Methodology:
I have reviewed and summarized 20 research papers by different authors along with other reference material in the form of articles and videos.
All data referenced is secondary data, and no primary data was involved due to the demography in question.
The inclusion of so many different papers was to be able to better understand the complexities of this topic and to gain a broader perspective on the different aspects of the topic at hand.
Introduction:
How obesity is defined in America:
BMI, or Body Mass Index, is defined as a person’s weight (in kg), divided by the square of said person’s height (in m; m2).
The Harvard School of Public Health bifurcates and defines their weight classes based on this measure of BMI as opposed to more standard height/weight charts. (As does the NIH, or The National Institutes of Health)
Accordingly, for adult men and women, a BMI of:
18.5-24.9 is defined as healthy.
25.0-29.9 is defined as overweight.
30+ is considered obese.
There are also classifications of lower BMI ranges, but these are the ones we are going to focus on for the purposes of this paper.
Since BMI itself does not account for muscle mass, or more technically, Fat Free Mass Index (FFMI), it is more than possible for a slightly to very muscular person to have a BMI similar to that of a morbidly obese person.
As is going to be highlighted further, obesity is an increasingly pressing problem among children as well as adolescents, however, since children tend to grow at varying rates based on their sex and age, the bifurcations as stated above would vary greatly for lower age groups.
For example, the definition of obesity in America is based on growth charts that have been developed by the Centers for Disease Control and Prevention.
Along the same lines, it can be noted that a BMI that falls between the 85th to 94th percentile, for age and gender among children and adolescents between 2 and 20 years of age, is considered overweight, and any greater than that is considered obese.
It should be noted that obesity is not measured only by the use of BMI, but also a metric that makes use of “abdominal obesity” that measures the fat around the midsection of a person as an important determinant of health, despite being independent of BMI.
This is used since it tends to be a very good indicator of visceral fat, which is of greater concern when it comes to being at risk.
As general guidelines, an individual is considered obese if their waist size is greater than 35 inches or more for females, and 40 inches or more for mails.
In terms of incidence, it is estimated that nearly 1.5 billion adults are considered either overweight or obese, and this number is expected to double to 3 billion by 2030.
“Economic Causes And Consequences Of Obesity”
Eric A. Finkelstein, Christopher J. Ruhm, Katherine M. Kosa
Figure 1:
The above graph shows the rising trend in percentage of obese adults, by gender, between 1960 and 2000 in America, as published by the National Health and Nutrition Examination Survey.
The incredible rise in obesity began around the 1980s, as stated in the graph above, and obesity rates have more than doubled over the last 25 years.
The prevalence diseases directly linked to obesity have also increased with this increase in obesity, such as cardiovascular diseases, type ΙΙ diabetes, different forms of cancers (including kidney, colon, and breast cancers), sleep apnea, musculoskeletal disorders and gallbladder disease.
Consequently, it is estimated that obesity is now the second leading cause of death, at 400,000 deaths a year, beaten only by tobacco.
This paper further elucidates how an increase in Energy Intake (measured in Kcal) rose by nearly 12%, or 300 Kcal/day, between 1985-2000 after remaining nearly constant all throughout 1910-1985, primarily due to an increased consumption of grains, as well as added sugars and fats.
The table as shown below (from the paper), summarizes the changing trends in caloric intake over the years and how that has affected the incidence of diseases related to obesity, which have been primarily driven by the following factors:
Decreased activity levels (resulting in a lower TDEE: Total Daily Energy Expenditure, as well as NEAT: Non Exercise Activity Thermogenesis)
Increased caloric intake (as summarized in the table below)
“The Effect Of Obesity On State Health Care Expenditures: An Empirical Analysis”
Kristen Collins
The Centers for Disease Control and Prevention (CDC) found that Americans are highly prone to the risk of contracting diseases and life threatening conditions ranging from strokes, type ΙΙ diabetes, cancer, heart diseases and hypertension among many other ailments.
Most of the health related costs tied to obesity arise from heart diseases, type ΙΙ diabetes and hypertension (from the Surgeon General’s Call to Action to Prevent and Decrease Overweight and Obesity).
