The effects of congressional appropriation committee membership on the distribution of federal research funding to universities.
In 1994 members of the Republican Party pledged to seek legislation to impose term limits on members of Congress. This pledge stemmed from the popular belief that senior members of Congress tend to promote personal interests or are more influenced by lobbying efforts that may not be representative of their constituents. Today, term limits continue to be discussed but have not been enacted; instead, many members have focused their energies toward minimizing the time spent on any particular committee of Congress, believing that tenure on a committee is a more serious concern than simple tenure in Congress. Implicit in these concerns is the issue whether as incumbent politicians plan to retire they will behave differently and, if so, whether tenure on a congressional committee exacerbates this behavior.
This article explores the role of membership on the appropriations committee on the distribution of federal research funding to universities. Specifically, it explores whether funding is diverted to these universities because politicians use their position on a committee to promote personal or constituent interests. Previous research on shirking compares the voting records of politicians on certain issues with demographic and economic characteristics of the politicians' constituents. This article explores the issue of shirking differently. I explore how membership on congressional appropriations committees affects the distribution of federal research funding to universities. I look at two types of relationships between the members and the universities. First, I consider the relationship between members and the universities that are located in the members' districts (states in the case of senators). Second, I examine the relationship between members and their undergraduate alma mater. I use district representa tion to proxy favoritism that reflects a politician's constituents. Given that in most instances an alma mater affiliation is not the same as district representation, I use alma mater affiliation to proxy favoritism that reflects the politician's personal interests.
Federal research funding accounts for more than 60% of research funding received by research universities. Previous research has shown a positive impact of federal funding on research outcomes; see Adams and Griliches (1998), Arora and Gambardella (1997), Connolly (1997), Payne and Siow (2002), and Payne (2001). Except for Payne and Siow (2002), however, these articles do not consider that political diversion of funds may promote or detract from research productivity as with any other federal program.
This article concentrates on the impact of membership on the House and Senate appropriations committees because they wield the greatest power in the allocation of funding. (1) Using a panel data set spanning 26 years, I explore how changes in the composition of the appropriations committees affects the distribution of research funding after controlling for the heterogeneity that exists across different universities.
The results suggest that both district representation and alma mater affiliation matter. Thus there is evidence of shirking for personal interests and evidence of members representing their constituents. With respect to the representation of one's constituents, the strongest results suggest that public universities benefit from representation on both the Senate and House committees. Private universities, however, only benefit from representation in the Senate. With respect to alma mater affiliations, the results suggest again that public universities benefit from representation on both the Senate and House committees. Private universities also benefit from representation on both the Senate and House committees. The results also suggest that the tenure of the member on the committee matters, as does whether the member is a part of the majority party in power in the congressional chamber. Given that a politician is likely to favor an institution only if asked, the results also suggest lobbying efforts by public and private universities may differ.
The results suggest that members of Congress influence the distribution of federal research funding. As much as 39% of research funding is diverted for reasons associated with the representation of one's constituents. As much as 47% of funding is diverted for reasons associated with shirking. Thus, although most funding is distributed through federal agencies using a peer-reviewed process, politics has a role in diverting funding that might be given to other institutions based on a more objective process. These diversions reduce the potential effectiveness of the funding on research activities. As such, similar to the finding of Alvarez and Saving (1997), this study provides additional evidence that members of Congress have much more control over federal dollars than is commonly believed.
The article is set forth as follows. Section II presents a conceptual framework, and section III provides an overview of the appropriations process as it relates to research funding. Section IV discusses the data and methodology used to measure the level of political influence over research funding. Section V discusses the results, and section VI provides a brief conclusion.
II. CONCEPTUAL FRAMEWORK
Since World War II the federal government has played a significant role in funding basic and applied research. The federal government became more heavily involved as a result of its recognition that research is important for economic growth and that the private sector was underengaged in the research process. (2) Most agencies have adopted a peer-reviewed process for distributing research funding to universities. This process attempts to elicit information from researchers engaged in similar research about the quality of the projects for which funding is sought, seeking to minimize the politics associated with federal agencies and Congress. The agencies, however, are not completely autonomous from Congress. Thus Congress may indirectly influence the actions taken by the agencies. Because agencies receive their funding from Congress (with the approval of the president), Congress has several avenues by which to monitor and/or control an agency's actions, ex-ante and ex-post. Thus, as introduced by Miller and Mo e (1983), Congress and agencies are likely to be strategic in their actions, thereby creating a principal-agent relationship. (3) As discussed in Calvert and Fenno (1994), the degree to which an agency reflects the preferences of Congress depends on the level of information asymmetries between Congress and the agencies.
With respect to research funding, assuming that most members lack the information needed to evaluate the quality of research proposals, potential areas of influence they may exert over an agency may include funding allocated to an agency. Thus if Congress is not satisfied with the distribution of funding to certain schools or to geographic areas, funding to agencies in future years may be affected. For example, if members believe that more research funding should be devoted to such things as cancer research or a "star wars" defense program, the budget can be adjusted to focus more funding on these areas, thus potentially minimizing the discretion an agency may have over the distribution of funding within the agency. Similarly, if a member is from a region that is known to be an expert in a particular area of research, that member may seek to promote funding for programs related to that research area.
In recent years, Congress has affected the distribution of research funding in two more direct ways. (4) First, Congress can directly appropriate funding by earmarking specific amounts to particular universities. Earmarks started being allocated to universities in large numbers in the early 1980s. (5) Despite much media coverage concerning earmarks, they represent between 5% and 10% of total federal research funding. A second way Congress has affected the distribution of research funding is by encouraging agencies to develop set-aside programs, whereby agencies seek more competitive research proposals from researchers affiliated with universities that are located in states that have historically received low levels of funding. Set-aside programs were established in the early 1980s as pilot projects and have grown in the past 30 years. These programs are designed to improve the research infrastructure within the state, with the expectation that this will promote more competitive proposals by researchers locate d in the state that receives the funding. (6)
Given that there are several ways in which Congress may affect the distribution of funding, the next issue concerns for what purpose may a member of Congress seek redistribution. As set forth in Peltzman (1976; 1984) and others, a politician's actions may be driven from an interest to represent all or part of his or her constituents or for personal reasons. Although these reasons could stem from the politician acting alone, in most instances, the politician's behavior is likely to stem from lobbying from the person or institution that is likely to benefit from the actions taken by the politician. If there are vehicles by which a politician is able to take actions that do not reflect the interest of his or her constituents, the politician is considered to be shirking responsibility, as discussed in Huber et al. (2001) and Rothenberg and Sanders (2000).
As illustrated by Adler (2000), little research has examined the role of shirking with respect to the appropriations process. With respect to federal research funding, evidence of shirking as well as evidence that suggests a politician has exerted influence with respect to his or her district potentially diverts the funding away from projects that may be viewed as more socially desirable. (See Goff and Grier , Greene and Munley , Levitt and Snyder , Lott and Bronars , and Poole and Romer .) Thus diversions associated with district representation or alma mater affiliation represent a social cost that affects the research activities undertaken by universities.
There are several reasons why a politician may want to have funding distributed to the universities located in his or her district. First, given that research funding benefits the university by increasing the level of university resources, this will benefit the community and/or promote growth of other sectors within the district. Second, constituents may judge a politician by his or her ability to bring federal funding to the district. Thus, if politicians can affect the distribution of research funding, one should see an effect with respect to those universities located in the district represented by the politician.
