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© 2022 International Monetary Fund
WP/22/184
IMF Working Paper
European Department
Structural Reforms and Economic Growth: A Machine Learning Approach
Prepared by Anil Ari, Gabor Pula and Liyang Sun
Authorized for distribution by Ivanna Vladkova Hollar
September 2022
IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
ABSTRACT: The qualitative and granular nature of most structural indicators and the variety in data sources poses difficulties for consistent cross-country assessments and empirical analysis. We overcome these issues by using a machine learning approach (the partial least squares method) to combine a broad set of crosscountry structural indicators into a small number of synthetic scores which correspond to key structural areas, and which are suitable for consistent quantitative comparisons across countries and time. With this newly constructed dataset of synthetic structural scores in 126 countries between 2000–2019, we establish stylized facts about structural gaps and reforms, and analyze the impact of reforms targeting different structural areas on economic growth. Our findings suggest that structural reforms in the area of product, labor and financial markets as well as the legal system have a significant impact on economic growth in a 5-year horizon, with one standard deviation improvement in one of these reform areas raising cumulative 5-year growth by 2 to 6 percent. We also find synergies between different structural areas, in particular between product and labor market reforms.
RECOMMENDED CITATION: Ari, A., Pula, G., & Sun, L. (2022). Structural Reforms and Economic Growth: A Machine Learning Approach. IMF Working Paper, WP/22/184
| JEL Classification Numbers: | E02, C54, C55, D58, O43, O47 |
| Keywords: | Structural reforms, institutions, economic growth |
| Author’s E-Mail Address: | aari@imf.org, gpula@imf.org, lsun20@cemfi.es |
| JEL Classification Numbers: | E02, C54, C55, D58, O43, O47 |
| Keywords: | Structural reforms, institutions, economic growth |
| Author’s E-Mail Address: | aari@imf.org, gpula@imf.org, lsun20@cemfi.es |
Title Page
WORKING PAPERS
Structural Reforms and Economic Growth: A Machine Learning Approach
Prepared by Anil Ari, Gabor Pula and Liyang Sun1
Contents
I. Introduction
II. Structural Indicators
A. Data overview
B. Synthetic structural scores via Partial Least Squares
C. PLS estimation procedure
D. Synthetic structural score as the predicted value from the PLS model
III. Structural Indicators
A. Impact of structural reforms on growth
B. Synergies of structural reforms on growth
C. The role of structural reforms during crises
IV. Conclusions
References
Appendix
A. List of structural indicators
B. Description of imputation for missing indicators
C. Comparison between the PLS structural score and simple-average score
The authors are grateful to Ivanna Vladkova Hollar, Ippei Shibata, Marina Mendes Tavares and seminars participants at the IMF for helpful comments and suggestions. Excellent research assistance was provided by Samuel Victor Romero Martinez. The dataset of synthetic structural scores is available upon request from the authors. All errors are our own.