@techreport{oai:grips.repo.nii.ac.jp:00001566,
 author = {CHEN, Stacey H. and CHEN, Yu-Kuan and WU, Huey-Min},
 note = {https://www.grips.ac.jp/list/jp/facultyinfo/chen-stacey/, Datasets of schools or hospitals often include an urban.rural divide drawn by government. Such partition is typically determined by subjective thresholds for a few variables, such as access to transportation and local population size, leaving aside relevant factors despite data availability. We propose to measure ‘remoteness’ by mapping a comprehensive set of covariates onto a scalar, and define an objective score of remoteness using a standard selection model. We apply the proposed method to data from Taiwanese public elementary schools. Our method replaces 35% and 47% respectively of the current official list of ‘remote’ and ‘extra-remote’ campuses, shifting the remoteness designation to those furthest from train stations, having the highest teacher vacancy percentages, and located in the least populous areas with the least well-educated populations. The campus- and district-level variables used are publicly available and periodically updated in most advanced economies, and the statistical model can be easily implemented.},
 title = {Measuring Remoteness Using a Data-Driven Approach}
}