Growth, Inequality and Poverty in Madagascar, 2001-2005 (World Bank- 2008)

8/6/2019 Growth, Inequality and Poverty in Madagascar, 2001-2005 (World Bank- 2008)

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

Abstracthe paper examines changes in poverty and inequality in Madagascar between theyears 2001 and 2005. During this period Madagascars economic progress hasbeen 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, andincome grew faster for the poor than the average. But poverty remains pervasive inMadagascar, with more than two thirds of the population below the poverty line. Andthough the incidence of poverty has barely changed, the number of the poor hasincreased by some two million individuals. Large disparities persist between urban andrural areas, as well as across provinces. Regression analysis shows that these disparitiespersist even after controlling for a wide range of socio-economic and demographichousehold characteristics. By matching household-level survey data from the Enqute

Priodique auprs des Mnages to community-level census data we identify three factorsthat largely explain the provincial variation in poverty rates: (i) infrastructure, (ii) landtenure and cropping patterns, and (iii) climate shocks. As for the future, simulations forbenchmark years 2007 and 2010 project incremental reductions in poverty rates on theorder of 0.5-2.0 percent per yearas estimates of earnings functions, provide supportingevidence of these barriers.

.JEL Codes: I31, I32, O1, O55.Key Words: Madagascar; poverty; inequality; growth incidence analysis.

Authors Affiliation and Sponsorship

Nicola AmendolaUniversity of Rome Tor Vergata

Giovanni VecchiUniversity of Rome Tor Vergata

The Africa Region Working Paper Series expedites dissemination of applied research and policy studies with potentialfor improving economic performance and social conditions in Sub-Saharan Africa. The Series publishes papers atpreliminary 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 appointedby the Regions Sector Directors. For additional information, please contact Paula White, managing editor of the series,(81131), Email: [email protected] or visit the Web site: http://www.worldbank.org/afr/wps/index.htm.

The findings, interpretations , and conclusions expressed in th is paper are entirely those of the author(s), theydo not necessarily represent the views of t he World Bank Group, its Executive Directors, or t he countries theyrepresent and should not be attributed to them.

T

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Growth, Inequalityand Povertyin Madagascar,2001-2005

Nicola AmendolaUniversity 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:[email protected]. We would like to thank Benu Bidani, Stefano Paternostro, Ken Simler and David Stifelwhose comments, advice and support have been invaluable. We are grateful to Tiaray Razafimanantena for usefulcomments, and to Elena Celada and Laza Razafiarison for help at various stages of the project. The usual disclaimerapplies. 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, ............... 6Figure 2 Sectoral growth rates, 1996-2006 ................................................................................ 7Figure 3 GDP shares (1984 prices), 1980-2006 ..................................... .................................... 7Figure 4 First-order stochastic dominance test, Madagascar 2001-2005 ................................. 11

Figure 5 First-order stochastic dominance by urban/rural area, 2001-2005 ............................. 12Figure 6 Second-order stochastic dominance test, Madagascar, 2001-05. ............................... 13Figure 7 Second-order stochastic dominance by urban/rural area ...................................... ..... 13Figure 8 Lorenz Curves for Madagascar, 2001 and 2005 .................................... .................... 15Figure 9 Changes in the poverty gap index, 2001-2005 ....................................... .................... 18Figure 10 The growth incidence curve for Madagascar, 2001-2005 ........................................ 20Figure 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 ...................................... ......................... 8Table 2 Poverty and Inequality Trends, Madagascar 2001-2005 (%) ...................................... 10Table 3 Inequality and mean consumption by province, 2001 and 2005 ................................. 14Table 4 Inequality Decompositions, Madagascar 2001 and 2005 ......................................... ... 16Table 5 Decompositions of the changes in aggregate inequality, 2001 and 2005 .................... 16Table 6 Poverty estimates by province in 2001 and 2005 ........................................... ............. 17Table 7 Comparison of poverty profiles, 2001 and 2005 ..................................... .................... 19Table 8 Growth rates (%) among the poor, Madagascar 2001-2005 ....................................... 21Table 9 Growth elasticities of poverty for Madagascar, 2001 and 2005 .................................. 22Table 10 Growth-inequality decompositions, Madagascar 2001-2005 .................................... 23Table 11 Sectoral decomposition of poverty, 2001-2005 ........................................................ 24Table 12 Impact of growth on poverty in Madagascar............................................................. 26Table 13 Regression Estimates of Consumption Models for Rural Households, .................... 30Table 14 Regression Estimates of Consumption Models for Urban Households, ................... 32Appendix Table 15 Provincial Deflators, Madagascar 2001 and 2005 .................................... 34Appendix Table 16 Regional vs. provincial deflators: Sensitivity of poverty estimates ...... 34


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1 Introduction

Madagascar is one of the worlds 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 examiningthe 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 tosubsistence level for most of Malagasy population, and other social indicators are far below the

Millennium Development Goal targets.

