State variation in health care spending and the politics of state Medicaid policy.

Journal of Health Politics, Policy and Law

State Variation in Health Care Spending and the Politics of State Medicaid Policy Gideon Lukens US Office of Management and Budget

Abstract This study investigates the factors that underlie large variation in Medicaid and Children’s Health Insurance Program (CHIP) policies among states. Both eligibility and provider payment policies are examined for low-income children and parents. I find that state variation in the cost of providing health care, due to variation in the intensity of health care use, is a key determinant of eligibility policies, and I also find tentative evidence of an effect for payment policies. Because rising health care spending increases the cost of providing health insurance coverage, state policy makers in high-spending states enact less generous Medicaid and CHIP policies. Results also indicate that the political environments of states are very important in determining their eligibility policies, but fewer political variables influence payment policies. In addition to including variables not yet examined in the context of Medicaid policy, this study uses an innovative measure of state-level health care spending and carefully constructed dependent variables that lend credibility to causal interpretations of relationships.

Introduction

Medicaid and the Children’s Health Insurance Program (CHIP) provide health insurance to more than 70 million recipients at a cost of over $400 billion (CMS 2012, n.d.; US Census Bureau 2012). From 1980 to 2010, the number of Medicaid and CHIP recipients grew from 22 million to over I conducted this work while a graduate student at the University of California, Los Angeles. Any views or statements in this article are my own and do not represent the views of the US Office of Management and Budget. I would especially like to thank Sarah Reber and Jim DeNardo for their helpful suggestions. I would also like to thank Karl Kronebusch, Jeff Lewis, and Mark Peterson for their comments. Finally, I am grateful to Janet Currie, Thad Kousser, and Kosali Simon for generously sharing their data. Journal of Health Politics, Policy and Law, Vol. 39, No. 6, December 2014 DOI 10.1215/03616878-2822634 2014 by Duke University Press

Published by Duke University Press


1214


70 million, and real spending grew from $75 to $443 billion (ibid.). As the Medicaid and CHIP programs have grown, they have become both a major source of insurance coverage and a large component of state budgets. Medicaid constituted an estimated 24 percent of state spending in fiscal year 2013, and it has been the largest category of state expenditures since fiscal year 2009 (NASBO 2011, 2013). Yet the size and scope of Medicaid and CHIP programs differ remarkably from state to state, with spending ranging from 9 to 36 percent of state budgets (NASBO 2008). Parents with incomes up to 300 percent of the federal poverty line are eligible for Medicaid in some states, whereas in other states, those earning more than 25 percent of the poverty line do not qualify. For children, eligibility thresholds for Medicaid and CHIP vary more than twofold (KCMU 2009). Provider reimbursements also vary tremendously among states, with physician fees for treating Medicaid patients ranging from 37 to 140 percent of Medicare fees for comparable services (Zuckerman, Williams, and Stockley 2009). A key question is whether this variation is solely a political phenomenon or whether the claim that rising health care spending constrains the generosity of Medicaid coverage holds some truth. I exploit variation across states and over time to identify the factors that underlie differences in Medicaid and CHIP generosity across states.1 I include separate analyses of Medicaid and CHIP eligibility policies and Medicaid payment policies. Eligibility policies determine who is eligible to receive Medicaid and CHIP and, ultimately, the number of recipients. Payment policies help determine the compensation of providers who treat Medicaid recipients and are an indicator of access and quality of care for Medicaid recipients. The first part of my analysis focuses on state eligibility generosity for low-income children from 1996 to 2005 and for low-income parents from 2000 to 2007. This analysis captures a period of rapid but uneven expansion in eligibility for children, including federal authorization of CHIP in 1997, and a period of highly divergent trends in Medicaid eligibility policies for parents. The second part of my analysis focuses on Medicaid provider payment policies, which also vary considerably across states. This analysis includes several years over the period from 1998 to 2008.2 1. While CHIP can be implemented as an expansion of Medicaid or as a separate program, it differs from Medicaid in some important ways. However, my analytical framework is applicable to both Medicaid and CHIP. For succinctness, I often use the term Medicaid to refer to Medicaid and CHIP. 2. Sample years for all analyses are chosen based on availability of data for key variables.



Lukens

-

State Variation in Health Care Spending

1215

I find that the cost of health care provision, due to differences in the intensity of health care use, is a key determinant of variation in state Medicaid generosity. Rising health care spending makes it more expensive to provide Medicaid coverage. As state policy makers face budgetary decisions, they respond to higher Medicaid costs partly by enacting cheaper and less generous Medicaid policies than they would otherwise. I use a measure of the intensity of health care use as a proxy for health care spending in order to avoid the endogeneity that would result from including other potential proxies. This measure of use is highly correlated with health care spending due to differences in the intensity of health care use, likely reflecting differences in practice patterns yet not reflective of other determinants of health care spending such as patient characteristics, insurance coverage, and medical input prices. I find a large and robust negative relationship between this proxy for health care spending and Medicaid eligibility generosity, and also tentative evidence of a negative relationship with payment generosity, indicating that the cost of health care has been an important constraint on the extent and quality of coverage. I also explore a diverse set of political determinants of state Medicaid generosity. I find that Medicaid eligibility policies are influenced by many aspects of the political environments in states and in particular are strongly reflective of partisan and ideological divisions. In contrast, Medicaid payment policies are relatively unrelated to many political variables. The political variables under study include two factors that have not been examined in previous studies—legislative supermajority requirements and the percentage of the legislature composed of women. I focus on methods and measures that are robust to omitted variables bias. First, I employ state fixed effects models, using within-state variation over time to identify determinants of state Medicaid generosity. Doing so controls for fixed differences between states that are unobserved. I supplement the fixed effects analyses with pooled ordinary least squares (OLS) models that use between-state variation in addition to within-state variation. The pooled OLS results complement my fixed effects findings by revealing long-standing differences among states that are not detected by the state fixed effects specifications. Second, I use dependent variables that capture changes in eligibility and payment policies, as distinct from nonpolicy factors such as economic and demographic conditions. The measure of payment policies has not been used in previous studies. Third, my measure of health care spending is chosen so as to minimize the likelihood of it being endogenous to Medicaid policies.



1216


I begin by discussing why health care spending is an important factor that influences Medicaid policies and present a proxy for health care spending that minimizes the potential for omitted variables bias. Next, I discuss the politics of Medicaid eligibility and payment policies and how I measure these two dimensions of generosity. I then proceed to my analysis and results, concluding by discussing what these results tell us about Medicaid policy making in the states. Health Care Spending

Medicaid is the largest category of state expenditures and has grown quickly as a result of rising health care spending and program expansions. That state policy makers would care about the costs of providing Medicaid coverage to existing and potential recipients is logical, and certainly many policy makers profess cost concerns. I test the hypothesis that policy makers respond to rising health care spending by pursuing less generous Medicaid eligibility and payment policies, using a measure of health care intensity as an innovative proxy for health care spending in order to circumvent the endogeneity of other potential measures. The following framework illustrates how I expect rising health care spending to negatively affect Medicaid generosity. First, rising health care spending increases the cost per beneficiary of providing Medicaid. As a result, the cost of the overall Medicaid program increases, putting pressure on state budgets. Second, state policy makers, generally aware of rising Medicaid costs, formulate and implement policies aimed at limiting this rise. These policy makers include state legislators, who regularly enact changes to Medicaid eligibility and payment policies, and governors, who typically can propose changes to Medicaid policies and veto legislation. The major policy levers available to minimize increases in Medicaid spending are (a) restricting Medicaid eligibility and (b) reducing Medicaid provider payments (Kronebusch 1993). States can react to rising Medicaid costs in other ways. First, state budget constraints are affected by other decisions of state policy makers and by the federal matching rate.3 A state may choose to respond to rising Medicaid costs by raising taxes, for example. Second, the allocation of state expenditures within the budget constraint can be altered. For example, a 3. Federal matching rates are the fractions of each dollar paid by the federal government and are formulated such that states with lower per capita income relative to US per capita income receive higher federal matches. The minimum matching rate is set at 50 percent, meaning that the federal government covers at least half of every state’s Medicaid expenditures.



Lukens

-


1217

state may respond to rising Medicaid costs by reducing spending elsewhere in the Medicaid program or in other state programs. These policy decisions are jointly determined and would best be modeled explicitly in a general equilibrium framework.4 Colleen M. Grogan (1994) is one author who takes a structural approach, incorporating Medicaid costs in her model of trade-offs between dimensions of Medicaid policy. This article instead takes a reduced-form approach, with a hypothesis that some of the incidence of rising health care spending is borne by the Medicaid program through reductions in eligibility and payment generosity for low-income children and parents.5 It asks whether increases in the cost of providing care—not confined to the Medicaid program but across the health care system more generally—affect Medicaid policies. Further, the approach focuses on spending due to differences in the intensity of care, most likely driven by geographic differences in physician practice patterns, as opposed to differences in patient characteristics, input prices, and other factors. Measuring the effect of health care spending on Medicaid policy poses a challenge. For the framework above, the most relevant measure of health care spending is the cost of providing health care coverage to the populations of interest: children and adults. However, reliable measures of health care spending for children and adults are not available by state and year.6 They would also be endogenous, as they are affected by a diverse array of factors, including disease prevalence, demographic characteristics, insurance coverage, and input prices. To infer a causal effect of private health care costs on Medicaid policy is difficult, as any association could result from omitted variables bias or reverse causation. For example, an increase in health care costs could result from an increase in input prices (e.g., physician salaries). But input prices are also affected by Medicaid policies, as well as having a direct effect on Medicaid policies outside of their effect on health care costs. Finally, Medicaid spending per beneficiary is highly endogenous, as it includes both causes and effects of Medicaid policies, and, moreover, it captures only Medicaid spending as opposed to health care spending more generally. Therefore, some proxy for health care spending on children and adults must be used, and the goal 4. Note that the issue of joint determination of policies applies not only to health care costs but also to other explanatory factors. 5. As a check on this assumption, I estimated seemingly unrelated regression (SUR) models that allow correlation across Medicaid dimensions in a simultaneous equations framework. Results were substantively the same. 6. Unpublished data are available in the Medical Expenditure Panel Survey (MEPS). But according to MEPS, separate health care spending data for children and nonelderly adults are unreliable by state and year.



