 

...
1!. “If

‘5 IA.

 

 

51A

.5.

“an"?

.. . [mummy-.1

lo‘ .

. . .vi-n‘ 1

 

THESIS

n

daog

This is to certify that the
dissertation entitled

EVALUATING THE EFFECT OF MEDICAID AND
STATE CHILDREN'S HEALTH INSURANCE PROGRAM
EXPANSIONS

presented by

Jason R. Davis

has been accepted towards fulﬁllment
of the requirements for

Ph.D . Economics

 

degree in

 

(UP‘YHQ‘é’V‘ﬁ.

Major professor

Date 3!”!D’L

MS U i: an Afﬁrmative Action/Equal Opportunity Institution 0- 12771

 

LIBRARY
Michigan State
University

 

 

 

PLACE IN RETURN BOX to remove this checkout from your record.
TO AVOID FINES return on or before date due.
MAY BE RECALLED with earlier due date if requested.

 

DATE DUE DATE DUE DATE DUE

 

JAN 8 O 2013

 

0429111

 

 

 

 

 

 

 

 

 

 

 

 

6/01 c:/CIRC/DateDue.p65-p. 15

 

EVALUATING THE EFFECT OF MEDICAID AND
STATE CHILDREN’S HEALTH INSURANCE PROGRAM EXPANSIONS

By

Jason R. Davis

A DISSERTATION

Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Department of Economics

2002

ABSTRACT

EVALUATING THE EFFECT OF MEDICAID AND
STATE CHILDREN’S HEALTH INSURANCE PROGRAM EXPANSIONS

By

Jason R. Davis

Title XXI of the Balanced Budget Act of 1997 resulted in increased eligibility
limits for children in all 50 states. Under Title XXI, states receive enhanced federal
matching rates to insure low-income children who would otherwise not qualify for
Medicaid. States have the option of expanding eligibility through Medicaid expansions,
through the creation of state programs, separate from Medicaid, or from a combination of
the two. The key differences between Medicaid and the state programs are that the state
programs have more freedom to impose minimal cost-sharing measures and to require
that children be uninsured for a speciﬁc length of time in order to become eligible.

This dissertation examines the impact these expansions have had on reducing the
number of uninsured children in the United States. A probit model is used to estimate the
change in insurance status, given the changes in eligibility limits for Medicaid and state
programs, based on March 1997-2001 Current Population Survey data. It is estimated
that the Title XXI expansions resulted in a 5.9% decrease in the number of uninsured
children, based on the simulations performed on the 2000 sample. For states using only
Medicaid expansions, the estimated decrease is 8.2%, compared to 5.0% in states using
only state program expansions and 7.0% in states using combination expansions.

Comparing the effects of the Title XXI expansions across different poverty level

groups, there was not a signiﬁcant effect for children with family income above 250% of

the federal poverty level. States using only Medicaid expansions had the largest impact
on uninsurance rates for children below the federal poverty level. For states using only
state program expansions or combination expansions, the largest impact on uninsurance
rates is observed for children with family income of 100-150% of the federal poverty
level.

Average partial effects are also estimated to provide a more direct comparison of
the effect of an average-sized expansion through either Medicaid or through a state
program. Overall, it is estimated that an average sized Medicaid expansion would result
in a 1.21 percentage—point reduction in the number of uninsured children, compared to a
0.65 percentage-point reduction through an equivalent state program expansion.

Based on speciﬁcation tests, there is some statistical evidence that Medicaid
expansions have a stronger impact on reducing the likelihood of being uninsured,
compared to state program expansions. However, the simulated changes in insurance
status and average partial effects of the expansions do not show any signiﬁcant

differences resulting from Medicaid expansions, compared to state program expansions.

ACKNOWLEDGMENTS

To begin, I’d like to thank the many teachers who have contributed to
my education. While there are far too many to mention here, I would like to
pay special thanks to Ranjana Bhandari, who ﬁrst inspired my study of
economics, and to my advisor, John H. Goddeeris, who took an early interest
in me as a student and has guided me through my graduate education. Thanks
also to the remaining members of my faculty committee: Charles Ballard, Jeff
Biddle, Leslie Papke, and Carol Weissert. I greatly appreciate your
encouragement and guidance, and the many comments and suggestions which
have helped to shape this work and contributed to my growth as a researcher.
I would also like to thank Kosali Simon, who was instrumental in the
developing stages of this research.

I would like to thank my parents for raising me well, for providing me with
everything I’ve needed to succeed in life, and for the freedom to make my own choices. I
sincerely hope that I can be as great a parent as you have been to me. Thanks also to the
rest of my family for the support you’ve offered over the years.

Finally, I’d like to thank my wife, Sarah, for joining me in this journey. Your
love and support has inspired me to keep chasing my dreams. While I may not always

know where I am headed, you make the ride worth taking.

iv

TABLE OF CONTENTS

LIST OF TABLES
CHAPTER 1: INTRODUCTION

CHAPTER 2: OVERVIEW OF MEDICAID AND TITLE XXI
EXPANSIONS
Section 2.1: Background on Medicaid Eligibility
Section 2.2: Medicaid Services
Section 2.3: Payment for Medicaid Services
Section 2.4: Title m of the Balanced Budget Act of 1997
Section 2.5: Overview of Title )Oﬂ Expansions
Section 2.6: Expected Effects of Title XXI Expansions

CHAPTER 3: PREVIOUS STUDIES
Section 3.1: Relationship Between Health Insurance and Health
Outcomes
Section 3.2: Previous Studies of Medicaid Expansions and
Crowding Out

CHAPTER 4: DATA

CHAPTER 5: STATISTICAL METHODS

Section 5.1: Model Design

Section 5.2: Possible Sources of Endogeneity

Section 5.3: Simulated Changes in Uninsurance Rates Resulting
from Title XXI Expansions

Section 5.4: Average Partial Effects of Medicaid and State
Program Expansions

Section 5.5: Expanding the Model to Allow for Diﬂ‘erent Effects
Across Poverty-Level Groups

Section 5.6: Robustness of the Model

vii

29
36
36
42
45
49
52

56

CHAPTER 6: IMPACT OF TITLE XXI EXPANSIONS ON
AVERAGE UNINSURANCE RATES

Section 6.1: Model Speciﬁcation

Section 6.2: Probit Model Estimation Results

Section 6.3: Simulated Effects of the Title XXI Expansions on
Uninsurance Rates

Section 6.4: Average Parial Effects of Medicaid and State
Program Expansions

CHAPTER 7: EFFECTS OF TITLE XXI EXPANSIONS ON
DIFFERENT POVERTY-LEVEL GROUPS

Section 7.1: Model Speciﬁcation

Section 7.2: Probit Model Results

Section 7.3: Simulated Effects of Title XXI Expansions on
Uninsurance Rates

Section 7.4: Average Partial Effects of Medicaid and State
Program Expansions

CHAPTER 8: EFFECTS ON PARENTS’ INSURANCE
STATUS
Section 8.1: Regression on the Likelihood of Having an
Uninsured Parent
Section 8.2: Regression on the Likelihood of Having an
Uninsured Parent using Policy/Poverty-Level Group
Interactions

CHAPTER 9: CONCLUSIONS AND POLICY
IMPLICATIONS

APPENDICES
Appendix A: Medicaid and State Program Eligibility Limits
Appendix B: Probit Regression Results from Chapter 6
Appendix C: Linear Probability Model Results ﬁom Chapter 6
Appendix D: Probit Regression Results ﬁ'om Chapter 7
Appendix E: Linear Probability Model Results from Chapter 7

BIBLIOGRAPHY

57
57
58
60

63

66
74
76

81

84

86

90

97
98
102
105
108
111

114

LIST OF TABLES

Table 1: Uninsurance Rates, 1996-2000

Table 2: Correlations Between Prior Uninsurance Rates/Trends
and Total Eligibility Changes

Table 3: Correlations Between Prior Uninsurance Rates/Trends
and Title XXI Eligibility Changes

Table 4: Comparison of Probit Model Speciﬁcations
Table 5: Probit Regression Results, Selected Variables
Table 6: Simulated Effects of Medicaid and State Program Expansions

Table 7: Changes in Elibility Limits for the 2000 Sample, 1996 Rules
versus Actual 2000 Rules

Table 8: Average Partial Effects from Medicaid Expansions versus
State Program Expansions

Table 9: Likelihood Ratio Tests for Adding Policy/Poverty-Level
Group Interactions

Table 10: Probit Regression Results for Policy Variables with
Policy/Poverty-Level Group Interactions

Table 11: Likelihood Ratio Tests for Different Effects from
Medicaid versus State Program Expansions

Table 12: Likelihood Ratio Tests to Combine Policy Variables
for Adjacent Poverty-Level Groups

Table 13: Probit Regression Results with Restricted Policy/Poverty-
Level Interactions, Selected Variables

Table 14: Simulated Effects of Medicaid and State Program
Expansions: Overall, by Expansion Type, and by
Poverty-Level Group

Table 15: Percent of Children in each Poverty-Level Group,
by Type of Expansion: 1996-2000

vii

34

43

58

59

61

63

64

67

68

70

72

75

77

79

Table 16:

Table 17:

Table 18:

Table 19:

Table 20:

Table 21:

Table 22:

Table 23:

Table 24:
Table 25:

Table 26:

Table 27:

Simulated Effects of Medicaid and State Program
Expansions: by Poverty-Level Group within
each Expansion Type

Average Partial Effects: by Poverty-Level Group

Comparison of Probit Model Speciﬁcations for the
Likelihood of Having an Uninsured Parent

Probit Regression Results for the Likelihood of
Having an Uninsured Parent with Policy/Poverty-
Level Group Interactions

Eligibility Thresholds for Infants, 1996 and 2000

Eligibility Thresholds for Children Ages 1-5, 1996 and 2000

Eligibility Thresholds for Children Ages 6 and Older,
Born After 10/1/1983, 1996 and 2000

Eligibility Thresholds for Children Born After 10/ 1/ 1983,
1996 and 2000

Complete Probit Regression Results
Complete Linear Probability Model Results

Complete Probit Regressions Results with Policy/Poverty-
Level Interactions

Complete Linear Probability Model Results with Policy/
Poverty-Level Interactions

80

82

85

87

98

99

100

101

102

105

108

111

CHAPTER 1

INTRODUCTION

Despite strong economic growth, the share of American children without health
insurance increased through much of the 1990’s. The percentage of children covered by
employment-related insurance increased from 58.1% in 1994 to 60.5% in 1998, and to
61.5% in 1999. In spite of this trend, Medicaid coverage was declining and the number
of uninsured children was increasing from 14.2% in 1994 to 15.4% in 1998. Between
1998 and 1999, however, the percentage of uninsured children ages 0-17 fell from 15.4%
to 13.9% (F ronstin, 2000b). While the continued growth of the US. economy helped
reduce the number of uninsured children, so did the increase in availability of public
health insurance for children.

Congress has taken several measures to assure access to health insurance for low-
income children and pregnant women, beginning with the creation of the Medicaid
program in 1965. Traditionally, Medicaid was available only to speciﬁc populations,
such as those who qualiﬁed for Aid to Families with Dependent Children (AFDC)
payments, low-income aged and disabled people, and those who were “medically needy”
(meaning that they had recently incurred large medical expenses, relative to their
income). Prior to 1984, low-income children were typically eligible for Medicaid only if
they were in families receiving AFDC. Beginning in 1984, the link between AFDC and
Medicaid eligibility was relaxed, so that Medicaid eligibility could be expanded to low-
income children who did not qualify for AFDC. From 1986 to 1992, there were

numerous expansions in Medicaid eligibility for pregnant women and children, which

occurred through both federal mandates and optional state expansions. The most recent
attempt to ensure that all children in the US. have adequate access to medical care was
through the passage of Title XXI, States Children’s Health Insurance Program (SCHIP),
of the Balanced Budget Act of 1997. Title XXI allows states to receive enhanced federal
matching funds to reduce the number of uninsured, low- income children through the
following options: 1) they can expand their Medicaid income eligibility threshold, 2) they
can create new state programs to insure low-income children not eligible for Medicaid, or
3) they can use a combination of the two approaches. States were allowed to receive the
enhanced federal matching funds for approved expansions beginning October 1, 1997; for
ﬁscal years 1998 and 1999, 46 states took advantage of these opportunities and received a
total of approximately $1 billion in enhanced federal matching funds (Kenney, Ullman,
and Wei], 2000). By the end of ﬁscal year 2000, all states had implemented Title XXI
expansions (Mathematica Policy Research, Inc.).

Despite the potential of these initiatives to have far-reaching effects on the health-
insurance coverage for children and the size of the budget allocation for this program, not
much is yet known about the effects of the Title XXI expansions. While advocates claim
that insurance coverage will increase, critics point out that such programs may ‘crowd
out’ private insurance and leave overall insurance rates unchanged.

The purpose of this research is to explore the impact that the Title XXI
expansions have had on reducing the number of uninsured children. In addition to the
overall effect, I examine the differential impact these expansions have had on reducing
the number of uninsured children, based on the type of expansion used. I ﬁnd that, as of

December 2000, these expansions had generated a 5.9% reduction in the number of

uninsured children. The expansions generated an 8.2% reduction in the number of
uninsured children in states using only Medicaid expansions, compared to a 5.0%
reduction in states using only state program expansions, and a 7.0% reduction in states
using a combination of the two approaches. After accounting for differences in the
average percent change in eligibility limits for states using each type of expansion, I ﬁnd
that the response of uninsurance rates to the changes in eligibility limits was greatest in
states with only Medicaid expansions, and least in states using only state program
expansions.

The larger effects on uninsurance rates resulting from Medicaid expansions
compared to state program expansions may be due, in part, to the fact that the Medicaid
programs do not typically use cost-sharing measures or waiting periods for enrollment.
Cost sharing measures are allowed in the state programs, which may provide a deterrent
for enrollment in these programs. State programs can also require that children be
uninsured for a speciﬁed length of time in order to be eligible for the programs. While
the use of these waiting periods should not affect the eligibility of uninsured children,
they may result in a temporary increase in the number of uninsured children if parents are
willing to drop private coverage in order to later enroll their children in the state
programs.

The reduction in the number of uninsured children, both overall and for each type
of expansion, is statistically signiﬁcant. The differences in the effects of different types
of expansions are not statistically signiﬁcant. However, based on speciﬁcation tests of

the model, there is evidence that an increase in eligibility limits through existing

Medicaid programs has had a greater impact than expansions through separate state
programs.

Looking at the effects of Title XXI expansions on children from different
socioeconomic backgrounds, Medicaid expansions had the largest impact on children
with family income below the poverty level, while state program expansions had the
largest impact on children with family income of loo-150% FPL. There was also a
signiﬁcant impact on children with family incomes of up to 200% FPL for both types of
expansions. Medicaid expansions also had a weak impact on children with family
income of ZOO-250% FPL, and no signiﬁcant effect for children above 250% FPL. State
program expansions had no signiﬁcant effect for children above 200% FPL.

This dissertation is organized as follows. Chapter 2 provides a brief history of
Medicaid eligibility for children, as well as a detailed description of the Medicaid and
state program expansions and their expected effects on the likelihood of being uninsured.
Chapter 3 provides a review of the literature on previous expansions to Medicaid.
Chapter 4 describes the data used in this study. Chapter 5 presents the statistical methods
used in evaluating the effects of Title XXI expansions. Chapters 6 and 7 summarize the
results of the empirical estimation of the effects of Title XXI expansions on children’s
uninsurance rates. In chapter 6, the model is used to estimate the average effect across all
children. In chapter 7, the model is expanded to allow for different effects for children of
different socioeconomic status. Chapter 8 estimates the effect of the Title XXI
expansions on the likelihood of having an uninsured parent. This is included to test that
the model is identifying effects unique to children, since the Title XXI expansions only

apply to children’s eligibility for public health insurance programs. Finally, chapter 9

summarizes the results and provides a discussion for how these results can be used by

policy makers.

CHAPTER 2

OVERVIEW OF MEDICAID AND TITLE XXI EXPANSIONS

2.1 Background on Medicaid Eligibility

Medicaid was initially designed to provide medical coverage to low-income
individuals who met speciﬁc categorical eligibility requirements. The three main
categories of Medicaid eligibility included AFDC recipients, the disabled, and the
elderly. As a result, low-income children were usually only eligible for Medicaid if their
families received AFDC payments. Children in families not receiving AFDC payments
were only eligible if they met one of the other categorical requirements (for example, a
disabled child in a low-income family). Because of the link between Medicaid and
AFDC eligibility, children in two-parent households were not categorically eligible for
Medicaid, since they were not eligible for AFDC payments.

In some states, children who did not qualify for Medicaid under AFDC eligibility
rules could still qualify for Medicaid if the state had a Medically Needy or Ribicoff
program. Medically Needy programs expanded Medicaid eligibility to some children in
families whose incomes exceeded the AFDC eligibility standards, but who otherwise
would have qualiﬁed for AFDC. These children could become eligible for Medicaid if
they had incurred large medical expenditures which brought their remaining income
(income net of medical expenditures) within the AFDC income-eligibility requirements.
The Ribicoff programs extended Medicaid eligibility to children in two-parent families

who met the AFDC income-eligibility requirements (Currie and Gruber, 1996).

Under the Deﬁcit Reduction Act of 1984, states were required to extend Medicaid
eligibility to all children who were born after September 30, 1983, and who met the
AFDC income-eligibility requirements (Currie and Gruber, 1996). This effectively
phased-in Ribicoff programs for all states and was the ﬁrst federally mandated Medicaid
expansion for children beyond those participating in AFDC. Following this legislation,
many states also implemented Ribicoff programs for children born prior to September 30,
1983, though they were not required to do so.

From 1986 through1990, several acts increased Medicaid income-eligibility
standards for children. These included: the Omnibus Budget Reconciliation Acts of
1986, 1987, 1989 and 1990; the Medicare Catastrophic Coverage Act of 1988; and the
Family Support Act of 1988. The end result of these acts was that states were required to
extend Medicaid eligibility to all children age 6 or under with family incomes up to 133
percent of the federal poverty level and all children born after September 30, 1983, with
family incomes under the federal poverty level. For children born prior to September 30,
1983, states were required to extend Medicaid eligibility to all children whose family
income met the AFDC income-eligibility standards. States had the option to expand
Medicaid eligibility to infants (less than one-year old) in families with income up to 185
percent of the federal poverty level (Currie and Gruber, 1996).

2.2 Medicaid Services

All Medicaid programs must include a set of services mandated by the federal
government. There are also a number of optional services which states can choose to
cover under Medicaid. As a result, the scope of Medicaid services varies by state.

Services which must be covered under a state’s Medicaid Program include:

Inpatient hospital services.

Outpatient hospital services.

Prenatal care.

Vaccines for children.

Physician Services.

Nursing facility services for persons aged 21 and older.

Family planning services and supplies.

Rural health clinic services.

Home health care for persons eligible for skilled-nursing services.
Laboratory and x-ray services.

Pediatric and family nurse practitioner services.

Nurse-midwife services.

Federally qualiﬁed health-center (F QHC) services, and ambulatory services of
an F QHC that would be available in other settings.

0 Early and periodic screening, diagnostic, and treatment (EPSDT) services for
children under age 21 (I-ICF A, “Medicaid: A Brief Overview”).

In addition, there are currently 34 optional services which may be included in a
state’s Medicaid program. The most common of these include:

Diagnostic Services.

Clinic Services.

Intermediate care facilities for the mentally retarded (ICFs/MR).
Prescribed drugs and prosthetic devices.

Optometrist services and eyeglasses.

Nursing facility services for children under age 21.
Transportation services.

Rehabilitation and physical therapy services.

Home and community-based care to certain persons with chronic impairments
(HCFA, “Medicaid: A Brief Overview”).

2.3 Payment for Medicaid Services

States are responsible for making payments to health care providers under their
Medicaid programs. Traditionally, these payments were made on a fee-for-service basis,
although many states have implemented prepayment arrangements, for example, through
health maintenance organizations (HMO’s). States are generally not allowed to use any

cost-sharing measures, such as deductibles, coinsurance rates, or copayments, in their

Medicaid programs for children, although they may use nominal cost-sharing measures
for other populations (HCFA, “Medicaid: A Brief Overview”).

The federal government reimburses states for a percentage of their Medicaid
expenditures, known as the Federal Medical Assistance Percentage (FMAP). The
FMAP’s are determined annually, based on a formula comparing a state’s average per
capita income level with the national average per capita income level. Based on this
formula, states with higher per capita income receive a lower FMAP than states with
lower per capita income. The FMAP’s are required by law to be no less than 50% and no
more than 83%.

2.4 Title XXI of the Balanced Budget Act of 1997

Starting in October of 1997, Title XXI allows states to receive enhanced federal
matching funds for programs to reduce the number of low-income uninsured children.
The enhanced FMAP for children covered under Title XXI is more generous than the
FMAP for Medicaid, so that the state’s share of medical expenditures under Title XXI is
70% of their share under the regular Medicaid F MAP. The total federal expenditure
made to a state in a given year is capped at a maximum absolute federal expenditure. For
a state using a Medicaid expansion under Title XXI, once it has reached the cap for the
enhanced FMAP, it may receive the regular Medicaid FMAP for any additional
expenditures stemming ﬁom those made eligible for Medicaid through the Title XXI
expansion. For states using a new state program, they may not receive any additional
federal ﬁinds for that program, once they have reached the cap for enhanced FMAP

(CMS, 1997).

If a state chooses to expand Medicaid eligibility, it must continue at least the same
level of coverage currently existing, with no new cost sharing or restrictions on services.
Ifa state develops a new state program, it has more ﬂexibility to create a new beneﬁt
package and to include cost-sharing provisions, such as premiums or copayments, though
cost-sharing is not to exceed 5% of a family’s annual income. For states implementing a
new state program, the beneﬁt package, at a minimum, must be equivalent to (or
actuarially equivalent to) one of the following benchmark coverages (CMS, 1997):

1) the Blue Cross/Blue Shield Preferred Provider option offered under the

Federal Employees Health Beneﬁts Program

2) a health beneﬁts plan that is offered and generally is available to state

employees

3) the HMO plan with the largest commercial enrollment in the state.

State programs may also include eligibility rules to discourage crowding out of
private insurance, such as enforcing a waiting period for children with private coverage.
For example, states can require that, in order to be eligible for the state program, a child
must not have had private coverage in the past year.

States must also ensure that children eligible for Medicaid who apply for either
Medicaid or a new state program are enrolled in Medicaid, not the state program. The
result is that most states have streamlined their application process. For example, states
with separate state programs have often developed a joint application for both Medicaid

and the new state programs. This may reduce barriers to enrollment in Medicaid.

