metadata: authors: 0: author: Christiaensen, Luc
1: author: Demery, Lionel
2: author: Paternostro, S
count: Africa
docna: 0: docna: Growth, distribution, and poverty in Africa
            : messages from the 1990s
docty: Policy Research Working Paper
subtopic: Consumption,Economic Theory & Research,Fiscal & Monetary Policy,Industrial Economics,Nutrition,Poverty Reduction Strategies,Economic Growth,Inequality
teratopic: Poverty Reduction
abstracts: cdata!: review recent evidence on the trends in
            household well-being in Africa during the 1990s. They draw
            on the findings of a series of studies on poverty dynamics
            that use the better data sets now available. The authors
            begin by taking a broad view of poverty, tracing changes in
            both income poverty and in other more direct measures of
            individual welfare. Experiences have been varied: several
            countries have seen a sharp decline in poverty, while some
            have witnessed a marked increase. Yet, in the aggregate,
            economic growth has been pro-poor. Nonetheless, the
            aggregate numbers also hide significant and systematic
            distributional effects which have caused some groups to be
            left behind. The authors draw four key conclusions: Economic
            policy reforms (improving macroeconomic balances and
            liberalizing markets) have been conducive to reducing
            poverty. Market connectedness is key for the poor to benefit
            from new opportunities generated by economic growth. Some
            population groups and regions, by virtue of their sheer
            remoteness, have been left behind when growth picks up.
            Education and access to land further condition the extent to
            which households can benefit from economic opportunities and
            escape poverty. Finally, rainfall variations and ill health
            are found to have profound effects on poverty outcomes in
            Africa underscoring the significance of social protection in
            a poverty reduction strategy.
repnme: repnme: Growth, distribution, and poverty in Africa
            : messages from the 1990s
display_title: Growth, distribution, and poverty in
            Africa : messages from the 1990s
listing_relative_url: /research/2002/03/1744001/growth-distribution-poverty-africa-messages-1990s
topicv3: Economic Growth m1326241 471,Poverty m1326687 695,Macroeconomic and Structural Policies
            m1327380 439,Income Inequality m1503891 2668,Malnutrition m1627088 4737
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Poverty Reduction
origu: AFTM3
no_of_pages: 44
model_extractions: POLIcy RESEARCH WORKING PAPER 2810

# Abstract

Christiaensen, Demery, and Paternostro review recent evidence on the trends in household well-being in Africa during thc 1990s. They draw on the findings of a series of studies on poverty dynamics that use the better data sets now available. The authors begin by taking a broad view of poverty, tracing changes in both income poverty and in other more direct measures of individual welfare. Experiences have been varied: several countries have seen a sharp decline in poverty, while some hlave witnessed a marked increase. Yet, in the aggregate, economic growth has been pro-poor. Nonetheless, the aggregate numbers also hide significant and systematic distributional effects which have caused some groups to be left behind.

The authors draw four key conclusions: e Economic policy reforms (improving macroeconomic balances and liberalizing markets) have been conducive to reducing poverty.

* Market connectedness is key for the poor to benefit from new opportunities generated by economic growth. Some population groups and regions, by virtue of their sheer remoteness, have been left behind when growth picks up.

* Education and access to land further condition the extent to which households can benefit from economic opportunities and escape poverty.

> Finally, rainfall variations and ill health are found to have profound effects on poverty outcomes in Africa underscoring the significance of social protection in a poverty reduction strategy.

This paper-a product of Poverty Reduction and Economic Management 3, Africa Technical Families-is part of a larger effort to review progress in poverty reduction in Africa. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Nadege Nouviale, roomJ7-269, telephone 202-473-4514, fax 202473-8466, email address nnouvialeCaworldbank.org. Policy Research Working Papers are also posted on the Web at http:/ /econ.worldbank.org. The authors may be contacted at lchristiaensen@o;worldbank.org, ldemery @worldbank.org, or spaternostro@worldbank.org. March 2002. (37 pages) _The Policy Research Workintg Paper Series disseminiates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings_ **_out_** _quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Research Advisory Staff
Growth, Distribution, and Poverty in Africa: Messages from the 1990s Luc Christiaensen, Lionel Demery, and Stefano Patemostro The World Bank, 1818 H Street NW, Washington, D.C. 20433 March 2002 Acknowledgements: This paper synthesizes and builds on the work of a large team of researchers who contributed to a series of _Poverty Dynamics_ country studies in Africa, coordinated by the Africa Region of the World Bank. It benefits enormously from their careful and competent analysis. The authors are grateful for helpful comments from Alan Gelb and especially acknowledge the responsive and enthusiastic research assistance of Angelica Salvi. The work was supported by a group bilateral donors: Italy, the Netherlands, Switzerland, the U.K., and the U.S.
# I. **Introduction**

Does the Dollar and Kraay (2000: 27) view, that 'anyone who cares about the poor should favor the growth-enhancing policies of good rule of law, fiscal discipline, and openness to international trade' apply to Africa in the 1990s? Or is the growth path the reforms induced characterized by increasing inequality, denying benefits to the poorest (Stewart, 1995; Mkandawire and Soludo, 1999; Forsyth, 2000). There is no simple answer to this question, given the real-world complexity of recent African history. The 1990s in Africa witnessed many changes that affected people's lives and livelihoods. In addition to economic and political reforms, external opportunities and constraints shifted during the decade, with many countries experiencing sharp movements in their terms of trade. Some countries faced internal civil strife and political instability. Others had to endure one of the worst droughts of the century. And there have been serious health shocks, such as AIDS and malaria, affecting rich and poor alike. The effects of these changes on growth and poverty were further conditioned by the private and public endowments households possessed-their physical assets, human capital, and their access to infrastructure and public services.

This complexity makes for considerable debate about the relationship between policy, growth and poverty in Africa-a debate that was previously not always well served with hard evidence (Stewart, 1995). This paper sheds light on this debate by utilizing the much-improved data base in Africa, and by addressing three central questions:

- First, what does recent evidence tell us about the evolution of overall poverty and inequality in Africa and its relation with economic growth (and recession)?

- Second, moving beyond the national averages, did particular population groups or geographical regions gain or lose from the episodes of reform-induced growth?

- Third, among the wide array of disparate events and factors affecting growth and poverty trends, which emerge as key in explaining changes in income distribution and poverty?

The paper builds on the results of a recent series of _Poverty Dynamics_ country studies' which exploit recent (1990s) household survey data in Africa. It examines the main factors behind observed poverty trends by first taking a _macro-perspective,_ linking the historical changes in ' Countries were selected based on the availability of comparable measures of consumption and include Ethiopia, Ghana, Madagascar, Mauritania, Nigeria, Uganda, Zambia, and Zimbabwe. The paper also draws
2 income poverty in our sample countries to changes in economic environment-the macroeconomic and sectoral policy frameworks, and the institutional setting. We then exploit the survey data to greater depth by taking a _micro-perspective._ This assesses how households (and poor households in particular) have been affected by the events of the 1990s, distinguishing between the effects of policies and of shocks. When available, household panel data have been used (Ethiopia and Uganda), though important insights were also obtained from repeated cross sections (Zimbabwe, Ghana, Madagascar). The paper highlights the main insights emerging from this selected sample of micro-econometric country studies on Africa.

Considering that well-being is multifaceted, the paper begins with a review (in section ID of the changes that have occurred in income, education, health and nutrition. We first examine how these four different dimensions of well-being have evolved during the 1990s at the aggregate level. We then move beyond the aggregates and look at their evolution across income quintiles, focusing particularly on how welfare of the poorest groups fared. The section concludes by describing the evolution of overall income poverty and inequality, and its relation with economic growth. In the two sections that follow we seek to explain the systematic changes in income distribution and poverty in Africa, taking both macro (Section III) and micro (Section IV) perspectives. Concluding observations are made in the final section.

# II. **Living standards during the 1990s**

To set the scene, Table 1 reports four basic measures of well-being: private consumption, primary school enrollment, child malnutrition, and child mortality. The first and obvious point to note is that living standards are very low in these countries. By the close of the decade, no country enjoyed an annual per capita consumption in excess of $500, and in Ethiopia it was just $87. All countries fall far short of universal primary enrollment, and in some (for example, Ethiopia) primary enrollments are unacceptably low. Malnutrition is also a very serious problem, especially in Ethiopia and Madagascar. In Ethiopia, about two thirds of children exhibit signs of stunting or long-term malnutrition (defined as the percentage of children with low height for age compared with a reference population). Even in Ghana, Mauritania and Zimbabwe, there is evidence of stunting in about a quarter of the population under _5_ years of age. Perhaps the most poignant indicator of the very low welfare levels of these countries is the incidence of child on an analysis of time series data from the Demographic and Health Surveys. References to these _Poverty_
3 deaths. Under-age-five mortality exceeds 100 (per 1000) in all countries. In Zambia, almost one in five children fail to survive to their fifth birthday. Too many children are dying needlessly.

