The Direct Effect of Taxes and Transfers on Changes in the U.S. Income Distribution, 1967-2015.

Scholars have increasingly drawn attention to rising levels of income inequality in the United States. However, prior studies have provided an incomplete account of how changes to specific transfer programs have contributed to changes in income growth across the distribution. Our study decomposes the direct effects of tax and transfer programs on changes in the household income distribution from 1967 to 2015. We show that despite a rising Gini coefficient, lower-tail inequality (the ratio of the 50th to 10th percentile) declined in the United States during this period due to the rise of in-kind and tax-based transfers. Food assistance and refundable tax credits account for nearly all the income growth between 1967 and 2015 at the 5th percentile and roughly one-half the growth at the 10th percentile. Moreover, income gains near the bottom of the distribution are concentrated among households with children. Changes in the income distribution were far less progressive among households without children.

Contextual explanations for numeracy and literacy skill disparities between native and foreign-born adults in western countries.

Using new direct measures of numeracy and literacy skills among 85,875 adults in 17 Western countries, we find that foreign-born adults have lower mean skills than native-born adults of the same age (16 to 64) in all of the examined countries. The gaps are small, and vary substantially between countries. Multilevel models reveal that immigrant populations' demographic and socioeconomic characteristics, employment, and language proficiency explain about half of the cross-national variance of numeracy and literacy skills gaps. Differences in origin countries' average education level also account for variation in the size of the immigrant-native skills gap. The more protective labor markets in immigrant-receiving countries are, the less well immigrants are skilled in numeracy and literacy compared to natives. For those who migrate before their teens (the 1.5 generation), access to an education system that accommodates migrants' special needs is crucial. The 1 and 1.5 generation have smaller numeracy and literacy skills gaps in more ethnically diverse societies.

The Health Effects of Income Inequality: Averages and Disparities.

Much research has investigated the association of income inequality with average life expectancy, usually finding negative correlations that are not very robust. A smaller body of work has investigated socioeconomic disparities in life expectancy, which have widened in many countries since 1980. These two lines of work should be seen as complementary because changes in average life expectancy are unlikely to affect all socioeconomic groups equally. Although most theories imply long and variable lags between changes in income inequality and changes in health, empirical evidence is confined largely to short-term effects. Rising income inequality can affect individuals in two ways. Direct effects change individuals' own income. Indirect effects change other people's income, which can then change a society's politics, customs, and ideals, altering the behavior even of those whose own income remains unchanged. Indirect effects can thus change both average health and the slope of the relationship between individual income and health.

The association of earnings with health in middle age: Do self-reported earnings for the previous year tell the whole story?

Research on earnings and health frequently relies on self-reported earnings (SRE) for a single year, despite repeated criticism of this measure. We use 31 years (1961-1991) of earnings recorded by the United States Social Security Administration (SSA) to predict the 1992 prevalence of disability, diabetes, stroke, heart disease, cancer, depression and death by 2002 in a subset of Health and Retirement Study participants (n = 5951). We compare odds ratios (ORs) for each health outcome associated with self-reported or administratively recorded earnings. Individuals with no 1991 SSA earnings had worse health in multiple domains than those with positive earnings. However, this association diminished as the time lag between earnings and health increased, so that the absence of earnings before approximately 1975 did not predict health in 1992. Among those with positive earnings, lengthening the lag between SSA earnings and health did not significantly diminish the magnitude of the association with diabetes, heart disease, stroke, or death. Longer lags did reduce but did not eliminate the association between earnings and both disability and depression. Despite theoretical limitations of single year SRE, there were no statistically significant differences between the ORs estimated with single-year SRE and those estimated with a 31-year average of SSA earnings. For example, a one unit increase in logged SRE for 1991 predicted a 19% reduction in the odds of dying by 2002 (OR = 0.81; 95% confidence interval: 0.72,0.90), while a similar increase in average SSA earnings for 1961-1991 had an OR of 0.72 (0.63, 0.82). The point estimates for the OR associated with 31 year average SSA earnings were further from the null than the ORs associated with single year SRE for heart disease, depression, and death, and closer to the null for disability, diabetes, and stroke, but none of these differences was statistically significant.

Inequality and mortality: long-run evidence from a panel of countries.

We investigate whether changes in economic inequality affect mortality in rich countries. To answer this question we use a new source of data on income inequality: tax data on the share of pretax income going to the richest 10% of the population in Australia, Canada, France, Germany, Ireland, the Netherlands, New Zealand, Spain, Sweden, Switzerland, the UK, and the US between 1903 and 2003. Although this measure is not a good proxy for inequality within the bottom half of the income distribution, it is a good proxy for changes in the top half of the distribution and for the Gini coefficient. In the absence of country and year fixed effects, the income share of the top decile is negatively related to life expectancy and positively related to infant mortality. However, in our preferred fixed-effects specification these relationships are weak, statistically insignificant, and likely to change their sign. Nor do our data suggest that changes in the income share of the richest 10% affect homicide or suicide rates.