For the expanded BRICS grouping, the researchers applied Bayesian hierarchical methods to improve inference stability in the presence of multicollinearity and parameter uncertainty. The authors describe this as the first Bayesian hierarchical application of the neo-Schumpeterian diffusion model. The supplied evidence consists of the study abstract, so the full methods, results and limitations cannot be assessed here.

The abstract identifies several factors associated with technological growth. Innovative human capital is linked to both imitation and domestic innovation, while institutional quality, tacit knowledge transfer, foreign direct investment attraction and information and communication technology diffusion are reported as significant drivers. The estimated effects of research and development intensity, digital investment, financial development and education investment are described as contingent on governance quality and absorptive capacity, indicating that financial inputs alone were not sufficient in the model without supportive institutions and capabilities.

These results are estimates from an observational modeling framework rather than direct experimental findings. The abstract reports convergence toward the frontier and identifies associated determinants, but it does not establish that individual factors caused the estimated technological changes. Its scope is limited to the specified BRICS groupings and the 1996–2022 period, and the supplied material does not provide country-level effect sizes, uncertainty intervals or details of robustness tests.

Subject of Research: Bayesian modeling of technology diffusion and convergence in BRICS economies

Article Title: Bayesian hierarchical modeling reveals how Schumpeterian technology diffusion drives BRICS convergence toward the United States frontier

Article References: Pham, K. X., & Thach, N. N. (2026). Bayesian hierarchical modeling reveals how Schumpeterian technology diffusion drives BRICS convergence toward the United States frontier. Discover Sustainability. https://doi.org/10.1007/s43621-026-04848-5

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04848-5

Keywords: BRICS, technological convergence, Bayesian hierarchical models, technology diffusion, domestic innovation, absorptive capacity, institutional quality, human capital, United States, Bayesian, hierarchical, modeling

The study combined three econometric approaches—Three-Stage Least Squares, Generalized Method of Moments and Full Information Maximum Likelihood—with machine-learning models. According to the abstract, the 3SLS model explained 96% of the variation in the Sustainable Development Index. The machine-learning component also showed strong predictive performance: Gradient Boosted Trees achieved an out-of-sample R² of 0.948.

The reported coefficient ranges were 0.023–0.030 for institutional quality, 0.011–0.025 for ICT and approximately 0.008–0.016 for resource efficiency. The authors described better resource utilization, improved institutional quality and more advanced ICT infrastructure as significant factors associated with sustainability outcomes in European countries. These are model-based associations, and the abstract does not establish that changing any of the measured factors would cause the reported changes in sustainable development.

The available source is an abstract for an open-access research article published online on 11 October 2026 in Discover Sustainability. It identifies the study population, period, methods, estimated coefficients and reported model performance, but does not provide enough methodological detail to assess the underlying full analysis, robustness checks or uncertainty around the estimates. The publisher’s early-access notice also states that the version may undergo further edits before replacement by the final Version of Record.

Subject of Research: Econometric and machine-learning analysis of sustainable development in 26 European countries from 2000 to 2023

Article Title: An intersection of machine learning and advanced econometric techniques to analyse sustainable development in Europe through resource efficiency, institutional quality, and ICT

Article References: Waqas, H., Ashfaq, M., Gill, A. R., Dadwal, S., Rauf, A., & Ahmad, U. (2026). An intersection of machine learning and advanced econometric techniques to analyse sustainable development in Europe through resource efficiency, institutional quality, and ICT. Discover Sustainability. https://doi.org/10.1007/s43621-026-04864-5

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04864-5

Keywords: sustainable development, European countries, resource efficiency, institutional quality, information and communication technology, econometrics, machine learning, carbon dioxide emissions, Gradient Boosted Trees, intersection, machine, learning

The centerpiece of the analysis is the prevalence of undernourishment, the standard indicator used to track progress toward SDG 2. Rather than treating all countries as a single homogeneous sample, the study first builds composite indices that capture four risk dimensions: institutional risk, demographic risk, environmental risk, and economic risk. These indices aggregate multiple underlying variables into coherent measures, allowing the researcher to test whether broad systemic pressures—not just isolated factors such as conflict or drought—are statistically associated with hunger. This multidimensional framing reflects a growing consensus in the food security literature that undernourishment is rarely the product of a single cause. Instead, it emerges from the interaction of fragile governance, population pressure, climate exposure, and volatile economic conditions, each capable of amplifying the others.

The methodological innovation lies in a two-stage empirical strategy. In the first stage, a K-means clustering algorithm—an unsupervised machine learning technique that partitions observations into groups based on similarity—classifies the 120 countries into three food insecurity profiles: high, moderate, and low. The high-insecurity cluster includes countries such as Afghanistan, the Democratic Republic of the Congo, Kenya, Liberia, Madagascar, Sierra Leone, Uganda, and Zambia. The moderate cluster contains a broad group including Bangladesh, Ethiopia, India, Nigeria, and Pakistan, among others. The low-insecurity cluster spans most of Europe, North America, parts of Asia and Latin America, and several African countries such as Ghana, Senegal, and South Africa. This data-driven grouping avoids imposing arbitrary regional or income-based categories and lets the underlying risk structure define the comparison groups.

In the second stage, the study applies pooled quantile regression within each cluster. Unlike ordinary least squares regression, which estimates the effect of a variable on the mean of the outcome, quantile regression estimates effects at different points of the outcome distribution—the 10th, 50th, and 90th percentiles, for example. This distinction matters enormously for food insecurity research. A country sitting at the 90th percentile of undernourishment is in a fundamentally different situation from one at the median, and the factors pushing it deeper into crisis may differ from those that matter for countries closer to the average. Mean-based studies, the paper argues, obscure exactly this kind of heterogeneity, potentially leading policymakers to design uniform interventions that fail where they are needed most.

The headline finding is stark: environmental risk is the most consistent factor associated with undernourishment across all three clusters. In the quantile estimates, the environmental composite index shows positive and statistically significant coefficients across most quantiles in the moderate and low-insecurity groups, with particularly strong associations in the low-risk cluster at the median and upper quantiles. When the composite is decomposed into its components, carbon dioxide emissions show positive and significant associations with undernourishment across nearly all quantiles in the high and moderate clusters, and across the upper half of the distribution in the low-insecurity cluster, where the coefficient at the 90th percentile reaches 1.20. Agricultural land use shows negative associations in the moderate cluster but positive ones in the low cluster, suggesting that the environmental dimension operates through different channels at different stages of development.