**Table 1: Evolving living standards selected African countries in the 1990s**

||_Real pr_<br>_consum_<br>_(consta_|_ivate_<br>_ptionpe_<br>_nt1995 _|_rcapita_<br>_US$)()_|_NetPri_<br>_Enrolm_|_marySc_<br>_entRates_|_hool_<br>_( _<sup>_2_</sup>_)_|_ChildM _|_alnutri_|_tion_**_(3)_**|_Child _|_Mortalit_|_y_**_(4)_**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||_Year_<br>_one_|_Year_<br>_two_|_Annual_<br>_growth_<br>_rate_<br>_(%)_|_Year_<br>_one_<br>_(%l)_|_Year_<br>_two_<br>_(%lo)_|_Change_<br>**_(/o_**<br>_points)_|_Year_<br>_one_<br>_(%)_|_Year_<br>_two_<br>_(%6)_|_Change_<br>_(%_<br>_points)_|_Year_<br>_one_<br>_(per_<br>1000)|_Year_<br>_two_<br>_(per_<br>1000)|_Change_<br>_(per_<br>_1000)_|
|**_Positive_**<br>**_growth:_**<br>Ethiopia<br>1994-1997|80|87|3.3|19|25|+6|66|55|-1].|190|175|-15|
|Ghana<br>1992-98|293|324|1.6|70|82|+12|26|26|0|119|104|_-15_|
|Mauritania<br>1987-95|296|353|3.3|28|41|+13|48|23|-25|-|149|-|
|Uganda<br>1992-97|211|259|4.8|68|86|+18|43|39|-4|165|162|-3|
|**_Stagnation_**<br>**_ordecline:_**|||||||||||||
|Madagascar<br>1993-1999|223|219|-0.2|48|64|+16|50|49|-1|170|149|-21|
|Zambia<br>1993-98|345|266|_-5.4_|73|66|-7|40|43|+3|194|189|_-5_|
|Nigeria<br>1992-96<br>Zimbabwe<br>1991-96|206<br>626|210<br>461|0.4<br>-6.2|94<br>83|98<br>86|+4<br>+3|38<br>30|-<br>23|-<br>-7|136<br>77|147<br>108|11<br>31|

Growth rates calculated based on least squared method, which is less sensitive to choice of base and terminal period.

**(2)** Net enrolment rates = percentage of children of school age enrolled in primary school as a fraction of the total number of children in that age group. Figures obtained from the surveys analyzed in the Poverty Dynamics studies. First year figure for Ethiopia refers to

1996. Figures for Nigeria reflect gross enrollment rates in 1994 and 1996 and are obtained from World Development Indicators.

**(3)** Child malnutrition defined as the percentage of children stunted, i.e. z-score of height for age which is less than -2; the reference periods for these figures approximate to those in column 1;

**_(4)_** Child mortality under 5 (per 1000 live births); the reference periods approximate to those in columnn 1.

_Source:_ World Bank data and country studies under _Dynamics of Poverty_ study.

Second, there are differences in the _changes_ in these indicators over time. In four countries economic living standards appear to have improved. But in Madagascar, average real consumption remained more or less unchanged, while it fell sharply in Zambia and Zimbabwe. Similarly, improvements in primary school enrollment in Ethiopia, Ghana, Mauritania and Uganda contrast with unsatisfactory outcomes in Zambia. Ethiopia and Mauritania experienced sharp reductions in long-term malnutrition, but there was little progress elsewhere. In all countries except Zimbabwe, the long term downward trend in child mortality appears to have continued through the decade. But child deaths have risen sharply in Zimbabwe, a result probably related to the AIDS epidemic (among other factors).

_Dynamics_ studies are given in the bibliography.
4 Third, the trends in the indicators are generally consistent with each other, though there are some important exceptions. In the four countries experiencing economic growth (Ethiopia, Ghana, Mauritania and Uganda) the trends in human development indicators match the improvement in economic well-being. But in those experiencing stagnation and decline, the signals are noisier. In some cases the education indicator improved despite the decline in economic living standards (Madagascar, Nigeria and Zimbabwe). Child mortality improved in Zambia and child malnutrition improved in Zimbabwe during episodes of deteriorating economic circumstance. Such outcomes serve as a reminder that focusing only on one dimension of well-being can be misleading when tracking poverty dynamics over time (World Bank, 2000).

# **_Inequality in human development_**

The indicators in Table 1 are averages for the population as a whole. We now review the _distribution_ of these indicators across the populations, identifying especially changes in the welfare of poorer households. We begin with the human development indicators. Primary school enrollments are particularly low in Ethiopia (Table 2), and to a lesser extent in Mauritania. The poorest households in these countries typically do not enroll their children in primary schools. But there have been major strides in raising primary enrollments during the decade in Ghana, Madagascar, Mauritania, and Uganda. And where there have been education enrollment gains, they have included the poor. Only Zambia seems to have lost ground.

**Table 2. Primary net enrollment rates by consumption quintile for seven African countries**

||_Ethi_|_opia_|_Gh_|_ana_|_Mada_|_gascar_|_Maur_|_itania_|_Uga_|_nda_|_Zam_|_bia_|_Zimb_|_abwe_|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|_SurveyYear_|_1996_|_1997_|_1992_|_1998_|_1993_|_1999_|_1987_|_1995_|_1992_|_1997_|_1993_|_1998_|_1991_|_1996_|
|Poorest quintile|15.0|17.0|54.4|70.1|29.3|53.2|19.4|25.0|54.4|79.6|57.5|49.8|77.9|80.8|
|Secondquintile|15.0|24.0|69.1|81.2|43.4|64.8|25.4|40.8|63.2|87.0|67.1|61.6|82.2|84.6|
|Thirdquintile|18.0|27.0|73.0|86.3|59.0|64.0|29.4|48.6|68.9|88.0|75.2|68.6|83.6|87.2|
|Fourth quintile|21.0|28.0|76.8|87.1|59.7|68.0|31.6|49.6|74.7|86.6|81.9|74.8|85.7|89.2|
|Richestquintile|30.0|33.0|87.2|90.2|59.6|77.7|40.9|60.0|85.7|89.4|86.0|80.7|89.0|90.9|
|_Ql/Q5_|_0.50_|_0.52_|_0.62_|_0.78_|_0.49_|_0.68_|_0.47_|_0.42_|_0.63_|_0.89_|_0.67_|_0.62_|_0.88_|_0.89_|

Source: Country studies under Dynamics of Poverty study (see bibliography).

Because income data were not collected in the Demographic and Health Surveys, Sahn et al. (1999) constructed a proxy index for income based on assets and household amenities. This enabled them to examine trends in child health capabilities (survival and nutrition) by wealth class. The poorest 20 percent of the populations appear to be the worst affected by the deterioration in pre-school child nutrition (Table 3). Stunting (measured by height-for-age) has
5 deteriorated among the poorest in four countries (Ghana, Mali, Senegal and Tanzania) and improved in four (Madagascar, Uganda, Zambia and Zimbabwe). But short-run malnutrition, or wasting (measured by weight for height), has increased among the poorest quintiles of six countries (Ghana, Madagascar, Mali, Senegal, Uganda and Zimbabwe). In general, the data indicate a major problem of increased wasting during the 1990s including among the poor. This is not fully understood, and clearly calls for further investigation.

**Table 3. Malnutrition by wealth quintile for eight African countries**

_Percent of children between 3 and 36 months of age with anthropometric z-score less than -2_

||Gha|na|Mada|gascar|M|ali|Sen|egal|Tanz|ania|Uga|nda|Zam|bia|Zimb|abwe|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|SurveyYear|1988|1993|1992|1997|1987|1995|1986|1992|1991|1996|1988|1995|1992|1997|1988|1994|
|_Heightforage:_|||||||||||||||||
|Poorestquinule|34|38|53|50|28|38|27|35|43|46|48|43|49|46|41|23|
|Secondquintile|33|30|45|40|29|39|23|30|44|44|45|40|45|49|37|24|
|Thirdquintile|30|29|51|51|25|34|24|30|43|42|44|40|39|43|27|_25_|
|Fourthquintile|27|23|50|49|26|32|25|20|40|39|42|33|30|33|25|22|
|Richestquintile|21|17|44|46|17|21|13|14|26|28|27|25|27|27|12|12|
|_QI/Q5_|_1.6_|_2.2_|_1.2_|_1.1_|_1.6_|_1.8_|_2.1_|_2.5_|_1.7_|_1.6_|_1.8_|_1.7_|_1.8_|_1.7_|_3.4_|_1.9_|
|_Weightfor height:_|||||||||||||||||
|Poorestquinble|7|16|6|10|12|28|7|15|9|8|2|6|7|_5_|1|_5_|
|Second quintile|9|10|8|7|11|22|4|14|7|10|4|7|7|7|2|4|
|Thirdquinb le|8|15|7|7|13|24|7|12|5|9|4|7|_5_|6|1|_5_|
|Fourtbquintile|8|10|4|5|10|23|8|12|6|9|0|4|6|5|1|6|
|Richestquintile|7|9|4|5|9|23|4|8|7|6|0|4|6|4|1|_5_|
|_QI/Q5_|_1.0_|_1.8_|_1.5_|_2.0_|_1.3_|_1.2_|_1.8_|_1.9_|_1.3_|_1.3_|-|_1.5_|_1.2_|_1.3_|_1.0_|_1.0_|

Source: Sahn et al (1999).

Most countries have experienced declines in mortality among the poor, the exceptions being Kenya, Zambia and Zimbabwe (Table 4). The trends are not always uniform across wealth groups, with a widening of the mortality gap between rich and poor. The ratio of mortality levels among the poorest to the richest quintiles has increased in most cases-where mortality has been falling, it has fallen faster among the richest group. The exceptions are Zambia and Zimbabwe.
6

**Table 4: Infant and under-age three mortality by asset index for nine African countries**

|_Forfive-year coho_|_rtsofchildr_|_en born o_|_neandthr_|_eeyearspr_|_iortothe_|_survey, _|_respectiv_|_ely.Per1 _|_000 bi_|_rths._||
|---|---|---|---|---|---|---|---|---|---|---|---|
||_Ghana_||_Keny_|_a_|_Madag_|_ascar_||_Mali_||_Senegal_||
|_SurveyYear_|_1988_|_1993_|_1988_|_1993_|_1992_|_1997_|_1987_|_1995_|_1986_|_1992_|_1997_|
|_Cohortatrisk_|_'83-'87_|_'88-'92_|_'83-'87_|_'88-92_<br>|_'87-91_|_'92-'96_|_'82-86_|_'90-'94 _|_81-85_|_'87-'91_|_'92-'96_|
|_Infantmortality_||||||||||||
|<br>Poorestquintile|120|90|78|90|121|128|173|157|114|<br>96|101|
|Third quintile|92|85|76|56|109|103|168|156|96|<br>76|70|
|<br>Richest quintile|74|48|_55_|_45_|88|73|102|98|81|38|47|
|_RatioQl/Q5_|_1.6_|_1.9_|_1.4_|<br>_2_|_1.4_|_1.8_|_1.7_|<br>_1.6_|_1.4_|<br>_2.5_|_2.1_|
|**_Under-age-threem_**|**_ortality_**|||||||||||
|Poorestquintile|160|152|93|128|200|191|318|266|224|<br>169|157|
|Thirdquintile|138|108|83|67|176|166|237|256|175|136|120|
|Richestquintile|113|80|60|54|135|85|184|148|114|<br>60|66|
|_RatioQl/Q5_|_1.4_|_1.9_|_1.6_|_2.4_|_1.5_|_2.2_|_1.7_|<br>_1.8_|_2.0_|<br>_2.8_|_2.4_|
||_Tanza_|_nia_||_Uga_|_nda_||_Z_|_ambia_||_Zimb_|_abwe_|
|_SurveyYear_|_1991_|_1996_||_1988_|<br>_1995_||_1992_|_1997_||_1988_|<br>_1994_|
|<br>_Cohortatrisk_|_'86-'90_|_'91-'95_||_'83-87_|_'90-'94_||_'87-'91_|_'92-'96_||_'81-'87_|_'89-93_|
|**_Infantmortality_**||||||||||||
|<br>Poorestquintile|114|116||141|107||134|143||66|57|
|Thirdquintile|97|89||115|100||129|101||69|54|
|Richestquintile|76|66||103|73||72|103||37|39|
|_RatioQJ/Q5_|_1.5_|_1.8_||_1.4_|<br>_1.5_||_1.9_|_1.4_||_1.8_|<br>_1.5_|
|**_Under-age-threem_**|**_ortality_**|||||||||||
|<br>Poorestquintile|156|144||189|182||217|224||84|71|
|Thirdquintile|152|138||184|168||187|184||92|70|
|Richestquintile|127|91||158|100||103|147||36|53|
|_RatioQl/Q5_|_1.2_|_1.6_||_1.2_|_1.8_||_2.1_|_1.5_||_2.3_|<br>_1.3_|

Source: Sahn et al. (1999).

# **_Income inequality_**

We turn now to income inequality and to the issue of whether episodes of growth in the 1 990s in Africa were associated with widening income distributions. On the one hand, increasing reliance on markets and the withdrawal of the state might be expected to increase income inequality (people with low levels of education, and limited access to public services and markets being less likely to take advantage of the opportunities growth presents). On the other hand, the previous tendency for the state to tax agriculture and the rural sector heavily, and the removal of such state intervention, might result in improved national income distributions.
7 We present Gini coefficients, a popular measure of inequality<sup>2</sup> , to describe how income inequality evolved in our selected sample of countries (Table 5). All our measures-except for urban Ethiopia-are based on real household expenditures per adult equivalent.<sup>3</sup> The surveys were designed to enable comparisons over time within a country, though due to different survey designs caution is warranted in making comparisons across countries. Nonetheless, the differences in the degree of income inequality in our sample of countries are striking. At one extreme, Zimbabwe has a highly unequal distribution (a Gini ratio of over 0.6)4, reflecting unequal land distribution, a result in part of its colonial past. Income distributions in Ghana and Uganda, are far more egalitarian.

In terms of evolution, the general picture is one of very little change in _overall_ income inequality in these countries. Reforms and growth have clearly not led to a significant deterioration in consumption inequality, as popular belief would hold (Forsyth, 2000). Nevertheless, these aggregate measures of inequality can be misleading. They may in fact mask a great deal of distributional change, an issue we review further in section IV below.

> 2 Recall that the Gini ratio varies from 0 (perfect income equality) to I (perfect inequality). The higher the value, the greater the inequality.

> 3 While the actual measures are based on expenditures, we use the terms 'income' and 'consumption' interchangeably.

> 4 Intuitively, the Gini index of a population represents the expected income difference between two randomly selected individuals or households. From Table 1 we know that in Zimbabwe real average per capita consumption in 1996 amounted to US$461. The corresponding Gini index is 0.64 (Table 5). Thus, in 1996 the per capita consumption of any two randomly selected Zimbabweans differed on average by US$295 (= 0.64*US$461)-a clear indication of high inequality given that average per capita consumption is only US$461.
8

> **Table 5: Consumption inequality(') during the 1990s in selected African countries**

|Ginicoefficient|_YearI _|_Year2 _|_Change_|
|---|---|---|---|
|_Ethiopia()_||||
|1994-1997(rural)|0.43|0.42|-0.01|
|1994-1997 (urban)|0.44|0.48|0.04|
|_Ghana1992-98_||||
|Rural|0.34|0.37|0.03|
|Urban|0.34|0.35|0.01|
|All|0.37|0.39|0.02|
|_Madagascar1993-99_||||
|Rural|0.42|0.36|-0.06|
|Urban|0.41|0.38|-0.03|
|All|0.43|0.38|_-0.05_|
|_Mauritania1987-95_||||
|Rural|0.43|0.37|-0.06|
|Urban|0.40|0.36|-0.04|
|All|0.43|0.39|-0.04|
|_Uganda1992-2000_||||
|Rural|0.33|0.32|-0.01|
|Urban|0.39|0.40|0.01|
|All|0.36|0.38|0.02|
|_Zambia1993-98_||||
|Rural|0.46|0.52|0.06|
|Urban|0.40|0.48|0.08|
|All|0.52|0.53|0.01|
|_Zimbabwe1991-96_||||
|Rural|0.58|0.57|-0.01|
|Urban|0.60|0.59|-0.01|
|All|0.68|0.64|-0.04|

> () Real expenditures per adult equivalent - real per capita expenditures for urban Ethiopia.

**(2)** Puiposively sarnpled villages and urban centers; not nationally representative. Source: Country Studies under Dynamics of Poverty study.

# **_Trends in poverty during the 1990s_**

If growth episodes were not associated with significant changes in inequality, did they lead to poverty reduction? Table 6 reports poverty estimates for the countries covered by the _Poverty Dynamics_ study. As with the inequality measures, real household consumption per adult equivalent is taken as the central economic welfare measure. Poverty lines in all cases (except Mauritania) are derived from a food consumption basket, estimated to yield a minimum caloric intake, with adjustments made for essential non-food consumption. We reiterate that because of differences in survey design, and in the specifics of how the welfare measure and poverty lines are derived, the data in Table 6 are not comparable across countries. But the research has been designed to ensure comparable estimates over time.
**9**

Table 6: Consumption poverty in eight African countries during in the 1990sl

||_Po_|_verty headcou_|_nt(PO)_|_Se_|_verityindex(_|_PJ_|
|---|---|---|---|---|---|---|
||_Yearl_|_Year2_|_Percentage_<br>_change_|_Year]_|_Year2_|_Percentage_<br>_change_|
||||_Perc_|_ent_|||
|Ethiopia2)|||||||
|1989-1995(rural)|61|51|-16|17|12|-29|
|<br>1994-1997(rural)|39|29|-26|8|6|-25|
|1994-1997(urban)|39|36|-8||||
|Ghana|||||||
|1992-1998|51|39|-24|9|7|-22|
|Madagascar|||||||
|1993-97|70|73|4|17|19|12|
|1997-99|73|71|-3|19|19|0|
|Mauritania|||||||
|1987-1995|58|35|-40|17|6|-65|
|Nigeria|||||||
|1985-92|46|43|-7|8|9|13|
|1992-96|43|67|56|9|17|89|
|Uganda|||||||
|1992-1997|56|44|-21|10|6|-40|
|1997-2000|44|35|-20|6|5|-16|
|Zambia|||||||
|1993-1996|74|69|-7|30|22|-27|
|1996-1998|69|72|4|22|26|18|
|Zimbabwe|||||||
|1991-1996|26|35|35|4|5|25|

1) Consumption measured as regionally deflated real household expenditure per adult equivalent. Poverty lines mostly calculated according to the cost of basic needs approach, which includes an adjustment for non-food needs (except for Zimbabwe). Poverty line for Mauritania based on US$1/day equivalent. Poverty lines are country specific, so that the data are not comparable across countries (only over time within countries).

2) Based on respectively six and fifteen purposively sampled rural villages for 1989-1995 and 1994-1997; urban figures are based on per capita household expenditures in seven large towns including Addis Ababa and Dire Dawa; not nationally representative _Source:_ World Bank data and country studies under _Dynamics of Poverty_ study The poverty measures we report here are derived from the familiar class of poverty indices after Foster, Greer and Thorbecke (1984). The general formula for these poverty measures is: <!-- Start of picture text --> Pa ) a 2 0(1<br>n j=1 z<br><!-- End of picture text --> where n is the total population, q the number of poor people, yi the income (consumption) of individual i, z the poverty line, and a a 'poverty aversion' parameter. The larger is _a,_ the greater weight is placed on the very poorest people. If a = 0, equation (1) becomes simply _qln,_ which is the _head-count ratio,_ or the incidence of poverty. Estimates of the _headcount_ (PO) are reported in the first data panel of Table 6. Setting a=2 involves taking the square of the proportionate poverty gap. This measure (P2) is given in the second panel in Table 6, and is sometimes known
10 as the _severity index._ We report this index because it is sensitive to the distribution of income _among_ the poor. It is particularly sensitive to changes in the living standards of the poorest of the poor. The data suggest the following:

- Most countries can be considered as having to deal with 'mass' poverty. Over 70 percent were estimated to be poor in Madagascar and Zambia. And 67 percent of Nigerians were estimated to be poor in 1996.

- There is no uniform trend. While consumption poverty incidence declined substantially in several countries (Ethiopia, Ghana, Mauritania and Uganda), it rose sharply in Nigeria and Zimbabwe. In Madagascar and Zambia, while fluctuating, the poverty headcount has remained largely unchanged.

