Replication data for: Spatial Enclaves or Regional Catalysts? A Spatial Econometric Typology of Special Economic Zones in Indonesia

1. Research Hypothesis
We hypothesise that Indonesia's Special Economic Zones (SEZs) primarily function as isolated "spatial enclaves" or generate negative backwash effects, rather than acting as equitable "regional catalysts", due to top-down governance and weak local supply chain linkages.

2. Data Description & Collection
This cross-sectional spatial dataset covers 514 Indonesian districts and 17 active SEZs in 2023. Official sources include BKPM (district investment), the National SEZ Council (SEZ data), and BPS (regional controls like GRDP and education). Spatial weights (Queen contiguity and KNN=7) were generated using BIG administrative boundaries.

3. Notable Findings
Spatial Durbin Model (SDM) estimations and LISA clustering reveal a highly unequal investment distribution. The data categorises SEZs into distinct typologies: most fail to generate positive spillovers (operating as enclaves), some drain nearby capital (backwash), and rare exceptions like KEK Sorong act as true regional catalysts.

4. Interpretation
Economic policy does not operate in an empty space. Without mandatory backward linkages, capital injected into SEZs remains trapped within zone boundaries or flows back to the economic core (Java), leaving peripheral host regions with minimal long-term prosperity.

5. How to Use & Replicate
Designed for full reproducibility. Raw data includes Excel files and zipped shapefiles. Users simply run the master_R_script.R script in R Studio. This single script automates NA filtering, multicollinearity (VIF) diagnostics, LISA calculations, SDM estimations, and robustness checks. Log-transformations for zero-investment districts are handled automatically to output a final results workbook.