This project seeks to determine whether proximity to major water sources (e.g. rivers or lakes) is associated with the public's perception of water scarcity. From a theoretical perspective, this project will aid our collective understanding of problem identification. From a practical perspective, this relationship could help inform decision makers about where support or resistance to a water policy may be concentrated and allow more targeted efforts to help inform citizens about short- and long-term water policy needs. Knowing the influence of local conditions on public support for policy action is particularly important as water policies are primarily the domain of local and state governments. We utilize Geographical Information Systems to quantify the distances between survey respondents and various water sources to attempt to determine the effect individuals' proximity to water sources has on their opinions about water scarcity. The analyses reveal that proximity is a predictor of water scarcity perceptions, and the implications of these findings are discussed.

Introduction

As populations continue to grow and short- and long-term weather and climate stresses increase, greater demand will be placed on global water supplies, which will make it increasingly necessary for members of the public to consider water conservation behaviors and policies. Without public support, conservation efforts will inevitably fail. Whether it be farmers using wasteful irrigation techniques, industry inefficiently using water, or members of the public using more water than necessary, without awareness of the problem it will be difficult to stretch water resources to meet all reasonable demands. Currently, demand is much greater than supply in many regions of the world, which can be exacerbated by population growth and the predicted increased frequency of droughts (Collins et al., 2013).

According to the US Drought Monitor, for the ten year period prior to 2015, Texas had been experiencing a significant and prolonged drought (United States Drought Monitor Tabular Data Archive (Texas), 2015). Even with the official break of the drought in 2015, numerous towns and small cities are beginning to examine less conventional methods to meet their water needs. Decision makers and the public are starting to conclude that stronger water management policies are needed. For decision makers to enact such policies, they must be seen as meeting the water needs of the population and be supported by the public. This requires public risk perceptions and levels of concern that can create opportunities for decision makers to act (e.g. Stone, 1989; Birkland, 1998).

Perhaps, not surprisingly, the residents of Texas are not optimistic about their current and future water supplies (Stoutenborough & Vedlitz, 2013). While this is an important step toward providing policy and decision makers the opportunity to place water issues on the policy agenda, much deeper analyses are needed to obtain a full understanding of the issue. It is important to understand who holds various viewpoints on water risks and possible solutions. Extant social science research suggests that these viewpoints are likely influenced by attitudes and demographic indicators (e.g. Bies et al., 2013; Robinson et al., 2013; Stoutenborough & Vedlitz, 2014b). However, this traditional approach ignores a potentially important causal mechanism – proximity. Indeed, if an individual lives near a river or lake that has water levels substantially lower than normal, conventional wisdom suggests that they might be more inclined to be pessimistic about current and future water supplies because they are constantly reminded how low the water level has dropped.

This project seeks to determine whether proximity to major water sources (e.g. rivers or lakes) is associated with the public's perception of water scarcity. From a theoretical perspective, this project will aid our collective understanding of problem identification. From a practical perspective, this relationship could help inform decision makers about where support or resistance to a water policy may be concentrated and allow more targeted efforts to help inform citizens about short- and long-term water policy needs. Knowing the influence of local conditions on public support for policy action is particularly important as water policies are primarily the domain of local and state governments (Stoutenborough & Vedlitz, 2014a). We utilize Geographical Information Systems (GIS) to quantify the distances between survey respondents and various water sources to attempt to determine the effect an individual's proximity to water sources has on their opinions about water scarcity. The analyses reveal that proximity is a predictor of water scarcity perceptions, and the implications of these findings are discussed.

Geographic proximity and problem identification

The problem identification literature has witnessed a renaissance in recent years as public opinion data on specific issues like climate change and drought have become more easily obtained. This research has revealed that a number of attitudes and beliefs frequently predict an individual's perception of a problem (e.g. Kellstedt et al., 2008; Stoutenborough & Vedlitz, 2014b).

One growing and particularly robust sub-domain within the problem identification literature focuses on risk. Risk assessments have seen a substantial increase in attention because of their consistent link to policy support (e.g. Lubell, 2002; Zahran et al., 2006; Lubell et al., 2007; Spence et al., 2010; Stoutenborough et al., 2013, 2014c, 2015; Stoutenborough, 2015a, 2015b). At its simplest, ‘those who perceive the risk associated with something as high should be more likely to oppose policies that would increase that risk, and, conversely, support policies that decrease this risk’ (Stoutenborough et al., 2015, p. 105).

