The Determinants of State Spending on Higher Education: How Capital Project Funding Differs from General Fund Appropriations

Transcription

1 The Determinants of State Spending on Higher Education: How Capital Project Funding Differs from General Fund Appropriations Erik C. Ness, David A. Tandberg The Journal of Higher Education, Volume 84, Number 3, May/June 2013, pp (Article) Published by The Ohio State University Press DOI: /jhe For additional information about this article Access provided by University of Georgia (8 Aug :08 GMT)

2 Erik C. Ness David A. Tandberg The Determinants of State Spending on Higher Education: How Capital Project Funding Differs from General Fund Appropriations Our fixed-effects panel data analysis of state spending on higher education fills a near void of studies examining capital expenditures on higher education. In our study, we found that political characteristics (e.g., interest group activity, organizational structure, and formal powers) largely account for differences between general fund and capital appropriations for higher education. State funding for higher education remains a perennial topic of interest for higher education researchers. In this decade alone, dozens of studies have examined factors associated with state appropriations for higher education, with emphasis on the role of politics (McLendon, Hearn, & Mohker, 2009; Tandberg, 2010a, 2010b), tax support for higher education (Archibald & Feldman, 2006), the balance wheel effect (Delaney & Doyle, 2007), differences in funding among research universities (Weerts & Ronca, 2006), and state financial aid spending (Doyle, 2010; Rizzo, 2007). Yet nearly all of this research activity has considered only the general fund appropriations to higher education rather than other streams of state funding, such as capital expenditures. Indeed, the empirical examination of state funding for higher education capital proj- Erik C. Ness is an Assistant Professor in the Institute of Higher Education at the University of Georgia; eness@uga.edu. David A. Tandberg is an Assistant Professor in the Department of Educational Leadership and Policy Studies at Florida State University; dtandberg@fsu.edu. The Journal of Higher Education, Vol. 84, No. 3 (May/June) Copyright 2013 by The Ohio State University

3 330 The Journal of Higher Education ects is virtually nonexistent. As a result, the research literature does not rise to the practical significance of capital spending on higher education, which includes: the substantial gross spending on higher education capital projects, the sizeable share of higher education projects of all state capital spending, and the variation in the process used to determine capital spending as compared to general appropriations for higher education, which seemingly leads to a more political process for capital expenditures. the gross higher education capital spending for all 50 states totaled $10.3 billion in 2009, which is 13 percent of state general fund appropriations (NASBO, 2010). This annual expenditure on capital projects equates to nearly $700 per student enrolled in a public higher education institution. 1 Analysts, however, rarely account for this significant state subsidy in their examinations of state higher education finance. In fact, much more scholarly attention has been paid to states per-student spending on need- and merit-based scholarship (e.g., College Board, 2011; Heller, 2002) and the implications on a range of higher education objectives and missions. Yet states spent roughly the same amount in 2009 ($10.3 billion) on higher education capital expenditures and on student financial aid (NASBO, 2010; NASSGAP, 2010). Granted, capital spending on higher education has a far less direct impact on college affordability and access than student financial aid. Nevertheless, with the same level of increasingly scarce state resources being allocated to higher education capital projects, an empirical examination of these expenditures seems long overdue. in addition to the gross amount of state capital spending, higher education as a sector represents a significant share of state capital expenditures. Other than transportation, higher education represents the largest sector for state capital projects, which despite a 2.5 percent decrease in 2009, represents slightly more than 12 percent of total state capital spending (NASBO, 2010). Moreover, in the aggregate, states spend more than five times as much on higher education capital projects as they do on corrections, which received $1.81 billion in 2009 (NASBO, 2010). In many states, higher education s share of all capital projects is even more pronounced than the national averages. The Utah System of Higher Education, for example, manages two-thirds of all state-owned facilities (Walthers, 2000). In fact, higher education s large share of total state capital spending may be based on the often different funding process for capital expenditures than for general appropriations to higher education. across the states, higher education capital funding decisions appear to be much less reliant on systematic processes than general fund ap-

