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Journal of African Economies, 2023, 32, ii228–ii245
https://doi.org/10.1093/jae/ejac050
Supplement Paper
Structural Change and Inequality in Africa
Hanan Morsya,Abebe Shimelesb,c,*and Tiguene Nabassagad
aUNECA, Addis Ababa, Ethiopia
bDepartment of Economics, University of Cape Town, Cape Town, South Africa
cIZA, Bonn, Germany
dHEC Montreal, Montreal, Canada
*Corresponding author: Abebe Shimeles. E-mail: abebe.shimeles@gmail.com
Abstract
This paper examines how inequality could be tackled through structural transformation using unit record
data from the Demographic and Health Surveys (DHS) for Africa. Results suggest inequality between
countries tends to be higher when the share of labour employed or value-added in the agriculture sector
is higher, while no association is observed for industry and services sectors contributions to GDP or
employment. Within-country inequality however tends to be strongly affected by structural change. A
1 standard deviation growth in the movement of labour from low- to high-productivity sectors could
decrease overall inequality by 0.5% and inequality of opportunity by 1.1%. Results from other data
sources strongly support these findings suggesting that positive structural transformation could lead
to sustained reduction in inequality in Africa. Other factors correlated strongly with inequality reduction
include human capital, which tend to have large and significant income or asset reducing effect in Africa,
particularly at higher level of education, while the pace of urbanisation exacerbates it incidence.
Keywords: labour productivity growth, inequality, structural transformation
JEL classification: D31, E24, I32, O55
1. Introduction
Inequality in Africa has been very high and persistent, compared with other parts of the
developing world1. Previous attempts to understand the dynamics and causes of inequality
in Africa have revealed only very limited information to guide policy, mostly identifying
issues such as ethnic fractionalisation as the cause of high inequality (Milanovic, 2003). At
the analytical level, we now have a better understanding of the growth–inequality nexus,
wherein the responsiveness of poverty to growth is largely driven by high inequality (see,
Fosu, 2015). High initial poverty could also be an important factor impeding subsequent
poverty reduction through its impact on growth and elasticity of poverty with respect to
growth (Ravallion, 2012), though the evidence for a sample of African countries tends to
suggest no such relationships exist (Ouyang et al., 2019). These studies amplify the role
of inequality, using the ‘identity’ relationships with growth and poverty, which provide
valuable information on the role inequality plays in impeding poverty reductions. Studies
that attempt to establish associations with key factors driving inequality have emerged
more recently and provide some insights into public policy (see, for example, Shimeles and
Nabassaga, 2018;Morsy and Levy, 2020).
1See, for example, Shimeles and Nabassaga (2018) and Chen and Ravallion (2012) and Bigsten (2014).
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Structural Change and Inequality in Africa ii229
Few African governments have consistent and coherent public policies and robust instru-
ments to address the persistence of high inequality. Some of the common macroeconomic
stabilisation measures, such as exchange rate adjustments, financial sector deregulations,
and other market-friendly reforms that tend to promote growth, may also turn out to exac-
erbate the state of inequality (Ostry et al., 2018). An attempt to capture the potential trade-
off between promoting growth and reducing inequality in macroeconomic policies could be
beneficial (see Berg and Ostry, 2011). The focus of this paper is on the extent to which the
structure of an economy could be associated with the dynamics of inequality in Africa. It
builds on the recent work of Baymul and Sen (2020), which examined empirically whether
structural transformation is associated with the pattern of inequality. This study attempts
to address the following research questions: Can structural change end the persistence of
high inequality in Africa? What policies tend to be effective in achieving inequality-reducing
structural changes? Are there trade-offs with growth-enhancing policies?
This paper extends the literature in several ways. First, it uses microlevel data drawn from
over 1 million household stories across Africa, using 129 waves in 37 countries for the period
1990–2018, to compute inequality indexes for the dimension of assets, instead of income—
offering an opportunity for temporal and contemporaneous comparability. Second, the
paper also reports results for components of inequality that are appealing to public policy,
such as inequality of opportunity, as it relates to structural transformation. In addition,
key variables, such as sectors of employment and education status attained, were computed
from the microdata, further enriching the analytical work. Third, in addition to the usual
measurements of structural change, such as share of employment or value-added, the paper
decomposes labour productivity growth in each country for each year into components of
structural change and within-sector productivity, following McMillan et al. (2016), giving
a richer discussion of the association between structural transformation and inequality.
Results indicate that only about 30% of the 37 countries in the sample had significant
structural change during the period 1990–2018, in which labour tended to move away
from either agriculture or services to industry. Some 20% experienced ‘deindustrialisation,’
where the share of employment in either agriculture or services increased at the expense
of industry. Close to 46% of the countries in the sample had the share of employment
in services increase during the period, either because of movement away from agriculture
or industry, or both. The patterns with respect to the share of value-added by the three
sectors remained similar. The decomposition approach offered a slightly clearer picture
of the pattern of structural transformation in Africa, with close to 45% of the countries
in the sample experiencing positive labour productivity growth during 1990–2018, and
for 33% of these countries, the mobility of labour from low- to high-productivity sectors
contributed positively to productivity growth. Hence, with heterogeneous experience in the
pattern of structural transformation, it may be possible to capture the implied effect on
inequality.
