A generalized double-hurdle model of Swedish gambling expenditures
James Rude,
Yves Surry and
Robert Kron
Applied Economics, 2014, vol. 46, issue 34, 4151-4163
Abstract:
This article estimates the relationship between demand for Swedish gambling, income and the other socioeconomic variables using empirical models of participation and gambling expenditure. It also indirectly attempts to account for the effect of a recent recession on gambling behaviour by examining gambling behaviour when the economy was growing versus the period immediately after a major recession. The aim of the article is to analyse the factors influencing gambling decisions. This is done using the double-hurdle method adjusted for the problems of heteroscedasticity and nonnormality using an approach that can handle extreme values and address skewness. The empirical results ruled out Tobit-type models and normally distributed double-hurdle models. The pattern on income elasticities across income classes implies that implicit Swedish gaming taxes are regressive. Income elasticities become smaller after the recession and the decision to participate changed with respect to the several of the demographic variables.
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)
Downloads: (external link)
http://hdl.handle.net/10.1080/00036846.2014.939376 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:applec:v:46:y:2014:i:34:p:4151-4163
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEC20
DOI: 10.1080/00036846.2014.939376
Access Statistics for this article
Applied Economics is currently edited by Anita Phillips
More articles in Applied Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().