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The Western Economics Forum is a peer reviewed publication. ... hubs and load centers necessarily traverse a variety of counties along their routes ...... Theoretically, preference reversals call into question fundamental behavioral assumptions.
Spring 2014: Volume 13, Number 1

A Journal of the Western Agricultural Economics Association

Western Economics Forum

Farm & Ranch Management Marketing & Agribusiness Natural Resources & the Environment Policy & Institutions Regional & Community Development

Western Economics Forum Volume XIII, Number 1 SPRING 2014

Table of Contents Julia Haggerty, Mark N. Haggerty, Ray Rasker, and Patricia Gude County Economic Development at a Glance: A Single Measure of Opportunity ...................................................................................... 1 Julia H. Haggerty, Mark Haggerty, and Ray Rasker Uneven Local Benefits of Renewable Energy in the U.S. West: Property Tax Policy Effects ............................................................................................... 8 Winslow D. Hansen, Julie M. Mueller, Helen T. Naughton Wildfire in Hedonic Property Value Studies .................................................................... 23 Christopher R. McIntosh Preference reversals: experimental review and a new idea for using arbitrage within the double bound dichotomous choice elicitation method ........................................................................................................... 36

The Western Economics Forum A peer-reviewed publication from the Western Agricultural Economics Association Purpose One of the consequences of regional associations nationalizing their journals is that professional agricultural economists in each region have lost one of their best forums for exchanging ideas unique to their area of the country. The purpose of this publication is to provide a forum for western issues. Audience The target audience is professional agricultural economists with a Masters degree, Ph.D. or equivalent understanding of the field that are working on agricultural and resource economic, business or policy issues in the West. Subject This publication is specifically targeted at informing professionals in the West about issues, methods, data, or other content addressing the following objectives: • Summarize knowledge about issues of interest to western professionals • To convey ideas and analysis techniques to non-academic, professional economists working on agricultural or resource issues • To demonstrate methods and applications that can be adapted across fields in economics • To facilitate open debate on western issues Structure and Distribution The Western Economics Forum is a peer reviewed publication. It usually contains three to five articles per issue, with approximately 2,500 words each (maximum 3,000), and as much diversity as possible across the following areas: • Farm/ranch management and production • Marketing and agribusiness • Natural resources and the environment • Institutions and policy • Regional and community development There are two issues of the Western Economics Forum per year (Spring and Fall). Editor – Send submissions to: Dr. Don McLeod Editor, Western Economics Forum Dept. of Ag & Applied Economics University of Wyoming Dept. 3354 1000 E. University Avenue Laramie, WY 82071 Phone: 307-766-3116 Fax: 307-766-5544 email: [email protected]

Note from the editor I gives me great pleasure to provide these four articles. The first piece is “County Economic Development at a Glance: A Single Measure of Opportunity.” It is actually the first of a pair of papers getting at methodology resulting in a community opportunity index. The companion piece, “Uneven Local Benefits of Renewable Energy in the U.S. West: Property Tax Policy Effects,” utilizes findings from the first article to determine how renewable energy could offer positive economic impacts. The third article is a summary of hedonic studies pertaining to the effects of fire on property values. Recommendations are offered to guide the methodology and its application moving forward. The final paper considers the case of preference reversals relevant to the double bounded dichotomous choice contingent valuation technique. The author draws from both field and experimental studies to examine the importance of accounting for preference reversals. Recommendations are offered as how to deal with the valuation problem. Regards Don McLeod, editor Western Economics Forum

Western Economics Forum, Spring 2014

County Economic Development at a Glance: A Single Measure of Opportunity Julia Haggerty, Mark N. Haggerty, Ray Rasker, and Patricia Gude1 Introduction The U.S. West is rich in renewable resources. Areas with high quality renewable energy resources as well as those targeted for new transmission facilities comprise a variety of economic circumstances. Proposed transmission lines that link remote renewable energy to grid hubs and load centers necessarily traverse a variety of counties along their routes, with longer transmission lines such as the TransWest Express crossing more than a dozen counties on its way from central Wyoming through Colorado and Utah to its destination in Clark County, Nevada. In a companion paper in this issue, we discuss why the revenue opportunity from future renewable energy development varies within the region. There are two main variables at play: differences in existing condition and differences in revenue collection. Here, we provide a new approach to describing county-level economic condition and discuss its implications for understanding the renewable energy opportunity in the rural West. The goal of this brief is to describe the county-level index and promote its practical application by economic development and policy specialists in the West.

A County-Level Index of Economic Opportunity In terms of existing conditions and economic opportunity, during the last four decades the fortunes of counties in the West have diverged significantly. Rural and micropolitan areas able to capture the dynamic growth associated with the new “knowledge” economy and amenitydriven migration have led the nation in population and income growth (Rasker et. al. 2009, Moretti 2012). In other counties, rapid growth in oil and gas development has added prosperity where it did not previously exist. Not all rural Western counties, however, have been able to create a diverse, robust, and resilient economy with a healthy tax base. Poverty, low-paying jobs, lack of education, isolation from markets, and difficulties competing in expanding service industries are persistent challenges for some counties (Gude et al. 2012). The coarse-scale county-level index we present here provides a quick way to categorize county opportunities and challenges according to the dynamics described above. This index is valuable as a basic screen that ranks counties relative to others according to multiple economic variables that consider performance as well as opportunity. It compares to other products like USDA’s county typology codes, but has the advantage of not being industry- or sector-specific.2 The companion article to this one uses this index to demonstrate that many, although not all, rural counties with high levels of renewable energy resources are also counties that have few other economic opportunities (see companion article, Table 2). The index is portable across a variety of policy analyses that benefit from a simplified, yet informed approach to differentiating western counties. 1

Authors are professional researchers at Headwaters Economics, a non-profit research group in Bozeman, Montana, except Haggerty, who is Assistant Professor of Geography, Department of Earth Sciences, Montana State University. 2 The USDA reports that it will issue a complete update to the county typology codes sometime in 2014. See: http://www.ers.usda.gov/data-products/county-typology-codes.aspx (access 8-9-2014).

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Methods: County Economic Opportunity Scores The methods explained below offer a straightforward approach to measuring economic need and development potential. The metrics used for the formula are readily available nationwide for all counties from data published by federal agencies. Measures of Economic Performance: A. Median Household Income: The sum of money received by household members 15 years old and over. It includes wage and salary income; self-employment income; interest, dividends, or net rental or royalty income from estates and trusts; Social Security and Railroad Retirement income; Supplemental Security Income, public assistance or welfare payments; and retirement, survivor, or disability pensions.3 The advantage of median household income is that is a comprehensive measure of all the sources of income, measured at the household level. The disadvantage is similar to the use of PCI in instances when household income is made up largely of non-labor sources. For this reason, an additional labor-related measure is needed. B. Average Earnings Per Job: The total earnings divided by total full-time and part-time employment.4 The advantage of this measure is that it indicates the relative quality of jobs available in a county. The limitation is that this metric does not measure whether the job is dangerous, high turnover, predictable and stable in the long term. C. Percentage of Families Above the Poverty Level: The U.S. Bureau of the Census uses a sophisticated technique for measuring poverty for different family configurations. For example, the poverty threshold in 1999 for a family of four with two children less than 18 years was determined to be an annual income of $16,954.5 A disadvantage of this metric is that it does not account for differences in local cost of living. Measures of Economic Potential: D. Percentage of the Population with a Bachelor’s Degree or Higher: The percentage of the population 25 years or older who have earned at least a bachelor’s degree. Education is one of the most important indicators of the potential for economic success, and lack of education is closely linked to poverty. Studies show that areas whose workforce has a higher-than-average education level grow faster, have higher incomes, and suffer less

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For the full definition of Median Household Income, see the U.S. Bureau of the Census, American Fact Finder Web Site Glossary, online: http://factfinder2.census.gov/help/en/american_factfinder_help.htm (accessed 8-9-2014). 4

For the full definition of Average Earnings per Job, see the Bureau of Economic Analysis, U.S. Department of Commerce: http://www.bea.gov/regional/definitions/ (last accessed 8-9-2014). 5

The U.S. Census Bureau follows the Office of Management and Budget’s (OMB’s) Directive 14, meaning that it uses a set of money income thresholds that vary by family size and composition to determine who is in poverty. If the total income for a family or unrelated individual falls below the relevant poverty threshold, then the family (and every individual in it) or unrelated individual is considered in poverty. See the U.S. Bureau of the Census, American Fact Finder Web Site Glossary, online: http://factfinder2.census.gov/help/en/american_factfinder_help.htm (accessed 8-9-2014).

