1

Is the Grass Always Greener on

the Other Side?

Analyzing the drivers behind the success of turf replacement programs in the

Metropolitan Water District.

Local Project

Faculty Project Manager:

Dr. Hal Nelson

Student Project Manager:

John Shideler

1

1 This picture was taken directly from the Water Smart San Diego website at

http://www.watersmartsd.org/news/watersmart-turf-replacement-program-0

2

Project Summary

The main goals of this research project are to identify variables that influence the household

decision to participate in turf rebate programs, investigate the potential for spatial and peer

effects in the decision making process, and create predictive analytics which can aid in future

policy implementation and design. The proposed project will combine survey data, turf rebate

program data, property data, and other demographic data to be employed via econometrics and

spatial analysis. While recent turf replacement programs have proven to be very successful,

outdoor irrigation remains as a major use of water which needs to be “curbed” in order for a

sustainable California to become a reality. The continued success of turf replacement programs

will require a better understanding of the household level incentives that drive the decision to

participate in these programs. We are specifically interested in understanding the role that

spatial and peer effects may play in relation to the classic pricing mechanisms of demand side

management. Is the household decision to participate in such programs driven more from the

desire for compensation, commendation, or community?

3

Contacts

College Claremont Graduate University

Department Division of Politics and Economics

Make Check Payable To:

Claremont Graduate University

Application Strand Policy Research

LOCAL Project Name Is the Grass Always Greener on the Other Side?

GLOBAL Project Name

Faculty Project Manager Dr. Hal Nelson

Title Research Associate Professor

Department Politics and Economics

Campus Address Harper East 209

Telephone / Email Address 909-621-8284 Hal.nelson@cgu.edu

Student Project Manager John Shideler

Undergraduate or Graduate Graduate

Department Politics and Economics

Cell Phone / Email Address 561-351-3174 John.shideler@cgu.edu

Contracts Manager / Officer Dr. Dean Gerstein

Title Vice Provost, Director of Research, and Research Professor

Department Office of Research and Sponsored Programs

Campus Address Harper 122

Telephone / Email Address 909-607-8069 Dean.gerstein@cgu.edu

MEMBER AGENCY(IES) / LOCAL WATER AGENCY(IES)

NAME TITLE / ORGANIZATION ADDRESS PHONE & EMAIL

1 Cindy DeChaine

Three Valleys MWD 1021 Miramar Ave, Claremont CA

909-621-5568

cdechaine@tvmwd.com

4

Organization Background The primary location for research and work related to this project will be on campus at Claremont Graduate University. Claremont Graduate University (CGU) is a private, graduate only research university which is part of the Claremont Colleges. Given the small size of the student body, and the focus on research, Claremont Graduate University allows for increased opportunities to collaborate between students and faculty of the same discipline and other disciplines. “In both teaching and research, our trademark is the interchange of ideas -inclusive of diverse points of view and attentive to evidence -to catalyze new ways to address tomorrow’s challenges.2” The Division of Politics and Economics (DPE) is housed within the School of Social Sciences Policy and Evaluation (SSSPE) at CGU. As a University CGU has a strong focus on the use of transdisciplinary research to address questions which are not always easily answered under a unidisciplinary research agenda. The DPE reinforces that focus with a strong emphasis on the synergy between Politics, Economics, Applied Methodology, and Evaluation. Students at CGU and within the department are encouraged to take coursework, and seek out research agendas, which involves mentorship and and collaboration with faculty and students from other departments. Within the Division of Politics and Economics there are a number of research institutes, two of which will have significant relevance to the research proposed in this project: The Center for Behavioral Economic Studies and The Claremont Institute for Economic Policy Studies. Of the projects conducted under these two centers the most relevant will be those related to the recent work of Dr. Hal Nelson, the Faculty Project Manager on this project. Dr. Nelson has worked on a number of projects over the last 5 years which will relate to the theory, methodology, and goals of this research project including: Georgetown University Energy Prize (GUEP) Collaboration with the City of Claremont The Georgetown University Energy Prize is a nationwide energy challenge to reduce energy consumption and increase energy efficiency by city for the calendar years of 2016-2017. This is a collaborative project involving representatives from the city of Claremont, local organizations, and representatives from the Claremont Colleges. Devon Hartman, a local energy conservation leader, is the Project Manager for the city of Claremont and has relied on Dr. Hal Nelson as a lead advisor. Many Claremont Colleges students have played significant roles as energy conservation leaders in the local community. Specific to this project, the team at Claremont Graduate University has worked on predictive analytics, spatial analysis, peer effects, and probabilistic estimation of household energy retrofit adoption in conjunction with the overarching goal of winning the Georgetown University Energy Prize. The Power Struggles Project This project maps the planned energy infrastructure projects in California allowing citizens and businesses easy access to visual and descriptive information related to upcoming projects. Further information on the project can be found at http://www.cgu.edu/pages/4546.asp?item=8166 Citizens and Social Sustainability of Energy Infrastructure Siting (SEI) This project is an extension of a previous project related to energy infrastructure siting and the complexities surrounding the potential for community organization related to the support or opposition of planned energy infrastructure projects.

