Conducting Public Policy Analysis

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CHAPTER 1

Incorporating Political Indicators into Comparative State Study of

Higher Education Policy Michael K. McLendon and James C. Hearn

Traditionally, the state policy literature on higher education has exhibited a major blind spot: Research has focused nearly exclusively on policy ef- fects, ignoring consideration of the determinants of state policy for higher education. A substantial empirical literature exists on the effects of state policies on students (e.g., impacts of financial aid regimes on college at- tendance), on campuses (e.g., consequences of state regulation for campus quality), and on society more broadly (e.g., the contribution of higher education to economic development). Yet, scholars have studied factors associated with interstate variation in public policy for higher educa- tion far less frequently. Thus, although the current era has witnessed dy- namic policy changes for higher education,1 our understanding of the forces that have led states to reform and adopt new policies remains rudimentary.

The ignoring of political determinants of policy is especially trou- bling. Until very recently, students of higher education largely over- looked political science as a framework for organizing state policy research. This omission seems vexing on a number of levels. First, re- searchers seeking to explain the policy choices state governments make for higher education should surely account for the governmental con- texts in which those choices are made, in much the same way as studies of the decisions of students (e.g., what college to attend, whether to drop out, etc.) or of colleges and universities (e.g., tuition setting) typically seek to account for key attributes of the individual or the institution of interest. One important context conditioning the policy choices of state

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State Postsecondary Education Research : New Methods to Inform Policy and Practice, edited by Donald E. Heller, and Kathleen M. Shaw, Stylus Publishing, LLC, 2011. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/capella/detail.action?docID=4438609. Created from capella on 2019-04-15 06:23:46.

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governments is the political context, which is defined by the constellation of state political institutions, actors, and processes within which govern- mental behavior is nested. Any serious attempt to explain governmental behavior requires, we believe, some systematic attention to be paid to the potential policy impacts of these state political forces.

Second, failure to account for the political context in which policies arise is a missed opportunity, since the American states provide one of the world’s most attractive venues in which to comparatively study the formation of public policy. Thus, across Alabama, California, Nebraska, Rhode Island, and beyond, the 50 states represent a system of constrained variation along key demographic, economic, organizational, and political dimensions that makes possible rigorous comparisons.2 From a compara- tive standpoint, in fact, the states represent an almost ideal ‘‘natural labo- ratory’’ for testing hypotheses about policies and the contextual conditions that produce them (Dye, 1990). Accordingly, political scien- tists in recent decades have ‘‘rediscovered’’ the American states as an arena in which to study policy, refining their theoretical and methodological approaches so as to better leverage across-state comparisons (Brace & Jewett, 1995; Moncrief, Thompson, & Cassie, 1996; Squire & Hamm, 2005). Contemporary researchers on state policy for higher education thus stand to benefit from two broader developments: a realization among policy scholars that the American states provide a unique opportunity for comparative analysis and the advent of new conceptualizations, measures, and analytic techniques with which to pursue comparative political analy- ses of state policy.

Our aim in this chapter is to build on these recent developments by providing a framework for, and describing some challenges attendant to, incorporating select indicators of political systems into comparative-state research on higher education policy. To help guide researchers, we first develop a conceptual framework for studying policy adoption and change in the American states. Our framework views the 50 states both as indi- vidual policy actors and as agents of potential mutual influence within a larger social system. It holds that states adopt the policies they do in part because of their internal demographic, economic, and political character- istics and in part because of their ability to influence one another’s behav- ior. In this discussion, we identify key political indicators and data sources commonly found in the comparative state policy literature but rarely incorporated into higher education research. We then discuss five challenges that researchers would face in integrating these data into their work.

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A Framework for Studying and Incorporating Political Indicators into Comparative-State Study of Higher Education Policy Although research on state policy innovation and diffusion can be traced to classic work in the fields of rural sociology, organizational theory, and communication studies (Katz, Levin, & Hamilton, 1963; Mohr, 1969; Rogers, 1962), much of the systematic scholarship in recent decades has been undertaken by political scientists, who have developed conceptual and analytical tools of increasing sophistication with which to examine governmental behavior in the states. The policy innovation and diffusion perspective holds that states adopt the policies they do partly because of the demographic, socioeconomic, and political features arising within them individually (i.e., innovation) and partly because of their ability to influence one another’s behavior (i.e., diffusion).3 Walker’s (1969) early investigation was the first to conceptually integrate both sets of considera- tions—intrastate and interstate. His factor analysis of the adoption of pol- icies over time indicated that certain characteristics internal to states influenced patterns of policy adoption, but that states also seemed to em- ulate the behavior of their neighbors, thus leading over time to the spread of policies along regional lines.

The more recent work of Berry and Berry (1990) produced concep- tual and analytical refinements through their pioneering use of event his- tory analysis. They found that states adopted new lotteries and taxes because of a combination of within-state and across-state forces. Numer- ous studies in recent years have built on the Berrys’s research, examining school choice initiatives, consumer protection laws, health insurance re- forms, and abortion and death-penalty statutes (Hays, 1996; Mintrom, 1997; Mintrom & Vergari, 1998; Mooney & Lee, 1999; Stream, 1999). Higher education researchers also have begun incorporating this frame- work into their studies (Doyle, 2005; Hearn & Griswold, 1994; McLen- don, Hearn, & Deaton, 2006; McLendon, Heller, & Young, 2005).

