The Economics of Childhood and Adolescent Obesity
Summary and Keywords
Obesity is widely recognized as a chronic disease characterized by an elevated risk of adverse health conditions in association with excess body fat accumulation. Obesity prevalence reached epidemic proportions among adults in the developed world during the second half of the 20th century, and it has since become a major public health concern around the world, particularly among children and adolescents. The economics of childhood and adolescent obesity is a multi-faceted field of study that considers the numerous determinants, consequences, and interventions related to obesity in those populations.
The central economic framework for studying obesity is a life-cycle decision-making model of health investment. Health-promoting investments, such as nutritional food, health care, and physical activity, interact with genetic structure and risky health behaviors, such as unhealthy food consumption, to generate an accumulation or decumulation of excess body fat over time. Childhood and adolescence are the primary phases of physical and cognitive growth, so researchers study how obesity contributes to, and is affected by, the growth processes. The subdiscipline of behavioral economics offers an important complementary perspective on health investment decision processes, particularly for children and adolescents, because health investments and participation in risky health behaviors are not always undertaken rationally or consistently over time.
In addition to examining the proximate causes of obesity over the life cycle, economists study obesity’s economic context and resulting economic burden. For example, economists study how educational attainment, income, and labor market features, such as wage and work hours, affect childhood and adolescent obesity in a household. Once obesity has developed, its economic burden is typically measured in terms of excess health-care costs associated with increased health risks due to higher obesity prevalence, such as earlier onset of, and more severe, diabetes. Obesity among children and adolescents can lead to even higher health-care costs because of its early influence on the lifetime trajectory of health and its potential disruption of healthy development.
The formulation of effective policy responses to the obesity epidemic is informed by economic research. Economists evaluate whether steps to address childhood and adolescent obesity represent investments in health and well-being that yield private and social benefits, and they study whether existing market structures fail to appropriately motivate such investments. Potential policy interventions include taxation of, or restricting access to, obesogenic foods and other products, subsidization of educational programs about healthy foods and physical activity inside and outside of schools, ensuring health insurance coverage for obesity-related preventive and curative health-care services, and investment in the development of new treatments and medical technologies.
A vast literature documents the trends, causes, and consequences of obesity in the late 20th and early 21st centuries. Although widespread concern about obesity first arose in the United States and other developed countries, obesity now has meaningful effects on societies worldwide. Between 1980 and 2015, countries ranking high on a sociodemographic index of income, educational attainment, and fertility (SDI) marked the highest prevalence of obesity among children, but there was a 20% increase in obesity prevalence in low SDI countries, and the increase was even greater for middle-income countries (The GBD 2015 Obesity Collaborators, 2017). Because childhood obesity is associated with a greater lifetime risk of type 2 diabetes, hypertension, and chronic kidney disease (Gregg & Shaw, 2017), disease burden and medical expenditures are expected to increase concomitantly.
The discipline of economics has contributed to the study of obesity along several dimensions. Economists conceptualize obesity in an economic framework, which acts as a guide when conducting theoretical and empirical analyses of household and individual-level behavior that relates to weight accumulation. Of particular importance is empirical research that identifies the causal influences of relevant existing and potential policies, and the economic framework of obesity can help inform discussion of policy design. Furthermore, economists contribute to understanding and accounting for medical and other costs associated with obesity.
Obesity in an Economic Framework
Economic analyses of obesity generally begin from the perspective of a utility-maximizing individual who invests material and time resources in health, leisure, and consumption. This approach follows the health capital model first described by Grossman (1972). In this framework, a forward-looking individual makes decisions about how time devoted to labor, leisure, and improving or maintaining health can be optimally allocated in current and future time periods. Investments are conceptualized as inputs in a health production function, the output of which is modeled as a capital stock, and health in turn yields services to the individual in the form of healthy time. This theoretical model sparks consideration of how the uses of time and other resources constitute direct investments in health through physical activity or other health-promoting activities, and how indirect investments are made in medical care, food, etc., that are purchased with labor earnings or wealth.
Economists studying obesity have adapted the health capital model to more specifically represent the process of weight gain. Instead of a capital stock of health, generally defined, the adaptations conceptualize body weight as a capital stock that decreases or increases in accordance with investment flows, such as food, exercise, and medical care. Although a capital stock (quantity) is an intuitive representation of weight, obesity is defined categorically. Among adults, obesity is measured as a body mass index (BMI) category, specifically defined as weight in kilograms divided by height in meters squared. An adult with a BMI greater than 30 is considered obese, and increasingly severe obesity is defined by higher BMI cutoffs (Centers for Disease Control and Prevention, 2012). In part because children can have a healthy weight at varying BMI ranges across ages, for children and adolescents, obesity is defined as a BMI of at least the 95th percentile for a given age and sex (Centers for Disease Control and Prevention, 2016). Consistent with the observation that weight gain is a cumulative process across childhood, evidence suggests that obesity status often persists for children after they enter primary school (Millimet & Tchernis, 2015).
The capital investment structure of the health capital model illustrates the importance of dynamic effects in the weight accumulation process. One implication highlighted by economists, because of its relevance for policy recommendations, is that an individual will settle on a steady-state weight for fixed rates of health investment (Bishai, 2015; Chou, Rashad, & Grossman, 2008; Cutler, Glaeser, & Shapiro, 2003; Schroeter, Lusk, & Tyner, 2008). This contrasts with simpler models of weight gain that assume a constant relationship between health inputs, such as total calories consumed, and weight accumulation (Lin, Smith, Lee, & Hall, 2011).
An appeal of the health capital model as a starting point is its emphasis on the relationship between resource allocation decisions and weight gain, but it imperfectly represents those decisions because of relatively strong assumptions about rationality, information availability, and a forward-looking perspective. This raises issues when adults are studied, for example, regarding the actual extent of forward-looking behavior with respect to consumption decisions (Chavas, 2013; Courtemanche, Heutel, & McAlvanah, 2015; Ikeda, Kang, & Ohtake, 2010) or information availability about nutrient content (Bollinger, Leslie, & Sorensen, 2011; Ellison, Lusk, & Davis, 2014; Just & Payne, 2009; Loureiro, Yen, & Nayga, 2012). Theoretical departures from the health capital model often focus on adults, but lessons from the departures are a good starting point for modeling children and adolescents because the issues are exacerbated in the young subpopulation.
A subset of the literature on the economics of child and adolescent obesity, which can be viewed as operating more or less with the health capital model as a foundation or touchpoint, focuses on the behavioral, social, and economic causes of obesity. Research in this area can be motivated by a specific behavioral model, such as the health capital model, and then typically proceeds by studying the relationship between health-related behaviors and obesity within that context. Other less structural approaches use policy changes or other quasi-experiments to estimate the causal influences of policies and other treatments, broadly defined, on obesity outcomes. Although only a few research areas are offered as examples in this article, they tie back to these modeling and analysis approaches along many dimensions.
