A geospatial statistical analysis of the density of lottery outlets within ethnically concentrated neighborhoods

Abstract

This study examines the density of lottery outlets within ethnically concentrated neighborhoods in Middlesex County, New Jersey, using geospatial statistical analyses. No prior studies have empirically examined the relationship between lottery outlet density and population demographics. Results indicate that lottery outlets were not randomly dispersed across the county but rather were significantly clustered in some census tracts. Using geospatial statistical analysis, percent poverty was found to be negatively related to the density of lottery outlets, while percent commercial use was highly positively related. Percent Hispanic was strongly significant in all the models predicting lottery outlet density and had the highest explanatory power other than percent commercial. Other independent variables, including median household income and race (African American and White) were not found to be significant predictors of lottery outlet density. Implications for policy and research are discussed.

Why the Poor Play the Lottery: Sociological Approaches to Explaining Class-based Lottery Play

“…Using macro panel data for US states between 1976 and 1995, Freund and Morris (2005) find that the increasing prevalence of state lotteries corresponds to rising income inequality in the USA. Another study employs a geospatial statistical analysis of the density of lottery outlets and finds a higher concentration of outlets within ethnically segregated neighbourhoods (Wiggins et al, 2010). Haisley et al (2008) demonstrate with experimental data that people are more likely to buy lottery tickets when they perceive their income as relatively low compared to others (see also Callan et al, 2011).…”

Do ‘environmental bads’ such as alcohol, fast food, tobacco, and gambling outlets cluster and co-locate in more deprived areas in Glasgow City, Scotland?

“…different categories of outlet found in similar areas) within poorer neighbourhoods. Previous research used a more traditional approach of comparing densities across geographical areas ( Chaiton et al, 2013 , Ellaway et al, 2010 , Thornton et al, 2016 , Wiggins et al, 2010 , Shortt et al, 2016 , Hay et al, 2009 , Wardle et al, 2014 , Wilson et al, 2006 ). Within these studies densities of outlets were generally calculated for pre-defined geographical and administrative boundaries e.g.…”

“…density of alcohol outlets across Scottish ‘Data Zones’ ( Ellaway et al, 2010 ), fast food outlet density at Australian ‘Local Government Area’ ( Thornton et al, 2016 ), density of tobacco outlets at Canadian ‘Public Health Unit’ level ( Chaiton et al, 2013 ), or gambling outlet density within US ‘Census Tracts’ ( Wiggins et al, 2010 ). The benefit of this approach was the potential to link these boundaries to additional key data such as poverty rates ( Ellaway et al, 2010 , Thornton et al, 2016 ), indicators of urbanicity/rurality ( Thornton et al, 2016 ), population ethnicity ( Wiggins et al, 2010 ), or number of smokers ( Chaiton et al, 2013 ). The cluster analysis approach applied within our paper has previously been used to detect geographic disparities in the incidence of disease cases, e.g.…”

Building Capacity for the Use of Geographic Information Systems (GIS) in Social Work Planning, Practice, and Research

“…Hetling and Zhang (2010) used spatial autocorrelation to examine the relationship between incidence of domestic violence, poverty status, and location of agency services focused on domestic violence. Wiggins, Nower, Mayers, and Peterson (2010) used spatial autocorrelation to examine the relationship that exists between certain population demographics and the density of lottery outlets at the census tract level.…”