The United States Department of Health and Human Services noted an average of a 12-pound increase, from 168lbs during the early 1960s to approximately 180lbs for the average adult male. This increased incidence of obesity has naturally begun placing a great deal of strain on our bodies, requiring the heart to work nearly twice as hard when compared to levels from a few decades ago.
As a result of this, obese individuals also tend to require more regular and frequent visits to doctors and health care facilities.
According to a study by the National Alliance for Nutrition and Activity from 2001, at least 310,000 Americans experienced premature deaths due to a combination of bad eating habits as well as reduced activity levels.
It is worth noting that this number is 5 times greater than the number of premature deaths as a result of HIV, AIDS, drug use and gun deaths, combined.
Obesity is also greatly affected by factors other than caloric consumption and activity. For example, demographic factors are also a determinant in the incidence of obesity. As stated by Lisa Mancino, Biing-Hwan Lin, and Nicole Ballenger, this is a result of the interaction of many different demographic variables, which contribute to obesity along with genetic factors, essentially stating that not everyone within the population has an equal level of risk, since genetic make up along with demographic variables tend to skew these observations greatly.
However, as far as demographic variables are concerned, the following interact to affect demographic factors:
Prices of goods and services
Income levels
Education levels
Gender
Age
Cultural background
Cooking skill
Time constraints
Ethnicity
Geographic location
All the above variables tend to contribute to regional variables that affect the incidence of obesity, over and above the standard factors such as activity, genetics and food consumption.
Researchers thus acknowledge that differences in weight are influenced greatly by personal choices, which may be molded by demographic factors.
“The Incidence Of The Healthcare Costs Of Obesity”
Jay Bhattacharya, M. Kate Bundorf
Moving onto the economic costs of obesity (Finkelstein, Flebelkorn et al. 2003), obese individuals experience an incremental $732 more in annual medical expenditures, in comparison to ‘normal’ individuals.
The existence of the option to pick between private and public health insurance implies that obese members of society who opt for the former are likely to pay for their increased utilization of medical care through higher levels of health insurance premiums.
In the United States, most of the population under the age of 65 tends to obtain their health insurance from private insurance firms; most of this coverage is obtained from their employers.
Consequently, the health care costs associated with obesity for this age group is a function of the incidence of costs of coverage that is sponsored by employers.
An extremely crucial factor is that the absence of risk adjusted health insurance premiums, especially for observable risk factors creates inefficiencies in this sector.
This absence may lead to inefficient quantities of insurance coverage being offered, since the population that the insurance is being offered in is one with heterogeneous risks, and therefore, a shift away from premiums based on the actuarially fair rate towards the average of the population would result in a mismatch in the quantity of health insurance that is purchased by customer, and the quantity being offered. This may also lead to the issue of adverse selection (Pauly 1970; Rothschild and Stiglitz 1976) due to this lack inherent lack of discrimination/differentiation.
An effect of having insurance may be the opposite that one might hypothesize. It may lead to increased rates of obesity due to the issue of moral hazard. Obese individuals may fail to pay for their higher medical expenditures through higher health insurance premiums due to a lack of risk ratings of premiums may reduce the incentive for individuals to maintain their weight within the ‘normal’ range (Bhattacharya and Sood 2006). Therefore, procedures to underwrite insurance that ignore body weight/BMI may result in inefficient outcomes among both non-obese as well as obese individuals, and may also lead to an increased incidence in obesity as individuals may engage in morally hazardous behavior due to a lack in incentive to maintain an ideal height-weight ratio.
“The Non-Linear Relationship between BMI and Health Care Costs and the Resulting Cost Fraction Attributable to Obesity”
Michael Laxy, Renée Stark, Annette Peters, Hans Hauner, Rolf Holle, Christina M. Teuner
In comparison to a BMI of 20, individuals with BMI values of 25, 30, 35, 40, 45 and 50 kg/m2 experience a greater mean health care costs by 4%, 15%, 35%, 64%, 105% and 160% respectively, thereby implying a non-linear relationship between health costs and Body Mass Index levels.
Especially in the case of individuals with a BMI of over 40, confidence bands tend to widen greatly, since the driving factor of this non-linear relationship that is observed with health care costs is the non-linear effects of both BMI on hospital costs as well as BMI on medical costs.
In stark contrast, there is an almost linear relationship between BMI and outpatient costs.
As a result of this non-linear relationship, a unit decrease in BMI for individuals with a high BMI would result in a high reduction in associated health care costs annually, than for individuals with lower BMI’s.
For example, a unit reduction in BMI for an overweight person is directly associated with a reduction in approximately €35. Similarly, a unit reduction in BMI for an obese individual (BMI of 40-45 kg/m2) would result in a cost reduction of nearly €135
A shift in BMI distribution of the entire sample by 1, 2 and 5 BMI points to the left (lowering individual BMI values of all classes by 5 points) is associated with an annual reduction in mean health care costs by 2.1%, 3.9% and 7.8% respectively, within the population.
Setting all obese individuals at a BMI level of 29.9 kg/m2 is done to help eliminate/prevent obesity, and can be associated with a 4.0% reduction in mean health care costs, annually.
Similarly, to help prevent/eliminate the incidence of being overweight would require setting all obese and overweight individuals at a BMI level of 24.9 kg/m2. This would be associated with an 8.7% reduction in mean health care costs, annually.
Figure 2:
The figure above helps visualize the non-linear relationship that is hypothesized by the researchers, and graphically gives the relationship that is estimated in the paper.
As shown in the figure above, direct health care costs tend to increase in a non-linear fashion with an increasing slope, and that the estimated fraction of costs associated with just obesity averages 4% and obesity and being overweight combined averages 8.7%
Extensive knowledge regarding the relationship between BMI (may be referred to as “Weight Status”) and medical costs are required for policy makers to know the exact value of this resultant burden to the society, and is extremely crucial in formulating cost effective strategies that may be used to prevent the same.
The paper goes on to discuss how a reduction in BMI may affect a reduction in health care costs in a skewed manner, however it is important to note that policies formulated on such hypotheses need to be certain that they have modeled the relationship between BMI and direct medical care costs correctly.
“Does Health Insurance Encourage Obesity? A Moral Hazard Study”
Elizabeth Robison Botkins
It is worth noting that despite ex-ante moral hazard being mentioned often in papers based on health insurance, it is usually considered as an insignificant issue.
However, Dave and Kaestner, 2009, states that poor health has an incredibly high associated cost to an individual, over and above the cost of standard health care.
Despite the possibility of insurance programs being extremely generous, people still always tend to take on the associated cost of not feeling well, such as taking off from work and so on and so forth.
This idea therefore suggests that the existence of moral hazard is extremely limited, as most people would already have enough incentive to maintain their health, however, when focusing on illnesses based on lifestyle choices in the US, this argument tends to fall apart.
This argument would imply that if the cost of falling ill were relatively high, even when not taking medical expenses into account, the expectation would be to see a very low incidence of obesity and other illnesses in tandem; since the opportunity cost of falling ill is supposedly very high.
However, due to the high incidence of obesity, we are able to note that this cost is not enough to incentivize individuals to remain in good health, thereby leading to the notion that moral hazard exists in decision making of individuals.
The RAND Health Insurance Experiment conducted in the 1970s has been considered the gold standard in health insurance studies as stated by Gruber. It involved the random assigning of two thousand families to insurance plans with differing levels of co-payment, and studied them for a period of five years.
The study found that the co-insurance rates did indeed have a notable impact on the quantity of care they received.
Dave and Kaestner, 2009, found that receiving health insurance had the following impacts:
Positive impacts on smoking and drinking
Negative impacts on exercise and physical activity
This is consistent with the factors that account for an increase in unhealthy behavior.
Kelly and Markowitz, 2009, tried deriving the impact that moral hazard had on obesity, and they found that there was a significant upswing in BMI associated with ex-ante moral hazard concerning health insurance sponsored by employers.
