Disclaimer: This assignment was written by a student and is not an example of our proffesional work. You can view professional samples here.

Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not reflect the views of BusinessTeacher.org. Information in this assignment should not be used to form the basis for any kind of financial or investment advice, as the content may contain inaccuracies or be out of date..

Economic Impacts of Obesity

3590 words (14 pages) Business Assignment

4th Nov 2020 Business Assignment Reference this

Tags:

Introduction

Obesity refers to excessive fat accumulation in individuals, that present a health risk. The World Health Organization defines obesity in adults as having a body mass index (BMI) of over 30.

However, it is commonly disputed that BMI is simply a weight-height ratio and does not measure fatness (Cawley, 2015). It is a crude measure of obesity that may not truly reflect the prevalence of obesity in a country. For example, muscular individuals might be incorrectly classified as obese. Moreover, countries with smaller than average body frame sizes, such as Asian countries, may find that they understate the obesity problem if BMI of 30 is the judging criteria.

Hence, more accurate measures of obesity should be based on body fat mass rather than the total body mass. Yet, most research defines obesity using BMI because most datasets include only weight and height.

Obesity has been rising in the US (Cutler, Glaeser, Shapiro, 2003; Bhattacharya, Sood, 2011). Refer to Fig.1 below, obesity has had a sharp increase since 1980.

Figure 1. Trends in overweight, obesity, and extreme obesity among adults aged 20-74 years: United States, 1960-2008 (Ogden, Carroll, 2010)

Obesity is especially prevalent in the US. In fact, US has the highest rate of adult obesity among the members of OECD (The Organisation for Economic Co-operation and Development).

Reasons for Rise in Obesity

Numerous factors contribute to the rise in obesity. Since there are so many possible causes, studies are only able to control a small number of these factors, hence, their results might overstate the importance of the explanatory variables they have chosen.  However, it is possible to measure the causal effect of specific factors on obesity at a given point in time (Cawley, 2015).

Calories expended have not changed significantly, but calories consumed have drastically increased. This can be attributed to technological and social changes that have lowered the price of calories and raised the opportunity cost of physical activities. However, the daily calorie surplus is not highly correlated with the increase in weights of US adults (Cawley, 2015). Since there is either little data on calories consumed and calories expended over time, or data that is plagued with errors, it is not easy to prove that the calorie surplus has a causal effect on obesity.

Improvements in agricultural technology have resulted in substantially cheaper food production and hence driven down food prices. Hence, people have responded to these lower prices by consuming more. The decline in food prices can account for up to 40 percent of the increase in BMI of adults since 1980 (Grossman, Rashad, 2004). Real prices of energy-dense foods have fallen relative to less dense foods, such as fruits and vegetables.

Hence, by economic theory, consumers likely responded to these changes by increasing the consumption of energy-dense foods and decreasing consumption of less energy-dense foods. However, regressing obesity on local food prices may not fully reflect the effect of obesity as geographical differences and changes in demand over time also plays a part and might make the estimates biased (Cawley, 2015).

Even rising portion sizes are a consequence of the fall in food prices. This explains why average body weight is higher in some countries like the US and UK since food is cheaper due to lower trade barriers. The popularity of processed food, which is generally unhealthier, has also contributed to the rising obesity rates (Cutler, Glaeser, Shapiro, 2003).

Changes in lifestyles such as women joining the workforce, eating out in restaurants more, widespread usage of automobiles, heating, and air-conditioning have also contributed. Physical exercise has declined since 1980, and urban sprawl provided more transportation choices (Grossman, Rashad, 2004). All of these factors have contributed to the prevalence of obesity.

Education also plays a role – those with higher education qualifications tend to have better health (Cawley, 2015; Cohen, Rai, Rehopf & Abrams, 2013). Education may help to promote a healthier lifestyle such as providing more information, promoting healthier diets, ‘forced’ recreational physical activities, higher income and better health insurance, etc. However, higher education could also lead to a higher likelihood of having a sedentary job, which has the opposite effect. Early childhood intervention has been found to reduce the risk of youth obesity, but only if is done at younger ages. The relationship between education and obesity may also vary across countries.

With an increase in income, there is a straightforward substitution effect. When wages are high, the opportunity cost of time spent on leisure activities is also higher. Hence, economic recessions may lead to more time investments in health such as increased physical activities and home cooking. The opposite is also true – when the economy is booming, there could be an increase in hours worked and hence less time spent on leisure and improving health.

