Recreatonal trp tmng and duraton predcton: A research note Ataelty Halu a and Le Gao a* a School of Agrcultural and Resource Economcs, The Unversty of Western Australa, Crawley, WA 6009, Australa *E-mal address: dr.legao@gmal.com 30 November 2010 Worng Paper 1002 School of Agrcultural and Resource Economcs http://www.are.uwa.edu.au Ctaton: Halu, A. and Gao, L. (2010) Recreatonal trp tmng and duraton predcton: A research note, Worng Paper 1002, School of Agrcultural and Resource Economcs, Unversty of Western Australa, Crawley, Australa. Contact authors for a revsed verson of the paper. Copyrght remans wth the authors of ths document.
Abstract: Ths paper presents models that predct two recreatonal fshng trp parameters: the length of a trp and the tmng of a trp wthn a year. A dscrete choce (logt) model lnng the choce of trp tmng to calendar events, the demographc characterstcs of anglers as well as the nature of the trp s econometrcally estmated. A Tobt model s used to evaluate the relatonshp between fshng trp length and personal and trp characterstcs. The results ndcate that tmng choce and trp length can be eplaned well n terms of observable personal and trp varables. Knowledge of these relatonshps s a useful nput to toursm/recreatonal fshng management as well as to the development of toursm/fshng actvty smulaton models. Keywords: recreatonal fshng, trp tmng, length of recreatonal trps, toursm smulaton, envronmental mpact management 1
1. Introducton Increasngly, models are beng used to smulate management outcomes and the effects of polcy measures (Lttle et al., 2009, Kramer, 2008, McClanahan, 1995, McDonald et al., 2008). By systematcally tracng the comple relatonshps between resource use and bophyscal components, models allow us to better evaluate management and polcy measures. For eample, a model estmatng the mpact of fshng on bophyscal stocs s lely to nclude sub-models lnng fsh speces or groups to each other as well as fshng actvtes. However, whle models of destnaton choce are usually modeled usng emprcal data, the tmng and duraton of recreaton are rarely adequately modeled. Instead, ad hoc approaches are used to determne these parameters. Trp tmng has receved lttle attenton n lterature. Some studes (Shales et al., 2001, Eugeno-Martn and Campos-Sora, 2010) ndrectly touch on the determnants of toursm trp tmng, ncludng the effects of congeston, clmate, and demographc varables. There are relatvely more studes focusng on toursts length of stay. Recently, some researchers (Alegre and Pou, 2006, Gooval et al., 2007, Martínez-Garca and Raya, 2008) have used econometrc models and dentfy dfferent nfluences, ncludng natonalty, age, ncome, employment status, vstaton rate, educaton, type of accommodaton avalable, daly spendng, and stage n the famly lfe cycle. These studes focus at a length of stay at a partcular destnaton. In ths paper, we used emprcal data from multple stes to develop a logt model of trp tmng and a Tobt model of trp duraton. The two models provde a general way of smulatng recreatonal tmng and duraton that are superor to smpler approaches, e.g. those based on hstograms or emprcal frequences. Unle our approach, trp tmng or length predcton methods that are based on observed frequences do not relate the varables of nterest to personal/trp characterstcs and, are, therefore, dffcult to etrapolate nto other envronments or perods. The 2
