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
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.
However, this assumption does not hold for our sample, since only 34.62 % of the households know the price schedule they face.
The tariff also includes discounts to those who are unemployed, retired, or have a certain minimum number of dependants.
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.
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).
Fortunately, since nowadays LCM routines are available through statistical packages such as Stata, estimating them should be within the reach of most analysts.
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.
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.
We thank an anonymous referee for suggesting the analysis included in this section.
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.
References
Agthe DE, Billings RB, Dobra JL, Raffiee K (1986) A simultaneous equation demand model for block rates. Water Resour Res 22(1):1–4
Angrist JD, Pischke JS (2009) Mostly harmless econometrics: an empiricist’s companion, Chapter 3. Princeton University Press, Princeton
Arbués F, García-Valiñas M, Martinez-Espiñeira R (2003) Estimation of residential water demand: a state-of-the-art review. J Socio-Econ 32:81–102
Arbués F, Villanúa I (2006) Potential for pricing policies in water resource management: Estimation of urban residential water demand in Zaragoza, Spain. Urban Stud 43(13):2421–2442
Ayyagari P, Deb P, Fletcher J, Gallo W, Sindelar J (2013) Understanding heterogeneity in price elasticities in the demand for alcohol for older individuals. Health Econ 22(1):89–105
Beaumais O, Briand A, Millock K, Nauges C (2010) What are households willing to pay for better tap water quality? A cross-country valuation study. Université Paris 1 Panthéon-Sorbonne (Post-Print and Working Papers), HAL
Blundell R, Powell JL (2003) Endogeneity in nonparametric and semiparametric regression models. Econom Soc Monogr 36:312–357
Bockstael NE, McConnell KE (1983) Welfare measurement in the household production framework. Am Econ Rev 73(4):806–814
Boter J, Rouwendal J, Wedel M (2005) Employing travel time to compare the value of competing cultural organizations. J Cult Econ 29:19–33
Cameron AC, Trivedi PK (2005) Microeconometrics: methods and applications. Cambridge University Press, New York
Campbell D, Hensher DA, Scarpa R (2011) Non-attendance to attributes in environmental choice analysis: a latent class specification. J Environ Plan Manag 54(8):1061–1076
Campbell HE, Johnson RM, Larson EH (2004) Prices, devices, people, or rules: the relative effectiveness of policy instruments in water conservation. Rev Policy Res 21(5):637–662
Clark WA, Finley JC (2007) Determinants of water conservation intention in blagoevgrad, bulgaria. Soc Nat Resour 20(7):613–627
Coleman EA (2009) A comparison of demand-side water management strategies using disaggregate data. Public Works Manag Policy 13(3):215–223
d’ Uva TB (2006) Latent class models for utilisation of health care. Health Econ 15:329–343
Dalhuisen JM, Florax R, Groot HD, Nijkamp P (2003) Price and income elasticities of residential water demand: a meta-analysis. Land Econ 79(2):292–308
Deb P, Trivedi PK (2002) The structure of demand for health care: latent class versus two-part models. J Health Econ 21:601–625
Dharmaratna D, Harris E (2012) Estimating residential water demand using the stone-geary functional form: the case of Sri Lanka. Water Resour Manag 26(8):2283–2299
Eshghi A, Haughton D, Legrand P, Skaletsky M, Woolford S (2011) Identifying groups: a comparison of methodologies. J Data Sci 9:271–291
Fernandez-Blanco V, Orea L, Prieto-Rodriguez J (2009) Analyzing consumers heterogeneity and self-reported tastes: an approach consistent with the consumer’s decision making process. J Econ Psychol 30:622–633
Fielding KS, Russell S, Spinks A, Mankad A (2012) Determinants of household water conservation: the role of demographic, infrastructure, behavior, and psychosocial variables. Water Resour Res 48(W10510). doi:10.1029/2012WR012398
García-Valiñas MA (2005) Efficiency and equity in natural resource pricing: a proposal for urban water distribution services. Environ Resour Econ 32(3):183–204
Garcia-Valiñas MA, Athukorala W, Wilson C, Torgler B, Gifford R (2014) Nondiscretionary residential water use: the impact of habits and water-efficient technologies. Aust J Agric Resour Econ 58(2):185–204
Gaudin S (2006) Effect of price information on residential water demand. Appl Econ 38(4):383–393
