Structural Reforms and Economic Growth: A Machine Learning Approach
IMF Working Papers, September 16, 2022
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- Structural Reforms and Economic Growth: A Machine Learning Approach
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Bibliographic details
- Authors: Anil Ari, Gabor Pula, Liyang Sun
- Published: September 16, 2022
- Series: IMF Working Papers
- DOI: https://doi.org/10.5089/9798400219955.001
Methodology
- Applied a machine learning approach: the partial least squares method (PLS).
- Combined a broad set of cross-country structural indicators into a small number of synthetic scores corresponding to key structural areas.
- Synthetic scores designed for consistent quantitative comparisons across countries and time.
Dataset and Scope
- Newly constructed dataset of synthetic structural scores in 126 countries between 2000-2019.
- Dataset enables establishment of stylized facts about structural gaps and reforms and analysis of reform impacts on economic growth.
Key Findings on Growth Impact
- 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.
- A one standard deviation improvement in one of these reform areas raises cumulative 5-year growth by 2 to 6 percent.
- The analysis establishes stylized facts about structural gaps and reforms across the sample.
Synergies Between Reform Areas
- Evidence of synergies between different structural areas.
- In particular, synergies identified between product and labor market reforms.
Policy Implications and Recommendations
- Prioritize reforms in product markets, labor markets, financial markets, and the legal system to achieve measurable gains in cumulative 5-year growth.
- Consider sequencing and combination of reforms to exploit synergies, notably between product and labor market reforms.
- Use synthetic structural scores for consistent cross-country benchmarking and to guide reform prioritization over time.
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- Working Paper