2 Taken directly from the CGU website at http://www.cgu.edu/pages/159.asp

5

While this is not a complete list of current projects which will be influential to the research proposed in this application; these three projects should give a good idea of the relevance of the work being done at CGU to the project at hand. Introduction The Metropolitan Water District of Southern California has had great success with the Socal Water Smart Turf Removal Program. According to the Socal Water Smart website, the waitlist for the program was officially closed on November 1, 2015 and turf removal is no longer being offered region wide. Demand for the program has been huge and the program will eventually be responsible for the removal of “over 170 million square feet of turf in southern California3”. While this is a big step in the right direction there is still much more to be done to ensure an environmentally and economically sustainable future for California. Drought, state mandated water reductions, and increased public awareness will continue to place turf rebate programs at the forefront of policy initiatives in the state of California. Therefore we intend to pursue research with a content strand related to policy at the local level. However, it should be noted that any information or knowledge gained through this research would provide useful outside of the local context as well. The focus of this research project will be to gain a better understanding of the incentives and causal effects related to the household decision to apply for turf rebate programs. Specifically we are interested in two main research questions: 1). What are the household level predictors of adoption in turf replacement programs? 2). What role, if any, do spatial patterns and peer effects play in the household adoption, and success, of turf replacement programs? Ultimately these two questions will help provide more information related to the goal of promoting further turf replacement and water conservation. While turf replacement programs have been very successful up to this point, research in this realm will become critical as the target audience becomes harder to reach. There is a growing need to better understand which behavioral, financial, demographic, and spatial variables play the biggest role in the household decision to participate in turf rebate programs. Although there is some overlap between the two research questions listed, studying them individually will allow us to better identify and validate causal effects in each class of variables. Data Collection and Methodology The primary use of funding from this application will be towards the collection and analysis of survey data. We have already attained turf rebate application data from The Metropolitan Water District for the one year period ranging from August 2014 through August 2015. This data was attained through a public records act request to the Metropolitan Water District. The research in this project will be broken down into three complementary projects, related to this data, which will help provide a more transparent picture of the household’s decision to participate in said program. The first phase of this research project will involve analysis of this turf replacement data combined with available household level demographic data and local property data. Using the combined sources of data, we will employ GIS and regression analysis in order to search for spatial adoption patterns, peer effects, rebate price sensitivity, and the power of behavioral and demographic prediction variables at the household level. The second phase of this project will involve sending out surveys and analyzing the resulting data.

3 The source of this information is http://socalwatersmart.com/?page_id=2967

6

The use of survey data will provide three primary functions which will set this research apart from many existing studies in this realm.

1. Survey data will allow us to perform a case control study which will allow us to provide further validation for or against our empirical findings related to the first phase of the project. As discussed we are particularly interested in analyzing the potential for spatial, peer, and behavioral effects; three causal variables that are difficult to validate without the use of survey data or focus groups.

2. We will also add data from the surveys to our existing data set, and restructure our empirical model according to the new data points and adjusted sample size, to see if any explanatory power can be added to our existing model.