models presented here have been used as components n an ntegrated economc and ecosystem model of recreatonal fshng for a marne envronment that ncludes trp demand and ste choce models n (Gao and Halu, 2010). The components of the smulaton model are shown n Fgure 1. How many trps n a year? Whch ste to go? Trp Demand Model Smulaton flow Ste Choce Model Management Strateges Recreatonal Angler When to go? Trp Tmng Model Recreatonal Anglng Ste How long s a trp? How many fshes caught? Trp Length Model Catch Rate Model Trophc-Dynamc Model Fgure 1. A set of econometrc models that underpn agent-based smulaton model of recreatonal fshng n a marne envronment. Source: (Gao and Halu, 2010) The paper s organzed as follows. The logt model for trp tmng s presented n Secton 2. Ths s followed by a presentaton of the Tobt model of trp length n Secton 3. The paper concludes n Secton 4. 2. Trp tmng model The trp tmng model focuses on the probablty that an ndvdual/household starts a trp on a gven date. A logt model s used. Ths probablty s hypotheszed to be a functon of three sets of factors: 1) the characterstcs of the day (e.g. weeend, holday, etc.), 2) the characterstcs of the person (e.g. employment status), and 3) the nature of the trp (e.g. drecton of the trp). 3
For eample, an employed person wll be nclned to choose a weeend or holday to start a trp. Ths wll partcularly be the case for a fshng trp that nvolves travel for a weeend or a day. For longer trps, an employed person s lely to start a holday at the end of the wee. The demographc characterstcs of the person are also mportant, e.g. retred people have more fleblty wth recreatonal trps than employed or worng people. Fnally, the nature of the trp (e.g. whether t s gong to north or south regons) wll affect the choce of the tmng because of weather effects. The probablty p that a person starts a trp on day among all possble sets of days d s gven by the followng logt formula: p = ep( d ep( β β + d l m + l β m lm l β lm m l ) md ) (1) where (or m ) s the -th (or m-th) characterstcs of day, l s the l-th characterstcs of angler, and β and β lm are the coeffcents to be estmated. In our case, we nclude the followng varables: β + l m β lm l m + β Employ PublcHolday 4 + β Employ Weeend 6 + β Retre Weeend 8 = β + β Weeend + β South Jan + β North Apr + β North May 12 0 1 13 + β PublcHolday + β Employ SchoolHolday + β KdProy SchoolHolday 9 7 5 2 + β North Jun + β SchoolHolday + β South Feb + β South Mar 14 10 3 + β North Jul 15 11 (2) These varables are defned n Table 1. The ey varables nclude whether the day s a weeend, a publc holday, or a school holday. However, the lelhood wth whch a person ntates a trp on a non-worng day also depends on other factors, such as whether they are employed or not, or whether they have chldren or not n the case of school holdays. The drecton of a trp and the month of the year together are an mportant nfluence. In the state where the data s collected, Western Australa, the northern half s warmer and thus attractve for recreatonal trps n the cooler months (le May to July). The reverse s true for trps headng south, where t s cooler n the south. 4
The reference pont for the defnton of a trp as south or north s the ndvdual s orgn or place of resdence. Table 1. Defnton of varables n recreatonal fshng trp date choce. Varables Descrpton Employ equals 1 f angler s employed, and equals 0 otherwse; Retre equals 1 f angler s retred, and equals 0 otherwse; KdProy equals 1 f two or more persons n a household go fshng wth angler, and equals 0 otherwse; South equals 1 f angler heads south to go fshng, and equals 0 otherwse; North equals 1 f angler heads north to go fshng, and equals 0 otherwse; Dstance dstance (lometers) between home locaton of angler and the fshng ste; Weeend equals 1 f day s a weeend, and equals 0 otherwse; PublcHolday equals 1 f day s a publc holday, and equals 0 otherwse; SchoolHolday equals 1 f day s a publc holday, and equals 0 otherwse; Jan equals 1 f day s n January, and equals 0 otherwse; Feb equals 1 f day s n February, and equals 0 otherwse; Mar equals 1 f day s n March, and equals 0 otherwse; Apr equals 1 f day s n Aprl, and equals 0 otherwse; May equals 1 f