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What Really Drives Economic Growth in Sub-Saharan Africa? Evidence from The Lasso Regularization and Inferential Techniques

Author

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  • Isaac K. Ofori

    (University of Insubria, Varese, Italy)

  • Camara K. Obeng

    (University of Cape Coast, Cape Coast, Ghana)

  • Simplice A. Asongu

    (Yaoundé, Cameroon)

Abstract

The question of what really drives economic growth in sub-Saharan Africa (SSA) has been debated for many decades now. However, there is still a lack of clarity on variables crucial for driving growth as prior contributions have been executed at the backdrop of preferential selection of covariates in the midst several of potential drivers of economic growth. The main challenge with such contribution is that even tenuous variables may be deemed influential under some model specifications and assumptions. To address this and inform policy appropriately, we train algorithms for four machine learning regularization techniques— the Standard lasso, the Adaptive lasso, the Minimum Schwarz Bayesian information criterion lasso, and the Elasticnet to study patterns in a dataset containing 113 covariates and identify the key variables affecting growth in SSA. We find that only 7 covariates are key for driving growth in SSA. Estimates of these variables are provided by running the lasso inferential techniques of double-selection linear regression, partialing-out lasso linear regression, and partialing-out lasso instrumental variable regression. Policy recommendations are also provided in line with the AfCFTA and the green growth agenda of the region.

Suggested Citation

  • Isaac K. Ofori & Camara K. Obeng & Simplice A. Asongu, 2022. "What Really Drives Economic Growth in Sub-Saharan Africa? Evidence from The Lasso Regularization and Inferential Techniques," Working Papers 22/061, European Xtramile Centre of African Studies (EXCAS).
  • Handle: RePEc:exs:wpaper:22/061
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    2. Azam, Muhammad & Hunjra, Ahmed Imran & Taskin, Dilvin & Al-Faryan, Mamdouh Abdulaziz Saleh, 2023. "Role of ethnic conflicts, regularization and natural resource abundance in sustainable development," Resources Policy, Elsevier, vol. 85(PB).

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    More about this item

    Keywords

    Economic growth; Elasticnet; Lasso; Machine learning; Partialing-out IV regression; sub-Saharan Africa;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • O4 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity
    • O55 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Africa

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