4 Capital and General Fund Appropriations 331 propriations and thus may be more susceptible to political influences. General fund appropriations are more often formula-driven and incremental; by comparison, state capital expenditure processes vary more widely and are less likely to be formula-driven and guided by statewide master plans (Manns, 2003; Manns & Katsinas, 2006; Potter, 2006). According to Manns (2003) survey of state capital budgeting practices, for instance, fewer than 20 percent of states use funding formulas for higher education capital projects. The capital funding process also differs by revenue source, especially since many states rely on bond initiatives to fund higher education capital. NASBO (2010) has indicated that 30 percent of capital projects are funded through state or local bonds. Although such bond initiatives have been shown to generate stronger public support than social morality initiatives (such as those dealing with the use of race and ethnicity in admissions decisions), reliance on direct democracy for funding support would seem to be much more volatile than general fund appropriations to higher education (McLendon & Eddings, 2002). Although this variation in capital expenditure procedures seems to emphasize political influences, studies of higher education general fund appropriations have shown political factors related to party control, organizational structure, and interest group activity to be significant determinants of state spending for higher education (Archibald & Feldman, 2006; McLendon, Hearn, & Mohker, 2009; Tandberg, 2010a, 2010b). Anecdotal and empirical evidence, however, has suggested that political factors may guide state capital spending as well. As an example, New Hampshire higher education supporters successfully embarked on an all-out grass-roots campaign for the university system to wring a record $100 million in capital funds (Curry, 2001, p. A20). On the other hand, the more recent account of a Western Michigan University capital project already underway not being included among the final legislative bill approving 23 higher education capital projects implies a political decision-making process (Carlson, 2010). Inspired in part by these observations, we empirically examined capital spending for higher education (Tandberg & Ness, 2011) and found that, indeed, many of the political factors that previous research has found to be associated with general fund spending are also associated with capital funding. Yet far fewer of the non-political variables (e.g., demographic and economic characteristics of the state) were shown to influence capital spending compared to recent studies examining general fund spending on higher education (Tandberg & Ness, 2011). Although our earlier study suggests a stronger political influence on capital spending, our model was limited to capital expenditures, so we could not directly compare the explanatory power

5 332 The Journal of Higher Education of political and non-political variables to general appropriations for higher education. Indeed, higher education researchers have rarely examined how determinants differ by revenue distribution. That is, are factors associated with state general fund appropriations different from those associated with capital expenditures? Although some studies have shown variables differing impact on state funding and tuition prices (e.g., Hearn, Griswold, & Marine, 1996; Koshal & Koshal, 2000), we are not aware of any studies that account for differences between two pools of state institutional funding. Such an examination seems appropriate for capital spending and general appropriations, especially given the similarities (the same campuses and systems vie to maximize state expenditures) and the differences (general fund appropriations are more often formula-driven and incremental whereas state processes vary more widely for capital spending). thus, our current study aimed to directly compare the determinants of state spending on general fund appropriations and capital expenditures for higher education. This examination fills a void in the research not only related to capital spending but also in comparing how factors might differently influence two state funding processes. After reviewing the literature that serves to conceptually and empirically ground our study and outlining our research design, we report findings highlighting the differences in factors and their magnitude between general fund and capital appropriations. Conceptual Framework Three strands of literature inform our study and are presented here. First, we discuss two classic theoretical perspectives from the social sciences new institutionalism and institutional rational choice that our study fundamentally draws on. Second, we review the modest and largely descriptive literature dealing directly with state higher education capital spending, which points to the influence primarily of political factors, but also includes factors related to states economic, demographic, and higher education system characteristics. Third, we summarize the spate of recent empirical studies related to overall state spending for higher education with particular attention to how these studies measure state spending and to the variables shown to be significant predictors. Then, given our study s emphasis on comparing capital and overall state spending, we review higher education studies with research designs that examine multiple policy or funding outcomes. Finally, we synthesize the broader conceptual framework with the empirical findings related

6 Capital and General Fund Appropriations 333 to state higher education spending to outline our analytic approach to examine the differences between general fund and capital spending. Theoretical Perspectives The conceptual underpinning of our examination of state spending on higher education is new institutionalism and institutional rational choice. According to new institutionalism, social outcomes or policy decisions are not only driven by actor behavior and optimization as advanced by rational choice theory but are also a result of institutional features (March & Olson, 1984; Shepsle, 1989). Within this framework, institutions are broadly defined to include the formal and informal rules, norms, and strategies of an organization; shared concepts used by actors in repetitive situations; the formal organizations and structures of government and public service; and patterns of behavior, negative norms, and constraints (Ostrom, 2007). ostrom has advanced a related perspective merging rational choice and new institutionalism: institutional rational choice (Kiser & Ostrom, 1982; Ostrom, 2007). Institutional rational choice argues that actions (or policy decisions) are a function of attributes of the individual and attributes of the decision situation. Based on assumptions of bounded rationality, the attributes of individuals (in the case of this study, policy makers) account for actors values, beliefs, information-gathering capabilities, and the internal mechanisms used to decide upon strategies (Ostrom, 2007). For studies of state spending on higher education, attributes of the individual could include measures related to policy makers, including their political party affiliation and the formal powers of their office. Attributes of the decision situation would include characteristics of the political setting, such as interest group activity, and of the electorate, such as citizen ideology and voter turnout. According to Kiser and Ostrom (1982), the decision situation also includes attributes of the community or environment, which for state higher education spending would include characteristics of the higher education setting, including structures (e.g., statewide governance, funding mechanism) and relevant trends (e.g., enrollment, tuition rates, private giving). Mechanisms of the broader state-level environment, such as economic and demographic characteristics, would also fit within the decision situation, or action arena (Ostrom, 2007). although differences in the determinants of capital and general fund appropriations were the primary interest for our study, the new institutionalism perspective serves to undergird our model specification as it did, at least implicitly, the previous empirical examinations of state spending on higher education. The measures included in our model of

