&
Deciphering the Relationship between
Structural Change and Urbanization in
Africa
Analyse de la Relation entre
Transformation Structurelle et
Urbanisation en Afrique
Louis de Berquin E YIKE M BONGO
University of Dschang
Laboratoire d’Économie et Géographie des Transports
eyike76@gmail.com/louis.eyike@univ-dschang.org
ORCID : 0000-0001-9910-8705
Auteur correspondant
Alexandre Turpin I ROUME A B OUEBE
University of Douala
Laboratoire d’Économie et Géographie des Transports
doniroume@yahoo.fr
ORCID : 0009-0001-2051-1264
Linda T IAGUE Z ANFACK
University of Dschang
zanfacktiaguel@yahoo.com
ORCID : 0000-0002-2950-3336
Mots clés : gouvernance, ressources naturelles, transformation structurelle,
urbanisation.
Keywords : governance, natural resource, structural change, urbanization.
Classification JEL : L16, R12, R23
rticle on line
2025 - N° 1 - pp. 25-51
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Deciphering the Relationship between Structural Change and Urbanization in Africa
Résumé
Le lien entre l’urbanisation et la croissance économique dans les économies en développement reste
difficile à cerner, en raison de facteurs tels que l’abondance des ressources naturelles et les niveaux
élevés de pauvreté qui incitent à l’exode rural en quête de meilleures opportunités. Reconnaissant que
la transformation structurelle est un élément clé de la croissance économique, cette étude examine le
lien entre le changement structurel et l’urbanisation dans 53 pays africains entre 2010 et 2020. En
utilisant la méthode des moments généralisés (GMM), des estimateurs de variables instrumentales
(IV) et des techniques de régression par quantile, notre recherche met en évidence une relation positive
constante entre les changements structurels et l’urbanisation. La démocratie semble avoir un impact
négatif sur les taux d’urbanisation. La présence de ressources naturelles, en particulier de pétrole,
influe sur la nature des changements structurels et de l’urbanisation.
Abstract
The nexus between urbanization and economic growth in developing economies, particularly in Africa,
remains elusive, attributed to factors such as abundant natural resources driving growth dynamics and
high poverty levels prompting rural-urban migration for better opportunities, posing a paradox yet to
be fully grasped. Recognizing that structural transformation is a key component of economic growth,
this study examines the link between structural change and urbanization across 53 African nations
from 2010 to 2020. Using the Generalized Method of Moments (GMM), instrumental variables (IV)
estimators, and quantile regression techniques, our research uncovers a consistently positive relationship
between structural change and urbanization. However, the analysis also indicates a troubling positive
correlation with corruption. In contrast, democracy seems to have a negative impact on urbanization
rates. The presence of natural resources, particularly oil, is found to affect the nature of structural
change and urbanization. The findings of this study have several implications for policymakers in
Africa. Firstly, fostering structural transformation, particularly through industrialization, should be
a key priority for promoting urbanization and economic development. Policies aimed at promoting
economic growth and reducing corruption are crucial for fostering sustainable urbanization. Moreover,
careful management of natural resource wealth is essential to avoid the adverse effects of the "resource
curse" on urban development.
Points clés
– Nous analysons l’impact de la transformation structurelle sur le processus d’urbanisation en Afrique.
– La transformation structurelle est positivement corrélée à l’urbanisation en Afrique.
– Les ressources naturelles modifient le sens de la relation entre transformation
structurelle et urbanisation en Afrique.
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L. de B. E YIKE M BONGO , A. T. I ROUME A B OUEBE , L. T IAGUE Z ANFACK
-1Introduction
Urbanization and economic growth appear to be very closely correlated, so
that urbanization is often used as a proxy for growth. While some studies present
urbanization as dependent on economic progress (Michaels et al., 2012; Gollin et al.,
2016), recent studies highlight the existence of an urban dividend (Brunt and GarcíaPeñalosa, 2021). This dividend involves access to basic infrastructure and education
for urban populations. However, in the context of developing economies in general,
and in the case of Africa in particular, the correlation between urbanization and
economic growth appears weak (Henderson and Kriticos, 2018; Diao et al., 2019).
The reasons for this "paradox" are not yet well understood. Firstly, Africa’s abundance
of natural resources helps to explain the growth dynamic, while the high level of
poverty may explain the migration from rural areas to cities in search of a better
situation.
According to forecasts by the Economic Commission for Africa, more than half
of Africa’s population will live in urban areas by 2035 (ECA, 2022), and more than
90% of the world’s urban population growth will occur in Africa. This sharp rise in
the pace of urbanization is associated with renewed growth, reviving hopes of an
association between urbanization and economic expansion (Rodrik et al., 2014). In
the same vein, structural transformation appears to be a prerequisite for economic
growth. According to the African Development Bank, structural transformation is
consubstantial to Africa’s economic development (AfDB, 2018).
Structural transformation refers to the progressive reallocation of factors of
production from low-productivity sectors to more productive sectors (Klinger and
Lederman, 2004; Avom and Nguekeng, 2020). However, there is no universally
accepted measure of structural change. For some economists, stuctural change can
be measured by industrialization (Nguimkeu and Zeufack, 2019; AfDB, 2018; Van
Neuss, 2019; Lectard, 2017), while others measure it by diversification and/or
economic complexity (Cadot et al., 2011; Hidalgo and Haussmann, 2009). Equating
it with industrialization, the African Development Bank shows that between 1995
and 2018, the share of the primary sector in African GDP fell from almost 40% to
31%, while the shares of the secondary and tertiary sectors rose respectively from
23% to 26% and from 36% to 41%. This evolution is consistent with the structural
transformation process (AfDB, 2018).