10

2.5 Overview of Title XXI Expansions:

As of March 31, 1997, the federally mandated eligibility thresholds for Medicaid
were 133% of the Federal Poverty Line (FPL) for infants and children less than 6 years
old, and 100% FPL for children 6 years old and older born after September 30, 1983. At
that time, there were 33 states with higher thresholds for infants, 10 states with higher
thresholds for children ages 1-5’, 10 states with higher thresholds for children ages 6-14’,
and 6 states with thresholds greater than 100% for children ages 15-18. The thresholds
were 200% FPL or greater in 6 states for infants, in 5 states for children ages 1-5, in 4
states for children ages 6-14, and in 3 states for children ages 15-18 (Health Care
Financing Administration, 2000).

Eight states began enrolling children in Title XXI expansion programs in 1997, 32
states in 1998, 8 states in 1999, and 2 states in 2000 (Mathematica Policy Research, Inc.,
2001)

As of March 31, 2000, there were 18 approved Medicaid expansion programs, 15
approved state programs, and 17 approved combination programs among US. states. At
that time, there were 28 states with thresholds of 200% FPL or greater for all children; 7
of these states had implemented expansions solely through Medicaid, 10 had
implemented expansions solely through state programs, and 11 had implemented
combination expansions. (Mathematica Policy Research, Inc., 2001).

For states using combination expansions, some had expanded Medicaid only to
accelerate the phase-in of Medicaid for older children (those born prior to September 30,
1983). For these states, regardless of any expansions resulting ﬁ'om Title XXI, the

Medicaid expansions required for older children would have been ﬁilly phased in for all

11

children ages 17 and under by 2000 (the last year included in this research). For the
purposes of this research, accelerated Medicaid eligibility for older children is ignored in
describing the type of expansion used, since these expansions would have taken place by
2000 anyway. Thus, states that use Medicaid expansions only to accelerate eligibility for
older children, and use state programs to ﬁirther increase eligibility under Title XXI, are
treated as states using only state program expansions. States described as using
combination expansions are those that increased Medicaid eligibility beyond the
requirements in 2000 as well as increased eligibility through state programs.

Overall, eligibility limits from public insurance programs increased from an
average of 128.96% FPL in 1996 to 205.75 in 2000. These averages are computed as the
average eligibility limit for each age, 0-17, within each state. The tables in Appendix A
provide a more detailed description of eligibility thresholds in place prior to the Title XXI
expansions, and the eligibility thresholds in effect as of December 31, 2000.

2.6 Expected Effects of Title XXI Expansions

About 1 million children were enrolled in Title XXI expansion programs in 1998,
increasing to about 2 million in 1999 (Mathematica Policy Institute, 2001). However, the
effect of these programs on reducing the number of uninsured children is still not known
due to the possibility of crowding out of private insurance. Additionally, it is uncertain
whether the best approach is through Medicaid expansions, state program expansions, or
a combination of the two.

Expanded eligibility for children through public insurance such as Medicaid or

the new state programs would be expected to increase the number of children enrolled in

 

‘ Eight of these states had higher thresholds for both the 1-5 and 6-14 age groups.

12

these plans. However, expanded eligibility for public health insurance may also reduce
the number of children enrolled in private health insurance plans, an effect referred to as
crowding out.

Crowding out can occur for a variety of reasons. Broadly deﬁned, crowding out
refers to those who do not hold private health insurance, but who otherwise would have
in the absence of expanded eligibility for public health insurance. While many of these
individuals enroll in the pubic health insurance programs, others may become uninsured.
The term direct crowding out is used to describe those who enroll in public health
insurance programs, but who otherwise would have held private health insurance. The
term indirect crowing out refers to those who become uninsured, but who otherwise
would have held private health insurance.

Direct crowding out occurs when newly-eligible children are dropped from
private coverage and enrolled in a public health insurance program. Additionally, direct
crowding out could result if a previously uninsured child is enrolled in a public health
insurance program, when they otherwise would have been enrolled under a private
policy.

Indirect crowding out may also occur if Medicaid expansions result in non-
eligible children being dropped from private coverage and becoming uninsured. For
example, many states have traditionally had different eligibility thresholds for children of
different ages. Thus, Medicaid expansions may result in some children in a family
becoming eligible for Medicaid, while other children in the same family do not. This

may give families a ﬁnancial incentive to change employment-related policies from

13

family to individual coverage. The eligible children may then be enrolled in Medicaid
(direct crowding out), leaving the non-eligible children uninsured (indirect crowding out).

Additionally, ﬁrms may react to expanded Medicaid eligibility by increasing the
employee share of premiums or, in a more extreme case, by discontinuing individual
and/or family health insurance from their beneﬁt package completely. This, again, may
result in direct crowding out for those eligible for Medicaid and indirect crowding out for
those that become uninsured.

Previous studies have focussed on the effect of earlier Medicaid expansions and
the resulting magnitude of crowding out of private insurance. However, expansions
through Medicaid may not have the same effects as expansions through the new state
programs, for a number of reasons.

First, promotion of a new state program might lead to higher enrollment for both
the state program and Medicaid, due to increased awareness of the availability of public
insurance. While promotion of Medicaid expansions could have a similar effect, we
might expect this effect to be stronger in states using state programs if there is less of a
welfare stigma associated with the state program, compared to Medicaid. These
differences may be small since most states had already renamed their Medicaid programs
prior to 1997 in an effort to reduce the welfare stigma associated with Medicaid.

Second, the state programs generally include cost-sharing measures, such as
premiums, copayments, or deductibles. These cost-sharing measures are not typically
allowed under Medicaid without a special waiver from the Health Care Financing
Administration. As a result, a state program expansion would likely lead to smaller

increases in enrollment, compared to an equivalent Medicaid expansion.

14

A third difference between state program expansions and Medicaid expansions is
that the state programs often have provisions to discourage crowding out. Many states
require a waiting period for children who previously had private insurance, before they
can become eligible for the state program. The goal of these provisions is to limit
participation in the state program to those children who would not otherwise have access
to affordable insurance. If successful, these provisions would result in smaller increases
in enrollment for a state expansion, compared to an equivalent Medicaid expansion, but
only for those children who would likely have had private insurance coverage in the
absence of eligibility expansions. However, these crowd-out provisions might have some
temporary adverse affects as well, if parents are willing to drop private coverage for their
children in order to obtain public coverage some time in the future. Overall, these
provisions would be expected to reduce the degree of crowding out, if not perfectly, as
long as some parents would not risk dropping their child’s health insurance for a period
of time.

Because of these differences between Medicaid and new state programs, the net
effect on enrollment is ambiguous. In addition, there are likely differences in the degree
of crowding out which results ﬁom either Medicaid or state program expansions. The
combined effect on the reduction of uninsured children is, thus, also ambiguous.

Prior to Title XXI, previous expansions of federal/state ﬁinded public insurance
always took place through Medicaid. It is expected that increased generosity would
result in an increase in Medicaid enrollment. However, these increases may have
attracted both those who were previously uninsured and those who would have private

insurance, in the absence of Medicaid expansions. Crowding out is usually deﬁned as the

15

percent of the increase in Medicaid enrollment which can be attributed to decreases in
private insurance coverage, resulting from an increase in Medicaid eligibility. If
crowding out occurs at a rate of 25%, then the number of children who otherwise would
have had private insurance, in the absence of Medicaid expansions, account for one-
fourth of those enrolled in Medicaid as a result of the expansions. In other words, the net
reduction in the number of uninsured children is only three-fourths of the increased
enrollment in Medicaid, resulting ﬁom expanded Medicaid eligibility. The result of
crowding out is that the government insures more children than simply the number of
children who otherwise would have been uninsured. Thus, the greater the extent of
crowding out, the larger the publicly-ﬁnanced cost of the expansions, in terms of the
average public cost per child who otherwise would have been uninsured.

Under Title XXI, states may choose to expand coverage through Medicaid
expansions, through state program expansions, or through a combination of the two. As a
result, there may be differences across states in the success of the expansion on the
intended goal of reducing the number of uninsured children, as well as differences in the
extent that crowding out occurs. The intent of this research is to provide a ﬁrst step in
understanding the effects of the Title XXI expansions, by estimating the impact these

expansions have had on reducing the number of uninsured children.

16

CHAPTER 3

PREVIOUS STUDIES

3.1 Relationship Between Health Insurance and Health Outcomes:

Much of the previous literature has focussed on whether Medicaid expansions
have increased insurance coverage. While increased insurance coverage is expected to
improve access to health care, it does not necessarily imply better health. Marquis and
Long (1996) have studied health-care service utilization, based on source of insurance.
They found few differences in utilization of health-care services, comparing Medicaid
recipients to the privately insured. However, they found that the uninsured had much
lower levels of utilization compared to those with Medicaid or private insurance.

Currie and Gruber (1996) have examined the effects of Medicaid expansions on
health outcomes for newborns. Using data ﬁ'om 1979 to 1992, they found that Medicaid
expansions not only increased Medicaid participation for pregnant women, but that the
Medicaid expansions had a signiﬁcant impact on reducing the incidence of infant
mortality and low birth weight. Thus, there is evidence that increasing health-insurance
coverage improves access to care, and may improve health outcomes as well.

3.2 Previous Studies on Medicaid Expansions and Crowding Out:

Previous studies of the effects of Medicaid expansions on insurance coverage
have focused heavily on the issue of crowding out, the extent to which increases in public
insurance are offset by reductions in private coverage. While I do not estimate
crowding out in this dissertation, I review the literature in some detail, because it is

relevant to how I identify policy-induced changes in uninsurance rates.

17

If we only look at changes in insurance source following an increase in Medicaid
eligibility thresholds, we ignore the fact that other factors may have contributed to these
changes regardless of the changes in Medicaid eligibility. For example, the 1991
recession would have resulted in increased Medicaid eligibility and decreases in
employment-related private coverage, regardless of any changes in Medicaid policy
which took place during that time. In order to control for these other factors, a control
group is used that is not affected by the Medicaid expansions, but is otherwise similar to
those who are affected. The success in this approach depends on the suitability of the
control group. The main differences in previous studies of Medicaid expansions result
ﬁom both differences in the control group used and ﬁ'om differences in the type of data
used. Many of the previous studies use cross-sectional data, such as the March CPS. The
main advantages of using cross-sectional data, and the March CPS in particular, are that
they have large sample sizes and are available fairly quickly. Other studies have used
panel data, which provide more detailed information on how individuals’ behavior
changes over time, though such data are not publicly available as quickly as cross-
sectional data sources.

Because of the time lag in the availability of panel data, only cross-sectional data
are currently available for the study of recent changes in eligibility for public insurance.
Cutler and Gruber (1996), Dubay and Kenney (1997), and Rask and Rask (2000) offer
three different approaches to measuring the impact of previous Medicaid expansions
using cross-sectional data. Their models and results are discussed in detail, since these
are most relevant to the model presented in Chapter 5. There is also a brief summary of

results for Shore-Sheppard, Buchmueller, and Jensen (2000) who examine whether ﬁrms’

l8

decisions to provide health insurance beneﬁts are affected by increased generosity of
public insurance programs. Finally, a brief summary is provided for studies by Thorpe
and Florence (1998), Yazici and Kaestner (2000), and Blumberg, Dubay and Norton
(2000) which use panel data to assess the effects of previous Medicaid expansions.

Cutler and Gruber (1996) provided the ﬁrst study of the Medicaid expansions
which took place in the late 1980’s and early 1990’s. Their study examined how changes
in individual eligibility for Medicaid affect the likelihood of holding private insurance,
public insurance, or of being uninsured. They use data on women of childbearing age
and children from the March CPS, collected in 1988-1993. Cutler and Gruber use state-
level eligibility variation to identify the effect of gaining eligibility on the choice of
insurance (Medicaid, private insurance, or uninsured). The variation in state-level
eligibility arises due to variation in eligibility thresholds over time as well as ﬁ'om
variation between states, both in the timing and size of the expansions, and from variation
within states for children of different ages. In essence, the control group consists of those
children not eligible for Medicaid. Because of the multiple sources of variation in
eligibility limits, the group of children not eligible for Medicaid includes children with
otherwise similar characteristics to the group of children eligible for Medicaid.

Cutler and Gruber use the linear probability model to estimate the effect of
changes in Medicaid eligibility on each source of insurance (Medicaid, private coverage,
or uninsured) for women of child-bearing age and for children. The regression for each
source of insurance is performed separately. Their regressions include demographic
controls for race, sex (for children), marital status (for women), type of

family(male/female head, male only, female only), number of persons and number of

19

workers in the household, as well as state, age, and year dummy variables. They
acknowledge that using eligibility itself as the “treatment” variable may create biased
estimates because of: 1) omitted individual characteristics which might be correlated with
Medicaid eligibility; 2) endogeneity bias resulting from the fact that jobs with employer-
sponsored private insurance coverage may offer lower wages, which possibly affects
Medicaid eligibility; and 3) measurement error in determining eligibility. In order to
remove these sources of bias, they create an instrument for Medicaid eligibility using a
national sample of women and children. This sample is used to compute the average
percent of children of each age who are eligible for Medicaid in each state for each year.
This instrument is correlated with the state policies in each year, but is not otherwise
correlated with the individual demand for insurance. Indicator variables for each age,
state and year are also included in the regressions. The treatment effect is thus identiﬁed
only by differential changes in eligibility across ages, states, and years.

While this technique does provide a correction for the problems listed above, it
does not address the question of whether the state policy changes, themselves, are
endogenous. For example, differences in the size of eligibility expansions across states
may be made in response to state-level differences in the insurance rates or differences in
the trends of insurance rates for children. Cutler and Gruber recognize this possible
source of bias, but point out that such bias is unlikely to exist since 90% of those made
eligible through the expansions gain eligibility under federal mandates, rather than state
choices to extend eligibility beyond the national mandates.

Cutler and Gruber estimate the rate of crowding out by taking the ratio of the

coefﬁcient on eligibility from the regression on private insurance coverage divided by the

20

coefficient on eligibility from the regression on Medicaid coverage. In other words, this
is the marginal change in private coverage divided by the marginal change in Medicaid
coverage, given a gain in eligibility for Medicaid. They ﬁnd a crowding out rate of 3 1%
for children and more than 100% for women.

The above estimates are all based on the changes in insurance coverage, given a
change in individual eligibility for Medicaid. Cutler and Gruber expand their basic
model to account for the fact that expanded eligibility for some family members may
cause other, non-eligible, family members to lose private coverage. As a greater
percentage of the household becomes eligible for Medicaid, it may be more likely that
private insurance is dropped not only for those eligible for Medicaid, but also for other
family members who may become uninsured. For example, for a family with three
children where the younger two are eligible for Medicaid, the primary wage earner may
switch from family to single coverage, leaving the oldest child uninsured. These
spillover effects are an important source of potential crowding out and should not be
ignored. Because Cutler and Gruber model a change in eligibility status for some
members of households, they need to model the effects of changes in eligibility for any
member of the household on the choice of insurance for all members of the household.

One approach for accounting for these spillover effects onto other family
members would be to include one variable indicating if an individual was eligible for
Medicaid, as before, and a second variable measuring the percent of the health insurance
unit which is eligible for Medicaid. The health insurance unit is deﬁned as the head of
the family, spouse, and children under age 19 (or children under age 23 who are full-time

students). The expected results are: 1) a gain in individual eligibility would lead to a

21

higher likelihood of enrolling in Medicaid, and a lower likelihood of holding private
insurance; and 2) an increase in the percent of the health insurance unit eligible for
Medicaid would lead to a lower likelihood of holding private insurance, even for those
not eligible for Medicaid, themselves. The problem with this approach is that families
may not value health insurance equally for all members of the health insurance unit. For
example, parents may have a stronger preference toward having health insurance for their
children, than for themselves. Additionally, parents may have a stronger preference for
insuring younger children, compared to older children who may have less need for
routine medical care, such as immunizations. Unfortunately, it is impossible to infer the
relative value placed on holding insurance for each family member with the available
data. As a proxy for the actual value, Cutler and Gruber use the expected annual health
spending for each age, based on the 1987 Nation Medical Expenditure Survey.

Rather than simply using the Medicaid eligibility variables, one for individual
eligibility and one for the percent of the health insurance unit eligible, Cutler and Gruber
convert these to the percent of expected annual health expenditures for the health
insurance unit which could be covered through: 1) individual Medicaid eligibility, and 2)
Medicaid eligibility for all other members of the health insurance unit.

Using this speciﬁcation, Cutler and Gruber estimate the rate of crowding out to be
40% for children and more than 100% for women of child-bearing age. This analysis
provides greater rates of crowding out since it accounts for parents and children who
become uninsured, rather than gaining public insurance, but who otherwise would have

been covered under private policies.

22

Other researchers have directly used comparison groups to measure the magnitude
of crowding out, based on cross-sectional data. Dubay and Kenney (Health Affairs,
1997), for example, use a comparison group of men ages 18-44 to measure the magnitude
of crowding out for women ages 18-44. Their data are based on the 1989 and 1993
March CPS. In order to estimate the rate of crowding out, they ﬁrst calculate the
percentage point changes in employer-sponsored coverage and Medicaid coverage for
both women and men, agesl8-44. They then take the differences in the percentage point
changes for women compared to men, using a difference-in-diﬁ‘erences approach.
Finally, they compute the rate of crowding out as the difference in percentage point
change in employer-sponsored coverage divided by the difference in percentage point
change in Medicaid coverage. Since adult men were generally not affected by changes in
Medicaid policy, this suggests that they would make a suitable control for how women’s
participation in public or private health insurance would have changed in the absence of
Medicaid expansions. One potential difficulty with this approach is that women typically
become eligible for Medicaid only when they become pregnant. Pregnancy, itself, is
likely to affect women’s demand for health insurance coverage in ways that are not
comparable to men.

Dubay and Kenney estimate an overall rate of crowding out for women of
childbearing age to be 45%. They also separate their population into further income
groupings and ﬁnd no evidence of crowding out for women with incomes <100% of
poverty, crowding out of 29% for women with incomes loo-133% of poverty, and 59%
for women with incomes 134-185% of poverty. Thus, they conclude that crowding out

becomes more likely as the eligibility rates expand further. Finally, they construct a

23

weighted average of these crowding out rates, based on the percent of women in the
sample that become eligible within each income group, to conclude that the total rate of
crowding out is only 14%.

Rask and Rask (2000) study the crowding out effect of not only Medicaid
expansions, but also the effect on private insurance coverage resulting from changes in
subsidies to health care providers, either through public hospitals or through
uncompensated care reimbursement funds. They ﬁnd that all three forms of public
insurance acted as substitutes for private insurance, and that more substitution resulted
from provider subsidies than from Medicaid expansions. This is likely due to the fact
that Medicaid expansions only extend health insurance to speciﬁc income groups, while
provider subsidies potentially affect all demographic groups.

Rask and Rask’s analysis of changes in Medicaid eligibility is based on cross-
sectional data from the 1989 and 1992 National Health Interview Surveys. They identify
whether individuals are eligible for Medicaid under AFDC eligibility, Medically Needy
eligibility, or AFDC-UP eligibility (which extends Medicaid to two-parent families who
otherwise meet the AFDC eligibility standards). Their dataset is stratiﬁed by poverty-
level groupings (<100% FPL, loo-200% FPL, ZOO-400% FPL, and >400% FPL). The
effects of differences in Medicaid eligibility are estimated for the ﬁrst three groups using
a mulitnomial logit regression, with possible outcomes deﬁned as: private insurance,
Medicaid, or no insurance. The effects for the highest income group are estimated using
a binomial probit model with possible outcomes of private insurance or no insurance
(since Medicaid is unlikely to be available to this group). These estimates are made

separately for 1989 and for 1992. Comparing the results from the two years, they

24

conclude that increases in Medicaid generosity result in increased Medicaid coverage,
along with decreased private insurance coverage (crowding out) and decreases in the
uninsured. For the below-poverty group, increased likelihood of having Medicaid was
offset mostly by a decreased likelihood of being uninsured. For the loo-200% FPL
group, increased Medicaid coverage was offset by both decreased private coverage and
decreases in the uninsured.

Rask and Rask’s model does control for many demographic characteristics,
including race, marital status, income, education, family size, and broadly deﬁned age
category indicators. However, the fact that the two years are estimated separately
indicates that the results are mainly observational. There is no attempt to account for
other changes over time which may affect insurance choice.

Shore-Sheppard, Buchmueller, and Jensen (2000) use ﬁrm-level data from
various surveys taken between 1989 and 1995 to assess employers’ responses to
Medicaid expansions. They ﬁnd that ﬁrms employing large ﬁ'actions of low-wage
workers were signiﬁcantly less likely to offer insurance, but that ﬁrms’ decisions to offer
insurance was unaffected by the percentage of workers eligible for Medicaid. They did
ﬁnd that ﬁrms with a higher percentage of workers eligible for Medicaid were
signiﬁcantly less likely to offer family coverage. They conclude that crowding out
occurs mainly through reduced take-up of employer-sponsored coverage, rather than
through reductions in the availability of employer-sponsored coverage, particularly for
workers who would be required to contribute directly toward premiums.

Many researchers have also used longitudinal data from the SIPP and NLSY to

measure both the magnitude of crowding out and the choice of insurance, both before and

25

after a change in eligibility. Thorpe and Florence (1998) use NLSY data from 1989 to
1994 to show that, although approximately one-third of children enrolled in Medicaid had
held private coverage the previous year, only 16% of children newly enrolled had access
to private insurance through a parent’s employment at the time of enrollment. This
indicates that Medicaid expansions are less likely to crowd out employment related
coverage than non-group coverage. This result would be expected due to the fact that: 1)
employment related coverage premiums are typically less than non-group coverage, and
2) employment related coverage premiums are often subsidized by the employer. Thus,
households may have a greater incentive to drop non-group coverage, compared to
employer-related coverage, in order to enroll in Medicaid. Yazici and Kaestner (2000)
use NLSY data from 1988 to 1992 to estimate that crowding out accounted for 18.9% of
the increased participation in Medicaid. They compare the insurance coverage for
children under age 8 in 1988 who were made eligible through expansions to those who
were never eligible. Blumberg, Dubay, and Norton (2000) use data from the 1990 SIPP
Panel to examine the change in insurance coverage between 1989 and 1992. They
estimate that 23% of the movement from private insurance to Medicaid was attributable
to crowding out as a result of the expansions. However, they ﬁnd no evidence of
crowding out among those who were initially uninsured. While using longitudinal data
certainly has many advantages in assessing changes in the choice of insurance, there is a
much longer lag in the availability of such data, making it currently inaccessible for
studying the effects of state programs.

