How are you going to improve the world?
As I left a Boston Celtics game in 2009, I watched as food stand attendants dumped excess food
leftover from the game in trash bags destined for the dumpster. Inspired to create a solution, I am
establishing a non-profit that reduces hunger by using the surplus food from large events and
distributing it to the local homeless population. The non-profit’s name is “Serving Those In
Need” (STIN), and is the initial endeavor of a larger organization I aim to form which increases
government efficiency through private sector participation. I plan to expand this institution into
an international services firm with the network and business acumen I acquire at Harvard
Business School.
STIN is a non-profit organization whose program transforms food that would otherwise
be wasted into sustenance for struggling individuals and families. STIN reduces the tax dollars
being spent in food shelters and the tax burden of large event holders. It reallocates tax dollars
that would have been spent on homeless shelters, food pantries, and soup kitchens to education,
NASA, and other critical sectors of government. Businesses which increase government
efficiency and create jobs should be supported by government.
STIN is my first endeavor as I build the credibility to form GAP, a professional services
firm that boosts growth and reduces inefficiency in economies. GAP is a twist on a traditional
venture capital firm which intends to incubate, fund, and finance businesses whose impacts
increase government efficiency, boost economic growth and create employment through the
private sector. If anything heals the wounds of our global economic problem it is going to come
from new ideas, products and services that create new industries which foster economic growth
and job creation. This is what GAP intends to find, create and finance. GAP and STIN are the
convergence of my professional aspirations: to build a global business that improves the world.
I am the type of individual who sees opportunity where others see inefficiency. In a
recent letter I sent to President Obama, I urged him to use my non-profit as an example of how to
inspire people to make the change he campaigned on in 2008. President Obama certainly created
change, but his biggest accomplishment may be future change. Firms such as GAP will
cultivate the future change we need to not only improve our economy, but to advance the global
economy.
Wednesday, October 12, 2011
Thursday, September 29, 2011
Perspectives on the Growing American Obesity Epidemic
Perspectives on the Growing American Obesity Epidemic
An Economic Literature Review by Dan Schiffman
If there were a mascot that represented the progression of American body weight over the past few decades, his proverbial pants would be bursting at the seams. It is no secret that the percentage of clinically obese Americans has climbed dramatically, but what eludes many researchers is the cause of this rise. Scholars have done studies linking the obesity rate to everything from genetics to the drop in food cost. In the discussion that follows, four authors’ perceptions on the cause of the obesity epidemic are analyzed, and their similarities are used to propose programs that can effectively treat this epidemic.
In the article, “What Explains Differences in Smoking, Drinking, and Other Health-Related Behaviors?” David M. Cutler and Edward Glaeser of Harvard University’s Department of Economics model health-related behaviors by genetic, environmental, personal, and socioeconomic indicators. They use surveys and regression analyses to model different determinants of health behavior. The initial models include measuring the correlation among smoking, heavy drinking, mammograms, and obesity to education, demographics, beliefs, and geography. Cutler and Glaeser’s original hypothesis was “those who value their health highly and care sufficiently about the future will have much better behaviors than those who do not” (Cutler 238). Using the variables mentioned above they found little correlation, nothing conclusive and proceeded to a new hypothesis: “Health behaviors differ because of differing information, genetic differences, and situational factors specific to the person” (Cutler 238). The authors conduct new surveys and construct new models in search of a significant correlation. After testing the new hypothesis, the authors find data with strong correlations enough to conclude that “variation in health behaviors results from two primary factors: genetics and behavior-specific situational influences” (Cutler 242).
The most relevant portion of Cutler and Glaeser’s study to the determinants of obesity lie in the table they provide on genetics and situational differences. The table illustrates the predictability of genetics and situational differences on different health behaviors. They find that 72% of variation in an individual’s BMI can be determined by genetics, and 88% determined by situational differences (Cutler 242). These percentages expose one of the few discrete methods that may successfully determine obesity, genetics. Seventy two percent predictability between genetic predisposition and bodyweight, in statistical analysis, is referred to as a ‘fairly strong’ relationship. Knowing at birth, that we can predict an individual’s body weight with only 28% probability of error opens floodgates of opportunity for attacking obesity problems before they occur. If an individual is genetically predisposed to becoming overweight, they can make numerous health decisions to minimize their chance of becoming obese.