7 334 The Journal of Higher Education state higher education spending for capital projects and general funds are fundamentally guided by new institutionalism and institutional rational choice. Given our emphasis on comparisons between these two funding decisions and the relative lack of attention to state higher education capital expenditures, our study is also informed by the descriptive and empirical trends reviewed below. Capital Spending on Higher Education Despite the modest empirical literature on state-funded higher education capital projects, the National Association of State Budget Officers (NASBO) annual reports show that capital spending for higher education represents the overwhelming share of all state education capital projects because K 12 school construction is most often funded at the local level. According to NASBO, state higher education capital expenditures include new construction, land purchases, infrastructure projects, major repairs and improvements, and the acquisition of major equipment and existing structures. These funds are considered separate from general fund expenditures for higher education, which are traditionally provided through the normal appropriations process to support the general operations of colleges and universities. In addition to their annual reports with information on capital expenditures and overall state spending for higher education, NASBO (1999) compiled a report summarizing state capital spending, planning, reporting, financing, and asset management based on questionnaires completed by state budget officers. The report offers good practices for each of these areas and includes 30 tables with data from all 50 states on topics ranging from the organization of the capital budget (in 23 states, capital requests come from the higher education sector), the oversight of capital projects (26 states have central state agencies to oversee and manage capital projects), cost estimating methods (29 states consider life-cycle costs), and changes in the capital planning process (31 states made significant changes between ). The tabular material in the NASBO (1999) report illustrates the variation between states on how capital expenditures are determined and managed. Perhaps as a result, the recommendations throughout the report call on states to develop and utilize systematic processes and to maintain centralized oversight for capital projects. Three additional survey projects specifically examined public higher education capital expenditures. During fiscal year , Manns (2003) surveyed chief fiscal officers of state higher education agencies in 41 states on their capital budgeting practices. Manns and Katsinas (2006) conducted a follow-up survey in 2003 with responses from 40

8 Capital and General Fund Appropriations 335 states. In a separate survey project on behalf of the Texas Council of Public University Presidents and Chancellors, Potter (2006) collected survey data from 37 states and more extensive data on the capital funding models of five states: California, Connecticut, Illinois, Pennsylvania, and Washington. Survey results from all three projects corroborate the variation of capital budget processes described in the NASBO (1999) report. In particular, these studies show capital spending processes to be less reliant than general appropriations on funding formulas and on statewide master planning. According to Potter s (2006) study, for example, only 11 of the 37 states indicated that state master plans included capital spending. In the earliest study we are aware of to directly compare capital expenditures and general fund appropriations for higher education, White and Musser (1978) examined the effect of business cycles on state government spending and found that state capital spending for higher education was more elastic relative to personal income than general fund appropriations. Moreover, state higher education expenditures in general were more elastic than any other state expenditure area. These findings appear to lend further support to the balance wheel trend, which seems to be exacerbated for capital spending. The 2008 NASBO report, for example, shows a 20 percent increase in higher education capital expenditures compared to only a 7 percent increase in overall state higher education spending. The most recent NASBO (2010) report projects an 11 percent decrease in higher education capital spending and a slight (0.8 percent) increase for overall higher education spending. However, Delaney and Doyle (2010), analyzing 20 years of panel data with state and year fixed effects, found that higher education capital spending followed a U-shaped curve rather than the S-shaped curve associated with the balance wheel effect on overall higher education spending. So, states spent more on capital projects during the best and worst fiscal climates. Their study also found no evidence of political influences, as measured by voter turnout, proportion of Republicans in the legislature, and political party of governor. Notwithstanding Delaney and Doyle s (2010) recent findings, the volatility of higher education capital spending has been largely attributed to political characteristics and to the funding process. In a study of all state capital expenditures, including higher education, Poterba (1995) found that having separate capital budgets, rather than lumping capital funding with general fund appropriations, resulted in nearly a one-third increase in education capital spending. Olivas s (1984, 1990) examination of state higher education governance reform in Ohio, which was sparked by the statewide coordinating board s inequitable distribution

