There have been more than 500,000 opioid overdose deaths since 2000. To analyze the opioid epidemic, a model is constructed where individuals, with and without pain, choose whether to misuse opioids knowing the probabilities of addiction and dying. These odds are functions of opioid use. Markov chains are estimated from the US data for the college and non-college educated that summarize the transitions into and out of opioid addiction as well as to a deadly overdose. A structural model is constructed that matches the estimated Markov chains. The epidemic's drivers, and the impact of medical interventions, are examined.
We study the effect of poor health on labor supply and its implications for lifetime earnings inequality. Using a dynamic panel approach, we provide empirical evidence that negative health shocks significantly reduce earnings. The effect is primarily driven by the participation margin and is concentrated among the less educated and those in poor health. Next, we develop a life cycle model of labor supply featuring risky and heterogeneous health profiles that affect individuals' productivity, likelihood of access to social insurance, disutility from work, mortality, and medical expenses. Individuals can either work or not work and apply for social security disability insurance (SSDI/SSI). Eliminating health inequality in our model reduces the variance of log lifetime (accumulated) earnings by 30 percent at age 65. About two-third of this effect is due to the impact of poor health on the probability of obtaining SSDI/SSI benefits. Despite this, we show that eliminating the SSDI/SSI
program reduces ex ante welfare.
The labor-force participation rates of prime-age U.S. workers dropped in March 2020—the start of the COVID-19 pandemic—and have still not fully recovered. At the same time, substance-abuse deaths were elevated during the pandemic relative to trend indicating an increase in the number of substance abusers, and abusers of opioids and crystal methamphetamine have lower labor-force participation rates than non-abusers. Could increased substance abuse during the pandemic be a factor contributing to the fall in labor-force participation? Estimates of the number of additional substance abusers during the pandemic presented here suggest that increased substance abuse accounts for between 9 and 26 percent of the decline in prime-age labor-force participation between February 2020 and June 2021.
Joint with Michale Darden, David Dowdy, Lauren Gardner, Barton H. Hamilton, Melissa Marx, Nicolas W. Papageorge, Daniel Polsky, Kimberly Powers, Elizabeth Struart, and Matthew Zahn.
Facing unprecedented uncertainty and drastic trade-offs between public health and other forms of human well-being, policy makers during the Covid-19 pandemic have sought the guidance of epidemiologists and economists. Unfortunately, while both groups of scientists use many of the same basic mathematical tools, the models they develop to inform policy tend to rely on different sets of assumptions and, thus, often lead to different policy conclusions. This divergence in policy recommendations can lead to uncertainty and confusion, opening the door to disinformation, distrust of institutions, and politicization of scientific facts. Unfortunately, to date, there have not been widespread efforts to build bridges and find consensus or even to clarify sources of differences across these fields, members of whom often continue to work within their traditional academic silos. In response to this "crisis of communication," we convened a group of scholars from epidemiology, economics, and related fields (e.g., statistics, engineering, and health policy) to discuss approaches to modeling economy-wide pandemics. We summarize these conversations by providing a consensus view of disciplinary differences (including critiques) and working through a specific policy example. Thereafter, we chart a path forward for more effective synergy between disciplines, which we hope will lead to better policies as the current pandemic evolves and future pandemics emerge.
We construct a unified objective measure of health status: the frailty index, defined as the cumulative sum of all adverse health indicators observed for an individual. Using this index, we document four stylized facts on health dynamics over the life cycle and show that they are robust to other ways of constructing the index. We also compare the frailty index with self-reported health status and find significant differences in their dynamics and ability to predict health-related outcomes. Finally, we propose and estimate a stochastic process for frailty dynamics over the life cycle accounting for mortality bias. Our frailty measure and dynamic process can be used to study the evolution of health over the life cycle and its economic implications.
