An evaluation of common explanations for the impact of income inequality on life satisfaction

This study explains how income inequality affects life satisfaction in Europe. Although research about the impact of income inequality on life satisfaction is inconclusive, authors suggest several reasons for its potential impact. In the literature section we discuss three types of explanations for the impact of inequality: pure aversion for inequality, aversion for inequality motivated by how an individual is personally affected by inequality and preferences for equality of opportunities. In order to test these explanations, we examine how three corresponding variables, respectively attitude towards redistribution, income and perceived mobility, interact with both actual and perceived income inequality in multilevel analyses using data from the European Values Survey. Our results reveal that there are significant differences between how people are affected by actual income inequality and how they are affected by perceived income inequality. The impact of perceived income inequality on life satisfaction depends on perceived mobility in society and income, while the impact of actual income inequality solely depends on perceived mobility. We conclude that traditional explanations often erroneously assume that people correctly assess income inequality. Moreover these explanations are more capable of clarifying the effect of perceived income inequality on life satisfaction than that of actual inequality

Tracking Oregon's Progress: A Focus On Income Inequality

Inequality in income, consumption, education, and quality of life across populations has become a growing concern in the United States. As the nation's attention shifts toward issues of inequality, it is important to understand the prevalence of inequality in Oregon. However, studying income inequality alone is not sufficient; counties with low income inequality can have high poverty, among other challenges. County and state variations in income inequality are partially due to differences in the population, their earning potential and their access to high-wage work. By examining poverty and inequality together, it is possible to gain a fuller understanding of the economic well-being of communities. Findings from this study reveal that:Oregon has consistently ranked 22nd in the nation for its level of income inequality since the mid-2000s, meaning that just over half of the states in the nation have more income inequality than Oregon.Within the western region of the U.S., Oregon has above average levels of income inequality.Within Oregon, counties vary in levels of income inequality.Multnomah, Benton, and Lane counties have consistently high income inequality. High income inequality is not unexpected in urban areas or small counties with large populations of university students.Hood River and Morrow counties maintain consistently low levels of income inequality. Low income inequality can indicate that an economy is providing a mix of jobs that support middle income earners, as in the case of Hood River. However, low income inequality can also result from a lack of high wage earners, as in Morrow and other rural counties in the state

Measuring Inequality Change in an Economy with Income Growth

This paper analyzes how to measure changes in inequality in an economy with income growth. The discussion distinguishes three stylized kinds of economic growth: high income sector enrichment, low income sector enrichment, high income sector enlargement, in which the high income sector expands and absorbs persons from the low income sector. The two enrichment types pose no problem for assessing inequality change in the course of economic growth: for high income sector enrichment growth, inequality might reasonably be said to increase, whereas for low income sector enrichment, inequality might be said to decrease. These adjustments are non-controversial and non-problematical. Where problems arise is in the case of high income sector enlargement growth. In that case, the two alternative approaches have been shown in this paper to yield markedly results: The traditional inequality indices generate an inverted-U pattern of inequality. That is, inequality rises in the early stages of high income sector enlargement growth and falls thereafter. The new approach suggested here, based on axioms of gap inequality and numerical inequality, generates a U pattern of inequality. That is, inequality falls in the early stages of high income sector enlargement growth and rises thereafter. The discrepancy between the familiar indices and the alternative approach based on axioms of gap inequality and numerical inequality bears further scrutiny. Two courses of action are possible. One might try to axiomatize inequality in ways that generate an inverted-U pattern in high income sector enlargement growth, thereby rationalizing the continued use of the usual inequality indices with the inverted-U property. Alternatively, one might retain the axioms proposed here, embed them into a more formal structure, and construct a family of inequality indices consistent with them. Others might wish to pursue the first course; I am at work on the second

Income Inequality, Income Mobility, and Economic Policy: U.S. Trends in the 1980s and 1990s

