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Out of Sight, Out of Mind? Measuring the Relationship Between Privatization and Medicaid Self-Reporting

This study uses Medicaid as a test case for measuring whether privatization of a

prominent government program influences awareness of program use. To measure whether privatization impacts the relationship between Medicaid enrollment and knowledge of Medicaid receipt, it uses Medicaid administrative records that have been linked to self-reports of Medicaid enrollment in the American Community Survey. It then tests whether these findings are replicated among self-reports in the National Longitudinal Study of Adolescent to Adult Health (Add Health). Variation in Medicaid at the state level enables this research to examine the consequences of privatization within the same policy area along a continuous, rather than dichotomous, scale. This analysis found that the reporting of Medicaid enrollment among a state’s Medicaid population decreases as commercial Medicaid managed care organization (MCO) penetration increases and as the Medicaid population in MCOs that share the name of private insurance companies grows. It also finds that when underreporting of Medicaid enrollment occurs, these respondents are more likely to report enrollment in private insurance, but not a lack of insurance. Having controlled for alternative explanations, these results identify when privatization, via Medicaid managed care, obscures the recognition of government's personal relevance.


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The American government is both expanding and disappearing from sight. These

paradoxical developments can be attributed to the growth of the “submerged” state: programs

that by nature of their design can be difficult for the mass public, as well as program recipients,

to identify as a government program and a social program (Mettler 2011). These programs, and

the policies that create them, are alternatively referred to as part of the “hidden,” “anti-

bureaucratic,” or “delegated” state (see Howard 1999; Skocpol and Finegold 1982; Morgan and

Campbell 2011). The size of the submerged state is staggering. Since the 1960s, government

spending has increased fivefold, adjusted for inflation, yet the number of federal employees has

remained roughly constant (DiIulio 2014). Submerged policy designs are particularly pervasive

in the healthcare arena (Morgan and Campbell 2011). Recent controversies, including the Health

Insurance Exchange rollout and mismanagement at Veterans Affairs hospitals, have highlighted

government’s reliance on private actors and the dearth of government personnel in these areas.

By definition, submerged state policies influence the visibility of government. Research

measuring the effects of submerged policy designs on a policy’s target population, however, has

overwhelmingly relied on self-reports of program enrollment to measure receipt of government

benefits (Earle and Gehlbach 2003; Mettler 2011; Morgan and Campbell 2011), creating a strong

potential for these enrollment estimates to be biased downward. Although this reliance is a

shortcoming of existing research, self-reports also present a unique opportunity. Rather than

interpreting self-reports as a measure of program use, I treat them as a consequence of program

design. Specifically, I use state-level variation in Medicaid administration to test whether and

when privatizing social policy decreases awareness of program enrollment and government

involvement.


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Using Medicaid for this inquiry also allows me to consider a potential cause of the

phenomenon known as the “Medicaid undercount.” The Medicaid undercount refers to the

finding that surveys of Medicaid enrollment drastically undercount the size of the Medicaid

population (Klerman et al. 2009). This paper probes whether Medicaid privatization can help to

explain the underreporting of Medicaid in surveys. To measure the relationship between reports

of Medicaid enrollment and Medicaid privatization, I utilize Medicaid administrative data on

program enrollment that has been linked to the American Community Survey (ACS), a Census

survey that contains questions on health insurance. This administrative data provides a measure

of Medicaid enrollment that is untainted by the biases of self-reports, which allows the linked

self-reported data to function as a measure of awareness of program enrollment. My analysis of

this linked data shows that commercial MCO penetration, and the naming of managed care plans,

are related to decreased Medicaid reporting. I then test for this relationship in self-reports of

Medicaid enrollment in the National Longitudinal Study of Adolescent to Adult Health (Add

Health). The results of the ACS analysis were mirrored in the Add Health survey data. Through

highlighting these relationships, this research calls attention to a profound consequence of

privatization: its ability to mask government’s personal relevance. These findings also

demonstrate the importance of disaggregating the policy mechanisms that are used in

privatization initiatives in order to clarify when and how privatization impacts government

visibility.

Medicaid and the Submerged State

“Submerged” policies can be distinguished from “direct” policies based on the potential

difficulties they create for public recognition of government involvement. The administrative

mechanisms used by submerged policies include deferral of administrative capacity to non-


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governmental organizations (i.e. privatization) and reliance on the tax code for wealth transfers,

rather than directly providing benefits (Mettler 2011).1 The implementation of these measures

has allowed government to address a range of policy agendas without a corresponding increase

in the size of formal institutions within the federal government (DiIulio 2014). The extent of the

submerged state in U.S. health care policy is exemplified by the “Mildred paradox.” Mildred

was the mother-in-law of political scientist Don Kettl. In the last few years of her life, a

combination of Medicaid and Medicare paid for Mildred’s extensive health care needs, including

her annual $85,000 a year nursing home. Despite the numerous government-funded health care

services she received, Mildred never encountered a government employee (Kettl 2009). Stories

like hers are increasingly common as governments shift more responsibility for service provision

to private actors.

The growth of submerged program characteristics has been attributed to two

contradictory demands: the need for adequate social provisions and a desire for smaller

government (Morgan and Campbell 2011). Morgan and Campbell use the 2009 health care

reform legislation and the development of Medicare Part D to show how powerful special

interests and conflicting public preferences helped to propel the delegation of administrative

authority to non-state actors (2011). Perceptual biases in favor of privatization can also bolster

support for the submerged state (Lerman Forthcoming). According to Lerman, people are more

likely to think that a well-performing municipal service is privately administered. Conversely,

poorly run services are more likely to be perceived as publically administered, regardless of

whether they were actually publicly or privately administered. These misperceptions were found

1 These policy mechanisms also contribute to the “delegated welfare state” (Morgan and Campbell 2011) and "government by proxy" (DiIulio 2014). Government-by-proxy refers specifically to the phenomenon whereby the federal government defers administrative responsibility to state and local governments, nonprofit organizations, and for-profit companies (DiIulio 2014).


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to be held by Democrats and Republicans alike and stemmed from stereotypes of government

incompetence.