9 336 The Journal of Higher Education of capital expenditures, shows that these state capital disbursements were overwhelmingly political. Olivas attributed the creation of a state higher education coordinating board largely to the inequitable distribution of state higher education capital spending and the attendant campus competition for these scarce resources (as compared to peer states). Our recent empirical examination of state capital funding (Tandberg & Ness, 2011) corroborates these earlier findings. As a measure of the funding process, we found that states with formula funding for higher education general fund appropriations were associated with increased higher education capital spending. An overwhelmingly share of the variance in state capital expenditures for higher education, however, was due to political factors, such as the ratio of higher education interest groups, the professionalization of legislatures, and the competitiveness of legislators districts. These political factors have also been commonly examined in research on state higher education general fund appropriations and are central to the current study s comparison of higher education capital and general fund expenditures. State Spending on Higher Education A steady stream of research examining state spending for higher education has shown funding levels to be associated with state-level economic, demographic, and higher education system characteristics (e.g., Hossler, Lund, Ramin, Westfall, & Irish, 1997; Kane, Orszag, & Gunter, 2003; Lowry, 2001; Peterson, 1976) and more recently has included political characteristics (e.g., Archibald & Feldman, 2006; McLendon, Hearn, & Mokher, 2009; Tandberg, 2010a, 2010b). Overwhelmingly, however, these studies have relied on overall measures of state spending on higher education rather than distinguishing between general appropriations, financial aid, and capital projects. Trostel and Ronca s (2009) unified measure, which accounts for state income, population, and high school graduates in addition to state higher education funding, serves as an exception but is still based on overall state spending rather than disaggregating between general appropriations and capital projects, for example. indeed, most studies of state spending on higher education have used one of two dependent variables both of which deal solely with general fund appropriations: higher education share of total appropriations (e.g., Delaney & Doyle, 2007; Okunade, 2004; Rizzo, 2007; Tandberg, 2010a) or higher education appropriations per $1,000 personal income (e.g., Archibald & Feldman, 2006; Kane et al., 2003; McLendon, Hearn, & Mohker, 2009; Tandberg, 2010b). The results of these studies are fairly consistent irrespective of different measures for state spending

10 Capital and General Fund Appropriations 337 on higher education. Favorable state economic conditions such as low unemployment rates and high gross state product, for example, have been found to be associated with increased higher education spending (McLendon, Hearn, & Mohker, 2009; Rizzo, 2007; Tandberg, 2010a). Studies have also generally found support for the balance wheel hypothesis (Hovey, 1999) that higher education competes for state appropriations with other sectors, such as K 12 education (Toutkoushian & Hollis, 1998) and health care (Delaney & Doyle, 2007; Kane et al., 2003; Okunade, 2004; Tandberg, 2010b). Political determinants, including Democratic Party control of legislatures and/or governors, strong governors powers, and higher levels of legislative professionalism, have been found to be associated with increased state spending on higher education (Archibald & Feldman, 2006; McLendon, Hearn, & Mohker, 2009; Rizzo, 2007; Tandberg, 2010a, 2010b). Taken together, these examinations of state higher education spending undergird our analysis of general fund appropriations and, in the absence of a similarly robust literature, of capital spending on higher education. Rationale for Comparative Research Design Although no studies have directly compared determinants of capital and general fund appropriations for higher education, there are a few education studies that have examined multiple policy or funding outcomes. For example, one of the most common comparative approaches to study higher education finance has been to use single models to examine influences on general appropriations and tuition levels (Hearn, Griswold, & Marine, 1996; Hossler et al., 1997; Koshal & Koshal, 2000). Breaking from the tradition of examining general fund appropriations, Hearn et al. (1996) examined the impact of social and economic resources, state higher education governance structures, and geographic region (which is shown to have the most consistent influence) on tuition and fee levels and state financial aid spending. Koshal and Koshal (2000), by contrast, examined the relationship between state appropriations and tuition and found them to be interdependent with appropriations affecting tuition, as is conventionally understood, and tuition influencing appropriations, specifically through the associations of median family income and out-of-state student enrollment. In addition to these examples of funding outcomes, McLendon, Hearn, and Deaton s (2006) study of the adoption of state finance policies serves as another compelling example of a comparative research design. Their event history analysis of the spread of accountability policies essentially used the same research design for the adoption of three related yet distinct accountability policies: performance funding, perfor-

