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This paper seeks to examine the impact of foreign direct investments (FDI) on air pollution in China using 286 cities from 2001 to 2007. It is a particular interest of this paper to observe the relationship between FDI and air pollution in particular after China joined to World Trade Organization in 2001. This paper provides a better understanding of economic growth and foreign investment while maintaining a sustainable environment. In order to achieve this task, this paper tests whether or not FDI inflow has impact on environmental deterioration in particular on air quality.

Since the data are both cross‐sectional and time series, panel data analyses (fixed effects and random effects) were applied. In order to detect the presence of serial correlation of error term, Durbin‐Watson test was used. As serial correlation problem was determined, generalized least square (GLS) using Ar(1) model was used to overcome serial correlation.

The findings show that FDI has no negative impact on the air quality in China. Contrary to expectations, the presence of FDI reduces the air pollution. This result can be attributed by the role of FDIs in the economy that FDIs are perceived as main sources of advanced technology in China. One of the striking findings of the paper shows that FDI has no significant impact on air quality in the central and western cities. The reason is that low level of FDI inflows to cities located in the Center and West. The findings are robust under both panel data (fixed effects and random effects) and GLS estimations.

The results provide a wide array of information useful to practitioners, policy makers. Since the paper shows that FDI has no negative impact on the air quality, this result is crucial in attracting FDI to China.

This paper provides the largest sample including 286 cities all over China from 2001 to 2007. Considering the distribution of FDI across China, the sample is divided into three regions. Making sub‐samples of the FDI distribution allowed us to examine how the impact of FDI differs on air quality in the East, Central and West regions.

In India, the operational performance of the refinery is influenced by many factors. It is important to identify those key drivers which can assist the refineries to uphold and succeed in day-to-day production activities. Therefore, the purpose of this study is to evaluate the operational efficiency of seven Indian oil refineries during the period 2010 to 2018.

In this work, a two-stage empirical analysis is proposed. In the first stage, the data envelopment analysis (DEA) – variable return to scale model is used to evaluate the operational efficiency of the Indian oil refineries. The ordinary least square (OLS), random effect generalized least square (GLS) and Tobit model are used in the second stage to identify the key determinants of efficiency and to explain the variation in refinery efficiency.

The first-stage DEA results showed that the Numaligarh Refinery Limited and Chennai Petroleum Corporation Limited are found to be more efficient than the rest of the sampled refineries and attained their efficiency scores of 0.993 and 0.981, respectively, during the study period. The second-stage regression analysis suggested three explanatory variables: refinery structure, utilization rate and distillate yield, which are found to be significant in explaining variations in refinery efficiency.

This study provides valuable information that would help policymakers to formulate policies toward improving the efficiency of underperforming Indian refineries, which reduces the excessive use of resources and gives a competitive advantage.

This study proposes the first-ever application of the profit frontier DEA model for assessing the operational efficiency of oil refineries and explains the variation in refinery’s efficiency using OLS, GLS as well as the Tobit model.

The purpose of this paper is to empirically examine the institutional structures and other predictors that determine bilateral trade between Africa and China from 1995 to 2017.

In line with the gravity model of trade, institutional, geographical and socio-economic determinants of China’s bilateral trade with 18 African oil/minerals exporting countries are examined by deploying Poisson pseudo-maximum likelihood and dynamic bias-corrected least squares dummy variable econometric techniques.

The results indicate that China’s oil/minerals imports from Africa are higher than imports of manufacturing and agricultural goods, and institutional structures indicate that a weak politically stable region with less control of corruption has a discernible effect on trade.

Further insight can be gained if the type of manufactured goods being exported to China is examined; this is necessary given that China crowds out Africa’s manufactured goods. Therefore, this study recommends the need for Africa to continually strengthen its institutional structures to stimulate trade from other regions.

This study examines the quality of the institutional structures (political stability and corruption) in African oil/minerals exporting countries, considering that China has been alleged for capitalising on Africa’s weak institutional structures to trade with the resource-endowed region. For the first time, the UN COMTRADE HS product-country-partner-year trade data is used to examine on bilateral sector trade China–Africa links rather than proxies used in the studies of Biggeri and Sanfilippo (2009), De Grauwe et al. (2012) and Foad (2011) that did not capture the real trade value.

Peter Christian (Chris) Murphy had worked his way up the “food chain” at AT&T for 19 years, culminating in his position as Sales Center Vice President for South Florida. When he and a coworker realized there was an opportunity in the marketplace for a small, nimble company to take advantage of emerging communications technology that a stodgy, lumbering corporation would have trouble integrating into its service package, Chris decided to jump into an entrepreneurial opportunity.

This study aims at investigating the impact of the disclosure and the Shariah governance on the financial performance in MENASA (Middle East, North Africa and Southeast Asia) Islamic banks.

We use the Generalized Least Squares (GLS) regression models to check the interdependence relationship between the disclosure, the Shariah governance and the financial performance of 47 Islamic banks (IBs) from ten countries operating in MENASA region. The sample period is from 2012 to 2019. In these regressions models, Return on Assets (ROA) and Return on Equity (ROE) are the dependent variables. The disclosure and the Shariah governance indicators are the independent factors. To measure the Shariah governance, we use the three sub-indices, which are the Board of Directors (BOD), the Audit Committee (AC) and the Shariah Supervisory Board (SSB). Size, Leverage and Age of the bank are used as control variables. We also used The Generalized Method of Moments (GMM) and the three-stage least squares (3SLS) estimations for robustness check.

Result shows a negative relationship between the disclosure and the two performance measures in IBs. Furthermore, as far as the governance indicators are concerned, we found that the BOD and AC, as well as the BOD and SSB, have a positive and significant impact on the ROA and ROE, respectively. This reveals that good governance had a significant association with higher performance in MENASA IBs.