Similarly, a politician may use the political process to encourage the distribution of research funding to a particular university for personal reasons. Distinguishing between the exertion of political influence for personal reasons and using this influence to help one's constituents is difficult with respect to most types of federal funding. With respect to funding to universities, however, we can use the alma mater affiliation of the politician as a measure of shirking and use the location of universities within a member's district as a measure of representation associated with one's constituents. Provided one's alma mater is not located within one's district, there is little reason to suggest that favoring one's alma mater promotes the interests of a politician's constituents. (7)
To explore the effect of political representation on the distribution of federal research funding, I examine the relationship between research and doctoral universities and the members of Congress that sit on the appropriations committee. As will be explained in more detail, for each member on the appropriations committee between 1972 and 1998, I identified the universities located in their district as well as the universities from which they received an undergraduate degree. In addition, I identified their party affiliation and tenure on the committee. With this information, I explore the questions of whether politicians affect the distribution of federal research funding and, if so, the extent to which the distribution is attributable to constituent interests or shirking.
III. APPROPRIATIONS PROCESS AND RESEARCH FUNDING
With respect to discretionary funding (funding that is not required to be allocated under mandatory entitlements, e.g., Social Security, Medicaid), the appropriations committee is responsible for the budgets of all agencies. (8) Much of the discussion concerning the structure of the budget is discussed and developed by the appropriations committees and subcommittees. In addition to determining the annual appropriations, these committees also provide funding guidance to agencies. Although agencies are not required to follow this guidance, it is expected that most agencies will comply with the wishes of the appropriations committees.
The classic work discussing the role taken by members of the appropriations committee is that of Fenno (1966). Members who are appointed to the appropriations committee are prevented from serving on other standing committees, thereby emphasizing the importance of their role on the appropriations committee. In general, research suggests members of this committee exert much power over the budget. Positions on the committee and the subcommittees are coveted. Provided a member is reelected, once on the appropriations committee, the member is likely to serve several terms on the committee. Because of the complexity of the government's budget and tenure on the committee, members develop a great deal of expertise with respect to the appropriations process. Thus other members of Congress tend to defer to the decisions made by the appropriations committees. The role of an appropriations subcommittee can be just as important (if not more so) as the role on the appropriations committee insofar as the subcommittee is res ponsible for the initial allocation to specific federal agencies.
With respect to the mechanics of the appropriations committee, the party in power of each chamber decides the number of members that will serve on the appropriations committee. Each party selects their members to the committee. The chair of the committee determines who serves on the 13 subcommittees. The budget process starts with the president submitting a proposed budget that includes each agency's request for funding. The level of detail for agency funding varies across the different agencies. The House appropriations committee reviews and changes the budget. The Senate acts second, acting more as an appellate body for the budget. (9) In the end, the two chambers and the president must approve the budget.
As discussed, there are several ways a university may receive special treatment with respect to research funding. The influence may stem from a member of Congress seeking special treatment for a particular university through its influence over the budget or over an agency. In this case, the member of Congress is not likely to seek special treatment for a given university unless that university actively lobbies the member for special treatment. The influence, however, could also come from an agency seeking some sort of favoritism (e.g., a better budget) from Congress. We should expect that most political influence is likely to be through the appropriations committees' interaction with the agencies responsible for distributing research funding to universities. Thus congressional influence over the direction of research funding is likely to be more through indirect means. (10) The specific relationship between the role of the university and the member of Congress with respect to lobbying is left for future resea rch. (11)
IV. DATA AND METHODOLOGY
The data for this project were gathered from two sources: congressional appropriations committee data and computer aided science policy analysis and research (CASPAR) data on federal funding and institutional characteristics. (12) For information on the congressional appropriations committees, I hand-collected data on congressional membership on the appropriations committee and subcommittees for both chambers of Congress for the period 1972 to 1998. Except for the occurrence of a death or resignation, both committees may change members every two years. (13) For each member that served on the appropriations committee during this period, I identified the state represented by the member, the political party affiliation of the member, the member's position on the committee, the undergraduate alma maters of the member, and the district of representation. (14) With respect to the member's position on the committee, there are three possible positions--majority and minority chairperson and general member. The majorit y and minority chairs are usually assigned to the senior members on the committee affiliated with the political party in power and the political party not in power, respectively.
I concentrate on the general members serving on the committee. (15) I study four effects on the distribution of research funding: first, the effect of having a member on the committee; second, the role of these members insofar as they are also a chair of one of the subcommittees that oversee the key agencies involved in research funding; third, the role of the members being a part of the majority or minority party that controls the chamber of Congress under study; and, fourth, the role of committee tenure of these members.
Using the CASPAR data set, I use the total annual federal research expenditures reported by the universities for the period 1973 to 1999. I combined this measure with the data on congressional representation and determined those universities for which there is alma mater and/or district representation for each year during the period under study. I limit my analysis to those universities with a Carnegie (1994) classification of research or doctoral university. (16) This leaves 220 universities that I can analyze. (17) Approximately 54% of these universities are classified as a research university.
Seventy-two of the universities have alma mater representation, and 186 of the universities have district representation at some point during the sample period. Of the 72 universities with an alma mater affiliation, 68 universities also have a district affiliation in the House or Senate during the sample period. Thus it is not uncommon for a university to have both district and alma mater representation during the sample period, although a given member is not likely to represent and have an alma mater affiliation with the same university. To the extent a member has both types of affiliations, this occurs most commonly with respect to representation on the Senate committees. Across chambers, a university with an alma mater affiliation with a member on the Senate committee also has an alma mater affiliation with a member on the House committee in 27% of the observations. For only 12% of these observations, however, is there a member affiliated with the majority party in power on both the House and Senate committees.
Of the 186 universities that are located in the district (or state for the Senate) represented by the committee members, 68 also have an alma mater affiliation during the sample period. Across chambers, a university with state representation on the Senate committee also has district representation on the House committee in 13% of the observations. For only 6% of these observations, however, is there a member affiliated with the majority party in power on both of the committees at the same time.
With respect to the correlation between district representation and alma mater affiliation, in most instances there is a low correlation (less than 15%) between the universities with an alma mater affiliation and a district representation in the same year. With respect to members on the Senate committee, for approximately 78% of the observations with an alma mater affiliation the university also has a member representing the state in which it is located on the committee at the same time. Thus it is likely that a fair number of these senators have an alma mater affiliation with one of the universities they are representing. A list of universities and their type of alma mater and/or district affiliation is provided in Appendix Table A-1.
Table 1 reports summary statistics on the annual federal research funding to research and doctoral universities during the period studied. (18) Across all 220 universities, the average level of funding is $41 million; the average is higher for private universities. For the universities for which there is at least one year of district representation during the sample period, the average level of funding is $44 million for the years for which there was representation and $45 million for the
years for which there was no representation. This suggests that district representation may not affect the distribution of research funding. For the universities for which there is an alma mater affiliation by a member for at least one year during the sample period, the average level of funding is higher in the years for which there was an affiliation with a member ($71 million) than in the other years ($49 million). This suggests that alma mater affiliation may affect the distribution of research funding.
Table 1 does not take into account two issues. First, it does not reflect that the level of funding allocated for research has varied over time. Second, it does not control for the heterogeneity in the universities receiving the research funding. For example, if one university has a better reputation than another, this could result in that university receiving more in research funding because its faculty submits higher quality proposals. Similarly, if a university has a medical school affiliated with it, the funding allocated to that university may be greater than the funding allocated to a university that does not have a medical school. To address these issues, Figures 1-4 reflect the average level of funding over time to universities in the years in which they have or do not have representation or an alma mater affiliation after controlling for non time-varying differences across the universities. (19) Because the averages are different for the public and private universities in Table 1, the figures depict the relationship between representation and nonrepresentation at public and private universities separately.