[Excerpt] Income inequality has been increasing in the United States over the past 25 years. Several factors have been identified as possibly contributing to increasing income inequality. Some researchers have suggested the decline in unionization and a falling real minimum wage as the primary causes. Others have argued that rising returns to education and skill-biased technological change are the important factors explaining rising inequality. Most analysts agree that the likely explanation for rising income inequality is due to skill-biased technological changes combined with a change in institutions and norms, of which a falling minimum wage and declining unionization are a part. Since most people are concerned with upward mobility, and given the central importance of income mobility to the debate over income inequality, this report examines the relation between income mobility and inequality. Income mobility studies are an important complement to income inequality studies — income inequality does not address the issue of whether or not the poor are getting poorer, whereas income mobility does. While there appears to be considerable relative income mobility (about 60% of individuals change income quintiles over 10 years), it is not far — about 60% of those individuals who changed income quintile in the 1980s or 1990s only moved to the next quintile. But most individuals in the poorest quintile in 1980 experienced an increase in their real income between 1980 and 1989 — half saw their real income increase by more than 36%. Of those in the richest quintile, almost half saw their real income fall by 10% or more during the 1980s. But there are differences in income changes between the 1980s and the 1990s: those in the poorest income quintile may have done slightly better in the 1990s than in the 1980s, while individuals higher up in the income distribution (quintiles 2-5) appear to have done better in the 1980s than in the 1990s. In both the 1980s and 1990s, income growth was progressive and had an equalizing effect on the income distribution, but the equalizing effect had a larger absolute value in the 1990s than in the 1980s. Mobility, however, had a disequalizing effect and, in fact, outweighed the progressivity effect, thus increasing the annual inequality. In both decades, the long-term income inequality is lower than the income inequality in the first year of the decade. The results suggest that mobility had a greater equalizing effect on long-term inequality in the 1990s than in the 1980s. Three broad types of government economic policy affect income growth and mobility, and hence income inequality: (1) regulation, (2) the tax system, and (3) government transfers. Economic policies to reduce the growth of income inequality may work, in part, through their effects on income mobility. Reducing income mobility (that is, stabilizing incomes) may reduce the rising trend in income inequality, but it could also increase inequality of longer-term income

The impact of education expenditures on income inequality: Evidence from US states

While the effect of various types of government expenditures on income inequality has been studied extensively, whether education expenditures impacts income inequality is less clear. The purpose of this paper is to examine the relationship between education expenditures and income inequality. Specifically, I explore the impact of tertiary versus primary and secondary education spending on income inequality using panel data for 50 US states over the period 1987-2015. Using an ordinary least squares model with time and state fixed effects, I find that total and disaggregated education expenditures have a significant inequality-reducing effect on the income distribution. The findings support continued spending policies at all levels of education as a way to reduce income inequality

Rural non-farm income and inequality in Nigeria:

"This paper investigates the contribution of rural non-farm income to income inequality by examining the contribution of specific income sources (farm income from irrigated agriculture, farm income from rainfed agriculture and non-farm income) to income inequality in Nigeria. The results reveal the relative importance of specific income sources to income inequality and the various determinants of income inequality in rural Nigeria. Although non-farm income is distributed more unequally than incomes from the other two sources, it contributes least to overall income inequality. Farm income from irrigated agriculture represents the most important inequality-increasing source of income." from authors' abstractNon-farm income, Inequality, Development strategies,

Income Inequality in Rural India: Decomposing the Gini by Income Sources

This paper examines income inequality in rural India in 1993 and 2005. It attempts to ascertain the contribution of different income sources to overall income inequality, and change in their relative importance between 1993 and 2005 through decomposition of Gini coefficient. The paper finds that income inequality has increased between 1993 and 2005. Agriculture income continues to contribute majorly in total income and income inequality; however its share in total income and total income inequality has declined significantly. A marginal increase in agriculture and salaried income leads to increase in inequality; however, a marginal increase in labor income (both agriculture and non-agriculture) lead to reduction in the income inequality.Gini decomposition, income inequality, income sources, India

Has the relation between income inequality and life expectancy disappeared? Evidence from Italy and top industrialised countries

Objective: To investigate the relation between income inequality and life expectancy in Italy and across wealthy nations.Design and setting: Measure correlation between income inequality and life expectancy at birth within Italy and across the top 21 wealthy countries. Pearson correlation coefficients were calculated to study these relations. Multivariate linear regression was used to measure the association between income inequality and life expectancy at birth adjusting for per capita income, education, and/or per capita gross domestic product.Data sources: Data on the Gini coefficient ( income inequality), life expectancy at birth, per capita income, and educational attainment for Italy came from the surveys on Italian household on income and wealth 1995-2000 and the National Institute of Statistics information system. Data for industrialised nations were taken from the United Nations Development Program's human development indicators database 2003.Results: In Italy, income inequality (beta = -0.433; p 0.05). In cross national analyses, income inequality had a strong negative correlation with life expectancy at birth (r =-0.864; p < 0.001).Conclusions: In Italy, a country where health care and education are universally available, and with a strong social safety net, income inequality had an independent and more powerful effect on life expectancy at birth than did per capita income and educational attainment. Italy had a moderately high degree of income inequality and an average life expectancy compared with other wealthy countries. The cross national analyses showed that the relation between income inequality and population health has not disappeared

Inequality and risk aversion in health and income: an empirical analysis using hypothetical scenarios with losses

Four kinds of distributional preferences are explored: inequality aversion in health, inequality aversion in income, risk aversion in health, and risk aversion in income. Face to face interviews of a representative sample of the general public are undertaken using hypothetical scenarios involving losses in either health or income. Whilst in health risk aversion is stronger than inequality aversion, in the income context we cannot reject that attitudes to inequality aversion and risk aversion are the same. When we compare across contexts we find that inequality aversion and risk aversion are both stronger in income than they each are in health