Standardized World Income Inequality Database Research Articles

Financial Development and Income Inequality in Nigeria: An Empirical Investigation

This study investigates the impact of financial development on income inequality in Nigeria using secondary data from the World Development Indicators database and the Standardized World Income Inequality Database (SWIIDspanning from 1990- 2021. The methodology employed includes the ADF test for unit root, the Johansen Test for Co-integration, the Ordinary Least Square(OLS) Technique, and diagnostic tests like the normality test, Stability test, Granger causality test.The explanatory variables were financial deepening (FID), credit to the private sector (CPS), bank capital to asset ratio (BCAR), population growth (POP), and trade openness (TOP) while the dependent variable is the Gini coefficient (GINI) proxy for financial development. The unit root test result shows that the variables are in the same order of stationarity, they were all stationary at first difference. The Johansen Test for Co-integration shows that there is a long-run relationship between the variables in the model. The OLS results of the analysis show that financial deepening (FID) and bank capital to asset ratio (BCAR) have a negative relationship with income inequality in Nigeria with financial deepening having a significant relationship with income inequality; and bank capital to asset ratio have an insignificant relationship with inequality. The results also show that Credit to the private sector (CPS), population growth (POP), and trade openness (TOP) maintain a positive and insignificant relationship with income inequality in Nigeria. Based on the findings,the study recommends that policymakers should focus on several key strategies, capable of promoting financial deepening through initiatives like expanding banking services to underserved areas.

Availability of Banking Services and Income Inequality: Evidence from Developing Economies of Asia Pacific.

This study aims to observe the impact of financial inclusion from a micro perspective, availability of banking services, on income inequality. The analysis is carried out by using an annual panel data set constructed with a sample of eight developing countries of the Asia Pacific region for the period of 2005-2019 to empirically estimate the impact of the availability of banking services on income inequality. A composite variable consisting of number of bank branches and automated teller machines is developed to measure the availability of banking services, whereas income inequality is measured by the latest release of the Standardized World Income Inequality Database (SWIID). The results show that greater availability of banking services robustly reduces income inequality across countries. Besides, the study generates evidence that greater women empowerment and better regulatory quality can reduce income inequality. In contrast, urbanization and globalization can deteriorate income equality in the short run. The central message of this study is in favor of further extending the formal banking services to the unbanked segments and females of the population and enhancing regulatory roles with better policy measures to maximize the grand social welfare and ensure sustainable development.

FINANCIAL INCLUSION FOR SELECTED OECD COUNTRIES

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

Do Technological Innovation and Financial Development Affect Inequality? Evidence from BRICS Countries

While technological innovation and financial development are broadly credited as important drivers of economic growth of developed nations, their impact on inequality (especially in emerging economies) remains understudied. Thus, the objective of this study is to investigate the impact of technological innovation and financial development on income inequality in BRICS (Brazil, Russia, India, China and South Africa) countries using panel dynamic ordinary least squares (PDOLS) and panel fully modified ordinary least squares (PFMOLS) with annual data sourced from the Standardized World Income Inequality Database, International Monetary Fund (IMF) and World Bank (1990–2017). The results suggest that technological innovation increases income inequality in the BRICS nations, while financial development has an income reducing effect on inequality. Our results are robust, using alternative estimation with various sub-indicators of financial development (such as financial markets and financial institution), including other measures proxied by access to credit provided by commercial banks. The study’s results have important implications for policy and practice in the BRICS countries. By providing a nuanced understanding of the relationship between technological innovation, financial development and inequality, the study will inform the design and implementation of policies aimed at reducing inequality and promoting inclusive growth in these emerging economies.

One stream, two channels? A parallel-process latent class growth model of homicide rates and suicide rates in 183 countries, between 2000 and 2019

