The study investigates the link between structural transformation and sustainable development in sub-Saharan Africa.
The study adopts the traditional ordinary least square method and the Driscoll and Kraay covariance matrix estimator to address every form of cross-sectional and temporal dependence in panel data.
The study finds the structural transformation of the SSA economy will engender sustainable development. Specifically, the study finds that knowledge exerts a positive and statistically significant impact on sustainable development in SSA. Similarly, we found that technology (mobile cellular subscription and fixed telephone line subscription) promotes sustainable development. The results also show that all the economic transformation promotes sustainable development in SSA. Further, we also found that economic development and physical capital are important drivers of sustainable development in SSA. However, trade openness does not contribute to sustainable development in SSA. This might be because the combined scale effect in trade outweighs the combined technology and composition effects in SSA. This suggests the technology component in total trade activities in SSA does not promote sustainable development. The study recommends that governments across SSA should invest more in ICT and mobile cellular infrastructure or create an enabling environment that encourages digitization and the development of financial technology in the manufacturing, mining, construction, agriculture and services sectors to enhance green and quality growth for sustainable development in SSA.
The study uncovers the role of structural transformation in promoting sustainable development in SSA.
1. Introduction
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.
2. Literature review
The literature reviewed has been summarized in Table 1.
A summary of review of literature on structural transformation and SDGs
| Author(s) and date | Topic | Estimation strategy | Findings |
|---|---|---|---|
| Structural transformation | |||
| Tasneem and Khan (2024) | Growth and structural transformation—options for Pakistan | Computable General Equilibrium (CGE) model | The results show 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 |
| Caldarola et al. (2023) | Mobile internet, skills, and structural transformation in Rwanda | OLS and 2SLS | The study found 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 |
| Abbas et al. (2023) | Structural transformation, urbanization, and remittances in developing countries: A panel VAR analysis | Vector Autoregressive (VAR) Model | The study reported that there is a two-way causal relationship between structural transformation and urbanization, structural transformation, and GDP growth |
| Fereira and Cateia (2023) | Trade reform, infrastructure investment, and structural transformation in Africa: Evidence from Guinea-Bissau | Dynamic Computable General Equilibrium model | The study found that structural transformation reduces inequality over time |
| Chen et al. (2023) | Do structural transformation and urbanization assist in enhancing sustainable energy technologies innovations? Evidence from ASEAN countries | Methods of Moments Quantile Regression | The findings show that structural transformation, urbanization, and renewable energy consumption significantly derive sustainable energy technology innovations |
| Ghosh et al. (2023) | Does economic structure matter for income inequality? | System-GMM (generalized method of moments) | The authors found that structural transformation improves income distribution |
| Zhao et al. (2022) | Does structural transformation in economy impact inequality in renewable energy productivity? Implications for sustainable development | CS-ARDL | The study shows that structural transformation helps to reduce the inequality in renewable energy productivity |
| Ali and Gninigué (2022) | Global value chains participation and structural transformation in Africa Are we advocating environmental protection? | Second-generation panel data | The study found that structural transformation influences the relationship between global value chain and environmental pollution |
| Grainger-Brown et al. (2022) | Exploring urban transformation to inform the implementation of the Sustainable Development Goals | A Systematic Qualitative Review | The authors note that urban transformation can be a tool to design a new transformative pathway to achieve SDGs in cities |
| Wang et al. (2022) | Has the Sustainable Development Planning Policy Promoted the Green Transformation in China’s Resource-based Cities? | Synthetic Method | The authors note that the implementation of the SDP policy in China has a weak positive promotion effect on the overall transformation and development of resource-based cities |
| Haldar and Sethi (2022) | Effect of sectoral foreign aid allocation on growth and structural transformation in sub-Saharan Africa—Analysing the roles of institutional quality and human capital | Driscoll–Kraay Fixed-Effect estimators, Fixed-Effects Panel Threshold regression and Method of Moments Quantile regression | The authors found that the agricultural and social sectors do not aid structural transformation |
| Muazu (2020) | Effects of trade and financial integration on structural transformation in Africa: New evidence from a sample splitting approach | Threshold Analysis | The study reported that the there is an optimal level above which trade openness and financial integration slows down structural transformation |
| López and Yoon (2020) | Sustainable development: Structural transformation and the consumer demand | Dynamic equilibrium | The findings reveal that a consumers’ composition effect plays a critical role in the structural transformation process |
| Atolia et al. (2020) | Rethinking development policy: What remains of structural. Transformation? | A Systematic Review | The review indicates that the manufacturing sector, which has been beneficial for most advanced economies for decades, may be incapable of delivering similar benefits to low-income countries |
| Cardinale and Scazzieri (2019) | Explaining structural change: actions and transformations | A Review | |
| Vu (2017) | Structural change and economic growth: Empirical evidence and policy insights from Asian economies | Generalized Method of Moments (GMM) | The result indicates that effective structural change (ESC) can be an important indicator for monitoring the impacts of structural reforms |
| Innovative technologies and sustainable development | |||
| Vaidya et al. (2018) | Industry 4.0— A Glimpse | A Systematic Review | The study notes that Industry 4.0 allows smart, efficient, effective, production at reasonable cost |
| Wen and Zhu (2021) | China's industrial revolution: A new perspective | A Simple Analysis | The author notes that the lack of industrialization in any nation is due to the lack of a mass market |
| Metu et al. (2021) | The Fourth Industrial Revolution and Employment in Sub-Saharan Africa: The Role of Education | System GMM | The result shows that 4IR exert a positive effect on industry labor employment in SSA |
| Digaru (2020) | The Main Goals of the Fourth Industrial Revolution. Renewable Energy Perspectives | A Simple Analysis | The authors note that the 4IR has the potential to increase revenue and raise standard of living |
| ElMassah and Mohieldin (2020) | Digital transformation and localizing the Sustainable Development Goals (SDGs) | A Simple Correlational Study | The study found that digital transformation boosts localization of sustainable development |
| Armoo et al. (2020) | The fourth industrial revolution: a game-changer for the tourism and maritime industries | A Qualitative Approach | The authors assert that the integration of the floating dry dock with 3D printing technology can make Jamaica a more economically viable country |
| Bhattacharyya and Mitra (2020) | Fourth industrial revolution and India’s “employment problem” | Fixed Effects (FE), Random Effects (RE), and OLS estimation Techniques | The authors found that innovation does not seem to enhance the performance index in a very significant manner across industry groups considered in the study |
| Erer and Erer (2020) | Industry 4.0 and its Role on Labour Market: A Comparative Analysis of Turkey and European Countries | A Simple Correlational Study | The authors concluded that Turkey have a higher risk at automation than European countries |
| Demirbağ and Yildirim (2018) | Industry 4.0: Literature Review and Thematic Analysis | A Review | The review shows that empirical methods are rare, with majority of the studies utilizing qualitative methods such as case studies |
| Ghobakhloo (2020) | Industry 4.0, digitization, and opportunities for sustainability | Interpretive Structural Modelling Technique | The results indicate that sophisticated precedence relationships exist among various sustainability functions of Industry 4.0 |
| Ghobakhloo et al. (2021) | Industry 4.0 ten years on: A bibliometric and systematic review of concepts, sustainability value drivers, and success determinants | A Bibliometric and Systematic Review | The findings reveal that Industry 4.0 transformation could address pressing issues of sustainable development goals, particularly manufacturing-economic development |
| Stock et al. (2018) | Industry 4.0 as Enabler for Sustainable Development: A Qualitative Assessment of its Ecological and Social Potential | A Qualitative Assessment Approach | The assessment uncovers that the value creation might positively contribute to a sustainable development in many instances |
| Dantas et al. (2020) | How the combination of circular economy and industry 4.0 can contribute towards achieving the Sustainable Development Goals | A systematic Literature Review | The authors note that the Circular economy- Industry 4.0 nexus is pivotal in the endeavours to achieve the SDGs |
Source(s): Created by authors
3. Theoretical framework, methodology and model specification
3.1 Theoretical framework
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).
3.2 Methodology
3.2.1 Materials and method
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.
Measurement and sources of variables
| Variables | Definition/Measurement | Source |
|---|---|---|
| 1. Structural transformation | ||
| i. Technology | This is measured by mobile subscription per 100 users, fixed telephone subscription per 100 users, and ICT goods imports | WDI/International Telecommunication Union |
| ii economic transformation | This is measured by output growth (gross value added at current basic prices) across various sectors, namely, agriculture, mining, manufacturing, utilities, construction, services, and real estate | ETD |
| iii. Economic complexity | Economic complexity assesses the current state of a country’s productive knowledge. A high ECI index implies a higher level of productive knowledge | Atlas Media databank |
| 2. Sustainable development | This is measured by a single index which considers the following five indicators: life expectancy, education, income, material footprint, and CO2 emissions | Hickel (2020) Sustainable development database |
| 3. Economic development | It is measured by per capita GDP | WDI |
| 4. Quality of government | This is measured by the mean value of the ICRG variables on “Corruption”, “Law and Order” and “Bureaucracy Quality”. The closer to 1, the higher quality of government | ICRG Quality of Government Dataset |
| 5. Trade openness | This is the degree to which nondomestic transactions (imports and exports) take place in a national economy | WDI |
| 6. Level of technology preparedness and literacy | This is measured by school enrolment, tertiary (% gross) | WDI |
| 7. Technology use | It is measured by the share of individuals using the internet | International Telecommunication Union |
| 8. Inflation | It is measured by consumer prices | WDI |
| 9. Physical capital | It is measured by gross capital formation | WDI |
Note(s): WDI denotes World Bank Development Indicators, ETD is Economic transformation Dataset (de Vries et al., 2021), ICRG is the International Country Risk Guide
Source(s): Created by authors
3.2.2 Model specification
The study models the relationship between structural transformation and sustainable development in line with extant studies. This is shown in Equation (1):
where subscripts 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)). 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 is the error term.
3.2.3 Estimation technique
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).
3.2.4 Cross sectional dependence test
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.
4. Empirical analysis
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.
4.1 Descriptive statistics
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.
Descriptive statistics of variables
| Panel A | sus. dev | tech | know | transf | pGDP | quality o G | techpre | inf | gcf |
|---|---|---|---|---|---|---|---|---|---|
| Summary statistics | |||||||||
| Mean | 0.50 | 31.43 | −0.77 | 10.62 | 7.01 | 0.45 | 7.58 | 11.14 | 22.60 |
| Median | 0.50 | 7.49 | −0.77 | 10.77 | 6.92 | 0.45 | 5.03 | 7.31 | 21.93 |
| Max | 0.71 | 161.19 | 0.36 | 15.27 | 9.27 | 0.89 | 41.59 | 183.31 | 58.18 |
| Min | 0.20 | 0.000 | −2.33 | 5.15 | 5.24 | 0.09 | 0.32 | −9.61 | 0.00 |
| std. dev. | 0.10 | 40.96 | 0.50 | 2.36 | 0.97 | 0.11 | 8.25 | 16.19 | 9.10 |
| Skewness | −0.22 | 1.24 | −0.29 | 0.02 | 0.39 | 0.64 | 1.92 | 5.70 | 0.31 |
| Kurtosis | 2.80 | 3.54 | 3.24 | 2.12 | 2.25 | 4.68 | 6.89 | 50.15 | 4.64 |
| Observations | 489 | 521 | 406 | 522 | 522 | 435 | 347 | 485 | 484 |
| Panel B | |||||||||
| sus. dev. | |||||||||
| tech | 1.000 | ||||||||
| know | 0.359 | 1.000 | |||||||
| transf | −0.010 | −0.125 | 1.000 | ||||||
| pGDP | 0.627 | 0.492 | −0.246 | 1.000 | |||||
| quality o G | 0.070 | 0.437 | −0.361 | 0.395 | 1.000 | ||||
| techpre | 0.855 | 0.332 | −0.141 | 0.816 | 0.203 | 1.000 | |||
| inf | −0.212 | −0.190 | −0.053 | −0.140 | 0.183 | −0.200 | 1.000 | ||
| gcf | 0.339 | 0.107 | 0.034 | 0.251 | 0.028 | 0.315 | −0.056 | 1.000 | |
Note(s): Table 2 presents the descriptive statistics for the variables used in the study. The study covers 18 sub-Saharan African countries over the period 1990–2018. sus. dev. tech, know, transf, pGDP, quality o G, techpre, inf, and gcf denote sustainable development, technology (captured by mobile subscription), knowledge (measured by economic complexity), transformation (measured by logarithm of financial services), quality of government, technology preparedness (measured by school enrolment, tertiary (% gross), inflation, and gross capital formation, respectively
Source(s): Created by authors
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.
Multicollinearity test
| Variable | VIF | 1/VIF | VIF | 1/VIF | VIF | 1/VIF |
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | |
| Agriculture | 204.10 | 0.004 | 4.09 | 0.244 | – | – |
| Manufacturing | 154.12 | 0.006 | – | – | – | – |
| Financial services | 120.57 | 0.008 | – | – | 2.36 | 0.42 |
| Utilities | 69.59 | 0.014 | – | – | – | – |
| Business services | 55.42 | 0.018 | – | – | – | – |
| Real estate | 47.34 | 0.021 | – | – | – | – |
| Construction | 44.66 | 0.022 | – | – | – | – |
| pGDP | 18.32 | 0.054 | – | – | – | – |
| School enrolment | 13.96 | 0.071 | – | – | 4.13 | 0.24 |
| Fixed tele | 12.71 | 0.078 | 4.14 | 0.24 | – | – |
| Mobile | 10.06 | 0.099 | – | – | 4.24 | 0.23 |
| Mining | 8.99 | 0.11 | – | – | – | – |
| Pop. using internet | 7.73 | 0.12 | 3.66 | 0.27 | – | – |
| Trade open | 7.55 | 0.13 | 3.41 | 0.29 | 3.25 | 0.30 |
| Complexity | 3.80 | 0.26 | 1.96 | 0.51 | 1.72 | 0.58 |
| QoG | 3.53 | 0.28 | 1.90 | 0.52 | 1.88 | 0.53 |
| gcf | 3.00 | 0.33 | 1.46 | 0.68 | 1.47 | 0.67 |
| ICT imports | 2.57 | 0.38 | – | – | – | – |
| Inflation | 1.90 | 0.52 | 1.29 | 0.77 | 1.23 | 0.81 |
| Mean VIF | 41.58 | 3.12 | 2.53 |
Note(s): VIF denotes Variance Inflation Factor
Source(s): Created by authors, authors computation
4.1.1 Unit root test
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).
Result of the panel unit root tests (individual effects)
| Variables | LLC | Breitung | IPS | PP-Fisher | ADF-Fisher | Order |
|---|---|---|---|---|---|---|
| tech | −2.14*** | −3.35*** | 74.80*** | 84.18*** | I(1) | |
| inf | −6.80*** | −6.37*** | 108.30*** | 147.01*** | I(0) | |
| sus. dev. | −2.45*** | – | −4.09*** | 78.74*** | 152.70*** | I(1) |
| pGDP | −6.41*** | −8.75*** | 149.25*** | 240.74*** | I(1) | |
| know | −1.99** | −2.77*** | 54.48*** | 85.02** | I(0) | |
| transf | −5.35*** | −8.85*** | 152.34*** | 304.63*** | I(1) |
Note(s): Table 4 shows the results of unit root tests under individual and linear trends
Source(s): Created by authors
4.1.2 Baseline regression
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.
OLS estimates
| Variable | Knowledge | Technology | Transformation | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| sus. dev | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
| Complex | 0.037*** (0.009) | – | – | – | – | ||||||
| Mobile | – | 0.0003*** (0.00009) | – | – | – | ||||||
| Fixed tele | – | – | 0.0018** (0.0008) | – | – | ||||||
| ICT imports | – | – | – | 0.007*** (0.001) | – | ||||||
| Agriculture | – | – | – | – | 0.007*** (0.002) | ||||||
| Mining | – | – | – | – | – | −0.004* (0.0022) | |||||
| Manufacturing | – | – | – | – | – | 0.0088*** (0.0022) | |||||
| Services | – | – | – | – | – | 0.011*** (0.002) | |||||
| Utilities | – | – | – | – | – | 0.011*** (0.002) | |||||
| Construction | – | – | – | – | – | 0.009*** (0.002) | |||||
| Real estate | – | – | – | – | – | 0.003*(0.002) | |||||
| Inflation | 0.001** (0.0004) | 0.0009** (0.0004) | 0.0006 (0.0004) | 0.0006 (0.0004) | 0.0004** (0.0001) | 0.0003 (0.0004) | 0.0009** (0.0005) | 0.0008* (0.0004) | 0.0008** (0.0004) | 0.0008* (0.0004) | 0.0007* (0.0004) |
| gcf | 0.0003 (0.0006) | 0.0001 (0.0005) | 0.001* (0.0005) | 0.001* (0.0005) | 0.0005 (0.0005) | 0.001* (0.0005) | 0.0007 (0.0005) | 0.0003 (0.0005) | 0.001** (0.0005) | 0.0001 (0.0005) | 0.0006 (0.0005) |
| pGDP | 0.062*** (0.006) | 0.066*** (0.005) | 0.066*** (0.006) | 0.066*** (0.006) | 0.081*** (0.004) | 0.076*** (0.004) | 0.075*** (0.004) | 0.070*** (0.004) | 0.079*** (0.004) | 0.074*** (0.004) | 0.074*** (0.004) |
| Trade | −0.0001 (0.0001) | −0.00001 (0.0001) | −0.0001 (0.0001) | −0.0001 (0.0001) | 0.0004** (0.0001) | −0.0002 (0.0002) | 0.0004** (0.0001) | 0.0005*** (0.0001) | 0.0005*** (0.0001) | 0.0005*** (0.0002) | 0.0002 (0.0001) |
| Pop/internet | – | – | – | – | – | ||||||
| QoG | – | – | – | – | – | ||||||
| School enrol | – | – | – | – | – | ||||||
| Constant | 0.091** (0.047) | 0.010 (0.036) | 0.011 (0.041) | 0.085** (0.04) | −0.208*** (0.059) | 0.004 (0.041) | −0.185*** (0.051) | −0.166*** (0.042) | −0.242*** (0.0455) | −0.180 (0.046) | −0.098** (0.047) |
| Adjusted R2 | 0.4701 | 0.4975 | 0.4824 | 0.4152 | 0.4997 | 0.4809 | 0.4964 | 0.5103 | 0.5647 | 0.5023 | 0.4807 |
| F-statistics | 55.82 | 75.45 | 71.27 | 37.92 | 74.30 | 70.84 | 75.33 | 79.57 | 97.52 | 77.11 | 70.78 |
| Prob. F-statistics | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
Note(s): Sus. dev, complex, mobile, fixed tele, services, gcf, pGDP, trade, pop/internet, QoG, school enrol are sustainable development, economic complexity, mobile cellular subscription, fixed telephone subscription, financial services, gross capital formation, share of population using the internet, quality of government, and school enrolment, respectively. ***p < 0.01, **p < 0.05, *p < 0.1
Source(s): Created by authors
4.2 Cross-sectional dependence
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.
Panel cross-section dependence tests (variable)
| Variable | Breusch-Pagan LM | Pesaran scaled LM | Bias-corr. scaled LM | Pesaran CD |
|---|---|---|---|---|
| sus. dev. | 2515.688***(0.0000) | 134.03***(0.0000) | 133.71***(0.000) | 28.91***(0.0000) |
| inf | 557.17***(0.0000) | 22.07***(0.0000) | 21.75**(0.0000) | 16.65**(0.0000) |
| tech | 4139.87***(0.0000) | 226.88***(0.0000) | 226.56***(0.0000) | 64.30***(0.0162) |
| know | 365.71***(0.0000) | 14.82***(0.0000) | 14.50****(0.0000) | 2.80***(0.0000) |
Note(s): The cross-section dependence test is set under the null hypothesis of cross-section independence, CD ∼ N (0,1) p-values close to zero indicate data are correlated across panel groups. ***p < 0.01, **p < 0.05, *p < 0.10. Abbreviations: CSD, cross-sectional dependence; LM, Lagrange multiplier
Source(s): Created by authors
Panel cross-section dependence tests in the model (regression)
| Test | Results |
|---|---|
| 1.Breusch-Pagan LM | 867.07***(0.0000) |
| 2. Pesaran Scaled LM | 56.48***(0.0000) |
| 4. Pesaran (2004) CD | 21.35***(0.0000) |
Note(s): ***p < 0.01, **p < 0.05, *p < 0.10
Source(s): Created by authors
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))
Results of panel unit root tests in presence of cross-section dependence
| CIPSa | CADFb | CIPSa | CADFb | |
|---|---|---|---|---|
| Level | 1st difference | |||
| sus. dev. | – | 4.64 | – | −2.52*** |
| transf | 2.14*** | −1.90 | – | −2.53*** |
| pGDP | −1.98 | −1.86 | −4.38*** | −3.285*** |
| Know | – | −1.66** | ||
| Tech | – | −4.17*** | ||
Note(s): aH0 (homogeneous non-stationary): bi = 0 for all I whereas b the null hypothesis assumes all series are non-stationary in a heterogeneous panel with cross-sectional dependence. ***p < 0.01, **p < 0.05, *p < 0.10
Source(s): Created by authors
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).
Driscoll-Kraay estimates (adjusted for fixed effects)
| Variable | Knowledge | Technology | Transformation | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| sus. dev | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
| Complexity | 0.034*** (0.01) | – | – | – | – | 0.024*** (0.008) | ||||||
| Mobile | – | −0.0002* (0.0001) | – | – | – | 0.0001 (0.0001) | ||||||
| Fixed tele | – | – | −0.006*** (0.001) | – | – | |||||||
| ICT imports | – | – | – | 0.0003 (0.001) | – | |||||||
| Agriculture | – | – | – | – | 0.142*** (0.018) | |||||||
| Mining | – | – | – | – | – | 0.050*** (0.004) | ||||||
| Manufacturing | – | – | – | – | – | 0.054*** (0.014) | ||||||
| Services | – | – | – | – | – | 0.0359*** (0.009) | 0.085*** (0.0102) | |||||
| Utilities | – | – | – | – | – | 0.015 (0.018) | ||||||
| Construction | – | – | – | – | – | 0.075*** (0.005) | ||||||
| Real estate | – | – | – | – | – | 0.039 (0.023) | ||||||
| Inflation | −0.003 (0.0002) | −0.0003 (0.0002) | −0.00003 (0.0001) | 0.0003* (0.0002) | 0.0005** (0.0002) | 0.0004 (0.0002) | 0.00001 (0.0002) | −0.00005 (0.0002) | −0.0002 (0.0003) | 0.0003 (0.0001) | −0.00007* (0.0002) | 0.0002 (0.0002) |
| gcf | 0.002*** (0.0008) | 0.002** (0.0009) | 0.001** (0.0001) | 0.001*** (0.0004) | 0.001** (0.0006) | 0.0008 (0.0005) | 0.002** (0.0009) | 0.001** (0.0009) | 0.002*** (0.0008) | 0.001*** (0.0005) | 0.002*** (0.0009) | −0.0007*** (0.0002) |
| pGDP | 0.135*** (0.011) | 0.208*** (0.022) | 0.207*** (0.011) | 0.175*** (0.007) | 0.042* (0.023) | 0.095*** (0.014) | 0.091*** (0.014) | 0.072* (0.035) | 0.145*** (0.023) | −0.013*** (0.004) | 0.093** (0.044) | 0.018 (0.028) |
| Trade | −0.0001 (0.0001) | −0.00001 (0.0001) | −0.00002*** (0.0001) | −0.0001 (0.0001) | −0.0001 (0.0001) | −0.0002 (0.0001) | −0.00003 (0.0002) | −0.00002 (0.0002) | 0.0002 (0.0001) | −0.00003 (0.0001) | 0.0001 (0.0001) | −0.0004** (0.0001) |
| Pop/internet | – | – | – | – | – | −0.0002(0.0003) | ||||||
| QoG | – | – | – | – | – | 0.09***(0.035) | ||||||
| School enrol | – | – | – | – | – | −0.004***(0.001) | ||||||
| Constant | −0.486*** (0.080) | −1.04*** (0.153) | −0.997*** (0.073) | −0.789** (0.062) | −1.59*** (0.100) | −0.760 (0.062) | −0.852*** (0.103) | −0.447*** (0.154) | −0.762*** (0.080) | −0.275 (0.062) | −0.670*** (0.089) | −0.560*** (0.135) |
| Within R2 | 0.5013 | 0.4375 | 0.5195 | 0.5102 | 0.5838 | 0.6002 | 0.4813 | 0.4892 | 0.4907 | 0.5516 | 0.4741 | 0.8710 |
| F-statistics | 489.15 | 355.50 | 222.71 | 419.54 | 597.16 | 579.06 | 709.43 | 474.70 | 399.64 | 472.60 | 523.16 | 1038.91 |
| Prob. F-statistics | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
Note(s): Sus. dev, complex, mobile, fixed tele, services, gcf, pGDP, trade, pop/internet, QoG, school enrol are sustainable development, economic complexity, mobile cellular subscription, fixed telephone subscription, financial services, gross capital formation, share of population using the internet, quality of government, and school enrolment, respectively. ***p < 0.01, **p < 0.05, *p < 0.1
Source(s): Created by authors
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).
4.2.1 The role of technology preparedness and technology use
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.
5. Conclusions, policy recommendations, and suggestions for further studies
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.
Notes
Danquah et al. (2024) defined structural transformation as the shift from an agrarian economy to a more industrialized economy as well as the redistribution of income to poor households.
The knowledge economy (captured by the services sector) can enhance green and quality growth, and promote sustainable development (Olaoye and Zerihun, 2023).
Schwab (2016) defines the 4IR as the fusion of technologies that blurred the lines between the physical, digital, and biological spheres.
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 growth and development.
Agriculture sector comprises Agriculture, forestry, and fishing.
Mining includes mining and quarrying.
Manufacturing sector comprises of all manufacturing-related activities.
The sector comprises electricity, gas, steam, and air conditioning supply; water supply; sewerage, waste management and remediation activities.
Construction refers to all activities relating to construction.
It includes all financial and insurance activities.
All Real estate activities.
Krueger and Maleckova (2003) and Choi (2010) argue that sound institutions (captured by corruption, bureaucratic quality, and law and order) promote sustainable development, while a deficient rule of law, high level of corruption, and low bureaucratic quality hinder sustainable development.
The authors affirmed that innovative technologies enhance sustainable development.
References
Further reading
Appendix
The infographic titled “Africa’s I C T development indicators” presents a comparison of Africa, all developing countries, developed countries, and world averages across several technology and education indicators related to the Fourth Industrial Revolution. The subtitle states, “Africa still lags behind both developed and other developing countries in several indicators essential for the Fourth Industrial Revolution, especially in infrastructure, technology access, and education”. The infographic is divided into three main sections titled “Technology access”, “Technology use”, and “Technology preparedness”. Each section contains grouped bar charts comparing four categories represented in the legend as “Africa”, “All developing countries”, “Developed countries”, and “World”. Under “Technology access”, the first chart is a horizontal bar graph displaying “International internet bandwidth per internet user (bits per second)”. The horizontal axis contains numerical values increasing from left to right, with labels ranging from 0 to 140,000 bits per second. Developed countries show the longest bar, followed by the world average and all developing countries, while Africa records the shortest bar. The remaining charts in this section are vertical bar graphs displaying “Fixed telephone subscriptions per 100 inhabitants”, “Percent of households with a computer”, and “Percent of households with internet access”. Their vertical axes contain numerical scales increasing upward, ranging from 0 to 100. Across these indicators, developed countries consistently show the highest values, while Africa records the lowest values. The largest gaps appear in internet bandwidth, household computer ownership, and internet access. Under “Technology use”, the section contains vertical bar graphs displaying “Fixed (wired) broadband subscriptions per 100 inhabitants” and “Active mobile broadband subscriptions per 100 inhabitants”. The vertical axes contain numerical values increasing upward, ranging from 0 to 100. Africa shows very low fixed broadband subscriptions compared with developed countries and lower mobile broadband usage than the other groups, although mobile broadband values are substantially higher than fixed broadband values across all regions. Under “Technology preparedness”, the vertical bar graphs display “Mean years of schooling”, “Secondary gross enrollment ratio”, and “Tertiary gross enrollment ratio”. The vertical axes contain numerical scales increasing upward, ranging from 0 to 12 for mean years of schooling and from 0 to 120 for the enrollment ratio charts. Developed countries show the highest education-related preparedness values across all three indicators. Africa records the lowest tertiary gross enrollment ratio and lower schooling and secondary enrollment values compared with world and developing-country averages. The bottom of the infographic includes a legend identifying the four comparison groups and source text citing Hebatallah Adam, the International Telecommunication Union, and the Africa Growth Initiative at Brookings, along with a logo.Africa’s technology development indicator
The infographic titled “Africa’s I C T development indicators” presents a comparison of Africa, all developing countries, developed countries, and world averages across several technology and education indicators related to the Fourth Industrial Revolution. The subtitle states, “Africa still lags behind both developed and other developing countries in several indicators essential for the Fourth Industrial Revolution, especially in infrastructure, technology access, and education”. The infographic is divided into three main sections titled “Technology access”, “Technology use”, and “Technology preparedness”. Each section contains grouped bar charts comparing four categories represented in the legend as “Africa”, “All developing countries”, “Developed countries”, and “World”. Under “Technology access”, the first chart is a horizontal bar graph displaying “International internet bandwidth per internet user (bits per second)”. The horizontal axis contains numerical values increasing from left to right, with labels ranging from 0 to 140,000 bits per second. Developed countries show the longest bar, followed by the world average and all developing countries, while Africa records the shortest bar. The remaining charts in this section are vertical bar graphs displaying “Fixed telephone subscriptions per 100 inhabitants”, “Percent of households with a computer”, and “Percent of households with internet access”. Their vertical axes contain numerical scales increasing upward, ranging from 0 to 100. Across these indicators, developed countries consistently show the highest values, while Africa records the lowest values. The largest gaps appear in internet bandwidth, household computer ownership, and internet access. Under “Technology use”, the section contains vertical bar graphs displaying “Fixed (wired) broadband subscriptions per 100 inhabitants” and “Active mobile broadband subscriptions per 100 inhabitants”. The vertical axes contain numerical values increasing upward, ranging from 0 to 100. Africa shows very low fixed broadband subscriptions compared with developed countries and lower mobile broadband usage than the other groups, although mobile broadband values are substantially higher than fixed broadband values across all regions. Under “Technology preparedness”, the vertical bar graphs display “Mean years of schooling”, “Secondary gross enrollment ratio”, and “Tertiary gross enrollment ratio”. The vertical axes contain numerical scales increasing upward, ranging from 0 to 12 for mean years of schooling and from 0 to 120 for the enrollment ratio charts. Developed countries show the highest education-related preparedness values across all three indicators. Africa records the lowest tertiary gross enrollment ratio and lower schooling and secondary enrollment values compared with world and developing-country averages. The bottom of the infographic includes a legend identifying the four comparison groups and source text citing Hebatallah Adam, the International Telecommunication Union, and the Africa Growth Initiative at Brookings, along with a logo.Africa’s technology development indicator
