Health Insurance and Labor supply: Joint
Decision-making within Households
Ling Wang
January 2008
Abstract:
This study proposed a dynamic model to capture the joint labor supply decisions within
household related with Employer-Provided Health Plan (EPHI). Based on the dynamic model, I
estimated the effect of EPHI on joint labor supply using multinomial logit model. Using data
from 1996 National Medical Expenditure Survey (MEPS), I have found that given spouse holding
health insurance from employers, the individual will decrease the labor supply. However, given
spouse offered health insurance from employers, the individual will higher labor force
participation, which illustrate that there might exist assortative mating in the couples. Also, the
estimates showed that children have a bigger effect on female’s labor force participation.
1. Introduction
In America, health insurance is largely employment based: nearly 80 percent of Americans
covered through their own or spouse’s employer-provided health insurance plan. During the
1992 presidential campaign, the issue of expanding access to health care system to everyonewhich is often referred to as universal coverage-was the focus of the debate. Although till now,
the universal health insurance failed to gain wide spread, the supporters still try to push for its
adoption. Partial reform was achieved in August of 1996, when the Health Insurance Reform Act
was signed into law. This law limited the insurer’s ability to deny coverage to people with preexisting health conditions, which to some extent extend the coverage of health insurance.
Because health insurance is the largest nonwage component of total compensation,
accounting 34 percent of expenditures on voluntary employee benefits and 7 percent of total
compensation (U.S. BLS 1994a), much research has been done on the relationship between labor
market outcomes with health insurance, such as job mobility (Madrian 1994,
Cooper and
Monheit 1993, Gilleskie and Lutz 2002) and retirement decisions (Gustman and Steinmeier 1994,
Gruber and Madrian 1995, Blau and Gilleskie 2006). A neglected potential effect of employerprovided health insurance is the labor force participation and working hours. The objective of this
study is to investigate the labor-supply implications of employer-provided health insurance and
measure the reduction in labor force given universal health insurance.
An intuitive effect of employer-provided health insurance on labor force participation would
be negative. However, due the joint decision of couples within household and endogeneity of
spouse’s health insurance on own labor supply, the effect of health insurance on labor supply
might be more complex.
Wellington and Cobb-Clark (1999) modeled working hours as a function of being covered
by spouse’s health insurance. They tried to answer three questions in the study: first, does a
1
worker’s decision about whether and how much to work depend on the availability of spousal
coverage? Secondly, how do these decisions differ across workers with different characteristics?
Thirdly, what can these estimates tell about the potential overall impact of universal coverage on
labor supply? Although they claim that “joint distribution implies that one can not separate the
effect of health insurance from simple heterogeneity in the preference for work”, they used linear
reduced-form equation to model the individual’s labor supply given the individual’s
characteristics and spouse’s health insurance status as exogenous. They also come up with the
method to calculate the magnitude of change in labor supply given universal health insurance.
Their results showed that wives with spousal coverage are 19.5 percentage points less likely to
participate in the labor market and work on average 7.1 to 14.8 percent fewer hours annually than
otherwise similar wives. Their estimates also suggests that universal coverage would reduce the
overall labor force participation rate by 3.3 percentage points and reduce the average hours of
workers by 46 hours per year.
Compared with Welling and Cobb-Clark’s simple reduced-form model, Buchmueller and
Valletta (1999) employed a more complex multinomial logit model to control for the
heterogeneity of husband’s health insurance on wives’ working hours. They separated the effect
of health insurance on working hours into threshold effect and heterogeneity effect of husbands’
health insurance on wives’ hours. The threshold effect is defined as the effect of employers’
health insurance offer on labor supply, that is, most employers only provide full-time workers
health insurance thus might change individuals’ decision about their labor supply. Based on their
theoretic model, they showed that difference-in-difference estimate of multinomial logit model
provides the unbiased threshold effect. Their analysis showed that the threshold effect on the
wives’ working hours is much larger than husbands’ and given universal health insurance, there
might be a non-trivial reduction in females’ labor supply. However, their unbiased estimations
are based on the two assumptions: first, the threshold effect does not apply to wives in jobs that
2
do not offer health insurance; second, the heterogeneity of husbands’ health insurance on wives’
working hours does not depend on wives’ insurance status.