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Western Economics Forum, Spring 2014 during economic downturns than other regions.6 Education rates make a difference in earnings and unemployment rates. In 2009, the average weekly earnings for someone with a bachelor’s degree was $1,025, compared to $626 per week for someone with a high school diploma. While in 2009 the unemployment rate among college graduates was 5.2 percent, for high school graduates it was 9.7 percent.7 E. County Typology—Degree of Isolation from Markets: Counties are classified as belonging to one of five categories: Central Metropolitan Statistical Area, Outlying Metropolitan Statistical Area, Central Micropolitan Statistical Area, and Outlying Micropolitan Statistical Area. A fifth category for all other counties is Rural.8 One of the principle determinants of economic success for a county is the ability of its businesses to trade with market centers and of its residents to work in centralized population centers. For example, someone living in a Core Metropolitan Area, or a nearby Outlying Metropolitan Statistical Area, has different employment opportunities from someone who lives in a Rural area. The five categories delineated above serve as a continuum from most densely populated to most sparsely populated. This typology serves as a measure of the degree of connection to markets, including labor markets.9 Definitions: Metropolitan Statistical Areas: counties that have at least one urbanized area of 50,000 or more population, plus adjacent territory that has a high degree of social and economic integration with the core as measured by commuting ties. Counties in Metropolitan Statistical Areas are classified as either central or outlying. Micropolitan Statistical Areas: counties that have at least one urban cluster of at least 10,000 but less than 50,000 population, plus adjacent territory that has a high degree of social and economic integration with the core as measured by commuting ties. Micropolitan Statistical Areas are classified as either central or outlying. Rural: counties that are not designated as either metropolitan or micropolitan. Central Areas: counties that contain the urban core of metropolitan and micropolitan areas. Outlying Areas: counties adjacent to metropolitan or micropolitan central counties that have a high degree of social and economic integration with the urban core, as measured by commuting to work.10 6

For information on the relationship between level of education, earnings, year-round employment, and unemployment rates, see Day and Newburger 2002. For a review of other metrics of economic well-being see Chapple and Lester 2010. 7

The wage and unemployment effects of education are available from the Bureau of Labor Statistics: http://www.bls.gov/emp/ep_chart_001.htm (last accessed 10/23/10). 8

For a discussion of the importance of access to markets, see Rasker st al. 2009. Also see Headwaters Economics’ “Three Wests” web page, which provides information on three distinct types of counties in the American West as measured by access to markets. www.headwaterseconomics.org/3wests.php. A useful article on metro and non-metro income levels and inequality is McLaughlin 2002. 9

Ibid, Rasker et al., 2009.

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Definitions of county typologies can be found at the U.S. Census Bureau web page for Metropolitan and Micropolitan Statistical Areas, http://www.census.gov/population/www/metroareas/metroarea.html (last accessed 8-9-14).

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Methods Used to Develop Maps Figure 1 (next page) maps the index scores of county opportunity in the West by quintiles, ranking economic performance and development potential, from best (black) to worst (lightest gray), based on the following approach. The five variables listed above are gathered for every county in the West. The variables are first normalized by recalculating each variable to a zero to one index by dividing the individual county values for each variable by the highest value for that variable for the latest year (for example, Index Household Income for Clark County, Idaho = Household Income (Clark County / Highest Household Income (Douglas County, CO). A combined economic performance index was calculated for each county as:

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Where: i = Local unit of government j = Year MHI = Median Household Income EPJ = Average Earnings per Job Poverty% = Percent of families above poverty Bachelors% = Percent of individuals with bachelor’s degree or higher Type = County typology To calculate the economic performance score, each unit’s combined economic index was assigned a percentile rank relative to all the other unit’s combined economic index. The percentile rank for each unit of local government’s combined economic performance index is calculated as: Percentile Rank = (100 * (i - 0.5)) / n Where: i = the rank of the unit’s combined index score n equal the total number of governmental units. The economic performance score is calculated based on the percentile rank as: Economic Performance Score = (Percentile Rank – 0.5) * 0.4 + 1 For example, the 75th percentile county receives a score of 1.1, calculated as: (0.75 - 0.5) *.4 + 1 = 1.1

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Western Economics Forum, Spring 2014 The result is an economic performance score for each county where the median county receives a score of 1, the highest performing county receives a score of 1.2, and the lowest percentile rank receives a score of 0.8. The map in Figure 1 sorts these scores into five quintiles. Figure 1. County Opportunity Index

Map by Patricia Gude.

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Implications for Economic Opportunities from Renewable Energy Facilities The economic opportunity index can be a useful tool for policy analysis. By way of example, consider renewable energy development, frequently touted as a potential boon for the rural West (Druckenmiiller 2012). In order to evaluate the representation of underperforming, or economically challenged areas in renewable energy landscapes in the West, we used the economic opportunity index to conduct a simple screening. We selected 20 counties in the West with the greatest number of acres of high quality solar resources and the 20 with the greatest wind resources, according to the Western Governor’s Association’s 2009 Western Renewable Energy Zone planning effort.11 We then sorted these counties into quintiles. According to this very basic exercise, we can quickly confirm that with regard to both wind and solar development, areas with limited economic opportunity are well represented in potential renewable energy development areas. We can also note some important differences between the solar and wind areas. Table 1 provides an overview of this exercise.

SOLAR)COUNTIES

WIND)COUNTIES)

Table 1. Economic Opportunity in Top Wind and Solar Counties

Laramie,)WY Weld,)CO

Albany,)WY Eddy,)NM Kern,)CA

Converse,WY Logan,)CO

Pondera,)MT Platte,)WY Sedgwick,)CO Lincoln,)NM Cheyenne,)CO Teton,)MT Torrance,)NM

Guadalupe,)NM Glacier,)MT Blaine,)MT Prowers,)CO Las)Animas,)CO Baca,)CO

1 2 3 4 ) 1 2 3 4 Los)Angeles,)CA Imperial,)CA Nye,)NV Luna,)NM Socorro,)NM Maricopa,)AZ San)Bernadino,)CA Iron,)UT Alamosa,)CO Hidalgo,)NM Riverside,)CA Mohave,)AZ Esmerelda,)NV Dona)Ana,)NM Lincoln,)NV Pima,)AZ La)Paz,)AZ Kern,)CA Millard,)UT Beaver,)UT

5 5

Of the top 20 counties in terms of total acres of high quality (class 4 and class 5) wind, more than half are among the West’s most challenged counties in terms of economic opportunity. Among the top 20 counties in terms of acres of high quality (greater than 6.5 Daily Normal Insolation values) solar resources, there is more widespread distribution among opportunity categories, with both the most challenged and the most advantaged represented. Still, nearly 11

This is not intended to predict the location of future development. The potential for renewable energy resources to support utility-scale projects is related not only to the quality of the resource, but many other factors as transmission access, demand, and so on. Still, this overlay serves as a way to emphasize the overlap between struggling remote rural areas in the West and high quality renewable energy resources. WREZ hub wind and solar data was obtained at the following URL: http://mercator.nrel.gov/wrez (username: wrez password: guest), 11/7/2012.

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Western Economics Forum, Spring 2014 half of the West’s best solar counties in terms of raw acreage are among the region’s worst in terms of economic opportunity. Together this information confirms the presence of a possible opportunity in areas that could really use them. The analysis should then prompt policy makers to ask how these challenged areas might best take advantage of renewable energy development—this is the subject of our companion paper.

Summary Coarse, county-level indices can be useful tools in policy analysis as simple ways to describe and differentiate county economies. The economic performance index described in this paper offers an aggregate, relative county ranking based on an analytically-informed set of variables that relate to performance as well as opportunity. The index complements, rather than replaces existing, more nuanced systems of county typologies though it has the advantage of not being industry- or sector-specific. As in the companion article to this one, the index can be used for policy analysis about county-level impacts of state policies.

