Existing data for South Korea's high-growth period stops at the province level — too coarse for studying the spatial dynamics of industrialisation, agricultural modernisation, or infrastructure expansion. Below provinces, municipal statistical yearbooks (시군 통계연보) recorded township-level data annually, but the physical volumes are deteriorating in provincial archives, printed in mixed Hangul/Hanja scripts, and largely uncatalogued. MIRACLE digitises, harmonises, and geocodes these yearbooks into an annual panel with time-consistent township identifiers at the 읍·면·동 level.
MIRACLE starts with South Korea's municipal statistical yearbooks, but the ambition extends in two directions. First, within Korea, we plan to incorporate additional administrative sources — expressway construction logs, agricultural extension records, Korea Forest Service archives, colonial-era household registries, and local personnel files — to deepen the panel and enable research designs that link infrastructure, agricultural modernisation, and environmental policy to local institutional conditions.
Second, across countries, the infrastructure we build is designed to accommodate other growth miracle economies with comparable subnational statistical traditions. If similar municipal records exist for Taiwan, or district-level yearbooks for post-war Japan, they belong in the same framework. The goal is a comparative subnational data platform for studying rapid development wherever it has occurred.
Municipal statistical yearbooks (시군 통계연보), published annually by county and city governments. Many survive only as single physical copies in provincial archives, uncatalogued and deteriorating.
Surviving yearbooks must be located across provincial archives and libraries, partnerships established with institutions like KDI and provincial governments, and fragile volumes scanned at high resolution. Many exist as single deteriorating copies that have never been photographed — making the archival discovery and scanning phases as critical as the technical pipeline that follows.
From scanned page to analysis-ready panel in four steps:
AI-OCR for mixed scripts
Custom pipeline fine-tuned for mixed Hangul/Hanja archival tables. 87% pilot accuracy, targeting 92–95%. This is what makes the project feasible — these documents were previously unusable at scale.
| 읍면 | 합계 | 논 (답) | 밭 (전) | ✓ | ||
|---|---|---|---|---|---|---|
| 소계 | 1모작 | 2모작 | ||||
| 남해 | 8,054 | 5,849 | 1,230 | 4,619 | 2,205 | ✓ balanced |
| 이동 | 10,993 | 7,544 | 1,175 | 6,369 | 3,449 | ✓ balanced |
| 삼동 | 12,785 | 7,349 | 1,239 | 6,110 | 5,436 | ✓ balanced |
| 남면 | 11,470 | 6,012 | 857 | 5,155 | 5,458 | ✓ balanced |
| 고현 | 8,310 | 5,680 | 743 | 4,937 | 2,630 | ✓ balanced |
| 창선 | 13,173 | 7,901 | 2,311 | 5,590 | 5,272 | ✓ balanced |
See structured output table above.
Variable harmonisation
Definitions, units, and table structures changed across editions and municipalities. We build crosswalks reconciling these into consistent time series.
Boundary concordances
Two major reorganisations (1963, 1973) plus dozens of smaller changes. We construct time-consistent miracle_id identifiers.
Geocoding & GIS
Every township linked to satellite, elevation, slope, soil, and transport network data. 196 Namhae-gun villages fully geocoded.
Township-year panel with time-consistent miracle_id identifiers. Modular structure by domain, CSV & Stata formats.
Each row is a township-year observation linked by miracle_id:
| miracle_id | year | prov | muni | twp | pop | hh | paddy_ha | schools | road_km |
|---|---|---|---|---|---|---|---|---|---|
| KR-48-840-010 | 1970 | 경남 | 남해군 | 남해읍 | 28,412 | 5,680 | 1,245 | 7 | 23.4 |
| KR-48-840-010 | 1975 | 경남 | 남해군 | 남해읍 | 25,891 | 5,320 | 1,198 | 8 | 31.7 |
| KR-48-840-010 | 1980 | 경남 | 남해군 | 남해읍 | 22,105 | 5,010 | 1,152 | 8 | 38.2 |
| KR-47-720-030 | 1970 | 경북 | 영주시 | 풍기읍 | 31,550 | 6,140 | 1,870 | 9 | 18.6 |
The full dataset is organised into modules by domain, each a flat panel at the township-year level with consistent miracle_id identifiers. Merge across modules using Core Keys. Full codebook and variable documentation will accompany each module release.
| Module | Description | ETA |
|---|---|---|
| Core Keys miracle_id · province · municipality · township · concordances | Geographic identifiers and boundary concordances across the 1963/1973 reorganisations. | 2026 |
| Demographics population · households · age structure | Population counts, household numbers, demographic composition. | 2026 |
| Agriculture paddy area · crop output · livestock | Cultivated area, output (harmonised to metric units), livestock. | 2026 |
| Industry establishments · employment · output | Industrial establishments, manufacturing employment, sectoral output. | 2027 |
| Infrastructure roads · electricity · water · telecom | Road length, electrification, public utilities. | 2027 |
| Public Finance revenues · expenditures · transfers | Municipal revenue/expenditure, central transfers, fiscal capacity. | 2027 |
| Education schools · enrolment · teachers | School counts, enrolment, teachers, educational infrastructure. | 2027 |
| Geospatial shapefiles · centroids · boundaries | GIS boundary files with consistent township geometries. | 2027 |
| Institutions clan concentration · bureaucratic capacity | Pre-treatment institutional measures from 1930 registries and personnel files. | 2028 |
MIRACLE enables research designs that were previously impossible — including the first causal analysis of the Gyeongbu Expressway, the Saemaul Undong, Korea's high-yield rice revolution, and one of history's largest reforestation programmes.
Seol (2026, R&R at Journal of Political Economy) — the Saemaul Undong paper — is built entirely on MIRACLE data.
Other applications. Geography of industrialisation, education expansion, fiscal transfers, land reform, environmental policy, developmental states. Using MIRACLE data? Let us know.
Seol, BooKang (2026). "The Saemaul Undong and Rural Development in South Korea." R&R at Journal of Political Economy.
Related projects:
- Growth Miracles knowledge bank — comparative subnational data platform
- Hyunjoo Yang (양현주, Sogang University) — DGM project, related digitisation work on Korean municipal records
- BooKang Seol's research page
Using MIRACLE data in your research? Let us know — we'd like to hear about it.
Digitisation proceeds province by province, constrained by the uneven survival of physical yearbooks across Korea's provincial archives. Hover over each province for details on coverage, year range, and scanning status.
Last updated March 2026
Changkeun Lee
Hyunjoo Yang
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@techreport{seol2026miracle,
author = {Seol, BooKang and Lee, Changkeun and Yang, Hyunjoo},
title = {{MIRACLE}: Subnational Economic Data for
South Korea's Developmental Period, 1960--1989},
institution = {London School of Economics},
year = {2026}
}