Impact of climate vulnerability and climate readiness on income inequality: Evidence from developing countries

The issue of income disparity has long plagued South Africa because of the political environment that existed before the country’s 1994 democratic transition. Based on the widely used Gini index, which gauges global inequality, the nation routinely has some of the highest rates of income disparity in the world. Income inequality in South Africa keeps rising even after a number of frameworks and policies have been put in place, which has a big influence on society. Thus, it is essential to comprehend the causes of income disparity and put suitable policies in place to remedy it. The purpose of this study is to look into the relationship between South Africa’s income disparity and its determinants. Using the Vector Error Correction Model (VECM) approach, this study empirically examines the effects of government spending on social grants, gross savings, population growth, and economic growth on income inequality from 1975 to 2017. Data on the Gini index are sourced from the Standardized World Income Inequality Database (SWIID). Findings reveal a statistically significant negative correlation between government spending on social grants and income inequality. Moreover, income inequality demonstrates a negative relationship with both gross savings and economic growth. However, population growth exhibits a positive correlation with income inequality. This study highlights the significance of implementing a comprehensive strategy to address income inequality in South Africa. This strategy should involve augmenting government expenditure on social grants, cultivating a savings culture within households, and enacting policies that incentivize job creation, particularly in areas with rapid population growth. In addition to making a substantial contribution to the body of evidence already available on income disparity, this study offers insightful information to policymakers working to improve the socioeconomic climate in South Africa.

ObjectiveSince 2008, the Standardized World Income Inequality Database (SWIID) has provided income inequality data that seek to maximize comparability while providing the broadest possible coverage of countries and years. This article describes the current SWIID's construction, highlighting differences from its original version, and reevaluates the SWIID's utility to cross‐national income inequality research in light of recently available alternatives.MethodsCoverage of inequality data sets is assessed across country‐years; comparability is evaluated in terms of success in predicting the Luxembourg Income Study (LIS), recognized in the field as the gold standard in comparability, before those data are released.ResultsThe SWIID offers coverage double that of the next largest income inequality data set, and its record of comparability is three to eight times better than those of alternate data sets.ConclusionsAs its coverage and comparability far exceed those of the alternatives, the SWIID remains better suited for broadly cross‐national research on income inequality than other available sources.

This study aims to examine the impact of economic growth on income inequality in Malaysia with special attention to the distribution of income among different ethnic groups. Twofold methodologies have been used in this analysis. The primary methodology is descriptive in nature where tables, charts, and diagrams have been extracted from the specific sources for analysis. Second, this paper applies the Granger noncausality test to estimate the causality and also applies the ARDL (autoregressive distributed lag) regression model to see the shot‐run and long‐run dynamic relationship between economic growth and income inequality in the context of Malaysia using the data of 1970–2018 from the household income survey, World Development Indicators, and the Standardized World Income Inequality Database. Additionally, it deploys panel Granger noncausality and dynamic pool mean group regression for the robustness of the results. This study reveals that the income gap among ethnic groups has been narrowed; although, intra‐ethnic income inequality is still very high, especially among Indians. The study further advises that income inequality does not Granger‐cause economic growth; rather, economic growth does Granger‐cause income inequality, and economic growth affects income inequality negatively, regardless of ethnicity, suggesting that economic growth significantly contributes to the reduction of income inequality in Malaysia. The paper concludes with a few policies which could significantly contribute to reducing income inequalities and achieving greater economic development goals, such that Malaysia can become a developed country by 2030.

Prior studies point to a significant correlation between income inequality and military expenditures. This study started by questioning whether the impact of terrorism on this relationship may have been neglected. The main purpose of the study is to demonstrate whether the terrorism variable has a significant effect on the direction and strength of the nexus between military expenditures and income inequality. As such, the effect of terrorism has been analyzed using as a moderator variable. Forecasts for 26 transition economies and the 2002–2020 period have been performed using three different models. The first two models are baseline models, and in the third model, the moderator effect is included with all the control variables. Three different income groups (top 10, middle 40, and bottom 50) have been selected as indicators of income inequality. We also confirm the results by utilizing the income inequality indicator (gini_disp) of the Standardized World Income Inequality Database (SWIID). All three model findings signalize a positive finding similar to previous literature in the relationship between income inequality and military expenditure. However, the stand-alone and moderator effects of terrorism vary between income groups. For the top 10 and middle 40 income groups, terrorism correlates inversely with income inequality according to Model 1 findings, and in the same direction for the bottom 50. For the top 10 and middle 40 income groups, the moderator effect of terrorism in the inequality-military expenditure relationship is positive (Model 2). The direction of the effect is positive when control variables are added (Model 3). For the bottom 50 income group, the moderator effect of terrorism is in the same direction (positive) for Models 2 and 3. Our results illustrate that decision-makers in terrorism-affected transition economies may be puzzled regarding the opportunity cost of social welfare expenditure for low incomes against military expenditure.

