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Price and Consumption Misperception Profiles: The Role of Information in the Residential Water Sector

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Abstract

Consumer misperceptions about key economic variables, such as price or consumption, often hinder the effectiveness of natural resources management policies. When facing increasing block rates, water users might fail to identify the marginal price of their water use and guide themselves by information from their past total bill and water use amounts. However, this information might not be correctly perceived or remembered. By comparing them with actual bimonthly billing data from 1465 households in Granada (Spain), we study the inaccuracy of the users’ recollections during an in-person survey that also asked them about their characteristics, environmental and conservation habits, and exposure to informational policies. A conditional mixed-process selection model is used to test the hypothesis that the degree of inaccuracy in the recollection of past water bill and consumption amounts is related to indicators of the costs and benefits of acquiring the relevant information. Then, a latent class model exploits unobserved household heterogeneity to sort households into two classes—based on whether and how accurately they recalled past bill and consumption amounts—and to estimate the probability of belonging to each class, based on observable characteristics. We derive policy recommendations and show that knowledge of consumption and bill size is rather poor but that informational policies could improve consumer knowledge and the effectiveness of pricing policies. Finally, we identify which informational policies might be most effective and what type of consumers are most likely to respond to such policies.

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Notes

  1. To our knowledge, so far only Brent and Ward (2019) have explored differences between actual data and perceptions about both water bills and consumption. However, they do not study how the level and type of information may influence the observed differences.

  2. Because the literature addressing this issue in the water sector is still scarce, we discuss studies about both water and electricity. These two sectors are linked and exhibit strong similarities. Indeed, the economic analyses of residential water and electricity have been linked from the start (Arbués et al. 2003), since demand modelling and price structures share similar features. However, the relative share of water bills on household budgets is usually much smaller than its electricity counterpart.

  3. Binet et al. (2014) provide a brief discussion of analyses of the price variable to which consumers react, in both the water and the energy sectors.

  4. According to INE (2019), Granada registered 223,208 inhabitants in 2018.

  5. The year when the survey, described below, was conducted.

  6. As defined by Law 40/2003 of 18 November, on Protection of Large Families.

  7. In a survey conducted by the water supplier in 2014, 82% of customers declared to be against receiving their bills online (EMASAGRA 2014).

  8. Unfortunately, we do not have enough information to develop independent measures of the components of perceived price. Respondents were asked simply to recall the total amount of their water bill. In theory, this recalling exercise involves the knowledge of the tariff and the knowledge of consumption. It would have been more interesting to isolate issues of imperfect perception due to lack of knowledge of the tariff from those due to misperceptions of the level of consumption.

  9. Actual values of the bill were calculated taking into account any applicable discounts.

  10. The perceived value is exceeded by the actual value, yielding a negative value for the deviation measures.

  11. This last variable is not used, though, as an information variable but as a proxy of unobserved heterogeneity, as we explain below.

  12. Note that this variable does not indicate knowledge of the price of each block and the amount of the fix component but only a very basic awareness of the type of tariff structure.

  13. A positive and significant estimate of \(\rho _{ij}\) would indicate that common unobserved factors tend to increase or decrease the errors in both equations. A negative correlation between two regression errors suggests, instead, that omitted common factors tend to increase the error in one equation and decrease the error of the other equation or vice versa.

  14. We implemented it with Stata’s (Statacorp, 2011) CMP routine (Roodman 2011).

  15. Further details about this methodology can be found in Ch. 18.5, Cameron and Trivedi (2005)

  16. Other approaches assume the unobserved heterogeneity to follow a continuum, rather than conceiving a discrete representation of heterogeneity leading to a finite number of latent components or classes.

  17. In principle, because of the nonlinearity of the selection equation, exclusion restrictions are technically not essential but they are usually considered a much safer strategy than relying on the, possibly slight, nonlinearity of a potentially misspecified functional form to identify the estimated parameters (Cameron and Trivedi 2010, p. 558).

  18. Table 7 does not directly report the estimated value of \(\rho \). Because \(\sigma \) and \(\rho \) are bounded, the CMP routine transforms them onto an unbounded scale by using the logarithm of the \(\sigma \)’s and atanh(\(\rho \)), the arc-hyperbolic tangents (inverse S-curve transforms) of the \(\rho \)’s, in order to prevent the possibility that the maximum likelihood search process submit impossible trial values for these parameters, such as a negative value for a \(\sigma \) (Roodman 2011).

  19. This is not surprising, since water consumption usually represents a very small proportion of total household expenses.

  20. This fractional model assumes that each household has a certain probability of belonging to each class.

  21. Or, as in the case of most of our covariates, discrete effects of the variable changing value from 0 to 1.

  22. As discussed before, unfortunately, in our survey we cannot rely on specific information about individuals’ psychological traits. We were only able to proxy them to some extent by including bill_not_remembered as an additional covariate and control.