Their research claimed that there was a confounding effect of doctor visits, and they attempted to remove this impact by excluding those that visited physicians within the last year, from their study. That being said, this led to a major flaw in their study, since anyone who had experienced moral hazard in a previous time period (which would be likely for individuals who formerly had health insurance) and become ill by virtue of having insurance is more than likely to go back to their doctor since moral hazard is founded in the idea of taking on risks that may lead to illness due to the expectation of receiving care.
The restriction thus placed should result in a downward bias of estimates of ex-ante moral hazard prevalence.
“The Influence of Obesity and Overweight on Medical Costs: A Panel Data Perspective”
Toni Mora, Joan Gil, Antoni Sicras-Mainar
It is important to note that this paper does not highlight the problem as in America, but highlights research in Spain, where the incidence of obesity is different and the medical care provided differs greatly, therefore this paper is used to allow an international perspective and highlight major differences regarding the two nations and if possible, draw parallels between the two.
The mean BMI of our sample for the period we are studying is 26.70, which corresponds to the prevalence of moderate or class I obesity of 23%.
Naturally, the mean BMI among men is slightly higher than that for women at 26.75 and 26.67 respectively with a greater prevalence of class I obesity among women, at 25% versus men at 21%.
Class II obesity or severe obesity is similarly more prevalent among women at 8.7% as opposed to men at 4.4% from within our sample size.
When drawing parallels to the United States, we find that this difference in incidence based on gender is also observed, albeit not at the same level as in Spain.
Along the same lines as our discussion on moral hazard, we find that the differences in gender also exist with respect to behavior such as smoking (17% for women versus 28% for men) and drinking (0.4% for women versus 3.5% for men), and public health insurance (82% versus 95%) and are related closely to a higher level of participation of men in the labor force.
It was found that severe obesity increased medical costs by €170.07 for women versus €145.64 for men, while moderate obesity increase costs by €115.83 for women and €84.61 for men, and finally, being overweight increased this cost by €60.73 for women and €43.49 for men.
“New Evidence on the Effect of Medical Insurance on the Obesity Risk of Rural Residents: Findings from the China Health and Nutrition Survey”
Jian Zhao, Chang Su, Huijun Wang, Zhihong Wang, Bing Zhang
As in the previous study, this paper offers a similar international perspective, however this paper aims to shed some light on the similar situation that exists in China.
The following table highlights the various descriptive differences among insured and uninsured groups for the different genders. Great deals of statistically significant differences were noted between the two sub groups, and it is worth noting that BMI of females in the insured group were significantly higher than that of females in the uninsured group, with a significant increase in both general as well as abdominal obesity for both the insured as well as uninsured groups between 2004 to 2011.
Participation in New Rural Cooperative Medical Insurance (NRCMS) increased the likelihood of abdominal obesity by 35% and general obesity by 12%, for women.
These findings are illustrated as in the table below:
Table 2:
“Annual Medical Spending Attributable to Obesity: Payer and Service-Specific Estimates”
Eric A. Finkelstein, Justin G. Trogdon, Joel W. Cohen, William Dietz
The analysis, study methods and data used here rely on data from the Medical Expenditure Panel Surveys (MEPS) from 1998 to 2006.
MEPS can be defined as a nationally representative survey of the non-institutionalized, civilian population that quantifies an individual’s total medical expenditure, annually, bifurcating it based on the service and sources of payment, that may range from private sources, Medicaid, Medicare or other sources.
This data is also important since it contains information regarding individuals’ status of health insurance and their socio-demographic characteristics that may include ethnicity, race and BMI from estimates of self reported height and weight numbers.
On carrying out regression analysis, the results we obtain allow us to compute the impact of obesity for each type of service on annual medical expenditure.
Using the given regression results, it can be noted that in 2006, across all payers, obese individuals experienced greater per capita medical expenses by approximately $1,429, 42% greater than the spending of normally weighted people.
