Measuring Remoteness Using a Data-Driven Approach

Stacey Chen (), Yu-Kuan Chen and Huey-Min Wu
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Stacey Chen: National Graduate Institute for Policy Studies, Tokyo, Japan
Yu-Kuan Chen: Teach-for-Taiwan Association
Huey-Min Wu: National Academy for Educational Research, Taiwan

No 17-03, GRIPS Discussion Papers from National Graduate Institute for Policy Studies

Abstract: 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 datavailability. We propose to measure eremoteness f 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.

Pages: 29 pages
Date: 2017-05
New Economics Papers: this item is included in nep-dcm
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