However, the income effect of change in wages could offset the substitution effect. A lower income could lead to a decrease in calories consumed, and the choice of low nutrient foods instead of more expensive nutritious food like fruits and vegetables. Economic downturns can increase stress, affecting health negatively and be one of the factors contributing to a rise in obesity.

Costs of Obesity

While a fall in food prices, technological innovations, and modern conveniences are all good things, the side effect that has resulted, obesity, is not. Obesity results in higher healthcare costs, reduced life expectancy, increased risk of chronic diseases (Bhattacharya, Sood, 2011), reduced physical mobility and function, lower wages and social stigma.

The decrease in life expectancy is even larger when one is young since they have more years left to lose. While the effects of obesity typically diminish with age, it is probably in part because those who are obese and older were likely to be obese when they were younger and survived longer.

There are numerous health risk factors, with obesity being a particularly important one. It is widespread and fuelled by powerful economic trends such as a fall in food prices and an increase in sedentary work. Obesity leads to a multitude of health risks, such as the risk of heart diseases, which leads to high healthcare expenditures (Cawley, Meyerhoefer, 2012). It is especially the case when costly chronic diseases such as diabetes result, placing a heavy financial burden on the healthcare system (Yang, Hall, 2007). However, such costs are not just borne by the obese individual but are likely to be shared with other people in the same insurance pool, or by taxpayers for government-provided healthcare. While reduced life expectancy is largely a private cost, increased healthcare expenditure is not.

Past papers done, however, are limited since they measure the correlation of obesity with medical care costs, rather than the causation effect. Some people might become obese after a serious medical condition and have higher medical costs because of the condition. Some might have less access to medical care and hence more likely to be obese. Moreover, many of the past studies done on the correlation of obesity and medical costs use self-reported data, which might also contain errors that makes the coefficient estimates biased.

In the paper done by Cawley and Meyerhoefer, they use the instrumental variable approach to investigate the relationship between obesity and medical costs. They found that the effect of obesity on medical costs is greater than found in existing literature, especially on medical expenditures for diabetes. Moreover, the causal effect of obesity has important implications. The healthcare costs of the obese and non-obese may differ not just because of their weight, but also because of differences in their income, accessibility to healthcare, pre-existing medical conditions and other sociodemographic factors (Biener, Cawley, Meyerhoefer, 2017).

Obesity could have serious consequences for the elderly. Evidence suggests that healthy elderly live longer and have lower lifetime medical spending than their less healthy peers (Lakdawalla, Goldman, Shang, 2005). Among the elderly population, the major payers of healthcare are not them but publicly financed health insurance, and unlike private insurance, public insurance cannot shift the higher healthcare expenditures by obese individuals to them by increasing their premiums (Yang, Hall, 2007). Hence, the bulk of the financial burden is borne by tax revenue, which could have been put into other policies to stimulate the economy.

However, it should be taken into consideration that as people age, the development of chronic diseases is common. Hence, it may not be as straightforward to link obesity as the reason for increased healthcare costs, especially since there is also a natural weight-loss that occurs.

Justifications for Intervention

Moral hazard arises with insurance coverage. Not only is there a potential distortion in people’s body weight choices, but the reduction of out-of-pocket health care also leads to a change in demand (Bhattacharya, Sood, 2011). An obese individual is likely to generate substantial medical costs, especially in comparison to healthy individuals. Everyone else in the same plan as that individual helps to ‘subsidize’ that individual’s costly habits, such as eating junk food every day. Hence, pooled insurance creates a negative externality that did not exist before through a reduction in the cost of unhealthy eating. The loss of social welfare implies a true public health crisis.

If health insurance premiums are adjusted to reflect the actual costs of obesity, then these costs would be internalized by the obese individuals themselves and not passed on to the others. However, premiums are rarely risk-adjusted for obesity and any other risk factor except family size. This is due to administrative burden and legal provisions, which tend to prohibit insurers from such forms of discrimination (Bhattacharya, Sood, 2011).

In the classic Pigouvian case, social welfare can be improved if the external costs are internalized by their creators. However, this does not apply well to obesity with employer-provided and public health insurance. Hence, the interventions that should be applied should be non-Pigouvian, such as through changing people’s behaviour. Humans tend to use something approximating hyperbolic discounting, which means that their preferences are time-inconsistent. Even with the best intentions, their past selves may alter a plan that the previous self deemed optimal, which result in lapses of self-control.  Public policies should give individuals incentives that push them towards their ‘true’ choice – a rational choice that maximises their lifetime utility.

