Pragmatism in a Time of Polarization?:

Implementing Health Insurance Marketplaces in the States

Simon F. Haeder

Doctoral Student

Department of Political Science

University of Wisconsin-Madison

haeder@wisc.edu

Abstract

The Affordable Care Act (ACA) has been one of the centerpieces of the Obama Administration’s

domestic policy agenda. Yet Republicans opposition has shown little subsiding even three years

after its enactment. Do standard accounts of Republican opposition actually square with reality?

Looking beyond the rhetoric and taking a more nuanced approach to analyzing the

implementation of insurance marketplaces, this paper shows that while Republicans in the states

are less eager than their Democratic counterparts to implement the ACA, they have nonetheless

made considerable headway in pragmatically participating in the shared governance of insurance

marketplaces. It appears as if even the staunchest opponents of the ACA remain eager to defend

long-time state prerogatives when it comes to the regulation of insurance.

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Introduction

The Affordable Care Act (ACA) has been one of the centerpieces of the Obama

Administration’s domestic policy agenda (see for example, Altman and Shactman 2011; Haeder

2012; Jacobs and Skocpol 2010, 2011; McDonough 2011; Starr 2011; The Staff of the

Washington Post 2010; Brennan and Studdert 2010). Implementation is a key component of

every policy initiative but the design of the ACA relies extensively on state and private partners

to cooperate with the national government over extensive periods of time (see Greer 2011; Kersh

2011; Miller 2010; Miller 2011; Skocpol 2010; Nichols 2010; Weil and Scheppach 2010;

Brennan and Studdert 2010). However, few pieces of legislation have proven as controversial

even after legislative passage, and doubts linger if the ACA could go the way of the ill-fated

Medicare Catastrophic Coverage Act of 1988 (Himelfarb 1995). Despite a Supreme Court

verdict and a presidential election, Republicans in Congress and across the states have, by and

large, shown few signs of accommodation even three years after the president’s signature,

particular with regard to the establishment of insurance marketplaces (Haeder and Weimer

2013b; Rigby and Haselswerdt 2013a). 1 Newspaper accounts are filled with stories of

grandstanding opposition to the various components of the ACA. Just recently, the U.S. House of

Representatives voted for the 46th

time to repeal or dismantle the ACA (Kapur 2013). Clearly,

the implementation of the ACA has confirmed that “the fight over policies does not end with the

presidential signature on a bill” (Haeder 2012, 66).

This essay seeks to answer two important sets of questions about the implementation of

insurance marketplaces. First, who is participating in the implementation of insurance

marketplaces? Specifically, are Republicans merely sitting on the sidelines, or worse, doing

1 The original terminology used by the Affordable Care Act is “health insurance exchanges.” However, in January

2013, citing concerns for non-English speakers, the Obama administration officially decided to rebrand the

exchanges as “health insurance marketplaces.” I use the new terminology in this paper.

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everything they can to impede implementation? Is implementation solely left to Democrats as a

result? Second, if Republicans are actively attempting to participate in the implementation of

marketplaces, how do they contribute and to what extent? And perhaps even more importantly,

how successful are they in influencing the implementation of marketplaces?

To answer these questions, I proceed as follows. First, I provide a brief review of the

implementation of shared governance programs like the ACA, with a particular focus on partial

pre-emption, and the conflicts during the process. Second, I illustrate the role states were

envisioned to play in the shared governance of insurance marketplaces. Third, I answer the

question, whether Republicans are participating in the implementation of insurance

marketplaces, and if so, how successful they are utilizing a variety of hierarchical models.

Finally, I conclude and provide a brief outlook for the future of the insurance marketplaces.

Looking beyond the rhetoric, this paper shows that while Republicans in the states are less eager

than their Democratic counterparts to implement the ACA, they have nonetheless made

considerable headway in pragmatically participating in the shared governance of insurance

marketplaces, particularly when it comes to lower profile issues. It appears as if even the

staunchest opponents remain eager to defend long-time state prerogatives when it comes to the

regulation of insurance.

Shared Governance Programs

Shared Governance and American Federalism

The debate about shared governance has long roots in the American and English

constitutional history (Beer 1993). This normative debate pits two opposing views of the

American federal system against each other. One the one side a strict dualists like David Walker

(1995) and Edward Corwin (1950) who are strongly opposed to federal interferences into states’

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affairs. They are supported, for instance, by those who worry about reduced competition

(Tiebout 1956), distortion of voter preferences (Tullock et al. 2002), government failure

(McKinnon and Nechyba 1997), the flypaper effect (Romer and Rosenthal 1980; Courant et al.

1979), a lack of accountability (Rivlin 1992, 2012), and potential fiscal defaults (Rodden 2006).

The opposing side is best exemplified by Morton Grodzins (1966) and Daniel Elazar (1962,

1984) and their partnership model, as well as scholars of fiscal federalism (Musgrave 1959,

1971; Oates 1972) and those worried about effects on equity and equality (Derthick 1970), a race

to the bottom (Cary 1974; Bailey and Rom 2004), and implications for democracy and

citizenship (Mettler 1998). Yet despite the vivid normative debate, the federal government and

the states have long shared in the governance for a wide variety of purposes (Clark 1938;

Bittermann 1938) including the state militia (Riker 1957) and a long list of healthcare programs

(Haeder and Weimer 2013a). However, a general increase in the polarization of American

politics (McCarty et al. 2006; McCarty 2007; Theriault 2008) has certainly revitalized the

debate.

Partial Preemption under Shared Governance Healthcare Program

A common form of shared governance between the federal government and the states can

be found in partial pre-emption (Crotty 1987; Hedge and Scicchitano 1994; Scicchitano and

Hedge 1993; Zimmerman 2007). Partial pre-emption is defined as “federal laws establishing

minimum national regulatory standards under which administrative responsibilities may be

delegated to states or localities provided they meet certain federal criteria” (Conlan 1991, 51).

Partial pre-emption, as utilized by the Obama Administration, can be considered as a form of

one-tail devolution, i.e. grants states significant opportunities for innovation and choice to

advance a more progressive agenda exceeding the national floor (Conlan and Posner 2011).

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While not without its problems, partial pre-emption is often considered a less controversial and

coercive approach compared to, for example outright mandates, restraints or total preemption

(Zimmerman 2001). At least in theory, “ partial preemption seemed to satisfy everybody; it

would achieve national regulatory goals and assuage those concerned about the intrusion of the

federal government into state affairs” (Scicchitano and Hedge 1993, 108).

Partial pre-emption is not a new development with regard to healthcare. One of the

earliest examples is the Federal Emergency Relief Administration (FERA) (Greenfield 1958).

