A primer on nonmarket valuation 2nd by champ

The Economics of Non-Market Goods and Resources Patricia A. Champ Kevin J. Boyle Thomas C. Brown Editors A Primer on Nonmarket Valuation Second Edition The Economics of Non-Market Goods and Resources Volume 13 Series editor I.J Bateman, School of Environmental Sciences, University of East Anglia, Norwich, UK More information about this series at http://www.springer.com/series/5919 Patricia A Champ Kevin J Boyle Thomas C Brown • Editors A Primer on Nonmarket Valuation Second Edition 123 Editors Patricia A Champ U.S Forest Service, Rocky Mountain Research Station Fort Collins, CO USA Thomas C Brown U.S Forest Service, Rocky Mountain Research Station Fort Collins, CO USA Kevin J Boyle Virginia Tech Blacksburg, VA USA ISSN 1571-487X The Economics of Non-Market Goods and Resources ISBN 978-94-007-7103-1 ISBN 978-94-007-7104-8 DOI 10.1007/978-94-007-7104-8 (eBook) Library of Congress Control Number: 2016958973 © Springer Science+Business Media B.V (outside the USA) 2003, 2017 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer Science+Business Media B.V The registered company address is: Van Godewijckstraat 30, 3311 GX Dordrecht, The Netherlands Contents Valuing Environmental Goods and Services: An Economic Perspective Kathleen Segerson Conceptual Framework for Nonmarket Valuation Nicholas E Flores 27 Collecting Nonmarket Valuation Data Patricia A Champ 55 Contingent Valuation in Practice Kevin J Boyle 83 Choice Experiments 133 Thomas P Holmes, Wiktor L Adamowicz and Fredrik Carlsson Travel Cost Models 187 George R Parsons Hedonics 235 Laura O Taylor Averting Behavior Methods 293 Mark Dickie Substitution Methods 347 Thomas C Brown 10 Experimental Methods in Valuation 391 Craig E Landry v vi Contents 11 Benefit Transfer 431 Randall S Rosenberger and John B Loomis 12 Reliability and Validity in Nonmarket Valuation 463 Richard C Bishop and Kevin J Boyle Index 499 Contributors Wiktor L Adamowicz University of Alberta, Edmonton, AB, Canada Richard C Bishop University of Wisconsin-Madison, Madison, WI, USA Kevin J Boyle Virginia Tech, Blacksburg, VA, USA Thomas C Brown U.S Forest Service, Rocky Mountain Research Station, Fort Collins, CO, USA Fredrik Carlsson University of Gothenburg, Gothenburg, Sweden Patricia A Champ U.S Forest Service, Rocky Mountain Research Station, Fort Collins, CO, USA Mark Dickie University of Central Florida, Orlando, FL, USA Nicholas E Flores University of Colorado-Boulder, Boulder, CO, USA Thomas P Holmes U.S Forest Service, Southern Research Station, Research Triangle Park, NC, USA Craig E Landry University of Georgia, Athens, GA, USA John B Loomis Colorado State University, Fort Collins, CO, USA George R Parsons University of Delaware, Newark, DE, USA Randall S Rosenberger Oregon State University, Corvallis, OR, USA Kathleen Segerson University of Connecticut, Storrs, CT, USA Laura O Taylor North Carolina State University, Raleigh, NC, USA vii Acronyms CE/CEs EA GIS i.i.d RUM TCM VSL WTA WTP Choice experiment(s) Equivalency analysis Geographical information system Independent and identically distributed Random utility model Travel-cost model Value of statistical life Willingness to accept Willingness to pay Use only in Equations ACS MLS NB PIN RDC TC American community survey Multiple listing service Net benefits Parcel identification number Research data center Transaction costs ix Chapter Valuing Environmental Goods and Services: An Economic Perspective Kathleen Segerson Abstract Nonmarket valuation, i.e., valuing environmental goods and services that are not traded in a market, has been increasingly used in a variety of policy and decision-making contexts This is one (but not the only) way that researchers and practitioners have sought to define and measure the values that individuals assign to environmental goods and services The idea of putting a dollar value on protecting the environment has been controversial, but often because the economic approach to valuation has not been well-understood This chapter provides a nontechnical overview of and rationale for the economic approach to valuation, starting from a broad conceptualization of values versus valuation It summarizes the economic concept of value and its key features It then discusses the use of economic valuation in decision making, followed by an overview of the steps involved in the valuation process and important issues that arise in implementing that process Finally, it identifies and briefly summarizes the principal non-market valuation methods used by economists In doing so, it sets the stage for the more detailed chapters on theory and methods that follow Á Á Á Á Keywords Preferences Market failure Externalities Ecosystem services Held versus assigned values Substitutability Economic versus commercial values