BackgroundSuicide and homicide have long been viewed in Western culture as moral, ethical and legal equivalents. This view has underpinned many theoretical and empirical explorations into their relationship over the centuries. However, there has been little evaluation of longitudinal heterogeneity. MethodsSuicide and homicide rates in 183 countries between 2000 and 2019 were collected from the World Health Organization Global Health Observatory Repository. Corresponding structural variables (i.e., GDP per capita, unemployment rate, percentage of urban population, percentage of elderly population, and Gini index) were acquired from The World Bank and Standardized World Income Inequality Database. Parallel-process latent class growth modelling was applied to identify different classes within the joint suicide and homicide rate trajectories. Multinomial logistic regression examined relationships between the structural covariates and trajectory classes. ResultsFour trajectory classes were identified, two with inverse relationships between suicide and homicide, and two with parallel relationships: 1) countries with increasing suicide rates and decreasing homicide rates (“suicide up, homicide down”: UD, n = 41) or 2) countries with decreasing suicide rates and increasing homicide rates (“suicide down, homicide up”: DU, n = 17); and 3) countries where suicide and homicide rates both trended up (UU, n = 19), or 4) both trended down (DD, n = 106). A higher average annual growth rate (AAGR) of GDP per capita was related to an increased possibility of being in DD than in DU. Countries with higher AAGR in unemployment rates were more likely to be in UD and UU than in DD, while those with higher AAGR in urbanization were less likely to be in UD than in DD. ConclusionThe over-time relationship between suicide and homicide is heterogenous and complex. It is influenced differently by GDP per capita, urbanization and unemployment in different countries, and it is not well described by a single theory.

Income inequality is unrelated to perceived inequality and support for redistribution

AbstractObjectivesThis study aims to contribute to our understanding of the relationship between income inequality, perceptions of income inequality, and support for redistribution. In particular, it asks whether income inequality affects support for redistribution by influencing perceptions of inequality.MethodsThis study combines the pay ratio measures from the International Social Survey Project with income inequality measures from the Standardized World Income Inequality Database. The analysis proceeds in three steps, asking whether (1) inequality is related to perceived inequality, (2) perceived inequality is related to preferences for inequality, and (3) perceived inequality is related to support for redistribution.ResultsIncome inequality is unrelated to perceptions of inequality. Perceptions of inequality strongly predict preferred inequality, reinforcing the prior conclusion that anchoring effects likely cause this close relationship. Perceptions of inequality also predict support for redistribution. However, because actual inequality is unrelated to perceived inequality, there is no link between actual inequality and either preferred inequality or support for redistribution.ConclusionThe overall pattern of results is consistent with the interpretation that perceptions of income inequality may be politically co‐determined with support for inequality and redistribution, instead of perceptions being mental antecedents of these attitudes.

Modelling the nexus between income inequality and shadow economy in Nigeria

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.

Do globalization progress and sectoral growth shifts affect income inequality? An exploratory analysis from India

AbstractThis paper examines the effects of globalization (i.e., economic, social, and political) and sectoral growth shifts (i.e., changes in relative shares of agriculture, industry, and services) on income inequality in India from 1976 to 2012. We have used two unique datasets: the Standardized World Income Inequality Database (SWIID) and the KOF Globalization Index – Revisited, and employed the Stock and Watson Dynamic Ordinary Least Square cointegrating regression model for empirical estimation. This study finds that economic globalization reduces income inequality, while social and political globalization increases income inequality in India. Additionally, socioeconomic factors, such as per capita income, fiscal spending, and agricultural dependency are pertinent factors that reduce income inequality. On the contrary, factors such as rapid urbanization, changes in society, international interference in public policy, and rapid services sector growth that have been triggered by the social and political globalization processes show a detrimental effect on income inequality, which merits special attention in future development policies and actions. Additionally, the growth in agricultural value added helps reduce income inequalities in India, which is a desirable trend and justified on the grounds of continued rural dependence on the sector for income and employment. This underscores the need to strengthen the agricultural sector through strategies including crop diversification, value‐added processing, and investments for expansion of rural infrastructure, so that growth in the primary sector is sustained despite the decline in its relative contribution to gross domestic product (GDP).

Do economic growth and institutional quality reduce poverty and inequality in West Africa?

Over the past two decades, the West African region has experienced much faster economic growth than other parts of the world. However, despite this economic upturn, the region has continued to experience high levels of inequality and poverty, yet economic growth is considered to be one of the main drivers of poverty reduction. An interesting literature indicates that local conditions may limit the expected effects of economic growth on poverty and income inequality. In this study, we are interested in the role of institutional factors that have been largely ignored in explaining poverty reduction and inequality outcomes in this region. Thus, this study empirically examines the role of economic growth and institutional quality on inequality and poverty reduction in West Africa. Using data from the World Development Indicators (WDI), the International Country Risk Guide (ICRG) and the Standardized World Income Inequality Database (SWIID), our results show that economic growth remains a necessary condition for poverty reduction and that the overall improvement in the quality of institutions contributes significantly to reducing poverty and income inequality in the long term. This contribution is made in particular through the improvement of democratic institutions, the alleviation of bureaucratic constraints, the quality of the judicial and regulatory system, the control of corruption and the quality of government stability. Furthermore, we show that improvements in the judicial system, low levels of corruption and better bureaucratic quality happen to be prerequisites for economic growth to significantly reduce income inequality. These results therefore call on policy makers in the West African region to improve their institutional framework and especially these dimensions with a view to enabling the region citizens to improve their living conditions.