Services covered under FERA were extensive, including physician care, emergency dental

services, bedside nursing, prescription drugs, and emergency appliances and the program “set a

precedent for increased participation of state and federal governments in financing medical care

for the needy” (Greenfield 1958, 25). FERA (and subsequent programs like the Civil Works

Administration (CWA) and the Works Progress Administration (WPA)) appear as a significant

break from past grant programs due to the relatively vast powers assigned to the federal

government in the form of “federal assumption” (Macmahon et al. 1941/1971; Williams and

Williams 1940). Whereas prior to FERA federal administrators had to rely solely on the

withdrawing of grant funding, the federal government now could choose to take over

administration of a program in case states were in non-compliance with grant requirements

(Maxwell 1946).2

More recently, partial pre-emption has occurred under the Health Insurance Portability

and Accountability Act of 1996 (HIPAA) and the Health Coverage Tax Credit (HCTC)

established under the Trade Act of 2002. While HIPAA is most widely known for its regulation

of medical record privacy and the partial pre-emption of state medical privacy laws (Ko 2005), it

2 Assumption occurred in Illinois, Kentucky, Oklahoma, North Dakota, Maine, Massachusetts, Ohio, Louisiana, and

Georgia (Clark 1938, 250-3). Moreover, several other states entered into a partnership with the federal government

(Clark 1938, 258).

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also established certain insurance portability requirements with regard to transitions from group

to individual coverage (Ladenheim 1997; Chaikind et al. 2005). HIPAA offered states a variety

of compliance options including the creation of qualified high-risk pools. At the same time,

HIPAA created a federal fallback option in case of non-compliance (Atchinson and Fox 1997;

Ladenheim 1997; Pollitz et al. 2000b). While many states chose to implement their preferred

program, various states failed to do so, forcing the federal government to administer programs in

nine states in 1998 and ten stats in 2005 (Chaikind et al. 2005; Fuchs et al. 1998).

Similarly, the HCTC provides tax credits to certain individuals that amount to 65 percent

of premium costs (Dorn and Kutyla 2004). The Trade Act, in addition to establishing fast-track

authority for trade agreements, established ten tax-credit eligible types of insurance plans, three

of which did not require any state action (Stone-Axelrad and Lyke 2005). State implementation

has been uneven and as a result solely federal options were available in 24 states in 2003 and 21

stats in 2005 (Stone-Axelrad and Lyke 2005). Arguably the increasing use of the 1915c and 1115

Medicaid State Waiver Program (Thompson 2012; Thompson and Burke 2009) could also be

considered a form of partial pre-emption with the traditional Medicaid program as the fallback

options.

Conflict in Recent Shared Governance Programs

Before moving on to a discussion of the implementation of insurance marketplaces, it is

worthwhile emphasizing that state opposition to federal programs has not been confined to the

Obama Administration or healthcare programs. Instead, research has described such opposition

with regard to the REAL ID Act (Regan and Deering 2009), No Child Left Behind (Loveless

2007; Shelly 2008), abstinence only programs (Doan and McFarlane 2012), various

environmental programs since the 1990s (Scheberle 2004), and OSHA (Thompson 1983). This is

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in addition to high-speed rail, the American Recovery and Reinvestment Act of 2009 and the

Race to the Top program under the Obama Administration (Nicholson-Crotty 2012).

The Role of States under the ACA in Setting up Health Insurance Marketplaces

Intended to provide coverage to millions of individuals, marketplaces are in many

respects the linchpin of ACA implementation. States have been tasked with a significant role in

this process and the ACA relies extensively on their cooperation for their shared governance. It is

not surprising that the ACA followed this precedent given the long tradition of shared

governance and partial pre-emption, particularly in the healthcare field, described above.

However, the establishment of health insurance marketplaces has been challenging as, with the

exception of Massachusetts and, to a very limited degree, Utah, marketplaces are new and hence

largely untested. As a result, many organizational questions remain to be answered and states

have little experience to rely upon in this process (Weil and Scheppach 2010).

Haeder and Weimer (2013b) provide an extensive treatment of the role of states in the

implementation of insurance marketplaces as they emphasize the extent of shared governance

envisioned under the ACA. Specifically, states are granted significant leeway in the

establishment of marketplaces with regard to governance and administrative structure, regulatory

power over the insurance market, financing mechanisms, and benefit structure. Most

importantly, the ACA offers states the opportunity to choose between a purely state-based

marketplace without any federal involvement at one end of the continuum, and a fully federal

marketplace without any state involvement at the other hand. Although not originally contained

in the ACA, federal regulations have also created an implicit compromise between these two

extremes in the form of Partnership Exchanges in which states and the federal government

essentially share in the regulation and administration of the various components of the

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marketplaces (Haeder and Weimer 2013b). While “HHS, by law, retains authority over each

FFE” (Center for Consumer Information and Insurance Oversight 2012, 6), the notion of a

partnership offers a variety of avenues for cooperation between the different levels of

government.

Overall, the relationship envisioned under the ACA in general, and the creation of

insurance marketplaces in particular, is thus one of shared governance in which the federal

government serves as a partner, providing funding and expertise while establishing a federal

floor. While the degree of state leeway can be debated, the ACA certainly grants states

significant opportunities for innovation and (Conlan and Posner 2011).

A Tale of Two Reponses: Creating vs. Regulating Marketplaces

Opposition to marketplaces has become a key issue for Republicans eager to verify their

anti-Obama credentials (Erickson 2012; Knickerbocker 2012). Sean Nicholson-Crotty has

described similar behavior for a variety of grant programs and differentiates between high-profile

issues, which are inherently partisan, and lower profile issues, which illicit much more pragmatic

responses (Nicholson-Crotty 2012). Nicholson-Crotty’s differentiation between high- and low-

profile responses provides the theoretical foundation for this research. Specifically, I expect state

responses to differ significantly in their response to the implementation of insurance

marketplaces with regard to (1) the establishment of marketplaces and (2) the regulation of

marketplaces. Given the strong opposition to the ACA, particularly by conservative voters,3 open

opposition the creation of insurance marketplaces seems a prudent political calculation for

Republican legislators. However, I argue that states, while being openly hostile with regard to

high-profile issues such as the actual creation of marketplaces, may very well be much more

3 The Kaiser Health Tracking Poll by the The Henry J. Kaiser Family Foundation provides more detailed insights. It

is available at http://kff.org/interactive/health-tracking-poll-exploring-the-publics-views-on-the-affordable-care-act-

aca/.

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cooperative on lower profile issues such as the regulation of marketplaces, i.e. the technical

implementation of their various components. As a result, and in direct opposition to assertions of

wide-spread state defiance, states may be much more cooperative and involved in the

establishment of marketplaces than commonly believed for a variety of reasons.

