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on Demographic Economics |
| By: | Breen, Casey; Seltzer, Nathan (University of California, Berkeley) |
| Abstract: | How accurately can age of death be predicted using basic sociodemographic characteristics? We test this question using a large-scale administrative dataset combining the complete count 1940 Census with Social Security death records. We fit eight machine learning algorithms using 35 sociodemographic predictors to generate individual-level predictions of age of death for birth cohorts born at the beginning of the 20th century. We find that none of these algorithms are able to explain more than 1.5% of the variation in age of death. Our results suggest mortality is inherently unpredictable and underscore the challenges of using algorithms to predict major life outcomes. |
| Date: | 2023–04–08 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:znsqg&r=dem |
| By: | Adam Blandin; John Bailey Jones; Fang Yang |
| Abstract: | Married men work substantially more hours than men who have never been married, even after controlling for observables. Panel data reveal that much of this gap is attributable to an increase in work in the years leading up to marriage. Two potential explanations for this increase are: (i) men hit by positive labor market shocks are more likely to marry; and (ii) the prospect of marriage increases men's labor supply. We quantify the relative importance of these two channels using a structural life-cycle model of marriage and labor supply. Our calibration implies that marriage substantially increases male labor supply. Counterfactual simulations suggest that if men were unable to marry, prime-age male work hours would fall by 7%, and if marriage rates fell to the extent observed, men born around 1980 would work 2% fewer hours than men born around 1960. |
| Keywords: | labor supply; family structure; marriage; marital wage premium |
| JEL: | D15 J1 J22 J31 |
| Date: | 2023–01–13 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedrwp:95483&r=dem |
| By: | Creedy, John; Subramanian, S. |
| Abstract: | This paper uses the concept of the M-Curve, which plots the cumulative proportion of deaths against the corresponding cumulative proportion of the population (arranged in ascending order of age), and associated measures, to examine mortality experience in India. A feature of the M-curve is that it can be combined with an explicit value judgement (an aversion to early deaths) in order to make welfare-loss comparisons. Empirical comparisons over time, and between regions and genders, are made. Furthermore, in order to provide additional perspective, selective results for the UK and New Zealand are reported. It is also shown how the M-curve concept can be used to separate the contributions to overall mortality of changes over time (or differences between population groups) to the population age distribution and age-specific mortality rates. |
| Keywords: | Mortality Curve, Mortality-inefficiency measure, Crude Death Rate, Lorenz Curve, Age-distribution of population, Age-specific death rates, M-Curve comparisons, Decomposition, Age and fatality effects, Decomposition, Mean and dispersion effects, |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:vuw:vuwcpf:22007&r=dem |