A simple model of COVID-19 that incorporates feedback from disease prevalence to disease transmission through an endogenous response of human behavior does a remarkable job fitting the main features of the data on the growth rates of daily deaths observed across a large number countries and states of the United States in 2020. This finding, however, suggests a new empirical puzzle: very large wedges that shift disease transmission rates holding disease prevalence fixed are required both across regions and within a region over time for the model to match the data on deaths from COVID-19 as an equilibrium outcome exactly.
We document four facts about the COVID-19 pandemic worldwide relevant for those studying the impact of non-pharmaceutical interventions (NPIs) on COVID-19 transmission. First: across all countries and U.S. states that we study, the growth rates of daily deaths from COVID-19 fell from a wide range of initially high levels to levels close to zero within 20-30 days after each region experienced 25 cumulative deaths. Second: after this initial period, growth rates of daily deaths have hovered around zero or below everywhere in the world. Third: the cross section standard deviation of growth rates of daily deaths across locations fell very rapidly in the first 10 days of the epidemic and has remained at a relatively low level since then. Fourth: when interpreted through a range of epidemiological models, these first three facts about the growth rate of COVID deaths imply that both the effective reproduction numbers and transmission rates of COVID-19 fell from widely dispersed initial levels and the effective reproduction number has hovered around one after the first 30 days of the epidemic virtually everywhere in the world. We argue that failing to account for these four stylized facts may result in overstating the importance of policy mandated NPIs for shaping the progression of this deadly pandemic.
This paper presents a procedure for estimating and forecasting disease scenarios for COVID-19 using a structural SIR model of the pandemic. Our procedure combines the flexibility of noteworthy reduced-form approaches for estimating the progression of the COVID-19 pandemic to date with the benefits of a simple SIR structural model for interpreting these estimates and constructing forecast and counterfactual scenarios. We present forecast scenarios for a devastating second wave of the pandemic as well as for a long and slow continuation of current levels of infections and daily deaths. In our counterfactual scenarios, we find that there is no clear answer to the question of whether earlier mitigation measures would have reduced the long run cumulative death toll from this disease. In some cases, we find that it would have, but in other cases, we find the opposite --- earlier mitigation would have led to a higher long-run death toll.
The 19th and 20th centuries saw a transformation in contraceptive technologies and their take up. This led to a sexual revolution, which witnessed a rise in premarital sex and out-of-wedlock births, and a decline in marriage. The impact of contraception on married and single life is analyzed here both theoretically and quantitatively. The analysis is conducted using a model where people search for partners. Upon finding one, they can choose between abstinence, marriage, and a premarital sexual relationship. The model is confronted with some stylized facts about premarital sex and marriage over the course of the 20th century. Some economic history is also presented.
previously Private Long-Term Care Insurance: Why is the Market so Small and Coverage Denials so Frequent?
Joint with R. Anton Braun and Tatyana Koreshkova.
Updated: January 2019.
Half of U.S. 50-year-olds will experience a nursing home stay before they die, and one in ten will incur out-of-pocket long-term care expenses in excess of $200,000. Surprisingly, only about 10% of individuals over age 62 have private long-term care insurance (LTCI) and LTCI takeup rates are low at all wealth levels. We analyze the contributions of Medicaid, administrative costs, and asymmetric information about nursing home entry risk to low LTCI takeup rates in a quantitative equilibrium contracting model. As in practice, the insurer in the model assigns individuals to risk groups based on noisy indicators of their nursing home entry risk. All individuals in frail and/or low income risk groups are denied coverage because the cost of insuring any individual in these groups exceeds that individual's willingness-to-pay. Individuals in insurable risk groups are offered a menu of contracts whose terms vary across risk groups. We find that Medicaid accounts for low LTCI takeup rates of poorer individuals. However, administrative costs and adverse selection are responsible for low takeup rates of the rich. The model reproduces other empirical features of the LTCI market including the fact that owners of LTCI have about the same nursing home entry rates as non-owners.
previously The Joint Effects of Social Security and Medicaid on Incentives and Welfare
Joint with R. Anton Braun and Tatyana Koreshkova.
