4.2. Analysis file In developing our analysis file, we first identify children born after September
30, 1983 and children born between September 30, 1978 and September 30, 1983. We then identify children living on their own and children living with unrelated
persons and exclude them from the analysis. We do this in order to identify Ž
. appropriate family level characteristics e.g., family income and structure . After
these exclusions and after excluding children in families with incomes above 185 of the federal poverty level and those with Medicaid coverage at the first
interview, the analysis file contains 2587 children with observations at both first and last interviews of the panel. Finally, we use the longitudinal weights devel-
oped by the Census Bureau to account for any SIPP attrition bias.
15
5. Results
Table 2 provides the mean values for the dependent and independent variables used in the six models. Each dependent variable is an indicator for a child’s health
insurance status at the last interview of the 1990 SIPP panel given their health insurance status at the first interview of the panel. We focus only on those children
Ž who were not enrolled in the Medicaid program i.e., were privately insured or
. uninsured prior to the time the children’s expansions were mandated, as these are
the children who may have been influenced to enroll in Medicaid due to the expansions.
Seventy-eight percent of the target group children and 80 of the comparison group children who began the panel with private insurance also had private
coverage at the last interview. Thirteen percent of the target group children starting in private had Medicaid coverage at the last interview while 9 was uninsured.
This dynamics was reversed for the comparison group children, with 6 of those starting with private coverage having Medicaid in the last period and 14 being
uninsured. Of those beginning the panel uninsured, 42 of the target children was also uninsured at the end as was 58 of the comparison group children. The
remaining target group children were fairly evenly divided between those with
15
To account for the complex sampling design of the SIPP, we inflate our standard errors by a factor of 1.32. According to the SIPP documentation, estimates using these data must be adjusted by a design
effect. While precise design effects for particular samples can be calculated using the SIPP cross-sec- tional files, the necessary information to do so is not available on the longitudinal files. The 1.32
adjustment was calculated by SIPP statisticians after personal communication. This figure takes into account the fact that our estimation was done correcting for correlations across children in the same
families. This adjustment, based upon design effects for the full population, is likely high, given that other subsamples of the population for which specific design effects have been calculated are smaller
than that for the full population and because we are conducting a multivariate analysis that controls for all but one of the factors used to draw the sample.
Table 2 Sample means of dependent and independent variables
Independent variables measured at first interview. Variable means
Children with private Children uninsured
insurance at first interview at first interview
Target Comparison
Target Comparison
group group
group group
Dependent Õariables Private insurance in 1st period and
0.778 0.803
Ž .
private in last 0,1 Private insurance in 1st period and
0.134 0.055
Ž .
Medicaid in last 0,1 Private insurance in 1st period and
0.088 0.141
Ž .
uninsured in last 0,1 Uninsured in 1st period and private
0.284 0.252
Ž .
insurance in last 0,1 Uninsured in 1st period and Medicaid
0.292 0.169
Ž .
in last 0,1 Uninsured in 1st period and uninsured
0.424 0.576
Ž .
in last 0,1 Independent Õariables
Income relative to poverty 1.177
1.195 0.796
0.849 Number of children in family
2.628 3.029
2.503 2.875
There is a full-time adult worker in 0.932
0.936 0.776
0.865 Ž
. the family 0,1
Age of high-earning adult 30.873
35.077 30.187
34.875 High-earner has high school diploma
0.440 0.444
0.391 0.428
Ž .
only 0,1 High-earner has some college
0.400 0.373
0.268 0.232
Ž .
education 0,1 Ž
. Child is in a two-parent family 0,1
0.796 0.735
0.722 0.723
Ž .
High-earner’s race is white 0,1 0.798
0.774 0.797
0.756 Ž
. Child lives in the midwest 0,1
0.280 0.250
0.218 0.182
Ž .