Economic impacts versus values Valuation process Aggregation Discounting Uncertainty Valuation methods Á 1.1 Á Á Á Á Á Á Á Making Choices As Jean-Paul Sartre put it, “we are our choices.” Choice is a fundamental part of our lives We are constantly making choices, often individually or among friends but also collectively Some individual choices are routine (e.g., about how to spend our income or time on a given day), but others involve major decisions (e.g., about K Segerson (&) University of Connecticut, Storrs, CT, USA e-mail: kathleen.segerson@uconn.edu © Springer Science+Business Media B.V (outside the USA) 2017 P.A Champ et al (eds.), A Primer on Nonmarket Valuation, The Economics of Non-Market Goods and Resources 13, DOI 10.1007/978-94-007-7104-8_1 12 Reliability and Validity in Nonmarket Valuation 489 high-ranking tool among revealed preference methods Many members of the body of researchers are cited in Chap 6, along with the large volume of peer-reviewed literature on the topic Still, a number of soft spots in current practice were identified in the conclusions of Chap For example, more realistic ways of valuing travel time are needed Overnight trip and multiple-purpose trip modeling need to be improved Practical, realistic models of intertemporal substitution need to be developed to account for the possibility that, if one cannot visit a preferred site today, one may, instead of going to a substitute site, visit the preferred site at a later date The Kuhn–Tucker approach still needs to evolve and be more widely applied Models need to be expanded to account for more choice features Standard practices have not yet developed to measure out-of-pocket trip costs More work is needed to integrate stated preference data into travel cost models Other loose ends are identified in Chap For example, Sect 6.3.8 highlights the components of trip costs (travel costs, the value of travel time, equipment costs, and access fees) and points out that the travel cost method assumes that these costs are treated as given by subjects, whereas they may involve choices by subjects As a case in point, consider the choice of where one lives, a determinant of travel expenses and time spent in travel At its heart, the travel cost method uses the behavior of those with higher travel costs to predict participation rates of people with lower travel costs if the price of visits were raised But what if people with different travel costs also have different preferences regarding the recreational activity in question? If some people choose where they live based in part on nearness to recreation sites, a serious bias could be introduced Rock climbing, fly fishing, and downhill skiing come immediately to mind as examples Other things being equal, the result would be to underestimate values As mentioned previously, still another issue is recall bias If people report visiting sites sites more frequently than they actually, this will bias values These are not trivial issues If any of these or other issues arise, the resulting values are likely biased Such soft spots reduce the content validity of the travel cost method This can be partially counterbalanced by carefully reporting assumptions used in travel cost studies Robustness tests of critical assumption are also helpful 12.4.2.2 Travel Cost Construct Validity The construct validity of the travel cost method can be considered from three different angles First, does it produce values that are economically plausible? Second, can it demonstrate relationships between value estimates and priors drawn from theory and intuition? And third, travel cost values demonstrate convergence with values derived from other methods? State-of-the-art travel cost studies normally produce value estimates that are intuitively reasonable or plausible, adding to the credibility of the method Of course, this test has limited potency because estimated values could be plausible but still contain bias However, if travel cost studies consistently produced outlandish 490 R.C Bishop and K.J Boyle value estimates—let us say thousands of dollars per day of recreation—researchers would have abandoned it long ago Instead they have looked at study after study and concluded that, yes, the recreational activities under study could be worth what the analyses concluded To the counter, we know of no literature that argues that these values are not plausible As for prior expectations, travel cost applications consistently find a negative relationship between travel costs and participation, satisfying negative price sensitivity Income sensitivity is more interesting Do values from travel cost models show a fairly consistent tendency to increase with subjects’ incomes? This question is not often asked Many studies in the peer-reviewed literature since 2000—including Boxall and