Similarly, Royalty and Abraham (2005) divides the husbands’ health insurance effect on
wives labor supply into two effects: assortative mating and unobserved income effect. Based on
two assumptions, they were able to separate the two effects by using difference-in-difference
instrumental variable model and using another fringe benefit, sick leave in the estimation. They
found that for males, assortative mating effect dominates, that is, higher educated men have a
higher tendency to choose a higher educated wife. While for females, the income effect
dominates, that is, women will have lower labor force participation given higher household
income.
These studies all found the effect of health insurance on wives’ labor force participation is
much larger than husbands. However, they either didn’t control the heterogeneity of husbands’
health insurance on wives’ labor supply or they get the unbiased estimators based on assumptions.
Secondly, they fail to consider the intra-household decision process. Given husbands and wives
might have same concern about the household such as household consumption and child care,
there might exist an intra-house bargaining process with respect to the labor force participation.
Thirdly, all the models did not consider the impact of health status on labor supply.
In order to overcome the drawbacks stated above, I proposed a dynamic model in this study
to model the joint decision of household labor supply considering the employer-provided health
insurance. The rest of the paper is organized as follows: The first part will introduce the dynamic
model of joint decision of household on labor supply as the theoretical model. Next, based on the
theoretical model, a multinomial logit model is derived to captures all the factors affecting the
labor supply decisions in the model. Part III and Part IV will present the descriptive analysis and
estimation results from the multinomial logit model. Part V concludes the paper.
3
2. Theoretical model
This section describes a dynamic model of joint labor supply considering the offer of health
insurance provided by employers to capture the sequential decision-making process in each
period of household. At each discrete time period, the individual faces an offer of wage and
health insurances provided by the employers. They also face two stochastic probabilities at the
beginning of each period: fertility probability and probability of health status change. Given
income, health insurance offers and wage offers and number of dependent kids in the family, the
individual decides each period’s labor supply and the method of acquiring health insurance to
optimize discounted household present value life utility. The decision-making process continues
until the divorce of the couple or retirement or death of either one of the couples. The children in
the model are only considered as providing utility to parents and taking the time and consumption
of the parents. The health and health insurance of children are not considered in the model.
2.1. Per-period Discrete Choices
At each period, the individual may experience health status change and fertility change in the
family, thus affect the household’s consumption. The individual makes optimizing decisions
about the labor supply and acquiring health insurance each period. In the model, those families
acquire health insurance through Medicaid or other forms of public insurance are not considered.
The alternative available to an individual are:
—Husband or wife Employment choices at period t;
If choose to be unemployed;
If choose to be a part-time worker;
If choose to be a full-time worker;
4
—Husband or wife health insurance choices at period t;
If choose to be uninsured;
If choose to be insured by individual purchased plan;
If choose to be insured through spouse’s Employer-Provided Health Insurance (EPHI);
If choose to be insured through own EPHI;
2.2. State variables and Laws of Motion
The individual’s state variables with the choices determined his utility at each period. And
previous choices will change the state when the individual enters a new period. The observed
states at period t are as following:
—Husband or wife health status at period t-1;
If dead;
If poor health with chronic disease;
If fair health with small disease;
If very well;
—Husband or wife insurance offer status at the beginning of period t;
Unemployed and not offered EPHI;
Part-time worker and not offered EPHI;
Part-time worker, offered EPHI but not cover spouse;
Part-time worker, offered EPHI and cover spouse;
Full-time worker and not offered EPHI;
Full-time worker, offered EPHI but not cover spouse;
Full-time worker, offered EPHI and cover spouse;
5
—Number of children in the family at period t;
—Husband or wife’s full-time work experience;
—Husband or wife’s part-time work experience;
—Husband or wife’s duration of unemployment;
The state variables evolve based on the following laws. I assume the fertility is exogenous
and stochastic. That is, with
, the family will have a new baby in next period and with 1- ,
the family size will remain the same.
Fertility change:
With
;
With 1-
The health status change will be assumed to depend on previous health status and the
preventive care is not considered here.
Health status change:
,
— Probability of observing husband or wife’s health
status changes to type k given health status at t-1.
At the beginning of each period, the individual will receive the wage offer from current
employer, the wage offer will depend individual education, full-time work experience, part-time
work experience and duration of unemployment.
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Wage offer:
If unemployed;
If employed;
Full-time work experience;
Part-time work experience;
Duration of unemployed;
2.3. Utility Functions and Budget Constraints
The household utility is stochastic which depends on the consumption of the household (
health status of husband and wife (
time of husband and wife (
The parameter
, number of children in the family (
and leisure
. The per-period linear additive utility function is:
captures the different effect of couples’ health status on household
utility and the children’s utility to parents are assumed to be same, which is captured by
parameter
7
,
At each period, the household face two budget constraints: Income constraint and time
constraint. In order to simplify the model, I assume the household can not borrow or save over the
periods.
Income constraint:
Time constraint:
Here
is the non-wage income of the family;
is Out-of-pocket expenditure for
medical care of husband or wife, which is the function of health insurance type and health
status;
husband;
is the expenses of children;
is the health insurance premium paid by
is the health insurance premium paid by wife and
is the time cost of
child care.
2.4. The Value function
The life value function of each alternative per period t conditional on health insurance offer is:
8
The life value function of each alternative per period t conditional on health insurance offer is:
The maximal expected value of life utility at period t unconditional preference error and
assuming each person make the optimized choice:
The dynamic of joint decision of labor supply ends when the household doesn’t exist or
exit the labor force, that is, when the couples divorce or death or retirement of the couples. At the
last period, the utility function will be a function of either one of the remaining couples:
2.5. Implementing the Theoretical Model
The dynamic model presents above capture almost every factor affecting the decision of
household labor supply. Based on the value function, the individual will choose his optimal
working status if the value function is greater the value function given other alternatives:
Solution of the model would indicate that the labor supply decision of the individual would
be a function of his own health insurance status(
9
, spouse’s health insurance status (
, his
own health insurance offer(
children (
, education level (
experience (
(
, spouse’s health insurance offer(
, his own wage (
, his own health care expenditures (
, his own health insurance premium (
income in the household (
, number of dependent
, spouse’s wage (
, his own working
, spouse’s health care expenditures
, spouse’s health insurance premium(
and other
. The labor supply function can be written as the follows:
3. Data and Descriptive Analysis
I use the data from Round 1 of the Household Component (HC) 1996 Medical Expenditure
Survey Panel (MEPS). The MEPS is a random sample of civilian non-institutionalized population
of the United States. The dataset contains the information of individual’s basic demographic
characteristics, health status, employment status and health insurance status. The sample in this
study consists of household in which both parents are between 25 and 64 years of age and I
eliminate those in the army, self-employed and insured by Medicaid or other forms of public
insurance from the sample. Based on these criteria, the sample consists of 2044 households.
The dependent variable is the individual working status, which is divided into three categories: 0
for out of labor force, 1 for unemployed, 2 for part-time worker and 3 for full-time worker. In the
sample of husbands, 169 are out of labor force, 24 are unemployed, 78 are part-time workers and
1773 are full-time workers. (Refer to table 2 for wives’ working status). The part-time work is
defined as working hour per week between 0 to 30 hours. The full-time workers are those who
work at least 30 hours per week. The education level is categorized as the highest degree acquired
10
by the individual. To capture the health status of the household members, I used the self-reported
or perceived health status by the individuals. In the questionnaire, they self-reported their own
health status as excellent, good, fair and poor. As stated in the theoretical model, children in the
family might affect the parents’ decisions about labor supply. In the model, I control the number
of children under 18 in the household.