Figures 1 and 2 depict the average level of funding for those universities that had at least one year of district representation during the sample period. I depict separately the average funding for those years in which there is representation and those years for which there is no representation. With respect to public universities (Figure 1), there is very little difference in the average level of funding based on representation over the sample period. To the extent there is a difference, this is seen in the early part of the period, prior to 1986. Given that earmarking of funding to universities became more prevalent in the latter part of the period, thus representing a more direct way of diverting research funding by Congress, it is interesting that there is little difference between the average funding when there is representation and when there is no representation subsequent to 1986.
With respect to private universities (Figure 2), for most of the sample period there is very little difference in average funding based on representation over the sample period. Subsequent to 1993, however, the gap between average funding for those universities with representation in those years and those universities without representation widens, providing some evidence that district representation may matter.
In Figures 3 and 4, I depict the average level of funding for those universities with an alma mater affiliation during the sample period. As with Table 1, both figures suggest a different relationship between alma mater affiliation and district representation with respect to the distribution of research funding. For the public universities (Figure 3) prior to 1985, the average level of funding is higher for those universities in the years without an affiliation. Between 1985 and 1989, there is very little difference between the average funding when there is and is not an affiliation. Subsequent to 1989, there appears to be a substantial premium for having an alma mater affiliation for most of the years. With respect to the private universities (Figure 4), the average level of funding is higher in the years when there is an alma mater affiliation in the early and later part of the sample, but the gap during these periods is not very big.
V. REGRESSION ANALYSIS
Table 1 and the figures suggest that alma mater affiliation matters but district representation may not, especially in the early part of the sample. To explore further the effect of committee membership further I use the following model:
(1) [G.sub.irt] = [[alpha].sub.i] + [[lambda].sub.rt] + [R.sub.irt-1][beta] + [delta][A.sub.irt-1] + [tau][O.sub.-rt] + [sigma][I.sub.r-it] + [v.sub.irt],
where G is research funding to university i, located in region r, averaged between years t and t - 1, R is the vector of Senate and House measures indicating whether the university has alma mater affiliation or district representation at time t - 1. (20)
Given that a member may have both a district and an alma mater affiliation, A indicates whether the university has a member with the other type of affiliation at time t - 1. Thus, if we are measuring the effect of an alma mater affiliation, R represents the vector of measures that identify the type of alma mater affiliation and A is a dummy variable equal to one if the university also has a member that represents the district in which the university is located.
I also include university fixed effects. The university fixed effects control for nontime-varying heterogeneity across the universities. Thus differences across universities (because some receive on average more research funding than others) will be captured by these fixed effects. Because I am including university fixed effects, however, the coefficients on the political measures represent the measurement of a change in committee membership for that university. (21)
I conduct separate analyses to measure the effect of alma mater affiliation and district representation. (22) I include only those universities with an alma mater affiliation during the sample period in the specification that looks at the effects of alma mater affiliation. Similarly, I include only those universities with district representation in the specification that looks at the effects of district representation. Because the specifications include university fixed effects, the coefficients on the political measures reflect changes in the committee composition within the university. Thus including universities that never have an affiliation would just make the estimates less precise because there is no within-university variation for these institutions.
Given that the sample period covers 26 years, one might expect the universities to have grown differently. To account for this, I could interact the university fixed effect with a time trend. This specification would allow universities to grow differently. A potential problem with this specification is if a university's growth includes changes in its relationship with politicians that are correlated to movement on and off the appropriations committee, then part of the effect of having a member on the appropriations committee will be captured by the university time trend effect. Although these results are not reported in this article, for the most part the conclusions that may be drawn from the specifications that use a university time trend are similar to those reported later; the magnitude of the coefficients, however, decreases.
In equation (1), [lambda] represents a year fixed effect interacted with a set of dummy variables representing the region in which the university is located. This effect helps control for changes in economic, demographic, or political environments across time that affect all universities in a region similarly. Such effects would include changes in the government's budget, changes in attitudes about research funding, macro-level economic changes, and changes in the political party in power in Congress and the executive office. (23)
In addition to these measures, I include measures to control for possible changes in government policy regarding research funding that may affect universities differently as well as to control for the impact of other universities on the actions taken by the university under study. The first measure is the average level of research funding to universities located outside of the region in which a university is located with the same type of ownership (public or private) and Carnegie (1994) classification. The second measure is the average level of research funding to universities located in the region in which a university is located with the same type of Carnegie (1994) classification after excluding the level of funding to the university under study. (24)
There are several ways to depict political affiliation in the regression analysis. I concentrate solely on the politicians serving as general members on the appropriations committees. As discussed above, I concentrate on whether the general member is a chair of one of the subcommittees that have a direct relationship with the agencies primarily responsible for research funding (25) whether the member is affiliated with the majority party in power in Congress, and the tenure of the member. In addition, I allow the affiliation to differ for public and private universities.
Table 2 reports the results from two specifications. The first specification measures congressional representation in the two chambers based on two measures: first, whether there is at least one member that is a chair of one of the key subcommittees, and second, the number of general members serving on the committee. The first measure is designed to capture the effect that Savage (1991) found that chairs of the subcommittees have power to block or to promote pork barrel politics. The number of general members serving on the committee ranges from zero to two for the Senate and zero to three for the House.
Column (1) of Table 2 reports the results for the institutions with at least one year of district representation during the sample period. With respect to the Senate, the results suggest that universities benefit from having representation on the committee. On average, having a member that is a chair of a key subcommittee increases average funding by $4.7 million; having a member that serves as a general member increases funding on average by $1.9 million. Thus a university with a member that is also a chair of a key subcommittee would benefit by $6.6 million. Given that average research funding is $41.3 million, this represents a diversion of 16%. The results are different with respect to committee membership in the House of Representatives. The results suggest no effect from having a member that is a chair of a subcommittee and a negative effect from having a general member on the committee. The effect of the university also having an alma mater affiliation is small and imprecisely measured.
In column (6), I report the results for the set of universities with at least one year of alma mater affiliation. These results are very different from the results measuring the effect of district representation. With respect to an alma mater affiliation, the strongest results are with respect to having an affiliation with a member on the House committee. The results suggest that on average having an affiliation with a member who is a chair of one of the key subcommittees increases research funding by $10.2 million and that having an affiliation with a general member increases research funding by $4.9 million. Thus a university having a member that is also a chair of a key subcommittee would benefit by $15.1 million, representing a 37% diversion of funding. With respect to the Senate committee, the results suggest that an affiliation with a committee member decreases funding by $4.4 million. The effect of the university also having a member that represents the district in which the university is located incre ases funding to that university an average of $3.6 million.
The negative coefficients are troubling. In general, any given coefficient on the member measures should not be viewed in isolation. With respect to the measures reflecting whether the member is on a key subcommittee, this member would also be a general member on the appropriations committee, and thus the coefficient on the subcommittee chair reflects the additional gain from having a member who is a chair. In addition, as discussed in the last section, many universities have members on both the House and Senate committees. Also, for the alma mater sample, most of the universities with a member with an affiliation on the Senate committee also have a member that represents the state in which the university is located. Given that the measures reflecting membership on the committees should not be viewed in isolation, the negative coefficient could reflect that some members have less power than others. Even taking this into account, however, the results still suggest that the net effect may be negative.
