higher education and congressional influence on administrative decisions: an examination of nsf and...

Higher Education and CongressionalInfluence on Administrative Decisions:An Examination of NSF and NIHResearch Grant Funding toFour-Year Universities∗

Thomas M. Rabovsky, University of Oklahoma

William Curtis Ellis, Auburn University, Montgomery

Objective. This study examines grant funding to four-year universities to determineif institutions with more powerful congressional delegations receive more in researchfunding from the National Institutes of Health (NIH) and the National ScienceFoundation (NSF). Methods. We analyze grant awards to public and private not-forprofit universities from 2000 through 2009. We employ panel-corrected standarderrors with panel-specific AR1 terms to determine the influence of representationon key congressional committees, in conjunction with institutional characteristics,such as size and mission, in shaping institutional success in securing grant revenues.Results. Time series, cross-sectional analysis suggests that members of the U.S. Houseand Senate may be able to influence the allocation of seemingly merit-based grantsand contracts made by the NIH and the NSF. Conclusion. We find some evidencethat congressional-bureaucratic relationships impact grant receipts, though the effectsare moderate in magnitude.

Higher Education and Congressional Influence on Administrative Decisions

As tuition and fees have skyrocketed at American colleges and universitiesover the past decade and a half, higher education finance has become increas-ingly salient in both popular and academic circles. Much of the literatureon higher education finance focuses on the extent to which current financialtrends, particularly regarding the finance of public institutions, impact studentoutcomes (Bettinger, 2004; Blose, Porter, and Kokkelenberg, 2006; Gansemer-Topf and Schuh, 2006; Ryan, 2004; Titus, 2006, 2009; Zhang, 2009) and theoverall quality of education provided by American colleges and universities

∗Direct correspondence to Thomas M. Rabovsky, Indiana University, Bloomington, 1315 E.10th St., Rm. 201, Bloomington, IN 47405 〈[email protected]〉. Thomas Rabovsky willshall share all data and coding for replication purposes. The authors thank Alisa Hicklin Fryar,Thaddieus Conner, Matthew Nowlin, and the rest of the policy group at the University ofOklahoma for their helpful comments and suggestions on this project.

SOCIAL SCIENCE QUARTERLY, Volume 95, Number 3, September 2014C© 2014 by the Southwestern Social Science AssociationDOI: 10.1111/ssqu.12001

Higher Education and Congressional Influence 741

(Bowen et al., 2005, 2009; Zumeta et al., 2012). Traditionally, studenttuition and subsidies from state governments have been the primary sourcesof funding for public universities in the United States, and as a result, thevast majority of the literature concerning higher education finance focuses onthese two streams of revenue. In recent years, however, as costs at institutionsof higher learning have increased faster than state governments can match,other revenue sources, such as funds related to research grants and contracts,have taken on an increased significance (Weisbrod, Ballou, and Asch, 2008).

Currently, scholars know very little about the systematic relationships thatinfluence decisions by federal agencies to award grants to colleges and uni-versities. Until recently, the higher education community tended to focus onnonpolitical forces in terms of explaining patterns in higher education finance(McLendon, 2003), but in the last decade there have been several notableworks that used theories of politics and public policy to explain patternsin higher education finance (Lowry, 2001; McLendon, Hearn, and Mokher,2009; McLendon, Mokher, and Doyle, 2009; Okunade, 2004; Richardsonand Martinez, 2009; Tandberg, 2006, 2010; Wong and Shen, 2002). We buildon this growing body of research by employing a framework of political con-trol and influence to better understand how federal agencies make decisionsabout grant applications.

Federal Support of Higher Education

The federal government is typically viewed as a secondary figure in highereducation finance, with state governments and student contributions gar-nering most of the attention. To the extent that scholars have focused onfederal support of higher education, they primarily research patterns in theway that indirect federal payments to institutions, via Pell grants and studentloan programs, impact higher education finance and enrollment fluctuations(Bettinger, 2004; Shin and Milton, 2006; St. John, Paulsen, and Carter, 2005).While these payments comprise a significant portion of federal support forhigher education, they are not the only form of federal financing provided tocolleges and universities.

In addition to indirect payments through student financial aid, the federalgovernment also disperses a significant amount of money directly to institu-tions. These direct funds come in two primary forms. First are federal appro-priations, which are essentially identical in function to state appropriationsin that they are specified via legislation rather than dispersed via grants, andthat universities have significant discretion in terms of using the money. Thesecond type of direct federal payment involves grants and contracts, which aregenerally related to specific research endeavors, and have greater restrictionson the ways the money can be spent (Paulsen and Smart, 2001).