Demographic risk tells a more complicated, and arguably more surprising, story. In countries with high and moderate food insecurity, the demographic composite index is positively associated with undernourishment at the lower quantiles, indicating that population pressure adds to hunger risk where food systems are already fragile. But the sign flips at the upper quantiles and in the more secure cluster, where demographic risk is negatively associated with food insecurity. Life expectancy, one of the demographic components, shows negative and significant coefficients at the upper quantiles in both the high and moderate clusters, consistent with the idea that longer-lived, healthier populations are better positioned to secure adequate nutrition. The lesson is that demographic dynamics are not inherently good or bad for food security; their effect depends on the vulnerability context in which they unfold.

Institutional quality, captured by the institutional composite index, emerges as a protective factor—but only in certain settings. In the moderate and low-insecurity clusters, the index shows negative and significant coefficients at the median and upper quantiles, with the strongest effects in the low-risk cluster, where the coefficient reaches −0.63 at the 90th percentile. In other words, better institutions are most strongly associated with lower undernourishment precisely in the countries that are already relatively secure, while in the most vulnerable group the institutional variable shows no significant association at any quantile. This asymmetry carries a sobering implication: governance improvements alone may not deliver measurable hunger reductions in the most fragile states, where environmental and demographic pressures may overwhelm institutional gains, or where institutions may be too weak for the index to register meaningful variation.

Economic risk, by contrast, is the least consistent of the four dimensions. Across most clusters and quantiles, the economic composite index fails to reach statistical significance. The exception is telling: in the most vulnerable group, at the 90th percentile, the index shows a negative and significant coefficient. This means that economic risk matters only at the extreme upper tail of undernourishment in the hardest-hit countries—exactly the situations of acute crisis that mean-based analyses tend to smooth away. The finding underscores the paper’s central methodological claim that quantile-specific, asymmetric associations are invisible to average-effect estimation, and that policies calibrated to the average country may miss the dynamics driving the worst outcomes.

What does this mean for the pursuit of SDG 2? The study’s answer is that interventions should be severity-specific rather than uniform. For countries in the high-insecurity cluster, environmental pressures and demographic stress dominate the picture, suggesting that climate adaptation, emissions mitigation, and investments in health and human capital are inseparable from food policy. For moderate-risk countries, strengthening institutions appears to be a viable pathway to reducing hunger, particularly for populations at the upper end of the undernourishment distribution. For relatively secure countries, the strong environmental signal—especially the carbon emissions component—suggests that even wealthy nations’ food security trajectories are tied to their environmental footprints. The clustering approach itself offers a practical template: by grouping countries according to their risk profiles rather than their geography or income alone, policymakers can identify peer countries facing similar systemic pressures and adapt strategies that have worked elsewhere in the same cluster.

The research also carries broader lessons for how the development community measures progress. Global hunger statistics are typically reported as averages, and international targets are set against those averages. But if the drivers of undernourishment differ systematically across the outcome distribution—as this study demonstrates—then average-based monitoring can mask deterioration among the most vulnerable even as headline numbers improve. The combination of unsupervised clustering and pooled quantile regression offers a replicable framework for other SDG indicators, from poverty to health access, wherever heterogeneity across vulnerability contexts is suspected. As climate change intensifies and geopolitical instability persists, the study’s core message grows more urgent: ending hunger will require understanding not just which risks matter, but for whom, and at what point of the crisis spectrum they bite hardest.

Subject of Research: Heterogeneous associations between multidimensional systemic risks and food insecurity across 120 countries

Article Title: Assessing food insecurity under global risks using a clustered pooled quantile framework for sustainable development goal 2

Article References: Essadik, E. (2026). Assessing food insecurity under global risks using a clustered pooled quantile framework for sustainable development goal 2. Discover Sustainability. https://doi.org/10.1007/s43621-026-04811-4

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04811-4

Keywords: food insecurity, Sustainable Development Goal 2, undernourishment, systemic risk, quantile regression, K-means clustering, environmental risk, institutional quality, demographic risk, economic risk, food security policy, Discover Sustainability

The research tackles one of the most persistent puzzles in development economics. Governments across Africa face mounting pressure to strengthen domestic revenue mobilisation to finance infrastructure, education, healthcare and private-sector development, yet many continue to grapple with narrow tax bases, weak compliance, rising public debt and institutional constraints. Traditional fiscal exchange theory holds that taxpayers comply more willingly when governments use revenues productively, and that stronger revenues should therefore support growth. But the empirical evidence has long been mixed, and the new study offers a systematic explanation for why: the fiscal-growth relationship appears to be institutionally conditioned rather than uniform.

Methodologically, the study is unusually ambitious. The core analysis employed a Two-Way Fixed Effects estimator with Driscoll-Kraay standard errors, which are robust to heteroskedasticity, serial correlation and cross-sectional dependence, the latter being a common feature of macro panels where countries experience shared global shocks. This baseline was complemented by Panel Threshold Regression to detect regime shifts, Panel Quantile Regression to examine whether associations vary across the conditional growth distribution, and Feasible Generalised Least Squares and Panel-Corrected Standard Errors as alternative robustness estimators. Crucially, the author also deployed two machine-learning algorithms, XGBoost and Random Forest, interpreted through SHAP feature-importance analysis, to assess the predictive contribution of each variable, while carefully distinguishing predictive importance from causal or econometric significance.

The baseline results were striking. Tax revenue mobilisation, measured as tax revenue as a percentage of GDP, showed a negative and statistically significant association with annual GDP per capita growth. Domestic credit to the private sector was also negatively associated with growth, while regulatory quality emerged with a strongly positive and significant coefficient. Most tellingly, the interaction between tax revenue and regulatory quality was negative and highly significant, indicating that the estimated tax-growth association varies systematically with the institutional environment. New business density, public debt, inflation, population growth and foreign direct investment did not reach statistical significance in the baseline specification.

The negative headline coefficients, however, tell only part of the story. The threshold analysis, using regulatory quality as the threshold variable, estimated a statistically significant breakpoint at a regulatory quality value of minus 0.412. Below that threshold, tax revenue mobilisation was negatively associated with growth; above it, the association turned positive. A similar pattern emerged for domestic credit to the private sector, which shifted from a negative association in weaker regulatory regimes to a weakly positive one in stronger regimes, and for new business density, which became positively associated with growth only in the higher-quality regulatory environment. Foreign direct investment likewise showed a positive association with growth only above the threshold.