A well-established approach to ascertaining risk perceptions comes from psychological research that has identified four psychometric components that compose risk – likelihood, severity, magnitude of harm, and level of understanding (e.g. Mumpower et al., 2013). Though not explicitly linked to any of these components, perceptions of water scarcity are clearly connected to likelihood. If there is not enough water to meet demand, either currently or in the future, then the likelihood of rationing or transporting water to meet demand must be higher.

This project deviates from the attitudinal based problem identification and risk perception studies by seeking to understand the influence of physical proximity on these perceptions. Proximity should influence issue perceptions because it brings an increased exposure to an issue. With increased exposure, the social construction of an issue and the way the problems associated with it are defined will differ from those who lack this close proximity. For example, an individual's support for community investment in a recycled water system is partially determined by how closely that individual lives to the waste water processing center (Hartley, 2006). The problem definition literature has long anticipated this kind of response (e.g. Rochefort & Cobb, 1994), which is also commonly found in the not-in-my-backyard, or NIMBY, literature (Ansolabehere & Konisky, 2009; Firestone et al., 2009).

There have been numerous efforts to incorporate distance analysis into policy models (e.g. Maantay et al., 2010; Sergi & Kley, 2010). Much of the focus has been placed upon the impact's proximity has upon individual behaviors (Tong & Chim, 2013) and the perceptions of a problem (Brody et al., 2008) and not necessarily on policy preferences. Proximity to water sources might also increase the public's knowledge of water issues, as they are more exposed to those issues. With knowledge being an important aspect in the process of solving problems (Hmelo-Silver, 2004), the knowledge brought by proximity could be critical to the support for a water policy.

The influence of proximity has been primarily examined within the broader context of environmental politics. For example, Brody et al. (2008) examined the relationship between proximity to locations commonly perceived to be at risk from climate change and the public's perception of this risk. Their findings indicate that the public's perceptions are influenced by proximity, where those closest to regions expected to be impacted by climate change are more likely to perceive climate change as a threat. A similar study found that proximity predicted the ability of an individual to evaluate the levels of pollution in creeks (Brody et al., 2004). This finding indicates that the usage of proximity to water bodies, streams, and rivers may hold some merit for understanding public perceptions of the water supply.

The research of Wood & Skole (1998) suggests that there are environmental impacts that direct proximity may not capture but could be caused by the socioeconomic behaviors of individuals living some distance from the plot of land being observed. This raises the possibility that the farther one is from a water source, the less the scarcity may matter to the individual. The uncertainty Wood & Skole (1998) raise increases the importance of determining whether this holds true for water scarcity in Texas as it effects how well informed any policy decisions will be. Similarly, Cutter et al. (2003) argue that geospatial information, when coupled with traditional social data, helps to provide a more comprehensive view of society's vulnerability towards natural disasters.

New technology and improved analytical methods have allowed researchers to implement advanced studies that establish a deeper understanding of many geospatial-human relationships. With these examples of the connection between humans, the environment, and physical location in mind, we expect that an individual's proximity to water sources may also affect their opinions and perceptions of water supply.

Recent psychological research has also begun to explain some of the importance of the relationship between cognitive reasoning and geospatial components. Simons & Chabris (1999) found that the closer a change is to an area of focus, the greater the likelihood that the change might be detected. This stands to reason, as changes to anything that we regularly use are likely to be noticeable because it might cause us to alter our behavior. However, this presumes that the change is to something to which we regularly pay close attention. What happens when we do not focus our attention?

Complicating this process is a phenomenon known as change blindness. This represents the inability to notice subtle changes between one scene and the next (Pashler, 1988). Minor changes may occur between two images shown one day apart, but the viewer may not recognize these changes (Simons & Levin, 1997). This is due to the ways that the mind processes imagery. The brain commits images into memory and utilizes these images as a baseline for future viewings, filling in minor differences with the baseline image. This allows the viewer to ‘see’ a location while utilizing less brain power. However, this mental process can lead to gaps in awareness and cause the viewers to overlook subtle changes in their surroundings (Pashler, 1988). Consequently, those living near water sources may not notice subtle changes in water levels, flow, or other features until those changes are so drastic that they cannot be overlooked (e.g. the stream is completely dry or the water level of a lake has dropped so much that docks are no longer in the water). This could be one aspect that influences the impacts of proximity on public water concerns, and it highlights a possible venue for which this research could be applied.

Consistent with the influence of change blindness, we hypothesize that those living closer to major bodies of water will be more likely to believe that their water supplies will be sufficient for current or future needs. Similarly, we hypothesize that those living closer to streams and similar aquatic features will be more likely to believe that their water supplies will be sufficient for current or future needs. The above discussions suggest that those living closest to water sources are going to be less likely to perceive any changes in their local environment because there is little reason to focus their attention on these physical environments that they frequently witness. Accordingly, the change blindness phenomenon indicates that those living closer to the water sources will be more likely to believe water supplies are sufficient. Conversely, those who live some distance from these sources may be more capable of noticing significant change over time in these locations and subsequently be less likely to perceive their supplies as being in a good condition. Understanding the impacts of this behavior matters because the capacity to detect the presence and nature of a problem by people closer to a water source (and subsequently the most impacted) will influence the speed at which a water issue can be framed and incentivized for policy makers to take action.

Methods

In order to quantify the relationship between perceptions of water scarcity and proximity, two sources of data were combined: public opinion data and geospatial data. Public opinion data were obtained through two identical surveys of the adult residents of Texas, 18 years and older, administered by GfK Custom Research, LLC (GfK). The two unique samples were drawn from GfK's web-enabled KnowledgePanel®, a probability-based panel designed to be representative of the population. The first survey was in the field 21 February 2013 to 12 March 2013 and the second from 2 April 2013 to 16 April 2013, resulting in 822 combined completed surveys for a 43.3% combined completion rate.

To understand the role of proximity, these data are coupled with geospatial data and put through spatial analysis to determine the distance between each respondent and a water source. For the geospatial component of the analysis, Arcmap was utilized to determine the geospatial distribution of Texas zip codes and water sources. Arcmap is a software program used in cartography, environmental research, and spatial analysis for a wide variety of applications ranging from archeology to civil engineering.

Publicly available GIS data sets contained the information used specific to Texas regarding: (1) the location of all streams, rivers, and other waterways (USGS, 2014a); (2) the location of every water body (e.g. lakes, reservoirs) (USGS, 2014b); and (3) the location of every zip code (US Census Bureau, 2014). The two public opinion surveys provided the zip code in which each respondent resides, making zip codes the smallest spatial unit from which we could analyze proximity. As we do not know where within a zip code a respondent resides, we assigned each respondent to the center-most point of each zip code. While this will place some respondents closer to a water source and others farther, on average these will cancel out and allow for a reasonable approximation of the distance between a respondent and each of these water bodies. Such an approach is commonly utilized in public health studies (e.g. Gregory et al., 2000; Zeger et al., 2008), and it has been shown that using zip code centers is an effective and appropriate approach to estimating proximity between the general public and specific locations or features (Bliss et al., 2012). Arcmap offers the ability to determine the exact distance between two points using coordinates based on the projection utilized by the selected GIS data sets, in a manner similar to that done using specific Global Positioning System (GPS) measurements. The distances used in this analysis are measured in metres. The spatial data were then integrated into the public opinion data1.

The emphasis of this project is on the ways spatial effects, or proximity, influence individual perceptions of whether there is enough water to satisfy all water needs. The dependent variable in this analyses comes from a battery of questions that asked respondents to ‘Please indicate whether you Strongly Disagree, Disagree, Neither Disagree Nor Agree, Agree, or Strongly Agree with each of the following statements.’ Two of these statements were, ‘There is enough water in my state to meet current needs,’ and ‘There is enough water in my state to meet future needs.’ The dependent variables are coded as an ordered scale from 0 to 4, with 0 representing ‘strongly disagree’ and 4 representing ‘strongly agree.’

Figure 1 illustrates the distributions of the responses for the two dependent variables. As illustrated, the two distributions appear different from one another. A paired t-test reveals that there is a statistically significant difference in the means of the two variables2. Consequently, it is appropriate to analyze both as separate dependent variables.
Fig. 1.

