-->
We present statistical software to implement imputation estimator for difference-in-differences.
Recent work has highlighted the difficulties of estimating difference-in-differences models when treatment timing occurs at different times for different units. This article introduces the R package {did2s}
which implements the estimator introduced in Gardner (2021). The article provides an approachable review of the underlying econometric theory and introduces the syntax for the function did2s
. Further, the package introduces a function, event_study
, that provides a common syntax for all the modern event-study estimators and plot_event_study to plot the results of each estimator.
The standard difference-in-differences estimator is modeled using the Two-way Fixed Effects Model. Unit $i$ and time $t$ has potential outcome:
where $D_{it}$ is an idicator variable that equals one when unit $i$ is currently experiencing treatment at time $t$. Researchers aim to estimate the effects of treatment and summarize it as the Average Treatment Effect on the Treatment, $\tau$.
If the two-way fixed effects model is correctly specified and the parallel trends assumption is satisfied, then OLS is fine and even BLUE! (woohoo 🎉🎉) So what’s with all these new Diff-in-Diff doesn’t work well problems?
The first problem is that $\tau$ is not typically constant across units and over time:
Treatment effects may depend on when you start treatment. For example, groups that benefit more from a policy implement it earlier
Treatment effects may depend on treatment duration like an event-study. For example, policy effects slowly take hold as the policy is adopted broadly.
Our TWFE model clearly needs to be enriched:
where now we have what the literature calls the Group-Time Average Treatment Effect, $\tau_{gt}$. This captures the two forms of treatment effect heterogeneity described above. The treatment effect size to depend on when you are treated, group $g$, and how many periods it has been since treatment determined by $t − g$. It will prove really hard to accurately estimate any particular $\tau_{gt}$, so researchers will try to estimate the Overall Average Treatment Effect:
the average of $\tau_{gt}$ across all $(g,t)$ observed in the sample.
If we knew $\mu_i$ and $\eta_t$, then we could move terms around in our model and have:
Then, if we ran this regression of an outcome variable ($y_{it} - \mu_i - \eta_t$) on an indicator variable $D_{it}$, then OLS algebra says that $\hat{\tau}$ will take the average of $\tau_{gt}$ for us and estimate the overall average treatment effect $\tau$.
Too bad we don’t know $\mu_i$ and $\eta_t$… Since we don’t know them, why don’t we instead estimate them? From the Frisch-Waugh-Lovell (FWL) theorem, we have:
where everything looks about the same, but now $\hat{\mu}_i$ and $\hat{\eta}_t$ are estimated and $\tilde{D}_{it}$ is the treatment indicator after being residualized by unit and time fixed effects. The left-hand side is a good estimate for $\tau_{gt}$, so what’s the problem then? This residualized treatment variable is actually the source of all the problems you’ve heard about. No really, go look at the proofs in those papers, you’ll see $\tilde{D}_{it}$ right away.
The problem is that since $\tilde{D}_{it}$ is no longer a simple dummy variable, OLS no longer estimates the simple average of $\tau_{gt}$.
Instead, OLS estimates
where the weights $w_{gt}$ are quite strange, but sum to 1. I’ll leave it to the paper to describe the weights, but no this: In some cases, $\hat{\tau}$ can be the opposite sign of the overall average treatment effect, $\tau$ 🚩🚩!
Okay, so why don’t you just regress $y_{it} - \hat{\mu}_i - \hat{\eta}_t$ on $D_{it}$? Great idea! You just thought of the two-stage difference-in-differences estimator (and Borusyak et. al.’s estimator too)
The above intution (hopefully) has lead us directly to the two-stage differences estimator as proposed by Gardner (2021). The two stages are as follows:
Stage 1: Estimate $\mu_i$ and $\eta_t$ using untreated/not-yet-treated observations ($D_{it} = 0$). Don’t include $D_{it} = 1$ since the treatment effects will be partially absorbed by unit and time fixed effects.
Stage 2: Regress $y_{it} - \hat{\mu}_i - \hat{\eta}_t$ on $D_{it}$, not $\tilde{D_{it}}$!.
Inference is a bit more complicated in this two-step estimator since your “outcome variable” is a noisy estimated object. Valid inference needs to account for this first-stage estimation. The details are in the paper and done properly in the {did2s}
package.
{did2s}
The paper then goes on to show off the {did2s}
package and provide some practical advice when implementing the estimator. For example, we will show how {did2s}
works for a dataset of simulated data with a treatment effect of 2.
library(did2s)
data("df_hom")
static = did2s(
df_hom,
yname = "dep_var", treatment = "treat", cluster_var = "state",
first_stage = ~ 0 | unit + year,
second_stage = ~ i(treat, ref=FALSE),
verbose = TRUE
)
Running Two-stage Difference-in-Differences
• first stage formula `~ 0 | unit + year`
• second stage formula `~ i(treat, ref = FALSE)`
• The indicator variable that denotes when treatment is on is `treat`
• Standard errors will be clustered by `state`
Since the package returns a fixest
object, you can use the suite of exporting tools that fixest
provides, like esttable
and iplot
/coefplot
. This makes, for example, exporting latex tables and event-study plots super simple.
fixest::etable(static)
static
Dependent Var.: dep_var
treat = TRUE 2.025*** (0.0307)
_______________ _________________
S.E. type Custom
Observations 31,000
R2 0.31846
Adj. R2 0.31846
Alternatively, if we use relative year indicator variables in the second-stage, we estimate event-study type coefficients.
dynamic = did2s(df_hom,
yname = "dep_var", treatment = "treat", cluster_var = "state",
first_stage = ~ 0 | unit + year,
second_stage = ~ i(rel_year, ref=c(-1, Inf)),
verbose = FALSE
)
Then, plutting an event study is super simple with fixest::coefplot
.
# plot rel_year coefficients and standard errors
fixest::coefplot(dynamic)