In June 1998 a 4.8-kilometre bridge over the Jamuna river connected 26 million isolated Bangladeshis to Dhaka and cut freight costs in half. This tutorial rebuilds the difference-in-differences evaluation of that bridge from the ground up in Python, using the Padma hinterland — a symmetric region left isolated by a river whose own bridge was not started until 2015 — as the comparison group. It teaches the 2x2 logic, parallel trends, two-way fixed effects, event studies and honest sensitivity analysis on satellite nighttime lights, then runs the same machinery over census employment shares, rice yields and a public-goods placebo. The two doubly robust estimators of the original paper are rebuilt by hand in NumPy and pushed through both diff-diff and pyfixest. All 122 published coefficients are audited side by side with the replication, and the defects found inside the shipped Stata package are documented in full.
Do industrial parks raise local economic activity — and for whom? A beginner's staggered difference-in-differences evaluation of Ethiopian industrial parks in Python, replicating Huang, Wang & Xu (2026) on synthetic calibrated data: TWFE and an event study with pyfixest, the modern Sun-Abraham, Borusyak/Gardner and Callaway-Sant'Anna estimators plus a Goodman-Bacon decomposition with diff-diff, survey-weighted repeated-cross-section DiD on DHS household welfare and women's empowerment, and Conley spatial standard errors.
Extend synthetic difference-in-differences to staggered adoption, where units adopt treatment at different times, and apply it in Stata to parliamentary gender quotas across 119 countries — deriving the per-cohort estimator, its aggregation into the overall ATT, the modern sdid_event event study, and bootstrap, jackknife, and placebo inference.