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April 17, 1996
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Abstract
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Introduction
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Overview
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Health Insurance Coverage
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COBRA Eligibility
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Measurement of COBRA Eligibility Using the April 1993
CPS
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COBRA Eligibility and Health Insurance Coverage
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Eligibility for Spouse Employer Coverage and COBRA
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Statistical Models of Health Insurance Coverage and
COBRA Eligibility: Theoretical Considerations
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Binary Logit Analysis
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Multinomial Logit Analysis
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Male-Female Differences Among the Unemployed
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Conclusions
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References
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Appendix
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Endnotes
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End Credits
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We use the 1993 April CPS to examine how health insurance coverage rates of
the unemployed differ by age, gender, marital status, education, number of
children, and length of time unemployed, and other characteristics such as
income and disability of the spouse. At the same time, we investigate the
effects of COBRA eligibility on health insurance coverage. We estimate
binary logit and multinomial logit models of the health insurance outcomes
of the unemployed as functions of COBRA and spouse's employer insurance
eligibility and worker characteristics. We find that after controlling for
these characteristics, COBRA eligibility increases the probability of health
insurance coverage among the unemployed by .075. Unemployed women have
higher health insurance coverage rates than unemployed men. However, women
are less likely to elect COBRA coverage than men, and therefore have lower
rates of coverage from their former employers than men.
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When health insurance is provided through the place of employment,
interruptions in the employment relationship disrupt health insurance
coverage. The Consolidated Omnibus Reconciliation Act (COBRA), enacted in
1986, contained provisions designed to partly remedy this problem. Most
employees are able to purchase health insurance from their former employer
for up to 18 months after their employment ends, at a premium not to exceed
102% of the group rate. If workers are reluctant to change jobs because of
health insurance considerations, COBRA can improve the efficiency of the
labor market. Perhaps more important, COBRA potentially increases the health
insurance coverage of the unemployed.
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While not universally elected, COBRA does appear to increase the health
insurance coverage of the unemployed. Using CobraServ data for 1990-91,
Flynn (1992) reports that 21% of workers who qualified, elected COBRA
continuation coverage. Others attempt to provide a more precise estimate of
the effect of COBRA on the health coverage of the unemployed by holding a
number of demographic characteristics and other factors constant. Using data
from the Survey of Income and Program Participation (SIPP) and holding
constant age, education, and months since job loss, Klerman and Rahman
(1992) find evidence of a positive effect of COBRA legislation on the health
insurance coverage of the non-employed.
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In an important recent study, Gruber and Madrian (1995a) examine health
insurance coverage among the non-employed, using longitudinal data from the
SIPP for 1983 to 1989 for men aged 25-54. They find that the likelihood of
having health insurance drops by approximately 20 percent after a worker is
separated from his job. However, they find that state and federal health
insurance continuation mandates such as COBRA increase the likelihood of
coverage among the non-employed by 6.7 percent. They also find that the
estimated effect of continuation mandates varies by the duration of the
spell of unemployment. The effect of continuation mandates is insignificant
for those with completed durations of one year or less. However, the effects
are substantially larger for those with durations of more than one year,
presumably the group with the greatest need. For instance, for those with
unemployment durations of more than one year, a continuation mandate of one
year increases the likelihood of insurance coverage by 9.4 percent (Gruber
and Madrian, 1995a, p.23).
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COBRA type legislation also appears to have the intended effect on labor
market efficiency. Gruber and Madrian (1995a) find that health insurance
continuation mandates increase turnover, and are associated with significant
wage gains in subsequent jobs. Thus, these mandates appear to reduce job
lock and to lead to more productive job search by individuals seeking new
jobs. Finally, COBRA type mandates influence workers decisions when to
retire. Using SIPP and the March Current Population Survey (CPS) data,
Gruber and Madrian (1995b) and Karoly and Rogowski (1994) find that health
insurance continuation laws increase retirement probabilities among older
workers.
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This previous work appears to show that, on average, COBRA legislation has
the intended effects on health insurance coverage and labor market
transitions. However, there are important differences in health insurance
coverage across the unemployed population. These differences may in part be
due to different responses to the availability of COBRA. The U.S. Department
of Labor (1994, p. F-27) uses the April 1993 Current Population Survey (CPS)
to provide some preliminary cross-tabular analysis of the health insurance
coverage of unemployed workers by various characteristics. Older workers and
higher income workers are more likely to have coverage through their former
employer.
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Perhaps the most interesting differences are by gender. According to the
1993 April CPS, the health insurance coverage rate of unemployed females
exceeded that of unemployed males (48.5% vs. 36.9%). Among those covered,
however, 36.2% of the males were covered by a former employer compared with
only 18.4% of the females. It is important to disentangle the reasons behind
this gender difference in health insurance coverage among the unemployed. In
this study, we use the April 1993 CPS to examine the reasons for this gender
difference in coverage rates by the former employer. Part of the difference
could be due to coverage under spouse's plans. Other studies have not
examined the effect of the availability of spouse coverage on COBRA
election. Alternatively, there may be higher coverage rates for women from
public sources such as Medicaid. Unemployed women may have
disproportionately been in jobs before they were unemployed that did not
have health insurance and thus not be eligible to take advantage of COBRA.
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We also examine differences in health insurance coverage rates and use of
COBRA by other characteristics such as age, income, and length of
unemployment. In addition to cross-tabular analysis of coverage rates and
COBRA eligibility among the unemployed, we estimate logit models of health
insurance coverage in order to provide a more precise estimate of the effect
of COBRA on coverage.
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This project offers an improvement over the existing literature in several
ways. First, the April 1993 CPS is the most current available data for
studying health insurance of the unemployed. Second, previous studies have
been limited in the sample used, the range of determinants of health
insurance coverage examined, or in the health insurance outcomes studied.
For example, Flynn (1992) is only able to examine COBRA coverage for ages
40-64 using the August 1988 CPS. The April 1993 CPS questions cover ages
25-64. Klerman and Rahman (1992) estimate a model of coverage for the
unemployed that includes measures of length of unemployment, age, education,
and COBRA eligibility. We examine these differences along with the effects
of a much broader range of characteristics than those employed by Klerman
and Rahman (1992). Gruber and Madrian (1995a) consider only males in their
analysis of health insurance of the unemployed. However, as already
mentioned, there are important gender differences in health insurance
coverage among the unemployed. Finally, unlike previous studies, the use of
the 1993 April CPS data allow us to consider multiple categories of health
insurance coverage of the unemployed including coverage by a former
employer, coverage through a spouse's employer, and other types of coverage.
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Health Insurance Coverage - The April Current Population Survey (CPS)
public use tapes contain the Employee Benefit Supplement as well as the
April-March CPS matched records for rotations 2, 3, 6, and 7. Those
individuals between the ages of 25 and 64 who were not employed but had
previously been employed and were actively looking for work in the last 4
weeks (the experienced unemployed) were asked questions about their pension
and health coverage. From the April 1993 CPS, we calculate that 41.62% of
the experienced unemployed have some form of health insurance coverage
(a_s84=1).(1)
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The CPS also contains demographic and other characteristics of the
unemployed. For example, the CPS contains information on a worker's gender,
age, education, race, number of children, income, amount of time unemployed,
and reason unemployed, in addition to many other characteristics.(2) In
Table 1, we show the mean proportions of the unemployed that fall into
various demographic and unemployment categories by health insurance status.
The insured sample has a higher proportion who are female than does the
sample without insurance. Similarly, the insured sample has a higher
proportion who are over 50 years old, a higher proportion who are white, a
higher proportion who have greater than a high school education, a higher
proportion who have one or more children, a higher proportion who are
married, and a higher proportion who have income more than $25,000 than does
the uninsured sample. The differences by income and marital status appear
particularly large.
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Table 1 - Means by Health Insurance Status
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Variable
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Insured
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Not Insured
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Definition
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female
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.4760664
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.3608687
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=1 if gender is female, =0 otherwise
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over50
|
.2408537
|
.1588362
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=1 if age over 50, =0 otherwise
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nonwhite
|
.1857875
|
.2814059
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=1 if race is black or other, =0 otherwise
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gthseduc
|
.5131650
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.3366575
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=1 if education > high school, =0 otherwise
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children
|
.8807072
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.7242192
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=1 if have 1 or more children, =0 otherwise
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married
|
.7298426
|
.3911447
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=1 if married, =0 otherwise
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lowinc
|
.3706291
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.8162309
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=1 if income < 25K, =0 otherwise
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look26wk
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.2412858
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.2584080
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=1 if unemployed > 26 weeks, =0 otherwise
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quitjob
|
.0992253
|
.1143216
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=1 if quit job, =0 otherwise
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Source: 1993 April Current Population Survey
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COBRA Eligibility - In 1987, the Consolidated Omnibus Budget
Reconciliation Act (COBRA) went into effect, which contained provisions
allowing certain former employees, spouses and dependent children to buy
temporary health insurance at group rates. "Employees" eligible
for COBRA can be full-time or part-time workers, agents, independent
contractors, directors, and certain self-employed individuals eligible to
participate in a group plan. A qualified employee is anyone who was covered
by a group health plan the day before a "qualifying event." Such
events include voluntary or involuntary termination of employment for
reasons other than gross misconduct, or a reduction in the number of hours
worked. Spouses and dependent children qualify for coverage by demonstrating
that either of these events were applicable to the covered employee (who was
either their spouse or parent), because of death or divorce of the covered
employee, or in the case of dependent children, if they lose dependent child
status under the plan's rules. If a covered employee becomes eligible for
Medicare benefits, their spouse and/or dependents qualify for COBRA
coverage. After a qualifying event, beneficiaries have up to 60 days to
elect COBRA coverage (U.S. Department of Labor, 1990, pp. 4-5, 9).