Jeitschko and Pecchenino in “Do You Want Fries With That? An Exploration Of Serving Size, Social Welfare, and Our Waistlines” create an economic model that determines the “socially optimal meal”. The authors define a socially optimal meal as one where the size of the meal, measured in calories, maximizes the utility of the consumer (Jeitschko 443). Prior to constructing their model to predict consumption choices, the authors discuss how portion size has increased in the past few decades. “Thus, on average, the portion size and energy intake has increased by 93 kCal for salty snacks, by 49 kCal for soft drinks, by 68 kCal for French fries, and by 97 kCal for hamburgers” (Jeitschko 443). Jeitschko continues to discuss how technological innovation in food processing has lead to lower total food costs, as well as lower marginal costs. The authors assert that the increases in portion sizing are actually restaurants passing on the cheaper cost of food to the consumer, in the form of larger portions (Jeitschko 444).
While the cost of food is decreasing, the authors postulate that the opportunity cost of preparing food has increased. The increase in female labor participation is used to substantiate the increased cost of food preparation; women are now more likely to be working, and are not the stay at home ‘chef wives’ of the 1970’s. This reasoning leads the reader to conclude that more individuals are eating out because the time spent preparing meals has become increasingly expensive, while the food itself has become cheaper. The heart of Jeitschko and Pecchenino’s argument lies in the construction of their model to determine the “socially optimal meal”. The economic model uses hunger, utility received from consumption, cost of consumption, type of food consumption, and the amount of food consumed, where the ideal scenario occurs when the utility received from consumption is maximized. A number of different applications of the model are demonstrated where different choices of either excess or inadequate portions of food are used. Eventually the authors conclude that with decreased marginal costs in food purchases, consumers will lose more utility from under-eating than they will gain from over-eating. In other words, the satisfaction one loses from consuming less than what satiates their appetite is more costly than consuming more than what is necessary. Eat more be happy. Eat less be very unhappy.
The results of this study, while fascinating, give little indication of how we can prevent people from over-consuming. The authors indicate in their analysis that people do not prefer to over-eat or consume in excess, but as a result of larger portions end up doing so. At the end of the article a restaurant is cited who changed its menu choices based on this preference in consumption: “Ruby Tuesday, a large restaurant chain, had to abandon an attempt to offer smaller, healthier portions because this move had angered customers and led to a 5% drop in sales” (Jeitschko, 449). We face an interesting dilemma as consumers. We do not want to overeat, but we want enough food so that we have the choice to overeat. Ultimately, it appears that the consumers are to blame for overeating, and less of a ‘clean your plate’ mentality may have some serious health benefits.
Seth Martin’s article "From Poverty To Obesity: Exploration Of The Food Choice Constraint Model And The Impact Of An Energy-Dense Food Tax” focuses on tackling the obesity problem among America’s lowest income groups. Martin discusses how a significant percentage of low income groups make up the population of obese persons, and programs targeting this demographic can have significant externalities. “The Centers for Disease Control and Prevention’s analysis of the National Health Interview Survey dataset, comprised of information from 68,556 adults living in the United States, confirmed that the lowest income groups contain a disproportionately higher share of obese persons” (Martin 78). Energy density is defined as the amount of energy per mass, or more easily explained as calories per kilogram. Energy cost is defined as dollars per unit of energy, again more easily explained as dollar per calorie. Martin proposes that as individual’s incomes decrease, they will consume foods with more energy density and lower energy cost. Foods high in fats and processed sugars, like cookies or soft drinks, have a much lower energy cost and higher energy density than vegetables or fruits, which have high energy costs and low densities (Martin 79).
Martin further models his energy density/cost hypothesis with graphs of different foods and corresponding density/costs. Indifference curves are graphs that measure the consumption of two goods as a result of their contribution to consumer utility. Martin uses indifference curves, with budget constraints, to describe how a decrease in income leads to substitution of higher energy cost foods for lower energy cost foods.