1218


is to find one that is correlated with health care spending but minimally correlated with other factors affecting Medicaid generosity. To deal with the potential endogeneity of health care spending, I use the Dartmouth Atlas of Health Care measure of the average number of hospital days for Medicare patients during their last six months of life as a proxy for health care spending.7 Because the Dartmouth measure is defined in terms of hospital days instead of dollar costs, it does not reflect input prices. It is risk adjusted to the degree that it includes only patients who are in their last six months of life and is adjusted for the age-sex-race group distribution of the Medicare population.8 Limiting the time frame to the last six months of life results in a more standardized distribution of patient health risks; the best argument for this adjustment is that the last six months of life is a period when health care spending is high, but a large and varied body of research finds that additional spending does not improve outcomes (e.g., Wennberg, Fisher, and Skinner 2002; Yasaitis et al. 2009; see CBO 2008 for a summary). Finally, insurance coverage and payment system are held constant, given that all patients are covered by fee-for-service (FFS) Medicare. Well-controlled studies in diverse settings have shown that the Dartmouth measure reflects spending resulting from intensity of treatment (i.e., greater use of care) rather than other factors affecting health care spending (e.g., Fisher et al. 2003a, 2003b; Baicker and Chandra 2004; Sandy et al. 2009). The Dartmouth measure was previously used as an instrument for Medicare spending (Hadley et al. 2011). In sum, the Dartmouth measure is less endogenous than other potential proxies for health care spending: it does not reflect input prices; it is not correlated with health outcomes; it holds constant the type of insurance coverage and payment system; and it is adjusted for demographic characteristics of the patient population. Using the Dartmouth measure enables a cleaner comparison of the average cost of providing care across states. 7. Specifically, it is the average number of hospital inpatient days during the last six months of life for all Medicare decedents aged sixty-five to ninety-nine with Part A entitlement and no home maintenance organization (HMO) enrollment. It is adjusted by the age, sex, and race of the Medicare patient population. Since many patients in this population (about 30 percent) are not hospitalized as inpatients, this measure incorporates both hospital admissions decisions and length of stay on admittance. 8. For the relevant end-of-life Medicare patient population, the national average inpatient days are calculated for each of twenty age-sex-race strata. These averages were applied to each state’s population to produce the expected number of inpatient days given the population distribution across age-sex-race strata. Standardized ratios were computed for states by dividing the observed numbers of inpatient days by the expected numbers of inpatient days. Finally, each standardized ratio was multiplied by the overall national average. The result is an inpatient days measure that is adjusted for the age, sex, and race of the Medicare population in the state. An example of this method of risk adjustment can be found in Dartmouth Atlas of Health Care (n.d.).



Lukens

-


1219

Important to note is that using health care intensity as a proxy for health care spending does not require (nor is it likely) that policy makers are aware of the intensity of health care use in their states. Instead, it requires that a substantial number of policy makers are generally aware of Medicaid costs in their states, as implied in the framework discussed at the beginning of this section. Also important to note is that the mechanism through which health care spending affects Medicaid policy is the same regardless of the sources of health care spending and regardless of whether the spending is beneficial to patients. The sources of health care spending could affect the interpretation of a negative correlation between health care spending and Medicaid generosity, however. If the additional health care spending were associated with better care, my results could be interpreted as a trade-off between quality and coverage within the Medicaid program. If, instead, the additional health care spending did not improve care, the interpretation is one of additional health care spending resulting in less Medicaid coverage while not improving societal welfare. Another interpretation is that the days-ofcare inpatient measure reflects norms or constraints on alternative means of end-of-life care, such as nursing homes, hospice, and home-based care. For example, in states with fewer nursing home beds, providers may have no choice but to keep patients in inpatient settings longer. In this example, it is other Medicaid policies (Medicaid sets constraints on the number of nursing home beds) that lead to costlier care and eventually less generous Medicaid policies for low-income recipients. Each interpretation is interesting and could be consistent with the use of health care intensity as a proxy for health care spending. This article deals only with the budgetary link between health care spending and Medicaid policy, which is interesting and important in itself. For the Dartmouth measure to be a valid measure of health care intensity for my populations of interest—children and nonelderly adults—geographic variation in the intensity of health care use for Medicare patients must be correlated with variation for other types of patients. Many studies support this assumption, as does my analysis below. Research has shown that practice styles of primary care physicians are similar for patients of different ages and types of insurance coverage and for patients with acute and chronic conditions (Sirovich et al. 2008). Doctors in regions with high health care intensity and spending, as ascertained by Dartmouth end-of-life measures, were found to practice highintensity care on nonelderly patients with chronic conditions. Intensity of



1220


care for nonelderly adults and elderly adults within the same hospital was found to be strongly correlated, as was intensity of care between FFS patients and managed care patients (Stiefel, Feigenbaum, and Fisher 2008; Baker, Fisher, and Wennberg 2008). Donald Young, Bruce Sachais, and Leigh Jefferies (2002) found that median hospital spending for adults and children was highly correlated (r = 0.88) within forty-three diagnosisrelated groups. John E. Wennberg and David E. Wennberg (2000) found wide variation in discharge rates that was highly correlated across children, adults, and Medicare patients. I further examined the relationship between the end-of-life hospital days measure and child health care intensity, using state-level data available from the Healthcare Cost and Utilization Project’s State Inpatient Databases. Figure 1a is a partial regression plot of twenty-eight states for which data were available in 2005. It plots the residuals of end-of-life versus pediatric (aged one to seventeen) inpatient hospital days after controlling for the inpatient child mortality rate, overall child mortality rate, poverty rate, race, and the percentage living in urban areas. In other words, it shows the relationship between average inpatient days for end-of-life Medicare patients and for children, after controlling for state demographics and child health characteristics. The positive relationship (r = 0.47) between end-oflife inpatient days and child inpatient days is highly statistically significant ( p = 0.002). Figure 1b shows a similarly robust relationship (r = 0.57; p = 0.002) using average pediatric inpatient days for the most common principal diagnosis among non-newborn children: pneumonia (Elixhauser 2008). My examination supports previous studies indicating that Dartmouth end-of-life intensity measures are correlated with child and adult health care intensity. The Politics of Medicaid Eligibility and Payment Policies

The politics of Medicaid differ across the dimensions of Medicaid generosity (Kronebusch 1993; Grogan 1994). To the degree that both lowincome recipients and health care providers benefit from both expanded eligibility and higher provider payments, I expect political variables to influence eligibility and payment generosity in the same direction. However, some factors—party control, ideology, and the preponderance of women legislators—are likely to have stronger effects on eligibility policies than on payment policies. While Medicaid was increasingly framed as a mainstream entitlement program during the 1990s, it continued to track



Lukens

-


1221

Figure 1a Relationship between Average Pediatric and End-of-Life Inpatient Days

Figure 1b Relationship between Average Pediatric Pneumonia and End-of-Life Inpatient Days Sources: Dartmouth Atlas of Health Care 2011; HCUP 2014 Notes: Observations are for 28 states in 2005. Units are the residuals of end-of-life inpatient days per decedent and of inpatient days per child (aged 1–17), after controlling for the following state characteristics: child mortality rate, inpatient child mortality rate, race, poverty rate, and the percentage living in urban areas.



1222


the patterns of welfare politics (Kronebusch 2001; Grogan and Patashnik 2003). Eligibility policies primarily benefit low-income families and are thus opposed by Republicans and conservatives and supported by Democrats and liberals. By comparison, the strength of provider interest groups is more likely to have a bigger impact on payment policies than on eligibility policies. Providers directly benefit from greater payment generosity, but how much low-income families benefit is unclear, and any such benefits are indirect (Fox, Weiner, and Phua 1992; Shen and Zuckerman 2005). Finally, the line-item veto and legislative supermajority requirements are institutional rules that serve to constrain spending, and these variables are expected to similarly affect both eligibility and payment policies. I expect Democrats to favor more generous Medicaid policies, and I test this hypothesis using a measure of unified party control of both houses of the legislature and the executive.9 The party in control of the state legislature can use its majority voting power and its control of committee appointments to achieve Medicaid policy outcomes closer to its preferences, and control of committees may be particularly important for Medicaid policy (Kousser 2002). The party of the governor could also be important, as governors typically propose budgets and hold veto powers. A number of studies have found that Democratic Party control, either control of the legislature or unified control of both the legislature and the executive, is positively related to Medicaid generosity (Kronebusch 1993; Grogan 1994; Kousser 2002; Baughman and Milyo 2008; Grogan and Rigby 2009). In addition to Democratic Party control, I also expect greater liberalism of state residents to result in increased government support for Medicaid. Ideology directly affects policy outcomes, as legislators respond to voter preferences. Ideology also indirectly affects policy outcomes by influencing the platforms, candidates, and strategies that parties pursue. Charles J. Barrilleaux and Mark E. Miller (1988) and John F. Camobreco (1996) found that state liberalism is correlated with higher Medicaid spending, but Thad Kousser (2002) found no relationship between ideology and discretionary Medicaid spending. Within the same party, ideology varies considerably across states, but to capture empirically the differences between party and ideology is not always easy (Erikson, Wright, and McIver 1989). 9. I include dichotomous variables for Democratic control and Republican control, where control is defined as the same party holding greater than a majority of seats in each house of the legislature, along with the governorship. In results not shown here, I estimated specifications using other common conceptions of party control. The unified control measure was most robust, possibly because party control is stronger using this measure and hence more likely to be detected given a fairly saturated model with limited observations.



Lukens

-


1223

I use a measure of ideology that was recently developed based on survey data and can effectively distinguish the ideology of state residents from party control. It is constructed by Julianna Pacheco (2011) using multilevel regression, imputation, and poststratification. In this method, demographic and geographic factors are used to predict survey responses across states using multilevel regression. Demographic-state groups are then weighted according to their preponderance in each state. Similarly constructed measures have been found to capture state public opinion accurately with relatively few surveys (Lax and Phillips 2009). The resulting measure of state resident liberalism is the estimated percentage of state residents who identify as liberal out of liberals, moderates, and conservatives. Studies suggest that a higher proportion of women legislators results in larger programs that benefit children and the family, such as support for state-sponsored child care (Thomas 1994; Besley and Case 2000). This result could be because women legislators have more liberal views on these issues or because voters expect women legislators to be more involved in issues traditionally considered important to women (Cammisa and Reingold 2004). However, while the effect of women legislators has been examined in other policy areas, it has not yet been applied to Medicaid. Because more generous Medicaid policies benefit children and parents, I expect a higher percentage of women in the legislature to result in more generous Medicaid eligibility and payment policies. A variety of political actors have stakes in Medicaid policy. One leading source of state interest group data comes from Ronald Hrebenar and Clive Thomas, who coordinated and combined the judgments of nearly one hundred state-specific experts on the relative influence of interest groups within each state (Hrebenar and Thomas 1987, 1992, 1993a, 1993b). Extensive work on state interest groups has also been conducted by Virginia Gray and David Lowery (see, e.g., Gray and Lowery 2000). Gray, Lowry, and Erik K. Godwin (2007a, 2007b) use a unique data set of lobbying registrations by health-related interest groups to construct measures of the size of the interest group community, diversity and competing interests in the community, and opposition groups with respect to state pharmacy assistance programs and managed care regulations. The Gray and Lowery measures are more objective, but to fully characterize the relationships between interest groups and policy makers using purely quantitative data remains difficult (Hrebenar and Thomas 1993b). The Hrebenar and Thomas approach is more subjective but may nevertheless provide a good approximation of the access, reputation, and overall effectiveness of interest groups.



