Estimating the Wage Differential for the Incidence of ...blogs.colgate.edu/economics/files/2013/05/Jakes-Thesis.pdf · Affordable Care Act (PPACA) signed into law by President Barack

Estimating the Wage Differential for the Incidence of Rising Premiums in Employer-Sponsored Health Insurance1 JAMES H. CALDWELL IV Faculty Advisor: Professor Jyoti Khanna Colgate University, 13 Oak Drive, Hamilton, NY, USA The introduction of legislature mandating that employers provide specific levels of health insurance to their employees brings into question the theory of wage differentials. Beginning with a basis hedonic model of wages, this paper focuses on the impact of rising health care costs for employers on worker wages. Utilizing data from the Medical Expenditure Panel Survey (MEPS), this study is able to combine household variables with employer-level information about health insurance offerings. Consistent with previous studies, this study fails to find a significant negative relationship between increases in the value of health benefits and wages using an ordinary least-squares regression and cross-sectional data. When considering other methods of adjustment that firms might undertake, this study concludes that higher health care costs are associated with non-proportionally higher annual deductibles and employee premium copayments. Taking advantage of the panel data this paper finds that 1% premium inflation in the cost of providing health benefits is associated with increases of .21% for employee premium copayments for single coverage and .57% for family coverage. While this study is not a policy analysis, the results show that rising health care costs associated with universal health care mandates may have a significant impact on American workers through reductions in take-home pay and increases in medical expenditure contributions, but not through adjustments to wages. The resulting offset in net take-home pay adversely affects lower and middle class workers and households as it limits their ability to finance other household expenditures. Keywords: Healthcare, Health Insurance, Occupational Wage Differential, Compensation, Fringe Benefits JEL classification: I13, I18, J08, J31, J32, J33, J40

1 This study was conducted with support from the Center for Learning, Teaching, and Research and the Department of Economics at Colgate University. Special thanks to Professor Jyoti Khanna, Professor Michael O’Hara, Professor Takao Kato and Peter Rogers for their guidance on this paper. The research in this paper was conducted at the CFACT Data Center, and the support of AHRQ is acknowledged. The results and conclusions in this paper are those of the author and do not indicate concurrence by AHRQ or the Department of Health and Human Services.

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I. Introduction

Employer-provided health insurance has taken the spotlight under the Patient Protection and

Affordable Care Act (PPACA) signed into law by President Barack Obama in March of 2010.

The law requires that U.S. employers with more than 50 full-time employees provide approved

insurance policies to their employees. Failure to do so results in penalties upwards of $3,000 per

worker.2 Under the employer mandate, many American businesses are likely to suffer from

rising health care costs in the United Sates. At the same time, small businesses providing

insurance will benefit from reimbursement and tax credits. Although these policies are likely to

reduce the number of uninsured Americans, it may also have an adverse effect on net worker

compensation.

The focus of this paper is to estimate the wage differential (the change in wages for rises

in the value/cost of providing health benefits) using cross-sectional data accounting for benefit

characteristics as well as worker characteristics. In doing so, I find that employers facing higher

costs of insurance generally offer benefit packages with higher employee premium copayments

(contributions to premium) and higher deductibles. I also find that 1% premium inflation is

associated with a .21% increase in the premium copayment for single coverage and .57% for

family coverage from 1997 to 1999. I fail to find a significant wage differential for firms facing

higher insurance costs and premium inflation over the period. This is not unexpected as the

“stickiness” of wages forces firms to find alternate ways to share the cost of providing fringe

benefits.

Employer-sponsored health insurance has a long history in the United States, first

appearing over 200 years ago with lower ranking officers in the United States Navy receiving

hospital care through compulsory wage deductions. It was not until 1929, with the development

of Blue Cross, that insurance plans took their current form. In the late 1940s and early 1950s, the

growth of employer-based health insurance and other fringe benefits led to the development of

record keeping mechanisms by the Bureau of Labor Statistics (BLS). Benefit packages became

commonplace during that time and employees began to consider both wages and benefits when

2 For firms with 50 full-‐time workers or more not providing insurance and with at least one employee receiving premium tax credits, the penalty is $2,000 per employee (exempting the first 30). For firms with 50 full-‐time workers or more providing a form of insurance and with at least one employee receiving a premium tax credit, the penalty is $3,000 per employee receiving the credit or the above (Kaiser Family Foundation).

Wage Differential For Employer-Sponsored Health Insurance

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entering the job market. In the 1970s, the rapid rise in health insurance costs led to the

establishment of managed care networks (HMOs, PPOs, etc.), with many medium and large

firms participating. The development of these benefit packages highlighted the wage differential

between jobs based on their attractiveness.

A recent report by the Kaiser Family Foundation (2012) shows that since 2002, employee

contributions to health insurance have risen by 102% while premiums have increased by only

97%. The report shows the growth of contributions vastly outpacing the 33% growth in worker

wages with these numbers varying greatly among regions. The survey conducted reported that

workers pay for 18% of the cost of single coverage and 28% of the premium for family coverage,

largely unchanged over the decade. The Kaiser Family Foundation finds that these numbers vary

greatly based on the type of plan that workers enroll in with regards to managed care networks.

The largest challenge to measuring the wage differential is accounting for unmeasurable

worker characteristics. As Miller (2004) highlights, the first sets of estimation used hedonic

wage models, which tried to capture the wage differential by considering negative job

characteristics. The major obstacle to these models using cross-sectional data was the omission

of worker characteristics. More recent work has used panel data to show that increases in

insurance costs may also lead to reductions in employee eligibility or adversely affect newly

hired employees. Under the individual mandate of the PPACA, a majority of Americans will be

required to purchase some form of health insurance by 2014. This will force workers to place a

value on health benefits and to decide whether to purchase plans through their employer or

through state exchanges. While this paper is not intended to be a policy analysis, discussion

around the incidence of rising health care costs is likely to provide some insight into the effects

of such policies. If the PPACA requires currently uninsured workers to gain insurance through

their employer, the results of this study suggest that it is likely that those workers gaining

insurance will be forced to bear a large percentage of the cost.