Updated: December 2015.
All individuals face some risk of ending up old, sick, alone and poor. Is there a role for social insurance for these risks and, if so, what is a good program? A large literature has analyzed the costs and benefits of pay-as-you-go public pensions and found that the costs exceed the benefits. This paper, instead, considers means-tested social insurance programs for retirees such as Medicaid and Supplemental Security Income. We find that the welfare gains from these programs are large. Moreover, the current scale of means-tested social insurance in the U.S. is too small in the following sense. If we condition on the current Social Security program, increasing the scale of means-tested social insurance by 1/3 benefits both the poor and the affluent when a payroll tax is used to fund the increase.
previously The Impact of Medical and Nursing Home Expenses and Social Insurance Policies on Savings and Welfare.
Joint with Tatyana Koreshkova.
Updated: December 2013.
We consider a life-cycle model with idiosyncratic risk in earnings, out-of-pocket medical and nursing home expenses, and survival. Partial insurance is available through welfare, Medicaid, and social security. Calibrating the model to the US we show that (1) savings for old-age, out-of-pocket expenses account for 13.5 percent of aggregate wealth, half of which is due to nursing home expenses; (2) cross-sectional out-of-pocket nursing home risk accounts for 3 percent of aggregate wealth and substantially slows down wealth decumulation at older ages; (3) the impact of medical and nursing home expenses on private savings varies significantly across the lifetime earnings distribution; and (4) all newborns would benefit if social insurance for nursing home stays was made more generous.
The Rouwenhorst method of approximating stationary AR(1) processes has been overlooked by much of the literature despite having many desirable properties unmatched by other methods. In particular, we prove that it can match the conditional and unconditional mean and variance, and the first-order autocorrelation of any stationary AR(1) process. These properties makes the Rouwenhorst method more reliable than others in approximating highly persistent processes and generating accurate model solutions. To illustrate this, we compare the performances of the Rouwenhorst method and four others in solving the stochastic growth model and an income fluctuation problem. We find that (i) the choice of approximation method can have a large impact on the computed model solutions, and (ii) the Rouwenhorst method is more robust than others with respect to variation in the persistence of the process, the number of points used in the discrete approximation and the procedure used to generate model statistics.
The welfare gain to consumers from the introduction of personal computers is estimated here. A simple model of consumer demand is formulated that uses a slightly modified version of standard preferences. The modification permits marginal utility, and hence total utility, to be finite when the consumption of computers is zero. This implies that the good won't be consumed at a high enough price. It also bounds the consumer surplus derived from the product. The model is calibrated/estimated using standard national income and product account data. The welfare gain from the introduction of personal computers is in the range of 2 to 3 percent of consumption expenditure.
A model with leisure production and endogenous retirement is used to explain the declining labor force participation rates of elderly males. The model is calibrated to cross-sectional data on the labor force participation rates of elderly US males by age, their median drop in market consumption and leisure good expenditure share in the year 2000. Running the calibrated model for the period 1850 to 2000, a prediction of the evolution of the cross-section is obtained. The model is able to predict more than 87 percent of the increase in retirement of men over 65. The increase in retirement is driven by rising real wages and a falling price of leisure goods over time.
Suburbanization in the U.S. between 1910 and 1970 was concurrent with the rapid diffusion of the automobile. A circular city model is developed in order to access quantitatively the contribution of automobiles and rising incomes to suburbanization. The model incorporates a number of driving forces of suburbanization and car adoption, including falling automobile prices, rising real incomes, changing costs of traveling by car and with public transportation, and urban population growth. According to the model, 60 percent of postwar (1940-1970) suburbanization can be explained by these factors. Rising real incomes and falling automobile prices are shown to be the key drivers of suburbanization.
Research in progress
Rising Cohabitation and Child Development, (draft coming soon).
Joint with Efi Adamopoulou, Anne Hannusch and Tim Obermeier.