11 338 The Journal of Higher Education mance budgeting, and performance reporting. The final policy did not yield significant results, but their analysis found that two independent variables (statewide governance structure and percent Republican in legislature) affected the adoption of two policy outcomes in different directions. They argued that the seemingly subtle policy differences between performance funding and performance budgeting were, in fact, substantively different, so we should not be surprised that variables affect adoption differently. For state higher education spending, it seems quite possible that the same variables would yield substantively different results as determinants of capital and general fund expenditures on higher education. Drawing on studies related to state higher education finance, we frame this examination as an exploratory study of the differences between general fund appropriations and capital spending. With such little scholarly attention paid to determinants of capital spending on higher education, we relied on the new institutionalism conceptual grounding to guide our selection of variables related to political, economic, demographic, and higher education system characteristics that have been tested in many of the recent studies reviewed above (e.g., Archibald & Feldman, 2006; Lowry, 2001; McLendon, Hearn, & Mohker, 2009; Rizzo, 2007; Tandberg, 2010a, 2010b). Thus, rather than identifying two models with distinct measures to analyze capital spending and general fund appropriations separately, our study used the same measures and focused primarily on the differences in significance, direction, and magnitude between the two models. Also, instead of presenting hypotheses for all independent variables in both models, below we outline what our expectations were of the differences in how the blocks of variables economic and demographic, higher education system, and political would influence funding for general fund and capital expenditures. Couched in terms of institutional rational choice, we expected these differences based on the attributes of different decision situations (Kiser & Ostrom, 1982) or action arenas (Ostrom, 2007). Put simply, we expected that economic and demographic characteristics and higher education system characteristics would have a greater impact on general fund appropriations than capital spending. We based this primarily on the fewer structural influences on the capital funding process (Manns, 2003; Manns & Katsinas, 2006; Potter, 2006). Conversely, we expected the political block of variables to explain more of the variance for capital expenditures. Below we briefly discuss our parsimonious selection of variables that constitute each block and outline examples of how these variables might differ between models. We detail more informa-

12 Capital and General Fund Appropriations 339 tion about variable construction and analytic techniques in the research design section to follow. Political Block of Variables. Consistent with new institutionalism and institutional rational choice, and as suggested by McLendon and Hearn (2007), we incorporated a series of state-level political indicators. In fact, the McLendon and Hearn (2007) framework cites each of the seven variables that constitute the political block. According to the institutional rational choice framework (Kiser & Ostrom, 1982; Ostrom, 2007), four of these variables relate to attributes of the individual actor or policy maker (budgetary powers of governor, legislative professionalism, and political party of the governor and legislature). Consistent with previous studies, we expected that actors with Democratic Party affiliation would be disposed toward increased spending on higher education general funds and capital projects. Likewise, we expected that greater gubernatorial powers and higher levels of legislative professionalism would be associated with increased analytic capacity available to actors that would also be associated with increased general fund and capital expenditures for higher education. As an example of a political variable that we hypothesized to differ in magnitude between models, we expected that more professionalized legislatures would have an even greater effect on higher education capital spending due to these state houses increased analytic capacity to formalize capital budget processes, thereby allocating more resources to capital projects. the remaining three measures relate to the attributes of the decision setting (higher education interest groups, political ideology, and voter turnout). These variables are also widely used in higher education finance studies (e.g., Archibald & Feldman, 2006; Delaney & Doyle, 2010; McLendon, Hearn, & Mokher, 2009; Rizzo, 2007; Tandberg, 2010a, 2010b). The political ideology and voter turnout measures relate to attributes of the electorate shown to influence policy decisions. We expected that state electorates with more conservative ideology and higher voter turnout would spend less on the higher education general fund and capital projects. finally, due to differences in attributes of the decision situation (i.e., capital expenditures and general fund appropriations), we expected that states higher education interest group ratio to non-higher education lobbying would have a greater impact on capital spending since these projects are less likely than general fund appropriations to be generated by formulas and thereby more susceptible to political influence. In fact, as Ostrom (2007) indicated, this may be due to the possibility of institutions free riding during the general fund action decision situation. In