Traditionally, state legislators of all parties have been eager to retain regulatory control of

their insurance markets. While the insurance industry, particularly in the healthcare field, is one

of the most heavily regulated industries in the United States, the regulation of insurance has

traditionally been the domain of the states (see Klein 2009; Field 2007; McCray 1993; Teske

2004; Gerber and Teske 2002; Gormley 1983; Berry 1984; Meier 1988, 1991). State primacy

was confirmed by the Supreme Court decision in Paul v. Virginia in 1869 which directly limited

federal regulatory efforts until its reversal in U.S. v. South-Eastern Underwriters Association in

1944 (McCray 1993). However, states were able to limit federal intrusion by lobbying Congress

to pass the McCarran-Ferguson Act of 1945 (Pub.L. 79−15) which reestablished state primacy

by statute. Nonetheless, recent increases in state regulation have been accompanied by extensive

federal regulations (see Pollitz et al. 2000a; Meier 1988) most prominently in the form of the

Employee Retirement Income Security Act (ERISA) of 1974 (Pub.L. 93–406), the Consolidated

Omnibus Budget Reconciliation Act (COBRA) of 1985 (Pub.L. 99–272), and the Health

Insurance Portability and Accountability Act (HIPAA) of 1996 (Pub.L. 104–191). The federal

government also indirectly regulates insurance through several benefits programs including the

Federal Employee Health Benefits Program (FEHBP), as well as Medicare, Medicaid, and

TRICARE. Despite this interference states, often against the preferences of insurers, have

eagerly guarded their regulatory powers (see Klein 2009; Field 2007; McCray 1993).

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In addition, democratic legislators throughout the country have a particular incentive to

assure that the implementation of insurance marketplaces progresses safely. Perhaps no other

piece of the legislation is as strongly identified with the Democratic Party as the ACA.

Democrats, whether they like it or not, own the ACA and will be held accountable for its

implementation, especially for its potential shortcomings. As a result, we would expect

significant implementation activities by Democratic legislatures. At the same time, Republican

legislators also have an incentive to participate in the regulation of insurance marketplaces, albeit

not equally as strong, given the alternative policy outcome: direct regulation by the federal

Department of Health and Human Services, i.e. the decidedly more liberal Obama

Administration (Haeder and Weimer 2013b). While it may be political expedient to openly

oppose the creation of the marketplaces, it appears futile to surrender all regulatory power as

well, given the inevitability of the establishment of marketplaces. As a result, Republican

legislators may very well participate in the regulation of insurance marketplaces, albeit in the

less visible manner hypothesized above.

Additionally, Republican legislators have an additional incentive to participate in the

regulation of insurance marketplaces in states with Democratic governors. Here Republican

legislators may seek to limit the independent implementation activities undertaken by the

executive. Without statutory guidance, Democratic governors are relatively free to cooperate

with the Obama Administration in the implementation of the marketplaces, again leading to

much more liberal outcomes than preferred to be Republican legislators. Only statutory guidance

offers the possibility of exerting any kinds of influence. Again, Republican participation appears

rather plausible on lower profile issues. Finally, states with unified Republican control may seek

to distinctively shape implementation and create the most conservative marketplace possible.

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Establishing Insurance Marketplaces: A High-Profile Issue

Very little empirical research has been conducted evaluating the implementation of health

insurance marketplaces.4 Nonetheless, two recent articles provide an initial analysis of the

developments (Rigby and Haselswerdt 2013b; Haeder and Weimer 2013b).5 However, both

analyses are very much focused on the relatively blunt question of whether states established

insurance marketplaces, i.e. the high-profile component of establishing insurance marketplaces.

Nonetheless, the articles show that, mostly driven by partisanship, state responses to the ACA

have varied significantly, ranging from enthusiastic support to outright hostility (Figure 1): given

a few outliers, strongly Democratic states have created insurance marketplaces while strongly

Republican states have failed to do so (Haeder and Weimer 2013b; Haeder 2013; Rigby and

Haselswerdt 2013a). Paradoxically, the implementation of insurance marketplaces appears to

have turned into a particularly maligned component of the ACA. This is surprising because

conservative reform plans have long advocated the establishment of such marketplaces in order

to improve the provision of private insurance products by alleviating information asymmetries

and facilitating consumer choice. (Haislmaier 2006; Haislmaier and Owcharenko 2006).

Moreover, the individual mandate is clearly based on the concept of personal responsibility.

Nonetheless, opposition to the creation of marketplaces has become a key issue for Republicans

4 However, there is no dearth of material released by various foundations, legal scholars, and think tanks offering a

wide variety of suggestions and advice for implementers yet providing no empirical assessment (Bachrach and

Boozang 2011; Blumberg 2011; Carey 2010; Corlette and Volk 2011; Gardiner and Perera 2011; Health Access

2010; Jost 2010; Lischko 2007; Wicks 2009). 5 Rigby and Haselswerdt (2013b) use elapsed-time conditional risk set models to assess the states’ progress in

establishing marketplaces. The authors find that public support, a Democratic governor, and Democratic seat share

in the legislature are positively related to progress whereas elected Republican insurance commissioners, resident

age and income are negatively related.5 They also provide a qualitative evaluation of 5 outlier states. Haeder and

Weimer (2013b) provide a very detailed and more nuanced overview of state activities with regard to the

implementation of insurance marketplace summarized under five distinct themes. They also illustrate the vast

differences in state responses. Further, they find empirical evidence that the probability of establishing a

marketplace is positively related to capacity of the insurance department as well as unified Democratic control of

state government

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thus making any outright cooperation, or mere appearance thereof, by Republicans a political

liability.

Regulating Insurance Marketplaces: A Low-Profile Issue

Analyzing the creation of insurance marketplaces, however, does not fully capture the

extent of states responses. Similarly to Haeder and Weimer (2013b) who also emphasize the less

visible activities states have undertaken, including upgrades to Medicaid eligibility systems, the

role of insurance commissioners, and foundation grants, states have been very active in

regulating the various components of the newly-created insurance exchanges. Descriptive

statistics do much to dispel the notion that insurance marketplaces have been exclusively the

domain of Democrats. Overall, 551 distinct bills were introduced from January 2011 through

May 2013: 215 in 2011 (39 percent), 128 in 2012 (23 percent), and 217 in the first 5 months of

2013 (38 percent). Bills were introduced in all states except Delaware. The number of bills

introduced is slightly skewed toward Democratic authors who introduced 292 bills (107/79/106)

compared to 238 bills (101/44/93) introduced by Republicans (Figure 1). The remainder was

introduced by non-partisan (Nebraska), independent, or bipartisan sponsors. Introductions are not

stable across time and there appears to be a significant dip in the presidential election year and

acceleration in 2013.6 About 55 percent of the bills were introduced in the lower chamber. This

pattern holds across all three years.

[Figure 1 about here]

Moreover, the 431 bills implementing the ACA (506 if counting abortion-related bills)

vastly outnumber those in opposition (51). Bills implementing the ACA numbered 152 in 2011,

6 Note that only bills up to May of 2013 are included here.

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105 in 2012, and 174 in 2013. Not surprisingly virtually all Democratic bills were supportive of

the ACA. All of the independent and bipartisan bills were in support as was a majority of the

non-partisan bills. Strikingly, also a majority (56 percent or 83 percent if counting abortion-

related bills) of Republican bills moved to implement the ACA. However, of the 51 bills that

clearly opposed implementation of the ACA, 47 were sponsored by Republican members.

