Digging Deeper--Evidence on the Effects of Macroprudential Policies from a New Database
By Z. Alam, Adrian Alter, Jesse Eiseman, Gaston Gelos, Heedon Kang, Machiko Narita, E. Nier, Naixi Wang. IMF Working Papers, 2019
385 citations recorded by the source corpus.
This paper introduces a comprehensive database of macroprudential policies, which covers 134 countries from January 1990. Using a novel numerical indicator of the tightness of loan‐to‐value (LTV) regulations, we estimate the policy effects of incremental tightening in LTV limits, employing a propensity score–based method to address endogeneity concerns. The results point to economically significant and nonlinear effects on household credit, with a declining per‐unit impact for larger tightening measures. The analysis indicates that policy leakage effects could be a factor behind the nonlinear effects. We finally find that the side effects of macroprudential policies on consumption and output are relatively small.
Subfields: Accounting, Finance, Information Systems
View Paper: https://www.semanticscholar.org/paper/fa2e848d391cb8e2a1e08936df4d9eccebc22bc4
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Citations in the corpus
- Economic uncertainty and bank risk: Evidence from emerging economies (2020, methodology): “Finally, we select the data provided by the recent research of Alam et al. (2019), which covers a more comprehensive set of macroprudential tools than previous data sources. Similar to Cerutti et al. (2017a), the authors record the innovations of macroprudential policies by using the dummy-type vari”
- Macroprudential policy and bank systemic risk (2020, methodology): “This database has several advantages compared to existing databases (for example the IMF database as used in Lim et al. (2011), the BIS database as used in Kuttner and Shim (2016), the GMPI database as used in Cerutti et al. (2017) and the iMaPP database as used in Alam et al. (2019)).”
- The benefits are at the tail: Uncovering the impact of macroprudential policy on growth-at-risk (2020, background): “This approach h s been followed also by other aut ors aggregat ng macr prudentia meas r s with inor variations (Bo r et al., 2017; Kim and Mehrotra, 2018; Dupre and Ueberfeld , 2018; Alam et al., 2019).”