Moral hazard also arises when health insurance causes people to take greater risks with their health or incur higher healthcare expenditures than they would otherwise (Einav, Finkelstein, 2017). People have lower incentives to take care of their health since the cost of an action that is risky to their health has decreased. For example, an individual might decide to consume more fatty meals since they know that if they fall sick, their healthcare costs will be covered, contributing to higher obesity rates. The fuller the insurance, the greater the price distortion between the socially efficient equilibrium price and the price with health insurance. Asymmetric information between insurers and individuals prevents the insurer from adequately pricing the action. The insured responds to this price distortion by taking more risks or demanding more covered goods and services. Hence, the resulting social loss creates a need for policies to address obesity.

Policies to Tackle Obesity

The most direct approach to tackle obesity would be through taxation of excess body fat, with the amount of the tax equivalent to the marginal external cost. However, this could be seen as discrimination against those genetically predisposed to obesity. Hence, most policies do not levy taxes directly on fatness, but rather take other forms, such as offering incentives, taxing specific food products, and subsidies on physical activities.

Obesity can be attributed to a failure in health insurance pricing (Cawley, Meyerhoefer, 2012). To eliminate the externality, one should charge a sufficiently high premium to obese individuals. Insurers can design incentives, that come as rewards or penalties to do so. However, while this tries to increase social welfare, it conflicts with the priorities of risk pooling and anti-discrimination. Moreover, the incentives would have to ideally be designed as a Pigouvian tax, which is hard to estimate the true value of.

In an experiment done by Belot, James, and Nolen in 2016, they tested the effectiveness of different incentives in increasing choice and consumption of fruit and vegetables at lunchtime. Other literature shows that incentives can increase motivation for exercise, losing weight, and eating more healthy food. Their results showed that competitive incentives had a stronger impact than individual incentives and more effective for all demographic groups. Hence, the usage of competitive incentives could potentially improve health and even encourage choice and consumption among groups that typically do not respond to health interventions.

The differential effects of each group suggest that different incentives may have to be developed for each of the groups. Demographic characteristics play an important role in the effectiveness of such incentives. For example, gamification of intervention would largely result in a positive impact on adolescents, as they find it more fun and enjoyable (Corepal R, Best P, O’Neill R, et al, 2018).

However, such effects of short-term incentives are found to not have a lasting impact on people’s behaviour. Theoretically, a small change in today’s behaviour could lead to long term changes in consumption patterns. Yet, in the experiment, they found no evidence of a long-run impact. In fact, incentives could also sometimes lead to discouraged behaviour (Belot, James, Nolen, 2016).

Other approaches to tackling obesity could be through information provision, as consumers often misjudge or lack important information about calories and nutrients. Typically, restaurants make nutrition information available only upon request, if at all (Burton, Creyer, Kees & Huggins, 2006). Laws that govern the provision of nutritional information could have potential health benefits on consumer health. Existing literature shows that consumers vastly underestimated the nutritional levels of food they ate.

In a study done by Burton, Creyer, Kees, and Huggins in 2006, on average, unhealthy items were underestimated by over 600 calories. If diners consumed the extra calories just once per week, it could cause a weight gain annually, and over time, the misestimation result in significant weight gain. Hence, providing nutritional information on menus could seek to improve consumer awareness and offers benefits to consumers’ health. In another study, providing nutritional information reduced the intent to purchase food that was less healthy than consumers expected. Such findings support the notion that nutrition information on menus is important and could be a crucial factor in reducing obesity. However, it is also limited as customized orders and portion size differences will make the provision of exact information for every item on the menu and every consumer difficult.

Insurers can also try to reduce the price distortion due to asymmetric information and moral hazard through coinsurance, co-payments, and deductibles. Coinsurance and co-payments are cost-sharing mechanisms that force the insured individual to pay a proportion of the incurred health care cost. Since they are no longer fully insured, the uncertainty they face increases. Out-of-pocket prices move closer to the socially optimal prices and reduce social loss. Deductibles set a minimum level of expenses that the insured must pay himself. However, if it is too low, moral hazard may persist. While private markets can navigate the trade-off between moral hazard and uninsurance if they are perfectly competitive, public insurance cannot.