Overall, crowding out is estimated to account for 15-40% of the increased

children’s enrollment in Medicaid, following the expansions in eligibility which occurred

26

in the late 1980’s and early 1990’s. In comparing the different approaches used, Cutler
and Gruber are the only researchers that have used cross-state variation directly to
identify the response to changes in Medicaid eligibility. This distinction does appear to
have an effect on the estimates of crowding out, since Cutler and Gruber’s estimates are
noticeably larger than that of other researchers. Part of this difference is due to the fact
that Cutler and Gruber explicitly allow for indirect crowding out which results ﬁ'om a
gain in Medicaid eligibility of other family members. However, the estimates from their
ﬁrst model, which does not account for such spillover affects, are still greater than those
of other researchers.

The accuracy of any of the diﬁ‘erence-in-differences models depends on whether
the comparison group (those not affected by the policy change) is otherwise similar to
those who are affected by the policy change. In Cutler and Gruber’s model, children not
eligible for Medicaid (based on their age, state, and the year of the observation), provide
a natural control for otherwise similar children who are eligible for Medicaid, due to the
multiple sources of variation in Medicaid eligibility. In the other difference-in-
differences models, the downfall is that there may be important differences between the
comparison groups, aside from the change in Medicaid eligibility. For example, women
of child-bearing age would likely have a stronger preference toward holding health
insurance (especially those who are pregnant), compared to single men of the same age
group. As a result, women of child-bearing age may not have been as likely to drop
private health insurance coverage, in response to the 1991 recession, compared to single
men. Thus, single men may not provide an equivalent comparison group for women of

child-bearing age. As another example, families may have a stronger preference toward

27

insuring young children (who may require more frequent medical attention), compared to
older children. Again, families may have been less likely to drop private coverage for
young children, in response to the 1991 recession, compared to older children. Thus,
older children, who were less likely to be affected by previous Medicaid eligibility
expansions, may not provide an equivalent comparison group for younger children. In
my opinion, it is the lack of an equivalent control group which has generated lower
estimates of crowding-out, compared to Cutler and Gruber.

All of the previous studies looked speciﬁcally at the extent of crowding out of
private insurance, rather than the net change in the number of uninsured children.
However, the calculations for crowding out can be related directly to the effect on
uninsurance rates. For example, Cutler and Gruber provide the highest estimate of
crowding out as 40%, meaning that 40% of those enrolled in Medicaid, in response to
eligibility expansions, can be attributed to reductions in private insurance coverage. In
terms of the effect on the number of uninsured, this implies that 60% of those enrolled in
Medicaid, in response to eligibility expansions, can be attributed to reductions in the

number of uninsured children.

28

CHAPTER 4

DATA

Data on the Medicaid and state program eligibility limits, and the effective dates
of these changes, is gathered from the following sources: a report issued by Mathematica
Policy Research, Inc. (2001); state reports made to the Health Care Financing
Administration (HCF A); and from the State Children’s Health Insurance Program
(SCHIP) Database, available from the US. Department of Health and Human Services,
Ofﬁce of the Assistant Secretary for Planning and Evaluation (1999).

Data from the March CPS Supplements taken in years 1997-2001 (representing
data from 1996-2000) are used to determine whether children have insurance, as well as
for household demographic data. The March CPS is the most widely used data set for
estimates of the number of uninsured. It is also the only data set currently available with
data recent enough to study the Title XXI expansions. In addition to household
demographic questions, the survey asks respondents about the type of insurance coverage
held by all members of the household during the previous calendar year. Thus, including
data beginning with the March 1997 CPS will allow for one ﬁrll year of data prior to the
implementation of Title XXI expansions.

March CPS survey asks respondents who, if anyone, in the household received
Medicaid coverage (using state-speciﬁc names as well as Medicaid) during the previous
year. Similar questions are asked about employer— or union-sponsored plans, non-group
plans, Medicare, Military and VA health plans. The survey later asks whether anyone in
the household was covered by any other type of health insurance plan (including state-
speciﬁc state program names) not already talked about. These questions make it possible

29

to determine whether each member of the household has health insurance, or if they are
uninsured. Coverage under state programs is reported as “other government health care”
which may include other state plans with limited coverage; the March 2001 CPS includes
new questions relating directly to state programs.

One potential problem with the March CPS data is that they typically under-report
Medicaid participation, compared to administrative data from the Health Care Financing
Administration (HCFA). In 1995, HCF A reported that 36.7 million non-elderly
individuals were enrolled in Medicaid at some point during the year, compared to only 29
million, based on March CPS data (Fronstin, 2000a). Part of this under-reporting may be
due to the fact that many states have renamed their Medicaid programs. The March CPS
uses the state-speciﬁc names for Medicaid in their interviews to minimize this sort of
confusion. However, as Fronstin (2000a) points out, the list used during the March 1999
CPS did not contain Maryland’s Medicaid program, which had been renamed
HealthChoice, resulting in a substantial under-reporting of the number of Maryland
Medicaid participants. Measuring Medicaid and state program participation accurately
has likely become even more diﬁicult now that more states have renamed Medicaid and
many have added state programs (some of which have the same name as the Medicaid
program, such as Indiana’s “Hoosier Healthwise”). Accurately reporting an individual’s
coverage as either public or private may have become more difficult as well, since many
states are using capitated payments to private insurance providers, making the public
programs appear more like private plans to the recipients. Granted, this difficulty in
assessing the correct source of health insurance coverage is likely not unique to the

March CPS, since other surveys are facing the same confusions.

30

In order to minimize the effects of inaccurate reporting of type of insurance
coverage, my model is based on whether an individual is uninsured. This will avoid bias
due to individuals reporting private insurance when they really have Medicaid, for
example.

Estimates of the number of Americans who are continuously uninsured over the
course of a year, using the March CPS, are also typically higher than estimates from other
surveys. Swartz (1986) has shown that March CPS estimates are much more similar to
other surveys’ point-in-time estimates of the number of uninsured. Thus, Swartz suggests
that respondents to the March CPS may be reporting their current source of health
insurance, rather than their source(s) of health insurance for the previous year.

In 1995, the March CPS survey was expanded to include questions about the
current source of insurance, as well as questions pertaining to the previous year.
Benneﬁeld (1996) has shown that both the point-in-time estimates and previous-year
estimates of the uninsured, using the March CPS, were greater than estimates from the
Survey of Income and Program Participation. In addition, the March CPS point-in-time
estimate was considerably greater than the previous year estimate. Thus, while the
March CPS tends to over-report the number of uninsured, compared to other surveys, it
does not appear that respondents are merely reporting their current source of health
insurance (Benneﬁeld, 1996).

One other difference between the March CPS and other surveys is that individuals
are identiﬁed as being uninsured if they do not respond afﬁrmatively to any of the health
insurance questions. That is, the March CPS never asked if an individual is uninsured,

prior to 2000. Beginning with the March 2000 CPS, a veriﬁcation question was added

31

for individuals who did not respond affirmatively for any source of health insurance. For
the March 2000 CPS, there are duplicate sets of responses for each source of insurance:
one set is based on the original responses to the questions, where the second set includes
updated information obtained through the veriﬁcation question. The inclusion of the
veriﬁcation question resulted in a reduction in the measured percent of uninsured children
ﬁ'om 13.9% to 12.6% (Nelson and Mills, 2001).

While the inclusion of the veriﬁcation question improves the accuracy of the
estimates of the number of uninsured, it is impossible to infer how this would have
affected responses for years prior to 2000. In order to avoid bias in the effects of the
Title XXI expansions, resulting from changes in the survey questions, I ignore the
updated responses due to the veriﬁcation question in determining health insurance status].
Failure to report insurance status consistently would create an artiﬁcial decrease in the
number of uninsured children for years including the veriﬁcation question, since part of
the decrease is due only to the change in the survey question.

For the March 2000 CPS, this is easily accomplished because of the duplicate sets
of variables for sources of health insurance. For the March 2001 CPS, these variables are
deﬁned only after accounting for responses to the veriﬁcation question.

The veriﬁcation question is only asked of those individuals who did not
previously report any source of insurance. As a result, those who respond afﬁrmatively
to the veriﬁcation question are identiﬁed as insured, based on the veriﬁcation question,
but would have been identiﬁed as uninsured, in the absence of the veriﬁcation question,
as in previous survey years. Those who respond negatively to the veriﬁcation question

are identiﬁed as uninsured regardless of whether the veriﬁcation question is asked; those

32

who are not asked the veriﬁcation question are identiﬁed as insured regardless of whether
the veriﬁcation question is asked.

In order to provide a consistent measurement of insurance status for data in the
March 2001 CPS, compared to earlier survey years, I designate anyone who responded
aﬁirmatively to the veriﬁcation question as being uninsured. This approach was tested
using the duplicate responses in the March 2000 CPS. The set of individuals who
respond afﬁrmatively to the veriﬁcation question is the same as the set who are identiﬁed
as insured, based on the post-veriﬁcation variables, and as uninsured, based on the pre-
veriﬁcation variables.

The sample used from the March CPS, reﬂecting years 1996-20002, includes all
never—married children ages 0-17 ﬁom civilian households. I have excluded children
who are identiﬁed as the head of a household, family, or subfamily; and children
identiﬁed as primary individuals. Heads of households are treated as adults in the CPS,
making it impossible to determine some variables, such as the number of parents present.
Children identiﬁed as primary individuals (meaning that they are unrelated to other
members of the household) are automatically recorded as having no income, making it
impossible to describe their income and poverty-level group accurately.

Table 1 shows the percent of uninsured children, ages 0-17, in years 1996-2000
and various subgroups. All of the percentages in Table 1 are based on insurance status,
assuming that the veriﬁcation question had not been asked, following the procedure
outlined above. The ﬁrst column shows the percent of uninsured children for each year.

The second column shows the percent of uninsured children from civilian households, for

 

‘ This adjustment did not noticeably change any of the results reported
”These data are from the March cps surveys collected in 1997-2001.

33

each year. The third and fourth columns show the percent of uninsured civilian children
based on the observations included and excluded, respectively, in the dataset. Finally the

ﬁfth column shows the percent of uninsured civilian adults, ages 21-64.

 

 

Table 1: Uninsurance Rates, 1 996-2000.
All Children Civilian Included Excluded Civilian Adults

Lear Ages 0-17 §h_ilg_r§p_ Qh_ilggr_i 911m Ages 21-65
1996 14.82% 15.04% 14.60% 41.56% 18.71%
1997 15.00% 15.20% 14.71% 43.63% 19.60%
1998 15.37% 15.57% 14.96% 48.17% 19.80%
1999 13.86% 14.03% 13.52% 42.37% 19.61%
2000 12.93% 13.10% 12.64% 35.53% 19.50%

Source: Author’s calculations, based on March CPS surveys from 1997-2001 .

 

 

The children excluded from the dataset are much more likely to be uninsured than
those included. However, their exclusion does not dramatically alter the percent of
uninsured children and does not change the year-to-year trends. Additionally, the
changes in uninsurance rates over time are very similar for both the included and
excluded children.

Pooling all years together, the omissions account for less than 1.8% of the total
number of observations, representing less than 1.8% of the total population of children.
In any given survey year, the omissions are always less than 1.9% of either the total
number of observations or the represented population. Thus, it is unlikely that the
omitted data have a signiﬁcant impact on the ﬁndings of this research.

Finally, the last column of Table 1 shows the percent of uninsured, civilian adults

for years 1996-2000. This is presented as a comparison to the trends observed in the

34

children’s data. For both children and adults, the percent of uninsured increases from
1996-1998, and then decreases from 1998-2000. Thus, this downward change in the
trend for children is not necessarily related to the increased availability of public
insurance, since the Title XXI expansions would not be expected to have the same effect
on adults. Part of the downward trend for both children and adults is likely attributable to
the economic expansion over this period. However, the larger decline for children’s

uninsurance rates may indicate an additional effect from the Title )OCI expansions.

35

CHAPTER 5

STATISTICAL NIETHODS

5.1 Model Design

Given that the primary objective of the Title XXI expansions is to decrease the
number of uninsured children, I develop a model that will assess which type of Title XXI
expansion has the largest impact in reducing the percentage of uninsured children.

The approach used is most similar to that of Cutler and Gruber (1996). However,
Cutler and Gruber analyze the effect of changes in eligibility (instrumented by state-level
variation in eligibility) on changes in insurance source. Eligibility for state programs
may depend on a child’s current or recent enrollment in private insurance policies, if
waiting periods for eligibility are used. Since this cannot be observed with March CPS
data, inference about actual eligibility is not possible. Instead of trying to infer eligibility
from the data, the model described below looks at changes in the likelihood of being
uninsured, given changes in the state eligibility limits. Thus, the model estimates the
average response across all children, both eligible and non-eligible, with the expectation
that larger changes in eligibility will have a stronger effect on uninsurance rates. Since
Cutler and Gruber use variation in state-level eligibility to instrument for estimated
eligibility, the identiﬁcation strategy is very similar. The use of state-level policy
changes, rather than actual changes in eligibility, has also been used by Yelowitz (1995)
to estimate changes in women’s labor supply and welfare participation in response to

expanded Medicaid eligibility, relative to AFDC eligibility.

36

Additionally, where Cutler and Gruber analyze changes in insurance source, this
model looks only at the likelihood of being uninsured versus uninsured. This is because
of difﬁculties in determining insurance source, as discussed in chapter 4.

One additional difference between this model and that of Cutler and Gruber is that
I do not control for changes in insurance status resulting from expanded eligibility for
siblings. This model only estimates the change in insurance status in response to changes
in the individual child’s eligibility for public insurance. Under previous Medicaid
expansions, the result was that young children were more likely to gain eligibility,
compared to older siblings. The Title XXI expansions had the effect of equalizing
eligibility limits across all ages, though in some states, it is possible that a younger child
is eligible for Medicaid, while the older siblings are eligible for the state program, if one
exists.

The fact that there is a larger change in eligibility limits for older children may
not have any effect on younger siblings, though. For example, suppose a family has
previously dropped private coverage, insured their younger child in Medicaid, leaving an
older sibling (who is not eligible for Medicaid), uninsured. If the older child gains
eligibility through Title XXI expansions, that child may be then be enrolled in Medicaid,
or the state program, while the younger child remains enrolled in Medicaid. In this case,
there is no change in the insurance status of the younger child, and thus, no spillover
effect ﬁ'om the older sibling gaining eligibility.

Another scenario is that the same family had chosen to continue private coverage,
even though the youngest child is eligible for Medicaid. Ifthe older sibling now gains

eligibility to a public program, both children may be enrolled in Medicaid and/or the state

37

program. However, since this model does not distinguish between public or private
insurance, the children would be designated as insured both before and after the change.
While there is a spillover effect onto the younger child’s source of insurance, the fact that
the child is still insured is reﬂected in this model.

Data on children ﬁom the March CPS Supplement representing years 1996-2000
is used to estimate the following probitl regression:
prob(U =1) = <I>(a + AX + ﬂzll/ILim + ,63SDif + ,64State+ ﬂ5Year) (1)

In the above equation, (1) represents the standard normal cumulative distribution
ﬁrnction. ‘X’ is a vector of socioeconomic controls including dummy variables for the
age, race, and gender of the child, as well as dummy variables representing the type of
family (both parents present, mother only, father only, or neither parent present). ‘X’
also includes family earnings (divided by 10,000), family non-earned income (divided by
10,000), the number of persons in the family, as well as the squares of each of these
variables. Finally, ‘X’ includes dummy variables for poverty level groupings, based on
this ratio(<50%FPL, 50-100%FPL, 100-150%FPL, 150-200%FPL, 200-250%FPL, 250-
300%FPL, 300-350%FPL, and >3 50%FPL). Together, this will account for any changes
in the likelihood of being uninsured which are attributable to changes in the
characteristics of families in the United States. Thus, to the extent that the number of
children being covered by employer-sponsored coverage increases over this period, this
may in part be explained by changes in the proportion of families in each income
grouping. The inclusion of state and year dummy variables will account for differences

in the likelihood of being uninsured due to economy-wide effects speciﬁc to particular

 

' For a thorough description of the probit model, see Wooldridge, 2000, pages 530-540.

38

years and due to state-level differences which are unrelated to the Title XXI expansions.
However, state variables do not control for any state-level differences which are related
to the Title XXI expansions. For example, differences in states’ policy changes may have
been made in response to differences in previous uninsurance rates or trends. This
possibility of policy endogeneity is discussed further in Section 5.2.

The goal is to establish the effect of the Title XXI expansions, after controlling
for changes in the socioeconomic characteristics of households, as well as ﬁxed-effect
time trends and state-level differences. A simpler speciﬁcation of this model would be to
include a set of dummy variables measuring if there was: 1) an expansion in Medicaid
eligibility, based on the child’s age, state, and year; 2) a new state program in place for
that state; or 3) a combination of both expanded Medicaid eligibility and a new state
program. However, this approach is only appropriate if we expect the impact on the
likelihood of a child being uninsured to depend only on the type of expansion used, and
not on the magnitude of the expansion itself. In reality, there is considerable variation
across states in the increased generosity resulting from the expansions. In addition, there
is variation across states in the timing of the expansions. Finally, there is variation within
states in terms of both initial Medicaid generosity and the size of the expansion for
children of different ages. By using the upper eligibility thresholds, deﬁned as a
percentage of the FPL and based on the age of the child, the state, and the year of the
observation, we can account for changes in eligibility thresholds based on all sources of
variation.

In the probit equation (1), the variable ‘MLim’ is deﬁned as the upper threshold

for Medicaid eligibility (expressed as a decimal), based on the child’s age, state, and year,

39

and the Medicaid policies in effect in the year of the observation. In states that do not
increase their Medicaid eligibility limits, ‘MLim' remains at the same level existing in the
base year, 1996. In states that do increase their Medicaid eligibility limits, ‘MLim’ will
identify not only that a change in eligibility took place, but also the magnitude of the
change. In a year in which an eligibility expansion takes place, ‘MLim’ is deﬁned as the
average eligibility limit over the year. In other words, ‘MLim’ is increased by the
increase in eligibility times the share of the year that it was in effect. This adjustment is
done because increases in eligibility which occur late in the year would not be expected
to have the same effect as increases in eligibility which occur earlier in the year.

The variable ‘SDif’ is deﬁned as the difference between the eligibility threshold
of the new state program and Medicaid (expressed as a decimal), based on the child’s age
and state, and the year of the observation. For states without a new separate state
program, this would be deﬁned as zero; for states that do have a separate state program,
‘SDif’ would identify not only the presence of a separate plan, but also the increased
generosity of the plan, relative to the state’s Medicaid program. New York and
Pennsylvania offered state-wide comprehensive coverage for children under state-
sponsored plans prior to Title XXI legislation’. These state programs will be treated
identically to any new state programs. In these cases, the value of ‘SDif’ will reﬂect the
eligibility limits of the existing state plan, minus those of Medicaid, based on the child’s
age and state, even in the base year observations for 1996. Failure to include these plans
would overstate the true expansion in the availability of public insurance prior to Title

XXI, and may lead to biased estimates. Again, in order to adjust for differences in the

 

‘ Florida also had a comprehensive state plan prior to Title XXI, though it was not yet available state-wide.

40

timing of the expansions, ‘SDif’ is calculated as the average increase in eligibility limits
through state programs over the year.

A simpler speciﬁcation would be to replace ‘MLim’ and ‘SDif’ with a single
variable which measured the maximum eligibility threshold of either a new state program
or the state’s Medicaid program (for states that do not have a separate state program in
that year). This approach would assume that expanded eligibility through a new state
program has the same effect as an equivalent expansion in Medicaid eligibility.

For example, suppose there are two states in which the Medicaid eligibility
threshold before the expansions is 100% FPL. State A expands its Medicaid eligibility
threshold to 200% FPL, while state B leaves its Medicaid program unchanged and creates
a new state program which covers children with family incomes between 100% and
200% FPL. In the year in which the expansions took place, my model would deﬁne
‘MLim’ as 200 for state A and 100 for state B; ‘SDif’ would equal 0 for state A and 100
for state B. Under the simpler speciﬁcation, there would be only one variable equal to
200 for both states. This alternate speciﬁcation is identical to my speciﬁcation only when
B; is assumed to be equal to [33, in equation (1). That is, the alternative speciﬁcation
would look at the average effect of any type of expansion, whereas my speciﬁcation
allows for different effects of Medicaid expansions and state program expansions.
Likelihood ratio tests are performed in order to determine if there truly is a difference in
the effects of increased Medicaid eligibility, compared to increased eligibility through

state programs.

41

5.2 Possible Sources of Endogeneity

One potential problem with the model described is the possibility of endogenous
regressors. In the Probit estimation model, the key relationship addressed is the effect of
changes in eligibility limits, through Medicaid or a separate state insurance program, on
the uninsurance rates for children. However, these policy changes may have been made
in response to the states’ previous experience with uninsurance rates. Ifthis is the case,
then the policy changes, themselves, are endogenous leading to biased estimates of the
changes in uninsurance rates. Cutler and Gruber discuss this possibility, but do not
correct for it, since most of the changes in eligibility are in response to federal mandates,
not to state options. However, the expansions made under Title XXI are not due to
federal mandates. As a result, analysis of Title XXI expansions may be more susceptible
to policy endogeneity than previous expansions to Medicaid.

In order to test if there is evidence of such endogeneity, it would be necessary to
ﬁnd an instrument which is partially correlated with the state policy changes, after
controlling for all exogenous regressors, but which does not, itself, inﬂuence the
probability of being uninsured. In the absence of such an instrument, I rely on a more
observational approach. While this is not a strictly valid test for endogeneity, it does
provide some insight into whether the policy changes are likely to have been chosen in
response to states’ previous experience with children’s uninsurance rates.

I use two measures of states’ previous experience with children’s uninsurance
rates. Using data ﬁ'om 1994 to 1996, I estimate each state’s average uninsurance rate

over the three-year period, as well as the change in uninsurance rates ﬁ'om 1994 to 1996.

42

The policy variables measure the change in eligibility limits through either
Medicaid or through a separate state program. Combination states are identiﬁed only by
the fact that they have changes in both types of eligibility limits. Further, many of the
states that only used SCHIP expansions did have some increases in the Medicaid
eligibility limits, due to the phasing in of coverage for older children.

In order to assess if policy endogeneity is likely to exist, I test whether there is a
signiﬁcant correlation between either the prior average uninsurance rates or the prior
uninsurance trends and the changes in eligibility through Medicaid and through separate
state programs. The eligibility changes are deﬁned as the average eligibility change,
across all ages, for each state based on the policies in effect in 2000 (the last year in the

sample). These correlations, along with the p-values measuring signiﬁcance, are listed in

 

table 2.
Table 2: Correlations Between Prior Uninsurance Rates/Trends and Total
Eligibility Changes
Average Uninsurance Rate Uninsurance Trends
Medicaid Change 0.0613 -0.0633
(0.67) (0.66)
State Program Change 0.0428 0.1359
(0.77) (0.35)
Total Change 0.1036 0.0925
(0.47) (0.52)
p-values measuring signiﬁcance shown in parentheses.

 

 

 

Based on these correlations, there is no evidence that states’ prior experience with
uninsured children is related to the size of the expansions, either through Medicaid, separate
state programs, or the combined change in eligibility limits. However, some of the changes

in Medicaid would have taken place without any expansions through Title XXI, due to the

43

phasing in of eligibility limits for older children. Additionally, some of the expansions
through separate state programs were in effect prior to the Title )Oﬂ legislation. Table 3
summarizes the correlations if only the expansions related to Title XXI legislation are

considered.

 

Table 3: Correlations Between Prior Uninsurance Rates/Trends and
Title XXI Eligibility Changes

Average Uninsurance Rate Uninsurance Trends
Medicaid Change -0.0257 -0.0724
(0.86) (0.62)
SCHIP Change 0.0668 0.1959
(0.65) (0.17)
Total Change 0.0500 0.1494
(0.73) (0.30)

p-values measuring signiﬁcance shown in parentheses.

 

 

 

Again, there is no statistical evidence that states’ past experience with uninsured children
is related to the Title XXI eligibility expansions. As noted earlier, this is not the ideal
measure of whether the policy variables are, in fact, endogenous. However, it does
provide some evidence that differences in state policy changes are not a result of
differences in the states’ previous uninsurance rates or trends.

Another possible source of endogeneity exists if changes in eligibility limits for
public health insurance affect not only the insurance status of children, but also their
observed demographic characteristics. While this would obviously not affect the child’s
characteristics such as gender, age, or race, it is possible that there is an effect on the
family income. For children who gain access to public insurance, this may affect the

labor-force participation or work effort of their parents. Thus, changes in eligibility for

public health insurance may affect both family income and a child’s health insurance
status simultaneously.

Testing for such endogeneity requires an instrument which is partially correlated
with the measures of family income, after controlling for all other regressors, but not
otherwise correlated with the children’s likelihood of being uninsured. In the absence of
such an instrument, another correction to remove bias from the coefficients on the policy
variables is to use an instrument partially correlated with the policy variables, after
controlling for all other regressors, but not correlated with family income. This would
still potentially result in biased estimates for other coefﬁcients, though these are not of
primary interest in interpreting the results. Since the policy variables are set at the state
level, the state indicators included in the probit equation will control for any differences
in the demographic make-up of each state, such as differences in the average family
income across states. To the extent that the state indicators are not correlated with
individual characteristics, their inclusion should help to control for the possible
endogeneity of any of the individual demographic characteristics, including family
income. However, state indicators are not sufﬁcient if state-level differences in the
change in eligibility limits generate state-level differences in the change in average
family income.

Cutler and Gruber control for the possibility of endogenous demographic
variables using their instrument based on state-level variation in eligibility. Thus, the

technique used to remove bias in their study is essentially the same as that used here.

45

5.3 Simulated Changes in Uninsurance Rates Resulting From Title XXI Expansions

Once the probit model from equation (1) has been estimated, the results can be
used to simulate the change in the probability of being uninsured, resulting ﬁom the Title
XXI expansions. This is done by obtaining the coefﬁcients from equation (1), based on
the entire sample of children, including sample weights to ensure a nationally
representative sample. The change in the probability of being uninsured for each
individual in the 2000 sample can then be calculated as:

prob(U =1 l 2000 policy) — prob(U =1 l 1996 policy) (2)

The ﬁrst term is found by using the estimated coeﬁicients from equation (1) to
calculate the estimated probability of being uninsured, based on the demographic
characteristics and the Medicaid and state program eligibility rules in effect for each child
in the 2000 sample. The second term is found by repeating this estimation, but
substituting the values of ‘Mlim’ and ‘SDif’ that would have applied to each child, based
on the child’s age' and state and the policy rules present in 1996’. This results in the
estimated probability of being uninsured, based on the demographic characteristics and
the Medicaid and state program eligibility rules in place in the base year, 1996, for each
child in the 2000 sample. In other words, this is an estimate of the probability of being
uninsured that would have existed in the absence of Title XXI expansions. The average
difference between these two estimates is then the estimated change in the probability of
being uninsured due to the changes in eligibility rules and introduction of new state

programs, holding all other variables constant.

 

‘ Because Medicaid eligibility would have been phased in for many children born after 10/1/1983, I do not
treat these increases in eligibility as part of the relevant change in eligibility limits. However, I do not
distinguish between Title XXI expansions in Medicaid eligibility limits and other expansions in Medicaid

46

After ﬁnding the average effect of the Title XXI expansions across all states, this
process is then repeated to ﬁnd the average effect of the Title XXI expansions, based on
the type of expansion used. This is accomplished by using the estimation procedure
above to calculate the average effect of the expansions across: 1) children in states with
only Medicaid expansions, 2) children in states with only state program expansions, and
3) children in states with combination expansions.

As stated earlier, for states which had not previously increased their Medicaid
eligibility limits for older children, beyond the AFDC eligibility limits, Medicaid
eligibility would have increased for older children regardless of the Title XXI
expansions. In many of these states, these increases in Medicaid eligibility for older
children were accelerated in response to Title XXI legislation. By the year 2000, though,
eligibility for these children would have been ﬁrlly phased in under the previous federal
requirements. In deﬁning the type of expansion used, the accelerated phase-in of
previous federal requirements are not treated as a Medicaid expansions, since they do not
affect the eligibility levels observed in 2000, the last year of the sample used.

Standard errors for these estimates are computed as follows. The estimator of

interest is:
Fa?) ﬁZDc/‘b (3)

where D.- (it) = «Xi-I?) — was?)
X1: is based on the actual observed values of the regressors for the 2000 sample,

while X2: substitutes the policy variables based on the 1996 policy rules. (I) is the

 

eligibility which took place after 1996, such as Arkansas’ Section 1115 expansion to Medicaid eligibility
effective September, 1997.

47

standard normal cumulative distribution ﬁrnction and 4), used below, is the standard

normal probability density ﬁrnction. The conditional variance of F ( B) arises from the
fact that this statistic is based on the estimated probit coefﬁcients. Using the delta

method], the estimated conditional variance is deﬁned as:

14:41:05)]: CVC’ (4)

am?) 1 " ~ ~
Where C =——A_:— [¢(X"ﬂ)X1i -¢(X2tﬂ)X2i]
606)‘ N Z;

and V = Var(,8) , the variance-covariance matrix from the probit regression.
This same procedure is used to ﬁnd the estimated conditional variance of

F ( ,8) for each type of policy change. The estimated unconditional variance of
F ( 3) could also be found by adding the variance of the mean value of the differences in

the likelihood of being uninsured, D, (,8) , to the estimated conditional variance of F ( ,8) .

Due to the large sample size, though, this has a negligible effect on the size of the
variance.

The comparisons of the simulated effects resulting from each type of expansions
are mainly included for descriptive purposes. The resulting differences in the reduction
in the number of uninsured children, based on the type of expansion used, can result from
two distinct sources. First, they may be due to differences in the estimated effect of
increases in eligibility through Medicaid, compared to increased eligibility through state
programs. Second, there may also be differences in the average size of the expansions

themselves.

 

' See Greene (1993), page 297, for a description of the delta method.

48

As a descriptive measure, the simulations estimate the actual average change in
the probability of being uninsured arising ﬁ'om both sources. However, the results of
option, Medicaid versus state program expansions, is more effective in reducing the
number of uninsured children. For example, suppose that a one percent FPL increase in
Medicaid eligibility limits results in a greater reduction in the probability of being
uninsured, compared to a one percent FPL increase in state program eligibility limits. If
eligibility limits increase by the same amount in all states, then we would expect the
greatest reduction in uninsurance rates in states using only Medicaid expansions and the
least change in uninsurance rates in states using only state program expansions. States
using combination expansions would fall somewhere in the middle, with the estimated
change in uninsurance rates depending on the relative share of the total expansion which
takes place through both Medicaid and state program expansions. However, it is possible
that states using only state program expansions could achieve similar results in the
change in uninsurance rates if the average size of their expansions exceed the average

size of the expansions in states using only Medicaid expansions.

5.4 Average Partial Effects of Medicaid and State Program Expansions:

The simulations described in Section 5.3 are based on a two-step process. In the
ﬁrst step, the change in the likelihood of being uninsured is estimated for each child in
the 2000 sample, based on the actual eligibility limits in place in 2000, compared to the
counterfactual eligibility limits that would have existed in the absence of the Title XXI
expansions. Second, the average change in the likelihood of being uninsured is

calculated across: 1) all children; and 2) children in states with each type of expansion.

49

The two sets of eligibility limits used for each child, in the ﬁrst step, vary for children in
different states, and for children of different ages within a particular state. Thus, it would
be nearly impossible to objectively adjust these sets of eligibility limits so that the
average change in eligibility limits is equal for each type of expansion.

In order to present a more direct comparison between the estimated effects of
increased Medicaid eligibility, versus increases in eligibility through state programs, the
average partial effect of each type of expansion is calculated’. Rather than taking the
average of the estimated change in the likelihood of being uninsured for each individual
child in the 2000 sample, the average partial effects estimate the change in the likelihood
of being uninsured for a representative child with average characteristics. Since this
estimate is based on a single observation, derived from the average characteristics of all
children in the sample, it is much easier and straight-forward to compare the effects of
increases in Medicaid eligibility, versus an equivalent increase in eligibility through a
state program.

Average partial effects are usually found by computing the expected change in the
probability of being uninsured, given a change in one of the independent variables, based
on the estimated probit coefficients evaluated at the mean value of all other regressors. In
general, for a discrete change in an independent variable, Z, the average treatment effect

is computed as:
¢<Yﬁ+2.r)—<I>(Yﬁ+zzr) (5)
where 21 and Z; are the two discrete values for Z, A7 is the vector of means for all other

regressors, and ,8 and )7 are the estimated coefﬁcients. In applying this procedure to the

 

' See Wooldridge (2002), pages 458-459 for a description of average partial effects.

50

model at hand, we use the fact that the average eligibility limit in 2000 is 205.75% FPL
(including both Medicaid expansions and state program expansions). The average
eligibility limit that would have existed in 2000, based only on the required expansions
for older children, is 131.62% FPL, a difference of 74.13% FPL. Thus, if the Title XXI
expansions had all taken place through Medicaid expansions, the average partial effect
can be simulated by using 131.62 and 205.7 5 for the two discrete values of ‘MLim,’
deﬁning ‘SDif’ equal to zero, and by using the mean value of all other independent
variables. Similarly, if the Title XXI expansions had all occurred through state program
expansions, the average treatment effect can be simulated using 0 and 74.13 for the two
discrete values of ‘SDif,’ deﬁning ‘MLim’ equal to 131.62, and using the average value
of all other regressors.

Standard errors for the average partial effects are again computed using the delta

method. The statistic of interest is:
F<ﬁ>=<b<Zﬁ>~¢<Y2ﬁ> (6)
where X, and X, are the vector of means for each of the non-policy variables,

and the policy variables are deﬁned as described above. The estimated asymptotic

variance is deﬁned as:

Var[F(,é)] = CVC' (7)
where C = 26% = ¢(X,,8)X, — ¢(Xzﬁ)X_2 , and V = Var-(,8), the variance-

covariance matrix from the probit regression.
This procedure is a variation of the standard transformation used to estimate the

marginal effects of a change in an independent variable on the change in the probability

51

of observing a positive outcome in the dependent, binary variable. The probit
coefﬁcients in Equation (1) measure the change in the value of the linear projection, XB,
given a one-unit increase in each independent variable, holding all other variables
constant. This is not the same as the change in the probability of being uninsured,
(130(8), given a one-unit increase in each independent variable, which is usually of more
interest than the coefﬁcient itself. The change in the probability of being uninsured,

given a one-unit increase in each dependent variable can be found as:

dram/9)] z
dX

¢(Xﬂ).3

This expression depends not only on the coefﬁcient, but the value of the linear projection,
XB, which depends on the value of each independent variable. This expression is most
often estimated at the mean value of each of the independent variables. Thus, it is
interpreted as the average marginal effect of a one-unit increase in an independent
variable on the probability of a positive outcome.

This type of transformation is used in the results of Chapters 6 and 7 to show the
average marginal effect of a one-unit increase in either Medicaid eligibility limits or state
program eligibility limits, on the probability of being uninsured. The only difference in
the average partial effects is that it looks at the average partial effect of an average-sized
increase in either Medicaid or program eligibility limits on the probability of being

uninsured, rather than a one-unit change in eligibility limits.

52

5.5 Expanding the Model to Allow for Different Effects Across Poverty Level
Groups

In Chapter 7, the basic model is expanded to allow for different policy effects for
children in families from different socioeconomic backgrounds. Speciﬁcally, the policy
effects are allowed to vary for children within different poverty-level groups, based on
the ratio of family income to poverty level ratio.

Rather than having a single set of policy variables, which capture the average
effect across all children, the policy variables are interacted with the poverty level group
indicators. This creates a set of policy variables speciﬁc to each poverty-level group. In
order to determine the optimal speciﬁcation for the policy variables, interacted with the
poverty-level indicators, the following process is used.

First, a set of policy variables is deﬁned for children in families with income less
than 50% FPL, and a second set of policy variables is deﬁned for all other children. A
likelihood ratio test is performed to determine if the added policy variables generate a
signiﬁcant improvement in the model. Under the null hypothesis, there is no signiﬁcant
difference in the effects of the policy variables on children in families with income less
than 50% FPL, compared to all other children. This process is then repeated, adding
additional policy variables for each poverty level group, as long as the null hypothesis is
rejected ﬁ'om the likelihood ratio test. This provides us with the poverty-level groups for
which there was a different effect of the policy variables, compared to wealthier children.

Second, likelihood ratio tests are performed to determine if there is a signiﬁcantly
different effect for Medicaid expansions and state program expansions for each speciﬁc

poverty-level group identiﬁed in the ﬁrst set of likelihood ratio tests. That is, the ﬁrst set

53

of likelihood ratio tests identify poverty—level groups for which there is a signiﬁcantly
different effect of the policy variables on the likelihood of being uninsured; the second
set of likelihood ratio tests identify if there is any different marginal effect for Medicaid
expansions, compared to state program expansions. This is accomplished by combining
the ‘NﬂJm’ and ‘SDif‘ variables for a speciﬁc poverty-level group, thus imposing the
restriction that either type of expansion has the same effect. A likelihood ratio test is then
used to determine if the restriction has a signiﬁcant effect on the model. Under the null
hypothesis, there is no signiﬁcant difference in the effect of Medicaid expansions and that
of state program expansions on the likelihood of being uninsured.

Finally, the policy variables from adjacent poverty-level groupings are combined,
where feasible to determine if the effects of the policy variables are signiﬁcantly different
between the adjacent groups. A likelihood ratio test is performed to test the null
hypothesis that the policy effects are the same across the two groups. For example,
suppose we are testing if the policy changes have a different effects for the 0-50% FPL
group and the 50-100% FPL. The unrestricted model would contain separate policy
variables for each poverty-level group identiﬁed in the ﬁrst set of likelihood ratio tests.
The restricted model would combine the policy variables for all children in families with
incomes 0-100% FPL. The likelihood ratio test would compare the log likelihood of the
unrestricted and restricted models to determine if the restriction has a signiﬁcant effect on
the model. Such a combination is not performed if the policy variables are deﬁned
differently for the two adjacent poverty-level groups. For example, if it is found that

there is not a signiﬁcant difference in the effects of Medicaid and state program

54

expansions for the 0-50% FPL group, but there is a signiﬁcant difference for the 50-
100% FPL group, the policy variables cannot be easily combine for these two groups.

The choice of ﬁrst testing whether to combine the policy variables within each
poverty-level group and then whether to combine the policy variables across two adjacent
groups is somewhat arbitrary. Ifthe tests were performed in the opposite order, the
results may not be same. For example, it is possible that combining the policy variables,
‘MLim’ and ‘SDif’ for the 0-50% FPL and 50-100% FPL groups would not impose a
signiﬁcant restriction on the model. However, in the previous example, this test would
not be performed due to the fact that the ‘MLim’ and ‘SDif’ variables had already been
combined into a single policy variable for the 0—50% FPL group, but not for the 50-100%
FPL group. The chosen order to perform these tests, although arbitrary, is based on the
fact that this research is primarily interested in whether there is a different effect from
Medicaid expansions, compared to state program expansions. Thus, it is reasonable to
ﬁrst test if such a difference exists within each poverty-level group, prior to restricting
the policy variables across adjacent poverty-level groups.

Once the probit model has been estimated, the simulated effects of the expansions
are found using the same methods described in section 5.3. Now, however, the simulated
effects are estimated for: 1) all children combined; 2) children living in states with each
type of expansion; 3) children in each poverty-level group; and 4) children in each
poverty-level group living in states with each type of expansion. Again, these estimated
effects may vary not only because of differences in the estimated coefﬁcients on the
policy variables, but also because of different average magnitudes of the expansions, for

states with each type of expansion.

55

Finally, the average partial effects are estimated using the same procedure
described in section 5.4. Now, however, the average partial effects are estimated for each
poverty-level group separately. The changes in policy variables are identical to those
described in section 5.4, but the mean value of all other regressors is identiﬁed separately
for each poverty-level group. This allows for the estimation of the average partial effects
of Medicaid expansions versus state program expansions, based on the average
characteristics of a child ﬁ'om a speciﬁc poverty-level group, rather than the average
characteristics of a child from the entire population.

5.6 Robustness of the Model

In order to test that the model really is identifying the change in the likelihood of
being uninsured, I exploit the fact the Title XXI expansions typically apply only to
children, and not to their parents. As a result, we would not expect the changes in
Medicaid or state program eligibility limits to decrease the parents’ likelihood of being
uninsured. Ifanything, the parents would be more likely to be uninsured through indirect
crowding out. For example, parents may choose to drop family health insurance
coverage if their child (or children) can be enrolled in the public insurance program,
leaving non-eligible family members uninsured. In order to test this, the dependent
variable identifying whether each child is uninsured is replaced by a variable indicating if
they have an uninsured parent. Children not living with their parents are necessarily
excluded from the sample. Children whose parents are under the age of 19 are excluded
as well, since the Title XXI expansions would apply to these parents, as well as their
children. Additionally, children with parents age 65 or older are excluded since their

parents would be insured through Medicare.

56

CHAPTER 6
IMPACT OF TITLE XXI EXPANSIONS ON

AVERAGE UNINSURANCE RATES

6.1 Model Speciﬁcation

In order to ﬁnd the best speciﬁcation for the model, it is important to ﬁnd if there
is a signiﬁcant difference in the effects of increased eligibility through either Medicaid
expansions or state program expansions. Ifthere is not, then it is more appropriate to
replace the two policy variables with the sum of these variables, representing the
maximum eligibility threshold.

Expansions through either existing Medicaid programs or through separate state
programs make these programs available to children in families with higher incomes.
The children who become eligible due to the expansions may be more likely to have
access to private health insurance policies. As stated earlier, Medicaid typically does not
allow any cost-sharing measures or waiting periods for eligibility, while these are often
included in separate state programs. As a result, we might expect that the marginal effect
of Medicaid expansions is different ﬁ'om the marginal effect of expansions through
separate state programs.

In order to test if there is any difference in the effect of Medicaid expansions and
expansions through separate state programs, likelihood ratio tests are performed. The
model is ﬁrst estimated with separate ‘MLim’ and SDif‘ variables, representing the

unrestricted model.

57

The model is then re-estimated imposing the restriction that the coefficients for
‘Nﬂim’ and ‘SDif’ are equal. This is accomplished by replacing these two variables with
their sum (the total eligibility through either type of expansion). The results are shown in
Table 4. Based on the likelihood ratio comparing the unrestricted and restricted models,
there is weak evidence (p<0.10) for rejecting the hypothesis that expansions of either type

have the same marginal effect.

 

Table 4: Comparison of Probit Model Speciﬁcations

 

 

 

 

 

 

 

I ll
Policy Variable: Coefﬁcient (Standard Error)
MLimit 00856007
(0.026623) 00006765
SDif 0.0454054 (0.0003209)
(0.021 085)
Log-Likelihood 63059641 -63061 .465

 

 

LR Statistic (one restriction): 3648*
*Statistically signiﬁcant at the 90% conﬁdence level
Model I is the unrestricted model

Model II is restricted so that the coefﬁents on MLim
and SDif are equal

 

 

6.2 Probit Model Estimation Results

The probit coefficients for selected variables of the estimated model are presented
in Table 5. The complete results can be found in Appendix B. For comparison purposes,
the coefficients have also been estimated under the linear probability model. These are
shown in Appendix C. The interpretation of the linear probability model coefficients are
analagous to the estimated marginal effects ﬁ'om the probit estimation.

As discussed in Section 5.4, page 49, the coefficients themselves are difficult to
interpret, except for the fact that the sign of the coefficients indicate the direction of the

change. The last column in table 5 shows the marginal effects of a change in the

58

 

 

Table 5: Probit Regression Results, Selected Variables
Marginal Effect
Coefﬁcient Standard Mean Value at Mean Value
Error

Policy Variables:
MLim 00856007” 0.026623 1 .34 0016568
SDif 0.0454054“ 0.021085 0.35 0008788

Demographic Variables:
Female -0.006155 0.008943 0.49 0001191
White 0.099542“ 0.049429 0.79 0019934
Black 0.124164“ 0.052383 0.16 0022822
Asian 0.034132 0.060298 0.04 0.006731
Both Parents -0.9071 13*" 0.030535 0.69 0.213352
Mother Only -1 .008200*** 0.031347 0.23 0.142731
Father Only 0.689998“ 0.037684 0.04 0.089274
# in Family 0.130560“ 0.022114 4.27 0025270
# in Family Squared 0.012723” 0.002142 20.31 0.002463
Family Earnings -0.0721202*** 0.00651 1 5.05 0.013959
Family Earnings Squared 0.0015065“ 0.000148 54.92 0.000292
Other Income 01761351“ 0.015570 0.47 0034091
Other Income Squamd 0.008234” 0.001348 1.52 0.001594
0-50% FPL 0.290364” 0.047792 0.08 0.064710
50-100% FPL 0.401608” 0.042003 0.11 0.092904
100-150% FPL 0.480721 *** 0.037482 0.11 0.1 14598
150-200% FPL 0.377220“ 0.034030 0.1 1 0.086366
ZOO-250% FPL 0.257503“ 0.031648 0.10 0.056157
250-300% FPL 0.077069“ 0.029842 0.09 0.015489
300-350% FPL 0.048875 0.030016 0.08 0.009696

Pseudo R-Squared 0.1082

Log-Likelihood 63059641

*Statistically signiﬁcant at the 90% conﬁdence level

“Statistically signiﬁcant at the 95% conﬁdence level

“Statistically signiﬁcant at the 99% conﬁdence level

Standard errors are robust to heteroskedasticity and correlation between observations within the

same household.

