TY - JOUR
T1 - Exploring the Association Between Minimum Wage Policy, Income Inequality, and Obesity Rates in US Counties
AU - Zare, Hossein
AU - Balsara, Khushbu
AU - Meyerson, Nicholas S.
AU - Delgado, Paul
AU - Delarmente, Benjo
AU - McCleary, Rachael
AU - Thorpe, Roland
AU - Gaskin, Darrell
N1 - Publisher Copyright:
© W. Montague Cobb-NMA Health Institute 2024.
PY - 2024
Y1 - 2024
N2 - Objective: To examine the interaction between minimum wage policy, income inequality, and obesity rates among U.S. counties, and how this relationship is shaped by policy, place, and racial/ethnic composition in a county. Methods: We used the County Health Rankings Data for obesity ratio (measured by Body Mass Index ≥ 30 kg/m2) in US counties and combined it with the American Community Survey to include the Gini coefficient (GC) and population characteristics. The analytical sample included 3129 counties between 2015 and 2019. We ran several sets of regression analyses, controlling for county characteristics, access to healthy foods, and minimum wage categories as a policy influencer on the obesity ratio. Results: In total, 31.7% of the population were obese, with wide variations across counties; during this time, counties’ average GC was 0.442. Our findings showed that in the lack of any other predictors, GC has a positive association with the county obesity ratio (OLS 0.147, CI 0.122–0.173). Counties with minimum wage between $7.26–$9.0 and $9 + had lower obesity ratios by − 0.6 and − 2.8 percentage points, respectively, and counties with lower access to healthy foods had higher obesity ratio (Coeff = 0.022, CI 0.019–0.025). Conclusions: Income inequality is positively associated with the obesity ratio in counties. Access to healthy foods and state minimum wage policy predict obesity rates, with a lack of healthy foods increasing the ratio, while a higher minimum wage reduces it.
AB - Objective: To examine the interaction between minimum wage policy, income inequality, and obesity rates among U.S. counties, and how this relationship is shaped by policy, place, and racial/ethnic composition in a county. Methods: We used the County Health Rankings Data for obesity ratio (measured by Body Mass Index ≥ 30 kg/m2) in US counties and combined it with the American Community Survey to include the Gini coefficient (GC) and population characteristics. The analytical sample included 3129 counties between 2015 and 2019. We ran several sets of regression analyses, controlling for county characteristics, access to healthy foods, and minimum wage categories as a policy influencer on the obesity ratio. Results: In total, 31.7% of the population were obese, with wide variations across counties; during this time, counties’ average GC was 0.442. Our findings showed that in the lack of any other predictors, GC has a positive association with the county obesity ratio (OLS 0.147, CI 0.122–0.173). Counties with minimum wage between $7.26–$9.0 and $9 + had lower obesity ratios by − 0.6 and − 2.8 percentage points, respectively, and counties with lower access to healthy foods had higher obesity ratio (Coeff = 0.022, CI 0.019–0.025). Conclusions: Income inequality is positively associated with the obesity ratio in counties. Access to healthy foods and state minimum wage policy predict obesity rates, with a lack of healthy foods increasing the ratio, while a higher minimum wage reduces it.
KW - Food access
KW - Gini coefficient
KW - Income inequality
KW - Minimum wage
KW - Obesity
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U2 - 10.1007/s40615-024-02210-x
DO - 10.1007/s40615-024-02210-x
M3 - Article
C2 - 39441522
AN - SCOPUS:85207000279
SN - 2197-3792
JO - Journal of Racial and Ethnic Health Disparities
JF - Journal of Racial and Ethnic Health Disparities
ER -