- Where the incidence of poverty has declined, the data suggest that the poorest sections of the population (in Lipton's phrase, the 'poorest of the poor') have also benefited. This is suggested by the significant downward trend in the severity index (P2). In most cases, the percentage fall in the P2 measure was greater than that in P0.

# _Poverty, inequality and economic growth_

In some cases these changes in poverty occurred in a context of economic decline (Zimbabwe and Nigeria, and Zambia during the later period). In others they accompanied overall economic progress (Ethiopia, Ghana, Mauritania and Uganda). To shed more light on the relation between poverty, inequality and growth, Table 7 presents a decomposition of poverty incidence into two components: changes explained by changes in mean consumption levels (keeping the _distribution_ of consumption unchanged); and changes arising from changing consumption distribution (with the mean kept constant). The poverty measure that is decomposed in the table is the elasticity of headcount poverty with respect to changes in mean household expenditure.<sup>5</sup> Overall, changes in poverty incidence are due predominantly to changes in mean expenditure. Where there has been economic growth, both mean and redistribution effects have the same sign, and have combined to reduce poverty (in Ghana, Mauritania and Uganda). But, the mean effect largely dominates the redistribution effect. In contrast, where there has been recession, mean and redistribution effects have opposite signs, and the redistribution effect substantially rnitigates the poverty increasing impact of lower mean incomes (Madagascar and Zimbabwe). Better-off
I1 groups clearly bear a heavier burden of income losses during periods of economic decline in Africa.<sup>6</sup>

**Table 7: Relative importance of mean and distribution in the evolution of poverty incidence**

||_Percentagechangein_<br>_meanpercapita_|_Percentage_<br>_changein_|_Poverty_<br>_Elasticity_|_Explainedb _|_ychanigesin:*_|
|---|---|---|---|---|---|
||_expenditure_|_poverty_<br>_headcount_|_wrtmean_<br>_expenditure_|_Mean_|_Distribution_|
|**Ghana**||||||
|1992-1998|23.7|-23.5|-0.99|-0.93|-0.06|
|**Madagascar**||||||
|1993-1997|-17.5|4.7|-0.27|-0.77|0.50|
|1997-1999|0.6|-2.7|-4.51|-0.79|-3.72|
|**Mauritania**||||||
|1987-1995|49.5|-39.7|-0.82|-0.74|-0.07|
|**Uganda**||||||
|1992-1997|17.1|-21.4|-1.21|-1.C07|-0.15|
|**Zimbabwe**||||||
|1991-1996|-28.8|34.6|-1.23|-2.22|0.99|

* Decompositions based on Kakwani and Pemia (2000); _Source:_ World Bank data and country studies under _Dynamics of Poverty_ study.

To assess further the extent to which these episodes of growth and recession are 'pro-poor' we follow Kakwani and Pemia (2000) in defining, <!-- Start of picture text --> 0 = 7<br>77g<br><!-- End of picture text --> where _i7_ is the _observed_ elasticity of headcount poverty with respect to changes in mean expenditure, and **_17g_** is the elasticity of headcount poverty assuming the distribution of income did not change during the period. 0 can be defined as an index of 'pro-poor ,growth.' Growth can be considered pro-poor if 4 > 1.7 Table 8 compares estimates of 4) for these five African countries with recent experience in Asia. On the basis of this sample of countries, growth and recession episodes in Africa have tended to be pro-poor, and indeed more so than the Asian experience.

> ' This is defined as the proportionate change in headcount poverty divided by the proportionate change in mean per capita household expenditure. For details of the method used see Kakwani and Pernia (2000). 6 The tendency for income inequality to narrow as higher income groups bear the brunt of economic recession was also noted by Grootaert (1996) in analyzing poverty changes in Cote d'Ivoire in the 1980s. 7 When mean household expenditures are declining, P= **Tlg/T1,** so that a recession would also be considered pro-poor if 4 > 1.
12

**Table 8: Pro-poor growth indices (0) in selected African and Asian countries**

||**_Growthepisod_**|**_es:_**||
|---|---|---|---|
|Ghana,1992-1998|1.07|Thailand,1992-1996|0.61|
|Mauritania,1987-1995|1.10|Lao PDR,1993-1998|0.21|
|Uganda,1992-1997|1.14|Korea, 1990-1996|1.03|
||**_Recession/stag_**|**_nadenepisodes:_**||
|Madagascar, 1993-1997|2.85|Thaland,1996-98|0.73|
|Zinbabwe,1991-1996|1.81|Korea,1997-1998|0.84|

For details of method see text.

Asian country estimates are simnple **means** across years within the sub-period shown. Sources: Table 7; **Kakwani** and Pemia (2000).

There is evidence from international cross section data that initial income inequality can be harmful for subsequent growth and poverty reduction (Alesina and Rodrik, 1994; Temple, 1999; Ravallion, 2001). Initial (income or asset) inequality tends to affect growth itself, with countries with lower initial inequality typically growing more rapidly in subsequent years. In addition, initial inequality reduces the poverty impact of subsequent growth. If initial inequality is large, the poor find themselves often further away from the poverty line and income increases (even when equi-proportionate) are less likely to lift them out of poverty.

The experience of this (albeit small) sample of African countries is consistent with this view (Figure 1). The countries that had lower levels of initial inequality (as evidenced by the Gini ratios), were more likely to experience declines in poverty in subsequent years. That said, it is worth noting that the three countries with identical initial year Gini ratios (of 0.43)-Ethiopia, Mauritania, and Madagascar -experienced subsequent annual poverty changes of (respectively) -8.7, -5.0, and +0.3 percent. While the broad pattern across countries suggests that higher levels of inequality are associated with lower subsequent growth and poverty reduction, there is also a lot of variation around this empirical regularity to counsel caution.
13

**Figure 1: Initial inequality and subsequent poverty trends**

<!-- Start of picture text --> 0.8<br>C~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br>0.7-<br>.~0.5-<br>0.4-<br>0.2 -<br>0.1<br>0<br>-10 -8 -6 -4 -2 0 2 4 6 8<br>Subsequent annual percentage change In headcount poverty<br><!-- End of picture text -->

# **MI. Growth and systematic changes in income distribution: a macro perspective**

Our review of the evidence so far suggests that growth has been pro-poor in the African countries under study. These changes have occurred during an era of economnic policy reform, institutional change and important internal and external shocks, such as droughts, disease, and fluctuating commodity prices. These events have effects at all levels-they influence the growth rate of the economy at large, they affect the functioning of markets and of government, they change village and community life, and they impinge directly on the lives of households and individuals. Understanding how these changes have influenced poverty outcomes therefore calls for knowledge at both the macro (economy-wide) and micro (household/individual) levels. And this is the approach we take here. First, we assess how macro changes (in economic and institutional environments) have affected poverty outcomes. This provides the context in which we then review (in section IV) the microeconomic evidence linking poverty outcomes to policies and shocks.

# **_Macro-economic reforms andpoverty trends_**

We begin by reviewing the relationship between macroeconomic policy reforms and income poverty. To do so, we elaborate and update the analysis of Demery and Squire (1997) who examined the empirical association between improvements in macroeconomic balances and
14 poverty reduction based on data of the late 1980s and the early 1990s. With better comparable household data now available (including emerging panel data), and with another decade of economic reform in many countries<sup>8</sup> , we are in a good position to revisit this issue.

Following Bouton et al (1994) we calculate a macroeconomic policy index or score, based on changes in three key elements of sound macroeconomic policy: fiscal, monetary, and exchange rate policy. The overall macro-policy score is a weighted average of these components, the weights being derived from international cross section growth regressions. These scores are computed for the three-year period prior to each survey, and changes in the index are then compared. The index is so computed that increases in the score (either lower negative values or higher positive values) indicate an _improvement_ in economic policy (Table 9).

**Table 9: Changes in macro-economic policy scores, selected countries**

||_Change_<br>_during._|_Fiscal_<br>_policy_|_Monetary_<br>_policy_|_Exchangerate_<br>_policy_|_Averag_<br>_Unweighted_|_eScore_<br>**_Weighted_**|
|---|---|---|---|---|---|---|
|C6ted'Ivoire|1985-88|-2|1|-2|-1.0|**-1.7**|
|Ethiopia|1989-95|-1|_-0.5_|2.5|0.7|1.0|
||1994-97|2|1.5|2.5|2.0|2.2|
|Ghana|1988-92|-I|_1.5_|2|0.8|0.8|
||1992-98|0|_-0.5_|_0.5_|0.0|0.2|
|Madagascar|1993-97|0.0|_-0.5_|0.0|-0.2|-0.1|
||1997-99|1.0|1.0|0.0|0.7|0.5|
|Mauritania|1987-95|3|_0.5_|2.5|2.0|2.4|
|Nigeria|1985-92|1|-1|3|1.0|1.8|
||1992-96|1|-I|-2.5|-0.8|-1.0|
|Uganda|1992-97|2|_1.5_|_-0.5_|1.0|**0.7**|
||1997-00|0|_0.5_|0.5|0.3|0.3|
|Zambia|1993-96|2|2|0.5|1.5|**1.2**|
||1996-98|1|1|-1|0.3|0.0|
|Zimbabwe|1991-96|-1|_-0.5_|_1.5_|0.0|0.3|

Sources: Demery and Squire (1997); authors' computations from World Bank data.

Given weaknesses in the underlying survey data, we prefer not to retain two countries included in the original Demery and Squire piece (Tanzania and Kenya). For Ethiopia, Ghana and Nigeria, we update the estimates by introducing trends in the 1990s. Finally, we add the cases of Madagascar, Mauritania, Uganda, Zambia and Zimbabwe, giving altogether a coverage of fifteen episodes of change in nine countries. Most countries experienced improvements in their macroeconomic policy indicators-those for the second period (i.e. the three-year period prior to the second survey) being generally better than those of the earlier period (the three years prior to

> 8 The data used in many previous assessments were often of doubtful quality and given the lags involved in implementing the reforms, the 1 990s might be a more appropriate decade to examnine the growth path induced by economnic policy reforms in Africa (Collier and Gunning, 1997).
15 the first survey). But there were only marginal improvements in Ghana (1]992-98) and Zimbabwe (1991-96), and no change in Zambia during 1996-98. Macroeconomic destabilization is observed in two countries-C6te d'Ivoire during the 1980s, and Nigeria in the 1990s.

Setting these against the trends in poverty reduction (Figure 2) confirms that countries achieving improvements in their macroeconomic balances in Africa typically have not experienced (in the aggregate at least) increases in consumption poverty-rather the reverse.<sup>9</sup> Ten of the fifteen episodes of change for which we have data indicate both macroeconomic policy improvement and subsequent poverty reduction. In the two cases where macroeconomic balances substantially deteriorated, poverty is indicated to have increased. Only one of the fifteen observations (Zimbabwe during 1991-96) is in the 'wrong' quadrant in Figure 2 (improved macroeconomic policy and increased poverty).

The _association_ between the macro-policy stance and poverty reduction does not necessarily imply any causative or direct behavioral link.'° Rather this evidence serves to highlight the close interactions between macroeconomic policies and economic well-being at the household level. The changes in the macroeconomic accounts took place alongside other sectoral reforms-mostly of a 'structural' nature (trade liberalization, agricultural marketing reforms, privatization, and so on)-and changing institutional environments. Both the institutional environment and the sectoral reforms are certain to be important as well, as is illustrated by the fact that quite similar poverty reductions occurred among some of the countries despite quite different changes in their macro-economic indicators (see south-east quadrant in Figure 2).

> 9 Ali (1998) gets quite different results, with reforms being associated with increasing poverty. This is probably due to the different poverty data sets he uses (derived from IFAD data). Our concern here has been to use only data where careful attention has been paid to over time comparability. Without further information about Ali's data, it is difficult to establish the specific reasons for the differences in results. **'1** Both poverty changes and macro-policy scores might be favorably affected by a third factor, movements in the terms of trade, for example.
16

