Higher Education in Review
The Politics of State Higher Education Funding David A. Tandberg
The Invisible Immigrants: Revealing 1.5 Generation Latino Immigrants and Their Bicultural Identities Holly Holloway-Friesen
The Spellings Commission on the Future of Higher Education: Global Competitiveness as a Motivation for Postsecondary Reform Casey E. George-Jackson
A Phenomenological Study of How Selected College Men Construct and Define Masculinity Jerry L. Tatum & Ralph Charlton
Getting It Almost, Approximately, Just About Right
Patrick T. Terenzini & Ernest T. Pascarella
Volume 5 2008
Volume 5 2008
Higher Education in Review
The Politics of State Higher Education Funding David A. Tandberg
The Invisible Immigrants: Revealing 1.5 Generation Latino Immigrants and Their Bicultural Identities Holly Holloway-Friesen
The Spellings Commission on the Future of Higher Education: Global Competitiveness as a Motivation for Postsecondary Reform Casey E. George-Jackson
A Phenomenological Study of How Selected College Men Construct and Define Masculinity Jerry L. Tatum & Ralph Charlton
Getting It Almost, Approximately, Just About Right
Patrick T. Terenzini & Ernest T. Pascarella
Volume 5 2008
Volume 5 2008
The Politics of State Higher Education Funding David A. Tandberg The Pennsylvania State University An analysis of the theoretical and empirical connections between state funding for public higher education as a share of the total state general fund budget and various political attributes (e.g., interest groups, political ideology, voter turnout) of the U.S. states is presented in this article. Based upon data covering all 50 states over 24 years, most of the included political attributes are found to have significant, theoretically predictable effects on the share of state funding public higher education receives. The inclusion of politics in the explanatory model results in a more robust and pragmatically useful model than those that ignore the politics of the appropriations process.
Tandberg, D. A. (2008). The politics of state higher education funding. Higher Education in Review, 5, 1-36.
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Higher Education in Review The Politics of State Public Higher Education Funding
Higher education provides students with an opportunity for upward mobility and personal development. In addition, higher education provides states with an educated workforce and citizenry, as well as economic stimulation. A major factor in determining how well colleges and universities can achieve these outcomes is the fiscal resources of the institutions. State support for public higher education impacts both student access and educational quality, yet great variation exists in the level of funding states provide for public higher education. This topic has received much attention in the higher education policy literature, but as of yet we do not understand all the factors that affect state public higher education funding. The appropriations process is complex, and it is difficult to determine why one state is generous to public higher education and why another is not. Likewise it is difficult to explain year-to-year or long-term change within states. During times of economic stability and economic recession, variation exists in the amount of funding that states appropriate to public higher education. Understanding why this variation exists is an important step in the process of developing a theory of state public higher education funding. This paper attempts to fill a void in the literature by analyzing the political variables that have been largely omitted from studies of an inherently political process—state public higher education funding. This study examines the comparative features of state political systems and addresses the research question: Do the political processes, state political institutions, and the political context of a state affect the percentage of the state’s expenditures that is appropriated to public higher education? In addition, this study attempts to determine whether, and to what extent, those political features help to explain variations in state public higher education funding. Understanding the political antecedents of the funding process is a necessary precursor to influencing the appropriations process. This study has immediate implications for college officials, policymakers, and others interested in influencing state public higher education appropriations. By focusing on the political factors that may affect the appropriations process, this study highlights the variables that can be influenced. While studies that attempt to explain public higher education funding using demographic and economic variables have important theoretical and empirical use, they have less practical utility. In contrast, many of the political factors considered in this study can be altered and influenced by interest groups
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and policymakers. Therefore, if the variables are shown to impact state public higher education funding, people interested in increasing that funding will be given important knowledge about how to do so. From a national perspective, state spending for public higher education has been declining as a proportion of state general fund expenditures. States must support multiple programs and demands such as Medicaid, K-12 education, and corrections. Compounding the problem of competition for state dollars, state legislatures have a great deal of discretion over spending for public higher education. In fact, higher education is the largest area of state discretionary funding. Colleges and universities have the ability to use alternative forms of revenue, such as tuition and private fund-raising, which makes them attractive targets for state funding cuts during economic downturns (Delaney & Doyle, 2004; Hovey, 1999; Humphreys, 2000; Rizzo, 2005). Declining state support and increasing operating costs often result in higher levels of tuition. This cost, when coupled with the political popularity of merit-based student aid, has begun to limit student access to public institutions of higher education. Public colleges and universities have traditionally been at the mercy of economic cycles (Kane, Orszag, & Gunter, 2003). Politicians tend to cut funding during economic crisis but do not always return the funding once the crisis has passed. This habit has caused public higher education to face a continual relative decrease in funding for three decades. When the specific dependent variable for this study (the percentage of state general fund expenditures devoted to public higher education) is examined, the decline in state support for public higher education is presented in stark terms. As Figure 1 shows, the average percentage of state general fund expenditures devoted to public higher education declined by 37 percent nationally from 1974 to 2001. In 1974, public higher education received 9.4 percent of the general fund expenditures. By 2001, that number had been reduced to only 5.9 percent. This decrease is the direct result of rapidly expanding state expenditures driven, in large part, by Medicaid and other government programs (Boyd, 2005). Higher education funding has been shown to be more responsive to the business cycle than other state budget items. In fact, one study showed that a 1 percent increase in the unemployment rate amounts to a $3.80 per capita decline in state appropriations for public higher education. Further, a 1 percent change in per capita income has been associated with a 1.4 percent change in real state appropriations per full-time equivalent student (FTE) (Humphreys, 2000). Therefore, during economic downturns, public
Higher Education in Review
4 10%
9%
8%
7%
6% 1975
1980
1985
Year
1990
1995
2000
Figure 1. Higher Education Funding as a Share of State General Fund Expenditures. Note. From Illinois State University’s Grapevine System, http://www.coe. ilstu.edu/grapevine/; U.S. Bureau of the Census, Statistical Abstract of the United States: 1974-2002. higher education can expect less state funding. This effect is compounded when one considers that public higher education normally experiences an increase in enrollment during economic downturns. Consequently, this means that institutions must do more with less appropriations (often the decline is at least partially made up for by increases in tuition and other forms of revenue) (Betts & McFarland, 1995). Data indicate that state appropriations are covering less of the cost of public higher education. In 1974 state appropriations covered 78 percent of the cost of schooling, while in 2000 state support only covered 43 percent of the same costs (Rizzo, 2005). Likewise, in 1977, state appropriations represented 46.5 percent of public university revenue and by 1996 that percentage had fallen to 35.9 percent. As Thomas Kane and colleagues noted, if the 1977 share had continued, state appropriations would have been approximately $13 billion higher in 1996 (Kane et al., 2003). This relative drop in funding has been compensated for largely by increases in tuition. Over the past decade, tuition and fees at public four-
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year colleges have risen at an average rate of 6.9 percent, or 4.4 percent per year after inflation. Likewise, tuition and fees at public two-year colleges have risen at an average rate of 5.1 percent, or 2.7 percent per year after inflation. At public four-year institutions, relatively large tuition increases occurred in the early 1980s and again in the early 1990s, and the rate of tuition increase was higher in the early 2000s than in the preceding decades (The College Board, 2005). Based on state fiscal projections completed by Boyd (2005) for the National Center for Higher Education Management Systems, future state budgetary gaps are projected to affect public higher education negatively. Boyd’s report projects that public higher education expenditures will grow less rapidly than total state and local spending. Higher education spending for the nation as a whole is projected to grow 34.4 percent over the eightyear period of the report, which is considerably less than the 41.1 percent growth projected for total spending. In most states, therefore, public higher education will continue to face considerable competition for state spending from other state offices and programs. States may be forced to cut funding in one area to maintain or increase funding in another. In order to influence appropriations to public colleges and universities, the variables that affect the funding process should be fully understood by the higher education community. Through an analysis of public higher education funding from the political perspective, this study will provide researchers, policymakers, and students of higher education policy with a broader and deeper understanding of state public higher education funding. Conceptual Framework and Theoretical Arguments While most studies of higher education funding have used frameworks that emphasize socioeconomic and demographic variables, this study developed a framework that focused on the politics of the appropriations process. While the higher education literature has yet to adopt widely this approach, the political science literature has a long history of studying the politics of state budgeting. Therefore. this study benefits by building on this tradition and applying what has been learned to the issue of state support of public higher education. Since this study attempts to fuse the two literatures and to introduce political variables into a framework describing public higher education to the study of higher education, it presents a general conceptual framework of state funding for public higher education. It then introduces theoretical
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arguments and associated political variables that have been taken from the political science literature. Each of the theoretical arguments and the associated variables fits within the general conceptual framework. The Budgetary Process The procedural path of state budgeting for public higher education is fairly similar across most states. The budgetary procedural path is depicted in Figure 2. An agency, office, or institution makes a request to the governor before the governor prepares his or her final budget request for the legislature. The request is normally a percentage increase over the previous year’s appropriation. The governor adjusts the agency’s request to fit his or her budget priorities, generally reducing the request by a certain percentage, and sends the final state budget request to the legislature. The legislature then considers the amount requested for public higher education along with the amount requested for other state budget areas. State budgeting for other areas normally follows a similar path (Layzell & Lyddon, 1990; Sharkansky, 1968; Thompson, 1987). As in all governmental budgetary decisions, politics plays a role in state budgeting for public higher education. There are multiple political forces acting upon the budgetary process that may influence public higher education appropriations each year. Kiel and Elliot (1992) explain that a proper understanding of budgeting should take into account the relationships between relevant institutional actors and other exogenous forces. This study adopts such an approach by focusing on various institutional actors active within government that may affect levels of public higher education appropriations and the various exogenous political forces that may also influence public higher education appropriations. State policy priorities, defined as “the component of governmental decision-making in which public officials allocate scare resources, in the form of expenditures, to different program areas” (Jacoby & Schneider, 2001, p. 545), essentially constitute the budget process and can be conceptualized as a function of state legislatures (Barrilleaux & Berkman, 2003), public demands (Erikson, Wright, & McIver, 1993; Raimondo, 1996), interest group pressures (Gray & Lowery, 1988; 1996), bureaucratic procedures (Barrilleaux, 1999; Elling, 1999), executive power and proposals (Barrilleaux & Berkman, 2003; Beyle, 1996), and the political culture of the state (Elazar 1984; 1972; 1966). The conceptual framework for this study places state funding for public higher education within this competitive political context and is depicted in Figure 3. The variables
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Constituents’ Requests/Needs
Agency Request s
Governor’s Budget
Legislative Consideration and Appropriations Bill Passage
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Governor Signs Appropriations Bill
Figure 2. State Budgetary Process.