This is not to say that the resulting moral hazard only has negative impacts on an individual’s health. It might also improve health since individuals consume extra preventive care, but this is only if people consume less than they ‘should’ - socially optimal level (RAND Health Insurance Experiment (HIE), n.d). The income effect of having health insurance also makes people ‘richer’ than they actually are by making expensive surgeries and treatments affordable when they previously may have not been.

Conclusion

In conclusion, numerous risk factors have contributed to the rise of obesity rates across the world, such as technological and lifestyle changes, that have led to lower food prices and lesser physical activity. The role of education and income levels also contribute to obesity rates. While obesity may have resulted from ‘good’ things, they come with several harmful costs that lead to a public health crisis and a cause for intervention. The increase in healthcare expenditures is especially crucial, as one considers the different forms of health insurance. When the costs of obesity are largely borne by the public instead of the obese individual, the externality could cause a failure in the market for insurance. Hence, interventions through policies such as taxations and information provision are important to mitigate the crisis.

While in theory, we assume people are rational and utility maximising, in reality, they are likely to have hyperbolically discounted preferences, which result in time-inconsistent preferences. The present bias makes it hard to define the overall utility function of the different ‘selves’ across time periods. Hence, this justifies the call for intervention in the fight against obesity.

Bibliography

  • 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.
  • Cutler, D.M.; Glaeser E.L. and Shapiro J. (2003), “Why Have Americans Become More Obese?”, Journal of Economic Perspectives, (17-3).
  • Ogden, C., & Carroll, M. (2010). Prevalence of Obesity Among Children and Adolescents: United States, Trends 1963-1965 Through 2007-2008. PsycEXTRA Dataset. doi: 10.1037/e582042012-001
  • Yang, Z., & Hall, A. G. (2007). The Financial Burden of Overweight and Obesity among Elderly Americans: The Dynamics of Weight, Longevity, and Health Care Cost. Health Services Research, 43(3), 849–868. doi: 10.1111/j.1475-6773.2007.00801.x
  • Einav, L., & Finkelstein, A. (2017). Moral Hazard in Health Insurance: What We Know and How We Know It. doi: 10.3386/w24055
  • Cawley, J., & Meyerhoefer, C. (2012). The medical care costs of obesity: an instrumental variables approach. Journal of health economics, 31(1), 219-230
  • Cohen, A. K., Rai, M., Rehkopf, D. H., & Abrams, B. (2013). Educational attainment and obesity: a systematic review. Obesity Reviews, 14(12), 989–1005. doi: 10.1111/obr.12062
  • Grossman, M. and Rashad, Inas, (2004), “The economics of obesity”, Research Report
  • Biener, A., Cawley, J., & Meyerhoefer, C. (2017). The High and Rising Costs of Obesity to the US Health Care System. Journal of General Internal Medicine, 32(S1), 6–8. doi: 10.1007/s11606-016-3968-8
  • Lakdawalla, D. N., Goldman, D. P., & Shang, B. (2005). The Health And Cost Consequences Of Obesity Among The Future Elderly. Health Affairs, 24(Suppl2). doi: 10.1377/hlthaff.w5.r30
  • Bhattacharya, J. and Sood, N., (2011), “Who pays for obesity?”, Journal of Economic Perspectives, (25-1).
  • Belot, M., James, J., & Nolen, P. (2016). Incentives and children's dietary choices: A field experiment in primary schools. Journal of health economics, 50, 213-229
  • Corepal R, Best P, O’Neill R, et al (2018). Exploring the use of a gamified intervention for encouraging physical activity in adolescents: a qualitative longitudinal study in Northern Ireland. BMJ Open 2018;8:e019663. doi: 10.1136/bmjopen-2017-019663
  • Burton, S., Creyer, E. H., Kees, J., & Huggins, K. (2006). Attacking the Obesity Epidemic: The Potential Health Benefits of Providing Nutrition Information in Restaurants. American Journal of Public Health, 96(9), 1669–1675. doi: 10.2105/ajph.2004.054973
  • RAND Health Insurance Experiment. (n.d.). Retrieved from https://www.rand.org/health-care/projects/hie.html.

Cite This Work

To export a reference to this article please select a referencing stye below:

Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.

Related Services

View all

DMCA / Removal Request

If you are the original writer of this assignment and no longer wish to have your work published on the UKDiss.com website then please: