Topic
Difference in differences
About: Difference in differences is a research topic. Over the lifetime, 330 publications have been published within this topic receiving 10557 citations. The topic is also known as: DiD & DD.
Papers published on a yearly basis
Papers
TL;DR: The difference-in-differences estimator is based on the simple idea that simple comparisons of pre-treatment and post-treatment outcomes for those individuals exposed to a treatment are likely to be contaminated by temporal trends in the outcome variable or by the effect of events, other than the treatment, that occurred between both periods as mentioned in this paper.
Abstract: The use of natural experiments to evaluate treatment effects in the absence of truly experimental data has gained wide acceptance in empirical research in economics and other social sciences. Simple comparisons of pre-treatment and post-treatment outcomes for those individuals exposed to a treatment are likely to be contaminated by temporal trends in the outcome variable or by the effect of events, other than the treatment, that occurred between both periods. However, when only a fraction of the population is exposed to the treatment, an untreated comparison group can be used to identify temporal variation in the outcome that is not due to treatment exposure. The difference-in-differences (DID) estimator is based on this simple idea. Card and Krueger (1994) assess the employment effects of a raise in the minimum wage in New Jersey using a neighbouring state, Pennsylvania, to identify the variation in employment that New Jersey would have experienced in the absence of a raise in the minimum wage. Other applications of DID include studies of the effects of immigration on native wages and employment (Card, 1990), the effects of temporary disability benefits on time out of work after an injury (Meyer, Viscusi and Durbin, 1995), and the effect of anti-takeover laws on firms' leverage (Garvey and Hanka, 1999). It is well known that the conventional DID estimator is based on strong assumptions.
2,327 citations
TL;DR: In this paper , the authors explain when and how staggered difference-in-differences regression estimators, commonly applied to assess the impact of policy changes, are biased, and present three alternative estimators developed in the econometrics and applied literature for addressing these biases, including their differences and tradeoffs.
1,576 citations
TL;DR: This paper develops an alternative approach to the widely used Difference-In-Difference (DID) method for evaluating the effects of policy changes by introducing a nonlinear model that permits changes over time in the effect of unobservables.
Abstract: This paper develops an alternative approach to the widely used Difference-In-Difference (DID) method for evaluating the effects of policy changes. In contrast to the standard approach, we introduce a nonlinear model that permits changes over time in the effect of unobservables (e.g., there may be a time trend in the level of wages as well as the returns to skill in the labor market). Further, our assumptions are independent of the scaling of the outcome. Our approach provides an estimate of the entire counterfactual distribution of outcomes that would have been experienced by the treatment group in the absence of the treatment, and likewise for the untreated group in the presence of the treatment. Thus, it enables the evaluation of policy interventions according to criteria such as a mean-variance tradeoff. We provide conditions under which the model is nonparametrically identified and propose an estimator. We consider extensions to allow for covariates and discrete dependent variables. We also analyze inference, showing that our estimator is root-N consistent and asymptotically normal. Finally, we consider an application.
1,001 citations
TL;DR: In this article, a group of 1724 randomly selected Minnesota taxpayers were informed by letter that the returns they were about to file would be "closely examined" and the effect was much stronger for those with more opportunity to evade; the difference in differences is not statistically significant for those who do not have self-employment or farm income, and do not pay estimated tax.
931 citations
TL;DR: In this article, the authors employ an international differences-in-differences approach to identify tracking effects by comparing differences in outcome between primary and secondary school across tracked and non-tracked systems.
Abstract: Even though some countries track students into differing-ability schools by age 10, others keep their entire secondary-school system comprehensive. To estimate the effects of such institutional differences in the face of country heterogeneity, we employ an international differences-in-differences approach. We identify tracking effects by comparing differences in outcome between primary and secondary school across tracked and non-tracked systems. Six international student assessments provide eight pairs of achievement contrasts for between 18 and 26 cross-country comparisons. The results suggest that early tracking increases educational inequality. While less clear, there is also a tendency for early tracking to reduce mean performance. Therefore, there does not appear to be any equity-efficiency trade-off.
928 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 12 |
| 2024 | 24 |
| 2023 | 68 |
| 2022 | 84 |
| 2021 | 29 |
| 2020 | 21 |