The impact of natural resource exploitation on the development of the manufacturing sector in Sub-Saharan Africa

  1. Introduction

The issue of industrialization is one of the major concerns that has always been at the center of economic debates around the world. Several literatures agree on the importance of industrial development in explaining long-term growth (Rodrik 2008). Its importance stems from the fact that industrial activities improve labor productivity and promote technological diffusion to other productive sectors, thus contributing to faster economic growth and job creation (Hidalgo 2007 and Hausmann et al. 2007).

Sub-Saharan Africa (SSA) countries have not yet begun their industrial development. Productive activity in this region is based primarily on a few primary products, such as agriculture and mining. Moreover, SSA remains the least industrialized region in the world.

However, Africa has now embarked on a new development paradigm by inscribing structural transformation as an imperative and a unique pathway for its economic emergence (The International Conference on Africa’s Emergence 2015).  Thus, over the past decades, African governments have adopted several initiatives at the national and regional levels to give themselves more chances to achieve their objectives. Indeed, the African Union (AU) through Agenda 2063 as well as the African Development Bank (AfDB) through its “high 5” priorities have made industrialization a priority for the sustainable development of the continent. More recently, the African Continental Free Trade Area Agreement (AfCFTA) was launched with the objective of increasing intra-African trade and stimulating industrialization and structural transformation in Africa.

Although, the success of such an industrial development challenge for SSA countries requires the removal of several major constraints, and the fundamental question facing researchers, practitioners, and policymakers today is how to accelerate the industrialization of these countries in a context where the exploitation of natural resources is expanding rapidly. In this region, the exploitation of natural resources occupies a very important place in the dynamics of most of these economies. 

Nonetheless, the exploitation of natural resources has consequences on the development of other sectors, especially manufacturing. There has been a contentious debate between proponents of the “natural resource curse” thesis and those who believe that the exploitation of natural resources is the main source of African economic growth.

The exploitation of natural resources can have both positive and negative effects on the development of other sectors and thus on the production structure of these countries. The positive effects of the exploitation of natural resources are because most of the countries in the region have abundant mineral and agricultural resources that can be used as inputs in the manufacturing process. In addition, natural resource rents can be used to invest in infrastructure, mechanization of other sectors, training of youth, etc.

However, the exploitation of natural resources could become a curse to the development of countries in the region if the resources derived from this exploitation are not well managed. This is the “Dutch disease”. This paper expects to analyze the impact of the exploitation of natural resources on the development of the manufacturing sector in the economies of this zone. 

Two main arguments justify the relevance of this work.  First, this paper examines the effect of natural resource rents on the level of industrial development in Africa, based on theoretical and empirical arguments. In essence, African countries can boost the development of their industrial sector if a large part of their natural resource rents are used to finance the development of the industrial sector. The dual approach to industrialization theory developed by Lewis (1954) and based on the availability of an abundance of raw materials and labor suggests encouraging the establishment of low capital-intensive firms that process local raw materials with available labor to benefit from economies of scale and agglomeration effects.  This could make it possible to move from an excessive dependence on natural resources that are perishable and a source of submission to the terms of trade to an industrialized economic system.  Second, the extent to which natural resources can stimulate industrial development through massive Foreign Direct Investment (FDI) inflows needs to be examined. As discussed above, massive FDI inflows can be a promising route to the industrial revolution in Africa if proactive domestic policies are pursued upstream to attract FDI into the manufacturing sector.

  • Literature Review between Natural Resources and Manufacturing Development

The argument on the curse of natural resources is no longer demonstrated because it has been debated too much in the literature. Indeed, this argument is based on the observation that countries rich in natural resources tend to have a poor economic performance, particularly in terms of economic growth and poverty reduction. Already present in the 1980s through the work of Corden & Neary (1982), the notion of the resource curse was pioneered by Auty (1993). The latter showed the paradox of low economic growth combined with a loss of competitiveness in the six countries in his sample despite the boom in their natural resources. Later, the concept of the resource curse was popularized by authors such as Sachs & Warner (1995), Gylfason et al. (1999), Smith (2004), etc., who explained how resource-rich countries tend to experience low economic growth.

There are several other arguments in the literature that explain the development gap in resource-rich countries. Indeed, these arguments can be organized around three channels of transmission of the natural resource curse.

(i) The institutional channel, which highlights the negative effect of natural resource abundance on the quality of institutions (Hodler 2006): Indeed, the abundance of natural resources leads to poor governance including the development of corruption and embezzlement in public administration, increased public spending (Yates 1997, Subramanian & Sala-i-Martin 2003, O’Higgins 2006, and Frankel 2010). Also, Fearon & Laitin (2003), Ross (2003), Humphreys (2005) Hinkkainen Elliott & Kreutz (2019) show that natural resource abundance leads to risks of civil war in resource rich countries. Resource abundance also leads to the risk of socio-political instability. Resource abundance, especially petrodollars, weakens the state and creates political instability (Karl 1997), income inequality and the consolidation of dictatorial regimes (Wantchekon 2002 and Jensen & Wantchekon 2004).

(ii) The commodity price volatility channel: the rapid exploitation of natural resources leads to a deterioration in the terms of trade (Stevens 2003), an appreciation of the national currency and a decline in the manufacturing sector in favor of the extractive sector (Carbonnier 2007). Moreover, the characteristic volatility of natural resource prices leads to increased macroeconomic volatility, which in turn leads to macroeconomic stabilities (Ades & Di Tella 1999, Blattman & Williamson 2007, Van der Ploeg & Poelhekke 2009, and Hayat & Tahir 2021).

(iii) Dutch disease: based on the experience of the Netherlands in the 1960s, this is a term that describes the situation in which a country rich in natural resources tends to experience a progressive destruction of its industrial fabric accompanied by an appreciation of its real exchange rate. In other words, the expansion of natural resources produces perverse effects in the country’s economy that result in the contraction of sectors producing tradable goods (industrial) outside the boom sector and the development of sectors producing non-tradable goods. Formalized by Corden & Neary (1982), the mechanism of Dutch disease is demonstrated in the context of an open economy with three sectors based on the exploitation of resources that generate devaluation and a sector of non-tradable goods.

The “Dutch disease” parody is based on the fact that the abundance of resources, which should be an opportunity, a boon for a harmonious and sustainable development for the country, ends up being a double-edged sword, because the revenues generated by the exploitation negatively affect the structure of the economy through certain sectors of production and income distribution. Several authors have sought to verify this phenomenon empirically, particularly in developing countries rich in natural resources (see Benjamin et al. 1989, Fardmanesh 199, Davis 1995, Hien et al. 2020).

(iv) Low levels of human capital: Another wave of literature on the resource curse studies the link between natural resource abundance, human capital and economic development. Several studies show that the effect of natural resource abundance on economic development would be negative for countries with low levels of human capital (Shao & Yang 2014; Gylfason 2001; Zallé 2019 and Kim & Lin 2017). Yet, resource-rich countries tend to invest very little in education compared to other countries. As an illustration, Oyinlola et al. (2020) analyze the role of natural resource rents on the link between human capital and industrial development in 17 sub-Saharan African countries over the period 1995-2015. They find that labor has a positive impact on industrial development but capital accumulation is not sufficient to promote the required investment effort in the industrial sector.

  • Methodology and Data Analysis
    • Specification of the model

The basic model we use to analyze the effects of FDI on the development of the manufacturing sector in resource-rich economies in Africa is the small dependent (open) economy model developed by Salter (1959) and Swan (1960). The model assumes an open economy with two sectors, one of which is the tradable goods sector and the other the non-tradable goods sector. The former is naturally exposed to foreign competition and is the channel through which price effects can influence the investment decision in each country.

From this basic model, we introduce a specific sector of natural resource exploitation. The reduced form of the model equation is written as follows:

 =  + +  + +                                                                      (1)

Where 𝑖 = 1 … 𝑁 is the individual (country) dimension and t = 1, … n the time dimension. The dependent variable  (Manufacturing Value Added). It is explained by the Rent variable indicating natural resource rent and a set of control variables𝑋 that the literature suggests impact manufacturing and potentially manufacturing value added, an individual fixed effect ѵ that is time invariant, and a set of unobservable factors ε.

  •    Estimation Method and Analysis Data

Analyzing the effects of natural resource rents on development in sub-Saharan Africa is not an easy task due to data availability and country specificities.

