Endogenous Fishing Moraliy in Life Hisory Models: Relaxing Some Implici Assumpions Marin D. Smih * Nicholas School of he Environmen and Earh Sciences Duke Universiy Box 90328 Durham, NC 27708 (919) 613-8028 marsmih@duke.edu Seleced Paper 2007 American Agriculural Economics Associaion Annual Meeings Porland, Oregon Absrac Life hisory models can include a wide range of biological and ecological feaures ha affec exploied fish populaions. However, hey ypically rea fishing moraliy as an exogenous parameer. Implicily, his approach assumes ha he supply of fishing effor is perfecly inelasic. Tha is, he supply curve of effor is verical. Fishery modelers ofen run simulaions for differen values of fishing moraliy, bu his exercise also assumes verical supply and simply explores a series of hese curves as differen scenarios. The seemingly innocuous assumpion of verical supply conflics wih a large body of empirical work on behavior of fishermen and fishing flees. Economiss and fisheries scieniss consisenly find ha fishing behavior is responsive o economic opporuniies over ime and space as well as across arge species. Accouning for his phenomenon requires ha fishing moraliy be made endogenous. This paper demonsraes an approach o endogenizing fishing moraliy in life hisory models by allowing he fish sock in he previous period and oher behavioral drivers o ener ino he equaion ha predics fishing effor in he nex period. The paper discusses condiions under which he sandard approach is approximaely accurae and when endogenous fishing moraliy dramaically alers model predicions. An empirical applicaion o he Gulf of Mexico gag, a species of grouper, illusraes he imporance of endogenizing fishing moraliy. Accouning for fishing behavior ulimaely will improve predicions from managemen models and avoid fisheries managemen failures. * Smih is an assisan professor of environmenal economics in he Nicholas School of he Environmen and Earh Sciences a Duke Universiy. The auhor hanks Junjie Zhang for valuable research assisance. Copyrigh 2007 by Marin D. Smih. All righs reserved. Readers may make verbaim copies of his documen for non-commercial purposes by any means provided ha his copyrigh noice appears on all such copies. 1
Endogenous Fishing Moraliy in Life Hisory Models: Relaxing Some Implici Assumpions Marin D. Smih Nicholas School of he Environmen and Earh Sciences Duke Universiy Inroducion There is lile doub ha human exploiaion of he marine environmen profoundly affecs marine ecosysems and he availabiliy of fish for fuure generaions (Bosford e al 1997; Pauly e al. 1998; Jackson e al. 2001; Myers and Worm 2003; NRC 1999; UNFAO 2000; Pew 2003). The dilemma is wha o do abou i. The gu reacion of mos scieniss is o resric where, when, how, and how ofen we fish. Bu when our resource base coninues o degrade in spie of our bes effors, we are lef wondering why. The answer is simple. Policies are pu in place, and humans adjus o hem. When we consrain people in one aciviy, hey do no simply go away bu insead engage in anoher aciviy. Policies ha fail o recognize his fundamenal fac do lile more han rea he sympoms of resource degradaion wihou regard for he underlying causes (Wilen 2006). Among he mos promising developmens in marine conservaion biology is our growing knowledge abou life hisory characerisics of fish and oher marine species. Fisheries scieniss and managers have long considered age-dependen growh and moraliy in modeling (Beveron and Hol 1957), and recen sudies have reinforced he imporance of life hisory feaures for marine conservaion (e.g. Heppell e al. 2000). Larger individuals end o produce more offspring han smaller ones (Palumbi 2004), while individual offspring of older fish can be more viable han hose of younger fish (Berkeley e al. 2004). Many species of fish are hermaphrodiic, and here is heoreical suppor ha harvesing pressure for sequenial hermaphodies can lead o 2
sudden collapses in populaions (Armsworh 2001). There is also some evidence ha sizeselecive fishing pressure can induce evoluionary changes in fish populaions over relaively shor ime horizons (Conover and Munch 2002). To promoe susainable fisheries and ecosysems, managers have a range of policy insrumens a heir disposal, including limied enry programs, individual fishing quoas, gear resricions, size limis, season closures, and marine reserves and oher area closures. These policies ofen aim o proec organisms a differen life sages. For insance, size limis may proec young, immaure fish o give hem an opporuniy o reproduce; season closures may proec fish during spawning; and area closures may allow some fish o grow larger and produce more offspring. In spie of growing awareness abou life hisory characerisics in fisheries managemen, we ofen fail o achieve conservaion goals. A grea challenge and opporuniy for marine conservaion biology is o combine he insighs from life hisory models wih insighs from economics and he social sciences abou how humans behave in response o policy. Wha everyone would like o know is wheher and under wha circumsances policy inervenions ha arge life sages are acually effecive in achieving heir goals. Alhough marine conservaioniss and fishing ineress may no always agree on managemen objecives, no one has a sake in desroying he producive capaciy of our oceans. When policies ha aim o conserve fish populaions fail o do so, fishing ineress lose ou. Similarly, when policies ha aim o generae fishing profis fail o do so, hey can exacerbae pressure on marine resources and ulimaely cause conservaion ineress o lose ou. A sysemaic consideraion of biological and economic forces ha inerac wih managemen offers a way ou of his predicamen. 3
