arXiv subjects

Dor Leventer

Publications and source records attributed to Dor Leventer.

3 recordsLinked to original sources

arXiv2026

Identification of Child Penalties

A large literature estimates child penalties using event studies by gender, normalizing by predicted earnings absent children and reporting the gender gap. This paper studies the identification framework underlying this strategy. Within gender, I argue that parallel trends is violated by selection into the timing of parenthood: higher human capital individuals delay childbirth and have steeper earnings trajectories. Between genders, I articulate the normalized design's identification assumptions, which I term normalized triple differences (NTD). Under NTD, I show that the conventional target, the gender gap in normalized effects, is not identified when parallel trends is violated. In contrast, a new causal estimand, the effect of parenthood on the gender earnings ratio, is point identified. Using Israeli administrative data, I find that parenthood's share of gender inequality is heterogeneous by age at first childbirth. Finally, I show that differences in fertility-timing distributions complicate cross-country comparisons of aggregate child-penalty estimates.

econ.EM↗

arXiv2025

Conditional Triple Difference-in-Differences

Triple difference-in-differences designs are widely used to estimate causal effects in empirical work. Surveying the literature, we find that most applications include controls. We show that this standard practice is generally biased for the target causal estimand when covariate distributions differ across groups. To address this, we propose identifying a causal estimand by fixing the covariate distribution to that of one group. We then develop a double-robust estimator and illustrate its application in a canonical policy setting.

econ.EM↗

arXiv2024

Correcting invalid regression discontinuity designs with multiple time period data

Regression Discontinuity (RD) designs rely on the continuity of potential outcome means at the cutoff, but this assumption often fails when other treatments or policies are implemented at this cutoff. We characterize the bias in sharp and fuzzy RD designs due to violations of continuity, and develop a general identification framework that leverages multiple time periods to estimate local effects on the (un)treated. We extend the framework to settings with carry-over effects and time-varying running variables, highlighting additional assumptions needed for valid causal inference. We propose an estimation framework that extends the conventional and bias-corrected single-period local linear regression framework to multiple periods and different sampling schemes, and study its finite-sample performance in simulations. Finally, we revisit a prior study on fiscal rules in Italy to illustrate the practical utility of our approach.

econ.EM↗