Chapter 9A. Gender Inequality and Growth in Sub-Saharan Africa

Kalpana Kochhar, Sonali Jain-Chandra, and Monique Newiak
Published Date:
February 2017
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Dalia Hakura, Mumtaz Hussain, Monique Newiak, Vimal Thakoor and Fan Yang 

Income inequality and gender inequality remain high in sub-Saharan Africa. Income inequality has changed little and remains the second highest in the world behind Latin America and the Caribbean region (Figure 9.1), although there is quite a bit of variation across countries (Bhorat, Naidoo, and Pillay 2015; Beegle and others 2016). Despite significant declines in income inequality in some countries, such as Niger and Sierra Leone, in a third of sub-Saharan African countries for which data are available, cumulative growth during 1995–2011 was associated with increases in income inequality. Similarly, gender inequality in sub-Saharan Africa remains one of the highest, just behind the Middle East and north Africa (Figure 9.2). It has declined slower than in other regions despite shrinking gender gaps in education, improving health outcomes, female labor force participation rates that are on average the highest in the world, and greater progress in eliminating legal gender-based restrictions (Demirgüç-Kunt, Klapper, and Singer 2013).

Figure 9.1.Gini Index of Net Income Inequality by Region, 1980–2011

Source: Solt 2016.

Figure 9.2.Gender Inequality Index by Region, 1990–2010

Sources: United Nations; and Gonzales and others 2015b.

Have these high levels of income and gender inequality been impeding economic growth? This analysis tests for the joint effects of both income and gender while also testing whether the growth-inequality relationship varies for low-income countries. To this end, it extends recent empirical work that has mainly focused on the effect of one dimension of inequality at a time (Ostry, Berg, and Tsangarides 2014; Gonzales and others 2015b) and has not specifically examined the implications for sub-Saharan Africa. A number of studies also note that the growth-inequality link is likely to be nonlinear at different levels of development (Castello-Climent 2010), and previous empirical work tends to find a negative association between growth and income inequality only below a certain threshold of income per capita (Neves and Silva 2014). To account for this possible nonlinearity, we allow for the relationship to be different between low-income countries and the other countries in the sample.

Income and gender inequality are found to jointly impede growth, mostly in the initial stages of development and resulting in large growth losses in sub-Saharan Africa. In particular, the average annual GDP growth per capita in these countries could be higher by as much as 0.9 percentage point if income and gender inequality were reduced to the levels observed in the fast-growing economies of the Association of Southeast Asian Nations (ASEAN). By contrast, the growth shortfall of Latin American and Caribbean economies vis-à-vis ASEAN is mainly explained by income inequality.

Inequality and Growth Revisited

There is growing evidence that income inequality hampers growth through various channels. Lower net income inequality has been robustly associated with faster growth and longer growth spells for a large number of advanced and developing economies (Berg and Ostry 2011; Ostry, Berg, and Tsangarides 2014). Similarly, increases in the income share of the poorest 10 percent have been associated with higher growth (Dabla-Norris and others 2015). With imperfect credit markets, the ability of low-income households to invest in education and physical capital is impaired, which limits income mobility (Galor and Zeira 1993; Corak 2013). High inequality can reduce private investment due to sociopolitical instability and poor governance. In contrast, inequality can spur growth by enabling rich households to invest more due to their higher marginal propensity to save; it can also create incentives for innovation and entrepreneurship; and differences in rates of returns to education may encourage more people to seek higher education (Cingano 2014).

Some of the channels by which income inequality hampers growth may have a stronger impact at early stages of development—for example, the extent of credit-constrained households, the impact of imperfect credit markets, or the extent of poverty and the resulting political implications of high inequality (Barro 2000). Our analysis therefore distinguishes between countries at different stages of development when exploring the association between income inequality and economic growth.

Gender inequality has also been associated with GDP losses across countries of all income levels. Losses from gender gaps in economic participation result from a less efficient allocation of resources due to a restricted talent pool (Cuberes and Teigner 2015; Esteve-Volart 2004). Mitra, Bang, and Biswas (2015) report that a greater presence of women in legislative bodies may alter the composition of public expenditures in favor of health and education, which can raise potential growth over the medium to long term. Education inequality affects the average quality of human capital and reduces growth (Klasen 1999). Female education contributes to improvements in children’s health, reductions in fertility rates, increases in labor force participation rates, and better quality of human capital of future generations (Mitra, Bang, and Biswas 2015). Restrictions on women’s rights to inheritance and property and legal impediments to economic activity are strongly associated with larger gender gaps in labor force participation (Gonzales and others 2015a).

