5.4 -7.0 Toliara 0.359 0.250 0.316 0.316 0.205 0.235 -12.0 -18.0 -25.6 Antsiranana 0.199 0.194 0.252 0.237 0.215 0.243 19.1 10.8 -3.6 Madagascar 0.338 0.330 0.371 0.276 0.190 0.224 -18.3 -42.4 -39.6 MEAN CONSUMPTION urban rural total urban rural total urban rural total Antananarivo 626882 412338 475759 463102 304758 352719 -26.1 -26.1 -25.9 Fianarantsoa 345506 172695 201548 296524 238171 247726 -14.2 37.9 22.9 Toamasina 332048 175721 208096 367884 268275 286627 10.8 52.7 37.7 Mahajanga 422524 216732 260147 421211 285374 314709 -0.3 31.7 21.0 Toliara 384209 204392 247397 329001 241362 259095 -14.4 18.1 4.7 Antsiranana 530288 240792 298486 517916 288922 324309 -2.3 20.0 8.7 Madagascar 475287 251770 302883 401471 269663 298644 -15.5 7.1 -1.4

Note: bootstrapped standard errors available from the Authors upon request. Source: Authors’ calculation based on EPM data.

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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 2005

0

.2

.4

.6

.8

1

0

.2

.4

.6

.8

1

Per

cap

ita e

xpen

ditu

re s

hare

of

poor

est

p100

%

0 .2 .4 .6 .8 1Cumulative population share, p

Legend:

0

.2

.4

.6

.8

1

0

.2

.4

.6

.8

1

Pe

r ca

pita

exp

en

ditu

re s

hare

of

poo

rest

p1

00%

0 .2 .4 .6 .8 1Cumulative population share, p

URBAN

0

.2

.4

.6

.8

1

0

.2

.4

.6

.8

1

Per

cap

ita e

xpen

ditu

re s

hare

of

poor

est p

100%

0 .2 .4 .6 .8 1Cumulative population share, p

RURAL

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 (ITOT) 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 (IW) that would be observed if there were no differences in mean levels of expenditures across population subgroups. Likewise, the between component (IB) 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 IW + IB = ITOT.

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 2005. 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.

16

Table 4 – Inequality Decompositions, Madagascar 2001 and 2005

2001 2005 Index Within Between Index Within Between

by urban/rural areaMLD (%)

0.371 (100)

0.332 (89)

0.040 (11)

0.224 (100)

0.209 (93)

0.015 (7)

Theil (%)

0.402 (100)

0.359 (89)

0.043 (11)

0.273 (100)

0.258 (95)

0.015 (5)

by provinceMLD (%)

0.371 (100)

0.306 (83)

0.065 (17)

0.224 (100)

0.213 (95)

0.010 (5)

Theil (%)

0.402 (100)

0.335 (83)

0.067 (17)

0.273 (100)

0.263 (96)

0.010 (4)

Source: Authors calculation on EPM data.

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.

Table 5 – Decompositions of the changes in aggregate inequality, 2001 and 2005

sub-group partition

change in MLD (%)

% change in MLD accounted for by changes in within-group inequalities

(A)

population shares

(B)

sub-group mean incomes

(C)

urban/rural -40 -33 0 -7 province -40 -25 0 -15

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 2005. 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).

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 2005

2001 2005 % change in index urban rural total urban rural total urban rural total

Headcount ratio Antananarivo 28.3 57.1 48.6 41.6 64.7 57.7 47.0 13.3 18.7 Fianarantsoa 59.8 87.9 83.2 71.6 78.7 77.6 19.7 -10.5 -6.7 Toamasina 60.8 87.9 82.3 55.8 75.6 71.9 -8.2 -14.0 -12.6 Mahajanga 50.7 78.4 72.5 47.0 76.6 70.2 -7.3 -2.3 -3.2 Toliara 53.3 83.3 76.1 64.3 77.4 74.8 20.6 -7.1 -1.7 Antsiranana 30.1 79.0 69.3 33.8 69.8 64.2 12.3 -11.6 -7.4 Madagascar 44.2 77.3 69.7 52.0 73.5 68.7 17.6 -4.9 -1.4 Poverty Gap Antananarivo 10.4 25.6 21.1 13.6 21.9 19.4 30.8 -14.5 -8.1 Fianarantsoa 25.5 49.5 45.5 28.8 30.9 30.6 12.9 -37.6 -32.7 Toamasina 28.5 48.3 44.2 21.4 33.1 30.9 -24.9 -31.5 -30.1 Mahajanga 17.4 40.0 35.2 16.1 28.9 26.2 -7.5 -27.8 -25.6 Toliara 25.5 43.6 39.2 28.3 34.0 32.9 11.0 -22.0 -16.1 Antsiranana 8.7 34.1 29.0 9.4 28.1 25.2 8.0 -17.6 -13.1 Madagascar 18.3 39.8 34.9 19.3 28.9 26.8 5.5 -27.4 -23.2 Poverty Gap Squared Antananarivo 5.3 13.9 11.3 5.8 9.8 8.6 9.4 -29.5 -23.9 Fianarantsoa 13.6 31.7 28.7 14.5 15.1 15.0 6.6 -52.4 -47.7 Toamasina 16.3 30.5 27.6 11.2 18.0 16.7 -31.3 -41.0 -39.5 Mahajanga 8.0 23.9 20.6 7.2 13.9 12.4 -10.0 -41.8 -39.8 Toliara 15.1 26.9 24.1 15.9 19.1 18.4 5.3 -29.0 -23.7 Antsiranana 3.8 18.9 15.8 3.5 14.2 12.5 -7.9 -24.9 -20.9 Madagascar 9.9 24.2 20.9 9.4 14.5 13.4 -5.1 -40.1 -35.9

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

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, 2001-2005

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 2005. The poverty incidence among households headed by self-employed 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 self-employed 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.

19

Table 7 – Comparison of poverty profiles, 2001 and 2005

2001 2005

Pop. share

Poverty share

H PG PG2 Pop.

