Economics of Physical Activity: Determinants and Mechanisms

Findings from kinesiology, medicine, and epidemiology have established the benefits of a physically active lifestyle. However, adolescents and adults in the United States are not sufficiently active. There are significant disparities observed in the prevalence of adolescent and adult physical activity by demographic and socioeconomic characteristics that are persistent over time. My research aims to propose a model of physical activity behavior that is capable of explaining the patterns and trends observed in physical activity levels of U.S. adolescents and adults. Studies in kinesiology and genetics indicate that physical activity is an outcome that starts from a biological foundation, changes in response to past levels of physical activity behavior, and then grows (declines) as a result of physiological feedback mechanisms. Psychology literature emphasizes cognitive and behavioral (i.e., noncognitive skills or personality traits) factors as important influences on physical activity. Specifically, physical activity is a complex behavior dependent on cognition and personality that make up psychic costs of physical activity. These biological and psychological processes underlying physical activity call for a dynamic modeling in which the past is an important determinant of the current level of physical activity. Human capital formation literature in Economics can accommodate these aspects of the behavior. Among alternative models of human capital formation, I propose to use a model of physical activity that is dynamic and is a modified version of the model described by Cunha and Heckman. Accordingly, the model I propose entails a physical activity production technology, the most important features of which are dynamic complementarities in early and late investments and self-productivities and cross-productivities in skill formation. Empirically, I examine whether there is evidence for the presence of the features of this technology that may yield dynamism in the formation of physical activity and inactivity behaviors during adolescence. To my knowledge, this research is the first attempt to treat various aspects of the physical activity behavior in a unified manner under theoretical guidance and to offer a dynamic model of physical activity in Economics that is potentially capable of explaining observations in adolescent and adult physical activity levels in the United States

Availability of Commercial Physical Activity Facilities and Physical Activity Outside of School Among High School Students

Background:Environmental factors may play an important role in the determination of physical activity behaviors.Methods:This study used the Child Development Supplement of the Panel Study of Income Dynamics to examine the association between the availability of objectively measured commercial physical activity-related instruction facilities and weekly physical activity participation among high school students outside of school physical education classes. A Negative Binomial count model was used to examine the number of days of vigorous physical activity (at least 30 minutes/day) per week and a Probit model was used to examine the probability of frequent (4 or more days/week) vigorous physical activity participation.Results:The results indicated that an additional instruction school per 10,000 capita per 10 square miles was associated with an 8-percent increase in the weekly number of days of vigorous physical activity participation and a 4 percentage point increase in the likelihood of frequent physical activity participation for female adolescents only. By income, associations were larger for low- versus high-income female youths.Conclusion:Increased availability of local area physical activity-related instruction facilities may help to increase female high school students’ physical activity levels, particularly among low-income female students.</jats:sec

Distribution of sugar-sweetened beverage sales volume by sugar content in the United States: implications for tiered taxation and tax revenue

AbstractThis study draws on data on sales volume, brand-level market shares, and sugar content to calculate the distribution of sugar-sweetened beverage (SSB) sales volume by sugar content, propose sugar content thresholds for a tiered tax structure, and estimate tax revenue. The most common SSBs sold had 26 g of sugar/8-oz serving; 70.8% had ≥ 25 g of sugar/8-oz serving, 16.9% were in the 10–15 g range, and 8.7% were in the 16–20 g range. A tiered tax with cut points at &lt; 20 g and &lt; 5 g of sugar/8-oz serving is proposed. A tax of 1¢/oz for SSBs in the second tier and 2¢/oz in third tier is projected to raise $18.2 billion in tax revenue similar to the 1.5¢/oz flat tax projection ($18.0 billion) but would yield 9% lower SSB volume. Understanding the distribution of SSB sales volume by sugar content informs policymakers on tiered tax structures, which may discourage consumption of SSBs with high levels of sugar and incentivize reformulation.</jats:p

Availability of Healthier Food Options in Fast Food Restaurants by Community Racial/Ethnic and Socioeconomic Composition in a National Sample

This research brief reports findings from a study that uses a nationwide dataset of fast-food restaurants from 2010-2012 to examine associations between the availability of healthier food options in both chain and non-chain fast-food restaurants and community racial/ethnic and socioeconomic composition

Clustering of Social Determinants of Health as an Indicator of Meaningful Subgroups within an African American Population: Application of Latent Class Analysis

Background: Health disparities between people who are African American (AA) versus their White counterparts have been well established, but disparities among AA people have not. The current study introduces a systematic method to determine subgroups within a sample of AA people based on their social determinants of health. Methods: Health screening data collected in the West Side of Chicago, an underserved predominantly AA area, in 2018 were used. Exploratory latent class analysis was used to determine subgroups of participants based on their responses to 16 variables, each pertaining to a specific social determinant of health. Results: Four unique clusters of participants were found, corresponding to those with &ldquo;many unmet needs&rdquo;, &ldquo;basic unmet needs&rdquo;, &ldquo;unmet healthcare needs&rdquo;, and &ldquo;few unmet needs&rdquo;. Conclusion: The findings support the utility of analytically determining meaningful subgroups among a sample of AA people and their social determinants of health. Understanding the differences within an underserved population may contribute to future interventions to eliminate health disparities