Building on this scholarship, we propose a framework for compara- tive analysis that conceptualizes state policy outcomes for higher educa- tion as being a function of many of the same factors that research has shown to influence policy in other arenas. Our framework includes policy influences arising both within states (e.g., higher education organization patterns, economic conditions, and a host of political factors, including political culture and ideology, legislative design, partisanship, gubernato- rial influence, and interest group climates) and between and among them (i.e., diffusion pressures).

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We issue two caveats, however. First, we would not characterize our framework as approximating a unified theory of governmental behavior. Instead, we propose a set of broad categories, variables, and indicators that are already staples of research in political science, but whose inclu- sion into the higher education literature would help to stimulate scholar- ship and capitalize on the unique contextual and comparative analytical advantages that attend state-level research. Second, given the space limita- tions of the chapter, our topical coverage is highly abridged; we focus on select variables, indicators, and data sources that we believe hold promise in building models capable of predicting and explaining policy outcomes for higher education across state settings.

Higher Education Demography, Organization, and Governance

Although most of the prospective influences on higher education policy that we discuss in this section focus on the broader political economies of states, a substantial body of empirical research supports the view that conditions and factors native to higher education might influence state policy outcomes. For example, certain demographic and postsecondary enrollment patterns (e.g., nonresident enrollments, state population share by age, etc.) and organization-ecologic features of higher education (e.g., the mix of two- and four-year and public and private institutions) have been shown to influence variation in public-sector tuition levels and stu- dent financing policies (e.g., Hearn, Griswold, & Marine, 1996; Heller, 1997; Rizzo & Ehrenberg, 2004; Zumeta, 1996). Data on these indicators are readily available to researchers via the Integrated Postsecondary Edu- cation Data Systems (IPEDS) and similar sources and have been routinely incorporated into previous studies of higher education policy.

A growing body of empirical research also points to connections be- tween postsecondary governance arrangements and policy outcomes for higher education at both the state and campus levels. Indeed, research continues to accumulate in favor of the view that various aspects of the governance climate for higher education, including the overall statewide approach to governance (e.g., coordinating or governing board), the num- ber of separately governed boards in a state, and the mode of trustee selec- tion (i.e., elected versus appointed), could help condition the higher education policies states adopt (Hearn & Griswold, 1994; Hearn et al., 1996; Knott & Payne, 2004; Lowry, 2001; McLendon, 2003; McLendon et al., 2006; Zumeta, 1996). Editions of McGuinness’s (e.g., 1997) widely cited handbook on postsecondary governance structures are the leading sources of comparative data on these and other governance dimensions.

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Ideally, an integrative model seeking to account for the determinants of state policy for higher education across the 50 states would include appropriate controls for the governance landscapes of state postsecondary systems, as well as the demographic and organizational features pre- viously noted.

Socioeconomic Climates One long-standing debate in the comparative-state policy literature in- volves the relative importance of economic and political factors as deter- minants of governmental behavior. One of the early, robust findings of research was that socioeconomic development patterns seemed to account for much of the interstate variation in public policy. In such classic studies as those by Dawson and Robinson (1963) and Dye (1966), researchers identified strong, positive relationships between levels of educational at- tainment, wealth, and industrialization and public expenditures. In some instances, evidence seemed also to point to a connection between higher levels of socioeconomic development and state adoption of new policies (e.g., Walker, 1969). Although much of the research of the past 20 years has dispelled the myth of economic determinism (e.g., Jacoby & Schnei- der, 2001), our read of the literature leads us to believe that both distal and proximal4 socioeconomic conditions are likely to play some role in shaping the policy choices states make for higher education.

Political Culture and Ideology Our first category of systemic political influences emphasizes variation across states in political culture and ideology. A staple of the comparative policy literature since Elazar’s influential work in the 1960s (Elazar, 1966), political culture refers to contrasting collective conceptions of U.S. political order that might shape both the structure of state political sys- tems and the policies arising within those systems (Gray, 2003). Elazar argued that early migration and settlement patterns produced several re- gional political subcultures in the United States, each producing a distinc- tive vision regarding the role of government in public life.5 Although interest in Elazar’s cultural theory of state politics and policy has waned, his ideas remain intuitively appealing. Additionally, some subsequent em- pirical work has demonstrated connections between political culture, as defined by Elazar, and the policies states adopt (Erikson, Wright, & McIver, 1993; Fitzpatrick & Hero, 1988; Sharkansky, 1969).

Numerous other scholars have attempted to map the political ideolo- gies of states’ citizenries and government officials. Broadly speaking, po- litical ideology can be understood as a coherent and consistent set of

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orientations or attitudes toward politics. Berry, Ringquist, Fording, and Hanson (1998) defined citizen ideology as the mean position on a liberal- conservative continuum of the electorate in a state and state government ideology as the mean position of the elected public officials in a state. In one creative undertaking, Erikson, Wright, and McIver (1993) pooled the results of more than 100 national telephone surveys from 1976 to 1988 to obtain measures of ideology by state, concluding (via multivariate analy- sis) both that the political attitudes of Americans vary according to where in the United States they live and that these attitudes appear to be linked with certain state policy choices. One criticism of the measures developed by Erikson et al. (1993) is that these cross-sectional snapshots ignore sig- nificant changes in ideological orientations over time, thus leading to out- dated measures (Berry at al., 1998, p. 328). In an effort to address these limitations, Berry et al. (1998) created what are perhaps now the leading indicators of state political, developing multiple, and longitudinal mea- sures of roll-call voting, outcomes of congressional elections, partisan bal- ance of state legislatures, and party of the governor. Using these measures, a number of studies have subsequently demonstrated strong empirical connections between the ideological propensities of citizens and politi- cians and certain policy outcomes in the states, notably in the areas of welfare and corrections (e.g., Soss, Schram, Vartanian, & O’Brien, 2001; Yates & Fording, 2005).