**Figure 2: Macroeconomic policy reform and poverty trends**

<!-- Start of picture text -->
*  ~~~~6<br>-2 0  -1.5  -1.0  -0.5  o.o  0.5  1.0  1.5  2.0  2.5  3.0<br><!-- End of picture text -->

**Change mscroecononice policy score**

# **_Institutional change andpoverty trends_**

There is an accumulation of convincing empirical evidence pointing to the importance of political stability and good governance for growth and poverty reduction (Alesina and Perotti, 1994; Knack and Anderson, 1995; Collier, 1999; World Bank, **2001).** While the construction and consolidation of good indicators of political stability and good governance remain work in progress, the composite political risk index of the International Country Risk Guide (ICRG), and subsets thereof, have been frequently used by researchers to exarnine the effect of govemance and institutional quality on growth and poverty. The composite index consists of 12 components covering different aspects of political stability (for example, government stability, internal conflict, external conflict), governance and institutional quality (for example, corruption, democratic accountability, bureaucracy quality). The key advantage of the ICRG index is its broad coverage across countries and over time (1985 to curTent).tl Evaluations of the different aspects of the index are provided by a private consultancy.

" The different cornponents of the ICRG political risk index (miaximwn scores in brackets) are govermnent stability (12), socio-econonmic conditions (12), investmnent **profile** (12), internal conflict (12), external conflict ( 12), corruption ( 12), military in politics (6), religion in politics (6), law and order (6), ethnic tensions (6), democratic accountability (6), bureaucracy quality **(4).** The maximumn score is 100 with a
17 We find an improvement in the political risk score during all episodes of poverty change covered by the _Poverty Dynamics_ study.'<sup>2</sup> In Ethiopia (1989-95) the improvement followed largely from reduced risk of internal and external conflict following peace agreements with Eritrea. Better overall governance (as captured by the corruption, law and order, democratic accountability and bureaucratic quality indices), as well as greater government stability and reduced risk of internal conflicts drove progress in institutional quality in Ghana (1992-98) and Uganda (1992-97). Increased government stability was responsible for the change in Madagascar. And in Zimbabwe (1991-96) the improvement followed from reduced risk of an external conflict, a result of the end of the Cold War and the peace process in neighboring Mozambique.

Plotting the changes in the average annual political risk scores of the survey years of our countries against annual changes in the observed poverty incidence (Figure 3) suggests that improvements in political stability and governance are generally associated with reductions in poverty, though experiences vary across countries."<sup>3</sup> In eight out of the eleven episodes these improvements were accompanied by poverty reduction. In one episode we observe almost no change in poverty (Madagascar) and in two other cases (Nigeria and Zimbabwe) poverty increased. In Nigeria the recorded improvement in the institutional environment was marginal (3.3 points) and was in all likelihood swamped by the adverse effects of the macroeconomic deterioration in the 1991-96 period. The other exception, Zimbabwe, is more of a puzzle. The macroeconomic balances also improved during this episode of poverty increase, so where did things go wrong? The answer to this cannot be provided here, but the very high initial inequality in Zimbabwe was a particularly serious challenge for growth and poverty reduction during the decade. We discuss the Zimbabwe episode of poverty increase in further detail below.

political risk score below 49.9 indicating very high risk; a score between 50 and 59.9 high risk; 60 to 69.9 moderate risk; 70 to 79.9 low risk; and 80 or more very low risk. Similarly, a score of 49.9 percent or below on an individual risk component, would imply that the cornponent can be considered as very high risk, a score in 50 to 59.9 percent range as high risk, and so on. For a detailed description of the ICRG rating system we refer to htto://www.icrzonline.comnicrgMethods.asp.

12 In all, eleven episodes of institutional change were examined. Political risk scores for our survey periods were not available for Mauritania and C6te d'Ivoire and we only retained one episode for Madagascar (1993-1999) and Zambia (1993-1998) given the short time span in between their second and third survey rounds.

**_'3_** Using two year averages of the survey year and the year prior to the survey year to account for lags in the effect of institutional change on poverty does not change the results. Our findings are also robust to the use of a subset of the political risk indicator focusing on indicators of political stability (government stability,
18

**Figure 3:** Change **in political stability and governance and poverty trends**

<!-- Start of picture text --> 6<br>4<br>2 *<br>0<br>U -2<br>-4<br>-2 0 .0 2 4 6 I~~~~~~~~~~~~~~~~~~~~~~~~.8 10 12 14 1 6 1 8 20 22 24<br>Change annual political risk score (ICRG)<br><!-- End of picture text --> While our measures of political stability and the quality of governance are admittedly crude, these findings would support the general observation that increased political stability and improved governance go hand in hand with poverty reduction. Nevertheless, many difficult questions remain to be resolved. Which of the different components of institutional change (for example, political, economic, civil rights or social stability), have had the most significant impact? And what is the direction of causality and the channels through which institutional improvements and poverty reduction may affect each other (Aron, 2000)? These fall beyond the scope of this study.

# IV. **Growth and systematic changes in income distribution: a micro-perspective**

The evidence from the African experience covered in this study indicates that growth (and recession) have been pro-poor. Yet, this conclusion must be qualified-it is true only in an aggregate sense. Further decomposition of national inequality and poverty measures-by geographical location and socio-economic group-indicates that the aggregate statistics often mask a wide variety of experience. Some groups and regions gained disproportionately from the newly created opportunities following economic reforms, while others lost out or even became impoverished. Similarly, overall Gini coefficients often appear stable over time despite substantial churning within and across geographical regions as illustrated by the experience in Ghana and Zambia (discussed below). This suggests that the positive association between intemal conflict, external conflict) and governance (corruption, law and order, democratic accountability and bureaucratic quality).
19 improved macro-environments and poverty reduction is conditioned by other factors such as location and infrastructure, households' private and public endowments, and the occurrence of shocks.

To disentangle the effects of these disparate events and factors on the different sections of African society it is tempting to use economy-wide modeling techniques which can generate counterfactuals, and provide insights into the respective impacts of policies and other shocks. Much of the serious work to date on policy reform and poverty in Africa has relied on such modeling approaches (Bourguignon and Morrison, 1992; Sahn et al, 1997). Yet despite their strengths, these approaches also have a number of important limitations. The models typically impose a strong structure which sometimes leads to questions about their realism. They are most often calibrated at one point in time. As a result, they cannot always confidently track changes over time-the economic history. Indeed, such history usually involves policy-induced structural changes in the economy that are not captured in such experiments.

Exploiting different experiences across households, this section places emphasis instead on the micro-econometric evidence emerging from the much improved and richer household survey data sets. We begin by highlighting two _Poverty Dynamics_ studies-Dercon (2001) on Ethiopia, and Deininger and Okidi (2001) on Uganda. These are particularly informative for two reasons. First, both involve the use of panel data, which track changes in the living standards of the same households over much of the 1990s. Although not identical, both the methodologies they adopt and their results are similar.

Second, both countries experienced far-reaching reforms in economic policy, inducing changes in market institutions, relative prices and producer responses. The rural sector in Ethiopia had previously been largely ignored and heavily taxed. But agricultural reforms initiated in the early 1990s included the abolition of food delivery quota for farmers, and a relaxation (and later abolition) of restrictions on private grain trades. These measures substantially reduced the food marketing margins between surplus and deficit regions. The Birr was devalued by 142 percent and the foreign exchange markets liberalized. This positively affected the farm-gate prices of tradables, such as coffee and chat, though the effect was somewhat muted due to the existence of parallel markets. Producer prices for coffee evolved favorably during the period, partly because of an increase in the world price.
20 Uganda's rural sector lost considerable ground during the period up to 1985. Adversely affected by state intervention, civil strife and agricultural price disincentives (through overvalued exchange rates and the implicit taxation of state marketing boards), rural producers retreated into subsistence. The production of cotton, tea and coffee suffered accordingly. From the late 1980s on, government policy changed, dismantling the biases against rural producers. Coffee marketing and exports were liberalized, and direct export taxation was abandoned. Similar measures were taken in the cotton sector. The foreign exchange market was liberalized, leading to real exchange rate depreciation. The weighted real producer price of export crops in Uganda (77 percent of which are coffee) increased by 78 percent between 1989-91 and 1995-97. Decomposition of this increase indicates that changes in the nominal protection coefficient (producer price/border price), changes in the real exchange rate, and changes in the real world price contributed respectively 58, 9 and 11 percent (Townsend, 1999). Over the past decade, agricultural output has recovered, averaging between 4 to 4.5 percent per annum in real terms. And this growth has played an important role in reducing poverty (Appleton et al, 1999).

In sum, economic policy reforms in both Ethiopia and Uganda had significant effects on agricultural markets and the prices farmers received for both food and export crops. At the same time, however, the period witnessed other changes, including rainfall variation. Both Dercon (2001) and Deininger and Okidi (2001) use the panel data to assess how these different changes affected household incomes and consumption, and rural poverty. Focusing on the factors they highlight as key for economic growth and poverty reduction, we then assess the evidence from the other case studies which use either repeated cross sectional regressions (Zimbabwe, Madagascar, Ghana) or simply an extensively documented narrative linking the macro-events to the observed evolutions in household welfare (Zambia and Mauritania).

Dercon (2001) uses panel data from six _rural_ communities in Ethiopia covering the period 1989 to 1995. The change in household real consumption per adult is explained through a reduced form regression model derived from an Oaxaca-Blinder type decomposition. In this approach changes in consumption and poverty can be explained by changes in endowments over time and changes in returns to endowments. The main regressors were changes in real crop producer prices (which Dercon shows to be closely related to the macro-economic and agricultural reforms that were implemented during the period), location (proxied by distance to an urban center), access to roads, private endowments (land, labor and education), and two shock variables, rainfall and ill health. His results are summarized in Table 10.
21 Household consumption increased on average by 32 percent between 1989 and 1995, and poverty incidence decreased by 29 percentage points. The growth in rural household incomes have been largely fueled by changes in relative crop prices'<sup>4</sup> and increased returns to location and access to road infrastructure. This is clearly illustrated by Dercon's simulations which show that consumption would have _declined_ by 13 percent and poverty would have _increased_ by 23 percent had there been no peace and no economic and agricultural reforms."<sup>5</sup> Interestingly, all poor households (even those who fell into poverty) benefited from the relative price changes that occurred. But those who escaped poverty benefited most. These findings suggest that the reforms and increased political stability substantially improved well-being of the poor, both directly through a favorable change in relative prices, and indirectly through an increase in the returns to market connectedness as determined by road infrastructure and distance to urban centers.

In addition to public endowments such as road infrastructure and location, private endowments are also found to be important for consumption growth and poverty reduction. Increases in land holdings or the quality of the land owned, and in adult labor reduced poverty by 14 percentage points. **16** Returns to land also increased,'<sup>7</sup> but because the poor typically possess little (and often low-potential) land, they profited much less than the average household from the increased returns to land. Finally, the occurrence of shocks (especially rainfall, but also illness shocks) had a large negative effect both on the growth process and poverty outconmes. If households had had access to full insurance protection from rainfall and health shocks, poverty would have declined by 42 percentage points compared with 29 percentage points in its absence. Dercon shows that the main reason why households fell into poverty during this period was mainly the combined effects of the rainfall and illness shock. Agricultural marketing reforms are shown to have benefited even the households that lost ground during the period.

