The medical care costs of obesity: An instrumental variables approach☆
Introduction
In the United States, the prevalence of obesity, defined as a body mass index1 or BMI > 30, has been rising for at least five decades (e.g. Burkhauser et al., 2009, Komlos and Brabec, 2010) and has more than doubled in the past thirty years (Flegal et al., 1998). In 2007–2008, 33.8% of American adults were clinically obese (Flegal et al., 2010). This is troubling because obesity is associated with an increased risk of myocardial infarction, stroke, type 2 diabetes, cancer, hypertension, osteoarthritis, asthma, and depression, among other conditions (Dixon, 2010, Hu, 2008).
Many previous papers have estimated the association of obesity with medical care costs (e.g. Finkelstein et al., 2009, Trasande et al., 2009, Thorpe et al., 2004, Finkelstein et al., 2003, Kortt et al., 1998). Typically, this involves estimating cross-sectional models using large secondary datasets such as the National Medical Expenditure Survey of 1987 (NMES) and the more recent Medical Expenditure Panel Survey (MEPS). These studies have made an important contribution to the literature by demonstrating the significance of medical costs associated with obesity and the diseases linked to obesity. As a result, these papers have been heavily cited and widely influential.2 For example, these estimates have been used to justify government programs to prevent obesity on the grounds of external costs (e.g. U.S. D.H.H.S., 2010).
However, the previous estimates have important limitations. The most significant is that they measure the correlation of obesity with, not the causal effect of obesity on, medical care costs. The correlation is an overestimate of the causal effect if, for example, some people became obese after suffering an injury or chronic depression, and have higher medical costs because of the injury or depression (which is likely to be unobserved by the econometrician). Conversely, the correlation is an underestimate of the causal effect if, for example, those with less access to care, such as disadvantaged minorities and the poor, are more likely to be obese (Fontaine and Bartlett, 2000). Another limitation is that these studies are usually based on self-reported, rather than measured, height and weight, and this reporting error biases the coefficient estimates (Bound et al., 2002).
This paper builds on the previous research by addressing both of these problems – endogeneity of weight and reporting error in weight – by estimating models of instrumental variables. Our instrument for the respondent's weight is the weight of a biological relative, an instrument used in the previous literature to estimate the impact of weight on other outcomes such as wages (e.g. Cawley, 2004, Kline and Tobias, 2008) and mortality (Smith et al., 2009). We estimate the IV model using the 2000–2005 MEPS, the leading source of data on medical care costs and utilization for the U.S. non-institutionalized population. Our results indicate that the effect of obesity on medical care costs is much greater than previously appreciated. The model also passes several falsification tests: it finds a stronger impact of obesity on medical expenditures for diabetes (clearly linked to obesity) than on medical expenditures for other conditions, does not find an impact of obesity on medical care costs for conditions that are unrelated to obesity, and biologically unrelated children (e.g. stepchildren) are not significant predictors of respondent weight.
The limitations of cost of illness studies are widely recognized (Shiell et al., 1987, Roux and Donaldson, 2004). For example, they are not useful for prioritizing the allocation of medical resources because that would amount to a circular argument: some conditions have a large amount of resources devoted to them and thus have a high cost of illness, but that does not imply that even more funding is needed (see, e.g., Shiell et al., 1987). This paper does not estimate the medical care costs of obesity in order to argue that treatment of obesity should be prioritized above treatment of other conditions, but to more accurately measure the marginal effect of obesity on medical care costs.
Section snippets
Identification: method of instrumental variables
Ideally, to measure the effect of obesity on medical care costs one would conduct a randomized controlled trial in which obesity was assigned by the investigator. Such an experiment would, of course, be unethical, so one must rely on natural experiments. We follow the previous literature (e.g. Cawley, 2004, Kline and Tobias, 2008, Smith et al., 2009) and use the weight of a biological relative as an instrument for the weight of the respondent.
There are two requirements for an instrument. First,
Data: medical expenditure panel survey (MEPS)
The medical expenditure panel survey (MEPS) is a comprehensive, nationally representative survey of the U.S. civilian non-institutionalized population that has been conducted annually since 1996 and uses an overlapping panel design. Respondents are surveyed about their medical care use and expenditures over the course of two years through five interview rounds. In addition, information from the household is supplemented by expenditure data collected directly from participants’ medical service
Summary statistics
Descriptive statistics for the main set of variables used in our empirical analysis are contained in Table 1 for men and Table 2 for women. (The samples are limited to adults with biological children, as they are the only MEPS respondents for whom we can estimate the IV model.) Among men, 79% incur some medical expenditures in the survey year, and the unconditional average medical expenditures in that year was $1999 (which includes zeros for those with no expenditures) in 2005 dollars. Among
Discussion
This paper provides the first estimates of the impact of obesity on medical costs that adjust for endogeneity and measurement error in weight. The impact of obesity on annual medical costs (in 2005 dollars) is estimated to be $2741 for men and women pooled, $3613 for women, and $1152 for men (which is not statistically significant). These averages are driven by relatively few individuals with very high BMI and very high medical expenditures. The estimated effects are much greater than those
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We thank Tania Andreyeva, Virginia Chang, Eric Finkelstein, Alan Monheit, Leo Trasande, Jessica P. Vistnes, and two anonymous referees their helpful comments. We are particularly indebted to Joe Newhouse, who provided many thoughtful and detailed suggestions.