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Age Related Health Costs and Job Prospects of Older Workers

Gary Burtless *

THE BROOKINGS INSTITUTION

October 22, 2017

________________________

* The author is a senior fellow and holds the Whitehead Chair in the Economic Studies

program at the Brookings Institution, Washington, D.C. This paper was prepared for the 2017

Working Longer and Retirement Conference, Stanford Institute for Economic Policy Research,

November 2-3, 2017. I gratefully acknowledge the excellent research assistance of Eric Koepcke

and Austin Drukker and the generous research support provided under the Alfred P. Sloan

Foundation’s Working Longer program. The views are solely my own and do not represent

those of Brookings or the Sloan Foundation.

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Age Related Health Costs and Job Prospects of Older Workers

by

Gary Burtless

MOST AMERICANS WHO WORK or are seeking work think age discrimination in employment is a

problem. More than a third think it is a serious problem (Wilson 2006). Among Americans past

age 50, one-third believe that they or people they know have been the actual victims of

workplace age discrimination (GS Strategy Group 2012). Though many forms of age

discrimination are difficult to document, at least one kind of discrimination—bias against older

job applicants—has been confirmed in experimental trials. Findings from resume audit studies

suggest that many employers prefer to interview, and presumably to hire, younger rather than

older job seekers who have identical qualifications.

None of the results from audit studies provide unambiguous evidence on employers’

motivations for discriminating against older applicants. Some managers may believe older

workers are more costly to train, harder to manage, less flexible in responding to workplace

change, or more likely to suffer some form of age-related cognitive or physical decline. In short,

these managers may assume older job seekers will be less productive than younger applicants

who have the same credentials. There is in fact evidence in the personnel and applied

psychology literatures that many managers apply negative age stereotypes when evaluating age-

related personnel issues in laboratory settings (Rosen and Jerdee 1977; Ng and Feldman 2012).

Some of the stereotypes accepted by managers who discriminate may of course have some

foundation in fact.

Another possibility is that employers think older workers will be more expensive to

employ, even if they are paid the same wage and are just as productive as identically credentialed

younger employees who hold the same jobs. Several kinds of employer costs rise as workers get

older. Expenses connected to a worker’s health, including employer premiums for health

insurance, can certainly increase. The increased risk of illness as workers age might also raise the

cost of sickness pay for employers offering paid sickness leave. Even employers that do not

provide paid leave can face higher costs as workers age if absences resulting from illness

increase firms’ operating costs. Serious health problems can also lead to an early end to a

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worker’s career, forcing employers to find or train a replacement workers. If career-ending

health episodes occur more frequently for older workers compared with younger ones, it can

increase the perceived cost of hiring or retaining older workers. Finally, some employer

retirement plans are more costly to fund for older workers than they are for younger workers who

earn the same wage. Defined-benefit (DB) pension plans, for example, might require larger

annual contributions for middle-age and older workers compared with workers who are under 35.

This paper focuses on age-related employee costs connected to the provision of health

insurance. Employer-sponsored insurance is by far the most important source of health coverage

for working Americans and their dependents. Half of children and approximately 60 percent of

nonaged adults obtain health insurance coverage under an employer-sponsored plan. For many

employers—and for the employer community at large—health insurance is the most costly non-

wage benefit they provide. In 2014 almost 60 percent of full-time workers between 18 and 64

obtained health insurance under a health plan sponsored by their employer. Employer-sponsored

insurance (ESI) is less likely to be offered and taken up by workers on part-time schedules.

Nonetheless, about 70 percent of all civilian employees are offered access to employer health

plans and slightly more than half participate in them (U.S. BLS 2017a, Table A). If we count

workers’ dependents, about 60 percent of all Americans under age 65 receive health coverage

under an employer-sponsored plan. The BLS estimates that 8.3 percent of employee

compensation consists of employer contributions for health insurance. This is equivalent to a

little more than 12 percent of total money wages (U.S. BLS 2017b, Table A).

The cost of paying for such plans is higher for older compared with younger workers.

Older people are more likely to experience serious illness and suffer adverse effects from injury

or chronic disease. As a result, health insurance plans make bigger annual payouts to older

compared with younger employees. Older employees do not bear the full extra costs of these

insurance reimbursements, because employee health premiums are not differentiated to reflect a

worker’s age or expected health care outlays. Nor do aged employees bear the costs indirectly

through lower money wages. Such wage differentiation is banned under the Age Discrimination

in Employment Act.

The gap in health care spending between older and younger working-age Americans is

sizeable. In the 2014 Medical Expenditure Panel Survey (MEPS), for example, mean health

expenditures per person were nearly four times greater for Americans between 60 and 64

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compared with adults between 25 and 29. Mean spending in the older group was nearly 2.4 times

the per capita spending on Americans between 40 and 44.1 Not all of these spending differences

are reflected in employer outlays on their employees’ health, of course. Insurance plans do not

cover the full cost of medical treatments. They reimburse only a fraction of the costs, a share that

is determined by a plan’s deductibles and employee cost-sharing requirements. Furthermore,

under the great majority of ESI plans employees must pay premiums to obtain coverage. These

charges partially offset employers’ cost of providing coverage.

Employers do not provide insurance coverage to all working-age adults. The highest

health expenditures often focus on people with disabilities and chronic conditions. Many adults

with these conditions do not work and are not covered by an ESI plan. Instead their health

expenditures are insured under public insurance programs, such as Medicare and Medicaid. The

relationship between workers’ ages and employers’ health expenses is also affected by the

distribution of reimbursable medical expenses among workers’ dependents. Workers who are

past age 50 are less likely to enroll in ESI family plans than are workers who are in their 30s and

40s. Older workers are more likely to enroll in single employee plans and employee-plus-one-

dependent plans. Since employer plans do not cover as many dependents per enrolled worker in

the case of their oldest employees, there may be an offset for the higher costs associated with

older employees’ own health care expenses. On the other hand, the percentage of workers that

accepts an employer’s offer of ESI coverage tends to increase as workers grow older, at least up

through age 65 when workers automatically become entitled to Medicare.

In the remainder of this paper I estimate the age-related costs of ESI plans in order to

determine whether employee cost differences associated with age can plausibly explain some of

the discrimination against older employees and job applicants.

I. Discrimination and age-related employment costs

Employer bias against older workers is widely suspected and, in the case of older job

applicants, has been confirmed in randomized trials. In these trials, commonly called

“correspondence studies” or “resume audits,” the experimenter responds to help wanted ads with

carefully crafted job applications from fictitious job seekers (Neumark 2016). The applications,

which typically include a formal resume, mention a variety of the applicant’s characteristics. The

1 MEPS, “Table 1: Total Health Services-Median and Mean Expenses per Person with Expense and

Distribution of Expenses by Source of Payment: United States, 2014” and author’s calculations.

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focus, however, is on one key characteristic, namely, the applicant’s membership in a group that

is the potential target of discrimination. In correspondence studies that examine age

discrimination, the key characteristic is the applicant’s age. Two applications may be prepared

by the experimenter that show identical qualifications for the advertised job. The only difference

between the two resumes is the applicant’s age. Appropriate fictitious resumes, selected at

random, are sent to employers who have published advertisements for a particular kind of job.

The experimenter then records the number of favorable responses received from employers for

each application. A favorable response might include a call-back from the employer or an

invitation to the applicant to visit the firm for a formal interview. If a smaller proportion of older

applicants than of younger applicants receives favorable responses from employers, there is a

strong presumption that age discrimination is hurting the employment chances of the older

fictitious applicants.

Most of the experimental resume audits provide evidence of age discrimination in the

hiring process (Neumark 2016). Results by Joanna Lahey (2008) are among the best known. She

submitted approximately 4,000 fictitious help wanted ad responses for entry-level positions

advertised in Boston, MA, and St. Petersburg, FL. All of the fictitious responses carried a

woman’s name and were in response to advertisements for positions typically held by women.

Women described in the responses were ages 35, 45, 50, 55, and 62. If we define applicants age

35 and 45 as “younger” and applicants age 50, 55, and 62 as “older,” Lahey found that younger

applicants were 42 percent more likely to be offered an interview in Boston and 46 percent more

likely to be offered an interview in St. Petersburg. Another correspondence study recorded

employer responses to applications from both male and female applicants, half age 32 and the

other half age 57 (Bendick et al. 1997). The older applicants were significantly less likely to

receive favorable responses from employers. Among employers whose size could be ascertained,

the researchers found that large employers were more likely to discriminate against older

applicants. It is notable that large employers are also more likely than smaller ones to offer

generous benefit plans whose cost increases with workers’ age (Wiatrowski 2013; U.S. BLS

2017b, Table 8). This may give the discriminating firms stronger financial reasons to favor

younger over older job applicants.