Finally, the incredibly small number of bipartisan bills deserves to be noted (0.9 percent).

Clearly, the ACA is a highly partisan issue and the parties have found little room for

compromise.

The most active states were California (36 bills), Illinois (25), Minnesota (25), Oregon

(23), Michigan (23), and New Mexico (23). Interestingly, neither Illinois not Michigan actually

established a marketplace and New Mexico only did so very recently. Thus even in states with

federal marketplaces, states are seeking to continue their regulatory role.7 However, 8 of the bills

in Michigan can be classified as seeking to prevent the implementation of the ACA.

Interestingly, Michigan is a significant outlier as the next highest number of opposition bills is 5

in Minnesota. All 36 bills in California moved to implement the ACA followed by Oregon (21),

Minnesota (20), Vermont (19) and Connecticut (19). The least active states are Delaware (0),

Alaska (1), Massachusetts (2), Oklahoma (3), and South Dakota (3). However, in all four of the

latter states at least one bill was introduced that was in support of the ACA. Finally, I note that

various states strongly opposing marketplaces like Texas (20 total, 13 implementing), Florida (9,

5), and Louisiana (5, 5) were nonetheless not completely inactive.

Looking at outcomes, I note that 288 bills (215 in support) did not move beyond the

introduction stage whereas another 90 (73) gained at least support from one committee and

7 For example, Ohio and South Dakota retained control over health plan management and Virginia will continue to

license and certify health plans in the marketplace (Haeder and Weimer 2013b)

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another 33 (25) were passed in one chamber. An additional 22 bills (19) then gained additional

support from at least one legislative committee in the non-originating chamber, and another 18

(15) passed both chambers but were not enacted either due to a veto or failure to agree on a

conference report. Finally, 100 bills (83) were enacted into law. Looking at party success we find

that about 18 percent of both Democratic and Republican bills were enacted while a majority

failed to move beyond the introduction stage.

Two specific bill topics stand out. Of the 551 bills, 76 bills deal with abortion and another

33 bills deal with marketplace navigators. The vast majority of abortion-related bills were

introduced by Republicans (64) and only 9 were introduced by Democrats. However, in a

majority of states no bills addressing the coverage of abortion were introduced. Again, Michigan

is an outlier with 11 abortion-related bills introduced. However, navigator bills are slightly more

likely to be introduced by Democrats than Republicans. Only 14 of the abortion bills and 6 of the

navigator bills have been enacted. In both cases, the vast majority of bills did not even gain

committee support. As most of the abortion bills were introduced by Republican sponsors, it

appears as if Republican tried to make a statement during the implementation process that could

gain the support of their constituencies, particularly Tea Party activists and social conservatives.

Later in the process, as the implementation of the ACA became more and more inevitable, both

Republicans and Democrats sought to be more pragmatic by participating in the implementation

generally, and to accommodate interest group pressure by addressing the issue navigators in

particular.

Finally, it is worth examining the role of the Supreme Court decision as well as the 2012

presidential election. Interestingly, between the Supreme Court affirmation of the ACA and the

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election, only a single bill was introduced in the entire country.8 It appears as if the decision was

made to hold out for the outcome of the presidential election before moving forward. Overall,

346 of the bills were introduced in the time prior to the election compared to 205 bills in the 7

months after the election. The ratio between Democratic and Republican bills has stayed largely

the same. However, 84 percent of abortion-related bills and only one third of navigator bills were

introduced prior to the election. Clearly, with increasing institutionalization, the persistence of

the ACA has become more and more settled. It appears as if Republicans in the states have

settled on the dual strategy of rhetorical opposition while pragmatically sharing in the

governance of the program.

Taking a Closer Look at State Regulation of Insurance Marketplaces

The preceding section established that both Republican and Democratic state legislatures

have been active on the lower profile issue of regulating insurance marketplaces. At the same

time, Republicans have been much more reserved in terms of the higher profile issue of creating

insurance exchanges. A more nuanced treatment offers an opportunity to more adequately

evaluate state responses. Data for the following section were obtained from a variety of sources.

First, information regarding the bills and their outcomes was obtained from the Federal Health

Reform State Legislative Tracking Database, which is maintained by the National Conference of

State Legislatures and covers the period from January 2011 through May 2013. This website

provides information about key variables including the author, the topic, the legislative history,

and a summary of the bill. The summary of the bill was used to code whether a bill was in

support9 or in opposition

10 to the implementation of the ACA. The coding decision was

8 The bill was introduced in New Jersey as NJ A 3186.

9 For example CO H 1245 is summarized as “Concerns funding mechanisms for the Colorado health benefit

exchange. On and after January 1, 2014, in order to fund the Colorado health benefit exchange, the bill allows: The

health benefit exchange board of directors to assess against small group and individual health insurers in an amount

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straightforward except in a handful of cases. For these, reading the bill in question made the

coding decision obvious.11

Moreover, I coded whether a bill dealt with abortions12

and whether a

bill dealt with the role of navigators and brokers13

in the marketplace. Additionally, I obtained

information about the partisan balances of the various chambers as well as the party of the

governor from The Book of States published by The Council of State Governments. Nebraska has

a non-partisan, unicameral legislature and is thus only included in the section on descriptive

statistics and omitted from all other estimations. Finally, information about the establishment of

insurance marketplaces was obtained from two recent articles published on the subject (Rigby

and Haselswerdt 2013b; Haeder and Weimer 2013b).

Hierarchical Poisson Models: Introduction of Bills

In order to assess the introduction of bills empirically and to decipher differences based

on party affiliation, I resort to multi-level analysis.14

“Multilevel analysis is a methodology for

the analysis of data with complex patterns of variability, with a focus on nested sources of

variability” (Snijders and Bosker 1999). These models are generalizations of various linear and

non-linear models that take account of the clustering or nesting of observations and treat the

underlying dependence not as a nuisance but rather as “an interesting phenomenon” (Snijders

necessary to provide for the sustainability of the exchange, and a credit against the tax imposed against insurance

companies.” 10

For example, TN S 666 is summarized as “Would prohibit, by state statute, health insurers doing business in the

state under insurance law from participating, selling or offering coverage in any American Health Benefit Exchange

or other exchange operated under the PPACA.” 11

In this sections, bills dealing with abortion issues were treated as neutral with regard to implementation. However,

it appears plausible to code the neutral bills as supportive because they move forward with implementation. As this

coding scheme is only used for descriptive statistics, I resorted to the more nuanced solution here and combine the

abortion-related bills and other bills in support in the following sections for estimation. 12

For example, IN S 116 is summarized as “Relates to abortion coverage by qualified health plans, prohibits

qualified health plans under the federal health care reform law from providing coverage for abortion.” 13

Navigators are individuals who support consumers in navigating through the insurance marketplace. Traditional

insurance brokers have actively sought to gain access to these marketplaces and to obtain compensation for their

activities. For example, 14

Other terms used include, for example, hierarchical linear models, nested models, mixed models, random

coefficient, random-effects models, random parameter models, or split-plot designs.

