Date of Award

12-31-2022

Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Sociology

First Advisor

Russell K. Schutt

Second Advisor

Reef Youngreen

Third Advisor

Michael Johnson

Abstract

Despite the widespread impact and negative effects of problem gambling (PG), limited attention has been paid to the environment where PG occurs. This study investigated the relationship between gambling on lottery and the zip code where gambling occurs, as well as the influence of individual-level characteristics that predict at-risk or problem gambling (AR/PG), among Massachusetts residents. A GIS analysis was conducted to identify vulnerable areas based on neighborhood characteristics, lottery sales, and AR/PG. Overall, residents of disadvantaged areas did not spend more money on lottery or have more lottery agents than residents of less disadvantaged areas. Some indicators of disadvantage (percent unemployed, percent single mothers) were associated with lower lottery sales and agent density, reflecting that communities with more single mothers and unemployed residents have less disposable income to spend on lottery. Areas where people work, shop, and vacation and communities on the Massachusetts border have higher per capita sales due to purchases of nonresidents being attributed to the residents of those zip codes. Thus, lottery sales aggregated by zip code do not provide an accurate picture of the gambling behavior of the residents. Individual characteristics hypothesized to predict AR/PG (gender, race, education, income, friends or family that gamble regularly) remained significant in nested analyses. Residents of disadvantaged areas did not have increased risk for AR/PG overall, although AR/PG likely has a greater adverse impact on disadvantaged areas due to lower levels of resources. Libraries were one community resource associated with lower risk for AR/PG; future research should investigate the value of other potential beneficial resources such as community or senior centers. An index of those community characteristics that increased or protected against risk for AR/PG in this study was used in a GIS analysis that demonstrates the potential for improved targeting of public health resources and interventions.

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