HEALTH INSURANCE POOLING BETWEEN THIN AND OBESE IN EUROPE. Cameron Mullen. June 1, 2011

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1 HEALTH INSURANCE POOLING BETWEEN THIN AND OBESE IN EUROPE Cameron Mullen June 1, 2011 Department of Economics, Stanford University Stanford, CA Under the direction of Professor Jay Bhattacharya ABSTRACT Numerous studies have found that obese workers are paid lower wages than the nonobese, even when controlling for skill level and productivity. Bhattacharya and Bundorf (2009) find that in the United States, this wage differential is only significant in jobs that provide health insurance to their employees and that this wage differential largely results from the increased cost of insuring obese workers. I examine European data from the Survey on Health Retirement and Aging in Europe (SHARE) to determine whether obese workers are paid lower wages than the non-obese, even where the majority of health insurance is provided by the state. I find that there is no significant wage differential between the obese and non-obese in Europe as a whole and that to the extent that wage differentials exist in individual countries, these are highly correlated with the prevalence of employer-provided health insurance in those countries. Keywords: Obesity, Health Insurance, Insurance Pooling, Europe, Survey on Health Retirement and Aging in Europe Acknowledgments: I thank Professor Jay Bhattacharya for his immense guidance and support throughout the process of the study. I also thank my friends and family for their support and encouragement.

2 Cameron Mullen 2 Introduction Obesity is a major and growing health concern in both developed and developing countries. The World Health Organization classifies overweight people as those having a Body Mass Index (BMI) 1 of greater than or equal to 25 and obesity as having a BMI of greater than 30 (WHO 2011). Worldwide obesity has more than doubled since 2008 and an estimated 500 million people around the world are obese (WHO 2011). Obesity has been found to be associated with type 2 diabetes, gallbladder disease, coronary heart disease, high blood cholesterol level, high blood pressure, osteoarthritis, and their associated co-morbidities (Must et al. 1999). In addition to the health effects of obesity, obesity imposes significant economic costs and has lately been the subject of much economic literature. The healthcare cost of obesity is significant: the average annual healthcare expenditure for obese persons in the United States is 17.9% higher than those with normal body weights (Finkelstein et al. 2003). In addition to direct healthcare costs, obesity also potentially imposes other economic costs through lost productivity due to morbidity and mortality, health insurance market failures due to moral hazard and adverse selection, and labor market inefficiencies due to hiring and wage discrimination. The purported external incidence of these costs has been used to justify the case for public policy intervention to reduce obesity; however, policy intervention based upon these costs is only justified if in fact obesity imposes external costs and these external costs and if the existence of the externality changes behavior. If behavior (both of obese and non-obese) is unaffected by the presence of external costs, these costs represent a costless transfer from the non-obese to the obese. As the obese tend to be poorer in developed countries (especially

3 Cameron Mullen 3 women), this transfer would represent not only a costless transfer, but also a progressive one (McLaren 2007). A recent paper by Jay Bhattacharya and Neeraj Sood ( Who Pays for Obesity? ) addresses the incidence of the costs of obesity. They apply the Future Elderly Model to estimate both the lifetime healthcare costs and years lost of life conditional upon becoming obese. By assigning a value of $200,000 to the value of a statistical life year, they conclude that the vast majority of the expected cost of becoming obese is borne by the obese individual through lost years of life (for a 50 year old, 1.65 years lost valued at $330,000). However, they show that the expected lifetime health cost of becoming obesity is also significant and decreases with age from $15,000 for 50 year olds who become obese to $5,000 for 65 year olds who become obese (Bhattacharya and Sood 2011). Even if the vast majority of the cost of obesity is internalized through years lost, the presence of increased medical expenditures leaves open the possibility that these costs are borne by others. There are many mechanisms by which these healthcare costs could be transferred to others; however the most direct mechanism would be pooled health insurance, including social insurance (like Medicare and Britain s National Health Service) and private pooled insurance, the most common of which is employer provided or subsidized health insurance. Two types of moral hazard are potentially generated by pooled health insurance. The first of which is higher healthcare expenditures induced by lower out-of-pocket costs, and the second of which is weight gain induced by lower out-of-pocket costs of obesity. The former is not unique to obesity, for most health insurance contracts have the potential to induce social loss this way. However, social loss increases with the elasticity of demand for healthcare; therefore, a higher elasticity of demand for healthcare associated with obesity would

4 Cameron Mullen 4 increase the magnitude of this social loss. Bhattacharya and Sood assert that it is unlikely that the obese and non-obese have different demand elasticities and that if they are different, the obese would have a lower elasticity (and less moral hazard) because of a higher propensity to use inpatient services, the demand elasticity for which is lower than outpatient services according to the RAND Health Insurance Experiment (Bhattacharya and Sood 2011) (Manning et al. 2007). The second type of moral hazard requires that pooled health insurance actually pool risks and that behavior changes as a result. Pooling of risks does not occur if insurance premiums adjust based upon the risk profile of the insured, and in a competitive market for insurance, insurers will adjust individual premiums based on easily-observable risk factors of which obesity is an obvious example (Arrow 1963). In social health insurance, pooling does occur because premiums do not reflect the risk of the individual; however in private insurance markets the situation is more complicated. Individual insurance plans premiums adjust based on healthstatus indicators of the insured, but group insurance plans (usually provided by an employer) rarely risk-adjust the premiums charged to individuals due to the difficulty and cost of administering risk-rating systems. In fact in the United States, federal premium requirements prohibit any employer-sponsored health coverage from charging employees a higher premium based on health-related factors than the premium charged to other similarly situated individuals (GAO 2003). However, the premium charged to the group by the insurance company is riskadjusted for the risk profile of the entire group (usually based upon historical cost to insure the group). Hence, at first glance, it would seem that pooling does occur at the group (or firm) level. Standard labor economics posits that in a competitive market, employees are paid their marginal revenue product; in other words, employees cost the firm what they are worth. In instances where fringe benefits such as pension plans, child-care, and health insurance are

5 Cameron Mullen 5 included, cash wages correspondingly decrease by the cost of providing the benefits. Hence it is theoretically possible that instead of adjusting employee health insurance premium contributions, employers adjust cash wages to reflect the cost of insuring individuals. Jonathan Gruber shows that the introduction of mandated maternity benefits in employer-sponsored health insurance decreased wages for married women, but not for single men, reflecting the increased cost of insuring married women (who were more likely to become pregnant) (Gruber 1994). Therefore, it is plausible that the obese receive lower cash wages to reflect the increased cost of insuring them. This is precisely what Jay Bhattacharya and Kate Bundorf found in their 2009 paper. Using data from the National Longitudinal Survey of Youth and the Medical Expenditure Panel Survey, they found that, controlling for measures of job-productivity, obese workers earn lower wages than their non-obese counterparts in jobs that provide health-insurance; however in jobs that do not provide health insurance, they found that obese and non-obese workers are paid the same wage. Furthermore, they conclude that the magnitude of the wage offset reflects the increased cost of insuring obese individuals. Hence, any pooling due to obesity is nominal only (Bhattacharya and Bundorf 2009). Firm level pooling of easily observable risks is highly problematic from a labor market efficiency perspective. If pooling exists, the non-obese are subsidizing the obese and are in effect paying greater than actuarially fair premiums for health insurance (in the form of higher health plan contributions, fewer other fringe benefits, or lower cash wages). If employees are free to move between firms and can observe the health-status of their co-workers (as is the case with obesity), non-obese, healthy employees will segregate into separate firms from the obese and sick to pay lower premiums, thereby creating potential inefficiencies. Augmenting this