ANALYSIS OF ECONOMIC GROWTH AND INCOME INEQUALITY IN ROMANIA IN THE PERIOD 1990-2019

The paper aims to analyze the relationship between economic growth and income inequality in Romania in the period 1990-2019. The “Gross Domestic Product” (GDP) per capita indicator from the World Bank database was used to show the evolution of economic growth, and the GINI Coefficient was extracted from the Standardized World Income Inequality database to analyze income inequality. Romania’s macroeconomic result (GDP per capita) registered a continuous growth trend in the period 1990-2019. The Gini coefficient has followed the same dynamics. At first sight, this fact highlights that the phenomenon of economic growth has widened the gap between the rich and the poor in Romania in the examined period.

HUMAN CAPITAL AND INCOME INEQUALITY LINKAGE IN SUB- SAHARAN AFRICA: PANEL DATA ANALYSIS (1984–2016)

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

Multiple Imputation for Missing Values with an Empirical Application

Abstract Missing data are the most common problem in many research areas. For cross-section and time-series data, imputation can be a challenging problem. The most widely used method for filling missing observations is the multiple imputation which increase the number of the available data and thereby reducing biases that may occur when observations with missing values are simply deleted. The main purpose of this paper is to employ a bootstrapping expectation–maximization (EM) algorithm in order to impute missing values mainly to economic data. In the application we use a dataset that is consisted by annual panel data for the 27 countries of the European Union covering the period 2000-2017. The data were obtained from the databases of World Bank and Eurostat namely the Global Financial Development Database, The Standardized World Income Inequality Database by Solt (2019) and the World Development Indicators. Different indicators were chosen representing the development of banking system and stock markets, economic growth, economic inequality, innovation, fiscal policy, physical and human capital, and trade openness. Finally, diagnostic tools are used inspecting the imputations that are created. Keywords: Multiple imputation, Amelia II, Economic data, Financial development, Inequality.

Impact of Income Inequality on Health Status in South Asian Countries: A Panel Data Analysis

Purpose: This study aims to analyze the impact of income inequality on health status in South Asian countries. These countries are Pakistan, India, Bangladesh, Sri Lanka, and Nepal. Design/Methodology/Approach: Fixed Effect Model is employed to find the impact of income inequality on these countries' health status using panel data from 1998 to 2017. The data is taken from the Standardized World Income Inequality Database, United Nations Educational, Scientific and Cultural Organization database, and World Development Indicators database. Finally, the Hausman test is used to differentiate between the Fixed and Random Effect Models. Findings: The findings of the study show that health is negatively affected by income inequality. Income and education have a significant impact on health status in this region. Further, results show that males are more affected by income inequality than females, and an increase in per capita income has more effect on female health status than males. It is revealed that education has more effect on female health condition as compared to male. The mean life expectancy of a male in this region is slightly higher than that of females. The outcome of the analysis explains that the coefficient value of education and income inequality is higher than income. Practical Implications: The unequal distribution of income has immense harmful effects on health. Health is the fundamental need of human beings, and it has effects on each activity of the individual. The increasing level of income inequality and its effects on health is a big challenge for South Asian countries. Reduction in income inequality is practically desirable. Originality: This research study explains an original empirical analysis based on panel data obtained from the World Development Indicators database for five South Asian countries.

Do macroeconomic indicators determine income inequality in selected African countries?

This study investigates the effect of macroeconomic variables on income distribution in Africa using panel data from 2001 to 2016. This is motivated by the high degree of income inequality and poverty in the region. Twenty‐eight (28) African countries were selected to capture every region. The selection of countries and the choice of this period were informed by data availability. The variables of interest were income inequality obtained from the Standardized World Income Inequality Database (SWIID), while the Gross Domestic Product, Inflation, and Unemployment were obtained from the World Bank database. A dynamic panel model using the Generalized Method of Moment (GMM) estimator was used to control for both individual and time‐specific effects. The Heterogeneous aspect of the Augmented Dicky‐Fuller (ADF), the Levine‐Lin‐Chu test and, the Im‐Pesaran‐Shin (IPS) panel units root process were utilized to test for the stationarity of the panel data. The results of the General Method of Moment (GMM) indicate a significant negative relationship between income inequality and economic growth. The study rejected the existence of the Kuznets curve hypothesis, and concludes that Inflation rate, Wage rate and labour force impact negatively income inequality, while unemployment and education impact positively.

How does economic growth impact on income inequality across ethnic groups in Malaysia?

AbstractThis 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.

Can innovation improve income inequality? Evidence from panel data

Income inequality is a source of social instability and armed conflict, which in turn are detrimental to economic development. This study examines the role of innovation in income inequality in twenty-three developed countries, using a panel mean group estimator that takes cross-sectional dependence into consideration. Three income inequality indicators are used: the Standardized World Income Inequality Database (SWIID), the University of Texas Inequality Project (UTIP), and the Estimated Household Income Inequality (EHII). The innovation indicators are patent applications and patents granted. The empirical results based on the common correlated effect mean group (CCEMG) reveal that innovation widens income inequality. We also investigate whether the innovation–income inequality nexus is subject to a country’s level of globalization and financial development. The findings suggest that the interaction terms between innovation with these two variables have positive effects on income inequality, whereas innovation failed to reduce income inequality. Globalization and financial development are found to drive income inequality. The empirical results are robust to different income inequality and innovation measures as well as estimation techniques.

Measuring Income Inequality Across Countries and Over Time: The Standardized World Income Inequality Database

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.MethodsTwo k‐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.

Clustering and regime dynamics for economic growth and income inequality

This study explores the dynamic relationship between income inequality and economic growth by using a non-parametric approach and numerical taxonomy as a research method based on data symbolization and clustering methods. The study uses annual data of the GINI index (considering two databases, i.e. the Standardized World Income Inequality Database (SWIID) and the Estimated Household Income Inequality Data Set (EHII)) and the Per Capita GDP Growth Rates (economic growth variable) for two samples, i.e. 38 countries between 1980 and 2015, and 23 countries during the period 1980–2010. This novel methodology is used to detect the existence of clusters of countries sharing similar economic performance that are characterized by the income inequality variable. Once the homogeneous clusters are fixed, using a dynamic econometric approach, we study the Granger causal relationship between economic growth and income inequality, and the positive or negative long-run effects. Our results show that in advanced economies there is an economic growth policy supporting income distribution, while in poor or developing economies economic growth is enhanced by income concentration.

Reciprocity in bank regulatory reforms and income inequality: first evidence from a panel vector autoregression analysis

Our study examines the dynamics between variations in bank regulatory policies and the income distribution, using an IMF database of quantified measures for financial reforms as well as the Standardized World Income Inequality Database. The unbalanced panel comprises 36 developed countries and covers a period over three decades from 1973 to 2005. We assess the potential endogeneity between bank regulations and inequality via a panel vector autoregression model. Among the regulatory reforms, we consider the deregulation of securities markets, entry barriers, credit and interest rate controls as well as the extent of privatization, international capital flows and banking supervision. We are able to provide support for the hypotheses that (i) overall, abolishing bank regulations enhances inequality in income and (ii) higher levels in inequality encourage laissez-faire policies in the banking sector. Moreover, our results highlight the importance of examining each regulatory reform individually. In particular, we endorse a relaxation of entry barriers for financial intermediaries while promoting interest rate controls, capital account restrictions and deliberate government intervention in securities markets.

The Goldilocks effect: Convergence in national income distributions, 1990–2015

Recent work shows that national income distributions are converging, with stratified nations becoming more equal and egalitarian countries becoming more unequal. I replicate this finding using more recent data from multiple sources, drawing from 3006 observations across 152 countries from the Standardized World Income Inequality Database (SWIID) and 238 observations across 46 countries from the Luxembourg Income Study (LIS) during the 1990–2015 period. I find that a country's initial Gini is negatively related to subsequent change in the Gini (i.e., β-convergence), with egalitarian and stratified countries both drifting towards a more moderate level of inequality. I then test several theoretical explanations for this trend, including (1) sectoral transitions, (2) economic development, (3) global diffusion, (4) public sentiment, and (5) statistical bias. Overall, the results are most consistent with the latter two accounts, indicating a public aversion to, and statistical bias against, extreme levels of (in)equality. By contrast, β-convergence is not explained by sectoral transitions, nor do development or globalization appear to accelerate the process, casting doubt on alternative interpretations. Overall, the findings suggest that scholars should begin to account for convergence dynamics when modeling income distributions within nations.