Parallel Trends in Interventions (Difference-in-Differences)

The credibility of a DiD design depends on parallel trends. Even with a continuous policy scaling up, we want to know if places adopting the seed faster were already growing their yield anyway. We can do a basic check by dividing the districts into “Early/High Adopters” vs “Late/Low Adopters”.

• Define “early” or “high” seed adoption districts vs “never/low” adoption.
  • Use bys district_id: egen max_seed = max(seed) to create a variable that captures the maximum level of seed adoption in each district over the entire period.
  • Use sum max_seed, detail to get the median value of max_seed and then r(p50) to store it in a local macro.
  • Use the sorted median value to create a dummy variable equal to 1 if the district’s max_seed is greater than the median value of max_seed, and 0 otherwise.
• Use collapse to calculate the mean evi_med by year for both the HighAdopt districts and the remaining districts.
• Plot the trend using twoway connected and put an xline at the year of the first seed introduction (2011).

1. Save the figure and input it into your Overleaf document.
2. Visually inspect the pre-adoption period. Do the two groups appear to have parallel trends? State your finding in comments.

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