Along with identifying the growth of and causes for submerged state policies, researchers

have begun to untangle these policies’ effects on the public’s attitudes and behaviors (see Bendz

2015; Mettler 2011; Pierce et al. 2014). In her influential work on the submerged state, Mettler

used this approach to test how the visibility of a social policy influences public opinion on the

policy (Mettler 2011). While this research analyzed a range of submerged state policies, it

focused on state programs involving wealth transfers located in the tax code rather than

privatization initiatives. Mettler found that compared to direct state policies, submerged state

policies were less likely to encourage participants to engage with government and make political

donations. This research also found that increasing knowledge of submerged state policies and

their distributional benefits resulted in changed attitudes and engendered greater opinion

formation. These conclusions were in part based on an assessment of the 2008 Social and

Governmental Issues and Participation Study. This survey, however, relied entirely on self-

reports of program use. Therefore, while this research did much to identify and further our

understanding of the submerged state, we still lack a rigorous test of when and how a proposed

mechanism of the submerged state – privatization – influences government visibility among a

program’s target population.

Although privatization is theorized to be a leading policy mechanism that obscures

awareness of government’s involvement in a program or service, a broad range of policies are

referred to under the label of privatization, further complicating how we understand the

relationship between privatization and the submerged state. At its most fundamental,

privatization involves a transfer of responsibility from government to the private sector


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(Lundqvist 1988). Lundqvist clarifies that in this formulation, “it is the direction that counts…

not some final or eternal location of that which is transferred” (1988: 4). In other words,

privatization is not a dichotomous phenomenon; rather, the degree to which programs are

privatized moves along a continuum. This suggests that privatization could have varying effects

on visibility based on how it is implemented. Some forms of privatization are more obvious to

recipients than others, which may serve to clarify government’s role in the delegation of

administration to non-state actors. A recent study of the Milwaukie school voucher program

showed that parents of children who utilized school vouchers were more likely to feel that

government impacted their child’s education and that this interaction taught them about

government (Fleming 2014).2 This finding suggests that privatization qua vouchers reminds

people that government is responsible for privatized public services. However, Mettler’s

research suggests that privatization of government social programs can diminish recipients’

awareness of their interaction with the state (2011). These divergent findings show that

additional research is needed to determine if and when privatization submerges the state. They

also indicate that inquiries into the consequences of privatization should account for the variety

of forms that privatization initiatives can take.

Medicaid presents a unique opportunity to assess the relationship between privatization,

program design, and awareness of government. Its prominence in state budgets, the health care

system, and political rhetoric underscore the program’s central role in U.S. social policy. This

suggests that Medicaid could be a difficult program to obscure from the public consciousness

(see e.g. Moore 2015). However, Medicaid can also be a confusing program: it is jointly

administered by federal and state governments as a federal-state partnership program; eligibility

2 This study also concluded that school vouchers may reduce support for public schools (Fleming 2014).


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and coverage differ by state and recipient group; and the name of Medicaid programs differ

between and within states. Similarity between the names of the Medicaid and Medicare

programs, as well as overlap between the two program populations, adds to the confusion over

what Medicaid is and who it serves. The confusion surrounding Medicaid suggests that if

privatization affects perceptions of government’s role in people’s lives, Medicaid is a policy area

where we should expect to find evidence of this trend. Given these divergent implications, this

paper tests whether the submersion of a program that looms large in government budgets and

national discourse, but whose administration is fragmented, can impact recipients’ awareness of

this government intervention in their lives.

Variation in Medicaid administration also allows this research to identify the

consequences of different degrees of privatization within the same policy area. I do so by

creating an index of the various elements of Medicaid privatization. Identifying these specific

features can help to clarify when, if at all, Medicaid privatization impacts government visibility.

This index also enables me to test whether elements of Medicaid privatization are implemented

in tandem. One of the earliest moves toward Medicaid privatization dates back to the 1980s with

the introduction of managed care plans. Under Medicaid managed care, states contract with

managed care organizations (MCOs). States provide MCOs with a set monthly payment

(capitation), and the MCOs provide health insurance benefits to the enrollees. MCOs currently

represent the most widespread form of Medicaid privatization in terms of enrollment (CMS 2013

Managed Care Enrollment Report).3 Accordingly, this study focuses on Medicaid privatization

via managed care. In a managed care arrangement, Medicaid enrollees receive their health

3 A voucher system did not become an approved form of Medicaid delivery until 2013, when states applied to the CMS for waivers to use vouchers in their plans to expand Medicaid eligibility in accordance with the Affordable Care Act (Kaiser Family Foundation “Medicaid Expansion in Arkansas” February 12, 2015).


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insurance from a nongovernmental entity called a Managed Care Organization (MCO). The

added institutional layer of the MCO moves Medicaid administration away from the government

by creating a new, nongovernmental (i.e. private), organization that directly interfaces with the

Medicaid recipient. The introduction of MCOs also made many of these Medicaid plans look

more like private insurance, which by this time had increased its reliance on a managed care

model.

States determine whether and how to implement managed care, and can require managed

care enrollment for a broader share of the Medicaid population under the Section 1915(b) waiver

program. Section 1115 of the Social Security Act allows states to apply for waivers that grant

additional flexibility in administering Medicaid programs. These measures provide states with

substantial latitude in managed care implementation, which has created significant between-state

variation in MCO reliance and administration. For instance, some states have nearly 100% of

their Medicaid population in MCOs while others have no Medicaid MCO penetration (CMS

Medicaid Managed Care Report 2015). In this paper, I test whether variation in Medicaid

managed care implementation affects whether recipients are aware that they are enrolled in

Medicaid. National surveys of Medicaid use, including Census surveys, drastically

underestimate Medicaid enrollment compared to administrative records (Call et al. 2013).

Inquiries into this Medicaid undercount have centered on two of the most prominent surveys

used to measure Medicaid enrollment: the Census’s American Community Survey (ACS) and the

Current Population Survey (CPS) (see, for instance, Abraham, Karaca-Mandic, and Boudreaux

2013; Boudreaux et al. 2015; Call et al. 2008; Davern et al. 2009). These studies show that

although it is seemingly straightforward to measure who receives Medicaid – just ask – the

resulting responses can underestimate the size of the Medicaid population by more than 40%


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(Davern et al. 2009). Leading explanations of the Medicaid undercount attribute this

mismeasurement largely to question wording and survey design. However, the theoretical

connection between privatization and confusion over government’s presence suggests that

Medicaid privatization may also contribute to the Medicaid undercount. There are a number of

studies that examine the relationship between Medicaid managed care and the Medicaid

undercount. Eberely et al. found that managed care usage reduced the accuracy of self-reports

(2009). Similarly, Chattopadhyay and Bindman studied Medicaid managed care enrollment

within the state of California and found that increased Medicaid managed care penetration

correlated with lower estimates of Medicaid enrollment (2006). Yet, other researchers

conversely found that including a variable for use of managed care plans versus fee-for-service

Medicaid failed to predict, or even lessened, the Medicaid undercount (Call et al. 2008;

Kincheloe et al. 2006). More recently, Plotzke, Klerman, and Davern (2010) used individual-

level data matched to administrative records and found that managed care usage did not have a

statistically significant effect on the accuracy of Medicaid reporting in the CPS. Among persons

who had Medicaid but did not use medical services in the past year, however, managed care

enrollees were more likely than fee-for-service enrollees to correctly report their Medicaid status.

