Stated preference methods for environmental management : recreational summer flounder angling in the northeastern United States

Transcription

1 College of Wllam and Mary W&M ScholarWorks Reports 2002 Stated preference methods for envronmental management : recreatonal summer flounder anglng n the northeastern Unted States Robert L. Hcks Vrgna Insttute of Marne Scence Follow ths and addtonal works at: Part of the Aquaculture and Fsheres Commons, and the Natural Resource Economcs Commons Recommended Ctaton Hcks, R. L. (2002) Stated preference methods for envronmental management : recreatonal summer flounder anglng n the northeastern Unted States. Vrgna Insttute of Marne Scence, College of Wllam and Mary. Ths Report s brought to you for free and open access by W&M ScholarWorks. It has been accepted for ncluson n Reports by an authorzed admnstrator of W&M ScholarWorks. For more nformaton, please contact scholarworks@wm.edu.

2 STATED PREFERENCE METHODS FOR ENVIRONMENTAL MANAGEMENT: RECREATIONAL SUMMER FLOUNDER ANGLING IN THE NORTHEASTERN UNITED STATES Fnal report prepared for Fsheres Statstcs and Economcs Dvson Offce of Scence and Technology Natonal Marne Fsheres Servce Requston Request# NFFKS-18 Aprl 2002 Robert L. Hcks Department of Coastal and Ocean Polcy Vrgna Insttute of Marne Scence The College of Wllam and Mary Gloucester Pont, VA 23062

3 Acknowledgements I would lke to thank several of my colleagues for ther efforts n ths project. The staff of the Fsheres Statstcs and Economcs Dvson, Offce of Scence and Technology n the Natonal Marne Fsheres Servce served a vtal role as the soundng board durng the developmental stages of the stated preference survey. In partcular, I am grateful to Brad Gentner, Krk Glls, Maury Osborn, and Amy Gautam for ther nvaluable assstance wth the desgn of the mal survey. Furthermore, ths research would not have been possble wthout the fundng and admnstratve commtment provded by Mark Hollday. Fnally, I am very apprecatve to Brad Gentner, Todd Lee, and Alan Lowther for ther assstance n refnng deas and approaches for the economc model and econometrc technques used n ths report.

4 Table of Contents I. Introducton... 1 II. A Revew of Approaches for Quantfyng Preferences for Fsheres Management. 5 The Revealed Preference Approach... 8 The Stated Preference Approach... 9 III. Revealed and Stated Preference Technques for Marne Recreatonal Fshng NMFS Data Collecton Efforts IV. Stated Preference Expermental Desgn Survey Feld Test and Focus Group Fnal Desgn Fnal Stated Preference Questonnare V. Model of Angler Behavor The Econometrc Model The SP Econometrc Model Combnng the and SP Models Welfare and Partcpaton Change Estmaton VI. Results SP and Model Estmates Jontly Estmated Model Results Choce-based sample models Welfare and Partcpaton Change Estmates VII. Recommendatons and Concluson References Appendx A. Focus Group Materals Focus Group Screener Focus Group Moderator s Gude Focus Group Sx Attrbute Survey Canddate Focus Group Fve Attrbute Survey Canddate Appendx B. Fnal Survey Cover Letters Appendx C. Fnal Survey Instrument

5 I. Introducton Envronmental managers are becomng ncreasngly aware that envronmental polces must be crafted n a way that ncorporates the human dmensons of the ecosystem. Falure to ncorporate stakeholder preferences nto management measures can lead to polces that fal because people s preferences, motvatons, and behavor concernng ther use of the envronment were not properly consdered even f defensble natural scence approaches were ncorporated n the management decson. In ths paper, we present a new method for quantfyng angler preferences for fsheres management. The method, called the Stated Preference Dscrete Choce Technque (SPDC) (Louvere et. al) s a partcular form of conjont analyss, whch has broad applcaton to measurng preferences for all sorts of goods ncludng both market and non-market goods. The method has been used appled n a wde varety of settngs (for example, applance choce (Ben-Akva et al.), yogurt (Guadagn et al.), and lght-ral transportaton (Preston), and envronmental valuaton (Adamowcz et al.)). For resource managers, the method provdes useful nformaton about new polces, non-observable ranges for management tools, and polces havng multple attrbutes. The SPDC technque does rely on respondents makng choces over hypothetcal scenaros. For the case of recreatonal fshng, respondents are asked to choose among hypothetcal trps, each completely descrbed by ste attrbutes (e.g., cost of travel to the ste, expected catch, etc.). The Natonal Marne Fsheres Servce (NMFS) has for some tme been collectng data on actual fshng choces made by recreatonal anglers. By observng these choces, analysts are able to use revealed preferences () technques to measure preferences. The prmary advantage of technques s the relance on actual 1

6 choces made by fshermen, avodng the problem of strategc responses (Blamey and Bennett) perhaps nherent wth SPDC technques. The strength of technques s also ts weakness. Relyng on observable trps lmts an analyss to observable states of the world. Therefore, technques may not be sutable for quantfyng preferences for attrbutes where no varaton exsts or for whch the attrbute cannot be observed. For summer flounder fshng, the fshery studed n ths report, ths was ndeed a problem. Summer flounder s one of the most sought after recreatonally caught fsh along the eastern seaboard of the Unted States. It s typcally n the top three speces n terms of anglers targetng t per year accordng to NMFS (personal communcaton, NMFS). NMFS has for some tme been concerned wth the overall explotaton level of summer flounder by both commercal and recreatonal fshermen along the Atlantc coast. The agency and councls have been gradually tghtenng regulatons for all fshng actvty n an effort to conserve the stock. Recently, nterest has shfted to understandng angler preferences and motvatons for fshng and fsheres management n an effort to comply wth admnstratve law requrements, and to craft more successful and acceptable polces. Ths nterest was the mpetus for ths study. Intal attempts at quantfyng behavoral responses to management regulatons focused on usng technques usng observable fshng choces coupled wth the effectve management regme at the angler s chosen fshng ste. methods faled largely because of very lttle spatal or temporal varaton n management regulatons. Ths s largely by desgn, however, as the agency and councls attempt to set unform spatal regulatons (across states) to avod confuson and enforcement problems. Table 1 shows regulatons n the Northeastern Unted States for summer flounder. Attempts by 2

