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Advances in Politics and Economic 
ISSN 2576-1382 (Print) ISSN 2576-1390 (Online) 

Vol. 3, No. 4, 2020 
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47 
 

Original Paper 

Urbanization, Gender and Economic Growth in the Waemu 

Zone: Evidence from Pooled Mean Group Estimation 

Prao Yao Seraphin1* 
1 Department of Economic Sciences and Development, Alassane Ouattara University, Bouaké, Ivory 

Coast  
* Prao Yao Séraphin, Department of Economic Sciences and Development, Alassane Ouattara 

University, Bouaké, Ivory Coast  

 

Received: November 5, 2020   Accepted: November 27, 2020  Online Published: December 1, 2020 

doi:10.22158/ape.v3n4p47        URL: http://dx.doi.org/10.22158/ape.v3n4p47 

 

Abstract 

This study empirically analyses the influence of urbanization and the participation of men and women 

in the labour force on economic growth in the countries of the West African Economic and Monetary 

Union (WAEMU). Using data from the World Bank (2017) on the member States between 1990 and 

2016, we show from Pesaran’s PMG estimator, Shin and Smith (1999) that in the short term, youth and 

women are very useful for economic growth. In the long term, urbanization, industrial added value and 

the elderly make a positive contribution to economic growth. The study urges governments to create 

better living conditions by ensuring adequate income levels and care, i.e., public policies should aim to 

increase employment, establish or improve social protection, social integration, health and the fight 

against discrimination. 

Keywords 

urbanization, economic growth, gender, WAEMU, PMG estimator 

 

1. Introduction 

In Africa, demography seems to be a source of concern. But the demographic problem that annoys 

African governments is rapid urbanization. According to Collier (2017), sub-Saharan Africa will 

continue to urbanize rapidly regardless of the policies implemented. According to the United Nations 

Population Office (2010), Africa’s population reached more than 1 billion in 2009, of which about 40 

per cent lived in urban areas. The share of urban dwellers increased from 14% in 1950, to 27% in 1980 

and 40% in 2015. By the mid-2030s, 50% of Africans are projected to live in cities. Urbanization is 

expected to continue and stabilize at around 56% by 2050. This considerable increase will also apply to 



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needs for urban services, infrastructure and jobs, whose availability is already severely limited. 

According to several studies and research reports, urbanization in Africa, unlike most other regions of 

the world, has not been associated with economic growth in recent decades. For example, Ravallion, 

Chen and Sangraula (2007) find that urbanization helps to reduce poverty in other regions, but not in 

Africa. Indeed, cities provide a large and diversified labour pool, a dynamic local market, more 

cost-effective access to suppliers and specialized services, lower transaction costs, more diversified 

networks of contacts and more opportunities for knowledge sharing, and an environment conducive to 

innovation (Krugman, 1991; Spence, 2012; World Bank, 2009; AfDB, 2010). Agglomeration 

economies can be beneficial to cities, as they allow fewer resources to be used to meet the needs of a 

larger population. The increasing yields of the agglomeration reinforce the attractiveness of cities that 

offer a cultural life and a diversified choice of services. This attractiveness also attracts talent and 

investment, creating a virtuous circle of urbanization and development. Post-industrial cities are the 

product of the rise of services, especially since the development of new communication technologies. 

The economic potential of post-industrial cities is based on new activities in the tertiary sector, such as 

financial services, Research and Development (R&D) and business services. In addition, the city itself 

is its main outlet: a large proportion of the goods and services produced in it are consumed by its own 

inhabitants. It is therefore an essential component of “its market”. This observation is all the more 

relevant since most services are by nature untransportable and must therefore be produced where they 

are consumed. As the export base often represents only a minority share of a city’s activities, local 

services are therefore a crucial factor in urban growth. In theory, the causal link between urbanization 

and growth is not sufficiently established, except for the early stages of development, to allow 

urbanization to become a general development domain (Henderson, 2003). Urbanization can occur 

without development, as has been observed in sub-Saharan Africa in particular (Ploeg & Poelhekke, 

2008). A United Nations study (UN, 2017) for Africa indicates that the least urbanized countries are 

those with the fastest growth. The annual growth rate for countries with more than 60% of the 

population urbanized is 2.23%, less than half the growth rate for countries with less than 30% of the 

population urbanized. However, urbanization could influence economic growth through physical 

capital, human capital, knowledge capital and industrial structure (Shen Kun-rong & Jiang Rui, 2007). 

In developing countries, at the same time as the low level of development, African countries have high 

gender inequalities. In the Comoros, for example, 72% of men hold managerial and executive positions 

compared to 28% of women. In Ghana, 91.7% of men sit in parliament compared to 8.3% for women. 

At the educational level, according to some authors, gender inequalities lead to an underutilization of 

human capital, which could also affect economic growth through channels whose effects are widely 

discussed in the literature (Hill & King, 1993; Klasen, 1999). From the above, it follows some 

questions. Do gender inequalities have an impact on the economic growth of WAEMU countries? Is the 

urbanization observed in the countries of the Union a source of economic growth in the WAEMU zone? 

A central question then emerges: to what extent has urbanization and the employment of men and 



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women affected economic growth in the WAEMU region? Thus, the objective of this study is to 

analyze the effect of urbanization and the share of men and women in the labour force on economic 

growth in WAEMU countries. Our general objective can be achieved through specific objectives. 

Specific objective 1: Analyze the effect of urbanization on economic growth in the WAEMU region.  

Specific objective 2: Assess the relative contribution of women and men to the economic growth of 

WAEMU countries.  

In relation to our objectives, we make the following two assumptions. 

Hypothesis 1: The rise of urbanisation is beneficial to the long-term economic growth of WAEMU 

countries.  

Hypothesis 2: The proportion of women in the labour force is favorable to economic growth in the 

WAEMU zone. 

