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Changing Consumer Willingness to Pay: a time series evaluation of factors impacting Floridians' desire to preserve water resources

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Changing Consumer Willingness to Pay: a time series evaluation of factors impacting Floridians' desire to preserve water resources
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Mckee, Brandon H.
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This study sought to gauge Florida's consumers' willingness to pay for protecting the future of Florida's water supply from 2013 to 2018. This study used a value approach for estimating consumer's willingness to pay for a 10 percent and a 50 percent increase in their water bill. The study also sought to identify dissonance between Florida's consumers to determine influencers of their willingness to pay. The study found an increasing percentage of consumers willing to support the protection of Florida's water supply from 2013 to 2016, with a drop in willingness in 2018. As well, income was a common factor influencing respondent's willingness to pay. Knowing this dissonance can help decision makers make informed polices and regulations about future water conservation strategies. ( en )
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Awarded Bachelor of Science, summa cum laude, on May 8, 2018. Major: Food and Resource Economics. Emphasis/Concentration: International Food and Resource Economics
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College or School: College of Agricultural and Life Sciences
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Advisor: Alexa Lamm, Brandon McFadden. Advisor Department or School: Agricultural Education and Communications, Food and Resource Economics

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Copyright Brandon H. Mckee. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.

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Changing Consumer Willingness to Pay: a time series evaluation of factors impacting Brandon McKee, Alexa Lamm, & Brandon McFadden Corresponding author: Brandon McKee Research assistant at the Center for Public Issues Education University of Florida Tel: (863)801 3499 Email: brandonmckee@ufl.edu Funding for this study was made possible by the Center for Public Issues Education, Department of Agricultural Education and Communications, University of Florida, IFA S

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Abstract s water supply from 2013 to 2017 This study used a value approach for estimating and a 50 percent increase in their water bill. The of their willingness to pay. The study found an increasing percentage of consumers willing to support the prote ction 2013 to 2016, with a drop in willingness in 2017 Knowing this dissonance can help decision makers make informed polices and regulations a bout futur e water conservation strategies.

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1 1. Introduction N early 80% of the world population faces potential threats to water security (Vorosmarty et al., 2010) a resource integral to food and energy production, transportation, infrastructure development, and health (Gleick, 1993). According to Young and Loomis (2014) water has four unique features that make it both difficult and necessary to esti mate its economic value, including: physical attributes, water demand, social attitudes, and political considerations. Therefore determining the value of water has been problematic and many allocation decisions have resulted in diverting the resource to l ess valuable purposes (Young & Loomis, 2014). To ensure proper resource management and to prevent exploitation of water it is imperative to determine the factors that affect valu ation of the resource C onflict and tension about water rights will become more challenging as an exponentially increasing population draws on the resource for agricultural, industrial, and economic development (Madani, 2010) This rising demand for water, coupled with a finite supply equates to an increas ed cost for the essential resource (Water Supply and Demand, n.d.) The state of Florida is particularly at risk because of several factors Projections provided by the Bureau of Economic and Business Research indicate Florida will grow from between 23,000,0 00 to approximately 29,000,000 by the year 2040 (Smith & Rayer, 2013). The state has also noticed frequent symptoms of poor water quality including algae bloom s and spring degradation (Drumm, n.d.) To help mitigate these issues Florida has an establishe d a water manage ment system designed around five districts : Northwest, St. Johns River, Suwannee River, South Florida, and Southwest Florida water management (Water management districts, n.d ) The districts work to provide programming,

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2 The increasing population combined with the wat er resource degradation make Florida a n ideal cast study to observe and the established water management districts are prime partners to determin e the effectiveness of various implementation strategies Water conservation efforts must be made by every industry in order to protect the resource from degradation and universally one aspect that can help curb consumption is through prices (Water for th e future, n.d.). It has been recognized that changes in prices and pricing system can have significant effects on water consumption (Water for the future, n.d.). Other factors associated with impacting water consumption include the adoption of water saving technologies, best management pract ices, and programs of education. H owever it has been shown that high water prices encourage investment in water saving technologies and an emphasis on management practices (Water for the future, n.d.). Therefore, future water demand strategies should focus on price for it not only discoura ges overuse, but also stimulates innovation. This study hopes to (WTP) for water conser vation efforts from 2013 to 2017 to determine if there are any shifts in support of water preservation. The researchers also will identify factors that influence a WTP for water preservation in Florida. By exploring the socioeconomic factors that WTP then decision make rs can make informed policies and programs that capitalize on this information. Recognizing the trends and changes seen in consumers annually and knowing the socioeconomic factors that consumers have dissonance in will better inform decision makers of appropriate water conservation strategies.