3. The results from the survey data will provide a basis for the design of extensions to this research in the way of focus groups and lab experiments. (see extensions section below)

From the list of applicants we will send a survey to 1,084 randomly selected residents. We will also send a survey to 1,084 randomly selected homeowners who did not apply for a rebate during this program. While the exact survey questions are still to be developed, they will be designed to help answer questions related to the motivation behind the choice to participate or not participate in the turf rebate program. In addition, the survey questions will help clarify incentives specifically related to potential peer effects and spatial effects. The third phase of this project will involve a price sensitivity analysis related to the cross section of turf replacement applications and the alternative rebate pricing structures that may have been employed by the different service areas within the metropolitan water district. Further information related to the methodology for each phase is given below. Spatial and Peer Effects There are a number of ways that the proposed survey data, along with the turf rebate application data, will allow us to isolate interesting peer effects and spatial effects. First, the use of GIS software will allow us to map the data points and look for clusters of applications by geographic area at the city and even street level. Since we have the dates that applications were approved, we can also calculate metrics such as distance to nearest residence with a turf replacement or number of turf replacements on a street or within high probability driving areas. When the spatial data is combined with demographic and property data we will have the ability to gain a better idea of which neighbors, on the same street or within a specified area, would be the most likely candidates for “peers” by matching on all available data points. This will allow us to draw inference related to influence not only via spatial considerations but also the nearest peers and how individuals identify with those peers. Predictive Analytics As discussed above, regression analysis which employs spatial, demographic, and property data will allow us to make inferences related to the probability of participation in turf rebate programs at the household level. While the spatial analysis will allow us to get a better visual of patterns and create proximity variables, regression analysis will allow us to estimate the causal effect that different household attributes have in the decision making process. From a methodological point of view, this type of model typically faces three primary challenges.

7

1. Given the nature of the policy/program the treatment may not be randomized and often includes a form of self selection bias from those households that participate.

2. There is often a lack of data at the household level 3. Estimated results are often difficult to validate

Our proposed research will allow us to overcome these challenges through the variety of data points we collect, and the methodology we will employ. For the turf rebate program mentioned above, one could argue that either:

a) All homeowners in the service area received the treatment since application was available to all households who qualified.

b) There is some self selection due to the fact that households who are more informed, care more about the environment, or lived in specific areas most likely received more information related to the program related to a higher level of salience for their specific household attributes.

In either case, the assignment of treatment would not be random and creates a large problem for correctly estimating the effect of the program by limiting the explanatory and predictive power of a model that simply compares the attributes of those who participated to those who did not. By using propensity score matching we can eliminate the selection bias mentioned above. We will take a random sample of households who participated in the turf rebate program, and use propensity score matching to create a random sample of households who did not participate by matching on all available and relevant data points. The random sample of households who did not participate in the turf rebate program will have been matched to households which did participate in the program according to probability of participation according to the data points we have. This process allows us to account for the covariates that predict receiving the treatment and eliminate the selection bias that would result from simply assigning a control group out of those households that did not participate. Once we have correctly assigned a control group through the matching process, we can evaluate the effectiveness of the program and predictive power of the variables in the model. In regards to challenge number two listed above, we are lucky enough to have already received turf rebate program data at the household level. Once combined with demographic and spatial data we can test the predictive power of those variables that we hypothesize play the most significant role in the household decision to participate in turf rebate programs. Validation is a final critical element that often provides challenges with program evaluation. Results from our survey data, and the potential to test our results alongside ongoing turf replacement programs like the “Save our Water” rebates program which is currently running, will provide opportunities for validation that are not available in similar studies. Price Sensitivity The wide scope of the Metropolitan Water District, and the large amount of data points that we have for rebate program applications will also allow us to do research related to the price sensitivity of the rebate offered. Given that we have data points for the different districts that have demographically different populations, and have potentially been offered different incentive structures for the rebate program, we have an opportunity to measure the rebate programs sensitivity to rebate amount per square foot offered. While we do not have all of the relevant data that we might need to construct a demand curve at the household level, we can use average water usage and income at the district level to measure the difference in percentage of applicable households that apply to the program in relation to the different incentive structures by district. This will allow us to estimate the price elasticity of demand for turf rebate programs across a variety of geographically and demographically similar service