7 the author to quantfy behavoral responses due to changng bag and sze lmts usng data faled, even after ntroducng varaton n bag and sze lmts usng varaton n open seasons. Table 1. Summer Flounder Regulatons, State Mnmum Possesson Open Season Sze Lmt (nches) Lmt Massachusetts May 10 - Oct. 2 Rhode Island May 10 - Oct. 2 Connectcut May 10 - Oct. 2 New York May 10 - Oct. 2 New Jersey May 6 - Oct. 20 Delaware May 10 - Oct. 2 Maryland Bays Maryland Coastal Potomac Rver 15 8 May 15 - Dec Aprl 15 - Dec May 15 - Dec. 31 Vrgna March 29 - July 23 Aug. 2 - Dec. 31 North Carolna Jan. 1 - Dec. 31 Source: Atlantc States Marne Fsheres Commsson, personal correspondence, May 14, Consequently, attenton shfted toward the use of SPDC technques to enable the nvestgaton of new management optons and to ntroduce varaton n bag and sze 1 For the perod , there was even less varaton n regulatons: states had no closed seasons and the dentcal mnmum sze and possesson lmts. Mnmum sze lmts ranges were from 14 to 15 nches and possesson lmts ranged from 8 to 10 fsh. 3

8 lmts so that management could explore what f scenaros before enactng regulatons. In addton to gudng the reader through the SPDC method, the paper wll offer some rgorous valdty testng for the method tself. Specfcally, we wll test whether there s a dvergence of parameters and welfare estmates from the SPDC method versus the method. Our fndngs show that the SPDC technque s very useful at quantfyng tradeoffs among varous summer flounder management alternatves and for recoverng welfare and partcpaton change estmates. Whle our fndngs ndcate that parameter and welfare estmates do dffer somewhat from that found from the method, the results demonstrate that these dfferences are qute small and that for practcal uses of the models, the dfferences are of such small magntude that polcy gudance comng from ether approach would be qute smlar. The reader should note that SPDC technques could be appled to a wde-range of polcy ssues facng the agency n addton to recreatonal ssues ncludng commercal fshery management n the context of area management, gear restrctons, etc. Smlarly, t could be appled to crtcal marne habtat or marne mammal ssues. The organzaton of the paper wll proceed as follows. We wll descrbe the complete process of SPDC development ncludng a theoretcal argument for the need to quantfy preferences and a revew of methods for quantfyng preferences (Secton II); compare technques to SPDC and showng how the SPDC method was adapted for a study of preferences for summer flounder management n the Northeaster Unted States (Secton III); descrbe the expermental desgn of ths project (Secton IV) and models of 4

9 angler behavor (Secton V); dscuss results and applcaton to evaluatng polcy (Secton VI); and conclude wth recommendatons for future SPDC studes (Secton VII). II. A Revew of Approaches for Quantfyng Preferences for Fsheres Management Why should managers care about ncorporatng angler preferences and behavor nto fsheres management? Fsheres management s somethng of a msnomer, snce polcy s drected at fshermen or the actvtes of humans havng some adverse affects on fsh populatons or habtat. Consequently, t s really people that we are managng. In the absence of man s nterventon n the fsheres ecosystem, there would not be a need for fsheres management. From ths perspectve, t s obvous that an understandng of people s behavor s mportant for effectve fsheres management. Such a perspectve does not preempt the role of sound natural scence nformaton n the polcy makng process. Knowledge of the natural system s obvously mportant to understand the mpacts of fshng and the capacty of the resource. However, n the absence of knowledge about those we are managng, placng lmts on fshng actvty can lead to management falure. Just as ndvduals and corporatons fnd and explot loopholes n tax laws, so to do affected fshermen react and change ther behavor once regulatons are mposed on them. It s vtal to understand these reactons when desgnng envronmental polcy. So how can natural/physcal scence and the human components of the management problem be reconcled? Fgure 1 shows a stylzed representaton of how these concepts can be combned to brng about effectve polcy. Suppose populaton dynamcs scentsts determne the combnaton of bag and sze lmt regulatons for a 5

10 speces that wll acheve a target mortalty level. Ths mortalty level s chosen to ensure the conservaton of the speces. In the absence of nformaton about angler preferences concernng bag or sze lmts, no pont on the fronter s more preferred to any other one from the physcal scence perspectve, snce all ponts both nsde and on the fronter ensure a sustanable fsh populaton. In such as settng, t s lkely that a non-optmal management level, such as pont a, wll be chosen. Pont a s non-optmal because for the same conservaton level, we could move to pont b and acheve a hgher level of wellbeng for a representatve angler snce 1 U > U 0, where curves 1 0 U and U represent levels of well-beng assocated wth dfferent levels of sze and bag lmts. These curves are termed ndfference curves by economsts, because anglers are equally well off wth any combnaton of bag and sze lmts mpled by a gven ndfference curve, U. At pont a, anglers are more restrcted wth regard to bag lmts than sze lmts. Anglers would prefer to tghten sze lmts and loosen bag lmts and move toward pont b. Fgure 1. Reconclng human preferences and envronmental constrants. Sze Lmt U 0 U 1 a Less regulaton b Better Less regulaton Tradeoff Fronter Bag Lmt 6

11 Another advantage of consderng stakeholder preferences n management decsons s the degree to whch t can foster buy-n nto management and stakeholder acceptance for polcy. Addtonally, there are legal requrements that the NMFS must consder stakeholders when formng management decsons. There are several ways of ncorporatng people s preferences of the natural system each havng t s own pluses and mnuses. For example, an approach requred by law for many federal envronmental polces- the publc meetng- allows affected partes to voce concerns about potental management optons. The approach allows all affected partes to partcpate f they wsh, but questons reman as to how representatve the nformaton s and f he who shouts loudest s heard most. Another approach s to ask anglers whether they favor or oppose management optons (publc opnon survey). For example, one mght ask a random sample of anglers the degree to whch they favor or oppose bag or sze lmts. These questons allow managers to gan nformaton on anglers preferences for bag or sze lmts, but does not reveal ther preferences to management optons where both bag and sze lmts mght be consdered nor are preferences revealed for how preferences for bag or sze lmts mght change as regulatons are tghtened. One could magne that anglers mght be more opposed to bag lmts that elmnate all take-home fsh but potentally more supportve of a slght decrease n bag lmts. Ths approach also reles on a representatve sample of anglers. However, vocal anglers may stll dspute results from such a survey f ther preferences are qute dfferent from the sample s. 7

12 The Revealed Preference Approach An approach used recently by the NMFS reles on observng actual angler behavor to nfer somethng about ther preferences for recreatonal fshng and fshng regulatons. The revealed preference approach (hereafter referred to as ), as t s called, requres a representatve sample of anglers. For recreatonal anglng, representatve can be thought of along several strata such as geographcal locaton of fshng, tme of year, and the type of fshng. To estmate models, data must exst on catch, locaton and tme of fshng, place of resdence, the degree to whch an angler gave up wages to take a trp, type of fshng, nformaton about envronmental characterstcs about the fshng ste, and fshng regulatons at the ste fshed. 2 Wth ths nformaton n place, statstcal models of the demand for recreatonal fshng trps are estmated that descrbe tradeoffs anglers make wth regard to expected catch, cost of travel to ste, management regulatons, envronmental condtons, and other factors deemed mportant to descrbe recreatonal ste choce (Hcks et al., Haab et al., McConnell et al.). The model, once estmated, allows preferences to be quantfed so that management optons can be ranked, anglers value of changng envronmental condtons can be estmated (useful, for example, to answer questons such as what s the value of recreatonal fshng? or what was the loss to recreatonal anglers due to an ol spll n Rhode Island? ). The methodology reles on varaton n the natural envronment so that the statstcal model can dscern how the varous factors mportant for descrbng recreatonal 2 For examples of applcatons and dscusson of some mportant ssues related to modelng relevant for sportfshng, see Bockstael et al., Green et al., Haab and Hcks, Hauber and Parsons, Jones and Lup, Kaoru and Smth, Klng and Thomson, Parsons and Needelman, Parsons et al., Parsons and Hauber, Pendleton and Mendelsohn, and Whtehead and Haab. 8

13 fshng stes nfluence the choce. If no varaton s found n the data (e.g., fsh stocks are unformly dstrbuted and catchable) then the model wll fal to quantfy the effect of that factor. For example, recreatonal anglng regulatons for bag and sze lmts n the Northeastern Unted States for most speces are set unformly across states, and open and closed seasons closely mrror each other: there s no varaton. Smlarly, approaches, based upon observable data at a ste, are lmted to analyzng the effect of actual factors at a ste. For example, f managers were consderng new management tools such as property rght regmes, then current marne recreatonal data of fshng behavor would provde lttle nformaton about anglers preferences for them snce anglers are not currently makng recreatonal fshng choces n the context of property rght management regmes. Therefore, observable data on angler behavor offer very lttle or no varaton wth regard to many management tools so that usng approaches to estmate angler preferences for management s problematc at best and mpossble at worst. The Stated Preference Approach Stated preference technques rely on anglers responses to hypothetcal scenaros. For example, the researcher mght descrbe a hypothetcal fshng trp to an angler and ask the angler whether they would take the trp or not. Stated preference technques have two major classes of elctaton technques to get at anglers preferences for fsheres management. The frst type, contngent valuaton, measures the value of a change from the status quo to some other state of the world. For example, one mght ask anglers to consder ther current trp and ask them ther wllngness to pay to avod a decrease n the 9

14 bag lmt for strped bass for that trp. Ths contngent valuaton queston s desgned to quantfy the economc loss of gong to a more restrctve management poston. The technque s not well suted to measurng preferences for all of the attrbutes of the fshng experence (expected catch, cost of travel to ste, management regulatons, envronmental condtons, etc.), but ths technque s useful for explorng new management tools or examnng wllngness to pay n the context of tghtenng or loosenng regulatons. Another stated preference methodology, Stated Preference Dscrete Choce (SPDC) technques have been appled to envronmental management problems such as Alaska fshng (Herman), huntng n Canada (Adamowcz et al.), and Mane fshng (Roe et al.). Lke contngent valuaton, SPDC technques appled to fshng management gan nformaton about preferences by analyzng responses to hypothetcal fshng trps. Further, SPDC consders a fshng trp as a bundle of attrbutes descrbng a trp. Usng expermental desgn technques, anglers are gven trp comparsons that are optmal n the sense that they requre the respondent to make tradeoffs across the dfferent trp attrbutes smultaneously. Therefore, t s possble to examne how preferences for a management measures such as bag lmts mght change as other management changes, as envronmental condtons change, or as the cost of the trp changes. Addtonally, new polcy-relevant attrbutes can be examned; for example, anglers mght be asked to consder a trp under the exstng management regme and one wth a new management tool n place (for example, gear or area restrctons). Lke contngent valuaton, SPDC s based upon hypothetcal, not real behavor. Consequently, questons could be rased about the veracty of results based upon ths type of data. 10