The quantile regressions reinforced this picture of heterogeneity. Tax revenue mobilisation was negative at the 25th and median conditional quantiles of growth but turned positive and weakly significant at the 75th quantile. Domestic credit followed the same trajectory, moving from negative at lower quantiles to positive at the upper quantile, while new business density became positive at the 75th quantile. Regulatory quality, by contrast, remained positive and significant across the entire distribution, with its coefficient strengthening from 4.128 at the 25th quantile to 9.371 at the 75th. The interaction between tax revenue and regulatory quality stayed negative throughout, though its magnitude declined at higher quantiles.

The machine-learning component added a distinct predictive perspective. XGBoost outperformed Random Forest, achieving an R-squared of 0.781 against 0.724, with lower root mean squared error and mean absolute error, under a country-blocked validation design designed to prevent information leakage across a country’s own observations. SHAP analysis ranked regulatory quality as the leading predictor of economic growth, followed by tax revenue mobilisation, domestic credit to the private sector and public debt, with broadly consistent rankings across both algorithms. The author is explicit that these rankings indicate predictive contribution within the fitted models, not causal importance, coefficient direction or economic effect size.

The study is equally candid about its limitations, a transparency that strengthens rather than weakens its contribution. The balanced panel is a harmonised analytical dataset rather than a fully observed one: 64 percent of new business density observations, nearly 43 percent of tax revenue observations, 30 percent of public debt observations and 24 percent of regulatory quality observations were completed through interpolation, backward or forward filling, or cross-sectional median imputation. A restricted sensitivity analysis using only directly observed data, comprising 252 observations across 28 countries, preserved the direction of several central coefficients but with reduced precision, and some coefficients changed sign. The author also stresses that tax revenue as a share of GDP captures the scale of mobilisation but not tax composition, administrative efficiency, compliance burdens or the productivity of the expenditure it finances, and that the observational design cannot eliminate reverse causality or omitted confounding. All estimates are therefore conditional associations, not causal effects.

The theoretical framing helps explain why the results look the way they do. The study integrates four complementary perspectives: fiscal exchange theory, which links revenue to accountable public spending; endogenous growth theory, which emphasises entrepreneurship, innovation and financial development; debt overhang theory, which identifies constraints from excessive indebtedness; and institutional theory, which explains how governance shapes incentives and resource allocation. Within this framework, new business density and domestic credit are treated as selected observable dimensions of entrepreneurial ecosystem development rather than exhaustive measures, and public debt as an indicator of fiscal-sustainability pressure rather than a complete measure of fiscal health. The unexpected negative baseline coefficients for taxation and credit are interpreted not as evidence that these factors are inherently growth-reducing, but as reflections of conditions, such as credit allocation efficiency and expenditure productivity, that the aggregate measures cannot separately identify.

The policy implications are suggestive rather than prescriptive, but they point in a clear direction. Rather than focusing exclusively on raising the tax-to-GDP ratio, the findings are consistent with reforms that pair revenue mobilisation with regulatory effectiveness, accountable resource management and the productive use of public funds. Similarly, expanding business registration or private-sector lending alone may be insufficient where surrounding institutional conditions do not support viable enterprises and productive allocation of credit. As African economies confront rising debt burdens and intensifying demands for domestic revenue, the study’s central message resonates: the same tax shilling can be associated with very different growth outcomes depending on the institutional machinery that collects and deploys it. Future research, the author suggests, should pursue richer measures of tax design, entrepreneurial ecosystems and fiscal sustainability, alongside stronger identification strategies capable of distinguishing genuine causal mechanisms from the heterogeneous conditional relationships documented here.

Subject of Research: The relationship between tax revenue mobilisation, entrepreneurial and financial conditions, institutional quality, and economic growth across African economies

Article Title: Tax revenue mobilisation and economic growth across African economies under entrepreneurial fiscal and institutional conditions

Article References: Mishiwo, S. H. (2026). Tax revenue mobilisation and economic growth across African economies under entrepreneurial fiscal and institutional conditions. Discover Global Society, 4(1), Article 272. https://doi.org/10.1007/s44282-026-00632-y

Image Credits: AI Generated

DOI: 10.1007/s44282-026-00632-y

Keywords: tax revenue mobilisation, economic growth, Africa, regulatory quality, institutional quality, fiscal policy, entrepreneurship, domestic credit, panel threshold regression, quantile regression, XGBoost, SHAP

The study, published in the journal Discover Sustainability by Emmanuel Bosomtwe and Samuel Gameli Gadzo of the University of Education, Winneba, together with Charles Omane-Adjekum, Joseph Nyame, and Emmanuel Kwasi Agyepong, set out to answer a question that has long hovered at the margins of environmental economics: does the way a country manages its money shape the way it manages its environment? To find out, the team assembled a dataset of 80 quarterly observations spanning 2005 to 2024, a period that covers Ghana’s commodity booms, its debt crises, its inflation surges, and its repeated encounters with International Monetary Fund programs. That twenty-year window gives the analysis unusual depth for a study of a single West African economy.

Methodologically, the researchers chose the classical autoregressive distributed lag model, known in econometrics as ARDL. The technique is prized in macroeconomic research because it can handle variables that move at different speeds and different orders of integration, and it can separate short-run dynamics from long-run equilibrium relationships. In practical terms, the ARDL framework allowed the team to ask whether changes in monetary policy today are associated with changes in ecological footprint in the following quarters, and whether a stable long-run relationship ties the two together across the full sample period. The explanatory, quantitative design means the study is built to test hypotheses rather than to generate narratives, which gives its conclusions a firmer statistical footing than descriptive accounts of environmental change.

The headline finding is an inverse relationship between monetary policy and ecological footprint. In plain language, when the Bank of Ghana tightens monetary conditions, raising policy rates, restraining credit growth, and pulling excess liquidity out of the financial system, the ecological footprint tends to fall. The mechanism the authors describe is intuitive once spelled out. Cheap money fuels consumption and expansion. When borrowing is easy, firms invest in energy-hungry production, households buy more fuel, appliances, and imported goods, and the economy as a whole draws harder on forests, fisheries, cropland, and carbon sinks. Tightening the monetary taps slows that appetite. Credit becomes expensive, speculative expansion stalls, and the pace of resource extraction and unsustainable production eases. Monetary policy, in this reading, acts as an indirect brake on environmental degradation, not because it was designed for that purpose, but because it governs the tempo of economic activity itself.