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and Bosker 1999, 7). The nesting structure in this case appears straightforward: bills are nested

within chambers, chambers within states, and states within years. Ignoring this multi-level

structure may have significant detrimental effect on estimates and inference (Snijders and Bosker

1999). In this case chambers are nested within states.15

Quite reasonably, bills introduced merely

to oppose the implementation of the ACA come from a different population than those

introduced to implement it. Hence, I only use the subsample of my data that contains bills that

implement the ACA.16

Given the nature of hierarchical models, a data-driven approach is often

recommended that begins with a simple model and then expands the model while accounting for

data constraints.17

Hence, I estimate a two-level hierarchical variance component model first

(Model 1).18

I also tested adding a third level for Years but the estimations were compromised as

they were asking too much of the data. Instead, I resorted to time fixed effects (Model 2). Next I

added various level-one covariates (Model 3) as well as a random slope for the Governor

variable (Model 4). The covariates include indicators for the Chamber (coded 1 for the

assembly), the Proportion of Republicans in the Chamber, and whether the Governor was a

15

Again, there were no bills introduced in Delaware; Nebraska is excluded for reasons stated above. The total

number of cases, i.e. chambers by year, in this case is 205. 16

Bills coded in opposition and excluded in the following include such summaries as “Would amend the Executive

Reorganization Implementation Act, by prohibiting an executive order of the governor from establishing or

providing for the administration or operation of a State-based health benefit exchange.” or “Would prohibit the state

and other entities from establishing, operating, or purchasing insurance from an American health benefit exchange

and would make such health insurance contracts void. Also would give an individual a tax credit if taxed under the

individual responsibility or coverage mandate of the PPACA, allows the Attorney General to bring an action against

a person or entity causing harm when implementing the PPACA.” 17

In all of the following models the multi-level structure was tested and compared to a single level structure. In

every case, the multi-level structure was strongly preferred. All models here were estimated with Stata/MP 12.1

(StataCorp 2011b). The command used was xtmepoisson. This is Stata’s command for mixed-effects models for

count responses containing both fixed and random effects. Fixed effects are interpreted similar to standard

regression coefficients but random effects are not directly estimated and variances are presented. As no closed form

of the log likelihood exists the model is estimated with adaptive Gaussian quadrature. The default number of

quadrature points is 7. For a detailed overview of the command see StataCorp (2011a).

18 In order to account for the potential non-normality of the random intercept √

the model is also estimated with

nonparametric maximum likelihood estimation (Model 1a). The results are largely similar. Nonparametric maximum

likelihood estimation assumes that the random intercept is distributed with discrete locations and probabilities. The

process is iterative as locations are added and assessed whether the likelihood is as large as possible. The process

stops when the maximum Gâteaux derivative is less than 0.00001.

Page 17 of 40

Republican. Next, I added a cross-level interaction between the Party of the Governor and the

Proportion of Republicans in the Chamber; first I estimated the model with a random intercept

only (Model 5) and then I added a random slope as well (Model 6).19

Finally, in order to account

for potential level-one over-dispersion and other model misspecifications I followed Rabe-

Hesketh and Skrondal (2012a,b) and estimated a robust model for the random intercept and the

random coefficient model (Models 5a and 6a).20

In both cases, the results are essentially similar.

Finally, I re-estimated Model 5 using nonparametric maximum likelihood estimation as

described earlier (Model 5b). A comparison of the predicted counts between the two versions

largely overlaps pointing to a good fit of the original model.21

All models were also estimated

with a larger number of integration points22

than the program default, in order to obtain more

precise estimates, and tested for various covariance structures. Likelihood ratio tests showed that

an unstructured variance was appropriate, i.e. all variances were distinctly estimated. Ultimately,

Model 5a was selected as the preferred model because it is parsimonious and coefficients are

essentially similar and relatively small.23

The fixed part of the model is rather consistent across specifications. The time fixed

effects (with 2011 as the base excluded) are negatively signed and statistically significant for

2012. The Chamber variable never reaches statistical significance. In the models without the

interactions, the proportion of Republicans is negatively signed and highly significant. However,

19

Other interactions were tested with likelihood ratio tests, as standard Wald tests are inappropriate, but did not

achieve significance. 20

For the robust models the gllamm command (Generalized Linear Latent and Mixed Models) was used as it is the

only command capable of estimating robust standard errors. For an overview of gllamm and its framework 21

The difference between the two has a mean of 2.53e-08 and a standard deviation of .397. More than 95 percent of

the difference in predicted counts is between -0.5 and 0.5. 22

This process is iterative. I began with a relatively low number of integration points to obtain a base set of

estimates and then used these as starting values in the next iteration with a higher number of integration point. The

process was repeated until estimates did not change further. 23

In addition to the most appropriate fit to theoretical considerations, I also conducted various tests to assess the

appropriateness of the model including residual analysis (deviance and Anscombe residuals). In addition, model

interpretation as described below was essentially similar.

Page 18 of 40

the models with the interactions appear most appropriate from a theoretical perspective. We

would expect the behavior of legislators to vary depending on the partisan balance in the

legislature as well as the party of the governor. Both strongly influence the probability of a bill

becoming law; the effect is conditional. Moreover, it seems plausible that legislators may be

particularly interested in curbing the power of governors by imposing statutory restrictions. The

case of New Mexico comes to mind where the Democratic legislature actively sought to rein in

the independent actions of a Republican governor. The opposite situation occurred in Kentucky.

Interactions in hierarchical count models pose challenges for interpretation. Hence, I only briefly

note that the party of the governor as well as its interaction with the proportion of the chamber

that is Republican are positively signed and significant in some of the specifications. Turning to

the random parts of the model, I note the relative stability across specifications.

[Table 1 about here]

A better illustration of the statistical results may be gained from a comparison of

predicted counts at various interesting values. 24

I used the predicted counts to extrapolate the

expected number of bills introduced nationwide including a 90 percent confidence interval and

present results for both chambers as there appear to be slight differences (Table 2). The

predictions are stratified by year.25

Several interesting patterns emerge. First, although not

24

i.e. the predicted mean of the Poisson distribution. The predicted mean is based both on the fixed and random

effects and is conditional on the random effects. For more details see StataCorp (2011a). For comparative purposes,

the observed data had a mean of 2.48, a median of 2.00, a standard deviation of 1.60, and included a total of 497

bills for all three years. 25

An illustrative example. The total for a Republican Governor and a Strongly Democratic Legislature in 2011 is

831 (599-1430) for the Senate and 895 (659-1544) for the assembly. This number is the total number of bills across

all states that we would expect to be introduced, given that there is a Republican governor with a strongly

Democratic legislature and the indicators for 2012 and 2013 switched off, i.e. the entire period of observation is

treated as if it were 2011 for all 29 months.