II. Literature Review

Previous work on this topic has centered around two main approaches. First, much of the

literature has looked at this question from the household side, analyzing the worker’s choice

between higher wages and health insurance costs. More recently, the literature has focused on the

employer side, analyzing firm decisions regarding the provision of health insurance versus

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wages. Within both approaches, researchers have focused on micro data sets with preference for

panel data. While much of the literature finds concrete conclusions regarding firm behavior in

the face of rising costs, most of the previous studies fail to find a wage offset because of

limitations within the data.

There are two primary pieces that influence the progression of studies on this topic over

the past three decades: Goldstein and Pauly (1976) and Rosen (1986). Goldstein and Pauly

developed one of the first models illustrating the trade-off between worker wages and employer-

based health insurance. The basic foundation for their model is a Tiebout-type model for public

goods. The major assumption being that health insurance acts as a public good. From this they

develop their own model that accounts for imperfect worker mobility. In the model, they analyze

the willingness of workers to accept lower wages in return for an increase in fringe benefits,

aiming to differentiate the reactions of unions and employers. They conclude that these two

parties do behave differently, although they are not able to directly test the implications of the

model.

Gruber (1994-1) is one of the first researchers to explore the wage differential for rising

health insurance costs, focusing on government mandated employer-provided health insurance

and its effectiveness. He builds upon Goldstein and Pauly’s work by directly testing the

implications of the model. Gruber focuses on “natural” experiments, analyzing the case for

mandated maternity benefits through employer-provided insurance. He finds evidence of cost

sharing among the target group and notes that specific workers’ wages do not adjust based on

their individual evaluation of the benefit. Gruber’s focus on the worker’s choice between health

benefits and wages has guided other research looking at worker job selection based on

compensation.

Simon (2001) analyzes a group of voluntarily unemployed workers observing their wage

level before and after switching jobs. Simon notes that his conclusions are questionable, finding

that workers who gain health insurance through their job selection gain higher wages and that

those who lose health insurance in their new job also gain higher wages. This study fails to

control for productivity factors that may lead to job and wage selection. Levy and Feldman

(2001) analyze the cost-shifting at an individual level and find that cost-shifting takes place at a

group level, independent of an individual’s specific health costs. At a group level, they find zero

compensating differences and conclude that health insurance status has exogenous components


5

(related to worker productivity) that they fail to account for using Medical Expenditure Panel

Survey (MEPS) and Consumer Expenditure Survey (CEX) data. Miller (2004) takes a similar

approach to Simon, but includes observable worker characteristics to control worker

productivity. Using Consumer Expenditure Survey cross-section data, Miller finds “nonsensical”

results pointing to an increase in wages for those workers with health insurance and for those

without. Miller speculates that his inability to control for changes in other fringe benefits may

have resulted in the bias observed in his results. More recently, studies have begun to account for

these exogenous determinants and have been successful in estimating the compensating

differential.

Sommers (2005) develops a new model accounting for the “stickiness” of nominal wages

in the short term. With the inclusion of sticky nominal wages, Sommers is able to estimate the

effect of rapid premium growth on worker wages. He finds that premium growth is not fully

offset with wage declines and that firms, newly employed workers, and premium-paying

employees bear the weight of premium inflation. His observation that some firms may reduce

worker eligibility and his ability to incorporate that into the model accounts for much of the error

in the studies conducted by Simon (2001), Levy and Feldman (2001), and Miller (2004).

Sommers’ work is limited by data, but he suggests that the incidence of rising premiums on high-

income and low-income workers is likely to be different. Vistnes and Selden (2011) expand

Sommers’ work using firm-level data from the Medical Expenditure Panel Survey Insurance

Component (MEPS-IC). Accounting for sticky nominal wages and various wage distributions,

they analyze employer methods for responding to rising health care costs. Vistnes and Selden

find that small firms with low-wage workers respond to premium inflation through adjustments

to employee contributions and deductibles. They also find that many of these firms are less likely

to provide health benefits to begin with. For large firms with low-wage workers, rising premiums

leads to a tightening of eligibility requirements while maintaining benefit offerings. Finally, for

firms with high-wage workers, rising premiums lead to increases in deductibles. Vistnes and

Selden’s results are consistent with Sommers’ model for fringe benefits.

Royalty (2008) develops a new method for measuring the value of marginal health

insurance for an individual worker by observing their choice of health plan instead of changes in

wage. Using data from the Robert Wood Johnson Employee Health Insurance Survey of 1993,

Royalty finds that employee behavior in response to rises in premiums varies drastically based

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on the perceived generosity of the plan. In general, Royalty finds that workers value an

additional dollar of benefits less than an additional wage dollar despite valuing an increase in

observable generosity at greater than a dollar. Lubotsky and Olson (2012) observe a similar

phenomenon among Illinois teachers, noting that unions reacted positively to increases in

observable benefits. Accounting for premium-copayments as an alternative to decreases in

salary, Lubotsky and Olson find that on average individual teachers pay 17% of increases in

premiums and that families pay 46% of the same increases through rises in premium-copayments

and not through wage offsets. Olson (2007) focuses on the trade-off between salary and fringe

benefits for married women employed full time. Using two uncorrelated variables, husband’s

firm size and union status, Olson finds that workers do in fact sort themselves among firms based

on benefit offerings and that married women accept a wage 20% lower than they could otherwise

make in return for benefits. Olson notes that the study fails to account for the eligibility of

workers with regards to health benefits. Much work has been done on the topic of wages and

health benefits, but most of it is limited in scope and fails to find statistically meaningful results.