The findings from resume audit studies confirm that many employers prefer to interview

younger rather than older job applicants. It is more difficult to determine whether they prefer to

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retain and promote younger workers already on their payrolls in preference to equally or more

qualified older workers, even though this preference is widely suspected by older workers. It is

also hard to identify employers’ specific motives for favoring younger job applicants, although

some of the audit studies have been designed to shed some light on this question. None of the

results obtained so far provide clear guidance regarding employers’ reasoning. As noted, some

managers may believe older workers are currently or soon will be less productive than younger

workers who have the same credentials. The connections between job performance, cognitive

aptitude, and physical ability, on the one hand, and age, on the other, have been topics of

extensive research in psychology and medicine (Ng and Feldman 2008; Skirbekk 2008;

Salthouse 2009; McGee and Wegman 2004). While there is considerable evidence that a number

of cognitive abilities and physical functions decline on average with age, it is a matter of

controversy whether these declines may be offset, fully or partially, with gains in wisdom or

good judgement, possibly arising from experience or greater knowledge. The effects on

productivity of these age-related changes are impossible to measure in many occupations, and

they are beyond the scope of this study. My focus instead is on a more easily documented effect

of aging, namely, its impact on employer costs and in particular on the employer cost of

providing health insurance to employees.2 To the extent that age-related cost differences are real

and are not offset by higher worker productivity or lower costs in some other part of the

compensation package, employers have a tangible reason to prefer younger and cheaper workers

to older and more expensive ones.

Health costs and age. A basic reality of health insurance is that personal health spending

increases as adults grow older. Figure 1 shows per capita health expenditures in 2014, by age, for

two groups of adults who are between 25 and 74 years old. The calculations are based on health

consumption patterns observed in the 2014 MEPS household survey file. Spending amounts

indicated by the light bars show expenditures among all adults in the noninstitutional population.

The spending totals reflect consumption of health care goods and services regardless of the

source of payment for the consumption. These include out of pocket outlays by the person or

family and reimbursements by public or private group health insurance as well as private

insurance obtained in the nongroup market. Because the MEPS sample excludes the long-term

2 Some of these and closely related issues have been examined in earlier research. See Hutchens

(1988); Scott, Berger and Garen (1995); and Mermin, Johnson, and Toder (2008).

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institutionalized, the health spending of some of the most costly adults is missed. The tabulations

show a dramatic increase in per capita spending between ages 40-44 and 60-64. Between these

ages annual expenditures increase by 137 percent or a bit more than $4,600 per year. For

purposes of comparison, the economy-wide average wage in 2014 was $46,480, so the difference

in annual health spending between Americans in their early 40s and those in their early 60s was

about 10 percent of average annual earnings.

The solid line in Figure 1 shows per capita spending in 2014 among employed workers,

whom we would expect to be healthier on average than nonworkers. Workers’ mean health

expenditures are indeed lower at every age compared with the spending amounts of all

noninstitutionalized adults. Moreover, the proportional difference between workers’ health

spending and that of the general population tends to widen at older ages, especially past age 45.

Thus, the population at work is increasingly selected from among relatively low spending adults

as it grows older. Past age 60, annual personal health expenditures among older workers increase

very slowly compared with spending in the general population. Still, workers who are between

60 and 64 have mean expenditures that are twice those of workers between 40 and 44. The

annual spending of the older group is more than three times that of workers in their late 20s.

These spending gaps are economically meaningful if employers’ ESI plans cover a large fraction

of the rising costs.

Figure 2 offers evidence that private insurance, which for the working population consists

mainly of employer-sponsored group insurance, is indeed financing a large percentage of the

rising health costs associated with aging. The chart shows per capita private insurance

reimbursements in 2014, by age, for workers who are covered by private insurance. The

tabulations show a sharp rise in average reimbursement payments after age 54. Privately insured

workers who are between 55 and 59 receive reimbursements that are $2,670 (or 112 percent)

larger than those received by 40-44 year-old workers. Note that the average reimbursement dips

slightly for 60-64 year-olds compared with 55-59 year-olds and falls noticeably for workers past

65. The small dip after 60 might be due to health selection effects among the population that

remains at work at older ages. The bigger drop after 65 is linked to workers’ eligibility for

Medicare starting at that age. Insured workers in smaller firms may have Medicare as their

primary insurance payer, even if they remain covered by their employer’s plan. In addition,

privately insured workers past 65 may obtain their private insurance through Medigap policies or

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as retired workers under a former employer’s retiree health plans. In both cases, Medicare rather

than the private policy would be the primary insurer. Thus, the sharp decline in private insurance

reimbursements after age 65 provides no assurance to large employers that their older workers

would become less expensive after reaching the Medicare eligibility age.3

For a couple of reasons the tabulations in Figures 1 and 2 do not give direct evidence

about the cost to firms of employing an older as opposed to a younger worker who fills the same

job and earns the same money wage. Even the estimates in Figure 2, which focus on workers

who are covered by a private plan, do not distinguish between the ESI reimbursements that are

paid under the employee’s own employer plan and those that are derived from a spouse’s plan.

This difference matters, because the cost of providing ESI insurance is obviously much greater in

the case of employers who insure workers and their dependents than it is in cases where a firm’s

employees choose to enroll in another employer’s plan. By focusing on the personal health

spending and insurance reimbursements of individuals, the estimates also miss the cost to

employers of supplying insurance to employees’ eligible dependents, including spouses and

children. ESI plans typically offer coverage to employees’ dependents as well as to the

employees themselves. The likelihood that an employee will have eligible dependents and that

the employee will elect to cover them under an employer’s plan varies over the life course. With

respect to dependent coverage, employees may actually become cheaper when they grow older,

as the number of their eligible dependents shrinks. However, an employee’s most likely

dependent is a spouse, and the spouse’s age and vulnerability to high health expenses usually rise

in line with the employee’s.

Public policy issues. Most Americans, especially those past 50, think workplace

discrimination against the aged is a problem. It is less clear whether they believe there are good

reasons, on cost grounds, for employers to prefer younger over older workers. One way to reduce

employers’ incentive to discriminate is to restructure or eliminate components of the

compensation package that are more expensive to provide to older employees. For example,

employers might eliminate paid sickness leave and replace it with a combination of paid

3 Under present law, employers with at least 20 workers must offer to current employees older than 65 the

same health benefits they offer to workers under 65. Similarly, they must offer coverage to employees’ spouses 65

and older the same coverage provided to employee spouses who are younger than 65. Employees in these firms who

are simultaneously insured under their employers’ ESI plan and Medicare receive insurance reimbursements first

under the ESI plan. Medicare is the secondary payer; that is, it only provides reimbursement based on the billed

amounts not reimbursed by the ESI plan.

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scheduled and unscheduled leave. Workers could still obtain paid leave when they are sick, but

sickness would no longer be the sole reason for taking paid unscheduled leave. If younger

workers are less likely to be sick than older ones, they could still take as much unscheduled leave

as older ones, though for reasons other than sickness. Employers might eliminate or reduce the

generosity of employee health insurance. However, if employees place high value on this benefit

or if the government mandates its provision, as is now the case for employers with 50 or more

employees, it may be impractical to eliminate the benefit or make it substantially less generous.

Assuming the benefits continue to be offered by employers, are there public policies that

might reduce age-related burdens on employers while preserving appropriate incentives for

worker selection and retention? The answer depends on the nature of the age-related cost that is

associated with a given employee benefit. In some cases it is feasible for employers to avoid

paying age-related compensation costs by hiring younger workers instead of older ones. From

the point of view of employers and the workers who get hired this may seem efficient. From a

social perspective it appears much less efficient if the age-related costs the employer avoids must

still be borne by the workers who fail to get hired, probably with generous help from society at

large. For example, because of differences in the cost of providing health insurance to young and

old workers, an employer may prefer to fill a vacancy with a less qualified 30-year-old rather

than a more qualified 60-year-old job applicant. The employer’s preference is rational from a

private perspective, but it is less rational (and less efficient) from a social perspective. If the

jobless 60-year-old will receive almost the same medical care with a public subsidy as she would

have received as a covered employee in the employer’s health plan, the employer’s hiring

decision has reduced the number of older workers who are gainfully employed but has not

appreciably reduced the total burden of paying for the older job applicant’s health care.

Assuming that many employers routinely discriminate against older workers and job

applicants in order to avoid paying for age-related costs that will ultimately be borne, in part, by

taxpayers, it may be preferable from a social perspective for public policy to reduce the age-

related costs that are borne by employers, possibly by subsidizing some of the age-related health

costs faced by employers. This issue is less relevant in most other rich countries, which provide

health insurance in a way that does not link a worker’s health coverage or its cost to the person’s

employer. Employers in these countries may face age-related costs that make it more expensive

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to employ an older rather than a younger worker, but the difference in the health insurance

expense of the two workers is not one of those costs.

II. Data and methods

In order to determine the added costs facing firms that employ older rather than younger

workers, it is necessary to measure employment costs for a representative sample of workers and

to determine how much those costs vary with age, holding constant other critical aspects of the

employment relationship, including the wage. In the case of health insurance costs, reliable

measurement of employer costs at the level of the employee was not feasible before 1996.

Publicly available data did not provide analysts with detailed and reliable information on

individual-level health care expenditures or the sources of payment for such expenditures. Large

private insurance companies probably had access to much of the needed information, but their

data were seldom accessible to academic researches. The introduction of the MEPS in 1996

greatly improved the availability of information on individual-level health care spending and the

sources of payment for the spending.

The MEPS is conducted by the Department of Health and Human Services’ Agency for

Healthcare Research and Quality (AHRQ). It collects comprehensive and detailed information on

health care utilization, spending on health care and insurance, and sources of payment for

personal health care goods and services. In addition, it gathers demographic, health status, and

employment information on family members in the sample. The MEPS research program has

three basic components, a survey of representative households, a survey of the medical providers

who supply services to these households, and a national survey of public and private employers

to gather information on the types and cost of employee health insurance offered (Bernard and

Banthin 2007). The first two surveys are the most important parts of the program for the current

study. They give detailed information on respondents’ utilization of medical providers, the cost

of health care goods and services supplied by providers, and the sources of payment for the

health care expenditures of people in the household sample. Researchers cross-check the reports

of household respondents against the responses of providers. As a result, the MEPS data files

provide much more accurate information about the cost and sources of payment for medical

services than would be possible in a survey that relied solely on the recall of household

respondents. The MEPS household survey gathers data covering about 15,000 families

containing 35,000 individuals every year.

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The household survey collects information from a nationally representative sample of the

noninstitutionalized population. Sample members are drawn from the respondents to the National

Health Interview Survey. Once enrolled in the MEPS sample, respondents are interviewed in five

separate surveys covering a total of two calendar years. The analysis in this paper is based on the

overlapping samples that provided information covering the 2010 through 2014 calendar years.

The data for each family and worker were organized into annual records showing ESI

availability, participation, employee premiums, dependent coverage, and insurance

reimbursements separately for each calendar year. The same families and workers could supply

information and be included in the analysis for up to two calendar years if they remain in the

MEPS panel for two years in 2010-2014. The MEPS household survey files give us information

on respondents’ employment, wages, insurance coverage, health premiums, medical spending,

and insurance reimbursement. This analysis covers employees who work for pay. The self-

employed are excluded from the analysis, as are adults who do not work for pay. However,

because of my focus on employer insurance costs, I gathered information on the health care

expenditures and insurance reimbursements of worker dependents who are covered by a

worker’s ESI plan. This information is attached to covered workers’ own spending and

reimbursement records in order to produce a comprehensive tally of total health care spending in

an insured family unit as well as all reimbursement payments under the worker’s ESI plan. Some

families obtain insurance coverage from multiple insurers. Their insurance coverage may be

episodic over the course of a year. This could be because one or more family members had

multiple jobs or interruptions in employment that required a change in insurer. To make the

analysis tractable, I excluded cases in which shifting insurance coverage made it impossible to

determine the exact source of private insurance covering part of a family’s medical expenses. If

two working members of a family had separate coverage under two ESI plans, I separately

tabulated the premiums, health expenditures, and insurance reimbursements under the two plans.

These restrictions yield a sample of about 58,000 employee person-years over the five

calendar years covered by the analysis (Table 1). This is approximately 11,700 individual

respondents per calendar year. On average the respondents’ records represent the experiences of

107,100,000 adult employees age 25 and older per calendar year. Not all of these employees

were offered employer sponsored insurance. Of the workers who were eligible for ESI coverage,

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many declined the employer’s offer of insurance and obtained their insurance from another

source or went uninsured.

The crucial calculations pertain to ESI reimbursements for health care received by an

employee and his or her dependents under a given employer’s insurance plan. There are two

ways to measure the sum of these reimbursements over a calendar year. The first is as the gross

reimbursement payments received by the insured family and the family’s health care providers

under the employer’s ESI plan. The second is as the net ESI reimbursements after subtracting the

employee’s annual premium contributions for enrollment in the employer plan. The net ESI

reimbursement is the best single measure available in the MEPS household survey to capture the

variation in employer cost associated with employing older rather than younger workers. The

resulting estimates of net ESI reimbursements clearly understate employers’ full cost of

financing ESI plans because they fail to account for the administrative cost of running the plan or

for the profit requirements of private firms that manage employer health plans. These costs are

not observed in the MEPS household survey. The CBO estimates that only 85 percent of

combined employee and employer health premiums is used to pay for medical reimbursement

(U.S. CBO 2016). The other 15 percent is absorbed by administration costs and insurers’ profits.

Among smaller firms, enrollees’ health care claims account for only 81 percent of combined

premium payments. Large employers enjoy economies of scale that increase the ratio of

reimbursement payments to total premiums. How administrative costs should be allocated across

individual employees is an open question. It seems likely these costs are higher in the case of

employees who have above-average covered expenses, but employers incur some ESI costs even

in the case of employees who do not receive any reimbursements in a given year. The Kaiser

Family Foundation estimates that in 2014 the combined employer-employee cost for an average

ESI individual insurance plan was slightly more than $6,000 a year (Kaiser/HRET 2017). The

cost of an average family plan was about $16,800. If 15 percent of average plan costs were

absorbed in administrative expenses, annual reimbursement payouts would have averaged about

$5,100 for individual employee plans and $14,300 for family plans.

The second and third rows in Table 1 show estimates in the analysis sample of the

weighted mean annual ESI reimbursement in three sub-samples of the MEPS analysis sample

used in this paper. The largest of the three subsamples consists of employed MEPS respondents

25 and older who meet the broad criteria to be included in the sample. The second subsample

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consists of employees in the larger sample who were offered ESI at their place of employment.

The smallest subsample consists of employees who took up the offer and enrolled in their

employer’s insurance plan. In the third sub-sample, which consists solely of ESI-insured

workers, the average gross ESI reimbursement for a calendar year is $5,261 per worker. Because

reimbursement amounts are calculated for five separate calendar years, 2010 to 2014, they are

converted into 2014 dollars before averaging. The average net ESI reimbursement is $3,181

which implies the average employee premium payment is $2,080. This is higher than the

Kaiser/HRET estimate of average premiums for ESI single-employee plans but below the

estimate of average premiums for ESI family plans. While the MEPS-reported employee

premium may appear low compared with the Kaiser/HRET estimates, some of the MEPS

respondents are reporting premiums for insurance coverage that does not last a full year.

The ESI reimbursements reported in the MEPS have shortcomings. Researchers have

found that the MEPS survey fails to capture all of the medical expenditures of Americans

covered by ESI plans. Aizcorbe et al. (2012) compared ESI-covered medical expenditures

reported in the MEPS with health care expenditures reported for an identically selected

population in the MarketScan® Research Databases. The researchers found that the MEPS

expenditure totals for 2005 were 10 percent lower on average than the comparable totals in the

far larger sample of ESI-covered people in the MarketScan database. While the authors conclude

that MEPS respondents underreported episodes of care in all ranges of the spending distribution,

a disproportionate share of the MEPS spending shortfall is traceable to missing observations at

the extreme upper tail of the distribution. The MEPS survey uncovers annual expenditure totals

for some individual cases in which spending exceeds $300,000 a year. These high-spending

cases, though rare, are relatively more common in the MarketScan database.

