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Despite the substantial economic potential, ASEAN faces challenges in meeting its infrastructure investment needs, resulting in suboptimal logistics performance for several countries. This study proposes an analysis of the contribution of logistics infrastructure and infrastructure investment in ASEAN countries to the overall economic growth of ASEAN. This study also aims to provide valuable insights for formulating effective policies that promote regional connectivity and accelerate economic integration in the ASEAN region. This study employs a fixed effect model with panel data from eight ASEAN nations (Cambodia, Indonesia, Malaysia, Myanmar, the Philippines, Thailand, Singapore, and Vietnam) gathered between 2021 and 2022. The results show that ASEAN countries’ logistics infrastructure is yet to affect ASEAN economic growth optimally. However, ASEAN countries’ domestic infrastructure investment significantly and negatively affects ASEAN economic growth. ASEAN governments must prioritize and actively develop logistics infrastructure, both nationally and regionally, to help develop regional linkages and promote sustainable economic growth rates. They should also intensify coordination efforts, optimize regional financial integration, identify alternative funding sources, and assess regulations restricting private sector participation to maximize private sector investments in infrastructure development, including public-private partnership (PPP). [ABSTRACT FROM AUTHOR]

Purpose: Few empirical studies examined the relationship between life expectancy and income in India. This study aims to examine the impact of life expectancy on economic growth in India by incorporating all the major states of India. Design/methodology/approach: This study is based on secondary data and includes 16 major states of India covering the periods 2000–2014. The author used panel fixed effect model (FEM) to examine the impact of life expectancy on economic growth. Findings: Empirical analysis revealed a positive trend in life expectancy in India. In association with life expectancy, the author found continuous growth in the elderly population. The result of the FEM shows that gains in life expectancy positively affect economic growth in India. The empirical findings do not support any negative impact of life expectancy gains on economic growth. Originality/value: This study is the outcome of the independent and original research work of the authors and contributes significantly to the literature on the demography–economic relationship. The findings of this study help the author to understand that life expectancy gain is in no way a constraint, rather the skill and experience of the workforce are crucial to determining economic growth. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2022-0422. [ABSTRACT FROM AUTHOR]

This paper explores the influence of corporate governance on the profitability and credit risk of a sample of listed banks in Tunisia. The methodology involves two main steps. Firstly, principal component analysis is employed to construct a novel governance index, assessing the quality of both internal and external bank governance. This index takes into account the degree of compliance and application of directives and laws mandated by the Tunisian Central Bank regarding banking governance. In the second step, panel data analysis is conducted to scrutinize the impact of internal and external governance mechanisms on the profitability and risk of Tunisian banks. The results reveal that as the governance index increases, the profitability of banks improves in terms of return on assets and stock market performance. Additionally, a higher governance index is correlated with a reduction in credit risks, as indicated by lower instances of non‐performing loans and an increased rate of coverage for classified debts. To enhance the robustness of our results, we calculate a standard governance score based on existing empirical literature. Furthermore, to account for potential endogeneity, we employ the two‐step system generalized method of moments. [ABSTRACT FROM AUTHOR]

The objective of this study is to examine the influences of institutions, globalization, and world uncertainty on bank profitability in small developing economies. Consequently, we emphasize the significance of both bank-specific and other external factors influencing bank profitability. The empirical estimation is based on seven banks in Fiji—a small island economy—over the period 2000–2021. Together with bank-specific and macro factors, we account for institutions, globalization, and world uncertainty in analyzing the determinants of bank profitability. The study uses the fixed-effect estimation method. From the results, we observe that bank-specific variables, like the net interest margin, non-interest income, bank size, and capital adequacy ratio, are positively associated with bank profitability. Non-performing loans and credit risk are negatively associated with bank profitability. Macro variables, such as real GDP growth and remittances, have positive effects on bank profitability. Institutional factors, such as government effectiveness and voice and accountability, are positively associated with bank profitability. Regarding globalization, we find that it supports bank profitability. Global uncertainty and the Global Financial Crisis (2007–2008) are positively associated with profitability, whereas the global pandemic (COVID-19) is negatively associated. This study underscores the need to analyze the bank performance with factors beyond those reported in financial statements to derive a comprehensive understanding and appreciation of the complex nature of banking operations. [ABSTRACT FROM AUTHOR]

