Wages and life expectancy, as well as labor market outcomes, savings, and consumption, differ by gender and marital status. In this paper we compare the aggregate implications of two dynamic structural models. The first model is a standard, quantitative, life-cycle economy, in which people are only heterogenous by age and realized earnings shocks, and is calibrated using data on men, as typically done. The second model is one in which people are also heterogeneous by gender, marital status, wages, and life expectancy, and is calibrated using data for married and single men and women. We show that the standard life-cycle economy misses important aspects of aggregate savings, labor supply, earnings, and consumption. In contrast, the model with richer heterogeneity by gender, marital status, wage, and life expectancy matches the observed data well. We also show that the effects of changing life expectancy and the gender wage gap depend on marital status and gender, and that it is essential to not only model couples, but also the labor supply response of both men and women in a couple.
Gender, Marriage, and Life Expectancy by Margherita Borella, Mariacristina De Nardi, Fang Yang :: SSRNDecember 1, 2016
The Measurement of Health Inequalities: Does Status Matter? by Joan Costa-Font, Frank Cowell :: SSRNDecember 1, 2016
The measurement of health inequalities usually involves either estimating the concentration of health outcomes using an income-based measure of status or applying conventional inequality-measurement tools to a health variable that is non-continuous or, in many cases, categorical. However, these approaches are problematic as they ignore less restrictive approaches to status. The approach in this paper is based on measuring inequality conditional on an individual’s position in the distribution of health outcomes: this enables us to deal consistently with categorical data. We examine several status concepts to examine self-assessed health inequality using the sample of world countries contained in the World Health Survey. We also perform correlation and regression analysis on the determinants of inequality estimates assuming an arbitrary cardinalisation. Our findings indicate major heterogeneity in health inequality estimates depending on the status approach, distributional-sensitivity parameter and measure adopted. We find evidence that pure health inequalities vary with median health status alongside measures of government quality.
Calculating Expected Social Security Benefits by Race, Education and Claiming Age by Geoffrey Sanzenbacher, Jorge D. Ramos-Mercado :: SSRNNovember 24, 2016
The option to claim Social Security before the full retirement age (FRA) has been around for over 50 years. But claiming benefits early has an inherent trade-off: more years of income are received in exchange for an actuarially reduced monthly benefit. The actuarial reduction is designed to be “fair” for the average worker in that, regardless of the age at which a person claims, he can expect to receive the same expected present value (EPV) of his lifetime benefits. Aside from a period of high interest rates in the 1980s, this equality has roughly held for the average worker since the inception of the actuarial reduction. But the key word here is average. Workers who live less long than the average might maximize the EPV of benefits by claiming early, while those who live longer than average might benefit more from delay. This paper analyzes this issue by calculating the EPV of Social Security benefits by race, education, and gender, all three of which are correlates of both mortality and earnings.
This paper found that:
– Non-Hispanic men, both black and white, who do not hold a college degree maximize their EPV of benefits by claiming before the full retirement age, especially using a 3-percent interest rate in the EPV calculation.
– On the other hand, white men with a college degree and white women with at least a high school degree maximize the EPV of their benefits when claiming after their FRAs.
– Within some groups, delayed claiming can result in a substantially higher EPV than early claiming, given today’s low interest rates. For white female college graduates, the maximum EPV occurs at age 70 and is 16 percent higher than the EPV at 62, assuming an interest rate of 1 percent.
The policy implications of this paper are:
– More educated workers have more incentive to delay claiming than less educated workers, and non-blacks have more incentive to do so than blacks.
– Since the EPV is not a welfare measure, this result does not necessarily advocate early claiming for some, but it does point to differential incentives across socioeconomic groups.
– Since some workers can maximize their EPV by claiming at 62, policies that delay the early eligibility age to 64 but hold the actuarial reduction constant would cause some workers to sacrifice expected lifetime benefits, although the decrease is small.
STATISTICAL BRIEF #497: Concentration of Health Expenditures in the U.S. Civilian Noninstitutionalized Population, 2014November 23, 2016
- In 2014, the top 1 percent of persons ranked by their health care expenditures accounted for 22.8 percent of total health care expenditures, while the bottom 50 percent accounted for only 2.8.
- Persons age 65 and older comprised 15.1 percent of the U.S. civilian noninstitutionalized population and accounted for 33.6 percent of total health care expenditures. In contrast, children under age 18 comprised 23.2 percent of the population and 10.2 percent of expenditures.
- While 14.4 percent of adults under age 65 were uninsured during 2014, this group accounted for only 5.7 percent of health care expenditures.
Preferences for Equality in Environmental Outcomes by Maureen Cropper, Alan Krupnick, William J. Raich :: SSRNNovember 1, 2016
Benefit-cost analysis judges health and safety regulations according to whether the monetized benefits of risk reductions, as measured by individual willingness-to-pay (WTP), exceed their costs. One way to complement such analyses is to take into account the distributional effects of health and safety policies. Our goal in this paper is to estimate the parameters of individuals’ social welfare functions (SWFs) defined over environmental health risks — specifically, risks of cancer and lung disease. We do this by confronting people with choices between environmental programs that result in higher average but more equally distributed health risks, and programs that would deliver lower average but less equally distributed health risks. We use the responses to parameterize an Atkinson SWF for cancer risks and a similar function for risks of lung disease. This SWF could be used to evaluate programs that would alter the distribution of environmental health risks in a population. The analysis also produces an inequality index (the Atkinson index) for health risks that reflects the preferences of our sample for equality of outcomes.
Our empirical estimates of public preferences for environmental health risk distributions come from a national internet survey with more than 900 completions, administered in August 2015. The survey asked respondents to choose between environmental programs that result in different mean health risks in a population and different distributions of these risks. Respondents made these choices (a) in a situation in which they (and their families) were not affected by the choices, and (b) in a situation in which they were affected, to see how this altered their preferences. We also used “leaky bucket” experiments to elicit respondents’ preferences for income inequality and a repeated coin toss question to gauge risk aversion. In addition to the base case survey, we used four alternative survey treatments to examine the effect of the scale of the risks, the nature of the health risks (lung disease versus cancer) and the effects of the order of questions on responses.
The results of our survey suggest that people are willing to accept a program that results in a higher total environmental health risk provided this risk is equally distributed across the population. Specifically, the median respondent is willing to accept a 50 percent increase in mean health risk (e.g., total environmental cancer cases) if these risks are distributed equally in the population. Interestingly, this result is the same whether the respondent and his family are affected by the program or not. When we compare preferences for income equality versus equality in the distribution of health risks, we find that the proportionate sacrifice people are willing to accept in the mean outcome to ensure equality in the distribution of outcomes is greater for health than for income: inequality aversion is higher for health risks than for income.
In 1932, Benjamin Malzberg, a New York epidemiologist, published a study showing that people with mental illness died, on average, 14 to 18 years earlier than otherwise similar people in the general population.1 This mortality gap persists today and may even have widened: a 2006 U.S. study suggested that it ranged from 13 to 30 years.2 Indeed, the gap persists worldwide,3 mostly owing to medical conditions, such as cancer and cardiovascular disease, rather than “unnatural” causes, such as accidents and suicide.
Tax-funded health expenditures totaled $1.877 trillion in 2013 and are projected to increase to $3.642 trillion in 2024. Government’s share of overall health spending was 64.3% of national health expenditures in 2013 and will rise to 67.1% in 2024. Government health expenditures in the United States account for a larger share of gross domestic product (11.2% in 2013) than do total health expenditures in any other nation.