Gaudin S, Griffin RC, Sickles RC (2001) Demand specification for municipal water management: evaluation of the Stone-Geary form. Land Econ 77(3):399–422
Gilg A, Barr S (2006) Behavioural attitudes towards water saving? Evidence from a study of environmental actions. Ecol Econ 57(3):400–414
Grafton RQ, Ward MB, To H, KT (2011) Determinants of residential water consumption: evidence and analysis from a 10-country household survey. Water Resour Res 47(W08537). doi:10.1029/2010WR009685
Greene W, Hensher D (2013) Revealing additional dimensions of preference heterogeneity in a latent class mixed multinomial logit model. Appl Econ 45(14):1897–1902
Grisolía JM, Willis KG (2012) A latent class model of theatre demand. J Cult Econ 36:113–139
Hensher DA, Greene WH (2003) The mixed logit model: the state of practice. Transportation 30:133–176
Hess S, Ben-Akiva M, Walker J (2011) Advantages of latent class over continuous mixture of logit models. Working paper, University Press, Harrisburg
Hewitt JA, Hanemann WM (1995) A discrete/continuous choice approach to residential water demand under block rate pricing. Land Econ 71:173–192
Howard G, Roe BE (2013) Stripping because you want to versus stripping because the money is good: a latent class analysis of farmer preferences regarding filter strip programs. In: 2013 annual meeting, August 4–6, 2013, Washington, D.C., Agricultural and Applied Economics Association
Hyppolite J, Trivedi P (2012) Alternative approaches for econometric analysis of panel count data using dynamic latent class models (with application to doctor visits data). Health Econ 21(1):101–128
Imbens GM, Wooldridge JM (2007, Summer) Control function and related methods. The National Bureau of Economic Research (NBER). http://users.nber.org/~confer/2007/si2007/WNE/lect_6_controlfuncs.pdf
Inman D, Jeffrey P (2006) A review of residential water conservation tool performance and influences on implementation effectiveness. Urban Water J 3(3):127–143
Kantola SJ, Syme GJ, Nesdale AR (1983) The effects of appraised severity and efficacy in promoting water conservation: an informational analysis. J Appl Soc Psychol 13(2):164–182
Krause K (2003) The demand for water: consumer response to scarcity. J Regul Econ 23(2):167–191
Lopez-Gunn E, Zorrilla P, Prieto F, Llamas M (2012) Lost in translation? water efficiency in spanish agriculture. Agric Water Manag 108:83–95
Mansur ET, Olmstead SM (2012) The value of scarce water: measuring the inefficiency of municipal regulations. J Urban Econ 71:332–346
Martínez-Espiñeira R, Nauges C (2004) Is all domestic water consumption sensitive to price control? Appl Econ 36(15):1697–1703
Miyawaki K, Omori Y, Hibiki A (2010) Panel data analysis of japanese residential water demand using a discrete/continuous choice approach. Global COE Hi-stat discussion paper series gd09-123, Institute of Economic Research, Hitotsubashi University
Nataraj S, Hanemann MW (2011) Does marginal price matter? A regression discontinuity approach to estimating water demand. J Environ Econ Manag 61(2):198–212
Nauges C, Whittington D (2010) Estimation of water demand in developing countries: an overview. World Bank Res Obs 25(2):263–294
Nguyen QH, Rayward-Smith VJ (2008) Internal quality measures for clustering in metric spaces. Int J Bus Intell Data Min 3(1):4–29
Nieswiadomy ML, Molina DJ (1988) Urban water demand estimates under increasing block rates. Growth Change 19(1):1–12
Nieswiadomy ML, Molina DJ (1989) Comparing residential water demand estimates under decreasing and increasing block rates using household data. Land Econ 65(3):280–289
Nylund KL, Asparouhov T, Muthén BO (2007) Deciding on the number of classes in latent class analysis and growth mixture modeling: a monte carlo simulation study. Struct Equ Model Multidiscip J 14(4):535–569
Olmstead SM (2009b) Reduced-form versus structural models of water demand under nonlinear prices. J Bus Econ Stat 27(1):84–94
Olmstead SM, Stavins RN (2007) Managing Water Demand. Price vs. Non-price conservation programs. A Pioneer Institute Water paper no 39
Patunru AA, Braden John B, Chattopadhyay S (2007) Who cares about environmental stigmas and does it matter? A latent segmentation analysis of stated preferences for real estate. Am J Agric Econ 89(3):712–726
Pint E (1999) Household responses to increased water rates during the California drought. Land Econ 75:246–266
Polebitski A, Palmer R (2010) Seasonal residential water demand forecasting for census tracts. J Water Resour Plan Manag 136(1):27–36
Renwick M, Archibald S (1998) Demand side management policies for residential water use: Who bears the conservation burden? Land Econ 74:343–359
