This paper investigates the impact of imported capital and intermediate inputs on structural change in African economies, focusing on the origin of imports—Global North versus Global South—and the moderating role of domestic absorptive capacity. The paper relies on a panel dataset comprising 52 African countries from 2000 to 2022 and a two-stage least squares (2SLS) estimation strategy. We find that imported inputs from the Global South consistently positively affect structural change, due to the alignment of those inputs with the technological realities and production structures of African economies. Disaggregated results further reveal that these effects are stronger when imported capital (intermediate) inputs are sourced from the emerging Global South economies (relatively less-industrialised Global South economies). Absorptive capacity dampens these positive effects, suggesting that the structural change effect of imported inputs from the Global South may be especially effective in lower-capacity environments. In contrast, while intermediate inputs from the Global North also foster structural change unconditionally, the benefits of capital imports from the Global North materialise only when African economies possess high levels of domestic absorptive capacity. The findings underscore the importance of a nuanced trade policy that leverages the strengths of both trade relations to advance Africa's structural change.

This paper draws on newly available data to examine the sources of labor productivity growth in Africa and to identify key emerging patterns. We show that while recent structural change in Africa has been growth-enhancing, overall labor productivity growth remains modest. Manufacturing—traditionally a cornerstone of structural transformation—has played a limited role since the 1990s. Instead, services, especially wholesale and retail trade, have been the main drivers. These trends are evident not only in value added and employment data but also in trade patterns, where services increasingly contribute to export growth. We argue that Africa’s modest productivity growth reflects deeper structural constraints, including limited physical and social infrastructure. The former category includes electricity access and roads; but we also argue that productivity growth is constrained by extreme poverty, poor health, and inadequate education. Against this backdrop, we argue for an African-led development strategy that recognizes both traditional and overlooked sources of productivity growth while addressing foundational barriers to transformation.

South Africa has been grappling with a persistent electricity crisis that has placed the country on an uncertain and turbulent economic trajectory. Based on a two-stage empirical approach, this paper unpacks the extent to which the country's manufacturing sector has been affected by this crisis. Notably, we first use South Africa's domestic input–output matrices to develop an index of manufacturing sub-sector energy vulnerability. The index reveals significant degree of heterogeneity across manufacturing sub-sectors, highlighting cross-sector differences in the level of exposure and susceptibility to the energy-related crises. Second, we employ the self-constructed energy vulnerability index in a flexible empirical framework to examine the effect of the electricity crisis on manufacturing sector jobs in the country. The empirical analysis results indicate that the electricity crisis is associated with significant job destruction, with this adverse effect severe for sectors with higher energy vulnerability intensity. The severity of this adverse effect holds irrespective of the nature of jobs, whether formal or informal. We discuss the implication of these findings in line with South Africa's economic recovery, resilience and development.

Firm-level innovation in developing countries is mostly incremental and depends on non-R&D activities. Integration into global production networks is one such activity that could help firms in developing countries innovate, particularly since new technologies and foreign knowledge diffuse through inter-firm linkages. Accordingly, this paper examines the relationship between Global Value Chain (GVC) participation and firm-level innovation in Africa, using data from the World Bank's Enterprise Survey (WBES). Employing different estimation strategies that enable us to address various empirical challenges, we find strong evidence suggesting that African GVC firms are highly innovative. They are not just more likely to introduce new products and processes but also more likely to jointly introduce both types of innovation as well as radical innovations. In an extended analysis, we found that integrating small and medium enterprises and younger firms into GVC enables them to overcome resource constraints, resulting in higher innovativeness. Finally, we document that the innovation gains from GVC trickle down to non-GVC firms in the same industry and region, implying that firms engaged in GVC activities generate positive spillovers to other firms in the economy. A proposed framework rationalizes our findings. The framework sheds light on the mechanisms that make firm-level innovation possible across African firms in an era where GVC is an important conduit for inter-firm learning, knowledge exchange, and technology transfer.

This paper examines the impact of Chinese foreign direct investment (FDI) on low-carbon industrialization in Africa, within the context of China's growing economic ties with the continent. The analysis relies on a panel dataset comprising Chinese greenfield FDI into the manufacturing sectors of 34 African countries from 2003 to 2014, employing the Lewbel Instrumental Variable approach to address potential endogeneity issues. The results show that these Chinese FDI inflows increased industrial carbon emissions in Africa. This adverse effect is particularly pronounced when Chinese FDI targets labor and resource-intensive manufacturing sectors. We attribute this finding to two mechanisms: the sector concentration on labor and resource-intensive manufacturing and the manufacturing processes of Chinese FDI characterized by suboptimal de facto implementation of environmental, social and governance (ESG) standards compared to the international best practices. Additional analysis underscores the potential moderating influence of FDI-host countries' environmental regulations, albeit statistically insignificant, highlighting the legacy of ineffective institutional enforcement that is prevalent on the Africa continent.

