Abstract

Policymakers are justifiably concerned about the relationship between economic growth and the distribution of income and, in particular, about the impact of growth on the incomes of individuals living below the poverty line. The conventional wisdom of recent years has been that growth leaves the relative income distribution unaffected, while policies aiming to redistribute income risk affect the growth rate adversely (Tanzi and Chu, 1998). The implication is that policymakers should concentrate on growth-promoting policies because the incomes of the poor will rise with growth, thereby contributing to poverty reduction. At the same time, the extent of poverty reduction in a country would depend on the initial income distribution, making the measurement of inequality an important indicator for evaluating the country’s prospects of reaching the income poverty MDG. Recent evidence of different rates of poverty reduction and rising inequality in sub-Saharan Africa points to the continued relevance of the growth-poverty-inequality nexus (Iradian, 2005).

Policymakers are justifiably concerned about the relationship between economic growth and the distribution of income and, in particular, about the impact of growth on the incomes of individuals living below the poverty line. The conventional wisdom of recent years has been that growth leaves the relative income distribution unaffected, while policies aiming to redistribute income risk affect the growth rate adversely (Tanzi and Chu, 1998). The implication is that policymakers should concentrate on growth-promoting policies because the incomes of the poor will rise with growth, thereby contributing to poverty reduction. At the same time, the extent of poverty reduction in a country would depend on the initial income distribution, making the measurement of inequality an important indicator for evaluating the country’s prospects of reaching the income poverty MDG. Recent evidence of different rates of poverty reduction and rising inequality in sub-Saharan Africa points to the continued relevance of the growth-poverty-inequality nexus (Iradian, 2005).

Increasing inequality is of particular concern when it undercuts the ability of growth to benefit society’s least well-off members. A set of recent studies has therefore sought to draw lessons on the type of growth process that is most effective at raising the incomes of the poor: “pro-poor” or “shared” growth. Focusing on the macroeconomic issues, this chapter reports the key messages of these studies for sub-Saharan Africa. The studies reaffirm that growth does not tend to widen inequality and that the strong impact of growth on poverty reduction far exceeds any offset from rising inequality. Nevertheless, a comparison of the drivers of growth across countries is informative about the interaction of growth with inequality and poverty. The studies highlight three important issues to be considered in assessing an economy’s ability to produce growth with significant poverty reduction. These are (1) the relative importance of the agriculture and rural sectors in growth, (2) the potentially wide-ranging impact of infrastructure investments, and (3) the management and allocation of aid inflows.

A. Meaning and Importance of Pro-Poor Growth

The notion of pro-poor growth captures the extent to which economic growth leads to increased welfare for the less well-off in a society. An assessment of whether growth is pro-poor thus requires knowledge of how the distribution of income shifts during growth, and how this affects the welfare of the less well-off. Studies of pro-poor growth identify the less well-off group as those who fall below the poverty line for income or consumption. If the welfare of the poor depends on the gap between their income and that of richer households, then pro-poor growth would involve more rapid income growth for the poor than the nonpoor. However, noting that global and national development targets call for reductions in the level of poverty, Ravallion (2004) has argued that pro-poor growth is best defined as growth that reduces the poverty measure of interest. This logic leads to a focus on the change in the income of the poor, which underlies changes in income poverty measures.

In accounting terms, a change in poverty over time contains components attributable to the rate of economic growth, the response of poverty to that growth, and changes in income distribution. This decomposition of changes in head-count poverty has proved to be very informative about the reasons for variations in the rate of poverty reduction across countries. A method proposed by Datt and Ravallion (1992) allows a decomposition of country-level poverty changes into a growth component and an inequality component. The growth component reflects the rate of growth and poverty response to it (the elasticity of poverty with respect to growth), whereas the inequality component reflects changes in distribution. Kraay (2005, 2006) presents a variance decomposition method that further attributes the variation in the Datt-Ravallion growth component to variation in the growth rate and the elasticity.48 These two components can then be combined with the inequality component to produce three-part decompositions of changes in poverty.