Introduction: Reproductive health service (RHS) helps for people to have a delighted and safe sex through their life journey. It enables especially for women to go safely through pregnancy and childbirth and provide couples with the best chance of having a healthy infant. Therefore, this study aimed to identify the significant determinants of RHS utilization among undergraduate regular class students in Assosa University by using advanced methodology. Methods: We used cross-sectional study design to collect RHS data from 362 students in Assosa University from 5 to 16, may 2021. These students were selected using stratified random sampling technique. We also used cross-tabulation to summarize the extents of RHS utilization across all predictors in terms of percentage and three varieties of multilevel binary logistic regression model to model the determinants of RHS. Results: 42.27% of undergraduate regular class students in Assosa University utilize at least one type of RHS during their time at Assosa University whereas, 57.73% of undergraduate regular class students in this University are not utilized it. Among three varieties of multilevel binary logistic regression models, the random slopes two-level model was selected as a best fitted model for the datasets. At 5% level of significance, awareness about RHS, gender, preference of service fees and student’s monthly average income were significant predictor variables in this model. In addition, the covariates; age, gender and preference of service fees have a significant random effects on utilization of RHS across all colleges/school. Conclusion: Students who; preferred service fee as usual rate, have awareness about RHS, are females and have high monthly average income were more likely to utilize RHS. RHS utilization among undergraduate regular students in Assosa University is likely to increase more effectively with interventions that address these factors. [ABSTRACT FROM AUTHOR]

The country urgently needs new impetus to achieve innovation, quality improvement, and sustainable development of the wheat industry. The agricultural service industry is a key strategic driving force for promoting the modernization and transformation of China's wheat industry and achieving high-quality development. This article is based on panel data from 11 major wheat producing provinces and regions from 2006 to 2021, and uses a fixed effects model to reveal the impact of agricultural services on the high-quality development of the wheat industry. The results showed that the overall high-quality development of the wheat industry is showing an upward trend; the increase in the proportion of agricultural service industry is conducive to the improvement of the quality of wheat industry development; further analysis shows that the agricultural service industry has a differential impact on the development of the four dimensions of wheat industry production efficiency, green development, economic benefits, and rural health, that is, it has a significant positive impact on production efficiency, economic benefits, and rural health, while it has a inhibitory effect on green development. Based on this, several policy suggestions are proposed: To achieve high-quality development of the wheat industry, attention should be paid to promoting the development of agricultural service industries; promote the provision of cross regional services in the main wheat producing areas; promote the development of agricultural service industry towards specialization. [ABSTRACT FROM AUTHOR]

Internet technology and data-driven innovations are changing the way people live and work, which could have an impact on real estate supply and demand. Therefore, this study focuses on the relationship between urban digital economy growth and urban house prices. First, the empirical model shows that the digital economy has an inverted U-shaped impact on urban housing prices. The mechanisms underlying this relationship were then examined. The results indicated that fixed asset investment, government expenditure, and the urban environment influence the relationship between digital economy growth and urban house prices. Finally, a heterogeneity analysis revealed that the digital economy does not affect house prices in Tier 1, New Tier 1, and Tier 2 Chinese cities, but increases house prices in Tier 3 cities. In Tier 4 and Tier 5 cities, this relationship was found to be inverted U-shaped. These findings offer valuable insights to policymakers in China in balancing the growth of the digital economy and the stability of house prices. [ABSTRACT FROM AUTHOR]

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms. [ABSTRACT FROM AUTHOR]

Abstract The primary objective of this research is to explore the elements that shape the progression of digital technology in Sub-Saharan African nations. The study employs data obtained from 16 countries, covering the period between 2000 and 2020. Employing fixed effect panel regression analysis, our research indicates that various non-technological factors significantly impact digital technology development in the region. The results highlight that variables including general government final consumption expenditure, inflation rate, employment growth rate, financial development, ease of doing business index, logistics performance index, international migration, access to electricity, and access to safe drinking water have a positive impact on the development of digital technology. Conversely, international trade is identified as a negative influence, primarily due to insufficient infrastructural development. These findings underscore the significance of non-technological elements, encompassing aspects like globalization, economic conditions, favorable digital ecosystems, and the fulfillment of basic human needs, in shaping the landscape of digital technology in the region. The study, while acknowledging limitations in terms of selected indicators, years, and countries, emphasizes the need for broader investigations in future research. Practically, the study suggests that governments in the region should prioritize addressing these non-technological factors to fully leverage the potential of digital technology development. The originality and value of this research lies in its exploration of non-technological determinants, shedding light on their pivotal role in shaping the digital technology landscape in sub-Saharan Africa.