Distribution of beliefs regarding the state's ability to meet current and future water needs. Source: Compiled by authors.

Fig. 1.

Distribution of beliefs regarding the state's ability to meet current and future water needs. Source: Compiled by authors.

Due to this coding scheme, an ordered logit is the most appropriate analytical tool to examine the non-continuous, ordered data (McKelvey & Zavoina, 1975). We will estimate four models (two for each dependent variable), one without the proximity measures, and one with them. The focus of the analysis will be on the full models, though the more traditional approach is reported to demonstrate how spatial information influences our understanding of the predicted impact of the control variables.

The primary independent variables are the two proximity measures. The first is associated with the distance in metres between the center-most point of the respondent's zip code and a body of water (i.e. lakes and reservoirs). The second is the distance in metres to rivers and streams. As outlined above, the change blindness phenomenon should cause those who live closer to either water source to be less likely to recognize changes in water levels. Accordingly, those who live closer to these water sources should be more likely to believe that there is sufficient water for the state to satisfy current and future water needs.

To ensure that the models were not ignoring characteristics that might also influence these perspectives, we control for attitudinal, experience, and demographic indicators3. We control for two potential attitudes that might influence a respondent's perception of the water needs of the state – membership in an environmental group and concern for water. Those who are a member of an environmental group are more likely to have access to information that would suggest that the state does not have enough water, as environmental groups have been advocating water conservation in Texas for years. Similarly, those who are generally concerned about water quality and availability should be more pessimistic regarding the ability of the state's water supplies to meet demand.

Individuals' personal experiences should also influence their perceptions of the capacity of the state to supply water. Those who believe they have experienced a drought more recently should be less likely to believe the water supply is sufficient to meet demand. Importantly, this variable assesses the respondents' beliefs that they experienced a drought, and not whether they lived in a region that was designated by scientists to be in a drought, as it is possible that the respondent was unaware or did not believe they were in a drought.

It is also possible that an individual will view current and future water supplies differently depending upon if they live in rural or urban areas. Urban respondents should be less aware of water supply issues, as their livelihood, or the livelihood of their friends and neighbors, is less likely to be connected with water supply concerns. Conversely, rural respondents, who may be farmers or ranchers, or know farmers and ranchers, should be more acutely aware of water supply concerns. Therefore, we control for respondents who live in rural areas.

Finally, consistent with a long line of research examining environmental attitudes (e.g. Bies et al., 2013; Stoutenborough et al., 2013; Bromley-Trujillo et al., 2014) and water management attitudes (Stoutenborough, 2015a, 2015b), we control for a common battery of demographic indicators – marital status, gender, education, race, political ideology, and income4. As the predictive influence of these indicators can vary from issue to issue, even within the same issue domain (e.g. Stoutenborough et al., 2014c), it is difficult to predict the directionality of these influences. Therefore, we operate under the basic expectation that these demographic characteristics will influence perceptions of the water supply.

Results

The results of the ordered logit analyses are presented in Table 1. All four models are presented in the same table to allow comparisons across models. To correct for heteroscedasticity, all four models were estimated with robust standard errors. We will begin with the examination of current water needs before turning our attention to future water needs.

Table 1.

The influence of proximity on public perceptions of the current and future water needs of Texas.

