The University of Massachusetts Amherst

Measuring the Economic Effects of Casinos on Local Areas: Applying a Community Comparison Matching Method

SEIGMA Expert Advisor Mark Nichols, Ph.D., University of Nevada, Reno highlights a recent white paper co-written with the UMass Donahue Institute, Economic and Public Policy Research Group

Since the inception of the SEIGMA study, we at the UMass Donahue Institute have been collecting data and refining our approach for measuring the economic and fiscal impacts of expanded gambling in Massachusetts. We recently released a white paper that provides an overview of community matching, one of the methods the SEIGMA economic and fiscal team will use to analyze the economic impacts of gaming venues in Massachusetts.

Community matching involves selecting a group of communities that are economically and demographically similar to the casino host communities in Massachusetts (Everett, Plainville and Springfield). Once casinos open in Massachusetts, comparisons of data trends between the casino host communities and their matched control communities (for example Springfield, MA compared with New Haven, CT) will provide a relative assessment of the impacts of casinos over time.  The selection process involved the use of matching variables to identify communities with similar economic and social profiles to the Massachusetts host communities (matching variables include household income, unemployment rate, population, employment growth and others). We also considered distance to the nearest casino to exclude potential matches that are likely to be influenced by gambling facilities.

To be clear, community matching is not the only method we will use to conduct our economic impact analysis.  Rather, it is intended to complement our other methods and enhance our overall assessment of the economic impacts of casinos.  While a brief description of our methods is outlined, the purpose and focus of the white paper is to provide detail about the community matching method. You can find the full white paper here.