Benefits of the Ballot Box for
Species Conservation
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Kailin Kroetz1, James N. Sanchirico2, Paul R. Armsworth3, H. Spencer Banzhaf4
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Department of Agricultural and Resource Economics, University of California, Davis, One Shields
Avenue, Davis, CA 95616 ; email: kkroetz@ucdavis.edu
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Department of Environmental Science and Policy, University of California, Davis, One Shields Avenue,
Davis, CA 95616 and University Fellow, Resources for the Future; email: jsanchirico@ucdavis.edu;
phone: (530) 754-9883
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Department of Ecology & Evolutionary Biology, University of Tennessee, Knoxville, TN 37996; email:
p.armsworth@utk.edu
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Department of Economics, Andrew Young School of Policy Studies, Georgia State University, 14
Marietta Street, NW, Atlanta, GA 30303, and Research Associate at the NBER, and a Senior Research
Fellow at the Property and Environment Research Center (PERC); email: hsbanzhaf@gsu.edu
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Running title: Ballot Box Conservation
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Keywords: Biodiversity, conservation, conservation movement, endangered species, integer
programming, open space, referenda, reserve site selection
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Type of article: Essay
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Manuscript length: Abstract (143 words), Body (5,000), References (43), Figures (4), Tables (1)
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Corresponding author:
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James N. Sanchirico
Department of Environmental Science and Policy
University of California, Davis
One Shields Avenue, Davis, CA 95616
Telephone: (530) 754-9883 Email: jsanchirico@ucdavis.edu
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Author Contributions: K.K, J.N.S., P.R.A., and H.S.B. designed research, analyzed results,
and wrote the paper.
The authors declare no conflict of interest.
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Benefits of the Ballot Box for
Species Conservation
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Kailin Kroetz, James N. Sanchirico, Paul R. Armsworth, H. Spencer Banzhaf
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Abstract
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Recent estimates reaffirm that conservation funds are insufficient to meet biodiversity
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conservation goals. Organizations focused on biodiversity conservation therefore need to
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capitalize on investments that societies make in environmental protection that provide ancillary
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benefits to biodiversity. Here, we undertake the first assessment of the potential ancillary
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benefits from the ballot box in the United States, where citizens vote on referenda to conserve
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lands for reasons that may not include biodiversity directly but that indirectly might enhance
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biodiversity conservation. Our results suggest that referenda occur in counties with significantly
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greater biodiversity than counties chosen at random. We also demonstrate that large potential
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gains for conservation are possible if the past and likely future outcomes of these ballot box
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measures are directly incorporated into national-scale conservation planning efforts. The possible
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synergies between ballot box measures and other biodiversity conservation efforts offer an
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under-utilized resource for supporting conservation.
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Introduction
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Global conservation funding needs to at least double to meet the 2020 biodiversity commitments
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of the Convention on Biological Diversity (McCarthy et al. 2012). The shortfall of funding
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heightens the importance of finding additional funding sources to support conservation. It also
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means that what resources are available need to be deployed efficiently and has led to calls for
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improving the coordination and planning of conservation organizations in a bid to capture
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potential efficiency gains (Mace et al. 2000; Kark et al. 2009). The idealized coordinated efforts
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that some authors have called for would prioritize sites that protect biodiversity at low cost
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(Margules & Pressey 2000; Naidoo et al. 2006; Wilson et al. 2009), engage in planning that
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operates at a number of scales (Erasmus et al. 1999; Meretsky et al. 2012), and have access to
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resources for conservation that are fungible over these scales (Balmford et al. 2003).
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Although the conservation biology literature includes pleas for more systematic planning
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(Margules & Pressey 2000; Wilson et al. 2009), these efforts often are not well-coordinated
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(Bode et al. 2011) or when coordinated, there is a mismatch between ecosystem and planning
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scale (Meretsky et al. 2012). Indeed, much of the support for conservation is locally sourced
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(Armsworth et al. 2012) and is intended to meet locally derived priorities (e.g. to provide open
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space, recreation opportunities and other ecosystem services). For example, in the United States
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there are over 1,600 active nonprofit land trust organizations that have varying objectives
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including open space preservation, but whose activities may provide ancillary benefits for
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biodiversity conservation (see e.g. Chang (2011)). As these groups have their own locally-
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derived objectives aside from biodiversity, their conservation activities might not be judged as
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efficient in terms of biodiversity conservation per dollar spent. Nevertheless, their efforts are
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likely beneficial to biodiversity. Understanding the magnitude of these potential gains and how
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best to capitalize on them in biodiversity planning is an important question for the conservation
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community.