To achieve this, we first assume that ordinary methods such as Ordinary Least Squares that assume the absence of heterogeneity between the countries in the panel are not efficient. We then use the panel data fixed effects (FE) estimator that accounts for autocorrelation problems between the explanatory variables and constant unobservable heterogeneities – the country fixed effects.

The first challenge is the reverse causality between resource rents and manufacturing sector development. Reforms in the resource sector may influence the expansion of manufacturing activity. To address these endogeneity issues, the existing literature relies on external instruments. Unfortunately, given the scarcity of data for the region of interest, such a series is difficult to construct. An additional challenge is the possibility that some omitted variables are correlated with both the dependent variable and or explanatory variables, which can also lead to biased estimates. We therefore use the fixed-effects (FE) estimator, which mitigates simultaneity and omitted variable bias by controlling for time-invariant idiosyncratic factors. The lagged dependent variable is correlated with the error terms, leading to the well-known Nickell (1981) bias. To assess the influence of the possible Nickell bias, estimation by the FE model of Equation (1) is conducted with and without the lagged dependent variable.

In addition, controlling for province and year fixed effects should limit the potential endogeneity problem by controlling for (i) time-invariant unobserved factors that affect both manufacturing value added and natural resource rents in a country and (ii) time-varying factors that affect both variables commonly across countries (or in the average country). All estimates are correct for autocorrelation and heteroscedasticity. For these reasons, we use the fixed effects (FE) method. The fixed effects method (intra estimator) allows for structural variables that are constant and country specific.

  •    Data and Sample

 This study uses panel data to estimate the effects of resource rents on manufacturing value added in sub-Saharan Africa. Indeed, the choice of panel data econometrics appeared obvious in view of the unavailability of time series over a long period in African countries on the one hand, as well as the double dimension, geographical and temporal. The advantage of panel data econometrics is that it allows for the aggregation of data into snapshots and time series and provides the possibility of analyzing the relationships between the dependent variable and the explanatory variables over time, for several countries. The study will cover approximately 17 countries in sub-Saharan Africa (SSA) and the period 1990-2018. The list of countries is presented in the appendix. The data are drawn from the secondary database of “The Economic Transformation Database is supported by United Nations University Institute for Development Economics Research (UNU-WIDER) and the World Bank’s World Development Indicators (WDI). All variables are presented in the table below.

Table 1: List of Variables Used in the Analyses

01ManufacturingGross value added of ManufacturingThe Economic Transformation Database UNU-WIDER
02MiningGross value added of Mining and quarrying
03AgricultureGross value added of Agriculture, forestry, and fishing
04ConstructionGross value added of Construction
05TradeGross value added of Wholesale and retail trade; repair of motor vehicles and motorcycles; Accommodation and food service activities
06TransportGross value added of Transportation and storage
07BusinessGross value added of Information and communication; Professional, scientific, and technical activities; Administrative and support service activities
08FinancialGross value added of Financial and insurance activities
09GovernmentGross value added of public administration and defense; compulsory social security; Education; Human health and social work activities
10CorruptionControl 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 “capture” of the state by elites and private interests.Worldwide Governance Indicators World Bank
11StabilityPolitical Stability and Absence of Violence/Terrorism measures perceptions of the likelihood of political instability and/or politically motivated violence, including terrorism.
12Rule of LawReflects perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence.

 Source : Author

  •   Analysis of Stylized Facts  Descriptive Statistics

Table 2 below shows that the different variables used have a mean that tends to be close to that of the dependent variable with a standard deviation that is less disparate from one another, except for the three governance variables. Indeed, the variables Corruption, Stability and Rule of law are measured in a range of -2.5 to +2.5 points, which gives them a low mean, but which are also close to each other. We can then conclude that the data are quite harmonious and that the results of the different analyses are statistically interpretable and without risk of dimensional problems.  