Adamowicz (2002), Lupi et al (2003), Haener et al (2004), Moeltner and Englin (2004), Kinnell et al (2006), Hynes et al (2007), Timmins and Murdock (2007), Scarpa et al (2008), and Hindsley et al (2011)—did not include income as a possible explanatory variable Of the exceptions, some found positive, significant income sensitivity (Boxall et al 2003; Landry and Liu 2009); others found no significant effect of income (Massey et al 2006); and some had more than one model with mixed results (Grijalva et al 2002; Murdock 2006) While it would be surprising if a thorough investigation failed to demonstrate positive income sensitivity, no such investigation appears to have been conducted Construct validity has not been confirmed in this dimension The counterparts of the contingent valuation scope effect are the many travel cost studies—including several cited in Chap 6—that value changes in the availability of recreation sites and/or the quality of those sites These studies often find statistically significant values See, for example, the beach case study in Sect 6.4 This is positive evidence for the construct validity of the travel cost method Meta-analyses provide some support for the construct validity of the travel cost method by documenting that value estimates demonstrate expected relationships to many independent variables For example, Smith and Kaoru (1990a) found that travel cost value estimates vary systematically with the type of recreation activity valued Shrestha and Loomis (2003) found recreational values were related to whether the site was a lake, river, or forest; whether the site was publicly owned; whether the site had developed recreational facilities (picnic tables, campgrounds, etc.); the number of different kinds of recreational opportunities available at the site; and the type of recreational opportunity being valued, which ranged from camping to big-game hunting to snowmobiling This supports the construct validity up to a point, but unfortunately the results are somewhat clouded by the fact that both travel cost and contingent valuation value estimates were included Following similar procedures but using only travel cost values would be useful Regarding convergent validity, recall the discussion in the preceding section of tests comparing stated and revealed preference methods, including the travel cost method While the contingent valuation discussion concluded that convergence has not been fully confirmed, travel cost and stated preference methods provide some mutually reinforcing evidence of convergent validity 12 Reliability and Validity in Nonmarket Valuation 12.4.2.3 491 Travel Cost Criterion Validity Only one study appears to have compared travel cost values with simulated market values McCollum (1986) compared travel cost estimates for a deer hunting experience with WTP estimates from simulated markets for the same deer hunting opportunity The main finding was that behavior captured in travel cost models was not statistically distinguishable from the behavior in cash markets when relatively low values for travel time (10-33% of the wage rate) were used Chapter notes that the most common opportunity cost of travel time used in the literature is one-third of the wage rate These results suggest that travel cost estimates of value might be valid for a one-time recreation activity when modest values are applied to the opportunity cost of travel time More studies comparing travel cost values to simulated market values would be helpful 12.4.3 Travel Cost Accuracy and the Weight of Evidence What can be said by way of conclusions about the reliability and validity of the travel cost method? Reliability may not be an issue if respondents are asked to recall trips over relatively short and recent periods This is good news for the reliability of studies that can acquire data with short, recent recall Still, more research is needed on the reliability of the travel cost method using test-retest methods On the validity side, consider each of the three legs of the validity stool While content validity of the travel cost method seems well established, concern remains about the assumptions that those applying the method must make without clear theoretical or empirical guidance In the meantime, carefully following the steps laid down in Chap 6, documenting the choices made, and conducting robustness tests can enhance content validity Regarding construct validity, several findings are encouraging: the plausibility of value estimates, the consistent finding of negative price sensitivity, successes in efforts to value quality changes and changes in recreation site availability, and successes in testing for significant associations between values and prior expectations support the construct validity of the method However, positive income sensitivity