As opposed to federal appropriations, which are only awarded to a hand-ful of schools (mostly land grant institutions, historically black colleges

742 Social Science Quarterly

and universities, and Hispanic-serving institutions), virtually every majoruniversity receives some federal funding for research grants and contracts.Such revenue has become increasingly vital to support university activities(Weisbrod, Ballou, and Asch, 2008). According to the Integrated Postsec-ondary Education Data System (IPEDS), federal operating grants and con-tracts constituted slightly more than 10 percent of revenues for public uni-versities during the 2007–2008 fiscal year. The other important differencebetween grants versus appropriations is that bureaucrats, rather than legisla-tors, are empowered to make funding decisions. Thus, conventional wisdomholds that the competitive grant process is primarily driven by the rational con-sideration of objective, merit-based criteria, rather than the political bargainsthat dominate the appropriations processes.

While significant attention has been paid to the potential tensions thatuniversities face in balancing the need for building research capacity to securecompetitive grants (in addition to increasing the institution’s national prestige)versus pressures for maintaining instructional support for undergraduate edu-cation (Grunig, 1997; Weisbrod, Ballou, and Asch, 2008), very little researchhas systematically analyzed variation in institutional success when it comes toreceiving federal funding for research. To the extent scholars have exploredfactors that predict success in receiving research funding, they have largelyfocused on individual professors within institutions as the unit of analysis,rather than on institutions as a whole (Ali, Bhattacharyya, and Olejniczak,2010; Liebert, 1977; Wood, 1990).

Because competitive grants are administered by federal agencies that aredesigned to evaluate applications for funding on the basis of merit, we expectthat competitive grants funding will primarily flow to those institutions thathave the greatest capacity to conduct high-quality research. We also recognizethat universities, like interest groups, keep tabs on local and national politicalevents and actively participate in lobbying activities to influence governmentpolicy (Cook, 1998; Slaughter and Rhoades, 2004). More importantly, theyrely on members from their congressional delegation to advocate on theirbehalf and help them secure financial resources. For their part, membersof Congress are often happy to aid in assisting universities in their districtsreceive grants, as this provides an opportunity for them to claim credit forsecuring additional federal spending that will flow to their constituents. Itis not at all uncommon to see elected officials appear at press conferencesalongside university presidents when grant awards are announced, boastingabout their role in supporting federal research programs and that help localinstitutions. For example, in February 2012, after the University of Marylandat Baltimore County (UMBC) received a $599,997 grant from the NationalScience Foundation (NSF), Senator Barbara Mikulski (Chairwoman of theSenate Appropriations Subcommittee that funds the NSF) released a pressstatement in which she said the following:


Education is the opportunity ladder of this nation and higher education is acritical rung in that ladder. STEM graduates are in demand to fill jobs now.Every student, regardless of race, deserves a chance to excel in those fields.Maryland has the resources and the infrastructure to guarantee any studenta rich experience, not to mention job prospects. I’m proud to put funds inthe federal checkbook to ensure that Maryland continues to be a leader inpreparing our future workforce for jobs today and jobs tomorrow. (Mikulski,2012)

Thus, while grant agencies often largely make decisions based on the meritof the proposals that institutions submit, we expect that congressional rela-tionships with those agencies responsible for awarding competitive researchgrants are also likely to influence the amount of funding institutions receive.

Politically Motivated Bureaucrats and Political Representation

Though not originally defined as a prominent actor in policy making bythe Constitution, the federal bureaucracy developed into a central player inshaping public policy (Meier and Bohte, 2007). Rather than mechanicallyimplementing legislation in a straightforward and neutral manner, scholarsnow argue that bureaucrats are often in a position to use substantial discre-tion in ways that fundamentally shape public policy and the policy agenda(May, Workman, and Jones, 2008). In large part, this discretion is a result oflimited congressional capacity or motivation to formulate specific statutoryrestrictions on administrative behavior (Epstein and O’Halloran, 1999) or toengage in aggressive oversight once legislation has been enacted (McCubbinsand Schwartz, 1984). Often, the regulatory activities engaging bureaucraciesinvolve highly complex technical questions that require considerable expertise,and the enforcement of sanctions is unpopular among interest groups and con-stituent populations. By crafting vague policy and creating passive “fire alarm”systems to monitor agencies, legislators are able to distance themselves fromundesirable regulatory mechanisms and can claim credit for positive out-comes when they correct administrative missteps (Epstein and O’Halloran,1999; McCubbins and Schwartz, 1984).