Martin’s analysis of energy dense/energy cost foods and their relative consumption as a function of income becomes less of a hypothesis, and more of an epiphany as the article concludes. Another government study that examines income decreases and the resulting daily calorie increases further corroborates the author’s claim: “…a U.S study of 371 low-income women enrolled in Expanded Food and Nutrition Education Program found that a $10-20 per month decrease in family food expenditures correlated with a net increase of 300 kcal/day in daily energy intakes” (Martin 79). As low income family’s income decreases their consumption of more energy dense food, with lower energy costs increases causing increased calorie intake per day.
Martin follows up his analysis of consumption with public policy programs that could decrease obesity among low income groups. He proposes, and models, that a tax on energy-dense food will decrease consumption of energy-dense (unhealthy) foods and increase consumption of non-energy-dense (healthy) food, resulting in reduced calorie intake (Martin 82). The author cites Kelly D. Brownell, Director of the Yale Center for Eating and Weight Disorders, for her invention of the “fat tax”. The fat tax uses Martin’s idea of taxing energy-dense foods, but broadens its effectiveness by using the tax revenue to subsidize the cost of non-energy-dense foods. Other policies discussed include income tax breaks for poor families who consume non-energy-dense foods, policies to raise income of low income groups, and allocating some of the tax revenues from energy-dense foods towards funding energy-dense food supplier’s development of healthier food items (Martin 84).
In “The Super Size of America: An Economic Estimation of Body Mass Index and Obesity in Adults” Michael Grossman et al. describe what may be the cause of the dramatic increase in obesity rates since 1980. Grossman first explains the debilitating effects obesity can have on the human body, and then proceeds to explain why obesity has increased. He models obesity as a function of number of restaurants, cigarette taxes, clean indoor air laws, tax on gasoline, gender, and household income (Grossman 138). Grossman explains the use of a frequency restaurant variable with reference to a previous study that correlated frequent fast-food restaurant use with higher fat intake and greater body weight (Grossman 139). Gasoline tax is used as a variable where the authors’ postulate that as gasoline becomes more expensive people will drive less often, and revert to another more physically demanding form of transportation. Cigarette taxes and clean indoor air laws are used in Grossman’s model to predict the effects of decreased cigarette consumption, and anti-smoking ordinances on obesity.
Since household income and gender are almost impossible to treat with public policy, and the author’s find no strong correlations with gasoline taxes, cigarettes and number of restaurants become our variables of interest. Grossman finds that “the rapid increase in obesity over time, especially during the 1980s, to be due in part to the great increase in the per capita number of restaurants, and partly an unintended consequence of the campaign to reduce smoking” (Grossman 145). As more restaurants, specifically fast-food restaurants, become available people eat there more often, and as a result consume more high-density food. Grossman explains that cigarettes are often used as a method of weight control (Grossman 145). So while government may be implementing more clean air laws reducing the number of smokers, smokers are now supplementing cigarettes with more food.
These four articles provide us with four very different hypotheses of the cause or causes of the obesity epidemic. While each article is almost completely unlike the next, each one uses sound economic analysis in hopes of demystifying the cause of the obesity problem. What becomes truly rewarding as the reader of these articles, is constructing the bridge that links them together. While Cutler found some interesting data on the genetic prediction of bodyweight, he expresses uncertainty over what type of situational differences account for 88% of the variation in bodyweight. As we read Jeitschko, Martin, and later Grossman’s articles we are presented with these situational differences that clarify Cutler’s statistics. The change in portion sizing, overeating, the ‘clean your plate’ mentality, incomes effect on food consumption, cigarette consumption, number of fast-food restaurants, and the pricing and availability of unhealthy foods make some of these situational differences more apparent. Jeitschko mentions in his article a statistic that found “when the density of fast food outlets rises in an area, the incidence of obesity rises as well” (Jeitschko 443). This makes perfect sense in the context of Martin’s findings. While I don’t have data to use concerning the locations of fast food restaurants, where do you tend to find most fast food chains? I know in Boston, where I currently reside, it’s much easier to find a fast food restaurant in the poorer portion of the city, as opposed to the wealthier. It is easy to acknowledge the reasoning behind this; poorer people eat more unhealthy (energy-dense) food, which is noticeably cheaper (low energy-cost) than healthier food. They get more ‘bang for their buck’ in terms of caloric value with fast food. What would happen if these low income neighborhoods had no fast food restaurants, would the poor people be forced to eat healthier? Or would an alternative unhealthy food establishment always be there due to the high demand for its food/service? Economics tells us with demand comes supply, and when one fast food outlet closes another will open in its place to grab that market share.