  Current water needs
 
Future water needs
 
 Reduced model
 
Full model
 
Reduced model
 
Full model
 
 Coefficient Prob. Coefficient Prob. Coefficient Prob. Coefficient Prob. 
Proximity 
 Water Bodies – – −0.000005 (0.000003) 0.092 – – −0.000004 (0.000003) 0.256 
 Rivers and Streams – – −0.00004 (0.00002) 0.077 – – –0.00005 (0.00002) 0.024 
Attitudes & Experience 
 Environmental Group 0.326 (0.437) 0.455 0.293 (0.449) 0.514 −0.228 (0.510) 0.655 −0.251 (0.511) 0.623 
 Concern for Water −0.145 (0.029) 0.000 −0.142 (0.029) 0.000 −0.178 (0.028) 0.000 −0.175 (0.028) 0.000 
 Drought Experience 0.013 (0.004) 0.001 0.013 (0.004) 0.001 0.011 (0.003) 0.002 0.011 (0.003) 0.002 
Demographics 
 Married −0.270 (0.145) 0.064 −0.283 (0.145) 0.051 −0.393 (0.151) 0.009 −0.397 (0.151) 0.009 
 Female −0.0006 (0.138) 0.996 −0.031 (0.139) 0.824 0.131 (0.139) 0.345 0.100 (0.139) 0.469 
 Education 0.049 (0.027) 0.073 0.052 (0.027) 0.056 −0.011 (0.028) 0.694 −0.009 (0.028) 0.737 
 White −0.191 (0.140) 0.175 −0.249 (0.144) 0.083 −0.346 (0.142) 0.015 −0.390 (0.143) 0.006 
 Ideology 0.060 (0.048) 0.208 0.055 (0.048) 0.250 0.099 (0.051) 0.051 0.094 (0.051) 0.069 
 Income 0.002 (0.017) 0.885 0.00005 (0.017) 0.997 −0.013 (0.017) 0.451 −0.016 (0.017) 0.347 
 Rural 0.145 (0.250) 0.560 0.153 (0.245) 0.530 0.081 (0.228) 0.723 0.100 (0.226) 0.656 
 Cut Point 1 −2.649 (0.497)  −2.896 (0.505)  −3.507 (0.485)  −3.783 (0.495)  
 Cut Point 2 −0.907 (0.479)  −1.143 (0.485)  −1.737 (0.472)  −1.999 (0.481)  
 Cut Point 3 0.383 (0.480)  0.160 (0.485)  −0.093 (0.466)  −0.343 (0.474)  
 Cut Point 4 2.983 (0.520)  2.770 (0.523)  2.138 (0.508)  1.894 (0.515)  
 Number of Cases 770  770  771  771  
 Wald Chi2 48.63 0.0000 58.48 0.0000 73.72 0.0000 89.30 0.0000 
 Log Pseudolikelihood −1069.698  −1065.549  −1048.834  −1044.623  
 McFadden's R2 0.0244  0.0282  0.0380  0.0418  
  Current water needs
 
Future water needs
 
 Reduced model
 
Full model
 
Reduced model
 
Full model
 
 Coefficient Prob. Coefficient Prob. Coefficient Prob. Coefficient Prob. 
Proximity 
 Water Bodies – – −0.000005 (0.000003) 0.092 – – −0.000004 (0.000003) 0.256 
 Rivers and Streams – – −0.00004 (0.00002) 0.077 – – –0.00005 (0.00002) 0.024 
Attitudes & Experience 
 Environmental Group 0.326 (0.437) 0.455 0.293 (0.449) 0.514 −0.228 (0.510) 0.655 −0.251 (0.511) 0.623 
 Concern for Water −0.145 (0.029) 0.000 −0.142 (0.029) 0.000 −0.178 (0.028) 0.000 −0.175 (0.028) 0.000 
 Drought Experience 0.013 (0.004) 0.001 0.013 (0.004) 0.001 0.011 (0.003) 0.002 0.011 (0.003) 0.002 
Demographics 
 Married −0.270 (0.145) 0.064 −0.283 (0.145) 0.051 −0.393 (0.151) 0.009 −0.397 (0.151) 0.009 
 Female −0.0006 (0.138) 0.996 −0.031 (0.139) 0.824 0.131 (0.139) 0.345 0.100 (0.139) 0.469 
 Education 0.049 (0.027) 0.073 0.052 (0.027) 0.056 −0.011 (0.028) 0.694 −0.009 (0.028) 0.737 
 White −0.191 (0.140) 0.175 −0.249 (0.144) 0.083 −0.346 (0.142) 0.015 −0.390 (0.143) 0.006 
 Ideology 0.060 (0.048) 0.208 0.055 (0.048) 0.250 0.099 (0.051) 0.051 0.094 (0.051) 0.069 
 Income 0.002 (0.017) 0.885 0.00005 (0.017) 0.997 −0.013 (0.017) 0.451 −0.016 (0.017) 0.347 
 Rural 0.145 (0.250) 0.560 0.153 (0.245) 0.530 0.081 (0.228) 0.723 0.100 (0.226) 0.656 
 Cut Point 1 −2.649 (0.497)  −2.896 (0.505)  −3.507 (0.485)  −3.783 (0.495)  
 Cut Point 2 −0.907 (0.479)  −1.143 (0.485)  −1.737 (0.472)  −1.999 (0.481)  
 Cut Point 3 0.383 (0.480)  0.160 (0.485)  −0.093 (0.466)  −0.343 (0.474)  
 Cut Point 4 2.983 (0.520)  2.770 (0.523)  2.138 (0.508)  1.894 (0.515)  
 Number of Cases 770  770  771  771  
 Wald Chi2 48.63 0.0000 58.48 0.0000 73.72 0.0000 89.30 0.0000 
 Log Pseudolikelihood −1069.698  −1065.549  −1048.834  −1044.623  
 McFadden's R2 0.0244  0.0282  0.0380  0.0418  

