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Atlanta Marriott Marquis, A707
American Economic Association
Income, Wealth and Inequality
Friday, Jan. 4, 2019 10:15 AM - 12:15 PM
- Chair: James X. Sullivan, University of Notre Dame
Estimating the Returns to Wealth in Disability Free Life Expectancy
AbstractRecent papers have documented a striking correlation between income and life expectancy: for example, using data from the U.S., Chetty et al. (2016) document that “at 40, the richest men could expect to live to 87 while the bottom 1 percent had a life expectancy of just above 72 – equal to the average in a developing country like Sudan.” While the literature has found that these gains in life expectancy that come with wealth include disability-free years (Meara et al. 2008), little is known about the ways in which these correlations have changed over time. In this paper, I combine data from the Health and Retirement Study with national life tables to show the cross-sectional wealth gap in disability-free life expectancy. I also show how this gap is changing over time by examining multiple cohorts turning age 65 in the data. These statistics have important implications for the progressivity of public programs such as Social Security and Medicare.
Income, Poverty, and Inequality over Two Decades
AbstractThis paper provides new estimates of income, poverty, and inequality in the United States since 2000 using a groundbreaking set of linked survey and administrative data. The administrative data cover earnings and asset income from IRS tax records and transfer income for a myriad of safety net programs including Social Security, SSI, SNAP, Public Assistance, housing assistance, Medicare, Medicaid, WIC, and energy assistance. We link these data to the Current Population Survey (CPS), the source of official poverty and inequality statistics. Previous research has shown that the CPS in recent years misses half of SNAP benefits, private pensions, and Public Assistance dollars and a third of unemployment insurance dollars – among other income sources (Meyer, Mok, Sullivan 2015; Bee and Mitchell 2017). Using these linked data, we examine the extent to which misreporting of various survey income sources biases the reported income of households. We also provide improved estimates of the change in the resources of the low-income population over time, documenting how these trends diverge from those calculated using the survey data alone. Finally, we present new evidence on the effectiveness of transfer programs in reducing poverty and targeting the needy. In particular, given the time period examined, we provide the most accurate evidence to date of the functioning of the safety net in the years leading up to the Great Recession and the time period during and after the recession.
Intergenerational Earnings Risk and the Distribution of Wealth
AbstractThis paper uses earnings data for parents and children to estimate the persistence in earnings profiles and risk across generations. Human capital accumulation and parent location help explain the persistence in both the level and risk in life-cycle earnings. Using the estimated inter-generational persistence in a parameterized earnings process, I calibrate a Bewley-Huggett-Aiyagari model to understand how persistence in income levels and risk affect the persistence in wealth across generations. Results suggest inter-generational persistence in income processes are quantitatively important drivers of wealth dispersion.
The Long Run Evolution of Absolute Intergenerational Mobility
AbstractThis paper combines cross-sectional and longitudinal income data to present the evolution of absolute intergenerational income mobility in several developed economies in the 20th century. We show that detailed panel data are unnecessary for estimating absolute mobility in the long run. We find that in all countries absolute mobility decreased during the second half of the 20th century. Increasing income inequality and decreasing growth rates have contributed to the decrease. Yet, growth is the dominant contributor to this decrease in most countries. We derive a model for the relationship between absolute mobility, growth, inequality and relative mobility. Ceteris paribus, absolute and relative mobility are inversely related.
- E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy