Consequently, we can expect the income gap between the rich and poor to be a better predictor of happiness.
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Indeed, empirical studies have shown a significant association between the income gap indexed by the Gini coefficient and happiness Brockmann et al. However, the shape of the income inequality-happiness function is still controversial, as the empirical results have been mixed. While some studies show income inequality and happiness to have a negative relationship Alesina et al.
In the current study, we aimed to resolve this inconsistency by exploring a new possibility: the relationship between income inequality and happiness is not linear, but curvilinear. Our hypothesis is based on previous theories that suggest the income inequality-happiness relationship is mainly affected by two competing psychological processes: the jealousy and signal effects Senik, , The jealousy effect suggests that, when income inequality is high, individuals tend to be unhappy because they are jealous of their wealthier peers Senik, By contrast, the signal effect posits that individuals might consider income inequality a signal of social mobility and expect upward mobility Senik, , Critically, we propose that both effects manifest in the presence of an income gap, and push the income inequality-happiness relationship into opposite directions.
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However, their relative strength depends on how large the income inequality actually is. When it is in the low range, it is easier for individuals to climb the social ladder, and the signal effect will be the main determinant in encouraging individuals to be more hopeful about their future.
At this stage, a positive relationship is expected between income inequality and happiness, because a higher income gap means higher possible status. However, as the income inequality level increases beyond a critical point, the top rungs of the social ladder become almost unreachable for most members of the society. As a result, individuals become less hopeful regarding upward mobility and, as the jealousy effect becomes the dominating factor, happiness decreases, while the income gap increases.
In summary, the dynamic competition between the jealousy and signal effects would convert the income inequality-happiness relationship into an inverted U-shaped curve. Previous studies have failed to identify this curvilinear relationship, because they mostly evaluated the fit of linear models. In the current research, we evaluate the fit of curvilinear models for the income inequality-happiness relationship based on two large datasets.
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It was launched in and was conducted almost annually until Since , it has been conducted in even years only. Here, we used GSS data on the household income and happiness for 50, United States residents during — During this period, 28 surveys were administered, but the data for , , and do not contain health information, which is an important covariate to predicting happiness.
Therefore, we used the data from the remaining 25 years. Students were excluded from the sample, as they typically do not have an independent income. Respondents who identified themselves as other than white or black were also excluded, since previous research has shown that race may affect happiness, and the size of these other racial samples were too small to make any comparison meaningful. However, the main results remained consistent even when respondents of all races were included in the analysis see Supplementary Tables S16—S Missing values were dealt with using a list-wise deletion approach.
The final sample size was 31, The yearly sample size varied from in to 2, in More details on the descriptive statistics of all pertinent measured variables are shown in the Supplementary Materials. Although there were occasional falls, an overall rising trend was registered. Moreover, during the same period, the Gini coefficient also rose from 0. Information on income distribution Gini coefficient was obtained from the United States Census Bureau. It is a widely used measure of income inequality in a country, with scores ranging from 0 to 1.
Individual-level control variables included age, education, gender, living with children or not, ethnicity, employment status, marital status, health status, and household size. Country-level control variables included year of data collection and per capita gross domestic product GDP growth rate. The results were consistent, even when several covariates were excluded from the model see Supplementary Table S Mixed-effects modeling Raudenbush and Bryk, was performed on the above described United States data.
The unit of analysis at level 1 was the individual respondent, level 1 measures including demographic variables such as gender, age, health status, education level, household size, whether the respondent had children or not, marriage status, income, and occupation. Several previous studies have found an inverted U-shaped relationship between age and happiness. However, since we excluded students from the analysis, we did not include the quadratic term of age in the model. Moreover, household annual income was measured as a continuous variable in the GSS.
Our results remained constant when we treated income in the United States data as a continuous variable or recoded income into five ordinal categories. The demographic variables were included in the model as control measures.
The units of analysis at level 2 were the survey years in the GSS. The GSS is a pooled cross-sectional dataset, with a different cross-sectional sample for each annual survey. The Gini coefficient, a level 2 measure, was the focal predictor of the model. We also included the GDP growth rate as a level 2 control variable. In the analysis, the year of the survey was also included as a control variable.
Demographic and income variables were controlled in the first-level analysis and GDP growth rates were controlled in the second level. The GSS model further controlled surveyed time. Critically, both the Gini coefficient and its quadratic terms were entered as predictors in the second level as to model the curvilinear relationship between the Gini and happiness for more details about the model specifications, see the Supplementary Materials.