8

areas. Once again, the use of survey data can provide a critical role in the validation of our estimates form this analysis. While we must be careful not to overburden our target audience with the survey design; short sequence of critically designed questions will also allow us to gain feedback related to the importance of the rebate structure as opposed to the power of peer and spatial influence. Potential Extensions The funding from this grant application is intended to cover the cost of the survey data collection, however it is important to point out that we will be seeking out additional funding which would provide a stipend for the student project manager and/or provide funding for valuable extensions to this project. At this point there are two main extensions to this research that we plan to pursue. First, we would like to design an experiment which would be carried out at the Center for Behavioral Economic Studies at Claremont Graduate University. The experiment would be designed to further test the validity of peer and spatial effects similar to those we propose to study here. Ideally, we would incentivize members from our control group and treatment group in the proposed research to participate in these experiments. In addition we will seek out students from the Claremont Colleges Community to participate in the experiment. The second extension to this research would be to include feedback from focus groups. The intimacy of the focus group would allow us to seek further depth on questions that come out of the initial rounds of survey and experimental research. While a focus group or case study does not provide the generalizability to the larger population that regression analysis will; it will allow us to have a more valid and rich data set for the sample that is chosen. These extension could have significant impacts on future research as experiments and focus groups could provides additional sources of validation; while the combination of predictive analytics, survey data, focus group feedback, and experimental data will also provide an interesting comparison of methodological approaches. I am mentioning this extension here, but it will not be included in the budget as suggested in the MWD college grant program webinar because we have not secured any additional funding at this point.

9

Project Timeline and Deliverables Quarter 1 January – March (2016)

Research and Develop Econometric Model

Develop Survey Questions

Combine relevant data sets

Clean data sets and prepare for analysis

Input data points into GIS software Quarter 2 April – June (2016)

Analysis of GIS mapping and visual spatial effects

Creation of spatial and peer distance variables

Assign Control and Treatment Group

Look for visual spatial patterns in GIS map

Deliverables -Progress report 1, GIS map and summary of progress to date Quarter 3 July – September (2016)

Funding received

Print and Mail Surveys

Purchase incentive prizes

Initial Econometric Analysis with predictive analytics

Initial price sensitivity analysis

Staff visit to CGU campus

Deliverables - Progress report 2, initial econometric analysis and updated progress report

Quarter 4 October – December (2016)

Receipt of Surveys

Clean, combine, analyze survey data

Secondary Econometric/Predictive Analysis

Secondary price sensitivity analysis

Validation measures compared to survey data

Deliverables – Progress report 3, Initial estimates for predictive measures Quarter 5 January – March (2017)

“Dry run” of presentation

Begin work on Final work and Presentation Quarter 6 April – June (2017)

Final Report and Presentation Due to MWWD

10

Project Team Faculty Project Manager Dr. Hal Nelson Research Associate Professor School of Politics and Economics Claremont Graduate University 160 E. 10th St. Claremont, CA 91711 909-621-8284 hal.nelson@cgu.edu Ten years teaching and research related to energy and water policy Specialization:

Energy and Water Policy

Computational Modeling

Software Development

Environmental Politics

Sustainability

Civic Capacity

Institutional Interactions

Environmental Politics

Climate Change Adoption and Mitigation Student Project Manger John Shideler PhD Candidate in Economics School of Politics and Economics Claremont Graduate University 160 E. 10th St. Claremont, CA 91711 561-351-3174 john.shideler@cgu.edu Relevant Experience:

Research assistant in DPE at CGU 1 year o Data preparation and analysis o Energy and Water Policy research

Student Project Manager for GUEP 1 year o Served as liaison between CGU, City, and Utility project managers o Cleaned, organized, and analyzed household energy retrofit data