1224


In this study, I use six measures of interest groups, covering two provider groups (physicians and hospitals) and one citizen group (seniors). Physicians who treat Medicaid patients benefit from higher Medicaid physician fees, and to the degree that hospitals’ Medicaid reimbursements are correlated with physician fees, we might also expect hospital interest group strength to be positively related to Medicaid physician fees. But the benefit to physicians and hospitals from greater eligibility generosity is ambiguous because Medicaid patients generally provide low reimbursement rates. Physicians can choose not to treat Medicaid patients, and hospitals that primarily treat low-income, uninsured patients are still reimbursed through uncompensated care funds (Kronebusch 1997; Grogan 1994). While the Medicaid recipients analyzed here—low-income families—are not well organized and are not represented by powerful interest groups, the opposite is true of seniors (Kronebusch 1997). The primary interest group representing seniors, the American Association of Retired Persons (AARP), includes members aged fifty to sixty-four, some (but probably not many) of whom are eligible beneficiaries for the policies in this study. More important, that many of those aged sixty-five and above benefit from other Medicaid programs may translate into support for Medicaid in general. Three of my measures come from the work of Hrebenar and Thomas and are based on categorizations of interest groups as most influential, increasing or decreasing in influence, or neither. Other studies of state Medicaid policy have estimated each of these variables as a continuous measure (Grogan 1994; Kousser 2002). I differ somewhat in that I treat each measure as categorical instead of continuous, which avoids the assumption, for example, that a group listed as most influential is twice as influential as a group listed as increasing or decreasing in influence. I create a simple 0–1 indicator variable for each group, where a group is categorized as most influential (1) or not (0).10 In addition to these measures of interest group influence, I also use measures of interest group size. I use the percentage of physicians within each state belonging to the American Medical Association (AMA), the percentage of hospitals belonging to the American Hospital Association (AHA), and the percentage of seniors over age fifty belonging to the AARP. These organizations are large, prominent, and rich in resources. Similar 10. An alternative strategy is to estimate separate effects for all three categories of influence. This strategy involves entering three additional indicator variables. In the interest of parsimony, I estimate only a single indicator for most influential or not, effectively combining the other two categories.



Lukens

-


1225

measures have been used in other research (e.g., Kronebusch 1993; Grogan 1994; Kousser 2002). More recently, the AARP measure was used by Gray, Lowry, and Godwin (2007b) to capture the level of seniors’ political activity. The interest group data are measured only once for each state and therefore can only be estimated in the pooled models. Despite this limitation, the membership data are unlikely to vary greatly from year to year, and the Hrebenar and Thomas data are generally stable over time (Thomas and Hrebenar 2004). Also, both these data and the Hrebenar and Thomas data precede the sample period and thus have the advantage of reducing the likelihood of endogeneity with the dependent variables (Kousser 2002 also makes this argument). For example, using a measurement of physician’s influence on health policy to estimate the influence of physicians on Medicaid policy over the same time frame would be problematic. Karl Kronebusch (1993) found no effect, and Kousser (2002) only a limited effect, of interest group measures on Medicaid generosity. Camobreco (1996) and Grogan (1994) reported substantial effects of interest groups, with Grogan (1994) using a set of interest group measures and interactions to capture the interest group strength of several groups of providers and senior citizens. For providers, these measures include influence, size, lobbying activity of health groups, and number of influential health groups; for senior citizens, influence and size are included (Grogan 1994). Barrilleaux and Miller (1988) found that interest group diversity positively enters into their equation for Medicaid spending in a simultaneous equations framework using cross-sectional data. Finally, Etienne E. Pracht and William J. Moore (2003) found that the percentage of pharmacists within each state belonging to the American Pharmaceuticals Association was positively related to Medicaid pharmaceutical drug reimbursement rates in their cross-sectional, simultaneous equations model. Legislative supermajority requirements, which have not been studied in the context of Medicaid policy, necessitate greater than majority votes in the legislature to enact tax or spending increases. Legislative supermajority requirements for tax or spending increases have been linked to lower tax rates (Knight 2000; Besley and Case 2003). By constraining spending, these requirements make legislatures less likely to expand Medicaid eligibility or raise provider payments. The line-item veto, which allows governors to eliminate items from a bill, is a powerful tool that enhances the ability of governors to constrain spending (Rosenthal 2004). To the degree that governors use the power of the line item to limit Medicaid spending, either through actual exercise of the veto or through the threat of its use, we might expect a negative relationship between Medicaid spending and presence of a line-item veto.



1226


Consistent with this hypothesis, Kousser (2002) found a negative relationship between existence of a line-item veto and discretionary Medicaid spending. Dimensions of Medicaid Generosity

State Medicaid policies can be assessed along several dimensions of generosity (Kronebusch 1993). This study focuses on enrollment eligibility generosity and provider payment generosity, each of which varies tremendously across states and over time. Eligibility generosity is the prime policy lever that states use to determine the number of Medicaid recipients, while payment generosity is the prime policy lever that states use to determine spending per recipient. Together these two forms of generosity are the major determinants of Medicaid spending that state policy makers control. Eligibility generosity refers to the policy rules governing who is eligible to receive Medicaid. Eligibility for low-income children and parents is primarily determined by income thresholds under which individuals are eligible. For children, these thresholds vary by state, year, and single year of age of the child. Typically, younger children are covered at higher income thresholds. In 2009 the income threshold for a child aged seventeen ranged from 100 to 300 percent of the poverty line (KCMU 2009). For parents, income thresholds vary by family size and employment status, with larger families and employed parents typically covered at higher thresholds. In 2009 the income thresholds for an unemployed, single parent with two children ranged from 11 to 300 percent of poverty (KCMU 2009). Payment generosity refers to the benefits to which Medicaid patients are entitled. One way to capture payment generosity is to consider the medical services included in Medicaid benefit packages. States vary in the optional services they cover, which groups are eligible for these optional services, what service use limits are imposed, and how care is managed. However, the most important and expensive benefits are mandated by the federal government. These include hospital services, physician and nurse services, federal health center and rural clinic services, laboratory and imaging services, family planning services, and nursing home care, among others. Optional benefits are relatively small in dollar value, especially for the nonelderly (KCMU 2005). Moreover, the most important optional benefits, such as prescription drugs, dental services, and vision services, are covered by almost all states (Gruber 2000; KCMU 2005). Instead, what varies tremendously across states is the amount that providers are reimbursed for treating Medicaid patients. Provider payments



Lukens

-


1227

for Medicaid typically fall well below those for other types of health insurance. For example, in 2008 Medicaid physician fees were on average 72 percent of Medicare fees for comparable services (Zuckerman, Williams, and Stockley 2009). But the generosity of provider payments varies considerably across states. In New Jersey, Medicaid fees were only 37 percent of Medicare fees, while in Wyoming, Medicaid fees were 143 percent of Medicare fees (ibid.). The generosity of provider reimbursements is important because studies have shown that lower provider reimbursements can lead to diminished access and quality of health care (Cohen 1993; Cohen and Cunningham 1995; Currie, Gruber, and Fisher 1995; Shen and Zuckerman 2005). This study therefore uses provider payments as measures of Medicaid payment generosity. Other forms of Medicaid generosity exist that are not included in this study. Some states require more cost sharing of Medicaid patients. Variation is also found in the ease of application procedures for those who are eligible to gain coverage. In addition, the majority of states also require that children be uninsured for a specified amount of time prior to receiving health coverage. Finally, an important consideration is that the size and scope of state Medicaid programs depend not only on state policies but also on nonpolicy characteristics of states. The number of Medicaid recipients, spending per recipient, and total Medicaid spending in a state depend on the income distribution, age distribution, employment, family sizes, and health characteristics of the population. This important point is discussed below. Measuring Medicaid Eligibility Generosity

Many existing studies use measures of Medicaid generosity that depend on Medicaid spending or the number of Medicaid recipients.11 However, a host of factors not policy related affect Medicaid spending and the number of recipients. For example, states with older or less healthy populations will spend more on Medicaid, holding eligibility policy constant. This effect makes Medicaid spending and the number of recipients indirect measures of Medicaid generosity and prone to omitted variables bias. For example, an economic downturn would likely both increase Medicaid spending and improve the election outcomes of the minority party, even with no policy changes. 11. Exceptions are Grogan 1994, Ullman and Hill 2001, Grogan and Rigby 2009, and Baughman and Milyo 2008.



1228


Like Reagan Baughman and Jeffrey Milyo (2008), I use simulated eligibility to measure Medicaid eligibility generosity. Simulated eligibility was first developed by Janet Currie and Jonathan Gruber (1996) for use as an instrumental variable and is constructed by applying each state’s eligibility rules to the US population as represented by the 1996 March Current Population Survey (CPS). For each state, simulated eligibility is the percentage of the relevant population that would be eligible given the state’s eligibility rules. For children, the relevant population is all children aged zero to nineteen, irrespective of family income. For adults, the population is all nonelderly adults, irrespective of income, in families of three who reside with their dependent children (aged zero to seventeen).12 Simulated eligibility is not directly affected by nonpolicy factors that change within states over time because it uses the same representative sample of the United States for all states. Besides decreasing the likelihood of biased estimates resulting from unobserved state characteristics, simulated eligibility also summarizes complex eligibility policies with a single measure. Figure 2 plots simulated eligibility for children over the years 1996– 2005 for the 25th, 50th, and 75th percentiles of states (corresponding to the 12th, 25th, and 37th states, respectively, ranked from least to most generous). Applied to a nationally representative sample, the median state’s eligibility rules would cover about 45 percent of children in 2006. The estimated percentage of children eligible for Medicaid or CHIP rises during the first ten years of this period before flattening out in the later years. A dramatic increase takes place from 1998 to 2001, reflecting state implementation of CHIP programs. While all states chose to implement CHIP in the four years following federal authorization in 1997, large variation was seen in the generosity and timing of their eligibility expansions. This variation resulted in both a wider and more skewed distribution of eligibility generosity across states by the end of the expansion. Over the sample period for simulated eligibility, from 1996 to 2005, about 17 percent of children became newly eligible in the median state. Parents are typically covered at much lower eligibility thresholds than children are. The thresholds are more variable as well, due partly to fewer federal mandates for low-income adults. Figure 3 plots simulated eligibility for parents for the 25th, 50th, and 75th percentiles of states in terms of generosity. Applied to a nationally representative sample, the median 12. The family size limitation stems from data limitations, which are discussed in the analysis section for parent eligibility policies.