Renwick ME, Green RD (2000) Do residential water demand side management policies measure up? An analysis of eight California water agencies. J Environ Econ Manag 40:37–55
Roibás D, García-Valiñas MA, Wall A (2007) Measuring welfare losses from interruption and pricing as responses to water shortages: an application to the case of Seville. Environ Resour Econ 38(2):231–243
Ruijs A, Zimmermann A, Berg AMvd (2008) Demand and distributional effects of water pricing policies. Ecol Econ 66(2–3):506–516
Russell S, Fielding K (2010) Water demand management research: a psychological perspective. Water Resour Res 46(W05302). doi:10.1029/2009WR008408
Scarpa R, Thiene M, Tempesta T (2007) Latent class count models of total visitation demand: days out hiking in the eastern Alps. Environ Resour Econ 38(4):447–460
Scarpa R, Willis KG, Acutt M (2005) Individual-specific welfare measures for public goods: a latent class approach to residential customers of Yorkshire Water. Econom Inf Nat Resour Manag 14:316–337
Scott D, Willits FK (1994) Environmental attitudes and behavior: a Pennsylvania survey. Environ Behav 26(2):239–260
Shen J (2010) Latent class model or mixed logit model? A comparison by transport mode choice data. Appl Econ 41:2915–2924
Shen J, Sakata Y, Hashimoto Y (2006) A comparison between latent class model and mixed logit model for transport mode choice: evidences from two datasets of Japan. Osaka University, Graduate School of Economics and Osaka School of International Public Policy (OSIPP). Discussion Papers in Economics and Business (06-05)
Stern PC (2000) New environmental theories: toward a coherent theory of environmentally significant behavior. J Soc Issues 56:407–424
Stigler GJ, Becker GS (1977) De gustibus non est disputandum. Am Econ Rev 67(2):76–90
Strong A, Smith VK (2010) Reconsidering the economics of demand analysis with kinked budget constraints. Land Econ 86(1):173–190
The World Bank (2012) Turn down the heat. Why a 4 C warmer world must be avoided. Technical report, The World Bank
Trumbo CW, O’Keefe GJ (2005) Intention to conserve water: environmental values, reasoned action, and information effects across time. Soc Nat Resour 18(6):573–585
UNDESA (2009) World population prospects, the 2008 revision—executive summary. Technical report, UNDESA (United Nations Department of Economic and Social Affairs)
Worthington AC, Higgs H, Hoffmann M (2009) Residential water demand modeling in Queensland, Australia: a comparative panel data approach. Water Policy 11(4):427–441
Worthington AC, Hoffman M (2008) An empirical survey of residential water demand modelling. J Econ Surv 22(5):842–871
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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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:
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.
The average price would be endogenous if and only if \(u_{1}\) is correlated with \(u_{2}\), being \(\gamma =E(u_{2}u_{1})/E(u_{2}^{2})\).
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):
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).
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?
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(a)
Do you recycle water, for example, making use of the water while you wait for the shower to get hot?
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(b)
Do you store drinking water in the refrigerator rather than letting the tap run every time you want a cool glass of water?
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(c)
Do you defrost food in advance in order to avoid using running hot water to thaw meat or other frozen foods?
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(d)
Do you fill the sink with water when washing dishes by hand?
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(e)
Do you operate automatic dishwashers and washing machines only when they are fully loaded?
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(f)
Do you slightly turn off the backflow valve to reduce the tap flow?
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(g)
Do you use a rubbish bin in the toilet rather than flushing the toilet unnecessarily?
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(h)
Do you avoid letting water run while brushing your teeth?
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(i)
Do you take shorter showers?
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(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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DOI: https://doi.org/10.1007/s10640-014-9846-0