WPS8494
Policy Research Working Paper 8494
The Devil Is in the Details
Growth, Polarization, and Poverty Reduction
in Africa in the Past Two Decades
F. Clementi
M. Fabiani
V. Molini
Poverty and Equity Global Practice
June 2018
Policy Research Working Paper 8494
Abstract
This paper investigates the distributional changes that lim- almost all the analyzed countries. Nineteen of 24 countries
ited pro-poor growth in the past two decades in Sub-Saharan experienced a significant increase in polarization, particularly
Africa; these changes went undetected by standard inequal- in the lower tail of the distribution, and this distributional
ity measures. By developing a new decomposition technique change lowered the pro-poor impact of growth substan-
based on a nonparametric method—the relative distribu- tially. Without this change, poverty could have decreased
tion—the paper finds a clear distributional pattern affecting an additional 5–6 percentage points during the past decade.
This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide
open access to its research and make a contribution to development policy discussions around the world. Policy Research
Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at
vmolini@worldbank.org.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
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Produced by the Research Support Team
The Devil Is in the Details: Growth, Polarization, and Poverty
Reduction in Africa in the Past Two Decades
F. Clementia, M. Fabiania, and V. Molinib,
Keywords: Sub-Saharan Africa; consumption distribution; polarization; relative distribution;
poverty change decomposition.
JEL classification: C14; D31; D63; I32.
The authors acknowledge financial support from the World Bank. We thank Andrew Dabalen for comments on an
earlier version of the manuscript. Of course, we are the sole responsible for all possible errors the paper may
contain.
a
University of Macerata, Macerata, Italy b World Bank, Washington DC, USA
Corresponding author Vasco Molini. Email addresses: fabio.clementi@unimc.it (F. Clementi),
m.fabiani9@studenti.unimc.it (M. Fabiani), vmolini@worldbank.org (V. Molini).
1 Introduction
Despite experiencing stable and sustained growth over almost two decades, several Sub-
Saharan African (SSA) countries have not experienced a commensurate reduction in poverty.
The present paper identifies and interprets the distributional changes that occurred in most of
these countries, which have offset the positive impact of growth in reducing poverty.
Recent estimates, based on an international poverty line of US$1.90 (in 2011 PPP U.S.
dollars), suggest that poverty declined only by 23 percent between 1990 and 2012 (from 56
percent in to 43 percent) (Beegle et al., 2016). This rate is much lower than those experienced
by countries with similar growth rates and similar poverty rates in other regions. World Bank
(2018) calculates that in a typical non-African developing country where 50 percent of the
population is living below the poverty line, a 1 percent yearly growth in GDP led to a reduction
of 0.53 percentage points a year in the incidence of poverty. In contrast, the same 1 percent per
capita GDP growth in a typical African country with the same poverty incidence reduced
poverty by only 0.16 percentage point.
The explanations for this lower growth poverty-elasticity in Africa are generally two: one
questioning the veracity of the recent African economic boom (the so-called African miracle)
and another looking at the role of inequality. Jerven (2013, 2015) has provided evidence on the
problems afflicting GDP calculations in Africa and argued that for many SSA countries the
recent high growth is merely statistical or, in other words, a feature of adding the informal sector
that previously was not counted (Jerven, 2015). Since growth is overstated, it is thus not
surprising that poverty did not fall so rapidly.
Although this argument has some validity, it does not completely solve the low elasticity
puzzle. Figure 1 compares the average annual GDP per capita growth and average consumption2
growth from available household surveys; consumption is the welfare measure typically used
to calculate poverty rates and growth is the factor that really matters in poverty reduction
(Adams, 2004; McKay, 2013). Indeed, the discrepancy between GDP per capita growth and
household consumption growth is higher in SSA than in the rest of the developing world, yet
SSA registers an average annual growth of household consumption of about 1.02 percent per
year, not much lower than the South Asia Region (SAR) and slightly higher than Latin America.
Therefore, household consumption increased in SSA not differently from other developing
regions but still poverty declined slower.
2
For Latin America we computed average income growth for the household surveys.
2
Figure 1: GDP per capita and household consumption average growth rates: 1999-2014.
Regarding inequality, literature has debated its relation with growth and poverty. Dollar
and Kray (2002) show that all income groups tend to benefit proportionally from increases in
economic growth and that income distribution does not really matter for poverty reduction.