15 III. Revealed and Stated Preference Technques for Marne Recreatonal Fshng The use of revealed preference methods n economcs s extensve. Applcatons nclude demand analyss (food demand, housng demand, and demand for other consumer goods), producton analyss (agrcultural and ndustral producton), and analyss of labor market choces. These models focus on observng choces made by ndvduals and attempt to relate choces to observed factors about the choce n order to estmate a quanttatve relatonshp. Recreaton demand analyss was the frst use of revealed preference methods for non-market goods. Hotellng was the frst to suggest that demand for natonal parks was probably a factor of the cost of accessng the park as well as envronmental and other factors assocated wth the choce to vst a park or not. In a marne recreatonal fsheres recreaton demand settng, the use of revealed preference methods requre extensve data on the ndvdual, the recreaton ste, the state of the envronment at that ste, and smlar nformaton for substtute recreatonal alternatves. The random utlty framework, n partcular, requres extensve data, on each and every recreatonal alternatve avalable to the ndvdual. Perhaps the most burdensome requrement n the context of recreatonal fshng s the characterzaton of the qualty of the fshng experence. Many studes have used the expected catch for the trp as a proxy for the qualty of a fshng trp. The formulaton of expected catch requres a tme seres of bologcal catch-effort data at a ste to produce a meanngful measure of expected catch (McConnell, Strand, and Blake-Hedges). 11

16 NMFS Data Collecton Efforts The NMFS Dvson of Fsheres Statstcs and Economcs, Offce of Scence and Technology has for some tme undertaken data collecton on recreatonal anglng. Snce 1994, ths data collecton effort has been expanded to nclude economc data to enable the estmaton of economc valuaton and mpact models n support of characterzng the economc mportance of recreatonal fshng and for fsheres management (see Hcks et al., 2000). The ntal analyss of the frst data collecton effort, undertaken n the Northeastern Unted States n 1994, revealed that developng speces-specfc models of angler behavor and economc value was severely hampered by data lmtatons. Addtonal research has shown that models aggregatng over speces, whle very useful for characterzng total economc value, are a relatvely poor proxy for speces-specfc models needed for gudance of management. Addtonal work usng data from other regons of the country has revealed smlar problems n developng speces-specfc management models. In response to these problems, the NMFS Fsheres Statstcs and Economcs Dvson (F/ST1) began a new data collecton effort n a way complementary to the ongong data collecton on recreatonal anglers. The effort conssted of addng a mal survey to the MRFSS feld survey. In the feld, anglers were asked questons enablng the estmaton of the total value models so that the hstorcal tme seres could be mantaned; n the mal survey anglers were presented wth questons about a specfc speces. These questons conssted of atttudes and awareness about catch and release fshng, management tools, and stated preference questons related to potental 12

17 management measures amed at summer flounder. These questons vared attrbutes relatng to a fshng trp; among the attrbutes were bag and sze lmts for summer flounder. The questons were framed n such a way that preferences for management tools could be estmated, welfare measures obtaned, and a partcpaton model could be estmated. The SPDC porton of the mal survey was created usng expermental desgn technques n order to mprove the effcency of the tradeoffs people had to make concernng fshng and fshng management. Clearly the ablty to control the tradeoffs respondents make s a major advantage to SPDC methods. Choce experments are desgned to ntroduce varaton n the factors researchers want to explore. Ths s obvously a major advantage relatve to methods where researchers are at the mercy of varaton and trade-offs that are observable n the feld. The ablty to desgn tradeoffs nearly places SPDC n the realm of expermental economcs. In SPDC, we can nvestgate new attrbutes (what f there were a recreatonal fshng tradeable quota) or attrbutes out of observable ranges (an 80 nch sze lmt)- wth SPDC we aren t lmted to the current state of the world when fndng out about people s preferences. In models we use real choces people make. To estmate models of behavor, researchers make assumptons about what nformaton s relevant for the person s recreaton choce. For example, the analyst must decde: the relevant substtute stes the ndvdual consdered, the envronmental qualty ndcators mportant to the ndvdual, the formaton of expectatons about qualty ndcators, and hope that mportant factors not observable are not correlated wth the observable varables. 13

18 In SPDC, all the nformaton s gven to respondents. It s a hypothetcal technque; people are not makng real economc choces. Therefore, t s mportant to frame questons properly (e.g., need the rght attrbutes, and the rght ranges of these attrbutes). The questonnare must be clear snce t s contanng all of the nformaton for the choce experment. In a travel cost settng, n order to get enough varaton n varables of nterest, e.g., bag and sze lmts, an analyst mght need tme seres or spatal data, whch opens up potental statstcal ptfalls. For the NMFS s needs, the SPDC technque s prmary advantage s the ablty to value new or out of range attrbute levels and for attrbutes wth lttle or no varaton. IV. Stated Preference Expermental Desgn To collect the SPDC data, the choce was made to leverage the Marne Recreatonal Fsheres Statstcs Survey (MRFSS) for two reasons. Frst, the MRFSS had already been used extensvely to obtan data for methods, those models exsted, and t was felt that t provded a mature methodology from whch to begn a plot project usng SP methods. Addtonally, there were cost advantages assocated wth gong wth the well-establshed MRFSS survey. The prmary advantage of leveragng the MRFSS was that t afforded the opportunty to collect both SP and data for the same fshermen. Havng ths data would allow hypothess testng on whether SP and data provded smlar results for both parameter and welfare estmates. Once the decson had been made to collect data va the MRFSS survey, the queston was how best to do t. The MRFSS has several vehcles for collectng data, each havng ts own strengths and weaknesses. The feld ntercept survey collects catch/effort and economc data from fshermen n the feld. It s well suted for methods because 14