Although these aforementioned studies present disparate findings, they are identical in

that they all treat Medicaid managed care as a dichotomous variable. They measure whether or

not a plan is an MCO, without accounting for variation between managed care plans. I attempt

to make sense of the conflict in the Medicaid undercount literature by identifying and measuring

the aspects of managed care plans that are the most theoretically relevant to the submerged state

literature and, in turn, the underreporting of Medicaid enrollment. The submerged state literature

theorizes that privatization should lead to the decreased visibility of the state. It predicts that as a


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state’s Medicaid program is privatized, government’s role in this health insurance becomes less

clear. Cantor et al. theorized that Medicaid MCO enrollees may misperceive the public nature of

their Medicaid plans because of their interactions with the managed care company and their

familiarity with their plan’s (non-Medicaid) name (Cantor et al. 2007). I test Cantor’s prediction

by measuring whether the name of the managed care company and the composition of who the

managed care company serves are related to the underreporting of Medicaid enrollment. Some

Medicaid MCO plans include the name of the insurance company administering the plan in the

plan name, other MCOs keep Medicaid or the name of the geographic region being served in the

plan name, and still other plans do neither. It stands to reason that plans which keep the

geographic unit served in the plan’s name, such as the Health Plan of San Joaquin (the Medicaid

managed care entity for California’s San Joaquin county) are more likely to be recognized as a

Medicaid plan than Medicaid MCOs that have the name of private insurance companies in their

plan name, such as Indiana’s Anthem MCO. In terms of Medicaid MCO enrollment, the Centers

for Medicare and Medicaid Services (CMS) divides managed care organizations into seven

different types of entities. Plans that provide full insurance coverage to an enrollee are classified

as comprehensive-care entities. This is in contrast to partial-care insurance plans, which only

provide limited forms of coverage, such as contraceptive coverage. The two most prevalent

types of comprehensive-care MCOs are commercial MCOs and Medicaid-only MCOs.

Medicaid-only MCOs provide services to Medicaid beneficiaries only. Commercial MCOs

provide services “to both Medicaid and commercial and/or Medicare” enrollees (CMS 2008

Managed Care Enrollment Report). Commercial MCOs may be more likely to cause confusion

about Medicaid enrollment because these MCOs are also providing services to non-Medicaid

enrollees and as such, their representatives and plan materials engage with and are targeted


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toward a broader population. Commercial MCOs also create the potential to allow Medicaid

enrollees to remain with the same insurance provider as they switch from private to public health

insurance.

Given the theorized relationship between submerged policy designs and awareness of

program use, I test the hypothesis that as Medicaid becomes increasingly privatized within a

state, the state’s Medicaid population is less likely to report their Medicaid enrollment. I

operationalize Medicaid privatization using the following indicators:

• MCO penetration in the Medicaid population • The construction of Medicaid MCO plan names • The member composition of the Medicaid MCO, i.e. commercial MCOs or Medicaid-

only MCOs.

Data and Methods:

To test this hypothesis, I build on a previously conducted linkage of Medicaid enrollment

records to survey self-reports. The American Community Survey (ACS) is a nationally

representative survey, conducted by the Census Bureau, which contains a battery of questions on

health insurance. The ACS questions on Medicaid enrollment describe Medicaid as a

government program. In 2013, the State Health Access Data Assistance Center (SHADAC), in

conjunction with the U.S. Census Bureau, matched ACS insurance data to Medicaid Statistical

Information System (MSIS) administrative records. They conducted this linkage using an

anonymized version of the internal Census ACS survey, which contains over 4 million personal

records for the 2008 survey. These records were then linked to MSIS records via Protected

Identification Keys (PIKs), which were developed by the Census Bureau. Through this linkage,

these researchers were able to estimate the size of the Medicaid undercount in the 2008 ACS

(Boudreaux et al. 2015). For each state and the District of Columbia they calculated the

Medicaid undercount using the following ratio:


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The number of persons who reported having Medicaid in the ACS ___and who were enrolled in Medicaid according to the MSIS___

The number of persons who were enrolled in Medicaid according to the MSIS

I use this output – the size of the Medicaid undercount in each state – as my dependent variable.4

Due to disclosure concerns, this linked data could not be released at any geographic unit smaller

than the state level (Boudreaux et al. 2015).

My key explanatory variable is a state-level measure of Medicaid privatization. This

variable is constructed from an index based on each state’s managed care arrangements. All

items in this index are calculated at the state-level and they include:

• The percent of the Medicaid population enrolled in managed care. • The percent of the Medicaid population enrolled in a commercial MCO. • The percent of the Medicaid population enrolled in a MCO with the private insurance

company’s name in the plan name. For clarity, this measure will be referred to as “privately-named Medicaid MCOs.”

The index of these three measures has a Cronbach’s alpha of .45. Given the moderate degree of

internal consistency shown by this measure of scale reliability, I analyze the components of this

measure together as an index, but I also analyze each item of the index separately. By testing the

measures separately, I can identify whether particular privatization design elements are driving

my findings.

Next, I specified relevant control variables to better ensure that any apparent correlation

between privatization and the Medicaid undercount is not the product of a correlated state-level

variable. One such potential feature is the size of a state’s elderly population. Persons aged 65

4 The published SHADAC paper recodes this variable so that higher values indicate lower levels of state-level Medicaid reporting (i.e. a larger Medicaid undercount). It also separates the state-level Medicaid reporting into two different categories for adult and child Medicaid recipients. To make interpretation of the data more easily comprehensible, I arranged the coding of the dependent variable so that higher values indicate higher levels of state-level Medicaid reporting. I also analyze all populations together, but include a variable to capture the percent of the state’s population that is over 65.


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and older are eligible for Medicare. Persons who are eligible both for Medicare, because of their

age, and Medicaid, because of their financial status, may be particularly vulnerable to

misreporting due to their dual-eligible status. However, their Medicare eligibility may also give

them a heightened awareness of, and attention to, government’s role in their health insurance.

To control for the potential that this population behaves differently than the population at large, I

included a variable that measures the percent of the Medicaid population that is 65 or older in

each state, as they would also be eligible for Medicare.

