spatial

Do Industrial Parks Work? Evaluating Place-Based Policy in Ethiopia with Difference-in-Differences

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.

Indonesia514

A Data Science Repository to Study Regional Development across 514 Districts in Indonesia

Bouncing Back Better? Evaluating the Economic Impact of the Aceh Tsunami

Evaluate the long-run economic impact of a localized natural disaster with causal inference in Python. A beginner's replication of Heger & Neumayer (2019) on the 2004 Aceh tsunami, using synthetic calibrated data: dynamic difference-in-differences with pyfixest, an event study with diff-diff, a night-lights dose-response, synthetic control with mlsynth, and Conley spatial standard errors.

Difference-in-Differences with Geocoded Microdata: When Distance Defines Treatment

When the 'treatment' is a point in space, distance becomes the running variable. We walk through the parametric ring DiD and a data-driven nonparametric alternative, first on a simulated world with a known answer, then on Linden and Rockoff's home-prices study, and reconcile a parametric −5.78 % with a nonparametric −20.6 %.

Bayesian Spatial Synthetic Control: California's Proposition 99 in R

Replicating the California tobacco case study from Sakaguchi & Tagawa in R: three estimators, one ATT, and a Nevada-sized spillover.

MGWFER: Causal Spatially Varying Coefficients via Panel Fixed Effects

A faithful Python tutorial on Li & Fotheringham (2026) — using a two-stage MGWFER algorithm to remove time-invariant spatial confounders from Multiscale GWR and recover both unbiased spatially varying slopes and intrinsic contextual effects from simulated panel data (225 units x 3 periods).

Spatial Dynamic Panel Data Modeling in R: Cigarette Demand Across US States

A hands-on guide to spatial panel data modeling using the SDPDmod package in R --- from Bayesian model comparison through static and dynamic SAR/SDM estimation with Lee-Yu bias correction to direct, indirect, and total effect decomposition --- applied to cigarette demand across 46 US states (1963--1992).

Spatial Dynamic Panels with Common Factors in Stata: Credit Risk in US Banking

Estimate spatial dynamic panel models with unobserved common factors using the spxtivdfreg package in Stata --- an IV approach that handles spatial lags, temporal persistence, endogenous regressors, and latent factors simultaneously

Exploratory Spatial Data Analysis: Spatial Clusters and Dynamics of Human Development in South America

An introduction to exploratory spatial data analysis using PySAL, covering choropleth maps, spatial weights, Moran's I, LISA clusters, space-time dynamics, and a Venezuela-Bolivia comparative analysis for 153 South American regions

Multiscale Geographically Weighted Regression: Spatially Varying Economic Convergence in Indonesia

Applying Multiscale Geographically Weighted Regression (MGWR) to reveal how economic catching-up varies across Indonesia's 514 districts, with each variable operating at its own spatial scale