The fast food example sheds light on some of the most crucial aspects of finding a palatable treatment for the obesity epidemic. We learn from Jeitschko and Pecchenino that restaurants are not to blame for consumers overeating. Consumers are to blame. When presented with the option of over or under eating consumers maximize their well being by overeating. Although Jeitschko and Pecchenino do not propose any clear solutions to this problem, maybe the solution lies in consumer’s understanding of nutrition. Knowing how much food is too much, and the caloric value or energy-density of foods will allow consumers to know what to eat and in what quantity.
It becomes clear that Martin’s proposal of the “fat tax” is most likely our best solution to the obesity problem. Without knowing more about food, consumers will be attracted towards eating healthier items as their relative price goes down. While this won’t affect the portion size of meals discussed in Jeitschko, healthy foods have low energy-densities; therefore, while portions may not decrease the calories consumed will.
These four authors provide us with four different aspects of a problem, and whether explicitly mentioned (Martin), or not (Cutler), they provide insight into how best we can treat the obesity epidemic. One may also wonder why the “fat tax” isn’t being implemented if it is the best solution to our growing obesity problem. I leave you with two explanations. The giant fast food chains embedded in our culture through financial markets and politics are huge contributors to our economic growth, and to political parties. Directly taxing the food that these enormous firms produce will not only affect their profits, but have detrimental effects on our aggregate output. Also, is it fair to impose a tax whose effects are isolated on the poor? Is it just to tax the only food that provides our poor with enough calories to live? It isn’t fair. It also may be the case that, in terms of proximity to residence, fast food is closer than traveling to a healthier establishment; and the time wasted traveling farther isn’t worth the tax break.
In economics, we call externalities the unexpected by-products of an activity or choice, sometimes negative and sometimes positive. The goal is always to minimize the negative externalities, so that the benefit to society is greater than the harm (which occurs where the additional cost to society is equal to the additional benefit). Every miracle drug has side effects, every solution its additional problems. Only when we fully understand the causes of obesity, along with the benefits and externalities associated with each solution, will government implement public policy that will effectively treat our epidemic.
Works Cited
Cutler, David M., and Edward Glaeser. "What Explains Differences in Smoking, Drinking, and Other Health-Related Behaviors?" American Economic Review 95 (2005): 238-242.
Grossman, Michael. "The Super Size of America: An Economic Estimation of Body Mass Index and Obesity in Adults." Eastern Economic Journal 32 (2006): 133-148.
Jeitschko, Thomas D., and Rowena A. Pecchenino. "Do You Want Fries With That? An Exploration Of Serving Size, Social Welfare, and Our Waistlines." Economic Inquiry 44(2006): 442-450.
Martin, Seth S. "From Poverty To Obesity: Exploration Of The Food Choice Constraint Model And The Impact Of An Energy-Dense Food Tax." The American Economist 49(2005): 78-86.
An Economic Literature Review by Dan Schiffman
If there were a mascot that represented the progression of American body weight over the past few decades, his proverbial pants would be bursting at the seams. It is no secret that the percentage of clinically obese Americans has climbed dramatically, but what eludes many researchers is the cause of this rise. Scholars have done studies linking the obesity rate to everything from genetics to the drop in food cost. In the discussion that follows, four authors’ perceptions on the cause of the obesity epidemic are analyzed, and their similarities are used to propose programs that can effectively treat this epidemic.
In the article, “What Explains Differences in Smoking, Drinking, and Other Health-Related Behaviors?” David M. Cutler and Edward Glaeser of Harvard University’s Department of Economics model health-related behaviors by genetic, environmental, personal, and socioeconomic indicators. They use surveys and regression analyses to model different determinants of health behavior. The initial models include measuring the correlation among smoking, heavy drinking, mammograms, and obesity to education, demographics, beliefs, and geography. Cutler and Glaeser’s original hypothesis was “those who value their health highly and care sufficiently about the future will have much better behaviors than those who do not” (Cutler 238). Using the variables mentioned above they found little correlation, nothing conclusive and proceeded to a new hypothesis: “Health behaviors differ because of differing information, genetic differences, and situational factors specific to the person” (Cutler 238). The authors conduct new surveys and construct new models in search of a significant correlation. After testing the new hypothesis, the authors find data with strong correlations enough to conclude that “variation in health behaviors results from two primary factors: genetics and behavior-specific situational influences” (Cutler 242).
