1

Benefits of the Ballot Box for 1

Species Conservation 2

3

Kailin Kroetz1, James N. Sanchirico

2, Paul R. Armsworth

3, H. Spencer Banzhaf

4 4

1 Department of Agricultural and Resource Economics, University of California, Davis, One Shields 5

Avenue, Davis, CA 95616 ; email: kkroetz@ucdavis.edu 6 2 Department of Environmental Science and Policy, University of California, Davis, One Shields Avenue, 7

Davis, CA 95616 and University Fellow, Resources for the Future; email: jsanchirico@ucdavis.edu; 8 phone: (530) 754-9883 9 3 Department of Ecology & Evolutionary Biology, University of Tennessee, Knoxville, TN 37996; email: 10

p.armsworth@utk.edu 11 4 Department of Economics, Andrew Young School of Policy Studies, Georgia State University, 14 12

Marietta Street, NW, Atlanta, GA 30303, and Research Associate at the NBER, and a Senior Research 13 Fellow at the Property and Environment Research Center (PERC); email: hsbanzhaf@gsu.edu 14

15

Running title: Ballot Box Conservation 16

Keywords: Biodiversity, conservation, conservation movement, endangered species, integer 17

programming, open space, referenda, reserve site selection 18

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Type of article: Essay 20

Manuscript length: Abstract (143 words), Body (5,000), References (43), Figures (4), Tables (1) 21

Corresponding author: 22

James N. Sanchirico 23

Department of Environmental Science and Policy 24

University of California, Davis 25

One Shields Avenue, Davis, CA 95616 26

Telephone: (530) 754-9883 Email: jsanchirico@ucdavis.edu 27

28

Author Contributions: K.K, J.N.S., P.R.A., and H.S.B. designed research, analyzed results, 29 and wrote the paper. 30 31

The authors declare no conflict of interest. 32

2

Benefits of the Ballot Box for 33

Species Conservation 34

35

Kailin Kroetz, James N. Sanchirico, Paul R. Armsworth, H. Spencer Banzhaf 36

37

Abstract 38 39

Recent estimates reaffirm that conservation funds are insufficient to meet biodiversity 40

conservation goals. Organizations focused on biodiversity conservation therefore need to 41

capitalize on investments that societies make in environmental protection that provide ancillary 42

benefits to biodiversity. Here, we undertake the first assessment of the potential ancillary 43

benefits from the ballot box in the United States, where citizens vote on referenda to conserve 44

lands for reasons that may not include biodiversity directly but that indirectly might enhance 45

biodiversity conservation. Our results suggest that referenda occur in counties with significantly 46

greater biodiversity than counties chosen at random. We also demonstrate that large potential 47

gains for conservation are possible if the past and likely future outcomes of these ballot box 48

measures are directly incorporated into national-scale conservation planning efforts. The possible 49

synergies between ballot box measures and other biodiversity conservation efforts offer an 50

under-utilized resource for supporting conservation. 51

52

53

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57 58

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Introduction 59

60

Global conservation funding needs to at least double to meet the 2020 biodiversity commitments 61

of the Convention on Biological Diversity (McCarthy et al. 2012). The shortfall of funding 62

heightens the importance of finding additional funding sources to support conservation. It also 63

means that what resources are available need to be deployed efficiently and has led to calls for 64

improving the coordination and planning of conservation organizations in a bid to capture 65

potential efficiency gains (Mace et al. 2000; Kark et al. 2009). The idealized coordinated efforts 66

that some authors have called for would prioritize sites that protect biodiversity at low cost 67

(Margules & Pressey 2000; Naidoo et al. 2006; Wilson et al. 2009), engage in planning that 68

operates at a number of scales (Erasmus et al. 1999; Meretsky et al. 2012), and have access to 69

resources for conservation that are fungible over these scales (Balmford et al. 2003). 70

71

Although the conservation biology literature includes pleas for more systematic planning 72

(Margules & Pressey 2000; Wilson et al. 2009), these efforts often are not well-coordinated 73

(Bode et al. 2011) or when coordinated, there is a mismatch between ecosystem and planning 74

scale (Meretsky et al. 2012). Indeed, much of the support for conservation is locally sourced 75

