{"title":"Economic Growth and Adult Obesity Rates in Rural America","authors":"Yancheng Li, Brian E. Whitacre","doi":"10.52324/001c.66201","DOIUrl":null,"url":null,"abstract":"Obesity has become an increasingly severe problem in the United States. From 2008 to 2018, the adult obesity rate rose from 33.8% to 42.4%, with rates that are notably higher in rural areas when compared to their urban counterparts. Meanwhile, rural regions have experienced relatively slower employment growth and higher poverty rates during the recovery from the Great Recession. Social scientists are interested in determinants of – and potential solutions to – this rise in obesity rates. The existing literature has focused on the relationship between obesity and social/economic factors, such as the number of fast-food restaurants, limited physical activity, and unemployment rates. However, one unexplored question is whether the level of economic growth experienced by a rural area plays a role in the obesity problem. This paper assesses the impact of economic growth (measured by county-level GDP per capita) on obesity rates (measured by the county-level percentage of adults with BMI higher than 30) in rural America. Nationwide, data is collected on a host of demographic and economic characteristics for all non-metropolitan counties from 2012 to 2016, resulting in a county-level panel data set (n=1,948, t=5). Control variables include age, race and ethnicity, unemployment rates, rates of physical inactivity, food assistance program participation, and an index measuring healthy food availability. Two different econometric approaches were applied: (1) a fixed-effects panel regression model and (2) a difference-in-difference model using propensity score matching (PSM). The results of both econometric models suggest there is no relationship between economic growth and future obesity rates. This suggests that programs focused on rural economic growth may not affect other quality-of-life metrics. The conclusion discusses these competing interests and how regional scientists can play a role in future research in this area.","PeriodicalId":44865,"journal":{"name":"Review of Regional Studies","volume":null,"pages":null},"PeriodicalIF":0.6000,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Review of Regional Studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52324/001c.66201","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
Obesity has become an increasingly severe problem in the United States. From 2008 to 2018, the adult obesity rate rose from 33.8% to 42.4%, with rates that are notably higher in rural areas when compared to their urban counterparts. Meanwhile, rural regions have experienced relatively slower employment growth and higher poverty rates during the recovery from the Great Recession. Social scientists are interested in determinants of – and potential solutions to – this rise in obesity rates. The existing literature has focused on the relationship between obesity and social/economic factors, such as the number of fast-food restaurants, limited physical activity, and unemployment rates. However, one unexplored question is whether the level of economic growth experienced by a rural area plays a role in the obesity problem. This paper assesses the impact of economic growth (measured by county-level GDP per capita) on obesity rates (measured by the county-level percentage of adults with BMI higher than 30) in rural America. Nationwide, data is collected on a host of demographic and economic characteristics for all non-metropolitan counties from 2012 to 2016, resulting in a county-level panel data set (n=1,948, t=5). Control variables include age, race and ethnicity, unemployment rates, rates of physical inactivity, food assistance program participation, and an index measuring healthy food availability. Two different econometric approaches were applied: (1) a fixed-effects panel regression model and (2) a difference-in-difference model using propensity score matching (PSM). The results of both econometric models suggest there is no relationship between economic growth and future obesity rates. This suggests that programs focused on rural economic growth may not affect other quality-of-life metrics. The conclusion discusses these competing interests and how regional scientists can play a role in future research in this area.