6 Cameron Mullen 6 segregation is the fact that by hiring exclusively non-obese employees, firms can earn positive profits in a competitive market by not hiring obese employees. As this type of segregation does not appear to be widespread, it would appear that true pooling does not exist; furthermore, because of the existence of the arbitrage opportunity in the presence of true pooling described above, firms face competitive pressure to adjust the wages of their obese employees to account for the increased cost of insuring them. It would appear that the healthcare costs of obesity are internalized by individuals under employer-sponsored health insurance, minimizing the moral hazard associated with obesity. If there is a wage penalty for obesity under employer-sponsored health coverage, one would expect that countries in which employer-sponsored health insurance is less prevalent to have lesser wage penalties for obesity. European countries tend to have very generous social health insurance plans available to the vast majority of their occupants. Due to the prevalence of social health care, European firms provide significantly less health insurance to their employees, and the insurance they do provide often does not replace social insurance, but rather complements it. This paper examines European data from 11 different countries on full-time employees older than 50 to test the hypothesis that greater levels of employer-provided health insurance lead to greater wage penalties. I find that on the aggregate, there is not a significant wage penalty due to obesity in Europe. I also find that to the extent that individual countries experience a wage offset due to obesity, it is highly correlated with the prevalence of employer provided health insurance in that country. Data

7 Cameron Mullen 7 This paper examines the Survey on Health, Aging, and Retirement in Europe (SHARE). In 2004, the study initially collected data on the individual life circumstances of about 27,000 persons aged 50 and over in 11 European countries, ranging from Scandinavia across Western and Central Europe to the Mediterranean (Börsch-Supan et al. 2005). The survey was restricted to those over 50 who spoke the native language of the country and who were neither living abroad or in an institution such as a prison. The survey also included the spouses and partners of the people surveyed independent of their age (Börsch-Supan et al. 2005). SHARE follows the design of the English Longitudinal Survey of Ageing (ELSA) and the U.S. Health and Retirement Survey (HRS) to make the data comparable. My analyses examined the first wave of data and restricted the sample to full-time workers (defined to be those who, in their primary job, were contracted to work more than 30 hours per week excluding meal breaks and paid or unpaid overtime). Employers typically only provide health insurance to full-time workers, so the inclusion of part-time workers would bias the study due to the potential heterogeneity of prevalence of full-time workers in different countries. I further restricted my sample to those individuals having only one job to ensure that yearly income from employment could be attributed to one job for the purposes of calculating wage. This restriction reduced the number of observations to approximately 3,300. All analyses used the study s calibrated sample weights. Dependent Variables

8 Cameron Mullen 8 My analyses examine the effect of obesity on the natural logarithm of before-tax hourly wages. Respondents were asked to report their previous year s earnings from employment. If the country did not use the Euro, this value was converted to Euros using the average exchange rate over the collection timeframe. Respondents were also asked about their typical hours per week worked (including paid and unpaid overtime) (data excluding unpaid overtime was unavailable) and typical number of months worked per year. Finally, the wage data was bottom coded at 1 Euro and top coded at 300 Euros. I used these variables to generate the natural logarithm of the hourly wage: Independent Variables Respondents were asked about their height and weight. These were to calculate Body Mass Index. Dummy variables were generated for the categories of overweight and obese. Individuals were classified as overweight if their BMI was greater or equal to 25 but less than 30 and obese if their BMI was greater or equal to 30. In addition to BMI, dummies were included to account for unobserved factors affecting wages. Respondents were asked about the highest level

9 Cameron Mullen 9 of education they received. A local expert used these responses for each country to code each response based upon the guidelines from the 1997 International Standard Classification of Education (ISCED-97) (Manheim 2011). These classifications were then further broken down into the following categories: pre-primary education or less, primary education, lower secondary education, upper secondary education, vocational education, and tertiary education. These categories are represented by dummy variables. Respondents were asked about their living situation whether they were single, married, or living with a partner. A dummy variable was generated to reflect whether an individual was either married or living with a partner. Respondents were also asked to report their overall health status as being excellent, very good, good, poor, or very poor. These responses were used to generate dummy variables for each. A summary of variables for the sample is given in Table 1.