Child lives in the south 0,1 0.365
0.387 0.460
0.526 Ž
. Child lives in the west 0,1
0.178 0.195
0.240 0.188
Sample size 968
717 509
393
private insurance and those with Medicaid at the last interview. Of the comparison group children, 25 who started the panel uninsured had private coverage at the
end and 17 had Medicaid coverage. Of the group starting with private insurance, 57 was in the target group. Of
the group starting out uninsured, 56 was in the target group. The private group had somewhat higher incomes, more children on average, and more highly
educated heads of household. Children beginning the panel uninsured were more likely to live in the south than children with private insurance.
5.1. Insurance status at last interÕiew conditional on haÕing priÕate insurance coÕerage at first interÕiew
Table 3 provides the results for the set of models that includes only those children who had private insurance at the first interview. Being in the target group
Ž .
children within the age and income constraints for Medicaid expansion eligibility is negatively associated with having private insurance in both the first and last
Ž .
interviews of the panel b s y0.0122 . This implies that there is a difference in
probability of y1.2 percentage points between the target and comparison groups. The negative coefficient on the target variable implies that some children may
have left private coverage as a simple result of the expansions. The estimated difference was not, however, statistically significant, which may be an artifact of
the low statistical power noted earlier.
16
Being in the target group increases the probability that a child will make a private-to-Medicaid transition by 5.3 percentage points, an effect that is statisti-
cally significant at the 0.01 level. Being in the target group lowers the probability of a transition from private insurance to uninsurance at the end of the panel by 3.9
percentage points, a difference that is not statistically significant.
17
To summarize the results of the first set of multivariate models, being a child in the age and income groups targeted by the Medicaid expansions:
Ø lowers the probability that a child with private coverage at the first interview also has private coverage at the last interview;
Ø increases the probability that a child with private coverage at the first interview has Medicaid coverage at the last interview; and
Ø decreases the probability that a child with private coverage at the first interview is uninsured at the last interview.
The negative coefficient on ‘target’ in the equation predicting whether a child would have private coverage in the first and last interviews of the survey indicates
that some displacement of private coverage may have occurred for those children who had private coverage at the beginning of the panel. How important is this
displacement? Clearly, not all transitions from private coverage to Medicaid coverage during this period were attributable to ‘‘crowd-out’’. Some children who
16
Given our sample size, in order to have an 80 probability of detecting a significant difference between the target and comparison groups in the probability of being in private in the last period, the
actual difference between the two populations would have to be at least 5.1 percentage points in comparison to the current 1.2 percentage points. In order to have an 80 probability of detecting a
difference of 1.2 percentage points as being statistically significant, the sample size for the target and comparison groups would have to be approximately 17,000.
17
The 3 percentage point differences across the three models do not sum exactly to zero because Ž
there were a small number of private coverage to non-Medicaid public coverage transitions e.g., .
Medicare, CHAMPUS . A regression for private to other public coverage transitions was not included here due to the very small number of such transitions.
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Table 3 Results of linear probability models including children with private insurance at first interview
Number of observations is 1685. Sample used in estimation includes those children in the target and comparison groups who had private insurance at the first interview.
Standard errors adjusted for clustering within family units and SIPP complex sampling design. Independent variables are measured at first interview.
Ž .
Income relative to poverty reflects definition of income used to determine Medicaid eligibility i.e., income less disregards . Dependent variables
Did child have private insurance in Did child have private insurance in first period
Did child have private insurance in first period Ž
. Ž
. Ž
. Ž
. Ž
. first and last periods? 0,1
and Medicaid any kind in last period? 0,1 and no insurance uninsured in last period? 0,1
Coefficient Standard error
Coefficient Standard error
Coefficient Standard error
Independent Õariables
UUU
Child is a member of the y0.0122
0.0293 0.0531
0.0202 y0.0387
0.0260 Ž
. target group 0,1
UUU UU
Income relative to poverty 0.1333
0.0450 y0.0858
0.0368 y0.0473
0.0312 Number of children in family
0.0036 0.0164
0.0116 0.0161
y0.0153 0.0105
There is a full-time adult 0.0095
0.0751 0.0074
0.0572 y0.0175
0.0650 Ž
. worker in the family 0,1
UUU
Age of high-earning adult 0.0048
0.0034 y0.0078
0.0027 0.0031
0.0026 High-earner has high school
0.0536 0.0674
y0.0551 0.0591
0.0012 0.0497
Ž .
diploma only 0,1
UU
High-earner has some college 0.1403
0.0656 y0.0840
0.0565 y0.0587
0.0459 Ž
. education 0,1
Ž .