ObjectiveThis article documents wide‐ranging revisions to the Standardized World Income Inequality Database (SWIID), which seeks to maximize the comparability of income inequality estimates for the broadest possible coverage of countries and years.MethodsTwok‐fold cross‐validations, by observation and by country, are used to evaluate the SWIID's success in predicting the Luxembourg Income Study (LIS), recognized in the field as setting the standard for comparability.ResultsThe cross‐validations indicate that the new SWIID's estimates and their uncertainty are even more accurate than previous versions, extending its advantage in comparability over alternate income inequality data sets.ConclusionGiven its superior coverage and comparability, the SWIID remains the optimum source of data for broadly cross‐national research on income inequality.

Income inequality means that one segment of the population has a disproportionately large share of income compared to the other. Disparities in income and wealth have tended to dominate the discussion on inequality because they contribute directly to individuals and families’ well-being and shape the opportunities people have in life. Therefore, addressing income inequality is essential to inspire each country’s population’s human and productive potentials to bring development. Therefore, this study examines the relationship between income inequality and human capital using static panel data analysis. Specifically, the study employs fixed effect panel data analysis using Least Square Dummy Variable for 25 sub-Saharan African countries. The World Bank data series was widely used as the data source for macroeconomic variables, while the Gini index has obtained from the Standardized World Income Inequality Database. The empirical results reveal that human capital in terms of secondary school enrollment rate has a negative impact on income inequality. The study also found a U-shaped relationship between real gross domestic product per capita and inequality, and it does not support the well-known concept of the Kuznets curve.Keywords: Income Inequality; Human Capital; Panel Data; Random Effect; Fixed Effect.JEL: C10, Q0, A10

In this study, we examine the relationships arising from the transition process to green economies and energy poverty in relation to income inequality across 22 Latin American countries during the period from 2000 to 2021. Our estimations are conducted using various estimators for energy poverty and the transition to green economies, employing feasible generalized least squares (FGLS) and panel corrected standard errors (PCSE) to ensure robustness. The results reveal a significant effect of both sets of variables on income inequality. Specifically, energy poverty contributes to higher levels of income inequality, while the ecological transition has the potential to address income inequality issues, provided an effective distribution of renewable energy resources among society is ensured. Failure to achieve equitable distribution of renewable energy resources may hinder the attainment of more egalitarian societies. To address this, a comprehensive set of policies is required to ensure equal access to renewable energy sources for the entire population, along with effective long-term measures that contribute to the equitable distribution of energy resources.

Objective. Cross‐national research on the causes and consequences of income inequality has been hindered by the limitations of existing inequality data sets: greater coverage across countries and over time is available from these sources only at the cost of significantly reduced comparability across observations. The goal of the Standardized World Income Inequality Database (SWIID) is to overcome these limitations.Methods. A custom missing‐data algorithm was used to standardize the U.N. University's World Income Inequality Database; data collected by the Luxembourg Income Study served as the standard.Results. The SWIID provides comparable Gini indices of gross and net income inequality for 153 countries for as many years as possible from 1960 to the present, along with estimates of uncertainty in these statistics.Conclusions. By maximizing comparability for the largest possible sample of countries and years, the SWIID is better suited to broad cross‐national research on income inequality than previously available sources.