10 Cameron Mullen 10 Table 1 Mean St. Dev. Wage ln(wage) Obese 13.69% 34.38% Highest Level of Education Primary Education 12.08% 32.60% Lower Secondary Education 14.25% 34.96% Upper Secondary Education 37.94% 48.53% Vocational Education 3.23% 17.67% Tertiary Education 29.83% 45.76% Female 41.47% 49.27% Age Married 80.62% 39.53% Country Austria 1.52% 12.22% Germany 27.57% 44.69% Sweden 6.58% 24.80% Netherlands 5.81% 23.40% Spain 8.39% 27.72% Italy 12.26% 32.81% France 25.54% 43.62% Denmark 4.30% 20.29% Greece 3.27% 17.79% Switzerland 2.42% 15.38% Belgium 2.34% 15.12% Self-Reported Health Status Excellent 14.48% 35.19% Very Good 30.08% 45.87% Good 41.28% 49.24% Fair 12.47% 33.05% Poor 1.69% 12.90% N 3,292 Observations Figure 1. Furthermore, the rates of obesity in each of the countries sampled are shown below in

11 Obesity Rate Cameron Mullen 11 Figure % 20.0% 15.0% 10.0% 5.0% 0.0% Austria Germany Sweden Netherlands Spain Italy France Denmark Greece Switzerland Belgium European Mean Health Insurance Data While the SHARE questionnaire asked respondents detailed information about their health insurance coverage under both private and public plans, it did not ask whether respondents received private health insurance from their employer. Employees wages should only be affected by health insurance status if the insurance is provided by the employer, as discussed above; therefore, in this data set, the effect of employer-provided health insurance on the wages of the obese cannot be ascertained at an individual level. Fortunately, the CEA, the European insurance and reinsurance federation, publishes data about the prevalence of employer-provided health insurance at the country level. The CEA publishes both the percentage of total healthcare

12 Cameron Mullen 12 expenditures financed by private insurance and the percentage of private health insurance benefits paid through group insurance contracts in each country. Using these two pieces of data, I calculated the percentage of total healthcare expenditures financed by private group insurance (CEA 2010). Method To estimate the effect of obesity on wages, I used ordinary least squares (OLS) regressions to estimate parameters in the following model: (1) In this specification, ln(w) is the natural log of the wage, O is a dummy variable representing obesity status, X represents measures of worker productivity and control variables, C represents a series of country dummy variables, and e is the unobserved error term. Furthermore, to estimate the effect of obesity on wages in individual countries, I alter the above model by interacting obesity status and country to produce the following model: (2) In the modified specification, O*C represents a series of interaction terms between country dummies and obesity status.

13 Cameron Mullen 13 The coefficients on the O*C terms (β j ) above represent the change in log wages and roughly the percentage change in wages attributable to being obese in a specific country. I modeled the impact of the prevalence of employer-provided health insurance on the wage penalty due to obesity in these countries (-β j ) again using an OLS regression: (3) In the above model, HI is the percentage of total healthcare expenditures financed by group insurance in country j. All models were estimated using sample weights where appropriate and robust standard errors were used. Results In Tables 2-4, I present the results from the SHARE data. Table 2 presents the results of the effect of obesity on log hourly wages for the entire sample using several different specifications of estimates of equation (1). It presents estimates for the entire sample as well as separate estimates for the sub-samples of men and women. I find that on average, obese workers earn between 1% and 3% less than non-obese workers; however these results are not statistically significant. Consistent with other literature, I find that obese women s wages are more negatively affected than that of obese men; however, none of these results are statistically significant. The lack of statistical significance in these regressions is unsurprising given that the majority of countries sampled have very generous social health insurance benefits and low rates of employer-provided health insurance.

14 Cameron Mullen 14 Table 2 Sample Controlling For All Men Women Country Effects, Education Country Effects, Education, Age, Health Status, Marital Status Obesity (3.08%) (3.85%) (6.40%) P-Value Obesity (1.01%) (0.06%) (3.40%) P-Value N Table 3 presents a modification of equation (1) that includes a dummy variable indicating whether the individual has private insurance and a dummy variable representing the interaction of private insurance with obesity. I obtained separate estimates for the entire sample, men only, and women only. I find a strong, significant correlation between wages and private insurance for all samples. This is unsurprising, as higher-income individuals are more likely to both receive health insurance from their employer and purchase private health insurance to supplement social insurance. I again find that obesity has no statistically significant impact on wages, and that to the extent to which obesity decreases wages, the effect is greater for women than for men. I also find that conditional upon having private insurance, obesity negatively impacts wages for all samples; however, the effect is not statistically significant. The lack of significance probably results from the fact that the data does not distinguish between employer-provided private insurance and individually purchased private insurance. Employer-provided insurance should affect wages as it is a component of workers total compensation, but individually-purchased insurance should have no effect on wages. Therefore, this data is inconclusive with respect to the effect of employer-provided health insurance on the wages of the obese.

