Assistance with Implementing Staggered DID for Unbalanced Panel with Repeated Treatment/Control Transitions

[Anand Kumar Finance]

Hello Stata Community,

I am a PhD scholar in finance and would greatly appreciate your help in implementing a staggered Difference-in-Differences (DID) analysis for my dataset. Data Context

1. Panel Structure:
   My dataset is an unbalanced panel covering multiple firms over several years. Firms are observed for different time periods, meaning the panel is not balanced.
2. Treatment and Control Assignment:
   • My research examines the impact of a law (captured by a binary variable year_dummy) on a dependent variable.
   • Treatment is determined at the country level:
     • In a given year, if a law is enacted, all firms within that country are treated (year_dummy = 1) for that year.
     • If no law is enacted, all firms in that country are in the control group (year_dummy = 0) for that year.
   • Note: Firms can transition between treatment and control groups depending on the enactment of laws in different years.
3. No "Always Untreated" Cohorts:
   All countries in the sample experience law enactment at some point, meaning there are no firms or countries that remain permanently untreated throughout the panel.
4. Objective:
   I aim to estimate the causal effect of these laws on the outcome variable while accounting for the staggered timing of treatment across countries and years.

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Challenges

1. Repeated Transitions Between Treatment and Control:
   • Unlike standard DID setups, firms can switch back and forth between treatment (year_dummy = 1) and control (year_dummy = 0) depending on whether a law is enacted in a particular year.
2. No Permanently Untreated Cohorts:
   • Since all countries experience law enactment at some point, there are no "always untreated" cohorts for comparison. The analysis needs to use "not-yet-treated" firms in a given year as the control group.
3. Unbalanced Panel:
   • My dataset is unbalanced, with firms observed for varying time periods. This adds complexity to ensuring proper identification of treatment effects.
4. Dynamic Effects:
   • I am particularly interested in estimating dynamic treatment effects (e.g., pre- and post-treatment effects) to understand how the laws impact over time.

Question:
1. Given that firms can switch between treatment and control, is eventstudyinteract or csdid the best tool for this type of staggered DID analysis? If not, what alternative approach or package would you recommend?
2. How can I account for the unbalanced panel structure in my dataset?
3. Are there specific steps or adjustments I should make to estimate dynamic treatment effects more effectively?

Any suggestions, corrections, or alternative approaches would be greatly appreciated. Thank you for your time and help!

[Nick Cox]

Duplicate post. Please don't post the same question repeatedly.

If interested, please follow https://www.statalist.org/forums/for...operly-regress