**1. Introduction**

Approximately 68% of the world's population is projected to live in cities by 2050, representing a 13% increase in demand for water services in urban areas [1]. In addition to this rise in demand, water service providers face additional pressures related to institutional fragmentation, the inability to defray costs to replace deteriorating infrastructure, and increased capital costs to mitigate the impacts of climate change [2,3]. In the face of these challenges, urban water providers struggle to balance the rising costs of providing quality water service while simultaneously keeping the cost of service low for customers [2,4,5].

In the United States, there is some indication individuals feel their water bills are too high. Anecdotal evidence from news stories cite a lack of billing transparency and a complex mesh of reasons for rising water costs from city to city [6–9]. In San Diego, CA for example, residents are confused about the sudden spike in water bills and meter readings, which they say cannot be explained by rate increases alone [7]. In Bayonne, New Jersey, the city cut a deal to have its water managed by a Wall Street firm that guarantees a rate of return on their investment, which has contributed to rising water costs for residents [9]. These consumer concerns and the rising cost of providing water services mean it is important to understand consumer perceptions of the cost of water services. To this point in time, research has not assessed perceptions of water costs. Instead, research has focused on analyzing perceptions of other aspects of water services including: water quality [10,11], water safety [12–16], and the propensity to consume water from different sources (e.g., tap water or bottled water) [17,18]. A Canadian study found for example that 72% of respondents in Toronto were 'somewhat' or 'extremely' concerned about chemical pollutants in the water [17].

**Citation:** Medwid, L.; Mack, E.A. An Analysis of Household Perceptions of Water Costs across the United States: A Survey Based Approach. *Water* **2022**, *14*, 247. https://doi.org/ 10.3390/w14020247

Academic Editor: Laura Bulgariu

Received: 6 December 2021 Accepted: 8 January 2022 Published: 15 January 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

A study of the state of Georgia, found that approximately half of the respondents rated drinking water quality as very safe, safe, or fair [19]. In a study within the state of Florida, respondents who had experienced water quality issues previously were more likely to perceive that water quality problems were becoming worse [18]. The same study also found that participation in extension programs improved the perceptions of water quality.

Studies also find that people's perceptions of quality are based on superficial characteristics or organoleptic properties (e.g., taste, hardness, color, odor) that do not pose health risks to people compared to invisible quality issues related to microbial or chemical contamination [12–14]. For instance, hardness of tap water was found to be a main reason individuals avoid consumption of tap water, despite the fact that hardness does not pose any health risk [14]. Studies of bottled water consumption also find a divergence or paradox between product characteristics and consumption preferences [20] that is tied to the perceptions of taste [21,22] and perceptions of water safety [23]. Research also points to perceptions of water safety as a reason that vulnerable populations, such as low-income households, females and racial/ethnic minorities are more likely to purchase bottled water [19,22,24,25].

In terms of research that examines water costs directly, several studies have conducted research on water resource valuation, demand and willingness to pay [26,27]. Though the overall demand for water is inelastic [19], several trends have emerged in the literature. For instance, in Jordan and Elnagheeb's [19] study, Black Americans were willing to pay more for improvements in water quality than non-Black Americans. Willingness to pay was also found to increase with the level of education. Community engagement also affects public willingness to pay for watershed services as well as the level of public engagement in watershed management [28].

One of the largest disconnects between the perception of water costs and actual costs is access to clearly delineated water bills for household water consumers [26,29]. Interestingly, research indicates that the public's understanding of water rates is affected by the clarity of water bills [30]. Specifically, studies find that progressive price schedules are difficult to understand for consumers [30–32]. For example, a nation-wide study of the U.S. found that only 17% of utilities provided information about marginal prices and 78% provided no information other than the total amount required for payment [30]. More recent studies suggest this lack of clarity about water pricing may be linked to water consumption practices. For example, Binet et al. [33] investigated the perceived price of drinking-water when consumers are imperfectly informed about pricing schedules and found that households underestimate the price of water and consume more than what is economically rational. To this point in time, however, research on water and public policy has not yet evaluated the public's perception of the fairness of water costs.

To address this gap, this study designed and collected nationally representative survey data from over 9000 United States households about a variety of water issues, including the cost of water. These one-of-a-kind data were incorporated into logistic regression models to assess household perceptions of water bills and the characteristics of households who perceive their water bills to be too high. In doing so, our study advances the water and public policy literature by making several contributions. One, it addresses the need for household resolution information about water issues, given the lack of data at this scale in the United States. Two, it uses one of a kind survey data to understand the perceptions of household water bills and the drivers of these perceptions. Three, we assess the impact of proposed solutions to improve water affordability on household perceptions of water costs.

