## Structural Reforms and Economic Growth: A Machine Learning Approach

_IMF Working Papers, September 16, 2022_

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**Canonical URL:** [Structural Reforms and Economic Growth: A Machine Learning Approach](https://www.imf.org/en/publications/wp/issues/2022/09/15/structural-reforms-and-economic-growth-a-machine-learning-approach-523396)

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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

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### 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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_Source: https://www.imf.org/en/publications/wp/issues/2022/09/15/structural-reforms-and-economic-growth-a-machine-learning-approach-523396_