The Effects of Trigger Events on Changes in Children's Health Insurance Coverage. 1. Regression Results

04/18/2000

In this section we present the findings from an application of logistic regression analysis to estimate the impact of a child's experiencing a possible trigger event on the likelihood that the child will make a transition from his current coverage. As in the preceding section, we present separate analyses of children whose initial coverage is ESI, uninsured, Medicaid, or other insurance.

Methodology. Analyses of transitions in health insurance coverage often focus on spell length and use proportionate hazard models to estimate the impact of fixed or time-varying characteristics on the exit rate from a particular coverage status. Typically, events have a limited role--if any--as predictors. 24 Given our interest in trigger events, we have approached the problem differently. Trigger events can occur at any point in the history of a spell, and by definition their impact is relatively quick. Rather than asking how the occurrence of such events affects the length of a spell or how it affects the monthly exit probability, we want to know how the occurrence of such an event affects the probability that a child will exit one state or enter another in the next few months. This is fundamentally different than wanting to know the impact of personal characteristics on spell length (or exit rates), and it requires a different approach. Our basic model utilizes a four-category "multinomial" dependent variable identifying exits from one of our four types of coverage into each of the other three versus a fourth category indicating no exit during the four-month time span. We estimated a separate model for each of the four original sources of coverage. The predictors are potential trigger events. Except for one additional variable added to adjust for the SIPP seam bias, the models include no other predictors. We opted for this reduced form rather than estimating structural equations in which we attempted to include all of the characteristics that may affect exits from particular types of coverage and transitions into others because our research is exploratory and we wanted to focus on the role of events as predictors of change in coverage.

Children with ESI. Table 14 presents the results of a logistic regression analysis of children's loss of employer-sponsored insurance. The regression model was estimated with a multinomial dependent variable indicating whether the child made a transition into uninsured, Medicaid, or other insurance or remained covered by ESI. 25 The predictors are the several trigger events, expressed as binary variables (coded 1 or 0 to indicate whether or not the event occurred in the reference month). 26

TABLE 14: LOGISTIC REGRESSION ESTIMATES OF THE EFFECTS (ODDS RATIOS) OF
TRIGGER EVENTS ON THE ODDS OF CHILDREN LOSING ESI
Trigger Event Child's Coverage After Losing ESI
Uninsured Medicaid Other
Insurance
Father Lost Employment 2.68 ** 4.58 ** 0.43  
Mother Lost Employment 1.94 ** 3.58 ** 1.62  
Father Reduced Hours below 30 3.60 ** 0.52   5.24 **
Mother Reduced Hours below 30 2.22 ** 1.17   1.23  
Father Changed Jobs 3.39 ** 0.97   1.54  
Mother Changed Jobs 2.78 ** 1.45   0.67  
Family Income Rose Markedly 1.16   1.13   2.12 **
Family Income Fell Markedly 1.55 ** 1.34   2.22 **
Parent Joined Family 1.51   3.84   0.33  
Parent Left Family 1.18   5.32 ** 0.06 *
Family Size Increased 1.42   1.72   1.58  
Family Size Decreased 1.71 ** 1.70   1.59  
Event Occurred in First Reference Month 1.22 ** 1.56 ** 1.41 **
 
SOURCE: Survey of Income and Program Participation, 1992 Panel.

* Statistically significant at the .05 level.
** Statistically significant at the .01 level.

NOTE: Coefficients were estimated from a multinomial logistic regression in which the dependent variable contrasted each of the three transitions with the alternative, no loss of ESI. The coefficients in the first row indicate that a child whose father lost employment was 2.68 times as likely to become uninsured as a child whose father did not lose employment. Similarly, a child whose father lost employment was 4.58 times as likely to enroll in Medicaid and .43 times as likely (that is, less likely) to obtain other insurance as a child whose father did not lose employment.

The logistic regression model necessitated by the nature of the dependent variable is non-linear, so the effects of the trigger events cannot be expressed simply as net changes in the probability of observing a transition. 27 We have elected to express the effects of the individual trigger events as odds ratios. An odds ratio indicates how much the likelihood or "odds" of a child losing employer-sponsored insurance is increased by the occurrence of a particular event. 28 With the multinomial dependent variable the odds ratios express the effects of the trigger event in terms of the likelihood of a child making the indicated transition versus remaining covered by ESI. For example, in the first row of Table 14 the coefficient of 2.68 in the uninsured column implies that the odds of a child becoming uninsured in the next four months are increased nearly 3 times by the father's losing employment. 29 The coefficient of 4.58 in the Medicaid column implies that the odds of a child enrolling in Medicaid in the next four months are increased between 4 and 5 times by the father's losing employment whereas the coefficient of .43 in the other insured column indicates that the odds of a child moving from ESI to other insurance are actually reduced by 57 percent (1 minus .43) by the father's loss of employment, although this particular effect is not statistically significant. 30

All six of the variables that represent actual or potential reductions in employment have significant and positive effects on the likelihood of a child's leaving ESI to become uninsured. The strongest effects are associated with the father's reducing his hours of work below 30 or changing jobs. The effects of changes in the mother's employment are consistently weaker than the corresponding changes in the father's employment, but they are still relatively strong. The only other events with significant effects on the likelihood of a child losing ESI and becoming uninsured are a drop in family income and a reduction in family size--both of which increase the likelihood of a transition to uninsured. These effects are weaker than the effects of employment changes. That the reduction in income continues to increase the likelihood of a loss of insurance after controlling for employment changes underscores the importance of the parents' ability or willingness to pay for coverage when free or heavily subsidized coverage is not available.

We have no explanation for the significant effect of a reduction in family size. We observed the appearance of this variable earlier as a prior event in transitions from ESI to uninsured, but we counted it as a relevant event solely on the strength of its empirical association with these transitions --that is, without a clear theoretical justification.

Turning to the next two columns of Table 14 we find, first, that only the parents' loss of employment and a parent's leaving the family affect the likelihood of a child's leaving ESI and enrolling in Medicaid. The parent's leaving the family may not only take away employer-sponsored coverage but place the family in a position where the child, at least, can qualify for Medicaid. This same event has a significant but negative effect on the child's moving from ESI to other insurance, and our interpretation is that the father's departure and associated loss of income may eliminate other insurance as a potential source of coverage. It is consistent with this interpretation that a marked increase in family income should also have a positive and significant effect on the likelihood of a child's obtaining other insurance, but we are at a loss to explain why a reduction in family income should have the same effect. Finally, the father's reducing his hours below 30 has a very strong positive effect on the likelihood of a child's replacing ESI with other insurance. It is not clear why the reduction in hours should so often result in an exit from ESI rather than the parent's assumption of the full costs of maintaining coverage, which we would continue to count as ESI. Further research is needed to understand the rationale behind such choices.

It is intuitively understandable that all six employment variables should have independent effects on the likelihood of transitions from ESI to uninsured, because each of these changes in employment carries the potential to change the employee's access to employer-sponsored coverage or the cost of maintaining that coverage. At the same time, children who enroll immediately in Medicaid rather than becoming uninsured must not only lose their ESI but qualify for Medicaid. Our results suggest that children whose parents lose their employment have some increased likelihood of qualifying for Medicaid whereas children whose parents change jobs or reduce their hours do not.

The strength of the coefficients on parents' employment loss may help to explain why the same two variables do not have a stronger effect on transitions from ESI to uninsured: rather than becoming uninsured, the children of parents who lose their employment may become covered by Medicaid. The results for other insurance, on the other hand, seem to underscore the fact that obtaining other insurance requires an ability to pay. That is, the parents' loss of employment has no effect on transitions from ESI to other insurance because parents who lose employment are not able to pay for other insurance. At the same time, fathers who change jobs or reduce their hours of work may retain their ability to pay for other insurance if they lose ESI. Nevertheless, we are surprised that the father's reduction in hours should have such a strong, positive effect on the likelihood of a child moving from ESI to other insurance.

Children without Insurance. Logistic regression analysis of the effects of potential trigger events on children who are without health insurance indicates that very few events were significantly associated with transitions out of uninsurance after controlling for other events. In Table 15 we see that increases in the hours worked by either parent had significant effects on transitions into ESI, as did a marked rise in family income and a parent joining or rejoining the family. This last event also had a very strong positive effect on the likelihood of a child enrolling in Medicaid. A parent's leaving the family also had a positive but much weaker effect on this same transition while the mother's changing jobs or losing employment had positive effects as well. The mother's losing employment presumably helps to qualify the child for Medicaid, but the fact that the child was previously uninsured suggests that the mothers that account for this effect held jobs that provided no insurance coverage but produced enough income to make the child ineligible for Medicaid. Income changes in both directions had significant, positive effects on transitions to other insurance, and the same was true of the mother's changing jobs. With respect to the income changes, recall that we saw the same result for transitions from ESI to other insurance. Here, too, it is difficult to explain why changes in both directions should affect transitions in the same way, but we saw this same phenomenon with respect to other insurance in our earlier analysis of events preceding transitions.

TABLE 15: LOGISTIC REGRESSION ESTIMATES OF THE EFFECTS (ODDS RATIOS) OF
TRIGGER EVENTS ON THE ODDS OF UNINSURED CHILDREN BECOMING INSURED
Trigger Event Child's Coverage after Becoming Insured
ESI Medicaid Other
Insurance
 
Father Increased Hours to 30 or More 2.34 ** 0.98   1.10  
Mother Increased Hours to 30 or More 2.35 ** 1.38   0.91  
Father Changed Jobs 1.54   1.14   1.03  
Mother Changed Jobs 1.41   1.88 * 2.22 *
Mother Lost Employment 1.30   2.55 ** 1.70  
Family Income Rose Markedly 1.34 ** 0.94   1.78 *
Family Income Fell Markedly 0.88   0.95   1.95 *
Parent Joined Family 2.57 ** 6.52 ** 0.00  
Parent Left Family 0.85   2.39 * 0.63  
Family Size Increased 0.88   0.90   0.10 *
Event Occurred in First Reference Month 1.47 ** 1.40 ** 1.41 **
 
SOURCE: Survey of Income and Program Participation, 1992 Panel.

* Statistically significant at the .05 level.
** Statistically significant at the .01 level.

NOTE: Coefficients were estimated from a multinomial logistic regression in which the dependent variable contrasted each of the three transitions with the alternative, remaining uninsured. The coefficients in the first row indicate that a child whose father increased his hours of work was 2.34 times as likely to become covered by ESI as a child whose father did not increase his hours of work. Similarly, a child whose father increased his hours of work was .98 times as likely to enroll in Medicaid and 1.10 times as likely to obtain other insurance as a child whose father did not increase his hours of work.

Children with Medicaid. Regression results for children who were initially covered by Medicaid are presented in Table 16. The odds ratios are strikingly similar for transitions into uninsured and transitions into Medicaid. The loss of AFDC is the single strongest predictor of transitions from Medicaid to uninsured, but it is also one of the strongest predictors of transitions from Medicaid to ESI. Trigger events that had significant effects on the transitions from Medicaid to uninsurance tended to have similar if not significant effects on transitions from Medicaid to ESI, and vice versa. The chief exceptions to this pattern are the mother's changing jobs, which had a significant if modest effect on the child's moving from Medicaid to ESI but no measured effect on the child's moving from Medicaid to uninsured, and the family's income falling markedly, for which

TABLE 16: LOGISTIC REGRESSION ESTIMATES OF THE EFFECTS (ODDS RATIOS) OF TRIGGER EVENTS ON THE ODDS OF CHILDREN LEAVING MEDICAID
Trigger Event Child's Coverage After Leaving Medicaid
Uninsured ESI Other
Insurance
 
Family Lost AFDC 3.52 ** 2.04 ** 0.83
Father Gained Employment 2.89 ** 2.95 * 0.85
Father Increased Hours to 30 or More 1.52   0.95   1.59
Mother Increased Hours to 30 or More 1.53 * 1.92 ** 1.54
Father Changed Jobs 1.85   1.33   0.47
Mother Changed Jobs 1.15   1.79 * 0.86
Father Lost Employment 2.91 * 1.43   9.23
Father Reduced Hours below 30 0.59   1.00   0.23
Family Income Rose Markedly 1.52 ** 1.39   2.76
Family lncome Fell Markedly 1.71 ** 1.00   3.75
Parent Joined Family 2.34   0.99   0.00
Parent Left Family 2.33   1.88   0.00
Event Occurred in First Reference Month 1.42 ** 1.31 ** 1.03
 
SOURCE: Survey of Income and Program Participation, 1992 Panel

* Statistically significant at the .05 level.
** Statistically significant at the .01 level.

NOTE: Coefficients were estimated from a multinomial logistic regression in which the dependent variable contrasted each of the three transitions with the alternative, remaining enrolled in Medicaid. The coefficients in the first row indicate that a child whose family lost AFDC was 3.52 times as likely to become uninsured as a Medicaid child whose family did not lose AFDC. Similarly, a child whose family lost AFDC was 2.04 times as likely to obtain ESI and .83 times as likely to obtain other insurance as a Medicaid child whose family did not lose AFDC.

the reverse was true. An interpretation of the overall pattern is that the principal effect of these events is to move children out of Medicaid rather than pull them into ESI or uninsurance.

Our estimates of the effects of trigger events on transitions to other insurance are affected by the very small sample size of these particular transitions. We included these transitions in our regressions only to obtain a complete accounting of transitions. Nevertheless, there are some similarities with the findings for the other two transitions--in particular, the estimated effects for either parent's increase in hours worked, the father's loss of employment or reduction in hours below 30, and the rise or fall in family income (where the resemblance is to transitions from Medicaid to uninsured but not Medicaid to ESI).

Children with Other Insurance. Regression results for children whose initial coverage was other insurance are presented in Table 17. Because of the relatively small sample size of children with other insurance, odds ratios that would be significant in the regression results that we have already reviewed are not significant here, and some of the odds ratios are quite large. Rather than viewing these as evidence of very powerful effects on transitions, we are more inclined to see them as the result of large standard errors. The father's increasing his hours of work or the mother changing jobs had significant effects on the likelihood of a child leaving other insurance for ESI. Both of these make intuitive sense, but we cannot explain the significant positive effects of either parent's gaining employment on the likelihood of a child leaving other insurance to become uninsured. On the other hand, the significant positive effects of the father's losing employment or family income falling markedly do fit our priors here, and they suggest that with a major employment loss or reduction in family income the family's ability or willingness to continue paying for other insurance tends to decline. Finally, as in the previous table the sample size for transitions between other insurance and Medicaid is very small. We included these transitions, again, so that we could fully account for transitions out of other insurance, but we find these odds ratios difficult to interpret.

TABLE 17: LOGISTIC REGRESSION ESTIMATES OF THE EFFECTS (ODDS RATIOS) OF TRIGGER EVENTS ON THE ODDS OF CHILDREN LEAVING OTHER INSURANCE
Trigger Event Child's Coverage After Leaving Other Insurance
ESI Uninsured Medicaid
 
Father Gained Employment 2.33   10.61 * 16.18 **
Mother Gained Employment 1.42   4.06 * 1.02  
Father Increased Hours to 30 or More 3.36 ** 0.80   1.11  
Mother Changed Jobs 2.91 ** 2.77   8.43 *
Father Lost Employment 2.55   6.97 ** 4.36  
Family Income Fell Markedly 0.80   1.79 * 0.45  
Parent Joined Family 0.90   0.00   24.04 *
Parent Left Family 1.04   5.15   0.00  
Event Occurred in First Reference Month 1.56 ** 1.20   1.24  
 
SOURCE: Survey of Income and Program Participation, 1992 Panel

* Statistically significant at the .05 level
** Statistically significant at the .01 level.

NOTE: Coefficients were estimated from a multinomial logistic regression in which the dependent variable contrasted each of the three transitions with the alternative, remaining covered by other insurance. The coefficients in the first row indicate that a child whose father gained employment was 2.33 times as likely to become covered by ESI as a child whose father did not gain employment. Similarly, a child whose father gained employment was 10.61 times as likely to become uninsured and 16.18 times as likely to obtain Medicaid as a child whose father did not gain employment.

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