Model results indicate that low-income and racial/ethnic minority households were more likely to perceive their water bills to be too high. There are also geographic variations in household water perceptions that may reflect widespread affordability issues in particular parts of the country [34,35]. For example, respondents in the Detroit and Flint regions were the most likely to report their water bills are too high compared to other regions in the U.S. From a public policy perspective, model results suggest two ways that utilities and city governments can affect consumer perceptions of water prices. In particular, model results

indicated that billing frequency and participation in payment assistance programs affects consumers' perceptions of whether water bills are too high. Compared to those billed monthly, households billed quarterly are more likely to say their water bills are too high. Yet, when extended to annual or semiannual billing, this trend reverses, and households are more likely to report their water bills are about right. These results indicate that monthly or annual billing may be ideal billing frequency options for utility companies. Participants enrolled in payment assistance programs were also less likely to perceive water bills were too high. This suggests the development of customer assistance programs (CAPs) could improve perceptions of the cost of water services.

#### **2. Materials and Methods**

To provide a first glance at perceptions of residential water costs across the United States, this study uses data from the Survey of Water Innovation and Socioeconomic Status of Households (SWISSH). This survey was designed by one of the authors to address the lack of household data in the United States about water issues and administered to a panel households by the Qualtrics survey firm [36]. The survey was administered to respondents at least 25 years of age in households across nine regions in the U.S. between December of 2017 and March of 2018. After data cleaning, 9250 responses were used in the analysis for this paper. These regions represent geographically, as well as socioeconomically and demographically diverse locations. Rim weights that combine race/ethnicity and income into one probability weight for each respondent are available so that the data are representative of households in the nine regions in terms of race/ethnicity and income, as indicated by 2011–2015 American Community Survey data from the U.S. Census Bureau [36].

The survey covers a variety of water issues, one question in particular asks respondents about their views on the amount of money they spend on water. The text of this question reads as follows: "In your opinion, is the amount you pay for water fair or unfair?" Respondents were given five response options to this question: (1) "unfair, the price of water should be higher", (2) "unfair, the price of water should be lower", (3) "fair, the price of water is about right", (4) "don't know", or (5) "prefer not to answer". Survey responses were coded with a "1" if consumers perceived them to be unfair and too high. The other responses were coded as a "0" if respondents indicated that the amount they pay for water is fair and about right or unfair because they were too low. Responses of "do not know" or "preferred not to answer" were excluded from our analysis.

Logistic regression models were estimated in STATA 14 [37] using the 'logit' command and were weighted with the 'svy' command. Rim weights were used to ensure representative samples that align with the demographic composition of the U.S. Census' American Community Survey. The probability that households report their water bills are too high is as follows:

$$\Pr(\mathbf{y} = 1 \mid \mathbf{x}) = \mathbf{e}^{\mathbf{x}' \boldsymbol{\beta}} / (1 + \mathbf{e}^{\mathbf{x}' \boldsymbol{\beta}}) \tag{1}$$

where y = 1 indicates water bills are too high. Vector β consists of slope coefficients corresponding to the independent variables and an intercept. The overall predicted probability, *Y*∗ is a ratio between the probability that households feel their water bills are either too high or not too high, as shown in Equation (2).

$$Y^\* = \ln\left(\frac{P(water\text{ cost too high})}{P(water\text{ cost not too high})}\right) \tag{2}$$

The base category (denominator) is any response in which households did not consider their water bills too high including a response of fair/about right, or unfair because they believe the cost could be higher. Vector x in Equation (1) includes the exogenous variables chosen based on prior research associated with water quality and risk perceptions, willingness to pay for water, and awareness of environmental issues [9,26]. This body of work shows that demographic and socio-economic factors such as income, education, employment and race/ethnicity, are important to understanding perceptions of a range

of water issues [23]. Independent variables in this model therefore include: (1) water bill characteristics such as water billing frequency and whether the household is enrolled in a water bill payment assistance plan, (2) socioeconomic characteristics including age and income, (3) demographic characteristics, (4) regional variables, and (5) other control variables. For example, we elected to include controls in the model, such as whether respondents have health insurance, because these factors may place them at financial risk. Therefore, health insurance status may affect their perceptions of financial issues, including the cost of water services. The complete list and description of variables are found in Table 1.


**Table 1.** Variable Names and Descriptions.


**Table 1.** *Cont*.

Note: response options in bold indicate the reference category for each variable.

Odds ratios are used to estimate the relative increase or decrease in the perception that water bills are too high associated with each explanatory variable. These odds ratios should be interpreted relative to reference groups for each variable, which are highlighted in bold in Table 1. In general, indicators of high socioeconomic status were selected as the base comparison category including those who are non-Hispanic White, earners over USD 100,000, male, college graduate or higher, and full-time or part-time employment.