Robust standard errors in parentheses. Two-tailed test.

Current water needs

Does proximity to water sources influence perceptions of current water needs in Texas? The results of our analyses are presented in Table 1. As the full model reveals both proximity measures are predictors of perceptions regarding the capacity of the water supply to fill the state's current needs. Consistent with the change blindness phenomenon, the closer an individual lives to a body of water (e.g. lakes or reservoirs), the more likely that person is to agree that there is enough water to fulfill current demand. Similarly, the closer an individual lives to a river or stream, the more likely that person is to agree that current water needs will be met.

The analysis also reveals that respondents with greater concern about water, who more recently experienced drought, are married, less educated, and are white are more likely to believe that there is not enough water to meet current needs. Interestingly, the influence of race was not observed in the reduced model. The additional information provided by the two proximity measures in the full model clarified the relationship between race and water perspectives.

Future water needs

Proximity to water sources appears to influence perceptions concerning the current ability to fulfill water needs. Does the same pattern hold when respondents were asked to evaluate whether there is sufficient water to meet future needs? The results of these analyses are also presented in Table 1. Unlike the previous analysis, proximity to water sources is predictive only when applied to rivers and streams. Those who live close to rivers and streams are more likely to believe that there is enough water to satisfy future needs. Interestingly, those who live closer to water bodies (e.g. lakes and reservoirs) are no more or less likely to believe water supplies will fulfill the state's future demands. This may reflect the fact that reservoirs are designed to provide water in the event of a future water shortage.

The analysis also reveals that those who are more concerned about water, more recently experienced drought, are married, white, and liberal are more likely to believe there is not enough water to supply future needs. These results are consistent with what we found in the reduced model.

Discussion

We began this project with the intent of determining whether public attitudes toward the ability of a state to supply its citizens with sufficient water are influenced by an individual's proximity to water sources. A review of the extant literature revealed that geographic proximity to various objects/places is frequently found to influence individual attitudes. We turned to the psychological literature to understand how proximity to water sources might influence individuals' perceptions of their state's water supply, which led us to examine the role played by change blindness. The results of this project have several important implications for the problem identification literature.

First, public attitudes can be influenced by an individual's unique geographical surroundings. We find that Texans who live closer to water sources (both bodies of water and rivers/streams) are more likely to believe that their state has enough water to meet current needs. Similarly, Texans who live closer to a river or stream are more likely to believe that their state will have enough water to meet future needs.

Second, these results are consistent with the psychological literature on the change blindness phenomenon. As discussed above, change blindness is the result of cognitive shortcuts that cause individuals to overlook slow or minor changes in their surroundings. In this case, those who live closer to water sources should be less likely to notice the lowering water levels, which should cause them to be overconfident in the water supply. While ‘overconfident’ might be a bit strong, there is clearly a connection between living near a water supply and believing that there is enough water.

A different way to think about these results is to reverse the interpretation. The results indicate that those who live farther from a source of water are far more pessimistic about future and current water supplies. However, we do find that those who live farther from lakes or reservoirs are no more likely to worry about future water needs. As noted, this may reflect the rationalization that reservoirs are there to provide a water supply even in the worst of times.

