Application of a hurdle negative binomial count data model to demand for bass fishing in the southeastern United States

Bilgiç, Abdülbaki; Florkowski, Wojciech J.

doi:10.1016/j.jenvman.2006.10.009

Cited by 34 publications

(24 citation statements)

References 33 publications

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“…While Hynes et al (2017) model all sea angling trips they find that anglers targeting sea bass undertake a higher number of angling trips than other anglers. The price elasticity estimates here are also in line with estimates for sea bass anglers in the southeastern United States, where Bilgic and Florkowski (2007) estimate a price elasticity -0.02. Results of the welfare analysis are shown in Table 5, with consumer surplus (CS ) per day calculated using equation 2.…”

Section: Econometric Modelssupporting

confidence: 84%

“…Although the species is closely managed to ensure sustainable stocks, there is relatively little research on angling activity. In the academic literature Bilgic and Florkowski (2007) is the only paper identified that specifically models demand for bass angling. This paper makes two contributions to the literature.…”

Section: Discussionmentioning

confidence: 99%

“…For every two additional sea bass caught the number of days sea bass fishing per annum increases by one. Bilgic and Florkowski (2007) also calculate marginal impacts of catch on sea bass trips in southeastern United States. In elasticity terms they estimate that a 1-percent change in the number of fish caught increases the number of bass fishing trips by 0.06 percent.…”

Section: Econometric Modelsmentioning

confidence: 99%

“…The CS estimates are somewhat higher than comparable estimates from the literature. For example, Bilgic and Florkowski (2007) estimate mean CS of $161 for bass angling in the southeastern United States, while Hynes et al (2017) estimate a CS for sea angling in Ireland of e242-261. Lawrence (2005), using a choice experiment methodology, find that English anglers are willing to pay more for additional catches of sea bass compared to cod, mackerel, or other species.…”

Section: Econometric Modelsmentioning

confidence: 99%

See 3 more Smart Citations

Sea Bass Angling in Ireland: A Structural Equation Model of Catch and Effort

Grilli

Curtis

Hynes

et al. 2018

Ecological Economics

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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. Terms of use: Documents in

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Section: Econometric Modelssupporting

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Section: Econometric Modelsmentioning

confidence: 99%

Section: Econometric Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Sea Bass Angling in Ireland: A Structural Equation Model of Catch and Effort

Grilli

Curtis

Hynes

et al. 2018

Ecological Economics

View full text Add to dashboard Cite

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“…The possibility to distinguish the participation from the integer level at which this participation takes place and the ability to correctly deal with overdispersion makes a case in favor of the Hurdle Zero-Truncated Negative Binomial model (HZTNB). Our analysis will thus be based on this model, following Bilgic and Florkowski (2007) and Shrestha et al (2002). This choice will be confirmed by the ad hoc statistical tests reported in Section 5.…”

Section: Empirical Approachmentioning

confidence: 78%

A travel cost assessment of the demand for recreation in Swiss forests

Borzykowski

Baranzini

Maradan

2017

Rev Agric Food Environ Stud

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This paper analyzes the demand for recreation in Swiss forests using the individual travel cost method. We apply a two-steps approach, i.e., a hurdle zerotruncated negative binomial model, that allows accounting for a large number of nonvisitors caused by the off-site phone survey and over-dispersion. Given the national scale of the survey, we group forest zones to assess consumer surpluses and travel cost elasticities for relatively homogeneous forest types. We find that forest recreation activities are travel cost inelastic and show that recreation in Swiss forests provides large benefits to the population. The most populated area is associated with greater consumer surpluses, but the lack of recreational infrastructure may cause a lower recreational benefit in some zones. For these zones, recreational benefits may be lower than costs caused by maintenance. More efficient management would require either improving recreational infrastructure thus increasing benefits, or switching the forest status from recreational to biodiversity forest hence decreasing management costs.

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Trends and Geographic Patterns in Drug-Poisoning Death Rates in the U.S., 1999–2009

Rossen

Khan

Warner

2013

American Journal of Preventive Medicine

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Background Drug poisoning mortality has increased substantially in the U.S. over the past 3 decades. Previous studies have described state-level variation and urban–rural differences in drug-poisoning deaths, but variation at the county level has largely not been explored in part because crude county-level death rates are often highly unstable. Purpose The goal of the study was to use small-area estimation techniques to produce stable county-level estimates of age-adjusted death rates (AADR) associated with drug poisoning for the U.S., 1999–2009, in order to examine geographic and temporal variation. Methods Population-based observational study using data on 304,087 drug-poisoning deaths in the U.S. from the 1999–2009 National Vital Statistics Multiple Cause of Death Files (analyzed in 2012). Because of the zero-inflated and right-skewed distribution of drug-poisoning death rates, a two-stage modeling procedure was used in which the first stage modeled the probability of observing a death for a given county and year, and the second stage modeled the log-transformed drug-poisoning death rate given that a death occurred. Empirical Bayes estimates of county-level drug-poisoning death rates were mapped to explore temporal and geographic variation. Results Only 3% of counties had drug-poisoning AADRs greater than ten per 100,000 per year in 1999–2000, compared to 54% in 2008–2009. Drug-poisoning AADRs grew by 394% in rural areas compared to 279% for large central metropolitan counties, but the highest drug-poisoning AADRs were observed in central metropolitan areas from 1999 to 2009. Conclusions There was substantial geographic variation in drug-poisoning mortality across the U.S.

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Application of a hurdle negative binomial count data model to demand for bass fishing in the southeastern United States

Cited by 34 publications

References 33 publications

Sea Bass Angling in Ireland: A Structural Equation Model of Catch and Effort

Sea Bass Angling in Ireland: A Structural Equation Model of Catch and Effort

A travel cost assessment of the demand for recreation in Swiss forests

Trends and Geographic Patterns in Drug-Poisoning Death Rates in the U.S., 1999–2009

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