Sarrrple Size is 174,004 observations. Estimation performed using sample weights. The

complete results for the probit coefficients are shown in Appendix C.

 

 

probability of being uninsured, given a change in the independent variable. These values
depend on the entire vector of independent variables; they are evaluated at the average
value of each variable. The direction of the change and statistical signiﬁcance is identical

to that of the estimated coefficients. However, the magnitude of the marginal effects is

59

easier to interpret since they are expressed as changes in the probability of being
uninsured.

The coefﬁcient on ‘MLim’ is negative and highly signiﬁcant, meaning that
children living in states with greater changes in Medicaid eligibility thresholds were less
likely to be uninsured. The coefficient on ‘SDif’ is negative and signiﬁcant, meaning that
increases in eligibility through separate state programs also result in a decrease in the
likelihood of being uninsured, as expected.

The coefficients on the earnings and non-earned income variables indicate that
higher income results in a decreased likelihood of being uninsured. The quadratics of
these variables indicate that this has a diminishing effect. A similar quadratic effect is
observed for the number of people in the family.

The poverty level groupings show that the likelihood of being uninsured increases
from the lowest poverty-level group through the 100-150%FPL poverty-level group.

This likely reﬂects the fact that children in the lowest poverty-level groups are more
likely to be eligible for Medicaid. For each poverty-level group beyond the 100-
150%FPL, the likelihood of being uninsured is decreasing. This likely reﬂects the fact
that children in higher income families are more likely to have access to employer-

sponsored coverage and are more likely to be able to afford non-group coverage.

6.3 Simulated Effects of Title XXI Expansions on Uninsurance Rates
In order to assess the impact of these expansions, I use the average predicted
probability of being uninsured, for all children in the 2000 sample [P(U|2000 policy)]. I

then recalculate the average predicted probability of being uninsured, substituting the

values of the policy variables with the 1996 policy variables, based on the child’s age and
state [P(U|1996 policy)]. This second prediction estimates what the probability of being
uninsured would have been in the absence of Medicaid and state program expansions.
The results are shown in Table 6.

The column labeled Actual P(U) shows the actual percentage of uninsured
children in the 2000 sample. The column labeled P(Ul2000) shows the average predicted
probability of being uninsured. The column labeled P(U|1996) shows the average
predicted probability of being uninsured, based on 1996 policy variables. Thus, the
difference between P(Ul2000) and P(U|1996), or %pt. AP(U), is the percentage-point
change in the probability of being uninsured that can be attributed to the Medicaid and

state program expansions.

 

 

Table 6: Simulated Effects of Medicaid and State Program Expansions

95% Conﬁdence Intemal
ExLTygg Actual P(U) P(Ul2000) P(Ul1996) % pt. Amy) Lower gm

All 12.64% 12.70% 13.49% -0.80** -o.24 -1.35
(0.28497)

Medicaid Only 10.86% 10.85% 11.82% 097*: -o.37 -1.56
(0.30348)

State Only 14.14% 14.50% 15.24% 074* -0.06 -1.42
(0.34655)

Combination 11.37% 10.56% 11.41% -0.85** 025 -1.46
(0.30934)

’Statistically signiﬁcant at the 95% conﬁdence level
”Statistically signiﬁcant at the 99% conﬁdence level

Sample size is 33,459 observations.

 

 

For all states combined (from the ﬁrst row of Table 6), this indicates that the

percentage of uninsured children is 0.80 percentage points lower due to the Medicaid and

61

state program expansions. This translates to a 5.92% reduction in the number of
uninsured children which can be attributed to the expansions].

For states with only Medicaid expansions, these expansions can be attributed to a
0.97 percentage point, or 8.19%, reduction in uninsured children. For states with only
state program expansions, there was a 0.74 percentage point, or 4.95%, reduction in
uninsured children. For states with a combination of Medicaid and state program
expansions, there was a 0.85 percentage point, or 6.97%, reduction in uninsured children.

The percentage-point reduction in the uninsurance rates is highly signiﬁcant for
all states combined, as well as for each type of expansion. Thus, we can conclude that all
three types of expansion had a signiﬁcant impact on reducing the number of uninsured
children. However, we cannot conclude that any expansion type has a signiﬁcantly
different effect from the others, based on the 95% conﬁdence intervals. The differences
are also not statistically different at the 90% conﬁdence level.

Based on the point estimates, it appears that the best results were achieved
through only Medicaid expansions and the worst through only state program expansions.
However, this result does not take into account the fact that the average change in
eligibility limits differs, based on the type of expansion used. Table 7 shows the average
eligibility threshold which would have existed in 2000, based on the 1996 policy rules,
the actual average eligibility threshold in 2000, and the percent change in these thresholds
for each type of expansion. The average eligibility thresholds are calculated using the
threshold for a child of each age, 0-17, for each group of states using the same type of

expansion.

 

' The percentage change is calculated by dividing the estimated change in the probability of being
uninsured, %pt. AP(U), by the estimated percent of uninsured children in the absence of the expansions,

62

 

 

Table 7: Changes in Eligibility Limits for the 2000 Sample, 1996 Rules versus Actual 2000

Rules
ggnsion Tygg Elig. Limit (1996 rules) Actual Elig. Limit %Change
All 131.62 205.75 56.32%
Medicaid Only 133.97 200.64 49.77%
State Only 129.47 196.60 51.85%
Combination 1 32.75 206.73 70.79%

 

 

The percent changes in the eligibility limits are used to calculate an elasticity of
uninsurance rates for children with respect to eligibility limits. This provides a measure
which can be more easily compared across expansion types. This elasticity is -0. 1646 for
children in Medicaid-only states, -0.0954 for children in states using only state program
expansions, and -0.0985 in combination states. Thus, the response of uninsurance rates
to changes in eligibility limits appears to be greatest in states using Medicaid-only
expansions, and similar in states using only state program expansions and states using
combination expansions. However, we cannot statistically conclude that there were any
differences in these elasticities for any of the expansion types.

6.4 Average Partial Effects of Medicaid and State Program Expansions

As noted in Chapter 5, differences in the simulated effects discussed in section 6.3
may arise from differences in the estimated probit coefficients on the policy variables or
differences in the average magnitude of the expansions, based on the type of expansion
used. In order to present a more even comparison of the effects of Medicaid expansions
versus state program expansions, the average partial effects of each type of expansion are

estimated. These estimates are based on the change in the probability of being uninsured,

 

Actual P(U) + %pLAP(U).

63

 

given the average change in eligibility through either Medicaid or state programs
expansions. All other non-policy variables are evaluated at their mean value. The

average partial effects are presented in table 8:

 

Table 8: Average Partial Effects from Medicaid Expansions versus State Program

 

Expansions
95% Conﬁdence Interval
ggansion Tyg Change in P(U) Standard Error Mgr 92m
Medicaid -1.21** 0.3634 -0.50 -1.92
State Program 065* 0.3026 -0.06 -1.25

*Statistically Signiﬁcant at the 95% conﬁdence level
“Statistically signiﬁcant at the 99% conﬁdence level

 

 

The average partial effect ﬁom either type of expansion is statistically different
than zero, meaning that both have a signiﬁcant impact on uninsurance rates. Based on
the point estimates of the change in the probability of being uninsured, the average partial
effect of a Medicaid expansion is nearly double the average partial effect of an equivalent
state program expansion. However, there is no evidence that the average partial effects
ﬁ'om Medicaid expansions are statistically different from those of state program
expansions.

The difference in the effect on uninsurance rates, comparing Medicaid expansions
and state program expansions, is more pronounced in the average partial effect estimates,
compared with the simulations in Section 6.3. This results, in part, ﬁom the fact that the
same change in eligibility is used for each of the average partial effect estimates. For the
estimated simulated effects, recall that the average change in Medicaid eligibility limits
was less than the average overall change in eligibility limits. The average change in

eligibility limits for states using only state program expansions was similar to the overall

64

average change. Thus, the average partial effect estimates, for Medicaid expansions, are
based on a somewhat larger average change in eligibility limits, compared to that used in

the simulations.

65

CHAPTER 7
EFFECTS OF TITLE XXI EXPANSIONS ON

DIFFERENT POVERTY-LEVEL GROUPS

7.1 Model Speciﬁcation

This chapter explores the differences in the effects of Title XXI expansions on
children in different poverty-level groupings, based on the ratio of family income to the
federal poverty level.

A priori, we would expect that children in wealthier families are not affected by
Title XXI expansions, since they are likely not eligible for public insurance progranrs at
all. In addition, we would also expect that there may be little effect on children in the
lowest poverty-level groups since they are likely eligible for Medicaid, regardless of the
Title XXI expansions. However, there may be an effect on these children if the Title XXI
expansions increase awareness of public insurance programs for those who were
previously eligible for Medicaid, but had not enrolled. In addition, there may be a
reduction in the perceived stigma of public insurance programs resulting ﬁ'om Title XXI
expansions. This effect may be larger for states using state program expansions, as
opposed to Medicaid, though most states have also renamed their Medicaid programs in
an effort to reduce the perceived stigma. The reduced stigma may increase enrollment
even for those previously eligible for Medicaid, since most states that have state
programs use a common application for both Medicaid and the state program. The
greatest effect on uninsurance rates would be expected for children with family income

loo-200% FPL, since these are the most likely to gain eligibility for public insurance as a

66

 

result of Title XXI expansions. In addition, there may be an effect on children with
family income above 200% FPL due to the fact that some states have expanded eligibility
beyond this level.

Beginning with the model used in Chapter 6, the policy variables are deﬁned
separately for children with family income at or below 50% FPL and those with income
above 50% FPL. A likelihood ratio test is used where the unrestricted model contains the
policy variables deﬁned separately for children in the lowest poverty-level group, and the
restricted model has one set of policy variables applying to all children. Under the null
hypothesis, the restriction does not signiﬁcantly affect the estimation, meaning that there
is not a different effect on very poor children, compared to children in higher income
families.

This process is then repeated by adding an additional set of policy variables for
children with family income 50-100% FPL, 100-1 50% FPL, ISO-200% FPL, etc.
Additional policy variables are added as long as the null hypothesis is rejected under the

likelihood ratio test. The results of these tests are shown in Table 9.

 

Table 9: Likelihood Ratio Tests for Adding Policy/Poverty-Level Group Interactions
Model

Modgl Descrim'on ng-Likelihood Comggrison Restrictions LR Statistic
I No Interactions 63059641
ll Add Interactions for <50% FPL -63051.351 Ivs. II 2 1658*
III Add Interactions for 50-100% FPL -63016.757 II vs. III 2 69188"
IV Add Interactions for 100-150%FPL 62975627 III vs. IV 2 82.26“
V Add Interactions for 150-200%F PL -62945.124 IV vs. V 2 61 .006"
VI Add Interactions for 200—250%F PL 62936465 V vs. VI 2 17.318*
VII Add Interactions for 250-300%FPL 62934910 VI vs. VII 2 3.11
*Statistically Signiﬁcant at the 99% conﬁdence level

 

 

67

Based on these results, there is a signiﬁcant improvement in the model for each
additional set of policy variables up to 250% FPL. Comparing model VI and model VH,
however, we ﬁnd that there is not a signiﬁcantly different effect of the policy variables

for children with family income 200-250%FPL, compared to children with family income

 

above 250% FPL.
able 10: Probit Regression Results for Policy Variables with Policy/Poverty-Level
Interactions
Marginal Effect
90mm 31.52% W W

Policy Variables:
MLim: 0-50% FPL 0.17837“ 0.04689 0.1017 0.03452
MLim: 50-100% FPL 0.21901“ 0.04858 0.1394 0.04238
MLim: 100-150% FPL 0.17790“ 0.04430 0.1447 0.03443
MLim: 150-200% FPL -0.15211*** 0.05080 0.1430 0.02944
MLim: ZOO-250% FPL 007334" 0.04115 0.1325 0.01419
MLim: >250% FPL 0.02237 0.03027 0.6744 0.00433
8011: 0-50% FPL 0.11104“ 0.05203 0.0244 0.02149
SDif: 50-100% FPL 0.13708” 0.04067 0.0378 0.02653
SDif: 100-150% FPL 0.15422“ 0.03784 0.0392 0.02984
SDif: 150-200% FPL 0.07604“ 0.03865 0.0357 0.01471
SDif: ZOO-250% FPL 0.04512 0.04459 0.0337 0.00873
SDif: >250% FPL 0.04022 0.02587 0.1820 0.00778

Pseudo R-Squared 0.11

Log-Likelihood 02936465

Actual Percent Uninsured 14.08%

Predicted % Uninsured, at Mean Values 11.45%

*Statistically signiﬁcant at the 90% conﬁdence level

“Statistically signiﬁcant at the 95% conﬁdence level

”Statistically signiﬁcant at the 99% conﬁdence level

Standard errors are robust to heteroskedasticity and correlation between observations within

the same household.

Sample Size is 174,004 observations. Estimation performed using sample weights. The

complete results for the probit coefﬁcients are shown in Appendix B.

 

 

Table 10 presents the estimated coefficients for the policy variables, deﬁned

separately for each of the poverty-level groups identiﬁed as having a signiﬁcantly

68

 

different response: 0-50% FPL, 50-100% FPL, 100-1 50% FPL, ISO-200% FPL, 200-
250% FPL, and greater than 250% FPL.

Based on the coefficients in Table 10, Medicaid expansions have a signiﬁcant
effect in reducing the likelihood of being uninsured, for children in all poverty-level
groups up to 250% FPL. The magnitude of the coeﬁicients increases from the 0-50%
FPL group to the 50-100% F PL group, and declines thereafter. The lesser response of the
0-50% FPL group is likely due to the fact that these children were most likely to be
eligible, regardless of the Title XXI expansions. In spite of this, there was a signiﬁcant
effect for the 0-50% group, which is likely attributable to increased awareness of public
insurance programs, as a result of Title XXI expansions, rather than expanded eligibility.

Again, based on the coefﬁcients in Table 10, the state program expansion have a
signiﬁcant effect in reducing the likelihood of being uninsured for all children in poverty-
level groups up to 200% FPL. The magnitude of the coefficients increases ﬁ'om the 0-
50% FPL group through the 100-150% FPL group, and declines thereafter. Again, the
signiﬁcant response of the lowest poverty-level group is likely attributable to increased
awareness of public insurance programs, as a result of Title XXI expansions, rather than
expanded eligibility.

The likelihood ratio tests shown in Table 9 determine if there is a different effect
for each additional set of policy variables, compared to children with higher family
income relative to poverty. However, this does not test 1) whether there is any difference
in the marginal effect of Medicaid expansions, compared to state program expansions, or

2) whether there is a signiﬁcantly different effect between adjacent poverty—level groups.

69

Likelihood ratio tests are performed to test if there is any statistical difference in
the marginal effects of Medicaid expansions, compared to state program expansions for
each of the poverty-level groups identiﬁed as having a signiﬁcantly different effect in
Table 9. The results are shown in table 11.

Under the null hypothesis, the effects of Medicaid expansions are not statistically
different from the effects of state program expansions. Based on the likelihood ratio tests
in Table 11, there is evidence that the effects of Medicaid expansions are different from
state program expansions for all children with family income 0-100% FPL and ISO-250%
FPL. However, there is no evidence of a different effect for children with family income
loo-150% FPL and above 250% FPL. The fact that Medicaid and state program
expansions have similar effects for children with family income above 250% is not

surprising, given that the neither coefficients on the policy variables for this group are

 

 

signiﬁcant.
Table 1 1: Likelihood Ratio Tests for Different Effects from Medicaid versus State

Program Expansions

I__gg; Model

Model Descrimion Likelihood Commn‘son Restrictions LR Statistic
I Unrestricted Model 02936465
ll MLim=SDif for 0-50%FPL 02937.967 I vs. II 1 3004*
III MLim=SDif for 50-100% FPL 02939.595 I vs. III 1 6260”
IV MLim=SDif for 100-150% FPL 02936739 Ivs. IV 1 0.548
V MLim=SDif for 150-200%FPL 02939074 I vs. V 1 5.218“r
Vl MLim=SDif for 200-250%FPL 02942.190 I vs. VI 1 11.450“
Vll MLim=SDif for >250%FPL 02936730 I vs. VII 1 0.530
VIII MLim=SDif for 100-150% FPL

MLim =SDif for >250% FPL 02936841 I vs. VIII 1 0.752

'Statistically signiﬁcant at the 90% conﬁdence level
“Statistically signiﬁcant at the 95% conﬁdence level
“Statistically signiﬁcant at the 99% conﬁdence level

 

 

70

For children with family income below the poverty level, there is a signiﬁcantly
different effect of Medicaid expansions, compared to state program expansions, on the
likelihood of being uninsured. The coefﬁcients in Table 10 indicate that the Medicaid
expansions have had a stronger effect for these groups, compared to state program
expansions. This is somewhat surprising given the fact that most of these children would
have gained eligibility for Medicaid, regardless of the Title XXI expansions. The
stronger effect of Medicaid expansions on children with family income below the poverty
level is explained by the fact that Medicaid eligibility was still being phased in for older
children in many states during the study period. As noted earlier, much of the response to
increased eligibility, for children with family income below the poverty level, was likely
due to increased awareness of public insurance programs. While promotion of a new
state program might be expected to generate greater awareness, similar promotion likely
took place in states that only expanded Medicaid eligibility. States are required to enroll
Medicaid-eligible children in their Medicaid program, regardless of the presence of a
state program. Families whose children are eligible for Medicaid, though, may be less
responsive to promotion of a state program, compared to promotion of a Medicaid
expansion, if they perceive that cost-sharing measures in the state program will apply to
them. This may also explain the stronger effect of Medicaid expansions, compared to
state program expansions, on the likelihood of being uninsured for children with family
income below the poverty level.

In order to test if the policy variables have a signiﬁcantly different effect for
adjacent poverty-level groups, a ﬁnal set of likelihood ratio tests is performed. Since this

builds off the previous restrictions, the unrestricted model does impose the restrictions

71

 

that the coefficients on ‘MLim’ and ‘SDif’ are equal for the 100-1 50% FPL group and
the greater-than 250% FPL group. Because of these previous restrictions, the policy
variables cannot be combined between these groups and the adjacent poverty-level
groups. Thus, the two restrictions to be tested is whether the policy variables have a
different effect for the 0-50% FPL group, compared to the 50-100% FPL group, and for
the ISO-200% FPL group, compared to the ZOO-250% FPL group. The results are shown

in Table 12.

 

Table 12: Likelihood Ratio Tests to Combine Policy Variables for Adjacent Poverty-Level
Groups
Log- Model
Model Descriwog Likelihood Commiison Restrictions LR gﬂgic
l Unrestricted Model 02936465
Il Same Policy Effects for 0-100%FPL 02937.812 I vs. II 2 1.942
lll Same Policy Effects for 150-250%FPL 02945.383 I vs. III 2 17.084*
*Statistically signiﬁcant at the 99% conﬁdence level

 

 

Based on the likelihood ratio tests in Table 12, there is no statistical evidence that
the effect of the policy variables are different for the 0-50% FPL group, compared to the
50-100% FPL group (comparing model I to model H). However, there is evidence that
the effect is uniquely different for both the ISO-200% FPL group and for the ZOO-250%
FPL group, since the null hypothesis is rejected when comparing Models I and 111.

Based on the results of all of the likelihood ratio tests, the model is deﬁned as
follows for all remaining estimates in this chapter. Policy variables are deﬁned for each
of the following poverty-level groups: 0-100% FPL, 100-150% FPL, ISO-200% FPL,

ZOO-250% FPL, and greater than 250% FPL. Further the policy variables are restricted so

72

that the coefﬁcient for ‘MLim’ is equal to the coefﬁcient for ‘SDif,’ for the 100-150%
FPL and greater than 250% FPL groups.

As noted in Chapter 5, the ﬁnal choice of restrictions on the model depends on the
order the restrictions are imposed. Ifthe order is reversed, so that we ﬁrst test for
different policy effects between adjacent poverty-level groups, and then for differences
between Medicaid and state program expansions within each poverty-level group, the
results do, in fact, differ. In this case the model would have been deﬁned so that there is
a separate set of policy variables for children with family income of 0-1 50% FPL, 150-
200% FPL, ZOO-250% FPL, and greater than 250% FPL. The only restriction that the
coefﬁcients on ‘MLim’ and ‘SDif’ are equal would be made for those with family income
greater than 250% FPL.

Again, the chosen order seems more reasonable since one of the central questions
of this research is whether there is a different effect on the likelihood of being uninsured
resulting ﬁ'om Medicaid expansions, compared to state program expansions.

Additionally, the results achieved under this approach more closely represent the
results achieved without imposing any of the restrictions, shown in Table 10. Namely, as
seen in the next section, the magnitude of the coefﬁcient on Medicaid expansions is
greatest for the 0-100% FPL group and declining for children in higher poverty-level
groups; the magnitude of the coefficients on state program expansions are greatest for the
100-150% FPL group, and declining for children in higher poverty-level groups. This is
consistent with the results in Table 10, where no additional restrictions are imposed. If

the order of restrictions were reversed, this difference in where the magnitude of the

73

coeﬂicients peak is lost, since all children with family income 0-150% FPL would be
, combined into the same group.
7.2 Probit Model Results

Results from the Probit estimation are presented in Table 13. The complete
results can be found in Appendix D. Again, for comparison purposes, the coefﬁcients
have also been estimated under the linear probability model. These are shown in
Appendix E.

The policy variables are signiﬁcant for family incomes up to 200% FPL, regardless of the
type of expansion used, with stronger point estimates for the Medicaid expansions.
Medicaid expansions also have a weakly signiﬁcant effect for children with family
incomes of ZOO-250% FPL. Based on the likelihood ratio tests in section 7.1, there is a
signiﬁcant difference in the effects of Medicaid expansions, compared to state program
expansions, within each poverty-level group up to 250% FPL. The coefﬁcient on the
policy variable for children with family income above 250% FPL is not statistically
signiﬁcant.