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3 2. Literature Review Researchers have explored a variety of methods for estimating the value of water. A study by Lund ( 1995 ) focused on using mathematical programming as an approach for determining WTP for water reliability. This approach could be effective for suggesting s economic st atus (Lund, 1995). Mathematical programming is considered superior to other approaches because it values an entire probability distribution of shortage levels, as opposed to examining single shortage levels with different probabilities (Lund, 1995). Lund notes that mathematical programming has disadvantages when compared to contingent evaluation, specifically revolving around the predetermination of customer rationale. Assuming customers will always react in a cost minimizing manner is a freque nt factor assumed by the mathematical method that is not fully supported by literature (Kahneman & Tversky, 1979). Lund concludes that this approach could be used for the design of water conservation programs, but this was out of range of his study. Natio nally the value of clean water for the public has been estimated on a variety of scales. T he Carson and Mitchell study (1993 ) is one of the most noteworthy assessments of a national study, which evaluated WTP for boatable, fishable, and swimmabl e quality water The study uses the contingent valuation method to approximate the monetary amount an individual is willing to give in exchange for a public good The national study used a scenario where estimated contributio ns reflected potential benefits and found that on average households were willing to pay a total of $242 for boatable, fishable, and swimmable quality water, with a 95% confidence interval ranging from $205 279 (Carson & Mitchell, 1993)

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4 Average WTP for the categories was $93 for boatab le water, $70 for fishable, and $78 for swimmable quality water (Carson & Mitchell, 1993). Agriculture and natural resource economists work diligently to manage natural resources by preventing overexploitation and developing strategies to maximize benefit verses cost. Properly managing resources is essential for resource longevit y (Why is NRM necessary?, 2007). However, a consistent issue facing natural resources longevity is when they are regarded as common property and are inevitably overused, such as wit (Bromley & Cernea, 1989). This ideology forms the basis of overexploitation for natural resources, as externalities unaccounted costs of resource use, arise and often lead to pollution and destruction of the resource (Clark, 1973). Therefore, to encourage conservation of water it is imperative that this common property resource have a cost associated with it that accounts for externalities. Butler and Memon (2005 ) discuss ed water consumption patterns and factors driving cons umption trends They noted that water consumption changes by location based on climate, availability, technology and innovation, water price structure, incentives, and legislative provisions (Butler & Memon, 2005 ). As well, Butler and Memon found that per capita consumption in households changed based on the number of people in the household, affluence, age, and recreation such as gardening. Water conservation technologies were heavily discussed, but fiscal policies were another strong proponent towards co nservation. This research uses a contingent survey approach to evaluate consumers WTP an additional cost water bills to pay for conservation efforts, hence c overing a portion of the external cost s (Clark,

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5 1973). By reviewing socioeconomics factors, such as in the Butler and Memon study (2005 ), WTP for conservation. 3. Purpos e & Objectives The purpose of t his study i s WTP to from 2013 to 2017 Florida is a central state to study because of the frequent water issues. These issues in part can be attributed to the growing false belief is that there is an over abundance of water available (Dell, 2010). Unfortunately, moments of stress, such as a severe water shortage are required for people to realiz e the finite availability of water. For exa mple, t he 2013 2017 drought in California attracted global attention as the state su ffered and fought over the limited amount of available water (U.S. Drought Monitor California n.d. ). However, the problems facing water not only include droughts, but als o wasteful water use and pollution from consumers (Geller, Erickson, & Buttram, 1983). Floridians have been exposed to algae blooms frequently, but some of the most notable blooms have occurred in the Lake Okeechobee watershed (Kennedy, 2016). The signific ance of these events cannot be understated and knowing the im pact these water scares have on consumers is important for future water conservation strategies. Therefore, research should be conducted perception on the importance of water conservation, especially since frequent exposure to poor water conditions can be a stimulus for Floridians to protect the resource.

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6 Floridians exposure to water degradation can be one of a few stimulants to encourage preservation, another could be economic growth. The United States has seen an increase in Economic growth since the 2008 2009 Great Recession. The state of Florida began seeing marginal growth in 2010 2011 as consumers were still recovering the recession and rebuilding their trust in the economy (Florida Economic Outlook, 2017). The state began to see further strides toward recovery between 2012 2013 and 2015 2016 (Florida Economic Outlook, 2017). 016 WTP The information gathered will allow decision makers to make informed choices on how be st to encourage water conservation. Specifically, the objectives of this study were : 1) Determine WTP for an increase of 10% in their water bill for the protection of ; 2) Determine WTP for an increase of 50% in their water bill ; 3) WTP h as changed between 2013 and 2017 ; and 4) Determine the socioeconomic factors that distinguishes a WTP 4. Methodology A WTP for the protection of The study questions were researcher developed and use d an online survey design that h as been conducted annually since 2012. To accompli sh the WTP for a 10% and a 50% increase in