Political culture and ideology is one area in which higher education researchers have made limited use of existing indicators (e.g., Doyle, McLendon, & Hearn, 2005; Hossler, Lund, Ramin, Westfall, & Irish, 1997; Nicholson-Crotty & Meier, 2003; Volkwein, 1987).6 Yet, most re- searchers who have pursued political ideology and culture have done so using cross-sectional measures, a problematical practice, given the dy- namic nature of public opinion. Fortunately, the index developed by Berry et al., which assigns ideology scores for all states for all years be- tween 1960 and 2002, has demonstrated high levels of validity and reliabil- ity and is available publicly (Berry et al., 2004).

Legislative Organization and Membership The second category of political influences involves certain structural fea- tures of state legislatures that might influence policy outcomes. Through constitutional and statutory provisions, and by tradition, state legislative bodies have been designed in ways that are broadly similar in form, but that vary by degree along important dimensions. For example, legislative professionalism refers to organizational properties of legislatures that are capable of shaping policy. State legislative assemblies that meet in ex- tended session, pay their members well, and provide ample staff resources

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(i.e., ones like the U.S. Congress) are considered professionalized. Legis- latures with session lengths of brief duration, low pay for members, and few staff are deemed as nonprofessional ‘‘citizen legislatures’’ (Squire, 2000). The variation that exists in the professionalism of legislatures holds implications for policy. Professionalism can directly influence policy in that greater analytical capacity (e.g., more staff) tends to produce higher volumes of legislation. Professionalism also can influence policy indi- rectly in that professionalized settings tend to attract better-educated leg- islators, ones who might be more inclined toward new policy approaches (e.g., Barrilleaux, Holbrook, & Langer, 2002; Squire, 1992, 2000).

State legislatures vary organizationally in other respects, including the powers accorded leadership, the means of allocating committee as- signments, the terms permitted officeholders, and the perquisites of in- cumbency that shape the goals, strategies, and behaviors of members. All of these factors might play some role in shaping policy differences among states, although the strength of evidence varies across settings and issues (Carey, Niemi, & Powell, 2001; Jewell & Whicker, 1994; Oppenheimer, 1985; Squire & Hamm, 2005). Research also indicates that certain demo- graphic differences among legislatures can hold important implications for policy development (Moncrief et al., 1996; Squire & Hamm, 2005). For example, women and men tend to perceive issues differently, and these differences influence the amount and the types of legislation passed in the states, including legislation relating to education (Thomas, 1991).

Higher education researchers have paid somewhat greater attention to legislatures than they have to other aspects of state political systems. One of the earliest across-state studies was Eulau and Quinley’s (1970) investigation of legislative norms toward higher education. These analysts interviewed nearly 100 legislators, identifying four principal political norms that seemed to govern state policy for higher education in the 1960s. Since the work of Eulau and Quinley, most research has examined the impact of certain institutional features of legislatures on higher educa- tion policy. For example, Volkwein’s (1987) cross-sectional analysis found correlational evidence of relationships between well-staffed legislatures and levels of state regulation of higher education. Recent longitudinal studies, however, point curiously to a set of bifurcated findings. On the one hand, studies examining the impact of state political climates on campus-level policy have tended to confirm the predictive value of certain legislative attributes. Nicholson-Crotty and Meier (2003) found legislative professionalism associated with higher tuition levels. Hicklin and Hawes’s (2004) hierarchical linear model of 500 universities over an 11-year period found that increased representation of African Americans and Latinos in legislatures positively influenced levels of minority student enrollment at

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public universities. Yet, studies of the determinants of state-level policies for higher education often have failed to find connections between legisla- tive organization or membership and state policy outcomes (Doyle, 2005; Doyle et al., 2005; McLendon et al., 2006; McLendon, Heller, & Young, 2005).

This divergence in findings suggests the desirability of further re- search aimed toward identifying the conditions under which certain legis- lative attributes help determine higher education policy outcomes. A number of publicly available measures of legislative professionalism exist to aid in such work. The most widely used measure of professionalism is Squire’s index (1992, 2000), which assigns states values ranging from 0 to 1. His measure relies on an index of Congress’s member pay, average days in session, and mean staff per member as a baseline against which to compare an index composed of those same attributes of state legislative bodies. One drawback of the Squire index is that it is updated only peri- odically and, thus, although published versions of the index are capable of capturing general trends in legislative capacity, they cannot account for short-term changes in legislatures that might impact policy in a given year. The individual, state-level elements comprising Squire’s index, how- ever, can be found in annual volumes of the Council of State Govern- ment’s Book of the States (e.g., Council, 2004), thus researchers could create similar metrics conveying the position of the states relative to one another. Although there are no data sets publicly available containing comprehensive, longitudinal information on most of the demographic in- dicators that we have discussed, select data on women legislators can be obtained publicly through the Center for American Women and Politics (2005) and data on black legislators can be purchased from the Joint Cen- ter for Political and Economic Studies (2005).