There is an abundant literature on the correlations between urbanization and
growth (Glaeser, 2014; Rodrik, 2016; Henderson and Kriticos, 2018; Diao et al., 2019,
2021; McMillan and Zeufack, 2022) on the one hand, and structural transformation
and growth (Nguimkeu and Zeufack, 2019; AfDB, 2018; Van Neuss, 2019; Lectard,
2017) on the other. However, the literature on the role that structural transformation
can play in explaining urbanization dynamics is fairly recent (Gollin et al., 2016;
Metha, 2018; Peng et al., 2023; Brunt and García-Peñalosa, 2022; Abbas et al., 2023;
Cœurdacier et al., 2022). By way of illustration, Michaels et al. (2012) show that the
urbanization of America between 1880 and 2000 is the result of the change in the
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Deciphering the Relationship between Structural Change and Urbanization in Africa
structure of employment between the agricultural and industrial sectors. Moreover,
literature on the specific context of Africa is almost non-existent. In this sense, Gollin
et al. (2016) analyze the effect of natural resource abundance on urbanization and
conclude that the correlation between urbanization and industrialization is not
robust in the specific case of Africa.
The main question addressed in this paper is: by which transmission channels
structural transformation affects urbanization in African context? We postulate that
African urbanization can be explained by structural transformation via two main
channels. Firstly, jobs in the formal sector, excluding the civil service, are virtually all
concentrated in urban areas. This contributes to the migration of people from rural
to urban areas. In addition, as industrialization improves infrastructure and living
conditions in urban areas, it helps to attract rural populations to the cities in search
of higher living standards. This paper makes a twofold contribution to the existing
literature. Firstly, it is one of the few empirical studies on the link between structural
change and urbanization focusing specifically on the African context. Secondly, this
study analyzes the impact of natural resource abundance on the relationship between
urbanization and structural change.
In addition to this introduction, the paper is organized around three sections.
Section 2 presents the data and the methodological approach used. Section 3
discusses the results, and the final section is devoted to the conclusion and policy
implications of the results.
-2Literature review
There is an abundance of theoretical and empirical literature on the causes and
consequences of urbanization, particularly in developing countries. Among the
works devoted to the explanatory factors of urbanization, the literature focuses on
the socio-economic causes, while with regard to its consequences, a significant part
of this literature analyzes the impact of urbanization on growth.
2.1. The urbanization-development nexus
A large body of literature is devoted to the study of the relationship between
urbanization and growth. There is no consensus on this relationship. For some, the
emergence of cities can promote economic growth through the industrialization
channel by increasing the market size, favoring emergence of a specialized workforce
and knowledge spillovers (Quigley, 2009). Moreover, this relationship depends of
productivity growth which is associated with the increasing size of the metropolis
and the emergence of economies of scale due to the presence of large companies
(OECD, 2016).
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L. de B. E YIKE M BONGO , A. T. I ROUME A B OUEBE , L. T IAGUE Z ANFACK
The theory of “districts” asserted that cities are innovative places conducive to
growth (Marshall, 1890). As a result, urbanization favors local scale effects and
facilitates knowledge transfer, increasing industrial specialization, leading to reduced
transaction costs, increased wealth and reduced poverty (Olsson, 2009).
Empirically, pioneer studies carried out on the urbanization-development nexus
support that economic growth and structural transformation (the shift from agriculture to industry) are accompanied not only by an increase in the rate of urbanization,
but also by an increase in metropolitan concentration (Kuznets, 1955; Williamson,
1965). Some studies focus on the impact of development on urban inequalities. For
example, Williamson (1965) argues the existence of an inverted-U curve between
economic growth and urban inequality. In the first stage of development, economic
growth is associated with an increase in urban inequalities. Above a certain threshold,
development reduces urban inequalities. On the same vein, Krugman (1991) shows
that increased urbanization affects economic development and favors inequalities
in their early stages.
Recent empirical studies have been carried out to analyze urbanization-growth
nexus (Ngounou et al., 2023; Pradhan et al., 2021; Leitão, 2013; Nguyen, 2018;
Jiang et al., 2022). For example, Pradhan et al. (2021), using a panel Vector Error
Correction approach on a sample of G-20 countries on the period 1961-2016,
conclude that there is a positive relationship between urbanization and economic
growth. A similar conclusion has been obtained by Leitão (2013) in Europe, the
United States, Japan, New Zealand and Mexico from 1990 to 2008 and Ngounou et
al. (2023) for African countries.
Some studies show mixed results. For example, in the context of ASEAN countries
and using a Granger causality test, Nguyen (2018) shows that urbanization has a
positive impact on economic growth, but after a certain threshold, urbanization can
impede economic growth. Analyzing the direct and indirect impact of urbanization
on sustainable development, Zhang et al. (2022) conclude that these effects depend
on the intensity of urban development, population density and background climate,
with more pronounced positive effects in cities with cold and arid environments.