Nonetheless, the studies that have focused on the issue suggest that the trade-off between wages

and benefits is not one-for-one and may not exist at all.

Most of the previous studies suggest that a simple model is not adequate for explaining

employee/employer decisions regarding compensation and fringe benefits. In many cases, the

productivity of the worker, the characteristics of the firm, and the type of insurance policy have

the largest impact on the decision. It is interesting to consider the progression of these studies

from the Gruber (1994) study of married females to the Lubotsky and Olson (2012) study,

mainly a result of the evolution of data keeping and surveys. The aim of my study is to continue

this progression accounting for total compensation, worker productivity, and insurance policy

characteristics.

IV. Wage Model and Specification

As conventional wage theory shows, increases in fringe benefit costs lead to reductions in

worker wages. Beginning with a simple labor supply model introduced by Goldstein and Pauly

(1976), it is easy to see the trade-of between money wages (

€

Yπ ) and health benefits (

€

π (k)):

€

MRPl = Y = Yπ +π (k) (1)


7

The first order conditions of profit maximization for the employer imply that the labor cost (

€

Y )

is equal to the marginal revenue product of labor (

€

MRPl ). In other words, the worker’s total

compensation is equal to his or her productivity level at the firm. For any given labor cost (

€

Y ),

employers offer a combination of money wages and health benefits. If the cost of insurance

increases, the employer must decrease the money wage in order to remain on the same iso-profit

line.

From an employee perspective, the marginal rate of substitution between health benefits

and wages is positive. If an employer reduces benefits then they must increase the wage.

Employees aim to maximize utility by gaining the highest total compensation. In this simple

model, there are many types of employees and employers. Each employer operates on a different

iso-profit line. Similarly, each employee operates with a different utility function. In equilibrium,

firms pick an optimal package of wages and benefits such that no excess labor supply exists and

workers pick an optimal package of benefits such that their utility is maximized.

This simplicity of the model requires making an assumption that must be accounted for in

analyzing the results. In equilibrium, all job characteristics are held constant with only wage and

health premiums adjusting. This is unrealistic and captures the difficulty in estimating wage

differentials. Taking the simple model above, I add observable worker characteristics, firm

characteristics, and other fringe benefits to try and avoid the major assumption made in equation

(1). From this I obtain the following empirical model:

€

ln(wage) = f (P,F,B) (2)

In equation (2), the money wage (ln(wage)) is a function of the health insurance premium (P),

the firm’s characteristics (F) and the fringe benefits offered by the firm (B). By assuming the f (·)

is linear in parameters, the relationship between wage and the parameters of interest can be

estimated using cross-sectional data. If however, this relationship is not linear and the coefficient

on P is not statistically significant, another parameter must be introduced to account for

unobservable worker and/or job characteristics. These characteristics might include intelligence,

competitiveness, or self-drive. I conjecture that these parameters are positively related with wage

levels and health insurance benefits. It is also important to think about lifestyle choices not easily

measured, such as appetite for risk. A firm with risk-loving individuals may in general have

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higher insurance costs. In both of these cases, there is likely to be a positive bias on the

coefficient for health premium that will affect the results of the model.

In addition to the model in equation (2), I also introduce a model that measures the

differential between deductibles and employee contributions to health insurance for both single

coverage and family coverage.

€

ln(contribution) = h(W ,P,F,B) (3)

In equation (3), I add wage (W) as a parameter of interest in estimating the factors that affect the

employee’s contribution to the health insurance. I do the same thing in equation (4) to capture

any relationship between wage and the annual deductible for health insurance.

€

ln(deductible) = g(W ,P,F,B) (4)

With these three models in mind, I implement a set of ordinary least squares regressions that

estimate the differentials for the three explained variables. I conclude that

€

π (k) from equation (1)

is not only the total premium for coverage, but that it is the total out-of-pocket expense to the

employee, a summation of the annual deductible and employee premium copayment:

€

π (k) =π (deductible + premiumcopayment) (5)

As a way of trying to control for the omitted variable bias I mentioned with regards to

equation (2), I also develop a fixed-effects model using panel data:

€

ln(wage)it = β0 + β1Pit + β2Fit + β3Bit + γ1Zi + δ1Yr98 + δ 2Yr99 + ε it (6)

In equation (6), I measure the difference between each worker in three consecutive time periods

(1997-1999). The vector Z is a set of time invariant worker characteristics (mentioned above)

that are observed in all time periods and drop out in the fixed effects model. The model includes

dummy variables for each time period (1998 and 1999). I also run a similar model considering

employee premium copayments as the dependent variable. This model is predicated on the


9

individual worker’s wage and health costs changing in-between the three time periods. This is a

fair assumption based on the report published by the Kaiser Family Foundation. By addressing

the omitted variable bias, I expect that the coefficient

€

β1 will be negative. I will address other

possible biases in my results section. It is also possible that if wages are sticky, that other fringe

benefits and eligibility may adjust instead.

V. Data

The data used in this study comes from the Medical Expenditure Panel Survey (MEPS)

Household (HC) and Insurance (IC) components collected by the Agency for Healthcare Quality

and Research (AHQR) from 1996 to 1999. The HC is a nationally representative survey of the

U.S. civilian noninstitutionalized population and collects detailed data regarding household and

personal medical expenditures, demographics, health conditions, health status, access to care,

and insurance coverage. The HC utilizes an overlapping panel design collecting data in a series

of five interviews over a 2½ - year period. The IC collects data on health insurance plans from

employers, unions, and private providers. This study focuses on the Household sample, which

collects data from employers of respondents to the HC. One of the downsides to using this data is

that the significant non-response bias in 1996 keeps this data from supporting national estimates.

When linking the HC and IC surveys, there are 15,884 unique observations, 6,981 employees,

6,071 firms, and 6,879 plans. After sorting the data to include only employees offered health

insurance plans at their place of employment and removing missing data from the variables of

interest, there are 4,225 unique observations remaining.