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Employees can receive coverage for up to 18 months at rates of up to 102
percent of the cost of the plan to similarly situated individuals who have
not incurred a qualifying event. This coverage can be extended for up to 11
more months if a qualified beneficiary is determined under Title II or XVI
of the Social Security Act to have been disabled at the time of termination
or reduction in hours. The cost for the additional 11 months of coverage can
be increased to 150 percent of the plan's cost. Spouses and dependent
children are also eligible for 18 months of coverage if the coverage
employee terminates employment or suffers a reduction in hours. Spouses and
dependent children can obtain up to 36 months of coverage if they become
eligible through the death or divorce of the covered employee, or if the
child loses his or her dependent status under the plan (U.S. Department of
Labor, 1990, pp. 6-7,15).
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Certain employers are exempt from providing COBRA benefits. The law
generally covers group health plans of employers with 20 or more employees
during the previous year. The law covers plans provided in the private
sector and by state and local governments. The law does not apply to
Federally sponsored health plans or the plans of certain church-related
organizations (U.S. Department of Labor, 1990, p. 2)
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Measurement of COBRA Eligibility Using the April 1993 CPS - While the
April 1993 CPS does not directly ask about COBRA eligibility, the series of
questions on health insurance coverage allow us to construct a measure of
COBRA eligibility for the unemployed. The most direct measure uses questions
a_s84, a_s85, and a_s88.(3) All of the experienced unemployed are asked this
series of questions. COBRA eligible could then be estimated to be those who
indicate that they have coverage from a previous employer (a_s84=1 and
a_s85=1) or that they had coverage on their last job (a_s88=1). This
potential measure indicates that 45.13% of workers are eligible for COBRA
coverage.
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This is an upper bound measure of the number of COBRA eligibles among the
experienced unemployed for a number of reasons. First, some may have been
unemployed for more than 18 months and thus no longer eligible for COBRA.(4)
Firms with less than 20 employees in the last year are not subject to COBRA.
COBRA also does not apply to plans sponsored by the federal government and
certain church-related organizations. Therefore, we adjust the COBRA
eligibility variable in the following way. Those individuals who are in an
unemployment spell longer than 78 weeks (a_wkslk>78) are coded as COBRA
ineligible (COBRA=0), as are those whose last job was in the federal
government (a_clswkr=2), or in a religious organization (a_ind=880). The
number of employees is not available as part of the April 1993 CPS for the
unemployed (it is available for the currently employed). However, as part of
the April-March CPS match, we have data for most of the sample on the number
of workers of the employer for the longest job held in 1992. We code as
COBRA ineligible those who worked for employers with less than 25 employees
in the longest job held last year (noemp=1 or 2). This revised measure
indicates that 33.59% of the unemployed are eligible for COBRA. We use this
measure of COBRA eligibility for the remainder of the paper.
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What are the characteristics of the COBRA eligible among the unemployed? In
Table 2, we show the means of various characteristics among those eligible
and not eligible for COBRA. A higher proportion of the eligible sample are
female than are the sample of those not eligible for COBRA. Similarly, the
eligible sample has a higher proportion who are over 50 years old, a higher
proportion who have greater than a high school education, a higher
proportion who have one or more children, a higher proportion who are
married, a higher proportion who have annual income greater than $25,000, a
higher proportion who have been unemployed less than 26 weeks, and a higher
proportion who quit their job than the sample of those who are ineligible
for COBRA. There are virtually no differences in the race makeup of the
eligible and ineligible groups.
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Table 2 -- Means by COBRA Eligibility |
Variable
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Eligible
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Not Eligible
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Definition
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female
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.4342093
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.3959584
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=1 if gender is female, =0 otherwise
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over50
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.2308315
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.1738128
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=1 if age over 50, =0 otherwise
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nonwhite
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.2407589
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.2420465
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=1 if race is black or other, =0 otherwise
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gthseduc
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.5024079
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.3634186
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=1 if education > high school, =0 otherwise
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children
|
.8217148
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.7729649
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=1 if have 1 or more children, =0 otherwise
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married
|
.5419932
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.5270878
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=1 if married, =0 otherwise
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lowinc
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.4885505
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.7027527
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=1 if income < 25K, =0 otherwise
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look26wk
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.2244352
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.2648648
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=1 if unemployed > 26 weeks, =0 otherwise
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quitjob
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.1388566
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.0924484
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=1 if quit job, =0 otherwise
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Source: 1993 April Current Population Survey
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COBRA Eligibility and Health Insurance Coverage - How many of the
current unemployed take advantage of COBRA eligibility? We answer this
question in Table 3 by showing various cross tabulations of COBRA
eligibility and health insurance coverage. Table 3A shows that 50.35% of
those who were eligible to elect COBRA at the beginning of their
unemployment are currently insured. This is substantially higher than the
37.20% coverage rate among those not eligible for COBRA. In Table 3B we see
that 40.64% of those with health insurance were eligible for COBRA compared
to 28.57% of those without health insurance. In order to see how many of
those eligible actually elected COBRA, Table 3C shows the breakdown by type
of coverage into four categories: 1) former employer coverage (a_s84=1 &
a_s85=1), 2) spouse's employer coverage (a_s84=1 & a_s85=2 & spouse
a_s62=1), 3) other coverage (a_s84=1 & a_s85=2 & spouse a_s62!=1),
and 4) no coverage (a_s84=2).
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Table 3A - Health Insurance Status of Individuals Eligible and Not Eligible for COBRA |
Health Insurance Coverage
|
COBRA Eligibility
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No
|
Yes
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Total
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No
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62.80
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49.65
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58.38
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Yes
|
37.20
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50.35
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41.62
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Total
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100.00
|
100.00
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100.00
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Table 3B - COBRA Eligibility Status for Individuals with and without Health Insurance |
Health Insurance Coverage
|
COBRA Eligibility
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No
|
YES
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Total
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No
|
71.42
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28.57
|
100.00
|
Yes
|
59.36
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40.64
|
100.00
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Total
|
66.41
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33.59
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100.00
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Table 3C - Coverage Categories by COBRA Eligibility |
Health Insurance Coverage
|
COBRA Eligibility
|
No
|
Yes
|
Total
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Employer
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4.96
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24.57
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11.55
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Spouse
|
15.41
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12.59
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14.46
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Other
|
16.83
|
13.18
|
15.60
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None
|
62.80
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49.65
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58.38
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Total
|
100.00
|
100.00
|
100.00
|
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Among eligibles, 24.57% have coverage from a former employer, presumably
through COBRA. Even this estimate is slightly higher than the 21% coverage
election rate reported by Flynn (1992). However, we are not calculating a
rate for a sample of new "qualifying events." Instead, we have a
sample of the stock of unemployed at a point in time. From a policy point of
view it may be the stock of the unemployed that is more relevant. It is not
clear whether the a stock of unemployed persons at a point in time or a flow
of new "qualifying events" would produce a higher coverage rate.
The coverage rate could be higher in the flow sample because over time those
in the stock sample may drop coverage due to the cost of maintaining
coverage or as individuals begin to obtain spouse coverage. On the other
hand, Gruber and Madrian (1995a) results suggest that the effect of
continuation mandates are highest for those who are in longer spells of
unemployment. In any event, it may be more important to know how much COBRA
is being used among unemployed at a point in time than among the newly
unemployed.
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We next examine how COBRA eligibility and health insurance coverage varies
across the unemployed. In Table 4, we show health insurance and COBRA
eligibility cross tabulations by gender, age category, education category,
number of children, marital status, race, income, time unemployed, and
reason unemployed.