References Chapple, K., and T. W. Lester. 2010. "The resilient regional labour market? The US case." Cambridge Journal of Regions, Economy and Society 3 (1):85-104. Day, Jennifer Cheeseman, and Eric C. Newburger. 2002. The Big Payoff: Educational Attainment and Synthetic Estimates of Work-Life Earnings. edited by U.S. Census Bureau U.S. Department of Commerce. Washington, DC. Druckenmiller, H. (2012). “At Wind Speed: How the U.S. Wind Industry is Rapidly Growing our Local Economies.” NRDC Issue Paper, September 2012. Gude, P. H., Rasker, R., Jones, K. L., Haggerty, J. H. and Greenwood, M. C. (2012), The Recession and the New Economy of the West: The Familiar Boom and Bust Cycle?. Growth and Change, 43: 419–441. McLaughlin, Diane K. 2002. "Changing Income Inequality in Nonmetropolitan Counties, 1980 to 1990." Rural Sociology 67 (4):512-533. Moretti, E. 2012. The New Geography of Jobs. Houghton Mifflin. Rasker, R., P.H. Gude, J.A. Gude, J. van den Noort. 2009. “The Economic Importance of Air Travel in High-Amenity Rural Areas.” Journal of Rural Studies. 25: 343-353

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Uneven Local Benefits of Renewable Energy in the U.S. West: Property Tax Policy Effects Julia H. Haggerty, Mark Haggerty, and Ray Rasker1 Introduction New utility infrastructure is a vital issue for the rural U.S. West, a region that has vast high quality renewable energy resources. Perceived project costs and benefits influence how rural residents and local governments engage with siting and permitting processes for new wind and solar farms and proposed high voltage transmission lines. Positive economic impacts feature strongly in the anticipated benefits of new utility projects (Slattery, M. C. et. al., 2012), creating a need for clear analysis of local economic development outcomes such as new tax revenue and employment. This paper estimates the amount and relative importance of new property tax revenue associated with renewable energy facilities in 17 rural counties in the contiguous, continental U.S. West. Most of study counties are areas with limited economic opportunities that stand to benefit from new utility infrastructure and also feature high quality renewable energy resources and/or potential for future new high voltage transmission development. The companion piece to this article titled, “County Economic Development at a Glance: A Single Measure of Opportunity,” describes and maps a county-level index of economic opportunity. Our study addresses the fact that although substantial effort has been spent on refining approaches to economic impact analysis for the benefit of promoting renewable energy development (see Reategui and Hendrickson, 2011), few (if any) studies have tackled the question of how benefits vary from place to place as a function of tax policies. The reality is that fiscal benefits to rural communities associated with new utility infrastructure are uneven. This paper provides revenue estimates and policy analysis to explain how and why revenue impacts vary across the rural West. We hope this work will inform project developers, policymakers, and local and state officials and will encourage discussion of policy approaches that prioritize local economic benefits in attracting and supporting new electricity infrastructure. This paper begins with brief discussion of how this study links to published research and an outline of our methods. Next, we offer the findings of potential size and scale of new property tax revenue in the study counties. What follows is a discussion of the uneven revenue benefits from new renewable energy infrastructure and possibilities for policy reform and future analysis. For the purpose of keeping the discussion clear and concise, major methodological considerations are presented in an accompanying appendix. According to published research, the key variable affecting the scale of local employment and income benefits from renewable energy development is the amount of local inputs such as capital, materials,

1

The authors are, respectively, Assistant Professor of Geography, Montana State University; Policy Analyst, Headwaters Economics; Executive Director, Headwaters Economics.

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Western Economics Forum, Spring 2014 and labor used in ach project (see Lantz and Tegen 2008; Torgeson et. al. 2006).2 Some states have recognized these advantages in policies that establish in-state or local labor requirements as a condition of qualification for tax credits and a few have created opportunities for community ownership of generation projects within existing Renewable Portfolio Standards. Generally speaking, however, opportunities for local investment and local supply of labor and material in large-scale renewable energy developments are limited. The hurdles for local investment in these expensive, logistically complex developments remain significant (Mazza 2008, Bolinger 2011). As recent studies have confirmed (Slattery et. al., 2012; Mulvaney 2013), lease payments to private land owners and new tax revenue figure prominently in the anticipated local benefits of large capitalintensive projects such as electric facilities. And while much has been made by popular publications of the “windfall” offered by new renewable energy projects (Van der Voo, 2011, Druckenmiller, 2012), very little has been documented with regard to measurable economic performance impacts (Brown, 2012). Furthermore, in many rural counties in the U.S. West renewable energy development is more likely to end up on public rather than private land meaning that landowner payments have less significance than taxes as a form of local revenue. For this reason, economic impacts from areas where projects are primarily on private land are not likely to be applicable to many parts of the West. These trends in published literature indicate that evaluating the link between tax incentives and potential local revenue opportunities is a key building block for a robust assessment of renewable energy economic impacts.3

Methods We selected seventeen non-metropolitan counties from eleven Western states with documented potential for utility-scale renewable energy facilities or where new interstate high voltage transmission systems have been proposed. We also targeted rural counties with low economic performance for analysis.4 Table 2 on the following page provides some context for the economic performance and potential renewable energy or transmission development in each of the study counties. As hypothetical examples, we considered the contribution to property tax collections of a $100 million of investment in both renewable energy generating and high voltage transmission facilities. Using generic figures, $100 million investment corresponds to about 35MW of utility-scale photovoltaic solar, roughly 50MW of wind, and about 50 miles of new, 500 kV single-circuit AC high voltage transmission (Black and Veatch 2012, Kahn et. al. 2013, Mason et. al. 2012).

2

Ensuring the local supply of labor and materials, especially the most expensive components like solar panels or wind turbines, greatly increases the local economic benefits during the construction period. Similarly, community investment in the project returns significantly more to the local economy during the operations phase than nonlocal ownership. 3

In economic impact studies, projected dollar value of new projects is often very large, particularly as a share of local tax base in areas with limited property values. Sales and use taxes associated with construction can figure prominently in calculations of local tax benefits (see Charnley et. al. 2012), but for this research, we focused exclusively local government property tax revenue as a first step in a difficult effort to analyze revenue and compare state policies. 4

Our economic opportunity index is described in the companion paper to this article. The opportunity index considers measure of opportunity (poverty, income, earnings, education and county typology) and ranks counties relative to all counties in the West.

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Table 2. Study County Attributes

1 2 3 4 5 6 7 8 9 10 11 12 13

County La$Paz$Co.,$AZ Navajo$Co.,$AZ Inyo$Co.,$CA Alamosa$Co.,$CO Baca$Co.,$CO Owyhee$Co.,$ID Glacier$$Co.,$MT Guadalupe$Co.,$NM Torrance$Co.,$NM Esmeralda$Co.,$NV White$Pine$Co.,$NV Union$Co.,$OR Sherman$Co.,$OR

14 15 16 17

Beaver$Co.,$UT Millard$$Co.,$UT Klickitat$Co.,$WA Platte$Co.,$WY

Wind*Resources* Associated*QRA* Rank*(by*total* Installed*Utility* (Utility*Interest* acreage) Scale*Wind Rank) AZ_WE$(1) 29 128$MW AZ_NE$(2) NV_WE$(1) CO_SO$(1) 1 CO_SE$(1) 26 ID_SW$(3) 7 189$MW MT_NW$(1) 2 90$MW NM_EA$(3) 9 100$MW NM_EA$(3) NV_WE$(1) 69 152$MW UT_WE$(1) 51 100$MW OR_WE$(4) 49 1,$057$MW WA_SO$(4) 200$MW 27 100$MW 28 1,247$MW 16

UT_WE$(1) UT_WE$(1) WA_SO$(4) WY_SO$(2)

Federal*and*State*Solar* Energy*Zone Brenda$(BLM) CREZ$25$Owens$Valley Fourmile$East$(BLM)

Installed*Utility* Scale*Solar Proposed*HVTL 100$MW Yes Yes 510$MW 100$MW Yes Yes 300$MW 50$MW

Yes Yes

Millers$(BLM) Yes Yes Milford$Flats$South$(BLM);$ Wah$Wah$Valley$(BLM)

Yes Yes Yes

Economic* Performance* Ranking* (Quintile) 5 4 3 4 5 4 5 5 4 5 4 3 5 4 5 4 5

Total* Population* Governmental** 2012 Revenue*FY*2011 20,281 $25,848,789 107,094 $65,699,835 18,478 $62,034,612 16,148 $27,025,847 3,751 $6,559,264 11,439 $6,481,168 13,711 $9,499,388 4,603 $15,075,572 16,021 $10,574,135 775 $4,080,300 10,098 $21,957,919 25,759 $17,453,450 1,732 $14,789,949 6,501 12,659 20,699 8,756

$15,645,143 $18,046,817 $5,166,551 $8,298,987

Data Description and Sources. Wind Resources Rank: ranking is the county’s ranking out of 152 Western counties with Class 3 or better wind resources. Installed Utility Scale Wind: Nameplate capacity in megawatts, source AWEA. Associated QRA: Qualified Resource Areas identified in the Western Governors’ Associations WREZ process, quartile of measured utility interest (1=01; 2=2-3; 3=4-5; 4=6-7) as reported by Black and Veatch 2012. Federal and State Solar Energy Zone: presence of a designated area for solar energy development in the BLM’s Solar PEIS or California’s REZ process. Installed Utility Scale Solar: Nameplate capacity in megawatts, source SEIA. Proposed High Voltage Transmission Lines: designation of county in proposed or sited route for one or more future high voltage transmission lines. Economic Performance Ranking: th value is ranking by quintile compared to all western counties, with 5 indicating lowest 20% of scores for economic opportunity, 1 the highest 20% of opportunity scores (see companion article). Population: source, U.S. Census Bureau, 2012 Population Estimates. Total Governmental Revenue FY 2011: All revenue available for governmental activities (taxes, licenses and fees, intergovernmental revenue, etc.), county Certified Audited Financial Statements.

