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This study examines the effect of digitalization on poverty and income inequality in developing countries. The study answers the question of whether digitalization is a way for developing countries to get out of poverty and income inequality.

The study uses data from 17 developing countries with data from 2005 to 2021. The study employs fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS), with an augmented mean group (AMG) for robustness. Digitalization, as the variable of interest, is proxied by the digitalization index (DI), constructed using principal component analysis (PCA). The dependent variables are poverty and income inequality, which are used in different models.

The evidence indicates that digitalization decreases poverty and income inequality in developing countries. These findings are justified when we use the AMG estimator, but the strength of the coefficients and significance levels are higher in the FMOLS and DOLS estimators. The results of the control variables also show that human development (LHDI), CO2 emissions and foreign direct investment (FDI) have decreasing effects on poverty and income inequality. Thus, digitalization is a good option for developing countries to get out of poverty and income inequality to achieve sustainable development goals (1&10).

This study provides rigorous empirical evidence on the effect of digitalization on poverty and income inequality in developing countries. Unlike the previous studies on developing countries, this study used a DI to proxy digitalization. In addition, the authors use FMOLS and DOLS estimators, with an AMG estimator for robustness, to provide long-run coefficients.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-08-2023-0586

This study aims to examine the impact of oil consumption on carbon dioxide (CO₂) emissions and total factor productivity (TFP) in highly oil-consuming countries of the world from 1995 to 2019.

For this purpose, fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) are applied.

FMOLS and DOLS models reveal that oil consumption, human capital, population, trade openness and nonrenewable energy have a significant positive effect on CO₂ emissions. While information and communication technology (ICT), as proxied by mobile and natural resources, has a significant negative effect on CO₂ emissions. In the case of TFP, oil consumption, ICT and natural resources have a significant positive effect on the TFP. On the other hand, trade openness, population, human capital and nonrenewable energy have a significant negative effect on TFP. The results of this study can help to provide policy recommendations to reduce CO₂ emissions in studied highly oil-consuming countries of the world.

Due to the threat to sustainable development, climate change has become a major topic for debate around the world. The influence of oil consumption on CO₂ emission and TFP is less known in the available literature. Another significance of this study is that many researchers considered aggregate energy consumption to study this relationship, but the authors have studied the effect of energy consumption, particularly from oil in the top oil-consuming countries, which is a significant shortcoming of the present research.

This study aims to examine the impact and contribution of priority and non-priority sectors, as well as their sub-sectors, on the gross non-performing assets of public, private and foreign sector banks.

The Reserve Bank of India's database on the Indian economy is used to retrieve data over 13 years (2008–2021). Public sector (12), private sector (22) and foreign sector (44) banks are represented in the sample. Two-way ANOVA, multiple regression and panel regression statistical techniques are used in SPSS and EViews to examine the data. Further, the results are also validated by using robustness testing by applying the fully modified ordinary least square (FMOLS) and dynamic least square (DOLS) regression.

The results showed that, for private and foreign banks, the non-priority sector makes up the majority of the total gross non-performing assets, although both the priority and non-priority sectors are substantial for public sector banks. The largest contributors to the total gross non-performing assets in public, private and foreign banks are industries, agriculture and micro and small businesses. The FMOLS displays robustness results that are qualitatively similar to the baseline result.

Based on the study's findings about the patterns of non-performing assets originating from these specific industries, banks might improve the way in which these advanced loans are managed.

There has not been much research done on the subject of sub-sector-specific non-performing assets and how they affect total gross non-performing assets across the three sector banks. The study's primary focus will be on the issue of non-performing assets in the priority’s and non-priority’s sub-sectors, namely, agricultural, micro and small businesses, food credit, industries, services, retail loans and other priority and non-priority sectors.

The main purpose of this study is to investigate the impact of housing price on mortgage debt accumulation while considering the structural break effects associated with the Global Financial Crisis (GFC).

To determine the existence of a long run relationship among the variables, this study used a Johansen cointegration test. The long run model was then estimated using the fully modified ordinary least square method and reported for both the model with and without a structural break associated with the GFC.

The findings demonstrate a moderate positive relationship between housing price and mortgage debt, with the impact of the GFC is positive but insignificant. The household’s lack of responsiveness to the GFC may be attributed to their optimistic expectations and confidence in the Malaysian housing market.

Findings of this study provide some guidance to policymakers and the banking sector in predicting household borrowing behavior during future economic crises.

The increase in housing prices and mortgage debt after the GFC has been a concern for many countries, including Malaysia. This study contributes to the literature by investigating the relationship between housing prices and mortgage debt in Malaysia and sheds light on the impact of the GFC on household borrowing behavior. The study’s contributions include providing new evidence to the underexplored topic, enhancing the robustness and reliability of the empirical results and providing insights into the importance of testing for structural breaks in time series analysis.

The objective is to assess the relationship between financial inclusion and bank profitability in emerging economies, i.e. “Bangladesh, Egypt, Indonesia, Mexico, Nigeria, Pakistan, Philippines, and Vietnam”.

The second-generation econometrics of panel data has been applied to examine the cross-section independence and control the heterogeneity between cross sections. Additionally, the authors employ the following tests for the analysis: “the unit root test, Westerlund's (2007) bootstrap cointegration, Pedroni cointegration, fully modified ordinary least square (FMOLS), and heterogeneous panel causality techniques”. The annual data consist of the period from 2000 to 2019.

The findings reveal that financial inclusion fosters bank profitability. Therefore, easier access to financial services and products will maximize banks' profitability. Additionally, the association between financial inclusion and bank profitability is unidirectional.

This research is a first attempt to bring a novel contribution to the subject of emerging economies by investigating the association between financial inclusion and bank profitability. Another unique addition to the literature is the use of a novel financial inclusion index. At last, a panel cointegration technique, FMOLS and heterogeneous panel non-causality tests are taken into consideration for the in-depth 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.

The purpose of this study is to empirically analyse the fiscal revenue implications for oil-dependent African countries in the face of low-carbon energy transition (LET).

The study combined the novel fully modified ordinary least squares, dynamic ordinary least squares and canonical cointegrating regressions estimators to analyse secondary data between 1990 and 2020 for the three major oil-dependent African Countries (Algeria, Angola and Nigeria).

The result shows that LET reduces oil revenue and non-revenue for specific countries (Algeria, Angola and Nigeria) and the panel, suggesting that low-carbon energy transiting is lowering the fiscal revenue of oil-dependent African nations.

The seeming weakness of this study is its inability to broaden the scope to include all oil-producing African economies. However, since the study selected Africa’s top three oil-producing states, the sample can serve as a model for others with lesser crude oil outputs.

Oil-dependent African countries must urgently engage in sincere economic diversification in sectors like industry and manufacturing, the service sector and human capital development to promote economic transformation that will enhance fiscal revenue.

With the pace of energy transition towards low-carbon energy, it is not business as usual for oil-rich African countries (Algeria, Angola and Nigeria) due to fluctuating demand and price. As a result, it becomes worthy to examine how the transition is affecting oil-dependent economies in Africa. Also, this study’s method is unique as it has not been used in a similar study for Africa.

The authors analyse the interactive influence of energy use, capital investment and finance on pollution in energy-dependent African countries.

The study analyses data from 5 selected energy-dependent African nations (i.e. Algeria, Egypt, Nigeria, Morocco and South Africa) between 1981 and 2020 using the fully modified ordinary least squares (FMOLS) approach.

The panel result reveals that capital investment and energy interaction and financial development and capital investment moderation reduce pollution in all the countries. However, for country-specific results, the interaction of investment and energy lowers emissions in Algeria, South Africa, Nigeria and Morocco but increases pollution in Egypt. Similarly, except for Egypt, financial development and capital investment interaction offset pollution in Algeria, Nigeria, South Africa and Morocco.

The limitation of the study stems from the inability to extend the scope to cover the entire African region. However, the fact that the authors selected the most prominent African nations in the sample to enable us to set the template for other smaller nations to follow makes the study tenable in its present form.

Energy-dependent African countries should invest in eco-friendly machines, technologies and equipment to lower pollution vis-à-vis production expansion.

The present research is more expansive by combining the finance and capital investment channels in the quest for decarbonising emerging African nations. Moreover, this is a comparative study, unlike past studies that mainly deploy a one-size-fits-all approach.

This study aims to determine how changes in macroeconomic indicators and the housing prices index (HPI) are related. These factors can cause short-term and long-term changes in the housing market in Spain.

The study used cointegrating regression, fully modified ordinary least squares and dynamic ordinary least squares methodologies. The models are trained using quarterly time series data for these parameters from 2010 to 2022. A comprehensive examination is conducted to explore the relationship between macroeconomic issues and fluctuations in the HPI.

The results indicate statistically significant short-run effects (p < 0.05) of economic growth, inflation, Spanish stock indices, foreign trade and the interest rate on HPI. The inflation variables, Spain’s stock indices, interest rate and monetary rate, have statistically significant long-run effects (p < 0.05) on HPI. The exchange rate, unemployment and money supply have no substantial impact on HPI in Spain.

The study’s findings significantly contribute to increased information concerning the level of investing activity in the Spanish housing sector. After conducting an in-depth study of both the long-run and short-run connections with HPI, the study proved to be highly effective in formulating appropriate policies.