On the other hand, the argument that urbanization promotes economic growth
has recently been challenged by a report showing that there is no evidence that the
level of urbanization affects the rate of economic growth (Pholo-Bala, 2009; Chen
et al., 2014). For example, Chen et al. (2014) analyzing the correlation between
urbanization and economic outcomes, conclude that there is no correlation between
the speed of urbanization and the rate of economic growth worldwide. Moreover,
Singh et al. (2014) show a negative impact of urbanization on economic growth in
the case of the Solomon Islands. More recently, Hong et al. (2021) empirically analyze
the impact of government-driven urbanization on economic growth with China’s
provincial data. Their results comfort that urbanization has mitigated effects on
economic growth and exhibit an “urbanization without growth” phenomenon. Also,
Castells-Quintana (2017) shows that the impact of urbanization on economic growth
depends of the urban environment in terms of infrastructures. A recent literature
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Deciphering the Relationship between Structural Change and Urbanization in Africa
analyzes the impact of urbanization on inequalities and shows that urbanization
leads to more inequalities in the African context (Ongo Nkoa & Song, 2019) and in
South Asia (Obaco et al., 2022).
2.2. Transmission channels of structural change on urbanization
There is no consensus on the definition and the measure of structural change.
For example, some economists equate it with industrialization (Nguimkeu and
Zeufack, 2019; AfDB, 2018; Neuss, 2019; Lectard, 2017), while others measure it
by diversification and/or economic complexity (Cadot et al., 2011; Hidalgo and
Haussmann, 2009). We adopt the definition of the African Development Bank and
consider industrialization as a proxy for structural change.
Budı-Ors (2023) pointed that industrialization and urbanization are two important transformations that have shaped the past, and these transformations will
continue into the future, creating political challenges as the use of land, materials
and energy increasingly respects natural limits or is constrained by arguments of
intergenerational equity.
For some authors, structural change, captured by industrialization, leads to
urbanization. For example, Chu (2020) indicated that the growth of the service
production sector, high-tech industries and green industries not only promotes
the process of a new type of urbanization but also accelerates the structural
transformation and sustainable development of the economy.
Using generalized moments and least squares methods, Peng et al (2023) find
that industry plays an important direct role in urbanization processes. But in the
context of developing countries, the links between urbanization and structural change
appear to be very weak (Gollin et al., 2016). Urbanization is not due to the growth
of manufacturing production, but to the migration of rural populations to capture
mining rents. Thus, urban growth is accompanied by growth in the service sector
(Gollin et al., 2016). Also focusing on developing countries, Huang et al. (2023)
analyze the determinants of divergences in urbanization and industrialization paths.
Their work also emphasizes the negative role of mining rents on industrialization
in Africa, leading to the emergence of cities without industrialization. But Gollin
et al. (2016) also explore theoretical channels through which structural change can
lead to urbanization. The main channel is the reallocation of labor from agriculture
to industry, which facilitates the emergence of modern cities producing tradable
goods. African countries are not homogeneous regarding urbanization and structural
change. For example, in Cameroon, the main industries are located in two towns,
Douala and Yaoundé. This industrial concentration leads to rural exodus and the
concentration of populations in these towns, which leads, according to Ongo Nkoa
& Song (2019), to deepened inequalities.
Two main gaps emerge from the above-mentioned empirical literature. First,
despite a recent growing literature on the structural change-urbanization nexus, no
study has focused on the African context. To our knowledge, only Gollin et al. (2016)
30
L. de B. E YIKE M BONGO , A. T. I ROUME A B OUEBE , L. T IAGUE Z ANFACK
analyzed the effects of industrialization on urban expansion in some developing
countries. Secondly, in this context, no study focuses on the impact of oil abundance
in the relation between structural change and urbanization. This is a major gap given
that it is well documented that oil rent has a negative impact on structural change in
developing countries.
-3Methods and Data
To test the hypotheses formulated above, we adopt an empirical strategy based
on the estimation of various econometric models using African data. In particular,
we’ll be using the system Generalised Method of Moments (GMM) and instrumental
variable estimators. These estimators have the advantage of solving the endogeneity
problem inherent in economic studies. We then use quantile regression estimation.
This section describes these empirical methods and the data used.
3.1. Data and sources
The analysis of the effect of structural change on urbanization in our context is
carried out for 53 African countries between 2010 and 2020. This period is justified
by the availability of data on the variables in our study. The variables in this study
are taken from World Bank data, in particular the World Bank’s World Development
Indicator (WDI) database for 2023. The variables used in this study are grouped into
two categories: the dependent variable and the independent variables.
3.1.1. The dependent variable: Urbanization
To capture the urbanization rate in Africa, we use the share of urban population
(% of total population). This indicator is calculated from World Bank population
estimates and urban ratios from the United Nations World Urbanization Prospects.
Urban percentages are the number of people living in an area defined as ’urban’ per
100 inhabitants.
3.1.2. The variable of interest: structural change
Following the African Development Bank (2018) and Nguimkeu and Zeufack
(2019), we capture structural change in Africa by the industrialization rate. Two
main proxies are used: manufacturing value-added (% of GDP) and the share of
industrial employment (% of total employment) defined as persons of working age
who were engaged in the production of goods or the rendering of services for pay or
profit, whether they worked during the reference period or did not work because of
temporary absence from a job or working time arrangements. The industrial sector
includes mining and quarrying, manufacturing, construction and utilities (electricity,
gas and water), in accordance with divisions 2 to 5 (ISIC 2) or categories C to F (ISIC
3) or categories B to F (ISIC 4).