Similarly, in 1998, the increase in per capita expenditure that could be attributed to obesity was several hundred dollars lower than the 2006 estimate, however, was found to not be statistically different.
In both cases (1998 as well as 2006), the cost increase due to obesity was estimated to be 37%.
“The Economic Analysis of Obesity”
Tahereh Alavi Hojjat
There is an obvious, yet complex relationship between obesity and poverty.
Being poor in a low-income country may be associated with poor nutrition levels, usually associated with malnourishment. However, being poor in a developed country, a middle income/high income country can be associated with a higher risk of obesity.
This hypothesis was confirmed by Sobal and Stunkard (1989). They stated that an inverse relationship exists between obesity and socioeconomic status in developed countries; where the higher classes are able to make up for their relatively sedentary lifestyles through access to high quality nutrition, better information and opportunities to engage in sport. The more affluent people in these countries are also able to purchase better quality food, allowing for higher nutrition levels, not simply measured by caloric intake.
In lesser-developed countries, such as those with a GDP per capita of less than $2,500, there is a direct relationship between excess weight among higher social classes (Monteiro et al., 2004).
It is a common suggestion of existing studies that the relatively high costs of healthy diets may be a contributing factor to the obesity epidemic. This is especially seen in groups with lower income and lower education.
On an individual level, incidence of obesity is linked to low levels of income (i.e. higher incidence of poverty), low levels of education and minority status.
Hammond and Levine (2010) measured the direct medical costs that are associated with obesity and illnesses that stem from the same, and they argued that relative medical spending for obese individuals could be up to 100% higher than that for normally weighted adults, and this “excess” medical spending on a nationwide basis would result in nearly $147.0 billion for adults, annually, and $14.3 billion for children, annually.
“Health Insurance And The Obesity Externality”
Jay Bhattacharya, Neeraj Sood
There is a known, stark contrast in the differences in medical expenditures, and a lot of literature on the same; however, very few of these papers and studies try determining the extent to which coverage in the form of health insurance leads to subsidies for an obese populous.
Finkelstein, Ruhm and Kosa (2005) estimate that “the government finances roughly half the total annual medical costs attributable to obesity. As a result, the average taxpayer spends approximately $175 per year to finance obesity related medical expenditures among Medicare and Medicaid recipients.”
In another study conducted by Daviglus et al (2004), Medicare claims records from the 1990s are linked to a sample of workers from Chicago between 1967-1973, and it estimates a substantial difference between Medicare expenditures between obese and non-obese individuals.
For instance, obese female workers between 1967-73 incurred a cost of nearly $176,947 on Medicare in the 1990s, while analogously; normally weighted female workers spent only $100,431 on the same.
Similarly, male workers that were obese spent $125,470 with normally weighted male workers spending only $76,866.
This being said, it is not enough to simply know the extent to which obesity related medical costs are financed through public insurance in trying to calculate the subsidy for obesity.
This would require estimating the payments made by both non-obese and obese individuals that are enrolling in health insurance.
For example, both non-obese and obese individuals pay similarly for Medicare while being under 65 years of age, and receive benefits, as they get older.
Due to the high incidence of obesity related diseases, obese individuals tend to work, earn, get taxed and die at different rates in comparison to normally weighted individuals, therefore, simply looking at differences in Medicare expenditure would give a very view idea of this notion regarding the subsidy provided by Medicare for the obese.
“Obesity Epidemiology”
Frank Hu
Depending on the methodology made use of, data sources and time period, it is estimated that the annual medical costs associated with obesity in the United States amount to roughly $75 billion (in 2003 dollars), and accounts for nearly 4.3% to 7% of total expenditure on health care.
Over and above these direct costs, there is a plethora of significant indirect costs in the form of decreased years of living without a disability, early retirement, increase in mortality before retirement, absenteeism from work, reduced productivity and many others.
Despite there being limited research in this area, it has been suggested that the magnitude of these indirect costs might even be larger than those of the direct medical costs.