Page 19 of 40

statistically significant, in all predictions the number of bills dips in 2012 and bounces back in

2013. Second, by far the most bills are introduced in Democratic legislatures with both

Democratic and Republican governors. Obviously, Democrats are seeking to move their states

into compliance with the ACA. Often this occurs despite the opposition of Republican

governors. The cases of New Jersey and, again, New Mexico come to mind. However, given the

limitation of the data we cannot find statistically significant differences across governors. Third,

states with strong Republican legislatures introduce the lowest number of bills, particularly if the

Governor is a Democrat. We could imagine that little compromise between the two is possible

and Democrats may try to use executive tools to move implementation forward given Republican

opposition in the legislature. Results for Republican legislatures are clearly statistically different

from those for Democratic legislature and, at times, even from split legislatures. Fifth, split

assemblies frequently produce statistically fewer bills than Democratic ones. Finally, despite the

lower numbers, it should be noted that Republican-controlled legislatures are nonetheless

expected to produce a significant number of bills ranging from annual point estimates of 157 to

218 nationwide if all legislatures were Republican. This is the more remarkable given the strong

rhetorical opposition presented in newspaper accounts and the Internet. Apparently, pragmatism

and accommodation prevails in the long run, at least to a certain degree.

[Table 2 about here]

Hierarchical Ordered Probit Models: Progress of Bills

Bill introductions are certainly an interesting part of the story of insurance marketplaces.

So far we have shown that while Democrats generally are more likely to introduce bills to

Page 20 of 40

implement the ACA, Republicans nonetheless are actively participating. However, if

Republicans or Democrats are introducing bills merely as symbolic actions, the effect on the

implementation of the ACA will remain negligible. We need to learn something about the

progress of the bills and under what conditions they become law. To do so I coded the progress

of a bill as follows. If it was introduced in either chamber, it was coded as 1. If it then was voted

upon by at least one committee of the same chamber it received a 2; if it passed the chamber of

origin it received a 3. Once the bill moved to the other chamber, it received a 4 if it gained the

support of at least one committee, if it passed that chamber it received a 5. Finally, if it was

enacted into law, it received a 6. In order to take advantage of the ordering and to account for the

nested structure, a hierarchical ordered probit model is appropriate. In the OLS environment,

ordered probit models usually show very similar results to standard OLS regressions. The same

holds for hierarchical models. With multi-level models, this can help with obtaining adequate

starting values as well as with model building, diagnosis, and interpretation.

[Table 3 about here]

Again, I began by estimating two-level standard hierarchical variance component models

with either state chamber or states as the higher level for all bills implementing the ACA. From a

theoretical and modeling perspective, the inclusion of the state-chamber level seemed most

appropriate (Model 7, other results omitted).26

Variance components can be interpreted in several

26

I also estimated various three-level (bills are nested in chambers are nested in states) and four-level (bills are

nested in chambers are nested in states are nested in years) variance components models. While these may seem

most appropriate from a theoretical perspective, hierarchical models are very data intensive. The estimations either

showed miniscule results for the intercept or failed to show reasonable results. Various estimation procedures and

algorithms were used. In both cases, the state level soaked up virtually all of the variance from the state chamber

level. Hence, it seems most appropriate in this case to resort to a lower level model with time fixed effects.

Page 21 of 40

ways but variance partition coefficients (VPC) and intraclass correlation coefficients (ICC) are

the most common. The former accounts for the proportion of the observed response variation at a

given level and hence point to the relative importance of the different levels. The ICC accounts

for the correlation within clusters. In the two-level model the ICC is .103, the chamber-level

VPC is .103 and the bill-level VPC is .897.27

Apparently, most of the variation occurs at the bill

level, emphasizing the need for a well-specified bill level model.

I subsequently added year fixed effects (Model 8) and other level-one covariates (Model

9). I also added a random slope for the Proportion of Republicans in the Chamber (Model 10).

The fixed part of the model included indicators for abortion-related bills, navigator-related bills,

committee-authored bills, and whether the bill originated in the assembly, whether the author

was a Republican, and whether the governor of the state was a Republican. In addition,

interactions between Republican governor and the proportion of the chamber that is Republican

and between Republican authors and the proportion of the chamber that is Republican were

added. This accounts again for conditional effects.28

I also estimated the model with restricted

maximum likelihood estimation with essentially similar results (omitted). Hence, for all other

models I resorted to standard maximum likelihood estimation in order to test various level-one

variables with likelihood ratio tests. For comparative purposes I also estimated several three-

level (Models 12-14a) and four-level models (omitted).29

The estimation results for the random

27

In the three level model, the ICC is 0.025 at the year level and .272 at the chamber level. The VPCs, from top to

bottom level, are .024, .266, and .684, respectively. The year fixed effects essentially eliminate the year-level VPC

and ICC and are left with .276 at the chamber-level for the ICC and .269 for the chamber-level VPC and .704 for the

bill-level VPC. In all cases, most of the variation occurs at the bill level. 28

Other interactions were tested, e.g. between abortion-related bills and the proportion of the chamber that is

Republican and navigator-related bills and the proportion of the chamber that is Republican, but likelihood ratio

tests did not warrant an inclusion. 29

Again, likelihood ratio tests showed that an unstructured variance was appropriate, i.e. all variances were

distinctly estimated. ICC and VPC results were presented in footnote 21.

Page 22 of 40

parts in these models are often compromised and imprecise and hence favor a more parsimonious

specification. However, the results are essentially similar.

[Table 4 about here]

Next, I utilized the estimates from linear hierarchical models as starting values for the

estimation of hierarchical ordered probit models. Specifications were again tested for multiple

levels but the most parsimonious structure, a two-level structure with a random intercept,

provided the most appropriate fit (Model 15). 30

A robust version of the model to account for

potential misspecifications (Model 15a) showed essentially similar results.31

The preferred model

used for illustration here is Model 15a.32

[Table 5 about here]

As ordered probit models are non-linear, little is gained by interpreting the coefficients

directly. Hence I merely note that estimates were consistent across specifications and several of

the coefficients in the fixed part of the model achieved or nearly achieved statistical significance

at conventional levels. In the random part of the model, I note that with added complexity the

random parameters became significantly more imprecise and hence it is advisable to utilize a

parsimonious structure if possible (models omitted).

30

For all order probit models I utilized adaptive quadrature instead of the default ordinary Gauss-Hermite

quadrature. I followed (Rabe-Hesketh and Skrondal (2012a), 2012b)) and iteratively increased the number of

quadrature points until estimates did not change anymore. All models converge relatively quickly. 31

Subsequently, I added a random intercept for the Proportion of Republicans in the Chamber (omitted). For

comparative purposes I also estimated several three-level models (omitted). Again, when illustrating the results, a

largely similar picture emerges. 32

In addition to the most appropriate fit to theoretical considerations, I also conducted various tests to assess the

appropriateness of the model including residual analysis.