Another concern is that potential language barriers could harm Medicaid reporting. One

item in the privatization index concerns Medicaid plan names. It is possible that non-English

speakers, or persons with only limited English-language skills, would be affected disparately by

these language-based name cues. To control for this possibility, I included a measure that

captures the percent of the state’s population that speaks English “not well” or “not at all”

according to ACS survey estimates.5

5 Given the correlation between the size of a state’s population that speaks English less than well and the size of a state’s Hispanic population, I also specified a version of this model that includes a measure of the percent of a state’s Medicaid population that identifies as Hispanic. The inclusion of this variable did not affect any of the substantive findings of this paper. I also tested for a number of additional control variables not listed in the main text, none of which impacted my findings. Regression output with these additional control variables can be found in Appendix A. These additional control variables are described as follows. (1) A variable measuring the percent of the population in each state that lives below 200% of the federal poverty level. Boudreaux et al. found that persons with incomes below 200% of the federal poverty level were more likely to report their Medicaid enrollment when they have Medicaid, according to the MSIS, than persons with higher incomes. (2) A measure of the percent of the population in a state that is on Medicaid. It is possible that living in a community where Medicaid is more commonly used can improve Medicaid reporting. Including this measure of Medicaid penetration also helps to control for the Medicaid generosity of each state, given that poverty levels are also included in this regression. Moreover, having already accounted for the percent of the population that lives below 200% of the poverty line, the Medicaid penetration variable captures Medicaid generosity (i.e. the expansiveness of the state’s Medicaid eligibility) and not simply the number of low-income residents in the state. Accordingly, this measure of Medicaid penetration helps to control for a potential correlation between Medicaid generosity and the decision to implement Medicaid managed care programs. I also address this concern by including a measure of average Medicaid spending per adult enrollee.


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I also include a measure of the adult population enrolled in Temporary Aid to Needy

Families (TANF). Until the Personal Responsibility and Work Opportunity Act of 1996

(PRWOA), Medicaid eligibility was linked to Aid to Families with Dependent Children (AFDC),

a federal program that provided financial assistance to low-income families. AFDC was a highly

stigmatized program and this stigma spread to Medicaid, impacting how Medicaid was

structured (Pracht 2007; Soss and Schram 2007). In particular, the size of the adult AFDC

population was shown to influence states’ decisions to switch to Medicaid managed care (Pracht

2007). According to Pracht, the social stigma toward adults on government welfare encouraged

politicians to promote Medicaid managed care, as it was considered a less generous alternative to

fee-for-service care (2007). As AFDC has since been restructured as TANF, and there is a close

correlation between the two populations (Gilens 2009), controlling for the size of the adult

TANF population can help to control for an element of the political calculus that is related to

privatization initiatives.

The historical development of Medicaid also suggests that racial identity has the potential

to influence willingness to report Medicaid enrollment. In the latter half of the 20th century,

attitudes on welfare became strongly correlated with attitudes on race (Kellstedt 2003). In this

process, welfare became a pejorative term that could conjure racialized images of an undeserving

non-white population dependent on government largess (Gilens 2000; Katz and Thomas 1998).

The race-coding of AFDC was ultimately a key motivating factor in the push to remake AFDC

into TANF (Meyerson 1996). However, TANF and Medicaid at least partially retained the

perception that they were programs for poor African-Americans (Gilens 2009; Soss and Schram

(3) A measure of the percent of each state’s employed population that is in a union. Strong union membership was shown to be correlated with resistance to managed care initiatives (Pracht 2007).


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2007). Given this continued racialization, I include a measure of the state’s Medicaid population

that lists their racial identity as black or African American in my regression analysis.

Education levels may also influence the accuracy of survey reporting. As education

levels increase, people should be increasingly capable of shouldering the cognitive burden

imposed by survey questions. Given this, I added a measure that captures the percent of the

population in each state that has at least a high school diploma. Lastly, I included a measure of

state ideology. This measure is taken from the state ideology index created by Tausanovitch and

Warshaw (Tausanovitch and Warshaw 2013). This is a particularly critical measure because it

allows me to establish that state privatization levels are not merely a proxy for state ideology.

The inclusion of this control variable is also necessary considering that another explanation for

the Medicaid undercount, aside from lack of awareness or confusion, is that respondents are

consciously misreporting their Medicaid use. It is possible that the stigma attached to this public

benefit may encourage the misreporting of Medicaid enrollment. This form of error is only a

concern for my analysis to the extent that these conscious misreports are correlated with the

state’s degree of privatization. The most plausible mechanism through which the two would be

connected is political ideology. Accordingly, the state-level political ideology variable helps to

control for the effects of this alternative cause of misreporting.

To summarize, I use the following model, model one, to measure the consequences of

privatization for Medicaid reporting:

Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 + β5X5 +β6X6 + β7X7 + ε

where

Y = Percent of the state’s Medicaid population that reports Medicaid in the ACS X1 = State’s degree of Medicaid privatization X2 = Percent of the Medicaid population that is over 65 years of age X3 = Percent of the state adult population that receives TANF


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X4 = Percent of the state population that speaks English less than well or not at all X5 = Percent of the state Medicaid population that identifies as African American X6 = Percent of the state population that received a high school diploma X7 = State political ideology My analyses of this model use robust standard errors. Estimating regression parameters using

robust regression analysis protects the resulting estimates from being unduly impacted by

deviations from modeling assumptions created by heteroscedasticity and outliers (Western

1995).

Next, I test whether these privatization measures relate to decreased levels of Medicaid

self-reporting in wave three of the National Longitudinal Survey of Adolescent to Adult Health

(Add Health). 6 This analysis uses state-level privatization variables as my explanatory variables

of interest. For the dependent variable, however, I use individual-level measures of Medicaid

reporting. I moved this variable to the individual-level by first constructing a measure of

Medicaid eligibility. I determined Medicaid eligibility based on whether the respondent met

state eligibility limits concerning total household income, parental status, employment, and

qualifying health conditions. I then limited my analysis to survey respondents who were eligible

for Medicaid in their state. The dependent variable in this analysis is a dichotomous measure of

whether or not the Medicaid-eligible respondent reported Medicaid, and as such, I use a logistic

regression model. In analyzing the Add Health data, I use the same control variables listed in

model one. A salient distinction from the ACS model, however, is that the Add Health survey

data allows me to measure the control variables – TANF enrollment, language skills, race,

education, and political ideology – at the individual level. Given the previously-discussed

theoretical connection between state-level political environment and Medicaid reporting, I

6 Wave 3 of the Add Health in-home survey was conducted from August 2001- April 2002.


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include both state and individual-level measures of political ideology.7 In the Add Health

analysis, I also omit the variable measuring recipients aged 65 and over because the Add Health

sample was limited to respondents aged 18-28. Accordingly, the model used to analyze the Add