Share Poverty share

H PG PG2

Gender Female 15.1 15.0 69.2 35.5 21.9 14.4 14.1 67.6 26.1 13.2 Male 84.9 85.0 69.8 34.8 20.8 85.6 85.9 69.0 26.9 13.4 Age 0-24 5.5 5.1 65.4 30.5 17.0 4.3 3.8 62.0 23.0 11.2 25-44 51.6 52.0 70.3 35.2 21.1 52.0 53.0 70.0 26.9 13.4 45-64 36.0 36.3 70.2 35.7 21.6 36.4 36.3 68.5 27.5 14.0 65+ 6.9 6.6 66.2 32.6 18.8 7.3 6.9 65.1 24.4 11.7 Status Married 83.1 83.5 70.1 34.9 20.8 83.0 83.9 69.5 27.1 13.5 Divorced 7.3 7.7 73.0 36.8 22.5 7.1 6.9 66.7 24.9 12.3 Widower 6.9 6.8 69.0 37.1 23.5 8.3 8.1 66.8 26.4 13.2 Single 2.6 2.0 53.0 22.9 12.5 1.7 1.2 48.5 19.8 10.6 Education None 47.0 57.1 84.6 46.2 29.1 52.3 58.9 77.4 31.9 16.6 Primary 29.5 30.3 71.5 33.7 19.2 30.0 30.3 69.3 25.7 12.4 Lower secondary 10.1 7.1 49.1 19.1 10.0 9.3 7.0 51.5 16.6 7.3 Upper secondary 7.5 4.4 41.1 14.4 6.5 4.9 3.0 41.5 13.4 5.8 Post secondary 5.8 1.1 13.4 4.1 1.7 3.4 0.9 17.6 4.0 1.4 Employment status Not employed 4.2 2.9 48.1 20.3 10.8 3.5 2.8 54.9 22.8 12.0 Wage earner 21.5 11.9 38.4 14.7 7.5 17.8 13.6 52.7 18.9 8.8 Self-employed 74.2 85.2 80.0 41.6 25.4 78.8 83.6 73.0 28.7 14.5 Economic sector Agriculture 70.8 85.1 85.0 44.7 27.4 77.9 84.8 75.4 30.1 15.2 Industry 5.9 3.1 37.8 14.1 7.1 2.8 1.8 44.8 14.7 6.5 Services 23.4 11.8 35.6 13.3 6.8 19.3 13.4 47.9 16.1 7.2 Size 1-2 6.1 3.2 37.2 12.5 5.7 5.1 2.0 27.5 7.5 3.4 3-4 26.5 22.5 59.2 26.6 14.6 25.0 19.5 53.6 17.6 7.9 5-6 31.5 31.1 68.8 32.9 19.3 33.0 32.7 68.3 25.1 12.0 7+ 35.9 43.1 83.8 46.6 29.6 36.9 45.7 85.2 37.2 19.8 Remoteness Most remote 7.2 8.4 81.0 41.0 23.7 14.6 16.2 76.3 30.8 15.5 4 15.0 18.9 88.0 48.6 31.1 15.2 16.9 76.4 32.8 17.9 3 17.4 22.6 90.3 50.0 31.6 14.6 16.9 79.7 33.0 17.2 2 10.9 12.5 79.8 39.8 24.1 14.5 14.8 70.5 24.8 11.4 Least remote 26.5 23.1 60.7 27.8 15.6 19.2 18.6 66.6 24.3 11.4 Second. city 11.9 9.8 57.1 25.3 14.2 12.1 11.2 63.3 24.8 12.6 Large city 10.9 4.7 30.2 10.7 5.2 9.9 5.4 37.9 12.6 5.5

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.

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 2001-2005, 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, 2001-2005

Average growth rate: -1.4%

-20

0

20

40

60

Gro

wth

rat

e of

per

cap

ita e

xpen

ditu

re 2

001

to 2

005

(%)

0 10 20 30 40 50 60 70 80 90 100Percentiles

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 2001-05 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.

21

Figure 11 – Growth incidence curves for urban and rural areas in Madagascar, 2001-05

Average growth rate: -15.5%

-20

0

20

40

60

Gro

wth

rat

e of

per

cap

ita e

xpen

ditu

re 2

001

to 2

005

(%)

0 10 20 30 40 50 60 70 80 90 100Percentiles

URBAN

Average growth rate: +7.1%

-20

0

20

40

60

Gro

wth

rat

e of

per

cap

ita e

xpen

ditu

re 2

001

to 2

005

(%)

0 10 20 30 40 50 60 70 80 90 100Percentiles

RURAL

Source: Authors’ calculations based on EPM data.

Table 8 – Growth rates (%) among the poor, Madagascar 2001-2005

percentile Madagascar Urban Rural 10 50.9 17.4 56.0 15 48.0 15.5 53.4 20 45.9 12.5 51.4 25 44.2 9.4 49.9 30 42.7 7.0 48.650 18.8 -12.8 32.9

mean -1.4 -15.5 7.1

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:

(1)

, , 10

,

,0

,

G

P t P tif

P tt

zf t zif

F t z

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 = 2005. 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 -3.0.

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.

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 2001.

Table 9 – Growth elasticities of poverty for Madagascar, 2001 and 2005

Elasticity to growth 2001 2005 Headcount ratio – 0.553 – 0.930 Poverty Gap – 0.998 – 1.567 Poverty Gap Squared – 1.337 – 2.002

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 (T0, T1) can be written as follows:

(2) td

dt

tdL

L

tPdt

dt

tdtPTPTPP

IC

T

T

GC

T

T

1

0

1

0

01

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 10 ,TT , and

therefore rely on the following approximation of equation (2):

(3) RLTPTPRdtdt

dLTPdt

dt

dTPP

CI

rL

CG

r

T

T

rL

T

T

r ˆˆ

1

0

1

0

where

1 0

1 0

1 0

1 0

, ,

, ,

r rr

r rL r

P T L T P T L TP tP T

T T

P T L T P T L TP tP T

L L T L T

approximate the partial derivatives in equation (2), Δμ = μ(T1) - μ(T0), ΔL = L(T1) - L(T0), and R is a residual term. Note that the decomposition depends on the arbitrary reference period Tr. Datt and Ravallion (1992) recommend the use of the initial period (Tr=T0), but other choices are

23

available. One is the ending period (Tr=T1), another is the so-called Shapley decomposition where the growth and inequality components are assumed to be an average of the approximated decompositions with Tr=T0 and Tr=T1.

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 2001-2005

reference period 2001 2005 Shapley Headcount (H) 69.728 68.748 ─ Change in H -0.980 -0.980 - 0.980 Growth component 0.506 0.731 0.619 Inequality component -1.711 -1.486 -1.598 Residual 0.225 0.225 0.000 Poverty Gap (PG) 34.906 26.780 ─ Change in PG -8.127 -8.127 -8.127 Growth component 0.491 0.591 0.541 Inequality component -8.718 -8.618 -8.668 Residual 0.100 0.100 0.000 Poverty Gap Squared (PG2) 20.923 13.383 ─ Change in PG2 -7.541 -7.541 -7.541 Growth component 0.396 0.375 0.385 Inequality component -7.915 -7.936 -7.926 Residual -0.021 -0.021 0.000

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:

(3) 1 0 0 1 0 01 1

K K

k k k k k kk k

WITHIN GROUP INTER GROUP

P P T P T n T n T n T P T R

where R denotes a residual term.

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

24

it not for the offsetting effect of the population shifts (+0.2), i.e. for the between-area movements of the population. This finding is consistent with the fact that during the observation period there was a net inflow towards rural areas, that is, towards areas with higher poverty incidence and poverty intensity.

The pattern found for the urban-rural decomposition is similar to the decomposition by province (mid panel in Table 11): it is the change of poverty within provinces that drives the overall decline in poverty rates in Madagascar. Populations shifts play a marginal role.

The bottom panel in Table 11 tells a slightly different story. While the decline in poverty continues to be driven by the within-group component, the population movements between economic sectors play a non negligible role: in the absence of population shifts we would have observed larger reductions in poverty rates.