11

Estimated Project Benefits Quantifying the benefits for this project is difficult at best. While there are many foreseeable benefits like attainment of knowledge, reduction of marketing costs for rebate programs, and increased uptake in future rebate programs; it is hard to place a direct and immediate impact on this research effort. Categorically, this project has the potential to improve equitable access to drinking water, improve the environment and sustainability benefits for people, reduce per capita use, make more water available, and provide technical training. Ideally, the understanding gained through this project will be used to help design future rebate programs which can target subsections of the service area more cost effectively. The reduction in water used for outdoor irrigation will lead to more equitable access to drinking water. This will also lead to a reduction in per capita usage. Another potential impact is through the potential sustainability benefits for people. This research has a focus on better understanding the role that peer effects and spatial patterns play on the household’s decision to pursue environmentally friendly, incentive-based programs. Sustainability must be a community wide effort in order to be truly successful. The benefits to programs such as these definitely display more of a non linear relationship in regards to participation. The more people that become involved, the greater the per-person impact will be. Technical training can also be considered as a benefit to this project because we feel that the results and methodology used in this research will be applicable in other realms and future policy implementation. With that being said, the best way to give an impression of a quantifiable metric for this project is create a realistic lower bound for the type of impact this project could have had for the Socal Water$mart program which is no longer accepting applications. The program is slated to remove 170 million feet worth of turf with a rebate value of up to two dollars per square foot. A section from page 10 of a report by The California Urban Water Conservation Council from March 2015, Turf Removal and Replacement: Lessons Learned, helps put the importance of this kind of study into context.

“While the decision on the dollar-value of a program’s rebate has real implications for customer attraction and retention, it alone does not dictate participation. For example, an agency with an eight-year old turf rebate program recently cut its rebate value in half when funding was getting low, from $1 to 50 cents per square foot, yet the program did not see a drop in participation. Since then, the agency has even grown its program participation and has effectively doubled its impact. Understanding local/regional costs for landscaping replacement, the marginal value of the anticipated water savings to your agency, and target customer demographics ‘willingness to pay’ can help with rebate selection.”

The report goes on to discuss the importance of social norms, marketing, and gaining a better understanding of the behavioral dynamics of the service territory. With this in mind, consider once again the Socal$mart program which is replacing 170 million square feet of turf. If we were to assume that the average turf rebate was $ 1 per square foot this would put the total rebate cost at 170 million dollars. In reference to the quotation above, it is not too much of a stretch to think that a better understanding of the incentives at the household level might allow an agency to reduce the average rebate amount. For instance, better knowledge of the household incentives for the MWD service area could have resulted in a 1 penny reduction in the average rebate amount, which would have effectively led to around a 1.7 million square foot replacement increase. At an average of 45 gallons per square foot per year (CUWCC 2015 report), this would have lead to an increase of 76 million gallons per year. At the same time, we do not know what the budget is for the current rebate programs that are taking place. While we want to provide an example of the savings potential of this type of research, it would

12

still be too presumptuous to assume that this is an effective measure of the future savings potential related to our research. For this reason, we will leave the quantitative outcomes as “Dependent upon program funding”. However it should be noted that while challenging to quantify, the potential impact of this research could be dramatic.

PERFORMANCE MEASURE

QUANTITATIVE OUTCOME

LOCAL / GLOBAL IMPACT

Makes More Water Available Dependent on future rebate program funding

Local

Reduces Water Treatment Costs

Reduces Per Capita Use

Provides Technical Training Potentially Local

Provides Water Conservation and / or Hygiene/Public Health Education

Improves equitable access to fresh drinking water and/or sanitation practices (e.g. by improving water quality)

Dependent on future rebate program funding

Local / Global

Improves the environment and sustainability benefits for people (e.g.- by improving watershed runoff)

Dependent on future rebate program funding

Local

Cost associated with each of the physical quantitative outcomes above

Financial Criteria The matching funds requirement for this project will be fulfilled by an in kind contribution which is equivalent to the difference between the standard Federal Indirect rate for Claremont Graduate University (38.4%) and the maximum allowable indirect rate for this proposal (10%). The difference of 28.4% amounts to a total in kind contribution of $2,555. A CGU template budget spreadsheet, a budget spreadsheet which breaks down the line items as suggested by the MWD, and separate match spreadsheet are also included at the end of the document.

DESCRIPTION AMOUNT NOTES

GRANT FUNDS REQUESTED

$9,897

ADDITIONAL SOURCE OF FUNDS (List all, if applicable)

$0 We are searching for additional

funding for extension to this

proposal

PROJECT TOTAL $9,897

13

14

15

16

17

18

19

20

21