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Much of the support for local land trusts derives from the direct democracy process, where
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citizens vote on ballot initiatives to conserve lands for a myriad of reasons (e.g., public access to
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open-space, conservation, groundwater protection, and recreation). According to the Land Trust
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Alliance (LTA), there have been approximately 2,400 land-vote referenda since 1988 occurring
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in over 46 states and setting aside more than $58 billion in conservation funds (Trust for Public
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Land 2012). Although larger conservation organizations (e.g., LTA, The Nature Conservancy)
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do provide support to help formulate initiatives and bring them to the ballot (Kline 2006;
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Kotchen & Powers 2006; Sundberg 2006; Nelson et al. 2007; Banzhaf et al. 2010), ultimately
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the success of the referendum depends on the preferences of the jurisdictional (e.g., municipality,
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county) residents towards land conservation as expressed through their votes (see, e.g., Deacon
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& Shapiro (1975)).
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To date, there is no systematic assessment of the potential ancillary benefits of the ballot box
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initiatives on biodiversity protection. Even though the local services citizens derive from land
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conservation are likely not the same as the value of a site assigned by a planner with the
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objective of maximizing biodiversity, the potential biodiversity benefits can be nonetheless large
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in aggregate because ballot initiatives are prevalent and the sums of money are substantial (e.g.,
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according to Jordan et al. (2007), the average yearly expenditure on these initiatives is
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approximately on par with the U.S. average annual expenditure of the U.S. Conservation Reserve
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Program).
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Furthermore, the potential for efficiency gains by incorporating these ballot measures into
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national-scale planning is an open question. For example, Abbitt, Scott, and Wilcove (2000)
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identified U.S. county-level hotspots of vulnerability across the United States as a type of area
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for central planning efforts to target. These hotspots where based on projected increases in
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populations and development and occur in areas near urban centers. These areas, however,
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might also be the places more likely to hold ballot measures for land conservation (see e.g. Press
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(2002)).
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We contribute to the literature by developing insights into the complementarity of these two
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processes: top-down national-scale biodiversity planning and bottom-up citizen voting.
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Specifically, our paper connects the political-economy research analyzing the occurrence and
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success of the land-vote referenda (e.g., Kline (2006), Kotchen & Powers (2006), Nelson et al.
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(2007) and Banzhaf et al. (2010)) and the conservation biology literature on the optimal
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conservation site selection that assumes a nationally-planned and well-coordinated set of
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activities. In particular, we compare the outcome of the direct democracy process with a
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hypothetical top-down planner to address the following questions: how well has direct
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democracy done at directing funding towards places that the top-down planner would have
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identified and how well is direct democracy likely to do by this standard in the future? We also
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illustrate the potential for efficiency gains by incorporating the spatial patterns of direct
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democracy directly into conservation planning.
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Materials and Methods
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We divide up our analysis into three parts. First, we undertake a retrospective analysis and
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examine the overlap of the location of past successful ballot measures with areas of high species
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concentration. We also compare the successful ballot measures with both a random selection
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process and one that corresponds to the recommendation of a hypothetical top-down biodiversity
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planner allocating a fixed conservation budget across the United States. The planner is
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represented by the solution of a reserve site selection algorithm (RSS). In the second part, we do
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a prospective analysis using a multivariate regression model to predict the likelihood of
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jurisdictions holding and passing land vote referenda. We compare the set of predicted counties
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to data on the presence of endangered species and to the sites selected by the top-down planner.
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Finally, we do an illustrative experiment where we include the past results of referenda directly
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into the reserve site RSS algorithm to investigate the potential efficiency gains from
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incorporating direct democracy outcomes in conservation planning.
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Our analysis uses a number of different data sources to capture the two processes. The three
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main data sets include; county-level USDA agricultural land values as a proxy for the cost of
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conservation land in a county; county-level data on the presence of endangered species; and
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county referenda ballot and outcome data between 1988-2006 come from the Trust for Public
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Land’s “Landvote database”. To focus on referenda that have potential ancillary benefits for
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conservation, we exclude measures that list only recreational and historical purposes (removes
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~10% of the total referenda from 1988-2006).
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To derive species presence/absence information, we utilize NatureServe’s GIS files and
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calculate, for each U.S. county, a list of the species that are present and the rating the species
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receives from NatureServe. NatureServe rates species on a G1 to G5 scale, where G1 is
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critically imperiled and G5 is secure. We focus on species classified as G1 (critically imperiled)
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and G2 (imperiled). We also use the same data on Federal endangered species that have been
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used in other site selection algorithms (see e.g. Ando et al. (1998)). NatureServe’s G1 and G2
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designations include 3,949 species, which is much more inclusive than the Federal endangered
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species list which only includes 874 species. The correlation between the number of
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NatureServe G1G2 species in a county and the number of ES is .74, suggesting there are
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differences in the spatial distribution of biodiversity represented by the two datasets (Stein et al.
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2000).
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Retrospective Analysis
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In the retrospective analysis, we examine two questions:
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Do successful ballot referenda occur in counties with more or fewer G1G2 species and
ES than the G1G2 species and ES in randomly sampled counties?
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Are successful ballot referenda more likely to have occurred in counties targeted by RSS
algorithms?
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To answer the first question, we compare ballot box outcomes to a random sample of counties.
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First we compare the number of species in ballot box counties to the number of species covered
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when randomly selecting 146 counties (equal to the number of counties with prior successful
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referenda; see Figure SI-14 and SI-15 for G1G2 species and ES). Then we compare the number
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of species in ballot box counties to the number of species covered by randomly selecting
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counties having the same overall area as those with successful ballot measures (see Figure SI-16
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and SI-17 for G1G2 species and ES).
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To answer the second question, we examine how outcomes of successful ballot box measures
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compare to the set of sites selected by a top-down biodiversity planner. In this case our
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benchmark is the outcomes of an RSS algorithm. While there are many possible variants of RSS
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formulations to consider (see, e.g. Sarkar et al. (2006) and our review in the SI), we choose for
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illustration purposes the simple yet seminal framework of Ando et al. (1998). Following Ando
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et al. (1998), we use two common site selection approaches to summarize the results of top-down
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conservation planning. Specifically, we solve the set covering problem (SCP) (Underhill 1994)
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and the maximum coverage problem (MCP) (Camm et al. 1996; Church et al. 1996) (see SI for
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mathematical formulation) from operations research. We explore several budgets in our
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analysis. Our base budget amount is consistent with that used in Ando et al. (1998), except that
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we account for the differences in farmland values (1992 in Ando et al. and 2002 here) by
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inflating the budget to reflect an 8% increase per year over the 10-year period.
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Prospective Analysis
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A key component of the prospective analysis is the development of a predicted probability of a
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successful referendum for each county in the U.S. that reflects the likelihood of the local citizens
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putting a measure on the ballot and passing it. We use these predicted probabilities to examine
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the overlap between predicted sites of successful referenda and species presence/absence and the
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RSS benchmark. Specifically, we address the following two questions:
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How do the predictions of our model of successful ballot box referenda compare to
counties with G1G2 species or ES?
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How do the predictions of our model of successful ballot box referenda compare to
counties targeted by RSS?
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To predict the probability of a successful referendum, we build off of the econometric analysis of
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Banzhaf, Oates, and Sanchirico (2010) and utilize the same set of covariates. They estimated a
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polychotomous sample selection model using Landvote data from 1988-2006. Their set of
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covariates included U.S. Census data, USDA Economic Research Service land use data, U.S.
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county election data, and data on state characteristics that may influence the occurrence of
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referenda.
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Based on their results, we make a number of simplifying assumptions. First, we use a probit
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model for estimating the probability of holding a successful referendum. Banzhaf, Oates, and
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Sanchirico (2010) used a multinomial logit model due to their interest in developing predictions
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specific to funding mechanism (e.g. bond, tax) for the referendum. The funding mechanism,
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however, is not of prime interest for our analysis. Second, we do not control for the potential
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selection issue (that is, we only observe counties that have held referenda), because they found
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along with Kotchen and Powers (2006) and Nelson, Uwasa, and Polasky (2007) that a two-step
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Heckman (1979) correction for sample selection is not necessary.
