Advances in Causal Inference
Paper Session
Friday, Jan. 3, 2025 10:15 AM - 12:15 PM (PST)
- Chair: Francis Vella, Georgetown University
Selection and Parallel Trends
Abstract
We study the role of selection into treatment in difference-in-differences (DiD) designs.We derive necessary and sufficient conditions for parallel trends assumptions under general classes of selection mechanisms. These conditions characterize the empirical content of parallel trends. We use the necessary conditions to provide a selection-aware decomposition of the bias of DiD and provide easy-to-implement strategies for benchmarking its components. We also provide templates for justifying DiD in applications with and without covariates. A reanalysis of the causal effect of NSW training programs demonstrates the usefulness of our selection-based approach to benchmarking the bias of DiD.
Overidentification in Shift-Share Designs
Abstract
This paper studies the testability of identifying restrictions commonly employed to assign a causal interpretation to two stage least squares (TSLS) estimators based on Bartik instruments. For homogeneous effects models applied to short panels, our analysis yields testable implications previously noted in the literature for the two major available identification strategies. We propose overidentification tests for these restrictions that remain valid in high dimensional regimes and are robust to heteroskedasticity and clustering. We further show that homogeneous effect models in short panels, and their corresponding overidentification tests, are of central importance by establishing that: (i) In heterogenous effects models, interpreting TSLS as a positively weighted average of treatment effects can impose implausible assumptions on the distribution of the data; and (ii) Alternative identifying strategies relying on long panels can prove uninformative in short panel applications. We highlight the empirical relevance of our results by examining the viability of Bartik instruments for identifying the effect of rising Chinese import competition on US local labor markets.Synthetic Difference-in-Differences via Distribution Regression
Abstract
N/AJEL Classifications
- C0 - General
- C2 - Single Equation Models; Single Variables