19 the economc add-on questons seek factual nformaton from the respondent about hs employment stuaton, ncome, and whether he s prmarly engaged n fshng. SP questonnares typcally requre respondents to dgest nformaton desgned to setup the hypothetcal queston they wll be asked. Addtonally, SPDC methods present multattrbute recreaton trps and ask respondents whch one they would have chosen. Taken n tandem, t s dffcult to mplement an SPDC survey n combnaton wth the MRFSS feld ntercept. If one factors the tme cost of the addtonal SPDC nformaton that one must read to respondents, and the tme t takes respondents to compare the hypothetcal trps, conductng the SPDC survey n the feld s not a sutable method for collectng the data. The MRFSS also collects data va a random phone survey. The advantage to ths approach s that one can collect data va a random sample of anglers. For many of the reasons lsted above, t s not possble to conduct the SPDC survey on the phone. One could conduct a mal follow-up to the random phone survey to obtan the SPDC data, but one would also need to collect data on actual trp choces f a rgorous comparson of and SPDC methods needs to be made. In 1999, a feld test was undertaken n Ocean Cty, Maryland. The feld test conssted of addng SPDC questons to the feld porton of the survey. Fndngs ndcated that fshermen responded well to the SPDC questons but t dd take them qute a bt of tme to dgest the trp comparson nformaton and make a decson. It was felt by survey statstcans that the resultng downtme for ntervewers could potentally jeopardze the scentfc ntegrty of the feld survey by basng the data collecton effort. Based on ths nformaton, t was decded that the ntercept survey should be used to collect data on 15

20 respondents (as t had been used n the past), and then a mal follow-up survey should be conducted to obtan SPDC data. Based upon the results of the ntal feld test, extensve survey revsons were undertaken. At ths tme, the focus was on properly dentfyng the attrbutes of the hypothetcal recreaton trp that were mportant for the angler s trp decson. It was clear that the SPDC model needed to be able to quantfy preferences for sze and bag lmts snce they were the prmary tools used by management (though season lmts are also used extensvely). To get at season lmt regulatons and to make the model amenable to predctng changes n partcpaton, the SPDC comparson, n addton to two hypothetcal trps, asked anglers to consder a Don t Go opton, whereby they could opt out of fshng f regulatons or some other factors moved n an unfavorable enough drecton (for more dscusson on the mportance of an opt out choce, see Banzhaf et al). Survey Feld Test and Focus Group Pretests were gven to employees of the Natonal Marne Fsheres Servce n the Offce of Scence and Technology. These surveys are avalable from the author. The ntent of these surveys was to further hone the nstrument, queston format, readablty of the questons, and meanngfulness of attrbutes and attrbute defnton. Ths was a hghly teratve process desgned to further the nstrument s development as far as possble before the focus group meetngs held n Baltmore, Maryland n March of The goal of the focus group was to further refne the entre nstrument and the SPDC questons. None of the prncpal nvestgators were present n the room durng the 16

21 focus group sesson; however, the prncpal nvestgators could vew respondents through a one-way mrror (of whch the respondents were made aware). A moderator s gude was prepared (see Appendx A). There were four focus groups each of approxmately 10 partcpants each. Focus groups were stratfed accordng to age and ncome. Respondents were randomly recruted and screened based upon ther knowledge and partcpaton n fshng and ther avalablty wthn the stratas descrbed above (the focus group screenng nstrument can be found n Appendx A). All portons of the survey were under consderaton for change as a result of feedback from the respondents. Two versons of the survey were prepared for the focus group. The prmary dfference between the two was factors ncluded n the hypothetcal choce comparsons (the two versons can be found n Appendx A). Table 2 contans the attrbutes and defntons consdered n the focus group experment. Our experence n the feld and n n-house pretests ndcated that Survey 1, whch dd not tell fshermen how many of the summer flounder they caught were of legal sze, was problematc, leadng to confuson among respondents who for the most part thought that all of the summer flounder caught were of legal sze. Under ths mproper assumpton, the respondents were not requred to make the proper trade-offs regardng mnmum sze lmts. Consequently, n the focus group we frst gave respondents Survey 1, and then probed whether they thought the descrbed trps gave them all the necessary nformaton to make a choce comparson. Next, we then gave them Survey 2 wth no explanaton other than t was a slghtly dfferent verson of the survey. Many respondents dd not notce that another attrbute had been added, but when probed about the dfference 17

22 between Surveys 1 and 2, notced that there was an addton of an attrbute. When probed about ther assumptons concernng the number of legal szed fsh n Survey 1, most had ndeed assumed that all of the caught fsh were of legal sze. Ths confrmed our suspcon that the addton of the attrbute n Survey 2 was necessary to get at the full range of preferences for fsheres management. Respondents were also asked about ranges of attrbutes ncludng the approprateness of the cost of the trp, catches for summer flounder, etc. Addtonally, respondents were probed about the appearance of the survey and cover letter, as well as how effectvely t conveyed nformaton to the reader. These steps were taken to nsure as hgh a response rate as possble. In addton to the SPDC porton of the survey, focus group partcpants were asked a varety of questons related to opnons about fsheres management, targetng habts, fshng habts and avdty, and catch and release practces. These questons were desgned to collect valuable nformaton for fsheres management, establsh a rough baselne of fshng behavor, and get respondents thnkng about ther fshng n preparaton for the SPDC questons. Placng these questons n sequence before the SPDC questons was done ntentonally. 18