Health Data, model two, is specified as follows:

Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 + β5X5 +β6X6 + β7X7 + ε

where:8

Y = Whether or not a Medicaid-eligible respondent reported Medicaid enrollment X1 = State’s Degree of Medicaid Privatization X2 = TANF Enrollment X3 = Language Proficiency9 X4 = Identification as African-American X5 = Highest Educational attainment10 X6 = Political Ideology X7 = State Political Ideology I account for heteroscedasticity created by the inclusion of state-level variables by clustering

standard errors at the state level. I also re-analyze model two as a multilevel model. Multilevel

modeling accounts for individual and group-level variation when estimating regression

coefficients, such as as state-level Medicaid privatization, at the group level (Gelman and Hill

2006: 246). It models the clustering of respondents within states by treating each state as a

separate context, the “fixed effects” component of the model, and specifying a variance

7 Although I include these variables because of their theoretical relevance, omitting them from the analysis does not impact the findings. 8 As the Add Health data does not include a measure of union membership, and as the sample size was already limited to persons under 200% FPL, I did not include these addition control measures from model one in re-analyzing the Add Health output. 9 The Add Health measures of language proficiency focus on frequency of use of a non-English language. This creates a discrepancy between the ACS and the Add Health models. As with model one, I re-ran model two with a measure of Hispanic identification. Output with this additional control variable is included in Appendix B. 10 I also re-measured education using a series of dichotomous variables that indicate one’s highest level of educational achievement (some high school, a high school diploma, some college, and a college degree or greater). These indicator variables did not change my findings. Output with these measures is included in Appendix B.


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component that allows the intercept to vary according to respondents’ state of residency, the

“random effects” component of the model. The multilevel analysis used in model 3 in defined

as:

Yi = αj[i] + β1Xi1 + β2Xi2 + β3Xi3 + β4Xi4 + β5Xi5 + β6Xi6 + β7Xi7 + εi

αj ~ N(µα, σ2α)

Model three uses the same dependent and independent variables specified in model two.

Findings

My first series of analyses use the linked aggregate ACS data. To ensure that any

observed relationship between privatization and Medicaid underreporting is not a product of

copious control variables, I first tested for a bivariate relationship between Medicaid reporting

and privatization. I found a statistically significant relationship between Medicaid reporting and

commercial MCO penetration (p=.022), with Medicaid reporting decreasing as commercial

MCO penetration increased. Recall, by decreased Medicaid reporting, I am referring to

decreased Medicaid reporting among persons who were enrolled in Medicaid according to MSIS

Medicaid administrative data. Similarly, increased penetration of privately-named MCOs

related to decreased Medicaid reporting (p=.027). The index variable, which was constructed

from all three privatization measures, was also statistically significant at the .05 level (p=.019).

However, I failed to find evidence of a statistically-significant bivariate relationship between

managed care penetration and Medicaid reporting (p= .948).

< Table One Here >

Next, I reanalyzed this data with the set of control variables listed in model one. These

results, detailed in table two, are consistent with my findings from the bivariate analysis. As the

percent of the Medicaid population that uses a commercial MCO grows, the size of a state’s


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Medicaid undercount (i.e. the underreporting of Medicaid enrollment) grows (p=.013).

Similarly, as the percent of the Medicaid population enrolled in privately-named plans grows, the

underreporting of Medicaid enrollment increases (p=.006).

< Table Two Here >

The privatization index also relates to Medicaid underreporting, with increased privatization

levels correlating with decreased Medicaid reporting (p=.007). However, I fail to find evidence

of a statistically significant relationship between managed care penetration and Medicaid

underreporting (p=.896).

These findings show that the form of the privatization matters. When I measure

privatization simply using managed care enrollment, which is the most widely-used measure of

Medicaid privatization in studies of the Medicaid undercount, I fail to find a significant

relationship. However, when I specify design features of Medicaid managed care that should

theoretically be expected to cause confusion about the public nature of Medicaid (specifically,

Medicaid plan names and commercial MCO penetration) these design features are related to

Medicaid underreporting. The significance of the commercial MCO variable is particularly

noteworthy given the construction of this variable. According to the 2008 CMS Managed Care

Report, an MCO is defined as commercial if it provides services to the non-Medicaid population,

the Medicare population, or both. This means that the commercial MCO variable also accounts

for plans that serve the Medicare population. I would expect this to attenuate the effects of the

commercial MCO variable because I cannot disaggregate the non-Medicare commercial MCOs

from Medicare commercial MCOs. Compared to plans that serve the general, non-Medicaid

population, I would not expect for plans that serve the Medicare population to look significantly

more like commercial plans than Medicaid-only MCOs. This finding is all the more notable


20

given that I control for the potential confusion created by the Medicaid-Medicare overlap, and

that a larger dual eligible population is related to improved Medicaid reporting.

With respect to the size of this impact, my findings suggest that, for every 1% increase in

the commercial MCO population, .06% fewer Medicaid recipients are reporting their enrollment.

Similarly, for every 1% increase in the number of Medicaid recipients in privately-named

MCOs, .082% fewer Medicaid recipients are reporting their enrollment. This suggests that the

size of the effect of these individual privatization measures is very modest. However, the

coefficient of the index variable is substantially larger. For every 1% increase in the degree of

privatization, Medicaid reporting decreases by approximately 1.07%. This indicates that the

impact of privatization grows as these different features of Medicaid managed care are

implemented in tandem.

These results, however, should be interpreted with caution. First, it is important to stress

that although these findings demonstrate a correlation between privatization and Medicaid

underreporting, this does not prove causation. While I have included theoretically-relevant

control variables in an attempt to account for other potential causal explanations, this does not

eliminate the possibility that the patterns demonstrated in this research are caused by a correlated

alternative factor. Moreover, this analysis uses aggregate data, which limits what these findings

can say about individual-level behavior with respect to privatization and Medicaid

underreporting. To address the concerns created by the use of aggregate data for these analyses,

I retest my hypothesis using Medicaid reporting in the Add Health survey. In this analysis, I use

individual-level measures of Medicaid reporting paired with state-level measures of Medicaid

privatization. This use of individual-level dependent variables allows me to make inferences

with respect to individual-level behavior. I begin my analysis by once again testing for a


21

bivariate relationship between Medicaid reporting and Medicaid privatization. As the

independent variable of interest is a state-level variable, I cluster the standard errors at the state

level to account for heterogeneity in the error terms. Here, I find a statistically significant

correlation between both commercial MCO enrollment and the size of the Medicaid undercount

(p=.025), and enrollment in privately-named plans and the size of the Medicaid undercount

(p=.022).