When is Policy Intervention Warranted?
Economic theory offers a compelling criterion that can be used when considering whether policy intervention is justified. Broadly defined, an activity or production process has an external effect, or externality, when it induces behavior change among nonparticipants for which they are not somehow compensated in a market transaction. A classic example is a factory that pollutes a river and does not compensate persons downstream who are forced to seek drinking water elsewhere, under the assumption that the river is not owned by the factory. Although externalities are not the only important considerations, notably with respect to children, they are generally useful and typically uncontroversial starting points for policy discussions among economists.
Bhattacharya and Sood (2011) questioned whether obese adults impose external costs on others, and they presented evidence that much of the cost of adult obesity should perhaps not be automatically a concern for policymakers. The key empirical question Bhattacharya and Sood asked is, do obese adults cause others to change their behavior in such a way that is uncompensated? They would presumably say “not evidently,” because, for example, some evidence suggests that obese workers earn a sufficiently low wage to compensate for their greater contribution to (group) health insurance costs (Bhattacharya & Bundorf, 2009). However, other evidence points to this effect’s being explained by weight discrimination or preferences for appearance among employers (Cawley, 2015). Bhattacharya and Sood (2011) also argued that public health insurance coverage of health-care expenditures attributable to obesity similarly does not in itself represent an obesity externality, with the exception that higher payroll taxes used to finance the transfer likely cause deadweight loss in labor markets. For the most part, public insurance coverage is a lump-sum transfer from the well to the sick, consistent with other treatments covered by public health insurance. Microeconomic theory typically finds that such transfers do not reduce total societal welfare (Feldman, 2008), so the question of how large they should be involves political considerations that are outside the scope of economic analysis.
An important question is, to what extent does the availability of health insurance induce ex ante moral hazard, or in this case, behavior change that increases the risk of obesity because the covered individual expects health-care costs will ultimately be paid by a third party? This is equivalent to asking whether obesity imposes external costs because ex ante moral hazard occurs when the presence of insurance affects the risk behavior of insurance participants, imposing additional costs divided across the insurance risk pool, and which are not otherwise compensated for. Bhattacharya and Sood (2011) cited evidence that health insurance does not impact obesity among adults—in other words, they found no evidence of ex ante moral hazard in this context, and some other evidence tends to support this finding. For example, Simon, Soni, and Cawley (2016) showed that state-level Medicaid expansions introduced by the Affordable Care Act had no impact on obesity or obesity-related behaviors, while at the same time they increased preventive care use.
Bhattacharya and Packalen (2012) nevertheless acknowledged evidence for Medicare-induced ex ante moral hazard, which arises if, knowing that Medicare will ultimately be available to pay for medical expenditures later in life, future Medicare recipients behave in a way that increases their obesity risk when young (Dave & Kaestner, 2009). Yet, Bhattacharya and Packalen estimated what they describe as the “innovation externality” of obesity, which has the opposite, and in their estimation, equivalent effect to Medicare-induced moral hazard. The innovation externality occurs when increased obesity prevalence spurs the (profit) reward for innovation in combating obesity through individual-level treatments, such as pharmaceuticals or other medical care. Although estimates of the innovation externality likely vary and can therefore have heterogeneous implications across populations and subpopulations, one can conclude that more attention to accounting for externalities in the context of obesity and related risky health behaviors is necessary to fully understand optimal obesity policy.
Much of the argument put forward by Bhattacharya and Sood (2011) refers to adults, so how should it be applied to children and adolescents? Although the same issues generally apply, the details of the discussion are markedly different. First, ex ante moral hazard is less likely to be a substantial concern for children and adolescents because they probably focus more on the immediate implications of their behavior rather than delayed health implications and health-care costs. In terms of externalities, this leaves the question of whether obesity among children and adolescents implies other external effects on the rest of society.
Although Bhattacharya and Sood’s (2011) labor market discussion is relevant because obese children who become obese adults similarly bear the burden of health costs, questions about the broader long-term impact on society remain. For example, if an important justification for investment in public education is to increase average state or national productivity and economic growth, might a similar justification apply to addressing childhood obesity, either due to obesity’s impact on education, or due to its direct impact on productivity? Indeed, an area of substantial interest for economists studying obesity is the extent of its impact on educational attainment and the development of both cognitive and noncognitive skills that affect health and productivity (Black, Johnston, & Peeters, 2015; Heckman, 2006, 2007; Kaestner & Callison, 2011).
An arguably clearer example that illustrates how obesity-reduction policies can be justified in this context is the decreasing share of the population who meet weight requirements for military service. Cawley and Maclean (2012, 2013) found that the proportion of civilians who exceeded weight and fat standards for military academy admission and regular military enlistment had increased substantially in the late 20th century. The government’s military capability is a textbook example of a public good, and a decrease in the availability of labor for the production of military capability generally increases the cost of its provision through capital input substitution. This is perhaps one of the clearest examples of how obesity has a negative macroeconomic impact that could be mitigated through obesity-reduction policies, but other examples follow similar logic.
Perhaps most importantly, children and adolescents cannot reasonably be expected to make forward-looking, rational decisions informed by perfect information, forecasting across their entire lifetimes. Children can be viewed as having an extreme form of present-bias, a version of which is known as hyperbolic discounting, in comparison to adults (Cutler et al., 2003; Smith, Bogin, & Bishai, 2005). Present-bias has been shown to predict BMI (Courtemanche et al., 2015). Although parents are assumed to make some decisions on behalf of their children with respect to investment in education and health, policy interventions are nevertheless justifiable when parent and child incentives are misaligned, for example, with respect to a decision’s time horizon, and because parents do not have entirely altruistic preferences with respect to their children. Justifications for age restrictions on the purchase of tobacco products, which generally exclude minors altogether, can be framed in these terms.
Determinants of Child and Adolescent Obesity
One of the most important categories, if not the most important category, of factors contributing to obesity is unhealthy eating or overeating. Economists typically measure net calorie surplus (or deficit) as a proxy for healthy (unhealthy) eating, where unhealthy eating can be represented as calorie intake, net of calorie expenditure from metabolic processes and physical activity, that is either too high or too low for maintaining a healthy weight (Riera-Crichton & Tefft, 2013). In the absence of detailed information about the types of foods consumed and how they interact with other factors to produce weight, researchers consider the total impact of policies expected to influence child and adolescent obesity through their effects on net calorie surplus.