15 Cameron Mullen 15 Table 3 Coefficient P-Value N All Men Women Obese 1.19% Private Insurance 21.68% Obese * Private Insurance (8.17%) Obese 4.32% Private Insurance 19.43% Obese * Private Insurance (7.89%) Obese (3.18%) Private Insurance 25.66% Obese * Private Insurance (8.08%) *Controlling for Country Effects, Education, Age, Marital Status, and Self-Reported Health Status Table 4 presents estimates of the coefficients of the obesity and country interaction terms specified in equation (2) using several different specifications. Specification A controls for individual country effects and education; Specification B controls for individual country effects, education, and gender; and Specification C controls for individual country effects, education, gender, age, marital status, and self-reported health status. I find a great deal of heterogeneity of the effects of obesity across different countries. Estimates of the wage effect of obesity range from -24.1% in Switzerland to 29.8% in Greece. I also find that for the majority of countries, the effect of obesity on wages is statistically insignificant. Again, this is unsurprising given the low prevalence of employer-provided health insurance. However, I did find statistically significant differences in Sweden, France, and Greece. Interestingly, in Sweden and Greece, obesity appears to increase wages; whereas in France it appears to decrease wages in each case by a substantial amount. This heterogeneity is surprising and difficult to explain. Perhaps there are unobserved

16 Cameron Mullen 16 productivity differences across individuals not captured by the model that are causing these results. Table 4 Specification A Specification B Specification C Coefficient P-Value Coefficient P-Value Coefficient P-Value Austria * Obesity (5.4%) 0.85 (4.2%) 0.88 (0.4%) 0.99 Germany * Obesity 6.5% % % 0.84 Sweden * Obesity 15.0% % % 0.03 Netherlands * Obesity (7.4%) 0.61 (7.4%) 0.61 (5.1%) 0.72 Spain * Obesity (12.9%) 0.56 (14.4%) 0.52 (14.5%) 0.52 Italy * Obesity 16.3% % % 0.29 France * Obesity (22.4%) 0.07 (21.8%) 0.07 (18.1%) 0.14 Denmark * Obesity (9.5%) 0.35 (12.2%) 0.23 (9.7%) 0.35 Greece * Obesity 25.4% % % 0.06 Switzerland * Obesity (24.1%) 0.40 (16.0%) 0.58 (15.5%) 0.59 Belgium * Obesity (8.3%) 0.64 (12.6%) 0.49 (13.0%) 0.47 Controlling For Country Effects Education Gender Age Marital Status Health Status *Estimates in bold are statistically significant at the 10% level Figure 2 plots the obesity wage penalty in each country against the percentage of total healthcare expenditures financed by group health insurance. The obesity wage penalty in Figure 2 is equal to -1 times the coefficient estimated in Specification A presented in Table 4. Furthermore, Table 5 presents estimates for equation (3) using coefficients generated by all three specifications. I find that there is a strong positive relationship between the obesity wage penalty and the prevalence of group health insurance. This relationship is statistically significant at the

17 Cameron Mullen 17 5% level for Specification A and is significant at the 10% level for Specifications B and C. Notably, the Netherlands is excluded from both the graph and the regressions. In the Netherlands, group insurance accounts for approximately 40% of medical expenditures, far greater than the other countries which have a range of between 0% and 4%. Because of this difference, it is difficult to compare the Netherlands with the other countries, so the Netherlands is excluded. The data supports the hypothesis that employer-provided health insurance induces lower wages for the obese; however, because the insurance data does not cover all of the countries sampled, these results should be treated with caution. Figure 2