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Table 4A - By Gender
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-> female=
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Male
|
Health Insurance Category
|
COBRA Eligibility
|
No
|
Yes
|
Total
|
Employer
|
6.67
|
27.47
|
13.35
|
Spouse
|
12.06
|
8.23
|
10.83
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Other
|
13.31
|
11.42
|
12.70
|
None
|
67.97
|
52.89
|
63.12
|
Total
|
100.00
|
100.00
|
100.00
|
->female=
|
Female
|
Insurance Category
|
COBRA Eligibility
|
No
|
Yes
|
Total
|
Employer
|
2.35
|
20.80
|
8.94
|
Spouse
|
20.53
|
18.27
|
19.72
|
Other
|
22.20
|
15.49
|
19.80
|
None
|
54.92
|
45.44
|
51.54
|
Total
|
100.00
|
100.00
|
100.00
|
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Table 4B - By Age |
->Over 50
|
<50 Yrs
|
Health Insurance Category
|
COBRA Eligibility
|
No
|
Yes
|
Total
|
Employer
|
4.07
|
20.80
|
9.43
|
Spouse
|
15.48
|
12.78
|
14.61
|
Other
|
15.71
|
13.82
|
15.11
|
None
|
64.74
|
52.60
|
60.85
|
Total
|
100.00
|
100.00
|
100.00
|
-> over50=
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50+ Yrs
|
Health Insurance Category
|
COBRA Eligibility
|
No
|
Yes
|
Total
|
Employer
|
9.17
|
37.14
|
20.41
|
Spouse
|
15.12
|
11.96
|
13.85
|
Other
|
22.13
|
11.06
|
17.68
|
None
|
53.38
|
39.84
|
48.06
|
Total
|
100.00
|
100.00
|
100.00
|
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Table 4C - By Race |
-> nonwhite=
|
White
|
Health Insurance Category
|
COBRA Eligibility
|
No
|
Yes
|
Total
|
Employer
|
5.44
|
26.79
|
12.62
|
Spouse
|
17.59
|
13.67
|
16.27
|
Other
|
16.46
|
14.46
|
15.79
|
None
|
60.51
|
45.09
|
55.32
|
Total
|
100.00
|
100.00
|
100.00
|
-> nonwhite=
|
Nonwhite
|
Health Insurance Category
|
COBRA Eligibility
|
No
|
Yes
|
Total
|
Employer
|
3.45
|
17.59
|
8.18
|
Spouse
|
8.60
|
9.19
|
8.80
|
Other
|
17.97
|
9.16
|
15.02
|
None
|
69.98
|
64.06
|
68.00
|
Total
|
100.00
|
100.00
|
100.00
|
|
Table 4D - Education Level |
->gthseduc=
|
<=HS
|
Health Insurance Category
|
COBRA Eligibility
|
No
|
Yes
|
Total
|
Employer
|
3.44
|
21.69
|
8.61
|
Spouse
|
13.51
|
10.50
|
12.66
|
Other
|
14.98
|
8.28
|
13.08
|
None
|
68.07
|
59.54
|
65.65
|
Total
|
100.00
|
100.00
|
100.00
|
-> gthseduc=
|
>HS
|
Health Insurance Category
|
COBRA Eligibility
|
No
|
Yes
|
Total
|
Employer
|
7.62
|
27.43
|
15.77
|
Spouse
|
18.75
|
14.66
|
17.07
|
Other
|
20.06
|
18.04
|
19.23
|
None
|
53.57
|
39.87
|
47.93
|
Total
|
100.00
|
100.00
|
100.00
|
|
Table 4E - By Number of Children |
-> children=
|
No Children
|
Health Insurance Category
|
COBRA Eligibility
|
No
|
Yes
|
Total
|
Employer
|
6.68
|
22.87
|
11.28
|
Other
|
11.67
|
13.84
|
12.29
|
None
|
81.65
|
63.29
|
76.43
|
Total
|
100.00
|
100.00
|
100.00
|
-> children=
|
1+ Children
|
Health Insurance Category
|
COBRA Eligibility
|
No
|
Yes
|
Total
|
Employer
|
4.45
|
24.94
|
11.62
|
Spouse
|
19.94
|
15.32
|
18.32
|
Other
|
18.34
|
13.04
|
16.49
|
None
|
57.26
|
46.70
|
53.57
|
Total
|
100.00
|
100.00
|
100.00
|
|
Table 4F - By Marital Status |
-> married=
|
Not Married
|
Health Insurance Category
|
COBRA Eligibility
|
No
|
Yes
|
Total
|
Employer
|
3.32
|
20.83
|
9.08
|
Other
|
16.02
|
12.77
|
14.95
|
None
|
80.66
|
66.40
|
75.97
|
Total
|
100.00
|
100.00
|
100.00
|
-> married=
|
Married
|
Health Insurance Category
|
COBRA Eligibility
|
No
|
Yes
|
Total
|
Employer
|
6.43
|
27.74
|
13.72
|
Spouse
|
29.24
|
23.23
|
27.18
|
Other
|
17.56
|
13.53
|
16.18
|
None
|
46.78
|
35.50
|
42.92
|
Total |
100.00 |
100.00 |
100.00 |
|
Table 4G - By Income Level |
-> lowinc=
|
>25K
|
Health Insurance Category
|
COBRA Eligibility
|
No
|
Yes
|
Total
|
Employer
|
9.76
|
34.15
|
21.11
|
Spouse
|
44.38
|
22.62
|
34.25
|
Other
|
18.85
|
11.81
|
15.57
|
None
|
27.01
|
31.41
|
29.06
|
Total
|
100.00
|
100.00
|
100.00
|
-> lowinc=
|
<=25K
|
Health Insurance Category
|
COBRA Eligibility
|
No
|
Yes
|
Total
|
Employer
|
2.93
|
14.55
|
5.95
|
Spouse
|
3.16
|
2.09
|
2.88
|
Other
|
15.97
|
14.62
|
15.62
|
None
|
77.94
|
68.75
|
75.55
|
Total
|
100.00
|
100.00
|
100.00
|
|
Table 4H - By Number of Weeks Unemployed |
-> look26wk=
|
=26 Wks
|
Health Insurance Category
|
COBRA Eligibility
|
No
|
Yes
|
Total
|
Employer
|
4.10
|
25.60
|
11.59
|
Spouse
|
15.52
|
11.51
|
14.13
|
Other
|
17.56
|
14.40
|
16.46
|
None
|
62.82
|
48.48
|
57.83
|
Total
|
100.00
|
100.00
|
100.00
|
-> lookwk
|
> 26 Wks
|
Health Insurance Category
|
COBRA Eligibility
|
No
|
Yes
|
Total
|
Employer
|
7.33
|
21.01
|
11.44
|
Spouse
|
15.11
|
16.31
|
15.47
|
Other
|
14.80
|
8.97
|
13.05
|
None
|
62.76
|
53.70
|
60.04
|
Total
|
100.00
|
100.00
|
100.00
|
|
Table 4I - By Whether the Worker Quit Job |
-> quitjob=
|
Other
|
Health Insurance Category
|
COBRA Eligibility
|
No
|
Yes
|
Total
|
Employer
|
5.46
|
25.06
|
11.82
|
Spouse
|
15.89
|
11.66
|
14.52
|
Other
|
16.96
|
13.04
|
15.69
|
None
|
61.69
|
50.24
|
57.97
|
Total
|
100.00
|
100.00
|
100.00
|
-> quitjob=
|
Quit
|
Health Insurance Category
|
COBRA Eligibility
|
No
|
Yes
|
Total
|
Employer
|
0.00
|
21.52
|
9.29
|
Spouse
|
10.73
|
18.36
|
14.03
|
Other
|
15.54
|
14.07
|
14.90
|
None
|
73.74
|
46.05
|
61.78
|
Total
|
100.00
|
100.00
|
100.00
|
Source: 1993 April Current Population Survey
|
|
There exist several interesting differences in health insurance coverage and
COBRA usage by worker characteristics. Males are less likely to be covered
by health insurance, but are more likely to elect COBRA than are females.
Females are more likely to be covered through the spouse or by other
coverage than are males. Among those eligible for COBRA, workers over 50
years old, who are white, have more than a high school education, have one
or more children, are married, or earn more than $25,000 are more likely to
elect COBRA coverage. Whites, those with more than a high school education,
and those with more than $25,000 income are more likely to have spouse and
other forms of coverage as well.
|
Eligibility for Spouse Employer Coverage and COBRA - Clearly spouse coverage
comes into play when individuals decide whether or not to exercise their
COBRA rights. However, in Tables 5 and 6, we only observe health insurance
outcomes, one of which is spouse coverage. What we would like to observe is
eligibility for spouse coverage and not the outcome of spouse coverage. We
construct an eligibility for spouse coverage variable (a_s61=1 or 3) and use
it in addition to COBRA eligibility in our cross tabulations. We construct
an eligibility variable with the categories: 1) COBRA eligibility, 2)
eligible for coverage through spouse's employer, 3) eligible for both COBRA
and spouse coverage, and 4) eligible for neither COBRA nor spouse coverage.
|
Table 5 shows eligibility for COBRA and spouse coverage for the entire
sample of unemployed and for various demographic groups.
|
|
|
Table 5A - Full Sample
|
COBRA and Spouse Insurance Eligibility
|
Percent
|
Cum.