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7 their water bill given it would protect the future water supply. These dichotomous variables can be found in T able 1 Respondents were asked the questions in pairs based upon the determinant of if the respondent was a home owner or a renter The reason for this distinction was to better represent what each of the parties would pay for their water bills. Respondents who considered themselves neither a home owner nor renter were not considered. Prices were based on a 10% increase in their water bill for the first variable and a 50% increase in the second. S ocioeconomic factors such as income, sex, race, education, politic al affiliation, and age were also measured and their measu rements can be found in Table 1 as well Table 1 Variables Dependent Variables Variable: Code : Description: Home owners 10% No=0 Yes=1 If your current water bill was $100 a month, would you accept it going up by $10 a month, for a total water bill of $110 a month, if the increase ensured a future water supply in Florida? Home owners 50% No=0 Yes=1 If your current water bill was $100 a month, would you accept it going up by $50 a month, for a total water bill of $150 a month, if the increase ensured a future water supply in Florida? Renters 10% No=0 Yes=1 If your current water bill was $50 a month, would y ou accept it going up by $5 a month, for a total water bill of $55 a month, if the increase ensured a future water supply in Florida? Renters 50% No=0 Yes=1 If your current water bill was $50 a month, would you accept it going up by $25 a month, for a tot al water bill of $75 a month, if the increase ensured a future water supply in Florida? Independent Variables Variable: Code : Age Category 1=18 19; 2=20 Sex 1=Male; 2=Female Race 1=White; 2=Black; 3=Asian; 4=Native American; 5=Multiracial Education 1= >12 th grade; 2=High school graduate; 3=Some college, no degree; 4=2 year college degree; 5=4 year college degree; 6=Graduate or professional degree Poli tical Affiliation 1=Republican; 2=Democrat; 3=Independent;

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8 Own or Rent 1=Own; 2=Rent; 3=Other Hispanic 0=No; 1=Yes Income 1=$24,999 or less; 2=$25,000 49,999; 3=$50,000 74,999; 4=$75,000 149,999; 5=$150,000 249,999, 6=$250,000 or more Prior to data collection the survey instrument was evaluated by a panel of experts. E xperts who specialized in water issues, survey design, and public opinion research were sought to ensure face and content validity. The panel of experts included an Extension specialist in water economics and policy, the Director of the UF Water Institute Nursery Growers, Chief Exe cutive Officer t he Director of UF/IFAS Center for Landscape Conservation and Ecology an assistant professor specializing in agricultural comm unications from the University of Florida and the As sociate Director of the Center for Public Issues Education Following this review a pilot test w as conducted with 50 respondents representative of the target population to confirm the re liability of the constructs. The survey was distributed by a public opinion survey research company and targeted state of Florida to residents aged 18 or older. Data was collected using a non probability opt in sampling method. The data was collected for five years with a total of 2 338 responde nts throughout the five year period ( Year one: 519 respondents, Year two: 749 respondents, Year three : 523 respondents, Year four : 547 respon dents Year five: 526 respondents ). Information about demographics for individual years can be found in the appendix. Quotas were set a priori and attention filters were integrated throughout the survey. Respondents who were not from Florida and did not enter have an address that registered with Florida were exited from t he survey. R espondent s who did not pass an attention filter were dismissed and their responses were not recorded. To negate issues that arise with non probability data collection, post stratification weighting methods were applied to ensure the responses w ere representative of

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9 the population of interest (Baker et al., 2013; Kalton & Flores Cervantes, 2003). Data was weighted based on the 2010 U.S. census. The data was analyzed using SPSS. Objectives one and two were compared using frequencies to show the p ercentage of consumers willing to pay for a 10 percent (Table 2 and Table 3) and a 50 percent (Table 4 and Table 5) increase by year Objective three used a chi square test to determine the significance of variations in varia bles from year to year (Table 6 ). Table 7 through 10 include the binary regression models used to evaluate factors that impa ct WTP A binary logit model was chosen given the binary nature of the four dependent variables (Moore, 2013) The categorical nature of the independent variables were also taken into consideration and the reference category for each of the variables is shown in the results. 5. Results 5.1 Objective 1: WTP for a t en percent increase Table 2 and 3 show the frequency and percentages of home owners and renters that are willing to pay for a 10 percent increase in a water supply. Homeowners show a trend of increasing willingness to support this i ncrease from 2013 to 2016 but then a decrease in 2017 during the five years, with a high in 2014 and a low in 2016 Table 2 2013 2014 2015 2016 2017 N % N % N % N % N % Yes 234 64 370 68 118 70 224 80 97 65 No 132 36 170 32 51 30 58 20 53 35

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10 Total 367 100 540 100 169 100 282 100 151 100 Table 3 2013 2014 2015 2016 2017 N % N % N % N % N % Yes 122 80 171 82 266 75 194 73 301 80 No 30 20 38 18 88 25 71 27 75 20 Total 152 100 209 100 354 100 265 100 375 100 5.2 Objective 2: WTP for a f ifty percent increase WTP for a 50 percent increase in their water bill to protect the und in T ables 4 and 5 There was an increase in homeowners WTP f rom a low of approximately five percent in 2013 to a high of 43 percent in 2016. Homeo wners have another decrease in 2017, simila r to that of T agreeance to a 50 percent increase was fairly different than in T able 3 with slightly more variation than before. T here was a high for renters in T able 5 during 2015, and a low in 20 13. Table 4 2013 2014 2015 2016 2017 N % N % N % N % N % Yes 26 5 91 17 46 27 122 43 39 26 No 340 95 449 83 123 73 160 5 7 112 74 Total 367 100 540 100 169 100 282 100 151 100 Table 5 2013 2014 2015 2016 2017 N % N % N % N % N % Yes 20 13 48 23 104 29 48 1 8 73 20 No 132 87 162 77 250 71 217 82 302 81 Total 156 100 209 100 354 100 265 100 375 100

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11 5.3 Objective 3 : Comparison of WTP over time A chi squared test was used to evaluate the statistical difference between years. The results are displayed in Ta ble 6 below. The test showed that the year evaluated was a significant in dependent variable in determi WTPF for three of the four variables. WTP WTP for a 50 percent increase in their water bill all show significant relat ionships with the year surv eyed The Phi values for the variables show the strongest relationship can be seen in homeowners WTP for a 50 percent increase (Phi Value=.372 ). Table 6 Chi Squared Table Chi Square P value Phi Home owners 10% 42.605 .000 .165 Renters 10% 4.271 .371 .057 Home owners 50% 216.913 .000 .372 Renters 50% 34.890 .000 .163 5.4 Objective 4 : Changing WTP T ables 7 through 10 WTP for a 10 percent and a 50 percent increase in their water bill. The models evaluate participants based on the socioeconomic factors discussed earlier. Reference categories are displayed next to the variable name. Significant variables are shown by asteris ks. WTP for a 10 percent increase in age category has significant groups in comparison with the reference category of 40 49 years old. Age groups 20 29 and 30 39 are s ignificantly more likely to be willing to pay for a 10 percent increase in their water bill, while 50 59, 60 69, 70 79, and 80 years and older are significantly less likely to agree to a 10 percent increase than compared to those who are 40 49

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12 years old. I ncome was significant for several groups compared to the reference group of those who make $24,999 or less. Respondents in income brackets of $50,000 to $74,999, $75,000 to $149,999, and $150,000 to $249,999 are more likely to agree to an increase of 10 pe rcent. The model shows that individuals who are black are less likely to agree to the increase in their water bill than compared to individuals who identify as white. As well, democrats when compared to republicans are more likely to be willing to pay for the increase in their water bill given it would Table 7 Binary Logit Model Variable Parameter Estimate Standard Error Age Category (Ref. 40 49 ) 18 19 .647 .530 20 29 .649** .263 30 39 .578** .240 50 59 .388** .197 60 6 9 .561*** .212 70 7 9 .804*** .239 80 8 9 .786*** .262 Income (Ref. $24,999 or less) $25,000 to $49,999 .311 .192 $50,000 to $74,999 .494** .203 $75,000 to $149,999 .885*** .217 $150,000 to $249,999 1.458*** .445 $250,000 or more 1.305 .819 Education (Ref. High school grad ) Less than 12 th grade education .326 .655 Some college .251 .190 2 year degree .317 .215 4 year degree .023 .205 Graduate .190 .240 Race (Ref. White) Black .599*** .185 Asian .647 .696 Native American .972 1.410 Multiracial 1.080 .702 Political Affiliation (Ref. Republican) Democrat .301* .154 Independent .083 .166

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13 Hispanic (Ref Yes ) No .190 .177 Sex (Ref. M ale) Fem ale .118 .127 Constant .691** .313 Model Fit Statistics Log Likelihood 1563.459 *** Cox & Snell R Squared .105 Note: *, **, and *** denote significance level at 0.10, 0.05, and 0.01 respectively. WTP for a 10 percent increase given it would protect the Eighteen to nineteen year olds when compared to indiv iduals who are 40 49 years old were more likely to be agree to a 10 percent increase in their w ater bill. The model shows that income is a significant factor in determining $74,999, and $75,000 to $149,999 were significantly more likely to agree to an increase t han compared to the reference group of respondents who make less than $24,999. Compared to those with a high school education, individuals who have some college education were more likely to be willing to pay for an increase. Individuals who identify as As ian were less likely to be willing to agree to an increase than when compared to those who identify as white. Table 8 Binary Logit Model Rent Variable Parameter Estimate Standard Error Age Category (Ref. 40 49 ) 18 19 1.548** .724 20 29 .330 .282 30 39 .262 .298 50 59 .402 .280 60 6 9 .204 .281 70 7 9 .327 .319 80 8 9 .387 .376 Income (Ref. $24,999 or less) $25,000 to $49,999 .447** .188 $50,000 to $74,999 .804*** .250 $75,000 to $149,999 .567** .279

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14 $150,000 to $249,999 .249 .604 $250,000 or more .412 1.103 Education (Ref. High school grad ) Less than 12 th grade education .014 .398 Some college .395* .224 2 year degree .092 .238 4 year degree .343 .247 Graduate .352 .318 Race (Ref. White) Black .040 .238 Asian .946* .486 Native American .241 1.129 Multiracial .069 .724 Political Affiliation (Ref. Republican) Democrat .091 .200 Independent .137 .207 Hispanic (Ref Yes ) No .327 .215 Sex (Ref. M ale) Fem ale .055 .163 Constant .650* .369 Model Fit Statistics Log Likelihood 1059.156 ** Cox & Snell R Squared .037 Note: *, **, and *** denote significance level at 0.10, 0.05, and 0.01, respectively. WTP for a 50 percent increase in that age was a significant variable to consider, with 40 49 year olds being more likely to agree to an increase in their water bill than 50 59, 60 69, and 70 79 year olds. Individuals who identify as 18 19, 20 29, and 30 39 years old were more likely than 40 49 year olds to be willing to pay for an increase. Income was also significant, with those who make $50,000 to $74,999, $75,000 t o $149,999, and $150,000 to $249,999 being more likely to pay for an increase of 50% than those who make $24,999 or less. The model shows that education was significant, with those who have some high school education, a 4 year degree, and a graduate degree all being more

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15 likely to pay for an increase of 50 percent in their water bill than those individuals who have a 12 th grade education. Individuals who identify as Hispanic were more likely than non Hispanic respondents to agree to an increase. The model a lso shows respondents who are male were more likely than females to agree to a 50 percent increase in their water bill. Table 9 Binary Logit Model Variable Parameter Estimate Standard Error Age Category (Ref. 40 49 ) 18 19 1.577*** .448 20 29 .757*** .235 30 39 .565*** .213 50 59 .984*** .271 60 6 9 1.639*** .352 70 7 9 1.315*** .392 80 8 9 .109 .296 Income (Ref. $24,999 or less) $25,000 to $49,999 .378 .285 $50,000 to $74,999 .581** .286 $75,000 to $149,999 .851*** .290 $150,000 to $249,999 1.504*** .382 $250,000 or more .864 .623 Education (Ref. High school grad ) Less than 12 th grade education 1.137* .649 Some college .198 .269 2 year degree .428 .315 4 year degree .596** .263 Graduate .610** .290 Race (Ref. White) Black .156 .213 Asian .270 .464 Native American .867 1.090 Multiracial .574 .482 Political Affiliation (Ref. Republican) Democrat .168 .181 Independent .089 .204 Hispanic (Ref Yes ) No .598*** .170 Sex (Ref. M ale) Fem ale .671*** .152 Constant 1.410*** .392 Model Fit Statistics

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16 Log Likelihood 1187.966 *** Cox & Snell R Squared .188 Note: *, **, and *** denote significance level at 0.10, 0.05, and 0.01, respectively. WTP for a 50 percent increase given it would protect 59 years old were less likely to pay for an increase of 50% than those who are 40 49 year old. The model shows that respondents who make between $50,000 to $74,999 and $75,000 and $149,999 were significantly more likely to be willing to pay for an increase of 50 percent than when compared to those who make less than $24,999. Individuals wh o had a 2 year degree, 4 year degree, and a graduate degree were less likely than individuals with a high school degree to be willing to pay for a 50 percent increase. As well, those who identify as multiracial were more likely than those who consider them selves as white to agree to an increase. Finally, individuals who are not Hispanic were more likely to be willing to pay for the increase. Table 10 Binary Logit Model Variable Parameter Estimate Standard Error Age Category (Ref. 40 49 ) 18 19 .534 .415 20 29 .191 .270 30 39 .013 .270 50 59 .613** .280 60 6 9 .406 .269 70 7 9 .963*** .336 80 8 9 20.143 4724.724 Income (Ref. $24,999 or less) $25,000 to $49,999 .208 .216 $50,000 to $74,999 .772*** .249 $75,000 to $149,999 .971*** .278 $150,000 to $249,999 1.164* .635 $250,000 or more .501 1.098 Education (Ref. High school grad ) Less than 12 th grade education .091 .428 Some college .146 .222 2 year degree .455* .269

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17 4 year degree .639** .265 Graduate .753** .352 Race (Ref. White) Black .298 .229 Asian .038 .557 Native American .133 1.111 Multiracial 1.408** .639 Political Affiliation (Ref. Republican) Democrat .056 .209 Independent .152 .222 Hispanic (Ref Yes ) No .662*** .244 Sex (Ref. M ale) Fem ale .050 .166 Constant 1.709*** .397 Model Fit Statistics Log Likelihood 974.716*** Cox & Snell R Squared .086 Note: *, **, and *** denote significance level at 0.10, 0.05, and 0.01, respectively. The models consistently show a disparity for homeowners and renters when it comes to income brackets compared to those who make less than $2 4,999. The models also show a disparity between age groups when compared to those who are 40 49 years old; however these also h ad some inconsistencies Other variables appeared significant throughout the models, but not as regularly as the variables listed above. For example, if considering the models for WTP by 10 percent then race was a significant va riable but the groups impacted by race were different between homeowners and renters. As well, the models WTP by 50 percent had education in common as a significant variable, but it appears education had contradicting effects on homeowners versus renters.

