This paper studies the spending response to news about a dividend tax reform to estimate the elasticity of intertemporal substitution (EIS). The Norwegian dividend tax reform was proposed in 2003, announced in 2004, and implemented in 2006, raising the dividend tax rate by 28 percentage points....

This paper evaluates the efficacy of the Secondary Market Corporate Credit Facility, a program designed to stabilize the U.S. corporate bond market during the COVID-19 pandemic. The program announcements on March 23 and April 9, 2020, significantly reduced investment-grade credit spreads across...

This is a replication study of Cook et al.(2023), a paper that investigates the determinants of access to nondiscriminatory public accommodations for African-Americans before the 1964 Civil Rights Act. They utilize the Negro Motorist Green Books and World War II casualty data to examine the...

This paper examines households' stock market participation responses to a critical life-cycle event, adult children's marriage. An event-study difference-in-differences approach is employed to facilitate identification, which compares changes in the stock market participation behaviors of...

We study a difference-in-differences (DiD) framework where groups experience unequal treatment statuses in the pre-policy change period. This approach is commonly employed in empirical studies but it contradicts the canonical model's assumptions. We show that in such settings, the standard DiD...

Staggered difference-in-differences designs are pervasive in policy evaluations but little is known about the mechanisms of policy diffusion: How and why do such policies spread across jurisdictions? In this study, we highlight the role of elections in policy diffusion in settings where...

Staggered difference-in-differences designs are pervasive in policy evaluations but little is known about the mechanisms of policy diffusion: How and why do such policies spread across jurisdictions? In this study, we highlight the role of elections in policy diffusion in settings where...

Two-way fixed effects (TWFE) regressions with period and group fixed effects are widely used to estimate policies' effects: 26 of the 100 most cited papers published by the American Economic Review from 2015 to 2019 estimate such regressions. Researchers have long thought that TWFE estimators...

The difference-in-differences (DID) approach that identifies the capitalization of amenities through changes in housing prices has been widely used in the literature of hedonic estimation in the past decade. However, concerns have been raised about how to interpret the estimated capitalization...

Staggered difference-in-differences designs are pervasive in policy evaluations but little is known about the mechanisms of policy diffusion: How and why do such policies spread across jurisdictions? In this study, we highlight the role of elections in policy diffusion in settings where...

In this paper, we describe a computational implementation of the Synthetic difference-in-differences (SDID) estimator of Arkhangelsky et al. (2021) for Stata. Synthetic difference-in-differences can be used in a wide class of circumstances where treatment effects on some particular policy or...

Atwood (2022) analyzes the effects of the 1963 U.S. measles vaccination on long-run labor market outcomes, using a generalized difference-in-differences approach. We reproduce the results of this paper and perform a battery of robustness checks. Overall, we confirm that the measles vaccination...

In this paper, we describe a computational implementation of the Synthetic difference-in-differences (SDID) estimator of Arkhangelsky et al. (2021) for Stata. Synthetic difference-in-differences can be used in a wide class of circumstances where treatment effects on some particular policy or...

Marketing scientists often estimate causal effects using data from pre/post test/control quasi-experimental settings. We propose a new, easy to implement Augmented Difference-in-Differences (ADID) method that complements existing approaches to estimate the average treatment effect on the treated...

We propose a difference-in-difference estimator with a continuous treatment. We consider an heterogeneous adoption design where no unit is treated at period one, all units receive a strictly positive treatment dose at period two, and there are units with a treatment dose close to zero at period...

Linear regressions with period and group fixed effects are widely used to estimate policies' effects: 26 of the 100 most cited papers published by the American Economic Review from 2015 to 2019 estimate such regressions. It has recently been shown that those regressions may produce misleading...

Linear regressions with period and group fixed effects are widely used to estimate policies' effects: 26 of the 100 most cited papers published by the American Economic Review from 2015 to 2019 estimate such regressions. It has recently been show that those regressions may produce misleading...

We study regressions with period and group fixed effects and several treatment variables. Under a parallel trends assumption, the coefficient on each treatment identifies the sum of two terms. The first term is a weighted sum of the effect of that treatment in each group and period, with weights...

We link newly-digitized personnel records of the U.S. government for 1907-1921 to census data to study the segregation of the civil service by race under President Woodrow Wilson. Using a difference-in-differences design around Wilson’s inauguration, we find that the introduction of employment...

This note formalizes the synthetic difference-in-differences estimator for staggered treatment adoption settings, as briefly described in Arkhangelsky et al. (2021). To illustrate the importance of this estimator, I use replication data from Abrams (2012), I compare the estimators obtained using...

We develop a framework for difference-in-differences designs with staggered treatment adoption and heterogeneous causal effects. We show that conventional regression-based estimators fail to provide unbiased estimates of relevant estimands absent strong restrictions on treatment-effect...

We study treatment-effect estimation, with a panel where groups may experience multiple changes of their treatment dose. We make parallel trends assumptions, but do not restrict treatment effect heterogeneity, unlike the linear regressions that have been used in such designs. We extend the...

This paper estimates the long-run impacts of banning affirmative action on men and women from under-represented minority (URM) racial and ethnic groups in the United States. Using data from the US Census and American Community Survey, we use a difference-in-differences framework to compare the...

What happens when the findings of a prominent medical study are overturned? Using a medical trial on breech births, we estimate the effect of the reversal of such a medical study on physician choices and infant health outcomes. Using the United States Birth Certificate Records from 1995-2010, we...

Digital advertising, which uses consumer data to target ads to users, now accounts for most of global ad expenditures. Privacy concerns have prompted regulations that restrict the use of personal data. To inform these policy debates, we develop an equilibrium model of advertising and market...