Lukens

-


1229

Figure 2 Simulated Eligibility for Children Source: Data provided by Kosali Simon Note: Percentage of all children aged 0–19, irrespective of family income, in the 1996 CPS who would be eligible for Medicaid or CHIP given state eligibility rules, varying by state, year, and single year of age.

state’s eligibility rules would cover approximately 6 percent of parents in 2008. Many states implemented year-to-year decreases in these thresholds, but overall they trended upward, especially in the top quartile of states. Measuring Medicaid Payment Generosity

Conceptualizing and measuring payment generosity presents several difficulties, some of which are similar to those encountered for eligibility generosity. Some previous studies have used measures based on actual Medicaid spending to measure payment generosity (e.g., Kronebusch 1993 and Camobreco 1996 use Medicaid spending per recipient). However, such measures conflate both policy and nonpolicy characteristics of states. For example, an older and sicker population will drive up spending per recipient even when actual payment policies remain unchanged. Ideally, I would like a measure of Medicaid payments that reflects state payment policies and that changes only when these policies change.



1230


Figure 3 Simulated Eligibility for Parents Sources: Author’s calculations based on 1996 CPS; KCMU 2000–2008; Broaddus et al. 2002 Note: Percentage of all nonelderly parents, irrespective of income, residing with their dependent children (aged 0–17) in families of three in the 1996 CPS who would be eligible for Medicaid given state eligibility rules, varying by state, year, and employment status.

I use a Medicaid physician fee index to measure payment generosity.13 This measure captures the generosity of state physician payments for Medicaid relative to the national average. The physician fee index is derived from Medicaid patients who are financed on an FFS basis. The Urban Institute constructed the index using fee data from surveys it sent to states in 1998, 2003, and 2008. Through these surveys, it obtained physician fees paid by state Medicaid programs for thirty-two representative medical services.14 These fees were then combined into a single index using weights that reflect the fraction of national Medicaid physician expenditures devoted to each medical service. The result is a single number that is scaled to reflect each state’s Medicaid fees relative to the national average for that year, where the national average for each year is normalized to 13. I also tried measuring payment generosity using capitation rates for Medicaid patients in HMOs. However, with only sixty-eight observations spanning two closely spaced years, estimates were too imprecise to be informative. 14. In 1998 only twenty-two services were included in the index.



Lukens

-


1231

equal 100. For example, Maryland’s fee index in 2008 is 127, meaning that its fees were 27 percent above the national average for 2008 (Zuckerman, Williams, and Stockley 2009). This measure has several advantages. First, the fee index may be representative of payment generosity even for patients financed using capitated payments in Medicaid HMOs. The reason is that many states with capitated payments base their rates on calculations of what FFS programs would have paid (Holahan and Suzuki 2003). Second, many Medicaid patients in my analysis are financed through an FFS system and thus subject to the fees used to create the index. In fiscal year 2008, 40 percent of children and 56 percent of adults were financed through an FFS system (MACPAC 2011). The physician fee index also has disadvantages. It directly includes only Medicaid patients in FFS systems at a time when capitated payment systems were becoming increasingly prevalent. Even for FFS patients, it only includes physician fees; in 2009 physician and clinical services accounted for about 11 percent of total Medicaid spending (CMS, n.d.). Figure 4 shows Medicaid physician fees over time. Instead of plotting the fee index that I use in the analysis, figure 4 plots the Medicaid-toMedicare fee ratio. The fee ratio was constructed by the Urban Institute using the same Medicaid fee data. Fees are expressed relative to Medicare fees for the same group of services and then are combined using the same Medicaid expenditure weights used to make the fee index. The Medicaidto-Medicare fee ratio is more useful as a descriptive measure of payment generosity because it reflects the generosity of Medicaid fees relative to alternative fees that physicians might expect to earn (data on private fees are unavailable). The reason I use the fee index and not the fee ratio in the analysis is to avoid a possible spurious correlation of Medicare fees with my health care spending measure, which is also based on Medicare patients. As figure 4 shows, Medicaid fees in the median state rose from 69 percent to 88 percent of Medicare fees between 1998 and 2008.15 This rise masks considerable variation across states, however. In 2008 Medicaid fees were only 37 percent of Medicare fees in New Jersey but 143 percent of Medicare fees in Wyoming.

15. In 2008, while Medicaid physician fees in the median state were 88 percent of Medicare fees, Medicaid physician fees for the average Medicaid patient were only 72 percent of Medicare fees.



1232


Figure 4 Medicaid Physician Fee Ratios Sources: Norton 1999; Zuckerman et al. 2004; Zuckerman, Williams, and Stockley 2009 Note: Medicaid physician fees as a percentage of Medicare physician fees

Analysis of Eligibility Policies

In this section, I present separate analyses of Medicaid and CHIP eligibility policies for children and for parents. Descriptions and sources for all variables are detailed in table S1 in the supplemental text file found online.16 Political variables in the final specification include unified Democratic control of both houses of the legislature and governorship, unified Republican control, state resident liberalism, the percentage of women in the legislature, an indicator for the presence of a line-item veto, an indicator for legislative supermajority requirements for tax or spending increases, and the set of six interest group variables. I conduct my analysis for low-income children on a panel of forty-nine states over the period from 1996 to 2005. This period corresponds to the

16. To access this table, please click on the ‘‘Supplemental Material’’ link that appears in the box to the right of this article online (doi.org/10.1215/03616878-2822634).



Lukens

-


1233

dependent variable. Nebraska is omitted due to its nonpartisan legislature. I estimate the following equation: ys, t + 2 = as + kt + bxst + gpst + ckst + est , where ys,t + 2 measures simulated eligibility for states in year t + 2; as and lt represent fixed state and time effects, respectively; xst are control variables describing the economic, demographic, and policy environments in the states; pst are political variables; kst proxies for health care spending per child; and est is a normally distributed error term. Observations are measured biennially to capture the two-year electoral cycles of state legislatures. States with even-year elections are measured in even years, while those with odd-year elections are measured in odd years. Time fixed effects, lt, are also measured in biennial election years, occurring in even years for most states but odd years for a few states. Time fixed effects control for factors that are common across states in each election year. Simulated eligibility is led two years because for policy makers to enact and implement changes in Medicaid rules in response to changes in explanatory variables takes time. The federal matching rate is an exception; it can be anticipated several years in advance and is therefore measured concurrently with the dependent variable. Results: Medicaid Eligibility Policies for Children

Estimates for simulated eligibility for low-income children are presented in table 1. The first model uses state fixed effects, while the second model uses pooled OLS with controls for region (nine census divisions). The dependent variable is the simulated percentage of children eligible for Medicaid or CHIP, given state eligibility rules. Both models include election year effects. The standard errors are generated using the ‘‘cluster’’ option in Stata, allowing for arbitrary correlations across observations within states. An advantage of the pooled models is that they identify parameters using both between-state and within-state variation, unlike state fixed effects models, which use only the latter source of variation. Effects of factors that vary greatly among states, but vary little within states over the ten-year period of the study, are more easily detected by the pooled estimates. Results should be interpreted with caution; without controlling for state fixed effects, estimates are more likely to be biased due to unobserved state differences. However, if regional controls are enough to capture the relevant omitted variables, the pooled results provide additional insights that are not detected using the more conservative strategy of state fixed effects.



1234


Table 1

Determinants of Medicaid Eligibility Generosity for Children State Fixed Effects Model

Variable Health care spending Unified Democratic control Unified Republican control State resident liberalism (%) Women legislators (%) Line-item veto Supermajority requirement AMA membership (%) AHA membership (%) AARP membership (%) Physician group influence Hospital group influence Senior group influence Log real income per capita Unemployment (%) Log real tax revenue per capita Unions (%) Poverty (%) Black/Hispanic origin (%) High school graduate + (%) Aged 65 + (%) Aged 0–17 (%) Single-parent households (%) Population (100K) Urban (%) Federal matching rate (%) Mid-Atlantic East north-central West north-central South Atlantic East south-central West south-central Mountain Pacific

B

SE

t

- 4.46 - 1.11 - 2.34 0.19 0.36 - 2.05 0.66

1.59 1.03 0.88 0.25 0.15 0.86 1.64

- 2.85* - 1.08 - 2.67* 0.77 2.34* - 2.38* 0.40

- 3.18 0.05 17.49 0.18 - 0.49 - 0.53 - 0.03 - 0.29 2.14 - 0.10 0.17 0.19 0.24

18.81 0.72 12.04 0.26 0.29 0.63 0.20 2.16 0.83 0.17 0.08 0.39 0.36

Pooled OLS Model B

- 3.44 2.21 - 2.80 0.81 0.22 - 2.71 - 2.56 - 0.02 0.07 - 1.14 - 3.12 0.68 0.27 - 0.17 0.47 0.08 0.02 1.45 0.00 0.69 0.76 - 1.66 - 0.24 - 0.83 0.19 - 0.12 0.24 - 0.14 0.77 2.58* 1.70 - 0.60 0.17 2.15* 0.02 0.49 - 0.08 0.67 - 0.18 0.84 - 10.75 3.75 - 1.03 1.20 2.49 - 12.32 - 17.10

SE

t

1.91 1.22 2.39 0.40 0.16 2.49 2.19 0.12 0.13 13.69 3.05 3.39 3.34 26.37 0.94 0.00 0.45 0.37 0.16 0.31 0.68 0.84 0.33 0.02 0.13 0.34 5.46 4.38 6.47 5.09 5.62 7.17 4.29 5.86

- 1.80 + 1.82 + - 1.17 2.03* 1.41 - 1.09 - 1.17 - 0.20 0.50 - 0.08 - 1.02 0.20 0.08 0.02 0.02 - 1.21 1.70 + - 0.64 1.20 0.77 1.12 2.03* 0.50 0.80 - 0.63 - 0.54 0.15 - 2.45* 0.58 - 0.20 0.21 0.35 - 2.87* - 2.92*

Notes: N = 245. Biennial observations for 49 states from 1996 to 2005. Robust standard errors clustered by state. Election year effects included. B = coefficient from ordinary least squares regression; SE = standard error. + p < 0.10; *p < 0.05; **p < 0.01



