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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), Chaisemartin, 2020.

Especially, Goodman, 2019 did a great job to decompose the single post-treatment dummy. In his note, 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?

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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), Gardner (2021), or Sun and Abraham (2020)

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  • $\begingroup$ Is there any reference or simple explanation for "* event-study style DD specification*" ? $\endgroup$
    – Louise
    Jul 6 at 19:41
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    $\begingroup$ This is a pretty user-friendly intro guide: mixtape.scunning.com/… $\endgroup$
    – Kyle Butts
    Jul 6 at 23:15

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