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|a 1018-5941
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|a Ari, Anil.
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|a Structural Reforms and Economic Growth :
|b A Machine Learning Approach /
|c Anil Ari, Gabor Pula, Liyang Sun.
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|a Washington, D.C. :
|b International Monetary Fund,
|c 2022.
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|a 1 online resource (32 pages)
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|a IMF Working Papers
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|a <strong>Off-Campus Access:</strong> No User ID or Password Required
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|a <strong>On-Campus Access:</strong> No User ID or Password Required
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|a Electronic access restricted to authorized BRAC University faculty, staff and students
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|a 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.
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|a Mode of access: Internet
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|a Computable and Other Applied General Equilibrium Models
|2 imf
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|a Institutions and Growth
|2 imf
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|a Institutions and the Macroeconomy
|2 imf
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|a Large Data Sets
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|a Quantitative Policy Modeling
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|a Pula, Gabor.
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|a Sun, Liyang.
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|a IMF Working Papers; Working Paper ;
|v No. 2022/184
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|z Full text available on IMF
|u https://elibrary.imf.org/openurl?genre=journal&issn=1018-5941&volume=2022&issue=184
|z IMF e-Library
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