Methodologically, the study uses the Pool Mean Group (PMG) estimator proposed by Pesaran, Shin 

and Smith (1999). Unlike conventional methods (fixed effects or generalized moments), the PMG 

method introduces heterogeneity into certain coefficients to be estimated. Indeed, the PMG method 

reconciles in the same specification, the usual approach imposing fixed coefficients and the one 

assuming country-specific coefficients. Thus, it is possible to specify that the long-term relationship 

between the variables is the same for all countries but that each country follows its own dynamic to 

converge towards this common relationship. This assumption seems reasonable for countries in a 

monetary union that aspire to strong long-term integration. This article contributes to the empirical 

literature on the link between urbanization and economic growth in the WAEMU region. The results 

obtained from this study are as follows. In the short term, only women’s contributions to the labour 

force and the proportion of young people (0-14 years old) appear to be statistically significant. In the 

long term, urbanization, industrial value added, the proportion of young people (0-14 years old) and the 

elderly (over 65 years old) have a positive influence on economic growth. 

This article is organized as follows: Section 2 is devoted to the literature review on the relationship 

between urbanization, gender and economic growth. Section 3 presents the data source and its 

descriptive and statistical analysis. In Section 4, we present the methodology of the study. Section 5 

will discuss the empirical results and their econometric and economic interpretations. Section 6 is 

reserved for the conclusion of the study. 

 

2. Literature Review of the Link between Urbanization, Gender and Economic Growth  

This section revisits the theoretical and empirical literature on the relationship between urbanization 

and economic growth. In a first subsection, we examine the link between urbanization, gender and 

economic growth. We will see that this relationship is still ambiguous. In a second subsection, we 

discuss empirical studies on this same relationship. 

 

 



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2.1 Urbanization, Gender and Economic Growth: Theoretical Contributions 

Urbanization can influence economic growth in a variety of ways, and the majority of studies suggest 

that urbanization should have a positive impact on economic growth. First, cities play a vital role in the 

economic and social fabric of developed and developing countries by providing educational, 

employment and health services opportunities. Educational capital determines a country’s ability to 

develop new technologies and adopt existing ones. The expansion of education systems in urban areas 

is easier and cheaper than in rural areas. The return on education is therefore generally higher in urban 

areas than in rural areas. In terms of public health, urban populations are more likely to reach hospitals, 

health centres and sanitation facilities. Health care systems are also more developed, which can lead to 

better health performance than those in rural areas. In addition, urban workers have better access to 

transport and other services such as water, internet and electricity. Enterprises and workers may have 

higher productivity in urban areas than in rural areas. Secondly, urbanization involves the 

agglomeration of people and companies, which reduces production costs, thus allowing economies of 

scale to be achieved. The resulting reduction in transaction costs allows companies to specialize, 

resulting in low production costs. The hypothesis is that urbanization, combined with a greater spatial 

density of economic activity, also brings greater proximity to suppliers and customers, better access to 

the market, and thus the benefits of agglomeration on costs and productivity for each individual 

enterprise. This “Marshallian” conceptualization underlies much of the recent work on the benefits of 

agglomeration in industrialized economies (Glaeser & Kerr, 2009; Jofre-Monseny et al., 2011; Dauth 

2011). Doubling the size of cities could even lead to an increase in productivity of about 3% to 8% 

(Rosenthal & Strange, 2004). Third, urbanization seems to be a key factor in entrepreneurship. Urban 

populations have access to financing and can easily promote their ideas and have a local market to 

some extent to do business. Loughran and Schultz (2005) show that geography affects business 

performance: all other things being equal, urban businesses are more profitable than rural businesses. 

Poverty reduction can be associated with the ability to become an entrepreneur and start your own 

business. This change in behaviour makes urban areas more attractive to entrepreneurs and 

entrepreneurship. In addition, a city’s prosperity and growth depend mainly on its ability to attract 

productive workers, match them appropriately to jobs and further develop their skills. Urbanization is 

driving the migration of talent and skilled people to large cities. This concentration causes interactions 

and generates spillover effects of knowledge and skills. Qualified people improve their skills and 

knowledge more effectively when exposed to similar profiles and qualified people (urban areas) than 

when they are not in contact with their peers (rural areas). This increases productivity in urban areas. 

Fourth, there are positive benefits or externalities of urban development on rural areas. Through 

migration, remittances and interactive activities between urban and rural areas, urbanization can have 

positive effects on finances and human capital. Through migration, the transfer of information, 

production skills and technology can be improved in areas of emigration. 

Regarding women’s labour force participation, many studies suggest a U-shaped relationship between 



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female labour force participation and economic growth (Tam, 2011; Lechman & Okonowicz, 2013; 

Olivetti, 2013; Tsani et al., 2013; Kaur & Tao, 2014). The logic behind this assumption is as follows. 

As the economy moves from subsistence agriculture to an industrialized economy, it requires a 

male-dominated workforce at the expense of women. The development of services in the 

post-industrial phase is associated with the increase in female employment (Olivetti, 2013). As a result, 

the feminization of the labour force in more service-oriented economies (Gaddis & Klassen, 2014; 

Olivetti, 2013). However, the relationship between women’s labour force participation and economic 

development may vary from country to country (Durand, 1975). This link may be weakened by 

discrimination and restrictions on women’s access to the labour market (Boserup, 1970) or religious 

restrictions (Wolch & Dear, 2014). 

2.2 Urbanization, Gender and Economic Growth: Empirical Contributions 

Urbanization and economic development have long been considered as interdependent processes. 

Indeed, the development history of many developed countries today has clearly demonstrated a 

dramatic increase in urbanization as their economies developed (Hughes & Cain, 2003). Studies have 

shown that the correlation coefficient between a country’s percentage of urban dwellers and GDP per 

capita is about 0.85 (Henderson, 2003), suggesting that urbanization is an inevitable component of a 

modern society. However, the phenomenon of urbanization in developing countries too often leads to 

disorganized hyperurbanization. It is therefore necessary to qualify the idea that density and 

urbanization would, in any case, be good for growth. The causal link between urbanization and growth 

is not sufficiently established (Henderson, 2003). In many developing countries, there is also a very 

high size of the largest city, especially if it is a political capital. The explanation for this “urban bias” 

would lie in the deplorable conditions of rural life and in a kind of political preference of the leaders 

who would somehow “buy” political and social peace with better quality facilities in the capital. On 

this point, empirical studies do not provide sufficiently precise estimates of the equilibrium size of 

cities to guide public policies. The “right” level of urbanization depends on the size of each country and 

its level of development (Henderson, 2003). Studies by Moomaw and Shatter (1993) have shown that 

urbanization is conducive to economic growth. (2006) also found a statistically positive and significant 

relationship between the level of urbanization and people’s standard of living, as measured by GDP per 

capita, in a sample of 35 countries covering the period 1985 to 2002. Daniel and Fhang (2010) also 

explored the link between urbanization and growth for 28 countries, including 14 developing and 14 

developed countries, over the period 1950-2000. 

