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.
Despite a substantial body of evidence to the contrary, many people believe hospitals shift costs in this way. For example, in 2014, Don George, MBA, the president and CEO of Blue Cross Blue Shield of Vermont wrote, “When government reimbursements are insufficient to cover the cost of the services a facility provides to Medicare or Medicaid beneficiaries, hospitals charge patients with private insurance enough to cover not only the cost of their services, but the shortfall created by government reimbursements as well.”In truth, it’s been nearly 2 decades since any rigorous study has found evidence of substantial cost shifting.
Recent work has found the opposite effect—when public programs pay hospitals less, so do private insurers. In a 2013 study published in Health Affairs, Chapin White, PhD, MPP, now a senior policy researcher at Rand Corporation, found that a 10% reduction in Medicare payments to hospitals was associated with a nearly 8% reduction in prices hospitals charge private insurers. Another study by him and Vivian Wu, PhD, now at the University of Southern California, published in Health Services Research in 2013, found that a reduction in hospital inpatient revenue from Medicare was associated with an even larger decline in total revenue, also suggesting hospitals cut prices charged to private payers.
He sees an epidemic of pain and a related flood of opioids into communities over the past decade as being, more than globalisation or economic dislocation, the real cause of rising mortality among middle-aged white Americans.
With Gallup’s help he has been collecting data on how many people report having felt physical pain in the past 24 hours and says the numbers are staggering in the US. What is causing that epidemic — and its links to Trump’s rise — remains unclear, he says. He seems more willing to blame pharmaceutical companies and doctors for overprescribing opioids. A surge in addiction (drug overdoses caused more deaths in the US last year than auto accidents) has, he argues, proved far more fatal than globalisation.
The Fading American Dream: Trends in Absolute Income Mobility Since 1940 by Raj Chetty, David Grusky, Maximilian Hell, Nathaniel Hendren, Robert Manduca, Jimmy Narang :: SSRNDecember 20, 2016
We estimate rates of “absolute income mobility” – the fraction of children who earn more than their parents – by combining historical data from Census and CPS cross-sections with panel data for recent birth cohorts from de-identified tax records. Our approach overcomes the key data limitation that has hampered research on trends in intergenerational mobility: the lack of large panel datasets linking parents and children. We find that rates of absolute mobility have fallen from approximately 90% for children born in 1940 to 50% for children born in the 1980s. The result that absolute mobility has fallen sharply over the past half century is robust to the choice of price deflator, the definition of income, and accounting for taxes and transfers. In counterfactual simulations, we find that increasing GDP growth rates alone cannot restore absolute mobility to the rates experienced by children born in the 1940s. In contrast, changing the distribution of growth across income groups to the more equal distribution experienced by the 1940 birth cohort would reverse more than 70% of the decline in mobility. These results imply that reviving the “American Dream” of high rates of absolute mobility would require economic growth that is spread more broadly across the income distribution.