Yves here. This study is carefully done for this sort of study…and yet…I’m left wondering about the fact that the researchers used observable variables, which may not be the key drivers of behavior. For instance, it finds that obesity is correlated with the amount of restaurants per capita, and it doesn’t matter whether they are fast food or higher price. That strongly suggests that eating out more leads to weight gain.
From personal experience, it is hard not to gain weight when eating out regularly because portion sizes are large, and most Americans are acculturated to clean their plates (in most social settings, it’s seen as tacky to get a doggie bag if you want to use getting another appetizer or meal out of your dining experience as a way to force portion control). But for those who aren’t subjected to regular eating out (say for business reasons), I wonder if mechanism by which eating out more often leads to obesity is not just the meals themselves, but that the large restaurant portion sizes lead regular diners to prepare home meals that have similarly large portion sizes.
By Josh Pinkston, Assistant Professor of Economics, University of Louisville, Christopher J. Ruhm, Associate Dean for Academic Affairs and Professor of Public Policy & Economics at the University of Virginia and George L Wehby. Associate Professor in the Department of Health Management and Policy, University of Iowa. Originally published at VoxEU
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Obesity is fast becoming a prominent global health issue. This column presents new evidence tentatively suggesting that variables related to the costs of eating – particularly whether there is a big discount warehouse nearby – are leading drivers of the rise in obesity occurring since the early 1980s. These findings should help policymakers work with businesses to find the best solution to tackle obesity.
The incidence of adult obesity in the US has increased to epidemic proportions over the past 50 years, reaching 35% in 2012 (Ogden et al. 2014). Obesity, defined as having a body mass index (weight in kilograms divided by height in squared meters) of 30 or greater is associated with chronic conditions such as heart disease, diabetes, and stroke (Sturm 2002). The estimated annual costs of obesity include 112,000 deaths and $190 billion in healthcare expenses (Flegal et al. 2005, Cawley and Meyerhoefer 2012).
The dramatic increase in obesity has raised the question of whether economic incentives can explain this trend. Previous studies have examined the roles of cheaper and more readily available food, variables related to physical activity, and a variety of other factors.1 However, most of this research has focused on only one or a few factors at a time, ignoring correlations with other variables that may have contributed to the trend. As a result, the previous literature likely overstates the contribution of some factors, and it is difficult to infer the combined effects of economic incentives without substantial double-counting.
Chou et al. (2004) provide the first attempt at a comprehensive economic model of obesity that includes several economic factors. They use the 1984-1999 Behavioral Risk Factor Surveillance System combined with state-level prices of grocery food, restaurant meals, cigarettes, and alcohol as well as restaurant density and clean indoor air laws. In models that control for individual demographic characteristics and state fixed effects, these state-level economic factors explain essentially all of the growth in body mass index and obesity during the period. However, Chou et al. (2004) do not control for time in any way, which likely introduces bias due to the strong upward trend in weight.
In a recent paper, we combine the various economic factors alleged to have influenced the rise in obesity in a single model (Courtemanche et al. 2015). We match state-level information on 27 economic factors to data on 2.9 million individuals from the 1990 through 2010 waves of the Centers for Disease Control’s Behavioral Risk Factor Surveillance System. We then estimate the effects of these economic factors on body mass, controlling for the effects of demographic characteristics, state, and year. The analysis is essentially a ‘statistical horse race’ to examine how much of the trend in obesity can be explained by each variable when all of the variables are considered simultaneously.
What Factors are to Blame for Obesity?
Changing economic incentives appear to play an important role in the trend of increasing body mass. The 27 economic variables jointly explain 37% of the rise in body mass index, 43% of the rise in obesity, and 59% of the rise in class II/III (severe) obesity (that is, having a body mass index equal to or over 35). The larger impact on the rise in severe obesity is especially important given the increased mortality risk at this threshold (Flegal et al. 2013). In other words, economic incentives affect your body mass index more at the higher thresholds of obesity where weight gain would result in greater harm to health than at lower thresholds.
Of the 27 economic factors, two stand out as having the largest effects. First is the increase in restaurants per capita, which explains 12%, 14%, and 23% of the increases in BMI, obesity, and severe obesity, respectively. Increased availability of restaurant food would likely encourage substitution away from home-cooked meals to relatively unhealthy restaurant meals. Furthermore, fast food is not the lone culprit. When we split the restaurant variable into fast-food and full-service restaurants, we find similar effects for each type.
The second major contributor is the increase in superstores and warehouse clubs per capita, which accounts for 17%, 16%, and 24% of the growth in body mass index, obesity, and severe obesity. The superstore variable combines Walmart Supercenters with the warehouse club chains Costco, Sam’s Club, and BJ’s Wholesale Club. A possible explanation for the impact of these stores on obesity is that they sell food at discounts of around 20% relative to traditional grocers. Alternatively, buying food in bulk at warehouse clubs could contribute to overeating. However, when decomposing the superstore variable, Walmart Supercenters are found to have roughly the same effect as warehouse clubs. Since Walmart Supercenters sell food in traditional package sizes, this reduces the likelihood that bulk buying is a primary explanation.
This analysis suggests that variables related to the costs of eating – particularly Supercenter/warehouse club expansion and increasing numbers of restaurants – are leading drivers of the rise in obesity occurring since the early 1980s. However, the source of these effects remains somewhat uncertain. One possibility, previously discussed, is that they lower food prices, particularly for energy-dense food products and restaurant meals, so that the utility-maximising level of weight has increased. An alternative is that the expansion of Supercenters/warehouse clubs and restaurants has reduced time costs because of the greater availability of such foods. When combined with time-inconsistent preferences (i.e. the inability to follow through on previously made plans) this could lead to weight gains beyond utility-maximising levels. Consistent with this, we find that Supercenter/warehouse club densities are correlated with increases in weight loss attempts, which may reflect eating mistakes.
While restaurants, Supercenters, and warehouse clubs appear to have contributed substantially to the rise in obesity, this does not necessarily mean that they are bad for society. The increased availability and affordability of food brought about by these businesses undoubtedly have substantial benefits for consumers. However, such progress comes at a cost. Future research should investigate the reasons why restaurants and superstores contribute to obesity with the aim of helping policymakers develop appropriately targeted solutions.
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