Table 11 – Sectoral decomposition of poverty, 2001-2005

sub-group partition change in national

index (%)

% change in MLD accounted for by changes in

within-group poverty

intergroup residual

by urban/rural H -1.0

(100) -1.2

(120) 0.3

(-30) -0.1(10)

PG -8.1(100)

-8.2 (101)

0.2 (-2)

-0.1 (1)

PG2 -7.6 (100)

-7.6 (100)

0.1 (-1)

-0.1 (1)

by province H -1.0

(100)-1.1

(114) -0.1 (13)

0.3 (-27)

PG -8.1(100)

-8.1 (100)

0.1 (-1)

-0.1 (1)

PG2 -7.6 (100)

-7.5 (99)

0.1 (-1)

-0.1 (2)

by economic sector: primary/secondary/tertiary H -1.0

(100) -3.4

(259) 3.5

(-268) -1.4

(109) PG -8.1

(100) -9.6

(112) 2.3

(-26) -1.2 (14)

PG2 -7.6 (100)

-8.4 (108)

1.5 (-19)

-0.9 (11)

Source: Authors calculation on EPM data.

7 A Model of Household Consumption

In this section we apply multivariate analysis techniques to the EPM data to identify the determinants of household consumption patterns in Madagascar. Following Razafindravonona et al. (2001), we estimate distinct rural and urban models of log-consumption for the years 2001 and 2005. Regression results are shown in the appendix. The main findings can be summarized as follows.

Demographics. Household composition has a major impact on consumption. The dependency ratio, defined as the number of household members aged below 15 or above 64 divided by the number of individuals aged 15 to 64, has a large and negative impact on per capita consumption.

25

This finding is robust to the econometric specification of the model; it holds true in urban as well as in rural areas, for both survey years. Living standards decrease with household size.9

Education. According to the 2001 estimates, educational attainments of household members are positively correlated with consumption, albeit with differences by gender, level of education and location. Estimates for 2005 are more blurred: the coefficients of education variables decrease, particularly in urban areas.

Occupational status. According to all models estimated, wage-earners and the self employed in the secondary and tertiary sectors are better off than their peers in agriculture. This finding is consistent with low estimates of agricultural productivity (Hoftijzer and Stifel 2007). The negative effect of informal sector employment is particularly strong.

Land. In a country like Madagascar, where a large fraction of the population is employed in the primary sector, land is perhaps the most important asset. Not surprisingly, land ownership decreases the risk of poverty, increasing consumption proportionately with the extent of land holdings.

Geography. Even after controlling for a wide range of socio-economic and demographic variables, provincial dummy effects are still large in size and significant. Living in provinces other than Antananarivo implies a higher risk of poverty. According to the 2001 estimates the households most at risk are those in Toamasina followed by those in Fianarantsoa, Toliara, Mahajanga and Antsiranana. The ranking hardly changes in 2005.

The non-negligible and significant provincial dummy effects suggest that our model fails to capture all relevant determinants of poverty. Following World Bank (2002), our strategy is to explore the roles of (i) infrastructure, (ii) climate shocks and (iii) land tenure patterns.

Unfortunately the EPM surveys do not provide information about these factors. However, community-level data from a 2001 census of communes can be matched to the EPM data. Adding variables from the census, we estimate a new model of household consumption with controls for crop mix, relevant infrastructure, and the natural environment. The estimates in Tables A1 and A2 indicate that these factors largely explain provincial differences not attributable to household variables; most provincial dummy effects disappear or lose statistical significance.

Considering individual variables we find that the major roles are played by public water provision, specialization in rice production, and remoteness of location, while adverse climate shocks have a negative but lesser impact.

8 Poverty and Growth Projections

This section presents the results of a simulation projecting poverty measures in Madagascar for two benchmark years, 2007 and 2010. The simulations are based on forecasts of sectoral value added and population growth (see Appendix 3).

9 As noted by Lanjouw and Ravallion (1995), “the existence of size economies in household consumption cautions against concluding that larger families tend to be poorer” (p. 1415). Further research is needed to address this issue; the analysis carried out in this paper, based on consumption per capita as the welfare metric, rules out by construction the existence of economies of scale.

26

Table 12 – Impact of growth on poverty in Madagascar

2007 2010 Urban Rural National Urban Rural National Headcount ratio 49.4 73.1 67.9 42.0 70.1 63.9 PG 18.2 28.8 26.5 15.4 27.2 24.6 PG2 9.1 14.5 13.3 7.7 13.6 12.3 Annualized change rates (%)

2005-2007 2005-2010

Headcount ratio -2.53 -0.27 -0.58 -4.18 -0.94 -1.44 PG -2.89 -0.17 -0.56 -4.41 -1.21 -1.70 PG2 -1.61 0.00 -0.37 -3.91 -1.27 -1.70

Source: Authors’ estimates based on EPM 2005 data and World Bank forecasts of sectoral value added and population growth.

The projections in Table 13 show poverty decreasing at a slow annual rate. At the same time, the pattern of poverty changes is reversed relative to the 2001-2005 period: poverty decreases faster in urban than in rural areas. This is due to projections of sectoral GDP growth rates which indicate more rapid growth in the largely urban secondary and tertiary sectors.

It bears emphasis that the simulation results are contingent on several assumptions. Of particular importance is the assumption that population shares remain constant across geographic regions and economic sectors. Other economic factors could affects poverty projections in Table 12. Similarly, GDP growth rates used in the analysis do not take into account the rise in world energy prices during the first half of 2006. Andriamihaja and Vecchi (2007) have estimated that a 17 percent rise in the price of energy products leads to a 1.75 percent average decrease in real expenditure (2.1 percent for low-income households, 1.5 percent for high-income households).

9 Summary and Final Remarks

The paper has accomplished three main tasks. First, it has documented the changes in poverty and inequality in the time period between 2001 and 2005. Second, it has examined the factors at play in determining the evolution of poverty and inequality over time. Third, it has forecasted poverty on the basis of population and sectoral GDP growth rate projections.

The availability of comparable surveys for the years 2001 and 2005 has made the updating of the poverty and inequality profiles a relatively straightforward task. The main findings can be summarized as follows.

The incidence of poverty at the national level has barely changed between the years 2001 and 2005. However, while the headcount ratio among rural households has not changed in a statistically significant way, it has unambiguously increased among urban households. This pattern reverses the tendency observed during the second half of the 1990s, when rural poverty was on the rise and urban poverty was falling precipitously – see World Bank (2002).

While the fraction of the population classified as poor has decreased, the absolute count of the poor has increased by some 2 million people. This leaves a fundamental question unanswered: has poverty in Madagascar decreased or increased during the first half of the decade? This finding suggests that population growth rates play a significant role in shaping poverty in Madagascar and in determining the risk of poverty. Families with low dependency ratios face an estimated poverty risk 60 percent lower than the average risk. Additional research on the impact of demographic transition on poverty in Madagascar should be awarded a high priority.

While the evidence on changes in the incidence of poverty is inconclusive, results of the analysis of the depth and severity of poverty are unambiguous. Both the poverty gap and the poverty gap squared point to a substantive and statistically robust reduction of poverty at the

27

national level. Moreover, in contrast to what observed with the headcount ratio, there is no contradiction when we consider total versus average poverty gaps; the absolute shortfall of the poor in monetary terms decreased by 7.8 percent between 2001 and 2005. With regard to the pattern of urban and rural poverty we find that rural poverty decreased while the change in urban poverty was not statistically significant.

Consistent with the trend in poverty is the finding that inequality decreases both nationally and within urban and rural areas, regardless of the index employed. Even if the growth-inequality decomposition of poverty leads to the conclusion that inequality dominates the explanation of the trend in poverty, we believe this result greatly over-emphasizes the role attributed to inequality. We argue that the political crisis of 2002 is largely responsible for this result.