Many managemen failures fundamenally sem from he lack of an inerdisciplinary perspecive. I is no pah-breaking economic analysis o sugges ha people fish more when prices are high, nor is i earh-shaering ecology o sugges ha age-dependen fecundiy is a deerminan of populaion growh, bu models rarely incorporae boh forces. Life hisory models in paricular ignore fishing behavior. Age-srucured and sage-based modeling infrasrucures are relaively advanced for incorporaing new insighs from ecology bu relaively primiive for incorporaing new insighs from social and behavioral sciences. As a resul, here is a disconnec beween managemen models and he effecs of managemen on fishing flees. Fishery managers only indirecly conrol fishing moraliy, and hese indirec conrols induce behavioral responses of fishermen and vessels ha may reinforce managemen goals or undermine hose goals (Wilen e al. 2002). Managers essenially plug one leak only o see anoher spring up somewhere else. Managemen models are unable o diagnose hese behavioral responses because hey assume direc conrol of fishing moraliy. Thus, prediced success of cerain policies becomes auological, bu acual oucomes are poorly undersood and may be successes or failures. Life hisory models of exploied fish populaions ypically rea fishing moraliy as an exogenous parameer. This approach implicily assumes ha he supply of fishing effor is perfecly inelasic. Tha is, he supply curve of effor is verical. Modelers run simulaions for differen values of fishing moraliy, bu his exercise also assumes verical supply and simply explores a series of hese curves as differen scenarios. The seemingly innocuous assumpion of verical supply conflics wih a large body of empirical work; economiss consisenly find ha fishing behavior is responsive o economic opporuniies over ime and space as well as across arge species (Bocksael and Opaluch 1983; Holland and Suinen 2000; Smih 2002; Egger and Tveeras 2004). Accouning for his phenomenon is crucial for predicing he effeciveness of 4
conservaion and fishery managemen policies and requires ha fishing moraliy be made endogenous. Tha is, fishing moraliy mus be dynamically linked in life hisory modeling such ha he fish sock in he previous period and oher behavioral drivers ener ino he equaion ha predics fishing effor in he nex period, and he resuling sock pos-fishing feeds ino he prediced fishing effor in he following period. Only models ha capure he dominan causal links in bioeconomic sysems will be successful in predicing and avoiding managemen failures. This paper builds on he work of Smih e al. (2006) o illusrae how radiional managemen models in fisheries go wrong and can compromise conservaion goals by ignoring economic behavior. Specifically, he resuls show how fishing effor supply shifs a various imes during he year in response o a seasonal closure in he Gulf of Mexico gag fishery and wha a model wihou he economic behavior implicily shows abou fishing effor supply. In he nex secion, I describe he empirical model and how i differs from a sandard marix model of fish populaion dynamics. The following secion reviews he empirical applicaion in he Gulf of Mexico. Nex, I presen resuls and focus paricularly on how a seasonal closure affecs fishing behavior hroughou an enire calendar year. In our discussion, I pu forward one possible explanaion for he seemingly paradoxical resul: ha oal fishing effor acually increases in response o a seasonal closure. Finally, I draw some conclusions abou he broader role of empirical bioeconomic modeling in marine conservaion. Model Descripion The biological model is a sandard age-srucured life hisory model ha incorporaes several unique feaures o reflec sage ransiions for a sequenial hermaphrodie and a hockeysick recruimen funcion (o reflec habia limied recruimen). There are four life sages in he 5
