【論文】 【スライド】

要旨：Labor force participation of prime-aged males in the U.S. has secularly declined for a half century since 1970s, threatening the formation of partnership and fertility. I test a hypothesis that long-run climate change nudged their labor market exits, a significant fraction of whom are working outdoors under heightened exposure to hot days. Combining granular daily temperature data and labor force participation across U.S. Commuting Zones during 1970-2019, I find that accumulated exposure to hot day with mean temperature 80F accounts for 20-30% of increased non-participation of prime-aged males. Climate change significantly accounts for market exits of black males, historically agglomerated in the Southeast, where warming was severest.

Abstract:
We show that the identification problem for a class of dynamic panel logit models with fixed effects has a connection to the truncated moment problem in mathematics. We use this connection to show that the sharp identified set of the structural parameters is characterized by a set of moment equality and inequality conditions. This result provides sharp bounds in models where moment equality conditions do not exist or do not point identify the parameters. We also show that the sharp identified set of the non-parametric latent distribution of the fixed effects is characterized by a vector of its generalized moments, and that the number of moments grows linearly in T. This final result lets us point identify, or sharply bound specific classes of functionals, without solving an optimization problem with respect to the latent distribution. We illustrate our identification result with several examples, and an empirical application on modeling children’s respiratory conditions.

Abstract:
Quantile regression is a method that allows to access the effect of predictors on the conditional quantile of the response in a regression framework. In this talk, we will present some recent theoretical and methodology developments for quantile regression in a panel data setting where repeated observations on individuals are available. On the theory side, we will discuss conditions that guarantee unbiased asymptotic normality of quantile regression with individual-specific intercepts and common slopes. From a methodological standpoint, we will discuss approaches to relax the common slope assumption and allow for groups of individuals that share the same slopes while leaving the intercepts unrestricted.