Lukens

-


1235

Health Care Spending

My proxy for health care spending is not measured in dollars. I therefore interpret the measure as a scale of health care spending and report its effects in standard deviation units. The results in table 1 demonstrate large and statistically significant negative effects of health care spending on Medicaid eligibility generosity. If we interpret the fixed effects estimates, moving 1 standard deviation up the scale of health care spending results in a 4.5 percentage point reduction in the share of children eligible for Medicaid. The size of this effect represents over one-third of 1 standard deviation of simulated eligibility and about one-quarter of the expansion in eligibility that took place in the median state over the sample period. The pooled model coefficient estimate, 3.4 percentage points, is similar in magnitude. The results provide strong evidence that state policy makers account for costs in setting eligibility policies. States experiencing greater increases in health care spending responded with substantially smaller expansions in Medicaid eligibility generosity. State Politics

The results show that party control matters for Medicaid eligibility policy. If we interpret the state fixed effects estimates, unified Republican control is associated with a 2.3 percentage point reduction in the number of children eligible relative to divided control. Unified Democratic control does not appear to differ from divided control in the state fixed effects model. The pooled estimates are larger in magnitude, with unified Democratic control increasing eligibility by 2.2 percentage points and unified Republican control decreasing eligibility by 2.8 percentage points, relative to divided control. In contrast to the state fixed effects results, in the pooled estimates unified Democratic control is statistically significant, while unified Republican control is not. This difference in results between fixed effects and pooled models could indicate that Republican Party effects are relatively more important for changes within states during the ten-year sample period, while Democratic Party effects are relatively more important for long-lasting differences among states. I find that liberalism of state residents is positively related to eligibility generosity, even after controlling for party control. Within the context of Medicaid, this finding provides additional evidence that ideology varies among states within the same party, and both ideology and party exert influences on policy (Erikson, Wright, and McIver 1989). But this effect is



1236


statistically significant only in pooled results. This difference between fixed effects and pooled model results may indicate that ideology is more apparent in long-lasting differences among states as opposed to changes within states. Given a relatively short time frame of ten years, this outcome makes sense. In the pooled estimates, a 1 percentage point increase in state resident liberalism corresponds to a 0.8 percentage point increase in the share of children eligible. Consistent with expectations, a greater prevalence of women legislators is associated with greater eligibility generosity for children. On average, women make up 22 percent of the state legislature, with a standard deviation of 7 percent. While pooled estimates are not statistically significant, according to the state fixed effects estimate, a standard deviation increase in the percentage of legislators who are women is associated with a 2.5 percentage point increase in the share of children eligible for Medicaid. These findings support the literature arguing that women legislators matter for policy and are the first findings to do so in the context of Medicaid policy (Thomas 1994; Besley and Case 2000). The results indicate that Medicaid eligibility coverage for children is considered a pro-family policy that women legislators are more likely to support, even after controlling for party control and ideology. The line-item veto is associated with a 2.1 percentage point reduction in the share of children eligible in the fixed effects results. This outcome supports Kousser’s (2002) finding that existence of a line-item veto results in less generous Medicaid policies, and it establishes this relationship specifically for eligibility policy. Pooled results, while larger in magnitude, are not statistically significant. Supermajority requirements are not statistically significant. Since only eleven states had supermajority requirements, with only three adding them during the sample period, insufficient variation to detect clear effects is likely, particularly in the fixed effects models. Finally, no evidence is found of a relationship between Medicaid eligibility generosity for children and the interest group variables. This finding is consistent with the hypothesis that hospitals and physicians do not generally benefit from a greater pool of Medicaid patients. Though the AARP and seniors may support the Medicaid program more generally, their benefits from expanded eligibility for children are likely to be indirect (e.g., intergenerational transfers) and not large. Economic, Demographic, and Policy Variables

Relative to the political variables and health care spending, the expected direction of effects for some of the economic, demographic, and policy



Lukens

-


1237

variables is less clear. For example, the models include several variables that are linked to business cycles, including logged per capita income, unemployment, logged per capita state and local tax revenue, and the poverty rate. While economic prosperity enhances a state’s ability to afford generous eligibility policies, prosperity also generates less political demand for generosity from state residents.17 In any event, these variables linked to business cycles are not statistically significant. The pooled model shows that a 1 percentage point increase in union density is associated with a 0.8 percentage point increase in simulated eligibility. The union relationship disappears in the fixed effects results, however. The reason may be that union density is proxying for unobserved state attributes that are related to higher demand or capacity for generous eligibility policies. These unobserved characteristics are controlled for by state fixed effects. Moreover, the positive relationship in the pooled estimates may reflect historical union support for some form of universal health insurance, which typically translates into support for Medicaid or a direct interest in expanding Medicaid on the part of health-related unions (Gottschalk 2007). These effects would most likely not change much during the sample period but would be evident in long-lasting differences among states. The only other economic or demographic characteristic strongly related to simulated eligibility is the percentage of children in the population.18 According to state fixed effects estimates, 1 percentage point more children out of the total state population corresponds to an increase in simulated eligibility of more than 2 percentage points. States with more children have more residents (i.e., parents or caretakers) who are sympathetic to government programs that support children. Though a higher federal matching rate makes expanding Medicaid more affordable, I found no relationship between the matching rate and eligibility policy. Previous studies also do not find the expected positive effect of the matching rate, and the reason may be due to confounding by nonlinear effects of state income, given that the federal matching rate formula includes the square of per capita state income (Kronebusch 1993; Baughman and Milyo 2008; Grogan and Rigby 2009). Alternatively, the 17. Local tax revenue data are not available for 2001 and 2003, and the state and local tax revenue variable is therefore linearly interpolated for these years. Note that this variable is highly endogenous, since tax rates are planned simultaneously with eligibility policies. Exclusion of this variable, however, has no substantive effects on results. 18. While overall state population appears to be related to eligibility policy, a closer look revealed that this relationship was driven entirely by a few outliers.



1238


federal matching rate may be a relatively minor factor explaining state Medicaid policies (Kronebusch 2004). Robustness

I estimated several other models to test the robustness of the results reported in the state fixed effects and pooled OLS specifications. First differences models, where biennial changes of simulated eligibility are regressed on changes in the explanatory variables, gave similar results for both political variables and health care spending. Long change models, which regress one change from an early year to a later year in the sample (equivalent to fixed effects with only one early year and one late year), also gave similar results. Finally, fixed effects with the addition of state-specific linear time trends were estimated, allowing states to have individual linear trends and identifying effects based on within-state deviations from these trends. These models gave similar results for health care spending but less statistically significant results for other variables. All of these additional specifications control for unobserved differences between states. Given the relatively small sample size, these robustness checks lend considerable confidence to the estimates reported in table 1. Results: Eligibility Policies for Low-Income Parents

In addition to evaluating the determinants of state Medicaid generosity for low-income children, I also performed a separate analysis for low-income parents. The politics surrounding eligibility policy for the two groups likely differs in significant ways (Kronebusch 1997). I expect the direction of effects of political variables and health care spending to be the same. However, I expect party control and ideology to have a larger effect on policy for adults. While CHIP has certainly been politically contentious, government aid for low-income children is likely less divisive than that for low-income adults. Health care spending should also have a larger effect, since adults are somewhat more expensive to insure than children are. The estimates for parents are less precise than those for children. First, the observations are fewer. I have four election years (2000–2007) of data on forty-nine states for a total of 196 observations. Second, the complex rules that determine parent eligibility are difficult to obtain and synthesize into a single measure. Consequently, my measure of simulated eligibility is based only on eligibility rules for parents in families of three. It does not capture how state eligibility rules vary with family size (typically, income



Lukens

-


1239

eligibility thresholds increase with family size). The dependent variable is the percentage of parents in families of three in the 1996 CPS who would be eligible for Medicaid under each state’s eligibility rules, which vary by year and parents’ employment status. Although this measure does not include eligibility thresholds for parents in all family sizes, it is likely to be generally representative of the generosity of state eligibility requirements for low-income parents and is published for that purpose by the Kaiser Family Foundation. Table 2 displays the results for the low-income parent analysis using state fixed effects and pooled OLS models. The variables and column formats are the same as for the child analysis except that the supermajority requirement variable is excluded. Supermajority requirements do not vary over the sample period. Health care spending again has a large and statistically significant effect on eligibility generosity. In the state fixed effects results, a 1 standard deviation increase in health care spending is associated with a 7 percentage point reduction in the share of parents eligible for Medicaid. The pooled estimate is somewhat smaller in magnitude, with a coefficient size of 5 percentage points. The negative effect of health care spending on Medicaid eligibility generosity is larger for parents than for children, especially considering that a far smaller percentage of parents than children are typically eligible. Party control and ideology again have important effects. In the fixed effects results, unified Democratic control is estimated to increase the share of parents eligible by 3.7 percentage points. In the pooled model results, unified Republican control is estimated to decrease the share of parents eligible by 2 percentage points. The fixed effects estimate for unified Republican control and the pooled model estimate for unified Democratic control are of the expected signs but do not meet the thresholds for statistical significance. Estimates for state resident liberalism are statistically significant and virtually identical in both models. A 1 percentage point increase in state resident liberalism corresponds to a 0.6 percentage point increase in simulated eligibility. These results are consistent with the findings for children’s eligibility, but the magnitudes are larger when one considers the relatively small share of adults who are typically eligible. This finding supports the hypothesis that the partisan and ideological divide is starker for adults than it is for children. Perhaps the more positive group image of poor children, which Kronebusch (1997) attributes to notions that they are undeserving of their poverty, have a right to equal opportunity, and represent a social investment, cuts to some extent across ideology and



1240


Table 2

Determinants of Medicaid Eligibility Generosity for Adults State Fixed Effects Model

Variable

B

SE

t

Health care spending - 6.99 3.96 - 1.77 + Unified Democratic control 3.69 1.65 2.23* Unified Republican control - 1.74 1.96 - 0.89 State resident liberalism (%) 0.63 0.28 2.23* Women legislators (%) 0.05 0.25 0.19 Line-item veto 1.10 1.45 0.76 AMA membership (%) AHA membership (%) AARP membership (%) Physician group influence Hospital group influence Senior group influence Log real income per capita 27.64 33.40 0.83 Unemployment (%) 1.99 1.32 1.51 Log real tax revenue per capita - 19.39 17.05 - 1.14 Unions (%) - 0.07 0.46 - 0.14 Poverty (%) - 0.95 0.46 - 2.07* Black/Hispanic origin (%) 4.38 1.83 2.39* High school graduate + (%) 1.14 0.45 2.54* Aged 65 + (%) 4.08 3.35 1.22 Aged 0–17 (%) - 1.41 1.49 - 0.94 Single-parent households (%) 0.23 0.24 0.96 Population (100K) 0.03 0.15 0.22 Urban (%) - 1.61 0.54 - 3.01** Federal matching rate (%) 0.10 0.41 0.25 Mid-Atlantic East north-central West north-central South Atlantic East south-central West south-central Mountain Pacific