remains to be demonstrated Turning to convergent validity, travel cost studies tend to give higher values than stated preference studies Given that the two approaches are so different and the value estimates are correlated, the closeness of the value estimates from the two methods is nevertheless encouraging There is not much evidence of criterion validity, pro or con, for the travel cost method Existing literature suggests that relatively simple travel cost models using modest allowances for travel time are the most likely to be valid More research is definitely needed and simulated markets comparisons are one promising line of attack 492 R.C Bishop and K.J Boyle In sum, the weak leg for criterion validity makes the three-legged stool for the travel cost method seem a bit shaky One could speculate that the confidence economists have in revealed preference data may have lulled practitioners into complacency about fully investigating the validity of the travel cost method 12.5 Conclusions The accuracy framework is an outline to assist investigators in systematically enhancing the reliability and validity of nonmarket valuation studies If you think of that framework as a skeleton, research to date has done much to flesh out the body, but the work is not finished The debate over the accuracy of nonmarket valuation methods has been most intense when focused on contingent valuation Much has been accomplished since Scott (1965) sarcastically wrote, “ask a hypothetical question and you get a hypothetical answer” (p 37) The weight of evidence suggests that much progress has been made since the early applications, and much more has been done since the NOAA panel’s pronouncements The contingent valuation debate has been healthy even if it has been uncomfortable at times Such rigorous debate needs to continue and expand to the other nonmarket valuation techniques Thinking about the evolution of nonmarket valuation, a disproportionately large share of the research appears to have focused on improving econometric estimation methods compared to other issues relating to study design and execution Taking a more holistic perspective on accuracy from initial study conceptualization through value reporting, which this book has as a goal, can enhance the overall credibility of nonmarket value estimates We believe that wide application of the accuracy framework introduced in this chapter should help to identify and balance research priorities for each of the valuation methods discussed in this book Criterion validity investigations are capable of providing the most potent tests of nonmarket valuation methods More of this research is needed with specific attention given to controlled experiments where treatments are credible and appropriate weight is given to the evidence provided by each treatment in the experiments (i.e., not being unduly swayed by long-standing beliefs and biases of people defending one side or the other of the investigations) A weakness of economics as a discipline compared to other disciplines is that, once a study is published, replications are very hard to get published; whereas in other disciplines, the publication of replication studies is a normal part of the scientific progress The consequence of this disciplinary parochialism is that our base of knowledge may be broad, but it is not very deep, which undermines the ability to assess accuracy This seems to be particularly true of nonmarket valuation Progress in improving the reliability and validity of nonmarket valuation methods has been slowed by lack of research funding, both from scientific funding 12 Reliability and Validity in Nonmarket Valuation 493 agencies and from those who use nonmarket values Thus, research is based on investigators’ ability to cobble together funding to conduct small experiments or to attach their research to practical, policy-oriented studies for which funding is more generally available This results in too few well funded studies specifically designed to address fundamental study design and implementation issues In spite of these limitations, the contingent valuation and travel cost examples illustrate the remarkable advances that have been made in the applications of stated and revealed preference methods While acknowledging that there are investigator choices that need documentation and robustness checks, this is true for any empirical method, not just nonmarket valuation Both examples have made great advances over the past 40+ years For contingent valuation, an example is the use of incentive-compatible question formats in the context of consequential valuation exercises For the travel cost method, this might be the use of random-utility models to more explicitly account for substitutes and effectively include resource quality in estimated models Thus, returning