The analysis of poverty at the provincial level confirms what was previously noted in World Bank (2002: iv), namely that Madagascar is characterized by a “... high degree of spatial heterogeneity in poverty levels across the country”. We find that provinces not only display variation in poverty levels but also in the changes of poverty over time. For instance, between the years 2001 and 2005 the incidence of poverty in Antananarivo increased by 18.7 percent, while it hardly changed in Toliara (minus 1.7 percent). In Toamasina it decreased by 12.6 percent.

Our second major aim in this paper was to explore the determinants of poverty. The strategy pursued in the paper capitalized on the regression analysis carried out in World Bank (2002). We find that even after controlling for a large number of socio-economic and demographic variables, the provincial dummies remain large in size and statistically significant. The interpretation of this result is twofold: (i) ceteris paribus, geographical location of households matters in determining poverty risks, (ii) regression analysis based on the EPM data alone, does not explain why geographical location for poverty matters so much.

By exploiting data from the Commune Census for 2001, we were able to assess the relative contribution of three sets of explanatory variables: (i) provincial endowments of infrastructures, (ii) the structure of the agricultural sector, and (iii) climatic factors. A new regression model, inclusive of factors (i)-(iii) was estimated separately for urban and rural households. In the rural model, controlling for (i)-(iii), rendered all provincial dummies, statistically insignificant. A similar result was obtained for the consumption model estimated for urban households.

Overall, the regression results support the view advanced in a number of recent World Bank documents, namely that for a country as remote as Madagascar, the lack of and/or poor quality of the transport and communication infrastructures is a major obstacle to development and poverty reduction. The above evidence is also consistent with the emphasis placed on the role of agricultural productivity in lifting people out of poverty. We find that land tenure (as captured by the ownership and distribution of land across the population) has a large and significant impact on living standards. Lastly, we find that the occurrence of adverse climatic shocks has a negative impact on poverty, though their magnitude is substantially lower compared to factors (i) and (iii).

The third and last aim of the paper was to project poverty to the present day, and to deliver a forecast of poverty into a not-too distant future. According to our estimates, poverty in 2007 has, largely, remained, at the same level as (and with the same structure of) in 2005. Our analysis points to a tendency towards improvement, especially in urban areas. Longer-term poverty forecasts predict that the incidence of poverty will fall from 68.7 percent in 2005 to 63.9 percent in 2010, with a marked pro-urban bias which characterizes the poverty reduction process.

28

List of References

Andriamihaja, N. and G. Vecchi (2007), “An Evaluation of the Welfare Impact of Higher Energy Prices in Madagascar”, in D. Go and J. Page (eds). Africa at a Turning Point? Growth, Aid External Shocks. Washington: World Bank.

Arndt, C. and K. Simler (2005), “Estimating utility-consistent poverty lines”, FCND Discussion Papers 189, International Food Policy Research Institute.

Chakravarti, S., R. Kanbur and D. Mukherjee (2006), “Population Growth and Poverty Measurement”, in Social Choice and Welfare, 26, 3.

Chaudhuri, S., J. Jalan and A. Suryahadi (2002), “Assessing household vulnerability to poverty from cross-sectional data: a methodology and estimates from Indonesia”, Discussion Paper 0102-52. New York: Columbia University.

Duclos J. and A. Araar (2006), Poverty and Equity. Measurement, Policy, and Estimation with DAD, Springer, New York.

Datt, G. and M. Ravallion (1992), “Growth and Redistribution Components of Changes in Poverty Measures: A Decomposition with Application to Brazil and India in the 1980’s”, Journal of Development Economics, 38.

Hoftijzer, M. and D. Stifel (2007), “Exploring The Role of Employment and Earnings in Poverty Reduction: The Case of Madagascar”, photocopy.

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Jenkins, S.P. (1995), “Accounting for inequality trends: Decomposition analyses for the UK, 1971-86”, Economica, 62: 29-63.

Kakwani, N. (1993), “Poverty and economic growth with application to Côte d’Ivoire”, Review of Income and Wealth, 39(2): 121-139.

Kakwani, N. (2000), “On Measuring Growth and Inequality Components of Poverty with Application to Thailand”, Journal of Quantitative Economics, 16 (1).

Kakwani, N. and H. Son (2004), Economic Growth and Poverty Reduction: Initial Conditions Matter, International Poverty Centre Working Paper n.2.

Lanjouw, P. and M. Ravallion (1995), “Poverty and Household Size”, Economic Journal, 105: 1415-1434.

Ligon, E. and L. Schechter (2003), “Measuring vulnerability”, Economic Journal, 113: C95-C102.

Mookherjee, D. and A.F. Shorrocks (1982), “A decomposition analysis of the trend in UK income inequality”, Economic Journal, 92: 886-902.

Muller, A. (2006) “Clarifying Poverty Decomposition”, Working Papers in Economics 213, Göteborg University, Department of Economics

29

Ravallion, M. (1988), “Expected poverty under risk-induced welfare variability”, Economic Journal, 98, 393: 1171-82.

Ravallion, M. and B. Bidani (1994), “How robust is a poverty profile?," World Bank Economic Review, 8, 75-102.

Ravallion, M. and M. Huppi (1991), “Measuring Changes in Poverty: A Methodological Case Study of Indonesia During an Adjustment Period”, World Bank Economic Review, 5.

Ravallion, M. and M. Lokshin (2006), “Testing Poverty Lines”, Review of Income and Wealth, 52(3): 399-421.

Shorrocks, A. (1980), “The class of additively decomposable inequality measures”, Econometrica, 48: 613-25.

Stifel, D., B. Minten and P. Dorosh (2003), “Transaction Costs and Agricultural Productivity: Implications of Isolation for Rural Poverty in Madagascar”, MSSD Discussion Poverty no. 56, International Food Policy Research Institute.

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30

Appendix 1 – Regression Analysis of Household Consumption

In this appendix we show the regression results of consumption models, estimated separately by urban ad rural households. The dependent variable is the log of per capita household consumption.

In Table 13 models (1) and (2) are comparable, models (1) and (3) are also comparable, while models (2) and (3) are not. Estimation of models (1), (2) and (3) uses sampling weights.

Table 13 – Regression Estimates of Consumption Models for Rural Households, Madagascar 2001 and 2005