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18 6. Conclusion s WTP for increases in WTP over four years and compared willingness year by year using a chi squared test As well, binary l ogit models were used to identify the socioeconomic factors that WTP over the course of time. The study found that the year the respondents were evaluated was a significant variable WTP given they were a homeowner or a renter willing to pay for a 50 percent increase lingness can be seen in WTP for a 50 percent increase in their wat er b ill. In 2013 approximately five percent of homeowners w ere willing to pay for the increase in their water bill ; however this number increased to 43 percent in 2016. The highest recorded WTP percentage is seen with renters willing to pay for a 10 percent increase. Eighty two percent of rente rs were willing to pay for an increase in their The increase that is WTP from 2013 to 2016 can potentially be attributed to the bolstering economy in Florida and the gro wing Florida Economic Outlook, 2017; U.S. Drought Monitor California n.d.; Kennedy, 2016 ). the state has reentered a state of normalcy (Florida Economic O utlook, 2017). However, while a strengthening economy could WTP seen during 2013 to 2016

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19 reentering normalcy would mean this increase could be shunted in the future This may attribute for the decre ase we see across T ables 2 5 for 2017. In order to continue this trend of increasing WTP focus future initiatives. Tables 7 through 10 show the binary logit models for increasing ho meowner and WTP for a 10 percent and a 50 percent increase in their water bill given the future A factor that appeared consistently as influencers of WTP is the income bracket, while o ther factors such as age and rac e inconsistently appeared throughout the models. However, age disparity did appear as a significant factor affecting homeowners and renters, it was noted that specific age groups were not always impacted. Tables 7 and 9 show that when compared with respondents 40 49 years old that younger groups were more likely to be willing to pay for the increases while older groups were less likely 7. Implications Decision makers should take into consideration the significance o f income brackets on WTP ; therefore future programs and initiatives that include price increases should be made in lite of this information. Since water consumption increases with affluence it is encouraging to see higher income groups more willing to pay for conservation efforts (Butler & Memon, 2005). In the future ado pting fiscal policies that increase water bills for the sake of water conservation could be an effective tool, as mentioned by Butler and Memon, and should be well received given the increasi ng

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20 willingness seen in T ables 2, 4, and 5. While the increase in water prices includ ing the cost of externalities, can also stimulate innovation by consumers to expand conservation efforts ( Clark, 1973; Water for the future, n.d.). As well, knowing the dissonance between income groups can help messages and marketing programs be intently developed and ta rgeted towards these groups. Audience segmentation can be an effective strategy for encouraging acceptance of increasing water bills (McKenzie Mohr, Lee, Schultz, Kotler, 2012) Audience segmentation allows you to focus upon the most important subgroups wi thin the larger population to create effective campaigns (McKenzie Mohr et al., 2012). Diminishing consumer externalities by focusing on those groups that most overuse water can be a strategy that highlights on audience segmentation and according to this study could be well received (Clark, 1973). This information may be most districts by c reating distinctive messages to educate the diverse income groups Consumers have shown similar trends in other studies that have focused on irrigation water conservation programs. Warner, Harder, Henry, Ganpat, & Martin (2017) research suggests audience segmentation can be utilized to focus on both groups that are and are not engaged in water conservation practices. 8. Recommendations Future recommendations for researchers would be to apply further elements from the studies and theories directed by Lund (1995), Carson and Mitchell (1993), an d Clark (1973) For example, Lund (1995) noted his mathematical approach could b e effective for suggesting This study

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21 suggests that income was a factor of dissonance between groups WTP for water conservation. Knowing mers future research should use a mathematical approach for determining WTP This will define specific amounts consumers will support and then they can be compare d to the cost of externalities. Carson and Mitchell (1993) determined WTP servation of fishable, boatable, and swimmable water, while this study evaluated consumer protect specific areas of Florida wate r, such as springs, lakes, rivers, or any area indigenous to WTP for externality costs. Comparisons can then be made for respondents in different areas around Florida to determine if prox imity to water issues impacts acceptance of costs. Future research should also consider using factors outside of socio demographic information to distinguish consumer groups. This study can be strengthened by including lues and personality characteristics as well (Verain, Bartels, Dagevos, Sijtsema, Onwezen, and Antonides, 2012). inclination towards water and environmental consciousness. This addition to future studies can be an WTP to protect district could show districts that can strengthen their efforts and show which subgroup s to focus upon.

