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The impact of perceptions in averting-decision models: An application of the special regressor method to drinking water choices

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  • Bontemps, Christophe
  • Nauges, Céline

Abstract

Individuals are commonly surveyed about their perception or assessment of risk and these variables are often used to explain individuals’ actions to protect themselves against these risks. Perceptions appear as endogenous variables in traditional theoretical averting-decision models but, quite surprisingly, endogeneity of perceived risk is not always controlled for in empirical studies. In this article, we present different models that can be useful to the practitioner when estimating binary averting-decision models featuring an endogenous discrete variable (such as risk perception). In particular we compare the traditional bivariate probit model with the special regressor model, which is less well known and relies on a different set of assumptions. In the empirical illustration using household data from Australia, Canada, and France, we study how the perceived health impacts of tap water affect a household’s decision to drink water from the tap. Individuals’ perceptions are found to be endogenous and significant for all models, but the estimated marginal effect is sensitive to the model and underlying assumptions. The special regressor appears to be a valuable alternative to the more common bivariate probit model.

Suggested Citation

  • Bontemps, Christophe & Nauges, Céline, 2014. "The impact of perceptions in averting-decision models: An application of the special regressor method to drinking water choices," TSE Working Papers 14-537, Toulouse School of Economics (TSE).
  • Handle: RePEc:tse:wpaper:28776
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    References listed on IDEAS

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    1. John C. Whitehead, 2006. "Improving Willingness to Pay Estimates for Quality Improvements through Joint Estimation with Quality Perceptions," Southern Economic Journal, Southern Economic Association, vol. 73(1), pages 100-111, July.
    2. Maurin, Eric, 2002. "The impact of parental income on early schooling transitions: A re-examination using data over three generations," Journal of Public Economics, Elsevier, vol. 85(3), pages 301-332, September.
    3. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, January.
    4. BONTEMPS Christophe & NAUGES Céline, 2006. "Carafe ou bouteille ? Le rôle de la qualité de l'environnement dans la décision du consommateur," LERNA Working Papers 06.07.200, LERNA, University of Toulouse.
    5. Yingying Dong & Arthur Lewbel, 2015. "A Simple Estimator for Binary Choice Models with Endogenous Regressors," Econometric Reviews, Taylor & Francis Journals, vol. 34(1-2), pages 82-105, February.
    6. Lewbel, Arthur & Schennach, Susanne M., 2007. "A simple ordered data estimator for inverse density weighted expectations," Journal of Econometrics, Elsevier, vol. 136(1), pages 189-211, January.
    7. Konishi, Yoshifumi & Adachi, Kenji, 2011. "A framework for estimating willingness-to-pay to avoid endogenous environmental risks," Resource and Energy Economics, Elsevier, vol. 33(1), pages 130-154, January.
    8. Bryan J. Hubbell & Jeffrey L. Jordan, 2000. "Joint Production and Averting Expenditure Measures of Willingness to Pay: Do Water Expenditures Really Measure Avoidance Costs?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 82(2), pages 427-437.
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    Citations

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    Cited by:

    1. Lanz, Bruno & Provins, Allan, 2017. "Using averting expenditures to estimate the demand for public goods: Combining objective and perceived quality," Resource and Energy Economics, Elsevier, vol. 47(C), pages 20-35.
    2. repec:eee:ecolec:v:141:y:2017:i:c:p:87-94 is not listed on IDEAS
    3. Bruno Lanz & Allan Provins, 2014. "The demand for tap water quality: Survey evidence on water hardness and aesthetic quality," CIES Research Paper series 23-2014, Centre for International Environmental Studies, The Graduate Institute.
    4. Whelan, Adele & McGuinness, Seamus, 2017. "Does a satisfied student make a satisfied worker?," Papers WP561, Economic and Social Research Institute (ESRI).
    5. Nauges, Céline & Wheeler, Sarah Ann, 2017. "The Complex Relationship Between Households' Climate Change Concerns and Their Water and Energy Mitigation Behaviour," Ecological Economics, Elsevier, vol. 141(C), pages 87-94.
    6. Gautam, Tej K. & Paudel, Krishna P. & Guidry, Kurt M., 2017. "Willingness To Pay For Irrigation Water In Louisiana," 2017 Annual Meeting, February 4-7, 2017, Mobile, Alabama 252821, Southern Agricultural Economics Association.
    7. Ahamad, Mazbahul & Gustafson, Christopher & VanWormer, Elizabeth, 2016. "Ex-post Livestock Diseases, and Pastoralists' Averting Decisions in Tanzania," 2016 Annual Meeting, July 31-August 2, 2016, Boston, Massachusetts 235764, Agricultural and Applied Economics Association.
    8. Bontemps, Christophe & Nauges, Céline, 2017. "Endogenous Variables in Binary Choice Models: Some Insights for Practitioners," TSE Working Papers 17-855, Toulouse School of Economics (TSE).
    9. Wang, Pengfei, 2017. "Syndication and Foreignness: Venture Capital Investments in Emerging and Developed Markets," Journal of International Management, Elsevier, vol. 23(1), pages 1-15.

    More about this item

    Keywords

    Discrete choice; special regressor; endogeneity; water consumption;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling

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