Less well understood are the effects of various gender gaps on growth, especially after controlling for income inequality. Most studies examine the effects of different dimensions of gender inequality in separate regressions (Klasen and Lamanna 2009; Elborgh-Woytek and others 2013). A few studies that explore the association between growth and a variety of gender gaps do not explore the possibility that income inequality could also capture other dimensions that impact economic growth such as the rural-urban income divide (see, for example, Mitra, Bang, and Biswas 2015; Amin, Kuntchev, and Schmidt 2015).

Inequality and Growth in Sub-Saharan Africa

An empirical analysis for 115 countries of the relationship between inequality and growth yields the following results (see Annex 9.1 for model specifications):

  • Income inequality is robustly related to lower growth in low-income countries, irrespective of the measure of income inequality. The negative association between growth and income inequality among low-income countries holds for various measure of inequality—the Gini coefficient, the income gap between the top 20 percent and the poorest 40 percent of the population, or the income share of the middle class (the 40 to 80 percentiles of population in the income distribution) (Table 9.1, Models 1–3). A 1 percentage point reduction in the initial Gini coefficient in low-income countries is associated with a 0.15 percentage point cumulative increase in growth over a five-year period.

  • Growth is also negatively associated with the multidimensional index of gender inequality, particularly in low-income countries and more generally with gender-related legal restrictions. A 1 percentage point reduction in gender inequality in low-income countries is associated with higher cumulative growth over five years of 0.2 percentage points in low-income countries, a result in line with previous estimates (Amin, Kuntchev, and Schmidt 2015; Table 9.1, Models 4–6).

  • The results highlight that inequality of income and gender affect growth individually and possibly through separate channels. For example, higher gender inequality may adversely impact gender gaps in educational attainment. Similarly, other aspects of household income inequality that are unrelated to gender inequality may be affecting growth in low-income countries such as rural-urban income inequality or inequality arising from countries’ dependence on natural resources exports whose revenues are appropriated by a few individuals.

Table 9.1.Growth, Inequality, and Gender Inequality in Sub-Saharan Africa
Measures of Inequality
Initial income inequality (top 20 to bottom 40)10.006–0.188***
Initial income inequality (top 20 to bottom 40)–0.207***
× low-income countries (LICs)1
Initial income inequality (net Gini)1–0.009
Initial income inequality (net Gini) × LICs1–0.030***
Initial income share of middle class20.081**
Gender inequality (lagged)–0.0170.005
Gender inequality × LICs (lagged)–0.029***–0.020**
Female legal equity (index)0.256**0.296**
Female legal equity (index) × LICs
Other Control Variables
Initial income per capita (log)–1.234***–1.347***–1.081***–1.746***–1.184***–1.608***
Fixed capital investment (% of GDP)0.134*0.184***–0.0140.093*0.1130.028
Schooling (years)0.1190.0680.159*0.0450.1020.154*
Dependent population growth (%)–0.356**–0.293**–0.539***–0.224–0.303**–0.286**
Infrastructure index0.238*0.1940.270*0.294*0.2410.334**
High Inflation (0.15%)–1.583***–1.627***–1.621***–1.228***–1.549***–1.552***
Terms of trade (percent change)0.068**0.076***0.091***0.098***0.063**0.094***
Institutional quality (index)0.047***0.063***0.040**0.080***0.064***0.054***
Number of instruments151514151417
Serial correlation (p-value)0.0710.0250.2020.2090.1670.274
Hansen test (p-value)0.2100.3350.3190.4450.9630.700
Country fixed effectsYesYesYesYesYesYes
Time (period) fixed effectsYesYesYesYesYesYes
Number of countries1101061041157878
of which: sub-Saharan African countries232323232020
Source: IMF staff calculations.Notes: The dependent variable is real GDP growth per capita, averaged over nonoverlapping five-year periods, for 1995–2014. Statistical significance: ***p < 0.01 (1 percent), **p < 0.05 (5 percent), and *p < 0.1 (10 pecent). Low-income countries (LICs) group include countries classified as low-income and lower-middle income groups by the World Bank. Robust two-step system general method of moments (GMM) estimator is used and all models include country and period effects. The p-values for second order serial correlation and Hansen test (for overidentifying restrictions) are reported.

Income inequality is measured by net Gini and ratio of income shares of top 20 percent to bottom 40 percent of population.

Income share of middle class is percent share of income attributed to the third and fourth quintiles of population.