In columns (3)-(5) and (8)-(10), I report the number of institutions with a change in representation and the number of changes for the district representation and alma mater affiliation regressions, respectively. For both measures for which the coefficient is negative, there are a fair number of universities and changes in representation, suggesting that the negative coefficients are not a problem associated with a small sample size. Another possible reason for the negative coefficients is that there is a university in the sample that is an outlier and is driving the results. I have, however, estimated these regression several times after excluding groups of universities based on their size, Carnegie (1994) status, and other characteristics. Although the coefficients change based on the sample group used, they do not change dramatically, suggesting that the problem with the negative coefficient is not due to an outlier.
The next reason for a negative coefficient I have explored has to do with whether the member is affiliated with the political party in the majority in the congressional chamber to which the member belongs. In the House of Representatives, the Democratic Party was the majority party from the beginning of the sample period until 1994. In the Senate, the Democratic Party was the majority party from the beginning of the sample period until 1980 and again from 1987 to 1994. Although one would not expect a member that is affiliated with the minority party to negatively affect the distribution of funding to a university, there is likely to be a significant difference between the effect a member can have depending on the political party with whom he or she is affiliated.
In columns (2) and (7), I report the results from the specification that changes the general member measure to one that identifies the number of members that are affiliated with the majority party and adds a third measure, a dummy variable equal to one if at least one member is affiliated with the minority party. The results for both groups of universities change. With respect to district representation, the significance of the subcommittee chair in the Senate is decreased and the coefficient on the majority general member measure is greater than the coefficient when we treat all general members as the same. These results suggest that being a member that is affiliated with the majority party plays a bigger role than being the chair of a key subcommittee. With respect to the House measures, the coefficients on the majority general member and the minority member measures are both negative and statistically significant. Although, it appears there is a different effect based on whether the member is part of the m ajority or minority party, this distinction fails to explain the negative coefficient.
With respect to alma mater affiliation, the results for the general members in the Senate also suggest that having a general member on the committee reduces funding. There is a greater reduction if the member is affiliated with the minority party. In the House, all three coefficients are positive, suggesting that regardless of party affiliation, membership affects the distribution of funding positively.
Despite separating the effects from party affiliation of the members, there are still negative coefficients. One aspect the discussion has not focused on is the fact that in most instances, to receive special treatment from Congress one has to ask for it. In this instance, if the members of Congress are able to influence agency behavior to distort the distribution of funding based on politics, most likely the member exerting the influence was lobbied to do so. If the efforts expended by the universities toward lobbying are relatively constant, the fixed effects in the empirical specification will control for differences across universities in the efforts expended. The specification, however, does not control for lobbying efforts that change over time. Plausibly, universities might change their lobbying efforts depending on who is in office as well as on the basis of their needs. For example, if a member affiliated with the majority party is replaced by a member that is affiliated with the minority party, a un iversity may choose to reduce its lobbying efforts. Similarly, the seniority of the committee member could affect lobbying efforts. If there is a correlation between lobbying effort and changes in the committee composition (as measured by the political variables), the coefficient on the political measures will capture the effect of both the change in composition and change in lobbying effort. To explore this further, however, we would need more information on the lobbying efforts of universities, as well as by associations that represent certain groups of universities. Until recently this information was not collected; thus, further investigation of this hypothesis will be quite difficult. (26)
The third specification starts with the second but allows the effect of committee membership to vary based on whether the university is public or private. Columns (1a)-(1c) of Table 3 report the results from this specification for the sample of universities with district representation. Interestingly, there are different effects from membership on the committees based on the ownership of the university. Private universities benefit from having a member that is a chair of one of the key subcommittees in both the Senate and the House. Public universities only benefit from members serving on the Senate committee and benefit more from those members that are affiliated with the majority party but are not a chair of one of the key subcommittees. The source of these differences are potentially attributable to differences in lobbying by public and private universities.
Columns (la)-(lc) of Table 4 report the results for the sample of universities with an alma mater affiliation. In the House, both types of universities benefit from having a member on the committee who is also a chair of one of the key subcommittees. Public universities also benefit from having a general member on the committee. In the Senate, the negative coefficient reported in Table 2 is driven by alma mater affiliations by public universities. This latter point raises an interesting question. Given that the universities with an alma mater affiliation with a member on the Senate committee are likely to be in a state also represented by the member, why does this result in a lower amount funding to the public universities than the funding distributed to the public universities that do not have an alma mater affiliation?
The last specification builds on the specification that allows for differences between public and private universities and between members affiliated with the majority and minority parties to study the effects of committee tenure on the distribution of funding. In this specification, I use three groups of measures to reflect the tenure of the members. Each measure identifies the number of majority party members on the appropriations committee based on the number of years the member has been on the committee. The first measure is for those members with 0 to 3 years on the committee, the second measure is for those members with 4 to 11 years on the committee, and the third measure is for those members with more than 11 years on the committee. Given that the preferences of members are likely to vary based on tenure and issues related to seeking reelection, the effects of lobbying and other activities on the actions taken by members are also likely to vary. Similarly, efforts toward seeking favoritism by universi ties may also vary with tenure. For example, if a member is concerned about reelection in the early years, we might see a preference away from shirking and toward representing one's constituents.
Columns (2a)-(2c) of Table 3 report the results with respect to district representation. Interestingly, the distribution of funding is diverted to public and private universities in the early years of committee membership. For public universities, on average, $3.6 million is diverted to universities if the member has less than 4 years of tenure and $2.1 million if the member has between 4 and 10 years of tenure. For private universities, on average, $8.0 million is diverted to universities if the member has less than 4 years of tenure and $10.1 million if the member has between 4 and 10 years of tenure. For both groups of universities, the coefficient for the members with more than 10 years of tenure is small and imprecisely measured. As in the other specifications, the effect in the House is imprecisely measured for the public universities and negative for the private universities. The negative coefficients are stronger for the more senior members.
Columns (2a)-(2c) of Table 4 report the results with respect to alma mater affiliation. For the Senate, the distribution of funding for public and private universities is negatively affected with members that have between 4 and 11 years of tenure. Private universities, however receive a large increase in funding by the members with more than 10 years of tenure. There are, however, only 3 private universities during the sample period with representation at this level. As such, this result should be treated gingerly. In the House, the distribution of funding to public universities is positively affected by members with more than 3 years of tenure. On average, funding is increased by $5.9 million if the member has between 4 and 11 years of tenure and by $13 million if the member has more than 10 years of tenure. The distribution of funding to private universities is positively affected by members with between 4 and 11 years of tenure. On average, funding is increased by $8.7 million.
All of the tables illustrate that membership on the committee has an effect on the distribution of research funding. Moreover, the characteristics of the politician's membership as well as the ownership of the university affects the average level of funding that is distributed to the university. Focusing first on public universities with district representation, the average effect of representation by a general member serving on the Senate committee is $2.9 million, representing an average diversion of 8%. The effect from representation on the House committee is imprecisely measured. With respect to alma mater affiliation, the average effect of an affiliation on the Senate committee is negative but very close to zero if we take into account that many members with an alma mater affiliation on the Senate committee also represent the university. The average effect in the House of a general member is $5 million, representing an average diversion of 14%. The average effect in the House of a general member that is also a chair of a key subcommittee is $17.4 million, representing an average diversion of 47%. Thus with respect to public universities, it is interesting to note that district representation is important in the Senate and alma mater affiliation is important in the House.