model: recruis, immaure females, maure females, and males. Surviving new recruis all become immaure females. The number of surviving fish in each age class depends on naural and fishing moraliy. Fish of each age grow according o a von Berallanfy growh funcion. Fishable biomass (he fish sock from he poin of view of fishermen) simply adds up he number of individuals in each age class above he size limi, applying allomeric parameers o conver fish lengh o weigh. Egg producion by individual maure female is an increasing and convex funcion of fish lengh, while ferilizaion success is a concave funcion of he percenage of males in he populaion. The disincion beween a sandard life hisory model (hereafer NOECON) and he model ha accouns for fishing behavior (hereafer ECON) involves he manner in which he model reas fishing moraliy. The basic approach follows Smih and Wilen (2003) bu is cusomized for his applicaion in Smih e al. (2006). NOECON incorporaes fishing moraliy as a fixed, exogenous parameer. As a consequence, if he fish sock increase, he rae of fishing moraliy remains consan, and he oal harves of fish is simply scaled up according o fishable biomass. Moreover, when a seasonal closure occurs, he fishing moraliy rae is simply scaled back according o he number of closed days. So, if here are 15 days ou of 30 closed in a paricular monh, fishing moraliy in he NOECON model is simply half wha i would be if here were no closure. When pu in his way, he sandard managemen model sounds simplisic, and ye his is he way ha managemen models are consruced. The ECON model, in conras, makes fishing moraliy a funcion of biomass and oher facors ha influence fishing effor. I no only accouns for he reducion in poenial days fished under a seasonal closure, bu also allows he daa o deermine how pronounced ha reducion acually is. 6
The fishery daa ener ino he ECON model in wo ways. Firs, here is a saisical rouine o link fishing effor o caches (a producion funcion). The parameers are esimaed using nonlinear leas squares. The parameer esimaes of his rouine are condiional on he how effor responds o sock, which is he second rouine. Here, he model implemens logisic regression (using maximum likelihood) o esimae fishing rips as a funcion of sock, weaher, regulaions, and monhly dummies (o capure ouside opporuniies). The esimaes are condiional on he producion funcion esimaed in he firs sep. Thus, an ieraive procedure is used o re-esimae each se of parameers unil he enire parameer vecor has seled down (based on relaive Euclidean disance). See Smih e al. (2006) for more echnical deails abou he biological and economic models. Empirical Applicaion The case sudy in his paper is he Gulf of Mexico gag fishery. Gag (Myceeroperca microlepis) is a long-lived, slow-growing grouper ha is a proogynous hermaprhodie. Beween he 1970s and he 1990s, he percenage of male gag declined from 17% o 2% in Gulf of Mexico (Coleman e al. 1996; 2000). Gag is an excellen case sudy for hree reasons. Firs, here is subsanial policy variaion in sample. In order o undersand he effecs of pas policies empirically, policy changes need o ake place in a ime horizon over which we can measure oucomes. The Gulf of Mexico Fishery Managemen Council (GMFMC) provides his in-sample policy variaion by forming wo marine reserves, changing he size limi for gag, and implemening a seasonal closure during he gag spawning season. Second, I have buil an exensive relaional daabase of fisheries micro-daa. The daabase conains U.S. NOAA Fisheries fishing logbook daa (en years of daily observaions), Florida landings ickes, NOAA 7
weaher buoys, online records of policy decisions of he GMFMC, couny-level unemploymen, and an original survey of reef-fish fishery capains ha I conduced in 2005. This daa se allows one o analyze economic subsiuion across space, ime, and arge species in response o policy changes. Here he focus will be only on iner-emporal subsiuion due o he echnical challenges of inegraing endogenous fishing moraliy ino a life hisory model, and he survey daa will no be used. Third, a published empirically-based life hisory analysis exiss for gag (Heppell e al. 2006). In previous work, colleagues and I adaped he Heppell e al. (2006) model o incorporae iner-emporal behavioral responses o changes in he fish sock and he policy environmen (Smih e al. 2006). Using an ieraive economeric rouine, we esimae behavioral parameers, a parameers from he fishing producion funcion, and one key biological parameer (an iniial condiion on he shape of he iniial abundance size disribuion). See Smih e al. (2006) for addiional echnical deails of he esimaion. The focus of he curren paper is o provide a heurisic explanaion of how endogenous fishing moraliy affecs managemen oucomes using an analogy o supply curves and supply funcions. Resuls Table 1 repeas he key qualiaive findings in Smih e al. (2006). The message here is simple; all of he qualiaive conclusions abou he policy reverse when one accouns for behavioral responses of he fishing flee. A sandard managemen model (NOECON) predics ha a seasonal closure will reduce fishing effor, increase biomass, and also reduce pressure on large, older fish. The laer effec would hus address concerns abou he gag sex raio. Incorporaing economic behavior (ECON), however, leads o he opposie conclusion for every 8
