Building Integrated Economies in West Africa
Chapter

Chapter 7. Growth Inclusiveness and Equality

Author(s):
Alexei Kireyev
Published Date:
April 2016
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Author(s)
Alexei Kireyev, Stefan Klos, Christina Kolerus and Monique Newiak 

Growth performance depends heavily on its distributional characteristics. The fundamental question is equality, that is, whether the benefits of growth are shared equally across different income groups and whether all income groups and both genders have an equal opportunity to contribute their fair share to growth. While poverty has fallen in the last two decades in most of the West African Economic and Monetary Union (WAEMU) countries, poverty reduction has slowed in recent years. Although available indicators sometimes give conflicting signals on distributional shifts, two case studies—one on Senegal and one on Mali—suggest that people in the middle of the income distribution usually received the most benefit of growth, and mainly in urban areas. Further progress in poverty reduction and inclusiveness would require sustained high growth and exploration of growth opportunities in the sectors with high earning potential for the poor. Better targeted social policies and more attention to the regional distribution of spending would also help reduce poverty and improve inclusiveness. The WAEMU’s growth could significantly benefit from the reduction in gender and income inequality.

Growth and Poverty Reduction

The high-growth episode in sub-Saharan Africa that started in the early 1990s has been fairly inclusive. The October 2011 Regional Economic Outlook: Sub-Saharan Africa found that although the pickup in growth has been accompanied by a fairly modest reduction in poverty, some progress has been achieved in terms of improving equality and social and health outcomes (IMF 2011). Meanwhile, the global financial crisis and social turmoil in different parts of the world have heightened global awareness of the potential impact of rising inequality on economic and social stability and on the sustainability of growth (Berg and Ostry 2011). The social and political dimensions make it important to look at inclusiveness of growth in individual African countries.

Senegal

The overall poverty level is relatively lower in Senegal than it is in most other sub-Saharan African countries. At 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 7.1). At the $1.25 a day poverty line (in 2005 prices), Senegal in 2011 was comparable to Ethiopia and Ghana but was behind other countries in the region, such as Gabon, Cameroon, and Côte d’Ivoire.

Figure 7.1.Poverty Headcount Rate at International Poverty Line

Source: World Development Indicators, World Bank.

Note: PPP = Purchasing power parity.

The 2011 household survey in Senegal indicated that poverty remains high, although it declined in the most recent two decades. More than 6 million people were living on a household income below the national poverty line. In 1994–2001, GDP growth in Senegal was about 5 percent a year; the poverty rate fell significantly, from 68 percent in 1994/95 to 55 percent in 2001/02. In 2002–05, GDP growth reached 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 in the past five years, poverty incidence has declined by only 1.8 percentage points to 46.7 percent.

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; 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 differs from country to country. For international comparison, two poverty lines are usually used: daily income of $1.25 and $2 at 2005 purchasing power parity. The poverty gap is the mean distance from the poverty line (counting the non-poor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty and its incidence.

The recent prolonged episode of growth has led to a significant reduction in poverty. Based on several household surveys, 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 7.1). The poverty gap declined from 17.2 to 14.5; 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 7.1Senegal: Poverty Indicators, 2001–11
200120052011
Poverty incidence55.248.346.7
Confidence interval (95%)52.9-57.546.1-50.644.1-49.3
Poverty gap17.315.514.5
Source: Agence Nationale de Statistique et de la Démographie, 2012, www.ansd.sn.
Source: Agence Nationale de Statistique et de la Démographie, 2012, www.ansd.sn.

Progress achieved in poverty reduction has been more pronounced in Senegal than it has in some regional peers. In 1994–2005, the share of population living on less than $1.25 a day declined by about 20 percentage points, and for people living on less than $2 a day, by about 19 percentage points (Figure 7.2). By the latter metric, which may be more appropriate for Senegal, given its per-capita income, Senegal’s poverty dropped faster than poverty dropped in other WAEMU countries (15 percentage points) in approximately the same period. The dynamics of poverty reduction in the region have been significantly affected by an increase in poverty in Guinea-Bissau and Côte d’Ivoire during political crises in these countries.