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Deciphering the Relationship between Structural Change and Urbanization in Africa
3.1.3. Others control variables
GDP per capita growth (GDPPC, annual %) is the annual percentage growth rate
of GDP per capita in constant local currency. GDP per capita is gross domestic
product divided by the mid-year population. GDP at purchasers’ prices is the sum of
gross value added by all resident producers in the economy, plus taxes on products
and minus subsidies not included in the value of products. It is calculated without
deducting the depreciation of manufactured assets or the depletion and degradation
of natural resources. This variable is used to capture the impact of economic growth
on urbanization.
Mineral rents (% of GDP): both theoretical and empirical literature suggest
that abundance in mineral resources tends to promote urbanization and reduce
industrialization in Africa (Gollin et al., 2016). We test this idea in this paper. The
mineral rent is calculated as the difference between the production value of a stock
of minerals at world prices and their total production costs. The minerals considered
in the calculation are tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite and
phosphate.
Incidence of malaria (Malaria per 1,000 population at risk): this is the number
of new cases of malaria per 1,000 people at risk per year. The population at risk is
defined as the population living in areas where malaria is transmitted. This variable
is used as a proxy of public health infrastructures.
Control of Corruption (Control_corr): Control of Corruption reflects perceptions
of the extent to which public power is exercised for private gain, including both
petty and grand forms of corruption, as well as the ’capture’ of the state by elites and
private interests. The estimate gives the country’s score on the overall indicator, in
units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5
(Kaufmann et al., 2010).
Voice and Accountability: (VoiceandAcc), captures perceptions of the extent to
which a country’s citizens can participate in the selection of their government, as
well as freedom of expression, freedom of association and freedom of the media.
Table one shows the main descriptive statistics of all variables used in econometric
analysis.
3.2. Empirical strategy
To analyze the effects of structural changes on urbanization in Africa, we adopt
a panel data framework and formulate the following model, consistent with the
empirical framework of Brunt and García-Peñalosa (2022), Gollin et al. (2016) and
Michaels et al. (2012):
Urbanizationit = f (Structural changeit , Xit )
32
(1)
2025 - N° 1
594
514
511
583
572
569
539
594
542
Urbanpop
Manufacture_va
Manufacture_vag
Employment_I
GDPPC
Min_rent
Malaria
Control_corr
VoiceandAcc
Source: authors calculations.
Obs
Variable
Table 1 – Descriptive statistics
1,025581
-0,6520288
182,3177
1,270645
0,974308
15,56865
3,289074
10,86117
44,50251
Mean
5,377856
0,6461065
150,2288
2,856333
7,115789
12,74522
10,26867
6,279062
18,29822
Std, dev,
0,1037159
-1,916457
0
0
-48,39245
2,98831
-52,80265
0,972748
10,642
Min
42,99517
1,420243
709,7933
24,82312
96,95642
31,19
84,13816
39,5935
90,092
Max
WDI
WDI
WDI
WDI
WDI
WDI
WDI
WDI
WDI
Sources
L. de B. E YIKE M BONGO , A. T. I ROUME A B OUEBE , L. T IAGUE Z ANFACK
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Deciphering the Relationship between Structural Change and Urbanization in Africa
Where X is the matrix of control variables. The empirical strategy relies on the
estimation of the following dynamic model using the GMM in which we investigate
the correlation between urbanization and structural change, while controlling for
mineral rent as a proxy of natural resources abundance.
Urbanit = β0 + γUrbanit−1 + β1 Struc_changeit + β2 Min_rentit + γj Xjit + εit
(2)
Traditional fixed effects and random effects estimators have been shown to be
inappropriate in dynamic models (Eyike-Mbongo and Djoumessi, 2024). In the case
of this study, this bias is pervasive. First, there is a simultaneity bias between certain
explanatory variables and the dependent variable. There is also a reverse causality
between urbanization and structural change. Thus, to tackle these problems, the
GMM is more appropriate (Roodman, 2009). Overall, there are two main advantages
to using the GMM method. In the case of a dynamic model, this method allows us to
consider temporal dynamics. The second advantage is that this estimation technique
allows us to treat all exogenous variables as potentially endogenous (Roodman,
2009). Also, this method is suitable because the ratio N/T between the number
of countries (N) and the length of the time span (T) is very high. But the major
problem of using GMM in this study is the very short time span, that does not allow
to consider many lags as instrumental variables.
There are two dynamic GMM estimators: first difference GMM developed by
Arellano and Bond (1991) and system GMM (Arellano and Bover, 1995; Blundell
and Bond, 1998). However, the literature has identified a problem with the use of
difference GMM: in some cases, series lags are not reliable instruments (Bond et al.,
2003). As a result, the GMM estimator in system seems better than the one in first
difference. We will therefore use this estimator in our empirical analyses. However,
there is always the question of validity and multiplication of instruments. We have
used the “collapse” command as recommended by Roodman (2009) in order to
ensure the validity of the instruments used. We perform two complementary tests:
the restriction overidentification and series correlation tests. Given the relative short
time span, we just consider two lags as instruments for the dependent variable.
We also use the instrumental variables estimator (IV) on panel data to estimate
the equation (3) below. This estimator has a number of advantages over the GMM
estimator: it is less sensitive to the problem of instrument multiplication (Baum et
al., 2003). However, the question of instrument quality is omnipresent when using
this estimator. We use lagged variables as instruments. To ensure instrument quality,
we perform DWH and Sargan tests (Baum et al., 2003).