An account of Madagascars more recent economic performance is provided by a study

conducted jointly by the Malagasy National Statistical Institute (INSTAT), Cornell Universityand 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 nationalpoverty rates remained relatively steady,

while significant swings both in urban and rural areas and across provinces were observed. Thispoint 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 ofliving standards, poverty and inequality in Madagascar during the first half of the 2000s.

The paper uses household survey data from the Enqute Priodique auprs des Mnages (EPM)

fielded in 2001 and 2005, with the aims to (i) update the poverty and inequality profiles, (ii) toidentify 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 isstatistically 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 recessions negative impact on the living standards potentiallyaffects 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 2001Recensement des Communes has allowed us to estimate a household

consumption model in which we control not only for demographic and socio-economichousehold 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, andclimatic 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 Madagascars recent

performance in terms of economic growth. Section 3 discusses the comparability of EPM 2001and 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 ofthe 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 withcensus 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. Inthis 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 capitaGDP 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

300000

350000

400000

450000

500000

3500

4000

4500

5000

5500

6000

1980 1985 1990 1995 2000 2006year

GDP (left y-scale)

GDP per head (right y-scale)

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 onlymodest (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 thegrowth rates in the secondary sector have declined in the recent years.

1For more see IMF (2007) and OECD (2007).


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Figure 2 Sectoral growth rates, 1996-2006

-20

-10

0

10

20

Annua

lgrow

thra

te(%)

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Primary

Secondary

Tertiary

Source:World Bank data.

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 andtertiary 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, theprimary 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 significantdecline 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

10

20

30

40

50

60

ShareofGDP(%)

1985 1990 1995 2000 2005 2007year

share primary sect share secondary sect

share tertiary sect

Source:World Bank database.


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From the evidence examined in this section, two main conclusions emerge. First, the impact ofeconomic 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, thesectoral 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 labourforce.

3 Household Surveys in Madagascar

The data used for this paper are drawn from the Enqute Priodique auprs des Mnages(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 analysisof the methodological problems arising from different survey designs and different choices

underlying the welfare indicator. Unfortunately, we lack a similar (systematic) assessment forthe 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 and2005.2

Table 1 compares the 2001 and 2005 EPM survey designs, showing that no major differences

exist between the two surveys.

Table 1 EPM 2001 versusEPM 2005: A Comparison

2001 2005

Sample design

Two-stage stratified: 12 strata, (1st) 303ZD (clusters), (2

nd) 5,080 household (16

per urban cluster, and 18 per ruralcluster).

Two-stage stratified: 44 strata, (1st)561 ZD (clusters), (2

nd) 11,781

household (21 per cluster).

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

Actual sample size23,170 individuals corresponding to5,080 households (3,040 urban, 2,040rural)

54,966 individuals corresponding to11,781 households (5,859 urban,5,922 rural)

Representativenessprovincial level (6 faritany), andurban/rural within each province

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

Time reference, recallperiod

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

Idem

ConsumptionExpenditures on both food and non-food

include in-kind consumption items.Idem

Of special concern to us is the consistency of the welfare indicators definition. This is crucial toguaranteeing 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

2Data 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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identicalmethodologies. This is the result of a strategy pursued in 2005, when inter-temporalpoverty 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 thediscrepancy 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 theanalysis 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 linebetween 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 theconstruction 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 thedynamics 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. Wefirst 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.

3In 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 2005Urban Rural National Urban Rural National

Headcount 44.2 77.3 69.7 52.0 73.5 68.795% 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 26.895% 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 13.495% 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 0.36595% 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 0.27395% c. i. [.305,.376] [.297,.441] [.362, .441] [.266, .362] [.179, .289] [.232, .314]

Background StatisticsTotal population 3,588 12,079 15,667 4,146 14,701 18,847Population share 22.9 77.1 100 22.0 78.0 100Mean PCE 475,287 251,770 302,883 401,471 269,663 298,644Mean 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 197,720Ariary/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 notchanged. While point estimates in Table 2

show that the headcount ratio decreasedfrom 69.7 percent in 2001 to 68.7 percent in 2005, thechange is not statistically significant. In contrast, the number of poor people has increasedby

some 2 million units during the same period. As recently noted by Chakravarti, Kanbur and

Mukherjee (2006), while the economists instinct is probably to conclude that poverty inMadagascar 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

0

.2

.4

.6

.8

1

0 500000 1000000 1500000per capita expenditure (2005 ariary)

2001

2005

Source: Authors estimates on EPM data.