Pooled OLS Model B

SE

t

- 5.01 1.86 - 1.97 0.62 0.08 0.64 0.15 0.06 - 5.78 - 0.36 0.34 1.55 - 29.07 0.41 8.46 0.16 - 0.25 0.14 0.17 - 0.74 - 0.93 0.24 0.02 0.11 - 0.11 4.50 - 12.45 - 7.66 - 10.02 - 6.81 - 9.05 - 11.92 - 11.77

1.19 1.87 1.17 0.31 0.13 1.86 0.07 0.07 8.22 1.27 1.73 2.03 12.17 0.75 3.75 0.21 0.41 0.13 0.17 0.56 0.71 0.16 0.01 0.10 0.21 4.16 3.36 2.62 3.30 4.85 3.96 3.45 2.53

- 4.21** 1.00 - 1.68 + 2.02* 0.62 0.34 2.13* 0.85 - 0.70 - 0.29 0.20 0.76 - 2.39* 0.55 2.26* 0.73 - 0.60 1.07 1.01 - 1.31 - 1.31 1.51 1.73 + 1.10 - 0.53 1.08 - 3.70** - 2.92** - 3.04** - 1.40 - 2.29* - 3.46** - 4.65**

Notes: N = 196. Biennial observations for 49 states from 2000 to 2007. Robust standard errors clustered by state. Election year effects included. B = coefficient from ordinary least squares regression; SE = standard error. + p < 0.10; *p < 0.05; **p < 0.01



Lukens

-


1241

party. While the effects of party control and ideology are strong for both child and parent eligibility, no such positive group image for parents is found that could mitigate those effects. Unlike in the analysis for children, the percentage of women in the legislature and the presence of a line-item veto do not seem to matter for parent eligibility generosity. The former result is consistent with the view that insuring low-income children fits better as a pro-family policy than insuring low-income parents. For parents, AMA membership is associated with more generous Medicaid eligibility rules. This result is indicative of the benefits to physicians when patients become insured and the demand for health care increases. It may also reflect an AMA that has grown less conservative over the years. A 1–standard deviation (13 percentage point) increase in the percentage of physicians who are AMA members corresponds to a 1.9 percentage point increase in simulated eligibility for parents. Other measures of interest groups are not statistically significant, echoing the results for child eligibility. Medicaid Payment Policies

The empirical approach used in this section is similar to that in the section on Medicaid eligibility policies. I estimate both state fixed effects models and pooled OLS models on a panel of states. All pooled models control for region in the form of dummy variables for nine census divisions, and the dependent variables are led two years to allow policy to respond to the independent variables. The states and years included in each set of analyses vary depending on data availability. The dependent variable used in this analysis is the Medicaid physician fee index, described extensively above. The independent variables for payment policies are almost identical to those for eligibility policies, but the key differences are fewer. First, the analysis does not include supermajority requirements because this explanatory factor does not vary during the sample years. Second, unified party control is replaced with a dummy variable measuring the party of the governor (equal to 1 for a Democratic governor), along with a continuous measure of the percentage of the legislature that comprises Democrats. The reason for this substitution is that most of the samples include only a few years spaced several years apart. This spacing of years does not allow the analysis to effectively capture party control because multiple election cycles are missing in years between sample years.



1242


I expect health care spending to have a negative effect on Medicaid payment generosity that is similar to the effect of health care spending on eligibility generosity. However, I expect Medicaid payment generosity to be less reflective of partisan and ideological divisions. To the extent that line-item veto power is used for ideological and/or partisan ends, I likewise expect it to have a smaller effect on payment generosity. Yet because some physicians and hospitals directly benefit from higher Medicaid payments, their interest group influence and size should have positive relationships with Medicaid payment generosity. I expect little effect of the fraction of women legislators on payment generosity because providing insurance to uninsured families (i.e., expanding eligibility) is probably more salient as a pro-family policy than increasing provider payments is. Results: Medicaid Payment Policies

Table 3 includes models using both state fixed effects and pooled OLS with controls for region. All specifications include year fixed effects. The number of observations in these specifications is 137, with forty-one states in 1998, forty-eight states in 2003, and forty-eight states in 2008. Little in the state fixed effects results is statistically significant. With only three time points, and far less variation over time compared to variation across states, the standard errors are too large for the state fixed effects results to impart much information. Alternatively, the reason may be that Medicaid physician fees were not an important policy lever over the sample period. In the pooled results, which take advantage of the substantial variation between states to identify effects, health care spending again emerges as important. A 1–standard deviation increase in health care costs is associated with Medicaid physician fees that are 6.2 percentage points lower relative to the national average. Thus, as in the case of eligibility thresholds, affordability appears to be important when it comes to setting Medicaid physician fees. However, these results rely only on pooled models, so they are more tentative than those for Medicaid eligibility policy. The effects of party, ideology, women in state legislatures, and the lineitem veto are not statistically significant. As discussed above, the effect of greater Medicaid provider payments on Medicaid recipients is indirect and likely weaker than the effect of Medicaid eligibility policies on Medicaid recipients. In the pooled model, measures of provider interest group size are positively related to Medicaid payment generosity, but measures of provider



Lukens

Table 3

-


Determinants of Medicaid Physician Fees State Fixed Effects Model

Variable

1243

B

SE

Health care spending 4.72 4.99 Democratic legislature (%) - 10.44 25.97 Democratic governor 3.98 2.59 State resident liberalism (%) 0.01 1.01 Women legislators (%) 0.60 0.49 Line-item veto - 1.13 8.27 AMA membership (%) AHA membership (%) AARP membership (%) Physician group influence Hospital group influence Senior group influence Log real income per capita 24.08 47.28 Unemployment (%) 2.34 2.55 Log real tax revenue per capita 54.85 26.98 Unions (%) 0.53 1.38 Poverty (%) - 0.69 1.11 Black/Hispanic origin (%) - 3.09 1.90 High school graduate + (%) - 1.45 0.92 Aged 65 + (%) 0.90 4.39 Aged 0–17 (%) 2.06 3.32 Single-parent households (%) 0.25 0.68 Population (100K) - 0.19 0.25 Urban (%) 0.99 2.42 Federal matching rate (%) 0.00 1.00 Mid-Atlantic East north-central West north-central South Atlantic East south-central West south-central Mountain Pacific

T

Pooled OLS Model B

- 6.23 - 20.50 1.93 0.98 - 0.41 - 10.32 0.51 0.71 0.75 - 4.16 - 5.92 - 12.49 0.51 137.47 0.92 3.27 2.03* 29.12 0.38 0.13 - 0.62 1.37 - 1.62 - 0.32 - 1.57 - 0.62 0.21 1.60 0.62 3.10 0.37 1.44 - 0.74 - 0.10 0.41 - 0.44 0.00 0.77 - 14.50 1.35 14.60 31.03 28.19 29.07 26.43 30.17 0.95 - 0.40 1.54 0.01 1.21 - 0.14

SE

t

3.38 25.24 3.00 0.88 0.42 7.02 0.23 0.31 0.37 5.36 6.61 6.33 47.09 2.50 9.51 0.91 1.43 0.45 0.71 2.29 2.95 0.81 0.05 0.37 1.11 15.56 10.18 11.59 10.16 11.75 14.34 11.17 12.68

- 1.84 + - 0.81 0.64 1.11 - 0.96 - 1.47 2.20* 2.28* 2.06* - 0.78 - 0.90 - 1.97* 2.92** 1.31 3.06** 0.15 0.96 - 0.72 - 0.88 0.70 1.05 1.77 + - 2.02* - 1.18 0.70 - 0.93 0.13 1.26 3.05** 2.40* 2.03* 2.37* 2.38*

Notes: N = 137. Observations for available states in 1998, 2003, and 2008. Robust standard errors clustered by state. Election year effects included. B = coefficient from ordinary least squares regression; SE = standard error. + p < 0.10; *p < 0.05; **p < 0.01



1244


interest group influence are not. A 1–standard deviation increase in the number of doctors belonging to the AMA and a 1–standard deviation increase in the number of hospitals belonging to the AHA are associated with respective increases of 6.6 and 6.5 percentage points in a state’s Medicaid payment generosity relative to the national average. Belonging to the AARP is positively related to Medicaid payment generosity, while senior group influence is negatively related to payment generosity. The reason for this contradictory result is not clear. While seniors support Medicaid in general, their level of support for higher provider payments for nonelderly patients is probably small relative to their support for Medicaid nursing home and medically needy payments. Discussion

Using robust methods and new and improved variables, this study examines the effects of health care spending and political variables on Medicaid and CHIP policies. It includes both the low-income child and low-income parent populations and examines both Medicaid eligibility policies and Medicaid payment policies. The findings enhance our understanding of how states formulate Medicaid policies. State policy makers frequently claim that rising costs of health care provision constrain the generosity of Medicaid coverage, but testing the validity of this claim is difficult. This article uses a measure developed from a separate literature as an innovative way to proxy for health care spending that avoids factors likely to confound other potential measures. The analysis provides evidence that rising health care spending, due to the intensity of health care use, substantially reduces Medicaid eligibility generosity, and it also provides tentative evidence of a similar effect on Medicaid payment generosity. As a result, fewer low-income families receive coverage, and those that do may experience diminished access to care. Other research has found that rising health care spending results in less private coverage (Kronick and Gilmer 1999; Chernew, Cutler, and Keenan 2005). Results of this study suggest that this phenomenon also plays out, indirectly through the political process, in the provision of public insurance. These results reflect the fact that policy makers make decisions among programs competing under budget constraints, and Medicaid is the largest component of state expenditures. For states with higher health care spending, to insure additional Medicaid patients and to increase provider payments for existing Medicaid patients is more expensive. Policy makers respond by pursuing less generous Medicaid eligibility and payment policies than they would otherwise.



Lukens

-


1245

Several additional interpretations are possible with regard to the health care spending variable, and these interpretations depend on how we construe this variable beyond its basic role as a proxy for health care spending. The variable was developed in the literature on geographic variations in care with the intention of measuring health care spending that does not reflect differences in health care quality. If health care spending is indeed what the variable captures, then the results of this study suggest that ‘‘wasteful’’ practices in the health care sector (e.g., ordering frequent CT scans in cases where simple X-rays would be just as effective) unintentionally and indirectly lead to less generous Medicaid policies. If instead the variable to some extent reflects greater quality of care, then the results suggest a trade-off between Medicaid generosity and the overall quality of health care in a state. Another interpretation is that the health care spending variable, which is based on inpatient end-of-life care, reflects norms or constraints on alternative means of end-of-life care such as nursing home bed availability, some of which themselves could be influenced by Medicaid policies. Preliminary analysis indicates that this interpretation is less likely, but it cannot be ruled out.19 To know which interpretation is correct is difficult, but all are consistent with the finding that rising health care spending leads to less generous Medicaid policies. The results also show that Medicaid and CHIP eligibility policies are clearly influenced by the political environments of the states. For Medicaid eligibility policies, party control, the ideology of state citizens, the prevalence of women in legislatures, the line-item veto, and physician interest group size emerge as important determinants. Among these, party control and ideology of state residents are particularly important in terms of the size and consistency of their effects. In contrast, Medicaid payment generosity is influenced only by some of the interest group variables. Kronebusch (1993) argued that liberals and Democrats favor generous eligibility policies relative to payment policies, while conservatives and Republicans favor payment policies relative to eligibility policies. In terms of relative partisan and ideological preferences for eligibility policies versus payment policies, my findings are consistent with this formulation. But my findings differ from Kronebusch’s (1993) in that I uncovered little partisan or ideological differences in preferences for Medicaid payment generosity. My findings support the hypothesis that any support of 19. Using data on nursing home beds, nursing home occupancy rates, and covered use of hospice care, I found no evidence that these variables could underlie the results. However, this interpretation cannot be ruled out because the variables are available for only some of the sample years in this study.



1246


conservatives and Republicans for medical providers is counterbalanced by their opposition to increased funding for Medicaid. While the relative impacts of these opposing factors may have changed since the time of Kronebusch’s (1993) study, at least more recently, payment policies have not clearly manifested partisan or ideological divisions. The choices of state policy makers will loom large over the next several years as states decide whether and how much to expand Medicaid coverage. On the eve of implementation of the Patient Protection and Affordable Care Act (ACA), governors in twenty-one states appeared intent on not expanding Medicaid coverage; meanwhile, governors in twenty-five states planned to move ahead with expansions (Advisory Board Company 2013). State policies regarding the ACA will likely continue to evolve during its initial years, and any prediction of state behavior is highly uncertain. The politics of Medicaid have been particularly intense and divisive in the postACA period, and state policy makers cite cost considerations as crucial in their decisions (Lieb 2012; Pear 2012). As in previous efforts to expand Medicaid, the budgetary and political environments in states will be critical in shaping the future of the program.

n

n

n

Gideon Lukens is an economist in the Economic Policy Division at the US Office of Management and Budget (OMB). He supports the OMB on a variety of issues in health, labor, and demographic economics, as well as benefit-cost analysis for federal programs and policies. His research interests include health economics and policy and labor economics. He received his PhD in political science and his MA in economics from the University of California, Los Angeles.

References Advisory Board Company. 2013. ‘‘Where the States Stand on Medicaid Expansion.’’ Daily Briefing, December 11. www.advisory.com/daily-briefing/resources/primers /medicaidmap. Baicker, Katherine, and Amitabh Chandra. 2004. ‘‘Medicare Spending, the Physician Workforce, and Beneficiaries’ Quality of Care.’’ Health Affairs 23, no. 3: w184– w197. Baker, Laurence C., Elliot S. Fisher, and John E. Wennberg. 2008. ‘‘Variations in Hospital Resource Use for Medicare and Privately Insured Populations in California.’’ Health Affairs 27, no. 2: w123–w134.



Lukens

-


1247

Barrilleaux, Charles J., and Mark E. Miller. 1988. ‘‘The Political-Economy of State Medicaid Policy.’’ American Political Science Review 82, no. 4: 1089–1107. Baughman, Reagan, and Jeffrey Milyo. 2008. ‘‘How Do States Formulate Medicaid and SCHIP Policy? Economic and Political Determinants of State Eligibility Levels.’’ Working paper, University of Missouri. Besley, Timothy, and Anne Case. 2000. ‘‘Unnatural Experiments? Estimating the Incidence of Endogenous Policies.’’ Economic Journal 110, no. 467: 672–94. Besley, Timothy, and Anne Case. 2003. ‘‘Political Institutions and Policy Choices: Evidence from the United States.’’ Journal of Economic Literature 41, no. 1: 7–73. Broaddus, Matthew, Shannon Blaney, Annie Dude, Jocelyn Guyer, Leighton Ku, and Jaia Peterson. 2002. ‘‘Expanding Family Coverage: States’ Medicaid Eligibility Policies for Working Families in the Year 2000.’’ Washington, DC: Center on Budget and Policy Priorities. www.cbpp.org/archiveSite/1-2-02health.pdf. Cammisa, Anne Marie, and Beth Reingold. 2004. ‘‘Women in State Legislatures and State Legislative Research: Beyond Sameness and Difference.’’ State Politics and Policy Quarterly 4, no. 2: 181–210. Camobreco, John F. 1996. ‘‘Medicaid and Collective Action.’’ Social Science Quarterly 77, no. 4: 860–76. CBO (Congressional Budget Office). 2008. ‘‘Geographic Variation in Health Care Spending.’’ Washington, DC: CBO. Chernew, Michael, David M. Cutler, and Patricia Seliger Keenan. 2005. ‘‘Increasing Health Insurance Costs and the Decline in Insurance Coverage.’’ Health Services Research 40, no. 4: 1021–39. CMS (Centers for Medicare and Medicaid Services). 2012. Medicare and Medicaid Statistical Supplement: 2012 Edition. Table 13.4. www.cms.gov/Research-Statistics -Data-and-Systems/Statistics-Trends-and-Reports/MedicareMedicaidStatSupp/2012 .html. CMS (Centers for Medicare and Medicaid Services). n.d. National Health Expenditures Tables. Table 3. www.cms.gov/Research-Statistics-Data-and-Systems/Statistics -Trends-and-Reports/NationalHealthExpendData/Downloads/tables.pdf (accessed June 27, 2014). Cohen, Joel W. 1993. ‘‘Medicaid Physician Fees and Use of Physician and Hospital Services.’’ Inquiry 30, no. 3: 281–92. Cohen, Joel W., and Peter J. Cunningham. 1995. ‘‘Medicaid Physician Fee Levels and Children’s Access to Care.’’ Health Affairs 14, no. 1: 255–62. Currie, Janet, and Jonathan Gruber. 1996. ‘‘Saving Babies: The Efficacy and Cost of Recent Changes in the Medicaid Eligibility of Pregnant Women.’’ Journal of Political Economy 104, no. 6: 1263–96. Currie, Janet, Jonathan Gruber, and Michael Fischer. 1995. ‘‘Physician Payments and Infant Mortality: Evidence from Medicaid Fee Policy.’’ American Economic Review 85, no. 2: 106–11. Dartmouth Atlas of Health Care. 2011. ‘‘Hospital Discharges and Post-acute Care.’’ September 28. www.dartmouthatlas.org/tools/downloads.aspx?tab=41. Dartmouth Atlas of Health Care. n.d. ‘‘Indirect Adjustment.’’ www.dartmouthatlas .org/downloads/methods/indirect_adjustment.pdf.



1248


Elixhauser, Anne. 2008. ‘‘Hospital Stays for Children, 2006.’’ Health Care Cost and Utilization Project, Statistical Brief No. 56. July. Rockville, MD: Agency for Healthcare Research and Quality. www.hcup-us.ahrq.gov/reports/statbriefs/sb56 .pdf. Erikson, Robert, Gerald Wright, and John McIver. 1989. ‘‘Political Parties, Public Opinion, and State Policy in the United States.’’ American Political Science Review 83, no. 3: 729–50. Fisher, Elliott S., David E. Wennberg, Therese A. Stukel, Daniel J. Gottlieb, F. L. Lucas, and Etoile L. Pinder. 2003a. ‘‘The Implications of Regional Variations in Medicare Spending, Part 1: The Content, Quality, and Accessibility of Care.’’ Annals of Internal Medicine 138, no. 4: 273–87. Fisher, Elliott S., David E. Wennberg, Therese A. Stukel, Daniel J. Gottlieb, F. L. Lucas, and Etoile L. Pinder. 2003b. ‘‘The Implications of Regional Variations in Medicare Spending, Part 2: Health Outcomes and Satisfaction with Care.’’ Annals of Internal Medicine 138, no. 4: 288–98. Fox, Michael H., Jonathan P. Weiner, and Kai Phua. 1992. ‘‘Effect of Medicaid Payment Levels on Access to Obstetrical Care.’’ Health Affairs 11, no. 4: 150–61. Gottschalk, Marie. 2007. ‘‘Back to the Future? Health Benefits, Organized Labor, and Universal Health Care.’’ Journal of Health Politics, Policy and Law 32, no. 6: 923– 70. Gray, Virginia, and David Lowery. 2000. The Population Ecology of Interest Representation: Lobbying Communities in the American States. Ann Arbor: University of Michigan Press. Gray, Virginia, David Lowery, and Erik K. Godwin. 2007a. ‘‘The Political Management of Managed Care: Explaining Variations in State Health Maintenance Organization Regulations.’’ Journal of Health Politics, Policy and Law 32, no. 3: 457–95. Gray, Virginia, David Lowery, and Erik K. Godwin. 2007b. ‘‘Public Preferences and Organized Interests in Health Policy: State Pharmacy Assistance Programs as Innovations.’’ Journal of Health Politics, Policy and Law 32, no. 1: 89–129. Grogan, Colleen M. 1994. ‘‘Political-Economic Factors Influencing State Medicaid Policy.’’ Political Research Quarterly 47, no. 3: 589–622. Grogan, Colleen M., and Eric Patashnik. 2003. ‘‘Between Welfare Medicine and Mainstream Entitlement: Medicaid at the Political Crossroads.’’ Journal of Health Politics, Policy and Law 28, no. 5: 821–58. Grogan, Colleen M., and Elizabeth Rigby. 2009. ‘‘Federalism, Partisan Politics, and Shifting Support for State Flexibility: The Case of the U.S. State Children’s Health Insurance Program.’’ Publius 39, no. 1: 47–69. Gruber, Jonathan. 2000. ‘‘Medicaid.’’ NBER Working Paper No. 7829. Cambridge, MA: National Bureau of Economic Research. www.nber.org/papers/w7829.pdf. Hadley, Jack, Timothy Waidmann, Stephen Zuckerman, and Robert A. Berenson. 2011. ‘‘Medical Spending and the Health of the Elderly.’’ Health Services Research 46, no. 5: 1333–61. HCUP (Healthcare Cost and Utilization Project). 2014. ‘‘Overview of the State Inpatient Databases SID.’’ June. www.hcup-us.ahrq.gov/sidoverview.jsp.