Using causality tests in the Granger sense, he highlights a causality that goes from urbanization to 

economic growth for developing countries and in the opposite direction for developed countries. There 

is no shortage of studies on women’s contribution to economic growth. Women now represent more 

than 40% of the global workforce (UN-HABITAT, 2013). However, women’s economic opportunities 

are often still limited, especially when age, education and socio-economic status are taken into account. 

Urban economic sectors have opened many doors for women, especially in trade and domestic service. 



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The latter employs between 4 and 10 percent of the workforce. In developing economies, women 

represent 74 to 94 percent of domestic workers (World Bank, 2011). Women represent 70 to 90 percent 

of workers in multinational electronics and garment factories alone, most of whom are located in urban 

areas (UN Habitat 2013). In addition, women are the preferred workers for manufacturing 

jobs—traditionally low-paying, low-skilled, labour-intensive and precarious jobs—because they are 

considered docile, cheap and efficient according to stereotypes (Chant, 2013). In Africa, where 

agriculture remains the backbone of the economy, employing 70% of the population, women play a 

major role. They constitute almost two thirds of the agricultural labour force and produce the majority 

of foodstuffs. Outside agriculture, female labour force participation rates are high throughout Africa, 

with the exception of North Africa. They reach 85 to 90% in countries such as Burundi, Tanzania and 

Rwanda. In many countries (Nigeria, Togo, Burundi), the participation rates of men and women are 

equal or very close. However, African labour markets are characterised by very high gender segregation, 

with women generally working in low-paying occupations. Women are much more likely to work as 

self-employed entrepreneurs in the informal sector than to earn a regular wage in formal employment. 

In the formal sector, women hold 4 out of 10 jobs and earn on average two thirds of the salary of their 

male colleagues. Women are increasingly participating in paid work without a significant change in 

their domestic and family work responsibilities (Chant, 2014). This dual responsibility could prevent 

them from taking advantage of all the economic opportunities offered by urban areas. Thus, women 

contribute substantially to general economic well-being by performing a large number of unpaid tasks, 

such as childcare and household chores, which are often not included in GDP. On average, women 

spend twice as much time on household chores and four times as much time on childcare as men (Duflo, 

2012). Gender disparities in access to social and financial services have an impact on women’s 

economic productivity. Globally, women have less access to banking and other financial services than 

men. For example, less than 53% of women have an account in a financial institution in middle-income 

countries, compared to nearly 62% of men (Demirgüç-Kunt et al. 2015). It follows from the above that 

gender equality is in itself a development objective. There is evidence that when women are able to 

achieve their full potential in the labour market, there are significant macroeconomic gains (Loko & 

Diouf, 2009; Dollar & Gatti, 1999; McKinsey, 2015; Cuberes & Teignier, 2016). Potential losses in 

GDP per capita that can be attributed to gender gaps in the labour market can reach about 27 per cent in 

some regions (Cuberes & Teignier, 2012). Aguirre et al. (2012) estimate that increasing the 

participation rate of women to the level of men would increase GDP by 5% in the United States, 9% in 

Japan, 12% in the United Arab Emirates and 34% in Egypt. Very recently, Lechman and Kaur (2015) 

reexamined for 168 countries, over the period 1990-2012, the U-shaped curve between women’s 

participation in the labour market and economic growth. The results confirm the U-shaped curve 

hypothesis, which implies that in the early stages of economic growth, women’s participation in the 

labour market tends to decline, but as the country progresses in its economic development, with a 

significant share of the service sector, women’s contribution increases. But in Africa, an econometric 



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study on this relationship has a reduced U-shape, indicating an incomplete transition of women from 

agricultural and manual labour to higher-income services (UN, 2017). In North Africa, for example, 

high socio-cultural norms keep women’s participation rates at their lowest: 15-31% of working-age 

women are in the labour market, compared to an average of 69% in the rest of Africa. Based on panel 

data from several African and arable countries, Baliamoune-Lutz (2007) empirically assesses the 

impact on growth of two primary indicators, namely the ratio of girls to boys in primary and secondary 

education and the literacy ratio of women aged 15 to 24 years compared to men in the same age group. 

The results of the study indicate that gender inequalities in literacy have a significant negative effect on 

growth. What about the WAEMU countries? 

 

3. Data Sources and Descriptive Statistics 

The empirical study uses annual data from the eight (8) WAEMU countries, namely Côte d’Ivoire, 

Senegal, Niger, Mali, Burkina Faso, Togo, Benin and Guinea-Bissau. The study data are mainly from 

the World Development Indicator (WDI) database and the study covers the period 1990-2016. 

GROWTH refers to the growth rate of gross domestic product, URBAN the urbanization rate, INDUST 

the industrial added value as a percentage of GDP, MEN the share of men in the labour force, WOMEN 

the share of women in the labour force, YOUNG the share of the population aged 0-14 years (% of the 

total) and OLD the share of the population aged 65 years and over (% of the total). The descriptive 

statistics of all variables are recorded in Table 1. The Pearson correlation coefficient matrix is 

summarized in Table 2. 

 

Table 1. Descriptive Analysis of Variables 

VARIABLE OBS. MEAN STD. DEV. MIN MAX 

GROWTH 

URBAN 

INDUST. 

