Illegal gambling: measuring the market using the MIMIC model

ABSTRACT

Using a multiple indicators and multiple causes (MIMIC) model, this paper estimates the extent of illegal gambling in Italian regions over the period 2013–18. By treating illegal gambling as an unobserved latent variable directly related to its causes and effects, this model gives information about the relationship between cause and indicator variables and the latent variable from covariance structures. From the analysis, it emerges that the share of illegal gambling increases with the value of the winnings paid; it decreases when the number of authorized machines increases. We also find that individuals with low levels of education and low income show a greater propensity to gamble illegally.

KEYWORDS:

• illegal gambling
• MIMIC model
• market gambling
• amusement machine

JEL:

• L83
• O17
• R11

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1. Following the main literature, the choice of the MIMIC model was based on several model-fit characteristics. Overall, a perfect model fit requires an insignificant chi-square (i.e., p > 0.05), a comparative fit index (CFI) closer to 0.95, a standardized root mean square residual (SRMR) closer to 0.09 and a root mean square error of approximation (RMSEA) closer to 0 (Hassan & Schneider, 2016).

2. The authors thank an anonymous referee for very helpful comments.

3. If we had known an exogenous value (i.e., in 2013 euros for each region), we could have multiplied it and obtained an index of the scale (e.g., if your IG_base, k, 2013 = €IG, then the index would have measured illegal gambling in euros for each region).