Borrower-based macroprudential measures and credit growth: How biased is the existing literature?

MALOVANÁ, Simona, Martin HODULA, Zuzana GRIC and Jozef BAJZÍK. Borrower-based macroprudential measures and credit growth: How biased is the existing literature? Journal of Economic Surveys. ENGLAND: John Wiley & Sons Ltd, 2024, p. 1-37. ISSN 0950-0804. Available from: https://dx.doi.org/10.1111/joes.12608.

Basic information

• Original name: Borrower-based macroprudential measures and credit growth: How biased is the existing literature?
• Authors: MALOVANÁ, Simona, Martin HODULA, Zuzana GRIC and Jozef BAJZÍK.
• Edition: Journal of Economic Surveys, ENGLAND, John Wiley & Sons Ltd, 2024, 0950-0804.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 50206 Finance
• Country of publisher: United Kingdom of Great Britain and Northern Ireland
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 5.300 in 2022
• Organization unit: Faculty of Economics and Administration
• Doi: http://dx.doi.org/10.1111/joes.12608
• UT WoS: 001137177000001
• Keywords in English: Bayesian model averaging; borrower-based measures; macroprudential policy; meta-analysis; publication bias
• Tags: International impact, Reviewed

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.