The sensitivity of health spending estimates to cases with high outlays is obvious in the

MEPS data. Figure 3 shows the cumulative probability distribution of gross ESI reimbursements

for ESI-insured employees who were between 55 and 64 years old in the 2010-2014 calendar

years. The chart shows the cumulative share of total ESI reimbursements disbursed on this

population, with recipients ranked from lowest to highest spenders. Cumulative ESI

reimbursements to the 75 percent of ESI enrollees with the lowest spending accounted for 18.4

percent of total reimbursements received by this population. The bottom three-quarters of

spenders received an average of $1,760 in gross ESI reimbursements per year, while the top

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quarter of spenders received average annual reimbursements of $23,470. Another line in the

chart shows ESI reimbursements to the bottom 95 percent, who accounted for 55.9 percent of

total reimbursements. By implication, the top 5 percent of spenders received 44.1 percent of total

reimbursements, an average of $63,470 per year. The top 1 percent of spenders received an

average reimbursement of about $130,400 accounting for 18.1 percent of all ESI

reimbursements. The concentration of spending is important to bear in mind for two reasons.

First, if the MEPS sample underrepresents high spenders, even slightly, the resulting omission

can have a sizeable impact on estimates of the average reimbursement payment. Further, even if

the overall MEPS sample achieved perfect representation of high spending cases, the rarity of

such cases would mean they are almost certainly over-represented in a few MEPS subsamples

while under-represented in others. The pattern of over-representation and under-representation

can have sizeable effects on relative spending patterns across cells. Median spending levels are

little affected by the problem of outliers. However, employers’ and health insurers’ costs are

driven by the average, not the median, covered health care spending of insured workers. Among

all ESI-insured workers in the 2010-2014 MEPS sample, about 8 percent of ESI-insured workers

between 55 and 64 are in the top 5 percent of ESI-insured spenders. In comparison, less than 3

percent of ESI-insured workers between 25 and 34 are among the top 5 percent of spenders.

Although instances of very large ESI reimbursements are comparatively rare for both the young

and the old, the fact that they are much more common among the old makes older workers much

more expensive to insure.

The importance of high-spending cases to health insurance costs makes it helpful to

analyze costs using the largest possible sample. For that reason, I have combined information on

ESI reimbursements across five calendar years rather than analyze costs within only a single

year. The disadvantage of the procedure is that about two-thirds of the unique families analyzed

supply information for two successive calendar years.

III. Results

The cost to employers of providing health insurance to their employees depends on the

percentage of their workforce that is eligible to enroll in their plan, the fraction of eligible

workers that enrolls, and the cost of plan administration plus reimbursement payments for

covered care less any premiums collected from insured workers. The MEPS data file permits us

to identify employees who are offered enrollment in an ESI plan by their employers. For

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employees in the 2010-2014 MEPS sample who are at least 25 years old, the probability that

workers will be offered enrollment in ESI is only modestly affected by the worker’s age once we

account for an employee’s weekly wage. Workers with very low weekly wages, either because of

low hourly pay or short weekly hours, have very low ESI offer rates. Only 22 percent of

employees in the bottom one-tenth of the weekly wage distribution are eligible to enroll in an

employer’s ESI plan. In contrast, more than 90 percent of wage and salary workers in the top

three-tenths of the wage distribution are offered eligibility for an employer plan.

Employer offers of ESI. Figure 4 shows estimates of ESI offer rates at successive ages for

workers in three weekly earnings groups: Workers who are in in the bottom 30 percent of weekly

earners, workers in the middle 40 percent of earners, and workers in the top 30 percent of

earners. It is plain in the chart that an overwhelming share of employees past age 25 who earn

average or above-average pay also receive an offer of employer-provided health coverage. For

middle and high earners, workers’ ages appear to have very little influence on the probability

their employers will offer them an ESI plan. Only at ages past 65 do we see a fall-off in the ESI

offer rate. This is almost certainly because workers in this age group, who are entitled to

Medicare, can afford to choose employers that do not offer ESI. Among workers in the bottom

pay group there is a stronger relationship between workers’ ages and the offer of an ESI plan.

Workers between 40 and 64 are more likely to be offered ESI than workers who are younger or

older. Except among wage earners at the bottom of the wage distribution, there is little evidence

of noticeable difference between the sexes in the likelihood an employee will receive an ESI

offer. For employees in the middle and at the top of the weekly earnings distribution, the

probability a worker will receive an ESI offer is virtually the same at each age for women as for

men. Among wage earners in the bottom 30 percent of the earnings distribution, however, female

employees are less likely to receive an ESI offer (Figure 5). This may be because a larger

fraction of women who earn low weekly pay are on part-time schedules and hence may be

excluded from participating in their employers’ ESI plan.

The differences between men and women may also be traceable to distinctive patterns of

the two sexes’ industrial attachment or job tenure. The MEPS data file permits us to identify an

employee’s industry and job tenure and the employer’s establishment size. Ideally, we would

like to know the employer’s overall size as well as the size of the establishment in which the

employee works. This information is not ascertained in the MEPS interview, but respondents are

- 15 -

asked whether their employer has other locations besides the one where the employee works.

Basic statistics about these variables are presented in Table 1.

The effects of these employee-specific and employer-specific factors on ESI offer rates

can be ascertained in weighted logit analysis. Table 2 displays these results. The β parameters

confirm that a worker’s age has only modest effects on the likelihood of receiving an ESI offer,

except in the oldest age group. In contrast, the employee’s weekly earnings rank has a consistent,

large, and statistically significant impact. Workers at the top of the wage distribution have a

greater probability of receiving an ESI offer compared with workers in the middle and especially

at the bottom. Note that, unlike the estimates displayed in Figures 4 and 5, the odds ratios

displayed in the right-hand column of Table 2 control for other factors in addition to workers’

age and gender. One of these factors is the industry in which an employee works. Controlling for

other factors in the specification, offer rates are significantly higher in manufacturing and public

administration; they are significantly lower in construction, transportation, leisure and

hospitality, and professional business, among other industries. The findings with regard to

employer size are not surprising. Smaller establishments and employers that have only a single

location are less likely to offer their employees an ESI plan. Employees’ job tenure also has the

expected effect. Employees with under a year’s tenure are much less likely to be offered

employer-sponsored health coverage, even controlling for other aspects of the worker’s

employment. This result may explain the small apparent effect of a worker’s age on the offer of

employer health insurance. Many young workers have begun to work with their employer only

recently, and consequently may not meet an eligibility test for coverage under the employer’s

plan. As employees’ job tenures lengthen, employees are more likely to be offered enrollment in

an ESI plan. This pattern is similar both for young and old employees of firms of equal size and

in the same industry. A key difference between young and old employees, therefore, may be that

the young are more likely to be in the early years of their job tenure.

Employee enrollment in ESI. An employer’s ESI costs are affected by their employees’

take-up of the offered benefit. Are older employees’ more likely than younger ones to enroll in

an employer plan? The raw statistics certainly suggest this is the case. Figure 6 shows ESI take-

up rates, by employee age, among the workers in the MEPS sample who are offered an ESI plan.

The take-up rate reaches a peak among 55-64 year-old workers, precisely the ones likely to face

the highest spending burdens for medical care. After age 65 there is a sharp fall-off in the take-up

- 16 -

rate. Many employees past 65 who are offered an ESI plan at their place of work may have

access to lower cost insurance under Medicare or a previous employer’s retiree health plan.

Between ages 40-44 and 55-59, however, the take-up rate of ESI climbs from 77 percent to 83

percent, which suggests employers with an older workforce may face higher costs as a result of

higher enrollments.

Table 3 presents weighted logit estimates of ESI take-up among MEPS employees who

are offered an ESI plan. The explanatory indicator variables in the analysis are the same as those

predicting ESI offers in Table 2. As in Table 2, the results in Table 3 imply that wage levels are

much more important than age in forecasting outcomes. Controlling for the other factors

included in the specification, workers earning the lowest wages are much less likely than well-

paid workers to take-up an employer’s offer of ESI. Many low-paid workers may have cheaper

insurance alternatives than the plan offered by their employer. The alternatives might include

public insurance or dependent coverage under the ESI plan of another family member. In

addition, the generosity of an employer’s ESI plan may be linked to the average wage paid to its

workforce. Low-wage employers on average probably offer less generous insurance, and this

may discourage some low-paid employees from enrolling in the employer’s plan.

