Race to the Next Income Frontier

Chapter 5. Inclusiveness and the Social Dimensions of Growth in Senegal

Ali Mansoor, Salifou Issoufou, and Daouda Sembene
Published Date:
April 2018
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Alexei Kireyev

Growth and Poverty Reduction

Historical and Regional Perspectives

Ever since its independence, Senegal’s growth has been uneven. Two clear phases can be identified: before the 1994 devaluation and after. In the first period, Senegal’s real GDP per capita declined on average by about 0.8 percent a year and recorded large gyrations. This phase was highly unstable, with drastic drops in per capita income associated with periodic droughts, financial crises, oil shocks, and a worldwide recession. These declines were partly offset by temporary growth related to increases in international demand for key export commodities, such as groundnuts and phosphates.

After the 1994 devaluation, growth in per capita GDP became less volatile and was on average almost 2 percent a year. The devaluation increased the competitiveness of Senegalese exports by cutting domestic costs, and it marked a turnaround in per capita GDP, which has sustained an upward trend for much of the past two decades. Even with the onset of the international financial crisis in 2008, Senegal’s growth has remained positive in absolute terms, although its GDP per capita growth has been below trend (Figure 5.1). This more recent period will be the focus of the rest of this chapter.

Figure 5.1.Evolution of Real GDP Per Capita, Senegal, 1963–94 and 1995–2012

Source: IMF, World Economic Outlook database.

The overall poverty level is relatively lower in Senegal than in most other sub-Saharan African countries. Based on the revised international poverty line, which usually differs somewhat from the national poverty line, Senegal is in the top quarter of sub-Saharan African countries for which data are available (Figure 5.2). At the US$1.25 a day poverty line (in 2005 prices), Senegal in 2011 was comparable to Ethiopia and Ghana but behind other countries in the region, such as Gabon, Cameroon, and Côte d’Ivoire.1

Figure 5.2.Poverty Head Count Rate at International Poverty Line, Selected Sub-Saharan African Countries

Source: World Bank, World Development Indicators.

The 2011 household survey in Senegal indicates that poverty remains high, although it has declined in the most recent two decades. More than six million people were living on a household income below the national poverty line in 2011. During the period 1994–2001, GDP growth in Senegal was about 5 percent a year, and the poverty rate fell significantly, from 68 percent in 1994/95 to 55 percent in 2001/02. During 2002–05, average GDP growth was 4.7 percent, allowing the poverty rate to decline further to about 48.5 percent. However, since 2005/06, repeated shocks have contributed to reducing per capita income growth to little more than the rate of population growth. The 2011 household survey suggests that over the preceding five years (2006–11), poverty incidence had declined by only 1.8 percentage points to 46.7 percent.

This chapter uses both national and international estimates of poverty and inequality in Senegal. The distributional and poverty-related data are drawn from nationally representative household surveys published by the National Statistical and Demographic Agency of Senegal.2 However, for international comparisons, the chapter uses the data published by the World Bank, including through PovCalNet,3 an interactive, online computational tool that allows one to calculate poverty measures across countries using comparable data. In PovCalNet, all poverty rates are based on the international poverty line of US$1.25 a day at 2005 purchasing power parity, which is different from the poverty line used in Senegal and therefore not directly comparable with the national poverty rate. Moreover, because PovCalNet uses grouped data for each income group, there might be differences from the national data in measurements of the Gini index, poverty head count ratios, consumption by decile of population, and other poverty indicators.4

The Impact of Growth on Poverty Reduction

Growth is usually defined as pro-poor if it reduces poverty. Several metrics are used to measure the change in poverty: the change in the share of population living below the poverty line; the change in monthly per capita consumption, income, or expenditure; and the change in the poverty gap. The poverty line is the minimum level of income deemed adequate for meeting basic consumption needs in a given country, and it therefore differs from country to country. For international comparisons, two poverty lines are usually used: daily income of US$1.25 and daily income of US$2, both at 2005 purchasing power parity. The poverty gap is the mean distance from the poverty line (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This second measure reflects the depth of poverty and its incidence.

Senegal’s recent prolonged episode of growth has led to a significant reduction in poverty. Based on several household surveys,5 poverty in Senegal—defined as the share of people below the national poverty line—declined from 55.2 percent in 2001 to 46.7 percent in 2011 (Table 5.1). The poverty gap declined from 17.3 to 14.5 percent; other metrics also point to a continued trend in the reduction in poverty, although the pace of improvement declined during the second half of the decade and may not be statistically significant between 2006 and 2011.