Gubernatorial Influence

The separation-of-powers system that characterizes American state gov- ernment also ensures governors a prominent role in shaping public policy. Although governors everywhere exert considerable sway, the precise ex- tent of their influence over public policy processes and outcomes varies from one state to the next, depending in part on the governors’ institu- tional and personal powers (Beyle, 2003). In some states, for example, gov- ernors wield stronger influence over policy through the line-item veto, broad appointment powers, and robust tenure potential. Elsewhere, gov- ernors hold fewer formal instruments of policy control, thus limiting their influence (Barrilleaux & Bernick, 2003; Beyle, 2003; Dometrius, 1987). The policy influence of governors overall, however, appears to rest on a

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combination of contextual factors, including the professionalism of the legislature, the strength of political parties, and the strength of the econ- omy (Dilger, Krause, & Moffett, 1995).

Only a handful of published studies have sought to account systemat- ically for the influence of governors on higher education policy (e.g., Lowry, 2001; Nicholson-Crotty & Meier, 2003; McLendon, Deaton, & Hearn, 2005; McLendon et al., 2006; Volkwein, 1987). Lowry’s (2001) ap- proach, linking governors, bureaucracies, and the behaviors of public uni- versities, warrants extended discussion.7 Drawing on principal-agent theory, Lowry conceptualized state governance structures for higher edu- cation as, in effect, systems of political representation, because the differ- ent institutional arrangements ‘‘affect the ability of different actors to influence decisions’’ (p. 846). Lowry reasoned that regulatory coordinat- ing boards8 essentially are extensions of governors’ capacity to supervise, because they appoint board members. Consequently, regulatory boards should behave in a manner generally consistent with the preferences of governors (and voters), leading to lower tuition levels. By contrast, gover- nance structures lacking such direct political oversight should tend to in- stitutionalize the preferences of faculty and administrators, resulting in higher tuition levels. Estimating a series of models using data on 407 pub- lic universities for a single year, 1995, Lowry found that universities lo- cated in states with regulatory boards in fact charged significantly lower prices.

Although Lowry’s work illustrates one creative approach to studying gubernatorial influence in higher education,9 the conventional measures of gubernatorial power are those indices created originally by Schlesinger (1965) and subsequently revised and periodically updated by Beyle (2003).10 Beyle’s institutional-powers variable is a metric combining scores on six individual indices: governor’s tenure potential, appointment power, budget power, veto power, extent to which the governor’s party also control the legislature, and whether the state provides for separately elected executive branch officials. The personal-power variable also is a metric combining separate measures for electoral mandate, ambition lad- der, personal future, personal style, and job performance rating. Beyle (2005) has made his ratings of gubernatorial power for the years 1980, 1988, 1994, 1998, 2001, 2004, and 2005 publicly available, and these mea- sures could be integrated straightforwardly into comparative research on state policy for higher education.

Party Strength and Control of Governmental Institutions Our fourth category of political-system influences on higher education policy involves partisan balance of state government. The linkage between

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party control of state governmental institutions and policy outcomes has been studied from a number of vantage points. Research suggests that several factors can mediate the effects of partisanship on state policy out- comes, including district-level competition, differences in the constitu- ency bases of party support, the governing party’s margin of control, and whether the party pursues policy commitments consistent with its true preferences or adjusts its positions strategically in order to attract voters (e.g., Barrilleaux et al., 2002). Nonetheless, numerous empirical studies find that party strength and control can influence the policy postures of states (Alt & Lowry, 2000; Barrilleaux et al., 2002; Berry & Berry, 1990; Holbrook & Percy, 1992; Stream, 1999; Yates & Fording, 2005). For in- stance, Democratic Party strength has been linked with higher levels of state taxation, higher overall spending, higher spending on certain educa- tion and welfare programs, and with abortion access and gay rights initia- tives. Republicans, on the other hand, have been associated with higher levels of spending on law enforcement, higher incarceration rates, regula- tory and tax policies that are viewed as more favorable to business inter- ests, and with opposition to lotteries and abortion access.

A recent series of studies has produced intriguing evidence of connec- tions between the partisan complexion of state government and policy outcomes in the higher education arena (McLendon et al., 2006; McLen- don, Deaton, & Hearn, 2005; McLendon, Heller, & Young, 2005).11 In all three studies, statistically significant coefficients for partisanship with- stood rigorous statistical controls for state socioeconomic conditions, at- tributes of higher education systems, and the influence of other political- systems. Researchers in one study found Republican legislative control associated with state adoption of certain college-financing policies in the 1980s and 1990s, but found no such relationships involving accountability policies (McLendon, Heller, & Young, 2005). In a second investigation, researchers returned to the accountability arena using event history analysis to examine the factors that led states to adopt performance- accountability mandates from 1979 to 2002 (McLendon et al., 2006). Their analysis identified Republican legislative party strength as a primary driver of adoption, but the direction of the influence varied across the three policies studied. McLendon and colleagues also examined reforms in state governance of higher education from 1985 to 2000 (McLendon, Deaton, & Hearn, 2005). In this third study, the authors tested the ‘‘polit- ical-instability hypothesis,’’ or the proposition that governance change is most likely to occur in states where there has been greatest turbulence in political institutions. Their event history analysis yielded strong support for the hypothesis, pointing to rates of change in Republican legislative

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membership and to shifts from divided to unified party control of legisla- tures as predictors of the initiatives.12

These initial studies suggest the desirability of incorporating diverse measures of partisan balance of government into research on higher edu- cation policy. One may wish to operationalize partisan influences in a variety of ways, depending on the underlying relationships that one hy- pothesizes to exist. A few examples include party control of legislatures (i.e., a binary variable designating which of the two major parties control the institution in a given year), year-to-year changes in party control, compositional strength (i.e., the percentage of seats across chambers of a legislature that belongs to either of the two parties), rates of change in compositional strength over time, and interparty competition (i.e., an in- dicator of the degree of competition for control of government or the legislature). Note that indices of interparty competition, such as the Ran- ney index,13 differ from more straightforward measures of partisan bal- ance in that the former attempt to capture the degree of Democratic or Republican control of government over time. The Ranney index also can be recalculated to indicate the level of competition between the parties for control of government, a subtle but important distinction (Bibby & Holbrook, 2003).14 All of the operationalizations noted require annual measures of partisanship, the leading source of which traditionally has been the Book of the States. Recently, Klarner (2004) made a signal contri- bution to the literature by organizing detailed data on the partisan balance of state government from 1959 to 2000.

Interest Group Climates Interest group climates represent a rich source of variation in state sys- tems, yet higher education researchers have largely ignored rigorous study of interest groups in policymaking. The states exhibit remarkable variety in terms of the numbers, diversity, activities, and power of the interest groups operating within them (Hrebenar & Thomas, 2003; Nownes & Freeman, 1998). Traditionally, much of the work on interest groups has sought to explain differences in lobbying styles and activities and in the ecology of state interest-group systems—in effect treating groups as a dependent variable. A substantial empirical literature also has arisen on the impact of interest groups on the policy choices of states. For example, recent work has shown that interest group strength and diversity help shape the allocative decisions of state governments (Gray & Lowery, 1988, 1996; Jacoby & Schneider, 2001), although a host of factors (e.g., party strength and cohesion, governors, lobbying strategy) appear to me- diate the effect (Wiggins, Hamm, & Bell, 1992). Higher education special- ists have been very slow in investigating the policy impacts of state

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interest group climates. A handful of case studies and cross-sectional anal- yses suggest the value of this line of inquiry (deGive & Olswang, 1999; Sabloff, 1997), but few rigorous and systematic across-state studies exist.

A variety of indicators of interest group strength are readily available. Despite limitations, these indicators could be readily incorporated into policy research on higher education in the states. Two such measures com- bine quantitative and qualitative data in a 50-state assessment of individual group influence and overall system power (Hrebenar & Thomas, 2003). The Hrebenar-Thomas ranking of the most influential interests organizes 40 interests into 3 categories, from most to least effective.15 The analysts also developed a classification of states according to the overall impact of interest groups relative to other actors. This fivefold typology arrays states along a continuum, ranging from systems in which interest groups as a whole are the overwhelming influence on policymaking to systems in which interest groups are consistently subordinate to other actors.16 Both rankings are based on similar sets of measures collected in 1989, 1994, 1998, and 2002, thus permitting comparisons over time.

Three other indicators, ranging from generic to higher education spe- cific, merit discussion. First, studies have used Gray and Lowery’s (1996) ‘‘relative density’’ variable as a measure of general interest group strength in a state. This variable is defined as the ratio of gross state product to the number of organizations registered to lobby within the state, with larger values indicating economically stronger groups.17 Higher education re- searchers may find a second indicator even more useful. Public-agency officials often are the most effective advocates for specific programs (Gorm- ley, 1996; Mintrom, 1997). To account for this potential influence on the policy choices of governments, some studies have used a ratio of public- sector employees to population. Although the size of some segment of a state’s bureaucracy does not necessarily translate into lobbying success, it does capture the relative prevalence of one possible source of influence on governmental behavior (Gormley, 1996). An indicator like this might be used to test hypotheses on the growth of the state higher education bureaucracy, appropriations levels and trends across sectors of higher ed- ucation, state policies toward the private (or for-profit) sectors, or other policy outcomes for which the size of the labor force in public higher education could conceivably shape government’s choices.18

McLendon, Hearn, and Deaton’s (2006) recent work on the emer- gence of new accountability mandates for higher education suggests yet a third approach to measuring interest group influence. Their analysis re- vealed an inverse relationship between university-dominated governance systems and the probability of states adopting rigorous accountability

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mandates. Building on Lowry’s (2001) work on principal-agent theory, the authors surmise that certain governance arrangements for higher edu- cation (i.e., consolidated governing boards) might advance the interests of certain stakeholders (i.e., academic) more than others (i.e., elected offi- cials) on particular issues. Thus, the model of governance a given state practices could condition the strength of the higher education lobby rela- tive to other political actors in the state. As suggested in our earlier discus- sion of Lowry’s (2001) research, one advantage to viewing different governance models as distinct forms of interest group representation is the availability of a consistent set of proxy measures over time (McGuin- ness, 1997).

Interstate Policy Diffusion

The final category composing our framework focuses on the influences states exert upon one another—often termed policy diffusion pressures. Several distinct diffusion models exist. The most prevalent one holds that states are likely to emulate those neighbors with which they share a con- tiguous border (Berry & Berry, 1990; Mintrom, 1997). A second popular approach operationalizes one’s ‘‘neighbor’’ as other states to which one is regionally bound (e.g., the South). Recently, scholars have begun ex- ploring other relationships, including politico-geographic connections (e.g., distances between state capitols) and economic connections that might transcend geographical boundaries, or other networks only semi- related to geographical considerations (McLendon et al., 2006).