listed above are the political factors identified that play a role in the state appropriations process. Variations between state political systems have been shown to affect the outcomes of state budgetary processes. This study extends this notion to hypothesize that variations in state political systems will specifically affect the proportion of state spending that is directed toward public higher education. The conceptual framework places various aspects of state political systems, each theoretically affecting the proportion of the state budget that public higher education receives, into two categories: internal structural political factors and external environmental political factors. The internal political factors include budgetary powers of the governor, party of the governor, party of the legislature, legislative professional, unified institutional control, and public higher education governance structures. The external political factors include electoral competition, public opinion, state political culture, interest groups, and voter turnout. The factors that are included in this study have been conceptualized and measured in several ways. This study seeks to emphasize the particular aspects of each factor that have the greatest potential to influence state funding for public higher education as a percent of the state’s expenditures. The choice to use funding as a percent of the state’s expenditures was intended to capture the dynamics of the political process through which budget makers manifest their priorities through the appropriation of certain percentages of the state budget to different policy areas. It also captures the competition that exists between possible funding priorities. In addition, by using this dependent variable, the study will begin to explain the political dynamics of the documented trade-offs between higher education and other policy areas (e.g., Medicaid). The specific theoretical and conceptual arguments for each political variable that may affect state funding for public higher education are discussed next. Since these concepts, theory, and variables are relatively new to the area of higher education research, substantial discussion is
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Higher Education in Review
Figure 3. State Public Higher Education Budgetary Picture.
included here. The economic, demographic and higher education sector variables are discussed as well. Internal Political Factors Budgetary powers of the governor. Powers of the governor refers to the ability of the governor to influence the political and budgetary processes in his or her state. These powers may be institutional, statutory, or constitutional. Research in higher education has repeatedly shown the importance of the governor in state higher education policy making (Heller, 2002; Marcus, 1997; McLendon, 2003a, 2003b; McLendon & Ness, 2003). In his 1976 study, Peterson found that greater institutional powers of the governor were associated with increased higher education appropriations. Since this study is concerned with the allocation of state resources, a measure of the governor’s budgetary powers is an appropriate measure of the powers of the governor. Barrilleaux and Berkman (2003) developed
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a budget powers index to measure the relative power of the governor versus the legislature over the state budgetary process. There is evidence to suggest that governors with greater budgetary powers would divert funds away from public higher education and toward other redistributive policy areas (Garand & Hendrick, 1991; Hendrick & Garand, 1991). For example, Hendrick and Garand (1991) found that governors with greater powers were more willing to engage in expenditure tradeoffs. Hendrick and Garand theorized that tradeoffs were more likely to occur when decision making is centralized, as opposed to fragmented and decentralized, and that decision makers in centralized decision environments are better able to coordinate the reciprocal changes in spending priorities that are implied by the tradeoff concept. They further argued that governors with greater powers provide the best opportunity for centralized budgetary and expenditure decision making, particularly in comparison to relatively decentralized legislative bodies. Therefore, governors with strong powers (budgetary or otherwise) are in a better position to coordinate spending decisions across expenditure categories to get their proposals enacted into law. They are also more able to make the necessary tradeoffs to accomplish their objectives. Thus, tradeoff behavior is expected to be more common and greater when governors have stronger budgetary power. Consequently, because higher education is particularly susceptible to tradeoffs, greater budgetary powers of the governor may be associated with less funding for public higher education. Party of the governor. Another variable that may influence state public higher education appropriations is the party of the governor. Studies have shown a relation between party strength in governmental institutions and the policy posture of the state. For instance, market-oriented policies have been associated with Republicans and greater spending on education has been associated with Democrats (McLendon, Hearn, & Deaton, 2004). Subsequently, changes in spending priorities have been associated with shifts in partisan control of the governorship (Garand, 1985). Alt and Lowry (1994) found that Democrats tend to tax and spend more. Other studies on the effect of party control of the executive have, however, produced mixed results, most likely due to their use of different measures of state support of higher education (Archibald & Feldman, 2004; Bailey, Rom, & Taylor, 2004; Kane et al., 2003). From a theoretical perspective, the idea of tradeoffs provides reason to hypothesize that a Democratic governor may be associated with more funding for public higher education. As noted above, greater spending
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and taxing has been associated with Democratic control. As spending and taxing is expanded, there may be less need for policymakers to engage in budgetary tradeoffs between public higher education and other areas. Therefore, Democrats may create situation where budgetary tradeoffs are less needed. Party of the legislature. Kane et al. (2003) found that increases in higher education appropriations as a percent of gross state product (GSP) and per capita were significantly associated with Democratic control of the house and senate. Archibald and Feldman (2004) found that the effects of having a Democratic majority in the lower house had the same effects as having a Democratic governor, although having a Democratic majority in the upper house was consistently associated with increased funding for public higher education. These studies suggest that having a Democratic majority in the legislature will be associated with increased appropriations for public higher education. As noted previously, however, greater spending and taxing have been associated with Democratic control. Therefore, tradeoffs may be less likely as spending and taxing is expanded and there is less reason to draw from public higher education to fund other areas. Legislative professionalism. In line with their arguments about executive budgetary powers, Barrilleaux and Berkman (2003) argued that legislators are compelled by their constituents to pursue geographically concentrated distributive benefits (i.e., provide aid or resources to certain geographic areas, such as where their constituents live, to benefit as many people as possible in those areas). The authors further argue that “as legislatures become increasingly professionalized, the value of a seat increases and members have stronger incentives to produce benefits that are apparent to the voters at home than do members in less professionalized legislatures” (p. 412). Barrilleaux and Berkman’s (2003) results confirmed this hypothesis. Since state governments treat higher education as a redistributive policy area, and the benefits of funding higher education are geographically diffuse, more professional legislatures may be associated with less spending for public higher education. Further, because making tradeoffs involves making difficult decisions and negotiating different demands, more professionalized legislatures, with possibly greater power, may be more able to make tradeoffs to balance the budget and benefit their constituents. Unified institutional control. Unified institutional control refers to the same party controlling both houses of a legislature. It can also refer