Bourgignon (2003) and Ravallion (2007) find that inequality reduces the poverty-reducing
effects of economic growth. Thorbecke (2013) argues that the combination of high endemic
poverty and inequality is in general responsible for low growth elasticity of poverty. High initial
poverty and inequality reduces directly the growth rate but also indirectly the poverty-reducing
effect of this growth. Looking at SSA, Fosu (2009, 2015) finds that economic growth reduces
poverty while growth elasticity is a decreasing function of initial inequality. Therefore, the low
elasticity registered in SSA in the last two decades could potentially be attributed to an increase
in inequality that limited the pro-poor content of growth.
Unfortunately, when measured with standard indicators like Gini, there is no clear
evidence of an increase in inequality in the last two decades. Pinhovskiy and Sala-i-Martin
(2014) show that the recent SSA growth spurt was, in fact, accompanied by a generalized
decrease of inequality. Beegle et al. (2016), analyzing the SSA countries for which there are
two comparable surveys, conclude that about half of them experienced a decline in inequality
while the other half saw an increase. Cornia et al. (2017) find a bifurcation in inequality trends
3
in SSA: 17 countries experienced declining inequality, whereas 12 countries, predominantly in
Southern and Central Africa, recorded an inequality rise.
At first glance therefore, there is no clear pattern in SSA that can explain through
increasing inequalities the low elasticity of poverty. From a distributional point of view, thus,
it is still unclear why growth did not translate into greater household consumption at the bottom
of the income distribution at rates comparable with those experienced in other regions of the
world (Christaensen et al., 2014; Thorbecke, 2013).
The central argument of this paper is that significant distributional changes against
poverty reduction have, in fact, taken place in most of the SSA countries we analyze. These
changes affected predominantly the lower part of the welfare distribution and went undetected
by standard inequality measures. The reason is simple. Summary measures like the Gini do not
assign a weight to the different percentiles; if a pro-inequality change in one part of the
distribution is more than compensated by a pro-equality change in the rest of the distribution,
the Gini will decline. The distributional changes that matter most for poverty reduction,
however, are those localized in the lower part of the distribution but can be detected only if we
are able to focus on this part only.
To analyze these changes, this paper develops a novel yet simple decomposition based on
the “relative distribution” method (Handcock and Morris, 1998, 1999). The strength of this
decomposition consists of providing a non-parametric framework for taking into account all the
distributional differences that could have affected the variation in the poverty rate and countered
the pro poor effect of growth. In this way, it enables to summarize multiple features of the
welfare distribution that a standard decomposition based on summary inequality measures
would not have detected (Datt and Ravallion, 1992; Kolenikov and Shorrocks, 2005).
The paper is organized as follows Section 2 outlines the distinctive features of the relative
distribution approach and presents the proposed decomposition. Section 3 discusses the data
and provides summary statistics. Section 4 details the main findings of the study. Section 5
provides summary conclusions.
4
2 Methodology
2.1 Polarization and relative distribution
Over the last two decades, the issue of polarization has gained increasing importance in the
analysis of income distribution (Foster and Wolfson, 1992; Levy and Murnane, 1992; Esteban
and Ray, 1994; Wolfson; 1994, 1997) and now it seems to be widely accepted that polarization
is a distinct concept from inequality.
A general notion of income polarization (Esteban and Ray, 1994) regards it as
“clustering” of a population around two or more poles of the distribution, irrespective of where
they are located along the income scale. The notion of income polarization in a multi-group
context is an attempt at capturing the degree of potential conflict inherent in a given distribution
(see Esteban and Ray, 1999, 2008, 2011). The idea is to consider society as an amalgamation
of groups, where the individuals in a group share similar attributes with its members (i.e. have
a mutual sense of “identification”) but they are different from the members of the other groups
(i.e. have a feeling of “alienation”).
Political or social conflict is therefore more likely the more homogeneous and separate
the groups are, that is when the within-group income distribution is more clustered around its
local mean and the between-group income distance is longer (see, inter alia, Gradín, 2000,
Milanovic, 2000, D’Ambrosio, 2001, Zhang and Kanbur, 2001, Reynal-Querol, 2002, Duclos
et al., 2004, Lasso de la Vega and Urrutia, 2006, Esteban et al., 2007, Gigliarano and Mosler,
2009, and Poggi and Silber, 2010).
The use of summary measures of income polarization is common in literature. The
approach used in this paper, the so-called “relative distribution”, combines the strengths of
summary polarization indices with details of distributional change that the kernel density
estimate yields. The relative distribution method has been employed by Alderson et al. (2005),
Massari (2009), Massari et al. (2009a,b), Alderson and Doran (2011, 2013), Borraz et al. (2013),
Clementi and Schettino (2013, 2015), Clementi et al. (2017, 2018), Molini and Paci (2015),
Petrarca and Ricciuti (2016), Nissanov and Pittau (2016), and Nissanov (2017).