As noted, several studies have examined relationships between geo- graphical regions and state policy for higher education (Hearn et al., 1996; Hossler, 1997; Volkwein, 1987; Zumeta, 1996). Yet, only recently have in- terstate diffusion pressures overtly been tested. These recent analyses have provided mixed evidence on diffusion. For example, analyses of state adoption of college-savings, prepaid-tuition, and merit-scholarship pro- grams have yielded conflicting results, and studies examining accountabil- ity mandates consistently have failed to find diffusion-like effects at work (Doyle, 2005; Doyle et al., 2005; McLendon, Deaton, & Hearn, 2005; McLendon et al., 2006; McLendon, Heller, & Young, 2005).

One clear advantage to studying diffusion-like pressures is that one state’s policy influence on another can be rather easily measured and in- corporated into a multivariate model. Diffusion traditionally has been measured as the proportion of a state’s ‘‘neighbors’’ that had already adopted a particular policy at the time the given state adopted the policy.

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This approach seeks to capture the cumulative pressures for policy adop- tion arising from the prior behaviors of a state’s neighbors or peers, how- ever neighbor or peer might be defined.19 Thus, diffusion can be measured simply by counting the number of states constituting a predetermined social network that have taken a particular action by a specific point in time. Although measurement of policy diffusion might seem straightfor- ward, the information upon which its calculation depends might not be clear-cut or easily obtained—for reasons we discuss in the following sec- tion, determining what constitutes a ‘‘policy’’ and when one has been adopted can sometimes prove daunting.

Summary of the Framework

To summarize, although our framework does not purport to provide a unified theory of governmental behavior, it does encompass a set of fac- tors we propose to be critical in state policy action in higher education. Those factors—state socioeconomic climates, political culture, and ideol- ogy, legislative organization and membership, gubernatorial influence, party strength and control of governmental institutions, interest group climates, and interstate policy diffusion—each contribute in important ways to the emergence of policy in a given state. Some do so via indirect channels. For example, the socioeconomic conditions in a state can argua- bly drive citizens and legislators toward certain ways of thinking, and thus toward certain policy choices. Thus, discussions preceding the devel- opment of merit-aid programs in several Southern states prominently fea- tured attention to lagging educational quality indicators in those states. Other factors in the list suggest more direct influences. For example, when the parties in power in a state change, there is likely to be movement toward signature legislative proposals, and such proposals have often fo- cused on education, thus suggesting a direct link from change in partisan control toward action in postsecondary policy. Regardless of the mecha- nisms involved, whether indirect or direct, the factors enumerated have a logical and, increasingly, an empirical rationale for inclusion in models of policy development.

Challenges of Incorporating Political Indicators into Comparative-State Study of Higher Education Policy

Clearly, there are numerous advantages in incorporating political indica- tors into comparative-state policy research on higher education. Yet, re- searchers also face notable challenges in building data sets and conducting

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analyses along the lines we have discussed. In the remainder of the chap- ter, we describe five such challenges, drawing on lessons that we have learned as a result of our own recent work.

1. Challenges of Theoretical Relevancy. One of the most glaring chal- lenges in building a comparative-state research literature on the determinants of public policy for higher education involves the challenge of theoretical relevancy: What is the basis theoretically for one to presume that certain political features of the states might have distinctive connections with public policies for higher education? For example, what would be the theoretical justifica- tion for presuming that differences among states along a contin- uum of liberal-conservative political attitudes should help explain interstate variation in higher education funding trends, regulatory climates, or financial aid regimes? In our view, such rationales exist, they appear plentiful, and they are taking a variety of creative forms in the accumulating literature (e.g., Doyle, 2005; Hicklin & Hawes, 2004; Lowry, 2001; McLendon et al., 2006). Given our field’s prior inattention to systematic political analysis, it is essen- tial that analysts continue to build the theoretical underpinnings of this line of work.

2. Challenges in Defining and Operationalizing the Dependent Vari- able. In our work, we have confronted four challenges relating to the definition of policy and policy adoption. Each of these ques- tions holds important implications for comparative research. First, what is a policy? When are the actions of governments deemed as constituting public policy—when an initiative is enacted, funded, implemented, or something else? For example, Georgia’s HOPE scholarship was fully operational prior to the program’s formal enactment into law. When studying longitudinally the factors as- sociated with the creation of state merit-scholarship programs, what year in one’s data set should Georgia’s program be coded as date of adoption? Clearly, ‘‘policy’’ should be determined contex- tually (depending on the goals of one’s study), but also consis- tently and with some clear rationale. Second, and relatedly, whose behavior constitutes public policy—the elected ‘‘principals’’ (i.e., legislators and governors), or the administrative ‘‘agents’’ (i.e., co- ordinating board officials), or both? If one chooses not to distin- guish between the actions of principals and agents, how can a single explanation account equally for the behaviors of both par- ties, when there is evidence suggesting that elected principals and bureaucratic agents hold different goals, operate from differing

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bases of authority, and respond to distinctive environmental pres- sures (Moe, 1987)? These challenges present in turn a third one: Because many data sources fail to define policy in clear operational terms, the researcher might be unable to place much confidence in reported dates of ‘‘policy adoption’’ for use as dependent vari- ables. Consequently, labor-intensive searches of state statutes might be necessary to cross-validate dates of important policy activity.

