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Calculating Confidence Intervals for Regional Economic Impacts of Recreation by Bootstrapping Visitor Expenditures

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  • Donald B. K. English

Abstract

In this paper I use bootstrap procedures to develop confidence intervals for estimates of total industrial output generated per thousand tourist visits. Mean expenditures from replicated visitor expenditure data included weights to correct for response bias. Impacts were estimated with IMPLAN. Ninety percent interval endpoints were 6 to 16 percent above or below the original sample's point estimate depending on the calculation method. Due to the linearity of input‐output a shortcut method that estimates confidence interval endpoints from the distribution of mean expenditure profiles yields nearly identical results.

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  • Donald B. K. English, 2000. "Calculating Confidence Intervals for Regional Economic Impacts of Recreation by Bootstrapping Visitor Expenditures," Journal of Regional Science, Wiley Blackwell, vol. 40(3), pages 523-539, August.
    • Handle: RePEc:bla:jregsc:v:40:y:2000:i:3:p:523-539
    DOI: 10.1111/0022-4146.00186
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    Cited by:

    1. Daniel C. Monchuk & Dermot J. Hayes & John A. Miranowski & Dayton M. Lambert, 2011. "Inference Based On Alternative Bootstrapping Methods In Spatial Models With An Application To County Income Growth In The United States," Journal of Regional Science, Wiley Blackwell, vol. 51(5), pages 880-896, December.
    2. David M. Brasington, 2022. "Local economic growth and local government investment in parks and recreation, or five cheese pizzas for $2.6 million," Journal of Regional Science, Wiley Blackwell, vol. 62(1), pages 81-95, January.
    3. English, Donald B.K., 2000. "A Simple Procedure for Generating Confidence Intervals in Tourist Spending Profiles and Resulting Economic Impacts," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 30(1), pages 1-16.
    4. Philip Watson & Steve Davies & Dawn Thilmany, 2008. "Determining Economic Contributions in a Recreational Industry," Journal of Sports Economics, , vol. 9(6), pages 571-591, December.
    5. Natalie Stoeckl & Alastair Birtles & Marina Farr & Arnold Mangott & Matthew Curnock & Peter Valentine, 2010. "Live-Aboard Dive Boats in the Great Barrier Reef: Regional Economic Impact and the Relative Values of Their Target Marine Species," Tourism Economics, , vol. 16(4), pages 995-1018, December.
    6. Todd M. Gabe & Nicholas A. Lisac, 2014. "A Note on the Effects of Popular Music Concerts on Hospitality Sales: The Case of Waterfront Concerts in Bangor, Maine," The Review of Regional Studies, Southern Regional Science Association, vol. 44(1), pages 61-74, Spring.
    7. Adam Orens & Andrew Seidl, 2009. "Working Lands and Winter Tourists in the Rocky Mountain West: A Travel Cost, Contingent Behaviour and Input–Output Analysis," Tourism Economics, , vol. 15(1), pages 215-242, March.
    8. Chang Seung & Daniel Lew, 2013. "Accounting for variation in exogenous shocks in economic impact modeling," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 51(3), pages 711-730, December.
    9. Weiler, Stephan & Loomis, John & Richardson, Robby & Shwiff, Stephanie, 2002. "Driving Regional Economic Models with a Statistical Model: Hypothesis Testing for Economic Impact Analysis," The Review of Regional Studies, Southern Regional Science Association, vol. 32(1), pages 97-111, Winter/Sp.
    10. Eugene Choi, 2012. "Urban amenities as determinants of selecting a logo type in Korea: the multinomial logit approach with the bootstrap sample," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(1), pages 391-404, January.

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