Abstract Background The overall level of negative affect (NeA) has been linked to impaired health. However, whether the diurnal timing of NeA matters and whether the NeA-health relationship is mediated by sleep quality remain unclear. Methods Using a longitudinal dataset (2006, 2009 and 2014 waves) consisting of 1959 participants, we examined the within-person impact of both bedtime NeA and non-bedtime NeA measured by Day Reconstruction Method (DRM) on subjective health measured by Visual Analogue Scale (VAS), and the mediating effect of sleep quality on the NeA-health relationships by fixed effect models. Results Bedtime NeA predicted poorer health, while non-bedtime NeA was unrelated to health. The deleterious impact of bedtime NeA reduced and became non-significant after sleep quality was controlled for. Bedtime NeA also significantly predicted impaired sleep quality. Conclusions Bedtime NeA is a stronger predictor of poorer health than non-bedtime NeA, and the deleterious influence of bedtime NeA on health seems to operate through poor sleep quality. Therefore, interventions to reduce bedtime NeA could potentially improve subsequent sleep quality, thereby protecting people to some extent from impaired health status.

While modeling panel data, identifying outliers is crucial, yet visually spotting them can be challenging. However, not many methods exist for outlier detection in panel data models other than robust estimation methods. This study uses the variance shift outlier model (VSOM) approach to identify and handle outliers in panel data modeling, both in single and simultaneous equation modeling. This study applies the VSOM approach to GDP and FDI data from ten countries that are part of the ASEANChina Free Trade Area (ACFTA). The results demonstrate that the VSOM approach effectively handle outliers in panel data modeling, in both single and simultaneous equation model. Specifically, the VSOM model shows a lower sum of squared residuals (SSR) compared to the null model. This indicates that VSOM successfully reduces the impact of outliers, leading to improved model fit and accuracy in capturing the underlying relationships within the data. Some of the highlights of the proposed approach are: • Identifying the square of the standardized residual. • Generate the distribution of the squared standardized residuals by parametric bootstrapping. • Reducing the impact of observations identified as outliers by downweighting their variance through the addition of a D matrix.

The primary objective of this research is to explore the elements that shape the progression of digital technology in Sub-Saharan African nations. The study employs data obtained from 16 countries, covering the period between 2000 and 2020. Employing fixed effect panel regression analysis, our research indicates that various non-technological factors significantly impact digital technology development in the region. The results highlight that variables including general government final consumption expenditure, inflation rate, employment growth rate, financial development, ease of doing business index, logistics performance index, international migration, access to electricity, and access to safe drinking water have a positive impact on the development of digital technology. Conversely, international trade is identified as a negative influence, primarily due to insufficient infrastructural development. These findings underscore the significance of non-technological elements, encompassing aspects like globalization, economic conditions, favorable digital ecosystems, and the fulfillment of basic human needs, in shaping the landscape of digital technology in the region. The study, while acknowledging limitations in terms of selected indicators, years, and countries, emphasizes the need for broader investigations in future research. Practically, the study suggests that governments in the region should prioritize addressing these non-technological factors to fully leverage the potential of digital technology development. The originality and value of this research lies in its exploration of non-technological determinants, shedding light on their pivotal role in shaping the digital technology landscape in sub-Saharan Africa. [ABSTRACT FROM AUTHOR]

The improvement in urban production efficiency has led to income distribution being skewed towards urban labor, thereby widening the urban–rural income gap. However, integration of the agricultural industry at the county level can accelerate the flow of production factors between industries. Therefore, this study evaluates the degree of agricultural industry integration at the county level using the entropy weight method and explores its impact on the urban–rural income gap, based on sample data from 1122 counties in China spanning from 2014 to 2021. The research findings reveal the following: (1) The fixed model demonstrates that enhancing agricultural industry integration can significantly narrow the urban–rural income gap; (2) The mediating model indicates that this narrowing effect can be achieved by improving the green total factor productivity of agriculture; (3) Regional heterogeneity analysis indicates that the impact of agricultural industry integration is more pronounced in the central region and main crop production areas; (4) The results of the spatial Durbin model demonstrate that agricultural industry integration also exhibits a significant positive spatial spillover effect on neighboring areas. The outcomes of this study contribute to enriching the research on agricultural industry integration for green and low-carbon agricultural development, further promoting the development of county-level agricultural industry integration, and providing valuable insights for other similar countries. [ABSTRACT FROM AUTHOR]

Background: The overall level of negative affect (NeA) has been linked to impaired health. However, whether the diurnal timing of NeA matters and whether the NeA-health relationship is mediated by sleep quality remain unclear. Methods: Using a longitudinal dataset (2006, 2009 and 2014 waves) consisting of 1959 participants, we examined the within-person impact of both bedtime NeA and non-bedtime NeA measured by Day Reconstruction Method (DRM) on subjective health measured by Visual Analogue Scale (VAS), and the mediating effect of sleep quality on the NeA-health relationships by fixed effect models. Results: Bedtime NeA predicted poorer health, while non-bedtime NeA was unrelated to health. The deleterious impact of bedtime NeA reduced and became non-significant after sleep quality was controlled for. Bedtime NeA also significantly predicted impaired sleep quality. Conclusions: Bedtime NeA is a stronger predictor of poorer health than non-bedtime NeA, and the deleterious influence of bedtime NeA on health seems to operate through poor sleep quality. Therefore, interventions to reduce bedtime NeA could potentially improve subsequent sleep quality, thereby protecting people to some extent from impaired health status. [ABSTRACT FROM AUTHOR]

Internet technology and data-driven innovations are changing the way people live and work, which could have an impact on real estate supply and demand. Therefore, this study focuses on the relationship between urban digital economy growth and urban house prices. First, the empirical model shows that the digital economy has an inverted U-shaped impact on urban housing prices. The mechanisms underlying this relationship were then examined. The results indicated that fixed asset investment, government expenditure, and the urban environment influence the relationship between digital economy growth and urban house prices. Finally, a heterogeneity analysis revealed that the digital economy does not affect house prices in Tier 1, New Tier 1, and Tier 2 Chinese cities, but increases house prices in Tier 3 cities. In Tier 4 and Tier 5 cities, this relationship was found to be inverted U-shaped. These findings offer valuable insights to policymakers in China in balancing the growth of the digital economy and the stability of house prices.

This study utilized panel data from 132 countries spanning from 1996 to 2019 to examine the effect of government efficiency on carbon emission intensity. Using a fixed effect model, the study found that stronger government efficiency is associated with a significant decrease in carbon emission intensity. Robustness tests were performed, the results of which consistently supported the main findings. Additionally, the study investigated the mechanisms underlying the linkage between government efficiency and carbon emission intensity, revealing that improved government efficiency can inhibit carbon emission intensity by fostering environmental innovation and promoting renewable energy consumption. Finally, the study examined the moderating effects of national income level, economic freedom, democracy, and ruling party ideology on the nexus of government efficiency and carbon emission intensity, and found empirical evidence supporting these moderating effects. These results provide new insights for governments seeking to reduce carbon emission intensity. [ABSTRACT FROM AUTHOR]

Despite much impetus on economic growth, educational expansion, health and infrastructural development, women participation in labour force market is not improving. Women participation in politics, leadership positions, national parliaments and local government, judiciary and police force is very essential for achieving Sustainable Development Goals (SDGs) by 2030. The said target is clearly mentioned as SDG 5.5 which entails—"Ensure women's full and effective participation and equal opportunities for leadership at all levels of decision-making in political, economic and public life". In India, percentage of female elected representatives in parliament rose from 9.0% in 1999 (13th Lok Sabha general elections) to 14.4% in 2019 (17th Lok Sabha general elections) as per the statistical reports published by the Election Commission of India. Thus, the paper tries to study the relationship between women in parliament (MP) and female labour force participation rates (FLPR) using panel data across Indian states for the period 1999 to 2019. The objective of the paper is to empirically examine the relationship between female (elected) political representation and FLPR, using fixed effect panel data model across 15 Indian states excluding Union Territories (UTs), North-Eastern States (NEs), Delhi, Goa, and Jammu & Kashmir (J&K). The panel data is created by taking data of female elected candidates of different states from the statistical reports of general elections published by Election Commission of India and that of FLPR is extracted from various rounds of National Statistical Office (NSO) and Periodic Labour Force Survey (PLFS) reports, respectively. The empirical results suggest that higher political representation of women is associated with favourable female labour outcomes. Our result implies that Female Political Representation (FPR) has a positive and significant effect on FLPR under fixed effect model. However, the random effect model suggests net state domestic product (NSDP) per capita has a negative and significant effect on FLPR, while, Fixed effect model suggests that female literacy rate has a negative and significant effect on FLPR which are consistent with the established literature. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Vibration, Measurement & Diagnosis is the property of Nanjing Hangkong Daxue and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper aims to investigate the effect of the gross domestic product growth rate on inflation in these four selected ASEAN countries. This study is worthy as there is a need to understand the variance of national inflation for these ASEAN countries, particularly when Singapore's inflation rate is the lowest among them. The Keynesian Theory and Static Panel Data analysis are put to the test in this empirical study. The investigation is carried out on yearly balanced panel data involving four ASEAN member countries. The secondary data is extracted from the World Bank database over a study period of sixty years from 1961 to 2020. The methodology is based on static panel data analysis involving both Fixed Effect and Random Effect models. The empirical results show that there is a negative significant relationship between economic growth and inflation within the four countries sampled. As such, the policy implication from this study may suggest that it is important for ASEAN-4 governments to continue beefing up their economic growth in order to curb inflationary pressure. [ABSTRACT FROM AUTHOR]

Copyright of Jurnal Ekonomi Syariah Teori dan Terapan is the property of Universitas Airlangga and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article explores the factors that drive student mobility to developing and developed countries for studying abroad. Economic factors, such as employment opportunities and GDP per capita, are found to be the main determinants of global student flow. Other factors include the reputation of universities, cost of living, and cultural and social similarities. The study highlights the importance of considering return on investment, the value of a degree, and skill development when choosing a study destination. The findings provide policy implications for attracting foreign students and contribute to the existing knowledge on student mobility. [Extracted from the article]

In this paper, the one-sided hypothesis testing and interval estimation problems for the fixed effect and variance component functions are considered in the skew-normal unbalanced heteroscedastic one-way classification random effects model. Firstly, the Bootstrap approach is used to establish test statistic for fixed effect. Secondly, the test statistics and confidence intervals for variance component functions are constructed by Bootstrap approach and generalized approach, and their theoretical properties are discussed. The Monte Carlo simulation results indicate that the Bootstrap approach performs better than the generalized approach in most cases. Finally, the above approaches are illustrated with two real examples of the annual average concentrations of fine particulate matter and nitrogen dioxide. [ABSTRACT FROM AUTHOR]

The economic costs of tobacco use are substantial particularly in developing countries. While much has been written on healthcare costs from tobacco use related diseases, little is known on the macro-economic impacts of tobacco-related productivity loss. The objective of the current research is to understand the macroeconomic burden of tobacco use in developing countries. Using World Bank's development indicators database for 44 low-and lower-middle-income countries, most of which are African, this paper assesses the relationship between tobacco consumption and economic growth. Specifically, using the fixed effect regression approach, we examined whether tobacco prevalence affects long-run growth rate in the real gross domestic product (GDP) per capita. We adopted an empirical framework from the growth literature by including the national tobacco prevalence rate as one of the determinants of long-run growth in the real GDP per capita. The tobacco prevalence rate varies across countries ranging from 5% in Ethiopia to almost 50% in Myanmar. The prevalence rate is much higher among males than females in almost all countries, with Myanmar males having above 70%. The regression results indicate the existence of a statistically significant negative relationship between the prevalence of tobacco and economic growth after controlling for other determinants of growth, including the initial level of GDP per capita, gross fixed capital formation, trade, resource revenues, institutional quality, a human capital indicator, and infrastructure capacity. A 1% increase in the total tobacco prevalence rate reduces the economic growth rate by almost 2%, all else equal. These findings are robust even after running separate models for male and female tobacco prevalence rates. The results on the other determinants of growth are consistent with the previous empirical studies on economic growth in the context of developing countries. The findings of this study provide additional support to the existing body of empirical economic research on the role of better health, as a form of human capital, in increasing economic output. Thus, actively targeting tobacco control interventions, both in and outside of the workplace, can likely improve workers' productivity and hence economic growth. [ABSTRACT FROM AUTHOR]

The construction of agricultural composite teaching and research bases plays an important role in promoting rural revitalization and strengthening agricultural education. This paper emphasizes the role of agricultural information technology innovation in rural revitalization and proposes the impact of agricultural technology innovation on the development of rural revitalization. The variables of agricultural technology innovation as well as rural revitalization are selected, the benchmark regression model is set, the local impact model is set for the seven secondary indicators under agricultural technology innovation, and the evaluation index model is constructed. Through the empirical analysis method, the model is sequentially subjected to preliminary analysis and nonlinear characterization. Benchmark regression test using the fixed-effects model, model (7) shows that agricultural technological innovation at the 1% significant level of β =2.6548, P < 0.01, indicating that agricultural technological innovation has a positive and positive impact on rural revitalization. The threshold test shows that when the level of financial activity is lower than the threshold effect of 0.0965, the coefficient of the impact of agricultural technological innovation on rural revitalization is 0.0458. The effect of agricultural technological innovation on rural revitalization is affected by the intensity of financial activity, and there is a moderating effect of economic activity on both.