The Implications of Inequality

A growth decomposition analysis suggests that addressing high inequality could significantly affect growth in sub-Saharan Africa (Figure 9.3). Compared with the ASEAN-5 countries (Indonesia, Malaysia, Philippines, Thailand, Vietnam), which have a strong track record in terms of growth, sub-Saharan Africa’s average annual real GDP growth per capita has been about 1½ percentage points lower over the past decade. Weaker infrastructure, lower levels of investment in fixed and human capital, higher dependency ratios, and lower quality of institutions were key factors explaining this growth shortfall. But the contribution of inequality is also substantial. More precisely, reducing the three inequality indicators to the level currently observed in the ASEAN-5 countries could boost the region’s annual GDP growth per capita by on average about 0.9 of a percentage point, roughly the same order of magnitude as the impact on annual growth per capita from closing the infrastructure gap between the two regions. Moreover, compared with Latin America and the Caribbean, which has a growth differential with the ASEAN-5 of a similar order of magnitude as sub-Saharan Africa, the growth effects from reducing gender inequality and legal gender-related restrictions are sizable for sub-Saharan Africa. Notwithstanding its higher levels of income per capita relative to the ASEAN-5, income inequality is found to be a key factor holding back growth in Latin America and the Caribbean—not surprising given that the region has the highest levels of income inequality.

Figure 9.3.Growth Differentials between Sub-Saharan Africa, Latin America and Caribbean, and ASEAN-5

(Percentage points)

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

Notes: The annual average growth differential with the ASEAN-5 (Indonesia, Malaysia, the Philippines, Thailand, Vietnam) is 1.5 percent for SSA and 1.7 percent for LAC. The estimated regression coefficients of Model 6 in Table 9.1 are applied to the differences between the average values of the factors associated with growth for the past 10 years for SSA and LAC and comparator ASEAN-5, respectively. A bar with a negative value denotes what share of the growth shortfall in SSA is explained by a particular variable.

The impact of income and gender inequality on growth varies across sub-Saharan Africa (Figure 9.4). Using the same approach as for the whole region, the growth decomposition analysis for the subgroups yields the following additional lessons:

  • In low-income countries (excluding fragile states) the catch-up effect from a low initial income relative to the ASEAN-5 countries contributes about 2½ percentage points of real GDP growth per capita. However, this catch-up effect is more than undone by weak infrastructure and lower human capital accumulation. Gender inequality accounts for ¾ percentage point of the growth differential.

  • For fragile states, the lower quality of infrastructure and political institutions explains the largest fraction of the growth differential. Reducing gender inequality could boost annual GDP growth per capita by ⅔ percentage point, whereas the potential effects of a reduction in income inequality and legal gender-based restrictions are smaller.

  • For middle-income countries—where infrastructure and educational attainment gaps tend to be smaller—and for oil-exporting countries, reducing income inequality to the levels observed in ASEAN-5 countries is an important factor to raise growth. The growth payoff from removing legal gender-related restrictions also appears particularly strong for oil-exporting sub-Saharan African countries.1

Figure 9.4.Growth Differentials with the ASEAN-5 for Subgroups of Sub-Saharan African (SSA) Countries

(Percentage points)

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

Notes: The estimated regression coefficients of Model 6 in Table 9.1 are applied to the differences between the average values of the factors associated with growth for the past 10 years for SSA and comparator ASEAN-5 countries (Indonesia, Malaysia, the Philippines, Thailand, Vietnam). Blue bars represent the three inequality indicators included in the regression. A bar with a negative value denotes what share of the growth shortfall in SSA is explained by a particular variable.

1 Terms of trade.


Income and gender inequality impede growth in particular in countries at earlier stages of development, with large growth losses for sub-Saharan Africa. Examining the effects of gender inequality and income inequality jointly in a large global panel over the last two decades shows that further progress in reducing income and gender inequality could deliver significant sustained growth dividends, particularly for low-income countries. The fact that both gender inequality and income inequality matter for growth implies that gender inequality affects growth via different channels than income inequality. The implications for sub-Saharan Africa are particularly striking. Despite some progress in the last 20 years, there remain comparatively high levels of income and gender inequality in the region. The empirical analysis highlights that annual economic growth in sub-Saharan African countries could be higher by as much as 0.9 percentage point if gender and income inequality were reduced to the levels observed in the fast-growing countries of ASEAN, with variations across country groups. This contrasts with the findings for Latin America and the Caribbean, where gender inequality does not appear to be a main contributor to the region’s growth differentials with ASEAN.


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Annex 9.1. Model Specifications

The econometric analysis relating growth in GDP per capita involves a sample of 115 countries and indicators of income and gender inequality as well as commonly used growth determinants.2 Annex Table 9.2 provides exact definitions of the variables and data sources, but two dimensions are worth highlighting.