More formally,3 let Y0 be the income variable for the reference population and Y the
income variable for the comparison population. The relative distribution is defined as the ratio
3
Here we limit ourselves to illustrating the basic concepts behind the use of the relative distribution method.
Interested readers are referred to Handcock and Morris (1998, 1999) for a more detailed explication.
5
of the density of the comparison population to the density of the reference population evaluated
at the relative data r:
, 0 1, 0, (1)
where f (∙) and f0 (∙) denote the density functions of Y and Y0, respectively, and yr = is
the quantile function of Y0. When no changes occur between the two distributions, g(r) has a
uniform distribution; a value of g(r) higher (lower) than 1 means that the share of households
in the comparison population is higher (lower) than the corresponding share in the reference
population at the rth quantile of the latter.
One of the major advantages of this method is the possibility to decompose the relative
distribution into changes in location and changes in shape. The decomposition can be written
as:
. (2)
F0L (yr) is the median-adjusted density function:
, (3)
where the value is the difference between the medians of the comparison and reference
distributions—alternative indices like the mean and/or multiplicative location shift can also be
considered.
The relative distribution approach also includes a median relative polarization index,
which is a measurement of the degree to which the comparison distribution is more polarized
than the reference one:
∑ 1. (4)
The values of the MRP index range between -1 and 1: positive values represent more income
polarization and negative values represent less polarization; a value of 0 indicates no difference
in distributional shape. The MRP index can be additively decomposed into the lower relative
polarization index and the upper relative polarization index, which behave similarly as the
MRP.
6
2.2 Decomposition
The relative distribution is a well-established approach to distributional analysis, whereas novel
is the polarization-poverty and growth decomposition we develop for showing how the
distributional changes we observed in many SSA countries have effectively limited the impact
of growth on poverty reduction.
In general terms, poverty , , is expressed in terms of poverty line, , mean income
level, , and the Lorenz curve, , representing the structure of relative income inequalities.
Assuming the poverty line is fixed at a given level, poverty is given by , . The total change
in poverty, ∆ , is then decomposed into two components. The first component is the growth
component due to changes in the mean income while holding the Lorenz curve constant at some
reference level, and the second is a redistribution component due to changes in the Lorenz curve
while keeping the mean income constant at some reference level.
Following Heshmati (2007), one can compute growth and inequality decompositions in
various ways. Kakwani and Subbarao (1990) introduced the following decomposition:
∆ , , , , , , , (5)
where and are mean income and the Lorenz curve characterizing the distribution of income.
The subscripts 0 and 1 denote the two (consecutive or non-consecutive) initial and final periods
of observation, and and are contributions from the growth and redistribution components.
Jain and Tendulkar (1990) suggested an alternative formulation:
∆ , , , , , , , (6)
which differs from the previous decomposition by the reference point (base year versus final
year) that is initially chosen for computation of growth and redistribution components.
Kakwani (2000) suggested a simple averaging of both the growth and inequality
components from Equations (5) and (6), which is:
1
∆ , , , ,
2
, , , , . (7)
7
Datt and Ravallion (1992) found the above decompositions of poverty changes as being
time path dependent, arising through and dependent on the choice of reference levels. To make
the changes path independent they proposed adding an extra residual as follows:
∆ , , , , , , . (8)
The residual in (8) can be interpreted as the difference between the growth (redistribution)
components evaluated at the terminal and initial Lorenz curves (mean incomes), respectively.
The above decompositions compute the growth and redistribution effects of poverty
change through an analysis of mean incomes and relative inequalities. However, results would
be different if the analysis is carried out through median incomes and absolute income gaps—
as it is in the spirit of the relative distribution approach.4 In such an eventuality, the poverty
change between two periods, and , into growth and redistribution components is
decomposed as follows:5
, (9)
Variation
when is the period of reference, and:
, (10)
Variation
when is the period of reference. In the above:
∑
: poverty headcount ratio of the first period.6
∑
: poverty headcount ratio of the second period.
Variation : difference in poverty headcount ratio between and .
: growth component when is the period of reference; is
the poverty headcount ratio of the first period when all incomes of the first period
4
On the importance of paying more heed to absolute difference as well, rather than to relative difference only,
see e.g. Atkinson and Brandolini (2010) and references therein.