Comparison between countries, using pooled regressions, suggest that an asset-based
Gini coefficient tended to increase significantly in countries where either the share of
employment or value-added in agriculture increased, with no detectable effect observed
for similar changes in industry or services, on inequality. However, when time-varying and
time-invariant unobserved factors are controlled, within-country structural changes tended
to have large and significant effects on inequality. Describing structural change as a driving
force behind growth accelerations, the paper also documents that countries, which have
completed two or three growth accelerations, benefited in the form of significant reductions
in inequality, and hence, poverty—reinforcing the potential role of structural change in
tackling inequality.
The rest of the paper is organised as follows. Section 2 outlines the conceptual framework
that motivates the potential relationships between structural transformation and inequality;
Section 3 describes data sources; and Section 4 reports the main results. Section 5 concludes.
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ii230 Hanan Morsy et al.
2. Conceptual framework
The link between structural change and inequality in the process of development has
been well articulated in early works of Arthur Lewis (1954) and Simon Kuznets (1955),
in which they formulated a working hypothesis that, at the initial level of development,
economic growth accelerates in the ‘modern’ sector, keeping wage rates relatively lower in
the traditional sector due to ‘unlimited labour supply,’ and hence, expanding the degree
of income inequality at the national level. Here, the assumption is that inequality in the
traditional sector is much lower (due to undifferentiated productivity levels), while the
modern sector tends to have high inequality. However, as demand for labour in the modern
sector increases faster than its supply, wages start to rise, productivity growth stabilises;
hence, inequality declines. Kaldor (1961) further expanded these insights by linking capital
accumulation with higher inequality at the initial period, because the marginal savings
rate is higher among the rich than the poor, which implies higher inequality, as the
return to capital becomes higher, favouring the rich. These insights have been a subject
of large empirical literature that reported inconclusive evidence regarding these predictions.
Influential empirical papers by Dollar and Kraay (2002) and Dollar et al. (2016) suggested
that growth generally tends to be neutral with respect to inequality, where the pattern of
growth may not matter to inequality. As new data became available,2the issue of structural
change took centre stage in decomposing per capita growth into two components: growth
in within-sector productivity and mobility of labour from low- to high-productivity sectors
(structural change component); see McMillan (2013),Rodrik (2013),andMcMillan et al.
(2016), which formed a framework to look at long-term growth in developing areas with a
potential association with inequality.
This approach has the benefit of capturing the processes underpinning structural transfor-
mation according to Timmer (2012, p.2) that encompass the following ‘(1) a declining share
of agriculture in gross domestic product (GDP) and employment, (2) the rapid process of
urbanisation as people migrate from rural to urban areas, (3) the rise of a modern industrial
and service economy, and (4) a demographic transition from high to low rates of births
and deaths.’ Which combined tends to increase the average productivity of labour in the
economy.
The link between structural change and inequality is hence captured mainly through the
decomposition of labour productivity growth, which can be broken into growth of within-
sector productivity for a given level of employment; growth in employment in each sector;
and interaction of growth between productivity and employment. Equation (1)provides
such a decomposition:
gy=
i
wigyi +
i
yigli (1)
where gyi is the growth rate of labour productivity of sector i;gli isthegrowthrateof
the share of sector iin total employment and wiis the share of employment of sector iin
period t–k. The two components measure contributions to aggregate productivity growth.
The first component measures the contribution of productivity growth of the different
sectors to aggregate productivity growth. The second component measures the contribution
of reallocation of labour from low-productivity to high-productivity sectors, called the
component of structural change by Diao et al. (2017). Following McMillan et al. (2017),the
last term is treated here as constituting structural change, involving employment shifts away
from sectors with lower labour productivity growth and levels.3The link between structural
2For example, the ten-sector decomposition of GDP, including employment, by Groningen University.
3In cases where this part of the labor productivity growth is negative, it means labor has moved from high-
to low-productivity sectors.
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Structural Change and Inequality in Africa ii231
transformation and inequality is not straightforward, as so many interacting factors are at
aplay.
In the process of growth, for instance, within-sector productivity growth (the first
component of Equation (1)) can contribute to higher or lower inequality because of
variance in labour productivity growth between sectors, even assuming equal levels of initial
inequality and no change in the movement of labour between sectors. For inequality to
remain unchanged or to decline, it is necessary for all sectors to grow at the same pace, or
for the low-inequality sector to grow faster than the high-inequality sector (assuming no
productivity variance within workers in each sector). A positive structural change, which is
defined as a movement of labour from a low- to high-productivity sector, reduces inequality
if the recipient sector has lower inequality than the releasing one. In a two-sector framework,
if the modern sector (high productivity one) has lower inequality than the traditional sector
(low productivity one), then movement of labour from the traditional sector to the modern
sector not only improves overall productivity but also reduces inequality. Bringing the two
together, the likelihood of overall inequality declining depends on whether the sector that
grows faster has low within-sector inequality and attracts more workers into its ranks in
the process of growth. If this assumption does not hold, then, we have multilayer scenarios
to establish the direction of change in inequality following structural transformation.
Households in the fast-growing sectors tend to have higher earnings than those in slow-
growing sectors. Again, this is a heavy simplification, as within-sector productivity growth
may not necessarily benefit everyone equally in the same sector. There is high degree of
inequality among people employed in the same sector. For instance, rural inequality tends
to be high in Africa, largely, due to inequality in land ownership rather than productivity
differentials among framers. Similarly, in the extractive sector, inequality is very high,
because return to capital is much higher than labour, which is paid very low wages due to
its abundance and fungibility from other sectors. A significant inequality shift can be seen
when a country is experiencing rapid structural change wherein income growth is driven
largely by shifts in employment from low- to high-productivity sectors.