The most relevant portion of Cutler and Glaeser’s study to the determinants of obesity lie in the table they provide on genetics and situational differences. The table illustrates the predictability of genetics and situational differences on different health behaviors. They find that 72% of variation in an individual’s BMI can be determined by genetics, and 88% determined by situational differences (Cutler 242). These percentages expose one of the few discrete methods that may successfully determine obesity, genetics. Seventy two percent predictability between genetic predisposition and bodyweight, in statistical analysis, is referred to as a ‘fairly strong’ relationship. Knowing at birth, that we can predict an individual’s body weight with only 28% probability of error opens floodgates of opportunity for attacking obesity problems before they occur. If an individual is genetically predisposed to becoming overweight, they can make numerous health decisions to minimize their chance of becoming obese.
Jeitschko and Pecchenino in “Do You Want Fries With That? An Exploration Of Serving Size, Social Welfare, and Our Waistlines” create an economic model that determines the “socially optimal meal”. The authors define a socially optimal meal as one where the size of the meal, measured in calories, maximizes the utility of the consumer (Jeitschko 443). Prior to constructing their model to predict consumption choices, the authors discuss how portion size has increased in the past few decades. “Thus, on average, the portion size and energy intake has increased by 93 kCal for salty snacks, by 49 kCal for soft drinks, by 68 kCal for French fries, and by 97 kCal for hamburgers” (Jeitschko 443). Jeitschko continues to discuss how technological innovation in food processing has lead to lower total food costs, as well as lower marginal costs. The authors assert that the increases in portion sizing are actually restaurants passing on the cheaper cost of food to the consumer, in the form of larger portions (Jeitschko 444).
While the cost of food is decreasing, the authors postulate that the opportunity cost of preparing food has increased. The increase in female labor participation is used to substantiate the increased cost of food preparation; women are now more likely to be working, and are not the stay at home ‘chef wives’ of the 1970’s. This reasoning leads the reader to conclude that more individuals are eating out because the time spent preparing meals has become increasingly expensive, while the food itself has become cheaper. The heart of Jeitschko and Pecchenino’s argument lies in the construction of their model to determine the “socially optimal meal”. The economic model uses hunger, utility received from consumption, cost of consumption, type of food consumption, and the amount of food consumed, where the ideal scenario occurs when the utility received from consumption is maximized. A number of different applications of the model are demonstrated where different choices of either excess or inadequate portions of food are used. Eventually the authors conclude that with decreased marginal costs in food purchases, consumers will lose more utility from under-eating than they will gain from over-eating. In other words, the satisfaction one loses from consuming less than what satiates their appetite is more costly than consuming more than what is necessary. Eat more be happy. Eat less be very unhappy.
The results of this study, while fascinating, give little indication of how we can prevent people from over-consuming. The authors indicate in their analysis that people do not prefer to over-eat or consume in excess, but as a result of larger portions end up doing so. At the end of the article a restaurant is cited who changed its menu choices based on this preference in consumption: “Ruby Tuesday, a large restaurant chain, had to abandon an attempt to offer smaller, healthier portions because this move had angered customers and led to a 5% drop in sales” (Jeitschko, 449). We face an interesting dilemma as consumers. We do not want to overeat, but we want enough food so that we have the choice to overeat. Ultimately, it appears that the consumers are to blame for overeating, and less of a ‘clean your plate’ mentality may have some serious health benefits.