A fourth challenge concerns how policies across states should be meaningfully compared and scored. Much of the research both in higher education and political science presumes that a policy is a policy is a policy—but is it? States rarely adopt identical initia- tives, yet studies often fail to differentiate among similar policies on the basis of their scope or content. For example, the states’ merit-scholarship programs differ significantly in their eligibility requirements and along other substantive dimensions. Differences in policy design and content provide rich opportunities for empir- ical analysis when viewed both comparatively and over time. To what extent are the factors associated with the adoption of more robust policies similar to the factors associated with the adoption of less robust ones? What accounts for the broadening or narrow- ing of policy scope over time? These questions demonstrate the important conceptual implications that can stem from simple seeming definitional matters.

3. Challenges of Sample Selection and Delimitation. Much of our dis- cussion in this chapter presumes the inherent desirability of 50- state research designs—the more states the better, it would seem. There are notable advantages to this approach. Yet, the questions of which states one should study comparatively and why are not necessarily clear-cut. Ideally, sample selection should serve larger theoretical purposes and methodological necessities. It is conceiv- able that a sample of states (rather than all 50 of them) might pro- vide sufficient variability along the key dimensions of interest.

Indeed, even a handful of states—or one of them alone— might prove adequate under some circumstances (Nicholson- Crotty & Meier, 2002). Truly in-depth exploration of a single state can provide richer, more grounded, and better-contextualized in- formation. Comparisons among systems within individual states can be especially fruitful. Systems within states sometimes vary in the governance, legal frameworks, and financing policies affecting them. For example, in California, Michigan, and Minnesota, states with a constitutionally autonomous flagship institution, the

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research-university sector is immune from some of the political oversight present in the state-university and community college systems (Hearn, McLendon, & Gilchrist, 2004). Different public higher education sectors within a given state can have significantly different policy climates, personnel, students, missions, and fund- ing, and those differences can also provide needed bases for com- parative analysis.

A different sort of challenge involves the need for delimiting one’s sample when one is faced with differences across state politi- cal systems that inhibit meaningful comparisons. For example, Nebraska’s unique nonpartisan, unicameral legislature poses prob- lems for the testing of many party-related hypotheses. Thus, stud- ies often omit Nebraska from analyses in which partisan control is a critical theoretical consideration. What, then, does one do when relatively few dependent observations (i.e., policy adoptions) exist for a given analysis and one of those occurred in Nebraska? The researcher’s dilemma becomes one of trade-offs among conceptual clarity, external validity, and statistical power. Alaska and Hawaii also can prove problematic, but for different reasons. Studies in which spatial conceptions of diffusion are hypothesized to be im- portant policy influences often omit the two states from analysis, because they do not share a land border with the other 48 states— who, after all, is Hawaii’s ‘‘neighbor?’’ These questions point out the need for higher education researchers to carefully weigh both theoretical and methodological considerations when choosing states to study comparatively.

4. Challenges of Data Collection and Measurement. The avenue of research we have outlined throughout this chapter holds substan- tial implications for data collection. Most studies today examining policy outcomes in the American states employ some form of lon- gitudinal analysis. Often, the reason for doing so extends beyond the statistical problems that commonly attend cross-sectional de- signs (e.g., few observations; little power). For one, the time sensi- tivity of certain political variables suggests the need for data sets capable of capturing both spatial and temporal dimensions of pol- icy adoption. Some facets of state political systems, such as institu- tional powers of governors, are unlikely to change notably over the short run. Other political-system features, such as partisan control, can change markedly in the span of a single year. State economic conditions and conditions within the higher education system itself can also fluctuate from year to year. Thus, researchers

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need to develop data sets that, for each state, include annual indi- cators of the factors believed to influence the focal policy activity. Such multistate, longitudinal data sets are naturally ideal, but dig- ging deep into conditions, processes, and issues in each state over time can pose insurmountable costs. Many of the data elements that we have discussed are now available via reliable secondary sources, but the tasks of identifying, organizing, cross-validating, and cleaning data across states and over time can be quite labor intensive.

Other data-related challenges exist. For example, one often finds data on certain political indicators covering only a brief time period. Although the Klarner-produced data files at the State Poli- tics and Policy Data Archive now stand as a definitive source of information on partisan control of state government dating back to the 1950s, many indices of conceptually relevant variables exist only for particular years. When conducting longitudinal analysis, what are the appropriate strategies for handling missing data in such instances, so as to reduce biased estimates? Does one impute the missing values? Does one create trend curves? Does one trun- cate the time series (and thus one’s study) to accommodate gaps in the independent-variable data? These questions indicate the major challenges awaiting researchers even when many of the needed data elements are publicly available.

5. Challenges of Analytic Capacity. A fifth challenge to conducting an across-state study of higher education policy involves the ana- lytic methods required. The tools most appropriate for investigat- ing many of the relationships we have explored in this chapter are ones often not emphasized in many graduate education programs in higher education. For example, event history analysis (EHA) has become the standard technique for use in studying many forms of governmental behavior. Essentially a logistic regression technique applied to grouped data over time, EHA examines both whether and when a particular event occurred, permitting the analyst to make inferences concerning the influence of certain independent variables on the duration and timing of an event (e.g., when a state adopted a particular policy). For those studying state-level policy phenomena, one distinct advantage of the technique is that the co- efficient estimates EHA generates can be used to calculate proba- bilities that a state with certain attributes will adopt a policy in a given year (Berry & Berry, 1990; Box-Steffensmeier & Jones, 1997).

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Although the technique has found application in numerous studies of college student careers (see DesJardins, 2003), only a handful of studies thus far have used EHA in examining state-level policy outcomes for higher education (Doyle, 2005; Doyle et al., 2005; McLendon et al., 2006). Other analytic techniques might also be appropriate, depending on the nature of the outcome studied (e.g., Nicholson-Crotty & Meier, 2003). Yet, thorough grounding by higher education specialists in event history analysis and in other techniques similarly well suited for analyzing the behavior of state governments over time seems essential.