< Table Three Here >

Table four shows that this relationship holds when I run this analysis with the full list of

control variables detailed in model two. As the percent of a state’s Medicaid population in a

commercial MCO increases, Medicaid-eligible respondents become less likely to report

Medicaid enrollment (p=.010). Likewise, as the percent of a state’s Medicaid population in

privately-named MCOs increases, Medicaid-eligible respondents become less likely to report

Medicaid enrollment (p=.021).

< Table Four Here >

The results of my analysis using the multilevel model, model three, are detailed in table

five. These findings confirm my previous results. Increased penetration of commercial MCO

enrollment and increased penetration of privately-named plans relate to a decrease in Medicaid

reporting among Medicaid-eligible recipients in the bivariate model (p =.042, and p =.032,

respectively), as well as the model with the full set of control variables (p = .044, and p = .038,

respectively).

< Tables Five and Six Here >

These results, however, do not tell me how these Medicaid eligible respondents are

misreporting their insurance status. Are the respondents who do not report Medicaid enrollment


22

reporting that they are uninsured or are they reporting a different type of insurance coverage,

such as private insurance? To get at this question, I keep the sample limited to Medicaid-eligible

respondents, but I create a new dichotomous dependent variable that measures whether a

respondent reports Medicaid or the respondent reports having private insurance. By private

insurance, I am referring to health insurance that is provided by a source other than government.

Using this measure, I find that increased commercial MCO enrollment, as well as increased

enrollment in privately-named plans, relate to a greater propensity to report private insurance

(compared to reporting Medicaid enrollment) among the Medicaid-eligible population. This

output is detailed in table six.

I also re-ran this analysis using a dependent variable that measures whether a respondent

reports Medicaid or a respondent reports having no health insurance. With this measure of the

dependent variable, I failed to find a statistically-significant relationship between commercial

MCO enrollment, or enrollment in privately-name plans, and Medicaid reporting (see table

seven). In other words, among the Medicaid-eligible population, neither an increase in

commercial MCO enrollment nor increased enrollment in privately-named plans correlates with

an increased likelihood of reporting being uninsured. This finding holds for both the logistic

regression model and the multilevel analysis. Output from these models is detailed below.

< Table Seven & Eight Here >

Viewed together with my previous finding, this suggests that Medicaid privatization may

increase the number of Medicaid enrollees who report having private insurance, but that I do not

find a connection between privatization and estimates of the uninsured.11 This distinction is

11 This finding is in line with Boudreaux et al.’s conclusion that respondents who misreported their Medicaid status were more likely to report having private health insurance rather than reporting no health insurance (2015). This suggests that my finding is not merely an artifact of how I constructed the Medicaid-eligibility variable.


23

consistent with my expectation for why Medicaid privatization should impact the Medicaid

undercount. I theorized that privatization had the potential to influence Medicaid reporting by

obscuring the visibility of government’s presence. Rather than making people think that they

lack health insurance, the relationship between Medicaid privatization and the increased

reporting of private insurance suggests that privatization hampers Medicaid recipients’

recognition of the public origins of their health insurance.

Discussion

Overall, this research demonstrates that the privatization of a major social program can be

related to decreased awareness of program use. Just as critically, it also shows that not all

privatization initiatives have the same effects and that the form of the privatization matters. The

finding that increased reliance on commercial MCOs and privately-named MCOs correlate with

higher levels of Medicaid underreporting makes intuitive sense. It indicates that forms of

privatization that scramble signals of government involvement drive the relationship between

privatization and underreporting Medicaid. This finding emphasizes the necessity of treating

privatization as more than a dichotomous measure in research on the effects of program

privatization. However, the analysis used in this research establishes a correlation, but not

necessarily causation. Accordingly, I cannot definitively claim that privatization causes this

underreporting. Rather, this research identifies a pattern showing that an increase in design

features that obscure government’s role in Medicaid corresponds with a larger portion of the

Medicaid population failing to recognize and report their enrollment. That this pattern persisted

in two different data sets and with multiple modeling specifications, however, indicates the

robustness of this relationship. This finding also cautions more broadly against interpreting self-


24

reports as an unbiased measure of program use, particularly for programs that are theorized to be

a part of submerged state policies.

In identifying this relationship, this research suggests that estimates of Medicaid

enrollment which rely on self-reports should be corrected to account for states’ levels of

privatization, specifically their joint reliance on commercial MCOs and privately-named MCO

plans. The Congressional Budget Office (CBO), along with state governments, use Medicaid

enrollment statistics from Census surveys to estimate the cost of, and compliance with,

legislation (see, for example, CBO Long-Term Budget Outlook, 2015; Legislative Budget Board

Staff Estimate, 2013). Inaccurate measurement can lead to inefficient, and less effective, policy

administration. Although the size of the aggregate misreporting identified in this research is

modest, the growth in Medicaid managed care reliance is not. Some of the most populous states

in the country, such as Florida, are transferring their Medicaid population almost entirely to

MCOs, meaning that these privatization initiatives affect a rapidly growing portion of the

population (CMS 2016). This paper provides guidance on which elements of managed care

policy designs to account for when generating corrected estimates of Medicaid enrollment.

The civic consequences of this research, however, extend beyond inaccurate

measurement. Medicaid is the largest source of public health insurance in the country. It is

directly responsible for the health care of communities at the margins of American society:

children, their parents, the elderly, and the disabled living below or near the poverty line. My

findings suggest that Medicaid enrollees may have an increasingly difficult time recognizing

their enrollment, and consequently, government’s personal relevance. Aside from creating

unrecognized material changes, submerging the public origins of people’s health insurance

makes it more difficult for Medicaid recipients to have informed civic responses to this major


25

government intervention in their lives. When people fail to recognize that they are interacting

with government, conceptions of government’s personal relevance are – by definition –

distorted. This research suggests that this distortion is connected to policy design. The hope is

that calling attention to this phenomenon, and government’s role in its creation, encourages

scholars to investigate the ways in which this submersion impacts the target population’s civic

understandings and behaviors.


26

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Tables for Paper Manuscript: Out of Sight, Out of Mind? Measuring the Relationship Between Privatization and Medicaid Self-Reporting

Table One: Privatization and Medicaid Reporting (bivariate analysis)

VARIABLES (1) (2) (3) (4)

% of State Medicaid Population in a Commercial MCO

-0.0932*

(0.0383) 0.0187 % of State Medicaid Population in a Privately-Named MCO

-0.123*

(0.0471) 0.0118 % of State Medicaid Population with Managed Care

-0.00208

(0.0281) 0.941 State's Degree of Privatization -1.631* (0.644) 0.0145 Constant 79.40* 79.50* 78.57* 78.42* (0.742) (0.793) (1.791) (0.615) 0 0 0 0 Observations 51 51 51 51 R-squared 0.103 0.096 0.000 0.107

Robust standard errors in parentheses * p < 0.05

!