Child is in a two-parent family 0,1 0.0445
0.0532 0.0055
0.0427 y0.0511
0.0434 Ž
. High-earner’s race is white 0,1
0.0624 0.0586
y0.0309 0.0495
y0.0330 0.0451
Ž .
Child lives in the midwest 0,1 y0.0234
0.0464 0.0139
0.0336 0.0098
0.0321
UUU UU
Ž .
Child lives in the south 0,1 y0.1453
0.0507 0.0472
0.0375 0.0959
0.0386 Ž
. Child lives in the west 0,1
y0.0595 0.0529
0.0250 0.0402
0.0347 0.0369
UU UUU
Constant 0.3716
0.1576 0.4364
0.1469 0.1901
0.1173
U
Coefficient to the right is significant at the 0.10 level.
UU
Coefficient to the right is significant at the 0.05 level.
UUU
Coefficient to the right is significant at the 0.01 level.
moved into Medicaid from private coverage would have been uninsured in the absence of the expansions, as the recession and other unrelated trends in health
insurance coverage which occurred over the same period decreased the overall level of private health insurance coverage.
To answer this question, we divide the decreased probability attributable to the expansions that a child would have private coverage in both the first and last
Ž .
periods by the increased probability also attributable to the expansions that a child would have private coverage in the first period but Medicaid coverage in the
last period. The resulting calculation, which uses the probabilities shown in Table Ž
. 3 y0.0122r0.0531 s y0.2298 , implies that 23 of these private-insurance-to-
Medicaid transitions attributable to the expansions was made by children who Ž
. otherwise would have had private insurance coverage standard error s 0.55 . This
estimate focuses exclusively on change over a 28-month period, ignoring short-term transitions that may have occurred in the interim or following the end of the panel.
Thus, even if the estimated coefficients on the ‘target’ variable reflect a real crowd-out effect that the small sample size is preventing from being statistically
Ž significant, the magnitude of the point estimate is relatively small i.e., 77 of the
private coverage to Medicaid transitions was not attributable to those who would .
have been privately insured in the absence of the expansions . 5.2. Insurance status at last interÕiew conditional on being uninsured at first
interÕiew Table 4 provides the results for children who were uninsured at the first
interview. Being in the target group implies a higher probability of moving from Ž
. uninsurance to private coverage
b s 0.0108 , a relationship that is not statisti- cally significant but certainly gives no sign of any crowd-out. The fact that target
children were not less likely to move out of uninsurance and into private coverage Ž
. and might be more likely to do so indicates that uninsured children were not
opting for Medicaid coverage as opposed to moving into private insurance. The probability of moving from being uninsured to Medicaid coverage was also higher
for those in the target group, as one would expect, with a difference of 7.9 percentage points between the target and comparison groups which is statistically
significant at the 0.05 level. Being in the target group lowers the probability that a
Ž .
child uninsured at the first interview was uninsured at the last b s y0.0861 , a
modestly significant result. To summarize the results of the second set of multivariate models, being a child
in the age and income groups targeted by the Medicaid expansions: Ø increases the probability that a child who was uninsured at the first interview
had private coverage at the last interview; Ø increases the probability that a child who was uninsured at the first interview
had Medicaid at the last interview; and Ø decreases the probability that a child who was uninsured at the first interview
was also uninsured at the last interview.
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Table 4 Results of linear probability models including children who were uninsured at first interview
Number of observations is 902. Samples used in estimation includes those children in the target and comparison groups who were uninsured at the first interview.
Standard errors adjusted for clustering within family units and SIPP complex sampling design. Independent variables are measured at first interview.
Ž .
Income relative to poverty reflects definition of income used to determine Medicaid eligibility i.e., income less disregards . Dependent variables
Was child uninsured in first period but had Was child uninsured in first period but had
Was child uninsured in the first Ž
. Ž
. Ž
. Ž
. private health insurance in the last period? 0,1
Medicaid any kind in last period? 0,1 and last periods? 0,1
Coefficient Standard error
Coefficient Standard error
Coefficient Standard error
Independent Õariables
UU U
Child is a member of the target 0.0108
0.0411 0.0791
0.0386 y0.0861
0.0498 Ž
. group 0,1
U
Income relative to poverty 0.1278
0.0763 y0.1088
0.0795 y0.0062
0.0277 Number of children in family
y0.0298 0.0283
0.0366 0.0223
y0.0161 0.0849
There is a full-time adult worker y0.0085
0.0835 y0.0675
0.0822 0.0826
0.0889 Ž
. in the family 0,1
UU UU
Age of high-earning adult y0.0017
0.0041 y0.0096
0.0038 0.0113
0.0046
UU
High-earner has high school 0.1315
0.0620 y0.1025
0.0806 y0.0330
0.0829 Ž
. diploma only 0,1
High-earner has some college 0.2693
UUU
0.0940 y0.1608
U
0.0950 y0.1099
0.1032 Ž
. education 0,1
Ž .
Child is in a two-parent family 0,1 0.0072
0.0750 y0.1036
0.0666 0.0929
0.0737 Ž
. High-earner’s race is white 0,1
0.0827 0.0687
y0.0320 0.0622
y0.0534 0.0795
Ž .
Child lives in the midwest 0,1 0.1462
0.1000 y0.0200
0.1179 y0.1257
0.1275
U
Ž .
Child lives in the south 0,1 0.1593
0.0824 y0.1290
0.1020 y0.0327
0.1144 Ž
. Child lives in the west 0,1
0.0927 0.1029
y0.0806 0.1409
y0.0130 0.1438
UUU
Constant y0.0184
0.1762 0.8156
0.1795 0.1973
0.2085
U
Coefficient to the right is significant at the 0.10 level.
UU
Coefficient to the right is significant at the 0.05 level.
UUU
Coefficient to the right is significant at the 0.01 level.
These estimates provide no support for the conclusion that the Medicaid expansions displaced private coverage for those children who were uninsured prior
to the implementation of the program mandates. In fact, there is some weak evidence that those initially uninsured in the target group transitioned to private
coverage in greater proportions than their comparison group counterparts.
5.3. Combined effect for children in priÕate coÕerage or uninsured at first interÕiew
We next estimated a set of models which would generate a combined estimate of the effects of the expansions on private coverage for children who had either
private coverage or were uninsured the first interview of the SIPP panel. The results of these models are presented in Table 5. Being in the target group has a
negative but statistically insignificant effect on the probability that a child had private insurance in the last period. The difference in probability between the
target and comparison groups was approximately 0.3 percentage points. Being in the target group had a positive and highly significant effect on the probability that
a child would have Medicaid coverage in the last period of the panel, with the difference in probability equal to 6.3 percentage points. In addition, being in the
target group had a negative and significant effect on the probability that a child would be uninsured at the end of the SIPP panel, with target children being 5.6
percentage points less likely to be uninsured than the comparison children.
Taking the ratio of the coefficients for the target variables in the first two equations, as we have done in the previous set of models, yields an overall
estimate of the Medicaid expansion displacement effect. In this way, we estimate Ž
. that 4.4 y0.0028r0.0633 of the children who moved into Medicaid from
either private coverage or uninsurance over the course of the SIPP panel would Ž
have had private coverage in the absence of the expansions standard error is .
