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The Price of Purity: Willingness to Pay for Air and Water Purification Technologies in Rajasthan, India

Abstract

Diarrheal illnesses and acute respiratory infections are among the top causes for premature death and disability across the developing world, and adoption of various technologies for avoiding these illnesses remains extremely low. We exploit data from a unique contingent valuation experiment to consider whether households in rural Rajasthan are unwilling to make investments in “domain-specific” environmental health technologies when faced with health risks in multiple domains. Results indicate that demand for water-related risk reductions is higher on average than demand for air-related risk reduction. In addition, households’ private health benefits from mitigating diarrheal (respiratory) disease risks are higher (no different) when community-level air pollution risks, rather than community-level water pollution risks, have previously been mitigated. This asymmetric response cannot fully be explained by survey order effects or embedding, but rather suggests that that the broader health environment and the salience of particular risks may be important in households’ decision to adopt environmental health technologies.

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Notes

  1. 1.

    POU water treatment technologies have gained prominence largely due to the continuing high reliance in many rural and low-income settings on community sources located outside the home. Water obtained from such sources varies in quality, and there is strong empirical evidence that water safety can be compromised between the time of collection and the time of consumption due to transport and storage, even when source water quality is fairly good. For example, Gasana et al. (2002) find that water tested at various source points indicated no risk to consumers, but that contamination levels were significantly higher at the point of use; there is similar evidence from other settings (Kremer et al. 2011; Rufener et al. 2010). In addition, investments in water infrastructure may also crowd out preexisting private prevention behavior, offsetting expected health benefits (Bennett 2012; Jessoe 2013; Jeuland et al. 2015b).

  2. 2.

    About 20% of households reported no instances of cough or cold in the two weeks prior to the survey, while around half reported no instances of diarrhea over the same period. Averaging over all households, approximately 9 and 4% of monthly expenditure were spent treating the most recent bouts of cough/cold and diarrhea, respectively.

  3. 3.

    The remainder (approximately 75%) stated that acute respiratory infections and diarrheal diseases are “equally dangerous.”

  4. 4.

    These patterns are entirely consistent when different groups of variables are excluded from model (3) (results not shown).

  5. 5.

    Interestingly, neither of the water-related perceptions variables (IMPROVEDWATER and UNSAFEWATER) are statistically significant predictors of the water purifier purchase decision, although they do have the “expected” signs. These results for perception variables suggest that clean-fuel use and consideration that respiratory diseases are particularly dangerous may simply be correlated with WTP for environmental health improvements in general, rather than indicating something specific about differential demand for respiratory versus diarrheal disease risk reductions.

  6. 6.

    We assume an exponential demand curve primarily for empirical tractability and, in particular, to enable comparability of our estimates with the related literature that looks at WTP for environmental quality in low-income settings (e.g., Orgill et al. 2013). The results we present are qualitatively unchanged if we assume that demand for purification technologies is linear.

  7. 7.

    As the dichotomous variable WATERFIRST captures the assignment of a scenario that is “mirrored” across technologies, the appropriate comparisons to make across the WTP estimates for air and water purifier are as follows: Air Purifier, \({WATERFIRST}=1\) with Water Purifier, \({WATERFIRST}=0\); and Air Purifier, \({WATERFIRST}=0\) with Water Purifier, \({WATERFIRST}=1\).

  8. 8.

    Specifically, we bootstrap at the village level by randomly sampling villages from our original study sample. For each of 1000 bootstrapped samples of villages, we then repeat our analyses to obtain 1000 sets of results.

  9. 9.

    Because the disease frames were perfectly correlated with the order in which alternatives are presented to respondents, our interpretation of the results depends on the assumption that respondents took the information about the community-level intervention into account when answering the subsequent questions about the air and water purifier (though this is also of course a general critique of any stated preference design that assumes that respondents consider the information with which they are provided). As noted by a reviewer, respondents may have disregarded this information. In this specific instance, our results would not highlight the salience of complementarities in disease-risk reductions in respondents’ minds. However, they would still provide informational value on (i) the relative importance households in rural India place on air- and water-based risk reductions; and (ii) an upper bound for their WTP for technologies that achieve these risk reductions on their own (because their budget constraints bind less). That said, we do not believe respondents ignored the information about the community-level intervention. That we observe the typical “order effect” result seen in the literature (whereby respondents are more likely to accept a price offer for alternatives presented earlier in the sequence) consistently only for the water purifier suggests that disease frame and, in turn, complementarities in disease-risk reductions matter. A more complete experiment would also have randomly varied the inclusion of the community-level risk reduction programs, but budget and sample size limitations prevented us from including the two additional arms that would have been required to test this formally.

  10. 10.

    For instance, a nominal fixed surcharge assessed as part of a household’s water tariffs may, counterintuitively, make households more likely to connect to the water network (and reduce water-quality-related health risks) if proceeds from this surcharge are used to drive efforts to reduce complementary health risks in other domains. Utility payments, however, are not relevant in the context of rural Rajasthan, where a more straightforward payment vehicle–charging fees directly for the provision of specific risk-reducing technologies–may be most appropriate. We note, however, that at the community scale, even these simple payment approaches may require efforts to overcome collective action problems related to collective payments.

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Correspondence to Marc Jeuland.

Additional information

We thank the joint research program between Duke University and the Indian Institute of Management in Udaipur, which provided financial support for this study. Seva Mandir and Chitra provided support with field data collection. We also thank seminar participants at Duke University and the University of North Carolina Water and Health Conference for valuable comments. Usmani is grateful to the Duke Global Health Institute and the Duke University Energy Initiative for their generous support. Any remaining errors are our own.

Appendices

Appendix

Survey Scenarios

The following pages, extracted from the English translation of our survey instrument, provide additional insight about the two scenarios—Option “A” and “B”—randomly allocated to survey respondents.

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Shannon, A.K., Usmani, F., Pattanayak, S.K. et al. The Price of Purity: Willingness to Pay for Air and Water Purification Technologies in Rajasthan, India. Environ Resource Econ 73, 1073–1100 (2019). https://doi.org/10.1007/s10640-018-0290-4

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Keywords

  • Household air pollution
  • Diarrheal diseases
  • Technology adoption
  • Contingent valuation

JEL Classification

  • Q51
  • Q53
  • Q56