<p>It depends on a few things. First, if you expect treatment effects change over time, then you want to estimate an event-study style DD specification.</p>
<p>If you have a single treatment timing (all treatment starts at the same time), then an event-study will unbiasedly estimate the treatment path. If you have variation in treatment timing, you want to use a modern method, e.g. <a href="https://pedrohcgs.github.io/files/Callaway_SantAnna_2020.pdf" rel="nofollow noreferrer">Callaway and Sant'Anna (2020)</a>, <a href="https://jrgcmu.github.io/2sdd_current.pdf" rel="nofollow noreferrer">Gardner (2021)</a>, or <a href="http://economics.mit.edu/files/14964" rel="nofollow noreferrer">Sun and Abraham (2020)</a></p>

CC BY-SA 4.0
2021-07-06T19:41:46.017Z
Is there any reference or simple explanation for "* event-study style DD specification*" ?
Phil Nguyen
CC BY-SA 4.0
2021-07-06T23:15:19.047Z
This is a pretty user-friendly intro guide: https://mixtape.scunning.com/difference-in-differences.html#providing-evidence-for-parallel-trends-through-event-studies-and-parallel-leads
Kyle Butts
CC BY-SA 4.0
2021-07-06T16:55:56.240Z
2021-07-06T16:55:56.240Z
Kyle Butts
<p>Recently, there is an emerging line of the study said that the traditional two-way fixed effect(TWFE) is failed in a lot of case because of the heterogeneous effects of laws over time, follow some paper <a href="http://goodman-bacon.com/pdfs/ddtiming.pdf" rel="nofollow noreferrer">Goodman-Bacon (2019)</a>, <a href="https://arxiv.org/abs/1803.08807" rel="nofollow noreferrer">Chaisemartin, 2020</a>.</p>
<p>Especially, Goodman, 2019 did a great job to decompose the single post-treatment dummy. In <a href="https://cdn.vanderbilt.edu/vu-my/wp-content/uploads/sites/2318/2019/10/09023516/so_youve_been_told_dd_10_9_2019.pdf" rel="nofollow noreferrer">his note</a>, he answered the question &quot;<strong>Is DD wrong</strong>&quot;:</p>
<blockquote>
<p>Not in general. The DD research design—comparing outcomes for groups
whose treatment status changes to groups whose treatment status does
not change—still can be a good idea. The DD specification—estimating
the coefficient a single post-treatment dummy—is a bad idea when your
treatment effects vary over time (<strong>get bigger with time since
treatment</strong>). In this case, just summarize your findings in a different
way—event-study or a linear trend-break, for instance.</p>
</blockquote>
<p>My question here is, if I expect and argue that the treatment effects get <em><strong>smaller</strong></em> with time since treatment, so whether DD specification now is a bad idea?</p>

<econometrics><causal-inference>
Can Difference-in-Difference be used when the treatment effects get smaller with time since treatment?
Phil Nguyen
# Can Difference-in-Difference be used when the treatment effects get smaller with time since treatment?

(Asked by: username_0 on 2021-07-05)

Recently, there is an emerging line of the study said that the traditional two-way fixed effect(TWFE) is failed in a lot of case because of the heterogeneous effects of laws over time, follow some paper [Goodman-Bacon (2019)](http://goodman-bacon.com/pdfs/ddtiming.pdf), [Chaisemartin, 2020](https://arxiv.org/abs/1803.08807).

Especially, Goodman, 2019 did a great job to decompose the single post-treatment dummy. In [his note](https://cdn.vanderbilt.edu/vu-my/wp-content/uploads/sites/2318/2019/10/09023516/so_youve_been_told_dd_10_9_2019.pdf), he answered the question "**Is DD wrong**":

> Not in general. The DD research design—comparing outcomes for groups
> whose treatment status changes to groups whose treatment status does
> not change—still can be a good idea. The DD specification—estimating
> the coefficient a single post-treatment dummy—is a bad idea when your
> treatment effects vary over time (**get bigger with time since
> treatment**). In this case, just summarize your findings in a different
> way—event-study or a linear trend-break, for instance.

My question here is, if I expect and argue that the treatment effects get ***smaller*** with time since treatment, so whether DD specification now is a bad idea?

**Tags:** econometrics, causal-inference

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## 1 answers

### Answer by username_1 on 2021-07-06. Score: 2 | Accepted answer

It depends on a few things. First, if you expect treatment effects change over time, then you want to estimate an event-study style DD specification.

If you have a single treatment timing (all treatment starts at the same time), then an event-study will unbiasedly estimate the treatment path. If you have variation in treatment timing, you want to use a modern method, e.g. [Callaway and Sant'Anna (2020)](https://pedrohcgs.github.io/files/Callaway_SantAnna_2020.pdf), [Gardner (2021)](https://jrgcmu.github.io/2sdd_current.pdf), or [Sun and Abraham (2020)](http://economics.mit.edu/files/14964)

> **Comments:**
> **username_0**: Is there any reference or simple explanation for "* event-study style DD specification*" ?  
> **username_1**: This is a pretty user-friendly intro guide: https://mixtape.scunning.com/difference-in-differences.html#providing-evidence-for-parallel-trends-through-event-studies-and-parallel-leads  

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