Food prices and other constraints on food availability can impact total food consumption and the relative consumption of healthy and unhealthy foods, which in turn has important implications for child and adolescent obesity. Notable examples of closely studied food categories are fresh fruits and vegetables, as well as fast food. As expected from the economic framework describing weight accumulation, studies often find that higher food prices can impact obesity, presumably through changes in food consumption patterns, and depending on how elastic the responses to price changes are (Grossman, Tekin, & Wada, 2014; Powell, 2009; Powell & Chaloupka, 2009). In addition, physical access to food, as constrained by its geographic distribution and availability, may affect food purchases, consumption, and obesity. Commonly studied examples of this are food deserts, or a lack of nearby supermarkets with fresh fruits and vegetables (Powell & Bao, 2009; Thomsen, Nayga, Alviola, & Rouse, 2016), fast food restaurants (Currie, DellaVigna, Moretti, & Pathania, 2010; Dunn, 2010; Lhila, 2011), and the availability of fast foods in schools (Anderson & Butcher, 2005).
If consumers lack information about available food products, or if their demand for specific food products can be influenced, food advertising can also change consumption patterns and impact obesity. Because advertising is often used to promote unhealthy food products, such as fast food (Chou et al., 2008; Grossman, Tekin, & Wada, 2012), restrictions on advertising for these products could have a beneficial impact on obesity prevalence, particularly among children and adolescents (Berning & McCullough, 2013; Silva, Higgins, & Hussein, 2015).
Policies designed to improve the population’s nutrition have generally been found to lower obesity prevalence among children and adolescents. Participation in the National School Lunch Program, School Breakfast Program, and Supplemental Nutrition Assistance Program generally reduces obesity risk for children (Baum, 2011; Burgstahler, Gundersen, & Garasky, 2012; Hofferth & Curtin, 2005; Kreider, Pepper, Gundersen, & Jolliffe, 2012; Millimet & Tchernis, 2013; Millimet, Tchernis, & Husain, 2010; Schmeiser, 2012), although the results are not uniform for all population subgroups (Millimet et al., 2010; Robinson & Zheng, 2011; Schanzenbach, 2009). Studies have identified a reduction in high-calorie or otherwise unhealthy foods in response to targeted food policies (Bauhoff, 2014; Fan & Jin, 2015).
Policies that have gained considerable momentum for their potential to combat obesity are those that restrict consumption of sugar-sweetened beverages (SSB) through taxation or restrictions on SSB availability in schools (Brownell et al., 2009). Recent examples demonstrate the potential to increase SSB prices via taxation while also inducing a behavior response when demand is not perfectly inelastic (Cawley & Frisvold, 2017). Taxes on SSB could also reduce SSB purchases among vulnerable population subgroups, but more research is needed to understand the long-term consumption and obesity effects (Colchero, Popkin, Rivera, & Ng, 2016).
As with restrictions on tobacco product purchases that have affected adolescent tobacco consumption, restrictions on SSBs or other unhealthy foods have the potential to meaningfully impact obesity. An important difference, however, is the potential substitutability of relevant products when some, but not all, are targeted by obesity policies. Contributions from the economics literature have highlighted this issue, recommending comprehensive policies that account for substitution in the context of an SSB tax (Fletcher, Frisvold, & Tefft, 2010a, 2013) or food and beverage restrictions in schools (Fletcher, Frisvold, & Tefft, 2010b).
On the other side of the calorie-expenditure balance equation are exercise and other physical activities, which receive a great deal of attention in obesity research. Some research directly examines the relationship between physical activity and obesity: Dhar and Robinson (2016) found that being physically active reduces the likelihood of obesity among children. Beyond the overall association, some research has attempted to identify contextual factors related to physical activity and its subsequent obesity effects, including the built environment, with mixed results (Fan & Jin, 2014; Morales, Gordon-Larsen, & Guilkey, 2016; Sandy, Tchernis, Wilson, Liu, & Zhou, 2013).
Other studies consider the effects of policies designed to influence childhood physical activity, although there are relatively few in the economics literature. A pair of studies considered whether state minimum physical education requirements in schools impact reported physical activity, and they found evidence that stricter standards increase physically active time and, in some cases, reduce BMI among youth (Cawley, Frisvold, & Meyerhoefer, 2013; Cawley, Meyerhoefer, & Newhouse, 2007). These results highlight the importance of understanding the heterogeneous effects of policies related to obesity. Increased physical activity because of stricter requirements is found to complement other physical activity among boys, thus increasing the policy’s effectiveness for that population. However, the same set of policies is found to induce substitution away from other physical activity among girls, thus rendering it less effective (Cawley et al., 2013).
Overall, the study of policies related to combating obesity through changes in physical activity is less well developed in the economics literature. This is perhaps due to a relative lack of available data on detailed physical activity. Another possibility is that physical activity could have ambiguous effects on BMI, such as through building muscle in the place of body fat, or that effects occur over a longer term than is readily detectable. As a result, there is still much to be learned not only about how physical activity affects obesity, but also about how social and economic factors influence physical activity.
In addition to mechanisms directly related to net calorie surplus, broader categories of childhood and adolescent experiences are a useful as potential targets of policy intervention. Classen and Hokayem (2005) found that maternal obesity and other demographic characteristics are separately and similarly predictive of youth obesity status. This suggests that childhood experiences, such as the household environment, can affect the risk of childhood obesity, despite there also being evidence for a substantial genetic component in intergenerational obesity transmission (Classen & Thompson, 2016).
Several studies have considered the effects of parental employment, particularly maternal employment, on a variety of child outcomes, including childhood overweight and obesity. Parental employment can affect obesity through a variety of channels because employment uses time resources to generate labor income. If time spent preparing healthy food and monitoring food consumption, encouraging physical activity, or otherwise organizing the household to promote health is used instead to earn income, child health and obesity may worsen (Cawley & Liu, 2012; Courtemanche, 2009; Fertig, Glomm, & Tchernis, 2009; Fiese, Hammons, & Grigsby-Toussaint, 2012; Roy, Millimet, & Tchernis, 2012). At the same time, labor earnings can be used to purchase healthier food, medical care, and access to health-promoting activities, which could lessen the obesity risk for household members (Jo, 2014).
That these mechanisms are ultimately impactful is sometimes evident in the economics literature. Ruhm (2008) found evidence that maternal employment increases obesity risk among youths of higher socioeconomic status but no evidence that it affects the body weight of disadvantaged children. Chia (2008) reported that a mother’s returning to work after a child’s birth is associated with an increased risk of obesity. Although Chowhan and Stewart (2014) found that maternal employment affects youth behaviors expected to be related to obesity, they found no evidence for effects on obesity itself.
Some policies could affect a household’s wealth, or income earned from sources other than labor, so it is important to understand how changes in wealth separately impact obesity. Akee et al. (2013) found that cash transfers increase the BMI of children in the poorest households of a sample drawn from western North Carolina, with less of an impact in wealthier households. However, Cesarini et al. (2016) found the opposite effect on obesity among lottery winners in Sweden, specifically that increased wealth could reduce obesity risk. They noted that this result represents an outcome for an otherwise wealthy country with a strong safety net, so more research is needed to identify causal influences in other populations.