(Armsworth et al. 2012) and is intended to meet locally derived priorities (e.g. to provide open 76

space, recreation opportunities and other ecosystem services). For example, in the United States 77

there are over 1,600 active nonprofit land trust organizations that have varying objectives 78

including open space preservation, but whose activities may provide ancillary benefits for 79

biodiversity conservation (see e.g. Chang (2011)). As these groups have their own locally-80

derived objectives aside from biodiversity, their conservation activities might not be judged as 81

efficient in terms of biodiversity conservation per dollar spent. Nevertheless, their efforts are 82

likely beneficial to biodiversity. Understanding the magnitude of these potential gains and how 83

best to capitalize on them in biodiversity planning is an important question for the conservation 84

community. 85

86

Much of the support for local land trusts derives from the direct democracy process, where 87

citizens vote on ballot initiatives to conserve lands for a myriad of reasons (e.g., public access to 88

open-space, conservation, groundwater protection, and recreation). According to the Land Trust 89

4

Alliance (LTA), there have been approximately 2,400 land-vote referenda since 1988 occurring 90

in over 46 states and setting aside more than $58 billion in conservation funds (Trust for Public 91

Land 2012). Although larger conservation organizations (e.g., LTA, The Nature Conservancy) 92

do provide support to help formulate initiatives and bring them to the ballot (Kline 2006; 93

Kotchen & Powers 2006; Sundberg 2006; Nelson et al. 2007; Banzhaf et al. 2010), ultimately 94

the success of the referendum depends on the preferences of the jurisdictional (e.g., municipality, 95

county) residents towards land conservation as expressed through their votes (see, e.g., Deacon 96

& Shapiro (1975)). 97

98

To date, there is no systematic assessment of the potential ancillary benefits of the ballot box 99

initiatives on biodiversity protection. Even though the local services citizens derive from land 100

conservation are likely not the same as the value of a site assigned by a planner with the 101

objective of maximizing biodiversity, the potential biodiversity benefits can be nonetheless large 102

in aggregate because ballot initiatives are prevalent and the sums of money are substantial (e.g., 103

according to Jordan et al. (2007), the average yearly expenditure on these initiatives is 104

approximately on par with the U.S. average annual expenditure of the U.S. Conservation Reserve 105

Program). 106

107

Furthermore, the potential for efficiency gains by incorporating these ballot measures into 108

national-scale planning is an open question. For example, Abbitt, Scott, and Wilcove (2000) 109

identified U.S. county-level hotspots of vulnerability across the United States as a type of area 110

for central planning efforts to target. These hotspots where based on projected increases in 111

populations and development and occur in areas near urban centers. These areas, however, 112

might also be the places more likely to hold ballot measures for land conservation (see e.g. Press 113

(2002)). 114

115

We contribute to the literature by developing insights into the complementarity of these two 116

processes: top-down national-scale biodiversity planning and bottom-up citizen voting. 117

Specifically, our paper connects the political-economy research analyzing the occurrence and 118

success of the land-vote referenda (e.g., Kline (2006), Kotchen & Powers (2006), Nelson et al. 119

(2007) and Banzhaf et al. (2010)) and the conservation biology literature on the optimal 120

5

conservation site selection that assumes a nationally-planned and well-coordinated set of 121

activities. In particular, we compare the outcome of the direct democracy process with a 122

hypothetical top-down planner to address the following questions: how well has direct 123

democracy done at directing funding towards places that the top-down planner would have 124

identified and how well is direct democracy likely to do by this standard in the future? We also 125

illustrate the potential for efficiency gains by incorporating the spatial patterns of direct 126

democracy directly into conservation planning. 127

128

129

Materials and Methods 130

131

We divide up our analysis into three parts. First, we undertake a retrospective analysis and 132

examine the overlap of the location of past successful ballot measures with areas of high species 133

concentration. We also compare the successful ballot measures with both a random selection 134

process and one that corresponds to the recommendation of a hypothetical top-down biodiversity 135

planner allocating a fixed conservation budget across the United States. The planner is 136

represented by the solution of a reserve site selection algorithm (RSS). In the second part, we do 137

a prospective analysis using a multivariate regression model to predict the likelihood of 138

jurisdictions holding and passing land vote referenda. We compare the set of predicted counties 139

to data on the presence of endangered species and to the sites selected by the top-down planner. 140

Finally, we do an illustrative experiment where we include the past results of referenda directly 141

into the reserve site RSS algorithm to investigate the potential efficiency gains from 142

incorporating direct democracy outcomes in conservation planning. 143

144

Our analysis uses a number of different data sources to capture the two processes. The three 145

main data sets include; county-level USDA agricultural land values as a proxy for the cost of 146

conservation land in a county; county-level data on the presence of endangered species; and 147

county referenda ballot and outcome data between 1988-2006 come from the Trust for Public 148