In contrast to the effects of workers’ wages, the effects of their age on take-up are less

consistent. For reasons already mentioned, the workers who are least likely to enroll in an

employer’s plan are those past 65. However, after controlling for other factors that influence

take-up, employees between 25 and 29 are the ones with the highest probability of enrolling in

an employer plan. Enrollment rates among 55-64 year-old employees are about the same as those

among 30-34 year-olds. Among workers who are offered insurance and less than 65, those who

are between 40 and 49 have the lowest ESI take-up rates. So, while it is true that enrollment rates

tend to rise with age among workers past 50, it is hardly the case that workers in this age group

have exceptionally high take-up rates once the effects of wages, job tenure, and employer

characteristics are taken into account.

The number of employees’ dependents covered by an employee’s plan does not rise

noticeably with age. Figure 7 shows the proportion of ESI participants enrolled in single

employee plans, in employee-plus-one-dependent plans, and in family plans, by age. The

tabulations cover only employees in the MEPS sample who participate in their employers’ health

plans. The age group between 40 and 44 is the one with the highest combined enrollment in

- 17 -

family plans and employee-plus-one-dependent plans. Combined enrollments in these two plans

represent 56 percent of total enrollments in the age group. Enrollments in these costly plans

gradually decline to just 36 percent of total enrollments among ESI-covered employees who are

between 60 and 64. As we shall see, the total cost of providing insurance coverage to older

workers is higher than it is to provide coverage to workers in their 40s, but the reason is not the

large number of dependents covered by the older workers’ policies.

Employers’ reimbursement costs. The association between employers’ net reimbursement

costs and the employee-policyholder’s age is displayed in Figure 8. The numbers reflect the

estimated gross ESI reimbursements less employee premium payments per ESI-insured

employee in each age group. Per-employee reimbursement costs reach a peak of $5,540 for

workers between 60 and 64. Costs for all three of the age groups past 55 are well above those of

any of the age groups under 55. The net reimbursement cost associated with an average

employee between 55 and 59 is almost $1,260 (40 percent) higher than that of employees

between 45 and 49. The average net reimbursement for 60-64 year-old insured employees is

$2,300 (70 percent) higher than that for 45-49 year-old employees. The differences may be due

to characteristics of workers’ employment situation that make older employees more costly to

insure. For example, the older workers may disproportionately be employed in industries

providing more generous insurance. Holding constant workers’ earnings, job tenure, and

industrial attachment, are older workers more expensive to insure than younger ones?

The weighted least squares regression results in Table 4 offer an answer. The regression

is estimated with a MEPS sample that contains all employees 25 and older who are enrolled in

their employers’ ESI plans. The specification includes controls for workers’ gender, age, position

in the weekly wage distribution, industrial attachment, establishment size, and job tenure. ESI-

insured workers who are in the lowest earnings group receive significantly lower ESI

reimbursements than workers earning higher ages, holding all the other factors constant. A

bigger influence on ESI reimbursements, however, is the employee’s age. Insured workers in the

youngest age group receive net reimbursements that are about $1,900 per year below those

received by insured workers in the reference group, which contains insured workers between 45

and 49. Insured workers who are 55 and older receive significantly larger net reimbursements

than younger workers, even controlling for the other factors in the specification. The estimated

effects of employee age on net ESI reimbursements are very similar to the age-related spending

- 18 -

differences shown in Figure 8. Thus, inclusion of a variety of statistical controls, including

industry, employer size, and workers’ position in the earnings distribution does not affect the

basic conclusion that workers past 55 and their dependents are significantly more expensive for

employers to insure compared with workers who are under 55.

The estimates just reported show employers’ net cost of insuring older workers

conditional on workers’ decision to participate in the employer’s health plan. A more useful

estimate of the extra cost of employing older workers would account for differences in the

probability that younger and older workers will enroll in an employer’s plan if ESI coverage is

offered. Figure 9 shows employers’ average net reimbursement costs for employees who are

offered enrollment in the employer’s ESI plan. The numbers in the chart show the estimated net

ESI reimbursements per employee offered eligibility for employer-provided insurance. Note that

I assume the net reimbursement amount per employee who is offered insurance, but who

declines to take it, is $0. (For employees who accept the employer offer, the average net

reimbursement costs are indicated in Figure 8.) Of course, employers incur some administrative

costs in making ESI offers to employees who decline insurance, but these costs are presumably

low relative to the costs of actually providing insurance.

The tabulations displayed in Figure 9 show sizeable differences in the cost of offering

insurance to older compared with younger workers, though the differences are smaller than the

ones displayed in Figure 8. Workers offered insurance who are between 55 and 59 cost $1,220

(50 percent) more per employee than workers between 45 and 49. Workers between 60 and 64

cost $2.060 (82 percent) more in net ESI reimbursement payments compared with similar

workers in the younger age group. The weighted regression results displayed in Table 5 show

employers’ expected costs of offering insurance to workers of different age controlling for the

effects of workers’ wage rates, job tenure, and industry and their employers’ establishment sizes.

Workers’ rank in the wage distribution has a striking impact on expected reimbursement costs.

Employees who are in the bottom deciles of the weekly earnings distribution are far less costly

than those in the middle and at the top of the distribution, even controlling for age and other

characteristics of the worker and employer. The estimates also show, however, that the offer of

insurance coverage is significantly less expensive in the case of workers in the youngest age

group and significantly more expensive in the case of workers past 55 compared with the

expected cost of offering insurance to 45-49 year-old employees. The estimated effects are very

- 19 -

similar to the cost differences displayed in Figure 9. By implication, inclusion of a variety of

statistical controls to capture the effects of other factors that influence worker and employer

differences does not change our basic interpretation. It is significantly more costly for employers

to offer health insurance to workers past 55 than it is to offer insurance to their younger

employees.

The extra compensation cost of older workers. The results in Table 5 can be compared to

workers’ money wages to determine whether they appear large enough to influence employers’

hiring and employee retention decisions. The estimates in the table only reflect the net ESI

reimbursement payments that employers make for different classes of workers. They do not

include the administrative cost of managing the ESI plan. If we assume administrative costs are

strictly proportional to reimbursements, the estimates in the table should be boosted by about 18

percent (U.S. CBO 2016). This implies that, relative to the ESI compensation costs of 45-49

year-olds, those of employees between 55 and 59 would be $1,440 per year higher, those of

employees 60-64 would be $2,440 higher, and those of employees 65 and older would be $1,300

higher.

The economic significance of the expected added compensation clearly depends on an

employee’s wage. Among workers 25 and older who are offered ESI, the median annual wage in

the 2010-2014 was $46,770. Using this wage as a benchmark, the extra ESI cost associated with

55-59 year-olds and 65+ year-olds is about 3 percent of the median earner’s money wage. The

extra cost of employing a worker between 60 and 64 is about 5 percent of the median earner’s

pay. The 90th percentile worker earns about $107,410, and for this high earner the extra

compensation cost of offering a typical ESI plan amounts to only 1 percent or 2 percent of the

annual money wage. On the other hand, for a worker earning the 25th percentile wage, the

additional cost of paying for expected ESI reimbursements is a considerably bigger percentage of

the worker’s annual pay. For workers between 55 and 59, the added expected cost is 5 percent of

pay; for those between 60 and 64 it is 8 percent of pay; and for those past 65 it is 4 percent of

pay.

These calculations suggest expected ESI costs could weigh heavily on the choice between

an older and a younger job applicant when filling a vacant position, particularly in the case of a

firm filling a low-wage position. Of course, firms that offer ESI might arrange work schedules so

that the lowest pay positions are part-time jobs, in which case no ESI might be offered to

- 20 -

affected employees. Under the Affordable Care Act (ACA), covered employers do not have to

offer ESI to employees who on average work less than 30 hours a week. Even before

implementation of the ACA, many firms only offered health insurance to workers who were

employed more than a specified number of hours per week. Employers with a large proportion of

low-wage workers on their payrolls might opt to offer an ESI plan that is relatively unattractive,

either because the reimbursement formula is unattractive to workers or the premiums are high. It

should be noted, however, that the ACA has put lower limits on the generosity of ESI plans that

covered employers can offer and imposes penalty payments on employers that do not make

affordable plans available to their employees who work at least 30 hours per week. (These

provisions were not fully in effect during most of the period covered by this paper.)

Do employers actually pay higher ESI costs for older workers? The MEPS

reimbursement data show that ESI plans make larger reimbursement payments, net of employee

premium contributions, to workers past 55 compared with those under 55. These data do not tell

us whether firms actually pay the added costs or whether they are borne by third-party payers.