5
Here, we assume that the headcount ratio is the poverty measure’s precise functional form. In Section 5, we shall
apply the decompositions to another common poverty measure, the poverty gap index, given by the aggregate
income short-fall of the poor as a proportion of the poverty line and normalized by population size, i.e.
∑ , where is the number of poor people in the population.
6
The “1” indicator at the numerator is a function assuming value 1 if the th individual has income below the
poverty line , and assuming value 0 otherwise. Note that is the size of total population, and not the total number
of poor individuals.
8
are additively shifted by , where and are the medians of the
two distributions.
: redistribution component when is the period of reference.
: growth component when is the period of reference; is
the poverty headcount ratio of the second period when all incomes of the second
period are additively shifted by , where and are the medians of
the two distributions.
: redistribution component when is the period of reference.
Taking the average of (9) and (10) yields the following decomposition of the variation in
the poverty headcount between the two periods and :
, (11)
Variation
which is the one we shall use in the subsequent empirical analysis.
3 Data and summary statistics
The data used in the paper are obtained from national household surveys from as many countries
as possible through PovcalNet.7 PovcalNet is the global database of national household surveys
compiled by the research department of the World Bank, and it is the source of the World
Bank’s global poverty estimates.
In our analysis, we use 48 comparable household surveys for 24 Sub-Saharan African
countries, the same Beegle et al. used (2016).8 For each country, we consider two survey years
distant enough in time to allow for meaningful comparisons of consumption distributions. This
distance varies between 5 and 14 years, because the household surveys are not released every
year in every country but take place in different periods for each country. Overall, the period
observed covers two decades, since the late 1990s to the early years of this decade.
7
GLOBAL TSD/GPWG ([year of access (2017. As of [date of access (12/10/2017)] via Datalibweb Stata Package.
8
Namely, the countries analyzed are: Botswana, Burkina Faso, Cameroon, Chad, Democratic Republic of Congo,
Côte d’Ivoire, Ethiopia, Ghana, Madagascar, Malawi, Mauritius, Mauritania, Mozambique, Namibia, Nigeria,
Rwanda, Senegal, Sierra Leone, South Africa, Swaziland, Tanzania, Togo, Uganda, Zambia.
9
We use (per capita) household expenditure as the main welfare indicator throughout the
analysis.9,10 In that, we depart from the literature using income as a measure of well-being. In
economies where agriculture is an important and established sector, consumption has indeed
proven preferable to income because the latter is more volatile and more highly affected by the
harvest seasons, so that relying on income as an indicator of welfare might under- or over-
estimate living standards significantly (see, for instance, Deaton and Zaidi, 2002). On the
theoretical ground, as consumption gives utility to individuals, the analysis of its distribution
should be the most natural approach to study well-being. Income matters insofar as it gives
access to consumption, which is the ultimate source of individual welfare. Consumption is a
better measure of long-term welfare also because households can borrow, draw down on
savings, or receive public and private transfers to smooth short-run fluctuations.
As the data show, for many of the countries studied average household consumption
increased over time, following the significant economic growth Sub-Saharan Africa
experienced over the last decades (e.g. Beegle et al., 2016). We can see this trend in Figure 2.
Figure 2: Household consumption mean growth rates for each country.
9
To enhance comparability among the very different surveys, all consumption values are expressed in 2011
international dollars (PPP).
10
For Ghana, we use the national poverty line in local currency. For Nigeria, we estimate the consumption
distribution for 2003/04 using a “survey-to-survey” imputation method. For more details, see Clementi et al.
(2015).
10
Standard measures of inequality seem not to capture this widening gap between rich and
poor. As shown in Figure 3, both the Gini and Theil indices declined for most of the analyzed
countries, even though they start from a very high level.
Figure 3: Gini index and Theil index variations for each country.
As for polarization, calculation of the Foster-Wolfson (FW) and Duclos-Esteban-Ray
(DER) indices produced evidence that is mixed and thus hard to interpret.11
4 Empirical results
In this section, we first provide an overview of the results of the standard relative distribution
decomposition into growth effect and shape effect showing that in most analyzed countries, the
consumption distribution polarized, in particular, in the lower tail of the distribution; in other
words, these countries faced a significant process of lower polarization. In the second part, by
decomposing the poverty variation into growth and shape effects, we show how this lower
polarization offset the potential gains stemming from growth in consumption.
