Drawing from neoclassical growth theories, this study explores the interplay between financial development, gender-specific human capital, and total factor productivity (TFP) growth in India, addressing a gap in prior literature by examining their interactive effects on emerging economies' productivity trajectories. Employing the ARDL Bound test model, we estimate the productivity growth equation using annual data from 1980 to 2019. Variables such as government spending on education and foreign direct investment serve as crucial control variables in the TFP growth framework. Our findings reveal nuanced dynamics: while financial development enhances productivity growth in the absence of gender-specific human capital considerations, its impact varies significantly with the inclusion of male and female education levels. Notably, financial development positively influences productivity growth when male education levels are high. Surprisingly, financial development hampers productivity growth when female education level is high. These insights underscore the disproportionate influence of male education on productivity growth vis-à-vis female education in India. This study contributes to the literature by highlighting the differential impacts of financial development on India's productivity growth in the presence of gender-specific human capital. This analysis emphasizes the role of gender dynamics in educational attainment for policymakers aiming to leverage financial development as a catalyst for productivity growth. In addition, the policymakers in India are urged not to downplay the significance of male education in fostering financial development and augmenting productivity growth. Furthermore, the policymakers are advised to scrutinize the adverse repercussions of financial development on productivity growth within the context of female education at higher levels.

Purpose: This study endeavors to elucidate the divergent conclusions encountered in empirical research regarding the interplay of Economic Growth (EG) and Energy Consumption (EC). Design/methodology/approach: For this purpose, we employ the Panel Smooth Threshold Regression (PSTR) model to intricately examine the non-linear impacts of independent variables on EC and EG within a dataset encompassing 46 countries over the period from 1996 to 2021. Findings: The outcomes of our investigation can be summarized as follows: First, the findings underscore the positive impact of the logarithm of net capital formation on EG. This impact is particularly pronounced at low levels of Research and Development (R&D), gradually waning beyond a certain threshold. Second, the ratio of capital to labor exhibits a negative influence on EC at lower R&D levels. Notably, these detrimental impacts become more pronounced as R&D levels increase. Third, trade openness contributes positively to EG, particularly evident at low R&D levels. However, with increasing R&D levels, the incremental benefits from trade diminish. Finally, our findings lend support to the feedback hypothesis. Nevertheless, the impact of R&D expenditures in countries moderates these positive effects. Practical implications: Policymakers should strategically balance resource allocation between capital formation and research endeavors, considering diminishing returns at elevated levels of R&D spending, to ensure sustained EG. 

This Special Issue of the Journal of Productivity Analysis on African Productivity is the result of many years of collaboration and planning that began with Felicity Addo of the Institute for Sustainable Economic Development (Vienna) and her interest in expanding and broadening the scope of the International Society for Efficiency and Productivity Analysis’ (ISEaPA) conferencing venues to encompass the African continent. Although planning for such a conference began with discussions in 2016 at the European Workshop on Efficiency and Productivity in London, it was not until 2021 that formal planning for a virtual conference gained momentum. Almas Heshmati of the Jönköping International Business School (Sweden), whose experience in African higher education and research is quite extensive, and his former student Selamawit G. Kebede of the Poverty and Social Welfare Center (Ethiopia), joined with Christopher Parmeter (University of Miami), Robin Sickles (Rice University), and Felicity Addo to solicit papers and coordinate with then Editor-in-Chief William Greene on a Special Issue for the Journal of Productivity Analysis.

In the last few years, discussions and debates around environmental sustainability have become a key goal of the global agenda. This is because the latest studies and our direct experiences of the consequences of environmental degradation and climate change are showing that economic models need some urgent changes. The goal of this paper was to investigate the effects of foreign direct investment (FDI) flow on environmental sustainability globally. The balanced panel data of 100 countries from 2000 to 2020 were used for empirical analysis to understand the relationship between FDI inflow and environmental performance index (EPI) for countries with different income levels. The estimation is made using the two-step system GMM model as it gives more robust and efficient results in this study. The results of the dynamic panel model do not support the pollution haven hypothesis, i.e., FDI inflow was not found to have any statistically significant effect on EPI. The findings of the paper suggest that to improve environmental quality globally, it is important for governments to invest more in renewable energy projects as clean energy consumption can be one of the most efficient solutions for reducing the impact of climate change. Moreover, countries need to reduce the unemployment rate as it significantly improves the environmental quality according to our estimation results.

This study investigates the characteristics that contribute to elderly poverty, mainly focusing on individuals’ lifetime work experience. It adopts the heterogeneous relative poverty line. It calculates the work experience and obtains demographic variables using the Korean Labor and Income Panel Study’s survey data for 2006, 2009, 2012 and 2015. The objective is to estimate poverty among elderly and explain its variations in relation to individual characteristics and lifetime work experience. Poverty is measured in terms of the head count, poverty gap and the poverty severity indices based on monetary dimensions, namely income and consumption. The methodology used in this study is a logit model to explain the incidence of poverty and a sample selection model to analyze the depth and severity of poverty. The results show that an increase in the total work years and a decrease in the gap years between jobs reduce incidence and depth of poverty. 

