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Toward Proactive Policy Design: Identifying 'To-Be' Energy-Poor Households Using Shap for Early Intervention

Author

Listed:
  • Budría, Santiago

    (Universidad Nebrija)

  • Fermé, Eduardo

    (University of Madeira)

  • Freitas, Diogo Nuno

    (University of Madeira)

Abstract

Identifying at-risk populations is essential for designing effective energy poverty interventions. Using data from the HILDA Survey, a longitudinal dataset representative of the Australian population, and a multidimensional index of energy poverty, we develop a machine learning model combined with SHAP (SHapley Additive exPlanations) values to document the short- and long-term effects of individual and contextual factors—such as income, energy prices, and regional conditions—on future energy poverty outcomes. The findings emphasize the importance of policies focused on income stability and may be used to shift the policy focus from reactive measures, which address existing poverty, to preventive strategies that target households showing early signs of vulnerability.

Suggested Citation

  • Budría, Santiago & Fermé, Eduardo & Freitas, Diogo Nuno, 2025. "Toward Proactive Policy Design: Identifying 'To-Be' Energy-Poor Households Using Shap for Early Intervention," IZA Discussion Papers 17669, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp17669
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    More about this item

    Keywords

    Energy poverty; panel data; explainable AI; time-series analysis; public policy; temporal dynamics; feature importance;
    All these keywords.

    JEL classification:

    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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