31

Table Two: Privatization and Medicaid Reporting

VARIABLES (1) (2) (3) (4)

% of Medicaid Population in a Commercial MCO

-0.0599*

(0.0232) 0.0133 % of Medicaid Population in a Privately-Named MCO

-0.0816*

(0.0284) 0.00637 % of Medicaid Population with Managed Care

-0.00320

(0.0242) 0.896 State's Degree of Privatization -1.073* (0.380) 0.00715 % of Medicaid Population Over 65 Years of Age

0.559* 0.597* 0.523* 0.583*

(0.234) (0.237) (0.238) (0.236) 0.0214 0.0155 0.0337 0.0176 % of Medicaid Adult Population on TANF 2.647 2.467 1.595 2.643 (1.903) (1.801) (2.218) (1.802) 0.171 0.178 0.476 0.150 % of Medicaid Population: African American

-0.0833* -0.0754* -0.0780* -0.0795*

(0.0239) (0.0210) (0.0263) (0.0223) 0.00116 0.000848 0.00485 0.000895 % of State That Speaks English Less Than Very Well

-0.843* -0.849* -0.919* -0.840*

(0.118) (0.119) (0.137) (0.117) 7.77e-09 9.09e-09 3.37e-08 7.47e-09 % of State with High School Degree -0.422* -0.400* -0.419* -0.410* (0.138) (0.135) (0.154) (0.135) 0.00383 0.00503 0.00960 0.00410 State Ideology -0.0665* -0.0662* -0.0675* -0.0663* (0.0213) (0.0214) (0.0222) (0.0213) 0.00317 0.00342 0.00405 0.00327 Constant 117.0* 114.9* 117.3* 115.2* (12.47) (12.27) (14.18) (12.25) 0 0 1.95e-10 0 Observations 51 51 51 51 R-squared 0.624 0.627 0.591 0.629


!


32

Table Three: Medicaid Self-Reporting and State-level Medicaid Privatization (bivariate

analysis)

Table Four: Medicaid Self-Reporting and State-Level Medicaid Privatization

VARIABLES (1) (2)

% of State Medicaid Pop. in a Commercial MCO -0.0152* (0.00674) 0.0243 % of State Medicaid Pop. in a Privately-Named MCO -0.0518* (0.0226) 0.0219 Constant -0.175 -0.219 (0.165) (0.146) 0.289 0.134 Observations 570 570


!


33

VARIABLES (1) (2)

% of State Medicaid Pop. in a Commercial MCO -0.0188* (0.00727) 0.00984 % of State Medicaid Pop. in a Privately-Named MCO -0.0384* (0.0166) 0.0207 Frequency of English-Language Use -0.493 -0.374 (0.536) (0.463) 0.358 0.419 TANF Enrollment 1.518* 1.465* (0.477) (0.482) 0.00147 0.00235 Identify as African American 0.0381 0.354 (0.442) (0.332) 0.931 0.287 Identify as white -0.521 -0.335 (0.413) (0.327) 0.207 0.306 Highest Level of Education -0.208* -0.205* (0.0666) (0.0660) 0.00177 0.00187 State-level Political Ideology -0.0858 -0.0755 (0.195) (0.190) 0.661 0.690 Political Ideology -0.216 -0.248 (0.151) (0.157) 0.151 0.113 Constant 3.293* 2.900* (1.160) (1.034) 0.00454 0.00505 Observations 548 548


!


34

Table Five: Medicaid Self-Reporting and State-Level Medicaid Privatization (multilevel model)

VARIABLES

(1)

(2)

FIXED-EFFECTS ESTIMATES

% of State Medicaid Pop. in a Commercial MCO -0.00354* (0.00174) 0.0416 % of State Medicaid Pop. in a Privately-Named MCO

-0.00801*

(0.00373) 0.0318 Constant 0.443* 0.417* (0.0528) (0.0551) 0 0 ________________________________________

RANDOM-EFFECTS ESTIMATES

____________ _____________

State-Level Random Effects -26.03 -28.80 (46.85) (42.49) 0.578 0.498 σ2α -1.551* -1.596*

(0.240) (0.232) 1.08e-10 0 σ2ε -0.823* -0.817*

(0.0392) (0.0378) 0 0 Observations 532 532 Number of groups 117 117

Robust standard errors in parentheses * p<0.05

!


35

Table Six: Medicaid Self-Reporting and State-Level Medicaid Privatization (multilevel model with control variables)

VARIABLES (1) (2) !


% of State Medicaid Pop. in a Commercial MCO -0.00289* (0.00144) 0.0442 % of State Medicaid Pop. in a Privately-Named MCO 0.00513* (0.00247) 0.0381 Frequency of English-Language Use 0.0768 0.0811 (0.162) (0.159) 0.636 0.610 TANF Enrollment 0.374* 0.374* (0.0747) (0.0754) 5.54e-07 7.16e-07 Identify as African American 0.101 0.126* (0.0620) (0.0581) 0.103 0.0304 Highest Level of Education -0.0103 -0.0104 (0.00701) (0.00706) 0.142 0.141 State-level Political Ideology 0.00565 -0.000179 (0.0707) (0.0635) 0.936 0.998 Political Ideology -0.0332 -0.0370 (0.0357) (0.0364) 0.353 0.309 Constant 0.436 0.413 (0.228) (0.224) ____________________________________________

0.0562 ____________

0.0652 __________


State-Level Random Effects

-24.09

23.41* (50.12) (5.985) 0.631 9.19e-05 σ2α -2.136* -2.170*

(0.369) (0.373) 7.13e-09 5.74e-09 σ2ε -0.865* -0.861*


Robust standard errors in parentheses; * p < 0.05 !