|
COBRA
|
25.45
|
25.45
|
Spouse
|
14.64
|
40.09
|
Both
|
8.15
|
48.24
|
Neither
|
51.76
|
100.00
|
|
Table 5B - By Gender |
COBRA and Spouse Insurance Eligibility
|
Female
|
Male
|
Female
|
Total
|
COBRA
|
24.68
|
26.55
|
25.45
|
Spouse
|
13.20
|
16.73
|
14.64
|
Both
|
7.47
|
9.13
|
8.15
|
Neither
|
54.65
|
47.59
|
51.76
|
Total
|
100.00
|
100.00
|
100.00
|
|
Table 5C - By Age |
COBRA and Spouse Insurance Eligibility
|
50 Years Old & Over
|
<50 Yrs
|
50+ Yrs
|
Total
|
COBRA
|
24.07
|
31.19
|
25.45
|
Spouse
|
15.25
|
12.08
|
14.64
|
Both
|
7.95
|
8.99
|
8.15
|
Neither
|
52.73
|
47.74
|
51.76
|
Total
|
100.00
|
100.00
|
100.00
|
|
Table 5D - By Race |
COBRA and Spouse Insurance Eligibility
|
Nonwhite
|
White
|
Nonwhite
|
Total
|
COBRA
|
24.58
|
28.17
|
25.45
|
Spouse
|
17.00
|
7.23
|
14.64
|
Both
|
9.05
|
5.31
|
8.15
|
Neither
|
49.36
|
59.30
|
51.76
|
Total
|
100.00
|
100.00
|
100.00
|
|
Table 5E - By Education Level |
COBRA and Spouse Insurance Eligibility
|
More than High School
|
<=HS
|
>HS
|
Total
|
COBRA
|
21.97
|
30.44
|
25.45
|
Spouse
|
13.59
|
16.15
|
14.64
|
Both
|
6.37
|
10.71
|
8.15
|
Neither
|
58.07
|
42.70
|
51.76
|
Total
|
100.00
|
100.00
|
100.00
|
|
Table 5F - By Income Level |
COBRA and Spouse Insurance Eligibility
|
Low Income
|
>25K
|
<=25K
|
Total
|
COBRA
|
27.01
|
24.53
|
25.45
|
Spouse
|
31.42
|
4.82
|
14.64
|
Both
|
19.52
|
1.49
|
8.15
|
Neither
|
22.04
|
69.16
|
51.76
|
Total
|
100.00
|
100.00
|
100.00
|
Source: 1993 April Current Population Survey
|
|
A significant number of the unemployed are eligible for and take advantage
of coverage through their spouse's employer. In Table 3C we saw that 14.46%
of the unemployed opted for spouse's employer coverage. In Table 5A we find
that 14.64% of the unemployed are eligible for spouse coverage (and not
COBRA), and 8.15% are eligible for both COBRA and spouse coverage. Thus,
over 60% (14.46/(14.64+8.15)) of those eligible for spouse coverage use it,
compared to the 24.57% of those eligible for COBRA coverage who elect it.
Coverage through one's spouse is clearly an important way for the unemployed
to obtain health insurance.
|
Eligibility for spouse coverage varies across the unemployed. In Table 5, we
see that females and those with more than a high school education are
somewhat more likely to be eligible for spouse coverage than males and those
with less than a high school education. The differences by race and income
are striking. Nonwhites are much less likely to be eligible for spouse
coverage than are whites, while very few of the unemployed with incomes less
than $25,000 are eligible for coverage through an employed spouse. The
income variable used in this analysis refers to total husband and wife
income in 1992. Husbands and wives with combined incomes less than $25,000
in 1992 would have had relatively low income full-time jobs or a series of
part-time jobs. These types of jobs are unlikely to have offered health
insurance as a fringe benefit and therefore these jobs are an unlikely
source of coverage for an unemployed spouse.
|
In order to illustrate the differences in the effects of eligibility for
COBRA and eligibility for spouse coverage, we present cross tabulations of
eligibility and health insurance outcomes in Table 6.
|
|
|
Table 6A - Full Sample
|
Health Insurance Category
|
COBRA and Spouse Insurance Eligibility
|
COBRA
|
Spouse
|
Both
|
Neither
|
Total
|
Employer
|
23.92
|
4.67
|
26.62
|
5.04
|
11.55
|
Spouse
|
0.00
|
69.90
|
51.90
|
0.00
|
14.46
|
Other
|
16.26
|
5.95
|
3.58
|
19.90
|
15.60
|
None
|
59.83
|
19.47
|
17.90
|
75.06
|
58.38
|
Total
|
100.00
|
100.00
|
100.00
|
100.00
|
100.00
|
|
Table 6B - By Gender |
-> female=
|
Male
|
Health Insurance Category
|
COBRA and Spouse Insurance Eligibility
|
COBRA
|
Spouse
|
Both
|
Neither
|
Total
|
Employer
|
26.09
|
8.76
|
32.01
|
6.16
|
13.35
|
Spouse
|
0.00
|
61.99
|
35.43
|
0.00
|
10.83
|
Other
|
12.87
|
6.63
|
6.62
|
14.92
|
12.70
|
None
|
61.04
|
22.63
|
25.95
|
78.92
|
63.12
|
Total
|
100.00
|
100.00
|
100.00
|
100.00
|
100.00
|
-> female=
|
Female
|
Health Insurance Category
|
COBRA and Spouse Insurance Eligibility
|
COBRA
|
Spouse
|
Both
|
Neither
|
Total
|
Employer
|
20.99
|
0.00
|
20.26
|
3.18
|
8.94
|
Spouse
|
0.00
|
78.94
|
71.37
|
0.00
|
19.72
|
Other
|
20.82
|
5.19
|
0.00
|
28.18
|
19.80
|
None
|
58.19
|
15.88
|
8.38
|
68.65
|
51.54
|
Total
|
100.00
|
100.00
|
100.00
|
100.00
|
100.00
|
|
Table 6C - By Age |
-> over50=
|
>50 Yrs
|
Health Insurance Category
|
COBRA and Spouse Insurance Eligibility
|
COBRA
|
Spouse
|
Both
|
Neither
|
Total
|
Employer
|
19.07
|
4.18
|
26.04
|
4.04
|
9.43
|
Spouse
|
0.00
|
68.97
|
51.47
|
0.00
|
14.61
|
Other
|
17.33
|
5.10
|
3.20
|
18.78
|
15.11
|
None
|
63.60
|
21.76
|
19.30
|
77.18
|
60.85
|
Total
|
100.00
|
100.00
|
100.00
|
100.00
|
100.00
|
-> over50=
|
50+ Yrs
|
Health Insurance Category
|
COBRA and Spouse Insurance Eligibility
|
COBRA
|
Spouse
|
Both
|
Neither
|
Total
|
Employer
|
39.54
|
7.25
|
28.80
|
9.66
|
20.41
|
Spouse
|
0.00
|
74.87
|
53.46
|
0.00
|
13.85
|
Other
|
12.80
|
10.48
|
5.02
|
25.08
|
17.68
|
None
|
47.65
|
7.40
|
12.72
|
65.27
|
48.06
|
Total
|
100.00
|
100.00
|
100.00
|
100.00
|
100.00
|
|
Table 6D - By Race |
-> nonwhite=
|
White
|
Health Insurance Category
|
COBRA and Spouse Insurance Eligibility
|
COBRA
|
Spouse
|
Both
|
Neither
|
Total
|
Employer
|
26.12
|
5.30
|
28.61
|
5.49
|
12.62
|
Spouse
|
0.00
|
68.65
|
50.77
|
0.00
|
16.27
|
Other
|
19.08
|
5.73
|
1.93
|
20.16
|
15.17
|
None
|
54.81
|
20.32
|
18.70
|
74.35
|
55.32
|
Total
|
100.00
|
100.00
|
100.00
|
100.00
|
100.00
|
-> nonwhite=
|
Nonwhite
|
Health Insurance Category
|
COBRA and Spouse Insurance Eligibility
|
COBRA
|
Spouse
|
Both
|
Neither
|
Total
|
Employer
|
17.89
|
0.00
|
16.00
|
3.87
|
8.18
|
Spouse
|
0.00
|
79.15
|
57.93
|
0.00
|
8.80
|
Other
|
8.54
|
7.59
|
12.46
|
19.23
|
15.02
|
None
|
73.570
|
13.26
|
13.61
|
76.90
|
68.00
|
Total
|
100.00
|
100.00
|
100.00
|
100.00
|
100.00
|
|
Table 6E - By Education Level |
-> gthseduc=
|
<=HS
|
Health Insurance Category
|
COBRA and Spouse Insurance Eligibility
|
COBRA
|
Spouse
|
Both
|
Neither
|
Total
|
Employer
|
17.78
|
2.06
|
35.19
|
3.76
|
8.61
|
Spouse
|
0.00
|
71.21
|
46.71
|
0.00
|
12.66
|
Other
|
10.68
|
5.04
|
0.00
|
17.31
|
13.08
|
None
|
71.55
|
21.69
|
18.10
|
78.93
|
65.65
|
Total
|
100.00
|
100.00
|
100.00
|
100.00
|
100.00
|
-> gthseduc=
|
<HS
|
Health Insurance Category
|
COBRA and Spouse Insurance Eligibility
|
COBRA
|
Spouse
|
Both
|
Neither
|
Total
|
Employer
|
30.29
|
7.82
|
19.30
|
7.55
|
15.77
|
Spouse
|
0.00
|
68.32
|
56.33
|
0.00
|
17.07
|
Other
|
22.05
|
7.06
|
6.65
|
24.98
|
19.23
|
None
|
47.66
|
16.79
|
17.72
|
67.47
|
47.93
|
Total
|
100.00
|
100.00
|
100.00
|
100.00
|
100.00
|
|
Table 6F - By Income Level |
-> lowinc=
|
25K
|
Health Insurance Category
|
COBRA and Spouse Insurance Eligibility
|
COBRA
|
Spouse
|
Both
|
Neither
|
Total
|
Employer
|
41.64
|
5.89
|
23.79
|
15.28
|
21.11
|
Spouse
|
0.00
|
75.51
|
53.92
|
0.00
|
34.25
|
Other
|
17.43
|
5.70
|
4.05
|
37.59
|
15.57
|
None
|
40.93
|
12.90
|
18.24
|
47.13
|
29.06
|
Total
|
100.00
|
100.00
|
100.00
|
100.00
|
100.00
|
-> lowinc=
|
>=25K
|
Health Insurance Category
|
COBRA and Spouse Insurance Eligibility
|
COBRA
|
Spouse
|
Both
|
Neither
|
Total
|
Employer
|
12.49
|
0.00
|
48.37
|
3.13
|
5.95
|
Spouse
|
0.00
|
48.52
|
36.37
|
0.00
|
2.88
|
Other
|
15.51
|
6.92
|
0.00
|
16.60
|
15.62
|
None
|
72.00
|
44.56
|
15.26
|
80.27
|
75.55
|
Total
|
100.00
|
100.00
|
100.00
|
100.00
|
100.00
|
Source: 1993 April Current Population Survey
|
|
Table 6A shows the eligibility status and health insurance outcomes for the
full sample of the unemployed. 23.92% of those only eligible for COBRA are
observed to have coverage from their former employers while 69.90% of those
only eligible for spouse coverage elect to use it. Those eligible for both
types of coverage are more likely to elect spouse coverage (51.90% vs.
