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Labor Mobility and Loan Origination

Published online by Cambridge University Press:  11 May 2023

Sumit Agarwal
Affiliation:
National University of Singapore Business School [email protected]
Yupeng Lin
Affiliation:
National University of Singapore Business School [email protected]
Yunqi Zhang
Affiliation:
Nankai University School of Finance [email protected]
Zilong Zhang*
Affiliation:
Zhejiang University School of Economics
*
[email protected] (corresponding author)
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Abstract

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We find that mortgage loans originated after the adoption of the inevitable disclosure doctrine (IDD; a mechanism discouraging loan officers’ labor mobility) have a lower default probability, a higher loan modification rate, and a lower foreclosure rate. These effects are unaccompanied by any reduction in loan supply and contribute to more stable housing prices. Using the adoption of the Uniform Trade Secrets Act as an alternative identification generates consistent results. Overall, our findings suggest that restricting loan officers’ labor mobility leads to better ex ante screening and ex post monitoring, improving the origination efficiency for U.S. residential mortgage loans.

Type
Research Article
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

Footnotes

The authorship is in alphabetical order, and the authors contributed equally. We thank Guodong Chen, Haoyu Gao, Jianwen Li, Yan Liu, Changcheng Song, and seminar participants at the 2018 AREUEA International Conference, 2021 AsRES-GCREC-AREUEA Joint Conference, and 2019 China Finance Annual Meeting for the helpful comments. We are grateful to Jarrad Harford (the editor) and the anonymous reviewer for their valuable comments and suggestions. This research is supported by the National Natural Science Foundation of China (Grant No. 72203112), the MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Grant No. 20YJC790183), and Research Grants Council of Hong Kong (Grant No. 11503318). Lin acknowledges research support from the National University of Singapore and MOE Tier 1 Grant (Grant No. R-521-000-044-115).

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