The fact that there is no signiﬁcant effect for wealthier children supports the
expectation that the Title XXI expansions should not have had an effect on children in
higher-income families, who are not likely to be eligible for any public health insurance.
However, the strong effect on the lowest poverty-level group is somewhat unexpected.
The effect of the Title XXI expansions on uninsurance rates for children is largest for the
poorest children and is not statistically different for children with family income of 0-
50%FPL, compared to the 50-100% FPL and 100-150% FPL. The poorest children were

most likely to be eligible for Medicaid, regardless of the Title XXI expansions. In fact,

74

 

Table 13: Probit Regression Results with Restricted Policymommuml Interactions,
Selected Variables
Marginal Effect
Cosme—n! M32: W W
Policy Variables:
MLim: 0—100% FPL 01939005“ 0.035728 0.241 1 00375246
SDif: 0-100% FPL 01309103“ 0.033286 0.0622 00253344
Limit: 100—150% FPL -0.1642206*** 0.030784 0.1839 00317808
MLim: 150-200% FPL 01430038“ 0.049810 0.1430 00276748
SDif: 15020096 FPL 00797135“ 0.038106 0.0357 00154266
Mlim: ZOO-250% FPL 00644463‘ 0.038016 0.1325 00124720
SDif: zoo-250% FPL 0.0413475 0.044168 0.0337 0.0080018
Limit: >250% FPL 0.0333071 0.020904 0.8565 0.0064458
Demographic Variables:
Female 00064609 0.008950 0.4866 00012502
White 01034218“ 0.049572 0.7866 00207356
Black 01300948" 0.052516 0.1578 00238497
Asian 0.0275784 0.060413 0.0430 0.0054183
Both Parents 09061552“ 0.030585 0.6913 021 30654
Mother Only -1 .007975'" 0.031424 0.2342 01426861
Father Only 06877146” 0.037696 0.0443 00890846
# in Family 01300754W 0.022216 4.2725 00251729
# in Family Squared 0.0126613“ 0.002156 20.3136 0.0024503
Family Earnings 00728045“ 0.006480 5.0459 00140895
Family Earnings Squared 0.001517“ 0.000147 54.9178 0.0002936
Other Income -0.1770445*** 0.015564 0.4679 00342626
Other Income Squared 0.008316” 0.001347 1.5208 0.0016109
0-50% FPL 0.6409523“ 0.068885 0.0763 0.1653409
50-100% FPL 0.7530474“ 0.063871 0.1062 0.1983624
100-150% FPL 0.8072689“ 0.061755 0.1096 0.2158829
150-200% FPL 0.6472169“ 0.072865 0.1070 0.1643618
ZOO-250% FPL 0.3842641” 0.061267 0.0994 0.0884903
250-300% FPL 0.0788431 *** 0.029726 0.0893 0.0158574
SOD-350% FPL 00530599“ 0.029930 0.0772 0.0105471
Pseudo R-Squared 0.11
Log-Likelihood 02937.812
Actual Percent Uninsured 14.08%
Predicted % Uninsured, at Mean Values 11.45%
I'Statistically signiﬁcant at the 90% conﬁdence level
atistically signiﬁcant at the 95% conﬁdence level
”Statistically signiﬁcant at the 99% conﬁdence level
Standard errors are robust to heteroskedasticity and correlation between observations within the

 

me household.
ample Size is 174,004 observations. Estimation performed using sample weights.

 

 

75

by 2000, all children with family income below 100% FPL are eligible for Medicaid,
regardless of their age, state, or the presence of a state program. Thus, it appears that part
of the success in reducing children’s uninsurance rates was through increased enrollment
for those already eligible for Medicaid. This likely is in response to increased awareness

of public insurance programs as well as a reduction in the stigma associated with

Medicaid.

7.3 Simulated Effects of Title XXI Expansions on Uninsurance Rates

Table 14 shows the estimated average changes in uninsurance rates for each of the
following groups: 1) all children combined; 2) children in states with each type of
expansion; 3) children in each poverty level group; and 4) children in each poverty level
group for each type of expansion. This is based on the same calculation used in section
6.3, except that the policy variables are allowed to vary for different poverty-level
groups, as deﬁned in Section 7 .2. For all children combined, there was an estimated 0.90
percentage point, or 6.62%, decrease in the number of uninsured children, as a result of
the Title XXI expansions. This is slightly above the estimated 0.80 percentage point, or
5.92%, decrease found in section 6.3, though this difference is not signiﬁcant based on
the 95% conﬁdence intervals for the two estimates.

For states using only Medicaid expansions, there was an estimated 1.00
percentage point, or 8.45%, decrease in the number of uninsured children. Again, this is
very similar to the estimated 0.97 percentage point, or 8.19% decrease found in section
6.3. For states using only state program expansions, there was an estimated 0.93

percentage point, or 6.17%, decrease in the number of uninsured children. This is

76

somewhat higher than the estimated 0.74 percentage point, or 4.95% decrease found in
section 6.3, though this difference is, again, not signiﬁcant based on the 95% conﬁdence

intervals for these estimates. Finally, for states using combination expansions, there was

an estimated 0.82 percentage point, or 6. 76%, reduction in the number of uninsured

children, which is similar to the estimated 0.85 percentage point, or 6.97%, decrease

 

 

 

found in section 6.3.
Table 14: Simulated Effects of Medicaid and State Program Expansions:
Overall, By Expansion Type, and by Poverty-Level Group
95% Conﬁdence Interval
Actual P(U) P(Ul2000) P(UI1996) % m. [P(U) Lower Um
All 12.64% 12.64% 13.54% 090*“ -1.43 036
(0.2720)
By Expansion Type:
Medicaid Only 10.86% 10.87% 11.87% -1.00*** -1.46 055
(0.2320)
SCHIP Only 14.14% 14.38% 15.31% 093‘" -1.52 033
(0.3037)
Combination 11.37% 10.59% 11.41% 082*“ -1.40 025
(0.2921)
By Poverty Level:
0-100% FPL 22.06% 21.08% 24.19% -3.11*** -4.38 -1.85
(0.6469)
100-150%FPL 20.81% 21.58% 25.46% 088*“ -5.32 -2.45
(0.7323)
ISO-200% FPL 15.42% 17.98% 19.84% -1.86*** -3.23 049
(0.6989)
ZOO-250% FPL 15.60% 14.97% 14.57% 0.39 090 1.68
(0.6582)
<250% FPL 7.04% 6.80% 6.47% 0.33 007 0.73
(0.2035)
Statistical Signiﬁcance: *90% Conﬁdence, ”95% Conﬁdence, ”99% Conﬁdence
Sample size: 33459 observations

 

Based on the percent changes in the number of uninsured, the ranking is the same

as before, with the largest impact in states using only Medicaid expansions and the

smallest impact in states using only state program expansions.

77

 

While there is no evidence that any of these estimates are signiﬁcantly different
from those found in section 6.3, it is surprising that the estimated percentage-point
change in the number of uninsured children is now higher in states using only state
program expansions, compared to states using combination expansions. Based solely on
the probit coefﬁcients, the effect is always stronger for changes in Medicaid eligibility
than for changes in state program eligibility. Thus, we would expect a smaller effect in
states using only state program expansions, compared to states using both Medicaid and
state program expansions. The fact that we now estimate a larger effect for state program
expansions than for combination expansions results from differences in the distribution of
children in each poverty level grouping. Table 15 shows the distribution of children in
each poverty-level group for years 1996-2000.

In each year, the percent of children in the lowest poverty-level group is greatest
in the states using only state program expansions. Similarly, the percent of children in
the highest poverty-level group is lowest in states using only state program expansions.
The simulations in table 13 are based only on the 2000 sample. Because the effects of the
Title )OCI are greatest for the lowest poverty-level group, the change in the uninsurance
rates are more pronounced in the states using only state program expansions, since there
is a greater percentage of children in the lowest poverty-level group. Similarly, since
there is not a signiﬁcant effect for children in the highest poverty-level group, this effect
is less pronounced in states using only state program expansions. This explains why the
estimated percentage point changes are greater in states using only state program
expansions, compared to states using combination expansions, even though we would

predict the opposite, based solely on the probit coefﬁcients.

78

 

 

Table 15: Percent of Children in each Poverty-Level Group, by Type of

Expansionz1996-2000

Medicaid Only:
0-100% FPL
100-150% FPL
150-200% FPL
ZOO-250% FPL
>250% FPL

State Program Only
0-100% FPL
100-150% FPL
150-200% FPL
ZOO-250% FPL
>250% FPL

Combination:
0-100% FPL
100-150% FPL
150-200% FPL
ZOO-250% FPL
>250% FPL

Source: Author's calculations based on the March 1997-2001 CPS

1996

18.9%
11.4%
12.3%
10.8%
46.6%

22.0%
12.0%
11.4%
10.0%
44.6%

17.5%
10.0%
10.2%
10.2%
52.2%

16.3%
9.6%
9.8%

10.1 %

54.3%

18.2%
8.8%
10.9%
9.9%
52.2%

20.8%
11.5%
10.9%

9.6%
47.2%

15.0%
10.1%
9.6%
10.0%
55.3%

16.4%
10.8%
11.0%
11.2%
50.6%

18.4%
12.0%
10.9%
9.7%
49.0%

13.7%
10.3%
9.2%
9.6%
57.2%

15.8%
10.2%
10.7%
9.5%
53.8%

17.2%
11.9%
10.3%
10.6%
50.0%

13.3%
9.2%
10.2%
8.5%
58.7%

 

Looking at the estimates for children in each poverty-level grouping, the
estimated percentage-point changes in uninsured children are highly signiﬁcant for those
with family income below 200% FPL, as shown in Table 16.

There was a 3.11 percentage point, or 12.37%, reduction for those with family
income below the poverty level. There was also a 3.88 percentage point, or 15.72%,
reduction for those with family income of loo-150% FPL, and a 1.86, or 10.75%,
reduction for those with family income of ISO-200% FPL. The estimates for children in
wealthier families are slightly positive, though not signiﬁcantly different ﬁom zero.
Additionally, the estimated effect for children with family income below 150% FPL is

signiﬁcantly greater than that for children with family income above 200% FPL.

79

 

 

Table 16: Simulated Effects of Medicaid and State Program Expansions:
Overall, By Expansion Type, and by Poverty-Level Group
95% Conﬁdence Interval

 

Actual P(U) P(UIZOOO) P(Ul1996) % p_t. 13PM) Lower Um
By Expansion Type and Poverty Level:
Medicaid:
0-100% FPL 17.86% 18.80% 22.11% 031*“ -4.49 -2.13
(0.6027)
100-150%FPL 19.36% 19.95% 22.93% -2.98*** -4.09 -1.86
(0.5675)
150-200% FPL 12.05% 15.09% 17.23% -2.14*** -3.56 072
(0.7253)
ZOO-250% FPL 16.46% 12.48% 13.31% 083* -1.77 0.12
(0.4836)
<250% FPL 5.97% 5.69% 5.45% 0.25 005 0.54
(0.1520)
State Program:
0-100% FPL 24.49% 22.94% 25.94% -2.99*‘* -4.48 -1.51
(0.7597)
100-150% FPL 23.03% 23.23% 27.30% -4.07*'* -5.58 -2.56
(0.7699)
150-200% FPL 17.14% 20.20% 21.95% -1.74** 0.38 011
(0.8336)
ZOO-250% FPL 15.52% 16.58% 15.84% 0.74 082 2.30
(0.7947)
<250% FPL 7.56% 7.67% 7.33% 0.34 007 0.76
(0.2114)
Combination:
0-100% FPL 20.90% 18.97% 22.55% -3.58*** -5.04 -2.12
(0.7457)
100-150% FPL 17.57% 19.19% 23.48% -4.29*** -5.87 -2.71
(0.8045)
150-200% FPL 15.04% 16.25% 18.30% -2.05*** -3.58 052
(0.7814)
ZOO-250% FPL 15.76% 13.19% 12.72% 0.47 -1.02 1.95
(0.7559)
<250% FPL 6.95% 5.97% 5.60% 0.37 008 0.83
(0.2321)

Statistical Signiﬁcance: ‘90% Conﬁdence, ”95% Conﬁdence, ”99% Conﬁdence

Sample size: 33459 observations

 

These results are very similar when looking at the estimated effect for each

poverty-level group, given the type of expansion used, as shown in Table 16. Again,

there was a signiﬁcant effect for all children with family income below 200% FPL,

regardless of the type of expansion used. The estimated percentage-point change in

80

 

uninsured children is also signiﬁcantly greater for those with family income below 150%
FPL, compared to those with family income above 200% FPL, regardless of the type of
expansion used, based on the 95% conﬁdence intervals. For children in states using only
Medicaid expansions, there was also a weakly signiﬁcant effect for children with family
income ZOO-250% FPL, which is not seen when looking at the effects on each poverty-
level group for all states, combined.

All of these simulated effects are presented mostly for descriptive purposes.
While the differences are due in part to the difference in the probit coefﬁcients for the
policy variables, they are also due to differences in the average size of the expansions and
differences in the distribution of children across poverty-level groups, for each type of

expansion.

7.4 Average Partial Effects

Again, in order to present a comparison of the effect of Medicaid expansions,
relative to an equivalent state program expansion, average partial effects of each type of
policy change are estimated separately for each poverty-level group used in the probit
model. The interpretation of these estimates is more straightforward than for the
estimates in section 7.3, since they vary only with the type of expansion used, and not
because of differences in the size of the expansion or the distribution of children across
poverty-level groups. The policy variables are deﬁned as in section 6.4 for a particular
poverty-level group, and set to zero for all other poverty-level groups. The mean value of
all other regressors is deﬁned as the mean value observed in the sample for that particular

poverty-level group. This allows us to compare the average effect of the policy changes

81

on an individual with average characteristics within each poverty level group. The results
are presented in Table 17. The average partial effects of both Medicaid and state program
expansions are signiﬁcant for children with family income below 200% FPL. Medicaid
expansions also have a weakly signiﬁcant effect for children with family income of 200-

250% FPL. Neither type of expansion is signiﬁcant for children with family income

 

 

above 250% FPL.
Table 17: Average Partial Effects, by Poverty-Level Group
95% Conﬁdence Interval
Povegy Level Group Poligy Chagge Chagge in P(U) Std. Error Um Lgm
0-100% FPL Medicaid Expansion -4.16*** 0.7279 -5.59 -2.74
State Expansion 086*" 0.7253 -4.29 -1.44
100-150% FPL Either Expansion 072*“ 0.6924 -5.08 -2.37
ISO-200% FPL Medicaid Expansion -2.80*** 0.9257 -4.61 098
State Expansion -1.59** 0.7597 -3.08 010
ZOO-250% FPL Medicaid Expansion -1.03* 0.5969 -2.20 0.14
State Expansion 0.69 0.7409 076 2.14
>250% FPL Either Expansion 0.29 0.1837 007 0.65
*Statistically signiﬁcant at the 90% conﬁdence level
“Statistically signiﬁcant at the 95% conﬁdence level
”Statistically signiﬁcant at the 99% conﬁdence level

 

 

 

Comparing Medicaid and state program expansions within each poverty-level
group, the point estimates for Medicaid expansions are greater than those for state
program expansions. Based on the 95% conﬁdence intervals, however, there is no
evidence of a signiﬁcant difference between Medicaid expansions and state program
expansions, within a particular poverty-level group. For those with family income of
100-150% FPL and those with family income above 250% FPL, speciﬁcation tests in

section 7.1 have shown that there is not a signiﬁcant difference between the two policy

82

approaches. Thus, only one average partial effect statistic is estimated which applies to
either type of expansion.

Comparing the results across groups, the effects of both types of expansions are
stronger for the 0-100% FPL and 100-1 50% FPL groups compared to those with family
incomes above 250% FPL. This is a stronger result than simply stating that there was a
signiﬁcant effect for these groups, due to the fact that the conﬁdence interval for the over
250% FPL group includes some negative estimates. The effects of Medicaid expansions
are also signiﬁcantly greater for the 0-100% FPL and 100-150% FPL groups compared to
either type of expansion for the ZOO-250% FPL group; the effects of state program
expansions are signiﬁcantly greater for the 0-100% FPL and 100-150% FPL groups,

compared to state program expansions for the ZOO-250% FPL group.

83

Chapter 8

Effects on Parents’ Insurance Status

8.1 Regression on the Likelihood of Having an Uninsured Parent

In Section 6.2, the probit model is used to estimate the average eﬁ’ect of the Medicaid and
state program expansions on the likelihood of being uninsured. In Section 7 .2, the model is
expanded to estimate the average effect of the Medicaid and state program expansions on the
likelihood of being uninsured within different poverty-level groups. The probit coefﬁcients
found in section 6.2 indicate that the eligibility expansions, both through Medicaid and through
state programs, had a signiﬁcant impact on reducing children’s likelihood of being uninsured. In
Section 7.2, the probit coefﬁcients indicate that the Medicaid expansions had a signiﬁcant impact
on reducing the children’s likelihood of being uninsured for children with family income less than
250% FPL, while the state program expansions had a signiﬁcant impact for children with family
income less than 200% FPL. For children with family income above these thresholds, there was

no signiﬁcant effect on the likelihood of being uninsured.

In order to test that the change in uninsurance rates is truly a result of Title XXI
expansions, the probit model is used to test if the Title XXI expansions had an effect on
children’s likelihood of having an uninsured parent. Thus, the dependent variable
indicating whether a child is uninsured is replaced with a variable indicating whether he
or she has an uninsured parent. If the effect of the Title XXI expansions is unique to
children, then the policy variables should have no effect.

This test is ﬁrst implemented by using a single set of policy variables for all
children, as in Chapter 6. This addresses the question of whether there is any average

effect on the likelihood of having an uninsured parent, based on the full sample of

84

children ﬁom all poverty-level groups combined. The model is ﬁrst estimated using
separate variables for Medicaid eligibility thresholds (‘MLim’) and increases in eligibility
thresholds for state programs (‘SDif‘). The model is then re-estimated imposing the
restriction that Medicaid and state program expansions have the same marginal effect, so
that ‘MLim and ‘SDif’ are replaced with a single variable equal to their sum, or the total
eligibility threshold ﬁom either type of program. A likelihood ratio test is used to
determine if there is any evidence of a different marginal effect ﬁ'om Medicaid
expansions, compared to state program expansions. The results are shown in Table 18,

below.

 

Table 18: Comparison of Probit Model Speciﬁcations for the
Likelihood of Havingan Uninsured Parent

 

 

 

 

 

I 1 ll
Policy Variable: Coefﬁcient (Standard Error)
MLim 0.00869
(0.02865) 0.02594
SDif 0.03485 (0.01828)
(0.02147)
Log-Likelihood 08555.972 08556789

 

 

 

LR Statistic (one restriction): 1.634

 

Coefﬁcients and LR Statistic not Statistically Signiﬁcant

Model I is the unrestricted model
Model H is restricted so that the coefﬁents on MLim and SDif are equal

 

 

 

1n the unrestricted model, the estimated coefﬁcients for ‘MLim’ and ‘SDif’ are
not signiﬁcantly different from zero, as shown in Table 18. When the model is restricted
so that both Medicaid and state program expansions have the same marginal effect, the
resulting coefficient on the combined policy variable is also not signiﬁcantly different

from zero. Based on a likelihood ratio test, we cannot reject the null hypothesis that there

85

is any difference in the effect of changes in Medicaid eligibility, compared to changes in
state program expansions. Thus, there is no evidence that neither type of expansion has
had a signiﬁcant effect on the likelihood of having an uninsured parent.

These results support the hypothesis that the estimated changes in uninsurance
rates for children, shown in Chapter 6, were in response to changes in the eligibility for
public insurance. This is due to the fact that the expansions do have a signiﬁcant effect
on children’s likelihood of being uninsured, but not on their likelihood of having an

uninsured parent.

8.2 Regression on the Likelihood of Having an Uninsured Parent using
Policy/Poverty-Level Group Interactions

The basic probit model is expanded to allow the effects of the policy variables, on
the likelihood of having an uninsured parent, to differ across poverty-level groups.
Surprisingly, increases in children’s eligibility limits are correlated with a reduction in
the likelihood of having an uninsured parent, for children with family income below the
poverty-level. Based on likelihood ratio tests, there is no signiﬁcant difference in the
marginal effect of increased Medicaid eligibility, compared to increased eligibility
through state programs, for children with family income below 50% FPL. For children
with family income of 50-100% FPL, there is a statistically signiﬁcant difference in the
marginal effect of Medicaid expansions, compared to state program expansions. For
those with family incomes above the poverty level, there is no signiﬁcant effect for either
type of expansion, nor is there a difference between Medicaid and state program

expansions.

86

As shown in Table 19, both Medicaid and state program expansions appear to
have a signiﬁcant impact in reducing the likelihood of having an uninsured parent, for

children with family income below the poverty level.

 

Table 19: Probit Regression Results for the Likelihood of Having an Uninsured Parent
with Policy/Poverty-Level Interactions

Marginal Effect
CAM m Ms W
Policy Variables:
Limit: 0-50% FPL 0.09246“ 0.03702 0.12246 0.02118
MLim: 50-100% FPL 007251" 0.04005 0.13491 0.01661
SDif: 50-100% FPL 0.16859“ 0.03901 0.03657 0.03862
Limit: >100% FPL 0.00861 0.01926 1.39503 0.00197
Pseudo R-Squared 0.1534
Log-Likelihood 02843.353
Actual Percent Uninsured 18.65%
Predicted % Uninsured, at Mean Values 14.61%

*Statistically signiﬁcant at the 90% conﬁdence level
”Statistically signiﬁcant at the 95% conﬁdence level
“Statistically signiﬁcant at the 99% conﬁdence level

Standard errors are robust to heteroskedasticity and correlation between observations within
the same household.

Sample Size is 167,535 observations. Estimation performed using sample weights. The
complete results for the probit coefﬁcients are shown in Appendix C.

 

 

 

Recall that there was a signiﬁcant effect on the children’s likelihood of being
uninsured for those with family incomes up to 250% FPL for Medicaid expansions and
up to 200% FPL for state program expansions. Thus, even though there is an effect for
low-income parents, it is not identical to the effect for children. However, these results
may indicate there is something affecting both the children’s and the parents’ insurance
choices which is not identiﬁed in the model.