26.62%). Spouse coverage is more likely to be used among the unemployed than
COBRA coverage, perhaps because of lower out-of-pocket expense or because it
is potentially permanent coverage (as long as the spouse is employed) while
COBRA is not. The net result is that those with eligibility for spouse
coverage are much less likely to be uninsured than those with eligibility
for COBRA. However, those eligible only for COBRA (and not spouse coverage)
are much more likely to be insured than those eligible for neither spouse
nor COBRA coverage (40.18% vs. 24.94%).(5)
|
Gender differences are shown in Table 6B. Among those eligible only for
COBRA coverage, the difference in the COBRA election rate is not large.
However, women are much more likely than men to opt for spouse coverage if
they are eligible for it. When eligible for both spouse coverage and COBRA,
men are almost equally likely to elect COBRA as spouse coverage while women
are much more likely to opt for spouse coverage. Among those eligible for
neither COBRA nor spouse coverage, women are more likely to obtain other
coverage, perhaps because they are more likely to be eligible for public
programs such as Medicaid. However, as might be expected, this group has the
highest rate of non-insurance among the unemployed.
|
Those over 50 are more likely to elect COBRA and use spouse coverage and
have higher overall coverage rates. However, when eligible for both spouse
and COBRA coverage, those under 50 are more likely to elect COBRA coverage
than those over 50 (Table 6C). Similar patterns are observed for those with
a high school education or less relative to those with more than high
school, and for those with incomes less than $25,000 relative to those with
incomes more than $25,000 (Tables 8E and F). Perhaps those under 50, with a
high school education or less, or with incomes less than $25,000 are more
likely to elect COBRA because spouse coverage is less likely to be a
permanent alternative for them. Whites are more likely to elect COBRA and be
covered than nonwhites (Table 8D). However, the rate of non-coverage is
similar for whites and nonwhites who are not eligible for COBRA or spouse
coverage.
|
The tabulations shown in Tables 1 through 8 provide us with an initial
determination of the effects of COBRA eligibility on health insurance
coverage. However, they do not hold other variables constant that could be
affecting health insurance coverage of the unemployed. In order to provide
more precise estimates of the COBRA and spouse effects, we turn to the
estimation of binary and multinomial logit models of health insurance
coverage in the next section.
|
Statistical Models of Health Insurance Coverage and COBRA Eligibility:
Theoretical Considerations - In this section, we construct statistical
models to estimate the effects of COBRA eligibility on the health insurance
coverage of the uninsured. In the previous section, we saw that those
eligible for COBRA were more likely to be covered by health insurance.
However, we were only able to partially control for other factors that may
influence whether or not a given unemployed worker is covered. Here we
attempt to parameterize a COBRA effect within a multivariate framework.
|
In many ways the observed health insurance outcome of the unemployed can be
thought of as a standard consumer choice problem. The unemployed worker
chooses whether to purchase health insurance, given his or her income,
prices, and his or her tastes. The problem is complicated somewhat by the
fact that because we are dealing with health, outcomes are uncertain, and an
expected utility framework is usually employed. Within an expected utility
framework, the unemployed will have a higher demand for health insurance if
the expected loss from the event being insured against is higher, holding
constant the probability of the event. The expected loss could be monetary
or non-monetary. It could be lost earnings if the individual is unable to
work, although presumably for an unemployed worker this is less of a problem
than for an currently employed worker. Or the expected loss could be the
loss of well-being associated with bad health and untreated health problems.
Similarly, for relatively low probability events, the unemployed worker's
demand for insurance increases as the probability increases for an event
with a loss of a given magnitude. Thus, we need to consider prices, income,
tastes, expected loss, and probability of a loss when considering whether an
individual chooses to obtain health insurance.(6)
|
The problem is that we do not observe health insurance premiums (prices),
expected losses, or probabilities of loss in the April CPS. However, we do
observe a number of variables that are correlated with these variables. The
premium is effectively reduced for those eligible for COBRA or coverage
through their spouse's employer plan. In either case the premium an
individual faces is less than if he or she were to go out and purchase an
individual health plan. Thus, unemployed workers eligible for COBRA or a
spouse's plan are more likely to be covered.
|
Of course, there are other variables correlated with prices, incomes,
tastes, expected losses, and probability of loss that are available in the
1993 April CPS and can be included in our statistical models. Income can be
measured by the sum of own and spouse's total income in the previous year,
which is available using the April-March CPS match. Assuming that health
insurance is a normal good, we would expect that higher income workers would
be more likely to be covered by health insurance.
|
The frequency and severity of health problems increases with age so we would
expect health insurance to increase among the unemployed as age increases.
Those with children are likely to have a greater frequency of use of health
care services and the same may be true of females, leading to a higher
demand and a greater likelihood of coverage.(7) Those with disabilities or
whose spouses have disabilities would have smaller expected losses from
further health problems but a greater frequency of need for health services.
Premiums for purchased insurance are likely to be higher among this group or
they may be locked out of coverage due to preexisting conditions. The net
effect on coverage is uncertain. Those with more education are likely to be
more knowledgeable about health and thus need fewer health services. On the
other hand, expected losses are higher for this group. Of course, any income
losses are likely to be muted in a sample of unemployed. Until eligibility
ends or an individual is too sick to engage in job search, they continue to
receive unemployment and checks will continue whether sick or not.
|
In general, health outcomes tend to be worse among blacks than whites,
suggesting a greater demand for services. However, because earnings are
lower, earnings losses from poor health are lower among blacks. Also,
information about health insurance coverage may differ between blacks and
whites. In other words, it may be possible that the smaller losses when an
individual gets sick are canceled out by the greater demand and use of
health services. Thus, the direction of the net effect of race, like many of
the demographic variables available in the 1993 April CPS, is uncertain a
priori.
|
We use binary and multinomial logit models to estimate the effect of COBRA
eligibility on the health insurance coverage of the unemployed. The binary
logit models provide estimates of the determinants of whether an unemployed
individual has any form of health insurance coverage, and the multinomial
logit models the choice of alternative coverage categories (former employer,
other coverage, no coverage). The models use data from the regular portion
of the 1993 April CPS, the Employee Benefits Supplement, and the April-March
match.
|
Binary Logit Analysis - More formally, the 1993 April CPS data allow us to
estimate the probability of being unemployed and having health insurance, or
E(Pt|X,u=1), where Pt is the probability of having insurance after being
unemployed for a time t, X is a vector of characteristics, and u=1 if the
worker is unemployed and zero otherwise. We can estimate this conditional
probability using logit analysis. This allows us to calculate the
probability that an unemployed worker will have health insurance given a set
of observed characteristics X. The logit model also allows us to determine
the marginal effects of changes in the X variables on health insurance
coverage.
|
Included in X are our measure of COBRA eligibility, eligibility for spouse's
employer coverage, family income, and demographic characteristics of the
unemployed worker: age, education, race, gender, number of children, marital
status, and disability status of the unemployed worker and his or her
spouse. Formal definitions and means of these variables and the dependent
variable are shown in Table 7.(8)
|
|
|
Table 7 |
Variable
|
Mean
|
Definition
|
HI
|
.4328358
|
=1 if covered by health insurance
|
COBRA
|
.3239684
|
=1 if eligible for COBRA coverage
|
Spouse
|
.2396839
|
=1 if elig. for spouse coverage
|
female
|
.4363477
|
=1 if gender is female
|
age
|
39.55575
|
age in years
|
children
|
1.611062
|
# own children < 18 years old
|
married
|
.5522388
|
=1 if married spouse present
|
hsless
|
.1791045
|
=1 if has less than high school diploma
|
somecoll
|
.1992976
|
=1 if has some college
|
assoc
|
.0597015
|
=1 if has associate's degree
|
ba
|
.1009658
|
=1 if has bachelor's degree
|
baplus
|
.0491659
|
=1 if has more than bachelor's degree
|
black
|
.1395961
|
=1 if race is black
|
other
|
.0553117
|
=1 if race is other than black or white
|
disab
|
.0535558
|
=1 if wks. worked reduced due to illness or disability
|
spdisab
|
.0298507
|
=1 if spouse's wks. reduced due to ill. or disab.