Because bureaucrats are not directly accountable to the voting public viaelections (and often have legal protections that limit the extent to which politi-cal actors can hire or fire them), administrative discretion represents a potentialthreat to democracy (Bertelli and Lynn, 2006; Finer, 1941; Lowi, 1969). Asa result of this tension between the politically elected branches and the un-elected bureaucracy, the relationship between Congress and federal agenciesis often believed to be adversarial in nature. Members of Congress, who mustbalance their electoral and policy objectives against the threats posed by anuncooperative bureaucracy, seek to find an equilibrium between deferenceto administrative experts versus active oversight (Epstein and O’Halloran,1999; McCubbins and Schwartz, 1984; McCubbins, 1985), while actors in


the bureaucracy leverage their expertise to protect and expand programs andto advance their own policy goals (Niskanen, 1971).

Arnold (1979), however, argues that when agencies are in a position toallocate funding to local service providers, the relationship between legisla-tors and bureaucrats is likely to take on a more symbiotic character. Onceconstituents within a member’s district begin to apply for and receive federalfunding, congressmen have an electoral interest in maintaining the program’sexistence and, if possible, expanding its scope so they can increase the num-ber of constituents who benefit (Arnold, 1979; Stein and Bickers, 1995).Strategically, adept bureaucrats will then manipulate the grant process (withinreasonable limits) to build and maintain coalitions of support within the legis-lature. Thus, rather than deciding grant applications based solely on objectivemerit-based criteria, bureaucrats will attempt to build political capital by dis-proportionately awarding grants to applicants who come from districts thatare represented by influential members in Congress. Stein and Bickers (1995)extend this argument by illustrating the central role that the grant-makingprocess plays in allowing policy subsystems to maintain themselves over timeby reenforcing mutually supportive relationships between legislators, interestgroups, constituencies, and public agencies. As they write, the grant-makingprocess is a critical driver in the policy process at the federal level because suc-cessful use of this process ensures that “actors internal to the policy subsystemare able to mutually assist each other in gaining the resources to pursue theirindividual interests” (Stein and Bickers, 1995: 142).

Not all members of Congress are likely to benefit equally as a result of thisprocess. The committee system empowers certain legislators with a dispropor-tionate amount of control over programs within their jurisdiction, and thuspolitically motivated bureaucrats who are seeking to expand program supportwill tend to focus their energies on members who sit on the relevant commit-tees rather than on the chamber as a whole (Arnold, 1979). Further, the extentto which bureaucrats will divert grant awards to politically powerful districtsis in large part a result of the political environment. Agencies responsible foradministering programs that are more distant from political influence, widelypopular, and/or largely seen as legitimate have less need to build additionalpolitical capital, and are thus more likely to base grant decisions entirely onmerit criteria.

Subsequent scholarship has generally found support for Arnold’s frame-work (Bickers and Stein, 2004; Gryski, 1991; Shepsle and Weingast, 1981;Svorny and Marcal, 2002; Svorny, 1996), though it is important to note thatsome scholars have found strong evidence that the extent to which politicalinfluences will impact grant dispersal will vary significantly across issue ar-eas and between the two chambers of Congress (Copeland and Meier, 1984;Heitshusen, 2001; Lazarus and Steigerwalt, 2009). Currently, however, verylittle existing research systematically examines political influences on highereducation funding at the federal level. For reasons previously discussed, fed-eral funding is likely to be increasingly salient within the higher education


FIGURE 1

NSF and NIH Grants by Congressional Representation

community, and thus this analysis seeks to fill in an important gap in knowl-edge regarding the relationship between Congress and the agencies that areresponsible for funding research at America’s colleges and universities. To doso, we focus on two organizations that account for a large percentage of re-search grant and contract funding to colleges and universities: the NSF andthe National Institutes of Health (NIH).

Data and Methods

We downloaded data on grant awards to American colleges and universi-ties between 2000 and 2009 from the websites of the NSF and NIH, andincluded all public and private not-for-profit four-year institutions that aredegree granting and classified as baccalaureate or higher in our data set.1

Figure 1 provides a brief glimpse as to how this funding is allocated to institu-tions with and without representation on important congressional committees.Although we are cautious about putting too much emphasis on this displaywithout considering the impacts of potentially confounding variables, suchas institutional characteristics, Figure 1 suggests that for both the NSF andNIH, funding decisions are somewhat responsive to congressional influenceas measured by representation on various oversight and appropriations com-mittees. With this preliminary evidence in mind, we now turn toward moresophisticated statistical analysis to better understand these relationships.

1We exclude service academies and schools located in the District of Columbia becausethese institutions have an atypical relationship with the federal government.


One limitation with our grant data from the NIH and NSF is that thedistributions of these funds are highly skewed, with some highly influentialoutliers (in particular, there are a few high-intensity research institutions thatreceive hundreds of millions of dollars more in funding than the averageinstitution). Thus, we logged both of these variables in the multivariate analysisthat follows. Because one cannot take the natural log of zero, this approach alsohas the effect of dropping institutions that did not receive grant funding fromthe analysis. One possible solution to this would be to add a trivial number(i.e., $1) to grant totals for NIH and NSF and to then take the naturallog. Unfortunately, even doing so creates problems, both methodological andtheoretical, as there are a large number of institutions that receive no fundingat all. From a methodological standpoint, the problem arises from the factthat these grants are typically fairly substantial, such that there are very fewvalues between 0 and 5 for the logged variables (see Figure 2). In essence,there is a cutoff threshold, below which universities receive no funding, butabove which they receive considerable funding. From a theoretical standpoint,it may not make sense to include all institutions in the analysis to begin with.While many universities actively pursue grants for research, there is also alarge portion of institutions (primarily teaching colleges) for which this is nota major concern. In other words, there are many universities that do not “playthe game” so to speak, in terms of seeking research grants, and that wouldtherefore not be able to benefit from congressional representation. For thesetwo reasons, we restrict our analysis to institutions that actually received somefunding from the NIH and NSF, respectively.

In keeping with existing literature on congressional influence in the grant-making process, we employ several independent variables to predict NIHand NSF funding. In terms of political representation, we include a seriesof dichotomous variables that indicate whether the member representing thedistrict where the institution is physically located sits on relevant committees(Stewart and Woon, 2009). Following the lead of Arnold (1979), we focus ontwo types of committees in each chamber. First is the committee with oversightresponsibility. In the case of the NSF, we focus on the House Committee forScience and Technology and the Senate Committee on Commerce, Scienceand Transportation. For the NIH, we include the House Committee on En-ergy and Commerce and the Senate Committee on Health, Education, Laborand Pensions. In addition to these oversight committees, we also include di-chotomous variables to indicate whether the university’s district is representedon the Appropriations Committee in each chamber. Further, for each of thecommittees listed above, we also include a measure for whether the member isthe chair of a committee, as they are likely to wield even more influence thanregular committee members (Arnold, 1979; Denzau and Munger, 1986).

We also hypothesize that the seniority of their congressional delegationwill have a positive influence on the amount of funding that institutionsreceive. For the House, we measure seniority by the number of terms that themember representing the district where an institution is located has served. For


FIGURE 2

Distribution of NIH and NSF Grants

the Senate, measuring seniority is more complex because one must accountfor the delegation, rather than an individual member. One approach wouldbe to simply take the average of the number of years in the Senate that eachmember has served. One problem with this method is that it masks potentiallyimportant differences between Senate delegations. For example, a state withone senator who has zero years of experience and another with 12 years ofexperience would have the same average as a state with two senators, eachhaving six years of experience. Thus, we instead square the number of years inthe Senate for each member, sum the product, and then take the square root(i.e., Senate delegation seniority = �(YearsinSen12 + YearsinSen22)). Using


this approach, a delegation of two senators with six years each scores 8.48,while a delegation with one freshman and one 12-year incumbent scores a 12.

In addition to political representation, we also hypothesize that institutionalcharacteristics will influence the amount of funding that schools receive.Qualities such as the size and selectivity of the institution and its capacityto enroll graduate students who can aid in research endeavors are all likely tohave a significant influence on the amount of competitive research grants thatthe university is able to secure. We use the IPEDS, a publicly available dataset administered by the National Center for Education Statistics (NCES), toconstruct a measure of selectivity (average SAT2 scores of incoming cohorts),and to collect data on student body demographics (total enrollment, percentgraduate) and the number of full-time faculty that it employs. We also includevariables to measure a variety of institutional qualities that may be relevantfor research funding. We expect that universities for which research activityis a central part of the mission are likely to be much better positioned tocompete for grants than those who focus more heavily on the production ofbaccalaureate or masters degrees, so we include a dichotomous variable thatidentifies those institutions that are classified as research institutions accordingto the Carnegie 2005 basic ranking system.

There are also some reasons to believe that research funding will be differ-ent at public versus private not-for-profit institutions. First, public universitiesreceive state appropriations and other assistance from state and local govern-ments, and thus may have less need to pursue grants from the federal govern-ment. Second, many public universities (particularly nonflagship institutions)face pressures from state policymakers to focus primarily on undergraduateeducation and workforce preparation, rather than on research productivity.Thus, we include measures for the amount of funding that the institutionreceived in state appropriations,3 a measure for the extent to which an institu-tion is reliant on tuition for revenue,4 and a dichotomous variable for sector.We also include an interaction term for state appropriations and sector tomeasure whether public universities are more responsive to shifts in previouslystable sources of funding than their private counterparts. As was the case withour student body and faculty variables, these data come from IPEDS.

As state support for higher education has declined relative to other sourcesof revenue, some scholars have voiced concerned about threats to equity that

2In order to have a measure of selectivity that would be comparable for across regions,we weighted each institution’s median composite ACT score by the number of students whosubmitted scores, and then converted this number to the SAT equivalent.

3It should be noted that while state appropriations are primarily allocated to public institu-tions, there are many instances where private universities also receive this type of funding.

4Tuition reliance is calculated by dividing revenues from tuition and fees, minus institutionalgrants for student financial aid, by total revenues collected through tuition and federal, local,and private grants and contracts. Because some institutions actually allocate more money ininstitutional grants than they take in from tuition and fees, negative values indicate that an in-stitution actually loses money through enrollment and tuition, while positive numbers indicatethat the institution is more reliant on tuition to cover basic expenses. For more information onthis variable and others related to higher education finance, visit <www.deltacostproject.org>.


could potentially result from increased reliance on private funding and com-petitive research grants (Cheslock and Gianneschi, 2008). One mechanismthat many grant-making agencies employ to try and advance equity is theuse of targeted grants, such as the NSF’s Historically Black College and Uni-versity Undergraduate Program (HBCU-UP), that are specifically aimed atinstitutions with a history of serving minority students. Thus, we include adichotomous variable to control for HBCU status.

Finally, we include a variable that identifies those institutions that operate ahospital at the main campus, which we employ for two reasons. First, medicalresearch is the primary focus of the NIH and life science/biological discoveryresearch is a priority of the NSF. We expect that those institutions that operatea hospital on the main campus are better able to conduct this kind of research.Second, many universities and systems have separate health science centers, ormedical campus locations that are responsible for medical research. Althoughthese medical centers are generally affiliated with a particular institution oruniversity system, they are often located in a different town or congressionaldistrict than the main campus, and they are treated by the federal governmentas a separate administrative entity. Thus, grants to these locations are notcounted as part of the total for the main campus, and are therefore notincluded in our measures for research funding. Universities with a hospital onthe main campus, then, are also likely to receive more federal funding simplyby virtue of the fact that their medical research activities are not conducted ata separate location. Data on Carnegie classification, sector, and the operationof a hospital also come from IPEDS. Summary statistics for both of ourdependent, as well as our independent, variables can be found in Table 1.

Methods

Because these data consist of both time series and cross-sectional elements,they present both advantages and challenges. The advantage of time seriescross-sectional (TSCS) data is that it allows for comparison across both timeand units (institutions), rather than comparing changes within a single institu-tion over time, or comparing differences across all of the institutions at a singlepoint in time (as was done in the previous section). Unfortunately, TSCS dataoften violate several OLS assumptions regarding the structure and correlationof error terms, and thus requires modeling techniques that are robust againstboth autocorrelation and heteroskedasticity (Beck and Katz, 1995). There hasbeen a lengthy debate within the literature regarding the proper way to modelTSCS data (Wilson and Butler, 2007), and scholars have proposed a variety ofstrategies (Beck, Katz, and Tucker, 1998; Beck and Katz, 1995; Parks, 1967;Plumper and Troeger, 2007; Shor et al., 2007; Stimson, 1985).

We follow the advice of Beck and Katz (1995) and employ panel-correctedstandard errors and panel-specific AR1 terms. We employ a set of models that


TABLE 1

Summary Statistics

Mean SD Minimum Maximum

NIH grants (in dollar millions) 7.79 42.8 0 680.1NSF grants (in dollar millions) 2.74 11.0 0 204.4NIH grants (logged) 14.5 2.40 6.59 20.3NSF grants (logged) 13.7 2.15 6.22 19.1Total enrollment (in 1,000s) 6.37 8.25 0.014 68.1Percent undergraduate students 82.9 18.4 0 100HBCU 0.053 0.22 0 1Public 0.37 0.48 0 1SAT/ACT scores 1,060 125 515 1,530Research university 0.18 0.38 0 1Tuition reliance 0.504 .911 −58.65 54.24Hospital 0.023 0.15 0 1Number of full-time faculty (in 1,000s) 0.26 0.34 0.0010 3.44State appropriations (in dollar millions) 31.6 76.9 0 814.4House seniority 5.78 4.07 1 28Senate seniority 19.2 11.4 1.41 59.2

can be written as follows:

