The objective of this research is to measure and compare the importance of the contribution of inequality of opportunity in child health inequality. The latter is decomposed into within opportunity inequality and the between opportunity inequality using a non-parametric approach after building groups with deducted circumstance variables.
The results showed that the total health inequality experienced a decrease between 1998 and 2013 from 0.65 to 0.26 in 15 years unlike the inequality of opportunities which has increased. It goes from 0.14 to 0.18 respectively in 1998 and 2013. The relatively low levels of inequality of opportunity are interpreted as an estimate of the lower bound of the set of variables of circumstances that can influence child health. Considering the results, the increase in the level of inequality of health opportunity would come more from the increase of the “unfavorable opportunity” group’s contribution.
Opioid abuse rates and deaths vary considerably from state to state, as do the costs associated with this epidemic. But researchers have generally focused on the economic impact of the crisis in the aggregate, at the US level. In a new analysis, I estimate the cost at the state level and find substantial variation across the country. Here, I offer a preview of my findings, which will be released in full next month.
Long‐term care has profound intergenerational implications. It can be costly for those who need it and onerous for loved ones who provide it. We pinpoint three intergenerational aspects of long‐term care that require further research. One concerns the link between costs of private care and intergenerational wealth transfers. The second concerns the link between participation in care and the work and welfare of family providers. The third relates to intergenerational tensions that these and other late‐in‐life interactions create. We outline innovations in modeling and measurement that would improve understanding of intergenerational linkages and their implementation in appropriate panel data.
Recent headlines frequently refer to rising inequality and its implication on economic growth and social welfare. Addressing the latter is difficult and requires more than simply looking at GDP, as Kuznets long ago pointed out. In this paper we focus on the importance of the income measure underlying the inequality measure when examining the relationship between GDP growth and inequality. We create a mapping using Census Bureau household survey data and Bureau of Labor Statistics (BLS) consumer expenditure data to create distributional measures of the Bureau of Economic Analysis (BEA) personal income. We show that for the period 2000‐2012, inequality using personal income is substantively lower than inequality measured using Census Bureau money income, and the trends in both inequality and median income are different. This demonstrates the importance of using a measure a national accounts based measure of income when examining the relationships between inequality and growth.
This study uses German social security records to provide novel evidence about the heterogeneity in life expectancy by lifetime earnings and, additionally, documents the distributional implications of this earnings-related heterogeneity. We find a strong association between lifetime earnings and life expectancy at age 65 and show that the longevity gap is increasing across cohorts. For West German men born 1926-28, the longevity gap between top and bottom decile amounts to about 4 years (about 30%). This gap increases to 7 years (almost 50%) for cohorts 1947-49. We extend our analysis to the household context and show that lifetime earnings are also related to the life expectancy of the spouse. The heterogeneity in life expectancy has sizable and relevant distributional consequences for the pension system: when accounting for heterogeneous life expectancy, we find that the German pension system is regressive despite a strong contributory link. We show that the internal rate of return of the pension system increases with lifetime earnings. Finally, we document an increase of the regressive structure across cohorts, which is consistent with the increasing longevity gap.
It turns out that health care spending, at least in the underage-65 private markets for health insurance, has become less, not more, concentrated in recent decades. After a significant decline in spending concentration about two decades ago, it has stabilized
at that lower level. There is a significant decline in concentrated spending among individuals from one year to the next. That decline in the “persistence” of high spending continues in people’s later years, though at a less significant rate.
Nevertheless, the overall pattern remains that a majority of individuals below Medicare age, or people not redirected to other forms of public insurance coverage (primarily Medicaid) as a result of longer-term disabling and income-limiting health conditions, just don’t need to spend that much of their income on health care. Whether they still should be required to pay much more for their insurance under the ACA or some other government intervention is largely a matter of policymakers’ choice rather than economic necessity.
We describe research on the impact of health insurance on healthcare spending (“moral hazard”), and use this context to illustrate the value of and important complementarities between different empirical approaches. One common approach is to emphasize a credible research design; we review results from two randomized experiments, as well as some quasi-experimental studies. This work has produced compelling evidence that moral hazard in health insurance exists – that is, individuals, on average, consume less healthcare when they are required to pay more for it out of pocket – as well as qualitative evidence about its nature. These studies alone, however, provide little guidance for forecasting healthcare spending under contracts not directly observed in the data. Therefore, a second and complementary approach is to develop an economic model that can be used out of sample. We note that modeling choices can be consequential: different economic models may fit the reduced form but deliver different counterfactual predictions. An additional role of the more descriptive analyses is therefore to provide guidance regarding model choice.