Last week, the Census Bureau released some nice charts about the young and uninsured — that is, the folks that insurance companies are now falling over themselves to sign up because young people are cheap (and can be charged premiums higher than are actuarially fair in order to subsidize older people).
Thus, while the US spends more than twice as much on health care than the mean of other OECD countries, its greater GDP and higher prices explain most of it, and income inequality offers an explanation for the rest.
Jeff Young tweeted these Census Bureau maps showing where Americas low-income uninsured live, using the cutoffs for Medicaid expansion eligibility and for Obamacare exchange subsidy eligibility.
it’s clear that people with cancer diagnoses continued to have a higher rate of bankruptcy than people without such diagnoses.
How much higher? About 2 ½ times, on average, across all cancers.
Life Expectancy, Schooling, and Lifetime Labor Supply: Theory and Evidence Revisited by Matteo Cervellati, Uwe Sunde :: SSRNApril 1, 2013
This paper presents a theoretical and empirical analysis of the role of life expectancy for optimal schooling and lifetime labor supply. The results of a simple prototype Ben-Porath model with age-specific survival rates show that an increase in lifetime labor supply is not a necessary, nor a sufficient, condition for greater life expectancy to increase optimal schooling. The observed increase in survival rates during working ages that follows from the “rectangularization” of the survival function is crucial for schooling and labor supply. The empirical results suggest that the relative benefits of schooling have been increasing across cohorts of US men born 1840-1930. A simple quantitative analysis shows that a realistic shift in the survival function can lead to an increase in schooling and a reduction in lifetime labor hours.
Education and Health: The Role of Cognitive Ability by Govert Bijwaard, Hans Van Kippersluis, Justus Veenman :: SSRNApril 1, 2013
We aim to disentangle the relative contributions of (i) cognitive ability, and (ii) education on health and mortality using a structural equation model suggested by Conti et al. (2010). We extend their model by allowing for a duration dependent variable, and an ordinal educational variable. Data come from a Dutch cohort born around 1940, including detailed measures of cognitive ability and family background at age 12. The data are subsequently linked to the mortality register 1995-2011, such that we observe mortality between ages 55 and 75. The results suggest that the treatment effect of education (i.e. the effect of entering secondary school as opposed to leaving school after primary education) is positive and amounts to a 4 years gain in life expectancy, on average. Decomposition results suggest that the raw survival differences between educational groups are about equally split between a ‘treatment effect’ of education, and a ‘selection effect’ on basis of cognitive ability and family background.
Health care spending in three states – Maine, West Virginia and Mississippi – accounts for one out of every five dollars of state GDP. Conversely, Wyoming spends less one in ten, according to a new study by the National Center for Policy Analysis (NCPA).
“If every state could be like Wyoming, which they cannot, the country as a whole would be spending less of its income on health care than about three-fourths of the other developed countries,” said former Medicare Trustee and NCPA Senior Fellow Thomas R. Saving.
the gap between the health haves and the have-nots has narrowed dramatically in recent years.
The haves are those who enjoy great health into their 90s. The have-nots are those who suffer from serious health problems and do not live to see adulthood. As we pointed out in a recent study, among those Americans who were born in 1975, the unluckiest 1 percent died in infancy, while the luckiest 1 percent can expect to live to age 105 or longer. Now let’s fast forward to those born in 2012. The bottom percentile of this cohort can expect to survive until age 18. At the other end of the spectrum, the luckiest 1 percent can expect to live to age 108. That’s a much bigger gain in life expectancy among the have-nots than among the haves. Of course, life expectancy is but one measure of health and well-being, but understanding these trends offers a more complete picture than considering income alone.
Five months after the commission filed its final report, Governor Corzine introduced and New Jersey’s State Assembly passed Assembly Bill No. 2609. It limits the maximum allowable price that can be charged to uninsured New Jersey residents with incomes up to 500 percent of the federal poverty level to what Medicare pays plus 15 percent, terms the governor’s office had negotiated with New Jersey’s hospital industry.
The overwhelming majority of biological scientists agree that there is no such thing as race among modern humans. Yet, scientists regularly deploy race in their studies, and federal laws and regulations currently mandate the use of racial categories in biomedical research. Legal commentators have tried to make sense of this paradox primarily by looking to equal protection strict scrutiny analysis. However, the colorblind approach that attends this doctrine — which many regard as synonymous with invalidation — does not offer a particularly useful way to think about the use of race in research. It offers no way to address how current uses of race in science serve to reinforce biological notions of race long thought discarded. This Article, therefore, takes a different approach by shifting the debate from how strict scrutiny analysis can bear on race-based research, to asking a much deeper question: What normative aims motivate this jurisprudence and can they be instructive in mapping appropriate and equality-enhancing regulations for the use of race in biomedical research? Despite the Supreme Court’s apparent discomfort with government invocations of race, this Article locates in its equal protection race cases elements of an overlooked line of analysis that this Article terms “racial pragmatism,” according to which certain government race-conscious decisionmaking will not trigger strict scrutiny review. By parsing through the Court’s recent race cases, this Article identifies the goals and concerns that accompany racial pragmatist reasoning and brings them to bear in the biomedical research context to offer a framework for how regulators can mandate the use of race in research without dangerously “geneticizing” race.