TABLE 5.1Poverty Indicators, Senegal, 2001, 2005, and 2011
Poverty Incidence55.248.346.7
Confidence Interval (95 percent)52.9–57.546.1–50.644.1–49.3
Poverty Gap17.315.514.5
Source: ANSD 2012b.
Source: ANSD 2012b.

Progress achieved in poverty reduction has been more pronounced in Senegal than in some regional peers. In 1994–2005, the share of population living on less than US$1.25 a day declined by about 20 percentage points, and for people living on less than US$2 a day by about 19 percentage points (Figure 5.3). By the latter metric, which may be more appropriate for Senegal given its per capita income, poverty dropped faster in Senegal than in other West African Economic and Monetary Union (WAEMU) countries (15 percentage points) during approximately the same period. The dynamics of poverty reduction in the region have been significantly affected by increases in poverty in Guinea-Bissau and Côte d’Ivoire during political crises in those countries.

Figure 5.3.Change in Poverty Rate, Senegal and Selected Countries

Source: World Bank, PovCalNet, 2013 (http://iresearch.worldbank.org/PovCalNet).

Note: All rates measured in 2005 purchasing power parity prices. Years in parentheses indicate the latest year with available data. Change measured is change between the latest year and 1994 for Senegal, Burkina Faso, and Mali; 1985 for Côte d’Ivoire; 1991 for Guinea-Bissau; and 1992 for Niger.

The level of poverty also differs significantly among different regions of Senegal. In 2011, for example, the poverty incidence in the poorest regions (Kolda, Fatick, Ziguinchor) was 67–73 percent, whereas it was only 26 percent in Dakar.

This outcome reflects both higher growth and a higher sensitivity to growth of poverty reduction in Senegal. Unlike that in a number of countries in WAEMU, particularly those affected by internal conflicts or crises (such as Guinea-Bissau and Côte d’Ivoire in the 2000s), real per capita GDP growth in Senegal was always positive in 1995–2011, and in some years it was quite significant (Figure 5.4, panel 1). In addition, the elasticity of poverty reduction to per capita income growth has been significant in Senegal in regional comparisons. In 2001–11, this elasticity was about –1.3 in Senegal, above that of some other fast-growing WAEMU countries (such as Burkina Faso) (Figure 5.4, panel 2).

Figure 5.4.Factors Contributing to Pro-poor Growth

Sources: IMF, World Economic Outlook; World Bank, World Development Indicators; and IMF staff estimates.

Note: WAEMU = West African Economic and Monetary Union.

Although growth seems to have been a major factor behind the reduction of poverty, this conclusion should be treated with caution. First, an increase in real GDP per capita does not necessarily imply a reduction of poverty; conclusions about the causes of poverty reduction require supplementary information on the distribution of this additional income among different population groups. If the initial distribution of income is highly unequal, the impact of growth on poverty may not be significant. In an extreme case, if all benefits of higher growth were captured by the wealthiest part of the population, the impact on poverty reduction would be negative. Second, the elasticity of poverty reduction to growth in per capita income depends on the shape of income or consumption distribution and on the position of the poverty line with respect to this distribution. Normally, the closer the poverty line is to the median of the distribution, the higher will be the elasticity of the poverty rate to real per capita growth.

Finally, more regular household surveys that all employ similar methodology are needed to assess the evolution of growth inclusiveness through time. This impact assessment would be better served by the use of more advanced econometric techniques, which are difficult in the absence of high-frequency poverty data sets.

Growth Inclusiveness

Measures of Equality and Data Issues

Growth is usually considered inclusive if its benefits are widely shared across the population. Although there is no commonly accepted definition of inclusive growth, it usually refers to the goal of fostering high growth while providing productive employment and equal opportunities, so that all segments of society can share in the growth and employment, while redressing inequalities in outcomes, particularly those experienced by the poor (see IMF 2013 for an overview).

For analytical purposes, growth is usually considered inclusive if it is high, sustained over time, and broad based across sectors; creates productive employment opportunities; and includes a large part of a country’s labor force. Additional dimensions of inclusive growth include gender, regional diversification, and empowerment of the poor, including through inclusive institutions. This chapter focuses only on the distributional characteristics of growth, so in this chapter growth is considered inclusive if it helps improve equality.