Seth Martin’s article "From Poverty To Obesity: Exploration Of The Food Choice Constraint Model And The Impact Of An Energy-Dense Food Tax” focuses on tackling the obesity problem among America’s lowest income groups. Martin discusses how a significant percentage of low income groups make up the population of obese persons, and programs targeting this demographic can have significant externalities. “The Centers for Disease Control and Prevention’s analysis of the National Health Interview Survey dataset, comprised of information from 68,556 adults living in the United States, confirmed that the lowest income groups contain a disproportionately higher share of obese persons” (Martin 78). Energy density is defined as the amount of energy per mass, or more easily explained as calories per kilogram. Energy cost is defined as dollars per unit of energy, again more easily explained as dollar per calorie. Martin proposes that as individual’s incomes decrease, they will consume foods with more energy density and lower energy cost. Foods high in fats and processed sugars, like cookies or soft drinks, have a much lower energy cost and higher energy density than vegetables or fruits, which have high energy costs and low densities (Martin 79).
Martin further models his energy density/cost hypothesis with graphs of different foods and corresponding density/costs. Indifference curves are graphs that measure the consumption of two goods as a result of their contribution to consumer utility. Martin uses indifference curves, with budget constraints, to describe how a decrease in income leads to substitution of higher energy cost foods for lower energy cost foods.
Martin’s analysis of energy dense/energy cost foods and their relative consumption as a function of income becomes less of a hypothesis, and more of an epiphany as the article concludes. Another government study that examines income decreases and the resulting daily calorie increases further corroborates the author’s claim: “…a U.S study of 371 low-income women enrolled in Expanded Food and Nutrition Education Program found that a $10-20 per month decrease in family food expenditures correlated with a net increase of 300 kcal/day in daily energy intakes” (Martin 79). As low income family’s income decreases their consumption of more energy dense food, with lower energy costs increases causing increased calorie intake per day.
Martin follows up his analysis of consumption with public policy programs that could decrease obesity among low income groups. He proposes, and models, that a tax on energy-dense food will decrease consumption of energy-dense (unhealthy) foods and increase consumption of non-energy-dense (healthy) food, resulting in reduced calorie intake (Martin 82). The author cites Kelly D. Brownell, Director of the Yale Center for Eating and Weight Disorders, for her invention of the “fat tax”. The fat tax uses Martin’s idea of taxing energy-dense foods, but broadens its effectiveness by using the tax revenue to subsidize the cost of non-energy-dense foods. Other policies discussed include income tax breaks for poor families who consume non-energy-dense foods, policies to raise income of low income groups, and allocating some of the tax revenues from energy-dense foods towards funding energy-dense food supplier’s development of healthier food items (Martin 84).
In “The Super Size of America: An Economic Estimation of Body Mass Index and Obesity in Adults” Michael Grossman et al. describe what may be the cause of the dramatic increase in obesity rates since 1980. Grossman first explains the debilitating effects obesity can have on the human body, and then proceeds to explain why obesity has increased. He models obesity as a function of number of restaurants, cigarette taxes, clean indoor air laws, tax on gasoline, gender, and household income (Grossman 138). Grossman explains the use of a frequency restaurant variable with reference to a previous study that correlated frequent fast-food restaurant use with higher fat intake and greater body weight (Grossman 139). Gasoline tax is used as a variable where the authors’ postulate that as gasoline becomes more expensive people will drive less often, and revert to another more physically demanding form of transportation. Cigarette taxes and clean indoor air laws are used in Grossman’s model to predict the effects of decreased cigarette consumption, and anti-smoking ordinances on obesity.
Since household income and gender are almost impossible to treat with public policy, and the author’s find no strong correlations with gasoline taxes, cigarettes and number of restaurants become our variables of interest. Grossman finds that “the rapid increase in obesity over time, especially during the 1980s, to be due in part to the great increase in the per capita number of restaurants, and partly an unintended consequence of the campaign to reduce smoking” (Grossman 145). As more restaurants, specifically fast-food restaurants, become available people eat there more often, and as a result consume more high-density food. Grossman explains that cigarettes are often used as a method of weight control (Grossman 145). So while government may be implementing more clean air laws reducing the number of smokers, smokers are now supplementing cigarettes with more food.