indicaor. I is no surprising ha economic behavior offses some of he policy impacs, bu in his case, behavior more han offses he policy. The policy ha aims o proec socks acually harms hem. The dynamics of behavior are depiced in Figure 1. For he NOECON model, small flucuaions reflec only differences in number of days in each monh (e.g. January has 31, bu February has 28 or 29). The large dips illusrae he implied reducion in fishing effor during he seasonal closure. The ECON model ness all of hese feaures and illusraes boh shor- and long-run dynamics of effor. Shor dynamics reflec seasonal paerns in prices, openings and closings in he red snapper season, opporuniies ouside of fishing, and weaher. Long dynamics reflec adjusmens in he sock ha are endogenously deermined by fishing pressure. Because he qualiaive reversal in Table 1 is so surprising, i is worh exploring wheher he model predicions, and no jus he parameer esimaes, are saisically significan. To his end, he poin esimaes of he biological parameers are fixed, and Figure 2 explores he sensiiviy of model predicions abou average biomass o he behavioral model parameers. Specifically, Figure 2 depics 5,000 Mone Carlo simulaions in which behavioral parameers are drawn from heir empirical disribuion (by aking he poin esimae parameer vecor and adding a vecor of randomly drawn, independen sandard normal variables muliplied by he Cholesky facorizaion of he covariance marix). In all cases, he qualiaive predicions of ECON and NOECON reverse; he seasonal closure increases average biomass in he NOECON model bu acually reduces biomass in he ECON model. The hisograms for he Closure and No Closure scenarios do no even overlap in eiher he ECON or NOECON models. Figure 3 illusraes he implied average fishing effor supply curves for he ECON and NOECON model. Of ineres here is he supply of fishing effor in response o he sock of fish. 9
One can hink of he sock of fish as a price and number of fishing rips as a quaniy. A supply curve is disinguished from a supply funcion in ha he supply curve fixes all argumens of he supply funcion excep price, which in his case is he level of biomass. The shaded box illusraes he region over which here is in-sample variaion in gag biomass and fishing rips. In ECON, fishing moraliy is endogenous and responds posiively o he sock (he blue line). As such, here is some elasiciy in he supply curve of effor. The NOECON model, in conras, assumes exogenous effor supply (he green line). Thus, he NOECON supply curve is verical, i.e. perfecly inelasic. The qualiaive consequences for managemen are clear. Any increase in he biomass due o some managemen acion will simulae more fishing effor, and in he longrun, aenuae a leas par of he increased sock. The exen of his aenuaion will depend on he empirical applicaion. My applicaion in Figure 3 suggess ha his aenuaion will be subsanial because he supply curve is relaively fla (elasic in he sample region), implying ha effor will increase a lo in response o a sock increase. Figure 4 shows how modeling fishing effor endogenously affecs conclusions abou seasonal closures. The gag spawning season spans January hrough April, and since gag aggregae o spawn, all oher hings being equal, one expecs a concenraion of fishing effor during he spawning season. The op panel shows he monh of March. The shallow-waer grouper closure is in effec from unil March 15, so 15 ou of 31 fishing days are eliminaed. The assumed change in NOECON is hus a horizonal shif in he supply curve (solid green o doed pink). However, he real change empirically is shown by he ECON model (solid blue o doed red). To see wha a difference his makes, a horizonal black line depics he average biomass in sample. From his, one can see he shor-run effor change in he ECON model. Thus, he real 10
reducion (ECON model) in fishing effor due o he closure during March is less han half of he assumed reducion in he sandard managemen model (NOECON). The sory so far is ha he ECON model shows how he policy is less effecive han managers expeced, bu he sory does no end here. The boom panel depics fishing effor response in April (sill during he spawning season bu afer he seasonal closure). Because here is no seasonal closure in April, he NOECON model assumes ha here is no change in fishing effor, and he exogenous effor supply curve does no shif. Empirically, however, effor supply does shif in he ECON model (blue o doed red). Unlike in he op panel, effor supply in April shifs ouward. Tha is, here is more fishing effor in April due o he closure in February and March. Economically, his is no surprising in and of iself. Some of he reducion in fishing effor from earlier in he year is displaced ino laer pars of he year. Figure 5 repeas he analysis in Figure 4, bu here he monh of May is examined because i falls afer he spawning season. As in he boom panel of Figure 4, he NOECON model predics ha fishing effor will no respond o he seasonal closure. The ECON model, however, shows in increase in effor. The increase is less pronounced han he increase during April, bu i exiss noneheless. A he margin, his effec is no surprising economically, hough i runs couner o he way in which sandard managemen models incorporae fishing effor. Wha is surprising is ha he oal effor increase aggregaed over he year more han offses he reducion of effor due o he closure (Table 1). Discussion Naurally, he immediae quesion o ask is why fishermen increase oal effor in response o he seasonal closure. Afer all, Figures 3-5 show ha supply of fishing effor is 11
upward sloping wih regard o he fish sock, one of he key deerminans of he profiabiliy of fishing. This secion develops a hree-period model of effor supply for an individual fisherman. The goal is o show ha he seemingly counerinuiive resul of increased oal effor can be explained by consumpion smoohing combined wih a subsisence consrain. A he onse, i is imporan o noe a cavea: his explanaion is no direcly modeled in he empirical analysis. Thus, o formally es his explanaion would require more saisical analysis and possibly addiional daa on incomes of fishermen and employmen opporuniies in oher fisheries (and ouside of fishing). Previous analyses in he lieraure have focused on shor-run profi maximizaion in response o managemen. For insance, Anderson (1999) shows ha binding regulaions reduce profis in he shor run bu can, in some circumsances, increase oupu. Though no modeled direcly, he discusses individual preferences for leisure as anoher poenially imporan behavioral deerminan. In a more recen paper, Anderson (2004) argues ha moivaions beyond profi maximizaion can lead o fishing sraegies ha arge harves or profi goals, and he shows ha goal achievemen can explain seemingly perverse economic behavior. I focus on laborleisure radeoffs in a uiliy maximizaion framework and find ha binding regulaions, while reducing uiliy, can increase or decrease oal fishing effor. The implicaion is ha seasonal closures can poenially induce a perverse behavioral response in many fisheries hroughou he world. The empirical resul in he gag fishery is jus one example. Suppose ha fishermen derive uiliy in each period () from a composie consumpion good (C ) wih price normalized o one and leisure (L ). The hree periods ogeher consiue a calendar year, and for simpliciy, assume away ime preferences. The fisherman has a oal ime allomen in each period ( L ) o divide beween fishing effor (E ) and leisure. Assume ha 12
fisherman earn income (I ) only from fishing. In each period, revenue is he produc of he price of fish (p ), he fish sock (X ), effor exered, and a cahabiliy coefficien (q ). Time-varying cachabiliy explicily capures produciviy differences across periods. Coss are proporional o effor wih a parameer c. Noe ha c includes operaing coss like fuel and bai bu does no include he cos of a fisherman s ime, which is implici in he labor-leisure radeoff. Income from fishing can be wrien as: (1) I = pqex ce = we, where w = pq X c. In a simple model srucure like he one presened here, one can hink of cachabiliy differences also represening ime-varying opporuniy coss due o subsiue economic aciviies or differences in ypical weaher condiions during periods ha would affec an individual s paern of fishing effor over ime. Seasonal closures in each period consrain he individual s oal effor in he period ( E ). MAX To characerize he siuaion of many owner-operaor fishermen in indusrialized counries as well as arisanal fishermen in developing counries, I inroduce a subsisence consrain ( C MIN ). For arisanal fishermen, MIN C may represen he need o generae enough income o feed oneself and one s family. More likely in indusrial fishing is ha a minimum income is required in each period o make boa paymens, dock renal fees, home morgage paymens, and oher regular household expenses. To add more realism, he model allows saving and borrowing in periods 1 and 2 (S ). Essenially, fishermen can smooh consumpion in he hree periods by shifing heir incomes across periods. However, he calendar year budge is binding. Deb service on borrowing across years e.g. for a boa, home, or car is implicily folded ino he subsisence consrain. Fishermen in his model can save or borrow in he shor run o accommodae heir labor-leisure 13
preferences or o mee subsisence requiremens, bu hey canno carry debs for non-capial purchases across years. The resuling uiliy maximizaion problem is: (2) max U( C, L ) 3 = 1 subjec o: C1 = we 1 1 S1 C2 = w2e2 + S1 S2 C3 = w3e3+ S2 MIN C C, L = L E, 0 E L, MAX E E, This model is general enough capure spawning pre-closure, spawning pos-closure, and posspawning porions of a year hrough ime-varying ime allomens and cachabiliy coefficiens. I illusrae he model in equaion (2) wih a numerical simulaion. Suppose ha he uiliy funcion is Cobb-Douglas wih parameers 0.6 (for consumpion) and 0.4 (for leisure). Wihou loss of generaliy, I vary w o represen cachabiliy differences over ime. Periods 1 and 2 ake place during spawning, whereas period 3 akes place afer spawning. I se w1 = w2 = 1 and w 3 = 0.75 o capure he increased cachabiliy when fish aggregae o spawn. For simpliciy, we se L1 = L2 = L3 = 1. This assumpion can easily be relaxed o capure a siuaion in which he period afer spawning is longer han eiher of he periods ha divide he spawning season. This would mach he empirical realiy of he gag fishery. Gag aggregae o spawn during he firs 14
four monhs of he year, bu he season remains open he remainder of he calendar year. The resuls below also hold for siuaions in which ime allomens vary across periods. Assume ha he regulaor implemens a seasonal closure in period 2 bu no in periods 1 or 3. I explore how he exen of he closure affecs fishing effor decisions for hree differen values of he subsisence parameer. For each case, we opimize he model using Malab s nonlinear consrained opimizaion rouine (FMINCON). The resuls are depiced in Figure 6. The primary lesson from his numerical simulaion is ha oal fishing effor can increase, decrease, or remain he same in response o a seasonal closure. Fixing all oher parameers, he effec of he closure on oal fishing effor aggregaed across all hree periods depends on he exen of he closure and he subsisence requiremen. In all hree cases, he seasonal closure has no effec on oal annual fishing effor if he consrain on maximum period 2 effor does no bind (reducions ha are less han 50% of he period 2 season). Fishermen essenially can subsiue wihin period o mainain heir opimal labor-leisure radeoffs. While his resul seems obvious once saed, i illusraes how managers canno coun on effor reducions in response o marginal changes in seasons. In a real fishery, he desirabiliy o subsiue wihin a period will be limied by weaher condiions and oher economic opporuniies. Sill, if one imagines an exreme case say a single day closure in a wo-monh period i is difficul o believe ha fishing effor will no find an opporuniy o subsiue ino anoher one of he open days. For he region in which reduced period 2 effor binds, he hree cases diverge MIN qualiaively. When C = 0.4, subsisence never binds. As a resul, oal fishing effor is always lower han he baseline. This case corresponds o he convenional wisdom ha effor declines wih a seasonal closure. Neverheless, i is imporan o noe ha fishermen subsiue 15
some effor ino periods 1 and 3; he subsiuion is jus no enough o make up for he effor reducion in period 2. Subsiuion occurs because he marginal uiliy of consumpion in period 2 wih he closure is higher han he marginal uiliy of leisure in oher periods ha could be foregone in order o redisribue income o period 2. Thus, he sandard managemen model assumpion ha effor decreases proporionally o a seasonal closure is unenable. MIN When C = 0.6, subsisence always binds. Tha is, consumpion in each period is he subsisence level. In his case, oal effor increases as he resricion on fishing effor in period 2 ighens. This phenomenon is due o he fac ha fishing is less producive in period 3. Thus, fishermen mus more han compensae for reduced effor in period 2 wih increases in periods 1 and 3. MIN When C = 0.5, subsisence only binds once fishing in period 2 is resriced by more han 80%. Beween a resricion of 50% and 80%, oal effor declines. Afer 80%, oal effor sars o rise and evenually reaches a level ha is higher han he baseline. Eiher of he cases in which subsisence binds closely mirrors he empirical oupu in which he coefficiens on he closure dummies are posiive and large enough o increase oal fishing effor (Smih e al. 2006). Thus, a simple uiliy-heoreic model can explain why oal effor increases in response o a seasonal closure. The heoreical model in equaion (2) produces his resul wihou any coss of shor-run borrowing. If one considers he possibiliy ha shorrun borrowing is cosly for fishermen, he empirical resul emerges as an even more likely oucome. In order o mee long-erm capial deb service, e.g. boa paymen and home morgage, as well as basic household expenses, a fisherman may incur shor-run credi card deb during he seasonal closure. To repay his deb and he associaed ineres, he fisherman needs o generae more revenues in he periods afer he closure han would have been required wihou he closure. 16
This is exacly wha happens empirically, hough here is no way o es direcly he effec of shor-run subsisence consrains in he curren empirical modeling framework. Mos economiss, and probably mos fisheries managers, migh express discomfor wih he idea ha if you consrain people more, hey will fish more inensively. I may be easier o accep a subsisence consrain in a developing counry conex, for insance. Bu, is his reasonable for U.S. fishermen? One piece of supporing evidence ha is somewha orhogonal o our empirical analysis is how fishermen respond o quesions abou fishing in bad weaher. In a 2005 mail survey of Gulf reef fish capains (46% response rae ou of all regisered permi holders), I find ha 80% of fishermen agree or srongly agree wih he saemen Seasonal closures force fishermen o fish in bad weaher. Thus, reef fish fishermen in heir own claims suppor he noion ha a seasonal closure changes heir marginal values. Empirically, seasonaliy in prices also suppors he mechanism ha is operaing in his heoreical explanaion. Averaged over our sample period, gag price is 6.8% higher during spawning season han pos-spawning. For jus 2003, he spawning season premium is 5.2%. Thus, gag fishermen, in relaive erms, are losing more revenue wih a seasonal closure during spawning han if here were a closure some oher ime during he year. If fishermen really are aemping o mee some income arge, he empirical paern of price will exacerbae he oal effor inensificaion. Conclusion The long-erm goal of his research is o refine our undersanding of which species are vulnerable o overexploiaion and exincion by recasing vulnerabiliy as a bioeconomic phenomenon and no jus a biological one. To wha exen will marine reserves, seasonal 17
closures, size limis, and oher policies acually proec species a risk once we fully accoun for he behavioral responsiveness of fishing flees? This paper akes an imporan sep owards answering ha quesion by rejecing he reducionis approach of fishery managemen models in which humans are assumed o be unresponsive o changing economic condiions. Insead, fishing moraliy is made endogenous. All fisheries are coupled sysems wih biological and economic componens, bu wheher explici modeling of he economics is of firs- or second-order imporance is likely o depend on a wide range of facors. When will biological inuiion alone be sufficien for proecing criical life sages, and when will economic forces rump his inuiion? In his paricular applicaion, he behavioral response of fishermen is enough o undermine he inended oucome of a seasonal closure. This appears o resuls from boh a direc response o he policy iself and he elasic effor response o any policy ha grows socks. The science of marine conservaion currenly knows lile abou hese issues in general, bu my applicaion illusraes ha conservaion and economic behavior canno be separaed a priori. To harness he power of life hisory modeling for fuure marine conservaion, i is essenial o fill his knowledge gap. References Anderson, L.G. 1999. The Microeconomics of Vessel Behavior: A Deailed Shor-Run Analysis of he Effecs of Regulaion. Marine Resource Economics 14:129-50. Anderson, L.G. 2004. Open-access Fishery Performance when Vessels Use Goal Achievemen Behavior. Marine Resource Economics 19:439-58. Armsworh, P.R. 2001. Effecs of Fishing on a Proogynous Hermaphrodie. Canadian Journal of Fisheries and Aquaic Sciences 58: 568-78. Berkeley, S. A., C. Chapman, and S. M. Sogard. 2004. Maernal age as a deerminan of larval growh and survival in a marine fish, Sebases melanops. Ecology 85:1258-1264. Beveron, R.J.H. and S.J. Hol. 1957. On he Dynamics of Exploied Fish Populaions. London: Chapman and Hall (reprined in 1993). 18
Bocksael, N.E. and J.J. Opaluch. 1983. Discree modeling of supply response under uncerainy: he case of he fishery. Journal of Environmenal Economics and Managemen 10:125-37. Bosford, L.W., J.C. Casilla, and C.H. Peerson. 1997. The Managemen of Fisheries and Marine Ecosysems. Science 277: 509-15. Coleman, F.C., C.C. Koenig, G.R. Hunsman, J.A. Musick, A.M. Eklund, J.C. McGovern, R.W. Chapman, G.R. Sedberry, and C.B. Grimes. 2000. Long-lived reef fishes: he groupersnapper complex. Fisheries 25:14-21. Coleman, F. C., C. C. Koenig, and L. A. Collins. 1996. Reproducive syles of shallow-waer grouper (Pisces: Serranidae) in he easern Gulf of Mexico and he consequences of fishing spawning aggregaions. Environmenal Biology of Fishes 47:129-141. Conover, D.O. and S.B. Munch. 2002. Susaining fisheries yields over evoluionary ime scales. Science 297:94-96. Curis, R. and R.L. Hicks. 2000. The cos of sea urle preservaion: he case of Hawaii s pelagic longliners. American Journal of Agriculural Economics 82: 1191-97. Egger, H. and R. Tveeras. 2004. Sochasic producion and heerogeneous risk preferences: commercial fishers gear choices. American Journal of Agriculural Economics 86:199-212. Heppell, S.S., H. Caswell, and L.B. Crowder. 2000. Life Hisories and Elasiciy Paerns: Perurbaion Analysis for Species wih Minimal Demographic Daa. Ecology 81: 654-65. Heppell, S.S., S.A. Heppell, F.C. Coleman, and C.C. Koenig. 2006. Models o compare managemen opions for a proogynous fish. Ecological Applicaions 16(1):238-49. Jackson, J.B.C., M.X. Kirby, W.H. Berger, K.A. Bjorndal, L.W. Bosford, B.J. Bourque, R.H. Bradbury, R.E. Cooke, J. Erlandson, J.A. Eses, T.P. Hughes, S. Kidwell, C.B. Lange, H.S. Lenihan, J.M. Pandolfi, C.H. Peerson, R.S. Seneck, M.J.Tegner, R.R. Warner. 2001. Hisorical Overfishing and he Recen Collapse of Coasal Ecosysems. Science 293: 629-637. Myers, R.A. and B. Worm, Rapid Worldwide Depleion of Predaory Fish Communiies, Naure 423, 280-283, 2003. Naional Marine Fisheries Service (1999), Our Living Oceans: Repor on he saus of U.S. living marine resources. U.S. Deparmen of Commerce, NOAA Tech. Memo. NMFS-F/SPO-41 Naional Research Council, Susaining Marine Fisheries, Washingon, D.C.: Naional Academy Press, 1999. 19
Palumbi, S.R. 2004. Why mohers maer. Naure 430:621-22. Pew Oceans Commission. 2003. America's Living Oceans: Charing a Course for Sea Change. A Repor o he Naion. Pew Oceans Commission, Arlingon, Virginia. Pauly, D., V. Chrisensen, J. Dalsgaard, R. Froese, and F. Torres, Jr.1998. Fishing Down Marine Food Webs. Science 279: 860-863. Smih, M.D. (2002), Two Economeric Approaches for Predicing he Spaial Behavior of Renewable Resource Harvesers, Land Economics 78(4), 522-538. Smih, M.D. and J.E. Wilen. 2003. Economic Impacs of Marine Reserves: The Imporance of Spaial Behavior. Journal of Environmenal Economics and Managemen 46: 183-206. Smih, M.D., J. Zhang, and F.C. Coleman, 2006. Srucural Economeric Modeling of Fisheries wih Complex Life Hisories, Working Paper. Unied Naions Food and Agriculure Organizaion. 2000. The Sae of he World Fisheries and Aquaculure. online a hp://www.fao.org/docrep/003/x8002e/x8002e00.hm. Wilen, J.E. 2006. Why fisheries managemen fails: reaing sympoms raher han he cause. Bullein of Marine Science 78(3): 529-546. Wilen, J.E., M.D. Smih, D. Lockwood, and L. Bosford. 2002. Avoiding Surprises: Incorporaing Fishermen Behavior ino Managemen Models. Bullein of Marine Science 70(2), 553-575. 20
2000 1800 1600 Gag Fishing Effor Econ Closure NoEcon Closure Econ No Closure NoEcon No Closure Number of Trips 1400 1200 1000 800 600 400 0 50 100 150 200 250 300 Monh Figure 1 Dynamic Effec of he Seasonal Closure Policy Firs 120 Monhs are In-Sample 21
Frequency 3000 2500 2000 1500 1000 Biomass Accouning for Economic Behavior Closure No Closure 500 0 1.055 1.06 1.065 1.07 1.075 1.08 1.085 1.09 Average Biomass (kg) x 10 7 Biomass Assuming No Economic Behavior 2500 2000 Closure No Closure Frequency 1500 1000 500 0 1.02 1.03 1.04 1.05 1.06 1.07 1.08 Average Biomass (kg) x 10 7 Figure 2 Biomass Sensiiviy Analysis 22
18 Fishing Effor Supply Gag Biomass (million kg) 16 14 12 10 8 6 4 2 In-Sample Endogenous Exogenous 0 0 1000 2000 3000 4000 5000 Commercial Gag Fishing Trip Days Figure 3 Average Fishing Effor Supply in a Life Hisory Managemen Model - Wih Endogenous Fishing Moraliy Effor Supply is no Verical 23
15 Fishing Effor Supply in March Gag Biomass (million kg) 14 13 12 11 10 9 Endogenous, Closure Endogenous, No Closure Exogenous, Closure Exogenous, No Closure 8 0 500 1000 1500 2000 Real Change (-) Assumed Change (-) 15 Fishing Effor Supply in April 14 Gag Biomass (million kg) 13 12 11 10 9 Endogenous, Closure Endogenous, No Closure Exogenous, Closure Exogenous, No Closure 8 0 500 1000 1500 2000 2500 3000 Real Change (+) Assumed Change = 0 Figure 4 Fishing Effor Supply in a Life Hisory Managemen Model During he Spawning Season The x-axis repors prediced number of gag fishing rips in he monh. The wo panels show March (half of he monh closed) and April (none of he monh closed) 24
15 Fishing Effor Supply in May Gag Biomass (million kg) 14 13 12 11 10 9 Endogenous, Closure Endogenous, No Closure Exogenous, Closure Exogenous, No Closure 8 500 1000 1500 2000 2500 3000 3500 Real Change (+) Assumed Change = 0 Figure 5 Fishing Effor Supply in a Life Hisory Managemen Model Afer he Spawning Season The x-axis repors prediced number of gag fishing rips in he monh. The panel shows May (none of he monh closed) 25
Toal Effor Can Increase or Decrease in Response o a Season Closure 40% Change in Toal Effor for he Year 30% 20% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% -10% -20% Cmin=0.5 Cmin=0.6 Cmin=0.4-30% Reducion in Period 2 Season Figure 6 - A Possible Theoreical Explanaion for he Empirical Resuls Toal Fishing Effor in a Year Can Increase or Decrease in Response o a Seasonal Closure if Fishermen Face Minimum Income Consrains 26
Table 1 - Bioeconomic Policy Simulaion Using Poin Esimaes (from Smih e al. 2006) Comparing 1-monh Spawning Closure o No Closure 25 Years Toal, 10 Years in-sample No Grouper Spawning Qualiaive Closure Closure Change Change Modeling Economic Behavior Average Biomass (millions kg) 10.798 10.599-0.199 - Average Number of Big Fish (> 10 years) 66,160 64,387-1,773 - Toal Cach (millions kg) 15.719 15.958 0.239 + Toal Trip Days 293,180 306,500 13,320 + Weighed Cach Per Uni Effor 53.615 52.066-1.549 - Ending Percenage of Males (maure fish) 0.0616 0.0601-0.002 - No Economic Behavior Average Biomass 10.303 10.735 0.432 + Average Number of Big Fish (> 10 years) 61,609 65,277 3,668 + Toal Cach 16.215 15.542-0.673 - Toal Trip Days 315,650 296,980-18,670 - Weighed Cach Per Uni Effor 51.370 52.332 0.962 + Ending Percenage of Males (maure fish) 0.0603 0.0633 0.003 + 27