Several statistical metrics allow one to evaluate different aspects of inclusiveness following this narrow definition. The squared poverty gap6 assesses inequality, since it captures differences in the severity of poverty among the poor. The Watts index7 is a distribution-sensitive poverty measure because it reflects the fact that an increase in income of a poor household reduces poverty more than a comparable increase in income of a rich household. The Gini coefficient shows the deviation of income per decile from the perfect equality line. The mean log deviation index8 is more sensitive to changes at the lower end of the income distribution. The decile ratio is the ratio of the average consumption of income of the richest 10 percent of the population divided by the average income of the poorest 10 percent.

Finally, in dynamic terms the increase of income in the bottom deciles can be compared to the average income increase or the income increase in the highest deciles of the population. If the income of the bottom decile in the distribution tends to rise proportionately to or faster than the average income, growth is considered inclusive. Although the squared poverty gap and the Watts index take into account the distributional characteristics of growth indirectly, all other methods measure equality directly.

The quality of any analysis of growth inclusiveness depends on both data availability and data quality. Such analyses require at least two household surveys based on a comparable methodology as well as data on income and consumption by households, which are difficult to collect in Senegal because most of the population is employed in the informal sector (Foster and others 2013). The data may include outliers at both tails of the distribution. Although the outliers have been routinely corrected in Senegal’s household surveys, they may lead to negative growth rates of the incidence curve for both tails of the distribution in some years (see subsequent discussion). Also, some parameters, such as the size of households and other sociodemographic variables (household head, education level, marital status, employment sector, place of residence, regional distribution, etc.), can vary from survey to survey, affecting poverty measures. Finally, the timing and the definitions of key variables, including the coverage of rural and urban areas, should be the same in different surveys to achieve consistent poverty estimates.

Inequality Indicators

Different statistical measures suggest that, although poverty in Senegal has declined, overall inequality remains broadly unchanged. In 1994–2011, the squared poverty gap shrank by more than half, suggesting that poverty among the poorest people became less severe (Table 5.2). The Watts index also dropped substantially, suggesting a relatively faster improvement in the situation of people with the lowest incomes. At the same time, both the Gini coefficient and the mean log deviation index declined a bit in 1994–2005 and increased again in 2005–11, suggesting no major changes in the overall level of inequality.

TABLE 5.2Senegal: Inequality Indicators, 1994–2011
Square Poverty GapWatts IndexGini CoefficientMean Log Deviation Index

A simple decile ratio also suggests that the level of inequality remained broadly unchanged. The ratio of consumption in the top decile of the population relative to that in the bottom decile did not change much between 1994 and 2011. It stood at 12.9 in 1994, declined to about 11.8 in both 2001 and 2005, but then increased again to 12.5 in 2011, suggesting that the richest consume on average 12 to 13 times more than the poorest. In total, the richest two deciles of the population consume about half of all goods and services in the country, roughly the same amount as the seven bottom deciles of the population combined (Figure 5.5). This suggests a substantial level of income disparity and inequality, although it is lower than the average for sub-Saharan Africa.

Figure 5.5.Consumption Share by Income Deciles, Senegal, Selected Years


Growth in the level of consumption in 2006–11 was positive but low, and it was almost equal among different deciles of the population (Figure 5.6). No significant changes occurred in inequality during this period, because the growth in consumption of the bottom deciles was only slightly higher than that of the top deciles. In contrast, during 2001–05 the poorest fifth of the population experienced a decline in consumption, while all the middle deciles registered significant growth in consumption, although the increase in the consumption level of the richest groups was nonsignificant.

Figure 5.6.Consumption Growth by Welfare Groups, Senegal, 2001–05 and 2006–11


Sources: ANSD 2001, 2007, 2012a.

Growth Incidence Curves

A dynamic measure of the inclusiveness of growth can be derived from growth incidence curves. The estimation of growth incidence curves involves a methodology that helps identify the extent to which each decile of households benefits from growth (Ravallion and Chen 2003). In growth incidence curves, the vertical axis reports the growth rate of consumption expenditure, and the horizontal axis reports consumption expenditure percentiles (Foster and others 2013).

Growth incidence curves assess how consumption at each percentile changes over time. The part of the curve above zero signifies the deciles that benefit from growth, and the part below zero signifies the deciles that lose because of growth. The part of the curve that is above its own mean signifies the deciles of the population that benefit from growth relatively more than the average household does. The part of the curve below the mean, but still above zero, signifies the deciles that also benefit from growth but less than the average household.