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to one party’s control of both the executive and legislative branches of government. Unified institutional control has been associated with greater policy innovation because it removes partisan road blocks, and it has been linked to both tax policy adoption and K-12 educational reform (Hansen, 1983; Mintrom & Vergari, 1998). Rizzo (2005) found that uniparty governments, regardless of party, preferred to fund K-12 education as opposed to higher education. This result is understandable because the legislature and the governor are often inclined to do what is politically popular and when they are from the same political party they are more able to do so (Mintrom & Vergari, 1998). Increasing state funding for K-12 education tends to be a well-received initiative and frequently comes at the expense of higher education. Unified state governments may also be more willing to cut, or at least not increase, funding for higher education because they are more able than divided governments to react to exogenous shocks. When faced with income shocks, for example, unified governments react quickly by adjusting state spending priorities, but divided governments cannot. Likewise, unified governments may be more able to reduce or limit public higher education’s appropriation in order to fund the growing demand of Medicaid. Governance structures. More professional state agencies and agency heads (agencies that have more resources) have been shown to be more successful in budgetary matters (Thompson & Felts, 1991). Likewise, agencies have become far more assertive in arguing for increased state appropriations (Thompson, 1987; Thompson & Felts, 1991; Wilson & Sylvia, 1993). This assertiveness has implications for the budgetary process because the amount the agency requests from the state government has been shown to have a significant impact on the amount appropriated to the agency (Sharkansky, 1968). In addition, agency administrators have been shown to be among the most successful lobbyists within state political systems and to influence state spending priorities (Elling, 1999; Gormley, 1996; Jacoby & Schneider, 2001). Each state (except for Michigan) has some form of governance structure for higher education. The specific structure employed and the power granted to the structure varies from state to state, however. McGuinness (2003) developed a four-fold state governance typology which is as follows (in descending order of strength of control): consolidated governing board, regulatory coordinating board, weak coordinating board, and planning agency (McLendon, Heller, & Young, 2005). Theoretically, a more powerful centralized board would have more
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resources and more influence within state government. In other words, the greater the centralization, the more influence the structure has over the higher education institutions. Furthermore, the more centralized the board the greater autonomy the board has for developing state higher education policy. Centralized boards may have more influence during the appropriations process because of their greater power. Therefore, states with centralized higher education governance structures may appropriate more money to higher education than states with less centralized governance structures. In addition, centralized governance structures may be better able to protect higher education from tradeoffs. Lowry (2001) looked at a related issue and found that states with more governing boards (more in number) appropriated less money to public higher education. Lowry also found that states with more governing boards generally have a less centralized state governance structure. Consequently, Lowry concluded that it benefited higher education to speak with one voice. External Political Factors Electoral competition. This study makes use of Holbrook and Van Dunk’s (1993) measure of electoral competition. Greater electoral competition has been shown to result in an increased generosity by legislatures towards redistributive policies (Barrilleaux & Berkman, 2003; Plotnick & Winters, 1985). Plotnick and Winters (1985) concluded, “Probably the best known link in comparative state politics is between two-party competition and redistribution” (p. 463). When states are highly politically competitive, political leaders will vie for backing by offering services and support to as wide a range of constituents as possible, thereby causing them to favor redistributive policy areas, which cover a wider range of constituents. Since higher education offers diffuse benefits and is viewed by policymakers as a redistributive area, greater electoral competition should result in more funding for higher education. Further, as policymakers attempt to bring benefits to as many groups and individuals as possible, they may avoid making tradeoffs, therefore protecting public higher education from such actions. Public ideology. Erickson, Wright, and McIver (1993) argue that state policy is influenced by public ideology. The influence of public ideology on welfare policy has been demonstrated numerous times (e.g., Brown, 1997; Erickson, Wright, & McIver, 1993; Fellows & Rowe, 2004; Ringquist, Hill, Leighley, & Hinton-Anderson, 1997). Using Berry, Ringquist, Fording, and Hanson’s (1998) measure of state ideology, Archibald and Feldman (2004) found that more liberal states were more generous towards higher
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education. Further, Hendrick and Garand (1991) theorized that because the demand for public goods may be greater in states with liberal ideologies, and because there is a greater amount of spending in these states, tradeoffs would be more difficult and less likely. Their findings were mixed. Since public higher education is particularly susceptible to negative tradeoffs, however, there is good reason to expect less ambiguous results in the present study. Political culture. Elazar (1984) developed what is perhaps the most popular classification or typology of state political culture. He developed his measure by analyzing the historic migratory patterns of ethnic and religious groups between states and the general state orientation towards public policy. Elazar defined political culture as the “particular pattern of orientation to political action in which each political system is imbedded” (p. 79). According to Elazar, the orientation may be found among the mass and political elite, affect their understanding of what politics is and what can be expected from government, influence the types of people who become active in politics, and influence the way in which politics is practiced. Elazar classified states into three different types: individualistic subcultures, moralistic subcultures, and traditionalistic subcultures. His typology theorizes that the individualistic subculture emphasizes the marketplace and a limited role of government. The moralistic subculture promotes the commonwealth and expects government to advance the interest of the public. The traditionalistic subculture expects the government to maintain the existing social and economic hierarchy, and governance remains an obligation of the elite rather than the ordinary citizen. Several studies have found that state political culture influences a state’s approach to and spending on social programs and, in particular, education (French & Stanley, 2005; Gittell & Kleiman, 2000; Koven & Mausolff, 2002). Most often, greater funding for K-12 education was associated with moralistic and individualistic subcultures and less with traditionalistic subculture. A moralistic state promotes education because it is viewed as a public good. An individualistic state promotes education because it stimulates economic activity and provides individuals with the opportunity to get ahead economically. On the other hand, a traditionalistic state may not want to promote education because it threatens not only the status quo but also the elites’ place in society. The same theoretical argument could also be made for higher education funding. Therefore, increased funding for higher education may be associated with moralistic and individualistic states. As moralistic states tend to spend more, they may also be less likely to engage in tradeoff behavior.
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Interest groups. Since higher education institutions must compete for increasingly scarce and contested resources, they are called upon frequently to defend their autonomy and their use of limited state funds (Sabloff, 1997). One way in which they accomplish this task is through lobbying. Interest groups have been shown to affect state policy and state spending (Gray & Lowery, 1996; Heinz, Laumann, Nelson, & Salisbury, 1993; Jacoby & Schneider, 2001; Nice, 1984). Gray and Lowery (1996) argued that if we are to discern the real influence of special interests, “we need to examine specific interests at specific times in specific places” (p. 241). The authors conclude, “When such [organized] interests, as well a government interests, add their weight to efforts to pass legislation, it has a greater likelihood of passage, all other things being equal” (p. 242). All public institutions engage in some form of lobbying (Ferrin, 2003, 2005; Gove & Carpenter, 1997; Murphy 2001). Most universities have an in-house lobbyist, or they contract with an outside lobbyist. Many, if not most, large public universities have an office of government affairs that lobbies at the state and federal level. At the state level, one of the office’s primary responsibilities is to lobby for increased state funding (Tandberg, 2006). Even if the institution does not have an office or individual responsible for lobbying, as is the case for some smaller institutions, presidents frequently take that role upon themselves, as do others within the institution, including students. Although there is no available research on how effective the state public higher education lobbies are at garnering additional funding, the existing lobbying and interest group literature suggests that the larger the lobby is relative to the rest of the state lobby, the more effective it will be in accomplishing its legislative goals. Research has found that interests groups compete with each other (Heinz, et al., 1993; Truman, 1951). Therefore, when it comes to state funding, the larger a specific interest area is relative to the rest of the state lobby, the more successful it should be in procuring state dollars. This argument is consistent with Jacoby and Schneider’s (2001) finding that when there are fewer interest groups and less diversity, specific interests receive more funding. Further, recent literature has stressed that interest groups are most successful when there are relatively few of them within a state, when the groups are concentrated in particular substantive areas, and when the active interests possess economic power (e.g., Browne, 1990; Cigler, 1991; Gray & Lowery, 1996; Heinz et al., 1993). These findings appear to indicate that a concerted effort on the part of public higher education institutions should have a positive impact on funding.