Yit =a+Pi t+Si t+Ii t+At+εi t ,

where Yit is the amount of funding that an institution received at time t,Pit is a vector of independent variables measuring the political representa-tion (committee/chair assignments and seniority) of the institution at time t,Sit − 1 is a vector of independent variables for student characteristics (en-rollment, number of full-time faculty, incoming SAT/ACT scores, percentgraduate students) at t, Iit is a vector of independent variables for institutionalmission and funding characteristics (Carnegie classification, sector and stateappropriations) at t, At represents the average amount for grant awards tocolleges and universities at time t (which we include to control for time trendsin the data), and εit, which is the error term.

Findings

Table 2 lists the results for both NIH and NSF grants. One advantage tousing logged dependent variables such as ours is that it allows for relativelystraightforward interpretation in terms of substantive effects. Although thecoefficients for regression models on untransformed dependent variables canbe interpreted as the effect that a one unit change on the independent variablehas on the dependent variable (DV), when dealing with logged DVs, one caninterpret the substantive effect of a given independent variable in terms of


TABLE 2

Grant Funding for NIH and NSF

NIH Grant Awards NSF Grant Awards(Logged) (Logged)

Institutional characteristicsEnrollment (in 1,000s) 0.042∗∗∗ (0.01) −0.003 (0.01)Percent undergraduate students −0.011∗∗∗ (0.00) 0.008∗∗∗ (0.00)HBCU 0.650∗∗∗ (0.11) 1.010∗∗∗ (0.08)Public −0.173∗ (0.08) 0.024 (0.05)SAT/ACT scores (in 100s) 0.040∗ (0.02) 0.054∗∗∗ (0.02)Research university 1.286∗∗∗ (0.10) 1.410∗∗∗ (0.07)Tuition reliance −0.313+ (0.17) −0.084∗ (0.04)Hospital 2.757∗∗∗ (0.16) 0.450∗∗∗ (0.10)Full-time faculty (in 1,000s) 1.191∗∗∗ (0.19) 2.787∗∗∗ (0.16)State appropriations (in millions) 0.007∗ (0.00) 0.004 (0.00)Public ∗ State appropriations −0.006∗ (0.00) −0.003 (0.00)Congressional representationHouse seniority −0.007 (0.01) −0.000 (0.00)Senate seniority 0.007∗∗∗ (0.00) 0.008∗∗∗ (0.00)House oversight member 0.011 (0.06) −0.001 (0.06)House oversight chair −0.081 (0.15) −0.041 (0.32)Senate oversight member 0.089∗ (0.04) 0.074∗ (0.04)Senate oversight chair 0.085 (0.06) 0.374∗∗ (0.14)House appropriations member 0.000 (0.06) −0.076 (0.06)House appropriations chair −0.329 (0.28) −0.387 (0.29)Senate appropriations member −0.023 (0.04) 0.130∗∗∗ (0.04)Senate appropriations chair 0.023 (0.11) −0.040 (0.18)Average NIH/NSF award 0.071∗∗∗ (0.02) 0.287∗∗∗ (0.03)Constant 11.855∗∗∗ (0.36) 9.309∗∗∗ (0.24)Number of universities 536 816Observations 3432 5118R2 0.985 0.978

Panel-corrected standard errors in parentheses. Significant at +p < 0.10, ∗p < 0.05,∗∗p < 0.01, ∗∗∗p < 0.001. House Oversight Committees = Energy and Commerce (NIH);Science and Technology (NSF). Senate Oversight Committees = Health, Education, Laborand Pensions (NIH); Commerce, Science and Transportation (NSF).

percentage changes on the dependent variable, equal to 100 multiplied by thecoefficient. Thus, a coefficient of 0.1 would indicate that a one unit increaseon the IV results in a 10 percent increase on the DV (Wooldridge, 2009).