This is an important theoretical contribution for this growing body of research. There is limited utility in learning that one's physical environment influences one's attitudes. While these revelations are important, it is perhaps more important to understand why these physical environments make a difference. By unpacking the cognitive processes that drive this phenomena, we can begin to develop predictive models of attitudinal change. This can become increasingly important as the physical world looks to continue to be altered through changes in land use and as a result of climate change. By understanding the cognitive theory, we should be better prepared to evaluate how these changes will be interpreted by members of the public.

Third, the impact of geographic proximity is largely independent of common attitudinal and demographic indicators. Furthermore, it is independent of personal experience with droughts. This is an important revelation for our understanding of public attitudes, and this suggests that studies that do not account for appropriate geographical considerations may be underspecified and suffering from an omitted variable bias. While we recognize that many public opinion data sets do not provide information sufficient to allow such an analysis, these results should encourage future survey instruments to include geographical information.

Fourth, the analyses reveal that public attitudes regarding future and current water needs are not influenced by where an individual lives. Though we might expect there to be a difference between those living in rural areas as opposed to more urban areas, the results reveal that this is not the case. Importantly, this finding, or the lack thereof, should temper concerns about the sample size in relation to the geographic size of Texas. If there was a significant difference, we would have to worry about the proportion of urban to non-urban respondents. Additionally, the lack of a difference between urban and rural respondents indicates that our measurement of distance did not negatively impact the results. Here, urban zip codes tend to occupy a smaller geographic area, as they are more densely populated, while rural zip codes can be quite large. If there were a rural/urban difference, we would have to worry about using the central point for all of the zip codes to measure distances. However, the lack of a difference suggests that our measurement scheme is unlikely to have created artificial results.

Finally, it is important to note that this survey instrument was administered in the middle of a long drought. We decided to implement the survey at that time to allow us to capture the proximity influences that decreased water flow in rivers and streams and lower water levels of lakes and reservoirs might have on attitudes. While we attempted to control for attitudinal differences related to weather change with our measure of drought experience, it is important to note that these attitudes toward current and future water supplies may change following the end of the drought. This, according to change blindness, is to be expected. In addition, the influence of proximity to rivers and bodies of waters may also change depending upon the weather conditions. This, of course, is an empirical question that demands the use of a panel survey to capture attitudes during and after a drought to see if attitudes truly change.

Conclusion

There are important policy-related implications from these analyses. Perhaps the most important is that these results indicate that governments will have greater buy-in from those living farther from water sources when attempting to implement water management and conservation policies. Conversely, those who live closer to these sources of water are going to be more resistant to changes to the status quo. Policymakers are in the unenviable situation of needing to identify a manner to reach those who are less likely to alter their behaviors because they do not see a problem. We have seen this play out during the recent California drought where those with senior water rights and those closer to rivers have refused to alter their irrigation practices, while those with junior water rights and those farther from the river are being forced to stop irrigating (e.g. Alexander, 2015).

Acknowledgements

This material is based upon research conducted by the Institute for Science, Technology and Public Policy in The Bush School of Government and Public Service at Texas A&M University. This research was supported by Texas Sea Grant under Award No. NA10OAR4170099 from the National Oceanic and Atmospheric Administration, US Department of Commerce; by the Texas A&M University Office of the Vice President for Research; and the Institute for Science, Technology and Public Policy.

The statements, findings, conclusions, and recommendations are solely those of the authors and do not necessarily reflect the views of Texas Sea Grant, the National Oceanic and Atmospheric Administration, or the Department of Commerce.

1

Visualizations of the GIS data and the location of the respondents can be found in Appendix A (available with the online version of this paper).

2

The mean and standard deviation for the assessment of current water needs are 1.949 and 1.045, respectively. The mean and standard deviation for the assessment of future water needs are 1.653 and 1.013, respectively. The difference between the means, 0.295, is statistically significant at P > 0.0001. This indicates that the means are significantly different from one another.

3

Variable definitions can be found in Appendix B (available with the online version of this paper).

4

Respondent's age is not directly controlled in the analysis because it is indirectly controlled for with the variable measuring the respondent's experience with droughts. Drought experience is measured as the time since the respondent experienced a drought. If respondents indicated never having experienced a drought, the variable was coded as their age. To ensure that collinearity was not a concern, we chose to not model age.

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Supplementary data