Table 9.2.Variables and Data
Initial income inequality (top 20/ bottom 40)1Ratio of income distribution at the top 20 relative to that of the bottom 40 percent of population.World Development Indicators (WDI) database, augmented by the UNU-WIDER database.
Initial income inequality (Gini)The traditional Gini measure of inequality. In this paper, we use “net” Gini but find similar results with “market” Gini.Standardized World Income Inequality database (Solt 2016).
Initial income share of middle class Lagged gender inequalityThe sum of income shares of the third and fourth quintiles of population. Index calculated using the United Nations methodology, which covers the 1990–2010 period.WDI database, augmented by UNU-WIDER database. IMF staff estimates and others (2015b).
Female legal inequityThe sum of six binary indicators representing existence of restrictions on selected women’s legal rights. Takes on value of 0 (no restrictions) to 6 (all selected restrictions on rights).World Bank’s Women, Business and the Law (WBL) database.
Initial income per capita Fixed capital investmentThe logged real GDP per capita in the first year in each five-year period. Gross fixed capital formation in percent of GDP, averaged over five-year periods.Penn World Tables (PWT v.8.0).

PWT, augmented by World Bank, WDI database.
SchoolingAverage years of schooling (in each five-year period) for the population ages 15 and above.Barro and Lee 2003.
Dependent population growthAverage annual percentage change in the non-working-age population (under 15 or above 64).UN Population database.
Infrastructure indexComposite index based on electricity consumption, access to water, and access to any type of phones. A higher value corresponds to an overall greater level of infrastructure.Electricity consumption is taken from the International Energy Association database. Access to water and telephones are taken from WDI.
High inflationA dummy variable with value 1 if average annual inflation in consumer prices over a given five-year period is more than 15 percent.IMF, World Economic Outlook database.
Change in terms of tradeThe average annual change in the terms of trade over the five-year period (constant local currency units).WDI database.
Institutional qualityA composite index of political risk; higher values of the index (ranging 0–100) imply better quality of institutions and hence lower risk.This is the political risk index from the International Country Risk Guide.

Interactions of these variables with a low-income country (LIC) dummy variable are also included in selected models.

  • The Standardized World Income Inequality Database (Solt 2016) provides the broadest country coverage over time by incorporating a number of data sources to maximize the comparability and coverage across countries over time. However, missing observations are generated via model-based multiple imputation estimates. While the available data gives an indication of the trends across countries and regions, it should be interpreted carefully since household surveys in sub-Saharan Africa are less frequent and often are not comparable.

  • Gender inequality is measured by the United Nations’ Gender Inequality Index (GII), which captures gender inequality in health (maternal mortality ratio and adolescent fertility rate), empowerment (gap in secondary education and share of parliamentary seats), and economic participation (gap in labor force participation rates). In addition, we construct a female legal inequity index as the sum of six legal indicators representing women’s legal rights to earn and hold income and wealth: (1) unmarried women have equal property rights for immovable property, (2) married women have equal inheritance rights, (3) joint titling of property is default for married couples, (4) married women can get a job or pursue a profession, (5) adult married woman can open a bank account, and (6) married woman can sign contracts.

Key considerations in the empirical strategy were to include as many sub-Saharan African countries in the sample and address endogeneity concerns. Given data availability, the regressions were estimated for the 1995–2014 period and rely on nonoverlapping five-year averages to abstract from business cycle fluctuations in growth rates and deal with data gaps in certain years (for example, in the education and inequality measures). To account for possible endogeneity of the inequality and investment variables, the estimations use two-step system generalized methods of moments (system-GMM) and initial levels of inequality for each five-year period. We use various specification tests to ensure that the assumptions of no second-order serial correlation in the errors and that of the validity of the instruments hold. To show no second-order serial correlation, we use the Arellano–Bond test statistic, which fails to reject the null hypothesis of zero correlation. Moreover, since the Arellano–Bond (system GMM) estimators generate large numbers of instruments, this can lead to over-identification. To see whether this is a concern, we applied the Hansen J statistic to test for over-identification. In our model specifications, this statistic passes the criteria for no over-identification problem, leading us to conclude that the problem of excessive instruments is not too serious in our specification.3

The finding that the removal of gender-related restrictions affects growth positively in the oil-exporting countries may reflect correlation rather than causation given that oil-exporting countries can, if conditions are right, grow without much labor effort as oil and minerals are capital intensive. This would be the case if gender equality is correlated with other conditions, such as better property rights or a greater integration with developed-country capital markets, that make it easier for foreign companies to exploit mineral reserves.

This paper uses the World Bank’s classification of countries. However, the group of low-income countries includes lower-middle income countries, given their many similarities.

The Xtabond2 package for STATA (Roodman 2009) is used to estimate the system-GMM regressions.

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