day s n May, and equals 0 otherwse; Jun equals 1 f day s n June, and equals 0 otherwse; Jul equals 1 f day s n July, and equals 0 otherwse; The sample data used for the estmaton s drawn from the Australan Natonal Survey of Recreaton Fshng conducted n 2000/2001 (Henry and Lyle, 2003). A total of 3135 observatons for 778 ndvduals from Western Australa were used n the analyss (Burton et al., 2008). For the trp drecton varables, we dvded the state nto three zones (north, south and centre). These zones are used as ndcatons of the effect of weather on the tmng of trps. In the summer, the south s cooler and hence t s more lely that a trp to ths zone wll occur. Smlarly n the wnter the reverse wll be true and trps to the north wll be undertaen. We use 30 degrees south lattude (-30) and 33 degrees south lattude (-33) to classfy recreatonal fshng destnatons (48 fshng stes n total) as well as nformaton on 17 angler home regon locatons to classfy trps nto the three categores,.e. south, north, and center. To the reader get a sense of weather dfferences, we present n Table 2 the mean temperature data for three representatve ctes: Albany (south), Perth (central), and Geraldton (north). 5
Table 2. Mean temperature data ( o C) for three representatve ctes, Western Australa. Locaton (Regon) Mean Temperature Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual ALBANY Mamum 24.3 24.8 24.0 22.0 19.1 16.7 15.8 16.2 17.5 18.7 20.7 22.7 20.2 (South) Mnmum 13.7 14.3 13.6 11.7 10.0 8.2 7.4 7.6 8.3 9.2 10.9 12.4 10.6 PERTH Mamum 31.9 32.1 29.9 26.1 22.2 19.2 18.1 18.7 20.3 22.9 26.3 29.1 24.7 (Center) Mnmum 17.3 17.6 16.2 13.4 10.9 9.0 8.1 8.3 9.3 10.5 13.3 15.2 12.4 GERALDTON Mamum 31.6 32.8 31.2 28.2 24.5 21.2 19.6 20.0 22.0 24.7 27.5 29.4 26.1 (North) Mnmum 18.0 19.1 18.0 15.6 13.3 11.1 9.5 9.1 9.4 11.0 14.0 16.3 13.7 Note: The data s statstcs result from year 1981 to 2010 from the Bureau of Meteorology, Australa (http://www.bom.gov.au/) The mamum lelhood coeffcent estmates for equaton (2) are shown n Table 3. Almost all the varables are statstcally sgnfcant, ecept the North Apr nteracton varable. Aprl s the borderlne month, between hot and cool season n the north, and t s not surprsng that ths varable s nsgnfcant n the model. Table 3. Coeffcent estmates of trp tmng model. Varables a Estmated coeffcent Std. Err. z Constant -6.32503*** 0.03137-201.612 Weeend 0.49828*** 0.07353 6.776 PublcHolday 0.58434*** 0.12476 4.684 SchoolHolday 0.31548*** 0.05603 5.631 Employ Weeend 0.36363*** 0.08128 4.474 Employ PublcHolday 0.40508** 0.15587 2.599 Employ SchoolHolday -0.20619** 0.06381-3.231 Retre Weeend -0.53059*** 0.09943-5.337 KdProy SchoolHolday 0.29356*** 0.06550 4.482 South Jan 0.40984*** 0.09087 4.510 South Feb 0.33794** 0.10931 3.092 South Mar 0.44416*** 0.10080 4.406 North Apr 0.17936 0.10145 1.768 North May 0.33822** 0.10753 3.145 North Jun 0.23434* 0.11411 2.054 North Jul 0.28494** 0.10119 2.816 Note: log lelhood: -21326.31; Ch-square: 618.3177; p-value: 0.0. Three asterss (***) ndcate sgnfcance at the 0.1% level, whle ** and * ndcate sgnfcance at the 1% and 5% levels, respectvely. a varable defntons are provded n Table 1. The coeffcent estmates ndcate that recreatonal anglers are nclned to select a holday or non-worng day (such as weeend, publc holday, and school holday) to start a trp. If they are 6
headng south, they are lely to pc trp tmes n January, February, and March. If t s a trp to the north, on the other hand, they wll tend to have t n May, June, and July; August to October dummes are not sgnfcant n our model. Further, employed anglers are more lely to select a weeend or a publc holday for a trp whle retrees are more lely to select a weeday to go fshng. As epected, an angler wth chldren s more lely to undertae a trp durng school holdays. But t should also be noted that beng employed maes one less lely to go fshng durng school holdays, all else beng equal. Ths suggests that worng people wthout chldren prefer the queter recreatonal perods outsde the crowded school holdays. 3. Trp Length Model A Tobt model s used to ft a model predctng the length of a fshng trp taen by an ndvdual. Tobt (Amemya, 1984) s an econometrc model that s used to descrbe the relatonshp between a lmted dependent varable (e.g. non-negatve dependent varable) y ( = 1,2, L, n ) and an observed set of nfluences or eplantors. The model supposes that there s a latent (.e. unobservable) varable y *. Formally, the latent varable * y s related to the eplanatory varables as follows: y 2 = β u, u ~ N(0, σ ) (3) * + The latent varable s related to the observed varable y as follows: * * y, y > 0 y = * (4) 0, y 0 Here, β s the unnown vector of parameters that we want to estmate, s a nown vector of regresson varables for the -th observaton, and u s assumed to be ndependently dstrbuted wth a symmetrc error term. 7
Trp length n days (lengthoftrp) s assumed to be a functon of personal characterstcs and the characterstcs of the perod durng whch the trp s taen: lengthoftrp = β + l m + β Weeend + β South Jan β lm l = β + β Employ + β Retre + β KdProy 0 4 7 + β North Jul m + β PublcHolday + β South Feb + β South Mar + β South + β SchoolHolday + β North Apr + β North May + β North Jun 10 13 1 5 8 2 11 14 3 + β North + β Dstance 15 9 6 12 16 (5) where (or m ) s the -th (or m-th) characterstcs of the trp start day, l s the l-th characterstcs of angler, and β and β lm are the coeffcents to be estmated. The varables are defned n Table 1. We use the same 3135 observatons descrbed above. A mamum lelhood method s used to obtan the coeffcent estmates presented n Table 4. Table 4. Coeffcent estmates of the trp length Tobt model. Varables a Estmated coeffcent Std. Err. z p Constant 1.110485*** 0.0507 21.886 3.52E-106 Employ -0.054103 0.0402-1.346 0.178 Retre 0.016726 0.0463 0.361 0.718 KdProy 0.048794 0.041 1.189 0.235 Weeend -0.105353*** 0.0309-3.411 0.000648 PublcHolday 0.138226* 0.0619 2.232 0.0256 SchoolHolday 0.052224 0.0326 1.604 0.109 South Jan 0.115964 0.0789 1.47 0.141 South Feb 0.217101** 0.0932 2.33 0.0198 South Mar 0.089395 0.0862 1.037 0.3 North Apr -0.076393 0.0869-0.879 0.379 North Mar -0.064605 0.0921-0.702 0.483 North Jun -0.182671 0.0978-1.868 0.0618 North Jul -0.109620 0.0912-1.202 0.229 South -0.019860 0.0413-0.481 0.631 North 0.060258 0.0421 1.43 0.153 Dstance 0.000358*** 0.0000488 7.334 2.23E-13 Note: Number of observatons used s 3135; log lelhood (model): -3804.2; log lelhood (ntercept only): -3854.8; Ch-square: 101.16 on 16 degrees of freedom; p-value: 0.00. Three asterss (***) ndcate sgnfcance at the 1% level, whle ** and * ndcate sgnfcance at the 5% and 10% levels, respectvely. a Varable defntons are gven n Table 1. 8
The results ndcate that trp length s sgnfcantly affected by tmng, dstance of destnaton ste and an nteracton between the drecton of trp and tmng. The sgnfcant varables are: Weeend, PublcHol day, South Feb, and Dstance. The dstance from a recreatonal angler s home locaton to recreatonal ste s postvely correlated wth the trp length. Also, holdays (ncludng weeends) ncluded n a trp tme also affect the length of the trp. Trps ntated over a weeend tend to be shorter, all else beng the same. Recreatonal anglers who head south n February are lely to go on longer trps those travellng n other months. Other attrbutes of the person (e.g. employed, retred, or whether they have chldren) and other nteractons between drecton of trp and the tmng of the trp seem to be statstcally nsgnfcant. However, there s some evdence that the length s lely to be longer f the angler s unemployed or retred, or s accompaned by chldren. 