Although this data is not applicable on a national level, it is useful for analyzing the wage

differential for privately employed individuals. The survey shows the value of plans offered to

individuals through their employers. In addition, the 2½ year gap between the first and fifth

interview is useful in analyzing the first-difference fixed effects model. The downside to this

data is that it suffers from missing data based on the inability to locate employers or unions for

individuals. The data also provides multiple observations for each individual if they are

employed at more than one firm or their employer offers more than one health plan.

This data was ideal for this study because it provides detailed information from the

employer perspective regarding compensation packages, employee demographics, firm

characteristics, and other fringe benefits. Table (1) provides a summary of the data used in this

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Table 1. Summary Statistics Variable 1996 1996 Refined

Ln(wage) 9.616 [1.1085] 9.921

[0.892]

Age 43.45 [16.192] 41.59

[14.283]

Male 15.1% 49.0%

Blue Collar Professional 1.9% 1.9%

# of enrollees 3782.21 [22658.92] 5745.122

[27724.933]

# of female employees at firm 2504.035 [15967.49] 4076.398

[20218.811]

# of employees 50+ at firm 1161.357 [7654.617] 1962.711

[9873.185]

Hispanic 4.2% 12.7%

Black, non-hispanic 3.4% 11.8%

Employees with H.S. education 52.9% 86.3%

Employees with Undergraduate Degree 23.6% 41.1%

Employees with Higher Degree 5.5% 11.1%

Married 38.2% 59.6%

Plan flexible with provider 10.7% 53.7%

Total premium (SC) 1115.75 [4388.51]

1599.079 [5378.709]

Employer contribution (SC) 886.393 [1057.103]

1291.247 [1078.906]

Employee contribution (SC) 228.114 [4281.194]

318.895 [5343.705]

Total premium (FC) 2631.644 [5022.206]

3800.262 [5847.925]

Employer contribution (FC) 1852.852 [4342.351]

2694.859 [5197.471]

Employee contribution (FC) 758.691 [1276.270]

1095.467 [1448.920]

Annual deductible (SC) 99.155 [283.167]

176.246 [358.493]

Annual deductible (FC) 234.879 [596.471]

410.457 [758.151]

N 15,884 4,225


11

study, consistent with previous studies. The summary statistics for 1996 include all 15,884

observations as well as the summary statistics for the refined data set used in this study.

Compared with the master data set, the data used in this study includes a larger portion of males

(49% vs. 15.1%) and includes, on average, larger firms as measured by the number of

employees. In terms of the employee base, on average, the data set used in this study has a higher

percentage of educated employees on all levels (86.3% vs. 52.9% high school educated, 41.1%

vs. 23.6% college educated and 11.1% vs. 5.5% with higher degrees). Interestingly though, the

percentage of blue-collar occupations remains the same across both data sets. In terms of the

health insurance plans, on average, the total premium for the plans in the refined data set are

significantly higher and tend to be more flexible with regards to the choice of provider.

The variables of interest include annualized wage, health insurance premium,

employer/employee contribution, deductibles, and maximum out-of-pocket expenditures. With

regards to the firm, I include the number of enrollees in the firm’s insurance program, the

number of employees that are female and the number of employees above the age of fifty.

Building upon previous literature, this study includes details regarding the firm’s other benefit

offerings. I identify if firms offer paid vacation time and paid sick leave. I also identify if each

firm can refuse coverage based on pre-existing conditions and if the firm requires a mandatory

waiting period for new employees before offering health insurance coverage. These variables

account for some of the mechanisms firms use to combat rising health insurance premiums.

Table (2) summarizes the occurrence of these mechanisms in the firms surveyed in the IC.

Table 2. Other Benefits and Policies

Variable 1996

Paid sick leave 57.31%

Paid vacation 65.68%

Firm can refuse based on pre-existing condition 17.75%

Firm has waiting period for new employees 41.73%

In my study, the variable for Blue Collar Professional takes a 1 if the employee is employed as a

craftsman, foreman, operative, service worker or laborer. Finally, I account for the various

industries that respondents to the IC-HC survey are employed in. Table (3) summarizes the

industries covered by the survey. Some factors that this study fails to account for include

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variation between individual firms, whether an individual’s spouse has insurance, the number of

children/family members dependent on the individual, and the generosity of the health benefits

offered by the plan. While this study is missing important factors, it successfully includes

variables omitted from previous research that are thought to be significant.

Table 3. Professional Industries

Variable 1996

Retail 13.61%

Personal Services 1.54%

Mining 0.59%

Wholesale Trade 4.21%

Finance, Insurance or Real Estate 5.56%

Transportation, Communication or Electric 4.92%

Construction 2.34%

Agriculture or Forestry 0.88%

Public Administration 0.66%

Manufacturing 18.41%

Business Services 4.23%

Legal & Health Services 16.52%

*26.53% of respondents did not respond

VI. Results

As mentioned in Section II, most previous studies have failed to estimate a negative wage

differential when using cross-sectional data. Table (4) shows the results from running equation

(2) on the data. Age and the number of enrollees in the plan are associated with a higher wage. In

this model, age acts as a proxy for experience and shows that the older the worker, the more

experience the worker has, and hence the higher wage. Similarly, this size of the employee base

acts as a proxy for firm size; hence, larger firms pay higher wages. Additionally, firms with a

larger number of female employees are associated with lower wages on average. I also identify

that being a high school educated worker is associated with a 62% higher wage than being a non-

high school educated employee. Similarly, employees with a graduate degree on average have an

11% higher wage than those without a graduate degree. Finally, a higher


13

Table 4. Regression Table: ln(wage) Variable (1)

7.88998 Intercept [0.86632] 0.00815** Age [0.00308] 0.05802 Male

[0.06893]

