Benjamin Ly Serena

Abstract

We provide a novel decomposition of changing gaps in life expectancy between rich and poor into differential changes in age-specific mortality rates and differences in “survivability”. Declining age-specific mortality rates increases life expectancy, but the gain is small if the likelihood of living to this age is small (ex-ante survivability) or if the expected remaining lifetime is short (ex-post survivability). Lower survivability of the poor explains half of the recent rise in inequality in the US and the entire rise in Denmark. Declines in cardiovascular mortality benefited rich and poor, but inequality increased because of differences in lifestyle-related survivability.

Abstract

Over the past three decades, many countries have introduced iodized salt policies to eradicate iodine deficiency. Iodine deficiency in utero is detrimental to cognitive ability, but little is known about the consequences of iodine deficiencies after birth. This paper examines the impact of iodine deficiency in adolescence on school performance. I exploit the introduction of iodized salt in Denmark during 1998-2001 as a natural experiment. Combining administrative records on high school grades over a thirty-year period with geographic variation in initial iodine deficiency, I find that salt iodization increases the GPA of students by 6-9 percent of a standard deviation.

Abstract

This work proposes a method to compute the income gradient in period life expectancy that accounts for income mobility. Using income and mortality records of the Danish population over the period 1980–2013, we validate the method and provide estimates of the income gradient. The period life expectancy of individuals at a certain age, and belonging to a certain income class, is normally computed by using the mortality of older cohorts in the same income class. This approach does not take into account that a substantial fraction of the population moves away from their original income class, which leads to an upward bias in the estimation of the income gradient in life expectancy. For 40-y-olds in the bottom 5% of the income distribution, the risk of dying before age 60 is overestimated by 25%. For the top 5% income class, the risk of dying is underestimated by 20%. By incorporating a classic approach from the social mobility literature, we provide a method that predicts income mobility and future mortality simultaneously. With this method, the association between income and life expectancy is lower throughout the income distribution. Without accounting for income mobility, the estimated difference in life expectancy between persons in percentiles 20 and 80 in the income distribution is 4.6 y for males and 4.1 y for females, while it is only half as big when accounting for mobility. The estimated rise in life-expectancy inequality over time is also halved when accounting for income mobility.

Abstract

Depression and anxiety disorders are leading causes of disability worldwide, with enormous costs to society. Yet, insurance coverage for effective treatments remains limited and many patients are left untreated. This paper studies the effects of scaling up access to psychotherapy on mental health, health care use, and labor market outcomes. I study a 2008 reform of the Danish public health insurance, which introduced 60 percent coverage of the cost of psychotherapy for depression and anxiety patients below the age of 38. Using administrative data covering 1995-2019 and regression discontinuity and difference-in-difference designs, I show that psychotherapy coverage reduces the use of other mental health services, physical health care, and suicide attempts. However, I find no effect on labor market outcomes including employment, sickness benefits, and disability pension receipt. Still, the savings in health care exceed the cost of the policy. This suggests that scaling up access to mental health care is both cost-reducing and welfare-improving.

Abstract

Income gradients in life expectancy are similar in the US and Scandinavia, despite fundamentally different healthcare systems. This raises two key questions: Do equal-access healthcare systems ensure equal healthcare, or do healthcare factors contribute to inequality even in Scandinavia? Alternatively, is inequality in life expectancy driven by health factors beyond the control of the healthcare system, such as income disparities in disease incidence? We study these questions in the context of cancer. Applying a novel decomposition to detailed health records covering the population of Denmark, we document that income differences in healthcare factors, such as timely diagnosis, treatment, and hospital quality, contribute little to inequality in life expectancy. Rather, health factors, particularly income differences in cancer incidence and comorbidities, play a major role.

Abstract

The number of years individuals live is systematically related to their socioeconomic status (SES), often measured using single factors such as education or income. However, SES may be better measured by interacting several factors, e.g., high income and high education vs. low income and low education. This paper quantifies the extent of socioeconomic inequality in longevity by accounting for interactions between multiple SES factors. To do so, we exploit the predictive power of machine learning and measure inequality as the predictable differences in life expectancy based on education, income, occupation, wealth, and IQ test scores. Using longitudinal data over more than 40 years, we track mortality of birth cohorts 1942-1944 from age 40 and estimate their cohort life expectancy by SES. We find that individuals with the highest SES can expect to live 25 years longer than those with the lowest. SES explains 12\% of the variance in life expectancy for males and 9\% for females. We assess how well single SES factors approximate these estimates and find that our approach explains 2-4 times more of the variance than using only income or education. Among single factors, wealth is the best SES measure, explaining 8\% of the variance for males and 5\% for females.