Table 2: Descriptive Statistics of the Variables

VariableObsMeanStd. Dev.MinMax
Rule of law391-0.3296840.585150-1.5125101.0239560

Sources: Author based on UNU-WIDER data, (2023)

  • Analysis of the Structure of GDP Growth in SSA from 1990 to 2021

 When we analyze the structure of economic growth in SSA, we find a structural problem. Indeed, Kuznets & Murphy (1966) and Kuznets (1973) have identified five fundamental factors in the process of long-term growth, including structural transformation. According to these authors, structural transformation takes place in three stages, the first of which is materialized by the impetus of the agricultural sector, which provides the largest share of the labor force in the face of rapid population growth. In the second phase, the industrial sector is the main growth driver, thanks to a developed human capital that increases the sector’s productivity and improves its performance. Finally, the last phase is marked by the rise of the service sector. Thus, structural transformation is crucial in the productivity catch-up process of developing countries (Duarte & Restuccia 2010). Indeed, countries that tend to develop rapidly have driven their production and exports by relying on new, more productive economic activities (Hausmann & Klinger 2007).

However, in the case of SSA, the trajectory of its transformation does not follow these steps because it seems to skip the crucial phase for stronger and sustained growth, namely the second phase, that of intensifying production and exports through the industrialization of the country. Figure 1 clearly shows that the industrial sector in SSA has not expanded since 1981, even though the service sector has made a predominant contribution to growth. The manufacturing sector lags with the lowest contribution to SSA’s GDP growth. This result illustrates the problem of delayed industrial development, particularly in manufacturing, which should boost national production and absorb labor from the agricultural sector.

Figure 1 : Structure of GDP growth in SSA over the period 1981-2021

Source : Author based on WDI data

  • Results and Interpretation

The results of the fixed-effect model estimation are presented in Table 4 below. The estimates show that mining does not have a significant effect on the manufacturing value added of SSA economies. In other words, the exploitation of natural resources, particularly in the mining sector, does not have a significant effect on the development of the manufacturing sector. This result shows that natural resource rents have not been used to boost the manufacturing sector, particularly through investments in infrastructure, technology transfer, youth training, etc. This result could be justified by the theory of the resource curse, which refers to poor governance, particularly the development of corruption, political instability, etc., which leads to sandstone effects.

Furthermore, while the development of the construction, trade, transport, and business sectors are important factors in manufacturing development, the development of the agricultural and financial sectors seems to have a negative effect on it.  This result shows that the development of the manufacturing sector in SSA economies necessarily requires major achievements in infrastructure and the expansion of trade and business, particularly the business climate.   

Table 3: Estimation Result by the Fixed Effect Model

Dependante variable : ManufacturingModel IModel IIModel IIIModel IV
Mining0.02 (0.100)0.02* (0.091)0.02 (0.116)0.2 3* (0.079)
Agriculture-0.06*** (0.000)-0.06*** (0.000)-0.07*** (0.000)-.06*** (0.000)
Construction0.39*** (0.000)0.39*** (0.000)0.39*** (0.000)0.38*** (0.000)
Trade0.31*** (0.000)0.31*** (0.000)0.31*** (0.000)0.31*** (0.000)
Transport1.02** (0.000)1.00*** (0.000)0.99*** (0.000)1.03*** (0.000)
Business0.13*** (0.000)0.13*** (0.000)0.13*** (0.000)0.13*** (0.000)
Financial    -0.40*** (0.000)-0.41*** (0.000)-0.41*** (0.000)-0.40*** (0.001)
Government-0.01 (0.768)-0.01  (0.770)-0.001 (0.967)-0.005 (0.885)
Corruption-0.68* (0.082)-79** (0.014)  
Stability0.68 (0.182) -0.20** (0.028) 
Rule of Law-0.25 (0.579)  -0.67* (0.072)
Constante87922 (0.000)109578.1 (0.000)129906.32 (0.000)87922 (0.000)
Nombre d’observation493493493493
Nombre de pays17171717

Sources: Sources: Author based on UNU-WIDER (2023) and WGI (2023) data. The symbols ***; ** and * represent significance at the 1%, 5% and 10% thresholds, respectively. Values in parentheses are the p-values from the estimation.

Moreover, the expansion of the agricultural sector will naturally have a negative effect on the development of the manufacturing sector, since this sector should, according to Lewis (1954), give way to the manufacturing sector. The negative effect of the financial sector on the expansion of the manufacturing sector could be justified by the fact that there is a non-linear relationship between it and growth. By hook or by crook, a threshold of financial development must be reached before it has a beneficial effect on the growth of the manufacturing sector.