Hispanic 0.00374 [0.09677]

Black, non-hispanic 0.02989 [0.09248]

Married 0.07897 [0.06046]

Received H.S. education 0.62027** [0.11859]

Received Undergraduate Degree 0.11698 [0.06604]

Received Higher Degree 0.44363** [0.08559]

Blue Collar Professional -0.86122 [0.52508] 0.13656** Ln(# of enrollees) [0.02756]

-0.14874** Ln( # of women) [0.03536] 0.04589 Ln( # of employees 50+)

[0.03447] 0.11276 Plan flexible with provider

[0.12280] 0.25174 Firm offers paid vacation

[0.15473] 0.05721 Firm offers paid sick leave

[0.07543] Firm can refuse coverage 0.03429 based on pre-existing conditions [0.05751]

-0.00678 Waiting period for new employees [0.07534] 0.21625* Ln(total premium [SC]) [0.08779] -0.12857 Ln(total premium [FC]) [0.09497] 0.03174 Ln(annual deductible [SC])

[0.07961] -0.03830 Ln(annual deductible [FC]) [0.06952]

R-sqr 0.2161 N 650 Robust standard errors in brackets *Significant at 5% level **Significant at 1% level

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premium for single coverage is associated with higher wages on average. Although this does not

match economic intuition, it is consistent with previous studies using a similar model. One

general theory is that firms that offer higher wages may also offer benefits with higher costs.

Building on the basic model, I add dummy variables for each of the professional

industries listed in Table (3) as well as interaction terms to equation (2). The addition of the

industry dummy variables does not impact the significance of the economic controls established

in Model (1), but the inclusion of the interaction terms does highlight a significant wage

differential. The results of these regressions are shown in Table (5). I note that in Model (2),

employees of the retail industry had a significantly lower wage compared with employees of the

manufacturing industry, the base group in this case. When interacted with the total premium for

single and family coverage in Model (3) and (4), the wage differential for employees in the retail

industry is non-significant. But in Model (3), higher single coverage premiums for employees in

the business services, finance, insurance, and real estate industries were associated with lower

wages. One outlier is that higher premiums in the mining industry were associated with higher

wages – this may be biased depending on the number of mining firms surveyed. In Model (4),

the same outlier is observed, but the impact of higher premiums for family coverage is not

associated with lower wages for any industries. Even with the inclusion of industry dummies, a

higher premium for single coverage is associated with a higher total wage as shown in all three

models. Additionally, higher premiums for family coverage independent of industry are

associated with lower wages (although statistically non-significant). This points to higher wage

levels being associated with higher premiums as a result of better firms. In Table (6), I look at

the effect of specific occupations on wages. In model (5) and (6), I interact premium for family

coverage and premium for single coverage, respectively. Although small, I find a significant

negative wage differential for employees employed as craftsmen and foremen. On average, these

occupations were associated with lower premiums for a higher cost of insurance. I find a

significant positive wage differential for employees employed as salesmen and managers, likely

a result of better professions leading to both higher wages and more generous health benefits. Of

these occupations, the traditional white-collar workers experience the largest offset with higher

health insurance costs. The inclusion of interactions for blue collar professions fails to confirm

the report by the BLS in 1999 showing that blue collar workers are more likely to not be covered

by employee-sponsored insurance compared with white collar workers and the benefits offered


15

Table 5. Regression Table: ln(wage) Variable (2) (3)3 (4)4

8.0692 6.73574 8.02720 Intercept [0.60405] [1.12842] [1.52913]

-0.27789** -0.00848 -0.00604 Retail [0.08241] [0.19580] [0.24884] 0.19463 -0.34445 -0.08770 Personal services

[0.18061] [0.13114] [0.27672] 0.00529 -1.02033* -0.30964 Business services

[0.14818] [3.36106] [0.50111] 0.02900 -0.28417 0.01134 Legal or health services

[0.06099] [0.16706] [0.22236] 0.20919 1.77220** 2.14158** Mining

[0.23787] [0.61466] [0.48538] -0.02937 -0.18865 0.14864 Wholesale trade [0.08405] [0.29541] [0.24603] 0.14521 -0.63347* -0.10703 Finance, insurance, or real estate

[0.10123] [0.31246] [0.50266] 0.22845 -0.32753 0.15072 Transportation, communication or electric

[0.12725] [0.34996] [0.29924] 0.1058 -0.18579 -0.21177 Construction

[0.09833] [0.17839] [0.30709] -0.23701 -0.15196 -0.63222 Agriculture or forestry [0.28233] [0.86191] [0.88725]

-0.038 0.55979 0.39554 Public administration [0.18456] [0.34562] [0.48051]

Ln(total premium [SC]) 0.12900* [0.06743]

0.29841* [0.12967]

0.10518 [0.06554]

Ln(total premium [FC]) -0.03262 [0.08177]

-0.05045 [0.07845]

-0.03002 [0.17745]

Economic Controls Yes Yes Yes

Dummies for Industry Yes Yes Yes

R-sqr 0.1516 0.2136 0.2074 N 1362 1362 1362 Robust standard errors in brackets *Significant at 5% level **Significant at 1% level

are of lower quality on average. One explanation for the lack of significant results is that my

sample size of blue collar employees is small compared with those that did not identify an

occupation.