With respect to private universities, district representation has the biggest effect on the distribution of funding by members that are a chair of a key House subcommittee. On average, the effect is $19.1 million, representing an average diversion of 39%. With respect to alma mater affiliation, the biggest effect is by members that are a chair of a key House subcommittee and by members that are affiliated with the minority party in the House. On average, the effect from having an affiliation with a subcommittee chair is $6.6 million, representing an average diversion of 13%. Thus with respect to private universities, there appears to be a stronger effect from alma mater affiliation than district representation. Given that private universities on average are more active than public universities in keeping close ties with its alumni, this result should not be that surprising.
This article supports the theoretical literature that Congress and agencies behave strategically. This study suggests that research funding to universities is diverted to and from universities due to politics. Thus this work illustrates that as with any other discretionary program that requires appropriations from Congress, because of lobbying from agencies, research universities, or other entities, research funding may be diverted for political purposes.
This article finds that both alma mater affiliation and district representation of universities matter. With respect to district representation, membership on the Senate committee plays a bigger role than membership on the House committee. The net effect from representation on the Senate committee is approximately $4 million. On average, private universities benefit more from this type of representation than do public universities. The more junior members serving on the committee divert more funding for both groups of universities.
With respect to alma mater affiliation, membership on the House committee plays a bigger role than does membership on the Senate committee. The net effect of representation by a general member on the House committee is approximately $4 million. The net effect of representation by a member that is also a chair of one of the subcommittees responsible for the budget of the key agencies involved in research funding is approximately $15 million. On average, public universities benefit more from this type of affiliation than do private universities. More senior members on the committee, however, divert more funding for both groups of universities.
These results illustrate that members can influence the diversion of funding for both his or her constituents and his or her personal interests. In a broader context, this article illustrates the potential problems that develop when members of Congress have a long tenure on a committee. The diversions of funding associated with the more senior members on the committee are biggest for those universities with a personal affiliation (as measured by alma mater status), especially in the House. Thus this article provides further support to the notion that senior members are more susceptible than junior members to shirking. How this shirking compares with the advantage senior members may have on the committee because of their experience with the appropriations process, however, is left for future research.
APPENDIX TABLE A-1 Carnegie Universities Analyzed State Class University of Alabama AL D1 University of Alabama in Huntsville AL D2 University of Alabama at Birmingham AL R1 Auburn University AL R2 University of Arkansas AR R2 Northern Arizona University AZ D1 Arizona State University Main AZ R1 University of Arizona AZ R1 Loma Linda University CA D2 Pepperdine University CA D2 San Diego State University CA D2 University of San Diego CA D2 University of the Francisco CA D2 University of the Pacific CA D2 California Institute of Technology CA R1 Stanford University CA R1 University of California--Berkeley CA R1 University of California--Davis CA R1 University of California--Irvine CA R1 University of California-- CA R1 Los Angeles University of California--San Diego CA R1 University of California-- CA R1 San Francisco University of California-- CA R1 Santa Barbara University of Southern California CA R1 University of California--Riverside CA R2 University of California-- CA R2 Santa Cruz University of Denver CO D1 University of Northern Colorado CO D1 Colorado School of Mines CO D2 Colorado State University CO R1 University of Colorado CO R1 University of Connecticut CT R1 Yale University CT R1 University of Delaware DE R2 Florida Institute of Technology FL D1 Nova Southeastern University FL D1 Florida Atlantic University FL D2 Florida International University FL D2 University of Central Florida FL D2 Florida State University FL R1 University of Florida FL R1 University of Miami FL R1 University of South Florida FL R2 Georgia State University GA D1 Emory University GA R1 Georgia Institute of Technology GA R1 University of Georgia GA R1 Iowa State University IA R1 University of Iowa IA R1 Idaho State University ID D2 University of Idaho ID R2 Illinois Institute of Technology IL D1 Illinois State University IL D1 Loyola University of Chicago IL D1 Northern Illinois University IL D1 De Paul University IL D2 Northeastern University IL R1 Mississippi State University MS R2 University of Mississippi MS R2 Montana State University--Bozeman MT D2 University of Montana MT D2 University of North Carolina NC D1 at Greensboro Wake Forest University NC D2 Duke University NC R1 Nort Carolina State University NC R1 University of North Carolina at NC R1 Chapel Hill North Dakota State University ND D2 University of North Dakota ND D2 University of Nebraska at Lincoln NE R1 Dartmouth College NH D2 University of New Hampshire NH D2 New Jersey Institute Technology NJ D2 Seton Hall University NJ D2 Stevens Institute of Technology NJ D2 Princeton University NJ R1 Rutgers the State University of NJ NJ R1 New Mexico State University NM R1 University of New Mexico NM R1 University of Nevada--Reno NV D2 Adelphi University NY D1 Fordham University NY D1 Hofstra University NY D1 St. John's University NY D1 Clarkson University NY D2 Columbia University NY R1 Cornell University NY R1 New York University NY R1 Rockefeller University NY R1 SUNY at Buffalo NY R1 SUNY at Stony Brook NY R1 Univesity of Rochester NY R1 Yeshiva University NY R1 Rensselaer Polytechnic Institute NY R2 SUNY at Albany NY R2 Syracuse University NY R2 Bowling Green State University OH D1 Miami University OH D1 University of Akron OH D1 University of Toledo OH D1 Cleveland State University OH D2 Wright State University OH D2 Case Western Reserve University OH R1 Ohio State University OH R1 University of Cincinnati OH R1 Kent State University OH R2 Ohio University OH R2 University of Tulsa OK D2 Oklahoma State University OK R2 University of Oklahoma OK R2 Portland State University OR D2 Oregon State University OR R1 University of Oregon OR R2 Drexel University PA D1 Indiana University of PA PA D1 University of Chicago IL R1 University of Illinois at Chicago IL R1 University of Illinois IL R1 at Urbana--Champaign Southern Illinois University IL R2 Southern Illinois University-- IL R2 Carbondale Ball State University IN D1 Indiana State University IN D2 Indiana University IN R1 Purdue University IN R1 University of Notre Dame IN R2 Wichita State University KS D2 University of Kansas KS R1 Kansas State University KS R2 University of Kentucky KY R1 University of Louisville KY R2 Louisiana Tech University LA D2 University of New Orleans LA D2 University of Southwestern LA D2 Louisiana Louisiana State University LA R1 Tulane University LA R1 Boston College MA D1 Clark University MA D2 Worcester Polytechnic Institute MA D2 Boston University MA R1 Harvard University MA R1 Massachusetts Institute of MA R1 Technology Tufts University MA R1 Brandeis University MA R2 Northeastern University MA R2 University of Maryland MD D2 Baltimore County Johns Hopkins University MD R1 University of Maryland MD R1 at College Park University of Maine ME D2 Andrews University MI D1 Western Michigan University MI D1 Michigan Technological University MI D2 University of Detroit Mercy MI D2 Michigan State University MI R1 University of Michigan MI R1 Wayne State University MI R1 University of Minnesota MN R1 University of Missouri, Kansas City MO D1 University of Missouri, Rolla MO D1 University of Missouri, St. Louis MO D2 University of Missouri, Columbia MO R1 Washington University MO R1 St. Louis University MO R2 University of Southern Mississippi MS D1 Allegheny University of PA D2 the Health Sciences Duquesne University PA D2 Carnegie Mellon University PA R1 Pennsylvania State University PA R1 Temple University PA R1 University of Pennsylvania PA R1 University of Pittsburgh PA R1 Lehigh University PA R2 Brown University RI R1 University of Rhode Island RI R2 Clemson University SC R2 University of South Carolina SC R2 University of South Dakota SD D1 University of Memphis TN D1 Middle Tennessee State University TN D2 Tennessee State University TN D2 University of Tennessee at TN R1 Knoxville Vanderbilt University TN R1 Southern Methodist University TX D1 Texas Woman's University TX D1 University of North Texas TX D1 University of Texas at Arlington TX D1 University of Texas at Dallas TX D1 Baylor University TX D2 Texas Christian University TX D2 Texas Southern University TX D2 Texas A&M University TX R1 University of Texas at Austin TX R1 Rice University TX R2 Texas Tech University TX R2 University of Houston TX R2 University of Utah UT R1 Utah State University UT R1 Brigham Young University UT R2 College of William and Mary VA D1 Old Dominion University VA D1 George Mason University VA D2 University of Virginia VA R1 Virginia Commonwealth University VA R1 Virginia Polytechnic Institute VA R1 and State University University of Vermont VT R2 University of Washington--Seattle WA R1 Washington State University WA R2 Marquette University WI D1 University of Wisconsin--Madison WI R1 University of Wisconsin--Milwaukee WI R2 West Virginia University WV R1 University of Wyoming WY R2
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TABLE 1 Summary Statistics on Federal Research Funding to Universities # of Observations Mean SD SD/Mean All universities 5,671 41.3 65.2 1.6 Public universities 3,424 37.0 49.9 1.3 Private universities 1,947 48.9 85.1 1.7 Universities with some district representation District representation 2,634 43.9 72.5 1.7 Public universities 1,646 39.2 53.7 1.4 Private universities 988 51.6 95.5 1.8 No district representation 2,027 45.0 62.7 1.4 Public universities 1,209 40.2 50.5 1.3 Private universities 818 52.0 76.7 1.5 Universities with some alma mater affiliation Alma mater affiliation 972 71.2 65.3 0.9 Public universities 663 66.5 65.0 1.0 Private universities 309 81.2 65.0 0.8 No alma mater affiliation 874 49.1 57.8 1.2 Public universities 625 39.4 42.3 1.1 Private universities 249 73.4 80.1 1.1 Median Maximum All universities 16.5 741.7 Public universities 17.4 351.1 Private universities 14.5 741.7 Universities with some district representation District representation 17.3 741.7 Public universities 17.8 351.1 Private universities 16.0 741.7 No district representation 18.6 615.9 Public universities 20.7 301.2 Private universities 16.7 615.9 Universities with some alma mater affiliation Alma mater affiliation 51.0 351.1 Public universities 45.8 351.1 Private universities 63.2 279.9 No alma mater affiliation 25.4 337.6 Public universities 23.5 233.4 Private universities 37.4 337.6 Notes: All dollars are reported in millions ($1996). Universities studied are those with a Carnegie (1994) classification of research or doctoral universities. TABLE 2 Regression Analysis District Representation and Alma Mater Affiliation Dependent Variable: Federal District Research Expenditures Universities with Representation (2-Year Average) (1) (2) Senate appropriations At least 1 subcommittee chair 4.68 2.51 (2.07) (1.89) General member 1.92 (0.80) General member in majority party 4.01 (0.83) Representation in minority party 0.18 House appropriations At least 1 subcommittee chair 3.09 3.29 (2.09) (2.07) General member -5.55 (1.45) General member in majority party -6.49 (1.75) Representation in minority party -3.11 (1.60) F-test on all political measures 10.38 7.28 (p-value) (0.00) (0.00) District/alma mater representation 0.74 0.73 (0.95) (0.95) Average funding outside of region 0.65 0.64 (0.08) (0.08) Average funding within region 0.34 0.34 (0.06) (0.06) University fixed effects Yes Yes Regional year effect Yes Yes # of observations 4,227 4,227 # of Schools 186 186 R-squared 0.9241 0.9244 Dependent Variable: Federal # Universities w/ # Changes on Research Expenditures Representation Committee (2-Year Average) (3) (4) Senate appropriations At least 1 subcommittee chair 67 131 General member 157 338 General member in majority party 154 374 Representation in minority party 141 352 House appropriations At least 1 subcommittee chair 4 8 General member 82 150 General member in majority party 64 112 Representation in minority party 43 62 F-test on all political measures (p-value) District/alma mater representation Average funding outside of region Average funding within region University fixed effects Regional year effect # of observations # of Schools R-squared Dependent Variable: Federal Alma Mater Research Expenditures Universities with Affiliation (2-Year Average) (5) (6) Senate appropriations At least 1 subcommittee chair 0.27 -2.26 (1.87) (2.05) General member -4.38 (1.09) General member in majority party -2.18 (1.32) Representation in minority party -6.98 House appropriations At least 1 subcommittee chair 10.19 10.51 (2.39) (2.53) General member 4.85 (0.90) General member in majority party 4.34 (1.23) Representation in minority party 6.24 (1.41) F-test on all political measures 21.35 15.99 (p-value) (0.00) (0.00) District/alma mater representation 3.60 3.84 (0.87) (0.87) Average funding outside of region 0.41 0.41 (0.06) (0.06) Average funding within region 0.50 0.50 (0.06) (0.06) University fixed effects Yes Yes Regional year effect Yes Yes # of observations 1,678 1,678 # of Schools 72 72 R-squared 0.9624 0.9628 Dependent Variable: Federal # Universities w/ # Changes on Research Expenditures Representation Committee (2-Year Average) (7) (8) Senate appropriations At least 1 subcommittee chair 18 36 General member 38 71 General member in majority party 34 79 Representation in minority party 29 64 House appropriations At least 1 subcommittee chair 10 15 General member 53 102 General member in majority party 44 72 Representation in minority party 40 69 F-test on all political measures (p-value) District/alma mater representation Average funding outside of region Average funding within region University fixed effects Regional year effect # of observations # of Schools R-squared Notes: Robust Standard errors in parentheses, except where noted; General Member = number of members on the appropriations committee, excluding the majority leader and ranking minority member on the committee; Average Funding Outside of Region = average federal research funding for universities with same type of ownership (public or private) and Carnegie (1994) Classification (Research I, II, Doctoral I, II) located outside of the region; Average Funding Within Region = Average federal obligations for universities with same type of Carnegie (1994) classification located in the same region as the university under study; Regional Year Effect = dummy variable indicating which region (out of four) the university under studied is located interacted with a set a of year dummy variables; the District/Alma Mater Representation: a dummy variable equal to one if the university under study also has a member on the committee with an alma mater affiliation if the regression reflects the effect of representation on the distribution of funding and equal to one if the university under study also has a member on the committee representing the district/state in which the university is located if the regression reflects the effect of an lma mater affiliation on the distribution of funding. Coefficients in bold indicate p-value < 0.05; coefficients in italies indicate p-value < 0.10. TABLE 3 Regression Analysis District Representation: Differences Based on Ownership of University and Tenure on the Appropriate Committee Dependent Variable: Federal Research Expenditures Public Private (2-Year-Average) (la) (1b) Senate appropriations At least 1 subcommittee chair -2.43 21.63 (1.35) (6.78) General member in majority party 2.94 -2.51 (0.85) (1.51) Representation in minority party -0.54 (1.30) Tenure <4 years Tenure 4-11 years Tenure 11 + years House approriations