11
The inequality indices (Gini and Theil) and the polarization indices (Foster-Wolfson and Duclos-Esteban-Ray)
have been estimated using the Distributive Analysis Stata Package, which is freely available at
http://dasp.ucn.ulaval.ca/.
11
4.1 Changes in Sub-Saharan Africa’s consumption distributions
Figure 4 presents the overall distribution and the decomposition into location and shape for three
countries, one for each macro-region: Ghana for West Africa, Ethiopia for East Africa and
South Africa for the Southern cone.12 The left panel for each country depicts the overall relative
distribution, showing the fraction of households in the comparison year’s distribution that fall
into each decile of the reference year’s distribution. The location effect, i.e. the effect only due
to the median shift, is shown in the middle panels of Figure 4. Finally, the right panels of Figure
4 display the shape effect, which represents the relative distribution net of the median influence.
Figure 4: Relative distribution plots.
(a) Relative density (b) Location effect (c) Shape effect
Ghana
Ethiopia
South Africa
12
The analysis has been performed using the R package reldist (Handcock, 2016). The results for the remaining
34 countries are available upon request.
12
Looking at the shape effect graphs, we observe a clear concentration in the lowest decile.
Values above 1 indicate that, in relative terms, there are more households in that decile of the
distribution at the end of the period than there were at the beginning, vice versa less than 1
means there are less, and equal to 1 means that things have not changed: 10 percent of
households were in that decile at the beginning and 10 percent remained there. Therefore,
relative to the initial period, households in the lowest percentiles of each country increased by
14 percentage points (+1.4 over 1) in Ethiopia, 20 (+2 over 1) in Ghana and 15 in South Africa
(+1.5 over 1). In the three countries, this concentration in the lower tails (downgrading) is
paralleled by a similar but smaller concentration in the upper tails (upgrading). Overall, the two
effects produce a U-shaped relative density; households are concentrated in the tails of the
distribution while the middle of the distribution hollows out.
For the sake of space, we analyze the performance of the remaining countries using the
relative polarization indexes; these keep track of changes in the shape of the distribution and
measure their direction and magnitude. Figure 5 plots the median, lower and upper polarization
indexes for each country on a map. The null hypothesis of no change with respect to the
reference distribution is tested for each index and in 21 of the 24 countries, the variation in the
indexes is significant.13
The type of distributional change observed for Ghana, Ethiopia and South Africa is
closely replicated by 16 other countries; all of them experience a significant increase in
polarization that is predominantly driven by a downgrading of the consumption distribution, the
only notable exception being Nigeria where upgrading and downgrading are almost equivalent
(see Clementi et al., 2017). Interestingly, the polarization phenomenon appears widespread in
the region, while only in Madagascar and Zambia it decreased significantly.
13
Results are available upon request.
13
Figure 5: Relative polarization indices.
14
Another common feature of the group of polarizing countries is that the Gini index either
increased little or, as mentioned before, decreased. It is interesting to note that in the same
period, economies more advanced than the SSA African ones, but equally reliant on
commodities such as the Russian Federation and Brazil, experienced similar distributional
changes. Nissanov and Pittau (2015) find that during the commodity boom of the 2000s,
household net income restarted to grow after a long decline, Gini decreased while polarization
increased, driven mainly by a downgrading in the income distribution. Likewise, Clementi and
Schettino (2015) find that the decline in Gini experienced in Brazil between 2000-12 is
accompanied by a hollowing out of the middle of the distribution and accentuated concentration
of households in the lower tail.
4.2 Decomposition results
Once ascertained that there was a significant pro-polarization distributional change in the clear
majority of SSA countries analyzed, we now proceed by linking this change to poverty
reduction or lack thereof.
Figure 6 displays the results of the poverty “growth and polarization” decomposition (11)
that explicitly links the downgrading of the distribution to the reduced impact of growth on
poverty. Results are self-explanatory: in 13 of the 19 countries where the lower polarization
took place, it offset the poverty reduction benefits that could have arisen from growth. The
magnitude differs from a minimum registered by Senegal to a maximum registered by South
Africa; on average, this effect contributed to a 5-6 percentage points lessening in poverty
reduction.
15
Figure 6: Variation in the poverty headcount ratio and decomposition into growth and
redistribution components (our method).
The question then arises as to why standard measures/decompositions did not capture
this effect. Figures 7(a) and 7(b) compare our approach and the growth and distributive effects
of a widely-used decomposition, that of Datt and Ravallion (1992). As it appears, the negative
distributive effect (against poverty reduction) of this latter is always minimal or in many cases
the distributional change is pro-poor. Our point is that the distributional change SSA countries
went through could only be detected by the method we propose, and not by decompositions
based on standard summary tools to measure distributional changes.
