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Direct Data Manipulation for Local Decision Analysis as Applied to the Problem of Arsenic in Drinking Water from Tube Wells in Bangladesh

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  • Andrew Gelman
  • Matilde Trevisani
  • Hao Lu
  • Alexander Van Geen

Abstract

A wide variety of tools are available, both parametric and nonparametric, for analyzing spatial data. However, it is not always clear how to translate statistical inferences into decision recommendations. This article explores the possibilities of estimating the effects of decision options using very direct manipulation of data, bypassing formal statistical analysis. We illustrate with the application that motivated this research, a study of arsenic in drinking water in nearly 5,000 wells in a small area in rural Bangladesh. We estimate the potential benefits of two possible remedial actions: (1) recommendations that people switch to nearby wells with lower arsenic levels; and (2) drilling new community wells. We use simple nonparametric clustering methods and estimate uncertainties using cross‐validation.

Suggested Citation

  • Andrew Gelman & Matilde Trevisani & Hao Lu & Alexander Van Geen, 2004. "Direct Data Manipulation for Local Decision Analysis as Applied to the Problem of Arsenic in Drinking Water from Tube Wells in Bangladesh," Risk Analysis, John Wiley & Sons, vol. 24(6), pages 1597-1612, December.
    • Handle: RePEc:wly:riskan:v:24:y:2004:i:6:p:1597-1612
    DOI: 10.1111/j.0272-4332.2004.00553.x
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    Cited by:

    1. Lori Bennear & Alessandro Tarozzi & Alexander Pfaff & H. B. Soumya & Kazi Matin Ahmed & Alexander van Geen, 2010. "Bright Lines, Risk Beliefs, and Risk Avoidance: Evidence from a Randomized Intervention in Bangladesh," Working Papers 10-77, Duke University, Department of Economics.
    2. Matthew Krupoff & Ahmed Mushfiq Mobarak & Alexander van Geen, 2020. "Evaluating Strategies to Reduce Arsenic Poisoning in South Asia: A View from the Social Sciences," Asian Development Review, MIT Press, vol. 37(2), pages 21-44, September.

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