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Western Economics Forum, Spring 2014 Estimation Methods and Assumptions In order to generate estimates of the potential revenue benefit from the hypothetical investment, we first calculated a net taxable value by seeking out all state laws and administrative rules that determine the assessed value of the $100 million investment, including standard assessment practices and applicable property tax incentives. In states where tax incentive programs are offered on a discretionary basis, we conferred with state experts to come up with an average effect of the incentive on taxable value. More details on the estimation methods are provided in the Appendix. Second, we applied local tax rates to the net taxable value. We used the applicable tax rate for countywide services (e.g., general fund, roads, libraries, fire, but not schools, municipal, or state property tax mills or enterprise funds) in unincorporated county areas for fiscal year 2011. We chose this approach rather than an average county tax rate in order to capture an accurate tax rates in unincorporated county areas, where large-scale generation facilities are likely to locate. To restate: the revenue reported here is property tax revenue to county government. We did not include revenue to other jurisdictions, sales taxes, or impact fees.

Results: Property Tax Revenue Estimates The following four figures show the results of this exercise in estimating revenue impacts and comparing their size and potential impact across the seventeen rural study counties. Figure 1 charts the values of potential tax revenue associated with a $100 million investment in utilityscale renewable energy generating facilities in the study counties. The values shown are a product of Net Taxable Value and County Tax Rate (see Appendix). Figure 2 illustrates the revenue estimates shown in Figure 1 as a share of total governmental revenue in each county—essentially the scale of impact relative to existing collections. Figures 3 and 4 provide the same types of property tax estimates, but for $100 million investment in a high voltage transmission line. The revenue estimates shown in figures 1 – 4 point to several basic observations about property tax benefits from new renewable energy and utility infrastructure in the rural U.S. West. First, the same level of initial investment generates very different amounts of property tax revenue in otherwise comparable rural counties. For generation facilities, the range is from $32,000 to close to $850,000 for the hypothetical Year 1 revenue. For transmission investments the range is from $112,000 to $871,000. When depreciation enters into calculations of taxable value, the drop in revenue can be steep. Onetime spikes in valuation can create risk and challenges, especially in taxing jurisdictions where the new revenue is large relative to existing collections. As a result, the quality of the tax opportunity is highly uneven, and bears little relationship to the economic challenges and opportunities of counties. The scale of opportunity from new utility investments is substantial for some rural counties, especially those with small tax bases and high tax rates. In other rural counties that stand to benefit significantly from new revenue based on their existing economic performance, the opportunity is negligible.

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Western Economics Forum, Spring 2014

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$700,000#

$600,000#

$500,000#

$400,000#

$300,000#

$200,000#

$100,000#

$0#

Figure 2. Potential Impact: Revenue Estimate as Share of 2011 County Governmental Funds Revenue 12.000%$

10.000%$ Year$1$

Year$10$

8.000%$

6.000%$

4.000%$

2.000%$

0.000%$

12

Western Economics Forum, Spring 2014 Figure 3. Estimated Potential Property Tax Revenue from $100 Million Investment in Interstate Transmission Facilities

Figure 4. Potential Impact: Estimated Annual Revenue from Transmission as Share of 2011 County Governmental Funds Revenues

13

Western Economics Forum, Spring 2014 The explanations for these varying levels of revenue and impacts can be found in state tax incentives and rural tax rates, which are discussed in turn.

Incentives The net taxable value of the $100 million investment in a renewable energy generating facility is affected by state approaches to appraising utility property and the available of property tax incentives. The type of impact that renewable energy property tax incentives have varies according to their design. While all but one program tends to lower potential revenue compared to non-renewable energy facilities, in some cases they provide benefits by stabilizing revenue over time. Four states do not offer property tax incentives for renewable energy generating facilities—in other words, these states determine the value of renewable energy facilities using similar methods to any other utility properties. These are California, Utah, Washington, and Wyoming. One variation has to do with whether the state centrally appraises these properties (Washington and Wyoming) or the county conducts the appraisal (California and Utah16). In other states, property tax incentives are available to renewable energy facilities, though they vary in design. Montana and Arizona offer property tax incentives that use steeply reduced assessment rates to create lower property tax burdens for renewable energy generating facilities relative to comparable fossil fuel generating facilities. Qualifying wind energy and other clean energy generating facilities are assessed at 1.5% of their value in the first year compared to 12% for non-qualifying generating facility in Montana. By year 10, the assessment ratio adjusts up to 3% of depreciated value. One potential implication of this is that fossil fuel facilities may generate more tax revenue and thus more appeal from a fiscal standpoint to rural areas.17 In Arizona, the assessment rate for renewable energy facilities is 20% for renewable energy facilities versus 35% for other electric generating facilities in Year 1. The benefit is more pronounced in Year 10 when renewable generating facilities are still assessed at 20% of depreciated value whereas nonrenewable facilities are assessed at 100% of depreciated value. Colorado’s tax incentive for renewable energy generators aims to keep parity with non-renewable facilities. The state’s assessment process reduces the taxable value of a new renewable energy facility to the comparable value of a fossil-fuel generation facility using a revenue approach. In this model, renewable energy generators are not penalized for the relatively higher capital cost of their facilities (per unit of power generated), while local governments do not receive less than they otherwise would for fossil fuel facilities. One benefit of this approach is that the revenue value is stable and can in theory increase over time.18 Oregon, Nevada, and New Mexico offer negotiated property tax relief through state programs modeled after conventional enterprise zones, in which a government entity forfeits property tax for a predetermined period in order to recruit capital-intensive enterprises. 16

Utah State Tax Commission notes that in the past the state has appraised wind facilities, but going forward the counties will conduct appraisals, assuming the properties are “stand alone,” as opposed to being owned by a large utility that is centrally appraised. 17

However, there may be opportunities for certain modern fossil fuel facilities to capture similar tax breaks according to the State Department of Revenue (Personal Communication with Steve Cleverdon, 7/1/2013). 18

Assuming wind generators are able to negotiate for escalating rates in power purchase agreements, which is a changing dynamic in the utility market right now.

14

Western Economics Forum, Spring 2014 New Mexico uses an industrial bond model that eliminates property tax liability the life of the bond. The entity holding title to the property under the terms of the bond (e.g. county or municipality) can negotiate for payments in lieu of taxes that direct payments to key local entities, such as schools. Our estimate here replicates the terms of a 2009 agreement in Torrance County.19 In Nevada, the State Energy Office Director has the authority to offer property tax relief up to 55%. Oregon’s enterprise zone initiative is one of two property tax incentives available in the state. Counties or groups of counties can designate Rural Renewable Energy Development Zones that offer property tax abatement for 3 to 5 years (enterprise zones may not include the land within designated urban growth boundaries). The designating local governments can set a cap on the amount of investment that would be tax-free (with a maximum for any one project of $250 million). Another approach available in Oregon that is more attractive from the county point of view is the state’s Strategic Investment Program (SIP). Our chart estimates reflect the application of this program. The SIP works by limiting the conventional property tax liability of large industrial projects and assessing instead formula-based payments in lieu of taxes (Community Service Fees) that can be targeted to specific local governments. The SIP approach provides several benefits: it stabilizes tax revenue for 15 years, avoiding the declines typically associated with depreciation tables. Second, the local governments (and even quasi-governmental entities like 4-H) that are party to the agreement have more flexibility in using the payments in lieu of taxes than they would with property taxes. For their part, project developers also benefit through lower tax liabilities in early years when they are typically paying off financing costs and lower expenditures overall on total taxes paid. (The state offers a helpful stepby-step analysis of the program’s application).20 The estimate shown for Esmeralda County and White Pine County, Nevada assume that the state’s property tax incentive program is used to its full extent (as has been in the case of most recent large renewable energy projects in the state, including transmission and generation facilities). The program enables the Director of the Nevada Office of Energy to offer property (and sales and use) tax abatements up to 55% of what would be due over a period of 15 years.21 Transmission Property Tax Abatement In Arizona, California, Colorado, Idaho, Utah, Washington, and Wyoming there are no property tax exemptions offered to transmission projects. We assume that the challenges of applying the IRB or SIP programs to interstate transmission would preclude the use of these incentives in New Mexico and Oregon, although these programs may be pursued for major transmission facilities such as substations located in a specific county. This leaves Montana and Nevada as the states offering property tax abatement programs specifically targeting large transmission developers. In both states, the incentives target developments that 19

