Moderating Effect of Terrorism in the Income Inequality-Military Expenditure Nexus: Evidence from Transition Economies

This 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).

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.

This study empirically examines the impact of real interest rate on income inequality in India within a Kuznets Curve framework considering the role of economic growth, trade openness and technological innovation as the control variables. This study employs the ARDL bounds test for validating the long-run relationship over the annual data period 1995 to 2019. The results reveal the long-run relationship between the series in India. The findings suggest that the initial increase in interest rate significantly reduces income inequality. But, in a later stage, a threshold exists for such an increased interest rate to revert the prior beneficial impact. This finding further shows that Kuznets’ inverted U-shaped hypothesis is not valid for the relationship between income inequality and real interest rate in India. It shows that the real interest rate impedes income distribution in the long run. These findings are also found to be robust using FMOLS and DOLS estimators. We find that economic growth significantly reduces income inequality, whereas trade openness promotes it. Surprisingly, technological innovation enhances income inequality, but this effect vanishes in the long-run. However, these findings suggest that policymakers in India should not ignore the impeding role of real interest rates while aiming at achieving effective income distribution between haves and have-nots in the long run.

Spatial Income Inequality in India, 1993-2011: A District Level Decomposition

Spatial Income Inequality in India, 1993-2011: A District Level Decomposition

Can innovation improve income inequality? Evidence from panel data

Purpose The purpose of this study is to examine how infrastructure development contributes to economic growth and income inequality in India using annual data from 1990 to 2022. Design/methodology/approach The study applies the Autoregressive Distributed Lag (ARDL) model proposed by Pesaran et al. (2001) to estimate both the short-run and long-run growth impact of infrastructure development and other regressors. The Zivot–Andrews test is used to identify the structural break in the data. The Granger causality test is used to examine the direction of causality between infrastructure development and economic growth. A time series regression model is used to assess the impact of infrastructure development on income inequality. Findings The study presents several key findings. Firstly, the bounds test confirms the presence of long-run cointegrating relationships between economic growth and its regressors, including infrastructure development. Secondly, infrastructure development exerts a significant positive impact on economic growth both in the short and long run. Thirdly, the causality test indicates bi-directional causality between infrastructure development and economic growth. Fourthly, government expenditure has a negative and significant impact on economic growth in the long run. Fifthly, regression results indicate that infrastructure development plays a significant role in reducing income inequality by enhancing access to essential services. Sixthly, government expenditure and economic growth have no significant impact on income inequality in India. Practical implications This study offers empirical insights into how infrastructure development contributes to economic growth and income inequality in India. As India pursues its ambition of becoming a developed nation by 2047 and fulfilling its commitments to reduce income inequality (SDG-10), the findings underscore the importance of scaling up investments in infrastructure, an area where India still lags behind compared to developed nations and many emerging economies. The study emphasized the need for enhancing public spending on infrastructure development as a critical policy tool for achieving sustained economic growth and reducing inequality in line with Sustainable Development Goal-10 (SDG-10). Originality/value Previous research mainly concentrated on the growth impact of infrastructure development. There is a notable scarcity of literature assessing the impact of infrastructure development on income inequality in India. This study aims to address this research gap by investigating the impact of infrastructure development on income inequality in the context of the Indian economy. To the best of the author’s knowledge, this paper represents the first attempt to empirically study the impact of infrastructure development on income inequality. It contributes significantly to the existing knowledge by providing compelling evidence of the positive impact of infrastructure development on economic growth and income inequality in India.

PurposeThe purpose of this paper is to examine income inequality and income mobility, which have been central to understanding India’s recent economic development.Design/methodology/approachThis paper uses the first two waves of the India Human Development Survey data for the year 2004–2005 and 2011–2012 to analyze income inequality and income mobility using longitudinal data, and is the first to do so at a nationally representative level. In this research paper, we address three related research questions: How have been the patterns of income mobility in India? What are the trends, levels and sources of income inequality in India? and finally And What is the structure of household income mobility?FindingsThe paper examines the trends, levels, sources and factors of income inequality and income mobility in India between 2005 and 2012. The results further show the case for high persistence at the top of income distribution but lower persistence at the bottom.Research limitations/implicationsBecause of the chosen research approach, the research results may lack spatial analysis. Therefore, researchers are encouraged to test the proposed propositions further.Practical implicationsThe paper suggests that, in the end, the nature of longer-term well-being is crucial to designing policy interventions to effectively tackle inequality, and economic mobility can be seen as an avenue to long-term equality.Social implicationsThis study can further be extended to look at polarization issues at the national and sub-national levels.Originality/valueThis paper shows the analytical framework of additive decomposition of income mobility out of two sources, namely mobility due to the transfer of income within given structure and mobility due to economic growth or contraction in rural and urban India.

