Assessing the economic impact of waterfowl hunting in Mississippi

Santos, Xiana T.; Grado, Stephen C.; Hunt, Kevin M.

doi:10.1108/ijse-04-2014-0068

Cited by 6 publications

(9 citation statements)

References 14 publications

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“…It was beyond the scope of this research to determine what the optimal period before replication would be; however, it would be prudent to survey the angler population to generate new expenditure profiles more frequently than every 20 years. Quantity demanded for certain goods may be affected due to (1) changes in product availability as retail stores open or close, (2) technological advances, or (3) changes in preferences due to unknown influences (Santos et al 2016).…”

Section: Discussionmentioning

confidence: 99%

“…Quantity demanded for certain goods may be affected due to (1) changes in product availability as retail stores open or close, (2) technological advances, or (3) changes in preferences due to unknown influences (Santos et al. 2016).…”

Section: Discussionmentioning

confidence: 99%

“…As a result, this methodology could be a response to the critique that input–output models ignore changes over time, with current models having a short life span (Santos et al. 2016). With adjusted expenditures, properly collected data, and updated IMPLAN models, researchers would be able to account for changes to expenditures and sector interrelationships.…”

Section: Discussionmentioning

confidence: 99%

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Exploring the Use of Price Indices to Adjust Angler Expenditure Profiles over Time

Parker

Hunt

Grado

et al. 2023

N American J Fish Manag

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This research evaluated a price-adjusting methodology to update trip expenditure information annually for economic impact analysis. Historic expenditure profiles for nonlocal Texas anglers and nonresident (out-of-state) anglers were generated from 1995 angler survey data collected at Lake Fork Reservoir; these data were then adjusted over a 20-year period using price indices produced by the U.S. Bureau of Labor Statistics. To determine whether the price adjustment methodology was appropriate for angler expenditures, a replication of the 1995 angler survey was conducted in 2015 to compare adjusted expenditures to actual expenditures. Preliminary results concluded that expenditure profiles for nonlocal and nonresident anglers were different between the two periods but also suggested that significant differences within angler groups existed between the two angler surveys, so anglers were regrouped based on trip length rather than origin of trip. Adjusted average daily expenditure total for 1-d trips during the 1995 study (US$69.38) was similar to the average daily expenditure total of the 2015 study ($77.18), and the same conclusion was found for all item categories. For multiple-day trips, adjusted average daily expenditure ($130.48) was similar to the average daily expenditure ($128.04) in 2015. Expenditure profiles were then combined with effort data in the IMPLAN (Impact Analysis for Planning) software model to generate estimates of economic impacts. Total economic impacts of 1-d fishing trips to Lake Fork Reservoir were estimated to be $1.28 million and $1.43 million when using the adjusted and actual expenditure profiles, respectively. For multiple-day trips, economic impacts were estimated to be $10.82 million and $10.80 million for the adjusted and actual expenditure profiles, respectively. Although economic impact analysis must continue to identify where trip expenditures originate, results suggest that adjusting trip expenditures over time can be done using price indices and should be conducted based on trip length regardless of origin.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Exploring the Use of Price Indices to Adjust Angler Expenditure Profiles over Time

Parker

Hunt

Grado

et al. 2023

N American J Fish Manag

Self Cite

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“…Fourth, regarding visitor spending (Llop & Arauzo-carod, 2012;Kim & Chon, 2009;Santos, Grado, & Hunt, 2016;Seung & Lew, 2013;Li, Blake, & Thomas, 2013;Taks, Késenne, Chalip, Green, & Martyn, 2011;Pastor, Pérez, & de Guevara, 2013 ;Larsen, Mier, & Loop Capital Markets, 2012; Zheng & Hung, 2012), we considered not only the number of visitors, the number of equivalent full-time days, and the geographic origin but also the dissemination aspect with the number of days dedicated to scientific dissemination. Each partner measured its scientific dissemination outside GIANT and also, and more importantly, measured its' capability of hosting scientific meetings, conferences, etc.…”

Section: Methodsmentioning

confidence: 99%

“…Referring to the Regional Innovation System and to regional cluster, we want to examine the impact of public institutions on regional growth (Cooke & Morgan, 1998;Asheim & Isaksen, 2002;Krugman, 1998;Porter, 2000). The various expenses are direct, indirect, or induced (Connaughton & Madsen, 2007;Santos, Grado, & Hunt, 2016;Cassey, Holland, & Razack, 2011;Aries & Sclar, 1998;Venckus & Gaidelys, 2011). Total direct expenses are increased by a multiplier to consider interdependencies with local economic activity and to assess both indirect and induced expenditures.…”

Section: Methodsmentioning

confidence: 99%

Grenoble–GIANT Territorial Innovation Models: Are investments in research infrastructures worthwhile?

Scaringella

Chanaron

2016

Technological Forecasting and Social Change

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Over the past decades, the EU heavily invested in Research Infrastructures (RI). What are the expected returns of such investments? In the present article we address the question of returns on public funds/public infrastructures. We consider the role of RI and universities from an economic, social, and entrepreneurial perspective from various Territorial Innovation Models (TIMs): (1) Italian industrial districts, (2) innovative milieus, (3) regional innovation systems, (4) new industrial spaces, and (5) regional clusters. We conducted our empirical study on Grenoble Isère Alpes Nanotechnologies (GIANT), which is composed of large scientific instruments, universities, and engineering and management schools. Our microeconomic methodology measured the socioeconomic and entrepreneurial effects of GIANT with respect to budget, employment, and spin-off generation. We contribute to the existing body of knowledge on TIMs by (1) comparing the long-term investments to the generation of wealth, the creation of employment, and the development of start-ups; (2) adding new insights to the debate opposing positive and negative impacts empirical studies; and (3) offering recommendations for the use of public resources. In our discussion, we compare the GIANT model as a very localized RI-university club to the Grenoble model as localized cluster.

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