224 

224 

224 

3,813 

33,392 

19,165 

4,189 

10,297 

4,095 

-28,099 

13,815 

9,247 

15,376 

50,326 

29,724 

MEN 224 80,566 7,153 67,035 92,209 

WOMEN  

YOUNG 

OLD 

224 

224 

224 

60,225 

45,399 

2,908 

14,413 

2,152 

0,330 

34,219 

41,496 

2,299 

82,586 

50,231 

3,817 

Source: Author’s estimates, based on WDI data (2017). 

 

Table 1 requires some comments. It indicates that over the period 1990-2016, on average, the real GDP 

growth rate is 3.81%, the urbanization rate is 33.39% and industrial value added is 19.16%. As for the 

rate of participation of men in the labour force, it is around 80.56%, that of women 60.22%, thus 

marking a substantial gap between the contribution of men and women to the formation of the gross 



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domestic product. As for young people, on average, they represent a proportion of the order of 45.39% 

and a proportion of the elderly of 2.90%. The low standard deviation (0.33) observed in the elderly 

reflects a homogeneity of this category in the sample. Table 2 shows a low correlation between the 

explanatory variables. Of all these variables, the urbanization rate (Urban) and youth (Young) pair have 

the highest correlation coefficient (-0.79) but well below 0.8. The Urban and Men, Urban and Women 

pairs have correlation coefficients of -0.67 and -0.34 respectively. We still decide to include all the 

other variables in our model because of their theoretical interest. 

 

Table 2. Correlation Matrix   

 Growth Urban Indust. Men Women Young Old 

Growth 

Urban 

1,000 

-0,038 

 

1,000 

     

Indust. 0,054 0,285* 1,000     

Men -0,053 -0,676* -0,357* 1,000    

Women -0,028 -0,342* -0,284* 0,634* 1,000   

Young 0,024 -0,798* -0,120** 0,566* 0,015 1,000  

Old 0,028 0,314* 0,035 -0,504* -0,342* -0,261* 1,000 

Source: Author’s estimates, based on WDI data (2017). 

Note. * (**) refers to the significance of the parameters at the 5% (10%) threshold. 

 

4. Model and Methodology Research  

The methodology adopted in this study is essentially econometric modelling based on panel data. The 

analysis of the interaction between the real GDP growth rate and our explanatory variables covers all 

WAEMU countries observed over time. It is therefore a study based on panel data. In this section, we 

first present the specification of the model and then the methodology for estimating the PMG. 

4.1 The Specification of the Model  

The model to be estimated in this paper can be specified as follows: 

  
(1) 

From the point of view of economic theory, urbanization, industrial added value and human 

participation in economic life are expected to have a positive influence on economic growth. With 

regard to the share of women in the labour force, in view of the exclusion they experience in 

developing countries, it is expected that their contribution will be negative or, at best, very low. Based 

on Modigliani’s life cycle theory, the contribution of young and old must be marginal to economic 

growth, given their inactivity. 



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4.2 The Presentation of the PMG and MG Estimator 

The estimation technique used is that proposed by Pesaran et al. (1999), the PMG estimator. According 

to Pesaran et al. (1999), equation 1 can be seen as a staggered delay autoregressive model (ARDL) of 

the form : 

 
where 

 is a 

vector  of explanatory variables;  is a vector  of coefficients;  is a scalar and  

represents the fixed effect (country). From this model, the long-term relationship derives as follows: 

 
If the variables are cointegrated, then the term  is a stationary process. In this case, the model can 

be respecified as an error-correction model in which the short-term dynamics are influenced by the 

deviation from the long-term relationship: 

 
Where is the adjustment coefficient, is the vector of long-term coefficients and Δ is the variation 

operator between two successive dates. It is expected that . One of the advantages of the ARDL 

models is that the short-term and long-term multipliers are estimated jointly. In addition, these models 

allow the presence of variables that can be integrated of different orders, i.e.,  and , or 

cointegrated (Pesaran and Shin, 1999). The PMG estimator allows short-term coefficients and 

adjustment coefficients to vary from country to country, but long-term coefficients are the same for all 

countries ( . In this study, the PMG estimator is based on the following error-correction model: 

 
Where 



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It has been shown that the imposition of an identical coefficient for the return force could lead to biases 

(Kiviet, 1995). The MG estimator allows heterogeneity in both short-term parameters and long-term 

coefficients. The MG estimator estimates the equation for each country in the sample and then 

calculates the unweighted averages of the coefficients over the entire panel. The hypothesis of 

homogeneity of long-term coefficients is empirically tested. For this purpose, a Hausman test was used 

to determine the difference between the MG and PMG estimators. Under the null hypothesis, this 

difference is not significant and the PMG estimator is then preferable. 

 

5. Presentation of Econometric Results and Interpretations  

The empirical analysis follows the following approach. First, we apply unit root tests to the series to 

study the stationarity of the variables. Second, we apply the cointegration test to validate or invalidate a 

possible long-term relationship between the variables. Third, we estimate the long-term coefficients, 

using the PMG estimator. The order of integration of the variables is tested according to the tests of Im, 

Peseran and Shin (IPS, 2003), Levine, Lin and Chu (2002) and Maddala and Wu (1999). The results in 

Table 3 indicate that the variables Growth, Indust, Men, Young and Old are stationary in level at the 5% 

threshold. But all variables are stationary in first difference. It follows from the above that there is a 

presumption of a cointegrating relationship between the different variables. A cointegration test should 

therefore be applied. Pedroni’s cointegration test (1999; 2001) is performed for all variables and the 

results are reported in Table 4. 

 

Table 3. Stationarity Test Results  

 In Level In First Difference 

Variables LLC IPS MW LLC IPS MW 

Growth -10,464* 

(0,000) 

-11,249* 

(0,000) 

45,345* 

(0,000) 

-9,335* 

(0,000) 

-14,787* 

(0,000) 

78,435* 

(0,000) 

Urban 7,018 

(1,000) 

-1,423** 

(0,077) 

2,338 

(1,000) 

-116,949* 

(0,000) 

-83,648* 

(0,000) 

13,384 

(0,644) 

Indust. -1,781* 

(0,037) 

-2,791* 

(0,002) 

21,408 

(0,163) 

-10,810* 

(0,000) 

-11,139* 

(0,000) 

29,669* 

(0,019) 

Men -3,900* -0,694 27,322* -3,168* -1,218 13,463 

 

Women 

(0,000) 

-4,117* 

(0,996) 

-0,476 

(0,038) 

10,322 

(0,000) 

-1,729* 

(0,111) 

-2,885* 

(0,638) 

12,155 



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Young 

 

Old 

(0,000) 

-6,043* 

(0,000) 

-4,776 

(0,000) 

(0,316) 

-6,148* 

(0,000) 

-1,720* 

(0,042) 

(0,849) 

10,606 

(0,833) 

29,599* 

(0,020) 

(0,041) 

-2,175* 

(0,014) 

-3,643* 

(0,000) 

(0,002) 

-1,683* 

(0,046) 

0,009 

(0,503) 

(0,733) 

9,307 

(0,900) 

11,421 

(0,782) 

Source: Author’s estimates, based on WDI data (2017). 

Note. LLC, IPS and MW are respectively the Levin-Lin-Chu (2002), Im,-Pesaran-Shin (2003) and 

Maddala-Wu. 

 

Table 4 shows that of the seven statistics, four are in favour of a long-term relationship between the 

variables. As a result, we can conclude that the variables are cointegrated, allowing the choice of an 

error-correction model to estimate the long-term relationship. 

 

Table 4. Cointegration Test Results 

 Panel Tests Group Mean Tests 

 Statistiques P-values Statistiques P-values 

ν-stat -1,859 0,968 - - 

ρ-stat 0,070 0,527 0,884 0,811 

t-stat (PP) -10,116* 0,000 -20,792* 0,000 

t-stat (ADF) -2,813* 0,002 -3,670* 0,000 

Note. * indicates the significance of the test at the 5% threshold. 

Source: Author’s estimates, based on WDI data (2017). 

 

At this stage of the study, it is possible to present the results of the PMG and MG estimates. The results 

of the PMG and MG estimates are reported in Tables 5 and 6. To choose between the two models, it is 

recommended to apply the Hausman test. This test is applied to the differential between MG and PMG. 

Under the null hypothesis, the difference between the estimated coefficients MG and PMG is not 

significant and PMG is more efficient. 

 

Table 5. Results of the Short-Term Equations 

Variables PMG MG 

Coef. S.E p-value Coef. S.E p-value 



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Constante 

-26,226 

-0,095 

-13,155** 

34,363** 

23,246* 

-70,284* 

-155,834* 

35,213 

0,118 

6,950 

18,311 

7,617 

30,726 

24,438 

0,456 

0,420 

0,058 

0,061 

0,002 

0,022 

0,000 

0,781 

-0,201 

-12,244 

32,983** 

24,070* 

-23,329 

9,394 

53,367 

0,153 

8,000 

16,975 

9,178 

37,462 

412,735 

0,988 

0,191 

0,126 

0,052 

0,009 

0,533 

0,982 

Note. * (**) indicates that the null hypothesis of homogeneity of long-term coefficients at the 5% (10%) 

Source: Author’s estimates, based on WDI data (2017). 

 

Table 6. Results of the Long-Term Equations 

Variables PMG MG 

Coef. S.E p-value Coef. S.E p-value 

Urban 

Indust. 

Men 

Women 

Young 

Old 

Ajustement Coefficient 

Phi 

1,644* 

0,250* 

-0,263** 

-0,143** 

1,844* 

14,695* 

ficient 
-1,096* 

0,219 

0,073 

0,149 

0,084 

0,371 

2,344 

 

0,101 

0,000 

0,001 

0,078 

0,092 

0,000 

0,000 

 

0,000 

-0,127 

0,312 

-0,286 

3,768* 

-4,041* 

25,381** 

 

-1,208* 

1,210 

0,117 

1,377 

3,188 

4,330 

14,707 

 

0,084 

0,171 

0,274 

0,973 

0,009 

0,049 

0,084 

 

0,000 

Note. * (**) indicates that the null hypothesis of homogeneity of long-term coefficients at the 5% (10%) 

threshold has not been rejected. 

Source: Author’s estimates, based on WDI data (2017). 

 

The results of the haussman test, presented in Table 7, indicate that the assumption of homogeneity of 

the long-term coefficients can not be rejected. Indeed, the probability of the test is higher than the 5% 

threshold. In this case, the interpretation of the results will be based on those of the PMG estimator 

because it is more efficient. 

 

Table 7. Hausman Test Results 

Variables Coefficients Différence (b-B) 

MG (b) PMG (B) 

Urban -0,127 1,644 -1,772 



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Indust. 0,312 0,250 0,061 

Men -0,286 -0,263 -0,022 

Women 3,768 -0,143 3,911 

Young 

Old 

-4,041 

25,381 

1,844 

14,695 

-5,886 

10,686 

chi2(5) = (b-B)’[(V_b-V_B)^(-1)](b-B) = 2,38 

Prob>chi2 = 0,882 

Source: Author’s estimates, based on WDI data (2017). 

 

Now, we can interpret the results of the econometric analysis of the relationship between the real GDP 

growth rate and the explanatory variables used in this study.  

In the short term, the variables Men, Women, Young and Old show significant coefficients. The share 

of men in the labour force (Men) and the share of the population aged 65 and over, as a percentage of 

the total (Old), have a negative influence on the real growth rate in the WAEMU Zone. The negative 

impact of men’s share of the labour force is not very intuitive given their high weight compared to 

women. This could be explained by the low quality of jobs. According to the results of the 1-2-3 

surveys in seven capitals of the WAEMU zone, decent wage employment represents less than 30 per 

cent of urban employment (Zerbo, 2006). Indeed, for all the capitals studied, 23.8 per cent of the 

employed work in the formal sector (private, public and voluntary) with 8.4 per cent of the employed in 

the public and para-public sector and 15.4 per cent in the private formal sector. As for older people, 

their inactivity is not beneficial to economic growth. Most often in retirement, these elderly people are 

a burden on the economy, with no savings capacity. In this regard, a recent analysis has highlighted the 

economic threat to Africa posed by a growing population, low savings rates and low productivity, 

which could limit the demographic dividend (Eastwood & Lipton, 2011). On the other hand, the share 

of women in the labour force (Women) and the share of the population aged 0-14 years as a percentage 

of the total (Young) positively influence the real GDP growth rate in the area. Our results on women’s 

positive contribution to growth are similar to those of Baliamoune-Lutz (2007) and Klassen (1999). It 

appears that the increase in women’s participation in employment generates strong growth, the 

magnitude of which varies with the rate at which the participation rates of men and women 

converge.The proportion of informal employment in non-agricultural sectors is around 85% in the 

