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An investigation of income inequality through AutoRegressive integrated moving average and regression analysis

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The survival and nutrition of children and, to a lesser extent, adolescents have improved substantially in the past two decades. Improvements have been linked to the delivery of effective biomedical, behavioural, and environmental interventions; however, large disparities exist between and within countries. Using data from 95 national surveys in low-income and middle-income countries (LMICs), we analyse how strongly the health, nutrition, and cognitive development of children and adolescents are related to early-life poverty. Additionally, using data from six large, long-running birth cohorts in LMICs, we show how early-life poverty can have a lasting effect on health and human capital throughout the life course. We emphasise the importance of implementing multisectoral anti-poverty policies and programmes to complement specific health and nutrition interventions delivered at an individual level, particularly at a time when COVID-19 continues to disrupt economic, health, and educational gains achieved in the recent past.
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The present research examines why organizations with more unequal pay structures have been found to be characterized by a range of negative workplace outcomes. Drawing on the social identity approach, we propose that higher pay disparity can increase the comparative fit of pay categories whereby the organizational “haves” (the highest paid employees) and “have nots” (the lowest paid employees) are more likely to be categorized into distinct social groups. In turn, this can lead to poorer organizational functioning. In two studies, a field survey (N = 413) and an experiment (N = 286), we found that higher pay inequality increased the comparative fit of pay categories, which, in turn, was associated with lower superordinate (organizational) identification, higher perceived workplace conflict, higher leader toxicity, and lower perceptions of identity leadership (i.e., a leader who creates a sense of shared identity in the organization). Our research provides novel insights into how higher inequality affects employees’ categorization processes, thereby creating a psychological divide and contributing to organizational dysfunction.
Article
Although the effects of transport infrastructure on regional development have been widely discussed, the relationship between transport infrastructure and urban–rural income disparities has scarcely been examined. This study provides new evidence of those disparities by looking at 227 prefectural-level cities in China in 2016. We found that national, provincial and municipal roads played a positive role in narrowing the urban–rural income gap by facilitating rural labour mobility. The high coefficient of provincial and municipal roads indicates that they provide access to local and regional job markets for migrant farmers. The impact of roads is most remarkable in China's southwestern and middle regions, demonstrating that road infrastructure is more important for rural residents in these regions to increase their income. The least significant impact of road infrastructure was found in the northeast region, where road infrastructure is not the main restriction factor for its development. Policymakers should consider the impact of road infrastructure in different regions to reduce the urban–rural income gap.
Article
We build an endogenous growth model with automation (the replacement of low-skill workers with machines) and horizontal innovation (the creation of new products). Over time, the share of automation innovations endogenously increases through an increase in low-skill wages, leading to an increase in the skill premium and a decline in the labor share. We calibrate the model to the US economy and show that it quantitatively replicates the paths of the skill premium, the labor share, and labor productivity. Our model offers a new perspective on recent trends in the income distribution by showing that they can be explained endogenously. (JEL D31, E25, J24, J31, O33, O41)
Article
Social scientists have found income inequality is associated with an array of health and social problems, however the implications of income inequality for educational outcomes have not been investigated as thoroughly as other domains. In this study, I investigated how income inequality was associated with 4th grade academic achievement using state level data from the 1992 through 2019 rounds of the National Assessment of Educational Progress (NAEP). First, using an ordinary least squares modelling approach I found students in states with higher income inequality had lower average mathematics achievement, but not reading achievement. To control for stable, unobserved differences between states I estimated state fixed effects models that examined variation within states over time. States that experienced larger increases in income inequality experienced smaller increases in mathematics test scores, but not reading scores. I discuss the implications of income inequality for efforts to raise achievement among school children.
Article
Surprisingly, little is known about the cross-country effect of information and communication technology (ICT) on wealth inequality. At the same time, there is some tentative evidence suggesting that information and communication technology is positively correlated with income inequality. However, whether and how ICT affects wealth inequality is less explored, particularly because of the lack of reliable data on wealth inequality. This paper, therefore, fills this gap and contributes to this new literature by investigating the effect of ICT on wealth inequality in a sample of 45 developed and developing countries over the period 2000–2017. ICT is measured with six different indicators (including internet penetration, mobile penetration, ICT service exports, the ICT index, ICT quality, and ICT quantity), while wealth inequality is measured with three different indicators (comprising billionaire wealth to GDP, the Top 1% wealth share, and the Top 10% wealth share). The empirical analysis is based on the Generalised Method of Moments, and the results show that ICT increases wealth inequality. Furthermore, we show that democracy mitigates the increasing effect of ICT on wealth inequality. This result suggests that improving democracy in both developed and developing countries is an effective mechanism for mitigating the effects of ICT on wealth inequality. Therefore, we encourage efforts to implement democratic institutions that ensure respect for citizens' freedoms, greater democratic accountability, and executive constraints that allow for a more egalitarian distribution of wealth.
Article
Income inequality and economic complexity impacts on ecological footprint were researched for a panel of twenty-five countries, from 1970 to 2016, using the panel quantile regression approach. Results support that the economic complexity index in the 10th and 25th quantiles and pooled OLS regression positively affects ecological footprint, but not in the 75th and 90th quantiles. Gross Domestic Product in the 10th, 25th, 50th, 75th, and 90th quantiles have a positive effect on ecological footprint. Consumption of fossil fuels and population growth positively affects the ecological footprint in 10th, 25th, 50th, 75th, and 90th quantiles and the pooled OLS. Income inequality in the 10th, 25th, and 50th quantiles and the OLS model regression positively affect ecological footprint. Economic openness in 10th, 25th, 50th, 75th, and 90th quantiles and the pooled OLS negatively affect ecological footprint. Policymakers should promote policies to (i) encourage investment in green energy technologies and implement upgraded energy and environmental laws; (ii) diversify exports and sophisticate products in countries with a high ecological footprint; (iii) depth of human development to control for the population growth and stimulate the economic complexity; (vi) negotiate international trade agreements to open the economy; (v) implement measures to curb income inequality.
Article
The purpose of this study is to introduce a measure of inter-income inequality that complements the traditional ones proposed by Gini, Theil, and others. The latter are measures that account for the degree of income inequality within a given population of economic units (called here intra-income inequality ratios) while the former is intended to measure the degree of inequality between income distributions, which is called here economic distance ratio. The generalized mathematical form of this ratio is provided and two particular forms of economic distances ratios are identified. They are presented under both the discrete form, which is distribution-free, for a direct application to observed income distributions, and the parametric form corresponding to a given model of income distribution. Applications are made to the five economic regions of Canada and to white and black family income distributions of the U.S.A.
The measurement of income inequality: a survey
  • Atkinson
On the Dimensions of Human Capital: an Analytic Framework
  • G C Ruggeri
  • W Yu
Variability of distributions of income
  • Gini
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  • Foster
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  • van Genuchten
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  • Baumgartner
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  • J C Duque
  • G A García
  • N Lozano-Gracia
  • M Quiñones
  • K Y Montoya