Event Study Regression (Event Studies)

In this exercise, you’ll build an event study from scratch using panel_gis.dta. The goal is to trace out the dynamic effect of STRV seed adoption on crop yields over time, using the interaction-weighted estimator from Sun and Abraham (2021).

• Generate a cohort variable that records the first year each district received STRV seed. Create a temporary variable equal to year when seed > 0, then use bysort and egen min() to create first_seed that pushes the earliest adoption year to all observations within each district. Drop the temporary variable.
• Create a relative time variable ry equal to year minus the cohort variable. Bin any values greater than 10 to 10 by making ry = 10 when ry > 10 and also not missing (remember, Stata treats . as infinitely large).
• Identify the control cohort. Generate an indicator for districts that never received seed (never_seed) and a separate indicator for districts that received seed last (in 2019) (last_seed).
• Generate lead dummies using a forvalues loop counting down from 16 to 2. Name them g_k (e.g., g_16, g_15, …, g_2), where each equals 1 when ry == -k. Inside the loop, use label var g_k "-k" so the variable has a clean name for tables.
• Generate lag dummies using a forvalues loop from 0 to 10. Name them gk (e.g., g0, g1, …, g10), where each equals 1 when ry == k. Inside the loop, use label var gk "k" so it has a clean name for tables.

1. Run the regression using xtreg, specifying evi_med as the outcome, include all lead and lag dummies (g_* g0-g10), add year fixed effects (i.year), specify the fe option to absorb district fixed effects, and cluster standard errors at the district_id level. Store the event study results by prefixing the regression with eststo twfe:.

Now run the robust event study using eventstudyinteract. Specify evi_med as the outcome, include all lead and lag dummies (g_* g0-g10), set the cohort variable with cohort(first_seed), identify the control cohort with control_cohort(last_seed), include fld_cuml as a covariate with covariates(), absorb district and year fixed effects with absorb(i.district_id i.year), and cluster standard errors at the district_id level.

Because eventstudyinteract stores its estimates internally in e(b_iw) instead of e(b), commands like esttab won’t find them by default. Before exporting, you must move the results using erepost and store them:

* post results for esttab
	matrix          b_iw = e(b_iw)
	matrix          V_iw = e(V_iw)
	erepost         b = b_iw V = V_iw
	eststo iw

Export both stored results to a single LaTeX table using the code block below. Adjust \scalebox{} to make the table fit in your document.

* export results to latex
   esttab       twfe iw using "$answ/12-event-reg.tex", replace ///
                    b(4) se(4) ///
                    drop(*.year _cons) ///
                    star(* 0.10 ** 0.05 *** 0.01) ///
                    mtitles("TWFE" "Interaction-Weighted") ///
                    noobs booktabs nonum ///
                    eqlabels(none) collabels(none) ///
                    nobaselevels nogaps fragment label ///
                    prehead("\begin{tabular}{l*{2}{c}} " ///
                        "\\[-1.8ex]\hline \hline \\[-1.8ex] " ///
                        "& \multicolumn{2}{c}{Event Study} \\ \midrule") ///
                    postfoot("\hline \hline \\[-1.8ex] " ///
                        "\multicolumn{3}{p{\linewidth}}{\small " ///
                        "\noindent \textit{Note}: Dependent variable " ///
                        "is median EVI. Standard errors clustered at the " ///
                        "district level (in parentheses). " ///
                        "* p$<$0.10, ** p$<$0.05, *** p$<$0.01.} " ///
                        "\end{tabular}")

2. Looking at the two columns, do you see evidence of bias in the TWFE pre-trend coefficients (the negative lags) compared to the interaction-weighted estimator?