Figure 7.2.Change in Poverty Rate

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

Note: At 2005 PPP prices. In parentheses, the latest available year and corresponding headcount ratio at $1.25 a day and $2 a day, respectively. Change to 1994 for Burkina Faso, 1985 for Côte d’Ivoire, 1991 for Guinea-Bissau, 1994 for Mali, 1992 for Niger, and 1994 for Senegal.

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

This outcome reflects higher growth and a higher sensitivity to growth of poverty reduction in Senegal. Unlike a number of countries in the WAEMU, particularly those affected by internal conflicts or crises (for example, 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 7.3, 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 (for example, Burkina Faso) (Figure 7.3, panel 2).

Figure 7.3.Factors Contributing to Pro-Poor Growth

Sources: WDI; World Economic Outlook; and IMF staff estimates.

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 and requires supplementary information on the distribution of this additional income among different groups of the population. 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 of growth on poverty reduction may 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 based on consistent methodologies 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 is difficult in the absence of high-frequency poverty datasets.

Growth Incidence Curves

Growth is usually considered inclusive if its benefits are widely shared across the population. Although there is no commonly accepted definition, inclusive growth 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. Therefore, in this chapter growth is considered inclusive if it helps improve equality.

Several statistical metrics allow evaluation of different aspects of inclusiveness in this narrow definition. The squared poverty gap assesses inequality, as it captures differences in the severity of poverty among the poor. The Watts index is a distribution-sensitive poverty measure because it reflects the fact that an increase in income of a poor household reduces poverty more than does a comparable increase in income of a rich household. The Gini coefficient shows a deviation of income per decile from the perfect equality line. The mean log deviation index 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 with 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 or faster than does the average income, growth would be 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.

Senegal

Different statistical measures suggest that, although poverty declined, overall inequality in Senegal 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 in Senegal (Table 7.2). 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 (MLD) index declined a bit in 1994–2005 and increased again in 2005–11, suggesting no major changes in the overall level of inequality.

Table 7.2Senegal: Inequality Indicators, 1994–20111
Square Poverty GapWatts IndexGini CoefficientMLD Index
19949.090.2741.440.30
20016.180.1941.250.29
20054.670.1539.190.26
20113.770.1240.300.27
Source: PovCalNet; World Bank, 2013.

PPP-based calculations. The Gini index and income shares may differ from the aggregates used for the national poverty lines. The Gini index based on ESAM 2001/02, ESPS 2005/06 and ESPS 2011 household surveys was 39.2 in 2001, 38.1 in 2005, and 37.8 in 2011. All income/consumption shares by decile are based on estimated Lorenz curves. Households are ranked by income or consumption per person. Distributions are population (household-size and sampling expansion factor) weighted.

Source: PovCalNet; World Bank, 2013.

PPP-based calculations. The Gini index and income shares may differ from the aggregates used for the national poverty lines. The Gini index based on ESAM 2001/02, ESPS 2005/06 and ESPS 2011 household surveys was 39.2 in 2001, 38.1 in 2005, and 37.8 in 2011. All income/consumption shares by decile are based on estimated Lorenz curves. Households are ranked by income or consumption per person. Distributions are population (household-size and sampling expansion factor) weighted.

A simple decile ratio also suggests that the level of inequality remained broadly unchanged. The ratio of consumption in the top decile relative to the bottom decile of the population 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 increased again to 12.5 in 2011, suggesting the richest consume on average 12–13 times more than the poorest. The richest two deciles of the population consume about half the goods and services in the country, roughly the same amount as is consumed by the seven bottom deciles of the population (Figure 7.4), suggesting a substantial level of income disparity and inequality, although lower than the average for sub-Saharan Africa.

Figure 7.4.Distributional Dimensions of Poverty

Source: PovCalNet, World Bank, 2013.

Note: Purchasing power parity-based calculations. The Gini index and income shares may differ from the aggregates used for the national poverty lines. The Gini index based on Enquête Sénégalaise Auprès des Ménages (ESAM) 2001/02, Enquête Suivi de la Pauvreté au Sénégal (ESPS) 2005/06 and Enquête Suivi de la Pauvreté au Sénégal (ESPS) 2011 household surveys was 39.2 in 2001, 38.1 in 2005, and 37.8 in 2011. All income/consumption shares by decile are based on estimated Lorenz curves. Households are ranked by income or consumption per person. Distributions are population (household-size and sampling expansion factor) weighted.