WAEMU zone. Almost the entire labor market is informal. An estimate for Senegal indicates that only 

3.8 percent of jobs were formal. With 53% of active workers, the informal sector appears overall to be 

the most feminized sector in West African countries. This strong contribution of women could explain 

the positive effect of women’s activity in the short term in contrast to that of men. According to 

Thevenon et al. (2012), it can correspond to a potential output gain of 12% on average in the OECD by 

2030 if convergence is total—a gain of 0.6 percentage points in annual GDP per capita growth. For 

young people under 14 years of age, their positive contribution to economic growth must be supported 



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by their ability to increase the active population. Indeed, in most WAEMU countries, compulsory 

schooling up to the age of 15 or 16 leads almost all boys and girls to attend primary and secondary 

education. If this youth is trained then there will be more workers. However, more workers means more 

production, and when there are more workers in relation to the population, production per capita 

increases. 

In the long term, all varaibles are significant at the 10% threshold. Urbanization, industrial value added, 

the share of the population aged 0-14 years (% of the total) and the share of the population aged 65 

years and over (% of the total) have a positive influence on the real GDP growth rate. Urbanization and 

industrial added value are conducive to long-term economic growth. Indeed, the relative geographical 

concentration of research and innovation relative to production in most industries, and the strong links 

with the diversity of employment in technology-related industries, illustrate the role of cities in 

innovation (Duranton, 2015). Urbanization facilitates industry in this case. However, industrial 

development is not only the way forward, but also the corollary of structural transformation. The city 

increases the potential of the African manufacturing sector to generate growth. Young people and the 

elderly are also useful for the economic growth of WAEMU countries. 

The existence of a negative relationship between the share of men and women in the population and the 

long-term GDP growth rate, although surprising, is based on the structure of the labour market. In 

Africa, women’s participation rate is high in all age groups and remains high until the end of their 

productive years, which is only possible when women combine their family responsibilities with their 

work in the informal economy, especially on their own account. There is a clear trend towards a 

generalisation of self-employment among women (and men), especially when they are not in paid 

employment. More often than men, women prefer to work for themselves rather than employ staff and 

are more present in the informal sector than in the formal economy. However, informal workers earn, 

on average, lower wages than formal workers, thus facing a higher probability of falling into poverty, 

which could be considered as a brake on economic growth in the long term. As for the share of men in 

the labour force, its negative influence on long-term growth could be explained by the predominance of 

low-skilled workers, who receive low wages whether they are employed in the formal or informal 

sectors. It is also possible that the negative impact of the contribution of men and women to the labour 

force on economic growth can be explained by the importance of the informal sector in the economies 

of the WAEMU region. Indeed, according to the International Labour Office, this sector provides 72% 

of jobs in sub-Saharan Africa with 93% of new jobs generated (Adigbli, 2008), while the formal sector 

employs only 10% of the workforce across the continent. The lack of reliable data from this sector 

could lead to an undervaluation of GDP, which in the end may weaken the contribution of women and 

men to economic growth. 

 

 

 



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6. Conclusion  

The objective of this study was to show the empirical link between urbanization, the share of men and 

women in the labour force and economic growth. The data for this study come from the World Bank’s 

2017 Development Indicators Database. These data cover the period 1990-2016 and cover the eight 

WAEMU countries. Using Pedroni’s (1999; 2001) approach, the data indicate a long-term relationship 

between economic growth and urbanization. The results indicate that in the short term, young people 

and women are very useful for economic growth while older women are a burden on society. In the 

long term, industrial added value and urbanization have a positive influence on economic growth in the 

WAEMU region. However, the contribution of workers weighs on economic growth. The Union’s 

political authorities must reflect on the challenges posed by urbanisation in order to benefit from it. 

Improving access to basic services, infrastructure and public transit, as well as affordable and 

accessible childcare, would facilitate everyone’s ability to participate in the economy. In addition, the 

industrial policies pursued in almost all EU countries must continue. Our empirical analysis has 

highlighted the limitations of human capital formation in terms of the negative effect of the share of 

men and women in the active population on economic growth. But in the long term, older people are 

very useful for economic growth. Thus, measures will have to be taken by the political authorities in 

response to the negative growth effects of the active population. Policies to combat unemployment 

must go hand in hand with policies to build human capital. In this context, the empowerment of women 

must therefore be accompanied by changes in legislation, policies and socio-cultural norms in order to 

effectively address the gender disparities that impede their full economic, social and political 

participation in urban life. Governments will therefore have to create better living conditions by 

ensuring adequate levels of income and care, i.e., public policies should aim at intensifying 

employment, establishing or improving social protection, social integration, health and the fight against 

discrimination. The retirement pension system should enable individuals to maintain their standard of 

living after the period of employment and should prevent social exclusion. 

 

References  

Abdel-Rahman, A. N., Safarzadeh, M. R., & Bottomley, M. B. (2006). Economic growth and 

urbanization: A cross-section and time-series analysis of thirty-five developing countries. 

International Review of Economics and Business, 53(3), 334-348 

Adigbli, K. E. (2008). Comment rendre formel le secteur informel?. Economie-Afrique. 

AFDB. (2010). The Bank Group’s Urban Development Strategy: Transforming Africa’s Cities and 

Towns into Engines of Economic Growth and Social Development. African Development Bank. 

Aguirre, D. A. L., Hoteit, R. C., & Sabbagh, K. (2012). Empowering the Third Billion. Women and the 

World of Work in 2012, Booz and Company. 