|
totinc
|
25718.41
|
sum of own and spouse's income in 1992
|
Source: 1993 April Current Population Survey (n=1139)
|
|
In Table 8, we show binary logit estimates of health insurance coverage with
A) only the insurance eligibility variables and B) the insurance eligibility
variables and demographic characteristics.
|
|
|
Table 8A - Insurance
Eligibility Variables Only
|
Logit Estimates
Log Likelihood = -670.8202 |
Number of obs = 1139
|
chi2(2) = 216.73
|
Prob > chi2 = 0.0000
|
Pseudo R2 = 0.1391
|
HI
|
Coef.
|
Std. Err.
|
z
|
P>|z|
|
[95% Conf. Interval]
|
COBRA
|
.67821
|
.1399981
|
4.844
|
0.000
|
.4038187
|
.9526013
|
Spouse
|
2.143421
|
.1683546
|
12.732
|
0.000
|
1.813452
|
2.47339
|
_cons
|
-.9938632
|
.0906148
|
-10.968
|
0.000
|
-1.171465
|
-.8162615
|
|
Table 8B - Full Specification |
Logit Estimates Log Likelihood = -604.30624 |
Number of obs = 1139
|
chi2(2) = 349.76
|
Prob > chi2 = 0.0000
|
Pseudo R2 = 0.2244
|
HI
|
Coef.
|
Std. Err.
|
z
|
P>|z|
|
[95% Conf. Interval]
|
COBRA
|
.415881
|
.1541821
|
2.697
|
0.007
|
.1136897
|
.7180723
|
Spouse
|
1.45283
|
.2025905
|
7.171
|
0.000
|
1.05576
|
1.8499
|
female
|
.4058274
|
.1438808
|
2.821
|
0.005
|
.1238262
|
.6878287
|
age
|
.0335475
|
.0074741
|
4.489
|
0.000
|
.0188986
|
.0481963
|
children
|
.0720561
|
.0631101
|
1.142
|
0.254
|
-.0516373
|
.1957496
|
married
|
.4608835
|
.1958698
|
2.353
|
0.019
|
.0769857
|
.8447813
|
hsless
|
-.1231223
|
.2064497
|
-0.596
|
0.551
|
-.5277563
|
.2815177
|
somecoll
|
.3254417
|
.1911921
|
1.702
|
0.089
|
-.0492879
|
.7001712
|
assoc
|
.5209284
|
.2972679
|
1.752
|
0.080
|
-.061706
|
1.103563
|
ba
|
.5919875
|
.2531611
|
2.338
|
0.019
|
.0958009
|
1.088174
|
baplus
|
1.462033
|
.3818037
|
3.829
|
0.000
|
.713711
|
2.210354
|
black
|
-.0720482
|
.2191163
|
-0.329
|
0.742
|
-.5015083
|
.357412
|
other
|
.1630545
|
.310763
|
0.525
|
0.600
|
-.4460298
|
.7721388
|
disab
|
-.6363293
|
.3384751
|
-1.880
|
0.060
|
-1.299728
|
.0270697
|
spdisab
|
.0494727
|
.3947527
|
1.125
|
0.900
|
-.7242285
|
.8231739
|
totinc
|
.0000175
|
3.98e-06
|
4.406
|
0.000
|
.9.73e-06
|
.0000253
|
_cons
|
-3.25259
|
.3598647
|
-9.038
|
0.000
|
-3.957912
|
-2.547269
|
Source: 1993 April Current Population Survey
|
|
In Table 8A, we see that COBRA and spouse eligibility increase the
likelihood of health insurance coverage, mirroring the results from our
earlier cross tabulations. The estimated COBRA coefficient in Table 8A is
.678, which when converted to a probability is .140.(9) In Table 8B, after
controlling for a number of observed characteristics, the estimated COBRA
effect is reduced substantially. The estimated COBRA coefficient is .416,
which when converted to a probability and evaluated at the means of the
other variables is .075. Our estimated COBRA effect of .075 is similar to
the .067 estimated effect of Gruber and Madrian (1995a) using SIPP data.
|
Several observable worker characteristics in Table 8B are significantly
related to health insurance coverage. Age and income are significantly
positively related to coverage as expected. Females, married individuals,
and those with higher levels of education are more likely to be covered than
males, single individuals, and those with lower levels of education. There
is some evidence that disabled individuals have lower coverage rates.
|
Our estimated COBRA effects appear to be somewhat larger than that estimated
by Klerman and Rahman (1992). They estimate a COBRA coefficient of .052
using a probit specification and SIPP data. In order to provide a comparison
with Klerman and Rahman (1992), we estimate a probit model with a similar
specification to theirs using the April 1993 CPS.(10) In our estimated
probit model (results not shown), the COBRA coefficient is .228 (std. error=
.083). Evaluated at the means, the estimated increase in the probability of
being covered is .090. While we are unable to translate the Klerman and
Rahman (1992) estimate into a comparable probability without their sample
means, it appears from the magnitudes of the estimated coefficients that our
COBRA effect is larger than theirs.
|
Multinomial Logit Analysis - The binary logit estimates shown in Table 8
only consider whether an unemployed individual is covered or not and does
not distinguish among health insurance coverage from a former employer and
other sources. Therefore we next estimate multinomial logit models that
include these alternatives along with having no insurance coverage at all.
Let P1t denote the probability that a worker receives health insurance from
a former employer after being unemployed for a time t. A worker may have
coverage from an alternative source such as the spouse's employer, the
government or private health insurance, denoted by P2t. The probability that
a worker has no health insurance coverage is P3t. We use multinomial logit
to estimate E(Pit|X, u=1), i=1,2,3. This approach not only allows us to
track who has health insurance when unemployed, but also to examine the
determinants of the source of coverage.(11)
|
As before, we show two specifications in Table 9. In Table 9A we show the
multinomial logit estimates that include only the insurance eligibility
variables and in Table 9B we show the full specification, including a set of
observable worker characteristics. The multinomial logit estimates show the
effect of a change in the X variable on the natural log of the probability
of each outcome relative to the base outcome, in this case no insurance.
|
|
|
Table 9A - Insurance
Eligibility Variables Only
|
Multinomial Regression
Log Likelihood = -900.6442 |
Number of obs = 1139
|
chi2(4) = 327.94
|
Prob > chi2 = 0.0000
|
Pseudo R2 = 0.1540
|
hcat
|
Coef.
|
Std. Err.
|
z
|
P>|z|
|
[95% Conf. Interval]
|
COBRA
|
1.884316
|
.2129647
|
8.848
|
0.000
|
1.466913
|
2.301719
|
Spouse
|
1.012408
|
.2680134
|
3.777
|
0.000
|
.487111
|
1.537704
|
_cons
|
-2.660824
|
.176637
|
-15.064
|
0.000
|
-3.007026
|
-2.314622
|
Other
|
COBRA
|
.044253
|
.1651787
|
0.268
|
0.789
|
-.2794913
|
.3679974
|
Spouse
|
2.424341
|
.1741311
|
13.923
|
0.000
|
2.083051
|
2.765632
|
_cons
|
-1.237432
|
.0980825
|
-12.616
|
0.000
|
-1.42967
|
-1.045193
|
(Outcome hcat==None is the comparison group)
|
|
Table 9B - Full Specification |
Multinomial regression Log Likelihood = -815.62127 |
Number of obs = 1139
|
chi2(32) = 497.98
|
Prob > chi2 = 0.0000
|
Pseudo R2 = 0.2339
|
hcat
|
Coef.
|
Std. Err.