Understanding the effects of parental employment and income along several dimensions, including the number of hours worked, wage rate, other earnings, and the labor market participation of other household members, is important when considering the effects of policies like the Earned Income Tax Credit (EITC), parental leave, and subsidized child care. The EITC is a broadly impactful federal and state policy that has been shown to induce substantial employment effects, and in turn, effects on health outcomes, including obesity. Economists not only have studied the effects of the policy itself, but also have used changes in the EITC as quasi-experiments for studying causal economic relationships of interest, including the effects of employment or income on obesity. McGranahan and Schanzenbach (2013) found that a more generous EITC increases purchases of healthy food products. There is mixed evidence on the obesity effects of the EITC among adults (Gomis-Porqueras, Mitnik, Peralta-Alva, & Schmeiser, 2011; Schmeiser, 2009), and there is less evidence of its effects among youth.
Subsidized child care is another policy with potentially nuanced effects on obesity because it combines the opportunity for additional labor earnings for parents with a different child-care environment (i.e., in a group setting) than when parents provide care directly. Herbst and Tekin (2012) reported an adverse impact of child-care subsidies on child BMI and that nonparental child-care centers serve as the channel for this effect. However, Mandal and Powell (2014) reported that center care and other regulated care settings are associated with higher fruit and vegetable consumption. More research is needed to fully characterize the effects of child-care subsidies in various child care settings and among certain population subgroups.
School and Other Settings Outside the Home
Some national-level policies that affect nutrition in schools, such as the National School Lunch Program and School Breakfast Program, are discussed elsewhere in this article, but there also have been studies of direct incentives for children to improve food choices. The findings suggest that small incentives can lead to substantial, and perhaps persistent, changes in food choices during school (List & Samek, 2015), and the effect is strongest for the most vulnerable children (Just & Price, 2013). Providing students with information about healthy and unhealthy foods could also improve food choices, although in one experiment, positive impacts on healthy food consumption were negated by experimental advertising for unhealthy foods (Mora & Lopez-Valcarcel, 2017).
Aside from studies of nutrition-related policies and programs that were implemented in schools, there are relatively few studies of the overall impact of education on child and adolescent obesity in the economics literature. Frisvold and Lumeng (2011) compared full- to half-day attendance at Head Start, concluding that full-day attendance yields a lower obesity rate after 1 year. Also finding that school tends to improve outcomes related to obesity, Anderson et al. (2011) reported that similar age children who were in school longer, a determination made possible by birth-date cutoffs for school entrance, may have healthier weight outcomes among some population subgroups.
There is mixed evidence about whether peer effects are important in “transmitting” obesity, with much of the literature focused on how to separate causal factors from associations (Cohen-Cole & Fletcher, 2008). Renna et al. (2008) estimated that adolescents who have close friends also have a higher BMI, but they found a causal impact only for women. Yang and Huang (2014) showed evidence for asymmetric directional associations, where peer weight gain is associated with own weight gain, but peer weight loss is not similarly associated with own weight loss. Halliday and Kwak (2009) noted that the weight association between peers is particularly strong at the upper end of the BMI distribution, but they reiterate how difficult it is to identify causal relationships.
Although evidence on the relationship between obesity and other risky health behaviors among children and adolescents is relatively scarce in the economics literature, a notable body of work studies patterns of obesity and smoking. Overall, it suggests that obesity leads to smoking initiation or more smoking, in part to control appetite and weight gain, particularly among girls (Cawley, Dragone, & Von Hinke Kessler Scholder, 2016; Cawley, Markowitz, & Tauras, 2006; Rees & Sabia, 2010). Mellor (2011) similarly found that cigarette taxes increase BMI, but not obesity prevalence, among the children of mothers who smoke.
The Effects of Obesity on Selected Outcomes
Medical care plays a key role in the economic model of weight accumulation. Central to the model is that the demand for medical care is implied by the demand for good health because medical care is an important input in producing good health. Medical care expenditures related to obesity could be substantial, so economists work to account for costs that are specifically attributable to obesity (Finkelstein & Yang, 2011). For example, it is important that governments and insurers understand obesity-attributable costs, for example, by conducting economic evaluations, in order to weigh the benefits and costs of obesity-reduction policies.
The literature is mixed regarding the extent to which obesity among children and adolescents affects their medical care expenditures. Wright and Prosser (2014) discussed the variation in sampling and methods employed by researchers studying this association, and in their own analysis they found insignificant or marginal differences in health-care costs between normal-weight and obese youth. They pointed out that an important challenge when studying medical care cost impacts is that costs caused by obesity may accumulate over a long period of time, so significant differences may only be detected after childhood.
Another obstacle to understanding how obesity impacts medical care is the lack of controlled experimental data, or even high event frequency epidemiological data like that available for acute illnesses. Economists therefore seek to identify causal relationships through quasi-experimental studies when such data are unavailable. In the context of obesity and medical care, Cawley and Meyerhoefer (2012) took an instrumental variables estimation approach for adults. They used the BMI of a respondent’s oldest biological child as an instrument for the respondent’s own obesity status, under the assumption that the instrument has a random component that is associated with medical care costs only through its association with own obesity. They found that among this adult subpopulation, obesity increases annual medical care costs by $2,741 (in 2015 dollars), while an association study using the same data would conclude that obesity is associated with a cost increase of only $656.
Similarly, Biener, Cawley, and Meyerhoefer (2017) used the BMI of a child’s biological mother as an instrument for that child’s obesity status, effectively reversing the above policy quasi-experiment. They found that, among children, obesity increases annual medical care costs by $1,354 (in 2013 dollars). Although this is a smaller increase than for adults, the increase alone represents more than the average total medical care costs among non-obese children. This pair of estimates, for adults and children, could be used in economic evaluations of policies that impact childhood obesity but that appropriately account for expected total medical care costs across a child’s lifetime.
These examples highlight important questions that will benefit from future research. To effectively design policies to lower health-care costs among children, economists and other researchers can make substantial progress by identifying causal, long-term impacts of obesity on health-care costs among nationally representative populations and important subpopulations. This could be made possible with new quasi-experimental approaches or high-dimensional longitudinal data containing important covariates that have not previously been accounted for.
Education stands at the center of the economic model of health capital, where health in early life can influence one’s ability to acquire education, and education in turn affects health through a variety of channels. More education is typically associated with higher earnings, and with higher earnings an individual can better invest in his or her health. In addition, more education can help individuals navigate interactions with the health-care and health-insurance systems, or, more generally, better organize resources devoted to health investment.
The extent to which childhood obesity leads to worse education outcomes, or to which lower education levels impact obesity, has implications for policy design. Some mechanisms through which obesity may impact educational achievement include absenteeism or poor concentration due to ill health. In contrast to other illnesses that affect absenteeism and concentration, however, obesity can also affect a child’s socioemotional skill development, through discrimination or even bullying related to the stigma associated with obesity (Black & Kassenboehmer, 2017).
Economics research has considered the impact of obesity among children and adolescents on contemporaneous or lagged educational achievement, as well as on specific cognitive and noncognitive skills. When effects are identified, they have tended toward an adverse impact of obesity on education-related outcomes: grade point average, high school completion, and verbal skills among females (Cawley & Spiess, 2008; Okunade, Hussey, & Karakus, 2009; Sabia, 2007), verbal skills, social skills, motor skills, activities of daily living, and overall cognitive achievement among males (Black et al., 2015; Cawley & Spiess, 2008), overall reading proficiency (Gurley-Calvez & Higginbotham, 2010), and military academy admission (Cawley & Maclean, 2013).
Labor Market Outcomes
That obesity tends to adversely affect educational attainment is expected to translate into negative impacts on labor market participation and earnings when an obese individual enters the labor market. This based on the human capital model of individual utility maximization (Becker, 1962), which is closely related to the health capital model (Grossman, 1972). The human capital model describes investment in a theoretical capital stock of human capital through education, which yields services to an individual through higher productivity and earnings. When obesity is found to negatively impact educational outcomes, it is likely that labor market prospects will also worsen.
Several investigators have studied the relationship between adult obesity and labor market outcomes (Bhattacharya & Bundorf, 2009; Lindeboom, Lundborg, & van der Klaauw, 2010), and because youth obesity is correlated with adult obesity, labor market outcomes in adulthood will presumably also be related. Although longitudinal associations are more difficult to statistically establish, some studies have estimated the relationship. Allowing the impact of obesity to operate through education by not including measures of education in their estimation framework, Han, Norton, and Powell (2011) found that obesity among older adolescents is associated with adult wages that are lower by 3.5%. Amis, Hussey, and Okunade (2014) also showed that, more than 13 years later, adolescent obesity negatively impacts earnings for some population subgroups.
Amid the voluminous multidisciplinary literature on obesity, economics stands out by modeling obesity-related economic behavior and estimating causal relationships between policies and economic outcomes. Contributions from microeconomic theory include an investigation of utility-maximizing health investment, modified to study the determinants and consequences of excess weight accumulation over the life cycle. Applied econometricians, for their part, study quasi-experiments and use advanced statistical techniques to identify causal relationships, a useful strategy for studying real-world behavior when experimental approaches are not feasible.
Economists will surely continue their work to flesh out existing knowledge of policy-relevant obesity impacts, taking advantage of new quasi-experiments and data sets that contain additional information on determinants and other control variables. Although it is difficult when isolating causal effects, it is important that researchers present comprehensive evidence on policy effects simultaneously, so that the complex set of obesity’s causes and consequences can be effectively addressed by policymakers. For an extended discussion of upcoming research priorities in the economics of obesity, see Cawley (2015).
Some broad examples of ongoing, important research questions are: What are the long-term impacts of changes in the distribution of child and adolescent obesity on national income and medical care cost growth, two important economic outcome variables, when considering policy initiatives to combat obesity? Relatedly, what are the nonmonetary utility (or psychological) benefits and costs associated with obesity? In the specific context of childhood and adolescent obesity, how do we balance the immediate utility (enjoyment) children reap from their consumption of unhealthy foods or beverages against the long-term monetary and health costs sown by unhealthy weight gain? Starting with a comprehensive accounting for the external benefits and costs of obesity, and improving our understanding of the appropriate role of policy when children and adolescents have limited ability to make good long-term decisions, research in economics can help make headway in answering these questions.
An illustrative thought experiment is to imagine constructing a comprehensive set of policies to combat obesity that mirrors those currently in place for a comparable risky health behavior. The health effects of tobacco consumption are in some ways like those of unhealthy food consumption, with health risks generally occurring later in life. For children and adolescents, policymakers might be relatively more concerned about a lack of foresight about long-term health effects than they would be about externalities, a perspective that, for this population, would place obesity and tobacco policy more in line.
SSB taxes have been implemented in some jurisdictions, and economists and other researchers have begun to study their impacts. Setting aside concerns about substitution to other unhealthy beverage and food products, would an outright ban on the purchase of SSB by persons under age 18 or 21 be justifiable by analogy to similar bans on cigarettes or e-cigarettes? It potentially is, but economists and other researchers would likely be better equipped to explore such questions after conducting more comprehensive, integrated research. Such integration would involve some of the existing research strengths in economics, including modeling health capital investment and measuring causal impacts of obesity on economic variables, combined with knowledge from other disciplines, such as the psychological benefits and costs of unhealthy food consumption and obesity. Behavioral economics contributes directly to this intersection, so it will likely continue to be a relevant area of fruitful research. For example, there has been considerable research studying the effects of menu nutrition information on consumption behavior among the general population and adults, as discussed in this article, but less attention has been devoted to studying child and adolescent populations.
Finally, ongoing debates about the structure of the social safety net in the United States and other countries will likely continue for many years, with important implications for obesity policy. In the United States, how and to what extent health insurance coverage is delivered to the population will relate to how aggressively policymakers should combat obesity prevalence. Under discussion in countries across the developed world, substantial restructuring of the social safety net to feature a universal basic income would have important effects on children and adolescents living in families whose labor-market participation, labor-market earnings, and nonlabor income would substantially change. Economists and other health-policy researchers should continue to set their research agenda with an eye to these and other far-reaching social developments.
Cawley, J. (Ed.). (2011). The Oxford handbook of the social science of obesity. Oxford: Oxford University Press.Find this resource:
Cawley, J., & Ruhm, C. J. (2011). The economics of risky health behaviors. Handbook of Health Economics, 2, 95–199.Find this resource:
Akee, R., Simeonova, E., Copeland, W., Angold, A., & Costello, E. J. (2013). Young adult obesity and household income: Effects of unconditional cash transfers. American Economic Journal: Applied Economics, 5(2), 1–28.Find this resource:
Amis, J. M., Hussey, A., & Okunade, A. A. (2014). Adolescent obesity, educational attainment and adult earnings. Applied Economics Letters, 21(13–15), 945–950. London: Taylor & Francis Group.Find this resource:
Anderson, P. M., & Butcher, K. F. (2005). Reading, writing and Raisinets: Are school finances contributing to children’s obesity? National Bureau of Economic Research Working Paper Series, No. 11177.
Anderson, P. M., Butcher, K. F., Cascio, E. U., & Schanzenbach, D. W. (2011). Is being in school better? The impact of school on children’s BMI when starting age is endogenous. Journal of Health Economics, 30(5), 977–986.Find this resource:
Bauhoff, S. (2014). The effect of school district nutrition policies on dietary intake and overweight: A synthetic control approach. Economics & Human Biology, 12, 45–55.Find this resource:
Baum, C. L. (2011). The effects of food stamps on obesity. Southern Economic Journal, 77(3), 623–651.Find this resource:
Becker, G. S. (1962). Investment in human capital: A theoretical analysis. Journal of Political Economy, 70(5, Part 2), 9–49.Find this resource:
Berning, J., & McCullough, M. (2013). Advertising soft drinks to children: Are voluntary restrictions effective? Agribusiness, 29(4), 469–485.Find this resource:
Bhattacharya, J., & Bundorf, M. K. (2009). The incidence of the healthcare costs of obesity. Journal of Health Economics, 28(3), 649–658.Find this resource:
Bhattacharya, J., & Packalen, M. (2012). The other ex ante moral hazard in health. Journal of Health Economics, 31(1), 135–146.Find this resource:
Bhattacharya, J., & Sood, N. (2011). Who pays for obesity? Journal of Economic Perspectives, 25(1), 139–158.Find this resource:
Biener, A. I., Cawley, J., & Meyerhoefer, C. (2017). The medical care costs of youth obesity: An instrumental variables approach. National Bureau of Economic Research Working Paper Series, No. 23682.
Bishai, D. (2015). Generalized nutrient taxes can increase consumer welfare. Health Economics, 24(11), 1517–1522.Find this resource:
Black, N., Johnston, D. W., & Peeters, A. (2015). Childhood obesity and cognitive achievement. Health Economics, 24(9), 1082–1100.Find this resource:
Black, N., & Kassenboehmer, S. C. (2017). Getting weighed down: The effect of childhood obesity on the development of socioemotional skills. Journal of Human Capital, 11(2), 263–295.Find this resource:
Bollinger, B., Leslie, P., & Sorensen, A. (2011). Calorie posting in chain restaurants. American Economic Journal: Economic Policy, 3(1), 91–128.Find this resource:
Brownell, K. D., Farley, T., Willett, W. C., Popkin, B. M., Chaloupka, F. J., Thompson, J. W., & Ludwig, D. S. (2009). The public health and economic benefits of taxing sugar-sweetened beverages. New England Journal of Medicine, 361(16), 1599–1605.Find this resource:
Burgstahler, R., Gundersen, C., & Garasky, S. (2012). The Supplemental Nutrition Assistance Program, financial stress, and childhood obesity. Agricultural and Resource Economics Review, 41(1), 29–42.Find this resource:
Cawley, J. (2015). An economy of scales: A selective review of obesity’s economic causes, consequences, and solutions. Journal of Health Economics, 43, 244–268.Find this resource:
Cawley, J., Dragone, D., & Von Hinke Kessler Scholder, S. (2016). The demand for cigarettes as derived from the demand for weight loss: A theoretical and empirical investigation. Health Economics, 25(1), 8–23.Find this resource:
Cawley, J., & Frisvold, D. E. (2017). The pass-through of taxes on sugar-sweetened beverages to retail prices: The case of Berkeley, California. Journal of Policy Analysis and Management, 36(2), 303–326.Find this resource:
Cawley, J., Frisvold, D., & Meyerhoefer, C. (2013). The impact of physical education on obesity among elementary school children. Journal of Health Economics, 32(4), 743–755.Find this resource:
Cawley, J., & Liu, F. (2012). Maternal employment and childhood obesity: A search for mechanisms in time use data. Economics and Human Biology, 10(4), 352–364.Find this resource:
Cawley, J., & Maclean, J. C. (2012). Unfit for service: the implications of rising obesity for US military recruitment. Health Economics, 21(11), 1348–1366.Find this resource:
Cawley, J., & Maclean, J. C. (2013). The consequences of rising youth obesity for U.S. Military Academy admissions. Applied Economic Perspectives and Policy, 35(1), 32–51.Find this resource:
Cawley, J., Markowitz, S., & Tauras, J. (2006). Obesity, cigarette prices, youth access laws and adolescent smoking initiation. Eastern Economic Journal, 32(1), 149–170. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=21996404&site=ehost-live.Find this resource:
Cawley, J., & Meyerhoefer, C. (2012). The medical care costs of obesity: An instrumental variables approach. Journal of Health Economics, 31(1), 219–230.Find this resource:
Cawley, J., Meyerhoefer, C., & Newhouse, D. (2007). The impact of state physical education requirements on youth physical activity and overweight. Health Economics, 16(12), 1287–1301.Find this resource:
Cawley, J., & Spiess, C. K. (2008). Obesity and skill attainment in early childhood. Economics and Human Biology, 6(3), 388–397.Find this resource:
Centers for Disease Control and Prevention. (2012). Adult overweight and obesity. Retrieved from http://www.cdc.gov/obesity/adult/index.html.
Centers for Disease Control and Prevention. (2016). Defining childhood obesity. Retrieved from https://www.cdc.gov/obesity/childhood/defining.html.
Cesarini, D., Lindqvist, E., Ostling, R., & Wallace, B. (2016). Wealth, health, and child development: Evidence from administrative data on Swedish lottery players. Quarterly Journal of Economics, 131(2), 687–738.Find this resource:
Chavas, J.-P. (2013). On the microeconomics of food and malnutrition under endogenous discounting. European Economic Review, 59, 80–96.Find this resource:
Chia, Y. F. (2008). Maternal labour supply and childhood obesity in Canada: Evidence from the NLSCY. Canadian Journal of Economics, 41(1), 217–242.Find this resource:
Chou, S.-Y., Rashad, I., & Grossman, M. (2008). Fast-food restaurant advertising on television and its influence on childhood obesity. Journal of Law and Economics, 51(4), 599–618.Find this resource:
Chowhan, J., & Stewart, J. M. (2014). While mothers work do children shirk? Determinants of youth obesity. Applied Economic Perspectives & Policy, 36(2), 287–308.Find this resource:
Classen, T., & Hokayem, C. (2005). Childhood influences on youth obesity. Economics and Human Biology, 3(2), 165–187.Find this resource:
Classen, T., & Thompson, O. (2016). Genes and the intergenerational transmission of BMI and obesity. Economics and Human Biology, 23, 121–133.Find this resource:
Cohen-Cole, E., & Fletcher, J. M. (2008). Is obesity contagious? Social networks vs. environmental factors in the obesity epidemic. Journal of Health Economics, 27(5), 1382–1387.Find this resource:
Colchero, M. A., Popkin, B. M., Rivera, J. A., & Ng, S. W. (2016). Beverage purchases from stores in Mexico under the excise tax on sugar sweetened beverages: Observational study. BMJ, 352, h6704.Find this resource:
Courtemanche, C. (2009). Longer hours and larger waistlines? The relationship between work hours and obesity. Forum for Health Economics and Policy, 12(2).Find this resource:
Courtemanche, C., Heutel, G., & McAlvanah, P. (2015). Impatience, incentives and obesity. The Economic Journal, 125(582), 1–31.Find this resource:
Currie, J., DellaVigna, S., Moretti, E., & Pathania, V. (2010). The effect of fast food restaurants on obesity and weight gain. American Economic Journal: Economic Policy, 2(3), 32–63.Find this resource:
Cutler, D. M., Glaeser, E. L., & Shapiro, J. M. (2003). Why have Americans become more obese? The Journal of Economic Perspectives, 17(3), 93–118.Find this resource:
Dave, D., & Kaestner, R. (2009). Health insurance and ex ante moral hazard: evidence from Medicare. International Journal of Health Care Finance and Economics, 9(4), 367–390.Find this resource:
Dhar, P., & Robinson, C. (2016). Physical activity and childhood obesity. Applied Economics Letters, 23(8), 584–587.Find this resource:
Dunn, R. A. (2010). The effect of fast-food availability on obesity: An analysis by gender, race, and residential location. American Journal of Agricultural Economics, 92(4), 1149–1164.Find this resource:
Ellison, B., Lusk, J. L., & Davis, D. (2014). The impact of restaurant calorie labels on food choice: Results from a field experiment. Economic Inquiry, 52(2), 666–681.Find this resource:
Fan, M., & Jin, Y. (2014). Do neighborhood parks and playgrounds reduce childhood obesity? American Journal of Agricultural Economics, 96(1), 26–42.Find this resource:
Fan, M., & Jin, Y. (2015). The Supplemental Nutrition Assistance Program and childhood obesity in the United States: Evidence from the National Longitudinal Survey of Youth 1997. American Journal of Health Economics, 1(4), 432–460.Find this resource:
Feldman, A. M. (2008). Welfare economics. In S. N. Durlauf & L. E. Blume (Eds.), The new Palgrave dictionary of economics (2nd ed.). Basingstoke, UK: Palgrave Macmillan. Retrieved from http://www.dictionaryofeconomics.com/extract?id=pde2008_W000050.Find this resource:
Fertig, A., Glomm, G., & Tchernis, R. (2009). The connection between maternal employment and childhood obesity: Inspecting the mechanisms. Review of Economics of the Household, 7(3), 227–255.Find this resource:
Fiese, B. H., Hammons, A., & Grigsby-Toussaint, D. (2012). Family mealtimes: A contextual approach to understanding childhood obesity. Economics and Human Biology, 10(4), 365–374.Find this resource:
Finkelstein, E., & Yang, H. K. (2011). Obesity and medical costs. In J. Cawley (Ed.), The Oxford handbook of the social science of obesity. Oxford: Oxford University Press.Find this resource:
Fletcher, J. M., Frisvold, D. E., & Tefft, N. (2010a). The effects of soft drink taxes on child and adolescent consumption and weight outcomes. Journal of Public Economics, 94(11–12), 967–974.Find this resource:
Fletcher, J. M., Frisvold, D., & Tefft, N. (2010b). Taxing soft drinks and restricting access to vending machines to curb child obesity. Health Affairs, 29(5), 1059–1066.Find this resource:
Fletcher, J. M., Frisvold, D., & Tefft, N. (2013). Substitution patterns can limit the effects of sugar-sweetened beverage taxes on obesity. Preventing Chronic Disease, 10, E18.Find this resource:
Frisvold, D. E., & Lumeng, J. C. (2011). Expanding exposure: Can increasing the daily duration of Head Start reduce childhood obesity? Journal of Human Resources, 46(2), 373–402.Find this resource:
Gomis-Porqueras, P., Mitnik, O., Peralta-Alva, A., & Schmeiser, M. (2011). The effects of female labor force participation on obesity. Research Division, Federal Reserve Bank of St. Louis, Working Paper 2011-035A.
Gregg, E. W., & Shaw, J. E. (2017). Global health effects of overweight and obesity. New England Journal of Medicine, 377(1), 80–81.Find this resource:
Grossman, M. (1972). On the concept of health capital and the demand for health. Journal of Political Economy, 80(2), 223–255.Find this resource:
Grossman, M., Tekin, E., & Wada, R. (2012). Fast-food restaurant advertising on television and its influence on youth body composition. National Bureau of Economic Research Working Paper 18640.
Grossman, M., Tekin, E., & Wada, R. (2014). Food prices and body fatness among youths. Economics & Human Biology, 12, 4–19.Find this resource:
Gurley-Calvez, T., & Higginbotham, A. (2010). Childhood obesity, academic achievement, and school expenditures. Public Finance Review, 38(5), 619–646.Find this resource:
Halliday, T. J., & Kwak, S. (2009). Weight gain in adolescents and their peers. Economics & Human Biology, 7(2), 181–190.Find this resource:
Han, E., Norton, E. C., & Powell, L. M. (2011). Direct and indirect effects of body weight on adult wages. Economics and Human Biology, 9(4), 381–392.Find this resource:
Heckman, J. J. (2006). Skill formation and the economics of investing in disadvantaged children. Science, 312(5782), 1900–1902.Find this resource:
Heckman, J. J. (2007). The economics, technology, and neuroscience of human capability formation. Proceedings of the National Academy of Sciences, 104(33), 13250–13255.Find this resource:
Herbst, C. M., & Tekin, E. (2012). The geographic accessibility of child care subsidies and evidence on the impact of subsidy receipt on childhood obesity. Journal of Urban Economics, 71(1), 37–52.Find this resource:
Hofferth, S. L., & Curtin, S. (2005). Poverty, food programs, and childhood obesity. Journal of Policy Analysis and Management, 24(4), 703–726.Find this resource:
Ikeda, S., Kang, M.-I., & Ohtake, F. (2010). Hyperbolic discounting, the sign effect, and the body mass index. Journal of Health Economics, 29(2), 268–284.Find this resource:
Jo, Y. (2014). What money can buy: Family income and childhood obesity. Economics and Human Biology, 15, 1–12.Find this resource:
Just, D. R., & Payne, C. R. (2009). Obesity: Can behavioral economics help Annals of Behavioral Medicine, 38(1), 47–55.Find this resource:
Just, D. R., & Price, J. (2013). Using incentives to encourage healthy eating in children. Journal of Human Resources, 48(4), 855–872. Madison: University of Wisconsin Press.Find this resource:
Kaestner, R., & Callison, K. (2011). Adolescent cognitive and noncognitive correlates of adult health. Journal of Human Capital, 5(1), 29–69.Find this resource:
Kreider, B., Pepper, J. V, Gundersen, C., & Jolliffe, D. (2012). Identifying the effects of SNAP (food stamps) on child health outcomes when participation is endogenous and misreported. Journal of the American Statistical Association, 107(499), 958–975.Find this resource:
Lhila, A. (2011). Does access to fast food lead to super-sized pregnant women and whopper babies Economics & Human Biology, 9(4), 364–380.Find this resource:
Lin, B.-H., Smith, T. A., Lee, J.-Y., & Hall, K. D. (2011). Measuring weight outcomes for obesity intervention strategies: The case of a sugar-sweetened beverage tax. Economics and Human Biology, 9(4), 329–341.Find this resource:
Lindeboom, M., Lundborg, P., & van der Klaauw, B. (2010). Assessing the impact of obesity on labor market outcomes. Economics and Human Biology, 8(3), 309–319.Find this resource:
List, J. A., & Samek, A. S. (2015). The behavioralist as nutritionist: Leveraging behavioral economics to improve child food choice and consumption. Journal of Health Economics, 29(1), 1–28.Find this resource:
Loureiro, M. L., Yen, S. T., & Nayga, R. M. (2012). The effects of nutritional labels on obesity. Agricultural Economics, 43(3), 333–342.Find this resource:
Mandal, B., & Powell, L. M. (2014). Child care choices, food intake, and children’s obesity status in the United States. Economics and Human Biology, 14, 50–61.Find this resource:
McGranahan, L., & Schanzenbach, D. W. (2013). The Earned Income Tax Credit and food consumption patterns. Federal Reserve Bank of Chicago, Working Paper No. 2013-14. Retrieved from https://www.chicagofed.org/publications/working-papers/2013/wp-14.
