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Responses to Changes in Domestic Water Tariff Structures: A Latent Class Analysis on Household-Level Data from Granada, Spain

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Abstract

A problem that affects the estimation of water demand functions is the presence of unobserved individual heterogeneity, which means that a common demand function is unlikely to represent the behavior of all users. We implement Latent Class Models to estimate water demand functions for four groups of users who are classified according to their unobservable preferences. This more flexible approach makes it possible to distinguish four different response patterns to changes in the drivers of water use, including different price elasticities. These results should be of particular interest to regulators who would like to tailor water demand management policy to heterogeneous users. Our analysis exploits household-level panel data on residential water demand and consumers’ characteristics obtained by combining information from a survey of 1,465 domestic users in the city of Granada and bimonthly price and consumption data supplied by this city’s water supplier from the period 2009–2011.

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Notes

  1. On the other hand, the advantages of using LCMs could be not worthwhile if, after the process of estimating the model, water managers uncovered the determinants of class membership to be unobservable (that is, practically unobservable, rather than just part of the error component of the water demand functions) or that obtaining data about those determinants imposed a large informational burden. We thank an anonymous referee for pointing this out.

  2. See for instance, Agthe etal. (1986) in IV, Nieswiadomy and Molina (1988, 1989) in 2SLS or García-Valiñas (2005) for GMM.

  3. However, this assumption does not hold for our sample, since only 34.62 % of the households know the price schedule they face.

  4. The tariff also includes discounts to those who are unemployed, retired, or have a certain minimum number of dependants.

  5. To the best of our knowledge, there is only one previous work which deals with a change in the price structure similar to the one exploited in this paper. Martínez-Espiñeira and Nauges (2004) study residential water demand in Seville (Spain) for the period 1991–1999, having a slight change in the block size from 1996. Water demand is modeled using Stone–Geary utility function that allows identifying a threshold of water that is insensitive to price, however the change in the block is not directly analyzed.

  6. As shown in Beaumais et al. (2010), a water habit index was constructed by calculating the mean score on the answers related to the values of water use/conservation habits that were elicited by the survey (possible answers were 1 \(=\) yes or 0 \(=\) no).

  7. Fortunately, since nowadays LCM routines are available through statistical packages such as Stata, estimating them should be within the reach of most analysts.

  8. The sample could be split in arbitrary intervals by modeling the inverse of the cumulative distribution function (CDF) of the dependent variable using Quantile Regression.

  9. The potential endogeneity of habits or efficient electrical appliances is not considered in the paper, since we are not trying to make any type of causal inference about the impact of habits on water consumption. In the terminology of (Angrist and Pischke 2009, p. 68), these variables can be referred to as proxy control variables, in the sense that they are included in the regression in order to serve as a measure of the observed water behavior and in order to avoid omitted variable bias. Including this variable would not generate a regression coefficient of interest but it may be an improvement over the alternative of using no control. Moreover, we test the robustness of the results by excluding these variables, finding that the other coefficient estimates (not reported but available upon request) remain qualitatively unchanged.

  10. We thank an anonymous referee for suggesting the analysis included in this section.

  11. These groups are defined based on their sample average of the standard deviation in water consumption within a year and the two possible categories of the income variable.

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Acknowledgments

The authors would like to gratefully acknowledge the financial support given by the University of Oviedo (Grant: UNOV-11-BECDOC), the Spanish Ministry of Science and Innovation (ECO2009-08824) and the Spanish Ministry of Economy and Competitiveness (ECO2012-32189). The Collaborative Applied Research in Economics initiative at the Economics Department of Memorial University of Newfoundland also provided financial assistance to support part of this research. We are grateful for valuable comments by an anonymous referee and the journal editor. All remaining errors remain our sole responsibility.

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Correspondence to María Pérez-Urdiales.