Growth in the level of consumption in 2006–11 was positive but low and almost equal among different deciles of the population (Figure 7.5). No significant changes occurred in inequality during this period because growth in consumption of the bottom deciles was only slightly higher than that of the top deciles. In contrast, in 2001–05 the poorest fifth of the population experienced a decline in consumption, while all middle deciles registered significant growth in consumption, although the increase of the consumption level of the richest groups was insignificant.

Figure 7.5.Senegal Consumption Growth by Welfare Groups

(Percent)

Sources: Enquête Sénégalaise Auprès des Ménages (ESAM) 2001/02; Enquête Suivi de la Pauvreté au Sénégal (ESPS) 2005/06; Enquête Suivi de la Pauvreté au Sénégal (ESPS) 2011.

A dynamic measure of inclusiveness of growth can be derived from the growth incidence curve. The estimation of growth incidence curves is a methodology that helps identify the extent to which each decile of households benefits from growth (Ravallion and Chen 2003). In plotting 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). The growth incidence curve assesses how consumption at each percentile changes over time. The part of the curve above zero points at the deciles that benefit from growth, and the part below zero points at the deciles that lost because of growth. The part of the curve that is above its own mean points at the deciles of the population that benefit from growth relatively more than does an average household.

The part of the curve below the mean, but still above zero, points at the deciles that also benefit from growth but less so than does an average household. A negatively sloping growth incidence curve suggests that income or spending of the poorer deciles of the population grows faster than does income or spending of 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 one indication of inclusiveness of growth. Improvements in the degree of inclusiveness of growth would be signaled by the growth incidence curve changing the slope 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.

Although growth incidence curves give somewhat conflicting signals on distributional shifts in Senegal, they seem to confirm that growth benefited most those people in the middle of the income distribution. Between 2001 and 2005 (Figure 7.6), consumption increased on average, because the mean of the growth incidence curve is above zero, driven by the middle of the distribution (from the 3rd to the 8th deciles). 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. On average for 2001–11, a clear increase in mean consumption confirms the decline in poverty, as the middle class improved its relative position. However, for 2001–11 as a whole, the growth incidence curve has a slightly positive slope, which may point to some worsening of inclusiveness. This trend may not be statistically significant, indicating no substantial distributional changes during this period other than the improvement in the relative position of the middle class. This overall result, however, masks significant differences in growth inclusive-ness between urban and rural areas.

Figure 7.6.Growth Incidence Curve for Total Population, 2001, 2005, 2011

Source: World Bank, ESAM2001, ESPS2005, ESPS2011 databases processed using ADePT 5.1 platform for automated economic analysis, household-level data. The data may include outliers at both tails of the distribution.

Although available indicators sometimes give conflicting signals on distributional shifts, a statistical analysis of the distributional characteristics of growth suggests the following: (1) poverty in Senegal has fallen in the last two decades, although poverty reduction has slowed in recent years; (2) although available indicators sometimes give conflicting signals on distributional shifts, growth seems to have benefited most people in the middle of the income distribution; (3) the middle class has benefited from growth, mainly in urban areas, while both the poorest and the richest have lost ground; (4) growth in rural areas has been less inclusive than it has been in urban areas.

Mali

In Mali, fairly high growth has led to substantial poverty reduction. After a decade of relatively low per-capita growth in the 1990s, economic activity picked up and averaged 3.2 percent during 2001–10. Although GDP growth has been volatile, Mali’s share of poor households has decreased substantially from 55.6 percent in 2001 to 43.6 in 2010, taking into account the national poverty line at CFAF 453 per day of household consumption. Also, in comparison with other countries, Mali’s poverty reduction was remarkable. While per-capita growth was comparable in Mali and sub-Saharan Africa during 2001–10, poverty reduction was significantly stronger in the former. This is also shown by higher elasticities of poverty reduction relative to GDP growth in Mali than was the case in comparable countries (Figure 7.7).

Figure 7.7.Inclusiveness of Growth in Mali

Growth in Mali’s economic activity has been largely inclusive, meaning that it has not been associated with an increase in inequality (Rauniyar and Kanbur 2010) or with a reduction in the share of the bottom quintile of the income distribution. During 2001–10, the growth incidence curve—depicting the changes in household consumption according to consumption percentiles–features a clear downward slope, implying an increase in consumption of poorer households relative to richer households. Real consumption for households below the poverty line increased by 25 percent (“pro-poor growth”), while average consumption grew by 7.5 percent.

Poverty in Mali is mainly rural and concentrated among farmers. The results from a regression analysis pooling data of three household surveys (2001, 2006, and 2010) suggest that being a farmer implies a lower consumption by 33 percent (Table 7.3). In 2010 this effect was less pronounced, at 24 percent, reflecting an overall improvement of farmers’ consumption relative to the rest of the population. Subsequently, urban poverty increased in the second part of the decade, mainly due to migration to Bamako (Figure 7.8). Further, a higher number of household members and an older age of the household head affect consumption negatively while civil servants are clearly better off than others. The results of Table 7.3 are broadly similar to regression analyses performed on comparable sub-Saharan countries (IMF 2011). However, the rural-urban divide seems more pronounced in Mali, and household size and age have a positive influence in other countries studied in IMF 2011, as opposed to a negative effect in Mali. The latter might be partially explained by Mali’s higher population growth and more children per household than is the case in comparable countries.

Table 7.3Mali: Determinants of Household Consumption
(1)(2)(3)
Household size−0.0161***−0.0161***−0.0161***
Age−0.0587***−0.0629***−0.0632***
Sex−0.010−0.007−0.004
Urban0.284***0.283***0.284***
Farmer−0.279***−0.327***−0.282***
Civil servant0.299***0.307***0.297***
Self employed0.0200.0240.019
Unemployed−0.0714***−0.0659***−0.0676***
Year 20060.0337***−0.0030.012
Year 20100.171***0.116***0.191***
Farmer* 20060.0553***
Farmer* 20100.0868***
Urban* 20060.0805***
Urban* 2010−0.0879***
Constant12.33***12.37***12.34***
Observations184541845418454
Sources: Malian authorities; and IMF staff calculations.Note: *, **, and *** indicate statistical significance at the 90, 95, and 99 percent confidence interval, respectively.
Sources: Malian authorities; and IMF staff calculations.Note: *, **, and *** indicate statistical significance at the 90, 95, and 99 percent confidence interval, respectively.

Figure 7.8.Mali: Patterns of Poverty, Poverty Reduction, and Obstacles

Sources: Malian authorities; World Development Indicators; and IMF staff estimates.

Agriculture and Growth Inclusiveness

Poverty reduction in Mali was higher in the first part of the decade (2001–06) than it was during the second part (2006–10). While the magnitude of real GDP growth was broadly comparable throughout the decade, the number of households below the poverty line decreased more strongly during the first part. This is also reflected in a higher elasticity of poverty reduction to economic growth (Table 7.4). Moreover, in the first part of the decade Mali made more substantial progress toward achieving the millennium development goals than it did in the second part of the decade.

Table 7.4Mali: National Accounts and Household Survey Data
200120062010
National Accounts(period average annual growth rates)
Real GDP5.14.9
Real GDP per capita2.62.3
Real agricultural output4.68.2
Real industry output5.9−1.9
Real services output5.75.7
Household Surveys(in percent)
Poverty incidence (national poverty line)55.647.443.6
(period average annual growth rates)
Elasticity of poverty with regards to growth−1.8−1.5
World Development Indicators
Mortality rate under 5 (per 1,000)217.3199.5191.1
School enrollment primary (percent net)44.463.172.9
Sources: IMF; Malian authorities; World Development Indicators.Note: School enrollment 1999 and 2009, mortality rate 2009.
Sources: IMF; Malian authorities; World Development Indicators.Note: School enrollment 1999 and 2009, mortality rate 2009.

Consumption by the poorest rose and inequality decreased more strongly during 2006–10. As depicted in the growth incidence curves in Figure 7.8, the slope of the 2006–10 curve features a pronounced downward slope with steepening tails. Hence, the poorest quintile of the population benefited most, while the richest quintile lost relative to the rest of the population. The growth incidence curve of 2001–06 still implies higher consumption growth for the bottom half of households, but it is flatter and the poorest households are not better off than the average.

During 2001–06, the economy grew at an equal pace in all three sectors. Manufacturing contracted and agricultural production boomed during 2006–10. Particularly good weather conditions helped agricultural output in 2006–10 to increase by 8 percent on average per year. Since the very poor are mostly farmers, their consumption basket expanded during this part of the decade. But as most farmers produce on a subsistence level, these gains in agricultural production could not be translated into an overall increase in production and employment elsewhere. Hence, the impressive growth in agriculture during 2006–10 allowed the very poor to improve their lives relative to the rest of the population, but the balanced growth during 2001–06 helped more households to escape poverty.

Gender inequality

Gender inequality in the WAEMU remains among the highest in the world (Figure 7.9). The United Nations gender inequality index measures gender inequality of outcomes (the gap between and male labor force participation rates and the share of women’s seats in parliament) as well as inequality of opportunity (gender gaps in education and indicators of female health, such as the maternal death ratio and adolescent fertility). It shows that, when aggregating these categories, the WAEMU performs worse than most of the countries in the world. Surprisingly, female labor force participation is very low in some of the WAEMU countries even at very low levels of per-capita incomes (Mali, Niger). At these levels, countries usually observe higher labor force participation rates by women as women need to work for subsistence. Adult literacy rates, while lower in most WAEMU countries as compared to benchmark groups, remain particularly low for women. Health indicators remain poor in several WAEMU countries, especially in Mali and Niger.

Figure 7.9.Gender Inequality in the WAEMU

At the global level, there is evidence that higher income inequality can impede growth. Lower net income inequality has been associated with faster and more sustained economic growth in both advanced and developing countries (Berg and Ostry 2011; Ostry, Berg, and Tsangarides 2014). With imperfect credit markets, income inequality prevents an efficient allocation of resources by decreasing poorer households’ ability to make investments into human and physical capital (Galor and Zeira 1993; Corak 2013). Higher income and wealth inequality can also lead to socio-political instability and poor governance, thus discouraging investment (Bardhan 2005).

The evidence that gender inequality is impeding economic growth is also growing. Gender inequality has been associated with worse growth and development outcomes (WEF 2014; Elborgh-Woytek and others 2013; IMF 2015; Gonzales and others 2015). Gender gaps in economic participation restrict the pool of talent on the labor market and can yield a less efficient allocation of resources, lower productivity, and hence lower GDP growth (Cuberes and Teignier 2015; Loko and Diouf 2009). Women are more likely than men to invest a large proportion of their household income in the education of their children so that higher economic participation and earnings by women translate into higher expenditure on school enrollment for children (Duflo 2003; Duflo 2012; Heintz 2006; Miller 2008; Rubalcava and others 2004; Thomas 1990). IMF (2015) highlights reductions in gender inequality as one of the most promising avenues to boost growth in the region—together with closing gaps in infrastructure and education. It shows that decreasing income and gender inequality in sub-Saharan African countries to levels observed in the ASEAN 5 (Indonesia, Malaysia, the Philippines, Thailand, Vietnam) could increase real GDP per-capita growth by about 1 percentage point on average.

In particular, the WAEMU’s real GDP per-capita growth could significantly benefit from realistically implementable decreases in gender and income inequality (Figure 7.10). We follow the approach taken in IMF (2015) to decompose the differences in average real GDP per capita growth rates in the WAEMU and two benchmarks: a group of African benchmark countries (Ghana, Kenya, Lesotho, Rwanda, Tanzania, Uganda, Zambia) and a group of Asian benchmark countries (Bangladesh, Cambodia, India, Laos, Nepal, Vietnam), which have experienced around 2½ and 3½ percentage points higher real GDP growth compared to the WAEMU in the last two decades. The results of this approach reveal, that, in addition to large overall educational and infrastructure gaps, income and gender inequality can explain around 0.5 percentage points of the WAEMU’s real GDP per capita income shortfall compared to the Asian Benchmark group. The potential effects are even larger for more ambitious targets: Decreasing gender inequality, including legal restrictions to the fast growing ASEAN-5 (Indonesia, Malaysia, Philippines, Thailand, Vietnam) could accelerate growth by one percentage point.

Figure 7.10.Explaining the WAEMU’s Growth Differential to Benchmark Countries

The magnitudes of these effects vary across WAEMU countries (Figure 7.11). While income and gender inequality have on average significantly contributed to the growth shortfall of the WAEMU relative to benchmark groups, the magnitudes of the effect vary. For instance, in Burkina Faso, income and gender inequality have been lower than in benchmark groups which has positively affected growth in Burkina Faso vis-à-vis the benchmarks. In Mali and Niger, with a high share of the population living in poverty, income inequality is relatively low. However, gender inequality is high in absolute and relative terms in Niger and Mali. In particular, in Mali, a reduction of gender inequality and an increase of legal equality between men and women alone could have resulted in a real GDP per capita boost of 0.6 percentage points of GDP, if reduced to the African benchmark level, or as much as 1.25 points of GDP if reduced to the average level of the ASEAN-5.

Figure 7.11.Differential Effects of Closing the Gap in Income and Gender Inequality in the WAEMU to Benchmark countries

Policies to Increase Inclusiveness

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) showed that in developing countries, growth of average income explains 70 percent of the variation in poverty reduction in the short term. Berg and Ostry (2011) argue that longer growth spells are robustly associated with more equality in income distribution. Lopez and Servén (2006) suggest that for a given inequality level, the poorer the country, the more important the growth component is in explaining poverty reduction. Affandi and Peiris (2012) showed that growth is in general 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-headcount ratio. Therefore, any successful pro-poor growth strategy should have at its core measures to achieve sustained and rapid economic growth. Senegal’s experience is consistent with this cross-country evidence.

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. A 1 percent increase in incomes in the most unequal countries produces a mere 0.6 percent reduction in poverty, while in the most equal countries, it yields a 4.3 percent cut (Ravallion 2013). Because 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 is important in 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. For example, poor groups could be targeted through measures such as school lunches and public works programs, and tariffs could be better targeted for small quantities of electricity to protect some of the urban poor. In the medium term, a well-targeted and conditional cash transfer system is the best option for assistance for the poorest.

Structural policies promoting employment and productivity increases, in particular in agriculture, could also help increase inclusiveness. 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 took 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 to be 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.

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, creation of employment opportunities and increasing productivity in rural areas, in particular in agriculture, would prompt higher consumption growth among poorer households. For example, the stronger per-capita consumption growth observed in Cameroon and Uganda at the poorest levels seems to relate to high agricultural employment growth (IMF 2011). By contrast, rural agricultural employment fell in Mozambique and Zambia where the poorest experienced weaker or negative per-capita consumption growth.

Deepening the financial sector through policies that give better access to the poor for financial services would increase inclusiveness. A number of studies found that financial development generally increases incomes of the poorest households (Claessens 2005), whereas unequal access to financial markets can reduce incomes by impeding investments 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 and expanding credit information sharing could significantly expand credit availability. Some promising initiatives in this area are underway in Senegal.

Improving agricultural production is key to helping the poorest of the poor. Given the current opportunities, investments in agriculture can diminish the poverty gap and promote poverty reduction. Possible measures include: building and maintaining irrigation infrastructure (less than 15 percent of potentially irrigated land is actually irrigated), modernizing family farming and subsistence agriculture to agribusinesses and food processing, making public-sector support to agriculture more efficient, and improving access to finance.

Finally, the WAEMU’s real GDP per-capita growth could significantly benefit from the reduction in gender and income inequality. Decreasing income inequality and gender inequality are desirable from a political preference or human rights perspective. It is also smart economics, since the associated gains in real GPD per-capita growth are large for policy moves that are realistically implementable in shorter time horizons.

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