تجزیه و تحلیل اقتصادی از چاقی مفرط بزرگسالان: نتایج حاصل از سیستم نظارت رفتاری عوامل خطرزا
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|28284||2004||23 صفحه PDF||سفارش دهید||11520 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Health Economics, Volume 23, Issue 3, May 2004, Pages 565–587
This paper examines the factors that may be responsible for the 50% increase in the number of obese adults in the US since the late 1970s. We employ the 1984–1999 Behavioral Risk Factor Surveillance System, augmented with state level measures pertaining to the per capita number of fast-food and full-service restaurants, the prices of a meal in each type of restaurant, food consumed at home, cigarettes, and alcohol, and clean indoor air laws. Our main results are that these variables have the expected effects on obesity and explain a substantial amount of its trend.
Since the late 1970s, the number of obese adults in the US has grown by over 50%. This paper examines the factors that may be responsible for this rapidly increasing prevalence rate. We focus on societal forces which may alter the cost of nutritional and leisure time choices made by individuals and specifically consider the effect of changes in relative prices, which are beyond the individual’s control, on these choices. The principal hypothesis to be tested is that an increase in the prevalence of obesity is the result of several economic changes that have altered the lifestyle choices of Americans. One important economic change is the increase in the value of time, particularly of women, which is reflected by the growth in their labor force participation rates and in their hours of work. The reduction in home time has been associated with an increase in the demand for convenience food (food requiring minimal preparation time) and consumption in fast-food restaurants. Home time also has fallen and the consumption of the two types of food just mentioned has risen because the slow growth in income among certain groups has increased their labor market time. Another important change is the rise in the real cost of cigarette smoking due to increases in the money price of cigarettes, the diffusion of information concerning the harmful effects of smoking, and the enactment of state statutes that restrict smoking in public places and in the workplace. This relative price change may have reduced smoking, which tends to increase weight. A final set of relative price changes revolves around the increasing availability of fast-food, which reduces search and travel time and changes in the relative costs of meals consumed in fast-food restaurants, full-service restaurants, and meals prepared at home. Some of the changes just mentioned, especially the growth in the availability of fast-food restaurants, may have been stimulated by increases in the value of female time. To study the determinants of adult obesity and related outcomes, we employ micro-level data from the 1984–1999 Behavioral Risk Factor Surveillance System (BRFSS). These repeated cross sections are augmented with state level measures pertaining to the per capita number of restaurants, the prices of a meal in fast-food and full-service restaurants, the price of food consumed at home, the price of cigarettes, clean indoor air laws, and the price of alcohol (a potential determinant of weight outcomes given the high caloric content of beer, wine, and distilled spirits). Our main results are that these variables have the expected effects on obesity and explain a substantial amount of its trend. These findings control for individual-level measures of age, race, household income, years of formal schooling completed, and marital status.
نتیجه گیری انگلیسی
Table 3 contains ordinary least squares regressions of body mass index and the probability of being obese, for persons 18 years of age and older. Robust or Huber (1967) standard errors, which allow for state/year clustering, are obtained.16 In preliminary regressions we found evidence that most of the continuous variables had non-linear effects. Therefore, we employ a quadratic specification for each of these variables. Recent research by economists dealing with obesity estimates separate models by gender, race, and in some cases ethnicity (for example, Averett and Korenman, 1996, Lakdawalla and Philipson, 2002 and Cawley, 2004). We do not pursue this approach because the studies at issue focus on the relationship between obesity and labor market outcomes specific to an individual. We do not directly consider this relationship. Moreover, our aim is to provide an explanation of general trends in obesity rather than in trends for specific groups in the population.The two regressions in the table have low explanatory power, with R2 ranging from 4 to 8%. The main reason for this result is that body mass index and obesity have large genetic components. In this context it should be emphasized that our aim is to explain the increasing prevalence of obesity rather than to explain why a given individual is obese. This perspective is important because genetic characteristics of the population change slowly, while the incidence of obesity has increased rapidly. To be sure, some individual characteristics, such as years of formal schooling completed, may be correlated with genetic determinants of weight outcomes. 17 But there is little reason to believe that the state-specific variables that we consider are correlated with heredity. Of course, the regression disturbance term also may reflect tastes for different types of food. Our working hypothesis is that the mix of food consumption changes over time due to changes in prices and related determinants rather than to changes in tastes. Focusing on the effects of the individual characteristics, one sees that age has an inverted U-shaped effect. BMI peaks at an age of approximately 57, while the probability of being obese peaks at an age of 45 years.18 Black non-Hispanics and Hispanics have higher values of both outcomes than Whites, while persons of other races have lower values. Males have higher BMI levels than females, but females are more likely to be obese. Married and widowed persons have higher levels of BMI than single (never married) and divorced individuals. These relations carry over to the prevalence of obesity. Years of formal schooling completed and real household income have negative effects on BMI and the probability of being obese. There is little evidence that the schooling effect falls as the amount of schooling rises. Differentials between college graduates and those who attended college but did not graduate are almost as large as differentials between the latter group and persons who did not attend high school. Graduation from college appears to maximize the probability that BMI is in the range that minimizes mortality and morbidity risks since the differentials between those with some college and those who are high school graduates are small. Although the negative effect of household income on BMI or obesity falls as income rises, the effect remains negative throughout almost all the observed income range. At weighted sample means, the income elasticity of body mass index is modest (−0.03). The impact of income on the probability of being obese is