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Voter turnout. Voter turnout influences politicians’ perception of and attention to their constituents (Bibby & Holbrook, 2004; Bowler & Donovan, 2004). With greater voter turnout, elected officials may become more responsive to constituents’ needs and desires. Citizens are generally supportive of higher education. Moreover, with rising awareness of the link between increasing tuition and decreasing state funding (Dillon, 2005a, 2005b), the general population may grow more supportive of greater funding for higher education. If so, policymakers in states with greater voter turnout may feel compelled to appropriate more money to higher education. Sharkansky (1968) found a positive correlation between governors accepting agency requests for budget expansion and high voter turnout. Governor support of agency requests was essential for success in the legislature. Subsequently, voter turnout may also have an indirect effect on the level of appropriation by influencing the budgetary process at the early stage of the agency request. In fact, an early study covering 1960-1970 found a statistically significant positive correlation between voter turnout and state support of public higher education funding per capita (Lindeen & Willis, 1975). Variable Descriptions The factors that affect state budgeting for public higher education are many and often un-measurable. A researcher could continue to add variables until the model became unwieldy, but a parsimonious model should focus on only the most critical variables. The decision about which variables to include in this study was guided by the conceptual framework, past studies of state support of higher education, research from political science on state budgeting, and the availability of data. Future studies may build on this work by incorporating additional variables as suggested by new findings, building our understanding of this complex process. The dependent variable for this study is appropriations as a percentage of state general fund expenditures. The rationale for using this dependent variable is that it may capture the political dynamics of the state budgetary process. Two of these dynamics are critically important. First and directly related to the dependent variable, states are required to balance their budgets. Therefore, an increase in one area often necessitates a decrease in another area because of the reluctance of state policymakers to increase taxes. This dependent variable is an attempt to capture that trade-off.
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Second, the decision regarding who gets how much funding is a political one involving interests groups, give-and-take, different individual actors with their own interests and attributes, and numerous other factors. There are other ways of measuring state support of public higher education such as actual dollars allocated. Use of actual dollars as an outcome measure would not, however, control for other state budgetary areas nor capture the dynamics of the budgetary process. The control variables can be placed into three different categories: economic and demographic variables, other state budgetary area variables, and higher education context control variables. The economic and demographic control variables are drawn from past studies of state public higher education appropriations and have been shown to have a significant effect on the amount appropriated to public higher education (Archibald & Feldman, 2004; Kane et al., 2003; Rizzo, 2005). The specific variables included are (for source and description, see Appendix A; for summary statistics, see Appendix B): income inequality, share of population greater than 65 years old, share of the population age 18-24, tax revenue per capita, unemployment, total government transfers, federal government transfers, gross state product per capita, ratio of non-White college population and non-White K-12 aged population, and proportion of students below Pell Grant level. The state political context is made up of a variety of political variables including: budgetary powers of the governor, party of the governor and the legislature, legislative professionalism, state higher education governance structure, unified institutional control, electoral competition, state ideology, state political culture, state interest group activity, and voter turnout. The competing state interests construct control variables will include: spending on Medicaid, spending on other medical programs, court mandated K-12 finance reform, and spending on K-12 (Kane et al., 2003; Rizzo, 2005). The measures of the higher education context are those traditionally included in higher education policy research, which have been shown to influence the state funding of higher education (McLendon, 2003a, 2003b; Rizzo, 2005). The variables are: if a state uses a higher education funding formula, regional nonresident tuition, share of enrollments in private higher education, in-state tuition lagged one year, proportion of state merit aid, 1-period lagged dependent variable, and giving per student ($1,000). Some of the internal political variables deserve specific mention with regard to their construction beyond what was discussed in the conceptual framework section. The budget powers index of the governor developed
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by Barrilleaux and Berkman (2003) is a scale composed of seven items. The total score can range from 0 to 7. The seven items include: 1) whether the governor shares budgetary responsibility or has sole responsibility; 2) whether state agencies make requests directly to the governor or the legislature; 3) whether the executive budget document is the working copy for legislation or if the legislature can introduce budget bills of its own, or whether or not the legislature or the executive introduces another document later in the process; 4) whether revenue estimates are made by the governor, the legislature, or another agency or the process is shared; 5) whether revenue revisions are made by the governor, the legislature, or another agency or the process is shared; 6) whether the governor has the line-item veto; and 7) whether the legislature can override the line item veto with a simple majority. This variable is cross-sectional (budgetary powers of the governor was found to be fairly consistent over time). Holbrook and Van Dunk’s (1993) measure of electoral competition is based on several indicators of district level competition. The first indicator is the percentage of popular vote won by the winning candidate; second, the winning candidate’s margin of victory; third, whether or not the seat is “safe” (the authors conceptualize “safe” as a seat that is won by 55 percent of the popular vote or more); and fourth, whether or not the race was contested. Complete absence of competition is indicated by a score of zero. The scale ranges from 0 to 100, although a score of 100 is theoretically impossible as long as someone won the election. Erickson et al.’s (1993) state ideology measure is based on CBS/ New York Times surveys that have been collected since 1976. Ideology is assessed by the respondents’ answers to the following questions: “How do you describe your views on most political matters? Generally, do you think of your self as liberal, moderate, or conservative?” (p. 14). The number of respondents per state has been built up over time and, therefore, each state has an adequate respondent pool or large enough sample size (n), for statistical analysis and significant results. In addition, the respondents were fairly representative of each state’s population. The instrument does, however, tend to be slightly more representative of the active electorate rather than the population at large. It also captures shifts in state ideology over time. For this study, Elazar’s (1984) culture types are applied using Sharkansky’s (1969) numerical rating. These are cross-sectional data. The cultural types and Sharkansky’s ratings have been shown to be fairly consistent over time, and Sharkansky’s rating scale has been used
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Higher Education in Review
successfully in previous research (e.g., Fitzpatrick & Hero, 1988; Koven & Mausloff, 2002; Morgan & Watson, 1991). The scale assigns each state a culture rating on a scale ranging from 1 to 9. In this scale, 1 is a pure moralistic culture, 5 is a pure individualistic culture, 9 is a pure traditionalistic culture, and the values in between represent states with combinations of culture types (e.g., 2 indicates a very moralistic culture with very few attributes of a individualistic culture, and 8 represents a very traditionalistic state with few individualistic attributes, and so on). Legislative professionalism is measured using legislative salary, which has been found to be highly correlated with other measures of professionalism (Barrilleaux & Berkman, 2003). The state public higher education interest group density variable is constructed by dividing the total number of public higher education institutions by the number of state interest groups. This is consistent with Gray and Lowery’s (1996) construction of their interest group density measure. For variable names, descriptions, and sources, see Appendix A, and for general descriptions, see the appropriate subsection in the Conceptual Framework section. For summary statistics, see Appendix A. Research Design, Methods, and Analysis The study employs a pooled, cross-sectional times-series analysis. This approach is capable of developing a more powerful and accurate predictive model than a simple cross-sectional design because multiple states are examined over multiple points in time. This approach enables the researcher to increase the sample size and the predictive power. This study will use existing data on all fifty states from 1977 to 2001 to capture several recessions and recoveries. Because each state-year serves as the unit of analysis for this study, the data set consists of 1,200 observations (50 states by 24 years). Generalization is not an issue in this study because all 50 states are included. Instead, the issue is the predictive and explanatory power of the model, which is why the analysis covers such a large time period of 24 years. A general cross-section time-series model is as follows: yit = a + b1xit + b2xit + ui + vit In this model, y is the dependent variable, x represents the various independent variables, a is the intercept coefficient, b1 represents the coefficients for the various primary independent variables, b2 represents the coefficients for the various control variables, and i and t are indices for
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Table 1. Public Higher Education Appropriations as a Percent of State General Fund Expenditures Variable Coefficient and Standard Error Party of the Governor 7.722* (4.397) Party of the Legislature .1.343*** (0.297) Unified Institutional Control -10.663** (4.364) Electoral Competition 1.294*** (0.391) Higher Education Interest Group Density 178.438** -58.377 K-12 Education (in millions) -.00446** -0.000166 Medicaid -.0135*** (0.00325) Health Costs (medical CPI*share of pop > 65 years old) -.2.615*** (0.388) Medical CPI -4.036*** -0.786 Income Inequality 4.946** (1.75) Unemployment Rate -.4.2** (1.384) Proportion of Population below Pell Grant level -.398*** (0.116) Ratio of non-White college age pop. to non-White K-12 age pop. 13.163*** (3.704) Gross State Product Per Capita .00126*** (0.000332) Share of the Population College Age -.000152*** (0.0000342) Share of the Population > 65 Years Old -26.155*** (3.697) Regional Average 4-year Institution Tuition .00772* (0.00457) Share of State Enrollments in Private Higher Education 1.827*** (0.349) Lagged Dependent 1 year 2481.495*** (248.99) Total Giving to Public Research Univ. per FTE -.0228*** (0.00316) In-State Tuition Lagged 1 year -.0135*** (0.00386) R2=.79 (within) *.1 significance, **.5 significance, ***.01 significance Independent Variables
Note. Only variables with significant regression coefficients are included in the table.