Turning first to our models for NIH grant funding, there are several impor-tant findings. As expected, we find positive relationships for research missionsand the operation of a hospital with respect to NIH grant funding. Institu-tions that are classified as research institutions by the Carnegie classificationsystem receive approximately 127 percent more in NIH grant funding thanother universities, holding all else constant, while those that operate a hospitalon the main campus receive approximately 276 percent more. Additionally,public universities receive less NIH funding, all else equal, than their private


counterparts. We also find positive and statistically significant relationshipsfor HBCUs, total enrollment, faculty size, and selectivity. For total enroll-ment, a 1,000 student increase results in approximately 4.2 percent moreNIH funding, and similar increases in full-time faculty result in 119 percentmore funding. Conversely, every unit increase in the percentage of studentswho are undergraduates decreases predicted funding by about 1.1 percent.With regards to SAT/ACT scores of incoming freshmen, every 100 pointsresult in a predicted impact of 4.0 percent in NIH funding.

Finally, and somewhat contrary to the established narrative about the mo-tives for institutions to seek additional research funding, we find a positiverelationship between state appropriations and NIH funding. This impliesthat it may be the case that the adage “it takes money to make money” is truewhen it comes to higher education. In order to secure more outside funding,institutions may need discretionary resources to invest in activities, such aslobbying or hiring expensive star faculty members who are highly productive.Paradoxically then, as institutions lose money from other sources, they have agreater need for revenues from research and competitive grants, but may beless able to secure such funding. Interestingly, the interaction term for publicuniversities with state appropriations is negative, which indicates that publicuniversities benefit less from state appropriations with respect to NIH fundingthan do private institutions. We also find, as expected, a negative relationshipbetween institutional reliance on tuition and success in securing NIH funding.

With regards to our political representation variables, we find mixed supportregarding congressional influence. Two of our variables for political represen-tation are, as we hypothesized, positive and statistically significant, but it isimportant to note that the magnitude of the effect is often quite moderate.We find positive effects for Senate seniority and membership on the Senateoversight committee. Every year of service in the Senate that an institution’sdelegation serves results in a 0.7 percent increase in the amount of competi-tive research grants. For committee assignments, membership on the SenateHealth, Education, Labor and Pensions Committee results in an 8.9 percentpredicted increase in NIH funding.

Turning next to our findings with respect to the NSF, many of these rela-tionships persist, though there are some important contrasts. One differencethat emerges is that we see a slight positive effect for the percentage of stu-dents who are undergraduate. We also note that public universities are notstatistically different from private universities with respect to NSF funding.As one would also expect, universities that operate a hospital on the maincampus receive more NSF funds, all else equal, than those that do not, butthe magnitude of this effect is not as large as was the case for the NIH. Finally,we find no relationship between state appropriations and NSF funding.

With regards to the political representation variables, we again see mixed evi-dence in support of congressional influence on administrative decision makingwith respect to grant funding. As was the case with the NIH, Senats seniorityand Senate oversight membership are positively related to NSF funding. A one


unit increase in Senate seniority yields a 0.8 percent increase in NSF funding,while membership on the Senate Science and Technology Committee resultsin 7.4 percent more in grant funding. We also find that chairmanship of theSenate Oversight and membership on Senate Appropriations are positivelyrelated to NSF funding. Chairmanship on the Senate Science and TechnologyCommittee results in a 37.4 percent increase in predicted NSF funding, whilemembership on the Senate Appropriations Committee nets an additional 13percent in NSF grants.

Overall, the results in Table 2 provide some mixed support for our centralhypothesis that NIH and NSF grant awards are partially driven by consid-eration of the political clout of each institution’s congressional delegation.Assessing the substantive impact of political representation funding, however,is slightly more complex. In both cases, it appears that university character-istics, such as sector, mission, student body, and faculty characteristics, andwhether it operates a hospital on the main campus are often more importantthan is political representation. Nevertheless, the differences in grant fundingbetween those institutions with politically weak congressional delegations ver-sus those with experience and relevant committee membership are also clearlylarge enough to be substantively important for university finances, particularlygiven the current fiscal environment and the fact that university budgets havebecome increasingly constrained in recent years.

Limitations and Directions for Future Research

We do not argue that federal grants to higher education are overwhelm-ingly dominated by political influence. In fact, our data support the oppositeconclusion—that both the NSF and the NIH have implemented fundingprocedures that are primarily driven by merit-based criteria. We do, however,find that congressional bureaucratic relationships appear to directly impacthigher education grant receipts in ways that are likely to have substantivelymeaningful implications for how universities operate. While we expected con-gressional representation to affect NIH funding, we were somewhat surprisedto learn that political clout has even stronger impacts when it comes to theNSF. There are a number of reasons to expect that the NIH would be moresusceptible to congressional influence.