13 340 The Journal of Higher Education the capital spending decision situation, institutions may be more likely to advocate directly for campus-level capital project. Higher Education Block of Variables. The variables included in the higher education block relate to attributes of the decision situations (Kiser & Ostrom, 1982; Ostrom, 2007). This block of six variables include four that are commonly included in higher education finance studies (e.g., Lowry, 2001; McLendon, Hearn, & Mokher, 2009; Rizzo, 2007) higher education governance structure, enrollment (total and percent two-year), and tuition costs. Consistent with our contention that the higher education block of variables would hold greater explanatory power for general fund appropriations, we expected that higher total enrollment, lower proportion of students enrolled in twoyear colleges, and higher tuition would have a stronger impact on general fund than capital expenditures. With regard to governance structure, we expected that states with consolidated governing boards would have a negative impact on both general funds and capital expenditures but would more negatively affect capital fund despite findings from studies that suggest these centralized structures protect the interests of their academic cartels (McLendon, Hearn, & Deaton, 2006; Zumeta, 1996). Due to the relative lack of systematic procedures driving capital expenditures, we hypothesized that more decentralized higher education governance arrangements may be better suited to maximize capital expenditures. Moreover, as the institutional rational choice framework suggests, the attributes of the environment affect the general fund and capital expenditures differently because capital projects are fundamentally more connected to individual campuses whereas general funds are often appropriated in a block grant for the higher education system to distribute. our model also includes two additional variables that seem especially relevant to this comparative study use of funding formulas for general appropriations and giving to public universities, which Cheslock and Gianneschi (2008) found to be associated with higher general fund appropriations. Our previous studies have found funding formulas to be associated with increased general fund appropriations (Tandberg, 2010a, 2010b) and both funding formulas and private giving to be associated with higher capital spending (Tandberg & Ness, 2011). Because higher education capital projects in many states follow separate budget processes, we expected that states with funding formulas for general expenditures would have less influence on capital spending for higher education than for general fund appropriations. Economic and Demographic Block of Variables. As with the other blocks of variables, the four variables included in the higher education block relate to attributes of the decision situations (Kiser & Ostrom,

14 Capital and General Fund Appropriations ; Ostrom, 2007) and are all commonly included in higher education finance studies (e.g., Lowry, 2001; McLendon, Hearn, & Mokher, 2009; Rizzo, 2007; Tandberg, 2010a, 2010b) proportion of population older than 65, income inequality, unemployment, and share of state general fund to Medicaid. Studies that examine the state share of appropriations (e.g., Delaney & Doyle, 2007; Rizzo, 2007; Tandberg, 2010b) have consistently found increased state spending on health care to be associated with lower higher education expenditures. Based on the often separate capital funding processes, we expected this impact to be more pronounced for general fund expenditures. Indeed, because the attributes of the decision situation for general fund expenditures are better established through empirical examination than attributes of the capital spending decision situation, our overarching expectation was that nonpolitical variables would have greater explanatory power for the general fund appropriations than for capital expenditures. Research Design This study examined the determinants of both state general fund expenditures and capital spending for higher education between 1988 and In an earlier study, we highlighted the impact of political variables on state capital spending (Tandberg & Ness, 2011). The findings from that study, the theoretical frameworks discussed above, and previous work on predicting state appropriations for higher education led us to develop the more refined model of state support for higher education that we present in this article and use to predict both state capital expenditures for higher education and state general funds for higher education. The current model includes only the primary variables of interest (those variables that have repeatedly been shown to affect state finance decisions for higher education). The three blocks of variables are: political, higher education, and economic and demographic. The years covered, and the independent variables included, in each equation are exactly the same, which allows for comparability. In instances where data were not available for a particular year (i.e., voter participation data are only available every other year), data from the previous year were repeated to cover the missing year. 2 This is consistent with similar studies of state higher education spending and policy decisions (e.g., McLendon, Hearn, & Mokher, 2009; McLendon, Heller, & Young, 2005; Tandberg, 2010a, 2010b). Table 1 provides a list of all the variables included in this study and their descriptive statistics. 3 Appendix A includes a description of each of the variables, their sources, and their construction. Below we discuss in greater detail the dependent variables and several of the independent variables.

15 Table 1 Summary Statistics Variable Mean Std. Dev. Min Max Percent Change ( ) Log State Capital Expenditures for Higher Ed Log State General Fund Expenditures for Higher Ed Budget Power of Gov Higher Ed Interest Ratio Political Ideology Log Leg Professionalism Party of the Governor (1 = Dem) Party of Legislature (% Dem) Voter Turnout (%) Higher Ed Governance Structure (1 = consolidated governing board) % Enroll 2-Year Higher Ed (%) Total Enrollment 294, ,188 26,540 2,474, , , Funding Formula (1 = yes) Log Giving to Higher Ed per FTE Log Average Tuition Percent Elderly (%) Income Inequality Unemployment Rate (%) Share of State Spending on Medicaid (%)

