Structural transformation and sustainable development in sub-Saharan Africa

In 2015, world leaders under the United Nations umbrella adopted the 17 sustainable development goals (SDGs) as an action plan towards sustainable development. The aim is that in 15 years (that is, by 2030), the world would be transformed, and everyone would live a life of dignity. Unfortunately, socio-economic conditions across sub-Saharan African (SSA) countries continue to deteriorate. Some scholars (see Ferreira et al., 2010) argued that the continued deterioration of socio-economic conditions in SSA may not be unconnected to the fact that SSA economy may be structurally defective. For instance, the SSA economy relies heavily on the production of primary commodities—dominated by the manufacturing and agricultural sectors— which are characterized by the excessive utilization of non-renewable sources of energy. In SSA, due to the region's abundance of natural resources, the extractive sector is a major part of the economy in many economies. The international Monetary Fund notes that about 50% of SSA's exports and 15% of its annual output come from nonrenewable natural resources. Unfortunately, the region’s excessive reliance on non-renewable energy sources raises carbon emissions, creates extreme weather, and in turn damages homes and businesses; and it is often the trigger that tips the vulnerable into poverty (Baloch et al., 2020; Khan et al., 2019; World Bank, 2015). Specifically, SSA contributes about 2% of the world’s emissions, and perhaps it is now responsible for a large chunk of the world’s emissions today (Statista, 2022). This may hinder the actualization of the 2030 sustainable development agenda. Thus, to engender sustainable growth and development across SSA, there is a need for the structural transformation of the economy ^[1]—through innovation— towards a knowledge-based economy ^[2].

Consequently, in recent literature, scholars (see Schwab, 2016; Vaidya et al., 2018; ElMassah and Mohieldin, 2020; Digaru, 2020; Wen and Zhu, 2021; Metu et al., 2021; Abbas et al., 2023; Chen et al., 2023; Caldarola et al., 2023) have argued that the structural transformation of the economy aided by the innovative technologies such as information and communication technology (ICT) and the fourth industrial revolution technologies ^[3] (4IR hereafter) can aid the achievement of the sustainable development agenda by the year 2030 through various channels. First, technologies and their innovative applications, for instance, can play a significant role in facilitating access to loans, restructuring tax systems and aid resilient recovery and growth (Schwab, 2016; Metu et al., 2021), thereby, raising global income levels and improving the quality of life for populations around the world. Second, the authors assert that the application of innovative technologies can help to curb the excessive utilization of non-renewable energy, reduce carbon emissions, stop damage to home and businesses, and ultimately engender sustainable development (ElMassah and Mohieldin, 2020). Unfortunately, despite the importance of technology in promoting sustainable development, sub-Saharan Africa lags other regions on several indicators essential for technological revolution and structural transformation, specifically, in infrastructure, technology access and education (see Figure A1 in the  Appendix).

Although there have been a few attempts (see Schwab, 2016; Vaidya et al., 2018; ElMassah and Mohieldin, 2020; Digaru, 2020; Signé and Ndung’u, 2020; Wen and Zhu, 2021; Metu et al., 2021) to examine the effects of structural transformation and/or technological innovations on sustainable development, however, these studies suffer some significant shortcomings. One, the evidence presented thus far is limited and largely anecdotal, with most of the previous evidence on the relationship between innovations/structural transformation and SDGs based on survey and simple correlational study. Two, most of the existing studies have mostly focused on process and/or production engineering, system integration aspects of innovations, while the economic effects of these technologies have not been adequately investigated. Three, another important limitation of the existing studies is that extant studies have adopted a single metric approach, focusing on only one of the 17 SDGs per time. However, this single metric approach may not fully capture the full complexities in SDGs. Lastly, previous studies have not considered the transmission channels from structural transformation to sustainable development in SSA.

By addressing these gaps, we provide evidence-based strategies that will inform robust policy formulation on the structural transformation of sub-Saharan Africa’s economy for inclusive and sustainable development.

The rest of the paper is organized as follows. Section 2 presents the review of literature. Section 3 describes the data and Methodology. Section 4 presents the empirical findings, and Section 5 concludes the paper.

