Gambling nations: a study of the structural and cyclical macro-level correlates of gambling expenditure

ABSTRACT

This article studies the structural and cyclical macro-level correlates of betting, gaming and lottery expenditure par adult using data on 78 countries over the period 2003–2019. The results are different for each type of game, with significant similarities between betting and lottery. The main correlate of gaming expenditure is the median age of the population, with an inverted U-shaped relationship. For betting and lottery, the main positive correlates are the GDP per capita, with an elasticity greater than unity, the share of urban population and the quality of legal institutions. Patience is negatively related to betting expenditure. Religion plays a role for betting (positive link with being a Protestant country) and lottery (negative link with being a Muslim country). Gaming expenditure tends to be procyclical (negative link with unemployment) and lottery expenditure countercyclical (negative link with growth). These results can be useful to operators and governments in understanding the differences in the size of the gambling sector between countries and over time and defining appropriate business strategies and public policies.

KEYWORDS:

• Betting
• gaming
• lottery
• expenditure
• countries

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The dataset used for this study is not publicly available as it contains proprietary information that the author acquired through a license. Information on how to obtain it and reproduce the analysis is available from the author on request.

Ethics statement

No experiments on human participants or animals were conducted in this research.

Notes

1 https://h2gc.com/

2 Obviously, this can take on various shades depending on the regulation in the player’s jurisdiction. At the darker end of the spectrum, some activities are not included, such as offshore operators targeting Middle Eastern countries, such as Saudi Arabia or Iran.

3 https://data.worldbank.org/

4 To interpret this variable correctly, bear in mind that there are two variables expressed in monetary units in the analysis: gambling expenditure, expressed in US dollars, and GDP per capita, is expressed in PPP-adjusted current international dollars.

5 I have also considered using the United Nations inequality data (Gini index and Palma ratio). They do not alleviate the missing data issue and provide similar results.

6 https://population.un.org/wpp/Download/Standard/MostUsed/

7 https://hdr.undp.org/data-center/documentation-and-downloads

8 https://www.fraserinstitute.org/

9 https://scholar.harvard.edu/barro/data_sets

10 https://www.briq-institute.org/global-preferences/home

11 Since lottery expenditure par adult has some 0 values, the following transformation is used: log(lottery expenditure per adult+0.0001)

12 https://www.pewresearch.org/religion/2018/06/13/the-age-gap-in-religion-around-the-world/

Four cross-sectional variables are available: the percentage of the population that is affiliated, that attends weekly, that prays daily and that considers religion very important.