In order to test the effect employer-provided health insurance on labor supply, I also
include the health insurance held status and health insurance offer in the sample. Both are binary
variables, in which 0 of health insurance held indicates not holding health insurance through
employers and 0 of health insurance offer indicates the employer did not offer health insurance.
From table1, over 70% of husbands and over 50% of wives were offered health insurance through
the employers. However, the questionnaire didn’t capture the information whether the offer of
health insurance covered spouses.
Based on theoretic model, other factors might affect the labor supply decisions are the
medical expenditures and premium paid for the health insurance. The sample collected the
individual’s total expenditure on medical care in 1996 and the net medical care deduction in 1996.
Working experience and tenure also affect the labor supply of the individual. However, MEPS
didn’t collect the information on working experience and tenure. Table 2 presents the detailed
variables definitions and descriptive statistics.
4. Empirical results
Table 3 reported the multinomial logit model estimation results. For husband and wife the
base groups are the full-time workers. Column 2 reports the relative risk ration (RRR) with
respect to the group of full time workers. If RRR>1, it indicates with the increase of the
11
independent variable, the individual has a higher chance to fall into the comparison group (Out of
Labor force, Unemployed or Part-time work) relative to base group (Full-time work). If RRR<1,
the individual has a higher chance to fall into the base group (Full-time work) relative to
comparison group (Out of Labor force, Unemployed or Part-time work). All the regressions are
analyzed using the survey weights.
After controlling for education, health status, own health care expenditure, spouse’s wage
and own health insurance offer, it can be found that the marginal propensity of husband choose be
full-time worker is higher(RRRs are less than 1) given wife is offered health insurance from her
employers. The same pattern can be found with the wives. However, given wife is holding the
health insurance from the employers, the husband’s marginal propensity to be out of labor force is
higher (RRRS are greater than 1).
The opposite effect of spouses’ holding health insurance and spouses’ health insurance
offer might because health insurance offer captures the capability of the spouses which positively
correlated with own ability and thus higher own labor supply. While spouses’ holding health
insurance through employers might cover the whole household, thus decrease the individual own
labor supply.
Number of children is also an important factor determine the parents labor supply. For male
worker, as the number of children increases, they have a higher chance to fall into full-time
worker, as all RRRs are smaller than 1. However, for female worker, as the number of children
increases, they have a lower chance to fall into full worker, as all RRRs are greater than 1. It
indicates that the wives usually take care of children in the family.
Spouse’s hourly wage showed a negative effect on the labor supply and with hourly wage
increases for our of labor force females, as RRRs before hourly wage are greater than 1.Howeve,
the effects are not statistically significant.
12
The sign of RRRs before the health status is within expectations. With a poor health status,
the individual will have a higher tendency to participate in part-time job or out of labor force.
Another pattern that holds consistently across model is that total medical care expenditure has a
significant effect on part-time workers. With medical care expenditure increases, the part-time
worker will have a higher chance to change into full-time workers. Also, the effect is more
significant for females.
5. Conclusion
In this study, I have proposed a dynamic model to capture the labor supply decisions
within household with respect to employer-provided health insurance. The dynamic model
captures the factors that might affect the labor supply within household considering the health
insurance and health status of the household. The key feature of the dynamic model is that it
considered the probability of health shock and fertility over periods and individual make labor
supply decisions to optimize every period presented value of household utility.
Based on the dynamic model, I carried out a multinomial logit regression considering all
the factors in the dynamic model might affect the labor supply of couples. Using MEPS data, it is
found that offer of spouse’s health insurance have a positive effect on own labor supply, while
spouse’s holding health insurance have negative effect on own labor supply. Also, the children
have a different impact on mothers’ labor supply compared with fathers.
However, the estimators from the multinomial logit model are biased because of the
endogeneity. The assortative mating between couples might lead to a downward bias in the
estimators of spouses’ offered health insurance on labor supply. Also, the health status is strongly
13
correlated with the medical expenses. I will work on eliminating the endogeneity bias in the
future work.