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22 References Baker, R., Brick, J. M., Bates, N. A., Battaglia, M ., Couper, M. P., Dever, J. A., Tourangeau, R. (2013). Report of the AAPOR task force on non probability sampling. American Association for Public Opinion Research. Retrieved from http://www.aapor.org/AM/Template.cfm?Section=Reports1&Template=/CM/ContentDi splay.cfm&ContentID=5963 Bromley, D. W., & Cernea, M. M. (1989). The management of common property natural resources: Some conceptual and operational fallacies (Vol. 57). Wo rld Bank Publications. Butler, D., & Memon, F. A. (Eds.). (2005). Water demand management Iwa Publishing. Carson, R. T., & Mitchell, R. C. (1993). The value of clean water: the public's willingness to pay for boatable, fishable, and swimmable quality wa ter. Water resources research 29 (7), 2445 2454. Clark, C. W. (1973). The economics of overexploitation. Population 150 200 000. Dell, P. (2010). Protecting the planet: environmental activism Minneapolis, MN: Compass Point Books. Drumm, S. (n.d.). Multitude of water pollution, shortage issues facing Florida and Alachua County. Retrieved December 18, 2017, from https://www.wuft.org/news/2013/05/02/runoff water carries pollutants/ Florida Economic Outlook. (2017). Retrieved January 05, 2018, from htt p://edr.state.fl.us/Content/conferences/fleconomic/floridaeconomicsummary.pdf Geller, E. S., Erickson, J. B., & Buttram, B. A. (1983). Attempts to promote residential water conservation with educational, behavioral and engineering strategies. Population a nd Environment 6 (2), 96 112. Green Demer, I., Blanchard, C., Pelletier, L. G., & Bland, A. (1994). Perception of government environmental strategies by the citizens: The government style questionnaire (GSQ). (Research Paper No. 13). Ottawa: University of Ottawa Institute for Research on Environment and Economy. Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision Under Risk, Econometrica, vol. 47, pp 263 291. Kalton, G., & Flores Cer vantes, I. (2003). Weighting methods. Journal of Official Statistics, 19 (2), 81 97.

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23 Kennedy, M. (2016, July 02). Thick, Putrid Algae Bloom Overwhelms Miles Of Florida Coastline. Retrieved December 27, 2017, from https://www.npr.org/sections/thetwo way/201 6/07/02/484477038/thick putrid algae bloom overwhelms miles of florida coastline Lund, J. R. (1995). Derived estimation of willingness to pay to avoid probabilistic shortage. Water Resources Research 31 (5), 1367 1372. Madani, K. (2010). Game theory and water resources. Journal of Hydrology 381 (3), 225 238. McKenzie Mohr D., Lee N. R., Schultz P. W., & Kotler, P. (2012). Social marketing to protect the environment. Thousand Oaks, CA: Sage Publications. Moore, C. (2013). An introduction to logistic and probit regression models. Retrieved March 12, 2018, from https://liberalarts.utexas.edu/prc/_files/cs/Fall2013_Moore_Logistic_Probit_Regressio n.pdf Patterson, L. (2012). 2012 RBC Canadian water attitudes study RBC Blue Water Project. Retrieved from http: //www.rbc.com/community sustainability/environment/rbc blue water/index.html Smith, S.K., Rayer, S. (2013, March). Projections of Florida Population by County, 2015 2040, with Estimates for 2012. Bureau of Economic and Business Research, 46(165). Retrieved from https://www.bebr.ufl.edu/sites/default/files/Research%20Reports/projections_2013.pd f U.S. Drought Monitor California. (n.d.). Retrieved December 27, 2017, from https://www.drought.gov/drought/states/california Verain, M. C., Bartels, J., Dagevos, H., Sijtsema, S. J., Onwezen, M. C., & Antonides, G. (2012). Segments of sustainable food consumers: a literature review. International Journal of Consumer Studies 36 (2), 123 132. Retrieved from https://s3.amazonaws.com/academia.edu.documents/396 39496/Segments_of_sustaina ble_food_consumers_a20151103 4654 1s2zb55.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1516397264&Signat ure=1gMvJLUAxwV86IVpQ%2F2bP7RGqR4%3D&response content disposition=inline%3B%20filename%3DSegments_of_sustainable_food_consu mers_a.p df

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24 Vrsmarty, C. J., McIntyre, P. B., Gessner, M. O., Dudgeon, D., Prusevich, A., Green, P., ... & Davies, P. M. (2010). Global threats to human water security and river biodiversity. nature 467 (7315), 555. Warner, L. A., Harder, A. M., Henry, C. V., Ganpat, W. G., & Martin, E. (2017). Factors that Influence Engagement in Home Food Production: Perceptions of Citizens of Trinidad. Journal of Agricultural Education 58 (3). Water Management Districts. (n.d.). Retrieved December 27, 2017, from http ://www.stateofflorida.com/water management.aspx Water Supply and Demand. (n.d.). Retrieved December 18, 2017, from https://climatemodeling.science.energy.gov/research highlights/water supply and demand Why is NRM necessary? (2007, January 19). Retrieved December 18, 2017, from http://www.geo.fu berlin.de/en/v/geolearning/watershed_management/introduction_wm/natural_resourc e_management_planning/necessity_nrm/index.html Young, R. A., & Loomis, J. B. (2014). Determining the economic value of water: concepts and methods Routledge.