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Higher Education in Review
individual states and time. The error terms, ui and vit, are particularly important in this analysis. That is, the ui is the fixed, between, or random effect, and the vit is the pure residual. Assumptions about the first error term determine whether the model is a fixed-effects, between-effects, or random-effects model. Fixed-effects models control for omitted variables that differ between states but are constant over time. They allow the researcher to use the changes in the variables over time to estimate the effects of the independent variables on the dependent variable. Between-effects models control for omitted variables that change over time, but are constant between states. They allow the researcher to use the variation between states to estimate the effect of the omitted independent variables on the dependent variable. Random-effects models are used if the researcher has reason to believe that some omitted variables may be constant over time but vary between states, and others may be fixed between states but vary over time. Statistically, fixed effects are always assumed to be a reasonable model when dealing with panel data because they always give consistent results, but they may not be the most efficient model to run. Random effects will result in more accurate P-values as they are a more efficient estimator (Princeton University Library, 2006). Both random effects and fixed effects models were run and a Hausman test conducted in order to conclude which model would serve as the primary model. The results (χ2 = 907.98, df=26, p < .001) indicated that it was not safe to use a random-effects model, so the analysis relies on the results of the fixed-effects model (Princeton University Library, 2006) (see Appendix C for a detailed report of these results). As Table 1 shows, the fixed-effects model reported a within R2 of .79, which indicates that the model explains most of the variance in state support of public higher education as a percent of state general fund expenditures.1 Findings Primary Independent Variables In regard to the primary independent variables, the results are generally satisfactory and consistent with the study’s arguments (see Table 1). The small coefficients are understandable as the dependent variable is a ratio term, and they are relative to the size of the dependent variable, which has a range of .0211 to .1528. What is most interesting is that, with the exception of the previous year’s funding ratio, the political variables, on the whole, are the primary predictors of current year funding ratios. In fact, 1
All analyses were conducted using STATA SE 8.0.
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even with the removal of the lagged dependent variable and the interstate competition variables, the model still reports an R2 of .72. While past studies have shown public higher education funding to be primarily predicted by state economics, demographic variables, and competition from other state funding areas, this study highlights the influence of politics on the process. The difference between these findings and the findings of past studies may be due in part to the dependent variable. While past studies have looked at funding per capita, per FTE, and per $1,000 of personal income, this study attempted to isolate the politics of the process and, based on the results, did so fairly successfully by using state support of public higher education as a percent of state general fund expenditures. In most cases, the findings of this study do not necessarily negate or even challenge the findings of past studies of higher education funding. To the contrary, these findings elucidate an element of the process previously ignored (the politics) and, in most cases, support the findings of past studies. While past studies of state higher education funding have produced mixed results with regard to the effect of party control, the results of this study indicate that having a Democratic governor and a Democraticcontrolled legislature results in more funding for public higher education relative to other state expenditures. These findings confirm the study’s hypothesis and seem to indicate that Democrats are either less willing to sacrifice higher education appropriations to fund other areas, or that they are willing to raise taxes and are therefore less likely to engage in budgetary tradeoffs. This finding seems to indicate that ideology plays a part in shaping policymakers’ appropriations decisions. Having a unified state legislature appears to result in less funding for higher education relative to other state expenditures. This finding was both significant and in the predicted direction. Legislatures often desire to deliver localized benefits to their constituents, and when one party controls both houses, they are better able to do so. Having a unified legislature appears to remove at least some of the roadblocks, thus enabling legislators to accomplish more of their legislative goals. Generally, unified governments have been more generous towards K-12 education relative to higher education, and they are more able to react to times of income shock by cutting areas such as public higher education and engaging in tradeoff behavior (Alt & Lowry, 1994). The results also indicate that states with greater electoral competition are more generous toward higher education than states with less competitive
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Higher Education in Review
elections. This finding was both significant and in the predicted direction. The results support the hypothesis that when states are highly competitive, political leaders will vie for support by offering services and support to as wide a range of constituents as possible, thereby causing them to favor redistributive policy areas which cover a wider range of constituents. These circumstances may cause legislators to avoid tradeoff behavior. An interesting finding concerns state interest group ratio. As is shown in Table 1, interest group ratio has a significant and positive effect on the proportion of government expenditures devoted to public higher education.2 Aside from the lagged dependent variable, the interest group ratio variable has the largest independent effect on the dependent variable. Therefore, the relative strength of the public higher education lobby to the rest of the state interest group lobby is an important factor in determining the relative amount of state expenditures devoted to public higher education. This finding is compelling in light of the fact that the state higher education governance structures variable is not significant. It would seem that having one agency lobbying for public higher education may not be as effective as having many individual institutions all asking for more money for public higher education. This finding seems to indicate that lobbying by individual institutions is important and effective. What is clear is that the larger the public higher education lobby is relative to the rest of the state lobby, the more likely public higher education will be treated favorably at appropriations time.3 Likewise, these findings provide additional
evidence of the importance of state interest groups in state policy making and specifically budgeting. 2
As indicated earlier, some universities have offices that are registered to lobby on their behalf. Therefore, in some cases, universities are counted on both sides of the ratio equation.
3
Two other models were used, one in which the interest group ratio variable was replaced with the number of public institutions and another in which the interest group ratio variable was replaced by number of interest groups. In both cases they had significant, though somewhat small, negative effects. These findings tend to support the findings in regard to the interest group ratio variable. It may be surprising that number of public institutions had a negative effect, however, it should be remembered that the dependent variable is the percentage of state expenditures devoted to higher education. States with more public institutions are generally the larger states with more general expenditures. Larger states may or may not spend more on higher education relative to their total state general fund expenditures.
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Unfortunately, because a fixed-effects model was the safest approach, the cross-sectional variables were all dropped since they do not vary over time, and one cannot make accurate inferences as to their effect. The dropped variables included two of the political variables (budgetary powers of the governor and political culture), if a state has court mandated education reform and whether a state uses a funding formula for higher education. It is difficult to assess the effect of losing these variables. It is possible that their exclusion inflates the coefficients of some of the other measures, and it is also possible that the overall contribution of some of the categories of variables (e.g., internal political factors and external political factors) is not accurately accounted for. Control Variables As noted earlier, while past studies have shown public higher education funding to be primarily predicted by state economic variables, demographic variables, and competition from other state funding areas, this study has revealed the political side of the process. The findings of this study do not necessarily negate or even challenge, in most cases, the findings of past studies of higher education funding. The fact that economic, demographic, and competing state interests variables had any significant effect on the percentage of state expenditures devoted to public higher education supports past findings. The findings of this study give further evidence of the differential support lent to public higher education by state policymakers. If government officials treated public higher education as they treat the rest of the state budgetary areas, there would be no effect because the various economic, demographic, and competing state interests variables would affect both sides of the equation equally (total state general fund expenditures and appropriations to public higher education). The medical-related variables all have a significant negative effect and the rest of the findings are consistent with past studies and highlight the differential treatment public higher education receives. In regard to the higher education variables, two results in particular deserve attention. Both in-state tuition and total giving had small, though significant, negative effects. This finding seems to indicate that public higher education’s ability to generate alternative forms of revenue does have a negative effect on its ability to generate revenue from its state government. Consequently, the harder public higher education tries to get ahead through alternative means, the less support it receives from its primary source of revenue, the state.
