Borrower‐based macroprudential measures and credit growth: How biased is the existing literature?
Simona Malovaná,
Martin Hodula,
Zuzana Gric and
Josef Bajzik
Journal of Economic Surveys, 2025, vol. 39, issue 1, 66-102
Abstract:
This paper analyzes over 700 estimates from 34 studies on the impact of borrower‐based measures (such as loan‐to‐value, debt‐to‐income, and debt‐service‐to‐income ratios) on bank loan provision. Our dataset reveals notable fragmentation in the literature concerning variable transformations, methods, and estimated coefficients. We run a meta‐analysis on a subsample of 422 semi‐elasticities from 23 studies employing a consistent estimation framework to draw an economic interpretation. We confirm strong publication bias, particularly against positive and statistically insignificant estimates. After correcting for this bias, the effect indicates a credit growth reduction of −0.6 to −1.1 percentage points following the occurrence of borrower‐based measures, significantly lower than the unadjusted simple mean effect of the collected estimates. Additionally, our study examines the contexts of these estimates, finding that beyond publication bias, model specification and estimation method are vital in explaining the variation in reported coefficients.
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
https://doi.org/10.1111/joes.12608
Related works:
Working Paper: Borrower-Based Macroprudential Measures and Credit Growth: How Biased is the Existing Literature? (2022) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bla:jecsur:v:39:y:2025:i:1:p:66-102
Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0950-0804
Access Statistics for this article
More articles in Journal of Economic Surveys from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().