Institutional variables appear to have a negative effect on manufacturing growth. Indeed, control of corruption, political stability, and improved laws and institutional reforms each have a negative effect on manufacturing development in SSA economies. In other words, control of corruption, political stability, and institutional reforms have a detrimental effect on the development of the manufacturing sector. This result could be explained by the fact that the relationship between institutional quality and manufacturing development appears to be nonlinear, i.e., it is only after a certain threshold of institutional quality that the latter begins to have a positive effect.

  • Conclusion and Policy Implications

The objective of this paper was to analyze the impact of natural resource exploitation on the development of the manufacturing sector in sub-Saharan Africa. The study mobilized the fixed-effects model for econometric analyses using data from a panel of 17 SSA countries and over the period 1990-2018.

The analysis of stylized facts showed that the structural transformation trajectory of SSA countries appears to be skipping the key phase for stronger and sustained growth, namely that of intensification of manufacturing and export. These stylized facts show that the industrial sector in SSA has not expanded since 1981, even though the service sector has made a predominant contribution to growth. The manufacturing sector lags with the lowest contribution to SSA’s GDP growth. This result illustrates the problem of delayed industrial development, particularly in manufacturing, which should boost national production and absorb the labor force from the agricultural sector. This has led us to analyze the effect of natural resource exploitation, particularly mining, on the development of the manufacturing sector, given its weight in the economies of this zone.

The econometric analyses show that the exploitation of mining resources does not have a significant effect on the development of the manufacturing sector in SSA. This result shows that natural resource rents have not been used to boost the manufacturing sector, particularly through investments in infrastructure, technology transfer, youth training, etc. This result could be justified by the theory of the resource curse, which refers to poor governance, particularly the development of corruption, political instability, etc., which leads to sandstone effects.

In view of this result, it is imperative for the countries of SSA to put more mining resources at the service of the socio-economic development of the countries in the zone. Mining resources already make a significant contribution, especially in exports. Analyses show that the financial resources they generate are not sufficiently channeled into the development of other sectors, particularly manufacturing.


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Table 3: List of Sub-Saharan African countries selected for analysis

Botswana Burkina Faso Cameroon Ethiopia GhanaKenya Lesotho Malawi MauritiusMozambique Namibia Nigeria Rwanda  Senegal South Africa Uganda Zambia

Table 4: Result of the unit root tests in panel

VariablesMW (1999) StatisticsChoi(2002) StatisticsConclusion
Manufacturing166.36*** (0.000)-7.79***       (0.000)-10.03***       (0.000)16.05       (0.000)I(1)
Mining285.54***     (0.000)-12.75***       (0.000)-19.06***       (0.000)30.50***       (0.000)I(1)
Agriculture286.39***       (0.000)-11.61***       (0.000)-18.71***       (0.000)30.61***       (0.000)I(1)
Construction177.40***      (0.000)-7.38***       (0.000)-9.98***       (0.000)17.39***       (0.000)I(1)
Trade253.09***       (0.000)-10.84***       (0.000)-16.15***       (0.000)26.57***       (0.000)I(1)
Transport180.30***      (0.000)-6.88***       (0.000)-10.66***       (0.000)17.74***       (0.000)I(1)
Business308.54***      (0.000)-12.65***       (0.000)-19.83***       (0.000)33.29***       (0.000)I(1)
Financial274.84***      (0.000)-12.18***      (0.000)-18.04***      (0.000)29.21***      (0.000)I(1)
Government188.10***      (0.000)-8.25***      (0.000)-11.75***      (0.000)18.69***      (0.000)I(1)
Corruption294.56***      (0.000)-13.54***      (0.000)-19.76***      (0.000)31.60***      (0.000)I(1)
Stability49.27**      (0.044)-0.37***      (0.355)-1.02***      (0.153)1.85***      (0.032)I(0)
Rule of law249.65***      (0.000-12.79***      (0.000)-16.77***      (0.000)26.15***      (0.000)I(1)

Note: *** represents stationarity at the 1% thresholds.


Kirsi ZONGO, PhD is an Economist and member of the Laboratory of Applied Economics, University of Norbert ZONGO, Burkina Faso. He is interested in issues of economic development in Africa, particularly the industrial development of Africa through an endogenous mechanism.

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