|
z
|
P>|z|
|
[95% Conf. Interval]
|
COBRA
|
1.588409
|
.2295178
|
6.921
|
0.000
|
1.138563
|
2.038256
|
Spouse
|
.1672063
|
.3196103
|
0.523
|
0.601
|
-.4592184
|
.793631
|
female
|
-.1940584
|
.2270073
|
-0.855
|
0.393
|
-.6389846
|
.2508678
|
age
|
.0480965
|
.0113131
|
4.251
|
0.000
|
.0259232
|
.0702698
|
children
|
-.0714158
|
.1102133
|
-0.648
|
0.517
|
-.28743
|
.1445983
|
married
|
.4595111
|
.3024614
|
1.519
|
0.129
|
-.1333023
|
1.052325
|
hsless
|
-.5268852
|
.3827794
|
-1.376
|
0.169
|
-1.277119
|
.2233487
|
somecoll
|
.5057127
|
.2838788
|
1.781
|
0.075
|
-.0506794
|
1.062105
|
assoc
|
.2733529
|
.4502701
|
0.607
|
0.544
|
-.6091604
|
1.155866
|
ba
|
.4839196
|
.3612337
|
1.340
|
0.180
|
-.2240854
|
1.191925
|
baplus
|
1.364486
|
.4950886
|
2.756
|
0.006
|
.3941298
|
2.334842
|
black
|
-.178763
|
.3540869
|
-0.505
|
0.614
|
-.8727605
|
.5152345
|
other
|
-.4102342
|
.5561079
|
-0.738
|
0.461
|
-1.500186
|
.6797173
|
disab
|
-.6368233
|
.5640805
|
-1.129
|
0.259
|
-1.7422401
|
.4687541
|
spdisab
|
.071244
|
.6243361
|
0.114
|
0.909
|
-1.152432
|
1.29492
|
totinc
|
.0000225
|
4.81e-06
|
4.684
|
0.000
|
.0000131
|
.000032
|
_cons
|
-5.125466
|
.5776516
|
-8.873
|
0.000
|
-6.257643
|
-3.99329
|
Other
|
COBRA
|
-.1367382
|
.1773388
|
-0.771
|
0.441
|
-.4843158
|
.2108395
|
Spouse
|
1.779716
|
.2138468
|
8.322
|
0.000
|
1.360483
|
2.198748
|
female
|
.6214455
|
.1581918
|
3.928
|
0.000
|
.3113952
|
.9314958
|
age
|
.0273117
|
.0083285
|
3.279
|
0.001
|
.0109881
|
.0436353
|
children
|
-.1107654
|
.0676498
|
1.637
|
0.102
|
-.0218259
|
.2433567
|
married
|
.4848019
|
.219375
|
2.210
|
0.027
|
.0548348
|
.9147689
|
hsless
|
.0294785
|
.2253034
|
-0.131
|
0.896
|
-.471065
|
.4121081
|
somecoll
|
.2322449
|
.213849
|
1.086
|
0.277
|
-.1868915
|
.6513812
|
assoc
|
.6329582
|
.3254872
|
1.945
|
0.052
|
-.0049849
|
1.270901
|
ba
|
.6353141
|
.2772659
|
2.291
|
0.022
|
.091833
|
1.178745
|
baplus
|
1.518018
|
.4109095
|
3.694
|
0.000
|
.7126499
|
2.323386
|
black
|
-.0155238
|
.2421146
|
-0.064
|
0.949
|
-.4900598
|
.4590122
|
other
|
.2888596
|
.3292701
|
0.877
|
0.380
|
-.3564978
|
.9342171
|
disab
|
-.6338952
|
.3783407
|
-1.675
|
0.094
|
-1.375429
|
.1076389
|
spdisab
|
.0918728
|
.427573
|
0.215
|
0.830
|
-.7461549
|
.9299005
|
totinc
|
.0000146
|
4.27e-06
|
3.421
|
0.000
|
6.24e-06
|
.000023
|
_cons
|
-3.405367
|
.4006411
|
-8.500
|
0.000
|
-4.190609
|
-2.620125
|
(Outcome hcat==None is the comparison group)
|
Source: 1993 April Current Population Survey
|
|
As might be expected, COBRA eligibility does not significantly affect the
outcome of other coverage relative to no coverage, and spouse coverage does
not significantly affect the outcome of employer coverage relative to no
coverage. As with the binary logits, the addition of demographic variables
reduces the magnitude of the COBRA and spouse insurance effects. Table 10
shows the estimated effect of COBRA eligibility on the three outcomes
evaluated at the means of the variables.
|
|
|
Table 10 |
Model Specification
|
Health Insurance Outcome
|
Employer
|
Other
|
No Insurance
|
Total
|
A. Health Vbls. Only
|
.202
|
-.063
|
-.139
|
0.00
|
B. Full Specific.
|
.154
|
-.081
|
-.073
|
0.00
|
Source: 1993 April Current Population Survey (uses estimates from Table 9)
|
|
The estimated increase in the probability of employer coverage from COBRA
eligibility is .202 from the estimates in Table 9A and is .154 from the full
specification including demographic characteristics in Table 9B. The
estimated effect on employer coverage and no coverage is reduced by the
addition of individual characteristics to the model.
|
The effects of the individual characteristics are in many ways similar to
those estimated using the binary logit model. Increases in age, income, and
education significantly increase the probabilities of both employer and
spouse insurance relative to the probability of no coverage. Being female or
married significantly raises the probability of other coverage relative to
no coverage. The magnitudes of the effects vary across the two insurance
choices. Increases in age and income produce bigger effects on the relative
probability of employer coverage, being female produces a bigger effect on
other coverage, and the effects for being married are almost identical for
the two choices.
|
Male - Female Coverage Differences Among the Unemployed - As mentioned in
the Introduction, gender differences in health insurance coverage among the
unemployed remain an interesting puzzle. We return to this issue now. What
do we know so far? Unemployed women are more likely to have some type of
coverage than unemployed men (Table 4A). Among COBRA eligible, men are more
likely to have employer coverage than are women (Table 4A). On the other
hand, women are more likely to have coverage from their spouse's employer or
some other type of coverage among both those eligible and ineligible for
COBRA (Table 4A). Further, women are more likely to choose spouse's employer
coverage if eligible for it (Table 6A). Women also are somewhat more likely
to be eligible for COBRA coverage and spouse's employer coverage than are
men (Table 5A). Assessing the relative strengths of these various factors
will go a long way toward understanding male-female differences in coverage.
|
As a first step, we estimate separate binary logit and multinomial logit
models for males and females and present the estimated COBRA and spouse
coverage effects on the various outcomes in Table 11. The estimates shown
use models with the same specification as Tables 8A and 9A and are evaluated
at the mean of the other health eligibility variable.(12)
|
|
|
Table 11 |
Model, Gender, Variable
|
Health Insurance Outcome
|
Any Coverage
|
Employer
|
Other
|
No Ins.
|
Total
|
Logits
|
Males
|
COBRA Elig
|
.156
|
|
|
-.156
|
0.00
|
Spouse Elig
|
.454
|
|
|
-.454
|
0.00
|
Females
|
COBRA Elig
|
.120
|
|
|
-.120
|
0.00
|
Spouse Elig
|
.490
|
|
|
-.490
|
0.00
|
Mult. Logits
|
Males
|
COBRA Elig
|
|
.212
|
-.056
|
-.156
|
0.00
|
Spouse Elig
|
|
.013
|
.441
|
-.454
|
0.00
|
Females
|
COBRA Elig
|
|
.184
|
-.065
|
-.119
|
0.00
|
Spouse Elig
|
|
-.056
|
.544
|
-.488
|
0.00
|
Source: 1993 April Current Population Survey
|
|
Table 11 shows that the estimated effect of COBRA eligibility on employer
coverage is greater for males than for females. The estimated effect of
spouse insurance eligibility on spouse coverage is greater for females. In
fact, eligibility for spouse coverage actually reduces the probability that
they will be observed with employer coverage. These results suggest that
women's responses to spouse eligibility outweigh men's responses to COBRA
eligibility, producing higher overall coverage rates for women and higher
employer coverage rates for men. This would be the end of the story if
eligibility rates for COBRA and spouse coverage were the same for men and
women. However, we know from Table 5A that eligibility rates are somewhat
higher for women.
|
The relative contributions of differences in eligibility rates by gender and
gender differences in the estimated responses to COBRA and spouse insurance
eligibility are shown in Table 12. We perform "Blinder-Oaxaca"
decompositions on the observed unweighted raw difference in health insurance
coverage for unemployed men and women. The same set of logit estimates from
Table 11 are also used here. This exercise allows us to decompose the raw
differences into amounts due to differences in X's (in this case differences
in COBRA and Spouse eligibility rates) and differences in estimated
parameters, or responses to eligibility.(13)
|
|
|
Table 12 |
Model, Portion of Raw Diff.
|
Health Insurance Outcome
|
Any Coverage
|
Employer
|
Other
|
No Ins.
|
Logits
|
Due to X's
|
-.010
|
|
|
.010
|
Due to b's
|
-.058
|
|
|
.058
|
Total Raw
|
Difference (Male-Fem.)
|
-.068
|
|
|
.068
|
Mult. Logits
|
Due to X's
|
|
.002
|
-.013
|
.011
|
Due to b's
|
|
.048
|
-.105
|
.057
|
Total Raw
|
Difference
|
|
.050
|
-.118
|
.068
|
Source: 1993 April Current Population Survey
|
|
The decompositions illustrate that gender differences in eligibility rates
(X's) only explain a small portion of the raw differences in health
insurance outcomes. Instead, it is gender differences in the responses to
eligibility for COBRA and Spouse coverage. However, we are still left with
explaining why gender differences in the responses to COBRA and spouse
insurance exist. Perhaps women have higher responses to the possibility of
spouse coverage than men because the expectation is for longer job tenure
for men. Thus, spouse coverage is a more attractive alternative for women
than for men, all else equal. Why are men more likely to elect COBRA
coverage, even holding constant spouse coverage? The answer may be that
unemployed women are more likely to qualify for public sector health
insurance such as Medicaid than are men. Among some lower income women,
public sector alternatives would be preferable than paying for COBRA
coverage out of their own pocket.
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When confronted with a medical catastrophe, lower- or middle-income
households that lack health insurance face possible financial devastation. A
drawback of our system of employer-provided health insurance is that labor
force transitions may have the unintended side effect of eliminating the
worker's health insurance coverage. COBRA was enacted to provide workers and
their dependents with a safety net in the event of a job interruption.
Health insurance coverage can be maintained under COBRA during changes in
employment status.