Land’s “Landvote database”. To focus on referenda that have potential ancillary benefits for 149

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conservation, we exclude measures that list only recreational and historical purposes (removes 150

~10% of the total referenda from 1988-2006). 151

To derive species presence/absence information, we utilize NatureServe’s GIS files and 152

calculate, for each U.S. county, a list of the species that are present and the rating the species 153

receives from NatureServe. NatureServe rates species on a G1 to G5 scale, where G1 is 154

critically imperiled and G5 is secure. We focus on species classified as G1 (critically imperiled) 155

and G2 (imperiled). We also use the same data on Federal endangered species that have been 156

used in other site selection algorithms (see e.g. Ando et al. (1998)). NatureServe’s G1 and G2 157

designations include 3,949 species, which is much more inclusive than the Federal endangered 158

species list which only includes 874 species. The correlation between the number of 159

NatureServe G1G2 species in a county and the number of ES is .74, suggesting there are 160

differences in the spatial distribution of biodiversity represented by the two datasets (Stein et al. 161

2000). 162

163

Retrospective Analysis 164

In the retrospective analysis, we examine two questions: 165

Do successful ballot referenda occur in counties with more or fewer G1G2 species and 166

ES than the G1G2 species and ES in randomly sampled counties? 167

Are successful ballot referenda more likely to have occurred in counties targeted by RSS 168

algorithms? 169

170

To answer the first question, we compare ballot box outcomes to a random sample of counties. 171

First we compare the number of species in ballot box counties to the number of species covered 172

when randomly selecting 146 counties (equal to the number of counties with prior successful 173

referenda; see Figure SI-14 and SI-15 for G1G2 species and ES). Then we compare the number 174

of species in ballot box counties to the number of species covered by randomly selecting 175

counties having the same overall area as those with successful ballot measures (see Figure SI-16 176

and SI-17 for G1G2 species and ES). 177

178

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To answer the second question, we examine how outcomes of successful ballot box measures 179

compare to the set of sites selected by a top-down biodiversity planner. In this case our 180

benchmark is the outcomes of an RSS algorithm. While there are many possible variants of RSS 181

formulations to consider (see, e.g. Sarkar et al. (2006) and our review in the SI), we choose for 182

illustration purposes the simple yet seminal framework of Ando et al. (1998). Following Ando 183

et al. (1998), we use two common site selection approaches to summarize the results of top-down 184

conservation planning. Specifically, we solve the set covering problem (SCP) (Underhill 1994) 185

and the maximum coverage problem (MCP) (Camm et al. 1996; Church et al. 1996) (see SI for 186

mathematical formulation) from operations research. We explore several budgets in our 187

analysis. Our base budget amount is consistent with that used in Ando et al. (1998), except that 188

we account for the differences in farmland values (1992 in Ando et al. and 2002 here) by 189

inflating the budget to reflect an 8% increase per year over the 10-year period. 190

191

Prospective Analysis 192

A key component of the prospective analysis is the development of a predicted probability of a 193

successful referendum for each county in the U.S. that reflects the likelihood of the local citizens 194

putting a measure on the ballot and passing it. We use these predicted probabilities to examine 195

the overlap between predicted sites of successful referenda and species presence/absence and the 196

RSS benchmark. Specifically, we address the following two questions: 197

How do the predictions of our model of successful ballot box referenda compare to 198

counties with G1G2 species or ES? 199

How do the predictions of our model of successful ballot box referenda compare to 200

counties targeted by RSS? 201

202

To predict the probability of a successful referendum, we build off of the econometric analysis of 203

Banzhaf, Oates, and Sanchirico (2010) and utilize the same set of covariates. They estimated a 204

polychotomous sample selection model using Landvote data from 1988-2006. Their set of 205

covariates included U.S. Census data, USDA Economic Research Service land use data, U.S. 206

county election data, and data on state characteristics that may influence the occurrence of 207

referenda. 208

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209

Based on their results, we make a number of simplifying assumptions. First, we use a probit 210

model for estimating the probability of holding a successful referendum. Banzhaf, Oates, and 211