Reference:
Blau, D. and D. Gilleskie (2006) “Health Insurance and Retirement of Married Couples.” Journal
of Applied Econometrics, Vol. 21, No.7, pp935-953.
Buchmueller, T., and R. G. Valletta (1999) “The Effect of Health Insurance on Married Female
Labor Supply.” The Journal of Human Resources, Vol. 34, No.1 , pp42-70.
Cooper, Philip F., and A. C. Monheit (1993) “Does Employer-Related Health Insurance Inhibit
Job Mobility?” Inquiry, Vol.30, No. 4, pp400-416.
Dey, M. S. and C. J. Flinn (2005) “An Equilibrium Model of Health Insurance Provision and
Wage Determination” Econometrica, Vol.73 No. 2, pp571-627.
Gemici, Ahu (2007) “Family Migration and Labor Market Outcome” Job market paper
Gillesike, Donna (1998) “A Dynamic Stochastic Model of Medical Care Use and Work
Absence.” Econometrica, Vol. 66, No. 1, pp1-45.
Gilleskie, D. and B. Lutz (2002) “The Impact of Employer-Provided Health Insurance on
Dynamic Employment Transitions.” Journal of Human Resources, Vol. 37, No.1 pp129-162.
Gruber, J. and B. Madrian (1994) “Health Insurance Availability and Retirement Decision”
American Economic Review, Vol. 85, No. 4, pp938-948.
Gustman, A. L. and T. L. Steinmeier (1994) “ Limited Insurance Portability and Job Mobility:
The Effects of Public Policy on Jobe-Lock” Industrial and Labor Relations Review, Vol. 48, No.
1, pp124-140.
Madrian, Brigitte C. (1994) “The Effect of Health Insurance on Retirement.” Brookings Papers
on Economic Activity, Vol. 1, pp181-132.
Olson, Craig A. (1995) “Part-time work, Health Insurance Coverage and the Wages of Married
Women.” Mimeo. University of Wisconsin.
Royalty, A. B., and J. M. Abraham (2006) “Health Insurance and Labor Market outcomes: Joint
decision-making within households” Journal of Public Economics, Vol. 90 pp1561-1577.
Wellington, A. J. and D. A. Cobb-Clark (2000) “The Labor-Supply Effects of Universal Health
Coverage: What Can We Learn From Individual with Spousal Coverage?” Worker Well-Being,
Vol. 19, pp315-344.
14
Appendix:
Table 1:
Dependent Variable distribution:
Working status
Husband
Freq
Percent
Husband
169
24
78
1,773
2,044
0-out of labor force
1-unemployed
2-part-time worker
3-full-time worker
Sum
8.27
1.17
3.82
86.74
100
Wife
Freq
Percent
415
31
432
1,166
2,044
20.3
1.52
21.14
57.05
100
Cross analysis of work status and health insurance status.
Husbands:
0-out of labor force
1-unemployed
2-part-time worker
3-full-time worker
Sum
Husband hold health
insurance
Not hold
Hold
Wife offered health
169
0
insurance
Husband offered
health insurance
Not offer
Offer
Wife offered
169
0
health insurance
Wife hold health
insurance
Not hold
Hold
Wife hold health
insurance
Not hold
hold
Wife offered health
415
0
insurance
Wife offered
health insurance
Not offer
Offer
Wife offered
415
0
health insurance
Husband hold
health insurance
Not hold
hold
24
57
424
674
0
21
1,349
1,370
24
51
249
493
0
27
1,524
1,551
111
14
51
1,128
1,304
58
10
27
645
740
Wife offered
health insurance
Not offer
Offer
101
13
42
833
989
68
11
36
940
1,055
Wives:
0-out of labor force
1-unemployed
2-part-time worker
3-full-time worker
Sum
31
381
477
1,304
0
51
689
740
31
310
233
989
15
0
122
933
1,055
140
9
118
407
674
275
22
314
759
1,370
Husband offered
health insurance
Not offer
Offer
125
7
90
271
493
290
24
342
895
1,551
Table 2: Descriptive analysis
Husbands:
Variables
husband work status
Husband education
no degree
High school without degree
High school with degree
bachelor degree
master degree
doctor degree
Husband health status
excellent
good
fair
poor
Husband Health Insurance status
Husband held health insurance
Husband offered health Insurance
Husband Hourly wage
Husband medical care expense
Net medical expense deduction
Total medical expense
Number of children
Obs.