These four articles provide us with four very different hypotheses of the cause or causes of the obesity epidemic. While each article is almost completely unlike the next, each one uses sound economic analysis in hopes of demystifying the cause of the obesity problem. What becomes truly rewarding as the reader of these articles, is constructing the bridge that links them together. While Cutler found some interesting data on the genetic prediction of bodyweight, he expresses uncertainty over what type of situational differences account for 88% of the variation in bodyweight. As we read Jeitschko, Martin, and later Grossman’s articles we are presented with these situational differences that clarify Cutler’s statistics. The change in portion sizing, overeating, the ‘clean your plate’ mentality, incomes effect on food consumption, cigarette consumption, number of fast-food restaurants, and the pricing and availability of unhealthy foods make some of these situational differences more apparent. Jeitschko mentions in his article a statistic that found “when the density of fast food outlets rises in an area, the incidence of obesity rises as well” (Jeitschko 443). This makes perfect sense in the context of Martin’s findings. While I don’t have data to use concerning the locations of fast food restaurants, where do you tend to find most fast food chains? I know in Boston, where I currently reside, it’s much easier to find a fast food restaurant in the poorer portion of the city, as opposed to the wealthier. It is easy to acknowledge the reasoning behind this; poorer people eat more unhealthy (energy-dense) food, which is noticeably cheaper (low energy-cost) than healthier food. They get more ‘bang for their buck’ in terms of caloric value with fast food. What would happen if these low income neighborhoods had no fast food restaurants, would the poor people be forced to eat healthier? Or would an alternative unhealthy food establishment always be there due to the high demand for its food/service? Economics tells us with demand comes supply, and when one fast food outlet closes another will open in its place to grab that market share.
The fast food example sheds light on some of the most crucial aspects of finding a palatable treatment for the obesity epidemic. We learn from Jeitschko and Pecchenino that restaurants are not to blame for consumers overeating. Consumers are to blame. When presented with the option of over or under eating consumers maximize their well being by overeating. Although Jeitschko and Pecchenino do not propose any clear solutions to this problem, maybe the solution lies in consumer’s understanding of nutrition. Knowing how much food is too much, and the caloric value or energy-density of foods will allow consumers to know what to eat and in what quantity.
It becomes clear that Martin’s proposal of the “fat tax” is most likely our best solution to the obesity problem. Without knowing more about food, consumers will be attracted towards eating healthier items as their relative price goes down. While this won’t affect the portion size of meals discussed in Jeitschko, healthy foods have low energy-densities; therefore, while portions may not decrease the calories consumed will.
These four authors provide us with four different aspects of a problem, and whether explicitly mentioned (Martin), or not (Cutler), they provide insight into how best we can treat the obesity epidemic. One may also wonder why the “fat tax” isn’t being implemented if it is the best solution to our growing obesity problem. I leave you with two explanations. The giant fast food chains embedded in our culture through financial markets and politics are huge contributors to our economic growth, and to political parties. Directly taxing the food that these enormous firms produce will not only affect their profits, but have detrimental effects on our aggregate output. Also, is it fair to impose a tax whose effects are isolated on the poor? Is it just to tax the only food that provides our poor with enough calories to live? It isn’t fair. It also may be the case that, in terms of proximity to residence, fast food is closer than traveling to a healthier establishment; and the time wasted traveling farther isn’t worth the tax break.
In economics, we call externalities the unexpected by-products of an activity or choice, sometimes negative and sometimes positive. The goal is always to minimize the negative externalities, so that the benefit to society is greater than the harm (which occurs where the additional cost to society is equal to the additional benefit). Every miracle drug has side effects, every solution its additional problems. Only when we fully understand the causes of obesity, along with the benefits and externalities associated with each solution, will government implement public policy that will effectively treat our epidemic.
Works Cited
Cutler, David M., and Edward Glaeser. "What Explains Differences in Smoking, Drinking, and Other Health-Related Behaviors?" American Economic Review 95 (2005): 238-242.
Grossman, Michael. "The Super Size of America: An Economic Estimation of Body Mass Index and Obesity in Adults." Eastern Economic Journal 32 (2006): 133-148.
Jeitschko, Thomas D., and Rowena A. Pecchenino. "Do You Want Fries With That? An Exploration Of Serving Size, Social Welfare, and Our Waistlines." Economic Inquiry 44(2006): 442-450.
Martin, Seth S. "From Poverty To Obesity: Exploration Of The Food Choice Constraint Model And The Impact Of An Energy-Dense Food Tax." The American Economist 49(2005): 78-86.
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