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The probit model is 𝑃𝑟(𝑆𝑢𝑐𝑐𝑒𝑠𝑠𝑓𝑢𝑙 𝑅𝑒𝑓 = 1|𝑋𝑖 ) = 𝛷(𝛽𝑋𝑖 ), where Successful Ref is the
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dummy variable for whether or not the county has held at least one successful referendum from
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1998-2006, X is a matrix of explanatory variables, Φ is the cumulative normal distribution, and β
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is a vector of coefficients on the explanatory variables. The set of explanatory variables include
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those in Table 1 and public finance (e.g., type of measure, tax or bond), political-economy (e.g.,
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% voting for Bush in 2000, voter turnout in the election, home rule index, etc.), along with other
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controls (e.g., latitude and longitude, land area (sq. miles), % change in farmland, % of land in
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farming, % living in urbanized area, etc.). Estimation results are available in the SI. Using the
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estimated coefficients, we predict the probability of a successful referendum.
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Efficiency gains experiment
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In addition to illustrating the overlap between areas of conservation interest and those places that
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have passed or are likely to pass ballot measures, we construct a thought experiment to
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illuminate the potential gains from directly incorporating the outcomes of ballot measures into
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conservation planning.
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In particular, we ask the following question:
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How much would including counties with successful ballot referenda in RSS algorithms
improve the efficiency of conservation expenditures?
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The answer to this question depends in part on the nature of the conservation organization that is
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engaging in national-scale planning. We consider the case where the organization views any land
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preserved through referenda as exogenous to their efforts and as a potential substitute for their
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own land acquisitions (we discuss other possible scenarios in the Discussion section).
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Substitution is equivalent to assuming that the organization will count species as covered if
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counties with successful referenda coincide with species ranges. For such an organization, we
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illustrate the potential gain they could realize by adapting their prioritization of land purchases to
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account for the ballot box measures.
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We measure the gains by examining the change in number of species conserved and budget
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invested when (1) RSS is conducted independently of land vote and (2) RSS takes into account
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locations of past successful referenda and assuming species in these counties are covered at zero
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cost. The latter assumption implies that the hypothetical conservation planner can focus their
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limited budget only on the remaining, unprotected species.
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A possible extension could consider an organization that does not want to work directly within
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the land vote process but wants to invest resources in co-locating conserved and referendum
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sites. In this case, the spatial configuration of sites is important for measuring the potential gains,
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as groups can benefit from investing and/or partnering with other groups that are also conducting
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local conservation efforts, possibly supported by funding made available through the ballot box.
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Results
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Retrospective Analysis
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Analyzing the Land Vote data for U.S. counties from 1988-2006, we find that counties with at
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least one successful referendum tend to have a higher median household income, a higher
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median home value, and a higher population density than counties with none (see Table 1).
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These successful referenda counties, therefore, have similar characteristics to those used by
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Abbitt et al. (2000) to create their vulnerability index, suggesting there may be some overlap
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with counties important for biodiversity investments.
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To test this possibility, we compare the distribution of successful referenda counties with the
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G1G2 species and ES presence/absence data. Just over 20% of species classified as G1G2 occur
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in the set of 146 counties that had successful referenda from 1988-2006 (see Figure SI-1 for a
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map of all counties with at least one successful referendum from 1988-2006). Approximately
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35% of ES are present in counties that had successful referenda. These counties tend to be in the
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Northeast, Florida, and the West.
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To provide context for these percentages, we compare ballot box outcomes to a random sample
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of counties. We test whether the number of G1G2 species and ES in ballot box counties is
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greater than the number of G1G2 species and ES covered when randomly selecting 146 counties
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(equal to the number of counties with prior successful referenda). We find that counties with
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successful referenda cover more species than would be expected by a random sample: the p-
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values associated with the hypothesis test for G1G2 species and ES are .00018 and .00308,
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respectively. We also find that the number of G1G2 species and ES in ballot box counties is
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greater than the number covered by randomly selecting counties having the same overall area as
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those with successful ballot measures (the p-values associated with the hypothesis tests for G1G2
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species and ES are .00072 and .0031, respectively).