3 Results shown are the estimates from the interaction of the dummies and total premium for single coverage 4 Results shown are the estimates from the interaction of the dummies and total premium for family coverage

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Table 6. Regression Table: ln(wage) Variable (5) (6)5 (7)6

8.46369 8.58299 8.67318 Intercept [0.56509] [0.56387] [0.56493] -0.25719 1.47772 1.24199** Professional, Technical, and Kindred [0.34105] [6.40183] [0.35798] 0.29618 1.09348* 1.07282 Managerial and Administrative

[0.23054] [3.75771] [1.21423] -0.53017* 1.51293** 0.27763 Sales Workers [0.22022] [0.24432] [0.4898] 0.09363 -0.07587 -0.08181 Clerical and Kindred Workers

[0.06716] [0.35717] [0.34855] -0.28483** -0.03796** -0.03420** Craftsmen and Foremen [0.08690] [0.01159] [0.01036] -0.52294 -7.41157 -8.26836 Operatives [0.65375] [0.71303] [55.77901]

-1.38725** -0.04952 0.52242 Service Workers [0.32259] [0.5.50531] [5.27461]

-0.39907** -2.48318 1.02648 Laborers [0.10811] [5.22848] [2.21061]

Ln(total premium [SC]) 0.10546 [0.06378] 0.08844

[0.06256] 0.11120

[0.06335]

Ln(total premium [FC]) -0.05741 [0.07434] -0.05439 [0.07378]

-0.08642 [0.07433]

Other Economic Controls Yes Yes Yes

Dummies for Occupation Yes Yes Yes R-sqr 0.1856 0.1958 0.1911 N 1362 1362 1362 Robust standard errors in brackets *Significant at 5% level **Significant at 1% level

Considering the findings of Lubotsky and Olsen (2012), I introduce Model (7) and (8)

which treat the employee premium copayment (employee contribution to the premium) as the

dependent variable. Assuming that wages are “sticky” in the short term, I expected to find that

employers offset rises in health insurance by transferring the cost to employees via the premium

copayment. As shown in Table (7), the impact of a higher total premium for both family

coverage and single coverage on the employee premium copayment is significant. On average, a

1% higher total premium for single coverage is associated with a 0.68% higher premium

copayment. For family coverage this correlation is only 0.54%. In both cases, there is not a full

5 Results shown are the estimates from the interaction of the dummies and total premium for single coverage 6 Results shown are the estimates from the interaction of the dummies and total premium for family coverage


17

wage offset – this is significantly higher than the 0.17% and 0.46% estimated by Lubotsky and

Olsen. Although this is not a full wage offset, it shows a significant compensating differential for

employees with higher premiums. Also interesting, firms with a mandatory waiting period for

new employees on average had 14% higher premium copayments for single coverage and 12%

higher premium copayments for family coverage. While this may not seem consistent with

economic reasoning, it is consistent that firms concerned about the cost of insurance might have

these types of policies, as well as having employees bear a larger percentage of the cost. In the

same way, firms with policies of refusal based on pre-existing conditions were associated with

21.9% higher premium copayments on average for family coverage. Firms with these policies

may be impacted by the no refusal clause of the PPACA.

The results in Table (7) also point to an unexpected relationship. On average, a 1% higher

annualize wage is associated with a -.09% lower employee premium copayment for family

coverage. This result has two interpretations. First, firms offering higher wages are associated

with paying for a larger percentage of the total premium Second, firms offering higher wages are

associated with benefit plans that are lower in value (or cost). The explanation is not clear from

the regression and is an odd result. The study also finds that a higher number of enrollees in a

sponsored plan is associated with a lower premium copayment – a function of cost sharing

among risk groups. Similarly, married individuals were on average associated with a 9.5% lower

premium copayment – possibly a result of having coverage under their spouse’s employment. I

conclude from the results in Table (7), that employee premium copayment is a better cost-

sharing tool than wages for firms with regards to the cost of providing health benefits.

As a final component to testing how firms bundle wages and fringe benefits, I look at the

relationship between total premiums and annual deductible for both single and family coverage.

The results for Models (9) and (10) are shown in Table (8). Analyzing the results, the importance

of the plan’s flexibility in terms of the provider is significant with flexible plans, in general,

being associated with a 44% higher annual deductible for single coverage and 22% lower for

family coverage. This is important as it demonstrates how the type of care network (HMO, PPO,

POS, etc.) may have an impact on the real cost of healthcare for employees. Plans with a flexible

provider scheme will give incentivized rates to stay in-network, thereby transferring the cost to

the copayment and not the deductible (these are PPOs, and POSs). HMOs require that the patient

stay in-network and so they rely on deductibles. A surprising result in Model (8) is that firms

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Table 7. Regression Table: Ln(employee contribution)

Variable (7) Single Coverage (8)

Family Coverage

Intercept 2.03499 [0.90149] 3.80502

[0.67606]

Ln(wage) -0.05242 [0.03806] -0.09920** [0.02894]

Age 0.00290 [0.00239] 0.00115

[0.00192]

Male 0.09099 [0.06485] -0.05921 [0.05045]

Hispanic 0.23429 [0.08734] 0.19942* [0.07866]

Black, non-hispanic 0.13552** [0.10133] 0.09610

[0.07141]

Married -0.03107 [0.06358] -0.09589*7 [0.04934]

Received H.S. education 0.12470 [0.09548] 0.01696

[0.08356]

Received Undergraduate Degree -0.05746 [0.06763] 0.02057

[0.05480]

Received Higher Degree -0.09758 [0.10256] 0.12963

[0.08208]

Blue collar profession 0.15738 [0.13754] -0.12708 [0.19123]

Ln(# of enrollees) -0.03173 [0.02808] -0.09590** [0.02208]

Ln( # of women) 0.00092 [0.03316] 0.04635

[0.02858]

Ln( # of employees 50+) -0.06285* [0.03192] 0.00245

[0.02751]

Plan flexible with provider -0.04550 [0.06750] 0.03221

[0.05270]

Firm offers paid vacation -0.71641** [0.15637] -0.11541 [0.16034]

Firm offers paid sick leave 0.01524 [0.08284] 0.00998

[0.06923] Firm can refuse coverage based on pre-existing conditions

0.12628 [0.07055]

0.21899** [0.05337]

Waiting period for new employees 0.14436** [0.07366] 0.12027* [0.05891]

Ln(total premium [SC]) 0.68163** [0.10791]

Ln(total premium [FC]) 0.54383** [0.07093]

R-sqr 0.1931 0.1246 N 827 1117 Robust standard errors in brackets *Significant at 5% level **Significant at 1% level

7 It is possible for divorced individuals to still have family coverage, just as it is possible for married individuals to keep single coverage if their spouse has insurance.


19

with the ability to refuse coverage based on pre-existing conditions have on average 18% higher

annual deductibles for family coverage. More surprising though is that a 1% higher total

premium for single coverage is associated with a .22% lower annual deductible, while a 1%

higher total premium for family coverage is associated with a .26% higher annual deductible for

family coverage. These results suggest that firms may be more sensitive to the costs of family

coverage. This is consistent with findings in other studies. Combining the results of the models

using the cross-sectional data, I observe that higher wages are often associated with higher total

costs of health insurance and that annual deductibles and premium copayments for the employee

are an alternative response to the incidence of higher premiums.

`

Table 8. Regression Table: Ln(annual deductible)

Variable (9) Single Coverage (10)

Family Coverage

Intercept 7.32527 [0.70818]

7.31664 [0.76692]

ln(wage) -0.02491 [0.03276]

0.00748 [0.03616]

Age 0.00582** [0.00222]

0.00658** [0.00242]

Ln(# of enrollees) -0.03708 [0.02064]

-0.05938* [0.02811]

Ln( # of women) 0.02180 [0.02640]

0.02912 [0.03174]

Ln( # of employees 50+) -0.03280 [0.02884]

-0.02570 [0.03478]

Plan flexible with provider -0.42882** [0.12380]

-0.224493* [0.11040]

Firm can refuse coverage based on pre-existing conditions

0.05019 [0.05183]

0.18145** [0.05985]

Ln(total premium [SC]) -0.22494** [0.08815]

-0.41204** [0.10641]

Ln(total premium [FC]) 0.05612 [0.09846]

0.25993* [0.10481]

Other Economic Controls Yes Yes R-sqr 0.1176 0.1043 N 698 699 Robust standard errors in brackets *Significant at 5% level **Significant at 1% level

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20

Shifting from analysis of the benefit packages firms offer at various wage levels, I

combine panel data from 1997, 1998, and 1999 to analyze how premium inflation affects wages

and employee contributions to healthcare. Similar to Miller (2004) and Lubotsky and Olsen

(2012), I utilize a fixed effects model to control for time invariant worker characteristics. I also

include dummy variables for each time period. Once combined, the panel data from the three

years gives 950 observations. For the sample data, the total premium for single coverage

increased by 23.32% on average over the three-year. This rise in premium was matched by a

71.63% increase in the cost of the employee premium copayment for single coverage. For family

coverage, premiums rose by 17.58% on average followed by a 22.07% increase in the employee

premium copayment. Wages over the three-year period increased by 11.02% on average. This

summary statistics highlight the gap in growth rates between health insurance costs and

compensation. The results of the fixed effects model are reported in Table (9). Model (11) is the

regression of the total cost of insurance on wage for family and single coverage. The regression

does not yield significant results. Model (12) is a more complete model with variables measuring

the effects of changes in age, marital status, and other fringe benefits on wage. This regression

also fails to provide significant coefficients for the effects of premium inflation on wage. In

Model (13) and (14) I use employee premium copayment (the employee’s contribution to the

total cost of health insurance) as the dependent variable. Similar to the results from Model (7)

and (8) where higher premiums were associated with higher employee contributions, 1%

premium inflation over the three-year period is associated with a .21% increase in the employee

premium copayment for single coverage and .57% for family coverage. Interestingly, employees

who went from single to married over the period saw a 31% increase in premium copayment for

single coverage and 23% for family coverage. In addition, employees working at firms

introducing policies such as refusal based on pre-existing conditions and waiting periods for new

employees saw increases in their contribution to the policy. These results confirm that employers

facing higher costs of insurance share the burden of the cost with employees via the employee

premium copayment, but that wages in general do not decline as a result.


21

Table 9. Fixed Effects (11) (12) (13) (14)

Ln(wage) Ln(wage) Ln(Employee Contribution) Variable [SC] [FC]

10.11806 9.268674 2.054932 1.544293 Intercept [0.6568] [0.7721] [0.6906] [0.5786]

0.016642 0.0034 0.001965 Age [0.00800] [0.00715] [0.00599] -0.08959 0.308852* 0.233539* Married [0.1471] [0.1315] [0.1102] 0.126685 -0.1422 -0.07826 Firm offers paid vacation [0.0969] [0.0867] [0.0726] -0.01405 0.019376 -0.01441 Firm offers paid sick leave [0.0899] [0.0804] [0.0674]

Firm can refuse coverage 0.064027 0.098903* 0.030503 based on pre-existing conditions [0.0585] [0.0523] [0.0438]

-0.10566 0.012045 0.184367** Waiting period for new employees [0.0751] [0.0672] [0.0563]

0.080913 0.082668 0.319132** 0.106165* Ln(total premium [SC]) [0.0698] [0.0699] [0.0625] [0.0524] 0.012134 0.010594 0.208131** 0.566822** Ln(total premium [FC]) [0.0741] [0.0742] [0.0664] [0.0556]

1998 0.016282 [0.0269]

0.006904 [0.0278]

0.107883** [0.0249]

0.093001** [0.209]

1999 0.06221 [0.0529]

0.042441 [0.0541]

0.127919** [0.0484]

0.078401* [0.0406]

Employee Fixed Effects Yes Yes Yes Yes

R-sqr 0.8156 0.8177 0.8480 0.8629 N 950 950 950 950 Robust standard errors in brackets *Significant at 5% level **Significant at 1% level

VII. Conclusion Measuring compensating differentials for employer-sponsored health benefits has only become a

focus in labor economics over the last few decades. Spearheaded by Gruber in 1994, many

subsequent studies have failed to find a statistically significant offset for workers receiving

fringe benefits. Incorporating recent work, I focus on the wage offset for privately employed

workers in 1996, 1997, 1998, and 1999. I begin with a basic hedonic wage model and then add

variables controlling for other fringe benefits such as sick leave and vacation. In addition, I add

variables for worker professional industry and occupation, as well as employee premium

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22

copayment and deductible. Assuming that wages are “sticky’ in a downward direction, I control

for various ways in which employers might adjust worker compensation to account for rises in

the cost of health benefits.