The terms of the agreement were report in 2009 Albuquerque Journal story. “NM Attracting Wind Farms; Newest One with 40-Story Turbines.” Oct. 25, 2009. Albuquerque Journal. Under the terms of that agreement, payments would escalate slightly in Year 11 and also in Year 21. 20

See the Strategic Investment Program website at, http://www.oregon4biz.com/The-OregonAdvantage/Incentives/Strategic-Investment-Program/and specifically the tax treatment worksheet: http://www.oregon4biz.com/assets/docs/SIPexample.pdf. Additional information provided by Gillam County planner Susie Anderson and Sherman County Assessor Ross Turney. 21

The abatement does not apply to the property taxes due to school districts. Data on use of incentives to date provided by Stacey Crowley, Director of Energy Office, personal communication, 6/27/2013.

15

Western Economics Forum, Spring 2014 facilitate renewable energy generation. The applicable Montana property tax incentives for transmission are slightly different than those available for generation, but the discount can still be substantial, in the case of a 100 percent renewable energy transmission line, the incentive decreases the assessment ratio from 12 to 1.5 percent for up to twenty years. In Nevada, the abatement can be offered on a case by case basis, up to 55 percent of all applicable property taxes. Our estimates reflect this opportunity based on the fact that the property tax abatement was recently conferred to a large transmission project. However, the project in question was intra- not inter-state project, so it is not necessarily the case than an interstate line would qualify for such a large exemption. Rural Tax Rates Another factor significantly shaping these estimates is the differences in tax rates from county to county. In theory tax rates reflect the demand for government services distributed across the relevant tax base. In practice, there are many factors shaping tax rates (for a full overview, see Multari, et. al. 2012). State ‘tax and expenditure’ that dictate how local governments can budget, tax, and or assess properties in order to limit property tax burdens on individuals and businesses (Mullis and Wallin 2004). The distribution of responsibilities among types of government can vary from state to state or even county to county. In addition, the geography, population, and existing economy in a county affect how much the local governments need to collect to provide appropriate services. For example, a small county with a large municipality may have low service costs, whereas a rural county with a large service area and small tax base may be more likely to tax at a higher rate. In the latter type of county, new industrial projects can have real benefits by creating an opportunity to grow the tax base and lower overall tax rates accordingly. Figures 2 and 4 offer another level of insight into the revenue opportunities for different rural areas by charting impacts relative to existing governmental revenue. The first is, not surprisingly, that the biggest impact is in places where tax collections are relatively high and existing tax revenue is low. Platte County, Wyoming is a good example. Similarly, the smallest impact is in places where the revenue potential is low in size and compared to existing collections: for example, in Navajo County, Arizona, the $280,000 in potential revenue from a new renewable energy generating facility is more than in other counties, but it represents less than one-half of one percent of the county’s total revenue collections of nearly $66 million. In counties with high potential from new utility property, tax and expenditure limits imposed on local governments define the actual revenue opportunity. Each of the Western states except Montana, Wyoming, and New Mexico have laws that constrain the rate of growth in property values and/or the ability for local governments to increase tax rates or collections. This research did not delve into depth into the impact of these laws, however, as a general rule most states attempt to minimize the negative impact of these laws on local revenue from new industrial projects by putting those projects in a special (“new construction”) category for the purposes of budgeting and assessment. Exceptions include Idaho and Washington state, where state laws exclude non-generating utility property from the new construction allowance, meaning that many rural tax districts are likely to forfeit a large proportion of potential revenue from new transmission projects.22 Tax and expenditure limits also intersect with the depreciation issue. In areas that experience large increases in assessed value thanks to new development, lowering tax rates can be an appealing or even a required next step. However, if depreciation lowers values at rates that outstrip the ability for local governments to increase mill levies in order to capture required revenue, local governments can 22

Idaho Code 63-802. Washington RCW 84.55.010.

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Western Economics Forum, Spring 2014 face revenue shortfalls. We learned of one county assessor in a rural county who is conducting outreach with tax jurisdictions, including the county hospital district, to help them understand the impacts of depreciation of the county’s extensive wind development on their future collections.

Conclusions and Next Steps While property taxes are only one part of overall economic benefit of new utility projects, they have the potential to be highly influential in local reception of proposed development projects and in the actual experience of local areas that host large scale renewable energy generation and transmission facilities. The revenue estimates generated by this research demonstrate highly uneven property tax benefits in comparable rural counties. For generation facilities, the range is from $32,000 to close to $850,000 for the hypothetical Year 1 revenue. For transmission investments the range is from $112,000 to $871,000. When depreciation enters into calculations of taxable value, the drop in revenue collections can be very steep, posing risk and challenges, especially in taxing jurisdictions where the new revenue is large relative to existing collections. As a result, the quality of the tax opportunity is highly uneven, and bears little relationship to the economic challenges and opportunities of counties. The scale of opportunity from new utility investments is substantial for some rural counties, especially those with small tax bases and high tax rates. In other rural counties that stand to benefit significantly from new revenue based on their existing economic performance, the opportunity is negligible. In this analysis, Oregon’s Strategic Investment Program stands out as a potential model tax program that creates benefits for both developers and local communities. This work also raises questions about the importance of property tax incentives. Despite the popularity of industry-specific tax incentives in local economic development strategies, the expert literature questioning the effect of these “first wave strategies” is extensive and long-standing (see Zheng and Warner 2010: 326-327). The efficacy of incentives in terms of long term fiscal and income benefits has been shown to vary significantly as a function of the opportunities presented by the type of industry and the design of incentive programs (Bartik 2005).23 This work is a preliminary step that can support future efforts to consider the actual influence of tax incentives on location decisions by renewable energy companies. Future research should also consider actual case studies of construction projects that document rather than project actual revenue and the ways in which local communities are able to invest new revenue. In the rural West, the siting of new facilities on private versus public lands greatly affects the kinds of economic benefits to local areas. Rural economic opportunities are more limited when large areas of public land as opposed to private land are dedicated to renewable energy production; utility development on private lands merits an impartial, detailed analysis of specific case studies that document how these projects benefit landowners and their communities.

23

The small body of research on the link between industry incentives (typically production tax rebates) and the location of fossil fuel extraction in U.S. states suggests that while the industry benefits from tax incentives, it does not base production decisions on state tax incentives.

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Western Economics Forum, Spring 2014

Appendix: Tax Estimation Details Net Taxable Value We assume this is a privately- or investor-owned facility, given that many federally- or municipally-owned utility properties are tax-exempt. Most states use multiple appraisal methods to value transmission property as part of larger corporate units, but we do not have access to the detailed, proprietary data necessarily to replicate this process. So, we assume that both generation and transmission property are valued at installed capital cost less depreciation (or HCLD) in each state, except as noted below. We used recent published estimates of installed cost (Black and Veatch 2012). We apply assessment ratios based on statute for the following partial assessment rates (applicable statute and or administrative rule noted in parentheses): Arizona: Renewable Energy Generation: 20% (ARS 42-14155); Transmission 19.5% (for 2012-2013), ARS 42-12001; 42-14154; • Colorado: Transmission: 29% (CRS 39-1-104) • Montana: Generation: 1.5% in Year 1, 3% in Year 10 (MCA 15-24-1402). Transmission: 1.5% for half the value, 12% for the other half. This is a general approximation of a circumstance in which an interstate transmission facility garners half of its firm contracts with qualified renewable energy generators. (MCA 15-6-157; and ARM 17.80.201-203 and 17.80.225) • New Mexico: Transmission: 33% (NMRS 7-37-3) • Nevada: Renewable Energy Generation and Transmission: 35% (NRS 361.225) • Wyoming: Renewable Energy Generation and Transmission: (W.S. 39-39-11-101(a) (xvii) (B)) Washington State Department of Revenue provided an equalization factor for net taxable value for Klickitat County. •

For depreciation rates, we used 30 years to 10 percent for transmission facilities and 20 years to 10 percent for wind facilities. It is possible the wind facilities may depreciate less quickly; reinvestment in technology upgrades will counteract some of the depreciation; as new technologies are available to improve efficiency and production.24 Two appraisers observed that the application of federal investment tax credits had the effect of lowering the starting value of new wind projects in their states.25 While we did not adjust our starting value accordingly we note here this possible dampening effect on net taxable value. Exceptions to HCLD Approach: Colorado: In essence, renewable energy generation is assessed as though it were new gas generation according to CRS. 39-1-102-1-e. We utilized DOLA’s 2013 Renewable Energy Tax Factor worksheet26 to generate an assessed value for a 50MW wind farm, using the following assumptions: 24

Scott Sampson, Washington Department of Revenue, Personal Communication, 7/7/2013.