At least 1 subcommittee chair 2.54 4.37 (2.99) (2.01) General member in majority party -1.29 -9.83 (1.15) (3.12) Representation in minority party -2.55 (1.61) Tenure <4 years Tenure 4-11 years Tenure 11 + years F-test on all political measures 4.07 (p-value) (0.00) # of Observations 4,227 # of Schools 186 R-squared 0.9256 Dependent Variable: Federal Private = Public Research Expenditures F-test Public (2-Year-Average) (1c) (2a) Senate appropriations At least 1 subcommittee chair 11.57 (0.00) General member in majority party 0.34 (0.56) Representation in minority party Tenure <4 years 3.60 (0.96) Tenure 4-11 years 2.14 (1.02) Tenure 11 + years 0.38 (1.91) House approriations At least 1 subcommittee chair 10.72 (0.00) General member in majority party 5.65 (0.02) Representation in minority party Tenure <4 years -1.74 (1.69) Tenure 4-11 years -1.47 (1.49) Tenure 11 + years 2.84 (2.59) F-test on all political measures (p-value) # of Observations # of Schools R-squared Dependent Variable: Federal Private = Public Research Expenditures Private F-test (2-Year-Average) (2b) (2c) Senate appropriations At least 1 subcommittee chair General member in majority party Representation in minority party -0.44 (1.28) Tenure <4 years 8.02 2.09 (3.12) (0.15) Tenure 4-11 years 10.13 4.02 (4.06) (0.05) Tenure 11 + years 0.75 0.02 (2.69) (0.90) House approriations At least 1 subcommittee chair General member in majority party Representation in minority party -3.61 (1.64) Tenure <4 years -3.62 0.09 (5.19) (0.76) Tenure 4-11 years -16.52 .32 (5.70) (0.02) Tenure 11 + years -11.39 12.47 (3.14) (0.00) F-test on all political measures 3.86 (p-value) (0.00) # of Observations 4,227 # of Schools 186 R-squared 0.9254 # of Universities with Representation Dependent Variable: Federal Research Expenditures Public (2-Year-Average) (3a) Senate appropriations At least 1 subcommittee chair 49 General member in majority party 102 Representation in minority party 85 Tenure <4 years 91 Tenure 4-11 years 83 Tenure 11 + years 34 House approriations At least 1 subcommittee chair 2 General member in majority party 31 Representation in minority party 24 Tenure <4 years 30 Tenure 4-11 years 20 Tenure 11 + years 8 F-test on all political measures (p-value) # of Observations # of Schools R-squared # of Universities with Representation Dependent Variable: Federal # changes on Research Expenditures Private Committee (2-Year-Average) (3b) (3c) Senate appropriations At least 1 subcommittee chair 18 131 General member in majority party 52 374 Representation in minority party 56 352 Tenure <4 years 48 304 Tenure 4-11 years 42 300 Tenure 11 + years 12 76 House approriations At least 1 subcommittee chair 2 8 General member in majority party 35 112 Representation in minority party 19 62 Tenure <4 years 32 110 Tenure 4-11 years 22 83 Tenure 11 + years 16 47 F-test on all political measures (p-value) # of Observations # of Schools R-squared Notes: see note on Table 2. In addition, for each regression, the results are reported over several columns. The tenure measures represent the number of years serving on the appropriations committee. Although not reported, all regressions include the same fixed effects and other control measures reported in Table 2. The coefficients on the control measures are not different from the coefficients reported in Table 2. TABLE 4 Regression Analysis Alma Mater Affiliation: Differences Based on Ownership of University and Tenure on the Appropriations Committee Dependent Variable: Federal Research Expenditures Public Private (2-Year Average) (1a) (1b) Senate appropriations At least 1 subcommittee chair -4.85 2.91 (2.52) (3.64) General member in majority party -2.39 -2.16 (1.84) (1.47) Representation in minority party -7.47 (1.43) Tenure <4 years Tenure 4-11 years Tenure 11+ years House appropriations At least 1 subcommittee chair 12.41 6.61 (3.17) (3.47) General member in majority party 5.01 2.95 (1.46) (2.43) Representation in minority party 6.12 (1.42) Tenure <4 years Tenure 4-11 years Tenure 11+ years F-test on all political measures 10.93 (p-value) (0.05) # of Observations 1,678 # of Schools 72 R-squared 0.963 Dependent Variable: Federal Private=Public Research Expenditures F-test Public (2-Year Average) (1c) (2a) Senate appropriations At least 1 subcommittee chair 3.02 (0.08) General member in majority party 1.46 (0.23) Representation in minority party Tenure <4 years -2.53 (1.90) Tenure 4-11 years -7.20 (1.73) Tenure 11+ years 0.21 (2.83) House appropriations At least 1 subcommittee chair 0.01 (0.92) General member in majority party 0.51 (0.48) Representation in minority party Tenure <4 years 2.00 (1.67) Tenure 4-11 years 5.93 (1.66) Tenure 11+ years 13.14 (2.60) F-test on all political measures (p-value) # of Observations # of Schools R-squared Dependent Variable: Federal Private=Public Research Expenditures Private F-test (2-Year Average) (2b) (2c) Senate appropriations At least 1 subcommittee chair General member in majority party Representation in minority party -6.22 (1.36) Tenure <4 years 0.55 1.59 (1.58) (0.21) Tenure 4-11 years -5.37 0.39 (2.41) (0.53) Tenure 11+ years 17.56 21.87 (2.59) (0.00) House appropriations At least 1 subcommittee chair General member in majority party Representation in minority party 6.71 (1.46) Tenure <4 years 2.39 0.01 (4.07) (0.93) Tenure 4-11 years 8.67 0.97 (2.14) (0.33) Tenure 11+ years -1.53 14.79 (2.77) (0.00) F-test on all political measures 13.04 (p-value) (0.00) # of Observations 1,678 # of Schools 72 R-squared 0.9641 Dependent Variable: Federal # of Universities with Representation Research Expenditures Public (2-Year Average) (3a) Senate appropriations At least 1 subcommittee chair 12 General member in majority party 24 Representation in minority party 20 Tenure <4 years 22 Tenure 4-11 years 20 Tenure 11+ years 7 House appropriations At least 1 subcommittee chair 7 General member in majority party 30 Representation in minority party 26 Tenure <4 years 25 Tenure 4-11 years 18 Tenure 11+ years 14 F-test on all political measures (p-value) # of Observations # of Schools R-squared Dependent Variable: Federal # of Universities with # Changes on Representation Research Expenditures Private Committee (2-Year Average) (3b) (3c) Senate appropriations At least 1 subcommittee chair 6 36 General member in majority party 10 79 Representation in minority party 9 64 Tenure <4 years 8 65 Tenure 4-11 years 8 57 Tenure 11+ years 3 19 House appropriations At least 1 subcommittee chair 3 15 General member in majority party 14 72 Representation in minority party 14 69 Tenure <4 years 12 72 Tenure 4-11 years 10 61 Tenure 11+ years 5 35 F-test on all political measures (p-value) # of Observations # of Schools R-squared Notes: See note to Table 2. In addition, for each regression, the results are reported over several columns. The tenure measures represent the number of years serving on the appropriations committee. Although not reported, all regressions include the same fixed effects and other control measures reported in Table 2. The coefficients on the control measures are not different from the coefficients reported in Table 2.
(1.) Others have studied the effect of politics on the distribution of funding, but only Lichtenberg (1998), Lazear (1996), and Savage (1991) have studied the distribution of research funding. Lichtenberg (1998) studies the allocation process of biomedically funded research, examining the relation between the distribution of funds to research projects and the expected life-years lost associated with the diseases on which the research is being conducted. Lazear (1996) studies the incentives provided by agencies to researchers in the structure of their allocation process. Using an overlapping generations model, he examines such questions as what topics should be funded, whether small and large awards should be made, to what extent past research experience should be considered, and whether junior and senior researchers should be treated differently. Savage (1991; 1999) explores issues concerning congressional earmarking of funds to universities, focusing on the relationship between key members on the appropriati ons subcommittees. His study suggests that the chairs of the appropriations subcommittees possess the power to prevent or minimize the extent of pork barreling in the appropriations bills with respect to earmarked funding.
(2.) A 1945 government report by Vannevar Bush recommended the establishment of a single agency that would be responsible for allocating all federal funding appropriations for research. Although the National Science Foundation (NSP) was established as a result of the report, it did not become the sole agency responsible for allocating research funding.
(3.) Similar in this vein is the transaction cost framework. Huber and Shipan (2000) provide a discussion of how this framework explains legislative control of bureaucratic behavior.
(4.) Feller (2000) provides a complete description of the different methods used to allocate federal research funding.
(5.) Savage (1991; 1999) documents and explores the issues surrounding earmarked funding.
(6.) Lambright (2000), Payne (2002), and www.epscorfoundation.org provide information about set-aside programs.
(7.) One argument against this is if one's constituents are interested in a particular type of research and the best research is being conducted at the member's alma mater institution. For example, if a particular district or state has experienced an epidemic of some disease relative to other districts or states, and the best research related to the epidemic is being conducted by a university in another district that happens to be the member's alma mater. Although this scenario is certainly plausible, given the distribution of alma mater affiliated universities and the empirical specification used in this article, the likelihood of this type of phenomenon being the primary explanation of a relationship between alma mater affiliation the distribution of research funding is very low.
(8.) Detailed accounts of the appropriations process may be found in Fenno (1966) and Ferejohn and Krehbiel (1987). A history of the research funding process and the role of the federal government may be found in Drew (1985), Geiger (1993), and Kleinman (1995).
(9.) Although the U.S. Constitution dictates that revenue raising measures must be initiated in the House of Representatives, there is no such provision with respect to the appropriations process.
(10.) Congress, the president, and/or agencies could initiate this influence. The common perception is that a member of Congress may initiate a request for favoritism. Favoritism, however, could be initiated by the agency. Under the assumption that most agencies desire more funds for their activities, one way to "justify" a bigger budget could be through awarding grants to universities affiliated with the members of the appropriations committee. This article does not distinguish between favoritism initiated by members of Congress and favoritism initiated by agencies or other governmental entities. Similarly, universities may or may not seek favoritism from Congress members, either directly or through collective lobbying groups. Savage (1999) discusses reasons why a university may seek favoritism from Congress. Although it is common for a university or group of universities to maintain lobbyists in Washington to keep informed about proposed changes that would affect the operation of their universities, this ar ticle does not distinguish between those universities that actively seek special treatment from those that do not.
(11.) See Dc Figueiredo and Silverman (2002) for an analysis of lobbying expenditures by universities and earmarking to universities.
(12.) CASPAR includes several data sets collected by the NSF, National Center for Education Statistics, and other federal agencies. Information on CASPAR may be found at www.nsf.gov.
(13.) This is due to the fact that there are elections for both chambers every two years. In the House of Representatives, all members must be elected or reelected every two years. In the Senate, one-third of the members are elected or reelected every two years because a given member holds office for six years.
(14.) In some of the larger metropolitan areas, it was difficult to distinguish which members represented which universities. Therefore, I was over-inclusive in assigning the universities to representatives. For example, if there is a member on the House appropriations committee that serves a part of Manhattan, then Columbia University and NYU university (and all other universities located in Manhattan) would be treated as part of the member's district.
(15.) I do not look at the effect of being a minority or majority chair of the entire committee because during the sample period, there are few changes in these positions, thus providing little variation in the data analysis.
(16.) Research universities are defined as those that give high priority to research and award at least 50 doctoral degrees each year. Doctoral universities differ from research universities in that they do not meet minimum requirements with respect to federal research support or the number of doctorate degrees awarded. Though there are universities that have obtained the research or doctoral institution status subsequent to 1972, there is little or no attrition of universities from these classifications.
(17.) I excluded the following universities from my analysis because of inconsistencies in the data: City University of New York, International College, SUNY College of the Environment and Forestry, United States International University, University of Massachusetts at Amherst, Texas A&M at Commerce, Claremont Colleges, New School for Social Research, Clark Atlanta University, Polytechnic University, University of Massachusetts at Lowell, Pace University, Biola University, Union Institute, and University of Laverne.
(18.) All dollar amounts are reported in 1996 dollars. I use the higher education deflation index provided by CASPAR.
(19.) To get this measure, I ran a fixed-effects regression whereby I use a set of dummy variables that identify the university to allow for the average level of funding at each university to vary based on the nontime-varying differences. I then graph the average of the residuals of this regression. Thus, the residuals will capture aspects of the funding distributed to the universities that are not accounted for in the university fixed effects.
(20.) Because a research grant may be awarded in one year but then be distributed over several years, I average the funding over a two-year period to reflect this. The results, however, do not differ dramatically based on whether I do a two-year average, a three-year average, or do not average the data. I report the results from the two-year average because the standard errors are smaller with the average than when I do not average the research funding.
(21.) If fixed effects are not included in the regression, the results suggest a very strong affiliation between membership on the appropriations committee and alma mater or district representation.
(22.) One potential issue concerns the correlation between the alma mater and the district political measures. If many of the observations contain both alma mater and district representation then the coefficients may not be interpretable because of multicollinearity.
(23.) The following states are covered within each region. Region 1: Connecticut, Massachusetts, Maine, New Hampshire, Rhode Island, Vermont, New Jersey, New York, and Pennsylvania. Region 2: Illinois, Indiana, Michigan, Ohio, Wisconsin, Iowa, Kansas, Minnesota, Missouri, North Dakota, Nebraska, and South Dakota. Region 3: Delaware, Florida, Georgia, Maryland, North Carolina, South Carolina, Virginia, West Virginia, Alabama, Kentucky, Mississippi, Tennessee, Arkansas, Louisiana, Oklahoma, and Texas. Region 4: Arizona, Colorado, Idaho, Montana, New Mexico, Nevada, Utah, Wyoming, California, Oregon, and Washington.
(24.) By including these measures in the specification, the coefficients--in particular those on the alma mater measures decrease--suggesting these additional measures are picking up a time-varying measure that is correlated with research funding and the political measures.
(25.) NSF, National Institutes of Health, Department of Defense, and Department of Agriculture.
(26.) See De Figueiredo and Silverman (2002).
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RELATED ARTICLE: ABBREVIATIONS
CASPAR: Computer Aided Science Policy Analysis and Research
NSF: National Science Foundation
A. ABIGAIL PAYNE *
* I would like to thank Marie Rekkas, Patti Tilson, Gordon Davis, and Max Hollett for excellent research assistance. I also thank Tom Carsey, James Klukinski, Therese MeGuire, Angelo Melino, Barry Rundquist, James Savage, Aloysius Siow, and participants of the University of Toronto's SWEAT workshop for comments on earlier drafts. This article was funded through grants from the Andrew W. Mellon Foundation and the Social Sciences and Humanities Research Council of Canada.
Payne: Assistant Professor, University of Illinois, and Associate Professor, Department of Economics, McMaster University, 1280 Main St. W, KTH 426,Hamilton, ON L8S 4M4, Canada. Phone 1-905-529-9140 ext 23814, Fax 1-905-521-8232, E-mail email@example.com
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|Author:||Payne, A. Abigail|
|Date:||Apr 1, 2003|
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