Beginning with simple cross-sectional OLS regressions, I fail to find a wage differential

for higher health care costs, consistent with previous research. When I analyze the effects of

higher costs on worker contribution, I find that employees pay .68% of the 1% higher premium

for single coverage and .54% for family coverage. Similarly, higher premium costs for both

single and family coverage are associated with offsets in annual deductibles. From these linear

regressions, I conclude that lower wages do not offset the rise in premiums and employers tend

to account for rising costs through higher premium copayments and deductibles. I also find that

each firm’s reaction to higher costs is associated with their general demographics and risk

tolerance. For instance, firms with waiting periods for new employees or policies of denial based

on pre-existing conditions tend to also have higher premiums and consequently, higher premium

copayments. Using the panel data from 1997, 1998, and 1999, the fixed effects model confirms

that firms react to premium inflation through increases to the premium copayments for

employees. Over the three-year period, wages did not decrease as a result of premium inflation.

The effects of increases in premium copayments surely impact lower-middle class

workers and their families. Although it may appear that increases in premium copayments

instead of wages is a good thing, it has the same negative effect on other household expenditures.

The premium copayment is deducted from worker paychecks and although annual wages do not

decrease, take-home pay does. While this paper is not a policy analysis of the PPACA, it does

provide some commentary on the impact of government mandates on private insurance. The goal

of providing universal coverage is one that the majority of OECD countries have achieved, but

that the United States continues to pursue. Unlike most OECD countries, the United States has

decided to pursue this goal using employer-sponsored health insurance. First established to

attract and retain talent, employer-sponsored health insurance has become the delivery method

for universal coverage. This paper suggests that the cost of rising premiums is shared between

employer and employee. Mandating insurance coverage at an employer level will force further

cost sharing and does not guarantee that employees will not bear a larger percentage of that cost.

The real cost to employees may offset the value of insurance gained, bringing into question the

overall benefit of a mandate aimed at improving the general welfare of the Untied States.


23

VIII. References Auerbach, D. I. & Kellermann, A. L. (2011). A Decade of Health Care Cost Growth Has Wiped Out Real

Income Gains for an Average US Family. Health Affairs, 30(9), 1-7. doi: 10.1377/hlthaff.2011.0585.

Bureau of Labor Statistics (1999). The Editor's Desk, In small firms, blue-collar, service workers least covered by health care benefits. http://www.bls.gov/opub/ted/1999/may/wk4/art02.htm (visited April 10, 2013).

Gruber, J. (1994). State-Mandated Benefits and Employer-Provided Health Insurance. Journal of Public Economics, 55(3), 433-464.

Gruber, J. (1994). The Incidence of Mandated Maternity Benefits. American Economic Review, 84(3), 622-641.

Kaiser Family Foundation and Health Research and Educational Trust (2012). Employer Health Benefits: Summary of Findings. Available at http://www.kff.org.

Levy, H., & Feldman, R. (2001). Does the Incidence of Group Health Insurance Fall on Individual Workers?. International Journal Of Health Care Finance And Economics, 1(3-4), 227-247.

Lubotsky, Darren H. and Olson, Craig A., Premium Copayments and the Trade-Off between Wages and Employer-Provided Health Insurance (August 16, 2012). Available at SSRN: http://ssrn.com/abstract=2132963 or http://dx.doi.org/10.2139/ssrn.2132963.

Miller, R. r. (2004). Estimating the Compensating Differential for Employer-Provided Health Insurance. International Journal Of Health Care Finance And Economics, 4(1), 27-41.

Montagne, C. C. (2002). Bargaining Health Benefits in the Workplace: An Inside View. Milbank Quarterly, 80(3), 547-567.

Olson, C. A. (2007). Do Workers Accept Lower Wages in Exchange for Health Benefits?. In J. T. Addison (Ed.) , Recent Developments in Labor Economics. Volume 2 (pp. 26-49). Elgar Reference Collection. International Library of Critical Writings in Economics, vol. 207. Cheltenham, U.K. and Northampton, Mass.: Elgar.

Rosen, S. (1986). The Theory of Equalizing Differences. In O. Ashenfelter, R. Layard (Eds.) , Handbook of labor economics. Volumes 1 (pp. 641-692). Handbooks in Economics series, no. 5.

Royalty, A. (2008). Estimating Workers' Marginal Valuation of Employer Health Benefits: Would Insured Workers Prefer More Health Insurance or Higher Wages?. Journal Of Health Economics, 27(1), 89-105. doi:http://dx.doi.org/10.1016/j.jhealeco.2006.10.013

Simon, K. (2001). Displaced Workers and Employer-Provided Health Insurance: Evidence of a Wage/Fringe Benefit Tradeoff?. International Journal Of Health Care Finance And Economics, 1(3-4), 249-271.

Sommers, B. D. (2005). Who Really Pays for Health Insurance? The Incidence of Employer-Provided Health Insurance with Sticky Nominal Wages. International Journal Of Health Care Finance And Economics, 5(1), 89-118.

Vistnes, J., & Selden, T. (2011). Premium Growth and Its Effect on Employer-Sponsored Insurance. International Journal Of Health Care Finance And Economics, 11(1), 55-81. doi:http://dx.doi.org/10.1007/s10754-011-9088-4.

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