Structural Convergence between African Countries: Empirical Evidence

This paper aims to investigate structural convergence in selected African countries over the period 1994-2019. Using panel data for 48 African countries and several estimation methods [Panel-Corrected Standard Errors (PCSE), Feasible Generalized Least Squares (FGLS), tobit model, instrumental variable, and Granger non-causality], the results show the existence of the phenomenon of sectoral structural convergence in Africa, i.e. a greater similarity in sectoral structures while income gaps are narrowing. The paper also highlights the service sector's low relative productivity level and industrial sector's low labor force attractiveness despite a significant shift in labor from the agricultural sector and a higher level of relative productivity respectively. To address this issue, the development and acquisition of human and physical capital would be necessary to develop the industrial sector and increase the service sector's productivity.

The Impacts of FDI Inflows on Carbon Emissions: Economic Development and Regulatory Quality as Moderators

With the accelerated development of the global economy, environmental issues have gradually become prominent, which in turn hinders further high-quality economic development. As one of the important driving factors, cross-border flowing foreign direct investment (FDI) has played a vital role in promoting economic development, but has also caused environmental degradation in most host countries. Utilizing panel data for the G20 economies from 1996 to 2018, the purpose of this study is to investigate the impacts of FDI inflows on carbon emissions, and further explore the influence channels through the moderating effects of economic development and regulatory quality. To produce more robust and accurate results in this study, the approach of the feasible generalized least squares (FGLS) is utilized. Meanwhile, this study also specifies the heteroscedasticity and correlated errors due to the large differences and serial correlations among the G20 economies. The results indicate that FDI inflows are positively associated with carbon emissions, as well as both economic development and regulatory quality negatively contribute to the impacts of FDI inflows on carbon emissions. It implies that although FDI inflows tend to increase the emissions of carbon dioxide, they are more likely to mitigate carbon emissions in countries with higher levels of economic development and regulatory quality. Therefore, the findings are informative for policymakers to formulate effective policies to help mitigate carbon emissions and eliminate environmental degradation.

Modelling the balance of care: Impact of an evidence-informed policy on a mental health ecosystem

Major efforts worldwide have been made to provide balanced Mental Health (MH) care. Any integrated MH ecosystem includes hospital and community-based care, highlighting the role of outpatient care in reducing relapses and readmissions. This study aimed (i) to identify potential expert-based causal relationships between inpatient and outpatient care variables, (ii) to assess them by using statistical procedures, and finally (iii) to assess the potential impact of a specific policy enhancing the MH care balance on real ecosystem performance. Causal relationships (Bayesian network) between inpatient and outpatient care variables were defined by expert knowledge and confirmed by using multivariate linear regression (generalized least squares). Based on the Bayesian network and regression results, a decision support system that combines data envelopment analysis, Monte Carlo simulation and fuzzy inference was used to assess the potential impact of the designed policy. As expected, there were strong statistical relationships between outpatient and inpatient care variables, which preliminarily confirmed their potential and a priori causal nature. The global impact of the proposed policy on the ecosystem was positive in terms of efficiency assessment, stability and entropy. To the best of our knowledge, this is the first study that formalized expert-based causal relationships between inpatient and outpatient care variables. These relationships, structured by a Bayesian network, can be used for designing evidence-informed policies trying to balance MH care provision. By integrating causal models and statistical analysis, decision support systems are useful tools to support evidence-informed planning and decision making, as they allow us to predict the potential impact of specific policies on the ecosystem prior to its real application, reducing the risk and considering the population’s needs and scientific findings.

Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach

Reliable modeling of novel commutative cases of COVID-19 (CCC) is essential for determining hospitalization needs and providing the benchmark for health-related policies. The current study proposes multi-regional modeling of CCC cases for the first scenario using autoregressive integrated moving average (ARIMA) based on automatic routines (AUTOARIMA), ARIMA with maximum likelihood (ARIMAML), and ARIMA with generalized least squares method (ARIMAGLS) and ensembled (ARIMAML-ARIMAGLS). Subsequently, different deep learning (DL) models viz: long short-term memory (LSTM), random forest (RF), and ensemble learning (EML) were applied to the second scenario to predict the effect of forest knowledge (FK) during the COVID-19 pandemic. For this purpose, augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests, autocorrelation function (ACF), partial autocorrelation function (PACF), Schwarz information criterion (SIC), and residual diagnostics were considered in determining the best ARIMA model for cumulative COVID-19 cases (CCC) across multi-region countries. Seven different performance criteria were used to evaluate the accuracy of the models. The obtained results justified both types of ARIMA model, with ARIMAGLS and ensemble ARIMA demonstrating superiority to the other models. Among the DL models analyzed, LSTM-M1 emerged as the best and most reliable estimation model, with both RF and LSTM attaining more than 80% prediction accuracy. While the EML of the DL proved merit with 96% accuracy. The outcomes of the two scenarios indicate the superiority of ARIMA time series and DL models in further decision making for FK.

Econometric Assessment of Institutional Quality in Mitigating Global Climate-Change Risk

Background: Environmental deterioration is the alarming situation that results from rapid urbanization and development. The rising temperature and climate volatility are accounted for by the massive carbon dioxide (CO2) emissions. The research on climate-change mitigation is trying to curtail the situations before they become irreversible and unmanageable. This study explores the role of institutions in mitigating climate change by moderating the impact of environmental quality on climate change risk. Methodology: Global data sets have been collected from world big data depositories like the World Economic Forum (WEF), the World Development Indicators (WDI), and the International Country Risk Guide (ICRG). Countries that are listed in WEF were used as the sample of the study. An analysis was based on 114 countries that are based on the availability of data. For estimation, descriptive statistics, correlation analysis, change effects, and a Panel Feasible Generalized Least Squares (FGLS) model were used for estimating the results. Results: The global assessment indicates that CO2 emissions increase the climate risk, but its impact can be reduced by increasing the quality of institutions. Additionally, an increase in renewable energy consumption and economic growth reduces the climate risk. Implications: It is an instrumental study that empirically investigated the role of institutions in reducing climate risk by moderating CO2 emissions. The results of this study will help policymakers to formulate policies regarding environmental protection.

Sustainable Development Indicators and Their Relationship to GDP: Evidence from Emerging Economies

This paper aims to analyze the metrics the United Nations has set and called the Sustainable Development Goals (SDGs) and their association with the gross domestic product (GDP) in emerging economies. SDGs have been identified to measure healthy development, whereas GDP has historically been used to measure economic health and has been prioritized above many other indicators. This research deploys the feasible generalized least squares (FGLS) and the seemingly unrelated regressions (SUR) on panel data consisting of the five BRIC countries spanning 2000 through 2017 to estimate a regression model that shows the association of SDGs with GDP. The paper concludes that targeting GDP may not lead to achieving overall SDGs.

Corruption, Credit Risk, and Bank Profitability

The economies of African countries are generally characterized by inefficient management of resources, strong heterogeneity in the rate of economic growth, as well as high levels of corruption and embezzlement of public funds, clearly highlighting the need to consider the role of government in the performance of the economic environment. Corruption is characterized by three key behaviors—bribery, embezzlement, and nepotism—characteristics that can influence the performance of any financial system. The objective is to examine the effect of corruption on credit risk in Angola. The result of the feasible generalized least squares (FGLS) estimation suggests that corruption increases non-performing loans in the Angolan economy; additionally, the authors find that the larger the bank's assets (bank size), the more averse to credit risk they become, and the smaller the state's stake in the banking system, the lower the non-performing loans.

Microfinance institutions' risk and governance in Sub-Saharan Africa

PurposeThe purpose of this study is to explore the impact of MFI-level governance on microfinance institutions' (MFIs’) risk in Sub-Saharan Africa (SSA).Design/methodology/approachThe study uses data from a sample of 151 MFIs operating in 21 SSA countries during 2005–2014. The Feasible Generalized Least Squares (FGLS) regression model is applied to investigate the relationship between MFI level governance mechanisms and risk.FindingsThe study provides new evidence that board characteristics have differential effects on for-profit (FP) and not-for-profit (NFP) MFI risk. Board independence reduces credit risk of NFP MFIs. Foreign director presence increases MFI failure risk. Furthermore, greater female director representation reduces (increases) FP (NFP) financial risk whereas female CEOs are associated with higher (lower) FP (NFP) financial risk.Originality/valueThe paper contributes to existing literature on microfinance governance and risk, by exploring the impact of governance on MFI risk based on MFIs profit orientation. In addition, the study uses three different risk measures unlike previous microfinance studies.

Genome size distributions in bacteria and archaea are strongly linked to phylogeny

The evolutionary forces that determine genome size in bacteria and archaea have been the subject of intense debate over the last few decades. Although the preferential loss of genes observed in prokaryotes is explained through the deletional bias, factors promoting and preventing the fixation of such gene losses remain unclear. Moreover, statistical analyses on this topic have typically been limited to a narrow diversity of bacteria and archaea without considering the potential bias introduced by the shared recent ancestry of many lineages. In this study, we used a phylogenetic generalized least-squares (PGLS) analysis to evaluate the effect of different factors on the genome size of a broad diversity of bacteria and archaea. We used dN/dS to estimate the strength of purifying selection, and 16S copy number as a proxy for ecological strategy, which have both been postulated to play a role in shaping genome size. After model fit, Pagels lambda indicated a strong phylogenetic signal in genome size, suggesting that the diversification of this trait is strongly influenced by shared evolutionary histories. As a predictor variable, dN/dS showed a poor predictability and non-significance when phylogeny was considered, consistent with the view that genome reduction can occur under either weak or strong purifying selection depending on the ecological context. Copies of 16S rRNA showed poor predictability but maintained significance when accounting for non-independence in residuals, suggesting that ecological strategy as approximated from 16S rRNA copies might play a minor role in genome size variation. Altogether, our results indicate that genome size is a complex trait that is not driven by any singular underlying evolutionary force, but rather depends on lineage- and niche-specific factors that will vary widely across bacteria and archaea.

International Experts’ Influence on Company Internationalization

Purpose: The article evaluates the associative relationship between international supervisory board experts and foreign ownership, along with the experts’ influence on the financial and operating performance of firms. The study was based on data collected for 257 companies listed on the Warsaw Stock Exchange in 2010–2015. Methodology: The dataset was built as a panel, and then generalized least squares regression models with a fixed or random effect were employed to test hypotheses. Findings: The findings of the study clearly show that the presence of investigated firms in foreign markets positively affects company performance. Moreover, models with dependent variables ROA and ROS show that supervisory board members with foreign experience positively affect profitability indicators of firms that do not operate on foreign markets. The data analyses reveal that international experts are more effective advisors for companies that conduct no business activities on foreign markets. Furthermore, the results show a positive moderate association between the share of international experts in supervisory boards and the share of foreign ownership in the company. Originality: The article contributes to the understanding of determinants and consequences of the presence of international experts in supervisory boards and company internationalization.