Mean
Std. Dev.
Min
Max
2040
2.708
0.835
0
3
2040
2040
2040
2040
2040
2040
0.098
0.048
0.449
0.215
0.079
0.019
0.297
0.214
0.498
0.411
0.271
0.138
0
0
0
0
0
0
1
1
1
1
1
1
2040
2040
2040
2040
0.339
0.574
0.235
0.064
0.474
0.495
0.424
0.245
0
0
0
0
1
1
1
1
2040
2040
2040
0.693
0.778
16.294
0.461
0.416
16.452
0
0
0
1
1
369.52
2040
2040
2040
9.524
26.064
1.000
176.725
315.400
1.093
0
0
0
4500
7600
7
16
Mean
Wives:
Variables
wife work status
wife education
no degree
High school without degree
High school with degree
Bachelor degree
Master degree
Doctor degree
Wife health status
excellent
good
fair
poor
Wife Health Insurance Status
Wife held health insurance
Wife offered health Insurance
Wife hourly wage
Wife medical expense
Net medical expense deduction
Total medical expense
Number of children
Obs.
Mean
Std. Dev.
Min
Max
2040
2.164
1.164
0
3
2040
2040
2040
2040
2040
2040
0.077
0.034
0.522
0.182
0.071
0.008
0.267
0.181
0.500
0.386
0.257
0.087
0
0
0
0
0
0
1
1
1
1
1
1
2040
2040
2040
2040
0.313
0.592
0.238
0.072
0.464
0.492
0.426
0.259
0
0
0
0
1
1
1
1
2040
2040
2040
0.365
0.530
9.165
0.481
0.499
8.401
0
0
0
1
1
60.1
2040
2040
2040
41.957
98.045
1.000
544.818
745.746
1.093
0
0
0
14000
15000
7
17
Table 3: Multinomial Logit regression:
Husbands (base group: full-time workers)
Out of labor force worker:
Variables
No degree
High school without degree
High school with degree
Higher than Bachelor degree
Good Health
Fair Health
Poor Health
Wife held health insurance
Wife offered health Insurance
Net medical expense deduction/1000
Total medical expense/1000
Spouse’s hourly wage
Wife total medical expense/1000
Number of children
RRR
2.547
2.632
1.065
1.032
0.652
2.178
3.247
3.131
0.223
0.666
1.270
0.990
1.016
0.344
Std. Err.
Z
P>|Z|
0.719
0.926
0.251
0.360
0.161
0.537
0.931
1.226
0.088
0.620
0.538
0.014
0.111
0.047
3.31
2.75
0.27
0.09
-1.73
3.15
4.11
2.92
-3.81
-0.44
0.57
-0.73
0.14
-7.89
0.001
0.006
0.788
0.928
0.083
0.002
0
0.004
0
0.662
0.572
0.468
0.885
0
Obs.=2,040
Unemployed worker:
Variables
No degree
High school without degree
High school with degree
Higher than Bachelor degree
Good Health
Fair Health
Poor Health
Wife held health insurance
Wife offered health Insurance
Net medical expense deduction/1000
Total medical expense/1000
Spouse’s hourly wage
Wife total medical expense/1000
Number of children
Obs.= 2,040
18
RRR
Std. Err.
