Standard insurance models predict that people with high risks have high insurance coverage. It is empirically documented that people with high income have lower health risks and are better insured. We show that income differences between risk types lead to a violation of single crossing in an insurance model where people choose treatment intensity. We analyse different market structures and show the following: if insurers have market power, the violation of single crossing caused by income differences and endogenous treatment choice can explain the empirically observed outcome. Our results do not rely on differences in risk aversion between types.
Health Insurance Without Single Crossing: Why Healthy People Have High Coverage by Jan Boone, Christoph Schottmüller :: SSRNFebruary 8, 2017
A new poll from Gallup-Healthways shows which states had the highest and lowest well-being in 2016. Well-being scores are based on participants’ answers to questions about their sense of purpose, social relationships, financial lives, community involvement and physical health.
Sibling Spillovers by Sandra E. Black, Sanni Breining, David N. Figlio, Jonathan Guryan, Krzysztof Karbownik, Helena Skyt Nielsen, Jeffrey Roth, Marianne Simonsen :: SSRNFebruary 3, 2017
It is notoriously difficult to identify peer effects within the family, because of the common shocks and reflection problems. We make use of a novel identification strategy and unique data in order to gain some purchase on this problem. We employ data from the universe of children born in Florida between 1994 and 2002 and in Denmark between 1990 and 2001, which we match to school and medical records. To address the identification problem, we examine the effects of having a sibling with a disability. Utilizing three-plus-child families, we employ a differences-in-differences research design which makes use of the fact that birth order influences the amount of time which a child spends in early childhood with their siblings, disabled or not. We observe consistentevidence in both locations that the second child in a family is differentially affected when the third child is disabled. We also provide evidence which suggests that the sibling spillovers are working at least in part through the relative exposure to parental time and financial resources.
In 2014, half of the population accounted for 97% of health spending. The 5% of people who spend the most on health care spend an average of around $47,000 annually; people in the top 1% have average spending of over $107,000. At the other end of the spectrum, the 50% of the population with the lowest spending accounted for 3% of all total health spending; the average spending for this group was $264.
In other words, living longer doesn’t increase health care spending so much as it delays the large amount spent near death. Some health care spending is associated with those intervening, relatively healthy years, just not much compared with that spent in one’s final years.Living longer offers many benefits. That it isn’t, by itself, a major contributor to health care spending is a nice bonus.
Why are some people rich while others are poor? To what extent can governments affect inequality? Which instruments should they use? Answering these questions requires understanding why people save. Dynamic quantitative models of wealth inequality can help us understand and quantify the determinants of the outcomes that we observe in the data and to evaluate the consequences of policy reform. This paper surveys the savings mechanisms generated by the transmission of bequests and human capital, by preference heterogeneity, by rates of returns heterogeneity, by entrepreneurship, by richer earnings processes, and by medical expenses. It concludes that the transmission of bequests and human capital, entrepreneurship, and medical expense risk are crucial determinants of savings and wealth inequality.
Distributional National Accounts: Methods and Estimates for the United States by Thomas Piketty, Emmanuel Saez, Gabriel Zucman :: SSRNJanuary 7, 2017
This paper combines tax, survey, and national accounts data to estimate the distribution of national income in the United States since 1913. Our distributional national accounts capture 100% of national income, allowing us to compute growth rates for each quantile of the income distribution consistent with macroeconomic growth. We estimate the distribution of both pre-tax and post-tax income, making it possible to provide a comprehensive view of how government redistribution affects inequality. Average pre-tax national income per adult has increased 60% since 1980, but we find that it has stagnated for the bottom 50% of the distribution at about $16,000 a year. The pre-tax income of the middle class – adults between the median and the 90th percentile – has grown 40% since 1980, faster than what tax and survey data suggest, due in particular to the rise of tax-exempt fringe benefits. Income has boomed at the top: in 1980, top 1% adults earned on average 27 times more than bottom 50% adults, while they earn 81 times more today. The upsurge of top incomes was first a labor income phenomenon but has mostly been a capital income phenomenon since 2000. The government has offset only a small fraction of the increase in inequality. The reduction of the gender gap in earnings has mitigated the increase in inequality among adults. The share of women, however, falls steeply as one moves up the labor income distribution, and is only 11% in the top 0.1% today.