Abstract:Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in… Show more
“…Bass anglers are quite price sensitive; a one-percent change in trip costs diminishes number of bass fishing days by approximately 0.15 percent, ceteris paribus. This is in line with several previous elasticity estimates for angling in Ireland, though these are the first estimates specifically for sea bass angling (Curtis, 2002;Curtis and Stanley, 2016;Curtis and Breen, 2017). The price elasticity for sea angling from Hynes et al (2017) is an exception, where the implied elasticity is -0.6.…”
Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract: The relationship between angling effort and catch is well-recognised, in particular that effort influences catch rates. But increased catch, which can be considered an attribute of fishery quality, may influence effort in terms of number of fishing trips. This suggests bi-directional feedback between catch and effort. In many travel cost applications little attention has been given to this endogeneity problem. In this paper we expand the application of structural equation models to address this issue by jointly estimating demand (effort) and catch functions. Using a cross-section dataset of sea bass anglers we propose two separate joint models. First, we include expected catch as an explanatory variable in the demand equation. In the second, we reverse the causality and use the expected number of fishing days as a covariate in the catch function. The two approaches produce similar model estimates, and perform better at predicting anglers' catch and effort than standard models. The findings confirm that 'catch & release' does not curtail fishing activity and that sea bass angling is highly valued. Furthermore higher catches result more days fished, on average in a 2:1 ratio. Whereas on average, an additional fishing day results in 3-4 additional bass caught.
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“…Bass anglers are quite price sensitive; a one-percent change in trip costs diminishes number of bass fishing days by approximately 0.15 percent, ceteris paribus. This is in line with several previous elasticity estimates for angling in Ireland, though these are the first estimates specifically for sea bass angling (Curtis, 2002;Curtis and Stanley, 2016;Curtis and Breen, 2017). The price elasticity for sea angling from Hynes et al (2017) is an exception, where the implied elasticity is -0.6.…”
Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract: The relationship between angling effort and catch is well-recognised, in particular that effort influences catch rates. But increased catch, which can be considered an attribute of fishery quality, may influence effort in terms of number of fishing trips. This suggests bi-directional feedback between catch and effort. In many travel cost applications little attention has been given to this endogeneity problem. In this paper we expand the application of structural equation models to address this issue by jointly estimating demand (effort) and catch functions. Using a cross-section dataset of sea bass anglers we propose two separate joint models. First, we include expected catch as an explanatory variable in the demand equation. In the second, we reverse the causality and use the expected number of fishing days as a covariate in the catch function. The two approaches produce similar model estimates, and perform better at predicting anglers' catch and effort than standard models. The findings confirm that 'catch & release' does not curtail fishing activity and that sea bass angling is highly valued. Furthermore higher catches result more days fished, on average in a 2:1 ratio. Whereas on average, an additional fishing day results in 3-4 additional bass caught.
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“…Note that in our dataset, as reported in Table 3, the mean number of trips is 3.02, while the standard deviation is 3.67, placing the variance at 13.5, over four times the value of the mean. Several others also shown that the NB model performs better than the Poisson model [15,25,26]. Nonetheless, in our estimations the two model estimates are quite similar in magnitude and significance level.…”
There is growing public support for an outdoor, nature-based urban park, which offers the local population a wide range of recreational services for an improved quality of life. This study estimates the economic value of recreational benefits for the case of a lake-based urban park known as Taman Tasik Cempaka (TTC) in Bandar Baru Bangi in Selangor, Malaysia. The Travel Cost Method (TCM) was used to generate the demand function for park visitation and the recreational benefits were estimated using the Poisson and Negative Binomial (NB) models. The net benefits of recreation per visitor were evaluated at MYR 6.2 per trip while the price elasticity of demand was highly inelastic at −0.48. The result provides support for the imposition of an entrance fee and the subsequent revenue collection may be used for park upkeep and conservation.
“…Numerous studies have analysed the impact of water quality on recreational water-use demand. Topics have included angling (Bockstael, Hanemann, and Kling 1987;Curtis and Stanley 2016), swimming (Needelman and Kealy 1995), beach visits (Hanley, Bell, and Alvarez-Farizo 2003), boating (Lipton 2004) and many other water-based recreational activities (Binkley and Hanemann 1978;G€ url€ uk and Rehber 2008;Hynes, Hanley, and Scarpa 2008;Paudel, Caffey, and Devkota 2011). A contribution of this paper is its use of revealed user data to determine which water quality measures users are most responsive towards and whether the response varies by recreational activity.…”
This study combines routinely collected water quality data from Ireland and an on-site survey of waterway users to evaluate whether trip duration is responsive to changes in water quality. Four categories of recreational users are considered: anglers, boaters, other water sports (e.g. rowing, swimming, canoeing, etc.) and land-based activities at water sites, specifically walking and cycling. Water quality measures included in the analysis include Water Framework Directive (WFD) status, biochemical oxygen demand, ammonia, phosphorus and faecal coliform. The analysis finds evidence that higher levels of recreational demand (i.e. trips of longer duration) occur at sites with better water quality. However, we also find no statistical association between the overall WFD status and the duration of the recreational trip, which indicates that WFD status is of limited practical use for recreational users.
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