24
Higher Education in Review Conclusion
As Hendrick and Garand (1991) found, states vary in the amount of tradeoff behavior in which they engage, and policy areas vary in their susceptibility to tradeoff behavior. This study exposed some of the important political variables that affect how well public higher education fares in the competition for state dollars, and why some states are more willing to be generous toward public higher education relative to other expenditure areas. The findings of this study confirm that the structure of the state government and the political attributes of policymakers influence state budgeting. The findings show that governors and legislators act in theoretically predictable, partisan ways. Democrats tend to act more favorably towards public higher education. Likewise, the findings confirm that electoral competition and a unified legislature cause legislatures to act in theoretically predictable ways. When states are highly competitive, political leaders vie for support by offering services and support to as wide a range of constituents as possible, thereby favoring redistributive policy areas, covering a wider range of constituents, and supporting public higher education. More highly professionalized legislatures only slightly favor other areas more than public higher education as they try to deliver localized benefits to their constituents. Interest groups and the relative size of the public higher education lobby appear to influence significantly state funding for public higher education. Perhaps most importantly, the findings from this study reveal the influence of the political environment on the higher education budget allocation process. The findings of this study provide a missing element to our understanding of state public higher education funding. While past studies have shown the effect of state economic and demographic variables and the impact of competing state interests, such as Medicaid, they have not revealed why these variables have the impact that they do. The economic, demographic, and competing state interests assert their influence through the political process. States’ decisions to cut higher education funding disproportionately in response to economic downturns and to favor Medicaid funding over other programs, often pulling resources from public higher education, have to do with choices made by elected officials. Elected officials bring with them certain attributes and are influenced by multiple constituencies. This study highlighted some of the attributes and influences that appear to affect the relative amount of state funding devoted to public higher education. By using a percentage of state expenditures
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devoted to public higher education as the dependent variable, this study was able to isolate the relative impact of the various political factors. This study not only reveals how politics affect public higher education funding. It also provides empirical support for the notion that elected officials give deferential treatment to public higher education—especially during economic downturns and high unemployment. Likewise, it provides further evidence of the notion of tradeoffs and which state political attributes make state governments more or less likely to engage in tradeoffs with public higher education and other budget areas. The findings from this study reveal several avenues by which higher education officials and supporters may influence higher education budget allocations at the state level. Most importantly, this study highlights the importance of political action. Institutional lobbying affects funding outcomes. While higher education institutions can do little to influence the number of institutions within the states, they can control the amount of lobbying they do. Colleges and universities should not rely solely on their state governance structure to lobby for them, as it appears institutions may be their own best lobbyists. References Alt, J. E., & Lowry, R. C. (1994). Divided government, fiscal institutions, and budget deficits: Evidence from the states. The American Political Science Review, 88(4), 811-828. Archibald, R. B., & Feldman, D. H. (2004). State higher education spending and the tax revolt. College of William and Mary, Department of Economics, Working Paper # 9. Bailey, M. A., Rom, M. C., & Taylor, M. M. (2004). State competition in higher education: A race to the top, or a race to the bottom? Economics of Governance, 5, 53-75. Baker, J. R. (1990). Exploring the “Missing Link”: Political culture as an explanation of the occupational status and diversity of state legislators in thirty states. The Western Political Quarterly, 43(3), 597-611. Barrilleaux, C. (1999). Governors, bureaus, and state policymaking. State and Local Government Review, 31, 3-59. Barrilleaux, C., & Berkman, M. (2003). Do governors matter? Budgeting rules and the politics of state policy making. Political Research Quarterly, 56(4), 409-417. Berry, W. D., Ringquist, E. J., Fording, R. C., & Hanson, R. L. (1998). Measuring citizen and government ideology in the American states, 1960-1993. American
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Journal of Political Science, 41, 337-348. Betts, J. R., & McFarland, L. L. (1995). Safe port in a storm: The impact of labor market conditions on community college enrollments. Journal of Human Resources, 30, 741-765. Beyle, T. (1996). Governors: The middlemen and women in our political system. In V. Gray & H. Jacobs (Eds.), Politics in the American states: A comparative analysis (6th ed., pp. 207-252). Washington, DC: CQ Press. Bibby, J.F., & Holbrook, T. M. (2004). Parties and elections. In V. Gray & R. L. Hanson (Eds.), Politics in the American states: A comparative analysis (8th ed., pp. 62-99). Washington, DC: CQ Press. Bowler, S., & Donovan, T. (2004). The initiative process. In V. Gray & R. L. Hanson (Eds.), Politics in the American states: A comparative analysis (8th ed., pp. 129-156). Washington, DC: CQ Press. Boyd, D. (2005). State fiscal outlooks from 2005 to 2013: Implication for higher education. Boulder, CO: National Center for Higher Education Management Systems. Brown, R. D. (1997). Party cleavages and welfare effort in the American states. American Political Science Review, 89(1), 23-33. Browne, W. P. (1990). Organized interests and their issue niches: A search for pluralism in a policy domain. Journal of Politics, 52(4), 477-509. Cigler, A. J. (1991). Interest groups: A subfield in search of an identity. In W. Crotty (Ed.), Political science: Looking to the future (pp. 56-83). Evanston, IL: Northwestern University Press. Delaney, J. A., & Doyle, W. R. (2004, November). The role of higher education in state budgets. Paper presented at the annual meeting of the Association for the Study of Higher Education, Kansas City, MO. Dillon, S. (2005a, October 19). Tuition rise tops inflation, but rate slows, report says. The New York Times. Retrieved October 19, 2005, from http://www.nytimes.com/2005/10/19/education/19tuition.html? th&emc=th Dillon, S. (2005b, October 16). At public universities, warnings of privatization. The New York Times. Retrieved October 16, 2005, from http://www.nytimes. com/2005/10/16/education/16college.html Dynarski, S. (2004). The consequences of merit aid. In C. M. Hoxby (Ed.), College choices: The economics of where to go, when to go, and how to pay for it (pp. 63-100). Chicago: The University of Chicago Press. Elazar, D. J. (1966). American federalism: A view from the states. New York: Thomas Y. Crowell. Elazar, D. J. (1972). American federalism: A view from the states (2nd ed.). New York: Thomas Y. Crowell. Elazar, D. J. (1984). American federalism: A view from the states (3rd ed.). New
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York: Harper & Row. Elling, R. C. (1999). Administering state programs: Performance and politics. In V. Gray, R. L. Hanson, & H. Jacob (Eds.), Politics in the American states a comparative analysis (7th ed., pp. 267-303). Washington, DC: CQ Press. Erikson, R. S., Wright, G. C., & McIver, J. P. (1993). Statehouse democracy: Public opinion and policy in the American states. New York: Cambridge University Press. Fellows, M. C., & Rowe, G. (2004). Politics and the new American welfare states. American Journal of Political Science, 48(2), 362-373. Ferrin, S. E. (2003). Characteristics of in-house lobbyist in American colleges and universities. Higher Education Policy, 16(1), 87-108. Ferrin, S. E. (2005). Tasks and strategies of in-house lobbyists in American colleges and universities. International Journal of Educational Advancement, 5(2), 180-191. Fitzpatrick, J., & Hero, R. (1988). Political culture and political characteristics of the American states: A consideration of some old and new questions. Western Political Quarterly, 41, 145-153. French, P. E., & Stanely, R. E. (2005). Political culture and per pupil spending for education: Daniel Elazar revisited. Working paper. Retrieved from http:// www.angelfire.com/tn3/rstanley/Publications/WSSA.PDF Garand, J. C. (1985). Partisan change and shifting expenditure priorities in the American states, 1945-1978. American Politics Research, 3(4), 355-391. Garand, J. C., & Hendrick, R. M. (1991). Expenditure tradeoffs in the American states: A longitudinal test, 1948-1984. Western Political Quarterly, 44(4), 915-940. Gittell, M., & Kleiman, N. S. (2000). The political context of higher education. American Behavioral Scientist, 43(7), 1058-1091. Gormley, J. C., Jr. (1996). Accountability battles in state administration. In C. E. Van Horn (Ed.), The state of the states (3rd ed., pp. 196-221). Washington, DC: Congressional Quarterly Press. Gove, S. K., & Carpenter, J. (1977). State lobbying for higher education. Educational Record, 58(4), 357-373. Gray, V., & Lowery, D. (1988). Interest group politics and economic growth in the U.S. American Political Science Review, 82(1), 109-131.
Gray, V., & Lowery, D. (1996). The population ecology of interest representation: Lobbying communities in the American states. Ann Arbor: University of Michigan Press. Hansen, S. (1983). The politics of taxation. Westport, CT: Praeger.
