Growth, Inequality and Poverty in Sub-Saharan Africa: Recent Progress in a Global Context

Abstract

The present study employs recent World Bank data to shed light, in a global context, on the transformation of changes in income and inequality into poverty reduction for a large number of countries in sub-Saharan Africa (SSA). The study begins by discussing SSA's progress on poverty. Next, it presents data on how various African countries have fared in terms of the incidence of poverty relative to other countries, with special emphasis on the period since the mid-1990s, when SSA generally experienced a growth resurgence. The paper then decomposes performance on poverty into changes in income and inequality for a sample of SSA countries that have the requisite data. The paper finds that recent progress on poverty has been considerable, in contrast to the earlier, 1980–early 1990s, period. Compared with the progress in a global sample of countries, however, progress has been mixed: nonetheless, although African countries lag behind the Brazil, India, China and Russia group of countries as a whole, many of them have outperformed India. Furthermore, while income growth is found to be the main engine for poverty reduction in SSA in general, the role of inequality is crucial in certain countries. Viewed in a global context, moreover, the low levels of income have inhibited the effectiveness of income and inequality improvements in reducing poverty in many African countries.

Notes

¹ For a recent review of the literature, with a focus on SSA, see Thorbecke (2013).

² The specific data were sourced from World Bank (2009a, 2012). For a detailed discussion of this new, improved World Bank data, see Chen & Ravallion (2008).

³ This finding is based on the revised POVCALNET data from World Bank (2012, 2014), while World Bank (2009a) indeed showed SAS and SSA as having reduced poverty at the $1.25 level by 7 percentage points each.

⁴ One must, of course, heed the following warning:

Even though it [the World Bank database] is the most comprehensive and internally consistent data available on poverty and inequality, at the country level, it is particularly incomplete in its coverage of SSA and the World Bank apparently undertakes only a minimum of quality control. (Thorbecke, 2013, p. i16)

Indeed, the more recent World Bank (2012, 2014) databases have significantly revised the relevant data in previous databases such as World Bank (2009a), now showing faster poverty declines in SAS relative to SSA.

⁵ The global sample of 80 countries was selected based on the availability of data from the early-mid-1990s to the present (2000 or later), in order to be able to compute changes in poverty incidence over a reasonably comparable period across countries (for further details see Fosu, 2011).

⁶ The respective annualised growth rates of the headcount poverty between 1993 and 2009 for India-rural and India-urban are computed as − 4.015 and − 2.14 for the $1.25 standard, and − 0.480 and − 1.00 for the $2.50 standard.

⁷ More recently, Ravallion (2012) has found that initial poverty, rather than initial inequality, is the more pertinent variable explaining (the persistence of) poverty. What factors actually constitute initial poverty in such a structural model has yet to be identified, however. It should be noted, furthermore, that the Bourguignon model involving initial inequality is based on an “identity” specification, suggesting that initial inequality is indeed pertinent in the poverty equation.

⁸ For details of the application of the Bourguignon model, see, for example, Fosu (2009, 2011) and Kalwij & Verschoor (2007); see also Epaulard (2003) for a version of this model.

⁹ I ignore the sign and adopt the convention of referring to the income elasticity by its magnitude.

¹⁰ There are 320 and 392 usable observations for the $1.25 and $2.50 poverty standards, respectively. India has data beginning in 1977, but none of the other countries have data prior to 1981.

¹¹ Note that the estimation assumes the directional effects emanate from income and inequality to poverty; there is no assessment of the potential reverse “causality” from poverty to growth and/or inequality. For an excellent discussion of this reverse direction, see, for instance, Thorbecke (2013).

¹² For further estimation details, see Fosu (2011).

¹³ I restrict my analysis here to $1.25, which is most likely the more relevant poverty line for SSA countries, as MDG1 for instance is based on this poverty standard.

¹⁴ Instead of breaking the flow of the paper with a discussion of the elasticities, I opt to present instead the poverty decomposition immediately following the estimation.

¹⁵ See Fosu (2011) for the results for the global sample of 80 countries.

¹⁶ These countries are Cameroon, Ethiopia, Niger, Senegal, Burkina Faso, Mozambique, Nigeria, and Burundi.

¹⁷ These countries are Swaziland, Ghana, Uganda, South Africa, Mali, Guinea and Tanzania.

¹⁸ From Table 2, Cote d'Ivoire's income grew by an annualised average rate (in %) of 0.606. Thus, with E_y estimated at − 2.495 (see Table 5), the predicted growth in poverty when there is no change in inequality equals: (0.606)( − 2.495) = − 1.51.

¹⁹ This range excludes the perverse and inadmissible result that the income elasticity is positive for CAR (Table 5), a country where the mean income is considerably less than the poverty line.

²⁰ The negative inequality elasticity values are admissible: as mentioned [and also see in particular Fosu (2008, 2009, 2010a, 2010b, 2010c)], in very low-income countries, redistribution to reduce inequality might actually increase the poverty rate, since there is the increased risk of rendering poor those non-poor at the margin.

²¹ That the elasticities increase with the mean income relative to the poverty line may be made more apparent if one notes that either elasticity becomes larger with an increase in Y/Z, that is, a decrease in Z/Y (see Equations (2) and (3)).

²² Note that one could conceivably show a distribution of the elasticities around an “SSA” line, which would actually be flatter and fall below the global line; however, the SSA countries with usable data in the POVCALNET database could hardly be viewed as representative. Indeed, the paucity of data is such that there are likely to be substantial compositional differences over time, thus rendering such an “SSA” line quite unreliable.

²³ The sample mean of the Gini coefficient is larger for SSA than for any of the other five regions except for LAC (Fosu, 2011).

²⁴ As observed earlier, these would include infrastructural policies that improve market access for the poor. See, for instance, Thorbecke (2013) for more detailed examples of such policies, including those of social protection.