(1) (2) (3) 2001 rural 2001 rural 2005 rural

hh head age 0.002 0.002 0.001 (0.001) (0.001) (0.001) 1 if male hh head 0.136 0.101 -0.002 (0.058)* (0.054) (0.031) number of children aged 0-4 -0.172 -0.174 -0.148 (0.026)** (0.026)** (0.015)** number of children aged 5-14 -0.162 -0.147 -0.140 (0.023)** (0.023)** (0.014)** number of adults male -0.176 -0.174 -0.148 (0.026)** (0.025)** (0.015)** number of adults female -0.197 -0.209 -0.214 (0.028)** (0.028)** (0.016)**hh size squared 0.006 0.006 0.005 (0.001)** (0.001)** (0.001)** dependency ratio -0.596 -0.592 -0.532 (0.095)** (0.094)** (0.055)** hh head separated or divorces 0.029 0.035 -0.014 (0.064) (0.060) (0.033) hh head widower -0.021 -0.047 -0.048 (0.068) (0.061) (0.033) hh head single 0.124 0.092 -0.143 (0.067) (0.066) (0.046)**hh head with primary 0.096 0.109 0.030 (0.040)* (0.037)** (0.019) hh head with low secondary 0.370 0.333 0.107 (0.061)** (0.055)** (0.029)** hh head with upper secondary 0.341 0.333 0.144 (0.076)** (0.069)** (0.049)** number of male adults with at least primary 0.039 0.021 0.018 (0.024) (0.022) (0.012) number of female adults with at least primary 0.079 0.065 0.066 (0.022)** (0.020)** (0.010)** number of members attending school 0.042 0.024 0.018 (0.016)** (0.015) (0.009)* non-agricultural self-employment 0.300 0.234 0.388 (0.062)** (0.055)** (0.039)**wage employment (public sector) 0.282 0.288 0.230 (0.098)** (0.092)** (0.085)** wage employment (enterprise) 0.243 0.107 0.189 (0.067)** (0.058) (0.034)** wage employment (individual) 0.238 0.098 0.009 (0.090)** (0.083) (0.043) not employed 0.152 0.212 -0.216 (0.149) (0.178) (0.088)* informal -0.209 -0.183 -0.139 (0.063)** (0.057)** (0.073)hh owns no or little land -0.331 -0.189 0.040 (0.065)** (0.058)** (0.033) hh owns land (0.10-0.49 ha/head) 0.150 0.170 0.122 (0.035)** (0.032)** (0.020)**

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(1) (2) (3) 2001 rural 2001 rural 2005 rural

hh owns land (0.50-0.99 ha/head) 0.317 0.324 0.224 (0.048)** (0.045)** (0.025)** hh own land (more than 1 ha/head) 0.448 0.480 0.439 (0.055)** (0.050)** (0.032)** Fianarantsoa -0.354 -0.039 -0.166 (0.044)** (0.052) (0.019)**Toamasina -0.415 -0.088 -0.258 (0.045)** (0.053) (0.025)** Mahajanga -0.180 0.010 -0.133 (0.047)** (0.057) (0.022)** Toliara -0.208 0.097 -0.235 (0.051)** (0.058) (0.024)** Antsiranana -0.139 0.226 -0.234 (0.050)** (0.060)** (0.028)** drice1s 0.108 (0.032)** riceirrig 0.002 (0.001)** shareagr -0.003 (0.001)** post 0.021 (0.030) roadnat 0.061 (0.031) market -0.012 (0.029) phone -0.061 (0.045) waterjirama 0.451 (0.056)** time_quin== 2 -0.153 (0.046)** time_quin== 3 -0.212 (0.040)** time_quin== 4 -0.147 (0.045)** time_quin==Most Remote -0.007 (0.049) 1 if cyclones during previous 2 years 0.040 (0.017)* 1 if floud during previous 2 years 0.018 (0.015) 1 if drought during previous 2 years -0.036 (0.013)** 1 if phyto disease during previous 2 years -0.017 (0.009) intercept 14.429 14.237 13.248 (0.137)** (0.144)** (0.092)** Observations 1982 1963 5907 Adjusted R-squared 0.5116 0.5826 0.3539

Note: * significant at 5%; ** significant at 1%.

Source: Authors’ estimates on EPM data for 2001 and 2005, and Commune Census data for 2001.

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Table 14 – Regression Estimates of Consumption Models for Urban Households, Madagascar 2001 and 2005

(1) (2) (3) 2001 urban 2001 urban 2005 urban

hh head age 0.002 0.002 -0.000 [0.001] [0.001]* [0.001] 1 if male hh head 0.087 0.082 0.084 [0.055] [0.054] [0.044] number of children aged 0-4 -0.195 -0.187 -0.206 [0.031]** [0.029]** [0.027]** number of children aged 5-14 -0.200 -0.201 -0.217 [0.027]** [0.027]** [0.021]** number of adults male -0.205 -0.212 -0.301 [0.026]** [0.024]** [0.024]** number of adults female -0.232 -0.242 -0.326 [0.027]** [0.025]** [0.026]** hh size squared 0.005 0.006 0.009 [0.002]** [0.001]** [0.001]** dependency ratio -0.667 -0.704 -0.694 [0.108]** [0.103]** [0.086]** hh head separated or divorces -0.032 -0.024 -0.045 [0.060] [0.058] [0.047] hh head widower 0.023 0.028 -0.056 [0.062] [0.063] [0.053] hh head single 0.159 0.110 0.020 [0.064]* [0.062] [0.055] hh head with primary 0.085 0.085 -0.113 [0.041]* [0.039]* [0.031]** hh head with low secondary 0.425 0.428 -0.077 [0.046]** [0.044]** [0.037]* hh head with upper secondary 0.260 0.281 0.009 [0.054]** [0.051]** [0.044] number of male adults with at least primary 0.013 0.021 0.117 [0.020] [0.020] [0.018]** number of female adults with at least primary 0.091 0.090 0.136 [0.020]** [0.020]** [0.018]** number of members attending school 0.049 0.053 0.015 [0.015]** [0.014]** [0.013] non-agricultural self-employment 0.353 0.307 0.434 [0.052]** [0.050]** [0.039]** wage employment (public sector) 0.246 0.204 0.186 [0.064]** [0.062]** [0.069]** wage employment (enterprise) 0.317 0.239 0.172 [0.053]** [0.051]** [0.039]** wage employment (individual) 0.212 0.157 -0.004 [0.056]** [0.055]** [0.059] not employed 0.283 0.221 -0.114 [0.108]** [0.113] [0.066] informal -0.227 -0.210 -0.357 [0.036]** [0.036]** [0.046]** hh owns no or little land -0.240 -0.177 0.025 [0.049]** [0.047]** [0.031] hh owns land (0.10-0.49 ha/head) 0.079 0.075 0.105 [0.052] [0.049] [0.036]** hh owns land (0.50-0.99 ha/head) 0.153 0.160 0.182 [0.074]* [0.067]* [0.039]** hh own land (more than 1 ha/head) 0.404 0.364 0.253 [0.065]** [0.067]** [0.053]** Fianarantsoa -0.359 -0.267 -0.281 [0.037]** [0.043]** [0.029]** Toamasina -0.423 -0.434 -0.225 [0.038]** [0.043]** [0.032]** Mahajanga -0.204 -0.120 -0.003 [0.039]** [0.057]* [0.031] Toliara -0.203 -0.074 -0.200

33

(1) (2) (3) 2001 urban 2001 urban 2005 urban

[0.044]** [0.052] [0.032]** Antsiranana 0.049 0.065 0.119 [0.038] [0.050] [0.036]** 1 if bank in the community 0.093 [0.042]* 1 if airport in the community -0.205 [0.030]** 1 if harbor river in the community 0.200 [0.045]** 1 fi harbsea 0.028 [0.041] post -0.271 [0.072]** roadnat 0.150 [0.050]** market 0.056 [0.058] phone 0.174 [0.042]** waterjirama 0.135 [0.039]** redz 0.240 [0.044]** Intercept 14.707 14.464 13.881 [0.108]** [0.138]** [0.092]** Observations 2705 2704 5851 Adjusted R-squared 0.5671 0.5995 0.4510

Note: * significant at 5%; ** significant at 1%.

Source: Authors’ estimates on EPM data for 2001 and 2005, and Commune Census data for 2001.