16 Capital and General Fund Appropriations 343 Dependent Variables To measure state capital expenditures for higher education and state general fund expenditures for higher education, we used National Association of State Budget Officers (NASBO) data from 1988 to NASBO distinguishes between federal capital dollars that flow through state houses and actual state capital expenditures and also allows states to correct previously reported data. The ability to correct previously reported data is important, as capital projects frequently exceed projected costs and often take longer to complete than anticipated. Only the corrected data were used in our study. Additionally, NASBO distinguishes between general fund expenditures and state capital expenditures. Employing measures from the same data source is important in order to compare results across models. Other sources either do not report capital expenditures, separate federal dollars from state dollars, or do not allow for corrected data. We used the natural log of both state capital and general fund expenditures because neither follows a normal distribution. Independent Variables Some of the political variables deserve specific mention in regard to their construction beyond what is provided in Appendix A. All financial data were adjusted for inflation. The higher education finance data were adjusted by the Higher Education Cost Adjustment developed by SHEEO. 4 All other financial data were adjusted using the Consumer Price Index for all urban consumers (CPI-U). Budgetary powers of the governor was measured using a times series index developed by Tandberg (2010a) that closely resembles the cross sectional index developed by Barrilleaux and Berkman (2003). It is a scale of 0 to 7 and includes data from across all 50 states. The items included are whether state agencies make requests directly to the governor or to the legislature; whether the executive budget document is the working copy for legislation, the legislature can introduce budget bills of its own, or the legislature or the executive introduces another document later in the process; whether the governor can reorganize departments without legislative approval; whether revenue estimates are made by the governor, the legislature, or another agency, or if the process is shared; whether revenue revisions are made by the governor, the legislature, or another agency, or if the process is shared; whether the governor has the line item veto; and whether the legislature can override the line item veto by a simple majority. Each of these has a value of 0 or 1. The sources for the data are Council of State Govern-

17 344 The Journal of Higher Education ments The Book of the States, the National Association of State Budget Officers Budget Processes of the States, and the National Conference of State Legislatures data (various years). The higher education interest group ratio variable, introduced by Tandberg (2010b), was constructed by dividing the total number of state public higher education institutions and registered non-college or nonuniversity public higher education interest groups by the total number of interest groups in the state, minus any registered colleges and universities or other registered higher education interest groups that may lobby for more money for public higher education. The interest group data were retrieved from state websites and government archives, from the Council on Governmental Ethics Laws (CGEL) Blue Book (various years), and from data provided by David Lowery. Data on the number of public institutions were retrieved from the National Center for Education Statistics Digest of Education Statistics. We employed Berry, Ringquist, Fording, and Hanson s (1998) measure of citizen ideology. Berry et al. identified the ideological position of each member of Congress in each year using interest group ratings. Next, they estimated citizen ideology in each district (both house and senate districts) of a state using the ideology score for the district s incumbent, the estimated score for a challenger (or hypothetical challenger) to the incumbent, and election results that presumably reflect ideological divisions in the electorate. Finally, they used the citizen ideology scores for each district to compute an unweighted average for the state as a whole. Berry et al. have updated their measure to cover Consistent with previous research (Barrilleaux & Berkman, 2003; Carey, Niemi, & Powell, 2000; Fiorina, 1994; Tandberg, 2010a, 2010b), legislative professionalism was measured using legislative salary, which has been found to indicate important characteristics of the membership (Carey, Niemi, & Powell, 2000; Fiorina, 1994). The source for the data on legislative salary is the Council of State Governments The Book of the States. Higher education governance structures (consolidated governing board for higher education) was a dummy variable coded 1 if a state has a consolidated governing board for any given year and 0 otherwise. This dummy variable is based on McGuinness s (2003) typology of state higher education governance, which classifies states as: governing board states, coordinating board states, and planning/regulatory/service agency states. McGuiness then further classifies some governing board states as consolidated governing board states where all public institutions are governed by a single governing board. Although McGuiness s

