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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

The primary focus of this study is to examine the distributional consequences of the widespread increase in prices. The fundamental question the study aims to address is whether the dynamics of income distribution due to higher inflation differ in the short term compared to the long run.

The authors estimated a panel-data model (fixed effects) using inequality and inflation data available at a high frequency, i.e. on a quarterly basis for over 30 years, and found evidence that inflation causes rapid swings in income distribution.

The authors’ contribution to the literature lies in providing evidence that inflation rapidly causes swings in income distribution, even after controlling for the state of the economy. The authors also demonstrate that the magnitude and direction of the effect of inflation on income inequality depend on whether the initial inflation rate is below or above the Federal Reserve’s target of 2%.

To the best of the authors’ knowledge, the authors are the first to emphasize that the targets set by central banks can drive the strength and direction of the relationship between inflation and income inequality.

The principal aim of this study is to examine the relationship between financial development and income inequality in India using the financial Kuznets curve (FKC) hypothesis.

This study uses the autoregressive distributed lag (ARDL) model and the Toda–-Yamamoto causality test to investigate the long-run and short-run relationship and causality between financial development and income inequality. In addition, this study employs a principal component analysis (PCA) to construct a comprehensive financial development index.

The study found a long-run relationship between financial development and income inequality in India for the period under consideration. Trade is found to improve the income distribution, while inflation worsens income distribution. Moreover, the empirical results revealed a feedback causality between financial development and income inequality. The study results confirm an inverted U-shaped relationship between financial sector development indicators and income inequality, thus validating the FKC hypothesis for the Indian economy.

The study draws attention of the government and policymakers, urging them to focus on building a strong financial sector by improving its efficiency. This, in turn, will lead to enhanced financial stability and a reduction in income inequality. They should prioritise the development of high-quality and sustainable financial products and services to ensure the robust growth of the financial sector.

To the best of our knowledge, this study is the latest of its kind to empirically test the financial development on income inequality and the FKC hypothesis simultaneously for the Indian economy using financial proxy variables from financial institutions (FIs) and financial markets (FMs) for the measurement of financial depth.

This paper aims to examine the influence of financial inclusion (FI) on poverty, income inequality and financial stability from the perspective of public good (PG) theory in developing countries.

This study applies the fixed effects model (FEM), pooled ordinary least square (OLS) regression and generalized method of moment (GMM) across panal data of 69 developing countries from 2002 to 2020 inclusive.

Multiple regression analyses show that FI reduces poverty and income inequality while improving financial stability. Secondary enrolment ratio, GDP per capita, and trade openness reduce poverty and income inequality. However, a higher inflation rate increases poverty and income inequality while reducing financial stability. Finally, age dependency ratio and population do not affect poverty, income inequality or financial stability.

The regulators and policymakers in developing countries should raise the level of formal FI by expanding the size of the formal financial sector and improving the access of the large unbanked population to financial products/services. Improving FI enables the unbanked population to take over productive activities and ease consumption, which in turn complementing economic growth.

The increase in FI enables the developing countries to include the financially excluded population through formal financial products and services, which improve financial stability and eradicate poverty and income inequality in society. Thus, the FI enhances the social welfare of society.

This is the first study that examines the impact of FI poverty, income inequality and financial stability in the context of developing countries. This study contributes to the theoretical implications of the PG theory by examining the influence of FI on poverty, income inequality and financial stability in the context of developing countries.

The present study examines the significance of financial inclusion in reducing income inequality in the Asian context.

This study uses panel estimation techniques such as the Pedroni cointegration test, Kao residual-based test, FMOLS, ARDL and Granger causality, a dataset consisting of the Gini coefficient index, three dimensions of financial inclusion measures and one added variable on financial depth, spanning from 2005 to 2019.

The study finds that in the long-run, income inequality disparity is highly influenced by financial inclusion indicators, such as the number of bank branches, deposit accounts, outstanding loans and domestic credit to the private sector. Whereas in the short run, disparities in income are unaffected by all the indicators of financial inclusion. Further, unidirectional causality from financial inclusion indicators to income inequality necessitates the need for policymakers to design policies and programs that would enhance access to financial services as an essential mechanism to reduce income disparity.

Studies based on a panel of Asian countries that have undergone impressive growth of financial inclusion initiatives since the past decade—but are still facing widening income inequality—are conspicuously rare in the literature. The empirical analysis fills this void by showing the significant role financial inclusion indicators play in steering the Asian economies toward income equality throughout the study period.

Brazil’s regional inequality is an important topic due to the large and persistent differences in development between states and the high levels of inequality in the country. These variations in development can potentially render survey data inaccurate since the significance of capital income varies across the states. Besides, previous studies incorporating tax and national accounts data globally have mainly focused on measuring the income distribution at the country-level. This approach can limit the understanding of inequality, especially when considering large countries such as Brazil.

The methodology used to construct these estimates follows the guidelines of the Distributional National Accounts, whose core goal is to provide income distribution measures consistent with macroeconomic aggregates and harmonized across countries and time. The procedure has three main steps: first, it corrects the survey’s underrepresentation of top incomes using tax data. Then, it accounts for national income items not included in the survey or tax data, such as imputed rents and undistributed profits. Finally, it ensures that all components match the national income.

Compared to survey-based estimations, the results reveal a new angle on the state-level inequality. This study indicates that Amazonas, Rio de Janeiro and São Paulo have a more concentrated income distribution. The top 1\% of earners in these states receives around 28\% of total pre-tax income, while the top 10\% receive nearly 60\%. On the other end, Amapá (AP), Acre (AC), Rondônia (RO) and Santa Catarina (SC) are the states where the income distribution is less concentrated. There were no significant changes in the income distribution across the states during the period analyzed.

This study combines survey, tax and national accounts data to construct new estimates of Brazil’s state-level income distribution from 2006 to 2019. Previous results only considered income captured in surveys, which usually misses a significant part of capital incomes. This limitation may bias comparisons as capital income has different importance across the states. The new estimates represent the income of top groups more accurately, account for the entire national income and enable to compare regional inequality levels consistently with other countries.

This article analyzes the impact of monetary policy on income inequality across 16 advanced economies. The author investigates three key points: (1) the relationship between domestic monetary policy and domestic income inequality, (2) the spillover effect of USA monetary policy (including quantitative easing) on international inequality and (3) the quantitative influence of the monetary policies of both the USA and the Eurozone on the formation of domestic income inequalities.

The author employed the Global Vector Autoregressive (GVAR) model, which uses Vector Autoregressive with Exogenous Variables (VARXs) models of each economy to build an integrated system that enables us to evaluate individual responses to global shocks.

The author's analysis reveals that (1) contractionary monetary policy exacerbates domestic inequality and (2) USA monetary policy, including quantitative easing, affects international inequality. Furthermore, the author's variance decomposition analysis highlights that USA monetary policy is especially influential on income inequality in Norway and Sweden. Additionally, the cointegrating analysis confirms that monetary policy's impact on inequality persists in the long term.

Most of the studies focused on investigating domestic economies as closed economies. However, the author's approach differs in that the author uses the GVAR, which treats all economies as open. This allows us to incorporate the world economy into the domestic dynamics and connect the economies using bilateral trade. Another advantage of the GVAR is that it captures spillover effects by modeling each economy and constructing the international economy.

The measurement of women's economic welfare and exploring its underlying factors have been undervalued in the context of Khyber Pakhtunkhwa, Pakistan. This study addressed this gap by focusing on assessing women's subjective economic welfare and its socioeconomic and cultural determinants in the education and health sectors within Mardan, Northern Pakistan.

The study used stratified random sampling techniques for the selection of sample respondents and collected data through a well-structured questionnaire. To measure women’s economic welfare, the study utilizes Lorenz curves, the Gini index, the Sen Social Welfare function and an individual's gross monthly income. Furthermore, the ordinary least squares method was utilized to analyze the determinants of economic welfare.

The findings show greater income inequality and a lower welfare level for women in the education sector compared to the health sector. Likewise, the study identifies several key determinants, such as age, educational qualification, job experience, respect for working women, outside and work-place problems and the suffering of family members of working women for their economic well-being.

This study makes valuable contributions to the literature by focusing on the cultural perspective of Pakhtun women in Mardan and providing a context-specific understanding of subjective economic welfare. Additionally, the authors collected first-hand data, which gave an original outlook on working women's current economic welfare level. Furthermore, this study undertakes a comparative analysis of working women's welfare in the health and education sectors. This comparison offers a more accurate portrayal of the challenges and opportunities specific to these occupations.

The peer-review history for this article is available at: https://publons.com/publon/10.1108/IJSE-04-2023-0246

The main aim of this study is to investigate the role of fathers and mothers in the intergenerational educational persistence for sons and daughters under two dimensions that characterize the clusters of countries: redistributive policy and governance.

Data from the Global Database of Intergenerational Mobility (GDIM), hierarchical cluster analysis on principal components and panel regression are used in this study to estimate intergenerational educational correlation and to investigate its determinants related to the parents’ and descendants’ education variables in 93 countries grouped in four clusters. The empirical analysis is differentiated by gender combinations of parents and descendants.

In the clusters of countries characterized by high inequalities and poor governance, our findings show that the role of the fathers is stronger than that of the mothers in educational transmission; fathers and mothers are more influential for the daughters rather than for the sons; parental educational privilege is the main driver of intergenerational educational persistence; there is an inverse U-curve in the association between educational inequality of the parents and educational correlation for the sons. Differently, in the countries characterized by high income, low redistributive conflict and better governance, the role of the mothers is stronger and education mobility for the daughters is higher than that for the sons.

The authors’ results remark on the importance of social welfare policies aimed to expand a meritocratic public education system including schooling transfers for lower social class students and narrowing the gender gap in educational mobility between daughters and sons. Social welfare policies should also be oriented to spread high quality child care systems that help to foster greater women equality in the labor market, because the strength of educational persistence depends on the position of the mother in the economic hierarchy.

The distinctiveness of the paper can be found in the fact that this study investigates the parental role differentiating by gender and coupling hierarchical cluster analysis on principal components with panel regression models. This allows us to have a sample of 93 countries aggregated in four groups defined in two dimensions: redistributive policy and governance. Amongst the determinants of educational transmission, we consider not only education’s years of the parents but also other determinants, such as educational inequality and privilege of the parents. We also identify the effects of investment in human capital and educational inequalities for the descendants on education mobility.

In addition to being a violation of human rights, the practice of child labor can be related to criminality against young people. In view of this, the hypothesis tested in this article was that child labor aggravates youth homicide through educational level.

This study used annual data for the 26 states plus the Federal District for the period 2001 to 2014. To do so, the authors used the iterated feasible generalized least squares (IFGLS) estimator under the seemingly unrelated regressions (SUR) model.

The results showed that child labor positively affects the homicide of young people, showing education as a transmission channel through which the effect is materialized. The general conclusion, given this, that work is an alternative for children not to enter the world of crime due to its socializing character, cannot be sustained.

This evidence provides input to the formulation of policies and programs to eradicate or slow child labor. In addition to the social and economic rise of individuals, it is important to emphasize the role of education (human capital) in explaining economic growth.

So far, there is no record of national research that sought to empirically assess the effect of child labor on crime, in particular, on the homicide of young people, considering education as a transmission channel, and this assessment is the contribution of the present study to the economic literature on crime.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2023-0163