Sanchirico (2010) used a multinomial logit model due to their interest in developing predictions 212

specific to funding mechanism (e.g. bond, tax) for the referendum. The funding mechanism, 213

however, is not of prime interest for our analysis. Second, we do not control for the potential 214

selection issue (that is, we only observe counties that have held referenda), because they found 215

along with Kotchen and Powers (2006) and Nelson, Uwasa, and Polasky (2007) that a two-step 216

Heckman (1979) correction for sample selection is not necessary. 217

218

The probit model is 𝑃𝑟(𝑆𝑢𝑐𝑐𝑒𝑠𝑠𝑓𝑢𝑙 𝑅𝑒𝑓 = 1|𝑋𝑖) = 𝛷(𝛽𝑋𝑖), where Successful Ref is the 219

dummy variable for whether or not the county has held at least one successful referendum from 220

1998-2006, X is a matrix of explanatory variables, Φ is the cumulative normal distribution, and β 221

is a vector of coefficients on the explanatory variables. The set of explanatory variables include 222

those in Table 1 and public finance (e.g., type of measure, tax or bond), political-economy (e.g., 223

% voting for Bush in 2000, voter turnout in the election, home rule index, etc.), along with other 224

controls (e.g., latitude and longitude, land area (sq. miles), % change in farmland, % of land in 225

farming, % living in urbanized area, etc.). Estimation results are available in the SI. Using the 226

estimated coefficients, we predict the probability of a successful referendum. 227

228

Efficiency gains experiment 229

230

In addition to illustrating the overlap between areas of conservation interest and those places that 231

have passed or are likely to pass ballot measures, we construct a thought experiment to 232

illuminate the potential gains from directly incorporating the outcomes of ballot measures into 233

conservation planning. 234

235

In particular, we ask the following question: 236

How much would including counties with successful ballot referenda in RSS algorithms 237

improve the efficiency of conservation expenditures? 238

239

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The answer to this question depends in part on the nature of the conservation organization that is 240

engaging in national-scale planning. We consider the case where the organization views any land 241

preserved through referenda as exogenous to their efforts and as a potential substitute for their 242

own land acquisitions (we discuss other possible scenarios in the Discussion section). 243

Substitution is equivalent to assuming that the organization will count species as covered if 244

counties with successful referenda coincide with species ranges. For such an organization, we 245

illustrate the potential gain they could realize by adapting their prioritization of land purchases to 246

account for the ballot box measures. 247

248

We measure the gains by examining the change in number of species conserved and budget 249

invested when (1) RSS is conducted independently of land vote and (2) RSS takes into account 250

locations of past successful referenda and assuming species in these counties are covered at zero 251

cost. The latter assumption implies that the hypothetical conservation planner can focus their 252

limited budget only on the remaining, unprotected species. 253

254

A possible extension could consider an organization that does not want to work directly within 255

the land vote process but wants to invest resources in co-locating conserved and referendum 256

sites. In this case, the spatial configuration of sites is important for measuring the potential gains, 257

as groups can benefit from investing and/or partnering with other groups that are also conducting 258

local conservation efforts, possibly supported by funding made available through the ballot box. 259

260

Results 261

262

Retrospective Analysis 263

Analyzing the Land Vote data for U.S. counties from 1988-2006, we find that counties with at 264

least one successful referendum tend to have a higher median household income, a higher 265

median home value, and a higher population density than counties with none (see Table 1). 266

These successful referenda counties, therefore, have similar characteristics to those used by 267

Abbitt et al. (2000) to create their vulnerability index, suggesting there may be some overlap 268

with counties important for biodiversity investments. 269

270

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To test this possibility, we compare the distribution of successful referenda counties with the 271

G1G2 species and ES presence/absence data. Just over 20% of species classified as G1G2 occur 272

in the set of 146 counties that had successful referenda from 1988-2006 (see Figure SI-1 for a 273

map of all counties with at least one successful referendum from 1988-2006). Approximately 274

35% of ES are present in counties that had successful referenda. These counties tend to be in the 275

Northeast, Florida, and the West. 276

277

To provide context for these percentages, we compare ballot box outcomes to a random sample 278

of counties. We test whether the number of G1G2 species and ES in ballot box counties is 279

greater than the number of G1G2 species and ES covered when randomly selecting 146 counties 280

