Gambling and Financial Stress
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Connects with: @breunig2019 @muggleton2021 @oksanen2018
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koomson2022 - p. 474
Although a large body of research examines the implications and adverse effects of gambling, surprisingly, very little evidence exists on the impact of gambling on financial stress. Financial stress is the difficulty faced by an individual or a household in meeting basic financial commitments due to a shortage of money (Bray, 2001).
koomson2022 - p. 474
n the one hand, there is existing theoretical and empirical evidence to support the expectation that problem gambling could lead to financial distress and decreased financial inclusion (e.g., Muggleton et al., 2021; Oksanen et al., 2018). Based on the theoretical expositions of the pathways model of problem and pathological gambling (e.g., Blaszczynski & Nower, 2002; Tabri et al., 2022), it is implied that problem gambling, which is developed through loss chasing behaviour, can lead to financial losses, with negative implications for financial stress.
koomson2022 - p. 476
e use data drawn from the HILDA Survey, an Australian household panel survey that reports on household demographics, family, income and labour market outcomes, among others. The survey is nationally representative and has been administered yearly since 2001 (Watson & Wooden, 2012). To date, the survey has produced 19 annual waves of data, although only waves 15 and 18 provide information on gambling. Thus, we use only waves 15 and 18 for our empirical analysis.
koomson2022 - p. 477
Our main gambling measure is based on the PGSI, which captures problem gambling severity (Currie et al., 2013; Ferris & Wynne, 2001; Jackson et al., 2010). The PGSI is a validated measure of gambling behaviour widely used in the literature to assess the severity of problem gambling in population-based samples (see, e.g., Awaworyi Churchill & Farrell, 2019, 2020a; Gong & Zhu, 2019; Holtgraves, 2008; Korman et al., 2008; Loo et al., 2011; Raisamo et al., 2014).
koomson2022 - p. 477
e also capture gamblers’ risk status using their PGSI scores. We identify four risk statuses: (1) non-problem gamblers (i.e., those who did not engage in any problem gambling behaviour or never experienced the detrimental effects of gambling over the past year, and hence have a PGSI score of 0); (2) low-risk gamblers (i.e., those with PGSI scores of 1 or 2); (3) moderate-risk gamblers (i.e., those with PGSI scores of 3 to 7); and (4) problem gamblers (i.e., those with PGSI scores of at least 8).
koomson2022 - p. 477
We combine the approaches employed in previous studies to generate four binary measures of financial stress using the section of the HILDA survey that captures information on respondents’ experiences of financial stress and economic hardship.
koomson2022 - p. 478
The first measure, labelled ‘financial difficulty’ follows Wilkins and Lass (2015), who suggest that two or more of the seven conditions must be experienced for a person or household to be classified as being financially stressed The other three indicators are consistent with the literature that focuses on specific indicators of financial stress (Bray, 2001; Breunig & Cobb-Clark, 2006; Breunig et al., 2019).
koomson2022 - p. 478
Financial resilience is conceptualised as the ability to come up with an emergency fund equal to one year of income
koomson2022 - p. 479
ur baseline estimates are based on a model for financial stress as follows: where Fstressit is the measure of financial stress for respondent i at time t ; and GB is the indicator of gambling behaviour; X is a set of covariates likely to influence financial stress; αs and μt respectively represent state and wave fixed effects, while ε is the error term. For our baseline results, we use ordinary least squares (OLS
koomson2022 - p. 479
In the gambling-financial stress relationship, gambling is likely to be endogenous (Awaworyi Churchill & Farrell, 2019, 2020a). While we examine the impact of gambling on financial stress, it is also likely that financial stress can cause people to gamble (Buchanan et al., 2020), thus raising the issue of reverse causality, which may be a source of endogeneity bias. Endogeneity may also arise due to unobserved factors or omitted variable bias, which we are unable to control for but are likely to influence both individual problem gambling severity and financial stress. One way to address the endogeneity problem is to use the lag of PGSI. On the one hand, problem gambling severity in the previous period is expected to influence financial stress in the current period. On the other hand, we do not expect financial stress in the current period to influence gambling decisions in the past, which resolves the reverse causality problem but not the omitted variable bias. However, endogeneity can also be addressed using a two-stage least squares (2SLS) method in which an external instrument is employed.