openAccessArticle: FalsePage Range: 125-125doi: 10.1016/j.smr.2011.08.004Harvest Date: 2016-01-12 15:10:31issueName:cover date: 2012-02-01pubType

This article uses an extensive unique data set to investigate the efficiency of government subsides for minor league baseball teams and stadiums by measuring pecuniary gains in a local economy. Specifically, a dynamic panel data model incorporating 238 Metropolitan Statistical Areas (MSAs) that hosted affiliated or independent minor league teams between 1985 and 2006 shows that AAA teams, A+ teams, AA stadiums, and rookie stadiums are all associated with significant positive effects on the change in local per capita income. The presence of positive effects is strikingly different from decades of nonpositive results at the major league level.

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Publicly funded sport events are partially justified based on positive social impacts. Past research generally measured social impact for a generic and global “other” with claims such as “Events create new friendships in the community”. These other-referenced (OR) social impacts are generally higher pre-event than post-event and are inflated for both methodological and theoretical reasons. In the pre-event period of the Tokyo 2020 Olympic and Paralympic Games, we empirically tested OR items compared to self-referenced (SR) items, such as “Because of the event, I create new friends in the community” and allowed projection bias to vary between scales. Results of the experiment between an OR-Social Impact Scale (OR-SIS) and a similar SR-SIS confirmed OR-measures to be significantly higher than SR-measures. While artificially inflated OR scores may be useful for event organizers and politicians to gain support for hosting, estimates based on circumscribed self (SR) are a methodologically appropriate measurement of social impact.

The role of residents in the calculation of economic impact remains a point of contention. It is unclear if changes in resident spending caused by an event contribute positively, negatively, or not at all. Building on previous theory, we develop a comprehensive model that explains all 72 possible behaviors of residents based on changes in (a) spending, (b) multiplier, (c) timing of expenditures, and (d) geographic location of spending. Applying the model to Super Bowl 50 indicates that few residents were affected and positive and negative effects were relatively equivalent; thus, their overall impact is negligible. This leaves practitioners the option to engage in the challenging process of gathering data on all four variables on all residents or to revert back to the old model of entirely excluding residents from economic impact. From a theoretical perspective, there is a pressing need to properly conceptualize the time variable in economic impact studies.