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Predicting household resilience with machine learning: preliminary cross-country tests

Author

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  • Alessandra Garbero

    (International Fund for Agricultural Development)

  • Marco Letta

    (Sapienza University of Rome)

Abstract

Using a unique cross-country sample from 10 impact evaluations of development projects, we test the out-of-sample performance of machine learning algorithms in predicting non-resilient households, where resilience is a subjective metrics defined as the perceived ability to recover from shocks. We report preliminary evidence of the potential of these data-driven techniques to identify the main predictors of household resilience and inform the targeting of resilience-oriented policy interventions.

Suggested Citation

  • Alessandra Garbero & Marco Letta, 2022. "Predicting household resilience with machine learning: preliminary cross-country tests," Empirical Economics, Springer, vol. 63(4), pages 2057-2070, October.
    • Handle: RePEc:spr:empeco:v:63:y:2022:i:4:d:10.1007_s00181-022-02199-4
    DOI: 10.1007/s00181-022-02199-4
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    References listed on IDEAS

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    Cited by:

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    2. de Blasio, Guido & D'Ignazio, Alessio & Letta, Marco, 2022. "Gotham city. Predicting ‘corrupted’ municipalities with machine learning," Technological Forecasting and Social Change, Elsevier, vol. 184(C).

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    More about this item

    Keywords

    Resilience; Machine learning; Classification; Targeting; Predictive analytics;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development

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