Lukens

-


1249

Hirsch, Barry, David Macpherson, and Wayne Vroman. 2001. ‘‘Estimates of Union Density by State.’’ Monthly Labor Review 124, no. 7: 51–55. Holahan, John, and Shinobu Suzuki. 2003. ‘‘Medicaid Managed Care Payment Methods and Capitation Rates in 2001.’’ Health Affairs 22, no. 1: 204–18. Hrebenar, Ronald, and Clive Thomas. 1987. Interest Group Politics in the American West. Salt Lake City: University of Utah Press. Hrebenar, Ronald, and Clive Thomas. 1992. Interest Group Politics in the Southern States. Salt Lake City: University of Utah Press. Hrebenar, Ronald, and Clive Thomas. 1993a. Interest Group Politics in the Midwestern States. Salt Lake City: University of Utah Press. Hrebenar, Ronald, and Clive Thomas. 1993b. Interest Group Politics in the Northeastern States. Salt Lake City: University of Utah Press. KCMU (Kaiser Commission on Medicaid and the Uninsured). 2000–2008. ‘‘Annual Updates on Eligibility Rules, Enrollment and Renewal Procedures, and CostSharing Practices in Medicaid and CHIP.’’ Washington, DC: Kaiser Family Foundation. kff.org/medicaid/report/annual-updates-on-eligibility-rules-enrollment-and/. KCMU (Kaiser Commission on Medicaid and the Uninsured). 2005. ‘‘Medicaid: An Overview of Spending on ‘Mandatory’ vs. ‘Optional’ Populations and Services.’’ Washington, DC: Kaiser Family Foundation. kaiserfamilyfoundation.files.word press.com/2013/01/medicaid-an-overview-of-spending-on.pdf. KCMU (Kaiser Commission on Medicaid and the Uninsured). 2007. ‘‘Resuming the Path to Health Coverage for Children and Parents: A Fifty State Update on Eligibility Rules, Enrollment and Renewal Procedures, and Cost-Sharing Practices in Medicaid and SCHIP in 2006.’’ Washington, DC: Kaiser Family Foundation. kaiser familyfoundation.files.wordpress.com/2013/01/7608.pdf. KCMU (Kaiser Commission on Medicaid and the Uninsured). 2009. ‘‘Challenges of Providing Health Coverage for Children and Parents in a Recession: A Fifty State Update on Eligibility Rules, Enrollment and Renewal Procedures, and Cost-Sharing Practices in Medicaid and SCHIP in 2009.’’ Washington, DC: Kaiser Family Foundation. kaiserfamilyfoundation.files.wordpress.com/2013/01/7855.pdf. KCMU (Kaiser Commission on Medicaid and the Uninsured). 2013. ‘‘Getting into Gear for 2014: Findings from a Fifty-State Survey of Eligibility, Enrollment, Renewal, and Cost-Sharing Policies in Medicaid and CHIP, 2012–2013.’’ Washington, DC: Kaiser Family Foundation. kaiserfamilyfoundation.files.wordpress .com/2013/05/8401.pdf. Knight, Brian. 2000. ‘‘Supermajority Voting Requirements for Tax Increases: Evidence from the States.’’ Journal of Public Economics 76, no. 1: 41–67. Kousser, Thad. 2002. ‘‘The Politics of Discretionary Medicaid Spending, 1980– 1993.’’ Journal of Health Politics, Policy and Law 27, no. 4: 639–72. Kronebusch, Karl. 1993. ‘‘Medicaid Politics: Policymaking Contexts and the Politics of Group Differences in the American States.’’ PhD diss., Harvard University. Kronebusch, Karl. 1997. ‘‘Medicaid and the Politics of Groups: Recipients, Providers, and Policy Making.’’ Journal of Health Politics, Policy and Law 22, no. 3: 839–78. Kronebusch, Karl. 2001. ‘‘Children’s Medicaid Enrollment: The Impacts of Mandates, Welfare Reform, and Policy Delinking.’’ Journal of Health Politics, Policy and Law 26, no. 6: 1223–60.



1250


Kronebusch, Karl. 2004. ‘‘Matching Rates and Mandates: Federalism and Children’s Medicaid Enrollment.’’ Policy Studies Journal 32, no. 3: 317–39. Kronick, Richard, and Todd Gilmer. 1999. ‘‘Explaining the Decline in Health Insurance Coverage, 1979–1995.’’ Health Affairs 18, no. 2: 30–47. Lax, Jeffery, and Justin Phillips. 2009. ‘‘How Should We Estimate Public Opinion in the States?’’ American Journal of Political Science 53, no. 1: 107–21. Lieb, David A. 2012. ‘‘Some GOP States Want to Abandon Medicaid Expansion.’’ The Big Story, Associated Press, June 29. www.bigstory.ap.org/article/some-gop-states -want-abandon-medicaid-expansion. MACPAC (Medicaid and CHIP Payment and Access Commission). 2011. ‘‘Report to the Congress: The Evolution of Managed Care in Medicaid.’’ Washington, DC: MACPAC. Mullins, Daniel, and Bruce Wallin. 2004. ‘‘Tax and Expenditure Limitations: Introduction and Overview.’’ Public Budgeting and Finance 24, no. 4: 2–15. NASBO (National Association of State Budget Officers). 2011. State Expenditure Report: Examining Fiscal 2009–2011 State Spending. Washington, DC: NASBO. NASBO (National Association of State Budget Officers). 2013. State Expenditure Report: Examining Fiscal 2011–2013 State Spending. Washington, DC: NASBO. Norton, Stephen. 1999. ‘‘Recent Trends in Medicaid Physician Fees, 1993–1998.’’ Discussion Paper 99-12. Washington, DC: Urban Institute. Pacheco, Julianna. 2011. ‘‘Using National Surveys to Measure State Public Opinion over Time: A Guideline for Scholars and an Application.’’ State Politics and Policy Quarterly 11, no. 4: 415–39. Pear, Robert. 2012. ‘‘Uncertainty over States and Medicaid Expansion.’’ New York Times, June 28. Pracht, Etienne E., and William J. Moore. 2003. ‘‘Interest Groups and State Medicaid Drug Programs.’’ Journal of Health Politics, Policy and Law 28, no. 1: 9–40. Rosenthal, Alan. 2004. Heavy Lifting: The Job of the American Legislature. Washington, DC: Congressional Quarterly. Sandy, Lewis G., Thomas Bodenheimer, L. Gregory Pawlson, and Barbara Starfield. 2009. ‘‘The Political Economy of U.S. Primary Care.’’ Health Affairs 28, no. 4: 1136–45. Shen, Yu-Chu, and Stephen Zuckerman. 2005. ‘‘The Effect of Medicaid Payment Generosity on Access and Use among Beneficiaries.’’ Health Services Research 40, no. 3: 723–44. Sirovich, Brenda, Patricia M. Gallagher, David E. Wennberg, and Elliott S. Fisher. 2008. ‘‘Discretionary Decision Making by Primary Care Physicians and the Cost of U.S. Health Care.’’ Health Affairs 27, no. 3: 813–23. Stiefel, Matt, Paul Feigenbaum, and Elliott S. Fisher. 2008. ‘‘The Dartmouth Atlas Applied to Kaiser Permanente: Analysis of Variation in Care at the End of Life.’’ Permanente Journal 12, no. 1: 4–9. Thomas, Clive, and Ronald Hrebenar. 2004. ‘‘Interest Groups in the States.’’ In Politics in the American States, edited by Virginia Gray and Russell L. Hanson, 100–128. Washington, DC: CQ. Thomas, Sue. 1994. How Women Legislate. New York: Oxford University Press.



Lukens

-


1251

Ullman, Frank, and Ian Hill. 2001. ‘‘Eligibility under State Children’s Health Insurance Programs.’’ American Journal of Public Health 91, no. 9: 1449–51. US Census Bureau. 2012. Statistical Abstract of the United States: 2012. Table 145. www.census.gov/compendia/statab/2012/tables/12s0145.pdf. Wennberg, John E., Elliot S. Fisher, and Jonathan Skinner. 2002. ‘‘Geography and the Debate over Medicare Reform.’’ Health Affairs 13, no. 1: w96–w114. Wennberg, John E., and David E. Wennberg. 2000. ‘‘Variation in Hospitalizations for Medical Conditions.’’ In The Dartmouth Atlas of Health Care in Michigan. Hanover, NH: The Trustees of Dartmouth College; and Detroit, MI: Blue Cross Blue Shield of Michigan. www.bcbsm.com/content/dam/public/Consumer/Documents /about-us/dartmouth-atlas.pdf. Yasaitis, Laura, Elliot S. Fisher, Jonathan Skinner, and Amitabh Chandra. 2009. ‘‘Hospital Quality and Intensity of Spending: Is There an Association?’’ Health Affairs 28, no. 4: w566–w572. Young, Donald, Bruce Sachais, and Leigh Jefferies. 2002. ‘‘Comparative Costs of Treating Adults and Children within Selected Diagnosis-Related Groups.’’ Clinical Chemistry 48, no. 1: 150–60. Zuckerman, Stephen, Joshua McFeeters, Peter Cunningham, and Len Nichols. 2004. ‘‘Changes in Medicaid Physician Fees, 1998–2003: Implications for Physician Participation.’’ Health Affairs 23: w374–w384. Zuckerman, Stephen, Aimee F. Williams, and Karen E. Stockley. 2009. ‘‘Trends in Medicaid Physician Fees, 2003–2008.’’ Health Affairs 28, no. 3: w510–w519.


Explaining the Growth in US Health Care Spending Using State-Level Variation in Income, Insurance, and Provider Market Dynamics.

State mental health policy: Back to the future: New Mexico returns to the early days of Medicaid managed care.

Health care policy and politics in Italy in hard times.

The politics and analytics of health policy.

One approach to tracking state and local health spending.

Federal influence on state health policy.

The state of transgender health care: policy, law, and medical frameworks.

Politics of Health Policy: Yesterday and Today.

Health Care Use and Spending for Medicaid Enrollees in Federally Qualified Health Centers Versus Other Primary Care Settings.

Health policy basics: Medicaid expansion.

State and demographic variation in use of depot antipsychotics by Medicaid beneficiaries with schizophrenia.

Medicaid spending on contraceptive coverage and pregnancy-related care.

Sharpening the health policy analytical rapier Comment on "The politics and analytics of health policy".

The future of cardiothoracic care: policy, politics, and performance.

The Little State That Couldn't Could? The Politics of "Single-Payer" Health Coverage in Vermont.

Leveraging The Affordable Care Act To Enroll Justice-Involved Populations In Medicaid: State And Local Efforts.

A ghost in the machine? Politics in global health policy.

Medicaid on the eve of expansion: a survey of state Medicaid officials on the Affordable Care Act.

State regulation of health care costs.

Association between medical home enrollment and health care utilization and costs among breast cancer patients in a state Medicaid program.

Commentary: politics and the health care system.

The paradox of reducing health care spending.

Medicaid Spending: A Brief History.

ethnic variation in immediate postmastectomy breast reconstruction.