25

William Kowalowski, Utah State Tax Commission, Personal Communication 7/10/2103. Tony Ansolobahere, Kern County Assessor, 6/27/2013. 26

https://dola.colorado.gov/lgis/lg_finances.jsf;jsessionid=81818815c840b015f206b081abaa

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Western Economics Forum, Spring 2014 35% capacity factor $70/MWh PPA Value, based on NREL 2011 wind market report.27 1% escalation rate based on general market information suggesting escalation rates in PPAs are declining. Idaho: Wind producers in Idaho pay a gross production tax and are exempt from property tax (Idaho Statute 63-3503B.) The production tax is 3% of gross annual earnings, apportioned to the local taxing jurisdiction. Absent access to production data, we used a sample calculated through Colorado’s public worksheet to obtain an estimate of annual gross income of $10,731,000 (x .03 = 321,930).28 To derive the share of this that would accrue to county-wide governmental revenue for this, we calculated the proportion of countywide levy to all county levies (.49) in Owyhee County and applied this to the production tax. • • •

Wyoming: Wind farms pay a local production tax in Wyoming—in addition to state-assessed ad valorem. All Wind Farms generation—production tax of $1 per MWh based on annual production, MW rounded to the nearest whole, reported by company. Valued by the Department of Revenue in accordance with W.S. 39-13-102 (m) (iii). For generation facility, we assumed a 50MW facility at 30% average capacity factor, which generates an estimated 109,500MWh of energy per year. The resulting revenue is added to our generating facility property tax estimate, according to the distribution of 60/40 split between state and county that begins in the third year of production. Estimates of revenue from interstate transmission projects are a bit less straightforward than those for generation facilities because new high voltage transmission lines are rarely standalone projects. The majority of large interstate transmission projects are owned by large public or investor-owned utilities. When states “centrally assess” these properties, all of the company’s assets are considered and apportioned based on state laws. While it is not unusual to use a per mile value for assessment estimates, and even for local apportionment in some states, it should be recognized that estimated values could differ significantly from appraised values in the case of complex corporate holdings. The situation in which the estimation process used here might be best to track a real assessment would be the case of an independent merchant transmission project whose only property is the new interstate transmission line.

County Tax Rates and Revenue Comparison In order to generate an estimate for local revenue, we attempted to isolate the tax rate that generates revenue for countywide services in each county. This is imperfect given that counties have different service responsibilities in different states and types of areas, but we hope this is the best point of comparison and one that excludes some sources of variability, such as special districts and school districts. It is important to remember that the data shown are estimates for only this property tax, not all property taxes. In many cases, school district tax rates are equal to or exceed the tax rates for countywide services. Rates and sources are outlined in the table below.

27

http://www1.eere.energy.gov/wind/pdfs/2011_wind_technologies_market_report_slides.pdf

28

Idaho State Tax Commission indicated this estimate is relatively consistent with their experience to date. Jerott Rudd, Personal Communication, 7/17/2013.

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Western Economics Forum, Spring 2014 County La Paz Co., AZ Navajo Co., AZ Inyo Co., CA Alamosa Co., CO Baca Co., CO Owyhee Co., ID Glacier Co., MT Guadalupe Co., NM

Torrance Co., NM

Esmeralda Co., NV White Pine Co., NV Union Co., OR Sherman Co., OR Beaver Co., UT Millard Co., UT Klickitat Co., WA Platte Co., WY

2011 Tax Rate per $1 0.0197 0.011774529 0.0028 0.025238 0.024539 0.003573944 0.0219 0.01

0.010835

0.020995 0.01951 0.0029668 0.00871 0.00112 0.004032 0.00142 0.074432

Source Primary levy rates reported in Arizona Tax Policy Institute analysis. Primary levy rates reported in Arizona Tax Policy Institute analysis. Estimate. California BOE, share of total tax allocations v. city, school, other districts. County mill levy only from DPT Annual Report County mill levy only from DPT Annual Report Idaho State Tax Commission Budget Calculation Worksheets Montana Department of Revenue Annual Report, county wide levy. NM County Tax Values and Rates (2011) obtained from NM Department of Finance & Administration. We used only the non-municipal, non residential values to calculate an average mill levy rate for non-residential property in unincorporated county areas. NM County Tax Values and Rates (2011) obtained from NM Department of Finance & Administration. We used only the non-municipal, non residential values to calculate an average mill levy rate for non-residential property in unincorporated county areas. Nevada Department of Taxation "Redbook” Nevada Department of Taxation "Redbook" County Assessor County Assessor Utah State Tax Commission, Budget Rates by Entity (funds 1010 + 4090) Utah State Tax Commission, Budget Rates by Entity (funds 1010 + 4090) Washington State Auditor Office’s Local Government Financial Reporting System. Wyoming Dept. of Revenue, Tax Rates by District

To estimate the relative scale of impact of the estimated tax revenue, we compare our revenue estimate to revenue reported under “total governmental funds” in each county’s Certified Audited Financial Report on the “Statement of Revenues, Expenditures, and Changes in Fund Balances. Total governmental revenue includes taxes as well as other types of funds such as intergovernmental transfers, charges for services, and licenses and fees.

References Barnes, J., Laurent, C., Uppal, J. Barnes, C., Heinemann, A. (2013). “Property Taxes and Solar PV Systems: Policies, Practices, and Issues.” Report Prepared by North Carolina Solar Center and Meister Consultants. July 2013. Accessed online: http://ncsc.ncsu.edu/wpcontent/uploads/Property-Taxes-and-Solar-PV-Systems-2013.pdf. Black and Veatch. (2012). “Cost Report: Cost and Performance Data for Power Generation Technologies. Prepared for the National Renewable Energy Laboratory. Feb. 2012. Accessed online: http://bv.com/docs/reports-studies/nrel-cost-report.pdf Bolinger, M. (2011). Community Wind: Once Again Pushing the Envelope of Project Finance. Lawrence Berkeley National Laboratory: Berkeley, CA. Accessed online: http://eetd.lbl.gov/ea/emp. Brown, J. P., Pender, J. Wiser, R., Lantz, E. Hoen, B. (2012). "Ex post analysis of economic impacts from wind power development in US counties." Energy Economics 34(6), 17431754. Brown, J. P. (2013). “The Cycles of Wind Power Development.” Main Street Economist 3. Accessed online: http://www.kansascityfed.org/publications/research/mse/index.cfm?ealert=mse0713