(equal to the number of counties with prior successful referenda). We find that counties with 281

successful referenda cover more species than would be expected by a random sample: the p-282

values associated with the hypothesis test for G1G2 species and ES are .00018 and .00308, 283

respectively. We also find that the number of G1G2 species and ES in ballot box counties is 284

greater than the number covered by randomly selecting counties having the same overall area as 285

those with successful ballot measures (the p-values associated with the hypothesis tests for G1G2 286

species and ES are .00072 and .0031, respectively). 287

288

Next we ask: How do the outcomes of successful ballot box measures compare to the set of sites 289

selected by a top-down biodiversity planner? For each comparison of the RSS to referenda 290

outcomes, we consider the overlap in terms of the counties and the species covered. We present 291

results here for our base budget, which is consistent with that used in Ando et al. (1998). In 292

terms of the counties, when the objective is to maximize G1G2 species covered, 170 counties are 293

selected via RSS, of which only 10 counties (~7%) are also in the set of counties with prior 294

successful referenda (Figure 1). These RSS-selected counties are more concentrated in the west, 295

are not as concentrated in the northeast, and have a denser distribution in Appalachia than the 296

counties with prior successful referenda. 297

298

In terms of species covered, we find that 2,719 G1G2 species (~69%) are covered by RSS 299

selected counties compared to 846 G1G2 species in counties with past successful referenda 300

(~21%). Not surprisingly, the average number of G1G2 species and ES is higher in counties 301

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selected via RSS and the farmland and housing prices are lower (see Table 1). The RSS is by 302

design selecting counties with greater diversity at lower cost. 303

304

Prospective Analysis 305

While the focus of the retrospective analysis is on how well direct democracy has done at 306

passing land set-aside referenda in important locations for preserving species, the prospective 307

analysis asks how well it likely will do by comparing local citizen preferences for land 308

conservation measures with G1G2 species and ES data. To develop the predicted probabilities 309

associated with any county holding and passing a land vote referenda, we estimate a multivariate 310

cross-sectional regression (probit) model for 1998-2006 – the period over which we have a full 311

suite of covariates. We find a pseudo-R2 of .4735 (column 1 in Table SI-1), which is an 312

acceptable fit for a cross-sectional analysis. 313

314

We also run two sets of robustness checks. First, we do an out of sample test (see Methods) 315

where we compare the predicted probabilities for the 13 counties that hold successful referenda 316

from 2007-2011 to those for counties that never have a successful referendum from 1988-2011. 317

We find the average predicted probability for the counties with successful referenda from 2007-318

2011 is 24.92% compared to an average of 2.31% for the counties that never have a successful 319

referenda. Second, we omit the referenda that occurred in a given year and repeat the 320

estimation. We do this omitting each year one at a time for 1998-2006. The average predicted 321

probabilities for the counties that previously were designated as having had a successful 322

referendum before we dropped the year’s data are, on average, higher (24.1% average predicted 323

probability of holding a successful referendum for counties with prior successful referenda 324

versus 2.4% for counties without prior successful referenda). 325

326

In terms of statistically significant covariates, we find that voter turnout in the election, the % 327

voting for Bush in the 2000 presidential elections (proxy for Republican voters), and % without a 328

high school degree in the county are statistically significant at the 1% level and negatively 329

correlated with the probability of holding a successful referenda. Median income (1% level), 330

home rule index (1% level), and % living in an urbanized area (10% level) are all positively 331

correlated. 332

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333

To examine the potential contribution to biodiversity conservation from counties that are more 334

likely to hold successful ballot measures in the future, we examine species coverage over varying 335

thresholds of probabilities for both the comparison to G1G2 species and ES and the RSS 336

benchmark. That is, we assume that all counties with a predicted probability greater than the 337

threshold eventually will pass referenda that protect lands (and cover the species) in the county. 338

In this analysis we only generate predictions for counties that have not had prior successful 339

referenda. 340

341

We first examine the number of species that are present in counties with varying predicted 342

probabilities of having successful referenda (see Figure 2 for the G1G2 results and Figure SI – 7 343

for the ES results, which are similar). While most of the predicted probabilities are clustered 344

near zero, there are, however, 16 counties with a predicted probability of having a successful 345

referendum greater than 50%. Approximately 3% of G1G2 species and 8% of ES are covered by 346

this set of counties. If we assume all species in counties that had successful referenda in the past 347

are also conserved, then 23% of G1G2 species would be expected to be conserved overall. 348

Under the same assumption 37% of ES would be expected to be conserved overall. The number 349

of species covered varies with the probability threshold, for example, there are 82 counties with 350

predicted probabilities of having a successful referendum greater than 20%. These 82 counties 351

cover 11% of G1G2 species and 29% of ES. These results suggest that past and predicted future 352

referenda conserve lands in counties that overlap with the presence of species of concern. 353