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Using the 1993 April CPS, we estimate that 33.59% of the stock of unemployed
are or were eligible to elect COBRA. Of those eligible, 24.57% are observed
to have health insurance from a former employer. After controlling for
worker characteristics and eligibility for insurance from a spouse's
employer, our binary logit models imply that COBRA eligibilty increases the
probability of health insurance coverage by .075. Our multinomial logit
models provide a more detailed estimate of the COBRA eligibility effect:
COBRA eligibility increases the probability of employer coverage by .154
reduces the probability of other coverage by .081, and reduces the
probability of no coverage by .073. While many unemployed workers turn to
other sources of coverage such as through a spouse's employer, our results
clearly indicate that COBRA is an important part of the safety net for
unemployed workers.
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Flynn, Patrice. 1992. "Employment-Based Health Insurance:
Coverage Under COBRA Continuation Rules." In Health Benefits and
the Workforce, Pension and Welfare Benefits Administration, U.S.
Department of Labor, Washington, DC, pp. 105-116.
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Folland, Sherman, Allen C. Goodman, and Miron Stano. 1993. The
Economics of Health and Health Care. Prentice Hall, Englewood Cliffs,
NJ.
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Gruber, Jonathan and Brigitte C. Madrian. 1995a. "Non-Employment
and Health Insurance Coverage." NBER Working Paper No. 5228,
August.
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Gruber, Jonathan and Brigitte C. Madrian. 1995b. "Health
Insurance Availability and the Retirement Decision." American
Economic Review 85:4 (September), pp. 938-948.
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Karoly, Lynn A. and Jeannette A. Rogowski. 1994. "The Effect of
Access to Post-Retirement Health Insurance on the Decision to Retire
Early." Industrial and Labor Relations Review 48:1 (October), pp.
103-123.
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Klerman, Jacob Alex and Omar Rahman. 1992. "Employment Change and
Continuation of Health Insurance Coverage." In Health Benefits and
the Workforce, Pension and Welfare Benefits Administration, U.S.
Department of Labor, Washington, DC, pp. 93-104.
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Sindelar, Jody L. 1982. "Differential Use of Medical Care by
Sex." Journal of Political Economy 90:5 (October), pp. 1003-1019.
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U.S. Bureau of the Census. 1995. Statistical Abstract of the United
States, 115th edition. Washington, DC.
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U.S. Department of Labor. 1990. Health Benefits Under the Consolidated
Omnibus Budget Reconciliation Act (COBRA). Pension and Welfare Benefits
Administration, Washington, DC.
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U.S. Department of Labor. 1994. Pension and Health Benefits of
American Workers: New Findings from the April 1993 Current Population
Survey. Washington, DC.
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Current Population Survey Questions Used in the Analysis:
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Health Insurance Questions from the April 1993 CPS
a_s84 Are you covered by a health insurance plan?
1 = yes
2 = no
3 = don't know
a_s85 Is this plan provided by a former employer?
1 = yes
2 = no
3 = don't know
a_s88 Were you covered under a health insurance plan provided by your
employer on your last job?
1 = yes
2 = no
3 = don't know
a_s61 Does your employer offer a health insurance plan to any of its
employees?
1 = yes, employee and family coverage offered
2 = yes, employee coverage only
3 = yes, employee coverage, don't know family coverage
4 = no
5 = don't know
a_s62 Are you covered by this insurance plan?
1 = yes for myself and family members
2 = yes - for myself only
3 = no
4 = don't know
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Other Questions from the April 1993 CPS
a_age Age (years)
a_maritl Marital Status
1 = married, civilian spouse present
2 = married, armed forces spouse present
3 = married, spouse absent
4 = widowed
5 = divorced
6 = separated
7 = never married
a_sex Sex
1 = male
2 = female
a_hga Educational Attainment
00 = none
31 = less than 1st grade
32 = 1st - 4th grade
33 = 5th or 6th grade
34 = 7th or 8th grade
35 = 9th grade
36 = 10th grade
37 = 11th grade
38 = 12th grade - no diploma
39 = high school graduate
40 = some college but not degree
41 = associate degree - occupational/vocational program
42 = associate degree - academic program
43 = bachelor's degree
44 = master's degree
45 = professional degree
46 = doctorate degree
a_race Race
1 = white
2 = black
3 = American Indian, Aleut, Eskimo
4 = Asian or Pacific Island
5 = other
a_whylk Why did you start looking for work; was it because...
1 = lost job 2 = quit job
3 = left school
4 = wanted temporary work
5 = change in home or family responsibilities
6 = left military service
a_wkslk Weeks unemployed (# of weeks)
a_ind Industry of Employment (3 digit Census industry code)
a_clswkr Class of Worker
1 = private
2 = federal government
3 = state government
4 = local government
5 = self employed - incorporated
6 = self employed - unincorporated
7 = without pay
8 = never worked
a_pfnocd Number of own children <18 in primary family
0 = not in primary family
1 = no children
2 = 1 child
3 = 2 children
4 = 3 children
5 = 4 children
6 = 5 children
7 = 6 children
8 = 7 children
9 = 8+ children
a_supwgt April Supplement Sample Weight
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Questions from the March 1993 CPS
rsnnotw What was the main reason you did not work in 1992?
1 = ill or disabled
2 = retired
3 = taking care of home
4 = going to school
5 = could not find work
6 = other
pyrsn What was the main reason you were not working or looking for work
in the remaining weeks of 1992? (i.e. the weeks other than those spent
working or looking for work)
1 = ill or disabled
2 = taking care of home
3 = going to school
4 = retired
5 = no work available
6 = other
noemp Counting all locations where this employer operates, what is the
total number of persons who work for your employer?
1 = under 10
2 = 10-24
3 = 25-99
4 = 100-499
5 = 500-999
6 = 1000+
7 = other
ptotval Total income in 1992 (dollars)
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All of the counts and tabulations in this section through Table 6 are
weighted using the 1993 April CPS supplemental weights (a_supwgt). The
weights are designed to improve the accuracy of population counts and
distributions.
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All of these variables are constructed from questions in the April
1993 CPS except family income which is taken from the March 1993 CPS and
is included in the April-March match set of questions. The income
variable is constructed from questions concerning total income in 1992.
The April CPS includes data on current earnings but the question only
applies to currently employed workers and not the unemployed.
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The Appendix provides a listing of the CPS questions used in our
analysis.
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Those who qualify as disabled under the Social Security Act may be
eligible for 29 months. However, those who qualify as disabled will not
be looking for work and will not be counted among the unemployed, and
thus will not be in our data. Also, we do not consider the coverage
status of spouses or dependents of the unemployed. Therefore, the 36
month eligibility period for spouses and dependents after qualifying
events is also not a factor in our data.
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The rates of coverage are obtained by summing across the three
coverage categories in Table 6A. For those eligible for COBRA coverage,
overall coverage is the sum of 23.92% and 16.26% and for those eligible
for neither spouse nor COBRA coverage, overall coverage is the sum of
5.04% and 19.90%.
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See Folland, Goodman, and Stano (1993, Chapter 11) for a more
extensive treatment of determinants of the demand for health insurance.
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See Sindelar (1982) and U.S. Census Bureau (1995, Tables 178, 179,
189, 190, 191, 192, 194) for evidence on greater frequency of use of
health care services for females.
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The results presented in Tables 7 through 12 use unweighted data.
These tables show means, parameter estimates, and calculations for
behavioral models rather than population summary statistics. In this
case, weights serve primarily to correct for heteroscedasticity which we
have no reason to believe is present in these data.
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Our earlier cross tabulations produced similar results. In Table 3A,
the implied COBRA effect on the probability of any form of coverage is
13.15% (50.35% - 37.20%).
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Our specification includes weeks unemployed, a dummy for the first
month of unemployment, age, age squared, a series of education
completion dummies, and a COBRA eligibility dummy. Thus, our
specification is identical to Klerman and Rahman (1992) except that they
use months instead of weeks unemployed and years of schooling instead of
a series of schooling dummies.
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While we used four health insurance outcomes in our cross tabulation
analysis (employer, spouse's employer, other, no coverage), we were
forced to reduce the number of outcomes to three in the multinomial
logit analysis for identification reasons. In particular, only those
eligible for spouse coverage are observed to have spouse coverage. No
one eligible for neither spouse nor COBRA coverage is observed to have
spouse coverage. Thus, it is not possible to include spouse coverage as
a separate choice in the multinomial logit. Instead we aggregate spouse
and other coverage and there is no longer an identification problem.
This is not a problem with COBRA eligibility and employer coverage.
Individuals are observed with and without employer coverage in all
eligibility categories. For example, some of those not eligible for
COBRA are still be observed to have employer coverage.
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We also estimated multinomial logit models with specifications similar
to those in Table 11B and obtained similar though in general somewhat
smaller estimated effects. The specifications used differed from that
used in Table 9B in that the female, spdisab, and disab variables were
dropped. The spdisab and disab variables could not be used in the gender
specific analysis because no females with disab=1 or spdisab=1 were
observed to have employer provided insurance.
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Similar results are obtained when the full set of explanatory
variables from Table 9B (except for female, disab, and spdisab) are
used. The portions of the raw differences explained by differences in
X's increase slightly; however, the major portions of the raw
differences are still due to differences in the estimated b's.
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Mark C. Berger
Dan A. Black
Frank A. Scott
Carolyn Looff and Associates
1635 Ashwood Road
Lexington, KY 40502
The opinions expressed in this study are the sole responsibility of the authors and do not
represent the views of the U.S. Department of Labor. Amitabh Chandra provided capable
research assistance.
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