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Western Economics Forum, Spring 2014 Charnley, A. H., Rice, V., Vest, M., Popp, T., Peach, J., Delgado, L. (2012). “SunZia Southwest Transmission Project Economic Impact Assessment.” Prepared for SunZia Southwest Transmission Project. Appendix G1 to BLM Final Environmental Impact Analysis for the SunZia Southwest Transmission Project 2013. Accessed online: http://www.blm.gov/pgdata/etc/medialib/blm/nm/programs/more/lands_and_realty/sunzia /sunzia_feis/appendices.Par.23562.File.pdf/SunZia_FEIS_Appendix_G1Economic_Impact_Assessment.pdf. Druckenmiller, H. (2012). “At Wind Speed: How the U.S. Wind Industry is Rapidly Growing our Local Economies.” NRDC Issue Paper, September 2012. Gude, P. H., Rasker, R., Jones, K. L., Haggerty, J. H. and Greenwood, M. C. (2012), The Recession and the New Economy of the West: The Familiar Boom and Bust Cycle?. Growth and Change, 43: 419–441. Headwaters Economics (2013). “Policy Brief: Community-Ownership in Renewable Energy Projects.” Kahn, Shayne, et. al. (2013). “U.S. Solar Market Insight Report, Q1 2013 Executive Summary.” GTM Research and SEIA. http://www.seia.org/research-resources/us-solar-marketinsight-q1-2013 Lantz, E. and S. Tegen. (2008) “Variables Affecting Economic Development of Wind Energy” NREL/CP-500-43506. Mason, T. et. al. (2012). “Capital Costs for Transmission and Substations: Recommendations for WECC Transmission Expansion Planning.” Black and Veatch Project No. 176322. Mazza, P. 2008. Community Wind 101: A Primer for Policymakers. Energy Foundation. Accessed online: http://www.ef.org/docs/CommWind_web.pdf. Metcalf, G. E. (2009). “Investment in Energy Infrastructure and the Tax Code.” MIT Center for Energy and Environmental Policy Research Paper 09-020. Mullins, D. R., & Wallin, B. A. (2004). Tax and expenditure limitations: Introduction and overview. Public Budgeting & Finance, 24(4), 2-15. Multari, M., M. Coleman, K. Hampian, and B. Statler. (2012). Understanding Local Government Finance in California (and Everywhere Else). Solano Press Books. Mulvaney, K. K., Woodson, P. and Prokopy, L.S. (2013). "A tale of three counties: Understanding wind development in the rural Midwestern United States." Energy Policy 56, 322-330. Peters, A., and Fisher, P. (2004). "The failures of economic development incentives." Journal of the American Planning Association 70, 27-37. Reategui, S. and S. Hendrickson. (2011) “Economic Development Impact of 1,000 MW of Wind Energy in Texas.” NREL/TP-6A20-50400. Slattery, M. C., E. Lantz, and B. L. Johnson. "State and local economic impacts from wind energy projects: Texas case study." Energy Policy 39.12 (2011): 7930-7940. Torgeson, M., B. Sorte, and T. Nam (2006). “Umatilla County’s Economic Structure and the Economic Impacts of Wind Energy Development: An Input-Output Analysis.” Oregon State University Extension Service, Special Report 1067. Windustry (2006). “Wind Energy Easements and Leases: Compensation Packages.” http://www.windustry.org/sites/windustry.org/files/Compensation-2009-07-06.pdf

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Western Economics Forum, Spring 2014 Van Der Voo, Lee (2011). “Money Blows in to a Patch of Oregon Known for its Unrelenting Winds.” New York Times May 30, 2011. http://www.nytimes.com/2011/05/31/us/31wind.html?_r=0 Zheng, L. and Warner, M. (2010). "Business incentive use among US local governments: A story of accountability and policy learning." Economic Development Quarterly 24(4), 325336.

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Western Economics Forum, Spring 2014

Wildfire in Hedonic Property Value Studies Winslow D. Hansen1, Julie M. Mueller2, Helen T. Naughton3 Introduction An expanding wildland-urban interface (WUI)4 coupled with increased frequency and severity of wildfires has increased the importance of estimating the economic impacts of wildfires. Economic impacts need to be investigated to justify the rapid increase in wildfire suppression costs and inform other wildfire management decisions. Hedonic property modeling is a method that uses changes in property values to estimate the costs (or benefits) associated with wildfire. It is a unique tool that can help inform novel wildfire management and provide insight into ways to balance the social-ecological costs and benefits of wildfire. In this paper, we review hedonic property studies estimating the economic effects of wildfire. The market costs of wildfire suppression and protection are substantial. Federal fire suppression and protection costs average more than 3 billion dollars per year, consuming almost half of the U.S. Forest Service’s annual budget (Gorte 2013). In many western forests, wildfires are an integral ecological driver, resetting forest succession and fostering ecological heterogeneity (Turner 2010). However, western wildfires are becoming more frequent and larger as climate warms. These trends are projected to continue over the 21st century (Westerling et al. 2006, 2011). Wildfires burned approximately 2.75 million ha per year since 2000, more than double the 1990s average (Weeks 2012). Policymakers are therefore under increasing pressure to develop strategies that cost effectively balance protection of WUI property with maintaining the ecological necessity of wildfire (Stephens et al. 2013). The WUI are areas where at least six homes per square km are interspersed among natural vegetation (Radeloff et al. 2005, Stewart et al. 2007). WUI expansion in the western U.S. often occurs where wildfires burn at high intensity and are difficult to suppress (Theobald and Romme 2007). Developing cost-effective management strategies therefore requires improving understanding of how WUI homeowners perceive and respond to wildfires (Steelman et al. 2004, Sturtevant and Jakes 2008, Hansen and Naughton 2013). Often, policymakers assume humans respond negatively to wildfire. Yet, increasing evidence suggests people evaluate complex tradeoffs between amenities enhanced and degraded by wildfire (Donovan et al. 2007). Geographic variation in fire-regime characteristics also makes developing effective uniform policy problematic because diverse fire-regime characteristics likely influence WUI residents 1

[email protected], Department of Zoology, University of Wisconsin, Madison, WI 53706.

2

[email protected], The W.A. Franke College of Business, Northern Arizona University, Flagstaff, AZ 86011. 3

[email protected], Department of Economics, University of Montana, Missoula, MT 59803. This author acknowledges funding from the Joint Fire Science Program under Project JFSP 12-107-1. 4

WUI is defined as “the area where houses meet or intermingle with undeveloped wildland vegetation” (Radeloff et al. 2005).

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Western Economics Forum, Spring 2014 differently (Chapin et al. 2008). Finally, actually experiencing wildfire is often still rare, increasing the challenge of fostering public investment (Gill et al. 2013, Hughes et al. 2013). Wildfire is essential for shaping the structure and function of many ecosystems. It determines vegetation composition, creates wildlife habitat, and alters biogeochemical cycling (Turner et al. 1998, 2003, Turner 2010). Wildfires are often described by their severity (or ecological impact), size, and frequency of occurrence. These characteristics of wildfire can vary greatly by ecosystem (Stephens et al. 2013). For example, wildfires occur as frequently as once every one to five years in grassland ecosystems to once every 300 years, or longer in some coniferous forests (Turner et al. 2003, Schoennagel et al. 2006, Stephens et al. 2013). The characteristics of wildfire determine effects on environmental amenities and dis-amenities provisioned to people, such as carbon storage, timber production, and forest aesthetics (Chapin et al. 2003, Gallant et al. 2003, Hunt and Haider 2004, Balshi et al. 2009). Wildfire also responds strongly to changes in climate. Temperatures are expected to warm substantially over the 21st century, and will likely increase the frequency, size, and severity of wildfire (Westerling et al. 2006, 2011). For example, across the North American boreal forest, annual area burned is expected to increase by 74 to 118% by the end of the 21st century (Flannigan et al. 2005, Balshi et al. 2008). How ecosystems and environmental amenities are affected is therefore likely to vary in complex ways as a function of historical fire characteristics and the magnitude of climate-induced change in fire characteristics (Turner et al. 2013). These changes in wildfire will have important ecological ramifications that will affect people in profound ways. Despite growing need for high-impact economic research on wildfire-human interactions, the wildfire-economics literature is new and relatively sparse. Primary journals of the American Economics Association5 have currently published no articles on wildfire. Only six wildfire studies are published in two leading environmental economics journals The Journal of Environmental Economics and Management and Environmental and Resource Economics.6 Wildfire economics is better represented in other environmental journals such as Land Economics, Ecological Economics, and Journal of Forest Economics.7 Of note, high impact journals, such as Science magazine have published 26 articles containing both terms “wildfire” and “economics.” The lack of high-impact wildfire-economics publications in leading disciplinary journals, despite clear need, highlights extensive opportunities for research.8 The outline of this paper is as follows. We briefly introduce the ecological impacts and economics of wildfires. We then discuss current wildfire hedonic property model literature in detail. We conclude by identifying challenges and opportunities in employing the hedonic property method for wildfire valuation in the future.