18 Cameron Mullen 18 Figure 3 presents the obesity wage penalty plotted against the percentage of people in each country with private insurance. The explanatory variable was estimated from the SHARE data using the same criteria for private insurance as the analysis presented in Table 3. The obesity wage penalty is again derived from regression coefficients estimated under Specification A presented in Table 4. Furthermore, Table 5 presents regression statistics using coefficients estimated for Specifications A, B, and C. This analysis provides a robustness check for the relationship between the obesity wage penalty and the prevalence of employer-provided health insurance presented in Figure 2. Because the insurance data from the CEA is relatively sparse, it is necessary to confirm this relationship. By using estimates from the SHARE data, the sample size can be increased from 7 to 11; however the percentage of people covered by private insurance includes those who purchased their insurance individually. While by no means is this a perfect metric, in the context of confirming the relationship presented in Figure 2, this data provides a useful proxy. I find that the percentage of people covered by private insurance is indeed positively correlated with the obesity wage penalty, and this relationship is statistically significant at the 5% level across all 3 specifications. This finding confirms the relationship presented in Figure 2 that increased levels of employer provided health insurance induce a greater wage penalty on the obese.

19 Cameron Mullen 19 Figure 3 % of Healthcare Expenditures Financed by Group Insurance Table 5 Specification A B C P-Value % of People with Private Insurance P-Value Controlling For Country Effects Education Gender Age Marital Status Health Status

20 Cameron Mullen 20 Conclusion My findings show that obese workers and non-obese workers are not paid significantly different wages in Europe. This is in contrast to the United States, where obese workers are paid significantly lower wages than non-obese workers. Previous papers have found that differences in wages between the obese and non-obese in the U.S. result from the increased cost of providing healthcare to obese workers. However, in Europe, most employers do not provide healthcare to their workers, as the dominant mode of health insurance in Europe is social health insurance. The lack of differentiation in wages in Europe supports the theory that lower wages paid to obese workers in the U.S. result from the increased cost of providing health insurance to these workers. A potential criticism this argument is that the lack of differentiation between obese and non-obese workers in Europe results not from different health insurance institutions from the U.S., but rather different labor market institutions. This argument posits that the increased prevalence collective bargaining in Europe and European labor laws reduce the differentiation in wages in workers who differ along many dimensions, including obesity. However, my findings suggest that other differences among workers (education, age, gender, etc.) result in significantly different wages. This suggests that European labor markets do create significant wage differences between employees, and one would expect to find that employees who cost more or are less productive to be paid lower wages. Additionally, my findings suggest that although the differences between the wages of the obese and non obese are neither significant across all countries nor within most countries, to the extent that wage penalties for the obese exist in individual countries, these wage penalties are highly correlated with the prevalence of employer-provided health insurance within these

21 Cameron Mullen 21 countries. This further supports the argument that wage differences between the obese and nonobese result from employer-provided health insurance. Furthermore, I find that obese individuals are paid lower wages conditional upon having private insurance. However, it should be noted that this difference is not statistically significant. While no one piece of evidence I have presented is definitive, the body of evidence presented strongly indicates that employer-provided health insurance does reduce the wages of the obese. These findings complement those of Bhattacharya and Bundorf, who found that in the U.S. the obese are paid lower wages only in jobs that provide employer-provided health-insurance (Bhattacharya and Bundorf 2009). Pooling between thin and obese does not occur under employer-provided health insurance, even in the presence of the heterogeneous healthcare institutions of Europe. Risk adjustment for obesity does in fact occur in the presence of employer-provided health insurance. The lack of pooling suggests that obese European workers with employer-provided health insurance do bear some of the healthcare costs of their obesity. Individually purchased private insurance is explicitly risk rated for observable health characteristics and employer-provided health insurance has been shown to do so implicitly; therefore, obese workers with either type of private insurance do in fact pay for the increase in expected healthcare costs due to their obesity. This suggests that under private insurance, the healthcare costs of obesity are internalized, but under social insurance, they are externalized. The relatively low prevalence of employerprovided health insurance and relatively generous social insurance benefits in Europe result in the majority of the healthcare costs being borne by social insurance. The degree to which the externalization of the healthcare costs of obesity induces a moral hazard is an open question; however, to the extent that a moral hazard is generated by this

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