The key finding that emerges from poverty decompositions is that the bulk of the variation across countries in the rate of poverty reduction is due to variation in overall growth. At medium to long horizons, the proportion of poverty-reduction variation explained by growth is close to 100 percent. This means that, in the long run, whether growth is pro-poor is simply a matter of the rate of overall growth. At shorter horizons, variation in inequality and poverty elasticity can matter, but, on average, the effect is small. Moreover, as emphasized by Kraay (2005, 2006), the source of variation in the income distribution and elasticity components is poorly understood. Nonetheless, the elasticity of poverty (which is presumed to be negative) is larger in absolute value when inequality is lower and mean income is higher. The intuition, as explained in Heltberg (2004), turns on the fact that the elasticity refers to the percentage change in poverty and not the change in head-count poverty itself. Thus, when income distribution is more unequal, more households fall below any given poverty line and the percentage changes are thus smaller when the distribution shifts. Similarly, the higher mean income is above the poverty line, the fewer households there are (all other things equal) in poverty, generating bigger percentage changes in poverty from a change in mean.49

Despite the historically minor role of changes in inequality in explaining changes in poverty, sub-Saharan Africa has seen sizable changes in inequality since 1980. This finding is especially surprising given the belief that indicators of inequality are quite stable over time. Consider the Gini coefficient, which measures the skewness in the income shares accruing to groups of equal size in a population.50 Using data from the World Bank’s Global Poverty Monitoring database, Figure 12 shows the log change in the Gini coefficient for 27 growth-poverty spells in 20 sub-Saharan African countries between 1980 and 2001.51 There are cases of both large declines and increases in the Gini, with increases predominating (several changes in the Gini are near or above 20 percent). There is no obvious tendency for inequality to increase with growth, as a perusal of specific country episodes reveals. For instance, the largest increases in the Gini were experienced by Mali (1989–94) and Niger (1992–95), where mean income fell over the same period. Conversely, large declines in inequality occurred in Ethiopia (1995–2000) and Kenya (1994–97); in the former, mean income fell, and in the latter, it rose. This lack of correlation between growth and changes in inequality is demonstrated in large samples by Adams (2003) and Ravallion (2005b).

Researchers have found that sub-Saharan Africa has a low elasticity of poverty with respect to growth by global standards, reflecting high inequality and low per capita income in the region. Besley and Burgess (2003) report a global poverty elasticity to per capita GNP growth of–0.73, with elasticities ranging from a high of–1.14 in transition countries to a low of–0.49 in sub-Saharan Africa.52 Iradian (2005) constructs a large sample of poverty, growth, and inequality spells from multiple sources, and in a regression of the poverty head-count on GDP growth and the Gini, finds a global elasticity to growth of–1.1 and to inequality of 1.4. For sub-Saharan Africa, the corresponding numbers are–0.79 and 1.2.53 Epaulard (2003) confirms that the poverty elasticity to growth varies as one would expect with respect to initial equality and mean income in a global sample.

Figure 12.
Figure 12.

Changes in Inequality for Sub-Saharan Africa

(Log change in Gini)

Sources: IMF staff calculations; and World Bank Global Poverty Monitoring database. Note: The data spells differ by country with some multiple spells for a single country, and span 1980–2001 overall. Countries are indicated by ISO code, plus a number where there is more than one spell for a country.
Figure 13.
Figure 13.

Elasticity of Poverty in Sub-Saharan Africa

Sources: IMF staff calculations; World Bank Global Poverty Monitoring database.

Nevertheless, there is large country variation globally and across sub-Saharan Africa in the size of the elasticity around the global and regional averages. In addition, the empirical elasticities are only loosely correlated even with their proximate determinants, and more loosely with other measurable country-specific factors. Figure 13 shows a set of gross elasticities derived from the World Bank’s Global Poverty Monitoring data—that is, the percentage change in head-count poverty divided by percentage change in income growth for any country for which at least two household surveys are available. In the left panel of Figure 13, the elasticities are plotted against the initial Gini coefficient for the episode; in the right panel, they are plotted against initial income. Note the huge range of values for the elasticity and its lack of obvious correlation with either variable.

Active research seeks to clarify the role of policies and country conditions in explaining the evolution of poverty and inequality over time. However, relatively sparse data and multiple directions of causation linking growth, poverty, and inequality have impeded definitive findings. Kraay (2006) finds that it is difficult to provide a satisfactory empirical model for the elasticity of poverty or changes in distribution, consistent with the mixed record in explaining income distribution patterns across countries (probably because of the presence of poorly measured country-specific determinants thereof). Such unobserved heterogeneity also introduces the risk of bias in regressions explaining income distribution. Nevertheless, the formalization of pro-poor growth has provided a new set of diagnostic tools for analyzing poverty reduction within and across countries and guidance about where to look for explanations of different rates of progress on poverty reduction.

Despite the small role of inequality in explaining the average extent of pro-poor growth, rising inequality undercuts the ability of growth to reduce poverty. Thus, an indicator of inequality like the Gini coefficient is a valuable tool for monitoring a country’s poverty reduction prospects. In addition, the simple mathematics of income distribution and elasticity formulas implies that if African countries could find a painless way to reduce inequality, they would reap a double benefit: an immediate reduction in poverty and a higher elasticity of poverty to growth, meaning that any given rate of future growth would translate into more rapid poverty reduction than in the past (for example, Heltberg, 2004). As Besley and Burgess (2003) emphasize, this result is not a justification for a static redistribution of resources through taxes and transfers. Instead, it will be more important to address administrative or market imperfections that have an undue impact on the poor; property rights and access to financial services are two leading examples. Furthermore, while decompositions such as those reported here provide information on the proximate sources of variation in pro-poor growth, it is necessary to examine the drivers of growth to understand its distributional impact.

B. Channels of Pro-Poor Growth

Recent studies point to three related determinants of the effectiveness of growth in reducing poverty: generation of growth in the agriculture and rural sectors; enhancement of productive capacity, particularly in infrastructure; and management of aid inflows. The following subsections present the detailed evidence for this assertion, but the main points can be summarized here. Many studies of economic development in Africa highlight that development policy has tilted toward donor priorities in the social sectors and that, in the 1990s, it was assumed that the private sector would provide infrastructure.54 Donor and government budgets have tended not to make basic rural infrastructure and other investments to enhance agricultural productivity high priorities. Indeed, the lack of an agricultural productivity boom in Africa such as the one South Asia experienced underscores the different growth paths taken by the two regions. Countries in sub-Saharan Africa are further constrained by a weak domestic revenue base, which makes their economies vulnerable to both aid fluctuations and donor prioritization of spending needs.55 Thus, the legacy of a weakened agriculture sector as a result of traditional urban-biased development policies has not been fully offset.

Since pro-poor growth concerns the ability of the poor to participate in a country’s growth, an assessment of whether a policy is pro-poor should focus on its impact on the productive opportunities of the poor. This thinking leads to recognition of intertemporal trade-offs in the determination of policies. For instance, many health and education policies have a delayed productivity impact because beneficiaries may not be in the labor force until years after the policies are adopted. In addition, the effectiveness of provision of social services depends on the current productive constraints that hold back other sectors of the economy. Recent studies have demonstrated powerful synergies between the attainment of the poverty and sectoral MDGs when economy-wide links are taken into account. These links center on the role of infrastructure in increasing productivity—including that of the health and education sectors. Similarly, while labor-intensive growth in the sectors where the poor are employed would be associated with poverty reduction, the incomes of the poor also depend on the productivity of these sectors.

The exploration of country variation in the link between poverty, growth, and inequality poses significant methodological challenges. The basic problem is the number of possible explanations for the link between growth and poverty relative to the infrequency of household surveys that provide data on poverty and inequality. Thus, the analytical framework needs to be refined to derive more information from the limited range of experiences. Studies have used a variety of methodologies, including country case studies, structural economic models, statistical modeling, and hybrid statistical-narrative approaches. The case-study approach was the centerpiece of the World Bank’s Operationalizing Pro-Poor Growth (OPPG) research program. Structural models offer the ability to trace poverty levels to specific policy and exogenous factors, but their relevance for country experiences is questionable. Regression approaches offer the prospect of formal testable hypotheses, but will quickly exhaust available data unless very carefully specified. These considerations motivate hybrid studies.

Country Experiences

The OPPG program relies mainly on case studies and hybrid approaches and has also developed a specific set of metrics for pro-poor growth. These include the Datt-Ravallion decomposition of the change in poverty into growth and inequality components and a summary statistic rate of pro-poor growth.56 A closely related graphical tool, the Growth Incidence Curve (GIC), shows the growth rate of income or consumption across the entire income distribution, with the rate of pro-poor growth corresponding to the area under the GIC in the region below the poverty line.57 The OPPG research program contains two outputs of particular relevance for macroeconomic policymakers concerned with sub-Saharan Africa: five country case studies from the region (Burkina Faso, Ghana, Senegal, Uganda, and Zambia)58 and sectoral synthesis papers. The following paragraphs concentrate on those elements of the studies that reflect their focus, not just on overall growth but on pro-poor growth itself.

Although measurement of the rate of poverty reduction in case-study countries depends on the data source, the poverty-reduction record as shown by national data is important because of its use in policy formulation and its coverage of more recent periods. The World Bank’s Global Poverty Monitoring database meets the objective of measuring the MDG poverty indicator on a consistent basis across countries. But, although based on national data, the methodology and coverage of the poverty statistics differ from those of the national data. None of the World Bank data are more recent than 1998 for the case-study countries, and the data go up only to 2001 for any country in the region. National data provide a more recent picture than World Bank data, but have been open to measurement controversies.59 For completeness, Appendix Table A18 reports both World Bank and national data. The case-study countries, except for Zambia, have experienced sharp falls in poverty by their national measures. The finding that poverty has declined reflects the fact that the case-study countries have all participated to some extent in the improvement in African growth performance since the mid-1990s.

Trends in inequality are mixed across the case-study countries and show no obvious correlation with overall growth. There is rising inequality in Ghana and Uganda, declining inequality in Burkina Faso and Zambia, and an uncertain picture for Senegal. Inequality in Burkina Faso has remained steady or even declined slightly in the face of a significant macroeconomic adjustment in 1994 and steady subsequent growth. Ghana displays a modest increase in inequality according to its national survey.60 For Uganda, national and World Bank data sources both show a rise in inequality throughout the 1990s and, according to national data, into the current decade. Zambia is moving in the opposite direction, with a sharp decline in inequality sufficient to move the country away from a level of inequality more typical of Latin America at the start of the 1990s. Any definitive statement for Senegal is impeded by the lack of recent data and concern that the available surveys may not be comparable.

Countries’ experiences are consistent with recent evidence that growth is the main driver of poverty reduction, but the extent to which changes in inequality also contributed to poverty reduction varies from country to country. Burkina Faso experienced the highest rate of pro-poor growth, while Uganda’s progress by this measure in the 1990s was somewhat offset by its recent experience (Appendix Table A19). By this metric, Zambia is the most sluggish performer, an outcome that is consistent with macroeconomic and policy developments during the relevant period. Appendix Table A19 also shows the decomposition of the total change in head-count poverty into growth and inequality components.61 Although the contribution of growth tends to exceed that of inequality, inequality effects are often very sizable. While Burkina Faso has registered a sizable drop in poverty as a result of falling inequality, Uganda has experienced the opposite, and Ghana and Zambia show very small inequality effects.

Divergences in sectoral growth performance are a leading factor in explaining cases where inequality has increased. Ghana began the 1990s with low inequality by African standards. But, since then, growth and poverty reduction have been concentrated in Accra and the mineral-rich rural forest zone, with the rural savannah and rural coastal areas lagging behind. The case study estimates an elasticity with respect to the national poverty line of just under one, whereas the World Bank data show only a minuscule change in head-count poverty; that is, the implied elasticity from those data would be even lower. The broad picture for Uganda is quite similar: a rural versus urban divide, weak growth in agriculture, commodity price vulnerability, and social sector expansion are the important factors for understanding the pattern of growth. Although the country’s recovery in the 1980s and 1990s was broad-based, GICs reveal growth in incomes heavily skewed toward the top quintile in the distribution from 1997 onward, enough for the overall income growth experience since 1992 to have been characterized by faster income growth for those above the poverty line than below. In accounting terms, the increase in inequality can be traced both to increased inequality within urban areas and to the divergence between urban and rural areas. Thus, weakness in agriculture is only part of the explanation for rising inequality.62

The two countries in which inequality has fallen (Burkina Faso and Zambia) share an increased emphasis on agriculture as a source of growth that is mirrored in their adjustment of the role of urban formal sector employment in development policy. Sahn and Younger (2004) argue that the pricing reforms throughout African agriculture in the 1980s and 1990s were pro-poor because the poor tend to be net agricultural producers whereas the rents from price-management schemes accrued to better-off urban consumers. The case studies reflect this experience. In Burkina Faso, the 1994 devaluation favored traded-goods sectors, with the main employment response occurring in the informal sector. These changes tended to reduce poverty and inequality, although a study by the World Bank (Bernabé and others, 2005) points out that the higher prevalence of informal sector employment may represent increased vulnerability to poverty, relative to formal sector employment.63 In contrast to the singular role of the 1994 devaluation in Burkina Faso, policy reform in Zambia took place over a decade. Through its reforms the government has succeeded in reducing the bias toward the formal sector by progressively withdrawing subsidies to domestic industries linked to mining and by unwinding a food-pricing system that favored urban food consumers at the expense of producers. This reform program had distributional consequences, including negative ones for the previously protected sectors. Some urban households fell into poverty, but some of the initially poor households registered gains in income; thus, poverty rose but inequality fell. The recent household survey, not yet available, will be vital in ascertaining whether the steady growth recorded since 1999, based partly on a revitalized agriculture sector, has contributed to poverty reduction.

Channels of Pro-Poor Growth

Agriculture constitutes a significant part of the traded-goods sector for many sub-Saharan African economies, and the level and volatility of the real exchange rate are key determinants of this sector’s performance. It is well known that overvalued real exchange rates were part of the urban bias of development policies in the 1970s and 1980s. Recently, large increases in aid to particular sub-Saharan African countries as well as proposals for an overall large scaling up of aid to Africa have raised concerns about Dutch disease: constraints on export growth from real exchange rate appreciation. However, the stylized notion of an agriculture sector being severely crowded out by exchange rate appreciation is rarely observed (Adam, 2005). For example, empirical analysis by the IMF (2005a) of Ethiopia’s experience since 1991 did not find a link between real appreciation and aid inflows but did find that noncoffee exports had risen despite the increase in aid. Similarly, Nkusu (2004) reports a stable real exchange rate and strong growth in nontraditional exports for Uganda in recent years, despite high aid inflows. Evidence indicates that sound macroeconomic policies and important structural reforms protected these countries against the adverse consequences of aid inflows.

Theory and evidence confirm the lack of any clear link between aid and the exchange rate. The conventional mechanism arises because aid increases the demand for nontraded goods, so the only way that the non-traded-goods sector can respond is to draw resources from the traded-goods sector, induced by a real appreciation. However, as emphasized by Adam (2005), because this mechanism is contingent on the supply response, more elaborate responses are possible if aid can directly influence supply capacity. For instance, the above finding from Ethiopia suggests that aid flows were used to boost export supply capacity, mitigating the Dutch disease effect. In Adam and Bevan’s (2003a) computable general equilibrium model for Uganda, if aid is used to enhance the supply response of nontraded goods, then the relative price adjustment is moderated and export levels can be sustained. The model incorporates a role for infrastructure as the primary driver of the economy-wide supply response because of the complex range of scale, scope, and network efficiencies that infrastructure can offer. In general, the key factor in maintaining exports is the avoidance of rising costs as the economy’s output increases; the exchange rate is just one element of this relationship. For instance, Atingi-Ego (2005) documents relative price movements in Uganda that might be associated with Dutch disease, such as an increase in the relative price of nontradables in the 1990s. However, this could reflect a conventional Balassa-Samuelson effect of growth and may thus not be linked specifically to aid.