Page 23 of 40

One simple way to interpret ordered probit models are marginal probabilities.33

The best

way to interpret these generally are graphical displays. Figure 2 presents the predicted

probabilities for various interesting scenarios. The figure presents information about abortion-

related bills, bills that originate in the Senate and the Assembly, bills authored by Democrats and

Republicans, and Republican and Democratic governors. In all cases, results are presented for a

strongly Democratic, an evenly split, and a strongly Republican legislature. For abortion-related

bills, I note the unsurprising difference between Republican legislatures on the one hand and

Democratic and split legislatures on the other hand. The biggest difference here is between bills

that do not move beyond the introduction stage and those that are enacted, respectively.

Moreover, there appears to be little difference between the two chambers with regard to all levels

of outcomes. As expected, Democratic authors do well in Democratic legislatures and

significantly worse in Republican legislatures. The reverse holds true for Republican authors

who seem to be doing even worse when the opposing party controls the legislature. However,

Republicans do much better than Democrats when they control the legislature with a probability

almost twice as large. Finally, Democrats do slightly better under Democratic governors than

Republicans under Republican governors. Overall, the fewest bills get enacted under divided

government. Finally, I note that bills related to navigators were not assessed because of the

limited number available for predicting probabilities. As with the earlier count models we find

results that are inherently plausible: Democrats are more active than Republicans in

implementing the ACA. However, as in the earlier models, Republicans are far from inactive and

participate in the shared governance of the program.

[Figure 2 about here]

33

These are also referred to as population-averaged probabilities.

Page 24 of 40

Conclusion

The future of the ACA appears largely settled although funding concerns for federal

marketplaces as well as several ongoing court challenges have yet to be resolved (Haeder and

Weimer 2013b). Recent problems with the commencement of the enrollment period and the

significant problems with the IT infrastructure are also likely to be overcome eventually.

However, Republican opposition remains as staunch as ever and may even have increased as

potential contenders for the 2016 Republican presidential nomination, many of whom are

governors, are eager to verify their conservative credentials. However, looking beyond the

rhetoric, this paper shows that while Republicans in the states are less eager than their

Democratic counterparts to implement the ACA, they have nonetheless made considerable

headway in pragmatically participating in the shared governance of insurance marketplaces.

Even the staunchest opponents remain eager to defend the long-time state prerogatives when it

comes to the regulation of insurance. Hence while a significant number of states have failed to

create a completely state-based marketplace, virtually all of them appear to be sharing in its

governance in some way. Moreover, participation seems to have increased over time and we

have reason to suspect that this trend will continue. After all, in the first five months of 2013 we

have already seen a significant number of bills introduced. As with other joint programs, it is not

surprising that some states will hold out longer than others for political, financial, or some other

reasons. However, usually states will take up the invitation of the national government and take

advantage of its resources. The ACA appears to follow in these footsteps.

Page 25 of 40

Figures

Page 26 of 40

Figure 1: Implementation of Insurance Marketplaces: Creation of Marketplaces and Number of Bills Introduced

Number of Bills Introduced

Page 27 of 40

Figure 2: Predicted Probabilities for Ordered Probit Model - Progress of Bills

Note: If a bill was introduced in either chamber, it was coded as 1. If it then was voted upon by at least one committee of the same chamber it received a 2; if it

passed the chamber of origin it received a 3. Once the bill moved to the other chamber, it received a 4 if it gained the support of at least one committee, if it

passed that chamber it received a 5. Finally, if it was enacted into law, it received a 6. The figure presents the predicted probabilities for various interesting

scenarios: abortion-related bills, bills that originate in the Senate and the Assembly, bills authored by Democrats and Republicans, and Republican and

Democratic governors. In all cases, results are presented for a strongly Democratic, an evenly split, and a strongly Republican legislature. Confidence Intervals

are omitted to facilitate display.

0 .2 .4 .6 .8 1

Senate

Republican Governor

Republican Author

Democratic Governor

Democratic Author

Assembly

Abortion

Split

Republican

Democratic

Split

Republican

Democratic

Split

Republican

Democratic

Split

Republican

Democratic

Split

Republican

Democratic

Split

Republican

Democratic

Split

Republican

Democratic

1 2 3 4 5 6

Page 28 of 40

Tables

Page 29 of 40

Table 1: Results: Hierarchical Poisson Models – Number of Bills Introduced

(1) (1a) (2) (3) (4) (5) (5a) (5b) (6) (6a)

Support1 Var. Comp.

Model

Non-

parametric

MLE

RI(2)

Time FE

Covariates

RI(2)

RI(2)

and

RC(2)

Interaction

RI(2)

Robust RI(2)

Non-

parametric

MLE

Interaction

RI(2)

& RC(2)

Robust

Fix

ed P

art

2012 -0.271** -0.286** -0.288** -0.278** -0.263** -0.245** -0.280** -0.263***

(0.122) (0.121) (0.121) (0.121) (0.121) (0.120) (0.121) (0.0842)

2013 -0.0270 -0.0352 -0.0402 -0.0290 -0.0252 -0.0167 -0.0331 -0.0252

(0.102) (0.102) (0.103) (0.102) (0.102) (0.101) (0.103) (0.0708)

Chamber 0.123 0.121 0.126 0.105 0.0382 0.122 0.105

(0.0910) (0.0909) (0.0910) (0.103) (0.102) (0.0909) (0.101)

Republican

Governor -0.00489 0.0511 0.503 0.635* 0.882** 0.571 0.635*

(0.152) (0.169) (0.447) (0.380) (0.384) (0.437) (0.358)

Proportion

Chamber

Republican

-1.088*** -1.196*** -0.667 -0.673 -0.458 -0.693 -0.673

(0.414) (0.437) (0.534) (0.504) (0.409) (0.572) (0.422)

Rep. Governor x

Prop. Chamber

Rep.

-0.989 -1.155 -1.715** -1.034 -1.155*

(0.822) (0.721) (0.737) (0.815) (0.658)

Constant 0.817*** .856 0.887*** 1.407*** 1.438*** 1.241*** 1.259*** 1.232*** 1.238*** 1.259***

(0.0703) (.0587) (0.0891) (0.202) (0.210) (0.244) (0.231) (0.192) (0.261) (0.184)

Ran

dom

Par

t

Intercept √

.105 0.060 .109 .077 .104 .070 .055 .032 .095 .081

(.041) (.042) (.034) (.059) (.032) (0.030) (.055) (.060)

Slope √

.295 .256 .023

(.140) (.136) (0.060)

Covariance -.175 -.156 -.043

Observations 203 203 203 203 203 203 203 203 203 203

LR test vs. single

level model 0.000 0.000 0.000 0.003 0.001 0.009

Log likelihood -350 -347 -342 -341 -341 -343 -340 -341 -343

AIC 704 703 697 700 698 704 698 701 707

BIC 711 716 721 730 725 730 728 734 740

Integration Points 20 20 20 20 20 20 20 13 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Page 30 of 40

Table 2: Predicted Number of Bills Introduced Nationwide with 90 Percent Confidence Interval

