Rising Cohabitation and Child Development, (work in progress, draft coming soon).
Joint with Efi Adamopoulou, Anne Hannusch and Tim Obermeier.
Cohabitation rates of couples without children have steadily increased in the U.S. over the past 50 years. Yet, cohabitation rates of couples with small children have only increased for the less educated. What explains this differential rise in cohabitation rates by education and what are the implications for child investment and child outcomes? We show empirically that cohabiting women experience smaller childbirth penalties, work more in the labor market, and spend less time with their children as compared to married women. Subsequently, their children are less likely to obtain a college degree. To rationalize these facts, we build an overlapping generations model of marriage, cohabitation, and child development. Parents are altruistic towards their children and invest time and goods into their development. This, in turn, increases the probability that a child completes college. Couples can choose to separate in every period but married couples pay a divorce cost. Assets are split equally between spouses if couples were married prior to separation, but not if spouses previously cohabited.
Using a dynamic panel approach, we provide empirical evidence that negative health shocks reduce earnings. The effect is primarily driven by the participation margin and is concentrated in less educated and poor health individuals. We build a dynamic, general equilibrium, lifecycle model that is consistent with these findings. In the model, individuals, whose health is risky and heterogeneous, choose to either work, or not work and apply for social security disability insurance (SSDI). Health impacts individuals' productivity, SSDI access, disutility from work, mortality, and medical expenses. Calibrating the model to the United States, we find that health inequality is an important source of lifetime earnings inequality: nearly 29 percent of the variation in lifetime earnings at age 65 is due to the fact that Americans face risky and heterogeneous lifecycle health profiles. A decomposition exercise reveals that the primary reason why individuals in the United States in poor health have low lifetime earnings is because they have a high probability of obtaining SSDI benefits. In other words, the SSDI program is an important contributor to lifetime earnings inequality. Despite this, we show that it is ex ante welfare improving and, if anything, should be expanded.
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.
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. First,
we show that the frailty index has several advantages over self-reported health status,
particularly when studying health dynamics. Then we estimate a stochastic process
for frailty dynamics over the life cycle. We find that the autocovariance structure of
frailty in panel data strongly supports a process that allows the conditional variance
of frailty shocks to increase with age. Our frailty measure and dynamic process can be
used by researchers to study the evolution of health over the life cycle and its economic
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.