As previously mentioned, during —, the surveyed annual income of United States households increased from USD 28, According to psychophysical theories, happiness can be better predicted by the logarithm of income Kahneman and Deaton, Combining these two effects revealed an inverted U-shaped relationship between the Gini coefficient and happiness Figure 1.
For an individual with average income, the inflection point of the Gini-happiness relationship was 0. We further tested whether the relationship between the Gini coefficient and happiness was significantly positive before the inflection point, and significantly negative after the inflection point Simonsohn, Because the sample size at level 2 was too small to be splitted into two parts, here we used single-level models.
Therefore, only the second part of our hypothesis the jealousy effect was significant. In the model of slopes of household income and happiness, the intercept was In Study 2, we further tested the curvature relationship between the Gini coefficient and happiness in a large dataset from a cross-national survey on European countries. Data on personal happiness from European countries were obtained from the European Social Survey ESS , a large-scale sociological survey similar to the GSS, initiated by the European Science Foundation, which has been conducted biannually since in several European countries.
Ratio measures compare how much people at one level of the income distribution have compared to people at another.
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In very unequal societies, the ratio may be as large as 7. The Palma ratio addresses the Gini index's over-sensitivity to changes in the middle of the distribution and insensitivity to changes at the top and bottom . People in poverty are those who are considerably worse-off than the majority of the population. Their level of deprivation means they are unable to access goods and services that most people consider necessary to an acceptable standard of living . It can be an absolute term, referring to a level of deprivation that does not change over time, or a relative term in which the definition fluctuates in line with changes in the general living standard.
This is one of the agreed international measures used throughout the European Union. Inequality, by contrast, is always a relative term: it refers to the difference between levels of living standards, income etc. In practice, poverty and inequality often rise and fall together but this need not necessarily be the case.
Inequality can be high in a society without high levels of poverty due to a large difference between the top and the middle of the income spectrum. The section mainly uses gross household income. Where tax has been taken into account the graph will specifically state that it is looking at net income rather than gross. Gross is mainly used as this is the predominant focus in the economic inequality literature which is discussed elsewhere on the The Equality Trust guide to inequality.
It consists of earnings, self-employment and capital income and public cash transfers; income taxes and social security contributions paid by households are deducted. The income of the household is attributed to each of its members, with an adjustment to reflect differences in needs for households of different sizes. OECD Factbook Latin American data are for and , UK data for Cobham and Sumner Inequality begets greater inequality. In other words, disadvantages faced by children in low- and middle-income families and advantages held by their wealthy peers are two sides of the same coin.
The lack of opportunity for those in poverty is not some separate problem from the unequal distribution of wealth and income across society. Boosting mobility therefore requires directly addressing residential segregation, educational access, and other barriers described above. Here are some ideas that hold promise:.
One conclusion that stems from the Chetty et al. Housing analysts Barbara Sard and Doug Rice recently detailed a number of specific, straightforward changes that could eliminate barriers for voucher recipients to move into low-poverty areas, including tying voucher subsidies to rent in specific zip codes instead of entire metropolitan areas.
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Importantly, such a change could be implemented without significant funding or even congressional action, and HUD has just announced that it is considering doing so. To balance the educational playing field beginning at the starting gate, America needs a robust, publicly funded pre-K program. Still, research summarized by the Council of Economic Advisers suggests that the economic gains, higher tax revenues, and lower public costs from the children who would benefit from this investment would offset part of these costs in the long run.
America also needs more equitable funding for K education at both the federal and state levels. That means both funding increases—real overall state and local per pupil funding has declined in 35 states since —and changes in the distribution of funding, which is inequitable in part because of the reliance on local property tax revenue, and because of the political power that affluent communities have.
Evidence also shows that college enrollment and completion can be boosted by providing students with relief from the rapidly-escalating costs of tuition and other school-related expenses.heptorssa.pro/tyd-chloroquinphosphat-preis.php
It is thus important to at the very least protect recent improvements in Pell grant awards and eligibility which are under attack by Republican budgets , and to potentially consider more ambitious ideas for free college along the lines of what Bernie Sanders has proposed. While the above recommendations are important, growing up with less income significantly inhibits mobility even after taking educational access into account. As research by the Pew Economic Mobility Project indicates, children born into the bottom quintile who obtain a college degree are 2.
Policies that directly address inequality and poverty by boosting the incomes of poor and middle-class families are essential for maximizing mobility. As summarized by Marr et al. That evidence and similar evidence for food stamps and Medicaid reinforces the importance of maintaining and in many cases strengthening safety-net programs.
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