Baliamoune-Lutz M., & McGillivray, M. (2007). Gender Inequality and Growth: Evidence from 

Sub-Saharan Africa and Arab Countries. African Economic Conference 2007: Opportunities and 



www.scholink.org/ojs/index.php/ape                 Advances in Politics and Economic                   Vol. 3, No. 4, 2020 

62 
Published by SCHOLINK INC. 

Challenges of Development for Africa in the Global Arena. Addis Ababa, Ethiopia. 

Boserup, E. (1970). Women’s role in economic development. In St. Martin. New York. 

Chant, S. (2014). Exploring the “feminisation of poverty” in relation to women’s work and 

home-based enterprise in slums of the Global South. International Journal of Gender and 

Entrepreneurship, 6(3), 296-316. https://doi.org/10.1108/IJGE-09-2012-0035 

Chant, S. (2013). Cities through a “Gender Lens”: A Golden “Urban Age” for Women in the Global 

South?. Environment and Urbanization, 25(1), 9-29. https://doi.org/10.1177/0956247813477809 

Collier, P. (2017). African Urbanization: An Analytic Policy Guide. Oxford Review of Economic Policy, 

33(3), 405-437. https://doi.org/10.1093/oxrep/grx031 

Cuberes, D., & Teignier, M. (2016). Aggregate Effects of Gender Gaps in the Labor Market: A 

Quantitative Estimate. Journal of Human Capital, 10(1), 1-32. https://doi.org/10.1086/683847 

Cuberes, D., & Teignier, M. (2012). Gender Gaps in the Labor Market and Aggregate Productivity. 

Sheffield Economic Research Paper SERP 2012017. University of Sheffield, Sheffield, United 

Kingdom.  

Daniel, Y., & Fhang, L. (2010). Urbanization and Economic Growth: Testing for Causality. 16th Annual 

Pacific Rim Real Estate Conference. Wellington, New Zealand  

Dauth, W. (2011). The Mysteries of the Trade: Interindustry Spillovers in Cities. Working Paper, 

Institute for Employment Research. Nuremberg, Germany.  

Demirgüç-Kunt, A., Klapper, L., Singer, D., & Van Oudheusden, P. (2015). The Global Findex 

Database 2014: Measuring Financial Inclusion around the World. Policy Research Working Paper 

No. WPS 7255. World Bank Group, Washington, DC. https://doi.org/10.1596/1813-9450-7255 

Dollar, D., & Gatti, R. (1999). Gender Inequality, Income, and Growth. Are Good Times Good for 

Women?. In World Bank Gender and Development Working Paper (No. 1) (Washington). 

Duflo, E. (2012). Women Empowerment and Economic Development. Journal of Economic Literature, 

50(4), 1051-1079. https://doi.org/10.1257/jel.50.4.1051 

Durand, J. (1975). The Labour Force in Economic Development: A Comparison of International 

Census Data, 1946-1966. Princeton University Press. 

Duranton, G. (2015). Growing through Cities in Developing Countries. The World Bank Research 

Observer, 30(1), 39-73. https://doi.org/10.1093/wbro/lku006 

Eastwood, R., & Lipton, M. (2011). Demographic Transition in Sub-Saharan Africa: How Big Will the 

Economic Dividend Be?. Population Studies, 65(1), 9-35. 

https://doi.org/10.1080/00324728.2010.547946 

Gaddis, I., & Klasen, S. (2014). Economic development, structural change, and women’s labor force 

participation. Journal of Population Economics, 27(3), 639-681. 

https://doi.org/10.1007/s00148-013-0488-2 

Glaeser, E., & Kerr, W. (2009). Local Industrial Conditions and Entrepreneurship: How Much of the 

Spatial Distribution Can We Explain?. Journal of Economics and Management Strategy, 18(3), 



www.scholink.org/ojs/index.php/ape                 Advances in Politics and Economic                   Vol. 3, No. 4, 2020 

63 
Published by SCHOLINK INC. 

623-663. https://doi.org/10.1111/j.1530-9134.2009.00225.x 

Henderson, V. (2003). The urbanization process and economic growth: The so-what question. Journal 

of Economic Growth, 8(1), 47-71. https://doi.org/10.1023/A:1022860800744 

Hill, A., & King, E. (1993). Women’s education in developing countries: Barriers, benefits, and policies. 

In Johns Hopkins University Press. Baltimore. 

Hughes, J., & Cain, L. P. (2003). American economic history (6th ed.). Boston: Addison Wesley. 

https://doi.org/10.1016/S0304-4076(03)00092-7 

Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for Unit Roots in Heterogeneous Panels. Journal 

of Econometrics, 115(1), 53-74. 

Jofre-Monseny, J., Raquel, M-L., & Elisabet, V. M. (2011). The Mechanisms of Agglomeration: 

Evidence from the Effect of Inter-Industry Relations on the Location of New Firms. Journal of 

Urban Economics, 70(2/3), 61-74. https://doi.org/10.1016/j.jue.2011.05.002 

Kaur, H., & Tao, X. (2014). ICTs and the Millennium Development Goals: A United Nations 

Perspective. Springer. https://doi.org/10.1007/978-1-4899-7439-6 

Kiviet, J. F. (1995). On bias, Inconsistency and efficiency of various Estimators in Dynamic Panel 

Models. Journal of Econometrics, 68(1), 53-87. https://doi.org/10.1016/0304-4076(94)01643-E 

Klasen, S. (1999). Does Gender Inequality Reduce Growth and Development? Evidence from 

Cross-Country Regressions. In Policy Research Report, Engendering Development, Working 

Paper (No. 7). World Bank, Washington, D.C. 

Krugman, P. (1991). Increasing returns and economic geography. The Journal of Political Economy, 

99(3), 483-499. https://doi.org/10.1086/261763 

Lechman, E., & Kaur, H. (2015). Economic growth and female labor force participation—Verifying the 

U-feminization hypothesis. New evidence for 162 countries over the period 1990-2012. In 

Economics and Sociology, 8(1), 246-257. https://doi.org/10.14254/2071-789X.2015/8-1/19 

Lechman, E., & Okonowicz, A. (2013). Are Women Important for Economic Development?, An 

evidence on women’s participation in labor market and their contribution to economic growth in 

83 world countries. GUT Faculty of Management and Economics Working Paper Series A 

(Economics, Management, Statistics), 13. 