> **14** These reflect mainly changes in food crop prices. Coffee prices also improved, yet it was grown in only one of the six sarnpled villages, and the coffee harvest had failed that year in that particular village because of a pest attack and drought. The effect of changing export crop prices cannot be evaluated from this sample, but its importance has been assessed explicitly in the Uganda case study described below. **'5** Dercon (1995) shows that the cereal marketing margins mainly improved because of the liberalization of the grain markets and only on some routes did the end of the war have a significant effect.

**16** Adult education levels are extremely low, less than I year per adult, and they are assumed not to have changed. The effect of education as such, as opposed to changes in returns to education, has thus not been evaluated in this study.

17 As the direct effect of changing producer prices has been controlled for, changes in returns to land result from other factors such as shifts in the underlying production technology potentially induced by the reforms.
22 In sum, households that escaped poverty during the period not only benefited from better producer prices, they also enjoyed a more favorable location, and were endowed with good access to infrastructure and better land. Those who remained poor or who fell into poverty, did so in part because they were badly placed in terms of location and land. They were also at the receiving end of particularly bad luck-they suffered most from poor rainfall and from ill health.

**Table 10: Ethiopia, decomposition of consumption growth per adult and poverty gap ratio (percentage points)**

|_Realcroppricechange_|_Ac_<br>_Growth_<br>_15_|_tual_<br>_Poverty_<br>-18|_Counter_<br>_Noreform_<br>_Growth_|_factual:_<br> _&peace_<br>_Poverty_|_Counte_<br>_No_<br>_Growth_<br>15|_rfactual:_<br> _risk_<br>_Poverty_<br>-16|
|---|---|---|---|---|---|---|
|_Changereturnstoroad_|19|-23|||19|-21|
|_infrastructure/location_|||||||
|_Privateendowments_|||||||
|Increaseinland|7|-10|1|-2|7|-8|
|Changeinreturnstoland|3|0|||3|-I|
|Increasesinadult labor|3|-4|3|-4|3|-4|
|Changesinretumstoeducatedadults|0|0|||0|0|
|Changeinadultequivalentunits|-5|7|-5|7|-5|7|
|_Shocks_|||||||
|Relativerainfallshock|-8|13|-8|14|||
|Illnessshocks|4|_5_|_-4_|_5_|||
|Residual|0|0|0|3|0|0|
|Percentagegrowthandpercentagepoint<br>povertychange(sumofabove)|32|-29|-13|23|42|-44|

Source: Dercon (2001) Deininger and Okidi (2001) analyze changes in consumption and income observed for a panel of about 1,200 Uganda households during the period 1992-2000. They regress household level changes in consumption and income against variables representing the change in relative producer prices of coffee, their access to infrastructure, their initial endowments of physical and human capital, the initial health status of households, and their social capital. They found these variables to be significant in explaining growth in Ugandan household incomes during the 1990s. As in Ethiopia, the effect of changes in relative prices (in this case an increase in farm-gate coffee prices largely brought about by market liberalization, but also by the devaluation and favorable world prices) on consumption growth was substantial.

Initial private endowments of education and other assets (mainly land) were also crucial for consumption growth. For example, if households had had 6 years of completed schooling on
23 average (instead of the observed 3 years)-equivalent to completing primary schooling-growth in consumption would have been 2 percentage points higher. A difference of one standard deviation in terms of initial asset value (about half of which is accounted for by land) put households on a 2 percentage point higher consumption growth path. Households which in 1992 were afflicted by health problems-related to malaria in over 80 percent of cases-experienced consumption growth which was (other things constant) 1.8 percentage points lower than those not experiencing such problems. Households with access to electricity enjoyed consumption growth that was 6 percentage points higher than other households.

The above results offer insight into what determined the growth in income and consumption among Ugandan households. How did such growth affect poverty? To address this, Deininger and Okidi estimate a multinomial logit model of changes in poverty status (households are classified as either not changing their status, falling into poverty or escaping from poverty). They find that the relative coffee price changes had a powerful poverty-reducing impact, indicating that their effect was broad-based and that price changes in tradable commodities directly benefited poor producers (and not simply indirectly through the labor market.) Moreover, households with higher education, more initial assets (land), better health, and better access to infrastructure (electricity) and location (distance to municipality) were far less likely than others to fall into poverty, and more likely to escape from it.

These results from these micro-econometric analyses of panel data point to the following factors that appear to influence the relationship between economic growth and poverty reduction:

- First, many rural households stand to benefit directly from liberalization measures, as well as increased political stability and better governance. And the gains can be substantial. In so far as liberalization measures increase producer prices, rural producers will gain, and to the extent that food marketing margins tend to decline, rural consumers will gain as well. Nonetheless, some will gain more than others, depending on the product- and consumption-mix of the household.

- Second, a household's location is also key in conditioning the extent to which it will benefit from liberalization measures. Specifically, whether the household had access to infrastructure and urban markets was an immensely important factor in governing the growth in household income. It explains about half of household consumption growth and poverty reduction in Ethiopia during 1989-95, and it was also quantitatively
24 important for growth in Uganda household income. So, connectedness to markets as captured by access to infrastructure (especially roads, but also electricity) and distance to urban centers is likely to be a major factor in determining how growth in any country transmits it benefits to the population.

- Third, the potential for economic growth and poverty reduction further depends on a household's private endowments. Households with larger private endowments-be it more and better qualified labor or land-not only tend to be less poor, they are also better placed to profit from new opportunities generated by liberalization and institutional change.

- Finally, it is vital to separate out the effect of shocks when assessing the role of policy changes. Dercon highlights rainfall and health shocks, both of which are certain to be relevant to poor households in most African countries. The importance of health is also underscored by Deininger and Okidi for the Ugandan case.

We now examine the evidence on distribution and poverty changes in other countries covered in this review, looking for echoes of the findings from the panel data of Ethiopia and Uganda.

# _Distribution, poverty and liberalization_

The changes in relative prices through exchange rate devaluations, the opening of domestic markets, and changes in the structure of production are certain to lead to shifts in income distribution, with producers of tradable goods (mostly exportables) benefiting from the economic policy reforms. The Ugandan and Ethiopian studies show that these effects were evident during the 1990s, and that they directly benefited poor households. The experience of Ghana in West Africa echoes these East African findings. Ghana experienced sharp poverty reductions among cash (export) crop producers during the 1 990s, a result of more favorable world cocoa prices and an increase in cocoa production. Table 11 compares trends in poverty among crop producers in rural Uganda and Ghana.

In both countries about two fifths of the population are food-producing farmers, of whom about two thirds were poor in the early 1990s. And in both countries, poverty fell among food producers, but the decline was not as great as that experienced by export crop producers. Most of the rural poor appear to have benefited from growth, but those producing export crops have
25 benefited most. A much larger share of the population in Uganda grows cash crops (21 percent) than in Ghana (6 percent) which may explain the larger drop in poverty amongst food crop producers in Uganda. Reviewing the existing evidence on the experience with agricultural reforms in sub-Saharan Africa, Kherallah _et al_ (2000) arrive at a similar conclusion-export-crop producers seem to have benefited more than food crop producers. What needs to be better understood is the _transmission_ mechanism that led to economic gains of households not producing for export.

**Table 11: Poverty incidence by rural activity, Ghana and Uganda in the 1990s.**

|||_Uganda:_||||_Ghana._|||
|---|---|---|---|---|---|---|---|---|
||Population<br>share(2000)|1992|2000|Percent<br>reduction|Population<br>share(1998)|1992|1998|Percent<br>reduction|
|Foodcrop|45.9|63.3|45.7|-27.8|43.9|68.1|59.4|-12.8|
|Cashcrop|21.3|62.7|29.7|-52.6|6.3|64.0|38.7|-39.5|

Source: World Bank, _Poverty Dynamics_ studies.

Potential pathways include rural labor markets, with higher export crop prices stimulating export crop production leading to increased demand for agricultural wage labor and ultimately higher agricultural real wages. Abdulai and Delgado (2000) find that in Ghana a 1 percent change in the domestic terms of trade between agriculture and non-agriculture leads to a 0.83 percent change in the real agricultural wage rate in the long run, underscoring the importance of labor markets in transmitting the effects of economic reforms. Increased liquidity in rural economies from agricultural exports can also have important spin-off effects, through an expansion of both investment in export and food crop production, and increased consumption of goods and services produced with previously underutilized local labor, land or capital. As a rule of thumb Delgado et al. (1998) posit that any policy enhancing producers' income from agricultural exports increases local rural income by twice the amount of the increased exports.

To understand the different evolution in poverty among food- and cash-crop producers, it is important to keep in mind that the former group tends to be much more heterogeneous than the latter. In export-crop growing zones, the effects of favorable export crop prices were also transmitted to the food-crop growing households-either through the labor market or the input and product markets, or both. Transmission of such benefits to areas unsuitable for export crop production, especially when they are also remote, is much harder. For example, in Ghana food producers in more remote and less integrated regions (in the north) did not experience a similar reduction in their poverty as food growers in cash-crop (and better integrated) areas. Similarly,
26 food crop producers in northem Uganda, which is also less accessible, appear not to have benefited from recent growth.

Periods of economnic stagnation and recession also systematically affect some groups more than others. In Zimbabwe, for example, the increase in rural poverty during 1990/91 and 1995/96 was felt most keenly among the commercial farmers (Table 12). Disentangling exactly why some suffered more than others is a difficult undertaking. Some farmers might have suffered more than others from the drought (an issue taken up by Alwang and Mills, 2001 and discussed below). It is also likely that the fall in incomes among commercial farmers was due to the decline in real tobacco prices, estimated by Townsend (1999) to be -2.5 percent per annum during 1990 and 1996/97. Other features of real price changes during the period identified by Townsend (notably the increase in the real price of cotton and continued government intervention in the maize market) may also explain why the smallholder group of farmers have not suffered as much as the commercial farmers during this episode of drought and economic decline.

**Table 12: Zimbabwe, incidence of rural poverty by farming category, 1991-1996**

|Expenditure /adultequivale|nt<br>_1990_|_/91_|_1995/_|_96_|_Percentag_|_echangein:_|
|---|---|---|---|---|---|---|
||_Mean_||_Mean_||||
||_consumption_<br>_(Z$1990/month)_|_Poverty_<br>_headcount_|_consumption_<br>_(Z$1990/month)_|_Poverty_<br>_headcount_|_Mean_<br>_consumptio_|_Poverty_<br>_nheadcount_|
|Communal|65.54|38.5|50.17|52|-0.23|35.1|
|Small scalecommercial|93.15|18.7|65.95|34.4|-0.29|84.0|
|Large scale commercial|99.21|16.3|76.85|27.4|-0.23|68.1|
|Resettlementareas|57.51|47|46.47|50.6|-0.19|7.7|
|Rural|69.6|35.8|54.29|48|-0.22|34.08|

Source: Alwang and Ersado (1999)

# **_Distribution, poverty and location_**

The panel analysis of Ethiopian and Ugandan households provides strong empirical evidence that location is important in determining how growth influences income distribution. Other countries also experienced strongly divergent pattems in inequality across regions. In _Ghana,_ for example, inequality fell sharply in Accra, Urban Savannah, and Rural Forest, while it increased sharply in the Coastal zone and Rural Savannah. The stability in the overall Gini in _Zambia_ (at just over 0.5 in 1993 and 1998) also gives a misleading impression of little distribution change. In fact consumption inequality increased sharply in both urban and rural areas. But because mean living standards improved in rural relative to urban areas, the overall Gini remained unchanged (Table
27 5). Our conclusion then is that overall indices of inequality can mask important changes in distribution-particularly across and within geographic regions.

Geography is even more important in explaining poverty trends. In some countries the decline in poverty is observed in both the rural and urban areas (Uganda, Mauritania, Ghana-Table 13). In others, the change is confined mainly to rural areas (Zambia between 1993-1996). It is clear from the case studies that both within the rural and the urban sectors, poverty changes have varied considerably depending on geographical location. Some geographical areas have not benefited as much as others from growth, and some have even lost ground during the period of recovery. The different experience in the evolution of poverty seems closely related to the extent to which the region or village is integrated within the overall economy. The experiences of Ghana and Madagascar are illustrative.

**Table 13: Headcount poverty trends in rural and urban areas of six African countries**

|||**_Rural_**||||**_Urban_**||
|---|---|---|---|---|---|---|---|
||_Population._<br>_sharein_<br>_yearI ('Y)_|_YearI _<br>_(%/)_|_Year2_<br>_(M)_|_Change_<br>N<br>_points)_|**_Yearl_**<br>_(N)_|**_Year2_**<br>_(%)_|_Change_<br>C/<br>_points)_|
|Ghana<br>1992-1998|67|64|49|-15|28|19|-9|