This study aims to examine the impact of energy poverty on the aggregate and disaggregate material footprint components such as biomass, fossil fuel, metal ores and non-metallic minerals while considering the economic growth and tourism development during 2000–2014 for the BRICS countries (Brazil, Russia, India, China, and South Africa). By applying econometric tools, the study confirms a positive and significant impact of energy poverty on aggregate material footprint and its components. The same finding has been reached for tourism development. Moreover, this study finds a U-shaped Environmental Kuznets Curve (EKC) for all indicators used for material footprints. Based on the findings, this study proposes a set of policies for energy poverty alleviation to attain a sustainable environment and inclusive economic growth in the BRICS region.

Countries’ success in managing the Covid-19 pandemic was independent of their level of education or development and institutional quality. Similarly, different provinces in countries performed differently as compared to their neighboring provinces and the country. This study assesses the efficiency of Sweden’s different municipalities in managing the Covid-19 pandemic. Its aim is determining whether the relative efficiency of Sweden’s primary healthcare system during the Covid-19 pandemic was different in different in regions and whether the regions can learn from each other to improve their efficiency in the provision of healthcare in the future. Performance is measured as efficiency in keeping the number of infected cases and deaths low. We use a data envelopment analysis for measuring efficiency. Variations in the level of efficiency are attributed to the differences in the municipalities’ demography, population concentration, economic and industrial structures, labor markets, geographic locations, and national and local policies during the pandemic. The empirical part is based on a panel of the population of 290 Swedish municipalities observed on a weekly basis during April 2020 to February 2021. The results show large variations in municipalities’ performance that can, to some extent, be attributed to their observable socioeconomic, locational, and demographic characteristics.

This paper aims to contribute to the growing body of research literature on assessing environmental efficiency by introducing a new key performance indicator (KPIs) in more complete and dependable aspects of ecological footprint indices. For this purpose, the DEA model considering three inputs (energy consumption, labor force, and capital stock), one desirable output (GDP), and different undesirable outputs (CO2 emissions, ecological footprint indicators) are applied to 27 OECD countries from 2000 to 2017. According to the results, Norway, Luxemburg, and United Kingdom are the most environmentally efficient countries in terms of environmental efficiency and ecological footprint efficiency. On the other hand, the lowest environmental and ecological footprint efficiencies were in countries like Lithuania, Slovak, Czech, Estonia, and the USA. In addition, these nations fare poorly regarding their carbon footprint and farmland efficiency. In further detail, Lithuania, South Korea, Portugal, and Spain have a critical status in fishing ground efficiency, while the forest area efficiency is very acute in Estonia, Latvia, Lithuania, and Czech.

There are large variations between and within countries’ performance in coping with the Covid-19 pandemic. This study assesses the efficiency of different provinces in Sweden in managing the pandemic. Its objective to estimate the relative efficiency of provinces during the pandemic and identify the key determinants of the level and variations in their performance. Performance is measured as efficiency in keeping the number of negative outcomes low and the number of positive outcomes high for given resources. It uses a parametric distance function approach with multi-input, multi-output, and a flexible functional form for estimating the provinces’ efficiency and the variations in this efficiency over time. Variations in their performance are attributed to the observable characteristics of their socioeconomic, locational, demographic, and healthcare resources. The empirical part is based on a panel data of the population in 21 provinces observed on a weekly basis from January 2020 to September 2021. In particular, the paper estimates the effects of public support and vaccinations on a reduction in the number of deaths and the spread of new cases. The level and variations in outcomes are explained by various provinces and local and national policies. The results show large variations in provinces’ performance and their persistence/transitory nature attributed to their observable characteristics. The paper suggests some policy recommendations to help cope with the threat of the pandemic post the removal of restrictions.

Green transition is important for the economics of the OECD countries and their transition to cleaner production. This paper estimates a knowledge production function consisting of a system of innovation inputs, innovation outputs, and productivity with feedback effect from productivity on innovation investments. The model accounts for productivity shock, endogeneity of inputs, and their simultaneity and interdependence. Productivity shock is a latent unobserved component that is specified in terms of observable factors. The model is estimated using Bayesian methods organized around Marco Chain Sequential Monte Carlo iteration techniques also known as Particle Filtering. For the empirical part, the paper uses balanced panel data covering 27 OECD countries' green innovation and patents activities observed during the period 1990–2018. Our empirical results show evidence of significant heterogeneity in productivity and its relationship with its identified determinants. The paper also discusses the implications of these results for OECD countries’ green growth strategies.