23 Table 2. Focus group SPDC questons: attrbutes and defntons Attrbute Defnton Survey 1 Survey 2 Cost of travelng to a ste Includes gas, wear and tear on your vehcle and other expenses you mght have from travelng to and from a fshng ste. Ths cost does NOT nclude expenses for Yes Yes Bag lmt for summer flounder Mnmum sze lmt for summer flounder Lkely catch of summer flounder food, ce, or fshng equpment. The most summer flounder an angler can legally keep per day of fshng due to regulatons. Yes Yes Summer flounder smaller than a mnmum sze lmt must be released. Fshermen never know exactly how many summer flounder they wll catch when they take a trp. Often, they have an dea of how many fsh they are lkely to catch. Yes Yes Yes Yes Lkely fshng success for all other speces When takng a trp, fshermen mght also be nterested n fshng for speces besdes summer flounder. Fshng success refers to the expected number of fsh caught for all other speces that you mght encounter for a typcal trp n your area. Yes Yes Lkely Number of summer flounder of legal sze Fshermen also are never sure of the sze of summer flounder they wll catch. Often they mght be aware of dfferences n locatons that mght lead to dfferences n the szes of fsh caught. No Yes After analyzng the results of the focus group, t was found that even wth such a small sample, the model performed qute well wth regard to sgn and sgnfcance of coeffcents. The fnal lst of attrbutes was chosen based upon two presdng consderatons. Frst and foremost, attrbutes were chosen and defned to make the hypothetcal trp comparson meanngful for anglers. After meetng ths consderaton, attrbutes were defned to make the comparson consstent wth the models that have been used n past studes. Followng feedback from the focus group, the questonnare was fnalzed n March of Appendx B contans a fnal nstrument used for the 19

24 conjont study 3. Table 3 provdes the defntons and ranges of attrbutes used n the study. Table 3. Fnal Attrbutes, Defntons, and Ranges for SPDC Survey Attrbute Defnton Ranges Cost of travelng to a ste Bag lmt for summer flounder Mnmum sze lmt for summer flounder Lkely catch of summer flounder Lkely fshng success for all other speces Includes gas, wear and tear on your vehcle and other expenses you mght have from travelng to and from a fshng access ste (such as tolls, ferry fees, and parkng fees). Ths cost also ncludes expenses for food, ce, and fshng equpment used on ths trp. The cost does not nclude gude or boat fees. {$5, $20, $30, $40, $55} The most summer flounder an angler can legally keep per day of fshng. {1, 4, 6, 8, 12} (fsh) Summer flounder smaller than a mnmum sze lmt must be released. {12, 14, 15, 16, 18} (nches) Anglers never know exactly how many summer flounder they wll catch when they take a trp. However, they often have an dea of how many fsh they are lkely to catch. When takng a trp, anglers mght also be nterested n catchng speces besdes summer flounder. Fshng success refers to the expected number of fsh caught for all other speces that you mght encounter for a typcal trp n your area. {2, 5, 8, 11, 14} (fsh) {Below Average, Average, Above Average} Lkely Number of summer flounder of legal sze Anglers also are never sure of the sze of summer flounder they wll catch. However, they often mght be aware of dfferences n locatons that mght lead to dfferences n the szes of fsh caught. {0, 1, 3, 6, 10} (fsh) Fnal Desgn Once the attrbutes and attrbute levels were fnalzed, the fnal desgn needed to be created. Based upon our feedback from focus groups and other survey pre-tests, t was determned that respondents should only receve four of the SPDC questons. Ths level was determned because of two prmary reasons: 1) survey fatgue on the part of 3 The questonnare n Appendx B s only 1 of 18 versons dstrbuted to anglers. 20

25 respondents mght lead to poor responses f any more SPDC questons were offered to them and 2) for each two SPDC questons added, the survey s lengthened by one page. Any lengthenng of the survey mght sgnal to respondents that the survey s too tme consumng to complete. Upon openng a package, the prmary ndcator of how much tme a survey wll take to complete s the sze and thckness of the nstrument. The two factors taken n combnaton led us to the conservatve number of four SPDC trp comparsons per respondent. Gven these constrants, the challenge was to desgn a survey that would enable the quantfcaton of preferences for fsheres management tools and the other attrbutes dentfed n the prevous step. Snce each respondent was gettng a relatvely low number of SPDC questons, we decded to dvde the survey nto blocks (or unque versons of the survey), wth each block havng dfferent levels of attrbutes for the four trp comparsons. Usng the SAS QC module, we used PROC Factex to generate a Type V resoluton canddate desgn. Ths ensured that we could estmate all man and cross effects for attrbutes n the model. The canddate desgn created by PROC Factex s a startng pont desgn and s smaller than a full factoral desgn that would have exceeded the memory and dsk space avalable on the computer used for ths experment (6 ggabytes). The next step was to par down the canddate desgn nto the best desgn possble gven the fact that we were lmted to 4 (questons) x 18 (unque sets of questonnares)= 72 unque trp comparsons. Clearly, ncreasng the number of blocks ncreases the effcency of the desgn matrx snce ncreasng the number of unque trp comparsons allows for more tradeoffs by respondents. However, ncreasng the number of blocks ncreases survey costs 21

