The economics of obesity.

• Author: Cawley, John
• Position: Research Summaries

During the past three decades in the United States, many indicators of population health such as life expectancy, the prevalence of smoking, and drug and alcohol use among youths improved significantly. (1) In stark contrast to these trends, over the same period the United States also experienced a doubling of the prevalence of obesity, which is defined as a body mass index (BMI) of greater than or equal to thirty, which corresponds to a weight of 221 pounds for someone six feet tall. As of 2009 to 2010, more than one-third of adult Americans are obese. (2) The United States is not alone; many countries worldwide have experienced a significant increase in obesity, and the World Health Organization estimates that 2.8 million people die each year as a result of excess weight. (3)

This has led to considerable debate about the causes and consequences of obesity and what can be done to prevent and treat it. Answering these questions is complicated because in many cases researchers cannot conduct randomized experiments: it would be unethical to experimentally manipulate individuals' weight. For this reason the empirical methods of economics, particularly the attention to issues of selection and omitted variables, are especially useful for identifying causal effects.

My primary research interest is the economics of risky health behaviors, in particular the economics of obesity. In a series of studies, my co-authors and I have investigated the economic causes and consequences of obesity and evaluated policies and programs to improve diets and increase physical activity. This research summary provides an overview of several recent projects and findings. A broader review of the economics of risky health behaviors that I coauthored with Christopher Ruhm is also available. (4)

Measurement and Trends

An important limitation of BMI, the standard measure of fatness in epidemiology, is that it does not distinguish fat from lean mass: it simply measures weight for height. A study that I conducted with Richard Burkhauser (5) found that BMI, relative to more accurate measures of fatness such as percentage of body fat, misclassifies substantial percentages of individuals as obese and non-obese. BMI tends to be less accurate at classifying men (among whom there is more variation in muscularity) than women. The use of BMI also results in biased estimates of health disparities; the black-white gap in obesity among women is only half as large if one defines obesity using percentage of body fat rather than BMI. Moreover, the timing of the rise in obesity is sensitive to the measure of fatness used; Richard Burkhauser, Max Schmeiser and I find that if one uses skinfold thickness rather than BMI to define obesity then the rise in obesity becomes apparent 10 to 20 years earlier, which suggests that more gradual or long-run influences may be responsible. (6) It also suggests that the rise in BMI might have been detected earlier, and public health responses initiated sooner, if epidemiological surveillance had not relied so exclusively on BMI. Although many social science datasets continue to collect only self-reported weight and height, some innovative surveys such as the Health and Retirement Study (HRS) and the Household, Income and Labour Dynamics in Australia (HILDA) Survey are collecting additional measures of fatness such as waist circumference.

Economic Causes and Consequences of Obesity

Many theories have been advanced to explain the rise in obesity. To measure the extent to which income affects obesity, John Moran, Kosali Simon, and I exploit the natural experiment of the Social Security Benefits Notch. (7) The Notch is the result of a legislative accident that created variation in retirement income that was large...