The impact of aid expenditures on poverty will depend on the poor’s links to the labor market. These links may present policymakers with a growth-poverty tradeoff. In the Adam and Bevan model, for example, aid has the highest return and promotes the highest growth rate when it is used to enhance the supply response of the non-traded-goods sector, but the poor receive little benefit from this pattern of growth because their assumed links to the beneficiary urban sector are so weak. When aid is used to enhance the export supply response, the poor gain more because of their links to the export sector, but overall growth is lower as is the return on aid, and the relative price of nontradables increases substantially. However, the positive export effect may be even more important in the long run; Collier and O’Connell (2004) argue that productivity gains from export growth are the key missing driver of growth thus far for most sub-Saharan African economies.

How aid is allocated between social sector and productivity-enhancing expenditure has a substantial economic impact. The World Bank’s Maquette for MDG Simulations (MAMS) model of MDG attainment scenarios for Ethiopia (Sundberg, Lofgren, and Bourguignon, 2005) delineates some of the relevant effects.64 The short-run wage effects of increased education provision, for example, can be substantial because employment in the sector has to rise while young unskilled cohorts leave the labor force to receive schooling. The timing of the return on education investments is delayed until this cohort returns to the labor force with better skills. These shifts of cohorts out of and into the labor force come up against infrastructure deficiencies and risk producing serious bottlenecks, which would undercut the immediate benefits of aid flows. Because social sector jobs are typically found in the formal sector, labor shortages that result in higher formal sector wages will be of small benefit to the poor, but will create difficulties for employers, who must match wages in the formal sector, such as in manufacturing.65 On the other hand, a front-loaded infrastructure investment program raises productivity throughout the economy, improving the supply response and relaxing the need for relative price adjustment. It also lowers the cost of social service delivery, making the social sector MDGs easier to achieve. Without infrastructure investments, the model forecasts that the sectoral MDGs can be achieved in Ethiopia only with very sizable aid inflows but at the cost of a real exchange rate appreciation, which would squeeze export capacity substantially by 2015.

An evaluation of the impact of aid inflows should take account of the policy reaction to inflows and not just the inflows. This is especially important for understanding the link between interest rates and aid. To the extent that an exchange rate appreciation attributable to aid inflows is undesirable, a common policy response is to attempt to offset the inflows through sterilization. However, sterilization operations can lead to higher interest rates and domestic market portfolios that are heavily weighted toward government bonds. Determining the role of sterilization operations in explaining increases in interest rates is difficult because high real interest rates have multiple causes. For instance, in Uganda and Ghana (according to the OPPG case studies), high interest rates are attributed to a combination of high domestic deficits, tight monetary policy, and inflation risk.66 The case studies for these countries identify high real interest rates as an important factor in limiting pro-poor growth. The high borrowing rates were clearly a challenge for small firms and low-income borrowers, whom formal sector banks already view as high-transaction-cost borrowers.

Some studies have found that financial sector development benefits the poor, but the channels through which it occurs have not been determined.