16
Figure 7.a: Comparison of growth component between our methods and the Datt and
Ravaillon’s one.
Figure 7.b: Comparison of redistribution component between our method and the Datt and
Ravaillon’s one.
The reason is simple. Summary measure like Gini analyze the dispersion around the
mean of the distribution and this, as shown in Figure 7, either did not change or sometimes
improved in Africa. Also, most importantly, summary measures do not assign a weight to the
different percentiles; therefore, if a pro-inequality change in one part of the distribution is more
than compensated by a pro-equality change in the rest of the distribution, these measures will
decline. Yet, the distributional changes that matter most for poverty reduction are those
localized in the lower part of the distribution but can be detected only if one can look at changes
17
at a very granular level. The type of decomposition we propose can shed light on this, whereas
standard decompositions based on summary measures like Gini likely cannot.
The results of our analysis echo some recent findings from a recent World Bank (2018)
report on structural transformation in Africa. While our analysis focuses on distributional
changes, the report looks at changes in the labor markets and productivity that might explain
the low growth-to-poverty elasticity in Africa. The low contribution of employment growth to
poverty reduction, slow gains in agricultural productivity and a transition outside agriculture
towards sectors characterized by equally low productivity all contributed to characterize SSA
growth as little inclusive and consequently less able than other regions in the world in reducing
poverty. From a distributional point of view, our paper complements this analysis by showing
that this missed opportunity also translated into an increasing divide between the bottom 30-40
percent of the consumption distribution and the rest.
5 Summary conclusions
Since the end of the 1990s, two leading narratives prevailed when analyzing Sub-Saharan
Africa. The first, predominant, painted a picture of a continent on track in reducing poverty,
where middle classes were expanding, and prosperity was reaching large swaths of the
population (African Development Bank, 2011; The Economist, 2011, 2013; McKinsey, 2012).
The other narrative acknowledged the relatively robust growth, with a slow reduction in
poverty, without however conclusive evidence on the mechanisms that hindered growth from
trickling down.
This paper, to our knowledge, is the first attempt to provide a comprehensive explanation,
from a distributive point of view, of this low growth elasticity to poverty that characterized SSA
in a time when other regions in the world, growing as much as Africa, fared much better in
terms of poverty reduction.
To show that important distributional changes took place in SSA and that these played
against inclusive growth, this paper develops a novel yet simple decomposition based on the
“relative distribution” method (Handcock and Morris, 1998, 1999). Whereas the standard
“relative distribution” method enables to summarize multiple features of the welfare
distribution, our small innovation links these changes to the poverty reduction process
producing a poverty growth polarization decomposition.
18
In a nutshell, we find that the vast majority of SSA countries we analyze (about 80
percent) between the late 1990s and early 2010s experienced a very similar distributional
change of lower polarization—that is the clustering of the poorest 30-40 percent around a local
mean and an increasing divide between this group and the rest of the distribution. We also
observe an upgrading in the distribution that is the fattening of the upper tail of the distribution
(upper polarization), but only in the case of Nigeria this is commensurate to the lower
polarization.
This low polarization process has important implications for poverty reduction. The
proposed decomposition shows that polarization substantially reduces the positive effect of
growth on poverty reduction: on average without downgrading, poverty could have been 5-6
percentage points lower in SSA. Standard decompositions of poverty into growth and inequality
components fail to capture the impact of this distributional change on poverty also because there
is hardly a common Gini pattern in SSA, whereas we show there is a clear downgrading pattern.
The potential policy implications are numerous. First, we show that the type of growth
SSA experienced in the last decade was a sort of double-edged sword. It certainly reduced
poverty but at the same time it increased the divide between the bottom 40 percent (World Bank,
2012) and the rest of the population. Therefore, since SSA’s growth is not inclusive per se,
more efforts should be put to expand the benefits of growth by diversifying economies into
labor intensive sectors and reducing the divide between advanced and underdeveloped regions
within each country. Second, it looks like this divide is a slow-motion process that accumulated
over many years; evidence from Ghana and Nigeria for example (Bertoni et al., 2016; Clementi
et al., 2017, 2018) indicates that human capital, demography and basic infrastructures are the
main drivers of the polarization process. Reversing this trend will require time and resources in
a macroeconomic context that has substantially worsened after 2014; many SSA countries yet
again experienced sluggish growth, high inflation rates and falling fiscal revenues.
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