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Heinz, J. P., Laumann, E. O., Nelson, R. L., & Salisbury, R. H. (1993). The hallow core. Cambridge, MA: Harvard University Press. Heller, D. E. (2002). The policy shift in state financial aid programs. In J. Smart (Ed.), Higher education: Handbook of theory and research (Vol. XVII). New York: Agathon Press. Hendrick, R. M., & Garand, J. C. (1991). Expenditure tradeoffs in the U.S.: A pooled analysis. Journal of Public Administration and Theory, 1(3), 295318. Holbrook, T. M., & Van Dunk, E. (1993). Electoral competition in the American states. American Political Science Review, 87(4), 955-962. Hovey, H. A. (1999). State spending for higher education in the next decade: The battle to sustain current support. San Jose, CA: National Center for Public Policy and Higher Education. Humphreys, B. R. (2000). Do business cycles affect state appropriations to higher education? Southern Economic Journal, 67(2), 398-413. Jacoby, W. G., & Schneider, S. K. (2001). Variability in state policy priorities: An empirical analysis. The Journal of Politics, 63(2), 544-568. Kane, T. J., Orszag, P. R., & Gunter, D. L. (2003). State fiscal constraints and higher education spending: The role of Medicaid and the business cycle (Discussion Paper No. 11). Washington, DC: The Urban Institute. Kaplan, G. E. (2002). Between politics and markets: The institutional allocation of resources in higher education. Unpublished doctoral dissertation, Harvard University, Cambridge, MA. Kiel, L. D., & Elliott, E. (1992). Budgets as dynamic systems: Change, variation, time, and budgetary heuristics. Journal of Public Administration Research and Theory, 2(2), 139-156. Koven, S. G., & Mausolff, C. (2002). The influence of political culture on state budgets: Another look at Elazar’s formulation. American Review of Public Administration, 32(1), 66-77. Layzell, D. T., & Lyddon, J. W. (1990). Budgeting for higher education at the state level: Enigma, paradox, and ritual (ASHE-ERIC Higher Education Report No. 4). Washington, DC: The George Washington University, School of Education and Human Development. Lindeen, J. W., & Willis, G. L. (1975). Political, socioeconomic and demographic patterns of support for public higher education. The Western Political Quarterly, 28(3), 528-541. Lowry, R. C. (2001). The effects of state political interests and campus outputs on public university revenues. Economics of Education Review, 20, 105-119. Marcus, L. R. (1997). Restructuring state higher education governance patterns. The Review of Higher Education, 20(4), 399-418.
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McGuinness, A. C. (2003). Models of postsecondary education and governance in the states. Denver, CO: Education Commission of the States. McLendon, M. K. (2003a). Setting the agenda for state decentralization of higher education. Journal of Higher Education, 72(2), 1-37. McLendon, M. K. (2003b). The politics of higher education: Toward an expanded research agenda. Educational Policy, 17(1), 165-191. McLendon, M. K., Hearn, J. C., & Deaton, R. (2004, November). Called to account: An analysis of state performance-accountability policies for higher education. Paper presented at the annual meeting of the Association for the Study of Higher Education, Kansas City, MO. McLendon, M. K., Heller, D. E., & Young, S. P. (2005). State postsecondary policy innovation: Politics, competition, and the interstate migration of policy ideas. Journal of Higher Education, 76(4), 363-400. McLenden, M. K., & Ness, E. C. (2003). The politics of state higher education governance reform. Peabody Journal of Education, 78(4), 66-88. Mintrom, M., & Vergari, S. (1998). Policy networks and innovation diffusion: The case of state education reforms. Journal of Politics, 60, 126-148. Morgan, D., & Watson, S. (1991). Political culture, political system, characteristics, and public policies among the American states. Publius: The Journal of Federalism, 21, 31-48. Murphy, E. (2001). Effective lobbying strategies for higher education in state legislatures as perceived by governmental relations officers (Doctoral dissertation, Louisiana State University, 2001). Dissertation Abstracts International, 62(06), 2049. Nice, D. C. (1984). Interest groups and policymaking in the American states. Political Behavior, 6(2), 183-196. Peterson, R. G. (1976). Environmental and political determinants of state higher education appropriations policies. Journal of Higher Education, 47(5), 523542. Plotnick, R. D., & Winters, R. F. (1985). A polticoeconomic theory of income redistribution. American Political Science Review, 79, 458-473. Princeton University Library. (2006). Data and statistical services. Retrieved January, 25, 2006, from http://dss.princeton.edu/online_help/analysis/panel. htm#models Raimondo, H. J. (1996). State budgeting: Problems, choices, and money. In C. E. Van Horn (Ed.), The state of the states (3rd ed., pp. 53-81). Washington, DC: Congressional Quarterly. Ringquist, E. J., Hill, K. Q., Leighley, J. E., & Hinton-Anderson, A. (1997). Lower-class mobilization and policy linkage in the U.S. states: A correction. American Journal of Political Science, 41(1), 339-344. Rizzo, M. J. (2005). State preferences for higher education spending: A panel
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data analysis, 1977-2001. Paper presented at the Cornell Higher Education Research Institute’s annual conference, Ithaca, NY. Sabloff, P. L. W. (1997). Another reason why state legislatures will continue to restrict public university autonomy. Review of Higher Education, 20(2), 141162. Sharkansky, I. (1968). Agency requests, gubernatorial support, and budget success in state legislatures. American Political Science Review, 62, 1220-1231. Sharkansky, I. (1969). The utility of Elazar’s political culture. Polity, 2, 66-83. Tandberg, D. A. (2006). State-level higher education interest group alliances. Higher Education in Review, 3, 25-49. The College Board (2005). Trends in college pricing. Washington, DC: Author. Thompson, J. A. (1987). Agency request, gubernatorial support, and budget success in state legislatures revisited. Journal of Politics, 49, 756-779. Thompson, J. A., & Felts, A. A. (1991). Politicians and professionals: The influence of state agency heads in budgetary success. The Western Political Quarterly, 45(1), 153-168. Truman, D. B. (1951). The governmental process: Political interests and public opinion. Westport, CT: Greenwood. Wilson, L. A., & Sylvia, R. (1993). Changing revenue conditions and state budgetary decisions. Journal of Public Administration Research and Theory, 3(3), 319-333.
VARIABLES
Party of the governor Percentage of democratic legislators in both houses combined. Legislative professionalism (legislative salary) Unified institutional control, one party controls both houses of the legislature.
PARTYGOV
Political culture
POLCUL
VOTE
Interest group activity ratio, (total number of public institutions divided by the number of interest groups) Voter turnout, percent of eligible voters casting ballots
State ideology
IDEAOL
INTERGROUP
Holbrook and Van Dunk (1993); data provided by Barrilleaux
Electoral competition, district level competition data
ELECTCOMP
U.S. Bureau of the Census, Statistical Abstract of the United States: 1976-2002
State websites; Gray and Lowery (1996), data provided by Lowery; National Center for Education Statistics: Digest of Education Statistics: 1976-2004
Erickson, Wright, and McIver (1993), data retrieved from Wright’s homepage: http:// mypage.iu.edu/~wright1/ Elazar, 1984; Sharkansky (1969); Baker (1990); Koven and Mausolff (2002)
Education Commission of the States, State postsecondary education structures handbook; State postsecondary education profiles handbook: 1969-2003; Kaplan, 2002, some data provided by Kaplan
State higher education governance structures. 1 for least centralized to 4 for most centralized
U.S. Bureau of the Census, Statistical Abstract of the United States: 1974-2002
Council of State Governments, Book of the States: 1974-2002
U.S. Bureau of the Census, Statistical Abstract of the United States: 1974-2002
U.S. Bureau of the Census, Statistical Abstract of the United States: 1974-2002
Barrilleaux & Berkman (2003), data provided by Barrilleaux
Illinois State’s Grapevine System, http://www.coe.ilstu.edu/grapevine/; U.S. Bureau of the Census, Statistical Abstract of the United States: 1974-2002
SOURCE
GOVSTRUCT
UNICONTROL
LEGPROF
PARTYLEG
Budgetary powers of the governor
Higher education appropriation as a % of state general fund expenditures
DESCRIPTION
BUDGETPOW
Political Variables
HESHARE
Dependent Variable
Appendix A: Variable Descriptions
Tandberg 31
Ration of nonwhite college age population to K-12 non white population
Share of population > 65 years old
Share population age 18-24
Total federal transfers, per capita
Unemployment rate – entire population
Tax revenue per capita Total government transfers (federal + county + city) per capita
RACERATIO
ELDER
COLLAGE
FEDTRANS
UNEMP
TAXREV
PELL
Proportion of households below maximum Pell Grant eligibility level
Income inequality, Income of household at 75th percentile/Income of household at 25th
INCEQU
GOVTRAN
GSP per capita
GSP
Economic and Demographic Control Variables
U.S. Bureau of the Census, Current Population Survey (unpublished data), Estimates of Income of Households by State 1979-2004; American Council on Education, Status of the Pell Grant Report
U.S. Bureau of the Census, Statistical Abstract of the United States: 1976-2001
U.S. Department of Labor, Bureau of Labor Statistics. Employment Earnings. Regional Economic Information System-http:www.bea.doc.gov/bea/regional/gsp/ U.S. Bureau of the Census, Current Population Survey (unpublished data), Estimates of Income of Households by State U.S. Bureau of the Census, Population Estimates Program, http://eire.census.gov/ popest/archives/state/st_sasrh.php U.S. Bureau of the Census, Decennial Census Microdata Files: via IPUMS http:// www.ipums.org U.S. Bureau of the Census, Population Estimates Program, http://eire.census.gov/ popest/archives/state/st_sasrh.php U.S. Bureau of the Census, Decennial Census Microdata Files: via IPUMS http:// www.ipums.org U.S. Bureau of the Census, Population Estimates Program, http://eire.census.gov/ popest/archives/state/st_sasrh.php U.S. Bureau of the Census, Decennial Census Microdata Files: via IPUMS http:// www.ipums.org U.S. Bureau of the Census, Statistical Abstract of the United States: 1976-2004 U.S. Department of Labor, Bureau of Labor Statistics. Local Area Unemployment Statistics (published and unpublished data) U.S. Bureau of the Census, State Government Finance Files, 1974-2001
Appendix A, cont.