There are two basic ways for employers to insure their workers. Employers can purchase

coverage from an insurer in a so-called fully insured plan, or it can self-insure. The crucial

distinction is whether the employer or a third-party insurer bears the risk for unexpectedly large

employee health costs. In a fully insured plan, employers pay a fixed premium to a commercial

insurer for coverage, though the insurers’ rates undoubtedly take account of the predictable risk

factors associated with an employer’s eligible workforce, including the age distribution of its

workforce and their dependents. However, state insurance law or regulation may limit the

premium ratios employers must pay for workers of varying ages.

Employers who self-insure bear the risk that unexpectedly large health expenses may

drive up their compensation costs. State and federal regulation does not limit the ratio of costs

that self-insured employers may face for cost differences between older and younger employees.

Current estimates suggest that about 60 percent of all ESI-covered employees work for

employers who self-insure. Among ESI-covered employees who work in firms with 1,000 or

more employees, almost 90 percent work for employers who self-insure (Kaiser/HRET 2017).

Many self-insured employers purchase stoploss insurance to protect against unexpectedly heavy

expenses, but it seems doubtful whether this kind of insurance provides any protection against

the predictable reimbursement costs associated with an older workforce.

- 21 -

At a minimum, then, employers who self-insure pay for all of the extra reimbursement

costs connected to an older rather than a younger workforce, giving such employers an incentive

on the margin to hire or retain younger workers in preference to older ones. Since self-insured

employer plans cover the majority of ESI-insured workers, the estimates reported in Table 5

seem relevant to thinking about employer costs. For mostly smaller employers who are fully

insured and purchase coverage from commercial carriers, state insurance regulation may in some

cases limit the ability of carriers to charge employers for the full expected cost of older

employees. Even in these cases, however, carriers are permitted to charge higher premiums for

older workers, even if the premiums do not fully cover the extra reimbursements to workers past

55.

IV. Policy implications and conclusion

The ESI reimbursement data obtained in the MEPS survey strongly confirm the suspicion

that older employees on average receive larger reimbursements net of their premium payments

compared younger employees. The reimbursement differences are estimated with statistical

controls for other factors that influence employers’ plan generosity, employee take-up of

benefits, and employee characteristics apart from age that affect health care costs. I find that the

size of the cost difference between prime-age workers, on the one hand, and employees who are

past 55 is statistically significant and economically significant in the case of ESI-covered

workers who earn modest wages. The estimated age-related costs are more modest in comparison

to the wages of highly paid workers, say, those in the top quarter of the earnings distribution.

The estimates reported in this paper cover the period from 2010 through 2014. This is a

period that included passage of the ACA and the gradual implementation of the new law. The

future prospects of the ACA are now in question because a majority in Congress and the

President have pledged to repeal it. Nonetheless, the new law increases the importance of

understanding how age-related health costs may influence employer decisions with regard to

hiring and retaining older workers. The ACA imposed penalties on Americans who are offered

affordable insurance but fail to enroll, and it also introduced penalties on employers with 50 or

more employees if they did not offer their workers an adequate and affordable insurance plan.

The new rules have changed the incentives for employees to enroll in an employer health plan,

and they have increased the incentive for larger employers to offer adequate insurance to their

- 22 -

employees. An offer of employer-provided health insurance is now legally linked to most full-

time jobs.

Employers’ obligation to provide affordable insurance to their workers forces them to

offer a form of compensation that is more expensive to provide as workers grow older. It is hard

for employers to offset these age-related expense of health benefits by paying lower wages to

older workers, since age discrimination in wage setting is unlawful. To minimize labor costs,

some employers may therefore try to avoid bearing age-related ESI costs by preferring younger

to older workers in their hiring and employee retention policies. Even though these practices are

unlawful, they are much harder to detect than age discrimination in wages.

Both American law and public opinion regard age discrimination with disfavor. From a

society-wide perspective, age discrimination in hiring and employee retention is also

economically inefficient. It reduces the job opportunities (and probably the employment rate) of

older workers without much offsetting economic gain. One way to reduce employers’ incentive

to discriminate against older workers is to subsidize part of the age-related insurance costs that

employers’ face. For example, the government could provide employers with reinsurance for

ESI reimbursements that are larger than a specified amount. Since older workers are more likely

to incur large health expenses, public subsidies to a reinsurance plan could reduce age-related

cost differences faced by employers.

A tabulation of the gross ESI reimbursements to ESI-insured workers in the MEPS shows

that the average reimbursement to 55-64 year-old workers was $1,790 greater than the average

reimbursement to 45-54 year-olds. (The older group received an annual gross reimbursement

payment of $7,190; the younger group received an average reimbursement of just $5,400.)

Slightly more than three-quarters of the reimbursement difference between the two groups was

the result of spending differences in the top 15 percent of health care spenders. Less than a

quarter was due to spending differences between the bottom 85 percent of spenders in the two

age groups. If the reimbursements to high-spending cases were reinsured in a public program,

the ESI cost difference between older and younger workers would shrink. Consider a program

that covers one-half the cost of ESI claims that are greater than $10,000 per year and increases

this cost-sharing rate to 90 percent on claims greater than $20,000 a year. Based on ESI

reimbursement data for 2010-2014, this reinsurance plan would shrink employer reimbursement

costs by 29 percent in the younger age group and by 38 percent in the older age group. From

- 23 -

employers’ perspective, the annual cost difference between providing ESI coverage to 55-64

year-olds, on the one hand, and 45-54 year-olds, on the other, would shrink from $1,790 to $670,

a drop of 63 percent.

Adopting a reinsurance plan would reduce the age-related cost differences facing

employers, and presumably reduce their incentive to discriminate against older workers. At the

same time, it would force us to find a source of funding for a large fraction of the health costs

currently borne directly by employers and indirectly by workers.

- 24 -

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- 26 -

Table 1. MEPS analysis sample statistics

Continued on next page.

Full sample Offered ESI Enrolled in ESI

Total observations 58,336 41,806 31,630

Annual reimbursement from ESI (gross) $3,163 $4,104 $5,261

$1,913 $2,482 $3,181

Weekly wage (mean) $966 $1,102 $1,173

Female 49 48 46

Age group

25-29 14 12 12

30-34 13 13 13

35-39 13 13 13

40-44 12 13 12

45-49 12 13 13

50-54 13 14 15

55-59 11 12 12

60-64 6.9 7.2 7.7

65 and older 4.1 3.0 2.7

Employee's industry

Natural resources 0.97 0.53 0.52

Mining 0.48 0.56 0.65

Construction 5.2 4.1 3.9

Manufacturing 12.0 13.0 14.0

Wholesale and retail trade 12.0 12.0 11.0

Transportation and utilities 5.5 5.8 6.2

Information 2.2 2.4 2.7

Finance 6.4 7.0 7.1

Professional business 11.0 10.0 9.7

Leisure and hosptiality 6.5 4.2 3.2

Other 4.3 2.8 2.7

Public administration 6.7 8.1 9.2

Military 0.18 0.23 0.15

Unclassified 0.36 0.29 0.27

Education, health, social welfare 27 29 29

Annual reimbursement from ESI (net of employee

premiums)

Percent

- 27 -

Table 1. MEPS analysis sample statistics (continued)

Source: Author’s tabulations of MEPS household survey files as explained in text.

Full sample Offered ESI Enrolled in ESI

Employee's job tenure

Under 1 year 15.0 10.0 7.1

1 to 4.99 years 29.0 27.0 26.0

5 to 9.99 years 23.0 24.0 25.0

10 to 19.99 years 21.0 24.0 25.0

20 to 39.99 years 12.0 14.0 16.0

40 years or more 0.6 0.7 0.8

Establisment size / Other employer locations

Less than 25; no other location 16.0 8.5 7.5

25 to 49; no other location 3.9 3.6 3.4

50 to 99; no other location 3.5 3.5 3.5

Less than 25; other location(s) 19.0 18.0 16.0

25 to 49; other location(s) 8.5 9.0 8.8


100 to 499 employees 22.0 25.0 26.0

500 or more employees 18.0 21.0 23.0

Percent

- 28 -

Table 2. Weighted Logit Analysis of Employer Offer of ESI Coverage to Employees in MEPS Sample, 2010-2014

exp(β)

β SE β p- value [odds ratio]