One explanation for this effect on parent’s insurance status is the fact that parents
in low-income families are, in fact, eligible for Medicaid, though the Medicaid eligibility

limits for parents are typically not nearly as great as those for children. States are

87

required to extend Medicaid eligibility to all parents who meet the AFDC income,
resource, and family composition eligibility rules in place as of July 16, 1996. In
addition, states have the option to expand Medicaid eligibility limits for parents beyond
this limit and to relax the family composition rules which excluded married couples from
coverage under AFDC. Some states have also expanded Medicaid eligibility for parents
further under approved waivers from the Health Care Financing Agency. In fact, by June
2001, 19 states provided Medicaid eligibility for parents with incomes at or above the
poverty level (Guyer, 2002).

While the Title XXI expansions did not apply directly to parents, it is likely that
they were somewhat correlated with expanded eligibility for parents. In addition, the
large response for children who were likely eligible for Medicaid, even in the absence of
Title XXI expansions, indicates that part of the response was simply due to increased
awareness of public insurance programs, or reduced welfare stigma. Thus, there may
have been an increased awareness of public health insurance availability for parents as
well as for their children. Finally, in states that expanded eligibility for both children and
parents, though not necessarily to the same extent, this may have resulted in a stronger
impact for both children and parents than would be expected ﬁom either type of
expansion alone.

Since the model does not identify changes in eligibility for the parents, it is not
surprising that any resulting effect on both the parents’ and children’s insurance status is
picked up by the Title XXI policy variables. The fact that the probit regression for

children resulted in changes in uninsurance rates for children up to 200% FPL, rather

88

than up to the poverty level, shows that the model is identifying the effects of Title XXI

expansions, beyond the effects of changes in parents’ eligibility limits.

89

CHAPTER 9

CONCLUSIONS AND POLICY IMPLICATIONS

Changes in the availability of public insurance, following passage of Title XXI
legislation, have had a signiﬁcant impact on reducing the number of uninsured children.
Overall, Medicaid and state program expansions have led to a small, but signiﬁcant
reduction in the number of uninsured children. Comparing results by the type of
expansion used, all three approaches had a signiﬁcant effect on reducing the number of
uninsured children, with the greatest effect consistently seen in states using only
Medicaid expansions, although this difference is not statistically signiﬁcant.

The estimated effects, from Chapter 6, of the Title XXI expansions are that they
resulted in a 5.9% decrease in the number of uninsured children, based on the simulations
performed on the 2000 sample. For states using only Medicaid expansions, the estimated
decrease is 8.2%, compared to 5.0% in states using only state program expansions and
7.0% in states using combination expansions.

Comparing the effects of the Title XXI expansions across different poverty level
groups, states using only Medicaid expansions had the largest impact on uninsurance
rates for children below the poverty level. For states using only state program expansions
or combination expansions, the largest impact on uninsurance rates is observed for
children with family income of 100-150% FPL, though there is a similarly large impact
on children with income below the poverty level as well. The strong impact of the Title
XXI expansions on children with income below the poverty level is somewhat surprising,

given that all of these children would have gained eligibility by 2000 (the last year

included in the study), even in the absence of Title XXI expansions. This effect is
explained in part by the increased awareness of public health insurance programs as
states promoted their expansions. However, the response of children with family income
below the poverty level is also likely due to the fact that their parents were also more
likely to become eligible for Medicaid over the study period, though not as a direct result
of Title XXI expansions.

There was also a signiﬁcant impact for all children with family income up to
200% FPL, across all states, and for children with income up to 250% FPL in states using
Medicaid expansions. As would be expected, neither Medicaid nor state program
expansions had a signiﬁcant impact on children inwealthier families.

Based on the speciﬁcation tests in sections 6.1 and 7.1, there is some evidence
that increases in eligibility through Medicaid may have had a larger effect than increases
in eligibility through state programs. This difference may be due in part to the fact that
the state programs typically use cost-sharing measures, while the Medicaid programs
typically do not. The presence of cost-sharing measures may create a deterrent for
enrollment in the newer state programs.

Another difference in the state programs, compared to Medicaid, is the use of
waiting periods for eligibility. These waiting periods only apply to children with recent
coverage under private health insurance policies. The intent of these waiting periods is to
discourage crowding out of private insurance. However, if families are willing to drop
private health insurance coverage for children in order to enroll them in the public
program in the ﬁrture, there may be a transitory increase in the number of uninsured

children due to the use of waiting periods. This may partially explain the different effects

91

of a marginal increase in Medicaid eligibility, relative to a marginal increase in eligibility
through the state programs.

The estimated average partial effects offer a direct comparison of the effect of an
average-sized expansion through either Medicaid or through a state program. Overall, it
is estimated that an average sized Medicaid expansion would result in a 1.21 percentage-
point reduction in the number of uninsured children, compared to a 0.65 percentage-point
reduction through an equivalent state program expansion. For children below the poverty
level, it is estimated that an average sized Medicaid expansion would result in a 4.16
percentage-point reduction in the number of uninsured children, compared to a 2.86
percentage-point reduction for an equivalent state program expansion. For children
between 100-1 50% FPL, it is estimated that an average sized expansion, through either
Medicaid or a state program, would result in a 3 .72 percentage-point reduction in the
number of uninsured children. For children between ISO-200% FPL, it is estimated that
an average sized Medicaid expansion would result in a 2.80 percentage-point reduction in
the number of uninsured children, compared to a 1.59 percentage-point reduction for an
equivalent state program. For children with family income of ZOO-250% FPL, an average
sized Medicaid expansion results in a weakly signiﬁcant reduction in the number of
uninsured children, with no signiﬁcant change resulting from an equivalent state program
expansion. For all children above 250% FPL, there is not a signiﬁcant effect from either
program. However, none of the differences between Medicaid and state program
expansions is signiﬁcant.

From a state policy perspective, these results indicate that Medicaid expansions

may be the better alternative, since they seem to have the largest effect on reducing

92

uninsurance rates. However, this alone does not necessarily mean that Medicaid
expansions are the optimal policy choice. From a social welfare perspective, a true
measure of cost-effectiveness would compare the social cost of the Title XXI expansions
and the social gains resulting from the expansions.

The social gains would ideally reﬂect the gains in welfare for the participants and
their families. This would include any resulting improvement in health outcomes as well
as the reduction in expenditures used for health services which can be put to other use.
For families with children who would have been uninsured in the absence of the Title
XXI expansions, their welfare is increased by the fact that the children have access to
health services, which hopefully results in improved health, without having to sacriﬁce
other expenditures. For families that drop private insurance coverage in order to enroll
their children in public programs, there is also likely an increase in welfare even if there
is no effect on the utilization of health services, since expenditures on health insurance or
health care, if any, would be freed up for other uses. This may be offset, though if
indirect crowding out leaves other family members uninsured. From 8 revealed
preference perspective, families whose children are enrolled in the public programs must
be somewhat better off, or they would not have enrolled in the ﬁrst place.

The social cost of the Title XXI expansions would reﬂect not only the state and
federal expenditures for these programs, but also any resulting losses in economic
efficiency. The additional state and local expenditures would need to be ﬁnanced
through increased tax revenues, resulting in excess burden, or through reduced spending

elsewhere, resulting in welfare losses for those affected by the spending cuts.

93

In determining the best policy option, states must consider the both the beneﬁts
and cost of the policy change, although both of these may be difﬁcult to infer. This
research provides a ﬁrst step in understanding the beneﬁts of the Title XXI expansions, in
terms of the reduction in uninsured children. The implicit assumption is that such a
reduction will lead to better health for those children who would otherwise have been
uninsured. In terms of the ﬁnancial beneﬁts to participating families, the greatest beneﬁt
likely goes to those who would otherwise have been uninsured. However, there are also
likely ﬁnancial beneﬁts to all participating families, including those that crowd out
private insurance. Since the estimates presented in this research identify the decrease in
uninsured children as a result of Title XXI expansions, this provides some insight of the
number of children whose families are likely to gain the most beneﬁt from the policy
changes. However, this presents an incomplete picture of the total number of children
whose families receive at least some beneﬁt from the programs.

From the perspective of state policy makers, state programs have some potential
cost advantages. First, if state program expansions are successful in reducing the
magnitude of crowding out, compared to Medicaid expansions, this will reduce the
number of individuals whose health insurance is provided by public ﬁrnds. Second, the
inclusion of cost-sharing measures in state programs may lead to reduction in the use of
medical services. Finally, state programs are not required to be as generous as Medicaid
in terms of covered services, though they do have to be at least equivalent to certain
benchmark policies, as discussed in section 2.4. Thus, the two types of public programs

may have different effects on health or the value to beneﬁciaries.

94

Since there is no conclusive evidence that Medicaid and state program expansions
have a different effect on reducing uninsurance rates, the key advantages to state
programs is the likely reduction in the prevalence of crowding out, and the potential for
cost savings, on average, per enrollee. However, state programs would have additional
start-up costs in designing and implementing the program, which would not be necessary
to simply expand the existing Medicaid programs. State programs may also result in
some additional costs due to duplication of administrative costs.

It is important to note that any cost-saving through state programs, compared to
Medicaid programs, is limited to insuring those who gain eligibility through the Title
XXI expansions. Title XXI speciﬁes that any children eligible for Medicaid, based on
the eligibility limits in place as of March 31, 1997, are enrolled in the state’s Medicaid
program; states will only receive the regular Medicaid FMAP for insuring these children,
not the enhanced FMAP under Title XXI. The results in chapter 7 indicate that the
largest response to Title XXI expansions was in children with family incomes below
150% FPL. Since many of these children would have been eligible without the Title XXI
expansions, such children would be enrolled in Medicaid, regardless of the expansion
type used, and states would receive only the FMAP for Medicaid in insuring these
children. Thus, any potential cost savings through state programs would not be achieved
in insuring these children, nor would the states beneﬁt from the enhanced FMAP.

One potential danger in using state program expansions is the fact that federal
ﬁrnds available under the enhanced F MAP are capped. Once these funds have been
exhausted, states do not receive any additional ﬁrnds for providing insurance through the

state programs. In this event, states would be left with the decision to continue coverage

95

using only state funds, or to ﬁ'eeze enrollment in the state program. States using
Medicaid expansions have more protection ﬁom this occurrence, since they can continue
to receive the FMAP for Medicaid, once the enhanced FMAP has been exhausted.

In designing an optimal state policy, the results of this research indicate that the
reduction in the number of uninsured are not signiﬁcantly different for Medicaid
expansions and state program expansions. While this is not an ideal measure, it does
indicate that the beneﬁts resulting from either type of expansion are likely similar. Thus,
the use of state programs may be preferred if they succeed in reducing the public cost of
providing coverage. Such a reduction would also limit the excess burden associated with
ﬁnancing the additional public expenditures. However, due to the funding cap on federal
contributions for Title XXI expenditures, states would not want to make their state
program expansions so generous that they risk losing the federal match on a portion of
their expenditures. For large eligibility expansions, a safer route, from the state
perspective, is to use a combination expansion, since Medicaid expenditures will be
reimbursed at the regular Medicaid FMAP once the federal cap for Title XXI funds has

been reached.

96

APPENDICES

97

APPENDIX A: MEDICAID AND STATE PROGRAM ELIGIBILITY LIMITS

Table 20: Eligibility Thresholds for Infants, 1996 and 2000
SCHIP 1996

.8213:
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii

Idaho

Illinois
Indiana

Iowa

Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada

New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas

Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming

Medicaid 1996
133
133
140
133
200
133
185
185
185
185
185
133
133
150
185
150
185
133
185
185
185
185
275
185
185
133
150
133
185
185
185
185
185
133
133
150
133
185
250
185
133
400
185
133
225
I33
200
150
185
133

300

98

Medicaid 2000
133
200
140
200
200
133
185
185
185
185
185
150
200
150
185
150
185
150
185
200
200
185
280
185
300
133
185
133
300
185
235
185
185
133
150
185
133
185
250
185
140
400
185
133
225
133
200
150
185
133

SCHIP 2000
200

200

185

T $.KMAAACPUGDHGHVR

Table 21: Eligibility Thresholds for Children Ages 1-5, 1996 and 2000
SCHIP 1996

Slate
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii

Idaho

Illinois
Indiana

Iowa

Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada

New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas

Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming

Medicaid 1996
133
133
133
133
133
133
185
133
133
133
133
133
133
133
133
133
133
133
133
185
133
150
275
133
133
133
133
133
185
133
185
133
133
133
133
133
133
133
250
133
133
400
133
133
225
133
200
133
185
133

300 (age 2)

99

Medicaid 2000
133
200
133
200
133
133
185
133
133
133
133
150
133
150
133
133
150
150
150
200
150
150
280/275
133
300
133
185
133
185
133
235
133
133
133
150
185
133
133
250
150
140
400
133
133
225
133
200
150
185
133

SCHIP 2000
200

200

300/200
185
300
200
200
200

185

185

Table 22: Eligibility Thresholds for Children Age 6 and Older, Born After

10/1/1983, 1996 and 2000
_S_ta_te Medicaid 1996 SCHIP 1996
Alabama 100 -
Alaska 100 -
Arizona 100 -
Arkansas 100 -
California 100 -
Colorado 100 -
Connecticut 185 -
Delaware 100 -
Florida 100 -
Georgia 100 -
Hawaii 100 -
Idaho 100 -
Illinois 100 -
Indiana 100 -
Iowa 100 -
Kansas 100 -
Kentucky 100 -
Icuisiana 100 -
Maine 125 -
Maryland 185 -
Massachusetts 1 l4 -
Michigan 150 -
Minnesota 275 -
Mississippi 100 -
Missouri 100 -
Montana 100 -
Nebraska 100 -
Nevada 100 -
New Hampshire 185 -
New Jersey 100 -
New Mexico 185 -
New York 100 185
North Carolina 100 -
North Dakota 100 -
Ohio 100 -
Oklahoma 100 -
Oregon 100 -
Pennsylvania 100 235 (<age 10)
Rhode Island 100 -
South Carolina 100 -
South Dakota 100 -
Tennessee 400 -
Texas 100 -
Utah 100 -
Vermont 225 -
Virginia 100 -
Washington 200 -
West Virginia 100 -
Wisconsin 100 -
Wyoming 100 -

100

Medicaid 2000
100
200
100
200
100
100
185
100
100
100
100
150
133
150
133
100
150
150
150
200
150
150
275
100
300
100
185
100
185
133
235
100
100
100
150
185
100
100
250
150
140

100
100
225
100
200
100
185
100

SCHIP 2000
200

200
200
185
300
200
200
200

185
185
200
200

185

185
150

133

Fl; Cg—lft‘aIt}(((11(111111111111111111] ........

Table 23: Eligibility Thresholds for Children Born Before 10l1/1983, 1996 and 2000

m Medicaid 1996 SCHIP 1996 Medicaid 2000 SCHIP 2000
Alabama 15 - 100 200
Alaska 71 - 200 -
Arizona 30 - 30 200
Arkansas 18 - 200 -
California 82 - 100 300
Colorado 37 - 37 185
Connecticut 100 - 185 300
Delaware 100 - 100 200
Florida 28 - 100 200
Georgia 100 - 100 200
Hawaii 100 - 100 -
Idaho 100 - 150 -
Illinois 46 - 133 185
Indiana 24 - 150 -
Iowa 37 - 133 185
Kansas 100 - 100 200
Kentucky 33 - 150 200
Louisiana 10 - 150 -
Maine 125 - 150 185
Maryland 40 - 200 -
Massachusetts 86 - 150 200
Michigan 100 - 150 200
Minnesota 275 - 275 -
Mississippi 34 - 100 l 33
Missouri 100 - 300 -
Montana 41 - 41 150
Nebraska 33 - 185 -
Nevada 31 - 31 200
New Hampshire 185 - 185 300
New Jersey 4] - 133 350
New Mexico 185 - 235 -
New York 51 185 100 192
North Carolina 100 - 100 200
North Dakota 40 - 100 140
Ohio 33 - 150 -
Oklahoma 48 - 185 -
Oregon 100 - 100 170
Pennsylvania 41 - 41 235
Rhode Island 100 - 250 -
South Carolina 48 - 150 -
South Dakota 100 - 140 -
Tennessee 400 - 400 -
Texas 17 - 100 -
Utah 100 - 100 200
Vermont 225 - 225 300
Virginia 100 - 100 185
Washington 200 - 200 -
West Virginia 100 - 100 150
Wisconsin 45 - 185 -
Wyoming 55 - 55 133

Sources for all tables in Appendix A: HCFA State Reports (www.hcfa.gov/init/chpa-map.htm); Mathematica Policy
Research, Inc. (2001); and the State Children’s Health Insurance (SCHIP) Database from the US. Department of
Health and Hurmn services, Ofﬁce of the Assistant Secretary for Flaming and Evaluation
(aspehhs.gov/health/schipZIdefaulthtm).

101

APPENDIX B: PROBIT REGRESSION RESULTS FROM CHAPTER 6

Table 24: Complete Probit Regression Results

Policy Variables:
MLim
SDif
Demographic Variables:
Female
White
Black
Asian
Other Race
Both Parents
Mother Only
Father Only
Neither Parent
# in Family
# in Family Squared
Family Earnings
Family Earnings Squared
Other Income
Other Income Squared
0-50% FPL
50-100% FPL
100-1 50% F PL
150-200% FPL
200-250% FPL
250-300% FPL
300-350% FPL
<350% FPL
Age Indicators:
Newborn
Age=1
Age=2
Age=3
Age=4
Age=5
Age=6
Age=7
Age=8
Age=9
Age=10
Age=11
Age=12
Age=1 3
Age=14
Age=15
Age=16
Age=1 7

Coefﬁcient

-0.085601 “*
-0.045405**

-0.0061 55
-0.099542""
-0.124164**

0.0341 32

(omitted)
-0.9071 13*“
-1 .008200‘"
-0.689998***

(omitted)
-0.130560***
0.012723“
-0.072120***
0.001 507*“
-0.1 761 35*“
0.008233”
0.290364“
0.401608“
0.480721 ***
0.377220“
0.257503“

0.077069“
0.048875

(omitted)

(omitted)
-0.199195*‘*
-0.192641*"
-0.167063‘**
-0.159103***
0145800“
0.146512“
-0.141 329*“
-0.151296***
-0.160236*“'*
01 14640"
-0.101348***
0108353“

-0.058680"
-0.057978*
-0.048789
-0.048869
-0.021912

Standard Error

0.026623
0.021085

0.008943
0.049429
0.052383
0.060298

0.030535
0.031 347
0.037684

0.0221 14
0.002142
0.00651 1
0.000148
0.015570
0.001348
0.047792
0.042003
0.037482
0.034030
0.031648
0.029842
0.030016

0.029316
0.028460
0.028428
0.028299
0.028206
0.030756
0.030841
0.031475
0.031326
0.031215
0.031582
0.031713
0.031742
0.033086
0.034571
0.035722
0.035905

102

Mean Value

1.34
0.35

0.49
0.79
0.16
0.04

0.69
0.23
0.04

4.27
20.31
5.05
54.92
0.47
1.52
0.08
0.11
0.11
0.11
0.10
0.09
0.08

0.06
0.05
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.05
0.05
0.05

Marginal Effect
at Mean Value

-0.016568
-0.008788

-0.001191
-0.019934
-0.022822
0.008731

-0.21 3352
-O. 1 42731
-0.089274

-0.025270
0.002463
-0.013959
0.000292
-0.034091
0.001594
0.064710
0.092904
0.114598
0.086366
0.056157
0.015489
0.009696

-0.034559
-0.033539
-0.02951 1
-0.028235
-0.026068
-0.026186
-0.025336
-0.028963
-0.028432
-0.020857
-0.018569
-0.019774
-0.011004
-0.010876
-0.009197
-0.009212
-0.004191

Complete Probit Regression Results (Conﬁned)

Marginal Effect
Coefﬁcient Standard Error Mean Value at Mean Value
State Indicators:

1 (omitted)

2 -0.044448 0.090873 0.00 -0.008376

3 -0.144333 0.102890 0.00 -0.025571

4 -0.078047 0.077211 0.02 0014434

5 -0.229304“ 0.104011 0.00 -0.038510

6 0.026600 0.089129 0.01 0.00529

7 0.109541 0.067429 0.07 0.02425

8 0.240090“ 0.071973 0.03 0.052933

9 -0.195229*‘* 0.074663 0.04 -0.033831
10 -0.067572 0.071951 0.04 -0.012597
11 0.056408 0.081996 0.02 0.011275
12 0.064787 0.068673 0.05 0.012984
13 -0.088796 0.071926 0.04 -0.016349
14 -0.224681*"* 0.085523 0.02 -0.038035
15 0.031959 0.092170 0.02 0.006301
16 -0.126222 0.084294 0.01 -0.022648
17 -0.036020 0.086737 0.02 0006827
18 0.069610 0.086591 0.00 0.014039
19 -0.050444 0.083310 0.00 -0.009471
20 -0.152005* 0.082235 0.01 0026823
21 -0.010104 0.082790 0.01 -0.001944
22 -0.030309 0.088456 0.00 -0.005761
23 0.1 17819 0.089574 0.02 0.024383
24 0.128793 0.080859 0.02 0.026799
25 -0.164579* 0.086489 0.00 -0.028807
26 0.097976 0.073203 0.03 0.020036
27 0.186077“ 0.084489 0.01 0.040012
28 0.109032 0.078674 0.03 0.02424
29 0.281670“ 0.067743 0.05 0.063093
30 0.076916 0.081280 0.01 0.015563
31 0.166692 0.111101 0.02 0.035421
32 0.027774 0.083149 0.02 0.005463
33 0.158312" 0.081265 0.01 0.033541
34 0.246361 *‘* 0.078410 0.01 0.054759
35 0.292102“ 0.078491 0.02 0.066391
36 0.318726“ 0.077868 0.01 0.073531
37 0.449864“ 0.065390 0.08 0.107362
38 0.160366“ 0.078585 0.00 0.034061
39 0.243325“ 0.075103 0.01 0.054065
40 0.053444 0.080436 0.00 0.010678
41 0.276018“ 0.079338 0.01 0.062237
42 0.316486“ 0.077185 0.01 0.073012
43 0.401485“ 0.07221 1 0.02 0.096233
44 0.033824 0.079971 0.01 0.006678
45 0.370491 *** 0.076606 0.01 0.087859
46 0.028265 0.084769 0.02 0.005560
47 0.055439 0.081861 0.01 0.011082

103

Complete Probit Regression Results (Contiued)

Marginal Effect
Coefﬁcient Standard Error Mean Value at Mean Value
State Indicators
(Continued):
48 0.288714“ 0.065061 0.13 0.063203
49 0.343385“ 0.081689 0.00 0.080462
50 -0.265045**‘ 0.101185 0.00 -0.043517
Year Indicators:
1996 (omitted)
1997 0.029982 0.019735 0.199128 0.005866
1998 0.077320“ 0.020898 0.200076 0.015386
1999 0045789" 0.023881 0.201 189 0.009009
2000 0.016302 0.026002 0.201708 0.003174
Constant 0.326163“ 0.1 14073
Pseudo R-Squared 0.1082
Log-Likelihood 83059641
Actual Percent Uninsured 14.08%
Predicted % Uninsured, at Mean Values 11.45%

*Statistically signiﬁcant at the 90% conﬁdence level
“Statistically signiﬁcant at the 95% conﬁdence level
“Statistically signiﬁcant at the 99% conﬁdence level

Standard en'ors are robust to heteroskedasticity and correlation between observations within the
same household.