The paper considers both IBs that adopt mandatory as well as voluntary AAOIFI standards and the GLS method to investigate the impact of the AAOIFI disclosure and the Shariah governance on ROA and ROE. Also, it uses the GMM and the 3SLS estimations for robustness check. It is relevant for researchers, policymakers and stakeholders concerned with IBs' performance.

In the Iranian economy, investing in the housing market has been very important and beneficial for investors and households, because of inflationary environment, low real interest rates, underdeveloped financial and tax systems and economic sanctions. Hence, prediction of house prices is the main concern of housing market agents in the economy. The purpose of this paper is to test the stationary properties of Iran's provinces to improve the prediction of future housing prices.

In this paper, the authors have tested the stationary properties of 20 Iran’s province centers over the period from 1993 to 2017 using a novel Fourier quantile unit root test and conventional ordinary/generalized least squares (O/GLS) linear unit root/stationary tests.

According to conventional O/GLS linear unit root/stationary tests, most of the house prices series exhibit random walk behavior, whereas by applying the Fourier quantile unit root test, the null hypothesis of unit root is rejected for 15 out of 20 series. Other results indicated that house prices of cities responded differently to positive and negative shocks.

Previous studies only addressed conventional OLS or GLS linear unit root or stationary tests, but novel Fourier quantile unit root test was not used. New results were obtained based on this unit root test, that, as a priori knowledge, will help benefiting from the positive effects, or avoiding being victimized by the negative effects.

The objective of this research is to determine the influence of solvency and liquidity on the profitability [return on assets (ROA)] of Tunisian banks from Q2-2020 to Q3-2022 by considering asset quality as a moderating variable.

This study uses data on liquidity, solvency, ROA and asset quality for 12 banks. It also considers bank size, gross domestic product (GDP) growth and inflation as control variables. The methodology is based on panel data with generalized least squares (GLS) estimation to assess the moderate influence of the asset quality on solvency, liquidity and ROA. Also, the generalized method of moments (GMM) estimation is used as a robustness test.

The results of the GLS model estimation indicated a negatively significant moderating correlation between the liquidity and the solvency. The data from the GMM model indicate that the liquidity variable predicted by the liquidity has a positively significant influence on a bank's ROA as well as for the solvency variable, which is predicted by the capital capacity. Therefore, we conclude that these two variables had a positively significant impact on the ROA.

The studies have many implications for banks and their management in addition to the industry regulators. The results of this study will enable political decision-makers to determine the banks' profits based on their liquidity and solvency.

This analysis provides financial explanations and recommendations for stakeholders in Tunisian banks. Furthermore, these banks must also be able to maintain their liquidity and solvency to ensure their profits in times of COVID-19.

Despite India achieving many milestones under MGNREGA, the external and internal shocks result in below potential outcomes in employment demand and generation. This study examines how these shocks matter and how the migration-prone regions perform.

This study, employing district-level data from 2018 to 2021, investigates how climate change and COVID-19 have affected the employment demand and supply. We applied RE-GLS and IV-2SLS regressions to examine the effects of shocks on employment demand and generation, respectively. The difference in difference panel model is employed to test the spatial effects of the pandemic. Further, we used RE-GLS and extended regression model to examine how external shocks interacting with migration affect unemployment rates.

It was found that the pandemic increased employment demand and generation. This reflects the adverse effects of the pandemic and the swift action by the government. However, the responses were possibly different during climate shocks. The wage differential increased employment demand. However, demand decreased due to poor support from the support staff. The employment generation was higher in migration-prone districts, indicating that seasonal migration, being a lean-season phenomenon, continues to occur despite employment generation.

This study contributes to the literature in several ways. It captures spatial variations while examining the impact of climate change and COVID-19. It investigates the performance of MGNREGA in migration-prone areas. In effect, the findings provide policymakers with greater insight into the issues.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2024-0132.

This study aims to address a notable gap in the existing literature by exploring the relationship between gender diversity and dividend policy within the context of US Real Estate Investment Trusts (REITs).

The authors use a substantial data set comprising 1,398 firm-year observations across 209 US REIT companies from 2011 to 2021 to address the research aims. Fixed effects models and generalized least squares regression methods are used in the analysis.

The results demonstrate a significant positive association between board gender diversity and higher dividend payouts among US REITs. This relationship holds after controlling for corporate governance and other firm-level factors. The findings have strong implications that the presence of women on REIT boards contributes to a greater propensity for discretionary dividend increases in the USA.

This research contributes to the literature by empirically examining female directors’ role in influencing US REITs’ dividend policies, an area lacking adequate prior scholarship. The paper also considers the unique regulatory environment of REITs, highlighting the importance of the study for externally financed firms.

Recent research has examined the effect of the Financial Services Modernization Act of 1999, more commonly known as the Gramm–Leach–Bliley Act (GLB), on the market value of U.S. commercial banks, life insurers, property-liability insurers, thrifts, finance companies, and securities firms. This study fills a gap in our understanding of the Act by measuring the price and trading volume effects of the GLB on U.S.-listed foreign banks. A primary contribution of this study is to examine the role, if any, of two corporate governance perspectives, the stakeholder (code law), and shareholder (common law) models, in a cross-sectional analysis of foreign bank market reaction to the GLB.

Using a generalized least squares (GLS) portfolio approach, Corrado's rank statistic, and confirmed by the traditional market model approach, we find significant negative share price reactions to certain legislative announcements surrounding the passage of the GLB. Trading volume reactions corroborate the significant share price responses. In general, our results indicate that investors in foreign banks reacted negatively to key legislative action. In a cross-sectional analysis, younger, higher-risk foreign banks with less concentrated ownership and more subordinated debt from countries with higher quality accounting standards appear to have more positive (or less negative) share price reactions.