Constant 2.9916 *** 0.1133 <.0001 NA

Female -0.3364 *** 0.0432 <.0001 0.714

Age group

25-29 0.1517 * 0.0814 0.062 1.164

30-34 0.0251 0.0711 0.724 1.025

35-39 -0.0372 0.0878 0.672 0.963

40-44 -0.0872 0.0873 0.318 0.916

45-49

50-54 -0.0359 0.0899 0.690 0.965

55-59 -0.0884 0.0915 0.334 0.915

60-64 -0.1707 0.1193 0.152 0.843

65 and older -1.0795 *** 0.1140 <.0001 0.340

Wage decile

Bottom -2.4644 *** 0.0963 <.0001 0.085

2 -1.7083 *** 0.0696 <.0001 0.181

3 -1.2239 *** 0.0604 <.0001 0.294

4 -0.5542 *** 0.0675 <.0001 0.575

5

6 0.2720 *** 0.0725 0.0002 1.313

7 0.6730 *** 0.0832 <.0001 1.960

8 0.8861 *** 0.0854 <.0001 2.426

9 1.0016 *** 0.0947 <.0001 2.723

Top 1.3272 *** 0.1157 <.0001 3.770

Employee's industry

Natural resources -1.4545 *** 0.2411 <.0001 0.234

Mining 0.0701 0.2950 0.812 1.073

Construction -0.7485 *** 0.0913 <.0001 0.473

Manufacturing 0.2708 *** 0.0823 0.001 1.311

Wholesale and retail trade 0.0612 0.0698 0.381 1.063

Transportation and utilities -0.2951 *** 0.0951 0.002 0.744

Information 0.0327 0.2116 0.877 1.033

Finance 0.0173 0.0952 0.856 1.017

Professional business -0.2471 *** 0.0654 0.000 0.781

Leisure and hosptiality -0.6048 *** 0.0830 <.0001 0.546

Other -0.7441 *** 0.1023 <.0001 0.475

Public administration 0.7139 *** 0.1332 <.0001 2.042

Military 2.8692 ** 1.1553 0.013 17.623

Unclassified -1.0535 *** 0.3802 0.006 0.349

Educ., health, soc. wel. Omitted category

Omitted category

Omitted category

- 29 -

Table 2. Logit Analysis of Employer Offer of ESI Coverage (continued)


Note: Sample consists of employees 25 and older in the MEPS sample, regardless of whether they are

eligible to enroll in their employer’s ESI plan. This logistic regression predicts whether employees are

offered eligibility to enroll in an employer’s ESI plan.

exp(β)


Less than 25; no other location -2.0738 *** 0.0690 <.0001 0.126

25 to 49; no other location -0.8471 *** 0.0910 <.0001 0.429

50 to 99; no other location -0.6493 *** 0.1049 <.0001 0.522

Less than 25; other location(s) -0.5865 *** 0.0670 <.0001 0.556

25 to 49; other location(s) -0.1665 * 0.0921 0.071 0.847

50 to 99; other location(s) -0.1047 0.0828 0.206 0.901

100 to 499 employees

500 or more employees 0.0642 0.0765 0.401 1.066


Under 1 year -1.5490 *** 0.0631 <.0001 0.212

1 to 4.99 years -0.5194 *** 0.0651 <.0001 0.595

5 to 9.99 years -0.2152 *** 0.0664 0.001 0.806

10 to 19.99 years

20 to 39.99 years 0.3932 *** 0.1078 0.0003 1.482

40 years or more 0.4069 0.4310 0.345 1.502

Test df p

Model evaluation

Likelihood ratio test 44 <.0001

Score test 44 <.0001

Wald test 44 <.0001

No. of employee-year observations = 53,123

Of which: Weighted % offered ESI = 77.6%

199,517,742

195,904,020

8,041.83

Omitted category

χ2

Establisment size /

Other employer locations

Omitted category

- 30 -

Table 3. Weighted Logit Analysis of Employee Take-up of Employer Offer of ESI Coverage, 2010-2014

exp(β)


Constant 1.7249 *** 0.1099 <.0001 NA

Female -0.4729 *** 0.0451 <.0001 0.623

Age group

25-29 0.4640 *** 0.0766 <.0001 1.590

30-34 0.2549 *** 0.0740 0.0006 1.290

35-39 0.1255 * 0.0736 0.0881 1.134

40-44 0.0346 0.0837 0.6795 1.035

45-49

50-54 0.1549 ** 0.0722 0.0319 1.168

55-59 0.2746 *** 0.0870 0.0016 1.316

60-64 0.2613 ** 0.1058 0.0135 1.299

65 and older -0.5150 *** 0.1140 <.0001 0.598

Wage decile

Bottom -1.6653 *** 0.1560 <.0001 0.189

2 -0.9055 *** 0.0965 <.0001 0.404

3 -0.6440 *** 0.0827 <.0001 0.525

4 -0.2443 *** 0.0710 0.0006 0.783

5

6 0.1038 0.0678 0.1258 1.109

7 0.2793 *** 0.0800 0.0005 1.322

8 0.4708 *** 0.0729 <.0001 1.601

9 0.4516 *** 0.0854 <.0001 1.571

Top 0.5728 *** 0.0945 <.0001 1.773

Employee's industry

Natural resources 0.0179 0.3081 0.954 1.018

Mining 0.7331 ** 0.3680 0.046 2.082

Construction -0.1875 ** 0.0951 0.049 0.829

Manufacturing 0.1204 * 0.0717 0.093 1.128

Wholesale and retail trade -0.2209 *** 0.0727 0.002 0.802

Transportation and utilities 0.0531 0.0971 0.584 1.055

Information 0.3177 * 0.1670 0.057 1.374

Finance 0.0697 0.0942 0.459 1.072

Professional business -0.2492 *** 0.0691 0.0003 0.779

Leisure and hosptiality -0.4570 *** 0.1014 <.0001 0.633

Other -0.0571 0.1280 0.656 0.944

Public administration 0.5507 *** 0.0939 <.0001 1.734

Military -1.7944 *** 0.3832 <.0001 0.166

Unclassified -0.6090 0.7876 0.439 0.544

Educ., health, soc. wel. Omitted category

Omitted category

Omitted category

- 31 -

Table 3. Logit Analysis of Employee Take-up of ESI Coverage (continued)


Note: Sample consists of employees 25 and older in the MEPS sample who are eligible to enroll in their

employer’s ESI plan, although some employees in this sample decline to enroll.

exp(β)


Less than 25; no other location -0.5264 *** 0.0675 <.0001 0.591

25 to 49; no other location -0.1841 0.1212 0.129 0.832

50 to 99; no other location -0.0099 0.1024 0.923 0.990

Less than 25; other location(s) -0.2958 *** 0.0521 <.0001 0.744

25 to 49; other location(s) -0.1351 * 0.0770 0.080 0.874

50 to 99; other location(s) -0.0094 0.0739 0.899 0.991


500 or more employees 0.3182 *** 0.0605 <.0001 1.375


Under 1 year -1.4086 *** 0.0676 <.0001 0.244

1 to 4.99 years -0.4897 *** 0.0688 <.0001 0.613

5 to 9.99 years -0.2638 *** 0.0643 <.0001 0.768

10 to 19.99 years

20 to 39.99 years 0.1615 * 0.0844 0.0556 1.175

40 years or more 0.6133 0.4095 0.1343 1.847

Test df p

Model evaluation

Likelihood ratio test 44 <.0001

Score test 44 <.0001

Wald test 44 <.0001


Of which: Weighted % who accept ESI = 78.1%

48,045,821

50,130,128

3,152.43

Omitted category

χ2

Omitted category

Establisment size /


- 32 -

Table 4. Weighted Regression Analysis: Net ESI Reimbursement, Conditional on Employee’s Enrollment in ESI, 2010-2014

β SE β p- value

Constant 3,093.1 *** 596.1 <.0001

Female 370.0 * 200.2 0.066

Age group

25-29 -1,886.9 *** 338.0 <.0001

30-34 -533.3 439.5 0.226

35-39 -31.1 425.3 0.942

40-44 -513.9 477.8 0.283

45-49

50-54 -372.7 336.5 0.269

55-59 1,224.1 ** 517.0 0.019

60-64 2,397.3 *** 541.8 <.0001

65 and older 2,022.3 ** 848.2 0.018

Wage decile

Bottom -846.2 1,211.1 0.486

2 -1,731.0 *** 645.3 0.008

3 -1,369.3 ** 618.7 0.028

4 -963.2 601.5 0.111

5

6 -221.0 588.2 0.708

7 305.7 624.9 0.625

8 570.8 616.9 0.356

9 696.3 619.8 0.263

Top 582.7 639.8 0.364

Employee's industry

Natural resources -132.0 1,063.7 0.901

Mining 2,130.2 1,702.7 0.212

Construction -1,261.0 ** 607.9 0.039


Wholesale and retail trade -888.3 ** 369.7 0.017


Information -387.3 647.6 0.550

Finance 173.5 502.1 0.730

Professional business -470.8 453.5 0.300

Leisure and hosptiality -487.9 432.8 0.261

Other -573.1 683.0 0.402


Military -804.3 1,412.5 0.570

Unclassified -3,440.6 *** 631.5 <.0001

Educ., health, soc. wel.