2011

2012

2013

Chamber Low Point

Estimate High

Low Point

Estimate High

Low Point

Estimate High

Republican Governor and Strongly

Democratic Legislature

Senate 599 831 1430

479 699 1213

589 820 1413

Assembly 659 895 1544

531 756 1310

647 883 1524

Democratic Governor and Strongly

Democratic Legislature

Senate 342 550 951

255 453 796

336 542 935

Assembly 388 598 1023

292 495 863

380 590 1013

Republican Governor and Split

Legislature

Senate 330 536 925

243 441 776

322 528 915

Assembly 371 583 1000

281 481 838

366 574 987

Democratic Governor and Split

Legislature

Senate 154 337 585

102 272 474

153 331 573

Assembly 184 370 647

124 300 521

177 364 637

Republican Governor and Strongly

Republican Legislature

Senate 147 327 566

97 264 458

143 322 556

Assembly 173 359 627

117 291 506

170 354 615

Democratic Governor and Strongly

Republican Legislature

Senate 44 197 350

19 157 275

46 194 341

Assembly 63 218 385

29 174 306

59 215 379

Actual

152

105

174

Page 31 of 40

Table 3: Hierarchical Linear Models: Results for 2-Level Models - Progress of Bills

(7) (8) (9) (10) (11) (11a)

Var.

Comp.

Model

RI(2)

Time FE Covariates

RI(2)

RI(2)

and

RC(2)

Interaction

RI(2)

&

RC(2)

robust

2012 -0.441* -0.449* -0.452** -0.438** -0.438

(0.233) (0.230) (0.230) (0.222) (0.293)

2013 -0.818*** -0.878*** -0.876*** -0.891*** -0.891***

(0.222) (0.226) (0.226) (0.220) (0.262)

Abortion Bill -0.369 -0.377 -0.478* -0.478*

(0.277) (0.276) (0.267) (0.270)

Navigator or Broker Bill 0.116 0.122 0.132 0.132

(0.345) (0.345) (0.337) (0.366)

Assembly Origin 0.0236 0.0291 0.0932 0.0932

(0.211) (0.210) (0.187) (0.205)

Republican Governor -0.434 -0.440* -1.123 -1.123*

(0.268) (0.267) (0.787) (0.582)

Republican Author 0.0936 0.107 -3.407*** -3.407***

(0.225) (0.225) (0.758) (0.667)

Proportion Chamber

Republican 2.181*** 2.251*** -1.143 -1.143

(0.834) (0.834) (1.007) (1.108)

Authored by Committee 0.559** 0.558** 0.191 0.191

(0.283) (0.282) (0.274) (0.285)

Rep. Author x

Prop. Chamber Rep 6.687*** 6.687***

(1.397) (1.240)

Rep. Governor x

Prop. Chamber Rep 1.397 1.397

(1.502) (1.313)

Constant 2.509*** 2.988*** 2.070*** 2.023*** 3.443*** 3.443***

(0.112) (0.191) (0.402) (0.402) (0.457) (0.486)

L2 Intercept √

.395 .404 .334 .392 .145 .145

(.157) (.158) (.141) (.207) (.102) (.079)

L2 Coeff √

1.311

(.583)

.-717

Residual: √ 3.449 3.345 3.269 3.273 3.200 3.199

(.237) (.231) (.224) (.226) (.218) (.216)

Observations 497 497 497 497 497 497

Log Likelihood -1034 -1027 -1018 -1018 -1004 -1003

AIC 2073 2063 2061 2065 2036 2036

BIC 2086 2084 2111 2123 2095 2095

LR test vs. single level model 0.000 0.000 0.000 0.007 0.040 -- Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Page 32 of 40

Table 4: Hierarchical Linear Models: Results for 3-Level Models - Progress of Bills

(12) (13) (14) (14a)

Var. Comp.

Model

Covariates

RI(2) &

RI(3)

Interaction

RI(2) &

RI(3)

Robust

Abortion Bill -0.340 -0.397 -0.259

(0.270) (0.263) (0.262)

Navigator or Broker Bill 0.0300 0.0976 -0.00566

(0.329) (0.323) (0.346)

Assembly Origin 0.0252 0.0883 0.0852

(0.214) (0.201) (0.196)

Republican Governor -0.516* -1.163 -1.136*

(0.272) (0.842) (0.657)

Republican Author 0.248 -3.109*** -3.118***

(0.218) (0.759) (0.740)

Proportion Chamber Republican 1.863** -1.291 -1.149

(0.841) (1.076) (1.120)

Authored by Committee 0.539* 0.245 0.252

(0.275) (0.272) (0.290)

Rep. Author x

Prop. Chamber Rep

6.361*** 6.336***

(1.399) (1.347)

Rep. Governor x

Prop. Chamber Rep

1.374 1.243

(1.592) (1.408)

Constant 2.630*** 1.824*** 3.081*** 2.983***

(0.212) (0.416) (0.496) (0.427)

L3 Intercept √

.097 .112 .119 --

(.114) (.125) (.127) --

L2 Intercept √

1.063 .958 .726 .790

(.251) (.234) (.205) (.227)

Residual: √ 2.735 2.687 2.650 2.675

(.217) (.212) (.209) (.258)

Observations 497 497 497 497

Log Likelihood -1019 -1011 -998 -998

AIC 2046 2044 2021 2021

BIC 2063 2090 2076 2076

LR test vs. single level model 0.000 0.000 0.000 0.000

Integration Points -- -- -- 13 Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Page 33 of 40

Table 5: Hierarchical Order Probit Models: Results - Progress of Bills

(15) (15a)

Outcome Robust

Fix

ed P

art

2012 -0.403* -0.403

(0.243) (0.314)

2013 -0.684*** -0.684**

(0.243) (0.298)

Abortion -0.462 -0.462

(0.301) (0.312)

Navigator or Broker Bill -0.0690 -0.0690

(0.383) (0.420)

Assembly Origin 0.0510 0.0510

(0.202) (0.219)

Republican Governor -0.878 -0.878

(0.869) (0.764)

Republican Bill Author -3.931*** -3.931***

(0.917) (1.102)

Proportion Chamber Republican -1.680 -1.680

(1.079) (1.223)

Authored by Committee 0.196 0.196

(0.300) (0.317)

Rep. Governor x

Prop. Chamber Rep

0.993 0.993

(1.657) (1.616)

Rep. Author x

Prop. Chamber Rep.

7.747*** 7.747***

(1.664) (1.814)

Fix

ed P

art:

Thre

shold

s

κ 1 -1.100** -1.100**

(0.495) (0.531)

κ 2 -0.286 -0.286

(0.494) (0.537)

κ 3 0.0398 0.0398

(0.495) (0.535)

κ 4 0.289 0.289

(0.496) (0.533)

κ 5 0.511 0.511

(0.497) (0.539)

Ran -

dom

Par

t Intercept √

.158 .158

(.122) (.111)

Observations 497 497

Log Likelihood -649.6 -649.6

AIC 1333 1333

BIC 1405 1405

Integration Points 15 15

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Page 34 of 40

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