Purpose- Financial inclusion is defined as a process that ensures the ease of access, availability, and usage of the formal financial system for all members of an economy by emphasizing the use of accessibility and availability of financial services. A financial sector is measured and compared on four main features; debt is the size of financial institutions, access is the access and use of financial services by the users, efficiency is the efficiency in the provision of financial services, and stability is the stability in the provision of financial services. Financial inclusion, in short, is adults' access to and use of financial services. This study aims to measure the financial inclusion level for selected OECD countries from 2010-2021. Also, this study aims to estimate the effect of financial inclusion on economic growth and income inequality for selected countries. Methodology- The data used in this study cover a range of variables related to financial inclusion from various institutions, including the IMF-Financial Access Survey (IMF-FAS), the World Bank - World Development Indicators (WB-WDI), the World Bank - Global Financial Development Database (WB-GFDD) and the Standardized World Income Inequality Database (SWIID). These variables provide insights into the dimensions and determinants of financial inclusion and their impact on economic and social outcomes for selected OECD countries. In the study, we run panel data regressions for each group separately, using GDP per capita as the dependent variable to determine the impact of the Financial Inclusion Index on economic growth. We also construct two different models for each group of countries with and without the added control variables into the models. Findings- The analysis reveals that the effect of financial inclusion on economic growth is negative for all groups of countries. The impact is significant for Group 1 and Group 2. The magnitude of coefficients changes when we add control variables to the model. However, it does not change the significance level of the coefficients. The magnitude of the coefficients increases as countries’ per capita income increases. At the same time, the effect of financial inclusion on the GINI index is significant only in the model for Group 3 with control variables. The sign of the impact is negative. It implies that the GINI index decreases as the financial inclusion index increases. So, the effect of financial inclusion on income inequality is positive for countries in Group 3. Conclusion- The empirical results did not support the relationship between financial inclusion and economic growth (GDP per capita). These results may be explained by advocating the financial sector's quick and fundamental digital transformation. Hence, the rules for availability, accessibility, and usage of financial products and system are completely changed in the past ten years. On the other hand, the relationship between financial inclusion and income inequality, measured by GINI Index, is consistent with the literature only for Group 3 countries (developing countries). The increase in the gap between rich-developed and developing countries may explain these results. An increase in financial inclusion still supports adjustments in income inequality in developing countries, but its effect is disappeared in developed countries in the last 12 years. Keywords: Financial inclusion, economic growth, OECD countries, financial indicators, income inequity. JEL Codes: G20, G21, G23

PurposeThe reduction of income inequality and the ways to fight against it are source of debate among scientific communities and policymakers. Rents from natural resources that African countries are endowed with remain one way to cope with income inequality, but its influence on income inequality is mixed. Thus, the purpose of this paper is to explore the direct and indirect transmission mechanisms through which natural resources rents can affect income inequality in sub-Saharan Africa.Design/methodology/approachThis study obtained data on income inequality from the Standardised World Income Inequality Data database, natural resources rents from World Bank’s Development Indicators and education from United Nations Development Programme for the period 1990–2018. It was analysed using system generalised method of moments.FindingsThe results of this study showed that natural resources rents solely increased income inequality, but its interaction with education significantly reduced income inequality.Research limitations/implicationsThese findings suggest that the reduction of income inequality by natural resources rents passes through a good education system in sub-Saharan African countries.Originality/valueIn previous studies, authors analysed the role of education in the relationship between natural resources rents and income inequality by inserting the two variables separately in the model. But in this paper, the author analysed the role of education in the relationship between natural resources rents and income inequality by using the interaction of natural resources rents and education.

Can innovation improve income inequality? Evidence from panel data

Addressing climate vulnerability remains a priority for economies globally. This study used the panel-corrected standard error (PCSE) methodology to investigate the impact of adaptation financing on climate vulnerability. This analysis examined 52 African countries from 2012 to 2021 while considering their climate adaptation readiness. The impact was also assessed based on the Human Development Index (HDI) categories to reflect different levels of development. The findings showed that adaptation finance considerably influenced climate vulnerability reduction in Africa, particularly in nations with a moderate HDI. However, most countries still need higher levels of adaptation financing, resulting in a small impact on vulnerability reduction. Furthermore, the impact of readiness measures differed by HDI category. Economic and social climate readiness strongly impacted climate vulnerability in high-HDI nations, but governance preparedness was more critical in low-HDI countries. Based on the empirical facts, two policy proposals emerge. First, it is critical to reconsider the distribution of adaptation financing to reduce disparities and effectively alleviate climate vulnerability. Moreover, African economies should consider implementing innovative localized financing mechanisms to mobilize extra adaptation finance. Second, African governments should customize climate readiness interventions based on their HDI levels to improve the achievement of a positive impact on climate vulnerability.

ObjectivesThe aim of this study is to investigate if correlations exist between income inequality and antimicrobial resistance. This study’s hypothesis is that income inequality at the national level is positively correlated with antimicrobial resistance within developed countries.Data collection and analysisIncome inequality data were obtained from the Standardized World Income Inequality Database. Antimicrobial resistance data were obtained from the European antimicrobial Resistance Surveillance Network and outpatient antimicrobial consumption data, measured by Defined daily Doses per 1000 inhabitants per day, from the European Surveillance of antimicrobial Consumption group. Spearman’s correlation coefficient (r) defined strengths of correlations of: > 0.8 as strong, > 0.5 as moderate and > 0.2 as weak. Confidence intervals and p values were defined for all r values. Correlations were calculated for the time period 2003-10, for 15 European countries.ResultsIncome inequality and antimicrobial resistance correlations which were moderate or strong, with 95% confidence intervals > 0, included the following. Enterococcus faecalis resistance to aminopenicillins, vancomycin and high level gentamicin was moderately associated with income inequality (r= ≥0.54 for all three antimicrobials). Escherichia coli resistance to aminoglycosides, aminopenicillins, third generation cephalosporins and fluoroquinolones was moderately-strongly associated with income inequality (r= ≥0.7 for all four antimicrobials). Klebsiella pneumoniae resistance to third generation cephalosporins, aminoglycosides and fluoroquinolones was moderately associated with income inequality (r= ≥0.5 for all three antimicrobials). Staphylococcus aureus methicillin resistance and income inequality were strongly associated (r=0.87).ConclusionAs income inequality increases in European countries so do the rates of antimicrobial resistance for bacteria including E. faecalis, E. coli, K. pneumoniae and S. aureus. Further studies are needed to confirm these findings outside Europe and investigate the processes that could causally link income inequality and antimicrobial resistance.

This paper explores the relationship between entrepreneurship and income inequality, drawing from unbalanced panel data from 104 countries between 2001 and 2023, sourced from the Global Entrepreneurship Monitor, the Standardized World Income Inequality Database, and the World Bank. Employing system GMM and panel quantile regression (MM-QR), it fills crucial gaps in the literature, such as cross-country heterogeneity using panel data, endogeneity, and the varying effects of entrepreneurship across different quantiles of the income inequality distribution. The findings reveal that entrepreneurship reduces income inequality, with the effect being most pronounced at lower quantiles of the income inequality distribution. Moreover, women’s entrepreneurship emerges as a powerful force in reducing inequality, with a more pronounced effect than men’s entrepreneurship, particularly at the higher quantiles where income inequalities are most persistent. While opportunity-driven entrepreneurship narrows income inequality more than necessity-driven, it shows no distinct impact on income inequality quantiles. These findings challenge the predominant literature suggesting that entrepreneurship worsens income inequality and underscore the potential of entrepreneurship to empower marginalized groups, foster economic mobility, and promote social equity. Moreover, they provide critical insights for policymakers, emphasizing the need to promote inclusive entrepreneurial ecosystems necessary for equitable economic growth.

Purpose — This paper aims to examine the relationship between the shadow economy and income inequality in Nigeria.Method — The paper employed Autoregressive Distributed Lag (ARDL), Fully Modified Ordinary Least Square (FMOLS), and Granger causality. This methodology is used to avoid endogeneity and heterogeneity in the model. This paper gauged income inequality using two diverse indicators of the Gini coefficient: the Gini index in proportion to household disposable income and the Gini index in proportion to household market income. In accordance with the literature, our empirical analysis draws on data from the Standardized World Income Inequality Database (SWIID), the World Bank, World Development Indicators, and the International Country Risk Guide (ICRG) for Nigeria from 1991 to 2018.Result — The findings of ARDL and FMOLS suggested a positive relationship between income inequality and the shadow economy, based on both measures of income inequality. In the short term, however, the shadow economy and income inequality are negatively correlated. Furthermore, we discovered a one-way causal relationship exists in Nigeria between the shadow economy, household disposable income, institutional democracy, household market income, and corruption control (CCI).Recommendation — Shadow economy has been regarded as an avenue to create job opportunities and raise poverty-income levels. It is critical that, for the shadow economy to reduce income inequality in Nigeria, policymakers should develop much better policies aimed at addressing income inequality.Contribution — In order to understand the relationship between income inequality and shadow economy activities in Nigeria, this study employed three methodologies, namely: Autoregressive Distributed Lags (ARDL), Fully Modified Ordinary Least Squares (FMOLS), and Granger Causality. The result offers reliable recommendations for pro-poor interventions that aim to limit the growth of informality via redistributing incomes.