Poverty changes were not uniformly distributed across the national territory. Table 1 shows anasymmetric pattern of the incidence of poverty between rural and urban areas. This is a majorissue 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 thenumbers 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 asmeasured by the headcount ratio is notrobust to the choice of the poverty line.

5Note 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

0

.2

.4

.6

.8

1

empirica

lcdf

URBAN

0

.2

.4

.6

.8

1

empiricalcdf

0 500000 1000000 150000per capita expenditure (2005 ariary)

2001

2005

RURAL

Source: Authors estimates on EPM data.

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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poor benefited even more than the average poor during the period 2001 and 2005, and thisimprovement was, once again, more pronounced in rural areas.

4.3 Inequality

The evidence in Table 2 suggests that between 2001 and 2005 a substantial incomeredistribution 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 ruralareas. 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 andMahajanga, when using the Theil index). The provincial trends show, however, a significant

dispersion in inequality reduction rates, with Antananarivo, Fianarantsoa and Toliara standingout 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 % ChangeGINIurban 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 -24.2Fianarantsoa 0.411 0.369 0.406 0.385 0.278 0.300 -6.3 -24.7 -26.1Toamasina 0.424 0.356 0.402 0.392 0.390 0.398 -7.5 9.6 -1.0Mahajanga 0.395 0.365 0.402 0.390 0.366 0.385 -1.3 0.3 -4.2Toliara 0.450 0.386 0.433 0.431 0.348 0.373 -4.2 -9.8 -13.9Antsiranana 0.343 0.342 0.389 0.379 0.361 0.384 10.5 5.6 -1.3Madagascar 0.439 0.445 0.469 0.405 0.335 0.365 -7.7 -24.7 -22.2

THEILurban 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 -31.8Fianarantsoa 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 8.3Mahajanga 0.277 0.227 0.290 0.296 0.356 0.353 6.9 56.8 21.7Toliara 0.366 0.267 0.348 0.351 0.217 0.260 -4.1 -18.7 -25.3Antsiranana 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 DEVIATIONurban 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 -44.9Toamasina 0.311 0.209 0.268 0.265 0.255 0.265 -14.8 22.0 -1.1Mahajanga 0.255 0.224 0.272 0.250 0.236 0.253 -2.0 5.4 -7.0Toliara 0.359 0.250 0.316 0.316 0.205 0.235 -12.0 -18.0 -25.6Antsiranana 0.199 0.194 0.252 0.237 0.215 0.243 19.1 10.8 -3.6Madagascar 0.338 0.330 0.371 0.276 0.190 0.224 -18.3 -42.4 -39.6

MEAN CONSUMPTIONurban 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 22.9Toamasina 332048 175721 208096 367884 268275 286627 10.8 52.7 37.7Mahajanga 422524 216732 260147 421211 285374 314709 -0.3 31.7 21.0Toliara 384209 204392 247397 329001 241362 259095 -14.4 18.1 4.7Antsiranana 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.



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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

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1

Percapitaexpenditureshareof

poorestp100%

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

Legend:

0

.2

.4

.6

.8

1

0

.2

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poorestp100%


URBAN

0

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.8

1

0

.2

.4

.6

.8

1


poorestp100%


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 Mookherjeeand 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 thewithin- 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 ofinequality 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 EntropyIndices) 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 withincomponent 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 largestcontribution 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.


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Table 4 Inequality Decompositions, Madagascar 2001 and 2005

2001 2005Index 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 province

MLD(%)

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 staticdecomposition, 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 threecomponents: (A) pure inequality effectarising from changes in inequality within groups, (B)

population-share effect(or allocational effect) arising from changes in the number of peoplewithin different groups, and (C) income effect arising from changes in relative expenditures

between groups. Following Jenkins (1995), we decomposed thepercentage 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 andrural 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-grouppartition

change inMLD(%)

% change in MLD accounted for by changes inwithin-groupinequalities

(A)

populationshares

(B)

sub-group meanincomes

(C)

urban/rural -40 -33 0 -7

province -40 -25 0 -15


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 and2005. 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 betweenprovinces (urban/rural areas).


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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 ofpoverty. Focussing on the headcount ratio, estimated poverty changes between 2001 and 2005

are found to vary widely across provinces. The incidence of poverty increasedin Antananarivo(+18.7 percent), it decreasedin 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 theobserved 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. Antananarivois the laggard province.

Table 6 Poverty estimates by province in 2001 and 2005

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

Headcount ratioAntananarivo 28.3 57.1 48.6 41.6 64.7 57.7 47.0 13.3 18.7Fianarantsoa 59.8 87.9 83.2 71.6 78.7 77.6 19.7 -10.5 -6.7Toamasina 60.8 87.9 82.3 55.8 75.6 71.9 -8.2 -14.0 -12.6Mahajanga 50.7 78.4 72.5 47.0 76.6 70.2 -7.3 -2.3 -3.2Toliara 53.3 83.3 76.1 64.3 77.4 74.8 20.6 -7.1 -1.7Antsiranana 30.1 79.0 69.3 33.8 69.8 64.2 12.3 -11.6 -7.4Madagascar 44.2 77.3 69.7 52.0 73.5 68.7 17.6 -4.9 -1.4Poverty Gap

Antananarivo 10.4 25.6 21.1 13.6 21.9 19.4 30.8 -14.5 -8.1Fianarantsoa 25.5 49.5 45.5 28.8 30.9 30.6 12.9 -37.6 -32.7Toamasina 28.5 48.3 44.2 21.4 33.1 30.9 -24.9 -31.5 -30.1Mahajanga 17.4 40.0 35.2 16.1 28.9 26.2 -7.5 -27.8 -25.6Toliara 25.5 43.6 39.2 28.3 34.0 32.9 11.0 -22.0 -16.1Antsiranana 8.7 34.1 29.0 9.4 28.1 25.2 8.0 -17.6 -13.1Madagascar 18.3 39.8 34.9 19.3 28.9 26.8 5.5 -27.4 -23.2Poverty Gap SquaredAntananarivo 5.3 13.9 11.3 5.8 9.8 8.6 9.4 -29.5 -23.9Fianarantsoa 13.6 31.7 28.7 14.5 15.1 15.0 6.6 -52.4 -47.7Toamasina 16.3 30.5 27.6 11.2 18.0 16.7 -31.3 -41.0 -39.5Mahajanga 8.0 23.9 20.6 7.2 13.9 12.4 -10.0 -41.8 -39.8Toliara 15.1 26.9 24.1 15.9 19.1 18.4 5.3 -29.0 -23.7Antsiranana 3.8 18.9 15.8 3.5 14.2 12.5 -7.9 -24.9 -20.9Madagascar 9.9 24.2 20.9 9.4 14.5 13.4 -5.1 -40.1 -35.9

Note: poverty lines here.


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 featureof Table 7, however, is the substantial immobility that emerges from the comparison of the


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structure of poverty between 2001 and 2005 (column poverty share). For most of the povertycovariates 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 changesignificantly 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 whenheaded 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 forhouseholds 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 inagriculture. 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 beplaying a role. It is impossible to distinguish on the basis of simple correlation analysis, but wewill 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.

6In this situation, it was deemed needless to carry out formal tests for statistical significance of the

observed differences.


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Table 7 Comparison of poverty profiles, 2001 and 2005

2001 Pop.share

Povertyshare

H PG PG2Pop.

SharePovsha

Gender Female 15.1 15.0 69.2 35.5 21.9 14.4 Male 84.9 85.0 69.8 34.8 20.8 85.6

Age 0-24 5.5 5.1 65.4 30.5 17.0 4.3 25-44 51.6 52.0 70.3 35.2 21.1 52.0 45-64 36.0 36.3 70.2 35.7 21.6 36.4 65+ 6.9 6.6 66.2 32.6 18.8 7.3

Status Married 83.1 83.5 70.1 34.9 20.8 83.0 Divorced 7.3 7.7 73.0 36.8 22.5 7.1 Widower 6.9 6.8 69.0 37.1 23.5 8.3 Single 2.6 2.0 53.0 22.9 12.5 1.7

Education None 47.0 57.1 84.6 46.2 29.1 52.3 Primary 29.5 30.3 71.5 33.7 19.2 30.0 Lower secondary 10.1 7.1 49.1 19.1 10.0 9.3 Upper secondary 7.5 4.4 41.1 14.4 6.5 4.9 Post secondary 5.8 1.1 13.4 4.1 1.7 3.4

Employment status Not employed 4.2 2.9 48.1 20.3 10.8 3.5 Wage earner 21.5 11.9 38.4 14.7 7.5 17.8 Self-employed 74.2 85.2 80.0 41.6 25.4 78.8

Economic sector Agriculture 70.8 85.1 85.0 44.7 27.4 77.9 Industry 5.9 3.1 37.8 14.1 7.1 2.8 Services 23.4 11.8 35.6 13.3 6.8 19.3

Size 1-2 6.1 3.2 37.2 12.5 5.7 5.1 3-4 26.5 22.5 59.2 26.6 14.6 25.0 5-6 31.5 31.1 68.8 32.9 19.3 33.0 7+ 35.9 43.1 83.8 46.6 29.6 36.9

Remoteness Most remote 7.2 8.4 81.0 41.0 23.7 14.6 4 15.0 18.9 88.0 48.6 31.1 15.2 3 17.4 22.6 90.3 50.0 31.6 14.6 2 10.9 12.5 79.8 39.8 24.1 14.5 Least remote 26.5 23.1 60.7 27.8 15.6 19.2 Second. city 11.9 9.8 57.1 25.3 14.2 12.1

Large city 10.9 4.7 30.2 10.7 5.2 9.9

Note: H stands for headcount ratio, PG is the poverty gap index, PG2 is the poverty gap squared index.



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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 incidencecurves 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 identifythe 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 capitaexpenditure 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 nonpoor.

Figure 10 shows the GIC for Madagascar, based on household per capita expenditures from the2001 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 70percent 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

Growthrateofpercapitaexpenditure2001to2005

(%)

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


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 comparisonbetween 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 lesspenalized by the decline in average income.


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

Average growth rate: -15.5%

-20

0

20

40

60

Growthrateofpercapita

expenditure2001to2005

(

%)

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

URBAN

Average growth rate: +7.1%

-20

0

20

40

60

Growthrateofpercapita

expenditure2001to2005

(

%)

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

RURAL


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

percentile Madagascar Urban Rural10 50.9 17.4 56.0

15 48.0 15.5 53.4

20 45.9 12.5 51.425 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


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)

, , 1 0,

,0

,

G

P t P t ifP t

tzf t z

ifF t z

whereP(t,) is the Foster-Greer-Thorbecke poverty measure with parameter 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, andzis 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 thatin 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

7See also Duclos and Araar (2006).

8The result does not hold true for the headcount ratio, according to proposition 1.


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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 2005Headcount ratio 0.553 0.930Poverty Gap 0.998 1.567Poverty 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 growthand inequality to poverty reduction. In this section we decompose the observed changes in

poverty indices between 2001 and 2005 into two components: (i) thegrowth 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 equaldistribution of income.

Let P(t) be a poverty measure of the Foster, Green and Thorbeke (1984) class in period t.Following Muller (2006), the idealdecomposition of the variation ofPover the time interval

(T0, T1) can be written as follows:

(2)

tddt

tdL

L

tPdt

dt

tdtPTPTPP

IC

T

T

GC

T

T

1

0

1

0

01

where (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 , andtherefore rely on the following approximation of equation (2):

(3) RLTPTPRdtdt

dLTPdt

dt

dTPP

CI

rL

CG

r

T

T

rL

T

T

r

1

0

1

0

where

1 0

1 0

1 0

1 0

, ,

, ,

r r

r

r r

L r

P T L T P T L T P t

P TT T

P T L T P T L T P tP T

L L T L T

approximate the partial derivatives in equation (2), = (T1) - (T0), L = L(T1) - L(T0), andR

is a residual term. Note that the decomposition depends on the arbitrary reference period Tr. Dattand Ravallion (1992) recommend the use of the initial period (Tr=T0), but other choices are


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available. One is the ending period (Tr=T1), another is the so-called Shapley decompositionwhere 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 period2001 2005 Shapley

Headcount (H) 69.728 68.748 Change in H -0.980 -0.980 - 0.980Growth component 0.506 0.731 0.619Inequality component -1.711 -1.486 -1.598Residual 0.225 0.225 0.000

Poverty Gap (PG) 34.906 26.780 Change in PG -8.127 -8.127 -8.127Growth component 0.491 0.591 0.541Inequality component -8.718 -8.618 -8.668Residual 0.100 0.100 0.000

Poverty Gap Squared (PG2) 20.923 13.383

Change in PG2 -7.541 -7.541 -7.541Growth component 0.396 0.375 0.385Inequality component -7.915 -7.936 -7.926Residual -0.021 -0.021 0.000


Overall the inequality effect is dominant. The contribution of the growth component is low, aresult 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 inTable 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 acrosssectors. 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 k

k k

WITHIN GROUP INTER GROUP

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

whereR denotes a residual term.

Table 11 shows the results of the decomposition (3) for selected groups. The main result is thatthe 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) wouldhave caused a larger reduction in the aggregate PG index than the observed change (-8.1), were


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it not for the offsetting effect of the population shifts (+0.2), i.e. for the between-areamovements 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 povertycontinues 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 partitionchange in national

index(%)

% change in MLD accounted for by changes in

within-grouppoverty

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 provinceH -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/tertiaryH -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)


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 2001and 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.


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Table 12 Impact of growth on poverty in Madagascar

2007 2010Urban Rural National Urban Rural National

Headcount ratio 49.4 73.1 67.9 42.0 70.1 63.9PG 18.2 28.8 26.5 15.4 27.2 24.6PG2 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 -1.44PG -2.89 -0.17 -0.56 -4.41 -1.21 -1.70PG2 -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 andpopulation 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 decreasesfaster 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 particularimportance 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 atplay 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 besummarized 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 astatistically 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 anestimated 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 theanalysis 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


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national level. Moreover, in contrast to what observed with the headcount ratio, there is nocontradiction when we considertotalversus 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 inurban 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 ofthe 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 displayvariation 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 increasedby 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 householdsmatters 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 andpoverty 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 anegative 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 aforecast of poverty into a not-too distant future. According to our estimates, poverty in 2007has, 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.


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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 DiscussionPapers 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.

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DAD, Springer, New York.

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Poverty Measures: A Decomposition with Application to Brazil and India in the 1980s,

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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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1971-86,Economica, 62: 29-63.

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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 ConditionsMatter,International Poverty Centre Working Papern.2.

Lanjouw, P. and M. Ravallion (1995), Poverty and Household Size, Economic Journal, 105:

1415-1434.

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Ravallion, M. (1988), Expected poverty under risk-induced welfare variability, Economic

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Ravallion, M. and B. Bidani (1994), How robust is a poverty profile?," World Bank EconomicReview, 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,

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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

(0.030)

roadnat 0.061(0.031)

market -0.012(0.029)

phone

(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


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(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

[0.072]**roadnat 0.150

[0.050]**market 0.056

[0.058] phone

[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 Choiceof Different Deflators

This Appendix analyses the sensitivity of poverty estimates presented in the text to the choice ofdifferent (spatial) price deflators. The issue arises from the fact that in 2001 the welfareindicator 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 maderepresentative 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 inthe 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 werecalculated as population-weighted averages of regional prices.

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


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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 0.8598

Toamasina 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

2005regional deflator

2005provincial deflator

discrepancy(%)

province H PG PG2 H PG PG2 H PG PG2Antananarivo (U) 41.6 13.6 5.8 41.7 13.6 5.8 0.24 0.00 0.00Antananarivo (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 0.31Fianarantsoa (U) 71.6 28.8 14.5 71.4 28.7 14.5 -0.28 -0.35 0.00Fianarantsoa (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 1.14Toamasina (U) 55.8 21.4 11.2 57.0 21.6 11.2 2.15 0.93 0.00Toamasina (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 0.28Mahajanga (U) 47.0 16.1 7.2 48.4 15.9 7.1 2.98 -1.24 -1.39Mahajanga (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 3.52Toliara (U) 64.3 28.3 15.9 65.3 28.4 15.8 1.56 0.35 -0.63Toliara (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 0.14Antsiranana (U) 33.8 9.4 3.5 35.3 9.3 3.4 4.44 -1.06 -2.86Antsiranana (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 -1.01Urban 52.0 19.3 9.4 52.5 19.3 9.4 0.96 0.00 0.00Rural 73.5 28.9 14.5 73.5 29.0 14.6 0.00 0.35 0.69Madagascar 68.7 26.8 13.4 68.9 26.9 13.5 0.29 0.37 0.75



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

Sectors 2006 2007 2008 2009 2010Primary SectorAgriculture 2.6 2.3 3.5 3.5 3.5

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

Population 2.8 2.8 2.7 2.7 2.6

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


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Documents

Growth, Inequality and Poverty in Madagascar, 2001-2005 (World Bank- 2008)