A negatively sloping growth incidence curve suggests that income or spending among the poorer deciles of the population is growing faster than income or spending among the richer deciles. Because in this case the poorer groups of the population are catching up with the richer, a negatively sloping growth incidence curve can be viewed as an indication of inclusive growth. Improvements in the degree of inclusiveness of growth would be signaled by changes in the slope of the growth incidence from positive to negative, and progress in poverty reduction would lead to the mean of the growth incidence curve and the curve itself moving up (see Annex 5.1 for a suggested formal treatment).

Although growth incidence curves give somewhat conflicting signals on distributional shifts in Senegal, they seem to confirm that growth benefited most people in the middle of the income distribution. Between 2001 and 2005, consumption increased on average, because the mean of the growth incidence curve is above zero, driven by the middle of the distribution (from the third to the eighth deciles) (Figure 5.7). The growth incidence curve is positively sloped, suggesting some increase in inequality during this period. Between 2005 and 2011, the mean of the growth incidence curve is above zero, but the curve is broadly flat, suggesting no clear trend in changes in inequality.

Figure 5.7.Growth Incidence Curve for Total Population, Senegal, 2001, 2005, 2011

Sources: World Bank, Enquête Sénégalaise Auprès des Ménages (ESAM) 2001, Enquête Suivi de la Pauvreté au Sénégal (ESPS) 2005, and Enquête Suivi de la Pauvreté au Sénégal (ESPS) 2011 databases, processed using ADePT 5.1 platform for automated economic analysis, household-level data.

Note: The data may include outliers at both tails of the distribution.

The average for the entire period 2001–11 shows a clear increase in mean consumption, confirming the decline in poverty, as the middle class improves its relative position. However, the growth incidence curve over the full period has a slightly positive slope, which may point to some worsening of inclusiveness. This trend might not be statistically significant, indicating no substantial distributional changes during this period other than the improvement in the relative position of the middle class. However, this overall result also masks significant differences in growth inclusiveness between urban and rural areas.

Differences between Urban and Rural Growth

In urban areas, people in the middle of the distribution seem to have benefited the most from growth. Between 2001 and 2005, the growth incidence curve for urban areas is substantially above the mean for the whole distribution other than the top decile, but it slopes downward a little, suggesting somewhat reduced disparity between the rich and the poor (Figure 5.8). For 2005–11, however, the incidence curve hovers around zero and is upward sloping, pointing to some worsening of inclusiveness. For 2001–11 overall, again there is no clear trend, although consumption for the middle decile is very strong. Although the incidence curve is above zero, it looks broadly flat, pointing to unchanged inclusiveness.

Figure 5.8.Growth Incidence Curves for Urban Areas, Senegal, 2001, 2005, and 2011

Sources: World Bank, Enquête Sénégalaise Auprès des Ménages (ESAM) 2001, Enquête Suivi de la Pauvreté au Sénégal (ESPS) 2005, Enquête Suivi de la Pauvreté au Sénégal (ESPS) 2011 databases, processed using ADePT 5.1 platform for automated economic analysis, household-level data.

In rural areas, growth may have been less inclusive; here the improvement of the middle class is not very pronounced. Between 2001 and 2005, a clear trend of growing inequality is seen in rural areas, because the incidence curve is positively sloped and actually below zero for the first two deciles of the population (Figure 5.9). Again, in 2005–11 there is no clear trend, either in terms of inclusiveness (the incidence curve is broadly flat) or in terms of poverty reduction (the mean is about zero). Overall, in 2001–11 the incidence curve is positively sloped for the lower deciles but broadly flat in the middle, and the growth rate in the lower deciles is substantially lower than that in the median and highest deciles. This may point to an increasing gap between the poor and the rich in some rural areas.

Figure 5.9.Growth Incidence Curves for Rural Areas, Senegal, 2001, 2005, and 2011

Sources: World Bank, Enquête Sénégalaise Auprès des Ménages (ESAM) 2001, Enquête Suivi de la Pauvreté au Sénégal (ESPS) 2005, Enquête Suivi de la Pauvreté au Sénégal (ESPS) 2011 databases processed using ADePT 5.1 platform for automated economic analysis, household-level data.