to the glass of water metaphor, overall the nonmarket valuation “glasses” are at least half full and gaining volume (or accuracy) Our accuracy framework can enhance these gains when applied in a systematic fashion Acknowledgments We would like to thank Kerry Smith for his insightful comments that enhance this manuscript, and Vic Adamowicz, Rob Johnston, John Loomis, and George Parsons for providing suggested source material References Arrow, K., Solow, R., Portney, P R., Leamer, E E., Radner, R & Schuman, H (1993) Natural resource damage assessments under the Oil Pollution Act of 1990 Federal Register, 58, 4601-4614 Berrens, R P., Bohara, A K., Silva, C L., Brookshire, D & McKee, M (2000) Contingent values for New Mexico instream flows: With tests of scope, group-size reminder and temporal reliability Journal of Environmental Management, 58, 73-90 Bjornstad, D., Cummings, R & Osborne, L (1997) A learning design for reducing hypothetical bias in the contingent 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Reiling, S D (1995) Test-retest reliability of contingent valuation with independent sample pretest and posttest control groups American Journal of Agricultural Economics, 77, 613-619 Timmins, C & Murdock, J (2007) A revealed preference approach to the measurement of congestion in travel cost models Journal of Environmental Economics and management, 53, 230-249 Trice, A H & Wood, S E (1958) Measurement of recreation benefits Land Economics, 34, 195-207 Vossler, C A & Kerkvliet, J (2003) A criterion validity test of the contingent valuation method: Comparing hypothetical and actual voting behavior for a public referendum Journal of Environmental Economics and Management, 45, 631-649 Vossler, C A., Kerkvliet, J., Polasky, S & Gainutdinova, O (2003) Externally validating contingent valuation: An open-space survey and referendum in Corvallis, Oregon Journal of Economic Behavior & Organization, 51, 261-277 Zeller, R A & Carmines, E G (1980) Measurement in the social sciences: The link between theory and data New York: Cambridge University Press Index A Aboveground, 380 Ad hoc, 203, 273 Adding-up test, 120 Agglomeration, 257 Aggregate demand function, 188 Aggregate zonal data, 188 Alternative cost method, 348 Alternative specific constants/designs, 84, 148, 156, 158, 163, 173, 190, 210, 219 American Community Survey (ACS), 254, 256 Among, 1, 5, 9, 12, 21, 36, 50, 62, 69, 75, 91, 94, 110, 122, 137, 145, 152, 159, 168, 176, 209, 253, 271, 283, 294, 299, 301, 302, 305, 365, 445, 457 App (Computer application), 78, 135 ArcGIS, 256 Arms-length transactions, 252 Artefactual, 402 Asymptotic, 163 Attribute-based methods, 21 Averting behavior methods, 320 Averting-input demand studies, 336 B Baseload power, 364 Bednets, 392 Benefit-cost analysis, 134, 144, 285, 349, 437, 475 Benefit function transfer, 436, 442–445, 447, 450 Bid function, 43, 238, 240, 270, 271, 274–276 Binary contingent valuation questions, 134 Bird-watching, 205, 209, 215 Bivariate-logit model, 195 Blue ribbon panel, 85, 486 Bockstael–McConnell method, 317, 319, 331 Bound, 41, 94, 107, 116, 267, 268, 319, 323, 324, 353, 357, 417, 418, 424, 439 Box cox, 248 Box-cox estimation, 248 Bull’s-eye, 464, 467 Bycatch, 12 Byproduct, 4, 441 C Cancer-causing agents, 284 Car camp, 44 Catchall, 484 Choice experiment (CE), 45, 50, 63, 83, 121, 122, 134, 135, 137, 142, 144, 157, 159, 162, 163, 170, 172, 174, 176, 177, 179, 180, 405–407 Census Bureau, 253 Census-defined neighborhoods, 254 Choice-based sampling, 201 Choice-experiment studies, 423 Choice occasion utility, 202 Choice-theoretic tenet, 401 City-specific price functions, 249 Cleanup/clean up, 11, 12, 30, 262–264, 383 Clear-cut, 122, 355, 356, 479 Coal-fired, 364 Cobb-Douglas specification/form/production, 274, 312, 314, 315, 317, 325 Codebook, 76, 79 Coefficient, 114, 162, 164, 168, 169, 171, 202, 224, 247, 258–260, 281, 283, 326 Coherent-but-arbitrary perspective, 412 College-educated, 446 Collinear, collinearity, 134, 147, 166, 394 Common-level goods, 28 Compensating and equivalent surplus, 271 Complementarity, 34, 38–41, 192, 317, 328 Comprehensive Environmental Response, Compensation and Liability Act (1980), 84, Constrained-choice problem, 411 © Springer Science+Business Media B.V (outside the USA) 2017 P.A Champ et al (eds.), A Primer on Nonmarket Valuation, The Economics of Non-Market Goods and Resources 13, DOI 10.1007/978-94-007-7104-8 499 500 Consumptive-use value, 11 Contingent-behavior data, 193, 196 Contingent-valuation studies, 74, 86, 89, 94, 102, 109, 119–122, 473, 474, 477, 478, 480–484, 486 Count data models, 189, 190, 193 Counterbalanced, 489 Counterfactual, 85, 119, 262, 391, 409 Co-vary/co-variation/co-variates, 160, 237, 245, 329, 333−336, 338, 398, 399 Cross product, 145 Cross-sectional, 193, 263, 325, 327–329, 335, 