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Next we ask: How do the outcomes of successful ballot box measures compare to the set of sites
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selected by a top-down biodiversity planner? For each comparison of the RSS to referenda
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outcomes, we consider the overlap in terms of the counties and the species covered. We present
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results here for our base budget, which is consistent with that used in Ando et al. (1998). In
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terms of the counties, when the objective is to maximize G1G2 species covered, 170 counties are
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selected via RSS, of which only 10 counties (~7%) are also in the set of counties with prior
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successful referenda (Figure 1). These RSS-selected counties are more concentrated in the west,
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are not as concentrated in the northeast, and have a denser distribution in Appalachia than the
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counties with prior successful referenda.
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In terms of species covered, we find that 2,719 G1G2 species (~69%) are covered by RSS
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selected counties compared to 846 G1G2 species in counties with past successful referenda
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(~21%). Not surprisingly, the average number of G1G2 species and ES is higher in counties
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selected via RSS and the farmland and housing prices are lower (see Table 1). The RSS is by
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design selecting counties with greater diversity at lower cost.
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Prospective Analysis
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While the focus of the retrospective analysis is on how well direct democracy has done at
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passing land set-aside referenda in important locations for preserving species, the prospective
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analysis asks how well it likely will do by comparing local citizen preferences for land
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conservation measures with G1G2 species and ES data. To develop the predicted probabilities
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associated with any county holding and passing a land vote referenda, we estimate a multivariate
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cross-sectional regression (probit) model for 1998-2006 – the period over which we have a full
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suite of covariates. We find a pseudo-R2 of .4735 (column 1 in Table SI-1), which is an
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acceptable fit for a cross-sectional analysis.
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We also run two sets of robustness checks. First, we do an out of sample test (see Methods)
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where we compare the predicted probabilities for the 13 counties that hold successful referenda
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from 2007-2011 to those for counties that never have a successful referendum from 1988-2011.
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We find the average predicted probability for the counties with successful referenda from 2007-
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2011 is 24.92% compared to an average of 2.31% for the counties that never have a successful
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referenda. Second, we omit the referenda that occurred in a given year and repeat the
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estimation. We do this omitting each year one at a time for 1998-2006. The average predicted
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probabilities for the counties that previously were designated as having had a successful
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referendum before we dropped the year’s data are, on average, higher (24.1% average predicted
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probability of holding a successful referendum for counties with prior successful referenda
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versus 2.4% for counties without prior successful referenda).
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In terms of statistically significant covariates, we find that voter turnout in the election, the %
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voting for Bush in the 2000 presidential elections (proxy for Republican voters), and % without a
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high school degree in the county are statistically significant at the 1% level and negatively
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correlated with the probability of holding a successful referenda. Median income (1% level),
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home rule index (1% level), and % living in an urbanized area (10% level) are all positively
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correlated.
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To examine the potential contribution to biodiversity conservation from counties that are more
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likely to hold successful ballot measures in the future, we examine species coverage over varying
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thresholds of probabilities for both the comparison to G1G2 species and ES and the RSS
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benchmark. That is, we assume that all counties with a predicted probability greater than the
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threshold eventually will pass referenda that protect lands (and cover the species) in the county.
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In this analysis we only generate predictions for counties that have not had prior successful
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referenda.
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We first examine the number of species that are present in counties with varying predicted
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probabilities of having successful referenda (see Figure 2 for the G1G2 results and Figure SI – 7
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for the ES results, which are similar). While most of the predicted probabilities are clustered
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near zero, there are, however, 16 counties with a predicted probability of having a successful
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referendum greater than 50%. Approximately 3% of G1G2 species and 8% of ES are covered by
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this set of counties. If we assume all species in counties that had successful referenda in the past
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are also conserved, then 23% of G1G2 species would be expected to be conserved overall.
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Under the same assumption 37% of ES would be expected to be conserved overall. The number
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of species covered varies with the probability threshold, for example, there are 82 counties with
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predicted probabilities of having a successful referendum greater than 20%. These 82 counties
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cover 11% of G1G2 species and 29% of ES. These results suggest that past and predicted future
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referenda conserve lands in counties that overlap with the presence of species of concern.