36

Table Seven: Medicaid Self-Reporting and State-Level Medicaid Privatization (multilevel model with control variables) a

a Dependent variable = Reported Medicaid or Private Insurance

VARIABLES (1) (1)


% of State Medicaid Pop. in a Commercial MCO -0.00591* (0.00182) 0.00118 % of State Medicaid Pop. in a Privately-Named MCO -0.00818* (0.00245) 0.000838 Frequency of English-Language Use 0.117 0.124 (0.166) (0.162) 0.481 0.444 TANF Enrollment 0.488* 0.485* (0.0618) (0.0640) 0 0 Identify as African American -0.0292 0.0404 (0.0588) (0.0566) 0.620 0.476 Highest Level of Education -0.0132 -0.0138 (0.00828) (0.00878) 0.110 0.116 State-level Political Ideology 0.355* 0.277 (0.154) (0.160) 0.0212 0.0840 Political Ideology -0.0150 -0.0224 (0.0516) (0.0515) 0.771 0.664 Constant 0.541 0.480 (0.280) (0.271) 0.0531 0.0765 ________________________________________________ ______________ _______________ RANDOM-EFFECTS ESTIMATES State-Level Random Effects

-25.38

-18.89 (79.06) (48.64) 0.748 0.698 σ2α -2.615* -2.720*

(0.786) (1.090) 0.000876 0.0126 σ2ε -0.885* -0.870*




37

Table Eight: Medicaid Self-Reporting and State-Level Medicaid Privatization (multilevel model with control variables) b

b Dependent variable = Reported Medicaid or Reported Being Uninsured

VARIABLES (1) (2)


% of State Medicaid Pop. in a Commercial MCO -0.00260 (0.00288) 0.367 % of State Medicaid Pop. in a Privately-Named MCO -0.00544 (0.00449) 0.226 Frequency of English-Language Use 0.0834 0.0870 (0.144) (0.153) 0.562 0.569 TANF Enrollment 0.120 0.121 (0.0939) (0.0978) 0.202 0.218 Identify as African American 0.0573 0.0819 (0.0729) (0.0718) 0.432 0.254 Highest Level of Education 0.00834 0.00802 (0.0177) (0.0179) 0.639 0.654 State-level Political Ideology 0.482* 0.393* (0.182) (0.183) 0.00815 0.0314 Political Ideology -0.0702 -0.0809 (0.0605) (0.0625) 0.246 0.195 Constant 0.659* 0.662* (0.304) (0.313) 0.0301 0.0345 _________________________________________________ _______________ _______________


State-Level Random Effects -5.288* -22.61 (0.501) (31.06) 0 0.467 σ2α -2.866 -2.289*

(2.138) (0.706) 0.180 0.00119 σ2ε -0.822* -0.815*




38

Appendix A: Privatization and Medicaid Underreporting (with additional control variables)

!

VARIABLES (1) (2) (3) (4)

% of State Medicaid Population in a Commercial MCO

-0.0611*

(0.0208) 0.00563 % of State Medicaid Population in a Privately-Named MCO

-0.0667*

(0.0306) 0.0356 % of State Medicaid Population with Managed Care

0.000650

(0.0231) 0.978 State's Degree of Privatization -0.984* (0.368) 0.0112 % of Medicaid Population Over 65 Years of Age

1.047* 1.054* 1.006* 1.055*

(0.269) (0.284) (0.306) (0.275) 0.000411 0.000699 0.00227 0.000490 % of Medicaid Population: African American

-0.0401 -0.0342 -0.0295 -0.0376

(0.0279) (0.0278) (0.0297) (0.0277) 0.159 0.227 0.328 0.183 % of State with High School Degree 0.156 0.153 0.218 0.148 (0.287) (0.299) (0.302) (0.293) 0.591 0.612 0.476 0.617 % of State Adult Population on TANF 1.845 1.696 0.883 1.842 (1.595) (1.781) (2.201) (1.632) 0.255 0.347 0.691 0.267 State Ideology -0.0226 -0.0244 -0.0245 -0.0234 (0.0194) (0.0201) (0.0218) (0.0197) 0.252 0.233 0.268 0.241 % of State That Speaks English Less Than Very Well

-0.670* -0.681* -0.741* -0.670*

(0.178) (0.168) (0.234) (0.167) 0.000587 0.000257 0.00309 0.000297 % of State Population on Medicaid 0.374* 0.363* 0.381* 0.367* (0.103) (0.113) (0.123) (0.107) 0.000875 0.00289 0.00369 0.00154 % of State Population Under 200% FPL 0.188 0.179 0.213 0.180 (0.118) (0.128) (0.129) (0.124) 0.120 0.172 0.108 0.155 % of Medicaid Population: Hispanic 0.0283 0.0242 0.0392 0.0249 (0.0535) (0.0522) (0.0648) (0.0519) 0.600 0.647 0.549 0.634 % of State in Labor Union 0.107 0.0888 0.0848 0.0990 (0.0956) (0.0978) (0.114) (0.0963) 0.269 0.370 0.463 0.311 Average Spending Per Adult Medicaid Enrollee

0.000311 0.000360 0.000391 0.000333

(0.000256) (0.000274) (0.000309) (0.000262) 0.232 0.198 0.214 0.211 Constant 46.13 46.74 39.68 46.50 (29.95) (31.38) (31.81) (30.51) 0.132 0.145 0.220 0.136 Observations 49 49 49 49 R-squared 0.712 0.702 0.680 0.710


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39

Appendix B: Medicaid Self-Reporting and State-Level Medicaid Privatization (multilevel model with dichotomous education-based control variables)

VARIABLES (1) (2)


% of State Medicaid Pop. in Commercial MCO

-0.00281*

(0.00140) 0.0443 % of State Medicaid Pop. in a Privately-Named MCO -0.00458* (0.00230) 0.0466 Frequency of English-Language Use -0.0664 -0.0704 (0.125) (0.123) 0.596 0.568 TANF Enrollment 0.368* 0.369* (0.0724) (0.0732) 3.66e-07 4.57e-07 Identify as African American 0.103 0.127* (0.0625) (0.0585) 0.0980 0.0301 Highest level of education: some high school 0.166 0.169 (0.115) (0.112) 0.148 0.132 Highest level of education: high school diploma -0.0165 -0.00853 (0.107) (0.105) 0.877 0.935 Highest level of education: some college or greater -0.289* -0.303* (0.131) (0.132) 0.0274 0.0215 State-level Political Ideology 0.00254 -0.00315 (0.0667) (0.0598) 0.970 0.958 Political Ideology -0.0289 -0.0326 (0.0358) (0.0364) 0.420 0.372 Constant 0.433* 0.409* (0.196) (0.192) ______________________________________________ RANDOM-EFFECTS PARAMETERS

0.0268 _____________

0.0335 _____________

State-Level Random Effects -23.73 -19.72 (64.15) (29.75) 0.711 0.507 σ2α -2.094* -2.113*

(0.292) (0.290) 0 0 σ2ε -0.884* -0.880*

(0.0485) (0.0473) !

0 0 Observations 512 512 Number of groups 117 117


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