Skip to main content
Log in

Impact of macroprudential policy on economic growth in Indonesia: a growth-at-risk approach

  • Original Paper
  • Published:
Eurasian Economic Review Aims and scope Submit manuscript

Abstract

Macroprudential policy yields important benefits in terms of preventing and mitigating systemic risk, but it can also have an impact on economic growth, particularly on the left tail of the growth distribution. In this context, policymakers need to consider the effects of macroprudential policies on the entire growth distribution, and not only on average growth. The growth-at-risk (GaR) approach represents a useful framework for such an assessment. This paper describes the use of the GaR method and illustrates its implementation for assessing the impact of macroprudential policy on GaR in Indonesia. As a first step, I select 26 macrofinancial variables that are relevant for the Indonesian economy and build three partitions that capture financial conditions, macrofinancial vulnerabilities and other relevant factors. Results from quantile regressions have important policy implications, suggesting that an early tightening of macroprudential policy would reduce downside risks to Indonesia’s gross domestic product (GDP) growth by increasing the resilience of the financial system. Results further show that a materialization of risk, stemming from either a loosening of financial conditions, an increase of macrofinancial vulnerabilities or a deterioration of the macroeconomic environment have important effects on Indonesia’s GDP growth distribution and particularly on the left tail of the distribution, which represents the GaR. Under each of these scenarios, a tightening or loosening of the macroprudential stance, depending on the underlying vulnerabilities, yields high benefits in terms of improving Indonesia’s GaR, which range from 0.06 and 0.14 percentage points.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Source: Author’s calculations

Fig. 2

Source: Author’s calculations

Fig. 3

Source: Author’s calculations

Fig. 4

Source: Author’s calculations

Fig. 5

Source: Author’s calculations

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Notes

  1. This is done by assessing whether the respective observation is lower than (-1.5 * IQR) or higher than (1.5 * IQR); where IQR is the interquartile range and is defined as the difference between the third quartile and the first quartile.

  2. Refer to Wilcoxon et al. (1963) for a discussion.

  3. These include the following types of measures: countercyclical capital buffer; capital conservation buffer; capital requirements for banks (i.e., risk weights, systemic risk buffer, and minimum capital requirements); limits on bank leverage; liquidity requirements; and capital and liquidity surcharges applicable to domestic systemically important financial institutions (SIFIs).

  4. Refer to Azzalini and Capitanio (2003) for a detailed description of the skew t-distribution.

References

Download references

Acknowledgements

The author would like to thank the Managing Editor, Hakan Danis, and two anonymous referees for their useful comments and suggestions that helped improve the manuscript.

Funding

The author declares that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Raluca Maran.

Ethics declarations

Conflict of interest

The author has no relevant financial or non-financial interests to disclose.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publish

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix A

See Table 7.

Table 7 Description of partitions, variables, and data sources

Appendix B

See Table 8.

Table 8 Regression coefficients for the effect of FINC, MAF, OTH, MAP and their interaction on Indonesia’s GDP growth across different quantiles and horizons

Appendix C

See Tables 9, 10, 11 and 12.

Table 9 Robustness exercise #1: regression coefficients for the effect of FINC, MAF, OTH, MAP on Indonesia’s GDP growth across different quantiles and horizons with an exogenous MAP index
Table 10 Robustness exercise #2: regression coefficients for the effect of FINC, MAF, OTH, MAP on Indonesia’s industrial production growth rate across different quantiles
Table 11 Robustness exercise #3: regression coefficients for the effect of FINC, MAF, OTH, MAP on Indonesia’s GDP growth across different quantiles and horizons with inflation as an additional control variable
Table 12 Robustness exercise #4: regression coefficients for the effect of FINC, MAF, OTH, MAP on Indonesia’s GDP growth across different quantiles and horizons with lags of the dependent variable

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maran, R. Impact of macroprudential policy on economic growth in Indonesia: a growth-at-risk approach. Eurasian Econ Rev 13, 575–613 (2023). https://doi.org/10.1007/s40822-023-00236-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40822-023-00236-w

Keywords

JEL Classification

Navigation