The literature reviewed has been summarized in Table 1.

The study is premised on two relevant theories. They are Arthur Lewis Structural Change theory (sometimes referred to as the dual economic theory) and the compensation theory of technological change (CTTC). Arthur Lewis theory of structural change documented in his work “Lewis’s theory of economic development” tries to explain the growth of a developing country in terms of labour transition between two sectors. The theory focuses on labour being transferred from traditional activity to a modern capitalist sector under conditions of unlimited supply of labour (Gollin, 2014). To put it in a clear context, Lewis’s structural change theory primarily focused on the mechanism by which underdeveloped economies transform their domestic economic structures from a heavy emphasis on traditional subsistence agriculture to a more modern, more urbanized and more industrially diverse manufacturing and service economy. Thus, according to Arthur Lewis’s structural change theory, underdeveloped economies can only achieve sustainable development only if they undergo a structural transformation. However, Arthur Lewis recognizes that technology is crucial to the structural transformation of developing economies. Thus, we incorporated the compensation theory of technological change into the framework. The theory acknowledges that technological innovation may create some disruptions such as unemployment at the initial stage, however, it can compensate for the initial disruption through various compensation mechanisms. For example, technology innovation may create additional employment in the capital goods sector, which may help to increase productivity and economic growth, and engender sustainable development. In the alterative, the compensation mechanism may manifest through reduction in prices resulting from a decrease in the cost of production, and creation of new demand for product and employment, which may ultimately foster sustainable development (see Metu et al., 2021).

The study analysed the role of structural transformation in engendering sustainable growth and development in 18 SSA countries (due to data unavailability) over the period 1990–2018. For the list of 18 countries, see Table A1 in the  Appendix. Secondary data on ICT (proxied by mobile cellular subscription, fixed telephone subscriptions, and ICT goods imports), economic complexity ^[4], and economic transformation are used to measure structural transformation. Economic complexity assesses the current state of a country’s productive knowledge. The idea is that countries that can sustain a diverse range of productive know-how, including sophisticated, unique know-how would foster sustainable growth and development. Economic transformation is measured by output growth (gross value added at current basic prices) across various sectors, namely, agriculture ^[5], mining ^[6], manufacturing ^[7], utilities ^[8], construction ^[9], services ^[10], real estate ^[11])) ^[12].

Sustainable development is captured by a robust measure of sustainable development (which considers life expectancy, expected and mean years of schooling, GNI per capita, CO2 emissions, and material footprint). The study also adopts a more robust measure of sustainable development (which considers life expectancy, expected and mean years of schooling, GNI per capita, CO2 emissions, and material footprint). The control variables used in the study are the inflation rate used to capture macroeconomic fluctuations in the economy; and per capital gross domestic product (pGDP) to measure of economic development. Trade openness can also influence sustainable development by influencing carbon emissions through scale, composition, and technology effects. For instance, if the combined technology and composition effects (oriented towards clean goods) outweigh the scale effect, trade openness will improve environmental quality, and thus, sustainable development; and vice-versa (Fakher, 2019). Institutional quality (captured by the quality of government index) is used to capture the institutional environment. It is important to consider the quality of the institutional environment to reinforce the need for state and institutional capacity to drive and support innovation and create an enabling business environment, especially for developing countries who lack a comprehensive legal framework and institutional capacity to drive and support innovation for sustainable development. The measurement and sources of data are shown in Table 2.

The study models the relationship between structural transformation and sustainable development in line with extant studies. This is shown in Equation (1):

where subscripts $N$ is the time index, sd denote sustainable development (the measure considers life expectancy, expected and mean years of schooling, GNI per capita, CO2 emissions, and material footprint). ST denotes structural transformation (captured by a vector of variables, namely, (technology (measured by mobile subscription per 100 users, fixed telephone subscription per 100 users, and ICT goods imports)), economic transformation (measured by sectoral output growth)), and Knowledge (captured by economic complexity)). $Xt$ captures other control variables that could influence sustainable development, in line with extant studies, such as, the quality of institutions (proxied by the ICRG’s quality of government index, which is measured by the mean value of the ICRG variables on “Corruption”, “Law and Order” and “Bureaucracy Quality”). The ICRG variables have been re-scaled from 0 to 1— the closer to 1, the higher values indicate higher quality of government ^[13]; economic growth, tertiary school enrolment ratio, and inflation rate, and $εt$ is the error term.

