Presentation Title
Economic Growth and Weight Gain: A Dynamic Approach to the Obesity Epidemic
Abstract
Obesity is a health epidemic plaguing many Western countries today. According to an OECD (2019) study, 71% of Americans above the age of 15 are overweight or obese. This is the third-largest figure of the OECD countries, which include the regions of North America, Europe, and Oceania. Yet, the explanation for ever-increasing obesity rates is not well-defined. I propose an economic explanation behind this phenomenon. The work done in this thesis reviews income and obesity in six different countries to examine the positive relationship between income growth and higher obesity prevalence. In order to establish the link between income growth and the rise in obesity, I simulate an economy in Mathematica using a variant of Ramsey (1928), Cass (1965) and Koopmans’ (1965) (RCK) model. I present the model in its original form, which connects consumption to lifetime income and wealth. I adjust the RCK model by adding the Schofield (1985) equation, which describes weight gain as the net caloric intake of an individual, thereby connecting food consumption to body weight. I then present the steady-state equations with my addition of Schofield (1985) equation and begin calibration. I select six different countries for which I calibrate my Mathematica model in order to predict their relationships with obesity prevalence and income growth.
I study Egypt, Thailand, Mexico, Chile, Turkey, and the United States. These nations represent the highest obesity rates of their regions as well as some of the highest GDP/capita metrics as well. To calibrate my model, I use real-world long-run economic indicators, including all components of GDP, excluding net exports, as well as health metrics such as average body weight to GDP. I collect data from various databases including the World Bank national accounts data, the OECD, FRED, and WHO in order to calculate long-run averages of my economic indicators. Generally, my data ranges from the 1970s to 2018. I select this range so as to get a more accurate picture of the long-run trends in each economy. Then, I take the economic indicators and calculate parameter values for my model that best suits the structure of each economy in order to create a growth path of future obesity prevalence as the economies get wealthier over time. So far, my research suggests a positive relationship between obesity prevalence and income growth in all six of the nations. I simulate the model with Mathematica after calibrating it to match long-run economic indicators such as average income, interest rates, BMI, and weight. The end result is a quantitative analysis of the link between economic growth and a rise in obesity with numerical simulations. The model is used to predict macro-level obesity rates and, in an extension, potential welfare effects of growth when obesity is taken into consideration.
Primary Faculty Mentor Name
Nathalie Bolh
Status
Undergraduate
Student College
College of Arts and Sciences
Program/Major
Economics
Second Program/Major
French
Primary Research Category
Arts & Humanities
Secondary Research Category
Health Sciences
Economic Growth and Weight Gain: A Dynamic Approach to the Obesity Epidemic
Obesity is a health epidemic plaguing many Western countries today. According to an OECD (2019) study, 71% of Americans above the age of 15 are overweight or obese. This is the third-largest figure of the OECD countries, which include the regions of North America, Europe, and Oceania. Yet, the explanation for ever-increasing obesity rates is not well-defined. I propose an economic explanation behind this phenomenon. The work done in this thesis reviews income and obesity in six different countries to examine the positive relationship between income growth and higher obesity prevalence. In order to establish the link between income growth and the rise in obesity, I simulate an economy in Mathematica using a variant of Ramsey (1928), Cass (1965) and Koopmans’ (1965) (RCK) model. I present the model in its original form, which connects consumption to lifetime income and wealth. I adjust the RCK model by adding the Schofield (1985) equation, which describes weight gain as the net caloric intake of an individual, thereby connecting food consumption to body weight. I then present the steady-state equations with my addition of Schofield (1985) equation and begin calibration. I select six different countries for which I calibrate my Mathematica model in order to predict their relationships with obesity prevalence and income growth.
I study Egypt, Thailand, Mexico, Chile, Turkey, and the United States. These nations represent the highest obesity rates of their regions as well as some of the highest GDP/capita metrics as well. To calibrate my model, I use real-world long-run economic indicators, including all components of GDP, excluding net exports, as well as health metrics such as average body weight to GDP. I collect data from various databases including the World Bank national accounts data, the OECD, FRED, and WHO in order to calculate long-run averages of my economic indicators. Generally, my data ranges from the 1970s to 2018. I select this range so as to get a more accurate picture of the long-run trends in each economy. Then, I take the economic indicators and calculate parameter values for my model that best suits the structure of each economy in order to create a growth path of future obesity prevalence as the economies get wealthier over time. So far, my research suggests a positive relationship between obesity prevalence and income growth in all six of the nations. I simulate the model with Mathematica after calibrating it to match long-run economic indicators such as average income, interest rates, BMI, and weight. The end result is a quantitative analysis of the link between economic growth and a rise in obesity with numerical simulations. The model is used to predict macro-level obesity rates and, in an extension, potential welfare effects of growth when obesity is taken into consideration.