Mellor, J. M. (2011). Do cigarette taxes affect children’s body mass index? The effect of household environment on health. Health Economics, 20(4), 417–431.Find this resource:
Millimet, D. L., & Tchernis, R. (2013). Estimation of treatment effects without an exclusion restriction: With an application to the analysis of the school breakfast program. Journal of Applied Econometrics, 28(6), 982–1017.Find this resource:
Millimet, D. L., & Tchernis, R. (2015). Persistence in body mass index in a recent cohort of US children. Economics and Human Biology, 17, 157–176.Find this resource:
Millimet, D. L., Tchernis, R., & Husain, M. (2010). School nutrition programs and the incidence of childhood obesity. Journal of Human Resources, 45(3), 640–654.Find this resource:
Mora, T., & Lopez-Valcarcel, B. G. (2017). Breakfast choice: An experiment combining a nutritional training workshop targeting adolescents and the promotion of unhealthy products. Health Economics, 1–14.Find this resource:
Morales, L. F., Gordon-Larsen, P., & Guilkey, D. (2016). Obesity and health-related decisions: An empirical model of the determinants of weight status across the transition from adolescence to young adulthood. Economics & Human Biology, 23, 46–62.Find this resource:
Okunade, A. A., Hussey, A. J., & Karakus, M. C. (2009). Overweight adolescents and on-time high school graduation: Racial and gender disparities. Atlantic Economic Journal, 37(3), 225–242.Find this resource:
Powell, L. M. (2009). Fast food costs and adolescent body mass index: Evidence from panel data. Journal of Health Economics, 28(5), 963–970.Find this resource:
Powell, L. M., & Bao, Y. (2009). Food prices, access to food outlets and child weight. Economics & Human Biology, 7(1), 64–72.Find this resource:
Powell, L. M., & Chaloupka, F. J. (2009). Food prices and obesity: Evidence and policy implications for taxes and subsidies. Milbank Quarterly, 87(1), 229–257.Find this resource:
Rees, D. I., & Sabia, J. J. (2010). Body weight and smoking initiation: Evidence from Add Health. Journal of Health Economics, 29(5), 774–777.Find this resource:
Renna, F., Grafova, I. B., & Thakur, N. (2008). Symposium on the economics of obesity: The effect of friends on adolescent body weight. Economics and Human Biology, 6(3), 377–387.Find this resource:
Riera-Crichton, D., & Tefft, N. (2013). Macronutrients and obesity: Revisiting the calories in, calories out framework. Retrieved from http://dx.doi.org/10.2139/ssrn.2279503.
Robinson, C. A., & Zheng, X. (2011). Household food stamp program participation and childhood obesity. Journal of Agricultural and Resource Economics, 36(1), 1–13.Find this resource:
Roy, M., Millimet, D. L., & Tchernis, R. (2012). Federal nutrition programs and childhood obesity: Inside the black box. Review of Economics of the Household, 10(1), 1–38.Find this resource:
Ruhm, C. J. (2008). Maternal employment and adolescent development. Labour Economics, 15(5), 958–983.Find this resource:
Sabia, J. J. (2007). The effect of body weight on adolescent academic performance. Southern Economic Journal, 73(4), 871–900.Find this resource:
Sandy, R., Tchernis, R., Wilson, J., Liu, G., & Zhou, X. (2013). Effects of the built environment on childhood obesity: The case of urban recreational trails and crime. Economics and Human Biology, 11(1), 18–29.Find this resource:
Schanzenbach, D. W. (2009). Do school lunches contribute to childhood obesity? Journal of Human Resources, 44(3), 684–709.Find this resource:
Schmeiser, M. D. (2009). Expanding wallets and waistlines: The impact of family income on the BMI of women and men eligible for the Earned Income Tax Credit. Health Economics, 18(11), 1277–1294.Find this resource:
Schmeiser, M. D. (2012). The impact of long-term participation in the Supplemental Nutrition Assistance Program on child obesity. Health Economics, 21(4), 386–404.Find this resource:
Schroeter, C., Lusk, J., & Tyner, W. (2008). Determining the impact of food price and income changes on body weight. Journal of Health Economics, 27(1), 45–68.Find this resource:
Silva, A., Higgins, L. M., & Hussein, M. (2015). An evaluation of the effect of child-directed television food advertising regulation in the United Kingdom. Canadian Journal of Agricultural Economics/Revue Canadienne D’agroeconomie, 63(4), 583–600.Find this resource:
Simon, K., Soni, A., & Cawley, J. (2016). The impact of health insurance on preventive care and health behaviors: Evidence from the 2014 ACA Medicaid expansions. National Bureau of Economic Research Working Paper No. 22265.
Smith, P. K., Bogin, B., & Bishai, D. (2005). Are time preference and body mass index associated? Evidence from the National Longitudinal Survey of Youth. Economics and Human Biology, 3(2), 259–270.Find this resource:
The GBD 2015 Obesity Collaborators. (2017). Health effects of overweight and obesity in 195 countries over 25 years. New England Journal of Medicine, 377(1), 13–27.Find this resource:
Thomsen, M. R., Nayga, R. M., Alviola, P. A., & Rouse, H. L. (2016). The effect of food deserts on the body mass index of elementary schoolchildren. American Journal of Agricultural Economics, 98(1), 1–18.Find this resource:
Wright, D. R., & Prosser, L. A. (2014). The impact of overweight and obesity on pediatric medical expenditures. Applied Health Economics and Health Policy, 12(2), 139–150.Find this resource:
Yang, M., & Huang, R. (2014). Asymmetric association between exposure to obesity and weight gain among adolescents. Eastern Economic Journal, 40(1), 96–118.Find this resource: