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<p>Household-level water treatment is a vital health investment in developing economies, where access to safe drinking water remains limited. However, adoption, proper use, and sustained use of water treatment technologies remain low. This study investigates how risk and time preferences influence household investment in water treatment, using survey and incentivized lab-in-the-field experimental data collected in 2024 from a rural district in Tigray, northern Ethiopia. To address identification challenges, the analysis employs a control function approach to account for potential endogeneity, a double lasso machine learning routine for model selection, Oster’s coefficient stability test to assess unobserved heterogeneity, and enumerator dummies to correct for structural measurement errors. The findings reveal that risk-averse, impatient, and impulsive individuals are significantly less likely to adopt water treatment practices. While the study establishes a robust association with both risk and time preferences, external validity is confirmed only for time preferences, not for risk preferences. These insights inform targeted interventions to improve water treatment adoption.</p>

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Time preferences, risk preferences and the adoption of household level water treatment in Rural Tigray, Northern Ethiopia

  • 期刊论文

  • 发表日期：

    2026年3月9日

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  • Taddese Mezgebo,
  • Zenebe Gebreegziabher,
  • Haftom Bayray Kahsay,
  • Abrha Megos Meressa,
  • Lemlem Weldegerima Gebremariam,
  • Valentino Marini Govigli,
  • Marco Setti

摘要

Household-level water treatment is a vital health investment in developing economies, where access to safe drinking water remains limited. However, adoption, proper use, and sustained use of water treatment technologies remain low. This study investigates how risk and time preferences influence household investment in water treatment, using survey and incentivized lab-in-the-field experimental data collected in 2024 from a rural district in Tigray, northern Ethiopia. To address identification challenges, the analysis employs a control function approach to account for potential endogeneity, a double lasso machine learning routine for model selection, Oster’s coefficient stability test to assess unobserved heterogeneity, and enumerator dummies to correct for structural measurement errors. The findings reveal that risk-averse, impatient, and impulsive individuals are significantly less likely to adopt water treatment practices. While the study establishes a robust association with both risk and time preferences, external validity is confirmed only for time preferences, not for risk preferences. These insights inform targeted interventions to improve water treatment adoption.