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This study quantitatively assesses the fairness of military service in early Joseon Korea by analyzing household, population, and conscript data for 334 counties recorded in the Geographical Gazetteer of the Veritable Records of Sejong (1454, Sejong Sillok Jiriji, or Jiriji). Although the Joseon government declared a policy of “household-proportional conscription,” it has remained unclear whether this principle was enforced or how far regional disparities extended. To examine these issues, the Jiriji’s XML text was pre-processed and converted into a structured dataset, then analyzed using Pearson correlation coefficients, the Gini coefficient, the Theil T index, and GIS visualization. The study reveals extremely strong positive correlations (r ＞ 0.99) among households, population, and conscripts, suggesting that recruitment followed the proportional principle nationwide. Yet the conscripts-per-household ratio shows moderate nationwide inequality (Gini = 0.293), and the Theil T index attributes 61 percent of that inequality to within-province variation at the county level. Hamgil, Jeolla, and Pyeongan provinces bore both heavy burdens and high inequality due to border and coastal defense needs, while Gyeonggi province showed a low average burden but pronounced internal disparities. These results indicate that the early Joseon conscription system preserved an appearance of inter-provincial balance while operating unevenly at the county scale for strategic and political reasons.
As the first study to apply quantitative inequality indicators and spatial approaches to premodern Korean military institutions, this analysis provides nationwide numerical evidence for regional imbalances and reveals how digital history methods can transform premodern texts into structured data to map equity and inequality beyond the reach of conventional scholarship.
As the first study to apply quantitative inequality indicators and spatial approaches to premodern Korean military institutions, this analysis provides nationwide numerical evidence for regional imbalances and reveals how digital history methods can transform premodern texts into structured data to map equity and inequality beyond the reach of conventional scholarship.
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