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The purpose of this paper is to examine the contribution of financial development to poverty reduction in 11 South Asian developing countries using panel data set over the time period 1990-2012.

The stationarity properties are checked by using Levin-Lin-Chu and Im-Pesaran-Shin panel unit root tests. The paper applied the Pedroni’s panel co-integration test to examine the existence of long-run relationship. The coefficients of co-integration are examined by fully modified OLS (FMOLS) and the causal link is checked by panel causality test.

The empirical results of Pedroni co-integration test confirm a long-run relationship between financial development and poverty reduction in South Asian developing economies. The findings of FMOLS method confirm a strong and positive relationship between financial development, trade openness, inflation and poverty reduction. Results of panel causality test indicate that there is a unidirectional causality running from financial development to poverty reduction variable.

The present study recommends appropriate economic and financial reforms focussing on financial inclusion to reduce poverty in selected South Asian economies.

This paper is the first of its kind to empirically examine the causal relationship between financial sector development and poverty reduction in South Asian economies using modern econometric techniques.

The purpose of this study is to examine the effect of foreign aid (FA) on corruption in selected Asian countries (Pakistan, India, Srilanka and Bangladesh) using the panel data from 2000 to 2014.

The author used Levin-Lin-Chu and Im-Pesaran-Shin panel unit root tests to check the stationary properties of the variables. The Pedroni’s and Kao panel cointegration approach was applied to analyze the variable’s long-run relationship. The author used panel dynamic ordinary least squares (PDOLS) and fully modified ordinary least squares (FMOLS) framework to estimate the coefficients of cointegrating vectors. Additionally, the panel granger causality test was performed to check the causal relationship between the variables.

The results from PDOLS and FMOLS indicate that FA has a significant negative impact on the level of corruption. This infers that the foreign assistance decrease the level of corruption perception index, hence, more corruption in the country.

Overall, the study fulfills the need to understand the aid-corruption nexus, particularly in the case of the Asian region.

The recent dynamics of trade policy, especially that is associated with the United States of America (USA) and China, has not only triggered policy adjustments in two economies, it has also implied an uncertainty spillover to other economies across the globe. Consequently, the current study attempts to examine the effect of uncertainties in the USA–China trade policies on stock market indexes. In addition, the cointegration evidence between the USA–China trade policy uncertainty index and of the leading Global South fragile quintet (Brazil, Indonesia, South Africa, India and Turkey) stock market indices is investigated.

Mainly, the FMOLS and DOLS Granger causality analysis with cointegration coefficient estimators were employed for the dataset over the monthly data period of March 2003 and July 2019.

Accordingly, the study found a long-term relationship between the USA–China Trade Policy Uncertainty index and the stock exchange indexes. In addition, a causal relationship was established from the change in the USA–China Trade Policy Uncertainty index to the change in the stock market indexes of almost all of the examined countries (Brazil, Indonesia, South Africa, India and Turkey). In addition, the nonlinear Autoregressive Distributed Lag approach further offers evidence of asymmetric relationship among the examined indicators.

Moreover, this study contributed to the existing literature because it employed the indexes of BIST100, BOVESPA, BSE Sensex 30, IDX Composite and South Africa 40 in a novel approach. Thus, the study posited a useful policy guideline for associated economic uncertainties arising from the trade dispute, such as the case of the world’s two largest trading giants or partners (i.e. the USA and China).

The purpose of this paper is to investigate the relationship between financial development indicators and human capital for Asian countries using the annual data from 1984-2013.

The stationarity of the variables are checked by Levin-Lin-Chu, Im-Pesaran-Shin, Fisher-type augmented Dickey-Fuller and Philips-Perron panel unit-root tests. The Pedroni’s and Kao’s panel co-integration approaches are employed to examine the long-run relationship among the variables. To estimate the coefficients of co-integrating vectors, both panel dynamic ordinary least squares (PDOLS) and fully modified ordinary least squares (FMOLS) techniques are used. The short-term and long-run causality is examined by panel granger causality.

The Pedroni’s and Kao’s co-integration approaches support the existence of the long-run relationship among the indicators of financial development, economic growth and human capital. The PDOLS and FMOLS estimators revealed that both financial development indicators and economic growth variable act as an important driver for the increase in human capital. The results of panel granger causality indicate that causality runs from indicators of financial development, economic growth and public spending on education to human capital.

There is hardly any study that examine the impact of financial development indicators and economic growth on human capital in Asian economies, therefore the present study fill the research gap in the literature.

The study aims to empirically investigate the trends and determinants of labour productivity of the two broad sectors –industry and services – and their components, namely, manufacturing and market services sectors, in the case of major developing and developed economies of Asia-Pacific over the period 1980-2014 and make a comparison thereof.

The study uses econometric methodology of panel unit root tests, panel cointegration and group-mean full modified ordinary least squares (FMOLS).

The study finds that while capital deepening, government size, institutional quality, productivity of the other sector and financial openness affect productivity of all the sectors significantly, the impact of human capital and trade openness varies across sectors in the case of developing economies. Furthermore, the impact of technological progress becomes significant in the post-liberalization reforms period in the developing economies. The study further finds that capital deepening, human capital, government size, institutional quality, productivity of the other sector, government size and trade openness are significant determinants of productivity of all sectors of developed economies under consideration. However, the impact of technological progress is stronger for manufacturing sector than services and its components. Furthermore, while both equity and debt liabilities (as measures of financial openness) influence sectoral productivity of industry and manufacturing sectors positively and significantly in case of developed economies, only equity liabilities have a significant influence on the productivity of developing economies. This may indicate existence of more developed financial markets in the case of developed economies.

The study identifies important structural differences in determinants of productivity both across sectors and across developing and developed economies of Asia-Pacific.

The purpose of this paper is to examine the dynamic linkages amongst ecological footprints, fossil fuel consumption, real income, globalization and population in Egypt in the period from 1971 to 2014.

The paper uses fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) methods to investigate the long run relationships amongst ecological footprints, economic growth, globalization, fossil fuel energy consumption and population. Moreover, the Toda–Yamamoto approach is conducted to examine the causal relationships between variables.

Empirical results of FMOLS and DOLS methods show that real income and fossil fuel consumption are responsible for deteriorating the environment, while globalization and population are found to mitigate it. As for Toda–Yamamoto–Granger causal relationship results, unidirectional causal relation from globalization, population and fossil fuel energy consumption to the ecological footprint exists. Moreover, bidirectional causal relation between real income on the one hand and globalization and the ecological footprint on the other hand is found.

Using carbon dioxide emissions has major weakness as carbon dioxide emissions are considered only part of the total environmental deterioration so this study is the first study for Egypt that uses the ecological footprint as an indicator for environmental quality and environmental pollution and links it with globalization, economic growth, population and fossil fuel energy consumption. Moreover, realizing the direction of causality between these variables might help policymakers in designing the policies to promote the shift towards clean energy sources, especially that achieving sustainable economic growth with more contribution to the global economy depending on diversification of energy sources without deteriorating the environment is considered one of the most important objectives of Egypt’s National Vision 2030.

The paper investigates the effects of climate change along with ecological and carbon footprint on rice crop production in India during 1982–2016.

The autoregressive distributed lag (ARDL), canonical cointegration regression (CCR) and fully modified ordinary least square (FMOLS) models are used in the paper.

A long-run relationship is found between climate change and rice production in India. Results report that ecological footprint and carbon footprint spur long-term rice production. While rainfall boosts rice crop productivity in the short term, it has a negative long-term impact. Further, the findings of ARDL models are validated by other cointegration models, i.e., the FMOLS and CCR models.

This study provides insights into the role of ecological footprint and carbon footprint along with climate variables in relation to rice production.

In the literature, the effects of ecological and carbon footprint on rice production are missing. Therefore, this is the first study to empirically examine the impact of climate change along with ecological footprint and carbon footprint on rice production in India.

Flood damage to uninsured single-family homes shifts the entire burden of costly repairs onto the homeowner. Homeowners in the United States and in much of Europe can purchase flood insurance. The Netherlands and Asian countries generally do not offer flood insurance protection to homeowners. Uninsured households incur the entire cost of repairing/replacing properties damaged due to flooding. Homeowners’ policies do not cover damage caused by flooding. The paper examines the link between personal bankruptcy and the severity of flooding events, property prices and financial condition levels.