How inclusive growth is in rural areas has an important impact on the degree of inclusiveness in Senegal’s growth as a whole. The difference between the median and mean growth rates of household spending is smaller in rural areas than it is in urban areas. This may suggest that the overall change in the distribution of households’ consumption is heavily influenced by the changes in the distribution in rural areas and that it is skewed to the right, because most households are relatively poorer than the mean household in the country. By contrast, in urban areas the impact of changes in the consumption growth rates of relatively rich households on the overall inclusiveness of growth is less significant, because the distribution in urban areas is skewed to the left—most households are relatively richer than the mean household in the country.

Although available indicators sometimes give conflicting signals on distributional shifts, this statistical analysis of the distributional characteristics of growth suggests the following:

  • Poverty in Senegal has fallen in the last two decades, although poverty reduction has slowed in recent years.

  • Although available indicators sometimes give conflicting signals on distributional shifts, growth seems to have benefited most people in the middle of the income distribution.

  • The middle class has benefited from growth, mainly in urban areas, while both the poorest and the richest have lost ground.

  • Growth has been less inclusive in rural areas than in urban areas.

Are Senegal’s Public Policies Supportive of Inclusive Growth?

Public policies may be considered supportive of inclusive growth if they help to promote growth and to reduce poverty and inequality. Possible indicators that policies are supportive include (1) the overall level of social spending, because cross-country experience suggests that countries with relatively higher spending on human capital, health care, pensions, and other aspects of the social safety net tend to have more inclusive growth; (2) measures specifically targeted at raising the incomes of people in the bottom deciles of the income distribution relative to the average income; (3) development of social safety nets for the population in general and programs aimed at its poorest segments (social protection floor); and (4) the design of the tax system.

The aggregate level of health and education spending in Senegal is comparable to that in WAEMU countries generally, but the composition is different. Spending on education and health care has been higher in Senegal than for the WAEMU on average (Figure 5.10). Spending on education and health care should contribute to inclusiveness of growth, especially in urban areas, where the concentration of schools is high.

Figure 5.10.Social Expenditure

(Percent of GDP)

Sources: IMF, World Economic Outlook database; and IMF staff estimates.

Public expenditures, including in the social sectors, are concentrated in Dakar, the capital. The World Bank estimates that this capital region, where only about a quarter of the population of Senegal lives, absorbs more than half of all public resources. Other regions have less access to public resources, including in such critical areas as health care and education, which may also contribute to inequality (see Figure 5.11, panels 1 and 2, which are based on World Bank analysis).

Figure 5.11.Regional Distribution of Public Health and Public Education Expenditure, Senegal, 2009

Source: World Bank, Public Expenditure Review, 2012.

Ad Hoc Measures

Senegal has used ad hoc and untargeted measures to address the impact of shocks in the recent past. During the 2007–08 food and fuel crisis, for example, the authorities took several measures to limit price increases in food and fuel oil. They temporarily reduced the value-added tax and introduced excise tax exemptions and subsidies for butane for all consumers. The fiscal cost of these measures amounted to about 4½ percent of GDP during the two-year period, with about a third of this loss stemming from losses in revenue. Senegal’s 2008 poverty and social impact analysis (IMF 2012) revealed that ad hoc measures were in general poorly targeted, because almost 55 percent of the benefits accrued to households in the top 40 percent of the welfare distribution.

In February 2011, the government froze retail prices for six key food items to help the poor and temporarily limited price increases for petroleum products at the pump by reducing the value-added tax base. Some of these measures were reversed later in the year. In early 2012 and early 2013, the authorities temporarily introduced implicit subsidies for petroleum products through a mechanism of price stabilization, but later phased them out.

Social Safety Nets

The scope and coverage of the existing social safety nets in Senegal is limited, and most interventions are small and temporary. The safety net programs have three main benefits: offering general support for daily existence, providing nutritional support, and improving access to basic services. These programs are carried out through monetary transfers (cash grants and loans), food aid, and fee waivers for health services and are spread across several entities, each consisting of several projects (Box 5.1).

According to the World Bank’s (2013) social safety net assessment, formal social security coverage reaches 13 percent of the population. This includes 6 percent covered by formal pensions, 3 percent receiving social security benefits, and 3 percent having health insurance. Annual transfers under the safety net programs averaged about CFAF 17 billion a year in 2010–12, about 0.27 percent of GDP. Safety net funding remains largely dependent on donor financing, with the budget itself providing not more than one-fourth of the total.