The baseline estimation technique is the traditional ordinary least square (OLS). However, the OLS estimate is limited, in that it does not account for the potential cross-sectional or temporal dependency in panel data models. Thus, the study also adopts Driscoll-Kraay covariance matrix estimator to account for every form of cross-sectional and temporal dependence in panel data, and in technology diffusion (see Caldarola et al., 2023).

Before estimating the cross-sectional and spatial-dependence consistent models, the study tests whether the error terms in the panel models are truly cross-sectionally independent since it is affirmed that panel data models are likely to show various forms of cross-sectional dependencies (see Driscoll and Kraay, 1998). The test is set under the null hypothesis (H₀) the errors are cross-sectional independent.

This section presents the results from empirical analysis and discusses the results in relation to existing studies and theory. First, we present the descriptive statistics of the data used in the study.

Table 3 presents descriptive statistics. The data shows that the model is stable as indicated by the mean, median, minimum, and maximum values. For instance, the data shows that the mean and median values are close, which denotes a symmetric distribution, low variability, and a high level of consistency.

Table 4 presents the result of the multicollinearity test. By rule of thumb, the decision rule is that there is multicollinearity among variables when the variance inflation factor (VIF) is above 4.0. The result in Table 4, column 1 shows that there is multicollinearity among the measures of transformation (agriculture, mining, manufacturing, utilities, construction, services, and real estate), and technology (mobile subscription per 100 users, fixed telephone subscription per 100 users, and ICT goods imports). To sidestep this issue, we use each variable per time. The results in columns 3 and 5 show that the model is free from the problem of multicollinearity. Thus, we estimate the model using each of the proxies of transformation, and technology per time.

The study examined the order of stationarity of the variables. The result is presented in Table 5. The result shows that the variables are integrated either at levels, i.e. I(0) or at first difference, i.e. I(1).

Table 6 presents the baseline model. The method of estimation is the ordinary least square (OLS). The result indicates across all specifications that knowledge, technology, and transformation contribute positively to sustainable development in SSA. However, given that many panel data models exhibit some form of cross-sectional and temporal dependence, we test for cross-sectional dependence in the model.

The results of the cross-section dependence tests show that the units are cross-sectionally dependent (see Tables 7 and 8). Thus, the OLS estimation may not be adequate.

In the presence of cross-sectional and temporal dependence, we re-examined the order of stationarity using second-generation CIPS and CADF panel unit root tests. The model is set under the null hypothesis of homogeneous non-stationarity. The results are presented in Table 9. The result is consistent with the result from the first-generation unit tests. The result shows that the variables are integrated either at levels, i.e. I(0) or at first difference, i.e. I(1))

The result in Table 10 is robust to different estimation techniques. The study finds that knowledge (proxied by economic complexity) exerts a positive and statistically significant impact of sustainable development in SSA (see column 1), indicating that knowledge is very vital for sustainable development in Africa. Similarly, we found that technology (mobile cellular subscription, and fixed telephone lines subscription) promotes sustainable development (see columns 2 and 3). The study aligns with the findings of Dantas et al. (2020) and Ghobakhloo et al. (2021) ^[14][15]. Similarly, the result is consistent with the findings of Caldarola et al. (2023) who reported that an increase in mobile internet increases employment opportunities and contributes to changes in the composition of the labour market, education, and migration in Rwanda. However, we do not find that ICT imports engender sustainable development. The result is also consistent with the submission of the Nigerian Communication Commission that the achievement of the 2030 sustainable development agenda in SSA may be dependent on broadband availability and technology infrastructure (Olaoye and Zerihun, 2023). The Commission notes further that the security, economic, and educational development of Nigeria, and the region in general, may be contingent on the availability of state-based broadband structure and framework. The result is also corroborated by the central bank of Nigeria in its July 2022 report, that technology and innovation are crucial for positive economic transformation, output growth and sustainable development (Central Bank of Nigeria, 2022).

On the effect of economic transformation on sustainable development, the results show that all the economic transformation variables, except the utilities and real estate sectors promote sustainable development in SSA (see columns 5 to 11). This implies the rapid development of the services, manufacturing, agriculture, construction, and mining sectors can aid sustainable development in SSA. The results also align with the World Bank report (2023) that the rapid development of some sectors, particularly the services sector, driven by rapid growth and application of information and communication technologies (ICTs) has been the driving force behind the post-pandemic recovery in some parts of Africa. We also found that economic development (captured by per capital GDP) and capital (measured by gross capital formation) are important drivers of sustainable development in SSA. However, trade openness does not contribute to sustainable development in SSA. This might be that the combined scale effect in trade outweighs the combined technology and composition effects (oriented towards clean goods) in SSA. That is, the ICT component in total trade activities in SSA is not sufficient to engender sustainable development. This may also explain the insignificant impact of ICT goods import (a measure of ICT) on sustainable development (see column 4).

The results presented in Table 10 (column 12) show that quality of government exerts a positive and statistically significant effect of sustainable development, indicating that improvements in the quality of government in SSA will foster sustainable development. However, the level of technology preparedness (measured by tertiary school enrolment) exerts a negative and statistically significant effect on sustainable development in SSA, while technology use (proxied the share of population using the internet) has no statistically significant effect on sustainable development in SSA, indicating that SSA may not be ready to harness the gains of the ongoing technological revolution. This might mean that the number of people enrolled in tertiary school education, and the share of the population with access to internet may be too small to help drive the sustainable development agenda in SSA.

In summary, the results emanating from the empirical research indicate that the structural transformation of the SSA economy will aid the actualization of the sustainable development agenda in SSA. The results align with a recent study by Tasneem and Khan (2024) who found that structural transformation towards promising sectors (manufacturing and services) has a positive impact on the macro-economic variables as well as at the household level in Pakistan.

The study investigated the link between structural transformation (measured by knowledge, technology, and economic transformation) and sustainable development in SSA. The study adopted a cross-sectional and spatial-consistent model to account for every form of cross-sectional and temporal dependence in panel data, and more specifically, in technology diffusion. The study finds that knowledge (proxied by economic complexity) exerts a positive and statistically significant impact of sustainable development in SSA. Similarly, we found that technology (mobile cellular subscription, and fixed telephone lines subscription) promotes sustainable development. The results show that all the economic transformation variables, except the utilities and real estate sectors promote sustainable development in SSA. This implies the rapid development of the services, manufacturing, agriculture, construction, and mining sectors can aid sustainable development in SSA. The results also align with the World Bank report (2023) that the rapid development of some sectors, particularly the services sector, driven by rapid growth in information and communication technologies (ICTs) has been the driving force behind the post-pandemic recovery in some parts of Africa. We also found that economic development (captured by per capital GDP) and capital (measured by gross capital formation) are important drivers of sustainable development in SSA. However, trade openness does not contribute to sustainable development in SSA.

Based on the empirical findings, the study recommends the following:

One, government and policymakers across SSA should invest more in technology and knowledge-acquisition programmes to foster sustainable development in SSA.

Two, governments across SSA should provide state-based broadband and eco-friendly technologies to foster sustainable development.

Three, governments across SSA should ensure that ICT-based products constitute a larger component of their imports.

Four, governments should make education (particularly, at the tertiary level) affordable and accessible to all to improve the level of technological preparedness that will aid the structural transformation of SSA economies.

Five, governments across SSA should invest more in ICT and mobile cellular infrastructure or create an enabling environment that promotes digitization and the development of financial technology in the manufacturing, mining, construction, agriculture, and services sector (often referred to as the knowledge economy) since the knowledge economy can enhance green and quality growth and promote sustainable development in SSA.

The study examined the effect of structural transformation on sustainable development in SSA. One limitation of the study is that it did not consider the potential non-linear relationship in the model. However, this is not the focus of the study. Future research can investigate the optimal level of technology and knowledge that engenders sustainable development.