But the second finding is where the study makes its most distinctive contribution. Institutional quality, the researchers report, has a significant positive moderating effect on the monetary policy-ecological footprint relationship. Moderation is a specific statistical concept: it means institutions do not merely sit alongside monetary policy as a separate influence on the environment, they change how well monetary policy works. Strong governance frameworks amplify the environmental dividends of sound monetary management, while weak ones dilute them. A policy rate hike announced in Accra travels through commercial banks, credit markets, regulatory agencies, and enforcement bodies before it changes any real-world behavior. If those transmission channels are corrupted, politicized, or simply under-resourced, the signal degrades before it reaches the factories, farms, and households whose decisions determine the ecological footprint.

The theoretical implications reach into two established bodies of thought. The authors argue that their results extend Ecological Modernization Theory, the idea that modern economies can decouple growth from environmental harm through technological and institutional innovation, by empirically confirming that the effectiveness of monetary policy in reducing ecological footprint is conditional on institutional capacity. In other words, the greening of the economy is not automatic; it depends on the quality of the machinery that implements policy. The study also advances Institutional Quality Theory by demonstrating that institutions play a moderating role, not just a mediating one, in the transmission between monetary policy and environmental outcomes. A mediator carries an effect from cause to consequence; a moderator determines how strong that effect is in the first place. That distinction matters for anyone designing policy, because it implies that institutional reform is not merely another lever to pull but a multiplier that changes the force of every other lever.

For Ghana specifically, the findings arrive at a consequential moment. The country has battled persistent inflation, currency depreciation, and a debt restructuring program, all of which have kept the Bank of Ghana’s policy rate among the highest in Africa. The study suggests that this monetary tightening, whatever its costs for borrowers, carries an underappreciated environmental side benefit: it discourages the excessive resource utilization and unsustainable production that inflate the ecological footprint. At the same time, the moderating result warns that the benefit is not guaranteed. Ghana’s environmental governance depends on institutions such as the Environmental Protection Agency, and the authors’ analysis implies that the strength of those bodies, and of anti-corruption safeguards more broadly, directly conditions how much environmental protection the central bank’s decisions can purchase.

The policy recommendations that flow from the research are unusually concrete for a macroeconomic study. The authors call on the Bank of Ghana to incorporate measures of environmental sustainability into its monetary policy planning, effectively asking the central bank to treat ecological outcomes as a dimension of its mandate rather than an afterthought. They urge the Ghanaian government to deploy anti-corruption strategies, regulatory interventions, and digital surveillance tools to ensure that both environmental and financial policies are actually adhered to, an acknowledgment that rules on paper mean little without monitoring and enforcement. Most ambitiously, they recommend enhanced coordination among the Bank of Ghana, the Environmental Protection Agency, and the Ministry of Finance, so that monetary policy decisions actively promote green financing and long-term ecological resilience. The vision is of a policy triangle in which financial stability, fiscal planning, and environmental regulation reinforce one another instead of operating in silos.

The broader significance of the study lies in its challenge to how we categorize environmental tools. Climate policy is usually assigned to ministries of environment, carbon pricing schemes, and international treaties, while central banks are confined to inflation targets and financial stability. Yet the Ghanaian evidence suggests that the boundary is porous. Every decision about interest rates and liquidity ripples through consumption, investment, and production, and therefore through the ecological footprint. If tightening monetary policy measurably reduces environmental degradation, and if strong institutions magnify that effect, then central banks in developing economies may be sitting on an underused environmental instrument. The authors stop short of claiming monetary policy can replace fiscal and regulatory measures; rather, they conclude that well-implemented monetary policy can serve as an indirect environmental tool that complements them on the road to sustainable development.

There are, of course, limits to what a single-country time series can prove. Eighty quarterly observations capture one national experience, and the ARDL framework identifies associations within that record rather than randomized causal proof. The study’s own framing is careful on this point, presenting the results as empirical confirmation of a theoretical proposition rather than a universal law. Still, the research opens a line of inquiry that other economies, particularly resource-dependent developing nations with young institutional systems, could profitably pursue. If the Ghanaian pattern holds elsewhere, the implication is quietly revolutionary: the quality of a country’s courts, regulators, and anti-corruption agencies may be as important to its environmental future as any treaty or tax, because those institutions decide whether the levers of macroeconomic policy, once pulled, actually move the world. In an era when every basis point of policy is scrutinized for its effect on prices and jobs, Ghana’s researchers have added a third question to the central banker’s scorecard: what did that rate decision do to the planet?

Subject of Research: The moderating role of institutional quality in the relationship between monetary policy and ecological footprint in Ghana

Article Title: The role of institutions in shaping the effect of monetary policy on ecological footprint in Ghana

Article References: Bosomtwe, E., Gadzo, S. G., Omane-Adjekum, C., Nyame, J., & Agyepong, E. K. (2026). The role of institutions in shaping the effect of monetary policy on ecological footprint in Ghana. Discover Sustainability. https://doi.org/10.1007/s43621-026-04721-5

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04721-5

Keywords: Ghana, monetary policy, ecological footprint, institutional quality, ARDL, Bank of Ghana, Environmental Protection Agency, sustainable development, green financing, Ecological Modernization Theory, environmental economics, anti-corruption

The study, published in Environmental and Sustainability Indicators, addresses a stubborn measurement problem. Most research on green finance relies on single outcomes, such as counts of green patents or installed renewable capacity, which capture only one slice of a complex transition. The Vietnamese team instead constructed a composite Environmental Technology Transition index using principal component analysis, blending four theoretically grounded components: granted patents in the IPC Y02 climate-mitigation classes, the share of installed renewable capacity in each province’s power mix, environmental fixed-asset investment as a proportion of total investment, and energy-efficiency performance measured as economic output per unit of final energy use. The first principal component explains 54.5 percent of the variation across the panel, with component loadings between 0.61 and 0.78, indicating that no single metric dominates the composite.

The data reveal striking heterogeneity. Renewable-energy penetration ranges from zero to 68.9 percent across provinces, and environmental investment intensity spans from 0.3 to 22.6 percent of fixed-asset investment. That variation is precisely what makes Vietnam, with its 58 provinces and five centrally administered municipalities spanning eight socio-economic regions, an ideal laboratory for asking whether finance, geography, and governance jointly drive clean-technology upgrading. The study period is anchored by two policy milestones: the 2012 National Green Growth Strategy, which established national green-credit targets, and the country’s 2021 COP26 pledge to reach net-zero emissions by 2050.

Measuring the effect of green credit is notoriously difficult because banks do not allocate loans randomly. Lenders may simply channel green credit toward provinces that were already environmentally progressive, creating a correlation that looks causal but is not. The researchers confronted this endogeneity with two unusual instruments. The first draws on colonial history: the log count of French-chartered banking branches per 10,000 residents during the Indochina period from 1900 to 1954, reconstructed from French colonial archives and the Banque de l’Indochine’s provincial registries, interacted with a post-2015 policy indicator. The second exploits climate exposure, using annual flood-event frequency from disaster records as a source of financing demand that is plausibly unrelated to clean-technology outcomes once fixed effects and province-specific trends are controlled.

The headline result is a structural coefficient of 0.334 from a spatial two-stage least squares estimator: a one-standard-deviation increase in green finance intensity is associated with a 0.334-standard-deviation rise in the environmental technology transition index, conditional on controls, province and year effects, and province-specific linear trends. The instrument diagnostics are strong, with a Kleibergen-Paap first-stage F-statistic of 40.1, well above conventional weak-instrument thresholds, and an Anderson-Rubin confidence interval running from 0.212 to 0.461. A temporal placebo using pre-policy data from 2010 to 2013 finds no comparable association, and a spatial placebo that randomly permutes the geographic weight matrix across 1,000 permutations yields nothing, suggesting the pattern is specific to real geography and the post-2015 green-credit window.

Perhaps the most striking finding is spatial. Environmental technologies do not respect provincial borders. Using a spatial Durbin model with a row-standardised Queen contiguity matrix, the study finds significant positive spatial autocorrelation in both the transition index and green credit allocation, with Moran’s I statistics of 0.192 and 0.176 respectively. The indirect, cross-boundary impact of green finance is estimated at 0.189, meaning that a province’s neighbours’ green lending spills over into its own technology transition through knowledge flows, demonstration projects, supply-chain linkages, and skilled-labour mobility. The spatial feedback multiplier of 1.30 implies that outcomes reverberate through the network rather than stopping at each border. In practical terms, a province surrounded by green-finance-rich neighbours benefits even if its own banks stay cautious.

The transmission channels behind the aggregate effect are also decomposable. Bootstrap mediation analysis with 1,000 replications attributes 22.1 percent of the total effect to research and development investment and 15.7 percent to clean capital formation, leaving roughly 62 percent as the direct effect of relaxing financing constraints for cleaner capital goods. The R&D channel is far stronger in well-governed provinces, accounting for 28.4 percent of the effect there versus just 11.2 percent in weakly governed ones, consistent with the idea that directed technical change requires institutional infrastructure to translate financial flows into innovation output.

That institutional dependence is the study’s sharpest policy warning. Splitting the sample at the median of the Green PCI environmental governance score, the researchers estimate that high-institutional-quality provinces reap 0.421 standard deviations of transition gain per unit of green finance, while low-quality provinces see only 0.169, an approximately 2.5-fold differential. A Hansen threshold test further identifies a minimum green-credit intensity of roughly 0.287 standard deviations below which the transition mechanism is not fully activated. In weakly governed provinces, the authors argue, green credit risks being absorbed into low-additionality, compliance-only uses, a pattern the literature describes as institutional greenwashing, and the risk is most acute exactly where monitoring capacity is weakest. Substituting Rule-of-Law scores for the governance index produces a nearly identical pattern, suggesting the result is not an artefact of one survey.

The robustness battery is unusually thorough. The green-finance coefficient stays positive and significant across inverse-distance, road-network, and economic-proximity spatial weights, after excluding the two dominant metropolitan hubs of Hanoi and Ho Chi Minh City, after dropping the reconstructed 2014 to 2016 green-credit data, after excluding the Covid-disrupted years, after winsorising outliers, and in dynamic GMM and event-study designs built around Vietnam’s 2022 environmental protection law. Alternative outcomes tell the same story: patent-only measures, renewable-energy shares, and economic-only index variants all yield significant positive coefficients, while energy intensity and emission intensity both fall significantly as green finance rises.

For policymakers, the implications cut against simply expanding lending everywhere. The authors propose a tiered green taxonomy that distinguishes deep eco-innovation projects from incremental improvements and compliance-minimum spending, with concessional terms tied to verified innovation additionality and a project-level green credit registry linking loans to patenting, renewable adoption, and efficiency outcomes. Because spillovers are real, regional coordination of green-finance programmes is more defensible than isolated provincial efforts. And because the diagnosis matters, an indicator-based accountability system can distinguish provinces that need more credit from those that need better project screening and implementation capacity, and mobilise strong performers as regional diffusion hubs. The broader lesson travels beyond Vietnam: in emerging economies racing toward net zero, money is necessary but not sufficient, and the binding constraint on clean-technology transition may be the quality of the institutions that spend it.

Subject of Research: Green finance, spatial diffusion, and institutional absorptive capacity in provincial environmental technology transition in Vietnam

Article Title: A provincial environmental technology transition indicator for Vietnam: Green finance, spatial diffusion, and absorptive capacity

Article References: Tran, A. Q., Pham, M. T., Trinh, K. C., Pham, T. L., Pham, D. K., Pham, T. T., Le, K. C., & Nguyen, M. H. (2026). A provincial environmental technology transition indicator for Vietnam: Green finance, spatial diffusion, and absorptive capacity. Environmental and Sustainability Indicators, 32, Article 101554. https://doi.org/10.1016/j.indic.2026.101554

Image Credits: AI Generated

DOI: 10.1016/j.indic.2026.101554

Keywords: green finance, environmental technology transition, Vietnam, spatial econometrics, institutional quality, absorptive capacity, renewable energy, green patents, spatial spillovers, sustainability indicators, green credit, net zero

The research, published in the open-access journal Heliyon by Jerry Ogutu Sumba, Kennedy Nyabuto Ocharo and Paul Joshua Mugambi, is notable for how it frames the question. Instead of asking simply whether debt is good or bad for growth, it asks where the switch happens, whether the answer depends on a country’s income level, and whether the quality of a nation’s institutions changes how much debt an economy can safely swallow. Economists have debated these questions for decades, with studies variously reporting positive, negative or no relationship between borrowing and growth, largely because linear models obscure the tipping points that matter in the real world.

The historical backdrop makes the findings urgent. Sub-Saharan Africa’s external debt surged in the 1980s, peaking at about 58 percent of GDP in 1994 before the Heavily Indebted Poor Countries Initiative and successive Paris Club relief agreements wrote off large arrears and restructured obligations. The region then enjoyed a lull: external debt fell to a low of 21.85 percent of GDP in 2008, a stretch marked by relatively stable growth. That discipline did not last. Spurred by heavy borrowing for infrastructure and military spending, the median public debt-to-GDP ratio in the region jumped from 28.8 percent in 2012 to 59.1 percent in 2022, and by 2020 the regional external debt ratio had climbed back to 47.16 percent just as GDP growth sank to its lowest point, minus 2.01 percent, a decline the authors note was compounded by the COVID-19 pandemic but consistent with debt’s persistent negative influence.