Recently, two new projects have been announced. The government plans to implement a pilot project, Family Safety Grants (Bourses de sécurité familiale—BSF), to provide annual financial assistance to the poorest families. Also, the government intends to introduce universal health coverage (Couverture maladie universelle—CMU), which would provide basic medical care, particularly to the most vulnerable.

Obviously, a more comprehensive social safety net is needed. This could be funded by broadening the tax base and increasing some taxes, along with reallocating existing spending. The experience of other countries in the region suggests that a minimum social safety net could be provided at low cost. For example, in Burkina Faso a basic social safety net, including a minimum medical insurance coverage and government support for the poorest families, could be set up at a cost of about 1.5 percent of GDP (IMF 2012). For Senegal, this level of spending is within reach and would be worthwhile, since well-targeted social safety nets can help reduce inequality and poverty.

BOX 5.1Social Programs in Senegal

  • The Food Security Commissariat (Commissariat de la securité alimentaire—CSA) provides food aid assistance to vulnerable populations either in response to catastrophes or through rice distribution.

  • The National Solidarity Fund (Fonds de solidarité nationale—FSN) is responsible for providing immediate responses to emergency situations, including financial, medical, and material support.

  • The Community-Based Re-adaptation Program (Programme de réadaptation à base communautaire—PRBC) provides social, economic, and cultural integration for disabled persons through financial support and income-generating activities.

  • The Old Age Support Program (Projet d’appui à la promotion des aînés—PAPA) addresses the vulnerable elderly (over age 60) through capacity strengthening, grants, and subsidized loans for the elderly.

  • The National School Lunch Program (Programme d’alimentation scolaire) provides school lunches funded by the national budget.

  • The School Lunch Program (Cantines scolaires) supports the national school lunch program by providing primary school lunches in vulnerable rural areas.

  • Educational Support for Vulnerable Children (Bourses d’étude pour les orphelins et autres enfants vulnérables—OEV) provides schooling or professional training to vulnerable children through a program of the National HIV-AIDS Council.

  • The Sesame Plan (Plan Sesame) waives health service fees for all persons over age 60.

  • The Poverty Reduction Program (Programme d’appui à la mise en œuvre de la Stratégie de Réduction de la Pauvreté—PRP) supports grants for income-generating activities for vulnerable groups, primarily women, the disabled, and HIV-affected people.

  • A pilot Cash Transfers for Child Nutrition Program (Nutrition ciblée sur l’enfant et transferts sociaux—NETS) entails cash grants to mothers of vulnerable children under age five to mitigate the negative impacts of food price increases.

  • The WFP Vouchers for Food Pilot Program (WFP Bons d’Achat—WFP CV) addresses food insecurity among vulnerable households driven by high food prices.

Source: World Bank 2013.

Policies to Make Growth More Inclusive

Sustained overall economic growth is a precondition for further poverty reduction. A number of studies confirm that sustained growth is a key factor in enhancing inclusiveness. Kraay (2004) shows that in developing countries, the growth of average income explains 70 percent of the variation in poverty reduction over the short term. Berg and Ostry (2011) argue that longer growth spells are robustly associated with more equality in the income distribution. Lopez and Servén (2006) suggest that for a given inequality level, the poorer the country, the more important is the growth component in explaining poverty reduction. Affandi and Peiris (2012) show that growth is generally pro-poor, with growth leading to significant declines in poverty across economies and time periods. Specifically, a 1 percent increase in real per capita income leads to about a 2 percent decline in the poverty head count ratio. Moreover, Senegal’s experience is consistent with this cross-country evidence. Therefore, at its core any successful pro-poor growth strategy should include measures to achieve sustained and rapid economic growth.

Special attention should be given to the distributional dimension of growth. An increase in inequality may offset and even exceed the beneficial impact on poverty reduction of the same increase in income (Affandi and Peiris 2012). According to recent estimates, about two-thirds of poverty reduction within a country comes from growth, and greater equality contributes the other third. In the most unequal countries, a 1 percent increase in incomes produces a mere 0.6 percent reduction in poverty, whereas in the most equal countries the same increase in income yields a 4.3 percent reduction in poverty (Ravallion 2013).

Because the inclusiveness of growth is associated with a number of macroeconomic outcomes and policies, it is important to analyze growth and inclusiveness simultaneously. Increased inequality may dampen growth, but at the same time, poorly designed measures to increase inclusiveness could undermine growth. For instance, increasing farm productivity and broadening rural job opportunities are both important for addressing rural poverty. In the long term, attention to inclusiveness can bring significant benefits for growth.