Levine A., Lin, C. F., & Chu, C. (2002). Unit Root Test in Panel Asymptotic and Finite Sample 

Properties. Journal of Econometrics, 108(1), 1-24. 

https://doi.org/10.1016/S0304-4076(01)00098-7 

Loko, B., & Diouf, M. A. (2009). Revisiting the Determinants of Productivity Growth: What’s New?. 

IMF Working Paper 09/225 (Washington). https://doi.org/10.5089/9781451873726.001 

Loughran, T., & Schultz, P. (2005). Liquidity: Urban versus rural firms. Journal of Financial 

Economics, 78(2), 341-374. https://doi.org/10.1016/j.jfineco.2004.10.008 

Maddala, G. S., & Wu, S. (1999). A Comparative Study of Unit Root Tests with Panel Data and a New 

Simple Test. Oxford Bulletin of Economics and Statistics, 61(1), 631-652. 



www.scholink.org/ojs/index.php/ape                 Advances in Politics and Economic                   Vol. 3, No. 4, 2020 

64 
Published by SCHOLINK INC. 

https://doi.org/10.1111/1468-0084.61.s1.13 

Mckinsey Global Institute. (2015). The Power of Parity: How Advancing Women’s Equality Can Add 

$12 Million to Global Growth. In McKinsey and Company. London, San Francisco, and Shanghai. 

Moomaw, R., & Shatter, A. M. (1993). Urbanization as a Factor in Economic Growth. The Journal of 

Economics, 19(2), 1-6. 

Olivetti, C. (2013). The female labor force and long-run development: The American experience in 

comparative perspective. NBER Working Paper. https://doi.org/10.3386/w19131 

ONU. (2017). Rapport économique sur l’Afrique 2017: l’industrialisation et l’urbanisation au service 

de la transformation de l’Afrique. commission économique pour l’Afrique. 

Pedroni, P. (1999). Critical values for cointegration tests in heterogenous panels with multiple 

regressors. Oxford Bulletin of Economics and Statistics, 61(1), 653-670. 

https://doi.org/10.1111/1468-0084.61.s1.14 

Pedroni, P. (2001). Panel cointegration, asymptotic and fnite sample properties of pooled time series 

tests with an application to the PPP hypothesis. In Working Paper in Economics. Indiana 

University. 

Pesaran, M. H., Shin, Y., & Smith, R.J. (2001). Bounds testing approaches to the analysis of level 

relationships. Journal of Applied Econometrics, 16, 289-326. https://doi.org/10.1002/jae.616 

Pesaran, M. H., Shin, Y., & Smith, R.P. (1999). Pooled Mean Group Estimation of Dynamic 

Heterogeneous Panel. Journal o f  American Statistical Association, 94(446), 621-634. 

https://doi.org/10.1080/01621459.1999.10474156 

Ploeg, R. V. D., & Poelhekke, S. (2008). Globalization and the Rise of Mega-Cities in the Developing 

World. In CESIFO Working Paper (No. 2208). 

PNUD. (2011). Rapport sur le développement humain. 

PNUD. (2006). Rapport national sur le développement humain: Genre et développement humain, 

Union des Comores. 

Ravallion, M., Chen, S., & Sangraula, P. (2007). New Evidence on the Urbanization of Global Poverty. 

Policy Research Working Paper No. 4199 (Washington: World Bank). Retrieved from 

http://econ.worldbank.org/docsearch 

Rosenthal, S. and Strange, W. (2004). Evidence on the nature and sources of agglomeration economies. 

In J. F. Thisse., & J. V. Henderson (Eds.), Handbook of Urban and Regional Economics (Vol. 4). 

https://doi.org/10.1016/S1574-0080(04)80006-3  

Shen, K., & Rui, J. (2007). How does urbanization affect economic growth in China. Statistical 

Researc. 

Spence, M. (2012). The Next Convergence: The Future of Economic Growth in a Multispeed World. 

New York, Farrar, Straus and Giroux. 

Tam, H. (2011). U-shaped female labor participation with economic development: Some panel data 

evidence. Economics Letters, 110(2), 140-142. https://doi.org/10.1016/j.econlet.2010.11.003 



www.scholink.org/ojs/index.php/ape                 Advances in Politics and Economic                   Vol. 3, No. 4, 2020 

65 
Published by SCHOLINK INC. 

Thévenon, O., Ali, N., Adema, W., & Salvi del Pero, A. (2012). The Effects of Reducing Gender Gaps 

in Education and Labour Force Participation on Economic Growth in the OCDE. In OCDE Social, 

Employment and Migration Working Papers (No. 138). Paris, OCDE Publishing. 

Tsani, S., Paroussos, L., Fragiadakis, C., Charalambidis, I., & Capros, P. (2013). Female labour force 

participation and economic growth in the South Mediterranean countries. Economics Letters, 

120(2), 323-328. https://doi.org/10.1016/j.econlet.2013.04.043 

United Nations Human Settlements Programme (UN HABITAT). (2013). State of Women in Cities 

2012-2013: Gender and the Prosperity of Cities. Nairobi, KE: United Nations Human Settlements 

Program. 

United Nations. (2010). The World’s Women 2010: Trends and Statistics. New York, NY: United 

Nations, Department of Economic and Social Affairs.  

Wolch, J., & Dear, M. (2014). Power of Geography: How Territory Shapes Social Life. Routledge 

World Bank. (2011). World Development Report 2012: Gender Equality and Development. World Bank. 

Washington, DC. 

World Bank. (2009). World Development Report 2009: Reshaping Economic Geography. World Bank. 

Washington, DC. 

Zerbo, A. (2006). Marché du travail urbain et pauvreté en Afrique subsaharienne: Un modèle d’analyse. 

In Document de Travail 129/2006. Centre d’Economie du Développement, Université Bordeaux 

IV. 

 

 

 

 

 