26 because each respondent s tracked durng several stages of malngs accordng to ther assgned block (dscussed n detal below). Usng SAS Proc Optex, we took the canddate desgn set and created the best desgn set we could based upon the concept of D optmalty. Once attrbutes, ther levels, and model specfcatons are known then one needs to choose the fnal desgn. Table 4 shows some of the optmalty crtera that are commonly used when comparng desgn canddates. The frst two, A and D optmalty, are nformaton based canddates. That s, desgns are chosen n a way that maxmzes the nformaton matrx or equvalently, mnmzes the varance. U and S optmalty are known as dstance based crtera, snce they seek to spread or group canddates desgns accordng to the degree of coverage a gven desgn has over the attrbute space. D optmalty, the most wdely used crtera method, s used n ths study. We terated the PROC Optex procedure 1000 tmes and chose the best desgn out of those 1000 runs. Table 4. Optmalty Crtera* Crteron Goal Formula D-optmalty A-optmalty U-optmalty S-optmalty Maxmze determnant of the nformaton matrx Mnmze the sum of the varances of estmated coeffcents Mnmze dstance from desgn (D) to canddates (C) Maxmze dstance between desgn ponts mn *taken from the SAS/QC Usage and Reference Manual Volume I. max X X mn trace ( X X ) mn Y D x C 1 d ( x, D) d ( Y, D Y) 22

27 Fnal Stated Preference Questonnare Once these steps were completed, the fnal verson of the questonnare was produced usng Mcrosoft Publsher and mal merge technques. Fgure 1 shows an example of one of the actual trp comparsons used n the SPDC nstrument. Respondents were asked: Suppose last August that you could have chosen only from the recreatonal opportuntes descrbed below. Please revew the trp descrptons and answer the two questons at the bottom of the table. After respondents vewed the three optons, they were asked to ndcate Whch trp do you most prefer. All respondents were referred to consder the choce of trps relatve to August Ths was done to anchor all respondents to the same tme perod versus addng tme perod explctly as an addtonal attrbute n the choce experment. August was chosen because t s the generally the peak season for summer flounder fshng. Ths setup was chosen to avod havng respondents gettng an nstrument whose catch ranges were not belevable durng the perods n ether early sprng or late December. The chosen layout of the SPDC queston s very smlar to that used n Adamowcz et al. 23

28 Fgure 2. An actual SPDC trp comparson. Employees of F/ST1 used Mcrosoft Publsher to put together all opnon-related questons, SPDC questons, and demographc questons nto a booklet format n a sze very close to that recommended by Dllman, and Dllman and Salant and produced the fnal survey. Because a mal survey was used to contact people who had been ntercepted n the feld and who had agreed to partcpate, a modfed Dllman method approach was employed n an effort to maxmze the survey response rate (Table 5). The frst step was to recrut feld ntercept respondents at the tme of the feld survey. Once respondents agreed to partcpate n the follow-up survey they were gven a survey brochure that very brefly descrbed that they would soon receve a mal survey that would help the NMFS know more about what they thought about fsheres management. It was a full-colored tr-fold brochure that was prmarly desgned to help respondents recall at the tme of openng the mal survey that they had agreed to partcpate. 24

29 Table 5. Mal survey steps and response rates Acton Survey Brochure Frst Malng Post Card Second Malng Tme Admnstered At tme of feld ntercept No more than one month after ntercept Two weeks after the malng of the Frst Malng Two weeks after malng of the Post Card Overall response rates 4 Months Response Rate Wave 2 March-Aprl 58.4% Wave 3 May-June 56.3% Wave 4 July-August 55.7% Wave 5 September-October 59.6% Wave 6 November-December 53.5% Average Response Rates 56.8% At the end of each month, all ntercepted anglers who agreed to partcpate n the SPDC survey were maled the survey nstrument along wth a cover page that reterated many of the ponts made n the survey brochure and renforced the noton that each respondent s opnon mattered. Followng a two-week perod, respondents who had not yet responded to the frst mal survey were sent a postcard remnder that renforced the ponts made n earler cover letters and brochures. If after two weeks from the date of malng the postcard, respondents had stll not returned a survey, a second survey was sent to them along wth a slghtly dfferent cover letter that contaned smlar ponts as prevous nformaton, but n slghtly more forceful language. Pror to the begnnng of the ntal malng each survey respondent was randomly assgned a survey verson (also referred to as a block). A database tracked all subsequent malngs to ndvduals accordng to ther block number. Ths ensured that f the second malng was necessary, respondents would receve the same verson of the survey that they were assgned n the frst malng. 25

30 V. Model of Angler Behavor Both the and SPDC models employ dscrete choce statstcal technques to estmate models of behavor. The dscrete choce technque assumes that anglers must choose between a number of dscrete alternatves (or n the case of recreatonal fshng, fshng stes). Anglers utlty from choosng a partcular ste s dependent on the attrbutes assocated wth each ste. For models of recreatonal anglng, the angler s vector of ste-specfc attrbutes, X, s typcally assumed to be populated by data such as the cost of travelng to the ste, ndcatons of the ste s fshng qualty, and other stespecfc attrbutes. In the dscrete choce framework, the angler s assumed to choose the ste from among a set of stes S that maxmzes hs utlty. Assume that the angler s ndrect utlty functon for ste s gven by V( β, X + ε (1) ) = v( β, X ) where X s the vector of ste and ndvdual-specfc attrbutes assocated wth ste, β s a vector of preference parameters on the observable porton of the ndvdual s ndrect utlty functon, v( β, X ). Fnally, ε s the unobservable porton of the ndvdual s ndrect utlty functon and s assumed to be ste specfc. The angler then compares all potental choces n hs choce set, S, and chooses the best ste, : V( β, X ) > V( β, X ) j S, S (2) j The challenge s to take the model gven by (1) and (2) and develop a statstcal model that wll enable the recovery of the behavoral parameters, β. Of course, the structure of the model wll depend heavly on assumptons about the form of the ste- 4 Incorrect addresses are not ncluded n the calculaton of response rates. For the entre survey, there were 26

