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Shin-Yi Chou Michael Grossman Henry Saffer

An Economic Analysis of Adult Obesity: Results from the Behavioural Risk Factor Surveillance System. Shin-Yi Chou Michael Grossman Henry Saffer. Between the late 1970s and 2002, the number of obese adults in the US has grown by over 50%.

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Shin-Yi Chou Michael Grossman Henry Saffer

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  1. An Economic Analysis of Adult Obesity: Results from the Behavioural Risk Factor Surveillance System Shin-Yi Chou Michael Grossman Henry Saffer

  2. Between the late 1970s and 2002, the number of obese adults in the US has grown by over 50%. • Over 300,000 premature deaths per year are considered a result of obesity and ‘sedentary’ lifestyles. • Comparatively, tobacco is 400,000; alcohol is 100,000; ‘illicit’ drugs are 20,000. • In 1995, it was estimated that there was a $99.2 billion cost (5.7% of total cost of illnesses) for obesity. • The question posed is why has there been such a rapid increase?

  3. Data for this paper is taken from the 1984-1999 Behavioural Risk Factor Surveillance System providing micro-level data. • This paper predominantly makes use of the Body Mass Index (BMI). Where the ‘healthy’ BMI is considered within the range 20 to 22.

  4. There was a suggestion that a reduction in the strenuousness of work gave way to these trends (Lakdawallaand Philipson 2002). This does not appear to explain the trend between NHANES II and NHANES III. • Predictions in this paper that obesity would be approximately 33% appear to have come true. In 2009-2010, obesity was at 35.7%. (NCHS data brief, 2012).

  5. Over the last few decades, there’s been a movement towards preparation of food that can economise people’s time. ‘Convenience foods.’ • There has been growth of restaurants. • Particularly fast food restaurants which offer quick access to high caloric density foods. • Consumption of these foods are considered to be ‘habitual’.

  6. Since real incomes for families was stagnated; with only a percentage point higher real income in 1998 than 1970 for a single earner household. • There has, instead, been a 31% real increase between these years for married couples. • Bluestone and Rose (1997) say that the average number of two earner family working hours increased by 600 between 1970 and 1990. • Increased hours mean less time for food and physical activities. • The overall effect on income would appear to be stimulated demand for inexpensive, convenient goods.

  7. Smoking is adjudged to have a negative relationship with a person’s weight. • Since the 1970s, there has been a push for a reduction in the number of people smoking.

  8. Obesity is a function of an individual’s energy consumption over a given time: • If these are looked at over a given number of periods, then we will have a function of obesity: • Food also offers a benefit as well as energy, in health, taste, or entertainment of eating. • Furthermore, eating well requires an amount of time to prepare the food. This time is, of course, a cost. • The model used is therefore: • where C is calories consumed, L is active leisure, HC is (like leisure) household chores, EW is energy expended by an average person in the occupation performed by the individual, CS is cigarette smoking, A is age, G is gender, and R summarises racial/ethnic background.

  9. This can be reduced to the following: • The model therefore is: where C is calories consumed, L is active leisure, HC is (like leisure) household chores, EW is energy expended by an average person in the occupation performed by the individual, CS is cigarette smoking, A is age, G is gender, and R summarises racial/ethnic background.

  10. The data was split into eight groups, racially as there is a tendency for race/ethnicity to impact one’s predisposition to obesity. • As the sample is of considerable size, it is possible to fit a linear probability model. • The paper goes on to explain where they find information for each of the variables explained previously and how the data was treated to make it useful for the analysis.

  11. Tables 3 and 4 contains ordinary least squares regressions of BMI and the probability of being obese, respectively. • The three different methods allow for ‘state clustering’.

  12. Using the first model in each table, it can be said: • BMI peaks at approximately 57, while the probability of being obese is at 45. • Black non-Hispanics and Hispanics have higher values of both outcomes than whites, with all other races having lower still. • While males have higher BMIs, females are more likely to be obese. • Married/widowed individuals have higher levels of both BMI and obesity than those who are divorced/single. • Years of formal schooling and real household income have negative effects on both outputs.

  13. Using the second model in each table, there can be seen a number of state specific regressors: • The per capita number of restaurants and the real price of cigarettes have positive coefficients. • Similarly, fast-food price, price of food at home, and price of full-service establishments all have negative effects. • Surprisingly, the introduction of ‘clean indoor air’ laws do not seem significant.

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