Wildfire Economics A comprehensive review of forest-disturbance economics, with a heavy focus on wildfire economics, was compiled by Holmes et al. (2008) in The Economics of Forest Disturbances: Wildfires, Storms, and Invasive Species. The book advocates for improved connection between 5

These are American Economic Journal: Applied Economics, American Economic Journal: Macroeconomics, American Economic Journal: Microeconomics, American Economic Journal: Economic Policy, Journal of Economic Perspectives, Journal of Economic Literature, and American Economic Review. 6 Journal rankings according to Kodrzycki and Yu (2006). 7 The full content search for “wildfire” in these journals resulted in 20, 44 and 23 articles, respectively. 8 This need is also represented in the Joint Fire Science Program’s 2014 Funding Opportunities Notices to advance fire social sciences, see the following link for more information: http://www.firescience.gov/JFSP_funding_announcements.cfm?pass_fiscal_year=2014

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Western Economics Forum, Spring 2014 economic and ecological models. Economic valuation of forest disturbance is another major theme emphasized for its role in making management decisions and setting policy priorities. The review finds that too few valuation studies have been completed. Finally, Holmes et al. (2008) suggest improved fire management programs can lower costs and increase benefits to society. Further, management programs need to be evaluated in an integrated system that considers market and non-market values as well as ecological effects of wildfire. Wildfire effects span relatively easy to measure economic impacts, e.g., suppression expenditures, to more difficult to measure use and non-use values.9 Use and non-use values include foregone recreation opportunities such as hiking, hunting, and camping, and ecosystem services.10 To implement efficient wildfire policy, the nonmarket effects of wildfire need to be quantified. Economists appeal to non-market valuation to obtain the total economic effects of wildfire, including use and non-use values. Non-market valuation is based on revealed or stated preferences. Revealed preference models estimate non-market values based on observed data. Stated preference models estimate non-market values using survey instruments.11 While stated and revealed preference models each have strengths and limitations, a thorough analysis of how these methods apply to the non-market valuation of wildfire is beyond the scope of this paper, and yet another potential area of further research.12 However, it is well established that a large number of homeowners in the WUI are directly experiencing the impacts of more frequent wildfires in the US. Furthermore, the costs of wildfire suppression are positively related to protecting properties within the WUI (Liang et al. 2008). We therefore focus on wildfire in hedonic property models.

Hedonic Property Models The hedonic property model estimates the value of different environmental characteristics, such as proximity to wildfire, by examining their impact on nearby housing or land prices. Rosen (1974) formalized the theoretical framework for the hedonic property model. This framework is based on the proposition that identical houses in similar neighborhoods are valued differently if the houses have varying levels of an environmental amenity or dis-amenity. Hedonic property models are considered revealed preference models of non-market valuation because via the house price, the researcher observes the monetary trade-off a consumer is willing to make to obtain certain housing characteristics (Taylor 2003). The hedonic property model formalizes the familiar idea that we expect houses with an environmental amenity, such as an ocean view, to have higher selling prices than houses without an ocean view. Hedonic property models have been used to value a range of amenities and dis-amenities, including: heterogeneity in public lands (Ham et al. 2012), environmental amenities and agricultural land value (Wasson et al. 2013), open space and water resource ownership (Netusil 2013); water quality (Leggett and Bockstael 2000), and even nuclear waste transport (Gawande and Jenkins-Smith 2001). Forest-related HPMs include estimating the value of forest fuel reduction in Arizona (Kim and Wells 2005), and forest proximity and management practices

9

See Barrio and Louriero (2010) for a meta-analysis of contingent valuation forest studies.

10

See Loomis (2005) for an updated meta-analysis of recreational use values on national forests and public lands. 11

See Venn and Calkin (2011) for a wider of review research related to non-market effects of wildfires.

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Western Economics Forum, Spring 2014 (Kim and Johnson 2002). Despite the depth and breadth of hedonic property models in the environmental economics literature, we find little consensus on effects of wildfire from hedonic property studies. While hedonic property studies vary somewhat in method of estimation and specific variables, all follow the general format: Pit = f (Sit, Nit, Eit) (1) where the value of property i in period t (Pit) is estimated as a function of the structural characteristics of the property (Sit), neighborhood demographics (Nit) and environmental variables (Eit). In order to determine the marginal implicit price using a hedonic property model, it is necessary to control for other characteristics that determine house price or property value, including structural characteristics, neighborhood demographics, and housing market trends.13 The resulting house price differential between houses with varying levels of an environmental amenity or dis-amenity is homebuyers’ marginal willingness to pay, or the marginal implicit price. Specification and method of estimation of hedonic property models is a well-established area of research spanning decades (Cropper et al. 1998). Most hedonic property models estimate equation (1) above, which provides the marginal prices of environmental amenities. If sufficient data are available, these first-stage parameters can be used to estimate a second-stage model of inverse demand function for these amenities (Rosen 1974). Studies that estimate both stages of the hedonic property model include Garrod and Willis (1992), Chattopadhyay (1999), Mahan et al. (2000) and Netusil et al. (2010). None of the wildfire hedonic property models to date estimate the second-stage model. Most hedonic studies, including the wildfire hedonic studies we discuss below, use semi-log specifications with the natural log of Pit as the dependent variable. Furthermore, Ordinary Least Squares (OLS) and Maximum Likelihood (ML) are the most popular estimation methods. Of the wildfire hedonic studies discussed next, Loomis (2004) and Mueller et al. (2009) use OLS, and the remaining papers use ML.14

Wildfire in Hedonic Property Models Wildfire in the western U.S. has become increasingly prevalent on landscapes. Hedonic property models have been employed across many ecosystems ranging from southern California to Alaska. Thus far, results of the hedonic property model literature align with Spash and Vatn’s (2006) conclusion that non-market values of wildfires cannot be transferred across regions because social and cultural preferences associated with wildfire and characteristics of wildfires are heterogeneous. In the 1990s, the WUI near Los Angeles, California experienced five wildfires burning a total of around 1,100 ha. Employing property sales data from 1989-2003, Mueller and Loomis (2008), and Mueller et al. (2009) find negative effects of wildfire. Because of repeated fires in a small area, these studies are able to identify a drop in sales prices following the first and another drop following a second fire within 1.75 miles of a property. The consistent negative effects of fire may be explained by the moderate- to high-fire frequency in the region (Keeley 2006). Following a fire in the chaparral dominated landscape of Southern California, there is little decrease in 13

See Taylor (2003) and Palmquist (1991) for a comprehensive discussion of the theoretical aspects of hedonic property models. 14

A more detailed discussion of specification and methods of estimation is beyond the scope of this paper.

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Western Economics Forum, Spring 2014 subsequent fire risk. Thus, from a homebuyers’ perspective, the reminder of fire risk outweighs any potential long-term ecological benefits, resulting in a drop in house price. In Colorado, fire frequency varies with elevation, from lower elevation ponderosa pine forests, that burn relatively frequently, to higher elevation conifer forests that burn less frequently (Veblen et al. 2000, Romme and Knight 1981). In 1996, the Buffalo Creek fire burned almost 5,000 ha, destroying 10 houses two miles from Pine, Colorado. In the first published study estimating the impacts of wildfire on house prices, Loomis (2004) found that property values in the nearby town of Pine decreased by an average of 15%. The author cites an increased perceived fire risk and reduced recreational and aesthetic amenities as potential reasons for negative effects.15 In 2002 the Colorado Springs fire department used a website to make public estimated wildfire risk to 35,000 WUI homeowners. Risk was calculated based on environmental and home characteristics. Using home sale prices pre-website (1998-2001) and post-website (2002-2004), Donovan et al. (2007) estimate the economic value associated with property characteristics. Some determinants of wildfire risk, e.g. dangerous topography near homes, changed little post-website. The positive and statistically significant effect of dangerous topography on sales prices endures pre and post-website. This suggests that property owners on ridges with higher wildfire risk and better vistas may have been aware of the wildfire risks associated with living on the ridge, or once they became aware, the vistas still outweighed the risk. Conversely, the value of wood roofs and siding (important contributors to homes’ risk of catching fire) became negative post-website. The latter finding highlights potential opportunities for public education related to wildfire risk. In the northern Rocky Mountains, Stetler et al. (2010) estimate the impacts of 256 wildfires on house prices across 4 million ha of northwestern Montana, between 1996 and 2007. Similar to California and Colorado, the authors find that proximity to wildfire and view of burned area has a “persistent negative effect” on house prices. Interestingly, Stetler et al. (2010, pp. 2241-2242) find evidence of “out of sight, out of mind” mentality with respect to wildfire. Many coefficients estimating economic value associated with higher wildfire risk (e.g. canopy cover) are negative and statistically significant for the subsample of homes with views of past fire but become statistically insignificant for the subsample of homes without views of past fire. This suggests public education campaigns, similar to those in Colorado Springs, could improve knowledge, fostering private proactive fire-mitigation strategies, particularly for properties without views of past fire. Exploring the effect of 33 large wildfires (>3.3 ha) and 1160 small wildfires (