337, 398 D Damage cost estimates, 317 Data, 21, 22, 43, 44, 46, 55–57, 61, 64, 65, 68, 71, 77–80, 83, 84, 89, 92, 94, 102, 105, 109, 111, 112, 114, 115, 135, 136, 143, 147, 163, 164, 167, 169, 176, 177, 188, 190, 193, 194, 199, 201, 203, 205, 208, 210–212, 214, 217, 218, 223–225, 237, 241, 242, 248–256, 260, 263, 264, 269, 273, 281, 284, 285, 301, 305, 307, 325, 334–337, 339, 342, 359, 380, 393, 394, 407, 410, 418, 432, 434, 440, 443, 444, 447, 450, 452, 453, 457, 468, 469, 472, 487, 491 Data-demanding benefit function, 445 Data generating process, 155 Data-intensive approach, 286 Dataset, 76, 77, 255, 256 Daubert standard, 475 Day trip, 203, 206, 207, 213, 222 Day-trip analysis, 222 Decennial census, 78, 253, 256 Decision-making/er, 14, 23, 45, 52, 84, 88, 89, 91, 93, 106, 111, 116, 117, 139, 140, 145, 176, 189, 223, 280, 400, 402, 403, 412, 421, 432, 457, 458, 477, 481 Decision support tools, 143 Defensive expenditure function, 313, 319 D-efficient method, 152−155 Delta, 163, 167 Demand function estimation, 193 Demand-side data, 347 D-error, 152 Developing-country context, 284 Dichotomous-choice question, 74 Difference-in-difference designs, 263 Difference-in-differences measure, 300–302 Disamenities, 244, 246, 253, 257, 264 Discrete-choice random utility model, 28 Discrete-choice theory, 136, 406 Index Discrete, exogenous variation, 264 Distance-decay matrices, 260 Dose-response, 296 Double counting, 11, 22 Downside, 70, 73, 404 Downward-sloping demand function, 188, 191 Dummy-variable coding, 334 E Edward Elgar Effects-coded variable, 156 Endpoint, 139, 140 End state/end-state plan, Environmental Values Reference Inventory Database Reference, 438 Equilibrium hedonic price functions, 276 Equilibrium price surface, 261 Errors-in-variables problems, 257 Eutrophication, 241 Ex ante, 111, 176, 269, 368, 400 Executive order/Executive Order 12291, Exogenous equilibrium price structure, 238 Expected trip utility, 199, 200, 202, 203 Expected utility theory, 392 Expenditure minimization problem, 318 Ex post, 89, 176, 269, 368 Extreme value random variables, 198 F Farmland, 365 Finite mixture model, 166 First-order condition, 272, 310, 311, 334 First-stage analysis/es, 236, 237, 285 Fish consumption advisory, 204 Fixed effects estimator, 327, 336 Fixed time effects, 335 Fold-over procedure, 153 Four Corners region, 83 Framed field experiments, 402 F-tests, 249, 251 Full bath, 241, 244 Full factorial design, 145–147, 150 Full rank (of full rank), 277 G Gamelike, 400 GeoDa (software), 260 Geographic information system (GIS), 242, 255, 256, 449 H Habit-forming behavior, 224 Half-fraction, 147 Index Hazardous waste site, 252, 253, 257, 262, 264, 266 Health-enhancing procedure, 98 Health production demand studies, 336 Health-related risk, 20 Hedonic housing context, 263 Hedonic preferences model, 137 Hedonic price functions, 241, 249, 276 Hedonic regression analysis, 257 Hicksian, 31, 33–35, 89, 120, 192, 271, 318, 483 Hicks-Samuelson theory, 136 Higher order interaction terms, 147 High-quality, 56 High-rise, 219, 220 Homeowner, 258, 261, 264, 271 Homeowner-assessed values, 255 Home-produced output, 309–313, 315, 316, 319, 320, 322, 325, 328, 329, 334 Homoscedasticity, 160 Hotelling, Harold, 188, 225 Hypothetical-bias experiments, 484 I Incentive-compatible fashion, 142 Incommensurability, 394 In-depth, 113, 285 Indirect-use value, 11 Individual-based data, 188 Individual-defined choice sets, 206 Individual-level modeling, 137 Individual-specific characteristics, 219 Induced-value, 403 In-kind replacement, 375 In-person survey, 141 In-stream flow, 364 Instrumental variables approaches, 258 Integer nature (the), 190 Internet, 59, 62, 71, 73, 78, 88, 92, 94, 96, 141, 150, 213, 218 Interstudy, 447 Intertemporal, 28, 47, 189, 222, 224, 225, 489 Intragroup, 398 Inverse compensated demand function, 270, 275 Invisible-hand argument, Irrelevant alternatives property, 159, 166, 168 Iso-profit curves, 282 Iterative-bidding question, 102, 103, 107 J Jackknife, 469 501 K Krinsky-Robb method, 163 Krona/Kronor, 171 Kuhn-Tucker model, 190, 225 L Labor market setting, 284 Lagrange function, 310, 312 Lakefront, 236, 241, 243, 246 Lakeshore, 246 Lakeside, 146 Lake water, 241, 244, 274 Lake water clarity, 257 Landowners, 4, 262, 437 Land-use spillovers, 258 Land-use types, 257 Large-scale, 84, 189, 237 Latent class models, 155, 167, 190 Law of Comparative Judgment (Torgeson), 136 Least-cost alternative, 350, 353–355, 366, 387 Leisure/work model, 216 Leisure/work trade-off, 216 Likert scale, 137 Linear-in-parameters models, 145 Log-likelihood function, 194 Log-linear expected value, 193 Log-log, 193, 248 Log-normal distribution, 169 Log sum, 199, 220 Log-sum formula, 164 Long-form data, 254 Long-form survey, 253 Look-up, 72 Loss to trips, 219 Loss-to-trips ratio, 219 Lot size, 243, 246, 251, 273 Lump sum, 30, 171 M Mail-out surveys, 141 Main effects fractional factorial design, 150 Marginal price/bid function, 237, 238, 240, 246, 251, 272, 275–277 Marine Bill (UK Marine Bill) Market-based economy, Market-clearing equilibrium/prices, 266, 275 Marketplace, 3, 136, 245, 421 Marketwide, 266 Meta-analysis, 75, 76, 101, 120, 433, 447, 450, 453, 455, 457, 470, 480, 482, 483 Meta-regression analysis transfer, 436, 450 502 Microdata (plural, use plural verb), 251, 253, 255, 256, 327, 335, 337 Micro-level, 452 Midpoint, 378 Mis-specification, 105 Misstatements, 106, 109 Mixed logit model, 167, 200 Model parameter estimates, 407 Modulo arithmetic, 153 Monotonicity, 414 Motor boat(ing), 205 Multialternative, 134 Multiattribute, 140, 416 Multicollinearity, 152, 245, 257, 450 Multinomial logit model, 137, 155, 159, 160, 169, 171, 173, 199 Multiple alternative choice experiment, 180 Multiple-market approach, 286 Multiple-valuation treatments, 424 Multistage, 62 N National park (Yellowstone National Park), 144, 229 Natural field experiment, 402, 404, 405 Near-perfect match, 443 Negative binomial model, 194, 219 Neoclassical, 28, 412, 424 Nested logit model, 166, 199, 200, 279 NGENE (software program), 155 Nonavid, 487 Nonconsidered, 70, 207, 381 Nonconsumptive use/Nonconsumptive-use value, 10 Nonconvexity, 296 Non-economists, Non-environmental, 264 Nonhealth, 338, 342 Nonlinear/nonlinearly, 145, 148, 149, 152, 156, 162, 163, 246, 247, 259, 272, 274, 275, 290, 334, 339 Nonlinear-in-parameters models, 155 Nonlocalized, 242, 265, 268, 279 Nonmarginal, 44, 143, 242, 266–268, 279, 308, 317, 319, 331, 361 Nonmarket, 2–6, 10, 14, 17, 20–23, 27–33, 35, 36, 38–44, 47–49, 52, 55–57, 62, 64–69, 71, 73, 75, 78–81, 83, 88, 116, 119, 134, 178, 237, 245, 250, 254, 265, 282, 285, 286, 294, 295, 298, 299, 303, 310, 335, 336, 392, 393, 401, 403, 405, 411–414, 419, 420, 424, 426, 432, 434, 435, 443, 453, 457, 458, 464–467, 471–473, 475, 492, 493 Index Nonmarket valuation methods, 3, 5, 20, 22, 392, 393, 465, 466, 492, 493 Nonmelanoma, 320 Nonnegative, 193, 194 Nonoverlapping, 62 Nonparametric, 115, 116, 121, 290, 424 Nonpoint pollution, 241 Nonprobability, 57, 63, 64, 71, 74 Nonresponse, 61, 70, 73, 76, 92–94, 112 Nonstandard, 401, 402, 405, 438 Nonuse values, 10, 85 Nonzero, 10, 57, 60, 61, 106, 111, 154, 469 Nonzero priors design, 154, 155 Nordic, 64 No-trip utility, 195, 202, 203 N = sample size, 62, 63, 79, 87, 94, 95, 194, 201, 397–399, 468, 479 O Off-diagonals, 152 Offshore, 368, 377 Off-site, 194, 195, 201, 207, 213 Off-the-shelf analysis, 453 Oftentimes, 78 Old-growth forest, Omitted variable bias, 242, 245, 260, 264, 281 Omitted variables argument, 261 One-on-one interviews, 66, 68, 97, 101, 112 Ongoing, 71 On-site, 61, 70, 93, 110, 190, 194, 196, 201, 208, 209, 213, 216, 217 Open space amenities, 281 Optimal orthogonal designs, 153, 155 Opt-in panels, 142 Orthogonal coding, 146 Orthogonal fractional factorial designs, 147 Orthogonality, 153, 154, 393, 395, 407, 408, 425 Out-of-kind replacement, 375 Out of sample, 266 Out-of-sample prediction, 266 Overengage, Overestimation, 11, 121 Own-price elasticity, 274 P Panel-data framework, 283 Parcel identification number, 255 Parcel-level transactions, 255 Passive-use values, 10, 13, 21, 45–47, 59, 60, 85, 90, 99, 134, 139, 177 Payment vehicle rejection, 98 Payoff, 400 Index Peer-reviewed literature, 86, 89, 108, 137, 475, 488, 490 Peer-review screening, 448 Perceived exposure, 256 Per-choice occasion value, 202 Per-person, per-activity-day estimate, 433 Per-trip values, 189, 192, 193 Plant-level risks, 284 Point estimate transfer, 436, 437, 440, 452, 456 Poisson model, 193–196, 339 Policymaker, 3, 13, 15, 139, 176, 421, 484 Portfolio choice problem, 223 Post-change utility level, 33 Postenvironmental, 269 Postpolicy, 262, 263, 265, 269 Pre-existing, 335, 336 Preference order, 28, 29 Pre-injury, 372, 373 Prepolicy, 269 Pretest, 105, 113, 140, 154, 479, 497 Price determination process, 261 Price responsiveness, 432 Prisoner’s dilemma, 392 Probability-based sample, 58 Provision-mechanism effects, 98 Pseudo-demand model, 202 Public-good nature of, 27 Public-use microdata, 253 Public use of microdata area, 253 Purposive samples, 63 Q Quadratic box cox, 246, 248 Quasi-experiment(al), 248, 262–265, 268, 276, 283, 285, 303, 327, 337 Quasi-hyperbolic, 23 Quasi-linear, 311, 317, 326 Quasi/natural experiments, 264 Quasi-random assignment, 264 R Rain forest, 45 Random parameter model, 155, 166–168, 173–175, 200 Random utility maximization model (RUM model), 176, 188 Random utility models/modeling, 50, 136, 155, 280, 281 Rating-based conjoint, 136 Rational choice theory, 411 Ratio of choice probabilities, 159, 160, 165 Real estate market, 237 Realtor, 245, 252, 261 Real-world complications, 18 503 Recreation use value