Well-designed public policies are also important for promoting inclusiveness. The recommendations of the 2008 poverty and social impact analysis for Senegal remain broadly valid. Poorer households could be protected against food and fuel price increases in the short term at a lower budgetary cost and more effectively by redirecting resources to better-targeted measures: poor populations can be targeted through measures such as school lunches and public works programs and better-targeted tariffs for small quantities of electricity to protect some of the urban poor. Over the medium term, a well-targeted and conditional cash transfer system is the best option for assistance to the poorest.

Strong growth in agriculture is probably the single most important factor in improving inclusiveness of growth. The strong performance of agriculture in 2008–10 helps explain the improvement in consumption levels of the poor during this period in spite of low overall GDP growth.

Structural policies promoting employment and productivity increases, in particular in agriculture, could also help increase inclusiveness.9 According to the World Bank (2010), several policies have been successful in increasing the agricultural earnings of the poor in other low-income countries. These policies could be applicable in Senegal. They include improving market access and lowering transaction costs, strengthening property rights for land, creating an incentive framework that benefits all farmers, expanding the technology available to smallholder producers, and helping poorer and smaller producers handle risk. To expand nonagricultural and urban employment opportunities for poor households, other sub-Saharan African countries have taken steps to improve the investment climate; expand access to secondary and girls’ education; design labor market regulations to create attractive employment opportunities; and increase access to infrastructure, especially roads and electricity.

Inclusive institutions have also been found important for growth inclusiveness. Acemoglu and Robinson (2012) argue that rich countries are rich by virtue of having inclusive institutions, that is, economic and political institutions that include the large majority of the population in the political and economic community. An initial set of inclusive economic institutions would include secure property rights, rule of law, public services, and freedom to contract. The role of the state would be to impose law and order, enforce contracts, and prevent theft and fraud. When the state fails to provide such a set of institutions, growth becomes extractive.

Coherent labor market policies are also needed for increasing inclusiveness. The challenges of growth, job creation, and inclusion are closely linked, because creating productive employment opportunities throughout the economy is an important way to generate inclusive growth (IMF 2013). In Senegal, the creation of employment opportunities and increasing productivity in rural areas, especially in agriculture, would prompt higher consumption growth among poorer households. In Cameroon and Uganda, for example, stronger per capita consumption growth observed at the poorest levels seems to be related to high agricultural employment growth (IMF 2011). By contrast, rural agricultural employment has fallen in Mozambique and Zambia, where the poorest have experienced weaker or negative per capita consumption growth.

Social protection has been too narrowly limited in the past to formal systems. Senegal needs to focus on social inclusion as well as economic inclusion. All citizens need to have access to basic social services: water, sanitation, electricity, education, health, and the social safety net. Human capital needs to be built up through a variety of channels, including the development of local markets in addition to stronger social protection, which provides good incentives without negatively affecting the labor supply.10

Deepening the financial sector would also increase inclusiveness, specifically through policies that give the poor better access to financial services. A number of studies have found that financial development generally increases the incomes of the poorest households (Claessens 2005), whereas unequal access to financial markets can reduce incomes by impeding investment in human and physical capital. These barriers are widespread in Senegal, where most people lack access to the formal financial system. At the same time, microfinance and other rural finance, as well as the expansion of credit information sharing, could significantly expand credit availability. Some promising initiatives in this area are underway in Senegal.11

The authorities need to improve financial literacy as well as the availability of services, including electronic money. In Senegal, the establishment of electronic money has been abused by some providers, and regulatory authorities need to guard against this to protect savers. More progress is also needed concerning the geographic coverage of financial services, in particular in the north and the east. Finally, there is also a need to develop microinsurance products and to educate the public about them, to emphasize the importance of protection against shocks.

Annex 5.1. Assessing Inclusiveness of Growth: Some Theoretical Considerations

Inclusive growth should simultaneously reduce poverty and inequality. Growth reduces poverty if the mean income of the poor rises. Growth reduces inequality if it helps straighten the Lorenz curve, which plots the percentage of total income earned by various portions of the population when the population is ordered by size of income. More formally, starting from Ravallion and Chen 2003, the growth incidence curve, which traces out variability of consumption or expenditure growth by the percentile of the population, can be defined as

in which Lt(p) is the rate of change of the Lorenz curve,12p is the deciles of the population, and γt, is the growth rate of its mean. From the equation it follows that

  • gt(p) = γt, if Lt(p)=Lt1(p) : growth at each decile of the incidence curve will be equal to the average growth of the distribution at each decile of population, if the slope of the Lorenz curve does not change over time.