  23. Any other quantitative estimates are available upon request.

  24. See, for example, Olmstead et al. (2007), which reports a budget share of 0.5%.

Abbreviations

IBR:

Increasing block rate

LCM:

Latent class model

CMP:

Conditional mixed-process

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Acknowledgements

The authors would like to thank the European Commission and the Spanish Ministry of Science, Innovation and Universities for funding in the frame of the collaborative international consortium NEWTS financed under the 2018 Joint call of the WaterWorks 2017 ERA-NET Cofund. This ERA-NET is an integral part of the activities developed by the Water JPI. The views expressed in this paper are those of the authors and do not necessarily represent the views of Banco de España or the Eurosystem.

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Authors and Affiliations

  1. Oviedo Efficiency Group, Departamento de Economía, Universidad de Oviedo, Oviedo, Spain

    María Á. García-Valiñas

  2. Department of Economics, Memorial University of Newfoundland, St. John’s, Canada

    Roberto Martínez-Espiñeira

  3. Banco de España, Madrid, Spain

    Marta Suárez-Varela Maciá

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  1. María Á. García-Valiñas
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Correspondence to Roberto Martínez-Espiñeira.

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Appendices

Appendix A: Variable Descriptions

  • avpdcons_abs Average proportional misperception: use (absolute value).

  • avpdbill_abs Average proportional misperception: bill (absolute value).

  • billing_period_1 to billing_period_5 Binary indicators of bimonthly billing periods.

  • bill_not_descriptive Bill is received at home but lacks sufficient detail, according to respondent.

  • bill_not_remembered Bill is received at home but not remembered by respondent.

  • clearbill Bill is received at home and sufficiently clear to respondent.

  • college Either first or second household member has a postsecondary degree.

  • consultedbill User consulted bill while responding to the survey.

  • dcons Observed consumption (totalconsumption) minus the perceived (perceivedcons) consumption in each of the six billing periods for which information is available for both types of measure (cubic meters/period).

  • dbill Observed (totalbill) minus the perceived (perceivedbill) bimonthly bill for the six billing periods for which information is available for both types of measure (euros).

  • efficient_apps Household owns water-saving appliances, washer and dishwasher. The respondents were asked whether they had water-efficient washer and dishwasher. Since a dishwasher is itself a water-saving technology, we only considered the household as consistently enrolling in environmental behaviors of the “one-shot” type – installation of water-saving technologies- if they referred to have both water-efficient appliances.

  • enviro_concern user is “very concerned” about the environment.

  • estimatesinsurvey Number of estimates of bill and consumption provided, 0, 1, or 2.

  • estimatedcons: Respondent provided an estimate (a perceived value) of consumption.

  • estimatedbill Respondent provided an estimate (a perceived value) of bill.

  • hotshared Some of the hot water is not billed individually for each household but billed jointly through the contributions to the “comunidad de vecinos”, akin to “condo fees”.

  • householdsize Household size (number of members).

  • knowscampaign User knows of water saving campaign.

  • knowsweb Knowledge of the water suppliers’ webpage.

  • ownership User owns the dwelling.

  • p_age18 Proportion of household members 18 or younger.

  • p_age65 Proportion of household members 65 or older.

  • pdcons Proportional divergence, observed minus perceived consumption.

  • pdbill Proportional divergence, observed minus perceived water bill.

  • pdcons_abs Proportional divergence, absolute terms, observed minus perceived consumption.

  • pdbill_abs Proportional divergence, absolute terms, observed minus perceived water bill.

  • satisfied_with_income User claims to be satisfied with household income to cover needs.

  • waterhabitindex Index indicating proportion of eight water-saving behaviors practiced by the household.

Appendix B: Validity of Exclusion Restriction

See Table 11.

Table 11 Comprobations of validity of exclusion restriction (bill_not_remembered)
Full size table

Appendix C: Further Examples of Quantitative Effects on Probability of Class Membership

Figure 3 illustrates graphically how the marginal effects of consultedbill and clearbill (averaged through the sample households) vary depending on the values at which different binary variables are measured, depending also on the proportion of household members under 18 (variable p_age18).

Fig. 3
figure 3

Average marginal (discrete) effects of consultedbill, clearbill, and enviro_concern on the probabilities of belonging to Class 1, depending on combined values of knowstariff and knowsweb or ownership and hotshared by p_age18

Full size image

Figure 4 illustrates graphically how the marginal effects of consultedbill and clearbill (averaged through the sample households) vary depending on the values at which different binary variables are measured, depending also on the proportion of household members over 65 (variable p_age65).

Fig. 4
figure 4

Average marginal (discrete) effects of consultedbill, clearbill and enviro_concern on the probabilities of belonging to Class 1, depending on combined values of knowstariff and knowsweb or ownership and hotshared by p_age65

Full size image

Appendix D: Water Bill Sample

See Fig. 5.

Fig. 5
figure 5

Water bill sample

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García-Valiñas, M.Á., Martínez-Espiñeira, R. & Suárez-Varela Maciá, M. Price and Consumption Misperception Profiles: The Role of Information in the Residential Water Sector. Environ Resource Econ 80, 821–857 (2021). https://doi.org/10.1007/s10640-021-00611-8

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