Omitted category

Omitted category

Omitted category

- 33 -

Table 4. Net ESI Reimbursement Regression (continued)


Note: Sample consists of employees 25 and older in the MEPS sample who are enrolled in their

employer’s ESI plan.

β SE β p- value

Less than 25; no other location -732.0 545.9 0.181

25 to 49; no other location -571.0 577.7 0.324


Less than 25; other location(s) 82.8 368.0 0.822


50 to 99; other location(s) -204.6 523.3 0.696




Under 1 year 903.0 742.9 0.226

1 to 4.99 years -99.2 340.4 0.771

5 to 9.99 years -79.5 329.9 0.810

10 to 19.99 years

20 to 39.99 years 11.8 358.7 0.974

40 years or more -1,241.7 1,407.9 0.379


R 2 = 0.0112

Omitted category

Omitted category

Establisment size /


- 34 -

Table 5. Weighted Regression Analysis: Net ESI Reimbursement, Conditional on Employees Receiving an Offer of ESI, 2010-2014

β SE β p- value

Constant 2,530.1 *** 477.2 <.0001

Female 132.2 157.4 0.402

Age group

25-29 -1,153.6 *** 260.8 <.0001

30-34 -253.9 338.4 0.454

35-39 45.3 331.0 0.891

40-44 -365.4 369.7 0.324

45-49

50-54 -242.6 268.2 0.367

55-59 1,125.0 *** 431.7 0.010

60-64 2,073.1 *** 450.0 <.0001

65 and older 1,108.9 * 582.5 0.058

Wage decile

Bottom -1,404.3 *** 516.5 0.007

2 -1,456.5 *** 443.5 0.0012

3 -1,206.6 *** 451.9 0.008

4 -772.2 * 448.3 0.087

5

6 -114.1 443.0 0.797

7 398.4 478.7 0.406

8 697.4 476.3 0.145

9 795.9 483.2 0.101

Top 768.5 499.4 0.125

Employee's industry

Natural resources -102.8 814.7 0.900

Mining 2,194.5 1,581.8 0.167

Construction -1,017.9 ** 464.8 0.030


Wholesale and retail trade -762.0 *** 271.3 0.006


Information -214.3 554.9 0.700

Finance 134.3 395.5 0.735

Professional business -495.5 341.0 0.148

Leisure and hosptiality -454.4 290.3 0.119

Other -456.5 519.7 0.381


Military -1,339.7 * 721.0 0.065

Unclassified -2,828.0 *** 478.2 <.0001

Educ., health, soc. wel.

Omitted category

Omitted category

Omitted category

- 35 -

Table 5. Net ESI Reimbursement Regression (continued)


Note: Sample consists of employees 25 and older in the MEPS sample who are eligible to enroll in their

employer’s ESI plan, although some employees in this sample decline to enroll.

β SE β p- value

Less than 25; no other location -780.6 ** 392.1 0.048



Less than 25; other location(s) -56.9 279.2 0.839


50 to 99; other location(s) -158.0 417.0 0.705




Under 1 year -165.1 443.7 0.710

1 to 4.99 years -322.2 265.5 0.226

5 to 9.99 years -219.3 267.2 0.413

10 to 19.99 years

20 to 39.99 years 130.6 310.4 0.674

40 years or more -633.0 1,201.4 0.599


R 2 = 0.0119

Omitted category

Omitted category

Establisment size /


- 36 -

Figure 1. Mean Per Capita Personal Health Expenditures by Age, 2014

Source: Author's tabulations of Agency for Healthcare Research and Quality, Medical Expenditure

Panel Survey, Household Component files, 2014. [MEPS_2014_Share_of_Age-Group_in_Top_Quarter.xlsx]

Figure 2. Mean Private Insurance Reimbursements of Workers Who Are Covered by Private insurance, by Age Group, 2014


Panel Survey, Household Component files, 2014. [MEPS_2014_Employed-Privately-Insured_by-Age_CHARTS.xlsx]

2,070

3,110 3,200 3,3704,140

6,140

7,4807,990

9,710

1,8402,630 2,610

2,950 2,780

3,770

5,9706,060 6,250

0

2,000

4,000

6,000

8,000

10,000

25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-74Age

2014 $

All persons

All employed persons

1,500 2,350 2,190 2,380 2,290 2,650 5,050 4,790 3,3200

1,000

2,000

3,000

4,000

5,000

6,000

7,000

25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-74

Age group

2014 $

Mean expenditures

Mean + 2 SD

Mean - 2 SD

- 37 -

Figure 3. Cumulative Distribution of Gross ESI Reimbursements to Insured Workers Age 55-64, 2010-2014


Panel Survey, Household Component files, 2014. [Net and Gross Family ESI percentiles_Oct-19.xlsx]

- 38 -

Figure 4. Probability Employees in MEPS Sample Receive an Offer of ESI Coverage, by Age and Position in Wage Distribution, 2010-2014

Sample: Employees in 2010-2014 MEPS who are 25 and older. [offerESI Regr age incIntractn round53 6-7.xlsx]

Figure 5. Probability that Low-Pay Employees in MEPS Sample Receive an Offer of ESI Coverage, by Age and Sex, 2010-2014

Sample: Employees in 2010-2014 MEPS who are 25 and older and who earn a weekly wage in the

bottom 30 percent of the weekly earnings distribution. [offerESI Regr age incSexIntractn round53 6-21.xlsx]

80% 80% 81% 82% 84% 85%

68%

92% 95% 95% 94% 95% 94%

83%

40% 37% 37%44% 46% 45%

29%

0%

20%

40%

60%

80%

100%

25-29 30-34 35-39 40-54 55-59 60-64 65+

Probability of ESI offer

Middle 40% wage Top 30% wage Bottom 30% wage

43% 41% 40%

49%54%

49%

32%

37%33% 33%

40% 40% 41%

26%

0%

20%

40%

60%

80%

100%

25-29 30-34 35-39 40-54 55-59 60-64 65+

Probability of ESI offer

Men - Bottom 30% wage Women - Bottom 30% wage

- 39 -

Figure 6. Probability Employees in MEPS Sample Accept an Offer of ESI Coverage, by Age, 2010-2014

Sample: Employees in 2010-2014 MEPS who are 25 and older and who are eligible to enroll in their

employer's ESI plan. [Prob Offered,Held,Type,NetFam ESI,by Age 5-5_LATEST.xlsx]

Figure 7. Enrollment Patterns in Employee-Only and Family ESI Plans, by Age, 2010-2014

Sample: Employees in 2010-2014 MEPS who are 25 and older and are enrolled in their employer's ESI

plan. [Prob Offered,Held,Type,NetFam ESI,by Age 5-5_LATEST.xlsx]

74% 77% 77% 77% 78% 81% 83% 83% 67%50%

60%

70%

80%

90%

25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65+

Age group

Probability an employee offered ESI will enroll

Mean

UpperCLMean

LowerCLMean

77%

56%47% 44% 48% 52%

59% 64% 69%

12%

16%

13%12%

17%22%

27%29%

29%

11%

28%40% 44%

36%25%

14%6%

0%

20%

40%

60%

80%

100%

25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65+

Age group

Percent of ESI-covered employees enrolled in indicated plan

Family plan

Employee + 1

Singleemployee

- 40 -

Figure 8. Net ESI Reimbursement of Employees in MEPS Sample Enrolled in their Employer's ESI Plan, by Age, 2010-2014



Figure 9. Net ESI Reimbursement of Employees in MEPS Sample Eligible to Enroll in their Employer's ESI Plan, by Age, 2010-2014



1,110 2,630 3,140 2,770 3,240 3,010 4,500 5,540 4,710 0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65+

Age group

Net ESI Reimbursement (2014 $)

Mean

UpperCLMean

LowerCLMean

830 2,020 2,420 2,130 2,510 2,430 3,730 4,570 3,140 0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65+

Age group

Net ESI Reimbursement (2014 $)

Mean

UpperCLMean

LowerCLMean

age related health costs and job prospects of older workers · most of the experimental resume...

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