This paper is a contribution to understanding income generation and inequality in India's agricultural sector. We analyze the National Sample Surveys of agriculture in 2003 and 2013 using descriptive and regression based methods, and estimate income inequality in the agricultural sector at the scale of the nation and its 17 largest states. We show that: (a) there are significant state‐level differences in the structures/patterns of income generation from agriculture, (b) there is a negative relationship between the amount of land owned by the household and share of wages in total income, (c) income inequality in India's agricultural sector is very high (Gini Coefficient of around 0.6 during the period), and (d) about half of the income inequality is explained by the household‐level variance in income from cultivation, which in turn is primarily dependent on variance in landownership.

Purpose It is widely recognized that the relationship between income level, income inequality, and the impact of public goods on health and education is complex and mutually dependent. Yet, existing studies often fail to capture how increases and decreases in such investment have uneven effects on income distribution. This study aims to analyze the impact of human capital on income inequality in India, recognizing the multi-directional relationship between public investment in human capital and income distribution outcomes. Design/methodology/approach This research examines how the asymmetric nature of human capital influences income inequality in India by employing the nonlinear autoregressive distributed lag (NARDL) model, which utilizes data spanning from 1990–91 to 2019–20. Control variables include GDP per capita, poverty gap, inflation rate, and population growth rate. Findings The findings indicate a notable long-term asymmetric influence of human capital on income disparity. Notably, increased education expenditures in India are associated with rising income inequality, underscoring the role of quality and access disparities in education systems. Health expenditures show an inverse pattern, with spending cuts leading to heightened inequality. These findings underscore the importance of equity, access, and efficiency in public investment. Originality/value To the best of the authors’ knowledge, this is the first study to jointly examine the asymmetric effects of both education and health expenditures on income inequality in India using the NARDL framework. It contributes new empirical insights by extending the time series to 2019–20 and taking into account recent policy shifts. The study offers policy-relevant guidance on how direction-specific investment in human capital shapes inequality dynamics.

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.

Decomposition of progressivity and inequality indices: Inferences from the US federal income tax system

Using the annual data from 1980 to 2013, this study explores the effects of economic globalization on income inequality for a sample of two emerging economics, China and India, by endogenizing FDI inflows, remittances inflows, sectoral output, infrastructural development, human capital formation, government size, urbanization and economic growth as relevant determinants into the income inequality model. By applying combined cointegration method of Bayer–Hanck (J Time Ser Anal 34(1): 83–95, 2013) and ARDL bounds testing of cointegration approach of Pesaran et al. (J Appl Econom 16(3):289–326, 2001); it finds that there is the existence of a long-run relationship among the variables in our inequality model for both India and China. After confirming cointegration among the variables, the long-run results based on ARDL model surprisingly revealed that economic globalization widens the income inequality in India but the same factor reduces the income inequality in China. Contrastingly, both the FDI and remittances inflows significantly contribute to reduce income inequality in China, while the same worsens the income inequality in India. In examining the role and effects of structural changes in both the economies from the changing sectoral contributions of output in total output (industry sector and service sectors) in our model, it exposes the fact that the changing sectoral growth contribution has been leading to rising income inequality in China while the same has been resulting in reduction of income inequality in India. The infrastructural development has led to rising income inequality in both the countries, while human capital formation as expected reduces income inequality for both the countries. It also observes that economic growth, urbanization and government size enable both the economies to improve in their pattern of income distribution which have significant implication for public policy of both the economies while aiming at reducing poverty and inequality.

The paper, based on India Human Development Survey (IHDS) data, tries to address the question- how unequal is India in terms of income distribution? Accordingly, the paper examines the trends, levels, sources and factors of income inequality in India between 2005 and 2012. Three important results stemmed from our analysis. First, in this paper we use Gini as a measure of inequality and find that income inequality in rural India has increased from 0.50 to 0.54 between 2005 and 2012, whereas, in urban India income inequality has increased from 0.48 to 0.49 during the same period. Next and most importantly, we decompose income inequality by income sources and find that amongst different sources of income inequality; the contribution of farm income in total inequality has decreased from 35 percent in 2005 to 21 percent in 2012 in rural India. On the other hand, the contribution of salaried income in total inequality has plummeted drastically from 65 percent in 2005 to 16 percent in 2012 in urban India. Finally, we use Theil’s T index from the class of Generalized Entropy (GE) inequality measures, while decomposing income by four most important factors; namely, place of residence, social, educational and occupational groups. It is irrespective of these factors; the relative share of within-group inequality is not only much higher than that of between-group inequality, also its share has increased between these two periods. Thus, our paper suggests that these mutually reinforcing inequalities, in the long run, if not addressed effectively, will create a hard-hitting division between the privileged and the rest in Indian society.

Using income from nationally representative household surveys and district as the lowest level of aggregation, we examine the role of spatial factors in determining income inequality in India. In both rural and urban India, we find that within-district income differences account for majority of the income inequality in 2011. Moreover, between-state income differences are more important in explaining between-district inequality in rural India. In contrast, in urban areas it is the within-state income differences that play a more important role in explaining the between-district inequality. We find significantly smaller level of inequality but similar trends using the consumption expenditure. Finally, using data for 1993 and 2011, we find that although majority of the income inequality in rural India is explained by within-district income difference in both years, over time the share of between-district differences has increased and they account for a third of the total increase in rural income inequality between 1993 and 2011.