Z
P>|Z|
2.785
0.000
1.672
3.619
1.720
0.281
4.435
22.507
0.066
0.000
0.000
0.995
0.000
0.550
2.407
0.000
1.159
2.897
1.024
0.247
3.401
38.427
0.115
0.000
0.000
0.038
0.000
0.165
1.19
0
0.74
1.61
0.91
-1.44
1.94
1.82
-1.56
0
0
-0.12
0
-1.99
0.236
1
0.459
0.108
0.362
0.149
0.052
0.068
0.118
1
1
0.904
1
0.046
Part-time worker:
Variables
No degree
High school without degree
High school with degree
Higher than Bachelor degree
Good Health
Fair Health
Poor Health
Wife held health insurance
Wife offered health Insurance
Net medical expense deduction/1000
Total medical expense/1000
Spouse’s hourly wage
Wife total medical expense/1000
Number of children
RRR
0.647
0.749
1.815
1.269
0.909
1.065
0.887
1.586
0.586
0.215
1.799
0.980
1.141
0.492
Obs.= 2,040
19
Std. Err.
Z
P>|Z|
0.396
0.581
0.540
0.576
0.258
0.337
0.498
0.631
0.241
0.469
0.762
0.019
0.097
0.075
-0.71
-0.37
2
0.52
-0.33
0.2
-0.21
1.16
-1.3
-0.71
1.39
-1
1.55
-4.67
0.477
0.709
0.045
0.6
0.738
0.842
0.832
0.247
0.194
0.481
0.166
0.317
0.122
0
Wives (base group: full-time workers)
Out of labor force worker:
Variables
No degree
High school without degree
High school with degree
Higher than Bachelor degree
Good Health
Fair Health
Poor Health
Husband held health insurance
Husband offered health Insurance
Net medical expense deduction/1000
Total medical expense/1000
Spouse’s hourly wage
Husband total medical expense/1000
Number of children
RRR
Std. Err.
Z
P>|Z|
3.340
2.150
1.824
0.858
0.795
0.862
1.925
2.711
0.305
0.498
1.430
1.003
1.198
1.120
0.766
0.725
0.271
0.247
0.114
0.142
0.422
0.729
0.087
0.175
0.201
0.004
0.242
0.061
5.26
2.27
4.04
-0.53
-1.6
-0.9
2.99
3.71
-4.17
-1.98
2.54
0.7
0.89
2.08
0
0.023
0
0.594
0.11
0.366
0.003
0
0
0.047
0.011
0.483
0.372
0.037
RRR
Std. Err.
Z
P>|Z|
5.726
0.000
1.962
5.027
4.043
1.543
11.791
2.497
0.853
0.000
0.000
0.997
2.071
0.940
3.874
0.000
1.095
3.434
2.786
0.666
9.473
1.860
0.723
0.000
0.000
0.018
0.490
0.174
2.58
0
1.21
2.36
2.03
1
3.07
1.23
-0.19
0
0
-0.18
3.08
-0.34
0.01
1
0.228
0.018
0.043
0.315
0.002
0.219
0.851
1
1
0.858
0.002
0.737
Obs.= 2,040
Unemployed worker:
Variables
No degree
High school without degree
High school with degree
Higher than Bachelor degree
Good Health
Fair Health
Poor Health
Husband held health insurance
Husband offered health Insurance
Net medical expense deduction/1000
Total medical expense/1000
Spouse’s hourly wage
Husband total medical expense/1000
Number of children
Obs.= 2,040
20
Part-time worker:
Variables
No degree
High school without degree
High school with degree
Higher than Bachelor degree
Good Health
Fair Health
Poor Health
Husband held health insurance
Husband offered health Insurance
Net medical expense deduction/1000
Total medical expense/1000
Spouse’s hourly wage
Husband total medical expense/1000
Number of children
Obs.= 2,040
21
RRR
Std. Err.
Z
P>|Z|
1.044
1.833
1.131
1.246
0.899
0.877
1.055
2.106
0.499
0.649
1.487
1.005
1.310
1.234
0.280
0.568
0.150
0.271
0.121
0.136
0.272
0.488
0.128
0.125
0.192
0.003
0.233
0.064
0.16
1.95
0.93
1.01
-0.79
-0.85
0.21
3.21
-2.71
-2.24
3.08
1.37
1.52
4.07
0.871
0.051
0.353
0.311
0.429
0.397
0.835
0.001
0.007
0.025
0.002
0.169
0.129
0