A fully modified ordinary least squares (FMOLS) regression model is developed which uses personal bankruptcy filings as its dependent variable during the years 2000 through 2018. This time-series model considers the association between personal bankruptcy court filings and costly, widespread flooding events. Independent variables were selected that potentially act as mitigating factors reducing bankruptcy filings.

The FMOLS regression results found a significant, positive association between flooding events and the total number of personal bankruptcy filings. Higher flooding costs were associated with higher bankruptcy filings. The Home Price Index is inversely related to the bankruptcy dependent variable. The R-squared results indicate that 0.65% of the movement in the dependent variable personal bankruptcy filings is explained by the severity of a flooding event and other independent variables.

The severity of the flooding event is measured using dollar losses incurred by the National Flood Insurance program. A macro-case study was undertaken, but the research results would have been enhanced by examining local areas and demographic factors that may have made bankruptcy filing following a flooding event more or less likely.

The paper considers the impact of the natural disaster flooding on bankruptcy rates filings. The findings may have implications for multi-family properties as well as single-family housing. Purchasing flood insurance generally mitigates the likelihood of severe financial risk to the property owner.

Natural flood insurance is underwritten by the federal government and/or by private insurers. The financial health of private property insurers that underwrite flooding and their ability to meet losses incurred needs to be carefully scrutinized by the insured.

Prior studies analyzing the linkages existing between housing prices, natural disasters and bankruptcy used descriptive data, mostly percentages, when considering this association. The study herein posits the same questions as these prior studies but used regression analysis to analyze the linkages. The methodology enables additional independent variables to be added to the analysis.

Despite the huge financial resources associated with oil, Nigeria has consistently recorded poor growth performance. Therefore, this study aims to examine how corruption and oil rent influence Nigeria’s economic performance during the 1996–2021 period.

Various estimation techniques were used. These include the bootstrap autoregressive distributed lag (ARDL) bounds-testing, dynamic ordinary least squares (DOLS), the fully modified OLS (FMOLS) and the canonical cointegration regression (CCR) estimators and the Toda–Yamamoto causality.

The bounds testing results provide evidence of a cointegrating relationship between the variables. In addition, the results of the ARDL, DOLS, CCR and FMOLS estimators demonstrate that oil rent and corruption have a significant positive impact on growth. Further, the results indicate that human capital and financial development enhance economic growth, whereas domestic investment and unemployment rates slow down long-term growth. Additionally, the causality test results illustrate the presence of a one-way causality from oil rent to economic growth and a bi-directional causal relationship between corruption and economic growth.

Existing studies focused on the effects of either oil rent or corruption on growth in Nigeria. Little attention has been paid to the exploration of how the rent from oil and the pervasiveness of corruption contribute to the performance of the Nigerian economy. Based on the outcome of this study, strategies and policies geared towards reducing oil dependence and the pervasiveness of corruption, enhancing human capital and financial development and reducing unemployment are recommended.

This study examines the impact of profitability, firm size and leverage on corporate tax avoidance in Bangladesh, an emerging South Asian economy.

A balanced panel data of 62 firms from Dhaka and Chittagong stock exchanges in Bangladesh from 2009 to 2020 were used to run the regression. This study employed the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) to examine the hypotheses.

The findings show that large firms positively impact corporate tax avoidance. Similarly, profitability and leverage are positively associated with tax avoidance, and the results are significant. Furthermore, the study conducts robustness tests that confirm the findings.

The use of cash effective tax rate (ETR) to investigate firms’ tax avoidance practices poses some limitations, and the results should be interpreted cautiously.

The current study may help policymakers better enhance tax collection from business firms. The findings could serve as a valuable input for effectively monitoring tax collection from large profit-earning firms.

To the authors' best knowledge, this is the first historical attempt in Bangladesh to use panel data to examine the relationship between the firm’s level characteristics and corporate tax avoidance. Panel data often provides greater flexibility with large data, simplifying calculation and statistical analysis.