Measuring Remoteness Using a Data-Driven Approach
Stacey Chen (),
Yu-Kuan Chen and
Additional contact information
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
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
New Economics Papers: this item is included in nep-dcm
References: View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
https://grips.repo.nii.ac.jp/?action=repository_ac ... bute_id=20&file_no=1 (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ngi:dpaper:17-03
Access Statistics for this paper
More papers in GRIPS Discussion Papers from National Graduate Institute for Policy Studies Contact information at EDIRC.
Bibliographic data for series maintained by ().