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Finally, we compare the potential contribution of direct democracy toward biodiversity to our
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biodiversity planner benchmark. We do this by comparing the predicted probability of having a
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successful referendum with our RSS sets. The sets of sites selected via RSS are identical to the
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sets described previously. We focus on the overlap between counties with past successful
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referenda and various thresholds for the predicted referendum success, and sites selected via
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RSS. Figure 3 shows the distribution of the predicted probability of success of referenda in
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counties in the contiguous United States (darker colors indicate higher predicted probability)
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overlaid with the results from the base case RSS (black-hashed counties).
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We see evidence, for example in the Western United States, of overlap between counties with
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high predicted probabilities and the RSS set. However, we also observe counties, for example,
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inland counties in the Great Plains and Appalachia, which are selected via RSS but have a low
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probability of successful referenda. We also find a number of places where the referenda occur in
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counties neighboring those chosen by the biodiversity planner, implying that there might be
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agglomeration benefits especially for species with ranges that cover multiple counties
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(something we are not considering in this analysis; see e.g. Önal & Briers (2002)). These results
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assume a budget for the RSS consistent with Ando et. al (1998), which is 14% of the total
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necessary to conserve all G1G2 species; as we increase the budget the number of counties in the
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RSS increases as does the overlap (see Figure SI-12).
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Potential Efficiency Gains
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Here we illustrate one possible way a national conservation organization might use ballot box
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measures to utilize their budgets more efficiently. Specifically, we rerun for a range of budgets
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the previous RSS (MCP) analysis assuming land in counties with prior successful referenda is
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free (zero cost) to the central planner.
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Figure 4 panel A illustrates the relationship between a national conservation group’s budget and
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species (see Figure SI-9 for ES). The additional coverage, in terms of species, from taking into
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account sites covered via prior successful referenda is substantial, especially at low budgets. For
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example, if we focus on the base case budget, which is marked in the figure by the red dashed
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line and based on updating budgeting assumptions used in Ando et al.’s (1998) top-down
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planning study, we find that the base RSS budget could be reduced by 45% while still protecting
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the same number of G1G2 species (horizontal green line in the Figure; 47% with ES). Looked at
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another way, at the same budget level, the planner can protect 14% more G1G2 species (vertical
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blue line; 12% more ES).
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We also find that the location of conservation priorities change when accounting for the ancillary
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benefits available from successful referenda in national scale conservation planning (see Fig. 4
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panel B). For example, at the base budget, the national-scale planner omits 27 counties from the
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optimal solution, 10 of which had past successful referenda but 17 of which did not. These 17
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share species with counties protected by referenda and thus become a lower priority, as
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evidenced for example, by the new RSS no longer selecting counties in peninsular Florida. With
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the savings from not having to allocate funds to counties with past referenda, the planner adds 34
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new counties to their optimal set. When comparing sets of species in the omitted and new
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counties, we find that the planner substitutes toward counties that have a higher concentration of
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species not in referenda sites.
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Discussion
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The purpose of this paper is to examine the potential for the direct democracy process to
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contribute to biodiversity conservation in the United States. To our knowledge the ancillary
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benefits of citizen-supported initiatives have yet to be considered in this light. The values local
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residents derive from living near land set aside via referenda is analogous to the “human amenity
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value” that Fuller et al. (2010) suggested could be incorporated into top-down planning objective
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functions. Rather than requiring top-down planning to account for human amenity value though,
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the referenda process in the United States allows local residents to express their support for
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particular amenities and local biodiversity by voting in favor of conservation initiatives directly.
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Therefore, unlike other processes outside the control of the top-down planner that may serve as a
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source of inefficiency, such as political pressure (Pressey 1994; Margules & Pressey 2000), we
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demonstrate that the direct democracy process might actually enhance the efficiency of
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conservation budgets.
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While we illustrate one way in which conservation planners might be able to interact more
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systematically with the land vote process, our results also hint at other ways conservation
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planners might be able to interact more systematically with the land vote process. For example,
420
a conservation organization may find it cost-effective to allocate resources to help referenda with
421
a low probability of occurrence but a relatively high predicted percent voting yes to get on the
422
ballot. Resources could also be allocated to help referenda already on the ballot pass.
423
14
424
425
To inform such efforts, organizations could investigate the role of demographic, political,
426
economic, and other factors in predicting the probability of referenda occurring and the percent
427
voting yes as a means to better inform the allocation of their resources. Some groups including
428
The Conservation Fund and The Trust for Public Land already have manuals regarding how to
429
support conservation through ballet measures (see SI for more detail). As an illustration of how
430
statistical models could enhance current conservation efforts, we estimated a probit regression to
431
predict the probability of referenda occurring and a log-odds regression to predict the percent
432
voting yes (see Table SI-1, Figure SI-2, Figure SI–3). Our results, for example, suggest that
433
counties with higher percentages of the population with at least a bachelor’s degree have larger
434
number of voters voting yes. Previous econometric work related to open space referenda has
435
identified additional characteristics of a jurisdiction associated with having and passing a
436
referendum using a variety of different models (Kline 2006; Kotchen & Powers 2006; Nelson et
437
al. 2007; Banzhaf et al. 2010; Wu & Cutter 2011).
438
439
Given the diversity of RSS approaches in the literature, we use our formulations as a benchmark
440
for comparison and as a representation of “idealized” top-down planning, and do not view our
441
results as offering a management prescription (see the SI for a discussion of various elements to
442
account for when choosing an RSS-set). Some recent advances in RSS literature, for example,
443
formulate a return on investment approach that combines multiple attributes, such as
444
vulnerability to development, land prices, spatial contiguity and/or complementarity benefits (see
445
e.g., Murdoch et al. (2007), Polasky (2008), Underwood et al. (2009), Murdoch et al. (2010),
446
and Withey et al. (2012)). While our benchmark considers only the role of land prices, our
447
analysis can be tailored by organizations to particular conservation objectives and datasets to
448
make management decisions.
449
450
A possible concern about the potential use of ballot box measures for conservation is the
451
presence of taxonomic bias in the species covered. In RSS planning efforts, for example, the
452
species to use as a surrogate for biodiversity and the weights used in the objective are chosen
453
based on the objectives of the conservation exercise (Margules & Pressey 2000). In our RSS
454
analysis, we give all species, regardless of their taxa, equal weight. To check for potential
15
455
mismatches between the set of species conserved in the two processes, we calculate the
456
percentage of each taxa-type covered in the RSS (base budget) and the ballot measures. Our
457
results suggest that any taxonomic bias may be small (~10% for plants and vertebrates, see
458
Figure SI-19) at least for county-level referenda.
459
460
In this paper, we focus on the potential role of county-level ballot initiatives. Further work
461
should integrate data on land set aside via municipal and state level initiatives and other
462
protected lands into RSS type planning exercises to get a more holistic view of all of the
463
conservation activities being undertaken in the U.S. A possible hypothesis for such an analysis
464
might be that the potential gains from ballot measures are lower, after taking into account all of
465
these other types of protection. In our efficiency gains experiment, for example, we do not
466
account for land held by Federal and state governments. Using information from the Protected
467
Area Database (US Geological Survey 2012), we test the robustness of our findings by
468
conducting auxiliary analysis with protected area data. We assume that species in a county are
469
covered if greater than 25% or 50% of the county land area is protected (achieves GAP 1 or GAP
470
2 status). We find there are still large efficiency gains available through consideration of the
471
land vote process. For example, the budget gains when holding the number of species constant
472
and when only considering land vote are ~45%, when only considering protected areas are
473
~13%, and when both past referenda and protected areas are considered jointly are ~52%. (See
474
SI for additional details of the robustness check.)
475
476
We leave for future work incorporating complexities such as spatial configurations of reserves,
477
institutional arrangements such as partnering with local land trusts, and interactions between
478
conservation efforts such as attraction and repulsion of new reserves to current reserves and
479
crowding out by government (Albers et al. 2008a; Albers et al. 2008b; Parker & Thurman 2011).
480
We also do not consider the anti-growth/development ballot measures, which are the other side
481
of the coin to the land conservation referenda (see, e.g., Gerber & Phillips (2005)).
482
483
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16
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487
Acknowledgements
488
489
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494
We thank Andrew Chua for research assistance; Lynn Kutner, Data Management Coordinator at
NatureServe, for NatureServe species data; several colleagues, including Michael Bode, Martin Smith,
Alex Pfaff, Chris Timmins, Susan Harrison, and Andy Sih, as well as four anonymous referees, for
helpful comments and suggestions. Sanchirico acknowledges support from Agricultural Experimentation
Station project CA-D-ESP-7853-H. Kroetz acknowledges support of the National Institute for
Mathematical and Biological Synthesis at The University of Tennessee, Knoxville where she was a shortterm visitor.
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21
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Tables
712
Table 1: Summary statistics
Median Levels
Federal Endangered Species
NatureServe G1G2 Species
Median Household Income ($1,000s)
Median Home Value ($1,000s)
Percent in Poverty
Percent Age > 65
Percent Age < 18
Percent No High School Degree
Percent Bachelor's Degree
Population Density
Farmland Price per Acre
713
714
715
716
All
counties
3
1
33.69
71.80
15.08
14.43
25.34
20.80
14.40
42.21
$1,668
No successful
referenda
2
1
33.23
70.40
14.78
14.54
2534
21.10
14.10
39.73
$1,611
At least one
successful
referendum
4
3
48.66
140.00
26.96
11.38
25.06
13.80
28.50
435.82
$4,485
ES RSS
8
10
31.79
80.10
12.64
13.55
25.57
22.15
15.15
20.50
$1,183
G1G2 RSS
6
11
31.54
78.50
11.98
14.13
25.66
21.80
15.20
11.08
$763
Note: Counties are categorized according to whether the county has held at least one successful referendum from
1988-2006 and whether or not it was selected via RSS at the base budget that is ~14% of the amount needed to
cover all G1G2 species and ~33% of the amount need to cover all ES species (see Methods and SI).
717
718
719
720
721
722
723
724
725
726
727
728
22
729
730
Figures
731
Figure 1: Comparison of counties with past successful referenda and optimal RSS with
732
G1G2 species. This figure shows the overlap between the counties with prior successful referenda between 1988
733
and 2006, and the results of the RSS algorithm. The RSS results are based on maximizing the number of G1G2
734
species covered subject to a budget. We use a base budget similar to that of Ando et al. (1998) but adjusted to
735
account for differences in the cost of farmland. The base budget represents ~14% of the total budget needed to
736
cover all G1G2 species (see Methods).
737
738
739
740
741
742
743
744
745
23
746
Figure 2: Relationship between G1G2 species covered and predicted probability of
747
referendum success. We calculate, for counties with a predicted probability of a successful referendum greater
748
than the threshold probability, the number of G1G2 species covered under two assumptions: (1) species covered by
749
counties with prior successful referenda are included in the count; and (2) species covered by counties with prior
750
successful referenda are not included in the count.
751
752
753
754
755
756
757
758
24
759
Figure 3: Comparison of counties predicted to have future successful referenda and our
760
benchmark (RSS using G1G2 species data and base budget). The figure shows an overlay of the
761
RSS results on the predicted probability of having a successful referendum, by percentile group, along with the
762
counties that have had successful referenda from 1988-2006. The probabilities of holding a successful referendum
763
associated with the percentile groups are as follows: 75th percentile (1% or less), 76th-80th (1-2%), 81st-85th (2-
764
3%), 86th-90th percentiles (3-5%), 91th-95th (5-13%), 95th-100th (13% or greater).
765
766
767
768
769
770
771
772
25
773
Figure 4: Efficiency gains for G1G2 species covered. We solve a series of RSS problems maximizing
774
the number of species covered, but varying the budget. In panel A, we plot the species covered for each budget
775
under two scenarios. In our Without Ballot Measures scenario, a site must be purchased to preserve it. In our With
776
Ballot Measures scenario species in sites with successful referenda from 1988-2006 are preserved for free and all
777
other sites must be purchased to be preserved. We present the species as a proportion of the total number of G1G2
778
species and the budget as a percentage of the total budget required to cover all G1G2 species. Panel B represents the
779
changes in the set of sites chosen by the RSS planner when the ballot measures are incorporated directly into the site
780
selection problem at the base budget. In particular, the map corresponds to the gains in species covered labeled in
781
panel A.
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783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
26
A.
B.
803
27