Difference-in-differences in 2020
Common pitfalls and how to avoid them presented by Andrew Baker (Stanford University)
The difference-in-differences (DiD) research design is popular method for testing changes in outcome variables across treated and untreated groups. While the set-up is intuitive and easy to implement in the canonical setting of two time periods and two groups, most modern research using DiD exploits the staggered implementation of treatment across many units and different time periods.
Unfortunately, the common practice using unit and time fixed effects, along with an indicator variable for active treatment (the two-way fixed effects TWFE estimator) has known flaws that potentially biases the parameter estimates in most use settings. In this talk Andrew Baker discusses the pitfalls of the common approach. Using simple simulation analyses, Andrew will show how the bias arises, and where the potential for bias is largest.
In addition we will discuss new methods for conducting DiD analyses that overcome the flaws in the TWFE approach, and show the implications with an example from prior literature.
SoDa Labs webinar series
The SoDa Labs webinar series provides a platform for researchers around the world to present work that uses novel and alternative data and/or tools from data science and beyond to answer social science questions.
Event Details
- Date:
- 22 September 2020 at 9:00 am – 10:00 am
- Venue:
- Online
- Categories:
- Economics; Econometrics and Business Statistics; General
Description
Common pitfalls and how to avoid them presented by Andrew Baker (Stanford University)
The difference-in-differences (DiD) research design is popular method for testing changes in outcome variables across treated and untreated groups. While the set-up is intuitive and easy to implement in the canonical setting of two time periods and two groups, most modern research using DiD exploits the staggered implementation of treatment across many units and different time periods.
Unfortunately, the common practice using unit and time fixed effects, along with an indicator variable for active treatment (the two-way fixed effects TWFE estimator) has known flaws that potentially biases the parameter estimates in most use settings. In this talk Andrew Baker discusses the pitfalls of the common approach. Using simple simulation analyses, Andrew will show how the bias arises, and where the potential for bias is largest.
In addition we will discuss new methods for conducting DiD analyses that overcome the flaws in the TWFE approach, and show the implications with an example from prior literature.
SoDa Labs webinar series
The SoDa Labs webinar series provides a platform for researchers around the world to present work that uses novel and alternative data and/or tools from data science and beyond to answer social science questions.
Event Contact
- Name
- SoDaLabs@monash.edu
- Phone
- Organisation