  • gt(p) > γt, if Lt(p)>Lt1(p) : growth at each decile of the incidence curve will be higher than the average growth of the distribution at each decile of population, if the slope of the Lorenz curve increases.

  • gt(p) < γt, if Lt(p)<Lt1(p) : growth at each decile of the incidence curve will be lower than the average growth of the distribution at each decile of population, if the slope of the Lorenz curve decreases.

  • The slope of the incidence curve is positive if gt(p)=Lt(p)Lt1(p)Lt1(p)Lt(p)>1.

  • The slope of the incidence curve is negative if gt(p)=Lt(p)Lt1(p)Lt1(p)Lt(p)<1.

Therefore, based on the incidence curve, pro-poor and inclusive growth can be derived as follows. Assuming for simplicity of illustration that the incidence curve is linear (Annex Figure 5.1.1), (1) pro-poor growth shifts the mean expenditure (or consumption) of the poor up; the slope of the incidence curve is irrelevant and may be positive, suggesting that growth is not inclusive; (2) pro-poor inclusive growth shifts the mean expenditure up while the incidence curve is negatively sloped; (3) accelerations of pro-poor growth just shift the median income further up, while the slope of the incidence curve may remain positive, suggesting the growth remains noninclusive; and (4) an increase in the inclusiveness of growth suggests that the incidence curve becomes negatively sloped (g), the slope increases (g′) and/or the whole curve shifts to g″ as inequality declines, and Lt(p)<Lt1(p).

Annex Figure 5.1.1.Operational Definition of Inclusive Growth

Source: Author’s compilation.

From an operational perspective, to assess inclusiveness of growth, a country should take a number of actions: (1) establish the slope of the incidence curve based on the information of at least two sequential household surveys; (2) if the slope is positive, suggesting that growth has not been inclusive, identify measures that could increase income and spending of the lowest deciles, while increasing the mean growth rate, that is, not at the expense of higher deciles; (3) if the slope of the incidence curve is negative, suggesting that growth has been inclusive, identify measures to increase the slope by making growth of consumption of lower deciles even faster, without hampering any other deciles; and (4) alternatively or in addition, find a measure to reduce inequality in the Lorenz curve coefficient in the next period that would shift the entire incidence curve up.


The author is grateful to Herve Joly, Christina Kolerus, Doris Ross, and Rodrigo Garcia-Verdu for careful reading and helpful comments. The author thanks World Bank colleagues for providing databases and for useful discussions. Research assistance from Douglas Shapiro is gratefully acknowledged. Any remaining errors are the author’s. The views expressed are those of the author and do not necessarily represent those of the IMF or IMF policy.

Most comparisons in this chapter are based on the data from household surveys. The most recent survey for Senegal was conducted in 2011, whereas for most sub-Saharan African countries the latest surveys were published in 2005–10.

Methodological differences between national and internationally comparable poverty-related estimates are documented and discussed in detail at the World Bank’s PovCalNet site (http://iresearch.worldbank.org/PovcalNet).

Based on data from income, expenditure, household, and budgetary surveys conducted by the Senegalese authorities in 1991–2011 and processed by the World Bank through PovCalNet.

The squared poverty gap averages the squares of the poverty gaps relative to the poverty line. It takes into account not only the distance separating the poor from the poverty line (the poverty gap) but also the inequality among the poor, because it places a higher weight on households farther away from the poverty line.

The Watts index is defined as the logarithm of the quotient of the poverty line and the geometric mean of an income standard applied to the censored distribution in which the value of a measurement or observation is only partially known.

The mean log deviation index is an index of inequality given by the mean across the population of the log of the overall mean divided by individual income.

Part IV discusses structural reforms.

Chapter 18 discusses social protection in more detail.

Financial sector issues are discussed in Chapters 11 and 12.

Lt(p) is the fraction at time t of total income that the holders of the lowest pth fraction of incomes possess. This varies from 0 to 1, 0 ≤ p ≤ 1, presented as the inverse of the cumulative distribution function.

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