studies, 438 Recreation Use Values Database, 438, 439 Reduced-form equation, 311, 330, 337 Re-estimate, 160, 163, 469 Regression-discontinuity designs, 263 Regressors, 237, 243, 245, 258–260, 277, 278, 334, 408 Repeat sales hedonic property model, 411 Re-sale, 252 Research Data Centers, 253 Researcher-defined boundaries, 206 Researcher-sampling choice, 201 Resource-intensive benefit transfers, 21 Resource Planning Act, 433, 441 Respondent-reported estimate, 216 Revealed preference methods, 405, 478, 482, 484, 486–488, 490, 493 Risk-risk trade-offs, Round trip/Round-trip, 215, 217, 218 Rule(s) of thumb, 60 RUM model, 157, 159, 164, 165, 176, 188–190, 196–198, 202, 203, 206, 209, 212, 215, 218, 223, 225 S SAS (software program), 155 Sated-preference value, 119 Seat belt, 293 Second-most preferred, 137 Second-order conditions, 311 Second-stage analysis/es, 236, 237, 269, 271 Semilog, 242, 247, 248, 274 Semiparametric, 115 Show-up fee, 400 Simulated probability models, 190 Simultaneity, 253, 258, 402, 408, 409 Single-period hedonic price function, 276 Single site/Single-site model, 188–196, 201, 203, 215, 223–225, 437 Single-valuation treatments, 418 Site-characteristic data, 210, 218 Site choice models, 190 Site-specific data, 447 Small-scale test, 74 Snowmobilers, 64, 144 Socio-economic, 160, 163, 173, 269, 273, 276, 277, 403, 404 Socio-psychological, 412 SpaceStat (software), 260 Spatial autoregressive term/model, 260, 261 Spatial error correlation, 259 Spatial lag term, 260 Spatial multiplier approach, 261 Split-sample studies, 101 504 Sports card, 420 Stated-preference level/data/models, 142, 190, 192, 197, 204, 213, 224, 407 State-of-the-world experiment, 161 Status quo alternative/condition, 30, 88, 122, 140, 148, 150, 151, 156, 171 Step 1, 13, 86, 88, 89, 95, 203, 205, 206, 300, 331, 359, 363 Step 2, 13, 90, 213, 300, 332, 360, 361, 364, 366, 381 Stochastic, 157, 168, 198, 199 Straightforward, 52, 55, 61, 71, 101, 135, 168, 199, 237, 256, 261, 280, 285, 337, 363, 369, 376, 446, 456, 465 Streamflow, 363 Study site measures, 437 Study-specific factors, 447 Subpopulation, 62 Sunscreen, 295, 320, 332, 333 Supply-side methods, 388 Surfboard, 215 Surf fishing equipment, 218 Survey design features, 58, 86 Sweatshop, 97 Index U.S Census Bureau, 78, 91, 253, 256 User-friendly, 256 Use-value estimates, 438 U.S national parks, 137 Utility-constant nature, 32 Utility-increasing(be), 108 Utility-maximizing behavior/individual, 297, 311, 314, 325 Utility theoretic framework, 190 V Valuation-related survey, 21 Value elicitation protocols, 412 Value of a statistical life (VSL), 282, 283, 285, 320, 433, 441, 442 Variance-bias trade-off, 467 Variance-covariance matrix, 152, 154 Variance-minimizing estimator, 471 Variety-seeking behavior, 224 Versus (not vs.\; use v in court cases), 12, 18, 91, 94, 150, 161, 181, 189, 206, 215, 218, 224, 263, 416, 422, 426, 445, 446, 449, 455, 458, 487, 488 Vice versa, 57, 122, 154, 258, 467 T Tâtonnement, 392 Tax assessor’s office/tax assessors’ offices, 251, 252 Tax map, 255 Temporal fixed effects, 335, 339 Test–retest reliability, 118, 471 Thought experiment, 469 ”Three C’s”, 465, 472 Time-consuming, 211, 337, 431 Time frame, 67, 87, 100, 111, 197, 249, 354 Time-interaction variables, 250 Time-invariant characteristics/differences, 256, 263 Time period interactions, 250 Time-trend differences, 263 Time variation, 249 Tract-fixed effects, 256 Trade-off/trade off, 45, 98, 158, 216, 377, 384, 398 Travel cost model, 84, 119, 466, 487, 489–492 Trip frequency models, 201 Type I error, 397, 398 Type II error, 63, 397, 398 W Wage-based, 216 Wage-risk trade-offs, 298 Well-being, 5, 7, 13, 14, 18, 19, 387 Wellhead, 97 Whitewater, 205, 210, 437–440, 447, 449, 451, 452 Wi-Fi, 58, 60, 61 Willingness to accept (WTA), 8, 37, 457, 465, 466 Willingness to pay (WTP), 8, 37, 84, 87, 88, 94, 98, 100, 105, 106, 109, 115, 116, 120, 121, 155, 161–164, 171, 173–176, 236, 240, 265, 266, 269, 270, 436, 437, 442–445, 447, 457, 465–467, 469, 477, 480, 482, 483, 491 Willingness-to-pay functions, 64, 71, 72, 85, 94, 102, 190, 312, 315, 342, 433 Within-sample variability, 116 With-or-without scenario, Working-age healthy adults, 285 U Underprovide, Unweighted, 15, 17 Z Zero-inflated model, 195 Y Year-round, 205 ... used Although Chap is theoretical, the emphasis of the book is on the use and application of nonmarket valuation Data are a critical part of any application, and Chap provides a discussion about... Kathleen Segerson Conceptual Framework for Nonmarket Valuation Nicholas E Flores 27 Collecting Nonmarket Valuation Data Patricia A Champ 55 Contingent Valuation. .. the need for nonmarket valuation often arises in the context of missing markets or market failure 1.3 Development of Nonmarket Valuation Most nonmarket valuation techniques first appeared in the