Urbanit = β0 + β1 Struc_changeit + β2 Min_rentit + γj Xjit + εit
(3)
Finally, equation (3) is also estimated using quantile regression. This method
provides a richer and more detailed description of the dependent variable than
conventional regressions (Givard et d’Haultfoeuille, 2013). In fact, quantile regression models the relationship between a set of independent variables and specific
quantiles of the dependent variable. This method has two main advantages. First,
34
L. de B. E YIKE M BONGO , A. T. I ROUME A B OUEBE , L. T IAGUE Z ANFACK
it tends to resist the influence of outlying data. Secondly, it makes no assumptions
about the distribution of the dependent variable (Givard et d’Haultfoeuille, 2013).
There is also a reverse causality between urbanization and structural change.
In fact, structural change implies industrialization and urbanization. So, a strong
positive correlation between these two variables could not be considered as causality.
This paper does not test the causality link between industrialization and structural
change.
-4Results and discussions
First, we discuss the baseline results, followed by sensitivity and robustness tests.
Then, in addition to the regional analysis, we analyze the impact of natural resource
exploitation on the relationship between structural change and urbanization in
Africa. The question of the validity of these estimates is an important one. For this
reason, various instrument validation tests are proposed, in particular the AR1 and
AR2 tests for system GMM estimator.
4.1. Baseline results
Table 2 presents the baseline results for the effects of structural change on
urbanization using a GMM approach. Column 1 presents the results of a bivariate
model. The results of this estimation show that the coefficient associated with
structural change is positive and statistically significant at the 1% level. This result
suggests that structural change is associated with urbanization in Africa. In Columns
2 and 3, we present the results of estimations of equation (2) while introducing
GDP per capita growth rate, mineral rents, control of corruption, democracy and the
incidence of malaria as a proxy of public health infrastructure. Also, we find that
the coefficients associated with structural transformation is positive and significant
at the 1% level. This result is in line with studies showing the positive impact of
industrialization on urbanization (Brunt and García-Peñalosa, 2022; Abbas et al.,
2023; Cœurdacier et al., 2022).
As regards the control variables, we find that all coefficients associated with GDP
per capita growth rate are positive and significant, showing that economic growth is
positively linked with urbanization in the African context. This result is consistent
with the literature documenting a positive link between growth and urbanization
(Michaels et al., 2012; Gollin et al., 2016). In addition, the results suggest that
abundance of mineral resources is positively and statistically significant associated
with urbanization in Africa. In fact, all the coefficients associated with mineral rent
are positive and significant. This major result shows that abundance of mineral
resources explains the dynamics of urbanization in Africa. The main channel that can
be raised is the following: abundance in mineral resources tends to lead to corrupt
political regimes. This could lead to a neglection of rural areas in terms of furniture
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Deciphering the Relationship between Structural Change and Urbanization in Africa
of primary infrastructures. This result, consistent with Gollin et al. (2016), is also
consistent with the signs associated with corruption and democracy. Corruption
is positively linked with urbanization while democracy is negatively linked with
urbanization in the African context.
4.2. IV-Panel and quantile estimates
Table 3 presents the results of the estimation of equation 3 using the panel data
instrumental variable estimator. We perform the Durbin-Wu-Hausman test to check
the validity of the instruments used. The results of this test show that the instruments
used are not weak. The results show that structural change is also positively linked
with urbanization in the African context. Also, GDP per capita growth and corruption
are positively associated with urbanization. But the results suggest that the correlation
between mineral rent and urbanization is not robust.
Table 4 presents the results of the estimates of equation 3 using quantile
regressions. We perform regressions for the quartiles Q1 corresponding to α=25%,
Q2 corresponding to α=50% and Q3 corresponding to α=75%. Three main results
emerge from these estimates. First, the correlation between structural change and
urbanization is robust whatever the quartile level. Secondly, the relation between
economic growth and urbanization depends on the level of development. The more
urbanized a country is, the more growth leads to urbanization. Another important
result is that mineral rents do not affect urbanization in countries with a level of
urbanization close to the continental median.
4.3. Robustness checks
In this section, we test the robustness of the baseline results by using an alternative
proxy for structural change. We use industrial employment as a percentage of total
employment. Table 5 presents the results of the estimates using the GMM estimator
while table 6 shows the results of the estimates using panel instrumental variables.
Consistent with the main findings from Tables 2 and 3, the analysis reveals a
positive correlation between structural change, as measured by industrial employment, and urbanization in Africa. This reaffirms the initial conclusion that structural
transformation, particularly through industrialization, is indeed associated with
urbanization in the region. The consistency of this result across different proxies
for structural change adds robustness to the findings, suggesting that the relationship between industrialization and urbanization is robust to different measurement
approaches.
Furthermore, the results also highlight the strong correlation between economic
growth and urbanization. This relationship underscores the role of economic
development as a driver of urbanization in Africa. The positive coefficients associated
with economic growth indicate that regions experiencing higher rates of economic
growth tend to undergo more significant urbanization processes. This finding aligns
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L. de B. E YIKE M BONGO , A. T. I ROUME A B OUEBE , L. T IAGUE Z ANFACK
Table 2 – Baseline GMM results
GMMSYS
GMMSYS
GMMSYS
VARIABLES
Urbanpop
Urbanpop
Urbanpop
Urbanpop (-1)
0.985***
0.984***
0.977***
(0.000639)
(0.000594)
(0.000794)
0.00176***
0.000863***
0.00286***
(2.32e-05)
(3.24e-05)
(0.000164)
3.24e-05**
0.000748***
(1.55e-05)
(5.85e-05)
0.00137***
0.00220***
(0.000523)
(0.000778)
0.000101***
0.000129***
(2.63e-05)
(4.05e-05)
Structural change
GDPPCG
Min_rent
Incidence_malaria
Control_Corruption
0.101***
(0.00792)
Voice_Accountability
-0.00443**
(0.00182)
Constant
1.192***
1.299***
1.707***
(0.0464)
(0.0363)
(0.0802)
AR1
AR2
Hansen
Sargan
0.0000
0.1279
0.4321
0.000
0.0010
0.2541
0.5960
0.0001
0.000
0.1873
0.2749
0.000
Observations
417
372
312
Number of code
48
43
43
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Source: authors.
with the broader literature that emphasizes the positive link between economic
growth and urbanization.
On the other hand, the results demonstrate a noteworthy association between
corruption and urbanization. The positive correlation suggests that regions with
higher levels of corruption tend to experience more rapid urbanization. This finding
is consistent with the notion that corruption may influence urbanization dynamics
by skewing resource allocation and development priorities, potentially favoring
urban areas over rural ones. However, it’s essential to interpret this result cautiously
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Deciphering the Relationship between Structural Change and Urbanization in Africa
Table 3 – Results using IV-panel
IV-PANEL
IV-PANEL
IV-PANEL
VARIABLES
Urbanpop
Urbanpop
Urbanpop
Structural change
0.514**
0.987***
0.559***
(0.207)
(0.217)
(0.223)
1.311***
1.386***
(0.317)
(0.355)
0.298
0.496
(0.460)
(0.485)
0.0527***
0.0501***
(0.00758)
(0.00824)
GDPPCG
Min_rent
Incidence_malaria
Control_Corr
3.324***
(0.224)
Voice_Accountability
0.205
(0.161)
Constant
4.86***
5.15***
5.29***
(1.655)
(1.469)
(1.528)
Sargan
DWH
0.0056
0.001
0.001
0.000
0.000
0.000
Observations
511
503
467
R-squared
0.036
0.300
0.321
Robust Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Source: authors
and consider the broader socio-political context in which corruption operates within
African countries.
4.4. Heterogeneity analysis
4.4.1. Results by sub regions
In this section, we check for the robustness of the results considering African
subregional areas. Following the African Union, we divide the sample in five
subregions: Northern, Southern, Central, Western and Eastern. Tables 7 and 8
provide the results of the estimates using GMM and panel-IV respectively. Also, we
use the industrial employment share as a proxy of structural change.
38
L. de B. E YIKE M BONGO , A. T. I ROUME A B OUEBE , L. T IAGUE Z ANFACK
Table 4 – Results using Quantile regression
Q25
Q50
Q75
VARIABLES
Urbanpop
Urbanpop
Urbanpop
Structural change
0.453***
0.250***
0.121***
(0.060)
(0.0341)
(0.0922)
0.335
0.592*
0.556***
(0.297)
(0.344)
(0.197)
1.328***
0.146
1.085***
(0.326)
(0.257)
(0.132)
0.0303***
0.0302***
0.0574***
(0.00855)
(0.00938)
(0.00548)
4.311
6.768**
1.909
(3.414)
(2.952)
(1.384)
0.491***
0.137**
0.00144
(0.0874)
(0.0592)
(0.0622)
3.63***
5.57***
6.80***
(0.893)
(0.726)
(1.707)
Pseudo R-squared
0.316
0.337
0.2912
Observations
420
420
420
GDPPCG
Min_rent
Incidence_malaria
Control_Corr
Voice_Accountability
Constant
Robust Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
The findings, as depicted in Tables 7 and 8, reveal that the relationship between
structural change and urbanization remains robust across all considered subregions,
except Western Africa where there is no association between these two variables.
This consistency suggests that the positive association between industrialization and
urbanization holds globally true within Africa. Such robustness underscores the
significance of structural transformation, particularly through industrialization, as a
driver of urbanization across diverse subregional contexts.
Moreover, the results also indicate that the impact of economic growth and
corruption on urbanization remains robust across the various subregions. The
positive correlation between economic growth and urbanization suggests that regions
experiencing higher rates of economic growth tend to undergo more significant
urbanization processes, irrespective of their subregional categorization. Similarly,
the positive association between corruption and urbanization persists across different
subregions, highlighting the potentially influential role of corruption in shaping
urbanization dynamics on a regional scale.
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Deciphering the Relationship between Structural Change and Urbanization in Africa
Table 5 – Robustness tests for change in measure of structural change
using GMM
GMMSYS
GMMSYS
GMMSYS
VARIABLES
Urbanpop
Urbanpop
Urbanpop
Urbanpop (-1)
0.987***
0.982***
0.978***
(0.000440)
(0.000746)
(0.000682)
0.00231***
0.0191***
0.0125***
(0.000247)
(0.000792)
(0.000673)
0.000225***
0.000605***
(2.98e-05)
(6.14e-05)
0.00112
0.00138***
(0.000705)
(0.000423)
6.10e-05***
-4.80e-05
(1.42e-05)
(3.08e-05)
Employment_industry
GDPPCG
Min_rent
Incidence_malaria
Control_Corr
0.0695***
(0.00542)
Voice_Accountability
-0.00388**
(0.00152)
Constant
1.084***
0.998***
1.308***
(0.0337)
(0.0624)
(0.0721)
AR1
AR2
Hansen
Sargan
0.000
0.0128
0.000
0.000
0.000
0.1712
0.001
0.025
0.000
0.2119
0.000
0.0182
Observations
477
413
345
Number of code
53
48
48
Robust Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Source: authors
Interestingly, the analysis reveals that democracy tends to reduce urbanization
in the considered countries. This finding suggests that the relationship between
democracy and urbanization may vary across different subregional contexts within
Africa. While further exploration is needed to understand the underlying mechanisms
driving this relationship, it underscores the importance of considering political factors
alongside economic and social factors when examining urbanization dynamics.
40
L. de B. E YIKE M BONGO , A. T. I ROUME A B OUEBE , L. T IAGUE Z ANFACK
Table 6 – Robustness tests for change in measure of structural change
using Panel-IV
(1)
(2)
(3)
VARIABLES
Urbanpop
Urbanpop
Urbanpop
Employment_industry
0.208***
0.519***
0.491***
(0.0643)
(0.114)
(0.116)
GDPPCG
0.328**
0.364**
(0.157)
(0.163)
Min_rent
0.0924
0.0991
(0.286)
(0.290)
-0.0206***
-0.0193***
(0.00611)
(0.00651)
Incidence_malaria
Control_Corr
3.172**
(1.460)
Voice_Accountability
0.237*
(0.141)
Constant
4.28***
5.01***
4.14***
(1.277)
(1.462)
(1.674)
DWH
Sargan
0.000
0.000
0.000
0.002
0.000
0.000
Observations
530
463
419
R-squared
0.192
0.226
0.248
Robust Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Source: authors
4.4.2. Results following the oil wealth status
In this section, we analyze the incidence of oil wealth on the relation between
structural change and urbanization. To do so, we divide our sample according to
the criterion of oil wealth. Following the World Bank, we have 16 oil-producing
countries and 32 non-oil producing countries in our sample. The oil-producing
countries are those countries which benefit from an oil rent.
Table 9 provides the results of estimating Equation (2) using the GMM technique
for each category of countries, while Table 10 presents the results using panel
IV estimation to ensure the robustness of our findings. The results obtained are
consistent across different estimation techniques, indicating robustness. However, a
significant finding emerges from these analyses: the relationship between structural
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42
Voice_Accountability
Control_Corr
Incidence_malaria
Min_rent
GDPPCG
0.796
(0.0928)
(0.0262)
0.00654***
0.131
(0.000467)
(0.000168)
0.0593**
-0.000712
(0.00824)
(0.00104)
-8.84e-05
-0.00809
(0.00241)
(0.000591)
0.00347***
0.00745***
(0.00960)
(0.00464)
0.00552***
0.0159*
0.0168***
(0.00585)
(0.00181)
Structural change
0.975***
0.978***
Urbanpop(-1)
Urbanpop
Urbanpop
(South)
VARIABLES
(North)
Table 7 – Testing for regional factor using GMM
0.00119
(0.00768)
-1.19e-05
(3.16e-05)
9.37e-05***
(0.000443)
0.00314***
(0.000165)
0.00141***
(0.00120)
0.000626
(0.000909)
1.002***
Urbanpop
(West)
-1.701
(0.137)
-0.0241
(0.000686)
-0.00107
(0.0215)
0.0355*
(0.00218)
0.00365*
(0.0185)
0.0596***
(0.00964)
0.928***
Urbanpop
(Central)
-0.0793
(0.0114)
-0.0276**
(2.21e-05)
9.19e-05***
(0.00541)
-0.00137
(0.000174)
0.00792***
(0.00119)
0.00206*
(0.00139)
1.013***
Urbanpop
(East)
Deciphering the Relationship between Structural Change and Urbanization in Africa
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Robust Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Source: authors
Number of code
31
Observations
9
64
0.021
0.419
0.000
0.000
(0.247)
(0.178)
0.000
0.1732
0.000
0.000
1.460***
(1.268)
1.352***
(0.00205)
AR1
AR2
Sargan
Hansen
Constant
Table 7 – (suite)
15
9
68
0.000
0.3611
0.000
0.000
0.0015
0.0919
0.009
0.000
117
(1.420)
4.771***
(2.094)
(0.0433)
0.408***
(0.000778)
11
65
0.000
0.1082
0.000
0.000
(0.0458)
-0.0369
(0.0996)
L. de B. E YIKE M BONGO , A. T. I ROUME A B OUEBE , L. T IAGUE Z ANFACK
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44
Constant
VoiceandAccountability
ControlofCorruption
Incidenceofmalariaper
Min_rent
9.95***
(2.32)
(0.267)
4.40***
-11.0**
(3.007)
(6.616)
-1.948***
6.269**
(0.0209)
(0.0662)
12.21*
0.0921***
(0.797)
(0.254)
0.0289
0.433
(0.436)
(0.246)
-0.181
1.716***
0.410*
(0.469)
(0.321)
GDPPCG
1.434***
1.075***
Structural change
Urbanpop
Urbanpop
Southern
VARIABLES
Northern
Table 8 – Testing for regional factor using Panel-IV
5.39***
(0.0825)
0.171**
(1.605)
2.298
(0.00628)
0.0375***
(0.279)
-0.000930
(0.173)
0.448***
(0.163)
0.461***
Urbanpop
Western
1.23***
(0.301)
0.0211
(4.557)
29.74***
(0.0222)
0.138***
(0.720)
-0.461
(0.246)
0.916***
(0.250)
2.839***
Urbanpop
Central
5.28***
(0.871)
3.231***
(2.485)
-4.868*
(0.0109)
0.0512***
(0.489)
-0.108
(0.211)
0.561***
(0.121)
0.917***
Urbanpop
Eastern
Deciphering the Relationship between Structural Change and Urbanization in Africa
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0.774
Observations
R-squared
Robust Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Source: authors
0.000
0.002
(0.153)
DWH
Sargan
Table 8 – (suite)
0.389
77
0.000
0.0218
(1.384)
0.454
138
0.021
0.000
(0.253)
0.736
83
0.000
0.000
(0.454)
0.376
84
0.000
0.000
(1.475)
L. de B. E YIKE M BONGO , A. T. I ROUME A B OUEBE , L. T IAGUE Z ANFACK
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Deciphering the Relationship between Structural Change and Urbanization in Africa
Table 9 – Testing for the impact of oil using GMM
Non-petroleum
Petroleum
VARIABLES
Urbanpop
Urbanpop
Urbanpop(-1)
0.951***
0.985***
(0.00638)
(0.00215)
Employment_industry
0.00538
0.0185***
(0.00971)
(0.00367)
GDPPCG
0.00147
0.000105
(0.00138)
(0.000488)
-0.0156
-0.00185
(0.0159)
(0.00155)
-0.00106***
-4.31e-05
(0.000404)
(7.02e-05)
0.225***
0.00882
(0.0821)
(0.0242)
-0.0141**
0.0295
(0.00691)
(0.277)
3.428***
0.862***
Min_rent
Incidence_malaria
