MIRACLE integrates multiple archival source types into a single framework built around time-consistent geographic identifiers. Each township carries a miracle_id that tracks it through South Korea's two major boundary reorganisations (1963, 1973) and dozens of smaller changes, allowing researchers to follow the same unit across three decades without manually reconciling administrative maps. The municipal statistical yearbooks form the natural backbone — published annually by every county government, they provide the richest and most consistent subnational coverage for this period. Additional archival layers, from forest type maps to foreign loan records and colonial-era household registries, extend the panel into domains the yearbooks do not reach.
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 통계연보 Core — digitising now
Township-level demographics, agriculture, industry, infrastructure, public finance, and education. Published annually by county governments. - Forest Type Maps 산림유형도 Collected
Korea Forest Service spatial archives. Shapefiles available from 조선임야분포도 (1910) and forest type maps from 1974 onward. Enables studying one of history's largest reforestation programmes. - Future archival layers (not yet in active digitisation)
- Loan Relation Files 차관관계철 Planned
Foreign loan records linking firms to locations and financing sources. Geocoding firms and mapping industrial networks. - Household Registries 호적부 Planned
Colonial-era and early-Republic household records. Pre-treatment institutional measures including clan concentration and land ownership. - Agricultural Extension Records Planned
Farm-level adoption of high-yield rice varieties and extension programme participation. - Expressway Construction Logs Planned
Construction timelines and route data for the Gyeongbu Expressway and subsequent motorway network. - Personnel Files Planned
Local government personnel records. Bureaucratic capacity and institutional quality measures.
MIRACLE draws on four archival source types. Municipal statistical yearbooks form the backbone; additional layers are planned.
Municipal Statistical Yearbooks
Core — digitising nowTownship-level demographics, agriculture, industry, and public finance. Published annually by every county government.
Forest Type Maps
CollectedKorea Forest Service spatial archives from 1910 and 1974 onward. Enables research on large-scale reforestation.
Loan Relation Files
PlannedForeign loan records linking firms to locations and financing sources.
Household Registries
PlannedColonial-era household records. Pre-treatment measures of clan concentration and land ownership.
From archive to analysis-ready panel in six steps:
Archive discovery & source identification Done
Systematic survey of provincial archives, university libraries, and government collections to locate surviving yearbook volumes. Mapping what exists, what is missing, and where physical copies are held.
Outreach & scanning Done
Building partnerships with municipalities, counties, and provincial archives. Physical scanning of bound volumes into high-resolution page images — the raw input for digitisation.
AI-OCR for mixed scripts Current focus
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 Current focus
Definitions, units, and table structures changed across editions and municipalities. We build crosswalks reconciling these into consistent time series.
Boundary concordances Pilot complete
Two major reorganisations (1963, 1973) plus dozens of smaller changes. We construct time-consistent miracle_id identifiers.
Geocoding & GIS Pilot complete
Every township linked to satellite, elevation, slope, soil, and transport network data. 196 Namhae-gun villages fully geocoded.
The dataset is organised into modules by domain, each a flat township-year panel. Merge across modules using Core Keys. CSV & Stata formats, with full codebook and variable documentation.
| 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 |
| 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 |
Digitisation proceeds province by province, constrained by the uneven survival of physical yearbooks across Korea's provincial archives.
Last updated March 2026
The township-year panel structure, combined with time-consistent geographic identifiers, supports rigorous causal inference designs across Korea's major policy interventions. Several foundational debates in development economics can be addressed with unusual precision in this setting. The team is actively pursuing questions on transport infrastructure and spatial inequality, agricultural modernisation and structural transformation, large-scale reforestation under fiscal constraint, and the institutional determinants of programme effectiveness.
Does transport infrastructure reduce spatial inequality — or deepen it? Existing evidence points in both directions: connectivity can diffuse growth to lagging regions or accelerate concentration in leading ones. Korea's Gyeongbu Expressway, built through the core of an industrialising economy, offers a setting where township-level data can identify what local conditions determine which outcome occurs.
Can agricultural modernisation drive structural transformation without displacing rural populations? The standard model predicts that productivity gains push labour out of farming and into cities. Korea is the singular counterexample: rural incomes converged with urban levels during one of history's fastest industrialisations. MIRACLE can test which pathway — labour reallocation, demand linkages, or capital transfers — operated at the township level, and why the displacement prediction failed.
Is large-scale environmental restoration possible under fiscal constraint? The Environmental Kuznets Curve predicts that ecological recovery follows prosperity. Korea restored 2.8 million hectares of forest while industrialising from extreme poverty — a direct challenge to this assumption, and one whose enabling conditions remain unidentified at the subnational level.
Why do identical national programmes produce dramatically different local outcomes? Korea's developmental state applied uniform national policies to thousands of townships simultaneously — a setting that isolates local institutional variation as the source of divergent trajectories, without the confounding differences in programme design that plague cross-country comparisons.
Beyond academic research, the data infrastructure is designed to support applied policy tools. A diagnostic framework currently under development with KDI will use the historical relationship between institutional endowments and programme returns to help practitioners — at agencies like the World Bank and FCDO — assess where national investments are likely to succeed before committing resources.
- Seol, BooKang (2026). "The Saemaul Undong and Rural Development in South Korea." R&R at Journal of Political Economy.
- Hyunjoo Yang (양현주, Sogang University) — DGM project, related digitisation work on Korean municipal records.
If you are using or interested in using MIRACLE data, we would like to hear from you. Get in touch.
Changkeun Lee
Hyunjoo Yang
Hiring research assistants for 2026–27. Get in touch.
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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}
}