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Found 8 papers, 2 papers with code
Treatment Effects in Staggered Adoption Designs with Non-Parallel Trends
We mainly focus on the case where untreated potential outcomes are generated by an interactive fixed effects model and show that variation in treatment timing provides additional moment conditions that can be used to recover a large class of target causal effect parameters.
Difference-in-Differences for Policy Evaluation
Difference-in-differences is one of the most used identification strategies in empirical work in economics.
Difference-in-Differences with Time-Varying Covariates in the Parallel Trends Assumption
In addition to issues related to multiple periods and variation in treatment timing that have been emphasized in the literature, we show that, even in the case with only two time periods, TWFE regressions are not generally robust to (i) paths of untreated potential outcomes depending on the level of time-varying covariates (as opposed to only the change in the covariates over time), (ii) paths of untreated potential outcomes depending on time-invariant covariates, and (iii) violations of linearity conditions for outcomes over time and/or the propensity score.
Nonlinear Approaches to Intergenerational Income Mobility allowing for Measurement Error
This paper considers nonlinear measures of intergenerational income mobility such as (i) the effect of parents' permanent income on the entire distribution of child's permanent income, (ii) transition matrices, and (iii) rank-rank correlations when observed annual incomes are treated as measured-with-error versions of permanent incomes.
Difference-in-Differences with a Continuous Treatment
This paper analyzes difference-in-differences designs with a continuous treatment.
Policy Evaluation during a Pandemic
National and local governments have implemented a large number of policies in response to the Covid-19 pandemic.
Treatment Effects in Interactive Fixed Effects Models with a Small Number of Time Periods
That is, in addition to time-period and individual fixed effects, we consider the case where there is an unobserved time invariant variable whose effect on untreated potential outcomes may change over time and which can therefore cause outcomes (in the absence of participating in the treatment) to follow different paths for the treated group relative to the untreated group.
Evaluating Policies Early in a Pandemic: Bounding Policy Effects with Nonrandomly Missing Data
During the early part of the Covid-19 pandemic, national and local governments introduced a number of policies to combat the spread of Covid-19.