3. Data and methods
The data used for this paper were obtained primarily from 127 waves of Demographic
and Health Surveys (DHS) for 37 African countries covering the period 1990–2018, of
which 24 countries of the 37 had three or more waves with a maximum of six waves
(see Tabl e A 1 for the list of countries, number of waves and year of the surveys). The
microlevel data consisted of the history of over a million households on a wide range
of indicators relevant to this study. The data cover a wide range of variables, including
demographic characteristics, asset ownership, access to utilities and basic social services,
education and occupation of the head of a household and a wide range of health outcomes
(stunting, wasting, diseases burden). Also, the data are nationally representative. Since the
survey instruments and methods are generally standardised, they are comparable spatially
and temporally. To construct our measure of asset inequality, we reordered 10 items for
which data are available in all waves for all countries4. These are type of housing (number
of rooms; f loor material—perke, cement, ceramic, earth; roof material—bricks, tin, grass,
earth, etc.); sources of access to water (tap, water kiosk, well, etc.); access to electricity and
ownership of durable household assets, such as radio, television, refrigerator and car, etc.
The challenge is to generate a single asset index that could allow us to compute the Gini
coefficient for assets. Following Shimeles and Ncube (2015), we defined a welfare measure
4The details regarding the selection of the individual asset items and construction of the asset index is given
in Shimeles and Ncube (2015) and Shimeles and Nabassaga (2018) on which this paper draws heavily.
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ii232 Hanan Morsy et al.
for each household Wj, over individual constituents cij such that:
Wj=
k
i=1
aicij,(2)
where irepresents kassets that individual jpossesses to achieve a welfare level Wj.The
linearity in Equation (2) assumes that welfare is additive over the dimensions, allowing
for a possibility of a perfect substitution across the individual assets. In the case of assets
ownership, since there is no price information to aggregate the total value of asset or wealth
owned, aiwould have to be generated from the data with some assumptions. The common
approach in the empirical literature is to use data reduction methods to generate individual
weights as well as a single index and, in this study, we use multiple correspondence analysis
(MCA), which is closely related to factor analysis or principal components analysis. The
main difference is that the MCA is suitable for categorical variables. Formally, if we denote
ajas the weight of category jand Rij as the answer of household ito category j, then the
asset index score of households iis5:
MCAi=
J
j=1
ajRij (3)
This index can then be normalised between 0 and 1 to allow for intertemporal and cross-
country comparisons by the following formula:
normalised_MCAi=MCAi−min (MCA)
max (MCA)−min (MCA)(4)
The asset index hence serves as a ‘proxy’ to income or wealth with several limitations as a
measure of tracking income or inequality, which are discussed in Harttgen et al. (2013) and
others (Johnston and Abreu, 2016). One of the deficiencies is that the asset index is bounded
between 0 and 1, hence limiting by construction the ability to capture extreme inequality.
Secondly, asset inequality tend to exhibit little variation over time among countries that met
fully basic asset needs (housing, water, electricity and other amenities) to their population,
such as middle-income countries. One of the suggestions hence was to complement and
compare the asset inequality analysis with that of alternative ones, such as money-metric-
based inequality measures such as consumption or income inequalities. Figure 1 presents
a simple correlation of the measures of these two inequalities for African countries, which
suggests positive and strong associations (adjusted R2is about 40%) indicating some degree
of robustness of the asset-based measures of inequality.
3.1 Approach to compute spatial inequality6
Asset or income inequality is the consequence of inequality arising from differences in effort
between individuals or households, or inequality of circumstances beyond their control,
such as ethnicity or region of residence (Romer, 1998;Romer and Trannoy, 2016). The
basic idea of inequality of opportunity is that inequality of outcomes between households,
such as income, assets or education, are determined by two key factors: those over which the
individual has some degree of control or choice, called ‘effort,’ and those that are beyond
5Shimeles and Nabassaga (2018, pp. 6–7).
6This section draws heavily on Shimeles and Nabassaga (2018, pp. 9–10).
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Structural Change and Inequality in Africa ii233
35 40 45 50 55
Consumption based Gini Index
.3 .4 .5 .6 .7
Asset Gini Index
Figure 1. Correlation between Consumption Based and Asset-Based Gini Coefficients in Africa. Notes: Asset
Gini index is computed from unit record data of 129 waves for 37 countries, while the consumption-based
Gini index was obtained from World Bank PovcalNet. https://pip.worldbank.org/home
her/his control, called ‘circumstances,’ such as ethnicity.The outcome distribution, yhcan be
expressed as a function of these two factors, chand eh, respectively, and an unobserved factor
uhsuch that yh=f(ch,eh,uh), and the overall inequality is computed over the distribution
yh. Thus, the measure of inequality, such as Gini =Iyh, will be a function of effort as well
as circumstances.
Equality of opportunity occurs when household outcomes are independently distributed
from circumstances. The inequality of opportunity can be computed from a counterfactual
distribution function, F(y/C), which eliminates the effort effect. Two methods are widely
used in the literature—parametric and nonparametric (see, for example, Peragine, 2004;
Hassine, 2011). In this paper, we follow the parametric approach to decompose a measure
of inequality of an asset index into that of inequality of opportunities and effort. Following
Shimeles and Nabassaga (2018)7, the log-linear model can be expressed as follows:
ln yh=α∗Ch+β∗Eh+uh(5)
Since circumstance variables (Ch) are beyond individual’s control, they are exogenous,
but effort factors (Eh) may be endogenous to circumstances since an individual’s actions
may be influenced by the circumstances. This can be expressed as follows:
Eh=A∗Ch+εh(6)
By incorporating Equation (6) into Equation (5), the outcome distribution can be
expressed as:
ln yh=ω∗Ch+ϑh,(7)
where ω=α+A∗βand ϑh=uh+β∗εh.The counterfactual distribution ( ) can be obtained
by taking the predicted value after the regression of Equation (7) and, the inequality of
7See also Bourguignon et al. (2007).