To pin down the turning point rather than assume it, the researchers used a Dynamic Panel Threshold Model applied to an unbalanced panel of 44 countries from 2005 to 2022, deliberately starting after the debt relief era so the sample reflects a homogeneous, post-restructuring phase. The method, developed by Seo and Shin, solves a problem that has hampered earlier threshold studies: it allows both the threshold variable and the other regressors to be endogenous, meaning it does not pretend that debt and growth move independently of each other. Previous static approaches assumed strict exogeneity, while standard generalized method of moments estimation, though capable of handling endogeneity, forced a linearity that hid exactly the non-linear behavior the researchers sought.

The results confirm a regime-switching relationship. In the full sample, external debt carries a small positive coefficient of 0.09 below the estimated threshold of 47.23 percent of GDP, then flips to a significantly negative coefficient of minus 0.17 above it. Splitting the sample, the threshold sits at 42.64 percent for the 20 low-income countries and 49.81 percent for the 24 middle-income countries. Intriguingly, the 95 percent confidence intervals for these two groups overlap, which means the apparent income-based difference is not statistically distinguishable. That finding contradicts a common assumption, embedded in many debt sustainability frameworks, that middle-income countries can inherently carry proportionally heavier external debt because of stronger fiscal capacity.

Diagnostic tests support the robustness of the estimate. Arellano-Bond tests show first-order but no second-order serial correlation, the Hansen J statistic is insignificant, and excluding Liberia, whose extreme 2005 debt stock of nearly 498 percent of GDP towers over the sample mean of 42.43 percent, moves the threshold only marginally to 46.91 percent. The study also documents a consistently negative effect of domestic debt on growth across all groupings, though milder in middle-income countries, plausibly reflecting deeper and more liquid local financial markets. A strong negative pairwise correlation between external debt and GDP growth, minus 0.58, underscores the central tension in the data.

The most consequential findings concern institutions. Drawing on public choice theory, which holds that self-interested political elites determine whether borrowed funds are invested productively or siphoned into private utilities, the researchers measured institutional quality as the mean of six Worldwide Governance Indicators, spanning rule of law, corruption control, regulatory quality, government effectiveness, voice and accountability, and political stability. When institutional quality was modeled as a threshold variable, debt exerted a significant negative effect on growth in countries below an index level of roughly 0.77 and a positive effect above it. In other words, the so-called debt trap is not purely a function of how much a country borrows; it is also a consequence of how well its institutions function.

Stronger institutions do more than soften the blow of existing debt; they actively raise the ceiling. When the external debt and institutional quality interaction was added to the model, the effective threshold for the full sample shifted upward from 47.23 percent to 53.17 percent of GDP. Middle-income countries gained the most, with their threshold rising by 6.42 percentage points and the strongest interaction coefficient, while low-income countries saw a smaller shift of 3.86 points that was not statistically significant, reflecting persistently weak governance, pervasive corruption and limited state capacity to deploy borrowed funds well. The picture that emerges is of governance, not income, as the central, policy-actionable determinant of debt absorption capacity.

The practical implications are considerable. The authors recommend that sub-Saharan governments keep external debt below the estimated 47 percent benchmark and, crucially, treat institutional strengthening as an integral component of debt management rather than a separate governance aspiration, through sustained commitment to rule of law, transparency in public financial management, anti-corruption efforts and judicial independence. For multilateral lenders such as the IMF and World Bank, the results offer an empirical basis for linking financing terms to measurable governance indicators instead of relying solely on aggregate income-based ceilings. The authors also flag limitations: the model identifies statistical turning points rather than causal transmission channels, and it assumes a shared threshold within each sample, so the estimates should be read as regional benchmarks rather than country-specific limits, a gap future research with heterogeneous threshold models could close.

Subject of Research: The threshold effect of external debt on economic growth in sub-Saharan African countries and how income level and institutional quality condition debt sustainability

Article Title: External debt and economic growth among sub-Saharan African countries, a threshold analysis

Article References: Sumba, J. O., Ocharo, K. N., & Mugambi, P. J. (2026). External debt and economic growth among sub-Saharan African countries, a threshold analysis. Heliyon, 12(15), Article e45539. https://doi.org/10.1016/j.heliyon.2026.e45539

Image Credits: AI Generated

DOI: 10.1016/j.heliyon.2026.e45539

Keywords: external debt, economic growth, sub-Saharan Africa, debt threshold, debt overhang, institutional quality, Dynamic Panel Threshold Model, GDP, IMF, World Bank, public debt sustainability, governance

The timing could hardly be more consequential. Sub-Saharan Africa remains the world’s least gender-equal region, with an economic parity index estimated at just 68.5 percent and a gender inequality ratio exceeding 0.5. Burkina Faso sits squarely within this pattern. According to the World Economic Forum’s global gender gap data, the country achieves 90.3 percent parity in educational attainment and 97.8 percent in health and survival, but only 67.4 percent parity in participation in economic opportunities. The 2019 national census adds a sobering layer: the overall literacy rate stands at 31.9 percent, with men at 36 percent and women at just 27.9 percent, while women’s unemployment has averaged roughly 6 percent over the past five years according to World Bank figures.

To test whether the money flowing into and within the economy was changing this picture, the researchers assembled annual time-series data from the World Bank’s World Development Indicators and the International Country Risk Guide. They measured women’s economic participation through the ratio of female to male labor force participation, foreign direct investment as a share of GDP, and net national savings as an indicator of an economy’s self-financing capacity. Institutional quality was captured through a composite index built from six World Governance Indicators, spanning rule of law, voice and accountability, regulatory quality, political stability, control of corruption, and government effectiveness, with weights determined by principal component analysis to avoid the multicollinearity problems that plague these strongly correlated measures.

The econometric backbone of the study is an autoregressive distributed lag, or ARDL, model, a technique chosen for good reason. Unlike classical cointegration methods, ARDL can handle variables that are stationary at different orders of integration, a property confirmed here by Dickey-Fuller and Phillips-Perron unit root tests showing some series stationary in levels and others only in first differences. It also suits relatively small samples, and this one, spanning 35 annual observations, qualifies. The optimal lag structure, selected with the Akaike Information Criterion, produced a specification of ARDL(4, 3, 4, 2, 3), and the bounds test confirmed a long-run equilibrium relationship among the variables. The model fit was striking, with a coefficient of determination of 0.95, though the authors are careful to note the small sample when interpreting that figure.

The headline result is unambiguous. In the long run, foreign direct investment carries a positive coefficient of 0.482, but the effect on women’s economic participation is statistically insignificant. Net national savings, by contrast, delivers a coefficient of 2.716, positive and statistically significant, roughly five and a half times the size of the FDI effect. The explanation, the authors argue, lies in the structure of the Burkinabe economy. FDI flows have risen from less than 0.01 percent of GDP in the 1980s to a peak of 2.40 percent between 2011 and 2015 before easing to 1.32 percent, but they remain concentrated in the mining sector, an industry that is highly capital-intensive and generates few jobs for women. Weak linkages between foreign firms and the domestic productive base further dampen any spillover effects on female employment.

Domestic savings tell a different story precisely because of where they go. Women in Burkina Faso are overwhelmingly employed in agriculture and the informal sector, parts of the economy that attract little foreign capital but benefit directly from domestically financed development programs in education, vocational training, financial inclusion, women’s entrepreneurship, and small and medium-sized enterprises. Savings also represent a more stable and sovereign source of funding, insulating development finance from the volatility of global capital markets. The finding echoes earlier work by Bolarinwa and Obembe, who showed that domestic savings play a key role in financing productive investment across sub-Saharan Africa, and contrasts with Zandile and Phiri’s evidence of negligible direct FDI effects on growth in Burkina Faso.

The study’s most striking twist involves institutional quality, which the authors model not as a simple control but as a moderator interacting with both capital sources. Here the interactions between foreign direct investment and institutional quality, and between net national savings and institutional quality, are both positive and statistically significant in the long run. In plain terms, the same dollar of foreign investment or the same franc of domestic savings buys more economic opportunity for women when institutions are effective, transparent, and accountable. Graphical analyses of the interaction effects show the relationship clearly: at low institutional quality, additional FDI yields only modest gains, while at high institutional quality the positive correlation steepens dramatically. The pattern aligns with the institutional economics of North and of the 2024 Nobel laureates Acemoglu, Johnson, and Robinson, whose work argues that inclusive institutions determine whether resources are converted into broad-based prosperity.

The short-run dynamics add an important nuance. Net national savings and institutional quality show negative short-run effects but positive long-run ones, a divergence the researchers attribute to adjustment rather than contradiction. Higher savings temporarily reduce current consumption, and institutional reforms take time before their benefits materialize. Meanwhile, the negative and significant error correction term, estimated at roughly 0.503, indicates that about half of any short-run disequilibrium is corrected within a single period, implying a relatively rapid return to the long-run equilibrium path. Robustness checks using the fully modified ordinary least squares estimator and an alternative dependent variable, the index of women’s economic rights, broadly confirm the baseline conclusions.

The policy implications are concrete. The authors recommend directing foreign investment toward sectors with high potential for women’s employment, including agribusiness, agricultural value chains, local product processing, digital services, and light manufacturing. They call for domestic savings mobilization to be paired with expanded financial inclusion for women, through access to formal banking, affordable credit, digital financial services, and tailored savings products. Most fundamentally, they argue that institutional reform, from simplified administrative procedures to anti-corruption mechanisms and the digitization of public services, is the lever that amplifies everything else. In a country where government effectiveness has improved only marginally, from minus 0.85 to minus 0.83 on a scale running from minus 2.5 to plus 2.5, the message is that governance itself is a form of capital. The study does have limits, as its authors acknowledge: national aggregates mask regional and sectoral heterogeneity, and the analysis cannot capture structural breaks linked to Burkina Faso’s recent political upheavals. But its core finding stands as a challenge to both aid-dependent and investment-attraction strategies alike: empowering women financially may begin not with foreign money, but with what a nation manages to save, and with the institutions trusted to put those savings to work.

Subject of Research: The effects of foreign direct investment and net national savings on women's economic participation in Burkina Faso, moderated by institutional quality

Article Title: Do foreign direct investment and net national savings promote women’s access to economic opportunities in Burkina Faso? the moderating role of institutional quality

Article References: Ouedraogo, A. R., & Ouedraogo, S. S. (2026). Do foreign direct investment and net national savings promote women’s access to economic opportunities in Burkina Faso? the moderating role of institutional quality. Discover Global Society, 4(1), Article 257. https://doi.org/10.1007/s44282-026-00608-y

Image Credits: AI Generated

DOI: 10.1007/s44282-026-00608-y

Keywords: Burkina Faso, foreign direct investment, net national savings, institutional quality, gender economics, women's economic empowerment, ARDL model, governance indicators, development economics, sub-Saharan Africa, labor force participation, foreign

The central question addressed by the study is deceptively simple: does the level of a nation’s productive capacity condition the extent to which gender inclusion translates into better governance? Productive capacity, in the economic literature, refers to the combination of resources, infrastructure, technological capability, institutional quality, and human capital that determines what an economy can produce and how efficiently it can do so. Economies with high productive capacity tend to have diversified industrial bases, reliable energy and transport systems, and educated workforces, while low-capacity economies often depend on narrow resource extraction and face persistent institutional constraints.

The theoretical logic connecting these concepts runs in both directions. On one side, stronger governance—characterized by rule of law, government effectiveness, regulatory quality, control of corruption, and accountability—can expand opportunities for women by enforcing anti-discrimination protections, opening access to education and credit, and creating formal channels for political participation. On the other side, greater gender inclusion can improve governance outcomes by diversifying the perspectives represented in decision-making bodies, reducing corruption risks, and aligning public policy more closely with the needs of the full population rather than a narrow subset.

Productive capacity enters this relationship as a potentially decisive moderating factor. Where economies possess the infrastructure and institutional depth to convert inclusion into concrete participation—through functioning labor markets, accessible education systems, and administrative machinery capable of implementing policy—gender inclusion is more likely to produce measurable governance gains. In settings where productive capacity is weak, formal inclusion may remain largely symbolic, with women represented in institutions that lack the resources or authority to effect change.

This distinction matters for policymakers because it suggests that promoting gender inclusion in isolation may yield disappointing results if the underlying economic and institutional substrate is underdeveloped. Conversely, investments in productive capacity—industrial diversification, digital infrastructure, energy reliability, and skills development—may amplify the returns to inclusion policies such as gender quotas, leadership training, and legal reforms. The study’s framing implies that the two agendas are complements rather than substitutes: capacity builds the stage on which inclusion can perform.

The research sits within a broader empirical literature in development economics and political science that has documented correlations between women’s political representation and outcomes such as reduced corruption, greater investment in public goods like health and education, and more durable peace agreements. Cross-national studies have repeatedly found that higher shares of women in legislatures are associated with stronger governance indicators, although establishing causality remains methodologically challenging because gender norms, income levels, and institutional quality tend to move together over time.

By introducing productive capacity as a moderating variable, the study offers a way to account for heterogeneity in these findings. Countries at similar income levels may differ sharply in their structural capabilities, and the same inclusion policy may therefore produce different results depending on whether the surrounding economy can absorb and institutionalize the change. This perspective aligns with structuralist traditions in development economics, which emphasize that outcomes depend not only on policy choices but on the productive structure of the economy itself.

The publication appears in Humanities and Social Sciences Communications, an open-access journal published by Springer Nature that publishes peer-reviewed research spanning the social sciences and humanities. The article carries the DOI 10.1057/s41599-026-08982-3 and is available through the journal’s website. As with all work in this field, the findings invite further empirical testing across country samples, time periods, and measurement approaches, particularly as new datasets on economic complexity and institutional performance become available.

For researchers, the study underscores the value of interaction-based designs that test not just whether two variables are related, but under what conditions that relationship strengthens or weakens. For practitioners in international development and governance reform, the message is that gender inclusion strategies are most likely to succeed when they are embedded in broader programs of capacity building—ensuring that the institutions women join are equipped to deliver the improvements that inclusive governance promises.

One useful way to situate the study is within the long-running debate over how to measure both of its core constructs. Gender inclusion is typically operationalized through indicators such as female labor force participation, seats held by women in national parliaments, educational attainment gaps, and indices that composite these dimensions into a single score. Each measure captures a different facet of inclusion: parliamentary representation reflects formal political voice, while labor force participation reflects economic integration. Because these dimensions do not always move together—a country may have high female employment alongside minimal political representation—researchers must be explicit about which aspect of inclusion they are testing, and the moderating role of productive capacity may differ across them.

Productive capacity presents its own measurement challenges. Recent work by international organizations has developed composite indices that combine measures of energy infrastructure, transport and digital connectivity, structural change away from primary commodities, and the sophistication of exported goods. A related strand of literature on economic complexity infers productive knowledge from the diversity and ubiquity of the products a country exports, treating the export basket as a window into the accumulated capabilities of its firms and workforce. These approaches share the premise that what an economy can do is distinct from how much income it currently generates, which is precisely the distinction the study leverages when it argues that capacity, rather than income alone, conditions the governance returns to inclusion.

The historical record of gender quota adoption offers a concrete illustration of why this conditioning matters. Well over one hundred countries have now introduced legislative quotas of some form, ranging from reserved seats to voluntary party quotas, and the resulting variation in outcomes is striking. In some settings, quotas produced durable cohorts of legislators who reshaped committee agendas and oversight practices; in others, women entered chambers with limited committee assignments, short tenures, or weak party support, and the anticipated governance effects failed to materialize. Observers of these divergent trajectories frequently point to features that resemble productive capacity in the study’s sense: the professionalism of the civil service, the reliability of public administration, and the broader economic base that gives legislatures real resources to allocate.

The corruption channel deserves particular attention because it has generated one of the most cited empirical regularities in this literature. Cross-national analyses have repeatedly reported that higher shares of women in parliament are associated with lower perceived corruption, and early interpretations framed this as evidence that women are inherently less tolerant of corrupt exchanges. Subsequent scholarship has complicated that reading, suggesting that the relationship may reflect the institutional environments in which women gain office rather than any dispositional difference. Liberal democracies with strong rule of law both elect more women and exhibit less corruption, making it difficult to isolate the effect of gender itself. A moderating variable such as productive capacity offers a more structural interpretation: the same woman legislator operates within very different opportunity structures depending on the administrative and economic machinery surrounding her office.

There is also a macroeconomic dimension worth noting. Growth research over the past two decades has emphasized that sustained convergence depends on structural transformation—the reallocation of labor from low-productivity agriculture toward manufacturing and services—rather than on factor accumulation alone. If gender inclusion contributes to governance, and governance contributes to the quality of public investment and the enforcement of property rights, then the interaction the study examines may feed back into the very productive capacity that conditions it. This possibility of circular causation is a familiar challenge in development research, and it cautions against reading any single estimated coefficient as a complete description of the system.

Methodologically, studies of this kind typically rely on panel data covering many countries and years, with interaction terms used to test whether the slope linking inclusion to governance varies with the level of capacity. Such designs face well-known hurdles: omitted variables that correlate with both capacity and governance, reverse causality running from institutional quality to economic structure, and the sensitivity of results to sample composition and estimator choice. Robustness in this literature is usually assessed by varying measures, excluding regions, and testing alternative governance indices drawn from expert assessments, survey-based perceptions, and objective indicators. Readers interpreting the findings should keep in mind that interaction effects in cross-national panels are notoriously sensitive to these choices, even when the underlying theory is sound.

The policy translation also requires care. If capacity amplifies the returns to inclusion, then sequencing becomes a live question: should donors and governments invest in industrial and administrative capability first, or pursue inclusion and capacity simultaneously? The complementarity framing suggests simultaneous pursuit, since waiting for capacity to mature before opening political space risks entrenching exclusion, while pursuing inclusion without capacity risks symbolic representation. Practical programs that pair quota reforms with civil service professionalization, digital government infrastructure, and vocational education embody this logic, though rigorous evaluations of such paired interventions remain scarce.

Finally, the study contributes to a growing effort to bring structural economics and political sociology into a single analytical frame. Much of the governance literature has treated institutions as exogenous rules to be transplanted, while much of the gender literature has treated norms as the primary constraint. By foregrounding the productive structure of the economy as the terrain on which both rules and norms operate, the research points toward an integrated account in which economic capabilities, social inclusion, and institutional quality co-evolve—a perspective likely to inform the next generation of empirical work on development outcomes.

Subject of Research: The moderating role of productive capacity in the relationship between gender inclusion and governance quality.

Article Title: Gender inclusion and governance: the role of productive capacity

Article References: Emeka, E. T., & Asongu, S. A. (2026). Gender inclusion and governance: the role of productive capacity. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-08982-3

Image Credits: AI Generated

DOI: 10.1057/s41599-026-08982-3

Keywords: gender inclusion, governance, productive capacity, political participation, institutional quality, development economics, women in politics, economic complexity, public policy, corruption, human capital, Social Sciences Communications