The Socioeconomic Determinants of Obesity in the USA
While numerous studies have examined obesity in the United States of America, few have analyzed the leading socioeconomic determinants of obesity regarding demographics. According to The Organization for Economic Co-operation and Development (OECD), the United States has the 12th highest obesity rate in the world. In this paper, we will be analyzing the effect of different socioeconomic factors such as race, income, gender, education, chronic illnesses, and personal habits on rates in the United States. Obesity is viewed negatively when related to an individual’s health or personal attractiveness. Unlike other physical conditions, obesity can be avoided through behavioral changes. Economists expect individuals to address these behavioral changes if the benefits exceed the costs. Weight can result from personal tradeoffs and choices such as occupation, leisure-time activity or inactivity, residence, and food intake. Given the variation in weight preferences, being either fat or thin may be as desirable from the individual’s standpoint as adhering to the norms of importance set by doctors and the public health community.
COVID-19 and the global lockdown have affected the obesity rate in the United States. The lockdown has resulted in a trade cease, which has caused a lack of access to many goods that the U.S acquired from other countries, including healthy necessities, such as fruits and vegetables. Although the U.S has many farmers, they still faced many problems in the cause of the lockdown. For example, the demand for goods increased because everyone was stuck at home and the country did not have the amount of labor needed to keep up with this mass demand. Only grocery stores and essential businesses were open at the time. As the demand grew for these goods, so did the prices, which led to low-income individuals not being able to afford these healthier food options.
Additionally, during the global pandemic, many farmers struggled to get their fruits and vegetables into the grocery stores’ inventory, which likely caused an effect on obesity. This was exemplified in Fresno County as the economy experienced a sudden halt. The farmers at Harris Farms had to plow about 13 acres of newly grown lettuce because they had nowhere to send it. The demand from grocery stores had suddenly fallen. The opportunity cost was lower for them to let the crops rot rather than harvesting them during Covid. Aside from the difficulty of people acquiring healthy foods, they were now stuck in their homes with more to eat and less exercise. Data derived from Obesity, Seal A, et al. demonstrates that during the Covid-19 lock-down in the U.S., obese adults gained KG compared to those of standard weights. Adam Seal stated, “We observed that state stay-at-home mandates, designed to slow the spread of COVID-19, had unintended consequences of promoting weight gain that disproportionately impacted individuals with obesity.
Today more than ever, obesity is a topic that needs to be explored and discussed. According to the CDC, “having obesity increases the risk of severe illness from COVID-19. Since obesity may triple the risk of hospitalization due to a COVID-19 infection, it is linked to impaired immune function, decreases lung capacity and reserve, and makes ventilation more difficult (Center for Disease Control and Prevention, n.d).” The determination of contributing factors of obesity is essential, to eliminate and educate people about the barriers that may keep them from obtaining a healthy lifestyle. Obesity prevention and management should start early in one’s life to minimize long-term health issues. This is extremely vital because obesity has been linked to illnesses such as diabetes, gallbladder disease, coronary heart disease, high cholesterol, hypertension, premature death, and asthma. Therefore, it is essential to identify the determinants of obesity because it negatively impacts our society and economy.
Our research question is specific to the United States because it is the country with one of the highest rates of obesity. According to the CDC, “Since 1960, the prevalence of adult obesity in the United States has nearly tripled, from 13% in 1960–1962 to 36% during 2009–2010 (1,2). Since 1970, the prevalence of obesity has more than tripled among children, from 5% in 1971–1974 (3) to 17% in 2009–2010 “(Center for Disease Control and Prevention, n.d).” Due to the progressive rise in obesity, these statistics have been beneficial to economists and policymakers, studying and making hypotheses about broader subjects. According to the World Health Organization, “Worldwide obesity has nearly tripled since 1975 and 39% of adults aged 18 years and over were overweight in 2016 (World Health Organization, n.d). These findings are very similar to those of the United States.
With our research question, we can expand our research to incorporate additional dependent variables to achieve more reliable and representative data results if needed. [RSC1] [RSC2] The epidemic of obesity is a critical public health issue that has worsened during the COVID-19 pandemic. There is a need for transformational policies and substantial investment in programs that help reduce health inequities and address the socio-economic conditions that are a significant barrier to accessing affordable, nutritious food and physical activity. This point should be furtherly addressed due to the increasing economic costs of obesity. In just 2016 alone, obesity accounted for 480.7 billion dollars in direct health care expenses and 1.24 trillion dollars in indirect costs due to economic productivity loss. These costs will proceed to rise, and the nation will continue to incur these costs unless proper analysis and policies are implemented.
Our study uses BRFSS Prevalence and Trends Data and CDC Nutrition, Physical Activity, and Obesity: Data, Trend, and Maps from 2016 to 2020 to explore the socioeconomic determinants of obesity. The remainder of the research paper will be organized as follows. Section two will discuss the literary review of scholarly articles related to our topic. Section three is our economic theory. Section four will describe data, such as the variables obtained for the study. Section five is the empirical analysis and the conclusion.