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ii234 Hanan Morsy et al.
economic opportunity index, IEO, can be computed as:
(8)
IEO hare (IEOR) is expressed as EOR =, which gives the share of the overall
inequality due to inequality of opportunity. This measure gives an upper bound for
inequality of opportunity. Since equality of effort is not assumed, the decomposition will give
the lower bound for the proportion of inequality due to circumstances than the parametric
approach described in Equation (4). In recent work, variables that are frequently used to
capture inequality in opportunities include gender, race, ethnicity, family background, region
of residence and others that essentially act as barriers or advantages for individual effort
and shaping individual fortunes.
4. Results and Discussions
As indicated in Section 1, inequality, as measured by the Gini coefficient, showed that
Africa has been the second most inequitable continent, next to Latin America, for much
of the last four decades (see Figure 2). The Gini index was higher in 2018 than in 1980
for Africa, compared to Latin America, which experienced an almost 10% decline over
this period. The same trend of a relatively constant Gini index over time applies to East
and South Asian countries. During a time of rapid growth, the Gini remained unchanged
in Africa, declining in recent decades very marginally. Figure 2 displays the average Gini
by per capita GDP between African and non-African developing regions, suggesting that
high inequality is a feature of both relatively poorer and middle-income countries in Africa,
compared to other regions. The absence of variation across the income spectrum may also
suggest income sources could be highly bifurcated, with high- and low-income/productivity
economies motivating a lack of structural change, as one of the reasons for the persistence
of inequality.
The persistently high inequality presented in Figures 2 and 3for Africa translate into
the lack of inclusiveness of growth in recent decades, whereby many African countries
exhibited a relatively high and sustained economic performance. Figure 4 exhibits the
Growth Incidence Curve, proposed by Ravallion and Chen (2003), to measure the degree
of pro-poor growth for 31 African countries—for which it was possible to obtain two wave
data on consumption growth of percentiles between 2000 and 2016. It is evident that during
this period, the consumption of the poorest percentiles grew at much lower pace than the
average population. Hence, it is not surprising if the Gini remained constant or inched up
in some recent years in Africa.
Can part of the stagnation in inequality be explained by lack of structural transformation?
Deeper investigation of these issues requires detailed household and labour force surveys,
as well as administrative data to establish robust associations between inequality changes
and structural transformation. Preliminary results suggest that inequality tends to be lower
in countries that consistently increased the share of employment in industry, followed by
services. In places where agriculture stagnated and its employment share increased, there
seems to be high inequality (Figure 5).
To explore further the relationships between structural transformation and inequality, we
rely on data obtained from several waves of DHS. Ta ble 1 presents the key characteristics
of the data. Because of missing data in some waves, the asset-based Gini coefficient
was computed only for 114 waves and varied in range from 0.09 (indicating nearly all
households had the designated asset) to extreme inequality of around 0.76. The average Gini
coefficient for the entire period hovered around 0.46, indicating how Africa tends to exhibit
extreme inequality, as measured by the asset dimension. Similarly, the sector of employment
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Structural Change and Inequality in Africa ii235
Figure 2. Evolution of the Gini Coefficient in Selected Regions of the World. Source: African Development
Bank computations based on PovcalNet data. http://iresearch.worldbank.org/PovcalNet/povOnDemand.aspx
20 30 40 50 60
Gini coefficient (consumption)
3 4 5 6 7
Log of per capita consumption i n PPP
Africa Africa without the top 10 inequals countries
Non African devel oping countries
Figure 3. Inequality in Africa and other Developing Regions at Different Levels of Development (1980–2011).
Source: Authors’ computations based on data from PovcalNet.
by head of households indicated that only 5% of the population was engaged in industry,
38% in agriculture and 32% in services. Some households put either non-agriculture or
other sectors that could not be identified in either of these.
During the period under study, labour productivity has shown some growth of 1.4%, with
huge variations across countries. Structural change contributed to 10% of the productivity
growth, while the rest was attributed to within-sector productivity growth. This, in itself, is
suggestive of why inequality may tend to persist in Africa.
Figure 6 provides non-parametric trends for the share of employment in the three sectors
over time. The pattern clearly shows that share of employment in Agriculture continued to
have a declining trend until 2012, with services compensating by rising, while employment
in industry continued to decline. This ‘average’ scenario may only capture the typical trend,
as countries in our sample are sufficiently heterogeneous with respect to their experience on
structure of the economy. The trend for the share of value added in each of the sectors over
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ii236 Hanan Morsy et al.
2.7
2.8
2.9
3
3.1
3.2
3.3
3.4
3.5
3.6
3.7
5 7 9 11131517192123262830323537414345475052545658606264666870727476788082848688909294
Annualized growth rate
Growth incidence curve Mean growth rate Pro-poor growth rate
Figure 4. Africa Growth Incidence Curve: 2000–2005 and 2010–2016. Source: African Development Bank
computations based on PovcalNet data. Note: The reported growth incidence curve is truncated at the 5th
and 95th percentiles.