more substantial. Evaluated at sample means, a 10% increase in income is associated with a 0.5 percentage point decline in the percentage obese from 17.5 to 17.0%. In a fixed population, the number of obese people falls by 2%. It should be noted that the magnitude of the income effect may be overestimated due to the reverse causality from obesity to income (Averett and Korenman, 1996 and Cawley, 2004). Despite the relatively large number of state-specific variables in the set and the considerable amount of intercorrelations among them, most of their coefficients have the expected signs and are statistically significant. Regardless of the outcome considered, the per capita number of restaurants and the real price of cigarettes have positive and significant effects at weighted sample means. Along the same lines, the real fast-food restaurant price, the real food at home price and the real full-service restaurant price have negative and significant effects at weighted sample means. The effects of the clean indoor air laws do not show a consistent pattern. Restrictions on cigarette smoking in restaurants have no role in weight outcomes. This is surprising because these restrictions are most likely to encourage a substitution of food for cigarettes. One possible explanation is that smokers substitute consumption of food at home for consumption in restaurants in states that restrict smoking at the latter site. Restrictions in state and local government workplaces are associated with higher levels of BMI and higher prevalence rates of obesity, but the coefficients are not significant. Private workplace restrictions never are significant and are associated with higher levels of BMI and obesity. Restrictions in elevators, public transportation, and theaters (reflected by the dichotomous indicator other) raise both weight outcomes, with the obesity effect achieving significance. The absence of a clear pattern in the effects of clean indoor air laws may reflect in part their endogeneity. Evans et al. (1999) find that workplace smoking bans have very large negative effects on smoking participation. Moore (2001) reports this relationship reflects the underlying preferences of workers and employers rather than a direct causal process. In our context, state fixed effects may control for unobserved forces that influence smoking, obesity, and the enactment of clean indoor air laws. Table 4 contains elasticities of BMI with respect to the continuous state-specific variables at the points of weighted sample means. It also contains percentage point changes in the probability of being obese associated with 10% changes in the state-specific variables.19 As in the case of income, the elasticity of body mass index with respect to any of these variables is modest. The largest elasticity of 0.17 pertains to the per capita number of restaurants. This elasticity is six times larger than the absolute value of the income elasticity. When the probability of being obese is the outcome, the effects in Table 4 are much more substantial. For example, a 10% increase in the number of restaurants increases the probability of being obese by 1.4 percentage points. Put differently, evaluated at sample means, a 10% increase in the per capita number of restaurants is associated with a growth in the percentage obese from 17.5 to 18.9%. In a fixed population, the number of obese people rises by 8%. Note, however, that national or state-specific time varying unobservable changes in the demand for caloric intakes might be correlated with changes in obesity and the number of restaurants. In that case, the impact of the fast-food restaurants may be overestimated.With regard to the three direct food price variables, the greatest response to BMI occurs when the real fast-food restaurant price varies. The elasticity of BMI with respect to this price is −0.05. When obesity is the outcome, the fast-food and full-service restaurant price effects are about the same. A 10% increase in each price is associated with a 0.7 percentage point decrease in the percentage obese. Like Cawley (1999) and Lakdawalla and Philipson (2002), we find that weight outcomes rise when food at home prices decline. The elasticity of BMI with respect to this price is larger in absolute value than the full-service restaurant price elasticity but smaller than the fast-food price elasticity. When obesity is the outcome, the magnitude of the food at home price effect is slightly smaller than those of the other two food prices. The positive cigarette price effects in Table 4 indicate substitution between calories and nicotine. The magnitude of the cigarette price effect in the obesity equation is approximately two-thirds as large as any of the three food price effects in that equation. The elasticity of BMI with respect to the cigarette price is larger than full-service restaurant price elasticity. These results point to an unintended consequence of the anti-smoking campaign. In particular, state and federal excise tax hikes and the settlement of state Medicaid lawsuits have caused the real price of cigarettes to rise substantially. Our findings suggest that this development contributed to the upward trend in obesity. Finally, the negative alcohol price effects in Table 4 imply that calories and alcohol are complements. The magnitudes of these effects, however, are the smallest among the variables that we consider. The large elasticities with regard to the per capita number of restaurants emerge from models that hold the real fast-food restaurant price and the real full-service restaurant price constant. A simple supply and demand model predicts that these two variables should be negatively correlated if the demand function for restaurants is more stable than the supply function and positively correlated if the supply function is more stable. Only a minor change in the restaurant elasticity occurs when the price variables are deleted, implying that the supply function is very elastic. The reader should keep in mind that the per capita number of restaurants is employed as a proxy for the travel time and waiting time costs involved in obtaining meals at these eating places. The main purpose of this paper is to gain an understanding of the factors associated with the stability in obesity between the early 1960s and the late 1970s and the rapid increase since that time. Table 5 addresses the latter issue by examining how well selected models predict the increases in obesity and related outcomes between 1984 and 1999. The estimates in Table 5 are based on regression models in Table 3. The procedure simply is to multiply the change in a given variable between the initial and terminal year by the coefficient of that variable. In the cases of race/ethnicity, schooling, marital status, the clean indoor air laws, and variables in quadratic form, predicted changes associated with related variables (married, divorced, and widowed in the case of marital status) are summed to form a single factor. Note that national values of state-specific variables in 1984 are population-weighted averages of values for all states rather than for states in the BRFSS in that year. Note also that our conclusions are not altered when 1987 or 1990 is taken as the initial year.