354

Finally, we compare the potential contribution of direct democracy toward biodiversity to our 355

biodiversity planner benchmark. We do this by comparing the predicted probability of having a 356

successful referendum with our RSS sets. The sets of sites selected via RSS are identical to the 357

sets described previously. We focus on the overlap between counties with past successful 358

referenda and various thresholds for the predicted referendum success, and sites selected via 359

RSS. Figure 3 shows the distribution of the predicted probability of success of referenda in 360

counties in the contiguous United States (darker colors indicate higher predicted probability) 361

overlaid with the results from the base case RSS (black-hashed counties). 362

363

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We see evidence, for example in the Western United States, of overlap between counties with 364

high predicted probabilities and the RSS set. However, we also observe counties, for example, 365

inland counties in the Great Plains and Appalachia, which are selected via RSS but have a low 366

probability of successful referenda. We also find a number of places where the referenda occur in 367

counties neighboring those chosen by the biodiversity planner, implying that there might be 368

agglomeration benefits especially for species with ranges that cover multiple counties 369

(something we are not considering in this analysis; see e.g. Önal & Briers (2002)). These results 370

assume a budget for the RSS consistent with Ando et. al (1998), which is 14% of the total 371

necessary to conserve all G1G2 species; as we increase the budget the number of counties in the 372

RSS increases as does the overlap (see Figure SI-12). 373

374

Potential Efficiency Gains 375

376

Here we illustrate one possible way a national conservation organization might use ballot box 377

measures to utilize their budgets more efficiently. Specifically, we rerun for a range of budgets 378

the previous RSS (MCP) analysis assuming land in counties with prior successful referenda is 379

free (zero cost) to the central planner. 380

381

Figure 4 panel A illustrates the relationship between a national conservation group’s budget and 382

species (see Figure SI-9 for ES). The additional coverage, in terms of species, from taking into 383

account sites covered via prior successful referenda is substantial, especially at low budgets. For 384

example, if we focus on the base case budget, which is marked in the figure by the red dashed 385

line and based on updating budgeting assumptions used in Ando et al.’s (1998) top-down 386

planning study, we find that the base RSS budget could be reduced by 45% while still protecting 387

the same number of G1G2 species (horizontal green line in the Figure; 47% with ES). Looked at 388

another way, at the same budget level, the planner can protect 14% more G1G2 species (vertical 389

blue line; 12% more ES). 390

We also find that the location of conservation priorities change when accounting for the ancillary 391

benefits available from successful referenda in national scale conservation planning (see Fig. 4 392

panel B). For example, at the base budget, the national-scale planner omits 27 counties from the 393

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optimal solution, 10 of which had past successful referenda but 17 of which did not. These 17 394

share species with counties protected by referenda and thus become a lower priority, as 395

evidenced for example, by the new RSS no longer selecting counties in peninsular Florida. With 396

the savings from not having to allocate funds to counties with past referenda, the planner adds 34 397

new counties to their optimal set. When comparing sets of species in the omitted and new 398

counties, we find that the planner substitutes toward counties that have a higher concentration of 399

species not in referenda sites. 400

401

Discussion 402 403

The purpose of this paper is to examine the potential for the direct democracy process to 404

contribute to biodiversity conservation in the United States. To our knowledge the ancillary 405

benefits of citizen-supported initiatives have yet to be considered in this light. The values local 406

residents derive from living near land set aside via referenda is analogous to the “human amenity 407

value” that Fuller et al. (2010) suggested could be incorporated into top-down planning objective 408

functions. Rather than requiring top-down planning to account for human amenity value though, 409

the referenda process in the United States allows local residents to express their support for 410

particular amenities and local biodiversity by voting in favor of conservation initiatives directly. 411

Therefore, unlike other processes outside the control of the top-down planner that may serve as a 412

source of inefficiency, such as political pressure (Pressey 1994; Margules & Pressey 2000), we 413

demonstrate that the direct democracy process might actually enhance the efficiency of 414

conservation budgets. 415

416

While we illustrate one way in which conservation planners might be able to interact more 417

systematically with the land vote process, our results also hint at other ways conservation 418

planners might be able to interact more systematically with the land vote process. For example, 419

a conservation organization may find it cost-effective to allocate resources to help referenda with 420

a low probability of occurrence but a relatively high predicted percent voting yes to get on the 421

ballot. Resources could also be allocated to help referenda already on the ballot pass. 422