Sample Size is 174,004 observations. Estimation performed using sample weights.

104

APPENDIX C: LINEAR PROBABILITY MODEL REGRESSION RESULTS FROM CHAPTER 6

Table 25: Complete Linear Probability Model Results

Policy Variables:
MLim
SDif
Demographic Variables:
Female
White
Black
Asian
Other Race
Both Parents
Mother Only
Father Only
Neither Parent
# in Family
# in Family Squared
Family Earnings
Family Earnings Squared
Other Income
Other Income Squared
0-50% FPL
50-100% FPL
100-1 50% FPL
150-200% FPL
ZOO-250% FPL
250-300°/o FPL
300-350% FPL
<350% FPL
Age Indicators:
Newborn
Age=1
Age=2
Age=3
Age=4
Age=5
Age=6
Age=7
Age=8
Age=9
Age=10
Age=1 1
Age=12
Age=13
Age=14
Age=15
Age=16
Age=17

Coefﬁcient

-0.0167019
0.0106752

0001 0992
00221602
0031 2365
0.0002280
(omitted)
02736983
03056751
02234299
(omitted)
0.0370099
0.0033298
0.0087488
0.0001 983
00267623
0.001 5741
0.0984876
0.1 193256
0.1299610
0.091 3267
0.0545841
0.0147453
0.0066755
(omitted)

(omitted)
00374241
00358064
00305431
00283814
00259083
00258870
00240946
00267768
00276422
001 91462
00167642
001 761 35
00085044
00074483
00050199
0.0044232
0.0003899

105

Standard Error

0.005021
0.004283

0.001797
0.013307
0.013885
0.015340

0.010520
0.010609
0.012193

0.005959
0.000610
0.000912
0.000023
0.002432
0.000267
0.008918
0.007750
0.007063
0.006316
0.005581
0.004701
0.004484

0.006080
0.005912
0.005959
0.005981
0.005971
0.006312
0.006335
0.00641 1
0.006354
0.006416
0.006468
0.006455
0.006549
0.006752
0.006944
0.007121
0.007188

Complete Linear Probability Model Results (Continued)

State Indicators:

DQNGUI¥OONd

Coefﬁcient

(omitted)
00017236
00243822
00126052
0.0230665
0.0122255
0.0192293
0.0467249
00270888
001 1691 1
0.0106215
0.0125226
00122641
003071 12
0.01 12690
00200313
00048881
0.01 14341
00087950
00259355
00019607
0.0048356
0.0227522
0.0253266
0.0392704
0.0196270
0.0365903
0.0205227
0.0575270
0.0129915
0.0278386
0.0017689
0.031 161 1
0.0495396
0.0650814
0.0661888
0.1060213
0.0295091
0.0469745
0.0043548
0.0532540
0.0655505
0.0948609
0.0043484
0.0809481
0.0045774
0.0095031

106

Standard Error

0.014167
0.015290
0.012528
0.014609
0.014179
0.011845
0.012930
0.012217
0.012144
0.014772
0.011906
0.011837
0.012837
0.015024
0.013604
0.014424
0.016265
0.014223
0.013183
0.014180
0.015352
0.014933
0.014560
0.014873
0.013333
0.016808
0.014725
0.012466
0.015067
0.019909
0.015067
0.016063
0.016028
0.016474
0.016128
0.012106
0.015563
0.014760
0.014945
0.015039
0.015994
0.014852
0.014005
0.015630
0.014158
0.014879

Complete Linear Probability Model Results (Continued)

Coefﬁcient Standard Error
State Indicators (Continued):
48 0.0593762 0.01 1634
49 0.0658866 0.016001
50 00462345 0.016550
Year Indicators:
1996 (omitted)
1997 0.0052877 0.004115
1998 0.0153618 0.004353
1999 0.0086280 0.004812
2000 0.0025277 0.005052
Constant 0.5265899 0.025453
R-Squared 0.0895

*Statistically signiﬁcant at the 90% conﬁdence level
”Statistically signiﬁcant at the 95% conﬁdence level
“Statistically signiﬁcant at the 99% conﬁdence level

Standard errors are robust to heteroskedasticity and correlation between observations within the
same household.
Sample Size is 174,004 observations. Estimation performed using sample weights.

107

APPENDIX D: PROBIT REGRESSION RESULTS FROM CHAPTER 7

Table 26: Complete Probit Regression Results with Policy/Poverty-Level Interactions

Policy Variables:
MLim: 0-100% FPL
SDif: 0-100% FPL
Limit: 100-150% FPL

MLim: 150-200% FPL

SDif: 150-200% FPL
Mlim: ZOO-250% FPL
SDif: ZOO-250% FPL
Limit: >250% FPL

Demographic Variables:

Female

White

Black

Asian

Other Race

Both Parents

Mother Only

Father Only

Neither Parent

# in Family

# in Family Squared

Family Earnings

Family Earnings
Squared

Other Income

Other Income Squared

0-50% FPL

50-100% FPL

100-150% FPL

150-200% FPL

ZOO-250% FPL

250-300% FPL

300—350% FPL

<350°/o FPL
Age Indicators:

Newborn

Age=1

Age=2

Age=3

Age=4

Age=5

Age=6

Age=7

Age=8

Coefﬁcient

0.1939005
0.1 309103
0.1642208
0.1430038
0.0797135
0.0844463
0.0413475
0.0333071

0.0064609
01 03421 8
01 300948
0.0275784
(omitted)
09061 552
-1 .007975
06877146
(omitted)
01 300754
0.001 0871
00728045

0.001 5170
01 770445
0.008316
0.6409523
0.7530474
0.8072689
0.6472169
0.3842641
0.0788431
0.0530599
(omitted)

(omitted)
01 9791 07
01 948569

0168561
01607988
01474929
01504215
01456364
01 547557

Std. Error

0.035728
0.033288
0.030784
0.049810
0.038108
0.038018
0.044168
0.020904

0.008845
0.033653
0.035714
0.041299

0.023516
0.024077
0.029257

0.012611
11.647
0.008480

0.000147
0.015564
0.001347
0.049099
0.045897
0.044723
0.047478
0.048363
0.022536
0.022504

0.027703
0.027665
0.027627
0.027574
0.027272
0.027887
0.027648
0.028081

108

mean value

0.241108
0.082194
0.183872
0.142979
0.035656
0.132546
0.033748
0.856480

0.486639
0.78663
0.157817
0.042962

0.691256
0.234228
0.044271

4.27253
20.3136
5.04585

54.9178
0.467888
1 .52082
0.076263
0.106171
0.109576
0.108947
0.099377
0.089268
0.077176

0.042749
0.042289
0.03841 7
0.037241
0 .0351 24
0.035767
0.034969
0.036486

Marginal Effect
at mean value

0.037525
0.025334
0.031781
0.027675
0.015427
0.012472
0.008002
0.006446

0.001 2502
0.0207356
0.0238497
0.00541 83

0.2130854
0.1428881
0.0890846

0.0251729
0.0024503
0.0140895

0.0002938
0.0342826
0.0018094
0.1653409
0.1983624
0.2158829
0.1843618
0.0884903
0.0158574
0.0105471

0.004282
0.004291 9
0.0044239
0.0044576
0.0044808
0.0045631
0.0045548

0.004573

Complete Probit Regression Results with Policy/Poverty-Level Interactions (continued)

Marginal Effect
Coefﬁcient Std. Encr mean value at mean value
Age Indicators
(continued):
Age=9 01637089 0.028113 0.037879 0.0045362
Age=10 01177267 0.027957 -0.0307 0.0047556
Age=11 0.1046008 0.028347 0.028715 0.0048913
Age=12 01105604 0.028268 0.029844 0.0048443
Age=13 0.0807852 0.028378 0.021462 0.005142
Age=14 00609988 0.028931 0.021895 0.0052409
Age=15 0.0489723 0.029366 0.019793 0.0053897
Age=16 00500655 0.029824 0.020146 0.0054674
Age=17 00205017 0.029803 0.014983 0.0056423
State Indicators:
1 (omitted)
2 0.0598443 0.066902 0.03476 0.012035
3 01462878 0.071732 0.048479 0.0115311
4 00760787 0.056095 0.033516 0.0099146
5 02330182 0.073872 0.059889 0.0105378
6 0.0163609 0.064837 0.021871 0.0127898
7 0.1157748 0.048597 0.003068 0.0105638
8 0.2363849 0.052034 0.026819 0.012853
9 0.190756 0.052611 0.049048 0.0081173
10 00661584 0.051693 0.030532 0.0092812
11 0.0627704 0.058045 0011045 0.012059
12 0.068371 0.049914 0.008836 0.0103893
13 00880188 0.0522 0.034099 0.0091268
14 0.2185234 0.06219 -0.05503 0.0091343
15 0.0052353 0.061737 0.022543 0.0120204
16 01241495 0.060705 0.042044 0.0100734
1? 0.0462611 0.061923 0030951 00113451
18 0.0675772 0.060065 0.011035 0.0125746
19 00482803 0.060064 0.030552 0.0109597
20 0.1467683 0.060945 0.045196 0.0098047
21 00050379 0.059899 -0.023557 0.011523
22 00277409 0.063022 0.028396 0.0117942
23 0.1102191 0.064233 0.004845 0.0140593
24 0.133252 0.058431 0.002146 0.0130844
25 -0.171525 0.063446 0.049226 0.0098667
26 0.1001885 0.053014 -0.001944 0.0114565
27 0.1862747 0.060104 0.012138 0.0142429
26 0.1083933 0.056757 -0.001959 0.012368
29 0.2839017 0.049285 0.038966 0.0125962
30 0.0754371 0.059199 0.00922 0.0124844
31 0.173709 0.07053 0.00477 0.0164683

32 0.0307093 0.059727 0.017424 0.01 19765

109

Complete Probit Regression Results with Policy/Poverty-Level Interactions (continued)

State Indicators

(Continued):

Year Indicators:

Constant

Pseudo R-Squared
Log-Likelihood

33

35

37
38
39
40
41
42
43

45

47
48
49
50

1 998
1 997
1 998
1 999
2000

Actual Percent Uninsured

Predicted 96 Uninsured, at
Mean Values

Coefﬁcient

0.1 509127
0.2470678
0.2870454
0.31 94649
0.4481 735
0.1 553973
0.241 8975
0.0546488
0.2845789
0.3275972
0.3970744
0.036899
0.3776306
0.025291 4
0.055971 5
0.2941 536
0.3485181
0.2634079

(omitted)
0.0299316
0.078021 7
0.0470699
0.01 77758
0.1470662

Std. Error

0.058749
0.055895
0.05679
0.055727
0.0478
0.056299
0.053752
0.057556
0.056091
0.05371 3
0.051 704
0.054854
0.0551 41
0.060374
0.059064
0.047032
0.05746
0.0728

0.013822
0.014587
0.017091
0.018852
0.079

0.11

02937.8
1 4.08%

1 1.45%

*Statistically signiﬁcant at the 90% conﬁdence level
"Statistically signiﬁcant at the 95% conﬁdence level
”Statistically signiﬁcant at the 99% conﬁdence level

mean value

0.005502
0.027365
0.036079
0.044392
0.080277
0.007504
0.027275
0.01 2348
0.035888
0.047482
0.066089
0.01 4421
0.05931 9
0.01 8609
0.01 2703
0.041 857
0.050746
0.082784

0.000499

0.009681
0.00256

0.003781

Marginal Effect
at mean value

0.0134373
0.0140839
0.0147887
0.0149641
0.013588
0.012962
0.0134827
0.0118739
0.0145795
0.0145486
0.0147425
0.0110808
0.0155825
0.0120282
0.0121908
0.0115669
0.0158785
0.0099476

0.0027329
0.002983
0.0034206
0.0036953

Standard errors are robust to heteroskedasticity and correlation between observations within the

same household.

Sample Size is 174,004 observations. Estimation performed using sample weights.

110

APPENDIX E: LINEAR PROBABILITY MODEL RESULTS FROM CHAPTER 7

Table 27: Complete Linear Probability Model Results with Policy/Poverty-Level Interactions

Policy Variables:
MLim: 0-100% FPL
SDif: 0-100% FPL
Limit: 100-150% FPL
MLim: 150-200% FPL
SDif: 150-200% FPL
Mlim: ZOO-250% FPL
SDif: ZOO-250% FPL
Limit: >250% FPL
Demographic Variables:
Female
White
Black
Asian
Other Race
Both Parents
Mother Only
Father Only
Neither Parent
# in Family
# in Family Squared
Family Earnings

Coefﬁcient

0.05821 13
0.0392377
0.0523845
0.0401 507
0.0207082
0.01 70300
0.0090481

0.0083278

0001 1798
00238537
00333741
0002091 5
(omitted)
02727235
03044337
02216963
(omitted)
0.0367088
0.0032967
0.0089203

Family Earnings Squared 0.0002009

Other Income

Other Income Squared

0-50% FPL

50-100% FPL

100-150% FPL

150-200% FPL

200-250% FPL

250-300% FPL

300-350% FPL

<350% FPL
Age Indicators:

Newborn

Age=1

Age=2

Age=3

Age=4

Age=5

Age=6

Age=7

Age=8

0.0269448
0.001 5894
0.1990002
0.2202502
0.2283779
0.1624778
0.0848829
0.0148945
0.0072035
(omitted)

(omitted)

00389521
00382840
00327585
00308067
0028121 9
00291 812
00276191
00299422

111

Std. Error

0.00801 2
0.008960
0.008049
0.01 0980
0.009760
0.007688
0.01 0241
0.003557

0.001795
0.013321
0.013892
0.015348

0.010522
0.010616
0.012184

0.006002
0.000616
0.00091 1
0.000023
0.002426
0.000286
0.014506
0.013579
0.014934
0.016164
0.012310
0.004694
0.004479

0.005868
0.005783
0.005809
0.005851
0.005835
0.005901
0.005898
0.005966

Complete Linear Probability Model Results with Policy/Poverty-Level Interactions

(continued)

Age Indicators (continued):

Age=9

Age=10
Age=1 1
Age=12
Age=13
Age=14
Age=15
Age=16
Age=17

State Indicators:

.8
OOQNGUl-wa-F

QQQNNN NNN NN-l-e-F-l-e-l—‘d-B

Coefﬁcient

0.0307873
0.0223496
0.01 99160
0.0202846
0.01 13790
0.0107138
0.0076195
0.0072674
0.00181 72

(omitted)
00051101
00226085
00117733
0.0225176
0.0081933
0.0195287
0.0449176
00271056
0.0105865
0.0124754
0.0133002
00119315
0.0284423
0.0068183
00194466
0.0060379
0.0108083
00079729
00242042
00010412
0.0040934
0.0216637
0.0261166
0.0418689
0.0194397
0.0369106
0.0197367
0.0570292
0.0125135
0.0415749
0.0015218

112

Std. Error
0.005913
0.005997
0.006091
0.006023
0.006166
0.006279
0.006356
0.006433
0.006417

0.014079
0.015120
0.012508
0.014142
0.014183
0.011588
0.012877
0.011799
0.012142
0.014764
0.011875
0.011818
0.012835
0.014051
0.013573
0.014084
0.018238
0.014239
0.013199
0.014133
0.015254
0.014813
0.014490
0.014881
0.013235
0.016771
0.014618
0.012332
0.015007
0.017114
0.014950

Complete Linear Probability Model Results with Policy/Poverty-Level Interactions

(continued)
State Indicators Coefﬁcient Std. Error
(Continued):
33 0.0281580 0.015977
34 0.0505039 0.015929
35 0.0625144 0.016448
36 0.0665520 0.016094
37 0.1041319 0.012045
38 0.0269185 0.015517
39 0.0467067 0.014731
40 0.0041536 0.014919
41 0.0546434 0.014902
42 0.0710475 0.015867
43 0.0923193 0.014689
44 0.0044729 0.013913
45 0.0815760 0.015496
46 0.0051446 0.013946
47 0.0093132 0.014809
48 0.0600146 0.011417
49 0.0678448 0.015952
50 00451711 0.016580
Year Indicators:
1996 (omitted)
1997 0.0055738 0.004127
1998 0.0154861 0.004362
1999 0.0086523 0.004810
2000 0.0024220 0.005035
Constant 0.4931417 0.024677
R-Squared 0.0921

‘Statistically signiﬁcant at the 90% conﬁdence level
”Statistically signiﬁcant at the 95% conﬁdence level
“Statistically signiﬁcant at the 99% conﬁdence level

Standard errors are robust to heteroskedasticity and correlation between observations within the
same household.
Sample Size Is 174,004 observations. Estimation performed using sample weights.

113

BIBLIOGRAPHY

114

BIBLIOGRAPHY

Blumberg, L.J., L. Dubay, and SA. Norton. 2000. “Did the Medicaid Expansions for
Children Displace Private Insurance? An analysis using the SIPP.” Journal of Health
Economics, 19:33-60.

Besley, T., and A. Case. 2000. “Unnatural Experiments? Estimating the Incidence of
Endogenous Policies.” The Economic Journal, 1 10:F 672-F 694.

Centers for Medicare and Medicaid Services (CMS). 1997. “Title XXI Summary ﬁ’om
the Balanced Budget Act of 1997 (P.L.105-33) — August 5, 1997.” (Available online
at: www.cms.hhs.gov/schip/Iddssum.asp, access date: 6/7/02).

Currie, J ., and J. Gruber. 1996. “Saving Babies: The Efﬁcacy and Cost of Recent
Changes in the Medicaid Eligibility of Pregnant Women.” Journal of Political
Economy, 104: 1263-1296.

Cutler, D.M., and J. Gruber. 1996. “Does Public Insurance Crowd Out Private
Insurance.” Quarterly Journal of Economics, 1 1 12391-430.

Dubay, L., and G. Kenney. 1997. “Did Medicaid Expansions for Pregnant Women Crowd
Out Private Coverage?” Health Aﬂairs, J anuary/F ebruary, 185-193.

Fronstin, P. 20008. “Counting the Uninsured: A Comparison of National Surveys.” EBRI
Issue Brief, no. 225.

F ronstin, P. 2000b. “Testimony Before the Health, Education, Labor, and Pensions
Committee, United States Senate, Hearing on Health Insurance Coverage and
Uninsured Americans.” (Available online at: www.cbri.org/testimony/t123.pdf,
access date: 10/6/2000).

Guyer, J. 2002. “Low-Income Parents’ Access to Medicaid Five Years After Welfare
Reform.” Prepared for the Kaiser Commission on Medicaid and the Uninsured
(Available online at: www.kﬂiorg/content/2002/4052/4052pdf, access date: 6/22/02).

Health Care Financing Administration (HCFA). 2000. “The State Children’s Health
Insurance Program Annual Enrollment Report October], 1998 — September 30, 1999”
(Available online at www.hcfa.gov/init/enroll99.pdf, access date: 10/9/2000).

Health Care Financing Administration (HCFA). “Medicaid: A Brief Summary”
(Available online at www.hcfa.gov/pubforms/actuary/ormedmed/default4.htm, access
date: 6/7/02).

Health Care Financing Administration (HCFA). State Reports on Medicaid and SCHIP
programs. Available online at: www.hcfa.gov/init/chpa-map.htm access date 5/7/01.

115

Kenney, G.M., F.C. Ullman and A. Weil. 2000. “Three Years into SCHIP: What States
Are and Are Not Spending.” The Urban Institute, Assessing the New Federalism
Series A, No. A-44 (Available online at newfederalism.urban.org/pdf/anf_a44.pdf,
access date: 10/6/2000).

Nelson, C .T. and RI. Mills. 2001. “The March CPS Health Insurance Veriﬁcation
Question and its Effect on Estimates of the Uninsured.” Housing and Household
Economic Statistics Division, US. Bureau of the Census (Available online at:
http://www.census.gov/hhes/hlthins/veri£ htnrl, access date: 5/10/2002).

Mathematica Policy Research, Inc. 2001. “Implementation of the State Children’s Health
Insurance Program: Momentum Is Increasing After a Modest Start.” Prepared for the
Health Care Financing Administration, US. Department of Health and Human
Services, under HCFA contract number 500-96-0016 (3). (Available online at:
http://www.hcfa.gov/stats/schip1.pdf, access date: 5/2/2001).

Marquis, MS, and SH. Long. 1996. “Reconsidering the Effect of Medicaid on Health
Care Services Use.” Health Services Research, 30:791-808.

Rask, K.N., and K.J. Rask. 2000. “Public Insurance Substituting for Private Insurance:
New Evidence Regarding Public Hospitals, Uncompensated Care Funds, and
Medicaid.” Journal of Health Economics, 19: 1-3 1.

Shore-Sheppard, L., T.C. Buchmueller and G. A. Jensen. 2000. “Medicaid and Crowding
Out of Private Insurance: A Re-examination Using Firm Level Data.” Journal of
Health Economics, 19:61-91.

Swartz, K. 1986. “Interpreting the Estimates from Four National Surveys of the Number
of People Without Health Insurance.” Journal of Economic and Social Measurement,
14:233-242.

Thorpe, KB, and CS. Florence. 1998. “Health Insurance Among Children: The Role of
Expanded Medicaid Coverage.” Inquiry, 35:369-379.

US. Department of Health and Human Services, Ofﬁce of the Assistant Secretary for
Planning and Evaluation, 1999. “State Children’s Health Insurance (SCHEP)
Database,” availabe online at aspe.hhsgov/health/schipZ/default.htnr, access date:
10/5/01.

Yazici, E.Y., and R Kaestner. 2000. “Medicaid Expansions and the Crowding Out of
Private Insurance Among Children.” Inquiry, 37 :23-32,

Yelowitz, AS. 1995. “The Medicaid Notch, Labor Supply, and Welfare Participation:

Evidence from Eligibility Expansions.” Quarterly Journal of Economics, 1051909-
939.

116

Wooldridge, J.E. 2000. Introductory Econometrics: A Modern Approach. South-Western
College Publishing.

Wooldridge, J.E. 2002. Econometric Analysis of Cross Section and Panel Data. MIT
Press.

117