Control_Corr
Voice_Accountability
Constant
(0.414)
(0.0835)
AR1
AR2
Sargan
Hansen
0.000
0.6511
0.000
0.000
0.000
0.4871
0.001
0.000
Observations
109
236
Number of code
16
32
Robust Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Source: authors
change and urbanization appears to be more pronounced and stable in oil-producing
countries. This observation suggests a potentially adverse effect of oil wealth,
particularly on the quality of structural change processes in Africa.
The stability of the relationship between structural change and urbanization in
oil-producing countries implies that despite the economic benefits associated with
oil revenues, such as increased government revenue and investment opportunities,
there may be underlying challenges hindering structural transformation and urban
development.
46
L. de B. E YIKE M BONGO , A. T. I ROUME A B OUEBE , L. T IAGUE Z ANFACK
Table 10 – Testing for the impact of oil using panel-IV
Non petroleum
Petroleum
VARIABLES
Urbanpop
Urbanpo
Employment_industry
0.0399
0.466***
(0.199)
(0.169)
0.500***
0.277***
(0.0290)
(0.0183)
0.570
0.509**
(0.978)
(0.258)
-0.0395**
-0.0224***
(0.0183)
(0.00608)
3.860
6.316***
(4.005)
(1.483)
-0.0717
2.939***
(0.166)
(1.007)
5.52***
4.07***
(1.801)
(1.131)
DWH
Sargan
0.000
0.000
0.0001
0.0007
Observations
135
284
R-squared
0.390
0.460
GDPPCG
Min_rent
Incidence_malaria
Control_Corr
Voice_Accountability
Constant
Robust Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Source: authors.
One plausible explanation for this phenomenon is the phenomenon known as
the "resource curse," where reliance on natural resource extraction, such as oil, can
lead to a variety of negative outcomes including economic distortions, governance
challenges, and limited diversification of the economy away from the resource sector.
In the context of structural change and urbanization, the presence of oil wealth may
divert attention and resources away from investing in sectors that drive structural
transformation and urban development, leading to a less diversified and less resilient
economy.
This finding underscores the importance of understanding the nuanced impacts
of natural resource wealth on economic development and urbanization processes.
It also highlights the need for policymakers to carefully manage and diversify their
economies to mitigate the potential adverse effects of reliance on natural resources.
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Deciphering the Relationship between Structural Change and Urbanization in Africa
-5Conclusion
The objective of this study was to examine the effects of structural change on
urbanization in Africa using panel data from 53 African countries spanning the
years 2010 to 2020, sourced from the World Bank’s World Development Indicator
(WDI) database. To address endogeneity and other potential biases, we employed
a dynamic panel data framework. The Generalized Method of Moments (GMM)
and instrumental variables (IV) estimators were utilized to account for temporal
dynamics and address endogeneity concerns. Additionally, quantile regression was
employed to provide a nuanced understanding of the relationship between variables
across different quantiles of urbanization.
Our results revealed a robust positive association between structural change
and urbanization in Africa, supporting the notion that industrialization drives
urbanization. Furthermore, economic growth was found to positively influence
urbanization, while corruption exhibited a positive correlation with urbanization.
Interestingly, democracy was negatively associated with urbanization, suggesting a
nuanced relationship between political factors and urban development. The analysis
also highlighted the impact of natural resource wealth, particularly oil, on the dynamics of structural change and urbanization, with oil-producing countries exhibiting
a more pronounced relationship between structural change and urbanization.
The findings of this study have several implications for policymakers in Africa.
Firstly, fostering structural transformation, particularly through industrialization,
should be a key priority for promoting urbanization and economic development.
Policies aimed at promoting economic growth and reducing corruption are crucial
for fostering sustainable urbanization. Moreover, careful management of natural
resource wealth is essential to avoid the adverse effects of the "resource curse" on
urban development. Also, it is crucial for government leaders to facilitate the entry of
FDI into their countries, and to ensure there are no impediments to their operations.
While this study provides valuable insights into the relationship between structural change and urbanization in Africa, several avenues for future research exist.
Future studies could explore the mechanisms through which corruption influences
urbanization dynamics and investigate the role of governance reforms in fostering
sustainable urban development.
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