18 Capital and General Fund Appropriations 345 typology has been operationalized in a number of ways for analytical purposes, the use of a dummy variable has become the conventional approach (e.g., McLendon, Hearn, & Mokher, 2009; Nicholson-Crotty & Meier, 2003; Tandberg, 2010a, 2010b). We followed this approach because we were specifically interested in the impact the existence of a consolidated governing board has on state higher education capital appropriations relative to general fund appropriations for higher education. The dummy variable approach is also preferable because there is no underlying continuous scale for governance and because of the unique nature of each state s approach to higher education governance. This approach isolates the most centralized version of state governance of higher education as described by McGuinness. Data were gathered from the Education Commission of the States (ECS) website, ECS s State Postsecondary Education Structures Handbook, and State Postsecondary Education Profiles Handbook: Income inequality was measured using the Gini coefficient, which is a measure of inequality of a distribution and is defined as a ratio. The numerator is the area between the Lorenz curve (the cumulative distribution function of a probability distribution) of the distribution and the uniform (perfect) distribution line; the denominator is the area under the uniform distribution line (Dorfman, 1979). The Gini coefficient is often used as an income inequality metric, which is the way it is used here. Zero corresponds to perfect income equality (i.e., everyone has the same income), and 1 corresponds to perfect income inequality (i.e., one person has all the income, while everyone else has no income). The source for these data is the U.S. Census Bureau s Current Population Survey, Method For our study, two models were employed, one to predict state general fund expenditures for higher education and the other to predict state capital expenditures for higher education. The results of each model are compared below. The variables in each model were loaded in block mode (political variables loaded first, followed by the higher education related variables, and finally the economic and demographic variables) in order to compare the influence of each of the categories of variables. Each model includes panel data with all fifty states from 1988 to 2004 for an n of 800. the models were run using both raw scores and standardized scores (z-score). Standardizing both the independent and the dependent variables and reporting the standardized regression coefficients allowed us

19 346 The Journal of Higher Education to compare the relative contribution of each of the independent variables and to compare results across the two models, thereby allowing us to draw conclusions about how each variable may affect capital and general fund expenditures for higher education differently. The use of raw scores allowed for interpretation in the variables original matrix. The b coefficients represent the results using the raw scores, and the Beta coefficients represent the results using the z-scores (Tandberg, 2010a). In order to examine the determinants of state capital and general fund expenditures for higher education and the relative impact of the various state political measures, we employed panel data with fixed effects for both state and year. The fixed effects were implemented by utilizing the Stata statistical software. Fixed effects allowed us to control for unobservable characteristics about states and time that may affect state support for higher education. Our fixed-effects model may be specified as (Equation 1): OLS Fixed Effects Model y st = a + b 1 p st + b 2 h st + b 3 e st + τ t + δ s + v st (1) where y is the dependent variables; a is the intercept coefficient; p st represents the vector for the various political variables; h st represents the vector for the various higher education control variables; e st represents the vector for the various economic and demographic control variables; τ t represents the year effects; δ s represents the state effects; v st is the pure residual; s and t are indices for individual states and time; and b 1, b 2, and b 3 represent the coefficients associated with the variables included in each vector. In order to test whether the difference between the coefficients associated with significant variables across models are statistically significant, we employed two-tailed t-tests. We used a Variance Inflation Factor (VIF) test to evaluate the multicollinearity among the independent variables, and the results indicated that multicollinearity was not a concern (UCLA Academic Technology Services, 2006; Williams, 2005). Additionally, we conducted a Hausman test to determine whether a fixed effects or random effects model would provide for a better specification for our model. The results indicated that there was no improvement using the random effects, so we used a fixed effects model for our multivariate analysis. For descriptive statistics, please see Table 1.

20 Capital and General Fund Appropriations 347 Discussion of Findings The results from our analysis suggest convergence and divergence of the determinants of state spending on higher education between the general expenditures and capital spending models. Table 2 provides a summary chart of the statistically significant results for both models and reports the difference in magnitude for the eight variables that are significant determinants of general fund and capital project spending (see Appendices B and C for the full results of both models). The results in Table 2 are reported as standardized regression coefficients. Therefore, the size of the coefficients for the variables can be directly compared within and across the models. Overall, the findings suggest a high level of internal validity based on similarities between models, such as that 9 out of the 10 variables significant in both models are significant in the same direction. Likewise, each model has the same number of significant variables (11). Our findings also reveal differences between the effect of the variables from all three variable blocks: political, higher education, and economic and demographic. For the capital spending model, 6 of the 11 significant variables are in the political block compared to 5 of 11 significant variables in the general fund model. The differences in the results of the two models are primarily exhibited in the size of the effects rather than in the direction of the effect or in the level of significance. For example, in regard to the political variables, in each case where the variable is significant in both models the effect size is larger in the general fund expenditures model. The clear exception, however, is the higher education interest group ratio, which has a larger positive effect on capital expenditures. there are three additional differences in the results from the two models. First, budget powers of the governor has a positive and significant effect on general fund expenditures but has a negative and significant effect on capital expenditures. Further, political ideology does not have a significant effect on general fund expenditures while it has a positive and significant effect on capital expenditures. Finally, the share of state spending devoted to Medicaid has a negative and significant effect on general fund expenditures but not a significant effect on capital expenditures. the difference between R-square values shows clearly that our model explains more of the variance for general fund expenditures (.58) than for capital spending (.33). This is not surprising given that we drew the variables overwhelmingly from studies of overall state higher education expenditures, so we expected a stronger model-fit with general funds.