Impediments to financial intermediation can have a particularly adverse impact on the poor. Households with assets have access to the intermediation offered by the banking sector, but asset-poor households face much wider spreads between the return on savings and the cost of loans. Other channels linking financial development to poverty reduction are also possible, such as through the facilitation of private sector employment growth. Tsangarides, Ghura, and Leite (2004) report that financial sector deepening, as measured by the ratio of broad money to GDP, is positively associated with income growth for the bottom quintile of the income distribution, controlling for other factors. Beck, Demirguc-Kunt, and Levine (2004) find a similar link between the share of credit in GDP and income growth of the less well-off.67 Given that an evaluation of whether a policy is pro-poor should be based on direct measures of poverty, it is noteworthy that Beck and others (2001) confirm that the link also holds when the World Bank’s measures of poverty are used. By themselves, however, these studies shed no light on the channels linking aggregate financial development to poverty reduction. Although household-level studies, such as that by Khandker (1998), have been able to demonstrate that access to microcredit increases consumption, it has proved difficult to establish a similar causal link for overall financial sector development and poverty reduction.

C. Summary

Studies provide two types of guidance for policymakers on the sources of pro-poor growth. First, since growth is the most reliable long-run vehicle for poverty reduction, pro-poor growth policies overlap with growth policies. Second, there is large variation across countries in the rate of poverty reduction from a given rate of growth, and, as discussed above, useful tools and methods are available to better understand the nature of this variation. The complexity of the linkages between poverty, growth, and inequality means that only tentative lessons can be drawn for pro-poor growth. Nevertheless, some broad themes have emerged. The lessons build on the fact that the poor are disproportionately rural and depend on agriculture livelihoods, although past policies tended to center on employment in the urban formal sector. Countries have made substantial progress in unwinding explicit policy biases against the agriculture sector, but public spending on agriculture and rural sectors remains low. Because it is difficult for countries to mobilize additional domestic resources in the short run, they are faced with the closely intertwined challenges of raising agricultural productivity and allocating aid inflows prudently.

Recent studies highlight that countries stand to gain from spending aid inflows on public capital, because the induced increase in supply can protect sectors that would otherwise be constricted by relative price adjustments. To the extent that the economy’s general productivity is enhanced, the efficiency of social sector provision also rises. These findings create a case for front-loading aid expenditures toward building absorptive capacity: for example, “general purpose technologies” such as infrastructure and communications, as well as human capital.

Clearly, there is no single recipe for pro-poor growth. Because data on poverty and inequality are available only on an infrequent basis, policymakers will require more proximate indicators of whether growth benefits the poor. The evidence here suggests that such measures as growth in agriculture, productivity growth at aggregate and sectoral levels, wage and price differentials, and interest rate spreads may provide indicators of the extent to which the environment supports pro-poor growth. However, strategies for pro-poor growth should be formulated on the basis of an analysis of the factors that limit the participation of the poor in growth at the country level. Agence Française de Développement and others (2005) provide some guidelines on areas for analysis. The suggested lines of inquiry include, for example, sources of growth; the relationship between growth, changes in poverty, and changes in income distribution; comparison with other countries in the region on key initial conditions affecting pro-poor growth (fertility, population density, inequality, climatic instability, role of agriculture); distribution of spending across sectors and benefit incidence of these expenditures; sources of income for the poor; and access of the poor to productive assets. Further country-level study is therefore warranted.

48

The Datt-Ravallion method is an exact decomposition of the change in a poverty indicator into growth and inequality components, except for a small residual arising from the use of discrete rather than continuous time. Kraay’s variance decomposition requires an additional minor approximation.

49

Strictly speaking, the link between the Gini and the poverty elasticity of growth arises as a consequence of the function that is used to approximate the income distribution. It would be more precise to focus on the mass of households around the poverty line as a determinant of the elasticity (Kalwij and Verschoor, 2004).

50

The Gini coefficient ranges between 0 and 1, with 0 indicating equality and 1 indicating that all of the economy’s income accrues to the richest income group.

51

Each spell is constructed from changes between two comparable household budget surveys for a country. Household surveys are often conducted at irregular intervals, and not all have been processed for the World Bank’s Global Poverty Monitoring database. Thus, spells will differ in length and number across countries. See Chen and Ravallion (2004) for a description of these dollar a day head-count poverty measurement data, which are the basis for the World Bank’s monitoring of target 1 of the income poverty MDG.

52

A primary source of variation in estimated elasticities arises from the choice of growth measure. Because survey mean consumption or income tends to grow less rapidly than the corresponding national accounts measure (Deaton, 2003), the change in poverty will show a higher elasticity to growth in the survey mean than to a GDP-based measure.

53

The combination of multiple data sources into a single sample does raise concerns about comparability, however. One would expect the elasticity of poverty with respect to inequality to be highest where inequality is highest. In Iradian’s sample, this effect is most evident for Latin America. His regression includes other variables that may absorb some of the link between inequality and poverty, also explaining why sub-Saharan Africa’s elasticity with respect to inequality is lower than the global average.

54

See, for example, Chapter 7 of Commission for Africa (2005).

55

IMF (2005c) data for 2004 show that government revenue excluding grants is about 23 percent of GDP for sub-Saharan Africa overall, but just 16 percent for the Heavily Indebted Poor Countries (HIPC) subset, and 13 percent for Burkina Faso and Uganda.

56

Ravallion (2004, 2005) has argued that both intuitive appeal and theoretical elegance recommend the Watts index—the average of the growth in log income of those below the poverty line—as the measurement variable for the rate of pro-poor growth. This measure achieves a type of equal weighting of all household incomes below the poverty line (adjusted for the gap between household income and the poverty line).

57

The World Bank has disseminated software tools that calculate the GIC and the pro-poor growth rate, and such calculations are included in the OPPG country case studies.

58

See the following case studies: Uganda—Okidi and others (2004); Ghana—McKay and Aryeetey (2004); Senegal—Azam and Dia (2004); Burkina Faso—Grimm and Gunther (2004); Zambia—Thurlow and Wobst (2004). Links to all papers can be found at http://www.worldbank.org/propoorgrowth.

59

For two of the countries (Burkina Faso and Uganda), the measurement issues for developments since 1999 have been severe. There is a subtle risk of selection bias in the outcomes of controversies over poverty data. These tend to arise when a country is perceived as performing well in terms of GDP growth or compliance with policy advice, but poverty does not decline. The data are then approached with an expectation that something must be missing. Because African household surveys are imperfect, credible flaws that tend to overstate poverty may well be identified. But similar flaws in poorly performing countries might go unquestioned.

60

The increase in inequality for Ghana in the 1990s is larger when measured with World Bank data. However, longer spells of the World Bank data for Ghana also show only a small increase in inequality, because inequality seems to have fallen at the end of the 1980s before rising in the subsequent decade.

61

This is the Datt-Ravallion decomposition and hence, except for a small residual, the growth and inequality components will sum to the total change in head-count poverty over the indicated period.

62

Kappel, Lay, and Steiner (2004) provide a very similar analysis of weakness in agriculture but also point to constraints on small and medium enterprises as explaining Uganda’s slowing rate of poverty reduction.

63

The World Bank data show the country as experiencing a huge fall in head-count poverty during 1994–98, with the national data registering an increase in the same period. The country case study and recent work by the World Bank’s poverty analysis team have gone some way toward settling the controversies and provide evidence of a long-term downward trend in poverty reduction, albeit with an upward spike in 1998, along with stable inequality.

64

MAMS is one of several models developed by the World Bank to study Ethiopia’s development options.

65

This channel from aid to manufacturing wages is the central element in Rajan and Subramanian’s (2005) finding of negative effects of aid on recipient countries.

66

However, Prati, Sahayi, and Tressel (2005) show that under certain circumstances, a contractionary monetary policy response to aid might be optimal because a higher interest rate will help smooth aid inflows through saving.

67

Some qualifications can be noted with regard to the results in Beck, Demirguc-Kunt, and Levine (2004). Evidence is derived from cross-section regressions, which are vulnerable to bias because of unobserved heterogeneity. As with other studies, a policy is judged to be pro-poor if it has an effect on poverty or income distribution over and above that resulting from GDP growth alone. But this interpretation mingles two quite different data sources, and runs the risks explained in Deaton (2003).

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