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Applying Machine Learning Algorithms to Predict the Size of the Informal Economy

Author

Listed:
  • Joao Felix
  • Michel Alexandre
  • Gilberto Tadeu Lima
Abstract
The use of machine learning models and techniques to predict economic variables has been growing lately, motivated by their better performance when compared to that of linear models. Although linear models have the advantage of considerable interpretive power, efforts have intensified in recent years to make machine learning models more interpretable. In this paper, tests are conducted to determine whether models based on machine learning algorithms have better performance relative to that of linear models for predicting the size of the informal economy. The paper also explores whether the determinants of such size detected as the most important by machine learning models are the same as those detected in the literature based on traditional linear models. For this purpose, observations were collected and processed for 122 countries from 2004 to 2014. Next, eleven models (four linear and seven based on machine learning algorithms) were used to predict the size of the informal economy in these countries. The relative importance of the predictive variables in determining the results yielded by the machine learning algorithms was calculated using Shapley values. The results suggest that (i) models based on machine learning algorithms have better predictive performance than that of linear models and (ii) the main determinants detected through the Shapley values coincide with those detected in the literature using traditional linear models.

Suggested Citation

  • Joao Felix & Michel Alexandre & Gilberto Tadeu Lima, 2023. "Applying Machine Learning Algorithms to Predict the Size of the Informal Economy," Working Papers, Department of Economics 2023_10, University of São Paulo (FEA-USP), revised 11 Sep 2023.
    • Handle: RePEc:spa:wpaper:2023wpecon10
    as

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    File URL: http://www.repec.eae.fea.usp.br/documentos/Felix_Alexandre_Lima_10WP.pdf
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    References listed on IDEAS

    as
    1. Friedrich Schneider & Robert Klinglmair, 2004. "Shadow economies around the world: what do we know?," Economics working papers 2004-03, Department of Economics, Johannes Kepler University Linz, Austria.
    2. Schneider, Friedrich, 2005. "Shadow economies around the world: what do we really know?," European Journal of Political Economy, Elsevier, vol. 21(3), pages 598-642, September.
    3. Gabriel Ulyssea, 2020. "Informality: Causes and Consequences for Development," Annual Review of Economics, Annual Reviews, vol. 12(1), pages 525-546, August.
    4. James Alm & Abel Embaye, 2013. "Using Dynamic Panel Methods to Estimate Shadow Economies Around the World, 1984-2006," Working Papers 1303, Tulane University, Department of Economics.
    5. Ranis, Gustav & Stewart, Frances, 1999. "V-Goods and the Role of the Urban Informal Sector in Development," Economic Development and Cultural Change, University of Chicago Press, vol. 47(2), pages 259-288, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    : Informal economy; machine learning; linear models; Shapley values;
    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
    • O17 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Formal and Informal Sectors; Shadow Economy; Institutional Arrangements

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