|Madagascar<br>1993-1999|81|74.5|76.7|2.2|50.1|52.1|2|
|Mauritania<br>1987-1995|56|68|48|-20|**45**|17|-28|
|**Nigeria**<br>1992-1996|62|46|72|+26|37|59|22|
|**Uganda**<br>1992-1997|88|59|48|-11|28|16|-12|
|Zambia<br>1993-1996|62|92|83|-9|**45**|46|+1|
|1996-1998|62|83|83|0|**46**|**_55_**|+9|
|Zimbabwe<br>1991-1996|63|36|48|+12|3|8|_+5_|

Sources: Studies under the _Poverty Dynamics_ program.

From Figure 4, we see that poverty in Accra fell sharply, but not in other urban areas. In the Savannah zone poverty _increased_ in both urban and rural areas, and especially in the Northern Region and among subsistence farmers." The fact that growth in Ghana saw the Gini ratio 18 This finding was confirmed by the repeated cross-sectional multivariate analysis (Coulombe and McKay, 2001).
28 improve and aggregate poverty fall is very little comfort to food farmers and urban workers in the north of the country, who probably compare their fortunes with Accra residents. Important clues as to why Ghanaians in the north did not benefit from growth are found in recent papers by Badiane and Shively (1998) and Abdulai (2000), which conclude that markets (more specifically the maize market) in the remoter Northern Region are not very well integrated with the economy at large. This lack of integration most likely impeded the transmission of the benefits of growth to the region.

**Figure 4: Ghana, incidence of consumption poverty by zone, 1992-1999**

<!-- Start of picture text --> 73<br>7 l<br>62<br>6('<br>*IqqIl9.2 6~2 62<br>o .<br>85X'8 1 = 43<br>A.~~~~~~~~~~~~~~~~~~~~~~3<br>L 26<br>20 i<br>10<br>4<br>A-. U~b. Cotj U.6, Fores V.6, Rink CoAtal Ib-a Forut Rw,al Sa,...h Gha<br>sn,vah<br><!-- End of picture text --> Source: Coulombe and McKay (2001) 'Remoteness' is also important in understanding geographical differences in poverty outcomes in Madagascar. Paternostro, Razafindravonona and Stifel (2001) disaggregate poverty according to an index of remoteness, the latter being a weighted sum of indicators reflecting access to roads, bus stop, agricultural extension services, modem fertilizers, and distance to schools and health facilities (the weights were derived from factor analysis). Their findings (Table 14) indicate an association between the degree of remoteness and the likelihood of being in poverty. They also show that while rural poverty indicators were largely unchanged during 1997 and 1999, households assessed to be the most remote, experienced increased poverty-in contrast to the least remote quintile where poverty indicators actually improved.
29

**Table 14: Madagascar, rural poverty by 'degree of remoteness'**

||_Headco_|_unt(Po)_|_Depth(P)_|
|---|---|---|---|
||_1997_|_1999_|_1997_<br>_1999_|
|**_Rural_**|**76.0**|**76.7**|**34.7**<br>**36.1**|
|_Quintileof'remoten_|_essindex'_|||
|Mostremote|78.0|82.8|34.8<br>42.4|
|2nd quintile|78.2|78.9|38.1<br>35.6|
|3<sup>rdquintile</sup>|74.5|78.9|32.7<br>37.7|
|**4 **<sup>**th**quintile</sup>|77.0|77.7|36.6<br>36.5|
|<br>Least remote|72.6|65.9|31.6<br>29.0|

Source: Paternostro, Razafindravonona and Stifel (2001).

# **_Distribution, poverty and private endowments_**

The experiences in Ethiopia and Uganda demonstrated that better-endowed households, particularly more educated households and those with more (fertile) land, were not only less likely to be poor, but also more likely to benefit from favorable changes in the macroenvironment. The importance of education for poverty reduction is echoed by the microeconometric evidence from Ghana, Madagascar, and Zimbabwe.<sup>19</sup> Both in Ghana and Madagascar, real consumption levels increase with educational attainment. And the returns to education across the different education levels increased from the first to the second survey year. These observations hold for both urban and rural areas. In Zimbabwe, a more precipitous increase in poverty following the economic decline was prevented because of previous investments in schooling that increased the educational attainment of the population in the 1990s (Alwang and Mills, 2001). That incomes fell and poverty increased despite household efforts to invest in human capital, assets and migration (see Figure 6, panel B) can only be attributed to a reduction in the _rates of returns,_ which Alwang and Mills relate to an overall deterioration of the economic and institutional climate.

Evidence from Madagascar, the only other study which explicitly addresses the role of land holdings, confirms that consumption levels are higher for those who possess land, except for those with only a very small amount of land (less than 0.1 hectare per capita). Retums to land

> '9 One constraint these studies face is the absence of reliable price data (linked that is, to the household data), which would be needed to assess the direct impact of the reforms on consumption. Systematic changes in real producer prices are certain to have affected income distribution and poverty during this period. However, both the Madagascar and Zimbabwe studies control for rainfall shocks, an issue to which we return below.
*30

holdings also increase with the size of the plots owned. Returns to land holdings deteriorated from 1993 to 1999 for households with less than 0.4 hectares per capita, while they improved for those with more land. The changes in returns decreased poverty incidence among the latter group by 2 percentage points, while it increased poverty among the former by 0.82 percentage points. Paternostro, Razafindravonona and Stifel (2001) hypothesize that this difference follows from an extensification of land use by smallholders in the face of demographic pressures forcing small farmers to expand their fields into less productive and more fragile areas.

# **_Distribution,_** _poverty and shocks_

Poverty estimates provide a snapshot of the standard of living at a certain point in time and reflect both policy reforms as well as temporary external shocks such as droughts. When evaluating the evolution of poverty it is thus important to control for the effect of external shocks on comparative poverty figures. Controlling for all other factors, the Ethiopian panel analysis estimated that household income growth was reduced by about a fifth because of rainfall shortage (Dercon, 2001). The role of rainfall variations in influencing household income growth was also an important feature of the Zimbabwean and Madagascar experience.

That poverty increased sharply in Zimbabwe during the 1990s is without question (Alwang and Mills, 2001). The decline in economic well-being (and increase in poverty) is evident from the leftward shift in the distribution of real household consumption (Figure 5). The change occurred mainly in the vicinity of the poverty line (Z$30 per month)-a sharp increase in the numbers of people consuming just below, and a parallel decline in the numbers just above the poverty line. What is less clear is whether poverty increased because of the droughts that afflicted the country in 1991/92 and again in 1994/95, or because of the Economic Structural Adjustment Program (launched in 1991) which was being implemented at the same time.

Alwang and Mills (2001) apply non-parametric methods to simulate what the 1995 distribution would have been if the 1990 rainfall patterns had applied that year. This exercise confrmns that the drought led to an increase in poverty during the early 1990s, but it also indicates that the drought alone cannot fully explain the deterioration in economic well-being (Figure 6 Panel A). As discussed before, actual changes in household location, assets and individual characteristics (notably the levels of educational attainment) would actually, other things constant, have _raised_ consumption levels and reduced poverty (Figure 6 Panel B).
31 Figure 5: Zimbabwe, shift in welfare distribution, 1990-1995 <!-- Start of picture text --> 1995 D .... ly 1 990 D.n..ly<br>30 100 1000 S000<br>1;% O*oDifference 1995 and 1990<br>0~ o - 0<br>Per-Capita Consum,ption Expendit-re<br>.1 - _ _ __ _ _ _ _ __ _ _ _ __ _ _ _ _ __ _ _ _ _ __ _ _ _ 30 , 100 , 1000 . 0oo0 I__ _<br><!-- End of picture text --> Source: Alwang and Mills (2001) Figure 6: Zimbabwe, simulated effects of rainfall and household characteristics on changes in the welfare distribution, 1990 - 1995.

<!-- Start of picture text --> Panel A Panel B<br>Effects individual and household characteristics,<br>Effects of rainfall (rural distribution only) including location changes<br>0 0 ~~~~~~~~~~~~~~~~~~0 OA<br>00~~~~~~~~~~~~~~~~~~~~~<br>1- 0 0~~~~~~~~~~~~~~~~~~-0<br>05~~~~<br>A Observed difference between 1990 and 1995 o Difference with distributions adjusted to 1995<br>conditions<br><!-- End of picture text --> <!-- Start of picture text --> Source: Alwang and Mills (2001)<br><!-- End of picture text -->
32 Evidence from Madagascar further underscores the importance of weather shocks in comparing poverty over time. Simulations indicated that 75 percent of the predicted change in household economic well-being and poverty incidence could be traced back to the relative change in drought occurrence between 1993 and 1999. The insurance capacity of households against covariate shocks in many parts of Africa is clearly extremely limited.

# IV. **Concluding remarks**

The evidence of the 1 990s gives ground for cautious optimism. In the aggregate at least, episodes of growth have been pro-poor in Africa, and countries which have experienced a recovery in their macro-economic balances and the quality of their institutions have seen the numbers in poverty decline. But there are three serious qualifications. First, experiences have varied enormously. Some countries have enjoyed a decade of sustained growth, and others have had to cope with crisis and decline. In the eight countries covered by the _Poverty Dynamics_ study, four experienced significant declines in poverty (Ethiopia, Ghana, Mauritania and Uganda), two faced sharp increases (Nigeria and Zimbabwe), and in two (Madagascar and Zambia) there was no discernable trend, the outcome depending on the specific circumstances (rainfall, terms of trade) of the years in question.

The second qualification derives from the need to go beyond the averages. While it is true that _overall_ income distributions (evidenced by the Gini ratio) have not changed during African episodes of growth, and that such growth (or recession) can be characterized as pro-poor in this aggregate sense, this can be misleading. Beneath the aggregate numbers exists a variety of experiences. Neglect of this reality by policymakers-and sometimes also academics-has often impeded a constructive and fruitful dialogue with 'civil society' about appropriate poverty reducing policies (Kanbur, 2001). Third, the _Poverty Dynamics_ work highlights the importance of taking different perspectives of poverty. Although trends in human development indicators are generally consistent with economic well-being, their dynamics have been quite different in some countries. The multifaceted nature of poverty (emphasized in the 2000/1 World Development Report) calls for multivariate approaches to tracking and understanding its dynamics.

Focusing on income poverty, our review of the evidence shows that there have been systematic changes in income distributions and poverty in the countries covered. We have identified some of the main contours of these distribution changes, and highlighted four key policy messages: the
33 importance of economic reform and political stability for poverty reduction; the role of geography and remoteness in conditioning how the benefits of growth are distributed; the significance of private endowments (especially education and land) for the ability of households to take advantage of new opportunities, and the consequent poverty outcomes; and finally the need to account for shocks in understanding distributional outcomes and poverty changes over time.

The 'emerging picture' described by Demery and Squire (1996) appears to be confirmed with the better data (reflecting also a longer time perspective than previous work). Improvements in the macroeconomic balances are associated with reductions in poverty in the region. There is also an emerging micro-picture concerning the consumption poverty impact of market liberalization. The analysis of household panel data by Dercon (2001) for Ethiopia and Deininger and Okidi (2001) for Uganda provide the most systematic and empirically convincing cases that policy-induced changes in relative prices can have marked poverty-reducing effects. Micro-evidence from Ghana provides some corroboration from West Africa.

The second policy message is the need for a geographical perspective on poverty. Whilst the various rounds of poverty assessments have established that the incidence of poverty varies considerably across different regions of a country, this recent work on poverty dynamics has shown that some regions, by virtue of their sheer remoteness, have been left behind somewhat as growth has picked up. Households with limited access to markets and public services have not benefited from growth during the 1990s. Public policy, and the provision of public goods (notably infrastructure services-from the Ethiopian case, especially roads and from the Ugandan case, electricity) must address these fundamental regional inequalities.

Third, both education and access to land emerge as key private endowments to enable households to escape poverty. The importance of education for poverty reduction is brought out in all our case studies-in rural and urban areas-with the marginal returns to education typically increasing by educational attainment. While land redistributions may not be appropriate in all countries, as argued by Dercon (2001) for Ethiopia, it is ultimately the productive capacity of land which matters. A more efficient organization of agricultural services and agricultural inputs, such as fertilizer, could go a long way towards improving productivity of land (Kherallah et al., 1 999).

Finally, the empirical evidence reviewed here underscores the importance of social protection in a poverty reduction strategy. The impact of rainfall variations and ill health are the two risk factors
34 featured. Dercon (2001) estimates that poverty reduction in the sample of Ethiopian rural communities would have been 18 percentage points greater had households been protected from the effects of ill-health and rainfall shortages. The importance of weather shocks for poverty changes was also underscored by the findings from Zimbabwe and Madagascar. Deininger and Okidi (2001) find that ill-health amongst Ugandans back in 1992 noticeably increased the probability of being in poverty eight years later. And in light of households' greater exposure to the vagaries of world commodity prices following liberalization, policies to help the poor manage their risks have become even more important nowadays.