31 specfc error term, ε. In ths paper, we use two forms of the error structure, the Type II Generalzed Extreme Value dstrbuton (GEV) and the more restrctve Type I GEV dstrbuton (ndependent logt). The ndependent logt specfes the probablty of choosng ste as Pr ob() = e j S v( β, X ) e v( β, X j ) (3) A well-known restrcton assocated wth the model gven n (3) s that t mples the Independence of Irrelevant Alternatves restrcton (IIA). The mplcaton of ths s that the rato Pr ob() Prob( j) e = e v ( β, X ) v ( β, X j ) s ndependent of ste-specfc attrbutes for all other alternatves. Ths means that the probablty rato would reman unchanged as other stes n S are dropped or as addtonal stes are added. Many emprcal applcatons have demonstrated volatons of ths assumpton. To relax the IIA restrcton, analysts have turned to the nested logt model. The nested logt model dvdes the choce set S nto M subsets. Each subset s comprsed of stes/alternatves grouped accordng to smlarty. The IIA restrcton s bndng for stes wthn a subset m, but not for ste comparsons n dfferent subsets of the choce set. If the analyst desgns the choce structure approprately, then IIA restrctons can be elmnated for cases where t s thought to be problem. The nested logt model s 5009 surveys sent out and 150 bad addresses. 27

32 equvalent to assumng that the error terms are dstrbuted as Type II GEV. Gven ths assumpton, the probablty that an angler s observed choosng ste n can be wrtten 5 : Prob(n) = e s *(a + v( β, X )) n n M n m= 1 j S m j S e n n n s m *(a m + v ( β, X mj )) e e s *(a + v( β, X jn )) 1/s m (1/s ) 1 n (4) Notce that restrctng each scale parameter, s =1, and each alternatve specfc constant, a =0, collapses the model back to that found n (3). Therefore, the logt model s seen as a specal case of the nested logt model. The parameter s s referred to as the scale parameter and s the nverse of what McFadden terms the nclusve value parameter. The Econometrc Model Recent work usng revealed preference technques n a marne fsheres settng has attempted to provde nformaton that s useful for management and able to analyze ssues that are speces-specfc (Schumann; Hcks and Stenback). Fndngs for these models are two-fold: 1) If management measures or stock condtons change at a specesspecfc level, then speces-specfc models of angler behavor are mportant to develop snce aggregate speces models perform poorly, and 2) Speces-specfc models usng data are very hard or mpossble to estmate because of (a) the large number of speces targeted and 5 For the results presented later, s =1 f the Don t go opton s chosen, and s =s f ether of the the stated preference trps are chosen. 28

33 caught by marne anglers, (b) management measures do not vary much for a partcular speces, and (c) data requrements to characterze fshng qualty for all stes on a speces-by-speces bass are burdensome. Gven these factors, t was clear that developng a useful summer flounder model would be at best very dffcult to mplement. Attempts to estmate the dscrete choce model wth bag and sze lmts explctly ncluded as factors n the model faled because of a near complete lack of varaton n the management data. Therefore, a smpler model s developed that enables anglers to substtute between summer flounder and other speces they may want to target. We assume that when fshng, anglers choose stes based upon all speces regardless of what they choose to target. Consequently, anglers consder the fshng qualty for summer flounder as well as the fshng qualty for all other speces they could catch at the ste. Addtonally, anglers are concerned about the cost of takng a trp to ste. We expermented wth other varables thought relevant for explanng the decson, such as county of boat moorng and county-specfc varables descrbng the degree of tourst versus fshng destnatons, etc. Includng these varables dd not affect the fndngs of the paper, but dd greatly reduce the number of observatons for the model, snce the sample had to be reduced to nclude only those havng responded to the economc or SPDC survey. For these reasons, a smple choce structure was chosen to make the model as close to the SPDC model as possble, makng the statstcal comparson as transparent as possble. The varable defntons are gven n Table 6. The overall goal n developng the 29

34 model was to estmate a model that would be useful to enrch the SPDC experment and to test for parameter homogenety across the two technques. Table 6. Varable Defntons. Varable Name TC_ SF_ OC_ Defnton Travel Cost based on data to Ste. Equals roundtrp dstance to ste tmes the rate of $0.33 per mle. Average Catch per trp per wave at ste for summer flounder based on data. Average taken over the perod Average Catch per trp per wave at ste for all other speces based on data. Average taken over the perod The defnton of the ndrect utlty functon for the model s defned as follows: V( β, X ) = β *TC _ + β *SF_ + β *OC _ + ε (1 ) rp rp' t cost rp' sf rp' oc and the parameters to be estmated are gven by β rp' t cost, β, and β rp' sf rp' oc. Notce that ths ndrect utlty functon s lnear wth regard to the travel cost coeffcent. Ths assumpton ensures a closed form soluton for the welfare estmates that follow. For the model, we assume a non-nested choce structure mpled by (3) by estmatng a multnomal logt model usng maxmum lkelhood technques. It should be noted that the parameters lsted n (1 ) can be rewrtten as follows: rp' rp' rp' rp rp rp { β, β, β } = { λβ, λβ λβ } t cost sf oc tcost sf, oc. The parameter λ s often referred to as the scale factor and s ted drectly to the data source from whch the data are estmated. The parameter λ s nversely related to the varance of the error term n the model (Louvere et al.) and s mpossble to dentfy f one were only gong to estmate model (1 ). For ths reason, most applcatons of dscrete choce models do not explctly nclude the scale factor n ther model notaton. However, when combnng SPDC and models, the scale factor must be explctly accounted for durng estmaton. 30