II. Literature review
There have been numerous studies conducted on the causes and effects obesity has on Americans. Several studies have used statistics to demonstrate the rise in obesity rates and the links between obesity, disease, and death rates. The Division of Nutrition, Physical Activity, and Obesity, National Center for Chronic Disease Prevention and Health Promotion released their study on this topic on November 22nd, 2016. The CDC is recognizable for researching to save lives and protect people from health threats. In their research, they found that when controlling age and ethnicity in a regression model, there is an indication that obesity is more significant among men than women in 1992-2002 and 2007-2010. [RSC5] The CDC concludes in this study that there are several reasons that race, ethnicity, and gender could potentially be correlated with weight. A reason mentioned was the complex social and cultural factors that influence specific behaviors. E.g., body size preference in each racial group, previous experiences of undernutrition, different choices for physical activity, excess television and screen time, and high calorie, low nutrient foods and beverages. The CDC has specific methodologies in place for testing and analyzing trends. First, they analyze data from the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2008. They focus on sex, age, race/ethnicity, educational attainment, disability status, birth country, and language spoke at home. Geographic location is one variable they did not examine because the information was not available. However, for our investigation, geographic location will be a variable we consider as we are only investigating states in the United States. The CDC also analyzes educational attainment instead of income because of the plethora of education data they possess.
The data obtained was calculated by using standardized techniques and equipment to measure weight and height to get the individuals and body mass index (BMI) (weight [kg]/height [m]2). In addition to BMI, surveys of open-ended questions were also conducted. A limitation that this study encountered was that the NHANES did not sample a large enough number of individuals who were members of the racial/ethnic minority communities at hand, other than the non-Hispanic blacks and Mexican Americans. This resulted in them being unable to estimate or predict obesity in these communities on a larger scale. We will aim to eliminate this in our research. Also, even though the study adjusts for age, it does not allow for the assessment of covarying issues to further analyze independent effects.
The following study being addressed is The Geographic Distribution of Obesity in the US and the Potential Regional Differences in Misreporting of Obesity. [RSC7] This study, unlike the one previously mentioned, considers the effect of geographic location on obesity in the U.S. BRFSS is a CDC-sponsored domestic telephone survey that interprets state-level estimates of health-related factors. Using data from the Data from BRFSS, researchers find that the highest rates of obesity are in the East South Central Census division. A further method this study implements is direct measures from the NHANES data. However, the direct measures indicate that the West North Central and East North Central Census divisions had high rates of obesity. In this study, a limitation that they encountered, which we will aim to avoid in our study is self-reported bias. In many cases, the researcher finds that weight is under-reported, and height in many instances is over-reported. The BRFSS sample size is significantly large and contains 677,425 observations. The direct measure from NHANES however, only includes 6,615 observations. We gather that the BRFSS is most likely more reliable for estimating obesity prevalence on a larger scale. According to the analysis from the BRFSS, the state with the highest obesity in Mississippi. According to the study of the direct measures from NHANES the state with the highest obesity in Missouri. Considering this, it is clear there is not a [RSC8] general consensus in this study on which geographical location has prevalent obesity.
The next study I would like to introduce is Age, Socioeconomic Status and Obesity by Growth Charles L. Baum II and Christopher J. Ruhm.[RSC9] This study investigates and establishes that 31% of individuals between 18 and 74 years old were considered obese in
1999-2004, compared to the previous rate of 14% in 1976-1980. For this study, Baum and Ruhm acquired data from the National Longitudinal Survey of Youth (NLSY), in which they investigate body weight changes with age alongside Socioeconomic status (SES) differences and channels for SES differences. Their analysis reveals that obesity increases with age and is also inversely correlated to SES. This is because of the high prevalence of obesity rates in disadvantaged groups easily accessed relatively low-cost energy-dense foods. After all, they can easily reach caloric requirements by purchasing foods high in calories for a low cost. The NLSY data used in this study contains 12,686 participants aged 17 to 21, including samples of Blacks and Hispanics. In this study, low-income whites are not included, which could contribute to the findings of this study being less representative. Pregnant women who have previously been pregnant, have been excluded from this study for better representation. Similar to previous studies mentioned, the researcher used height and weight to calculate the BMI of each interviewee. BMI measurements are grouped into three, which are defined by the Federal and international guidelines; adults with a BMI less than 18.5 are considered “underweight,” while individuals with BMIs ranging from 18.5 to 25 are average weight, 25 to 30 are overweight, and greater than or equal to 30 are considered “obese.” Similar to the previous study, the error faced when conducting this research was interviewee bias when self-reporting height and weight.
The final study I will be referencing is The Impact of Education on Obesity Among Blacks and Whites Living In New York City by Chelsea Andrea Doub. The two key variables of this research were race/ethnicity and obesity. The source states that an additional year of education reduces Body Mass Index (BMI). The Community Health Survey is a cross-sectional telephone survey conducted every year by the New York City Department of Health and Mental Hygiene (DOHMH). The data were analyzed using SAS 9.4 Software and Microsoft Excel.
Using regression, chi-square, and predicted possibilities, the researcher confirms her hypothesis that an increase in education leads to a lower rate of obesity among black and white individuals, but the rate varies between both. A couple of mechanisms mentioned in this study that link education to obesity are “(1) work and economic conditions, (2) social-psychological resources, and (3) health lifestyle.” The research also states that they find “that education is highly and positively correlated with two measures cross-sectionally over time, self-reported health, and physical functioning. In addition, they find that after adjusting for economic conditions,social-psychological resources, and a healthy lifestyle, education persisted as being significantly associated with better health. A limitation encountered during this study is that it includes a random sample size of data that was only 8,400 individuals living in New York City, a highly metropolitan city, so the results may not be accurately representative of the United States. A further limitation is that the study does not consider the quality of education, economic security, or healthcare. Similar to all other studies mentioned above, the self-reporting of weight and height could also lead to reporting bias. Although it is clear there is a correlation between education and obesity. There are many other factors that need to be included to make the findings more reliable.
While all these studies highlight the importance of different socioeconomic and demographic factors of obesity, there is not one clear consensus. None of them are completed within the same time series, nor have they researched obesity alongside the same variables [RSC10]. These studies are limited because many explanatory variables are missing that could improve the results’ reliability and validity. Additionally, they have looked at only specific determinants, used a population that is not representative of the US, and used a single
cross-section of data. Self-reported data is not the most accurate when analyzing the health aspects of all the studies. In conclusion, an improvement we can incorporate in our research is using much larger sample size, including data from a more extended period, and containing newer and more recent data to have a more robust set of socioeconomic determinants.
III. Economic Theory
In Economics, economists have assembled several different theories that we can now use when analyzing economic activity. The Marxian, Macro, and Micro theories are the most relevant when testing our question. The effect of different socioeconomic factors such as race, income, gender, and education on obesity can be studied using Marxian theory, which is a study that focuses on the struggle between capitalists and the working class. Marxian and neoliberal economics also finds a link between obesity and capitalist societies. Several researchers have made the argument that the root of obesity is capitalism. In a capitalist society, working-class people are not merely paid enough to buy nutritious food.
Microeconomics can also be used to form predictions that will be tested in the empirical section of our paper. It has been shown that microeconomic factors are also linked with obesity. This can be demonstrated because of the influence of income on the cost of diet and dietary intake. In microeconomics, there is a hypothesis called the “food price- obesity hypothesis.” This assumes that when individuals have significantly limited purchasing power, they tend to shift their money to consume energy-dense foods and have many calories versus healthy consumption, the more expensive option. So in our empirical process, we will be able to test whether or not this microeconomic hypothesis holds. Other Macro topics that are relevant to our study of obesity are the link between health and education. This link is said to be associated with micro and macroeconomic topics such as work and economic conditions, social-psychological resources, and healthy lifestyles. The previously mentioned economic theories help us predict the possible effects of income, education, and socioeconomic status on obesity in the U.S.
IV. Data and Variables
Our data is obtained from the CDC Nutrition, Physical Activity, and Obesity: Data, Trend and Maps and the CDC BRFSS Prevalence and Trends databases. We have one dependent variable and six independent variables for our data set. Our dependent variable is all adults considered obese in the United States, which is extracted from the Nutrition database. Our analysis is conducted at the state level and we are looking at the percentage of the state population that is obese including all 50 states.
Our independent variables include a host of socio economic and demographic factors such as gender, income, race, education, chronic illnesses (specifically diabetes), and physical activity. For gender we will examine the percentage of men and women in each state. Income will be categorized by how much an adult makes annually and be measered by percentage. Race will be measured by percentage of the adults in each state by there race/ethnicity. Education will show the highest grade completed by an adult, measured by percentages. Chronic illness specifically diabetes will show adults who have been told by there doctor that they have diabetes, measured by percentages. Physical activity will show adults who have been phyically active for the last month, measure by percentages. All our independent variables were located in the BRFSS database. Our independent variables will indicate which demographic and personal habits can influence an individual being obese.
When we analyze the independent variables gender, income, race, and education we capture how significat the affect of demographics are on obesity. We will be able to see in which tax bracket or the level of a person’s education could be more prone to obesity. Same as when looking at other independent variables, such as gender or race. Different races and genders are stereotyped to being more or less obese which is one of the reasons demographics need to be included. When we examine the independent variables such as physical activity we observe how a personal habits and behavior can contribute to their weight. Finally, it is essential we analyze the effect of chronic illneses on obesity. To do this we include the percentage of the state population that is diabetic to control for the influence of chronic illneses since diabetes is stereotypically known to have a correlation with obesity.
There are about 53 observations since we are analyzing a 5-year period of time. Each of the years, 2016 to 2020. We chose a 10-year time period to have enough data to analyze the changes in the obesity rate in the United States, especially since there have been many technological and environmental changes in that time frame.
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