A large number of studies were conducted in Scandinavian countries on loss in productivity, for instance, obese individuals in Sweden were found to be 1.5-1.9 times more likely take sick leave, while 12% of obese female workers had disability pensions for the same, resulting in a cost of ~$300 for every adult female.
Similarly, in the United States, it was estimated by Thompson et al. that absenteeism stemming from obesity cost employers $2.4 billion in 1998.
Despite the incidence of obese and overweight individuals increasing in all segments of the American population, certain ethnic/racial groups have experienced a greater increase in the same.
For example, the prevalence of obesity among non-Hispanic black and Mexican American women is nearly 15% higher than non-Hispanic white women.
“The Medical Care Costs of Obesity: An Instrumental Variables Approach”
John Cawley, Chad Meyerhoefer
This paper highlights the impact that obesity has on medical costs.
It is estimated that its impact on annual medical costs is to the tune of $2,826 for men and women combined, $3,696 for women, and $1,171 for men (however, this is not statistically significant).
All these values are in 2005 dollars, and it is worth noting that these average values tend to be driven by a few individual with very high BMI levels, and consequently, very high medical expenditures.
The Instrumental Variables (IV) model constructed in the paper indicated that the effect of obesity on medical costs is a great deal higher than the correlation of obesity as found in existing literature.
The effect of obesity on medical care costs, as found in this paper is ~$3,115, nearly twice the value given by Finkelstein et al. (2009) at $1,429 (both in 2008 dollars)
This paper estimates that the incidence of obesity related illnesses on medical care costs in the nation is to the tune of $185.7 billion, which is more than twice the value of $85.7 billion, once again, suggested by Finkelstein et al. (2009).
Consequently, according to this paper, nearly 16.5% of US expenditure on health care is spent in treating obesity related illnesses.
“Parental Work Hours and Childhood Obesity: Evidence Using Instrumental Variable Related to School Eligibility.”
Charles Courtemanche, Rusty Tchernis Xilin Zhou
Among the many determinants of obesity, obesity among children is driven greatly by the amount of time their parents spend working.
After carrying out regression analysis (methodology is explained in depth in the paper) we discuss the OLS results thus obtained.
An increase in parental work hours by 10 hours per week is associated with a minute increase in the Z-score of 0.025-0.03 for BMI. Despite being small, this increase is statistically significant.
Similarly, even the Pr (Overweight) is affected in similar fashion on conducting said regression analysis.
We subsequently conduct subsample analyses based on parental education, marital status of the mother and ethnicity/race, with the objective being to identify the effect of parental work on child weight that differs by a household’s relative disadvantage level.
Anderson et al. (2003) states than the strongest, positive association between a mother’s workload and childhood obesity is among children of high income households, with highly educated mothers, and coming from a non-Hispanic white background.
This paper further explores the effect of maternal employment on the weight of a child. “The identification strategy exploits plausibly exogenous variation in parental labor supply coming from the youngest sibling’s age.”
There is little to no evidence proving that the effects of maternal versus paternal work are significantly different. Combined, these would imply that the contribution of a rise in maternal employment to the increase in childhood obesity is larger than the relatively modest estimates that prior literature would suggest.
“The Health Care Cost Implications of Overweight and Obesity During Childhood”
Nicole Au
Do note, this following article is based on Australian data, but it has been shown that the United States is fairly similar in that regard.
This study’s main aim is to investigate government funded costs on health care for children that are associated with being classified as overweight between the ages of 4-5.
Over the 5-year study period, overweight children aged 4-5 to 8-9 years, incurred a marked increase in PBS (pharmaceutical) and MBS (medical care) costs. These findings are in accordance with conventional literature on the same that finds that being overweight as a child is associated with increased health problems and supports the positive relationship as stated above (Hampl et al. 2007; Finkelstein and Trogdon 2008; Trasande and Chatterjee 2009).
The results of the study indicate that after controlling for maternal and child characteristics, overweight children at the age of 4-5 on average experienced an additional $93 in non-hospital Medicare costs over a 5-year period.
These findings can be extrapolated to all of the children that age, showing how the combined Medicare bill over the same 5-year period is 9.8 million AUD greater than normally weighted individuals.