Coronavirus Disease 2019 (COVID-19) is a disease caused by a new type of virus called Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). Based on data up to April 9th 2020, confirmed cases of COVID-19 in East Java are 6.78% of the total confirmed cases in Indonesia. Meanwhile, cases of death due to COVID-19 are 6.07% of the total cases of death in Indonesia. In handling it, the Ministry of Health of the Republic of Indonesia classifies COVID-19 patients into 3 status levels, namely ODP, PDP and Confirmed. The results of the meta-analysis using the risk ratio with the Mantel-Haenszel fixed effect model obtained a pooled risk ratio for the PDP and ODP groups (RR = 33.66, 95% CI: 20.88-54.27), the confirmed and ODP groups (RR = 56.31, 95% CI: 32.07-98.87), and the confirmed and PDP groups (RR = 1.75, 95% CI: 1.2-2.53). The order of the group with the highest risk of death is Confirmed > PDP > ODP.

The coronavirus disease 2019 (COVID-19) pandemic was defined by the World Health Organization (WHO) as a global epidemic on March 11, 2020, as the infectious disease that threatens public health fatally. In this study, the main aim is to model the impact of various air pollution causes on mortality data due to the COVID-19 pandemic by Generalized Linear Mixed Model (GLMM) approach to make global statistical inferences about 174 WHO member countries as subjects in the six WHO regions. “Total number of deaths by these countries due to the COVID-19 pandemic” until July 27, 2022, is taken as the response variable. The explanatory variables are taken as the WHO regions, the number of deaths from air pollution causes per 100.000 population as “household air pollution from solid fuels,” “ambient particulate matter pollution,” and “ambient ozone pollution.” In this study, Poisson, geometric, and negative binomial (NB) regression models with “country” taken as fixed and random effects, as special cases of GLMM, are fitted to model the response variable in the aspect of the above-mentioned explanatory variables. In the Poisson, geometric, and NB regression models, Iteratively Reweighted Least Squares parameter estimation method with the Fisher-Scoring iterative algorithm under the log-link function as canonical link function is used. In the GLMM approach, Laplace approximation is also used in the prediction of random effects. In this study, six different Poisson, geometric, and NB regression models with fixed and random effects are established for 174 countries all over the world to make global statistical inferences for investigating the relationships between “total number of deaths” by these countries due to the COVID-19 pandemic and “air pollution causes.” As a result of this study, “NB mixed-effects regression model” as the most appropriate GLMM is used to make global statistical inferences about the impact of the various air pollution causes on the mortality data due to the COVID-19 pandemic.

Climate change is one of several issues confronting the planet today. Addressing this problem will create a safer environment for humans and other species. Thus, this study explores how the urban population (UBNP) influences carbon dioxide emissions (CO2e) levels in BRICS–T from 1990–2021 (192 observations) using Pooled OLS and Fixed Effects techniques. In addition, energy intensity (ENIT) and economic growth (GDP) are utilized as control variables. The Pooled OLS result demonstrates that UBNP growth reduces CO2e by 0.19%; a rise in ENIT levels spurs CO2e by 1.10%, and an increase in GDP enhances CO2e by 0.61%. The Fixed Effects outcome shows that an upsurge in UBNP reduces CO2e by 1.19%, while ENIT and GDP rise boosts CO2e by 1.19% and 1.04%, respectively. This study recommends continuous urban planning, rural area development, renewable energy integration, and the use ofenergy–efficient buildings.

The use of panel data on spatial regression has many advantages. However, testing the spatial dependency and parameter presumption generated in spatial regression of panel data becomes inaccurate when applied to regions with large numbers of small spatial units. One method of overcoming problems of small spatial unit sizes is the bootstrap method. The research aimed to combine cross-section and time-series panel data. The analysis was performed to extract information based on observations modified by the influences of space or location, known as spatial analysis of panels. The influence of location effects on spatial analysis was presented in the form of weighting. The research applied the Fast Double Bootstrap (FDB) method by modeling poverty rates on Flores Island. The results of the Hausman test show the right model, which is a random effect. Meanwhile, spatial dependency testing concludes spatial dependence and poverty modeling in Flores Island, which is more likely to be the Spatial Autoregressive Random (SAR) model. SAR random effect in modeling value has R2 of 77,38% and does not meet the normality assumption. SAR effect in modeling the FDB approach can explain the diversity of poverty rate in the Flores Island with 88,64% and meets residual normality assumptions. The analysis with the FDB approach on spatial panels shows better results than the common spatial panels.

Credit risk is a significant factor affecting the financial stability of banks. Keeping the credit risk under control is essential to maintain a bank’s cash flow. This paper examines the various profitability, microeconomic and macroeconomic indicators that affect a bank’s credit risk. The study uses the dataset of 31 banks from 2012 to 2021 and employs a panel data modelling approach to account for any variations in risk-taking behavior. The results revealed a statistically significant negative relationship between return on equity and credit risk when nonperforming loans proxy credit risk. This finding was consistent across fixed effect, random effect, and pooled OLS methods, at 1 percent significance (P value < 0.00), indicating that the extent of credit risk decreases as profitability increases. It was further found that bank age and ownership type positively affect a bank’s credit risk, while factors such as bank size and operational efficiency negatively affect credit risk when nonperforming loans proxy credit risk. Further, macroeconomic variables showed that gross domestic product is positively associated with credit risk, while inflation negatively affects credit risk. Overall, the findings of this paper demonstrated that credit risk is affected by both micro and macroeconomic factors. The paper also addresses significant policy implications as it helps various stakeholders to examine the determinants of credit risk, make credit decisions, and ultimately lower their credit risk.

The Indonesian government has made various efforts to reduce poverty, including through policies in government spending. The spending realized by the government is expected to be able to encourage an increase in people's welfare and reduce poverty. This study aims to analyze the effect of government spending, especially social assistance expenditure and five types of capital expenditure, on the poverty rate, using data at the regional/city level in the Papua province from 2011 to 2019. This study also uses other supporting variables, namely the Gini ratio, Gross per capita Regional Domestic Product (GRDP), school participation rate, and ratio of community health centers. The data used is secondary data obtained from Statistics, The Audit Board of The Republic of Indonesia (Badan Pemeriksa Keuangan, BPK), and the Ministry of Health. The data analysis technique used in this study is panel data regression analysis with a fixed effect model. The analysis showed that out of the six types of government expenditure studied, only capital expenditure for roads, networks, and irrigation significantly reduces the poverty rate.

AbstractTechnological change in agriculture in land-scarce developing countries is required for land use intensification to feed the growing population. The purpose of this study is to analyze factors affecting adoption decision of improved wheat variety and its effect on households’ welfare in a semi-arid economy of Tigrai, northern Ethiopia. First, a correlated random effect double hurdle model with a control function approach is used to analyze determinants of improved wheat adoption decision and extent of adoption. Second, fixed effect instrumental variable method is used to assess the impact of area under improved wheat cultivation on households’ welfare. Double hurdle model results indicate that access to improved wheat, landholding, male adult and access to information have strong and positive effect on adoption decision and extent of adoption of improved wheat. The impact analysis shows that extent of improved wheat adoption has strong and positive effect on households’ income, consumption, and food security. Moreover, the gender and wealth disaggregation analysis show that extent of improved wheat adoption has a positive and significant effect on welfare of male and female-headed, land poor and land rich households. Thus, adoption of improved wheat is a pro-poor strategy and increasing access to the technology appears a promising pathway for rural development in a land scarce and semi-arid economy like Ethiopia.

The purpose of this article was to introduce 5 basic concepts, calculation methods, a clinical trial example and its SAS implementation related to the mixed model. The basic concepts include fixed effects, random effects, random coefficients, mixed models, and multilevel models. The calculation methods involved the classical calculation method and the Bayesian calculation method of the mixed model. An example of the clinical trial involved the effect of three drugs on the ability to breathe in people with asthma. This paper gave the whole process of using SAS software to implement the quantitative data in the example, including constructing 6 repeated measurement design quantitative data mixed models based on the mixed procedure and the bglimm procedure. The output results were explained, and all the mixed models constructed were compared based on the evaluation statistics of the model fitting effect, and the optimal mixed model suitable for the data in the paper was obtained. [ABSTRACT FROM AUTHOR]

Although new infrastructure investment is generally considered to have a high environmental improvement expectation, mismatched new infrastructure investment still has the potential to damage the environment. This study examines the environmental impact of the matching degree (NDC) between new infrastructure investment (NII) and digital economy (DECO), using comprehensive economic theory and the panel data of 30 provinces from 2011 to 2019 in China. We employ a range of models including fixed effect, lag effect, threshold effect, and spatial autoregressive models, as well as regional heterogeneity tests. Our results indicate that NII, DECO, NDC, and PM2.5 all have significant spatial spillover effects. In addition, our results indicate that both NII and NDC significantly promote the weakening of haze concentration, and NDC has a strong hysteresis effect. Moreover, the significant single-threshold effect reveals structural mutations in the environmental impact of NII and NDC in air pollution. All the results remained valid under a series of robustness tests. Based on these empirical results, we provide a theoretical and empirical basis about environmental protection for the government to implement new infrastructure investment and digital economy, and the research has implications for other developed or developing countries as well. [ABSTRACT FROM AUTHOR]

Using panel data from 269 Chinese cities, this study examined the impact of housing prices (HP) on cities' innovation capacity (IC) in China. Firstly, a fixed effect model was used to analyze the effect of HP on cities' IC in China, revealing that HP positively impacts cities' IC. Next, several robustness tests were conducted to verify the finding's reliability. Thirdly, the analysis empirically tested mediating mechanisms between HP and cities' IC in China. The results show that, on the one hand, higher HP can improve cities' IC by attracting talents and stimulating the growth of local fiscal revenue. On the other, increasing HP can inhibit cities' IC in China by attracting funds into the real estate market and impeding residents' consumption ability. Finally, the heterogeneous nature of the HP-IC link in China was further explored. This study's results provide recommendations for the government of China on how to promote cities' innovation performance. [ABSTRACT FROM AUTHOR]

This study examines the impact of corporate business strategy on sustainable growth of Chinese listed non-financial firms from 2010 to 2017. Supplier concentration is used as a moderator to examine how company strategy responses affect sustainable growth. This study measures sustainable growth using a comprehensive index based on nine financial indicators. Using fixed effect model, findings reveal that firms choose a conservative corporate strategy in the presence of higher supplier-concentration, whereas aggressive strategy significantly improves sustainable growth. The findings also indicate that higher supplier-concentration has a drag effect on a firm's growth sustainability as it weakens the positive relationship between corporates' business strategy and sustainable growth. Our results are robust to time and industry fixed effects as well as alternate proxies of sustainable growth. This research contributes to the literature on firm strategic strategies in the presence of supplier concentration to achieve sustainable growth, which may assist managers to keenly observe their relationships with their suppliers to maintain a healthy business environment. [ABSTRACT FROM AUTHOR]

In the context of China's synergistic development of import and export strategies and water environmental protection, this paper analyzes the role of two-way FDI synergistic development in water environmental governance efficiency based on panel data of 30 provinces, municipalities and autonomous regions in China from 2004 to 2021. The paper uses a fixed effect model and analyzes the role of heterogeneous technological innovation using the mediation effect model and the panel threshold model. The results of the study show the following: (1) overall, two-way FDI synergistic development helps to improve China's water environmental governance efficiency; (2) there is regional heterogeneity in the effect of two-way FDI synergistic development on China's water environmental governance efficiency, with a significant positive effect in areas along the coast and with high FDI synergistic development levels and an inhibitory effect in inland areas; and (3) the two-way FDI synergy enhances the efficiency of China's water environmental governance by improving the levels of independent innovation and imitation innovation. [ABSTRACT FROM AUTHOR]

This paper conducts an analysis of the impact of COVID-19 and digital transformation (DT) on the financial performance of European listed companies. Using a panel data regression model from 2015 to 2021, the study analyzed the financial performance of 2179 companies. The sample of companies was chosen based on the availability of financial statements and aimed to examine the effects of COVID-19 and DT on financial performance, as measured by return on assets (ROA). The study used a fixed-effect model and checked for robustness by introducing return on equity (ROE) as a dependent variable. The results indicated that COVID-19 had a negative significant impact on financial performance, while DT had a positive significant impact, consistent with previous research. This study provides valuable insights into the impacts of the COVID-19 pandemic and DT on the financial performance of listed companies. [ABSTRACT FROM AUTHOR]

In this work, panel data that were characterized by features of no first order autocorrelation was modelled using three estimation models: Pool Regression, Fixed Effect, Random Effect models. Panel data like other aspects of econometrics, exploits regression analysis as one of the statistical tools to formulate and illustrate models. The regression analysis requires some assumptions which, if violated, results to one problem or the other. In such case, the Pooling method of estimation remains linear, unbiased and normally distributed but might not be efficient as the estimates of the parameters might become indeterminate, the confidence intervals may be too wide and the standard errors might become large. Simulation studies were carried out at different panel structures and autocorrelation level. The experiment was repeated for 10,000 times and Root Mean Square Error (RMSE) was used to judge the performance of the models. The results from this work showed that for small sample panel structure N = 25, T = 5 and n = 5, irrespective of autocorrelation levels, fixed effect model is preferable at all level. But when moderate for N = 50, T = 10, n = 5, irrespective of autocorrelation level, random effect model is preferred, while for large panel structure for N = 450, T = 30, n = 15, irrespective of autocorrelation level, random effect model is preferred. [ABSTRACT FROM AUTHOR]