First, according to the mission statements of both organizations, the NIHand the NSF work toward very different ends. The NSF mission is to support“all fields of fundamental science and engineering, except for medical sciences”and to keep “the United States at the leading edge of discovery” (NSF—WhatWe Do). The NIH, by comparison, is “designed to improve the health of theNation by conducting and supporting research” (NIH—About NIH) and isinvolved in variety of public outreach efforts (NIH—Public Involvement withNIH). These statements reveal systematically different organizational goals.The NSF promotes science for the sake of science, whereas the NIH promotes


scientific research in the pursuit of social responsibility and public welfare thatare inherently political issues.

Additionally, the mechanisms these organizations rely upon to identify re-search priorities is markedly different. Unlike the NSF, the NIH openly statesthat it “seeks advice” from “the Congress and the Administration” in build-ing research priorities (NIH—Research Planning). In fact, political meddlingwith NIH definitions (Eskinazi, 1998), priorities (Drazen and Ingelfinger,2003), committee selection (Steinbrook, 2004), and the funding of specificprojects (Drazen and Ingelfinger, 2003; Marra, 2004) is an open concernin the medical research community. Thus, political representation may bothinfluence consideration of individual grants, and it could work in a moreindirect manner by shifting the criteria regarding which types of proposalsshould be viewed as most important toward programs and initiatives whereuniversities in better represented districts have a competitive edge. By con-trast, such concerns about political meddling with NSF priorities are not asnumerous. Correspondingly, the NSF boasts of a “bottom-up” process thatallows experts in various fields of science and engineering, who are employedby external research organizations (including universities), to identify cutting-edge research and areas of need in the research information community fromthe actual communities (NSF—How We Work).

We initially hypothesized that the direct link between political institutionsand building of NIH research priorities may create traceable links betweenpolitical institutions and the distribution of NIH funding to colleges anduniversities in ways that would not be present with the NSF. Instead, however,we found that congressional influences were actually stronger when it cameto NSF funding than the NIH, which implies that the relationships thatwe observe here are not the result of indirect influences on overall grant-making priorities, but are rather a result of more direct and narrowly focusedrelationships. Unfortunately, however, we are not able to fully disentanglethese causal relationships in this article given the data that we have, but itremains an important question for future research to consider.

One important variable that we are unable to explore in this article, but thatmay shed light on these findings, relates to issue salience and hostility in thelegislative environment that surrounds agencies and their missions. In recentyears, NSF funding, particularly funding for social science, has continued tobe a subject of debate within Congress, and this may have resulted in increasedresponsiveness on the part of the agency to political pressures within the pastdecade. Given these recent trends, it will be interesting to see if the politicalinfluences that we observe in this article persist and become more pervasive inthe years to come.

It is also important to note that our data are limited in their ability todirectly test the extent to which bureaucratic decisions are affected by politicalactors. It is possible that the relationships we observe are due to alternativeforces. For instance, there is a considerable body of literature that discusses themultitude of ways that members of Congress use staff resources to help educate


constituents about grant programs that they qualify for (Cain, Ferejohn, andFiorina, 1984; Fiorina, 1989). Perhaps universities that are represented onrelevant committees are better able to position themselves for research funding,not as a result of any political bargain between the committee member andthe bureaucracy, but rather by virtue of education and outreach efforts thatallow such institutions to better navigate the application process. Anotherpossibility is that legislators who serve districts with high research capacityuniversities are more likely seek membership on committees that overseeimportant grant programs. While this article marks an important initial steptoward understanding federal grant making with respect to higher education,more research is needed to explore the causal mechanisms that drive theserelationships.

Conclusion

Additional research funding not only helps institutions to increase in pres-tige, it also helps to supplement budgets in hard times. Colleges and uni-versities have a plethora of activities important to their core missions, andthe ability to perform research with external funding provides institutionsof higher education the opportunity to use internal resources on other im-portant endeavors that improve student outcomes and promote communitydevelopment (Weisbrod, Ballou, and Asch, 2008). We interpret our findingsto indicate that grants are largely awarded based on objective criteria relatedto institutional capacity to conduct research, but that political representationalso matters in important ways, particularly at the margins.

As higher education finance continues to trend away from reliance on stateappropriations, other sources of revenue, such as competitive grants for federalresearch, will continue to increase in importance. Our findings indicate thatinstitutional success in securing these funds will, to some extent, be influencedby characteristics of their congressional delegation. While further research isneeded to specify the exact causal mechanisms that guide the grant process,this article highlights the need for continued efforts to integrate theoriesfrom political science and public administration within higher educationscholarship.

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