BWA
ETH
GHA
MWI
NGA
SEN
TZA
ZAF
ZMB
BWA
ETH
GH
A
MUS
MWI
NGA
SEN
TZA
ZAF
ZMB
BWA
ETH
GHA
MUS
MWI
NGA
SEN TZA
ZAF
ZMB
30 40 50 60 70
Gini Coefficient
-.03 -.02 -.01 0 .01 .02
Change in the Share of employment
Agriculture Fitted values
Industry Fitted values
Services Fitted values
Figure 5. Growth in Share of Employment in Agriculture, Industry and Services and Gini Index. Source:
Computations based on Groningen Development Center (Timmer et al.,2015) and PovcalNet data sets.
Note: Figure used trends in the share of employment in the three main sectors from the Groningen data set
(Timmer et al.,2015) for 11 African countries and combined it with data on Gini coefficient obtained from
PovcalNet.
time mirrored similar trends, indicating that, on average, there has not been a large shift in
the structure of African economies, perhaps, except in some country cases.
It is not surprising, therefore, that the correlation between the Gini coefficient and
structure of the economy is not dictated, except in agriculture, which generally tended to
show a strong and significant positive correlation with the Gini coefficient. As shown in
Tabl e 2 , countries that tended to have a high share of labour employed in agriculture (or
high share of agricultural value added) contributed to high inequality in Africa, pointing to
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Structural Change and Inequality in Africa ii237
Ta b l e 1 . Descriptive Statistics
Var i a ble Number
of waves
Mean Standard
deviation
Min Max
Gini coefficient 114 0.463 0.137 0.081 0.758
Share of spatial inequality 114 0.346 0.126 0.077 0.618
Economic structure
Share of labour in agriculture 93 0.381 0.196 0.000 0.855
Share of labour in industry 93 0.050 0.060 0.000 0.244
Share of labour in services 93 0.324 0.177 0.051 0.777
Share of agriculture in TVA 81 0.257 0.122 0.041 0.546
Share of services in TVA 81 0.463 0.137 0.002 0.758
Share of industry in TVA 81 0.261 0.130 0.056 0.726
Labour productivity growth (within sector) 93 1.250 5.005 −10.131 35.341
Labour productivity growth (b/n sector) 93 0.135 6.101 −19.859 34.560
Labour productivity growth (total) 93 1.385 6.187 −10.279 35.714
Share of agriculture in TVA 81 0.257 0.122 0.041 0.546
Highest education attained by head of household
Primary 74 32.258 17.880 8.157 67.012
Secondary 74 19.729 12.893 4.666 58.936
Higher 74 4.399 3.782 0.341 14.369
Notes: The table reports descriptive statistics for key variables used in the study. The Gini coefficient and its
spatial component are computed from an asset or wealth index using survey and country weights for 37
countries in 114 waves of the DHS for the period 1990–2018. The economic structure used the employment
sector of head of households from the DHS. The share of TVA was computed from World Development
Indicators for various issues. The educational attainment refers to the head of the household, computed from
the DHS data.
0.1 .2 .3 .4 .5
1990 2000 2010 2020
year
Agriculture Industry
Services
Figure 6. Lowess Estimate of Trends in the Share of Employment in Agriculture, Industry, and Services.
Source: Authors’ computations based on DHS data. Note: Lowess = Locally Weighted Scatterplot
Smoothing.
the possibility that the preponderance of dualism in these countries tends to be associated
with high and persistent inequality.
This association between share of employment in agriculture and inequality remained
robust when controls such as education (Tabl e 3 ) and other time-varying factors were
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ii238 Hanan Morsy et al.
Ta b l e 2 . Pooled OLS Regression of Log Gini Coefficient on Sectors of Employment and Value-Added in Africa
Var i a ble Agri Industry Services Agri Industry Services
Share of employment in
agriculture
0.586∗∗
Share of employment in
industry
−0.712
Share of employment in
services
−0.012
Share of value-added in
agriculture
1.509∗∗∗
Share of value-added in
industry
−1.23
Share of value-added in
services
−0.652
Constant −1.053∗∗∗ −0.793∗∗∗ −0.821∗∗∗ −1.243∗∗∗ −0.529∗∗ −0.531∗
N78 78 78 68 68 68
r20.084 0.009 0.01 0.181 0.117 0.016
∗p<0.05. ∗∗p<0.01. ∗∗∗ p<0.001.
Source: Authors’ computations based on DHS data.
Note: Share of employment in agriculture, services, and industry were calculated from the DHS, while that of
share of value added by each sector for each country was obtained from World Development Indicators,
various issues.
included in the regression.8The role of education of the head of the household, in
explaining variation in inequality between countries, is substantial. As could be seen from
Tabl e 3 , including education into the OLS pooled regression increased the R2substantially.
Compared with Tabl e 1 , nearly 47% of the variation in inequality between countries
could be explained by differences in the highest levels of education attained by the heads
of households. In countries where the percentage of the heads of households whose
highest education attained was primary, inequality tend to be significantly higher, while for
secondary and tertiary education, inequality tend to be lower.A simple factor decomposition
of the regression in Tabl e 3 indicated that next to the residual, tertiary education accounted
for the largest variation in the Gini coefficient between countries, of approximately 30%
in most cases. This robust association between inequality and educational achievements9
points to an important dimension for promoting intergenerational mobility and a vehicle
for expanding economic opportunities.10
In addition, to appreciate the relationships between overall inequality and inequality of
opportunity (measured by taking factors that are deemed to be beyond the control of the
individual, such as ethnicity and gender, for example), Figure 7 shows strong and positive
association, which generally indicates that countries with high inequality tend to also have
high inequality of opportunity. Similarly, Figure A1 displays a negative correlation between
8This includes year dummies and country-fixed effects that are not reported. Also, including initial log per
capita GDP in constant PPP in the regression did not change the results.
9Shimeles and Nabassaga (2018) reported similar correlation using data obtained from povcalnet and World
Development Indicators, using controls, such as urbanization, governance, ethnic fractionalization, and other
potential correlates of inequality
10 Education is regarded as an important dimension in the inequality of opportunity literature that could
bridge the gap between inequality caused by circumstances beyond the control of a household or an individual
and one that for which it could be responsible because of less ‘effort.’ For example, households living in remote
areas could experience intergenerational poverty because schooling opportunities might not exist for generations.
Public policy to scale up education could then potentially reduce inequality by reducing returns to schooling as
well as improving earnings for those in the bottom of the income distribution (see, for example, Brunori et al.,
2013;Emran et al., 2020).
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Structural Change and Inequality in Africa ii239
Ta b l e 3 . OLS Regression of Log Gini Coefficient on Sectors of Employment and Value-added in Africa (Robust
Statistics), Conditional on Education Level Attained by the Head of a Household
Var i a ble 1 2 3 4 5 6
Share of employment in agriculture 0.319∗
Share of employment in industry −1.153
Share of employment in services 0.012
Share of value-added in agriculture 0.793∗
Share of value-added in industry −0.254
Share of value-added in services −0.998
Primary 0.010∗∗∗ 0.011∗∗∗ 0.011∗∗∗ 0.010∗∗ 0.010∗∗ 0.013∗∗∗
Secondary −0.012∗−0.014∗−0.013∗−0.011 −0.017∗−0.017∗
Tertiary −0.091∗∗∗ −0.096∗∗∗ −0.093∗∗∗ −0.090∗∗∗ −0.086∗∗ −0.092∗∗∗
Constant −0.770∗∗∗ −0.573∗∗∗ −0.642∗∗∗ −0.874∗∗∗ −0.550∗∗∗ −0.18
N78 78 78 68 68 68
r20.469 0.469 0.446 0.528 0.496 0.522
∗p<0.05. ∗∗p<0.01. ∗∗∗ p<0.001.
Source: Authors’ computations based on DHS data.
Notes:Tab l e 2 reports results from pulled regression of asset-based Gini coefficient on share of employment
and value-added in the three broad sectors. Data for the Gini coefficient, level of education attained by head of
asset, and share of employment in the three sectors were computed from DHS waves, and that for value-added
shares were computed from World Development Indicators.
0.2 .4 .6 .8
Asset-based Gini Coefficient
0.2 .4 .6
Inequality of Opportunity
Figure 7. Inequality of Opportunity for African Countries: 1990–2018. Source: Authors’ computations based
on DHS data.
inequality of opportunity and that of ‘effort’, suggesting a potential trade-off between the
two types of inequality.11
Finally, we report results on whether within-country inequality could respond to struc-
tural change in the economy. Tab l e 4 presents results from a fixed-effect panel regression
model, which controlled for time-varying and time-invariant unobserved effects using year
dummies. The results suggest that generally faster growth in labour productivity tends to
reduce inequality. Stronger effect was reported for the component of labour productivity
growth prompted by mobility of labour from low- to high-productivity sectors. The
decomposition is based on Equation (1) in which growth in output per worker (simple
11 A similar result was also reported in Brunori et al. (2013) that used a different data set, framework to
estimate inequality of opportunity, and country coverage.
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ii240 Hanan Morsy et al.
Ta b l e 4 . Effects of Growth in Components of Labour Productivity on Inequality: Fixed-effects Panel Regression
Log Gini Log inequality of opportunity
1 2 3 4 5 6
Total LP growth −0.006∗−0.018∗∗
Between sector productivity −0.009∗∗ −0.016∗∗
Within sector productivity 0.006 −0.009
Year dummy Yes Ye s Ye s Ye s Ye s Yes
Constant −0.463∗∗ −0.477∗∗∗ −0.508∗∗ −1.361∗∗∗ −1.419∗∗∗ −1.415∗∗∗
N78 78 78 78 78 78
Within r20.708 0.763 0.677 0.718 0.689 0.606
∗p<0.05. ∗∗p<0.01. ∗∗∗ p<0.001.
Source: Authors’ computations based on DHS data.
Notes: Labour productivity growth was computed using data from the share of sector employment in
agriculture, services and industry from DHS data and total value-added per person from various editions of
World Development Indicators. The decomposition was done using Equation (1) in Section 2. The Gini is
wealth- or asset-based, and inequality of opportunity is part of overall inequality due to such factors as ethnic
background of the household, gender and region of residence.
measure of labour productivity) was decomposed, in part, due to within-sector productivity
and the other mobility of labour across sectors. For African countries in our sample,
within-sector productivity growth accounted for nearly 85% of labour productivity growth
while the rest was due to mobility of labour, suggesting limited presence of structural
transformation in African countries. Still, where it occurred, the process allowed for a
significant reduction in wealth or asset inequality.Using results reported in Columns 2 and 5,
a growth in structural transformation of 1 standard deviation could lead to 0.5% reduction
in overall wealth inequality and 1.1% reduction in inequality of opportunity. The faster the
pace of structural change, the higher the chance for a country to rapidly reduce inequality.
