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Cited by 526

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The draw of home: How teachers' preferences for proximity disadvantage urban schools

Boyd, Donald, Lankford, Hamilton, Loeb, Susanna, Wyckoff, James·2005·Journal of Policy Analysis and Management

Abstract

This paper explores a little-understood aspect of labor markets, their spatial geography. Using data from New York State, we find teacher labor markets to be geographically very small. Teachers express preferences to teach close to where they grew up and, controlling for proximity, they prefer areas with characteristics similar to their hometown. We discuss implications of these preferences for the successful recruitment of teachers, including the potential benefits of local recruiting and training. We also discuss implications for the modeling of teacher labor markets, including the possible biases that arise in estimates of compensating differentials when distance is omitted from the analyses. This study contributes to the literature on the geography of labor markets more generally by employing data on residential location during childhood instead of current residence, which may be endogenous to job choice. © 2005 by the Association for Public Policy Analysis and Management.

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Metadata

Authors
Boyd, D., Lankford, H., Loeb, S., & Wyckoff, J.
Venue
Journal of Policy Analysis and Management
Publisher
Wiley
Published
2004
Volume
24
Issue
1
Pages
113-132
ISSN
  • 0276-8739 (ISSN-L)
  • 1520-6688
OpenAlex
W3021007182
Cited by
526
References
0
FWCI
63.52
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The draw of home: How teachers' preferences for proximity disadvantage urban schools. https://app.cochat.ai/papers/10.1002/pam.20072
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Boyd, D., Lankford, H., Loeb, S., & Wyckoff, J. (2005). The draw of home: How teachers' preferences for proximity disadvantage urban schools. Journal of Policy Analysis and Management. https://doi.org/10.1002/pam.20072

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.