Appendix 2 – Sensitivity Analysis of Poverty Estimates to the Choice of Different Deflators

This Appendix analyses the sensitivity of poverty estimates presented in the text to the choice of different (spatial) price deflators. The issue arises from the fact that in 2001 the welfare indicator was deflated at the urban-rural level for each province (faritany). At the time, this was the finest possible geographical disaggregation level. In 2005, however, the survey was made representative of each of the newly formed 22 regions (faritra) at the urban-rural level. Accordingly, deflation in 2001 used 13 different prices (Antananarivo being counted as a separate area), while in 2005 deflation exploited all the.45 regional prices available (again, Antananarivo counted as an additional region).

To assess the extent to which the change in the deflation method affects the results obtained in the text, we first calculated a set of provincial deflators for the year 2005 by aggregating the regional deflators, and next we used them to re-calculate poverty rates.

Table 16 compares the deflators for 2001, with the new deflators for 2005. The latter were calculated as population-weighted averages of regional prices.

Table 17 contains the key results of the sensitivity analysis. The main conclusion that we draw is that the poverty profile is robust to the choice of a regional versus provincial deflation method. The discrepancy in poverty rates, irrespective of the poverty measure, are – on average – lower than 1 percent.

34

Appendix Table 15 – Provincial Deflators, Madagascar 2001 and 2005

Province 2001 2005 Antananarivo – Urban 0.9132 0.8827Antananarivo – Rural 0.9375 0.8991Antananarivo – Capital 1.0000 1.0000Fianarantsoa – Urban 0.9015 0.8892Fianarantsoa – Rural 0.9208 0.8598Toamasina – Urban 0.9869 0.9030Toamasina – Rural 0.9395 0.8832Mahajanga – Urban 0.8943 0.8929Mahajanga – Rural 0.8398 0.8131Toliara – Urban 0.9250 0.9754Toliara – Rural 0.9386 0.8648Antsiranana – Urban 1.0998 0.9883Antsiranana – Rural 1.0893 0.9974

Source: Authors’ estimates based on EPM data for 2001 and 2005.

Appendix Table 16 – Regional vs. provincial deflators: Sensitivity of poverty estimates

2005

regional deflator 2005

provincial deflator discrepancy

(%) province H PG PG2 H PG PG2 H PG PG2

Antananarivo (U) 41.6 13.6 5.8 41.7 13.6 5.8 0.24 0.00 0.00 Antananarivo (R) 64.7 21.9 9.8 64.8 22.0 9.8 0.15 0.46 0.00 Antananarivo 57.7 19.4 8.6 57.8 19.5 8.6 0.17 0.38 0.31 Fianarantsoa (U) 71.6 28.8 14.5 71.4 28.7 14.5 -0.28 -0.35 0.00 Fianarantsoa (R) 78.7 30.9 15.1 79.1 31.0 15.3 0.51 0.32 1.32 Fianarantsoa 77.6 30.6 15.0 77.8 30.6 15.2 0.26 0.12 1.14 Toamasina (U) 55.8 21.4 11.2 57.0 21.6 11.2 2.15 0.93 0.00 Toamasina (R) 75.6 33.1 18.0 75.9 33.1 18.0 0.40 0.00 0.00 Toamasina 71.9 30.9 16.7 72.5 31.0 16.7 0.83 0.30 0.28 Mahajanga (U) 47.0 16.1 7.2 48.4 15.9 7.1 2.98 -1.24 -1.39 Mahajanga (R) 76.6 28.9 13.9 75.5 29.3 14.4 -1.44 1.38 3.60 Mahajanga 70.2 26.2 12.4 69.6 26.4 12.8 -0.85 0.82 3.52 Toliara (U) 64.3 28.3 15.9 65.3 28.4 15.8 1.56 0.35 -0.63 Toliara (R) 77.4 34.0 19.1 77.5 34.0 19.1 0.13 0.00 0.00 Toliara 74.8 32.9 18.4 75.0 32.9 18.4 0.27 -0.05 0.14 Antsiranana (U) 33.8 9.4 3.5 35.3 9.3 3.4 4.44 -1.06 -2.86 Antsiranana (R) 69.8 28.1 14.2 69.6 28.1 14.0 -0.29 0.00 -1.41 Antsiranana 64.2 25.2 12.5 64.3 25.2 12.4 0.16 -0.07 -1.01 Urban 52.0 19.3 9.4 52.5 19.3 9.4 0.96 0.00 0.00 Rural 73.5 28.9 14.5 73.5 29.0 14.6 0.00 0.35 0.69 Madagascar 68.7 26.8 13.4 68.9 26.9 13.5 0.29 0.37 0.75

Source: Authors’ estimates based on EPM data.

35

Appendix 3 – Sectoral Value Added And Population Growth Rates, Projections for 2006-2010

Sectors 2006 2007 2008 2009 2010 Primary Sector Agriculture 2.6 2.3 3.5 3.5 3.5 Farming & Fishing 1.9 3.2 3.2 3.2 3.2 Forestry 1.0 1.0 1.0 1.0 1.5 Other 2.1 2.6 3.2 3.2 3.2 Secondary sector Agro industry -12.5 6.0 6.0 6.0 6.0 Mining 9.1 10.5 11.5 11.5 11.5 Energy 4.4 5.6 5.6 5.6 5.6 Food industry 0.0 7.3 8.3 8.3 8.3 Chemical 2.2 3.5 3.5 3.5 3.5 Textile 0.8 6.0 6.0 6.0 6.0 Wood 4.1 5.0 5.0 5.0 5.0 Construction 11.6 15.9 16.9 13.0 12.0 Other -6.0 7.0 7.0 7.0 6.0 Tertiary sector Public Construction 22.5 22.8 18.1 18.0 14.0 Transport 8.7 9.5 11.1 11.0 10.0 Telecom 12.0 10.8 10.8 10.8 10.8 Trade 4.4 7.3 8.9 8.9 8.9 Banks & Insurance 14.2 8.9 7.6 7.6 7.6 Services 5.5 7.9 9.5 9.5 9.5 Public Administration 2.1 2.5 4.1 5.6 5.6 Population 2.8 2.8 2.7 2.7 2.6

Source: Ministère des Finances et de l'Economie, INSTAT, World Bank and IMF projections.

36

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Series # Title Date Author

Poverty, and Development in sub-Saharan Africa and South Asia

ARWPS 21 Handling Hierarchy in Decentralized Settings: Governance Underpinnings of School Performance in Tikur Inchini, West Shewa Zone, Oromia Region

September 2001 Navin Girishankar A. Alemayehu Yusuf Ahmad

ARWPS 22 Child Malnutrition in Ethiopia: Can Maternal Knowledge Augment The Role of Income?

October 2001 Luc Christiaensen Harold Alderman

ARWPS 23 Child Soldiers: Preventing, Demobilizing and Reintegrating

November 2001 Beth Verhey

ARWPS 24 The Budget and Medium-Term Expenditure Framework in Uganda

December 2001 David L. Bevan

ARWPS 25 Design and Implementation of Financial Management Systems: An African Perspective

January 2002 Guenter Heidenhof H. Grandvoinnet Daryoush Kianpour B. Rezaian

ARWPS 26 What Can Africa Expect From Its Traditional Exports?

February 2002 Francis Ng Alexander Yeats

ARWPS 27 Free Trade Agreements and the SADC Economies February 2002 Jeffrey D. Lewis Sherman Robinson Karen Thierfelder

ARWPS 28 Medium Term Expenditure Frameworks: From Concept to Practice. Preliminary Lessons from Africa

February 2002 P. Le Houerou Robert Taliercio

ARWPS 29 The Changing Distribution of Public Education Expenditure in Malawi

February 2002 Samer Al-Samarrai Hassan Zaman

ARWPS 30 Post-Conflict Recovery in Africa: An Agenda for the Africa Region

April 2002 Serge Michailof Markus Kostner Xavier Devictor

ARWPS 31 Efficiency of Public Expenditure Distribution and Beyond: A report on Ghana’s 2000 Public Expenditure Tracking Survey in the Sectors of Primary Health and Education

May 2002 Xiao Ye S. Canagaraja

ARWPS 33 Addressing Gender Issues in Demobilization and Reintegration Programs

August 2002 N. de Watteville

ARWPS 34 Putting Welfare on the Map in Madagascar August 2002 Johan A. Mistiaen Berk Soler T. Razafimanantena J. Razafindravonona

ARWPS 35 A Review of the Rural Firewood Market Strategy in West Africa

August 2002 Gerald Foley P. Kerkhof, D. Madougou

38

Africa Region Working Paper Series

Series # Title Date Author

ARWPS 36 Patterns of Governance in Africa September 2002 Brian D. Levy

ARWPS 37 Obstacles and Opportunities for Senegal’s International Competitiveness: Case Studies of the Peanut Oil, Fishing and Textile Industries

September 2002 Stephen Golub Ahmadou Aly Mbaye

ARWPS 38 A Macroeconomic Framework for Poverty Reduction Strategy Papers : With an Application to Zambia

October 2002 S. Devarajan Delfin S. Go

ARWPS 39 The Impact of Cash Budgets on Poverty Reduction in Zambia: A Case Study of the Conflict between Well Intentioned Macroeconomic Policy and Service Delivery to the Poor

November 2002 Hinh T. Dinh Abebe Adugna Bernard Myers

ARWPS 40 Decentralization in Africa: A Stocktaking Survey November 2002 Stephen N. Ndegwa

ARWPS 41 An Industry Level Analysis of Manufacturing Productivity in Senegal

December 2002 Professor A. Mbaye

ARWPS 42 Tanzania’s Cotton Sector: Constraints and Challenges in a Global Environment

December 2002 John Baffes

ARWPS 43 Analyzing Financial and Private Sector Linkages in Africa

January 2003 Abayomi Alawode

ARWPS 44 Modernizing Africa’s Agro-Food System:

Analytical Framework and Implications for Operations

February 2003 Steven Jaffee Ron Kopicki Patrick Labaste Iain Christie

ARWPS 45 Public Expenditure Performance in Rwanda March 2003 Hippolyte Fofack C. Obidegwu Robert Ngong

ARWPS 46 Senegal Tourism Sector Study March 2003 Elizabeth Crompton Iain T. Christie

ARWPS 47 Reforming the Cotton Sector in SSA March 2003 Louis Goreux John Macrae

ARWPS 48 HIV/AIDS, Human Capital, and Economic Growth Prospects for Mozambique

April 2003 Channing Arndt

ARWPS 49 Rural and Micro Finance Regulation in Ghana: Implications for Development and Performance of the Industry

June 2003 William F. Steel David O. Andah

ARWPS 50 Microfinance Regulation in Benin: Implications of the PARMEC LAW for Development and Performance of the Industry

June 2003 K. Ouattara

ARWPS 51 Microfinance Regulation in Tanzania: Implications for Development and Performance of the Industry

June 2003 Bikki Randhawa Joselito Gallardo

ARWPS 52 Regional Integration in Central Africa: Key Issues June 2003 Ali Zafar Keiko Kubota

39

Africa Region Working Paper Series

Series # Title Date Author

ARWPS 53 Evaluating Banking Supervision in Africa June 2003 Abayomi Alawode

ARWPS 54 Microfinance Institutions’ Response in Conflict Environments: Eritrea- Savings and Micro Credit Program; West Bank and Gaza – Palestine for Credit and Development; Haiti – Micro Credit National, S.A.

June 2003

Marilyn S. Manalo

AWPS 55 Malawi’s Tobacco Sector: Standing on One Strong leg is Better than on None

June 2003 Steven Jaffee

AWPS 56 Tanzania’s Coffee Sector: Constraints and Challenges in a Global Environment

June 2003 John Baffes

AWPS 57 The New Southern AfricanCustoms Union Agreement

June 2003 Robert Kirk Matthew Stern

AWPS 58a How Far Did Africa’s First Generation Trade Reforms Go? An Intermediate Methodology for Comparative Analysis of Trade Policies

June 2003 Lawrence Hinkle A. Herrou-Aragon Keiko Kubota

AWPS 58b How Far Did Africa’s First Generation Trade Reforms Go? An Intermediate Methodology for Comparative Analysis of Trade Policies

June 2003 Lawrence Hinkle A. Herrou-Aragon Keiko Kubota

AWPS 59 Rwanda: The Search for Post-Conflict Socio-Economic Change, 1995-2001

October 2003 C. Obidegwu

AWPS 60 Linking Farmers to Markets: Exporting Malian Mangoes to Europe

October 2003 Morgane Danielou Patrick Labaste J-M. Voisard

AWPS 61 Evolution of Poverty and Welfare in Ghana in the 1990s: Achievements and Challenges

October 2003 S. Canagarajah Claus C. Pörtner

AWPS 62 Reforming The Cotton Sector in Sub-Saharan

Africa: SECOND EDITION

November 2003 Louis Goreux

AWPS 63 (E)

Republic of Madagascar: Tourism Sector Study November 2003 Iain T. Christie D. E. Crompton

AWPS 63 (F) République de Madagascar: Etude du Secteur Tourisme

November 2003 Iain T. Christie D. E. Crompton

AWPS 64 Migrant Labor Remittances in Africa: Reducing Obstacles to Development Contributions

Novembre 2003 Cerstin Sander Samuel M. Maimbo

AWPS 65 Government Revenues and Expenditures in Guinea-Bissau: Casualty and Cointegration

January 2004 Francisco G. Carneiro Joao R. Faria Boubacar S. Barry

AWPS 66 How will we know Development Results when we see them? Building a Results-Based Monitoring and Evaluation System to Give us the Answer

June 2004 Jody Zall Kusek Ray C. Rist Elizabeth M. White

AWPS 67 An Analysis of the Trade Regime in Senegal (2001) and UEMOA’s Common External Trade

June 2004 Alberto Herrou-Arago

40

Africa Region Working Paper Series

Series # Title Date Author

Policies Keiko Kubota

AWPS 68 Bottom-Up Administrative Reform: Designing Indicators for a Local Governance Scorecard in Nigeria

June 2004 Talib Esmail Nick Manning Jana Orac Galia Schechter

AWPS 69 Tanzania’s Tea Sector: Constraints and Challenges

June 2004 John Baffes

AWPS 70 Tanzania’s Cashew Sector: Constraints and Challenges in a Global Environment

June 2004 Donald Mitchell

AWPS 71 An Analysis of Chile’s Trade Regime in 1998 and 2001: A Good Practice Trade Policy Benchmark