Conclusion

Understanding why states do what they do is both an important and daunting conceptual and analytic challenge. The data easiest to acquire often have proven inadequate to the task. Socioeconomic and structural characteristics of states, for example, fall far short of providing adequate explanations of the adoption of certain governance, accountability, and finance reforms in higher education. Veteran political observers and lead- ers rarely describe activity in their states without emphasizing the role of specific political institutions, actors, and processes. Trying to squeeze meaningful inferences out of models downplaying or ignoring ‘‘politics’’ is, for insiders, naive at best. Political factors are decidedly not easy to incorporate into modeling, but they are crucial to valid and reliable analy- sis of state-level policy phenomena.

Yet, it is important to think of policy development holistically. Years of trying to explain policy by emphasizing such indirect factors as struc- tural conditions and socioeconomic contexts have shown that, in this case at least, those factors are not enough. Similarly, ‘‘street-level’’ political analysis seems inadequate to the task of understanding why some states move toward certain policy choices and others don’t—how, for example, would such factors account for the fact that merit-scholarship programs have emerged mainly in the Southeast? What, then, is a minimal set of factors required for productive consideration of the phenomenon at hand? In the hard sciences, the standard for quality scholarship is parsi- mony, sometimes termed elegance and sometimes termed, more prosai- cally, simplicity. One aims for nothing less than the most straightforward possible explanation of a phenomenon. Clearly, a minimal set of factors for explaining policy development must include elements of the struc- tural, the socioeconomic, the political, and the regional contexts. To focus solely on one approach is to ignore the demands of good science. Ideally,

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this chapter presents a first step toward an appropriately inclusive ap- proach. For many higher education scholars, the approaches outlined in this chapter might lie in new territory, but it is territory that must be entered.

Notes

1. Consider as evidence the advent of new college-financing policies (e.g., merit- scholarship programs), accountability mandates (e.g., performance funding), governance regimes (e.g., charter colleges), and admissions policies (Top-10 percent plans).

2. Of course, there are also many differences among the states’ postsecondary systems and policies, encompassing a state’s total postsecondary enrollment and its distribution among two- and four-year institutions and the public and private sectors, a state’s total number of institutions and its distribution among two- and four-year institutions and public and private institutions, the nature of the state’s governance system, the pricing of colleges and universi- ties, and so forth.

3. The three leading explanations for emulative influences are that states look to their neighbors 1) as a shortcut in decision making, 2) so they can gain a competitive advantage or avoid being disadvantaged relative to their peers (e.g., the ‘‘race to the bottom’’ in state welfare benefits), and 3) because of competitive electoral pressures from within.

4. Year-to-year changes in the economic health of a state might be measured as three-year average change in gross state product, tax revenues, or unemploy- ment levels.

5. Elazar’s three major subcultures are moralist (predominant in New England, the upper Midwest, and the far West), individualist (comprising the Mid- Atlantic and lower Midwest regions), and traditionalist (primarily the South and Southwest).

6. Researchers sometimes have placed statistical controls on ‘‘region,’’ reasoning informally along the same lines as Elazar, if without explicit link to the partic- ular ideas underlying Elazar’s work.

7. Similar arguments appeared previously in work by Zumeta (1996). 8. Lowry classified states as practicing one of several basic models of statewide

governance of higher education, based on the McGuinness (1997) typology. 9. One advantage of this approach is the availability of the McGuinness gover-

nance typology, which could be used to assign states values representing the degree of control by governors.

10. But see Dometrius’s (1979) critique of the Schlessinger index. 11. See also Nicholson-Crotty and Meier (2003) and Volkwein (1987). 12. Divided government refers to the condition that exists when the legislative

and executive branches of a state are held by different political parties, or when one legislative chamber is held by one party and the other chamber and the executive are held by the other party. Much research has arisen on the

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policy effects of divided government (Alt & Lowry, 2000; Huber, Shipan, & Pfahler, 2001).

13. This index, developed in the 1970s by Austin Ranney, uses three different components to measure partisan control of government: proportion of elec- toral success, duration of success, and frequency of divided control. A score of 0 indicates complete Republican control and a score of 1 indicates complete Democratic control (Ranney, 1976). As noted, the index has subsequently been recalculated to capture a variety of other political phenomena in the states.

14. Recently, Holbrook and Van Dunk (1993) developed a measure of electoral competition, which includes an index based on district-level, state legislative election outcomes. Their district-level measure has been shown to be a better predictor of policy in states than the Ranney index.

15. The most recent assessment, in 2003, placed colleges and universities as the 20th most effective individual interest overall. This aggregate score is some- what misleading; colleges and universities ranked among the ‘‘least effective’’ interest in more than one-half of the states.

16. The existence of the two measures raises the possibility of interactions: To what extent do powerful individual interests in weak overall interest-group systems shape policy differently than do singularly powerful groups operat- ing in strong overall group systems?

17. Gray and Lowery have argued that this ratio is the most appropriate measure of group strength because it captures the ‘‘average economic base behind in- terest organizations in a state’’ (1996, p. 89).

18. Data on higher education’s share of the state workforce is available in the Book of the States.

19. Some older diffusion approaches pursued so-called leader-laggard models, whereby dates of adoption are factor analyzed to determine which conditions lead states to adopt policies earlier in time than other states.

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