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This paper develops a cross-sectionally augmented distributed lag (CS-DL) approach to the estimation of long-run effects in large dynamic heterogeneous panel data models with cross-sectionally dependent errors. The asymptotic distribution of the CS-DL estimator is derived under coefficient heterogeneity in the case where the time dimension (T ) and the cross-section dimension (N ) are both large. The CS-DL approach is compared with more standard panel data estimators that are based on autoregressive distributed lag (ARDL) specifications. It is shown that unlike the ARDL-type estimator, the CS-DL estimator is robust to misspecification of dynamics and error serial correlation. The theoretical results are illustrated with small sample evidence obtained by means of Monte Carlo simulations, which suggest that the performance of the CS-DL approach is often superior to the alternative panel ARDL estimates, particularly when T is not too large and lies in the range of 30–50.

The search for more effective policies, choice of optimal implementation strategies for achieving defined policy targets (e.g., cost-containment, improved access, and quality healthcare outcomes), and selection among the metrics relevant for assessing health system policy change performance simultaneously pose continuing healthcare sector challenges for many countries of the world. Meanwhile, research on the core drivers of healthcare costs across the health systems of the many countries continues to gain increased momentum as these countries learn among themselves. Consequently, cross-country comparison studies largely focus their interests on the relationship among health expenditures (HCE), GDP, aging demographics, and technology. Using more recent 1980–2014 annual data panel on 34 OECD countries and the panel ARDL (Autoregressive Distributed Lag) framework, this study investigates the long- and short-run relationships among aggregate healthcare expenditure, income (GDP per capita or per capita GDP^_HCE), age dependency ratio, and “international co-operation patents” (for capturing the technology effects). Results from the panel ARDL approach and Granger causality tests suggest a long-run relationship among healthcare expenditure and the three major determinants. Findings from the Westerlund test with bootstrapping further corroborate the existence of a long-run relationship among healthcare expenditure and the three core determinants. Interestingly, GDP less health expenditure (GDP^_HCE) is the only short-run driver of HCE. The income elasticity estimates, falling in the 1.16–1.46 range, suggest that the behavior of aggregate healthcare in the 34 OECD countries tends toward those for luxury goods. Finally, through cross-country technology spillover effects, these OECD countries benefit significantly from international investments through technology cooperations resulting in jointly owned patents.

This study is motivated by the fact that even though many African countries have witnessed rapid growth, they have also experienced high volatility in the form of severe financial crises, especially in the last two decades. These developments naturally lead to the issue of whether, in a more integrated global economy, the relationship between growth and output volatility has changed. The phenomena have also raised questions on whether the growth–output volatility relationship can be linked to the growing pains seemingly associated with rising trade and financial integration. This chapter attempts to provide answer to these questions by providing insights on how trade and financial integration affect the relationship between growth and output volatility using data from selected Africa countries. The study explores in detail the relationship between growth and the volatility of output components (consumption and investment). Our main result is that there is a positive growth and output volatility impact of trade openness and integration with the international financial market. The relationship between growth and financial integration and investment volatility is stronger in the long run than in the short run, while the consumption volatility impact of trade openness is higher in the long run than in the short run, suggesting that countries that are more open to trade appear to face less severe trade-off between growth and volatility.

Purpose: The main purpose of this chapter is to thoroughly investigate the diverse literature available concerning nonperforming loans (NPLs) and its determinants by studying and analyzing the empirical studies from 1985 to 2019.

Design/Methodology: A qualitative approach is being incorporated, and by using content analysis, various previous studies are reviewed and important issues like the objectives, methodology, key findings, and variables are reported.

Findings: The study tries to compile the main findings from the various studies done concerning NPLs and its determinants. The study shows how various determinants both bank-specific and macroeconomic affect the banking structure and thus the NPLs, in different countries and at different periods of time. The study also highlights how countries’ banking structure got affected by various economic phenomena like recession, contagious effect of the financial crisis, banking Basel norms, and NPL management strategies. Further major issues like data acquisition, lack of data reporting, countries specific banking conditions, methodologies used in the analysis, scarce resources, and disclosure hindrance which are faced by previous studies were also reported.

Originality/Value: As there are very few studies that provide a detailed viewpoint on NPLs and its determinants in this area, this research will provide a concise and detailed framework for the researchers to analyses the diverse literature on NPLs and its determinates.

This study focuses on examining the impact of energy consumption, economic structure, population, and manufacturing output on the CO₂ emissions of selected emerging countries by utilizing the Structural Time Series Model (STSM). Based on the annual data ranging from 1970 to 2019, the model is built up using total primary energy consumption, GDP per capita, population and manufacturing value-added, and, finally, a stochastic Underlying Emission Trend as explanatory variables. STSM is extended by the introduction of the notion of Underlying Energy Demand Trend (UEDT) as a factor for exogenous effects, including development in technical progress, energy efficiency improvements, changes in human behaviors, economy, and environmental regulations. In this context, STSM and the notion of UEDT are implemented to form a forecasting model for CO₂ emissions of the selected emerging countries. The model discovers the significant influences of all selected variables of CO₂ emissions. The results suggest that the most forceful factor in CO₂ emissions is the total primary energy supply. Furthermore, while the long-term impact of economic growth on CO₂ emissions is negative for some emerging economies, it is positive for several others. The model also measures the long-term manufacturing value-added elasticity of CO₂ emissions in these emerging economies.

The presence of economic power of BRICS nations could be felt from the late of nineteenth and beginning of twentieth century and during this period inflow of FDI also began to go up and spread across all the sectors. FDI has not only looked to capture the huge market of these economies, but while doing so, it has helped these nations in their economics progress. Our main contribution in this paper consists of analyzing both short-run and long-run interactions between status of knowledge and FDI in the form of inflow of FDI and proportion of GDP used for R&D activities accounting for possible development of knowledge in BRICS nations. For this purpose, our work is based on a sample of these five nations during the period 2006–2017. By the help of panel data analysis and having performed all the necessary tests, we have introduced both dynamic OLS and fully modified OLS to get the efficient long-run impact of FDI on knowledge. Our empirical results support long-run and short-run causality running from FDI to knowledge in all BRICS nations. Our policy recommendation includes encouragement of more FDI in development of knowledge-related activities as well as increase in proportion of GDP spent on R&D in BRICS nations.

Introduction: One of the main goals of the 2030 Agenda for Sustainable Development is to represent gender equality due to its essential role in sustainable progress. At the same time, the balance between women and men in management is explicitly mentioned as a desideratum, given that more women in leadership roles positively impact business performance and sustainability.

Purpose: The study investigates the dynamic relationship between gender inequalities in management positions and sustainable competitiveness. Our contribution is twofold: we examine this interrelationship and its causality.

Methodology: We used panel data of 350 observations for 2012–2021, and we employ a Vector Auto-Regression model and Granger causality method to examine the relationship between the gender gap in management and sustainable competitiveness. The panel VAR for analysing the impulse response function was enriched using Monte Carlo simulations with 5% and 95%.

Findings: The results highlighted that a bidirectional causality between the gender gap in management and sustainable competitiveness is manifested in the European countries. Our results are similar to other studies found in the literature, with gender equality and sustainability positively associated. As an element of originality, our study demonstrates that gender equality in management contributes to sustainable performance, and, on the other hand, a more competitive and sustainable environment contributes to eliminating the gap between men and women in management.