July 2004 Francesca Castellani A. Herrou-Arago Lawrence E. Hinkle

AWPS 72 Regional Trade Integration inEast Africa: Trade and Revenue Impacts of the Planned East African Community Customs Union

August 2004 Lucio Castro Christiane Kraus Manuel de la Rocha

AWPS 73 Post-Conflict Peace Building in Africa: The Challenges of Socio-Economic Recovery and Development

August 2004 Chukwuma Obidegwu

AWPS 74 An Analysis of the Trade Regime in Bolivia in2001: A Trade Policy Benchmark for low Income Countries

August 2004 Francesca Castellani Alberto Herrou-Aragon Lawrence E. Hinkle

AWPS 75 Remittances to Comoros- Volumes, Trends, Impact and Implications

October 2004 Vincent da Cruz Wolfgang Fendler Adam Schwartzman

AWPS 76 Salient Features of Trade Performance in Eastern and Southern Africa

October 2004 Fahrettin Yagci Enrique Aldaz-Carroll

AWPS 77 Implementing Performance-Based Aid in Africa November 2004 Alan Gelb Brian Ngo Xiao Ye

AWPS 78 Poverty Reduction Strategy Papers: Do they matter for children and Young people made vulnerable by HIV/AIDS?

December 2004 Rene Bonnel Miriam Temin Faith Tempest

AWPS 79 Experience in Scaling up Support to Local Response in Multi-Country Aids Programs (map) in Africa

December 2004 Jean Delion Pia Peeters Ann Klofkorn Bloome

AWPS 80 What makes FDI work? A Panel Analysis of the Growth Effect of FDI in Africa

February 2005 Kevin N. Lumbila

AWPS 81 Earnings Differences between Men and Women in Rwanda

February 2005 Kene Ezemenari Rui Wu

AWPS 82 The Medium-Term Expenditure Framework: The Challenge of Budget Integration in SSA countries

April 2005 Chukwuma Obidegwu

41

Africa Region Working Paper Series

Series # Title Date Author

AWPS 83 Rules of Origin and SADC: The Case for change in the Mid Term Review of the Trade Protocol

June 2005 Paul Brenton Frank Flatters Paul Kalenga

AWPS 84 Sexual Minorities, Violence and AIDS in Africa

July 2005 Chukwuemeka Anyamele Ronald Lwabaayi Tuu-Van Nguyen, and Hans Binswanger

AWPS 85 Poverty Reducing Potential of Smallholder Agriculture in Zambia: Opportunities and Constraints

July 2005 Paul B. Siegel Jeffrey Alwang

AWPS 86 Infrastructure, Productivity and Urban Dynamics in Côte d’Ivoire An empirical analysis and policy implications

July 2005 Zeljko Bogetic Issa Sanogo

AWPS 87 Poverty in Mozambique: Unraveling Changes and Determinants

August 2005 Louise Fox Elena Bardasi, Katleen V. Broeck

AWPS 88 Operational Challenges: Community Home Based Care (CHBC) forPLWHA in Multi-Country HIV/AIDS Programs (MAP) forSub-Saharan Africa

August 2005 N. Mohammad Juliet Gikonyo

AWPS 90 Kenya: Exports Prospects and Problems September 2005 Francis Ng Alexander Yeats

AWPS 91 Uganda: How Good a Trade Policy Benchmark for Sub-Saharan-Africa

September 2005 Lawrence E. Hinkle Albero H. Aragon Ranga Krishnamani Elke Kreuzwieser

AWPS 92 Community Driven Development in South Africa, 1990-2004

October 2005 David Everatt Lulu Gwagwa

AWPS 93 The Rise of Ghana’’s Pineapple Industry from Successful take off to Sustainable Expansion

November 2005 Morgane Danielou Christophe Ravry

AWPS 94 South Africa: Sources and Constraints of Long-Term Growth, 1970-2000

December 2005 Johannes Fedderke

AWPS 95 South Africa’’s Export Performance: Determinants of Export supply

December 2005 Lawrence Edwards Phil Alves

AWPS 96 Industry Concentration in South African Manufacturing: Trends and Consequences, 1972-96

December 2005 Gábor Szalontai Johannes Fedderke

AWPS 97 The Urban Transition in Sub-Saharan Africa: Implications for Economic Growth and Poverty Reduction

December 2005 Christine Kessides

AWPS 98 Measuring Intergovernmental Fiscal Performance in South Africa Issues in Municipal Grant Monitoring

May 2006 Navin Girishankar David DeGroot T.V. Pillay

AWPS 99 Nutrition and Its determinants in Southern Ethiopia - Findings from the Child Growth Promotion Baseline Survey

July 2006 Jesper Kuhl Luc Christiaensen

42

Africa Region Working Paper Series

Series # Title Date Author

AWPS 100 The Impact of Morbidity and Mortality on Municipal Human Resources and Service Delivery

September 2006 Zara Sarzin

AWPS 101 Rice Markets in Madagascar in Disarray: Policy Options for Increased Efficiency and Price Stabilization

September 2006 Bart Minten Paul Dorosh Marie-Hélène Dabat, Olivier Jenn-Treyer, John Magnay and Ziva Razafintsalama

AWPS 102 Riz et Pauvrete a Madagascar Septembre 2006 Bart Minten

AWPS 103 ECOWAS- Fiscal Revenue Implications of the Prospective Economic Partnership Agreement with the EU

April 2007 Simplice G. Zouhon-Bi Lynge Nielsen

AWPS 104(a)

Development of the Cities of Mali Challenges and Priorities

June 2007 Catherine Farvacque-V. Alicia Casalis Mahine Diop Christian Eghoff

AWPS 104(b)

Developpement des villes Maliennes Enjeux et Priorites

June 2007 Catherine Farvacque-V. Alicia Casalis Mahine Diop Christian Eghoff

AWPS 105 Assessing Labor Market Conditions In Madagascar, 2001-2005

June 2007 David Stifel Faly H. Rakotomanana Elena Celada

AWPS 106 An Evaluation of the Welfare Impact of Higher Energy Prices in Madagascar

June 2007 Noro Andriamihaja Giovanni Vecchi

AWPS 107 The Impact of The Real Exchange Rate on Manufacturing Exports in Benin

November 2007 Mireille Linjouom

AWPS 108 Building Sector concerns into Macroeconomic Financial Programming: Lessons from Senegal and Uganda

December 2007 Antonio Estache Rafael Munoz

AWPS 109 An Accelerating Sustainable, Efficient and Equitable Land Reform: Case Study of the Qedusizi/Besters Cluster Project

December 2007 Hans P. Binswanger Roland Henderson Zweli Mbhele Kay Muir-Leresche

AWPS 110 Development of the Cites of Ghana – Challenges, Priorities and Tools

January 2008 Catherine Farvacque-Vitkovic Madhu Raghunath Christian Eghoff Charles Boakye

AWPS 111 Is Africa’s Recent Growth Robust? January 2008 Jorge Saba Arbache John Page

AWPS 112 MADAGASCAR: Labor Markets, the Non-Farm Economy and Household Livelihood Strategies in Rural Madagascar

March 2008 David Stifel Stefano Paternostro

AWPS 113 Growth, Inequallity and Poverty in Madagascar, 2001-2005

April 2007 Nicolas Amendola Giovanni Vecchi

43

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