In this exercise, it is difficult to tell the specific sector to which labour had to move to obtain
a decline in inequality. We can only infer that movement of labour from the less to more
productive sector is beneficial to inequality reduction. The paper by Baymul and Sen (2020)
reported that for a sample of developing countries, inequality generally tended to decline
across or between countries when the share of labour or value-added in manufacturing
increased and it increased when the share of labour or value-added in agriculture or services
increased, echoing our result in Tab l e s 2 and 3. Hence, improvements in labour productivity
within any of the key sectors, by itself, would not lead to decline in inequality, rather in the
manufacturing sector.
The insights from Tabl e 4 seem to be supported by data used by Baymul and Sen (2020),
which relied mainly on the 10-sector disaggregation of national accounts, which provides
a series from 1950 to 2011 for 33 developing countries, of which 12 were from Africa
(Timmer et al., 2015). The strength of this data is that it uses comparable approach and
reports share of employment and share of value-added across 10-sectors of an economy
offering a unique opportunity to capture components of structural change in a country
over a long-period of time and has been used extensively by researchers to understand the
dynamics of structural transformation in Africa (Diao et al., 2017). The limitation of this
data primarily is the small sample of African countries covered, which is 12 and may not
represent significant variation to characterise the link between inequality and structural
transformation. It may be useful, however, to take advantage of the granularity in sectoral
decompositions of national accounts that these data present and investigate if in the long
term there is some association between rate of change in Gini coefficient computed from
household surveys using consumption expenditure and components of labour productivity
growth, with emphasis on the part that captures the structural change element. It is also
possible to address some of the potential limitations that exists in the use of asset-based
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Structural Change and Inequality in Africa ii241
Ta b l e 5 . Pooled OLS Regression of Average Rate of Change in Gini Coefficient on Key Correlates for a Sample
of Developing and African Countries
1234
Within sector productivity (%) 0.000278 0.00285∗
(0.95) (2.26)
Structural change (%) −0.00162∗−0.0203∗∗
(−2.00) (−2.90)
Log initial per-capita GDP 0.0217∗∗∗ 0.0215∗∗∗ 0.0383∗∗∗ 0.0335∗∗∗
(4.25) (4.27) (4.05) (3.56)
Log initial Gini −0.0684∗∗∗ −0.0689∗∗∗ −0.0997∗∗∗ −0.0978∗∗∗
(−5.70) (−5.80) (−4.12) (−4.45)
Index of Human Capital 0.159∗∗∗ 0.162∗∗∗ 0.230∗0.365∗∗∗
(3.72) (3.86) (2.52) −3.75
Index of Human Capital Squared −0.0351∗∗∗ −0.0356∗∗∗ −0.0510∗−0.0866∗∗
(−3.69) (−3.83) (−2.13) (−3.31)
Rate of urbanisation (%) 0.00731∗∗∗ 0.00743∗∗∗ 0.0198∗∗∗ 0.0177∗∗∗
(3.82) (4.00) (4.93) (4.66)
Constant −0.125∗−0.125∗−0.258∗−0.329∗∗
(−2.32) (−2.35) (−2.06) (−2.70)
N261 261 54 54
Number of countries 33 33 12 12
adj. R20.445 0.45 0.584 0.65
∗p<0.05. ∗∗p<0.01. ∗∗∗ p<0.001.
Notes: t-Statistics in brackets. Table reports pooled regression of average rate of change in Gini coefficient over
5 years for a sample of Developing and African countries. Variables within sector productivity growth and
structural change were computed from ten-sector data on value-added per person and share of employment
provided by Timmer et al. (2015) and using the decomposition formula given in Equation (1) to compute
labour productivity growth. Index of Human Capital was obtained from Penn World Tables, which is defined
as Index of Human Capital per person based on years of schooling and returns to education14Details of the
computation of the human capital index is given in the link here: https://www.rug.nl/ggdc/docs/human_capita
l_in_pwt_90.pdf. Gini coefficient, per capital real GDP, rate of urbanisation human capital index were
obtained, respectively, from PovcalNet and Penn World Tables.
inequality when linking with labour productivity growth some of which were mentioned in
earlier sections12. In this regard, Tabl e 5 reports results from a pooled regression of average
change in the consumption-based Gini coefficient and components of labour productivity
growth (with sector productivity) and structural change (between sector productivity) for
developing (columns 1 and 2) and African countries (columns 3 and 4). The results echo that
of Tabl e 4 in that labour productivity growth powered by movement of people from low-
to high-productivity sectors tend to be inequality reducing in both samples, in fact with
stronger magnitude for the Africa sample. Productivity growth taking place in respective
sectors tend to have no effect or in African case inequality increasing effect. The size of
the elasticity between Gini coefficient and structural change is very small. For Africa for
instance, a 10% increase in labour productivity growth arising from structural change
would lead to just 0.2% decline in inequality suggesting that growth process alone is not
enough to achieve rapid reduction in inequality, though in the long-term acceleration in
structural change could make significant impact. It is also interesting to note from Tab l e 5
that initial Gini tended to be negatively correlated with average growth of the Gini indicating
the possibility that countries that started out unequal tend to be equalising over time, while
on the other hand, initially richer countries tended to see rising inequality. Urbanisation
growth also tended to be a significant force in sustaining inequality over time in developing
countries, including Africa, confirming the arguments raised by Timmer (2012). Finally,
12 Very detailed and helpful discussion on the limitations of using the asset-index to track growth in household
income in the context of Africa is given in Harttgen et al. (201).
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ii242 Hanan Morsy et al.
Ta b l e 6 . Effect of Growth Acceleration on Inequality
Log of Gini coefficient 1 2 3
Log of real pc consumption 0.0132∗0.0117 0.0199∗∗∗
(0.00734) (0.00737) (0.00698)
Dummy (at least one growth acceleration) 0.0373
(0.0239)
Dummy (at least two growth acceleration) −0.115∗∗∗
(0.0242)
Dummy (at least three growth acceleration) −0.154∗∗∗
(0.181)
Constant 3.670∗∗∗ 3.742∗∗∗ 3.677∗∗∗
(0.0567) (0.0515) (0.0468)
R20.021 0.099 0.109
N254 254 254
∗p<0.10. ∗∗p<0.05. ∗∗∗ p<0.01.
Source: Author computation using Povcal and PWT data.
Notes: Pooled OLS, standard errors in parentheses. Growth acceleration episodes were obtained from African
Economic Outlook (AfDB, 2019); inequality per capita consumption data were obtained from povalnet.
human capital development seems a very important correlate of inequality. Even after
controlling for initial level of development, inequality, and other important factors such as
growth process, urbanisation pace, differences in human capital formation seem to explain
a significant portion of the variation in the Gini coefficient growth between countries. This
is remarkable as it is also what is reflected in the regressions for asset-based index inequality.
Here, as well, high level of human capital accumulation tends to be income equalising even
for the Africa sample.
The finding in Table 5 is further reinforced by noting that the benefit of structural change
is witnessed by the inertia it creates for a country to achieve growth accelerations. The
African Economic Outlook (AfDB, 2019) documented that countries that completed at least
one episode of growth accelerations13 did so through significant structural change, rather
than through within-sector productivity growth. Taking the growth acceleration episodes
as a dummy, Tab l e 6 reported the correlation with the Gini coefficient and number of
growth accelerations completed. We see that countries that managed to achieve at least
two or three growth accelerations during the period under study did manage to reduce
significantly compared with those with one or no growth accelerations, with the size of the
decline ranging from 12 to 15% points. This indicates the strong link between structural
transformation and inequality in the context of Africa.
5. Conclusions
This paper attempted to examine the association between inequality and structural trans-
formation in Africa. Evidence shows that inequality has been persistently high in Africa
in the last four decades and showed no significant decline, even at the time of relatively
faster and sustained growth. One possibility has been Africa has had very low structural
transformation in its economy—hence, the persistence of inequality. This proposition has
intuitive appeal in the sense that most African economies, particularly those South of the
Sahara exhibit a dual economy where a traditional, low-productivity sector coexisted with
a small, modern but high-productivity sector. Most of the growth in Africa’s economy
came from faster growth in all the sectors with different degrees of contribution to total
13 Growth acceleration was defined as per capita growth of higher than 3.5% achieved consequently in eight
years, which also leads to higher per capita incomes at the end of the growth acceleration than at the beginning.
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Structural Change and Inequality in Africa ii243
GDP growth in different countries and at different times. Examining the link between
structural transformation and inequality is constrained by availability of comparable data
across countries and over time. To overcome this constraint, the paper combined data from
the DHS to obtain several waves of asset or wealth inequality data that are estimated
consistently and comparable across time, along with sectors of employment drawn from
individual histories, which tend to be more accurate than those obtained from national
accounts.
The results indicated that inequality tended to be high in cases where share of employment
or value-added in agriculture increased. No difference in inequality was dictated for changes
in the structure of the economy regarding services or industry. The inequality between
countries tended to be driven by differences in the schooling levels attained by head of
households, which explained close to 50% of the variation in inequality. Higher proportion
of schooling at secondary or tertiary levels were associated with lower inequality, where in
the latter a 1% increase in tertiary education is associated with about 0.1% point decline in
inequality. The role of structural transformation on inequality within countries, however, is
significant. A 1 standard deviation increase in the growth of labour mobility across sectors
would contribute to 0.5% decline in inequality, and the effect on inequality of opportunity
(portion of inequality attributed to circumstances beyond the control of the household)
was almost twice the normal value, estimated at 1.1%. In addition, there is strong tendency
for sustained structural transformation could lead to a decline in inequality in Africa. This
finding is corroborated by a large decline in inequality associated with episodes of growth
accelerations achieved in a country in the growth process, which is driven in many cases
by structural transformation. Consistent with earlier findings of Baymul and Sen (2020),
structural transformation taking place in the manufacturing sector or in the case of this
study industry tend to reduce inequality significantly. The weight of evidence suggests that
tackling inequality within a country is tied closely with the sources of growth in average
labour productivity. The more it is driven by mobility of labour from a low- to high-
productivity sector, the better for a country to reduce fast inequality. Policies designed
to speed up structural transformation, mainly tilted towards movement of labour from
traditional to modern, especially to services, manufacturing or to related sectors could be
beneficial in tackling inequality. More research is needed to establish a robust relationship,
but the argument that expansion of manufacturing or a related sector could reduce poverty
can be understood from two perspectives. Here it is important to emphasise the pattern
of manufacturing expansion in Africa, which may have to be distinct from that observed
in many parts of Asia. The focus on transforming agriculture through agribusiness, agro-
industrialisation, and allied sectors, as well as linking up with global value chains in services
may have better chance of success. As shown in the paper, generally, inequality tends to
be higher in agrarian economies. There is also evidence that suggests inequality within
the modern sector, particularly that of manufacturing or related sectors tend to be lower.
Hence, structural transformation that allows labour to move from subsistence agriculture or
informal services, characterised by relatively high inequality, to modern and formal sectors
tends to bring higher wages but also lower variance in earnings. Combined, the tendency for
inequality to decline may not be surprising, following a positive structural transformation.
Supplementary material
Supplementary material is available at Journal of African Economies online.
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