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25 Appendix Table A1 Year 2013 N % Sex Male 251 48.3 Female 268 51.7 Age_Category 18 19 18 3.5 20 29 84 16.3 30 39 80 15.5 40 49 93 17.9 50 59 89 17.2 60 69 74 14.2 70 79 49 9.4 80 or older 32 6.2 Own_Rent Own 334 64.4 Rent 171 33.0 Other 14 2.6 Hispanic Yes 110 21.1 No 409 78.9 Race White 403 77.6 Black 75 14.4 Asian 13 2.5 Native American 2 0.4 Multiracial 10 1.9 Other 17 3.2 Education < 12 th Grade 2 0.5 High school graduate 108 20.8 Some college 135 25.9 2 year college degree 129 24.9 4 year college degree 68 13.1 Graduate 77 14.8 Political_ Affiliation Republican 118 22.7 Democrat 191 36.8 Independent 134 25.9 Other 76 14.6 Income

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26 $24,999 or less 126 24.3 $25,000 $49,999 181 34.9 $50,000 $74,999 116 22.3 $75,000 $149,999 82 15.7 $150,000 $249,999 14 2.7 $250,000 or more 0 0.1 Year 2014 N % Sex Male 362 48.3 Female 387 51.7 Age_Category 18 19 26 3.5 20 29 122 16.3 30 39 116 15.5 40 49 134 17.9 50 59 129 17.2 60 69 106 14.2 70 79 70 9.4 80 or older 46 6.2 Own_Rent Own 494 66.0 Rent 232 31.0 Other 22 3.0 Hispanic Yes 158 21.1 No 591 78.9 Race White 581 77.6 Black 108 14.4 Asian 19 2.5 Native American 3 0.4 Multiracial 14 1.9 Other 24 3.2 Education < 12 th Grade 35 4.6 High school graduate 130 17.3 Some college 211 28.1 2 year college degree 106 14.2 4 year college degree 188 25.1 Graduate 80 10.6 Political_ Affiliation Republican 179 23.9 Democrat 266 35.5

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27 Independent 190 25.3 Other 114 15.3 Income $24,999 or less 141 18.8 $25,000 $49,999 237 31.7 $50,000 $74,999 184 24.5 $75,000 $149,999 149 19.9 $150,000 $249,999 34 4.6 $250,000 or more 4 0.5 Year 2015 N % Sex Male 253 48.3 Female 270 51.7 Age_Category 18 19 18 3.5 20 29 85 16.3 30 39 81 15.5 40 49 93 17.9 50 59 90 17.2 60 69 74 14.2 70 79 49 9.4 80 or older 32 6.2 Own_Rent Own 256 49.0 Rent 235 45.0 Other 31 6.0 Hispanic Yes 110 21.1 No 413 78.9 Race White 406 77.6 Black 75 14.4 Asian 13 2.5 Native American 2 0.4 Multiracial 10 1.9 Other 17 3.2 Education < 12 th Grade 14 2.6 High school graduate 152 29.1 Some college 151 28.9 2 year college degree 66 12.7 4 year college degree 90 17.3 Graduate 49 9.4

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28 Political_ Affiliation Republican 141 26.9 Democrat 182 34.8 Independent 106 20.2 Other 95 18.1 Income $24,999 or less 110 21.0 $25,000 $49,999 180 34.5 $50,000 $74,999 126 24.0 $75,000 $149,999 77 14.6 $150,000 $249,999 22 4.3 $250,000 or more 8 1.6 Year 2016 N % Sex Male 264 48.3 Female 283 51.7 Age_Category 18 19 19 3.5 20 29 89 16.3 30 39 85 15.5 40 49 98 17.9 50 59 94 17.2 60 69 78 14.2 70 79 51 9.4 80 or older 34 6.2 Own_Rent Own 375 68.5 Rent 145 26.6 Other 27 4.9 Hispanic Yes 115 21.1 No 432 78.9 Race White 424 77.6 Black 79 14.4 Asian 14 2.5 Native American 2 0.4 Multiracial 10 1.9 Other 18 3.2 Education < 12 th Grade 7 1.2 High school graduate 116 21.2 Some college 93 17.0

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29 2 year college degree 75 13.7 4 year college degree 150 27.4 Graduate 106 19.5 Political_ Affiliation Republican 183 33.5 Democrat 220 40.2 Independent 107 19.6 Other 37 6.7 Income $24,999 or less 111 20.3 $25,000 $49,999 158 28.9 $50,000 $74,999 110 20.1 $75,000 $149,999 141 25.8 $150,000 $249,999 21 3.8 $250,000 or more 6 1.1 Year 2017 N % Sex Male 254 48.3 Female 272 51.7 Age_Category 18 19 18 3.5 20 29 86 16.3 30 39 81 15.5 40 49 94 17.9 50 59 90 17.2 60 69 75 14.2 70 79 49 9.4 80 or older 33 6.2 Own_Rent Own 281 53.4 Rent 181 34.5 Other 64 12.1 Hispanic Yes 111 21.1 No 415 78.9 Race White 408 77.6 Black 76 14.4 Asian 13 2.5 Native American 2 0.4 Multiracial 10 1.9 Other 17 3.2 Education

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30 < 12 th Grade 27 5.2 High school graduate 115 21.8 Some college 125 23.7 2 year college degree 81 15.4 4 year college degree 116 22.1 Graduate 62 11.8 Political_ Affiliation Republican 126 24.0 Democrat 190 36.1 Independent 134 25.5 Other 76 14.4 Income $24,999 or less 156 29.6 $25,000 $49,999 157 29.8 $50,000 $74,999 109 20.6 $75,000 $149,999 92 17.6 $150,000 $249,999 10 1.8 $250,000 or more 3 0.6