Structural Reforms and Economic Growth: A Machine Learning Approach

Anil Ari, Gabor Pula and Liyang Sun

No 2022/184, IMF Working Papers from International Monetary Fund

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 cross-country 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.

Keywords: Structural reforms; institutions; economic growth; C. PLS estimation procedure; machine learning approach; Gabor pula; Liyang sun; labor market composite; Business environment; Labor markets; Machine learning; Labor market reforms; Global (search for similar items in EconPapers)
Pages: 32
Date: 2022-09-16
New Economics Papers: this item is included in nep-big, nep-cmp, nep-edu and nep-gro
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