18
0.38 . This result, in effect, averages the two independent results calculated
18
In order to increase our sample size, we considered including children up to age 13 in our analysis. However, there are differences in health care utilization patterns and the consequent demand for health
insurance between younger and older children. Therefore, we would expect that the older the children included in the comparison group, the less comparable the two groups would be. In addition, we want
to avoid including pregnant teenagers in our comparison group because they were affected by the Medicaid expansions for pregnant women. The older the child, the higher the probability of pregnancy.
We did, however, test the sensitivity of our results to the inclusion of children who were 13 and younger at the start of the panel. This change leads to an overall crowd-out estimate of 14.9 as
compared to 4.4. The crowd-out effect for the group beginning the panel in private coverage was
Ž .
y40.7 y0.024r0.0589, as compared to 23 ; the effect for the group beginning the panel Ž
. uninsured was q14.7 0.014r0.092, as compared to 13.7 . Neither of the coefficients constituting
the numerator of these two ratios was statistically significant. The sample size of the comparison group beginning the panel with private coverage was 319 observations larger than for the main results
presented in this paper. The comparison group beginning the panel uninsured increased by 168 relative to the main results presented.
L.J. Blumberg
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Table 5 Results of linear probability models including children with private insurance or uninsured at first interview
Number of observations is 2587. Sample used in estimation includes those children in the target and comparison groups who had private insurance or were uninsured at the first interview.
Standard errors adjusted for clustering within family units and SIPP complex sampling design. Independent variables are measured at first interview.
Ž .
Income relative to poverty reflects definition of income used to determine Medicaid eligibility i.e., income less disregards . Dependent variables
Ž .
Did child have private insurance Did child have Medicaid any kind
Was child uninsured in Ž
. Ž
. Ž
. in the last period? 0,1
in last period? 0,1 the last period? 0,1
Coefficient Standard error
Coefficient Standard error
Coefficient Standard error
Independent Õariables
UUU UU
Child is a member of the target y0.0028
0.0246 0.0633
0.0197 y0.0577
0.0250 Ž
. group 0,1
UUU U
UUU
Child was uninsured at first y0.4312
0.0445 0.0803
0.0421 0.3513
0.0425 Ž
. interview 0,1
UUU UU
Income relative to poverty 0.1308
0.0410 y0.0907
0.0386 y0.0389
0.0363 Number of children in family
y0.0047 0.0142
0.0197 0.0135
y0.0148 0.0114
There is a full-time adult worker y0.0195
0.0537 y0.0434
0.0509 0.0664
0.0544 Ž
. in the family 0,1
UUU UUU
Age of high-earning adult 0.0025
0.0027 y0.0088
0.0023 0.0065
0.0024
U
High-earner has high school 0.0920
0.0480 y0.0822
0.0517 y0.0114
0.0464 Ž
. diploma only 0,1
UUU UU
U
High-earner has some college 0.1986
0.0521 y0.1193
0.0522 y0.0816
0.0476 Ž
. education 0,1
Ž .
Child is in a two-parent family 0,1 0.0325
0.0440 y0.0384
0.0367 0.0036
0.0395 Ž
. High-earner’s race is white 0,1
0.0664 0.0464
y0.0334 0.0396
y0.0347 0.0412
Ž .
Child lives in the midwest 0,1 0.0152
0.0462 0.0127
0.0401 y0.0276
0.0400 Ž
. Child lives in the south 0,1
y0.0583 0.0456
y0.0082 0.0392
0.0641 0.0421
Ž .
Child lives in the west 0,1 y0.0358
0.0523 y0.0019
0.0512 0.0375
0.0493
UUU UUU
Constant 0.4156
0.1219 0.5839
0.1137 y0.0044
0.1113
U
Coefficient to the right is significant at the 0.10 level.
UU
Coefficient to the right is significant at the 0.05 level.
UUU
Coefficient to the right is significant at the 0.01 level.
earlier from the two separate samples conditional on initial insurance coverage. Consistent with those results, this overall estimate takes into account both the
negative and insignificant effect seen from the sample of children beginning the panel in private coverage and the positive and insignificant effect seen from the
sample of children beginning the panel uninsured.
5.4. Children with Medicaid eligibility under non-expansion rules at first interÕiew Some children in our sample would have been eligible for Medicaid at the first
interview even in the absence of the expansions through the Ribicoff, DEFRA or medically needy provisions.
19
To the extent that these children behave differently Ž
. from the higher-income children eligible for Medicaid due only to the expan-
sions — and to the extent that they are differentially represented among the target and comparison groups — their inclusion could affect the direct applicability of
our results to the Medicaid expansions themselves. For example, the fact that these children were eligible under previous Medicaid rules but did not participate in the
program may simply indicate that their families do not have strong preferences regarding insurance coverage. We tested the sensitivity of our results to the
inclusion of these children by performing a re-estimation without them.
This re-estimation had virtually no effect on the probability of any particular Ž
last period status for those with private insurance at the first interview see Table .
6 , nor did it affect the probability that a child would move from being uninsured to having private insurance over the 28-month period. However, the re-estimation
did make a difference in the estimated probabilities of having Medicaid or being uninsured in the last period for those who were uninsured at the first interview
Ž
. see Table 7 .
The probability that a child in the target group would move from being uninsured at the first interview to having Medicaid at the last period increased
Ž .
from a b s 0.0791 significant at the 0.05 level for the broader sample of Ž
. children to b s 0.1190 significant at the 0.01 level for the smaller sample of
children. This implies that children who were uninsured and non-Medicaid-eligible at the first interview and became eligible through the Medicaid expansions were
12 percentage points more likely to have Medicaid at the last interview than similar children in the comparison group.
Similarly, the smaller sample estimates imply that children in the expansion- eligible group were 14 percentage points less likely than the comparison group
children to be uninsured at both the first and the last interviews, a result which was
19
For a full discussion of the legislative history and details regarding these other eligibility routes, Ž
. see Congressional Research Service 1993 .
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Table 6 Results of linear probability models including children who had private coverage at first interview: sample excludes children eligible for Medicaid through non-expansion routes of eligibility at
first interview Number of observations is 1502.
Sample used in estimation includes those children in the target and comparison groups who had private coverage at the first interview. Standard errors adjusted for clustering within family units and SIPP complex sampling design.
Independent variables are measured at first interview.
Ž .
Income relative to poverty reflects definition of income used to determine Medicaid eligibility i.e., income less disregards . Dependent variables
Did child have private insurance Did child have private insurance in first period
Did child have private insurance in first period Ž
. Ž
. Ž
. Ž
. Ž
. in first and last periods? 0,1
and Medicaid any kind in last period? 0,1 and no insurance uninsured in last period? 0,1
Coefficient Standard error
Coefficient Standard error
Coefficient Standard error
Independent Õariables
UU
Child is a member of the target y0.0117
0.0313 0.0517
0.0216 y0.0374
0.0274 Ž
. group 0,1
UUU U
U
Income relative to poverty 0.1618
0.0591 y0.0858
0.0477 y0.0756
0.0452
U
Number of children in family 0.0108
0.0167 0.0086
0.0164 y0.0196
0.0105 There is a full-time adult worker
y0.0882 0.0737
0.0384 0.0450
0.0488 0.0610
Ž .
in the family 0,1
UU
Age of high-earning adult 0.0029
0.0036 y0.0065
0.0027 0.0037
0.0027 High-earner has high school
0.0463 0.0740
y0.0709 0.0646
0.0244 0.0549
Ž .
diploma only 0,1
UU U
High-earner has some college 0.1412
0.0715 y0.1071
0.0626 y0.0368
0.0504 Ž
. education 0,1
Ž .
Child is in a two-parent family 0,1 0.0324
0.0557 y0.0027
0.0428 y0.0309
0.0462 Ž
. High-earner’s race is white 0,1
0.0461 0.0619
y0.0104 0.0511
y0.0374 0.0490
Ž .
Child lives in the midwest 0,1 y0.0328
0.0467 0.0372
0.0319 y0.0040
0.0344
UUU U
UU
Ž .
Child lives in the south 0,1 y0.1450
0.0520 0.0585
0.0349 0.0842
0.0419 Ž
. Child lives in the west 0,1
y0.0555 0.0545
0.0320 0.0379
0.0237 0.0404
UUU UU
Constant 0.4977
0.1863 0.3663
0.1729 0.1342
0.1314
U
Coefficient to the right is significant at the 0.10 level.
UU
Coefficient to the right is significant at the 0.05 level.
UUU
Coefficient to the right is significant at the 0.01 level.
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et al.
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Table 7 Results of linear probability models including children who were uninsured at first interview: sample excludes children eligible for Medicaid through non-expansion routes of eligibility at first
interview Number of observations is 650.
Sample used in estimation includes those children in the target and comparison groups who were uninsured at the first interview. Standard errors adjusted for clustering within family units and SIPP complex sampling design.
Independent variables are measured at first interview.
Ž .
Income relative to poverty reflects definition of income used to determine Medicaid eligibility i.e., income less disregards . Dependent variables
Was child uninsured in first period but had Was child uninsured in first period but had
Was child uninsured in the first Ž
. Ž
. Ž
. Ž
. private health insurance in the last period? 0,1
Medicaid any kind in last period? 0,1 and last periods? 0,1
Coefficient Standard error
Coefficient Standard error
Coefficient Standard error
Independent Õariables
UUU UU
Child is a member of the target 0.0194
0.0519 0.1190
0.0396 y0.1384
0.0565 Ž
. group 0,1
Income relative to poverty 0.0624
0.1086 y0.0377
0.0905 y0.0130
0.0337
U
Number of children in family y0.0288
0.0386 0.0418
0.0243 y0.0246
0.1148 There is a full-time adult worker
y0.0261 0.1416
y0.0672 0.1051
0.0933 0.1267
Ž .
in the family 0,1 Age of high-earning adult
y0.0042 0.0064
y0.0057 0.0046
0.0099 0.0061
High-earner has high school 0.1066
0.0852 y0.0194
0.0924 y0.0872
0.1028 Ž
. diploma only 0,1
High-earner has some college 0.2580
UU
0.1170 y0.1075
0.1173 y0.1506
0.1302 Ž
. education 0,1
Ž .
Child is in a two-parent family 0,1 y0.0108
0.1027 y0.0692
0.0780 0.0800
0.0904 Ž
. High-earner’s race is white 0,1
0.0929 0.0960
y0.0427 0.0717
y0.0502 0.1017
Ž .
Child lives in the midwest 0,1 0.1375
0.1208 y0.0317
0.1329 y0.1058
0.1463
UU
Ž .
Child lives in the south 0,1 0.2057
0.1022 y0.1476
0.1174 y0.0581
0.1318 Ž
. Child lives in the west 0,1
0.0949 0.1318
y0.0740 0.1768
y0.0209 0.1770
UU
Constant 0.1575
0.2989 0.5058
0.2504 0.3367
0.3051
U
Coefficient to the right is significant at the 0.10 level.
UU
Coefficient to the right is significant at the 0.05 level.
UUU
Coefficient to the right is significant at the 0.01 level.
Ž .
significant at the 0.05 level . The magnitude of this effect was roughly 60 higher than that of the effect estimated with the larger sample.
To summarize, limiting the sample to those children who were either not eligible for Medicaid at the first interview or who were eligible only as a result of
the expansions strengthens two of our major conclusions. First, it increases the probability of expansion-eligible children who were uninsured at the first inter-
view moving to Medicaid. Second, it decreases the probability that expansion- eligible children who were uninsured at the beginning were still uninsured at the
end of the 28-month period.
6. Conclusions