Appendices

Appendix 1

As explained in Sect. 5, the estimation using LCMs is nonlinear, therefore, two-stage least squares models are likely to be inconsistent (Howard and Roe 2013). Therefore, we used a control function approach to correct for price endogeneity.

Consider the model:

$$\begin{aligned} WD=\delta z_{1}+ \alpha AvgP+u_{1} \end{aligned}$$
(9)

where WD is residential water demand, AvgP the average price (i.e., the endogenous explanatory variable) and \(z_{1}\) a vector of exogenous explanatory variables.

This methodology uses the same first stage that would be used in 2SLS, that is, the endogenous explanatory variable is regressed on the exogenous explanatory variables and the set of instruments. Let \(z_{2}\) denote a vector of instruments.

$$\begin{aligned}&AvgP=\pi _{1}z_{1}+\pi _{2}z_{2}+u_{2}\nonumber \\&E(z_{1}^{\prime }u_{2})=0 \quad E(z_{2}^{\prime }u_{2})=0 \end{aligned}$$
(10)

The average price would be endogenous if and only if \(u_{1}\) is correlated with \(u_{2}\), being \(\gamma =E(u_{2}u_{1})/E(u_{2}^{2})\).

$$\begin{aligned} u_{1}=\gamma u_{2}+\epsilon \end{aligned}$$
(11)

Since \(u_{1}\) and \(u_{2}\) are uncorrelated with \(z_{1}\) and \(z_{2},\,E(u_{2}\epsilon )=0\) and \(E(z_{2}\epsilon )=0\). Substituting Eq. (11) into Eq. (9):

$$\begin{aligned} WD=\delta z_{1}+\alpha AvgP+\gamma u_{2}+\epsilon \end{aligned}$$
(12)

In this equation \(u_{2}\) is included as an explanatory variable. As explained above, \(\epsilon \) is uncorrelated with \(u_{2},\,z_{1}\) and \(z_{2}\). Moreover, AvgP is defined as a linear function of the explanatory variables, the instruments and the residual \(u_{2}\), so AvgP is uncorrelated with \(\epsilon \) and therefore, \(\delta \) and \(\alpha \) can be consistently estimated with Eq. (12).

Results from the first stage estimation as defined in Eq. (10) are presented in Table 12. Following Hewitt and Hanemann (1995) and Olmstead (2009b), we used the full set of marginal prices in each block as instruments. The Hansen test indicated that all the instruments are exogenous. The residuals were obtained from this estimation and then, the second stage estimation included the average price and the control function as defined in Eq. (11), that is, the residuals from the first stage and a standard normal random variable (Howard and Roe 2013).

Table 12 Control function estimation

Appendix 2

1.1 Selected questions from the survey used to construct a water habits index

P.17. In general, do you have any of the following water conservation habits in the household?

  1. (a)

    Do you recycle water, for example, making use of the water while you wait for the shower to get hot?

  2. (b)

    Do you store drinking water in the refrigerator rather than letting the tap run every time you want a cool glass of water?

  3. (c)

    Do you defrost food in advance in order to avoid using running hot water to thaw meat or other frozen foods?

  4. (d)

    Do you fill the sink with water when washing dishes by hand?

  5. (e)

    Do you operate automatic dishwashers and washing machines only when they are fully loaded?

  6. (f)

    Do you slightly turn off the backflow valve to reduce the tap flow?

  7. (g)

    Do you use a rubbish bin in the toilet rather than flushing the toilet unnecessarily?

  8. (h)

    Do you avoid letting water run while brushing your teeth?

  9. (i)

    Do you take shorter showers?

  10. (j)

    Do you avoid washing the cars with drinking water?

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Pérez-Urdiales, M., García-Valiñas, M.A. & Martínez-Espiñeira, R. Responses to Changes in Domestic Water Tariff Structures: A Latent Class Analysis on Household-Level Data from Granada, Spain. Environ Resource Econ 63, 167–191 (2016). https://doi.org/10.1007/s10640-014-9846-0

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