423

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424

To inform such efforts, organizations could investigate the role of demographic, political, 425

economic, and other factors in predicting the probability of referenda occurring and the percent 426

voting yes as a means to better inform the allocation of their resources. Some groups including 427

The Conservation Fund and The Trust for Public Land already have manuals regarding how to 428

support conservation through ballet measures (see SI for more detail). As an illustration of how 429

statistical models could enhance current conservation efforts, we estimated a probit regression to 430

predict the probability of referenda occurring and a log-odds regression to predict the percent 431

voting yes (see Table SI-1, Figure SI-2, Figure SI–3). Our results, for example, suggest that 432

counties with higher percentages of the population with at least a bachelor’s degree have larger 433

number of voters voting yes. Previous econometric work related to open space referenda has 434

identified additional characteristics of a jurisdiction associated with having and passing a 435

referendum using a variety of different models (Kline 2006; Kotchen & Powers 2006; Nelson et 436

al. 2007; Banzhaf et al. 2010; Wu & Cutter 2011). 437

438

Given the diversity of RSS approaches in the literature, we use our formulations as a benchmark 439

for comparison and as a representation of “idealized” top-down planning, and do not view our 440

results as offering a management prescription (see the SI for a discussion of various elements to 441

account for when choosing an RSS-set). Some recent advances in RSS literature, for example, 442

formulate a return on investment approach that combines multiple attributes, such as 443

vulnerability to development, land prices, spatial contiguity and/or complementarity benefits (see 444

e.g., Murdoch et al. (2007), Polasky (2008), Underwood et al. (2009), Murdoch et al. (2010), 445

and Withey et al. (2012)). While our benchmark considers only the role of land prices, our 446

analysis can be tailored by organizations to particular conservation objectives and datasets to 447

make management decisions. 448

449

A possible concern about the potential use of ballot box measures for conservation is the 450

presence of taxonomic bias in the species covered. In RSS planning efforts, for example, the 451

species to use as a surrogate for biodiversity and the weights used in the objective are chosen 452

based on the objectives of the conservation exercise (Margules & Pressey 2000). In our RSS 453

analysis, we give all species, regardless of their taxa, equal weight. To check for potential 454

16

mismatches between the set of species conserved in the two processes, we calculate the 455

percentage of each taxa-type covered in the RSS (base budget) and the ballot measures. Our 456

results suggest that any taxonomic bias may be small (~10% for plants and vertebrates, see 457

Figure SI-19) at least for county-level referenda. 458

459

In this paper, we focus on the potential role of county-level ballot initiatives. Further work 460

should integrate data on land set aside via municipal and state level initiatives and other 461

protected lands into RSS type planning exercises to get a more holistic view of all of the 462

conservation activities being undertaken in the U.S. A possible hypothesis for such an analysis 463

might be that the potential gains from ballot measures are lower, after taking into account all of 464

these other types of protection. In our efficiency gains experiment, for example, we do not 465

account for land held by Federal and state governments. Using information from the Protected 466

Area Database (US Geological Survey 2012), we test the robustness of our findings by 467

conducting auxiliary analysis with protected area data. We assume that species in a county are 468

covered if greater than 25% or 50% of the county land area is protected (achieves GAP 1 or GAP 469

2 status). We find there are still large efficiency gains available through consideration of the 470

land vote process. For example, the budget gains when holding the number of species constant 471

and when only considering land vote are ~45%, when only considering protected areas are 472

~13%, and when both past referenda and protected areas are considered jointly are ~52%. (See 473

SI for additional details of the robustness check.) 474

475

We leave for future work incorporating complexities such as spatial configurations of reserves, 476

institutional arrangements such as partnering with local land trusts, and interactions between 477

conservation efforts such as attraction and repulsion of new reserves to current reserves and 478

crowding out by government (Albers et al. 2008a; Albers et al. 2008b; Parker & Thurman 2011). 479

We also do not consider the anti-growth/development ballot measures, which are the other side 480

of the coin to the land conservation referenda (see, e.g., Gerber & Phillips (2005)). 481

482

483

484

485

17

Acknowledgements 486 487

We thank Andrew Chua for research assistance; Lynn Kutner, Data Management Coordinator at 488 NatureServe, for NatureServe species data; several colleagues, including Michael Bode, Martin Smith, 489 Alex Pfaff, Chris Timmins, Susan Harrison, and Andy Sih, as well as four anonymous referees, for 490 helpful comments and suggestions. Sanchirico acknowledges support from Agricultural Experimentation 491 Station project CA-D-ESP-7853-H. Kroetz acknowledges support of the National Institute for 492 Mathematical and Biological Synthesis at The University of Tennessee, Knoxville where she was a short-493 term visitor. 494

495

496

497

498

499

500

501

502

503

504

505

506

507

508

509

510

511 512

513

514 515

516

517

518

519

520

18

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665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709

22

Tables 710

711

Table 1: Summary statistics 712

Median Levels

All

counties

No successful

referenda

At least one

successful

referendum ES RSS G1G2 RSS

Federal Endangered Species 3 2 4 8 6

NatureServe G1G2 Species 1 1 3 10 11

Median Household Income ($1,000s) 33.69 33.23 48.66 31.79 31.54

Median Home Value ($1,000s) 71.80 70.40 140.00 80.10 78.50

Percent in Poverty 15.08 14.78 26.96 12.64 11.98

Percent Age > 65 14.43 14.54 11.38 13.55 14.13

Percent Age < 18 25.34 2534 25.06 25.57 25.66

Percent No High School Degree 20.80 21.10 13.80 22.15 21.80

Percent Bachelor's Degree 14.40 14.10 28.50 15.15 15.20

Population Density 42.21 39.73 435.82 20.50 11.08

Farmland Price per Acre $1,668 $1,611 $4,485 $1,183 $763 Note: Counties are categorized according to whether the county has held at least one successful referendum from 713 1988-2006 and whether or not it was selected via RSS at the base budget that is ~14% of the amount needed to 714 cover all G1G2 species and ~33% of the amount need to cover all ES species (see Methods and SI). 715 716

717

718

719

720

721

722

723

724

725

726

727

728

23

Figures 729

730

Figure 1: Comparison of counties with past successful referenda and optimal RSS with 731

G1G2 species. This figure shows the overlap between the counties with prior successful referenda between 1988 732

and 2006, and the results of the RSS algorithm. The RSS results are based on maximizing the number of G1G2 733

species covered subject to a budget. We use a base budget similar to that of Ando et al. (1998) but adjusted to 734

account for differences in the cost of farmland. The base budget represents ~14% of the total budget needed to 735

cover all G1G2 species (see Methods). 736

737

738

739

740

741

742

743

744

745

24

Figure 2: Relationship between G1G2 species covered and predicted probability of 746

referendum success. We calculate, for counties with a predicted probability of a successful referendum greater 747

than the threshold probability, the number of G1G2 species covered under two assumptions: (1) species covered by 748

counties with prior successful referenda are included in the count; and (2) species covered by counties with prior 749

successful referenda are not included in the count. 750

751

752

753

754

755

756

757

758

25

Figure 3: Comparison of counties predicted to have future successful referenda and our 759

benchmark (RSS using G1G2 species data and base budget). The figure shows an overlay of the 760

RSS results on the predicted probability of having a successful referendum, by percentile group, along with the 761

counties that have had successful referenda from 1988-2006. The probabilities of holding a successful referendum 762

associated with the percentile groups are as follows: 75th percentile (1% or less), 76th-80th

(1-2%), 81st-85th (2-763

3%), 86th-90th percentiles (3-5%), 91th-95th (5-13%), 95th-100th (13% or greater). 764

765

766

767

768

769

770

771

772

26

Figure 4: Efficiency gains for G1G2 species covered. We solve a series of RSS problems maximizing 773

the number of species covered, but varying the budget. In panel A, we plot the species covered for each budget 774

under two scenarios. In our Without Ballot Measures scenario, a site must be purchased to preserve it. In our With 775

Ballot Measures scenario species in sites with successful referenda from 1988-2006 are preserved for free and all 776

other sites must be purchased to be preserved. We present the species as a proportion of the total number of G1G2 777

species and the budget as a percentage of the total budget required to cover all G1G2 species. Panel B represents the 778

changes in the set of sites chosen by the RSS planner when the ballot measures are incorporated directly into the site 779

selection problem at the base budget. In particular, the map corresponds to the gains in species covered labeled in 780

panel A. 781

782

783

784

785

786

787

788

789

790

791

792

793

794

795

796

797

798

799

800

801

802

27

A.

B.

803