4. Concluson Models predctng trp tmng and trp length are useful. There has been lttle systematc research on trp tmng and lttle research on duraton modelng nvolvng multple destnatons. Potental areas of use nclude toursm promoton, congeston management as well as envronmental mpact management. These models are also useful for research purposes as they provde a ey nput nto smulaton models. Currently, models smulatng recreatonal behavour utlze ad hoc approaches. Even f these models utlze emprcal models that relate ste choce to personal and ste characterstcs (e.g. random utlty models of fshng ste choce), the tmng and length choce predctons for trps are rarely based on emprcally sound grounds. Ths paper has developed and econometrcally estmated two models. A trp tmng model for predctng the probablty that a persons/household ntates a recreatonal trp on a gven day was estmated as a logt model. The results show that a large number of varables (demographc and trp 9
related) can be used to eplan trp tmng. A Tobt model was econometrcally estmated to determne nfluences on trp length and the results show that demographc varable as well as calendar events are mportant n predctng trp duratons. Acnowledgements We than J. Durn for comments on the manuscrpt. The research reported here s part of a bgger proect funded through the Nngaloo Collaboraton Cluster, CSIRO Wealth from Oceans Flagshp Program. References Alegre, J. & Pou, L. (2006), 'The length of stay n the demand for toursm', Toursm Management, Vol 27, No 6, pp 1343-1355. Amemya, T. (1984), 'Tobt models: A survey', Journal of Econometrcs, Vol 24, No 1-2, pp 3-61. Burton, M. P., Raguragavan, J. & Halu, A. (2008), 'State-wde recreatonal fshng ste choce study', School of Agrcultural and Resource Economcs, Unversty of Western Australa, Techncal Report of Nngaloo Cluster Proect 4, Vol, No pp. Eugeno-Martn, J. L. & Campos-Sora, J. A. (2010), 'Clmate n the regon of orgn and destnaton choce n outbound toursm demand', Toursm Management, Vol 31, No 6, pp 744-753. Gao, L. & Halu, A. (2010), Integratng recreatonal fshng behavour wthn a reef ecosystem as a platform for evaluatng management strateges, n The 24th IEEE Internatonal Conference on Advanced Informaton Networng and Applcatons, Perth, Western Australa. Gooval, U., Bahar, O. & Koza, M. (2007), 'Determnants of length of stay: A practcal use of survval analyss', Toursm Management, Vol 28, No 3, pp 736-746. Henry, G. W. & Lyle, J. M. (2003). The Natonal Recreatonal and Indgenous Fshng Survey, Canberra, Australa, Australan Government Department of Agrculture, Fsheres and Forestry. Kramer, D. B. (2008), 'Adaptve Harvestng n a Multple-Speces Coral-Reef Food Web', Ecology and Socety, Vol 13, No 1, pp 17. [onlne] http://www.ecologyandsocety.org/vol13/ss1/art17/. Lttle, L. R., Punt, A. E., Mapstone, B. D., Begg, G. A., Goldman, B. & Wllams, A. J. (2009), 'An agent-based model for smulatng tradng of mult-speces fsheres quota', Ecologcal Modellng, Vol 220, No 23, pp 3404-3412. Martínez-Garca, E. & Raya, J. M. (2008), 'Length of stay for low-cost toursm', Toursm Management, Vol 29, No 6, pp 1064-1075. Mcclanahan, T. R. (1995), 'A coral reef ecosystem-fsheres model: mpacts of fshng ntensty and catch selecton on reef structure and processes', Ecologcal Modellng, Vol 80, No 1, pp 1-19. Mcdonald, A. D., Lttle, L. R., Gray, R., Fulton, E., Sansbury, K. J. & Lyne, V. D. (2008), 'An agent-based modellng approach to evaluaton of multple-use management strateges for coastal marne ecosystems', Mathematcs and Computers n Smulaton, Vol 78, No 2-3, pp 401-411. 10
Shales, A., Senor, M. L. & Andrew, B. P. (2001), 'Toursts' travel behavour n response to congeston: the case of car trps to Cornwall, Unted Kngdom', Journal of Transport Geography, Vol 9, No 1, pp 49-60. 11