“Demand Heterogeneity In Insurance Markets: Implications For Equity and Efficiency”
Michael Geruso
Presently, US employers are banned from governing bodies such as the ERISA from setting premium contributions that differ on basis such as age, sex or other observable workplace characteristics for their employee health plans.
In the marketplace for health insurance that has been established by the Affordable Care Act, price discrimination is allowed on the basis of age, but comes with its own set of restrictions, however, it isn’t allowed to discriminate on the basis of sex.
Within a local market, private insurers, under the US Medicare system must offer plans to all eligible beneficiaries at identical prices in terms of certain consumer characteristics such as age and sex.
Conventional wisdom tends to assume that with the use of proper tools such as consumer subsidies and risk adjustment, regulators may be able to undo the problem of adverse selection that arises through these non-discriminatory policies to be able to generate the most feasible allocation.
The tradeoff between equity and efficiency is a fundamental feature of a heterogeneous insurance market.
Policymakers may wish to pursue equity objectives, however, there exists an unavoidable cost of efficiency in doing this.
“Can Financial Incentives Help People Trying to Establish New Habits? Experimental Evidence With New Gym Members”
Mariana Carrera, Heather Royer, Mark Stehr, Justin Sydnor
We understand the necessity for decision making without the existence of moral hazard, and therefore we try to see if the existence of incentives and incentive structures might reduce the incidence of morally hazardous actions within a population.
Through the experimentation as elaborated on in the paper, it is possible to come to the conclusion that the provision of moderate sized financial incentives only slightly helped new members in establishing better habits regarding the same, suggesting that, at least as far as exercise is concerned, timing financial incentives to align with endogenous attempts at changing behavior may not be an entirely successful strategy in improving one’s exercise habits.
“Health Insurance and the Obesity Externality”
Jay Bhattacharya, Neeraj Sood
No one denies that obesity is most definitely a cause for concern when it comes to personal health, but is it necessary for obesity to be a concerning topic for the public, or should adults be free from government intervention and be allowed to determine their own desired body weight?
Legislation seems to swing both ways in trying to curb this problem of obesity, and yet sometimes finds itself unintentionally exasperating it.
However, it is worth noting that this dilemma can be solved through the use of externalities.
Individual choices regarding body weight, in the absence of government intervention, are optimal only if the individual faces the entire cost of their decisions i.e. all the externalities that may have existed are internalized.
“Without a market failure, there is no economic justification for government intervention. A high prevalence of obesity is not in itself proof of market failure.”
This paper states that it is not insurance itself does not lead to the existence of externalities, however, they arise when premiums are unable to fully adjust and reflect the weight choices of individuals, thereby implying that they do not find themselves bearing the entire cost of their choice.
It is worth noting though, that if premiums for the same were to reflect the decisions of individuals, then the change in premiums would internalize the cost of weight gain. Thus, even if an individual is completely insured, they may still be incentivized to decrease expected expenses on medical care through weight loss programs since consumers would recover lower expenses through lower premiums.
“Does Health Insurance have Influence on Obesity?”
Wenyao Zhou
The table as shown below shows the sample means for those individuals with and without health insurance.
Note, no confounding factors have been accounted for in these summary statistics.
From the table, it is possible to see that those individuals that have health insurance tend to have a higher BMI than those that don’t, on average.
Unsurprisingly, the table also highlights that people that have health insurance also tend to be older, possibly married, more educated and tend to have higher levels of income than those individuals without health insurance.
Moreover, the paper states that non-white men and poor people are less likely to have insurance. Along with this, an increase in educational attainment as well as personal earnings tends to reduce BMI levels.
With the ever increasing bodyweight of Americans from 2000 to 2010, the result shown in the paper also indicates that insurance is positively correlated with BMI, therefore insured individuals will tend to be heavier than uninsured people.
Note only that, but the presence of health insurance not only affects one’s chances of being overweight, but also of being obese to a much greater extent, since insurance tends to reduce an individuals’ responsibility, thereby reducing health consciousness.