Using both cross-quantilograms and Granger-causality in-mean and in-quantiles techniques, this paper investigates the dynamic dependence of transition minerals on global clean energy and technology markets for the period from December 28, 2018 to October 4, 2024. The results reveal asymmetric responses of transition minerals to changes in the global clean energy and technology markets, with a notably stronger and more consistent positive linkage to the global technology market. Transition minerals such as aluminum, cobalt, copper, and zinc tend to move in tandem with technology market returns, underscoring their strategic role in tech-driven growth. Interestingly, we found more compelling evidence of Granger causality from the clean energy market to transition minerals across several market states than we found from the technology market. For investors and commodity traders, these results suggest a forward-looking informational edge in the use of clean energy market signals to anticipate price movements in specific mineral markets, particularly in hedging or timing strategies. For policymakers, especially in mineral-rich economies, the results also highlight the importance of tracking global clean energy demand trends as early shock signal in the transition mineral market.

This paper analyzes the dependence and connectedness among fourth-industrial revolution technology markets (including big data and artificial intelligence, blockchain, and financial technology) and global and regional (US, Europe, and Asia) green energy markets. In particular, we consider the dynamic dependence among these markets in terms of both returns and volatility across different market conditions and investment horizons using the cross-spectral coherence and Quantile-VAR connectedness approach. Three main results emerge from our analysis. First, the return dependence is relatively stronger than volatility dependence and is stronger across most time scales among the technology markets and the European and Asian regional green energy indexes. Second, the return and volatility connectedness is stronger during extreme than normal market conditions. Unless under bullish market times, volatility connectedness appears smaller than return connectedness, implying that market volatility risks spread less forcefully among these markets than return risks under normal and bearish market periods. Third, geopolitical risks, business environment, economic policy, fixed-income, and oil and gold markets’ uncertainties are significant predictors of the degree of return and volatility connectedness. Overall, our findings offer crucial insights for short- and long-term investors interested in portfolios with modern technology and green assets. They also emphasize the roles of market and macroeconomic factors in shock propagation and their implications for low-carbon transition.

This paper examines return and volatility connectedness among Non-Fungible Tokens (NFTs) and (un)conventional financial assets across various market conditions using a Quantile-VAR connectedness technique. It also explores the predictive powers of major global macroeconomic and geopolitical indicators on both connectedness across these market conditions. First, we find that return and volatility connectedness vary across market conditions, with higher levels during extreme events. Except during bullish periods, return connectedness dominates volatility connectedness. Second, NFTs are decoupled from both (un)conventional assets during normal market condition but it is a net return shocks receiver except under bullish market period, where it is a net transmitter. However, it is a net volatility shocks receiver irrespective of the market situation. Lastly, geopolitical risks, business condition and economic policy uncertainty are important predictors of return and volatility connectedness, although the strength and direction are heterogeneous. We discuss the policy implications of these findings.

This paper employs wavelet coherence, Cross-Quantilogram (CQ), and Time-Varying Parameter Vector-Autoregression (TVP-VAR) estimation strategies to investigate the dependence structure and connectedness between investments in artificial intelligence (AI) and eight different energy-focused sectors. We find significant evidence of dependence and connectedness between the stock returns of AI and those of the energy-focused sectors, especially during intermediate and long-term investment horizons. The relationship has become stronger since the COVID-19 pandemic. More specifically, results from the wavelet coherence approach show a stronger association between the stock returns of energy-focused sectors and AI, while results from the CQ analysis show that directional predictability from AI to energy-focused sectors varies across sectors, investment horizons, and market conditions. TVP-VAR results show that since the COVID-19 outbreak, AI has become more of a net shock receiver from the energy market. Our study offers crucial implications for investors and policymakers.

We explore the heterogeneous effect of migrant remittances on citizens' support for taxation using a sample comprising 45,000 individuals from the Afrobarometer survey round 7 [2016–2018] across 34 African countries. To correct for unobserved heterogeneity, we endogenously identify latent classes/subtypes of individuals that share similar patterns on how their support for taxation is affected by their unobserved and observed characteristics, including remittance dependency. We apply the finite multilevel mixture of regressions approach, a supervised machine learning method to detect hidden classes in the data without imposing a priori assumptions on class membership. Our data are best generated by an econometric model with two classes/subtypes of individuals. In class 1 where more than two-thirds of the citizens belong, we do not find any significant evidence that remittance dependence affects support for taxation. However, in class 2 where the remaining one-third of the citizens belong, we find a significant negative effect of remittance dependence on support for taxation. Furthermore, we find that citizens who have a positive appraisal of the quality of the public service delivery have a lower probability of belonging to the class in which depending on remittance reduces support for taxation. The findings emphasize the need for efficient public services provisioning to counteract the adverse effect of remittances on tax morale.

Using the South African Revenue Service and National Treasury firm-level panel data for 2009–2017, this paper investigates how trade related to the global value chain (GVC) affects the performance of manufacturing firms in South Africa. The paper uses extant classifications of internationally traded products to identify different categories of GVC-related products and compares the productivity premium of international traders for these different categories. Also, the paper investigates possible differences in learning-by-exporting effects across the identified categories of GVC-related products by estimating the effect of exporting before and after entry into foreign markets. The results confirm that GVC-related trade is associated with a higher productivity premium compared with traditional trade. However, within the categories of exporters, only the firms that trade in GVC-related products and simultaneously engage in research and development in the post-entry periods appear to learn from exporting. Our results underscore the gains of GVC integration in terms of the associated productivity premia and highlight the need for GVC-integrated firms to invest in building technological capacity.

This paper examines the relationships among cryptocurrency environmental attention and clean cryptocurrencies prices using Time-Varying Parameter Vector Auto-Regression (TVP-VAR) and wavelets techniques. Results show strong connectedness among these variables, implying that the prices of clean cryptocurrencies are influenced by attention on cryptocurrency sustainability. Connectedness is stronger with positive shocks on environmental attention than negative shocks. Also, in the short-term, clean cryptocurrencies prices lead environmental attention, especially after 2021. However, there are notable periods when environmental attention led clean cryptocurrency prices before 2021. In the long-term, clean cryptocurrencies such as Hedera, Polygon, Cosmos, IOTA, TRON, Stellar, Tezos and Ripple lead environmental attention. In the presence of bitcoin, the degrees of connectedness increased across both shocks on cryptocurrency environmental attention. In all cases, the bitcoin market is the main destination of shocks from the system. We highlight some crucial implications of these results.

Productive efficiency has far-reaching implications on the direction of economic growth and welfare. While this has led to an expansive literature on the drivers of productive efficiency, this literature has proceeded without considering the role of energy poverty. Yet, energy poverty affects productive efficiency on several fronts. This paper fills this knowledge gap, u. sing a sample comprising 100 developing countries for the period 2000–2017. W e found robust evidence suggesting that energy poverty negatively affects productive efficiency—i.e., energy-poor countries become productively inefficient. Further analysis in the paper revealed that this negative effect persists largely across regions and is not sensitive to cross-country differences in income level. We also found that the negative effect of energy poverty on productive efficiency becomes more pronounced at a higher level of productive efficiency. We discuss the policy implications of our findings.

A substantial part of production and trade now takes place through global value chains (GVC), making it an essential conduit of knowledge spillover and technology transfer. Yet, extant studies examining how countries become productively efficient and catch-up to the global efficiency frontier through international trade have ignored the possible role of GVC in fast-tracking this process. This paper provides the first empirical evidence that fills this knowledge gap. We propose a two-stage empirical strategy to this end. First, we use a data envelopment analysis (DEA) approach to compute a measure of productive efficiency—defined herewith as a country’s relative productive efficiency to the global productive efficiency frontier. Second, we use the productive efficiency index as an outcome variable in a reduced-form equation that controls for GVC participation and its interaction with country characteristics that influence the gains from GVC participation. In addition to using the panel fixed effect method, we estimate the reduced-form equation with the difference-GMM to address endogeneity issues, and the Tobit and Fractional Response models to address the bounded nature of the productive efficiency index. We find strong evidence suggesting that GVC participation enables technology-lagging countries to become more productively efficient as well as catch-up to the global efficiency frontier. We also find that the productive efficiency and catch-up gains from GVC participation accrue more strongly to countries that have a high human capital stock, a well-functioning financial market, maintain stable macroeconomic conditions, and specialize in downstream activities in the value chain.

This paper uses the Quantile Vector-Autoregressive (Q-VAR) technique to examine the connectedness between three regional (North America, Europe and Asia-Pacific) sustainability indices and major natural resource commodities including energy commodities (crude oil and natural gas), precious metals (gold, silver, and platinum), and industrial metals (steel, aluminium, and copper). It also uses a linear regression model to investigate the macroeconomic and geopolitical factors that drive the connectedness among these investments. The QVAR results reveal asymmetric connectedness among these investment indices, with the levels of total connectedness during extreme downside and upside market conditions being significantly stronger than the level of connectedness during normal market condition. The results also show that, on average, the amount of shocks the regional sustainable investment indices each received from the studied energy and metal commodity markets are higher (lower) than what they transmit to the commodity market during the extreme upside (downside) market condition. During the normal market condition, however, only the Asian Pacific sustainable investment receives more shock than it transmits to the studied commodity market indices, making a net shock receiver. Finally, geopolitical risks, business environment conditions, gold and fixed income markets and economic policy uncertainty are important predictors of return connectedness, although the predictive strength and direction vary across market conditions. We discuss the implications of our findings.

The conventional portfolio design approach assumes Gaussian return distributions, but this is not accurate in practice. Asymmetric and heavy-tailed return distributions necessitate consideration of higher-order moments such as skewness and kurtosis, in addition to mean and variance. This study proposes a multi-objective approach using a mean-variance-skewness-kurtosis model to construct a diversified portfolio. A parametrized polynomial goal programming (PGP) method is used to optimize the portfolio by maximizing returns and skewness while minimizing variance and kurtosis. Empirical data from the S &P ESG index family is used, and PGP generates multiple portfolios reflecting investors’ preferences for the four moments. To compare between the obtained portfolios, we represent the empirical cumulative distribution of the portfolio returns for all studied values of weights and show how this can be used to assist the inverstor in selecting the best set of weights.

This paper examines the dependence between Artificial Intelligence (AI) and eight energy-focused sectors (including renewable energy and coal) across different market conditions and investment horizons. This paper adopts both linear and non-linear models such as quantile regressions and quantile cross-spectral coherency models. Evidence from the linear model suggests that the performance of energy-focused corporations, especially those in the renewable energy sector depends strongly on the performance of AI. Results from the non-linear model indicate that dependence varies across both energy sectors, market conditions as well as investment horizons. By considering both negative and positive shocks on AI, we demonstrate that the dependence of energy corporations on AI also varies according to the direction of shocks on AI. Interestingly, negative and positive shocks on AI impact differently on the performance of energy corporations across different sectors and market conditions. Besides, we found that the dependence became stronger during the first wave of the COVID-19 pandemic. Our findings hold profound implications for portfolio managers and investors, who may be interested in holding the assets of AI and those of energy corporations.

In 1987, Robert M. Solow hinted at the computer age and productivity statistics puzzle. While this puzzle persisted for many years, a growing literature has picked it up to examine the effect of ICTs on technical efficiency. However, this literature has focused mainly on quantity-based ICTs measures, which have come under severe criticism in recent times. We advance this literature in this paper by shifting the focus of analysis to quality-based ICTs measures; in this case, Internet quality. We also extend the literature by examining how the envisaged relationship between ICTs quality and technical efficiency is conditioned by a country's unique attributes. Our results show a significantly positive effect of ICTs quality on technical efficiency. We also find that the technical efficiency gains associated with ICTs quality are higher in skill-abundant countries, countries that engage more intensively in cross-border trade, have stronger contracting institutions, and are endowed with well-functioning and well-developed financial markets that ensure greater efficiency of capital allocation. We find a network effect in the nexus between ICTs quality and technical efficiency. That is, the marginal effect of ICTs quality on technical efficiency increases as the number of Internet users expands. We discuss the policy implications of our findings.

This paper examines the connectedness among 12 African equity markets and the global commodity, developed equity markets, paying particular attention to their evolution during the COVID-19 pandemic's peak period. We find that whilst African equity markets connect weakly to these markets, the levels of connectedness among these markets improved significantly during the pandemic. In addition, the energy market dominates the transmission of shocks in the system with commodity markets. Regarding the system with equity markets, the French and South African equity markets transmit the highest spillover in the full sample and during the pandemic's peak period, respectively.

We examine the dependence between volume and returns for the NFT market and three sub-markets (Cryptokitties, Cryptopunks, and Decentraland) using both quantile cross-spectral coherency and quantile regression techniques. Results from both techniques show significant evidence of dependence between NFT return and volume. Dependence between volume and return is weakest in the Cryptopunks market. Similarly, quantile regression results show that during extreme market conditions, equity and gold markets uncertainty, business condition and term-spread are important predictors of Cryptokitties returns, while oil, equity and gold markets uncertainty and geopolitical risks significantly predict Cryptopunks and Decentraland markets returns. In all cases, increase in Bitcoin prices reduces NFT market returns.