32 Higher Education in Review
Medical CPI
Spending on K-12
Court reform, = 1 in state-year after court decision mandated K-12 finance reform
CPI
K12
COURT
Share of FTE enrollments in private institutions
Regional nonresident tuition, weighted average nonresident tuition at 4-year institutions in geographic region in $1,000
Higher education funding formulas
In state tuition lagged 1 year
ENROLLPRIV
REGTUIT
FUND
INTUITION
U.S. Department of Education, National Center for Education Statistics, Digest of Education Statistics: 1976-2002. U.S. Department of Education's Integrated Postsecondary Education Data System (IPEDS) Surveys via WebCASPAR. http://caspar.nsf.gov; U.S. Department of Education's Higher Education General Information Surveys (HEGIS) via WebCASPAR; IPEDS Peer Analysis System www. nces.ed.gov/ipedspas/ U.S. Department of Education's Integrated Postsecondary Education Data System (IPEDS) Surveys via WebCASPAR. http://caspar.nsf.gov; U.S. Department of Education's Higher Education General Information Surveys (HEGIS) via WebCASPAR; IPEDS Peer Analysis System www.nces.ed.gov/ipedspas/ MGT of America U.S. Department of Education's Integrated Postsecondary Education Data System (IPEDS) Surveys via WebCASPAR. http://caspar.nsf.gov
Conference on State Aid to Education, Education Finance and Accountability Program, Center for Policy Research, The Maxwell School, Syracuse University (April 2002)
Medical CPI*Share or population > 65 years old
HEALTH
Higher Education Variables
U.S. Bureau of the Census, State Government Finance Files, 1980-2004
Spending on Medicaid, minus federal matching funds
U.S. Department of Health and Human Services, Centers for Medicare and Medicaid Services, Quarterly Expense Reports, http://new.cms.hhs.gov/ MedicaidBudgetExpendSystem/02_CMS64.asp, published and unpublished. U.S. Bureau of the Census, State Government Finance Files, 1980-2004 2003 Economic Report of the President; U.S. Bureau of the Census, Population Estimates Program, http://eire.census.gov/popest/archives/state/st_sasrh.php U.S. Bureau of the Census, Decennial Census Microdata Files: via IPUMS http:// www.ipums.org 2003 Economic Report of the President
Appendix A, cont.
MEDICAID
Competing State Interests Variables
Tandberg 33
Giving per student ($1,000), total giving per FTE student from all sources at public research universities
Dependent variable lagged by one year (previous year to predict current year)
GIVE
LAGDEP
U.S. Department of Education's Integrated Postsecondary Education Data System (IPEDS) Surveys via WebCASPAR. http://caspar.nsf.gov; U.S. Department of Education's Higher Education General Information Surveys (HEGIS) via WebCASPAR; IPEDS Peer Analysis System www.nces.ed.gov/ipedspas/; American Council on Education, Voluntary Support of Education, Various years Illinois State’s Grapevine System http://www.coe.ilstu.edu/grapevine/; U.S. Bureau of the Census, Statistical Abstract of the United States: 1974-2002
Dynarski (2004)
Note. The author would like to thank Michael Rizzo for his help in providing many of the variables in STATA spreadsheet format.
Note. For detailed descriptions of the political variables see the appropriate subsection of the Conceptual Framework section and the Variables section above as appropriate. Also see Appendix B for Descriptive/Summary Statistics.
=1 in state-year where there are substantial merit aid programs
MERIT
Appendix A, cont.
34 Higher Education in Review
Tandberg
35
Appendix B: Descriptive Statistics Variables
Mean
Std. Dev.
Range
DEPENDENT Hi Ed Funding/State Expenditures
0.076
0.021
0.021 - 0.15
Budget Powers Gov.
4.42
0.55
2.0 - 7.0
Party Gov.
58.63
0.49
0.0 - 1.0
Party Leg.
21388.4
17.65
11.43 - 100
Leg. Professionalism
22242.2
21388.39
0.0 - 119880.1
Unified Inst. Control
0.43
0.49
0.0 - 1.0
Governance Structure
3.26
0.84
1.0 - 4.0
Electoral Competition
47.84
19.76
3.63 - 86.71
Citizen Ideology
20.75
5.95
0.0 - 63.2
Political Culture
5.06
2.56
1.0 - 9.0
Interest Group Activity Ratio
0.075
0.057
0.002 - 0.48
Voter Turnout
44.67
11.55
3.5 - 71.8
K-12 Education
6.40e+09
7.24e+09
4.4e+08 - 5.4e+10
Medicaid
11346.4
16273.8
13.0 - 130750
Health Costs (medical CPI* Share pop > 65)
17.53
10.09
0.0 - 45.81
Medical CPI
41.4 - 278.8
INDEPENDENT
CONTROL
154.81
72.14
Court Mandated Education Reform
0.52
0.49979
0.0 - 1.0
Income Inequality
3.7
1.76
0.0 - 7.87
Unemployment Rate
6.12
2.11
2.2 – 18
Tax Revenue Per Cap
1423.22
609.15
483 – 9330
Proportion of the Population Below Pell Total Government Transfers Ratio of nonwhite college pop. to n-w K-12 Gross State Product Per Capita
41.4
21.81
0.0 - 76.79
1036.05
587.55
0.0 - 10572.44
0.78
0.72
0.0 - 12.85
33929.6
14136.5
0.0 - 148745.5
Total Federal Transfers
999.34
575.96
0.0 - 10467.62
Share of Population College Age
549045
590287
41735 – 3427000
Share of Population > 65 years Old
12.88
2.35
2.68 - 19.92
8378.07
2107.56
3410.53 - 16253.12
Share of State Enrollments in Private HI ED
20.21
13.83
0.0 - 61.9
Merit Aid
0.039
0.19
0.0 - 1
Higher Education Funding Formula
0.07
0.02
0.0 - .1
Total Giving to Public Res. Univ. per FTE
1279.98
1238.36
0.0 - 12236.57
Instate Tuition Lagged
2984.98
1392.87
0.0 - 8079.06
Regional Average 4-year Tuition
36
Higher Education in Review Appendix C: Results of Hausman Test Test: H0: difference in coefficients not systematic chi2(26) = (b-B)’[(Vb -VB)^(-1)](b-B) = 907.98 Prob>chi2
= 0.0000
(Vb-VB is not positive definite) **** Advisable to Use Fixed Effects ****
David A. Tandberg wrote this article as a PhD candidate in higher education and a MA candidate in political science at The Pennsylvania State University. Currently, Dr. Tandberg serves as a special assistant to the Deputy Secretary for Postsecondary and Higher Education at the Pennsylvania Department of Education. He can be reached at
[email protected].
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