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.

Predicting household resilience with machine learning: preliminary cross-country tests / Garbero, Alessandra; Letta, Marco. - In: EMPIRICAL ECONOMICS. - ISSN 0377-7332. - 63:4(2022), pp. 2057-2070. [10.1007/s00181-022-02199-4]

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

Garbero, Alessandra;Letta, Marco
2022

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.
2022
Resilience; Machine learning; Classification; Targeting; Predictive analytics
01 Pubblicazione su rivista::01a Articolo in rivista
Predicting household resilience with machine learning: preliminary cross-country tests / Garbero, Alessandra; Letta, Marco. - In: EMPIRICAL ECONOMICS. - ISSN 0377-7332. - 63:4(2022), pp. 2057-2070. [10.1007/s00181-022-02199-4]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1670739
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