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Examining Procedural Choice in the House Rules Committee Kenneth W. Moffett Doctoral Student Department of Political Science The University of Iowa 341 Schaeffer Hall Iowa City, Iowa 52242-1409 Voice: (319) 335-3381 Fax: (319) 335-3400 E-mail: ken-moffett@uiowa.edu Prepared for presentation at the annual meeting of the Southern Political Science Association, New Orleans, LA, January 8-10, 2004. I would like to thank Doug Dion, Charles Shipan, Don Wolfensberger and the graduate students within the Department of Political Science at Iowa for all of their extremely helpful comments and suggestions on earlier versions of this paper. I would also like to thank Andrew Bargen, Fred Boehmke, Keith Poole, Megan Shannon, Charles Shipan, Steve Smith and Matt Whittaker for their help in directing me toward appropriate data sources for this analysis. Any remaining errors and omissions in this paper are my own responsibility. 1 The House Rules Committee plays a vital role in the process by which a bill becomes a law because it places a rule on each bill before the Floor considers it. Its rules dictate the terms of debate, number of amendments, whether points of order are waived and whether certain types of amendments are admissible at all (Dion and Huber 1996, 25). This power is so important that if the Rules Committee does not place a rule on a bill, then the Floor will typically not consider that bill (Oleszek 1989, 122). Because of this power, political scientists have argued since the 1970s about what is behind the Rules Committee’s power to restrict the conditions under which members of Congress debate and amend bills. Distributive theories of legislative organization suggest that using restrictive rules enforces cross-jurisdictional bargains between committees (Shepsle and Weingast 1981, Weingast and Marshall 1988). Informational theories argue that restrictive rules encourage committees to specialize and transmit information to the Floor (Krehbiel 1991, 1997a, 1997b, 1998). Partisan theories suggest that the House Rules Committee will place restrictive rules on bills whenever the majority party wants (Cox and McCubbins 1993; Binder 1995, 1997; Dion 1997). To discover which theory most accurately explains the conditions under which the Rules Committee will place restrictive rules on bills, I will empirically retest and expand the most recent study on the subject, Dion and Huber’s (1996) article. I will do this for three reasons: 1) the House Rules Committee has placed more restrictive rules on bills since the 94th to 98th Congresses studied by Dion and Huber (1996); 2) I would like to see what has happened since the Republicans won the House in 1994; and 3) I would like to see whether Dion and Huber’s (1996) statistical methodology drives their empirical results. I will answer my research question in five parts. First, I will review distributive, informational and partisan theories to determine 2 what theoretical predictions most directly bear on my research question. Second, I will review prior studies that have tested the predictions of these theories on Rules Committee rule assignment. Third, I will state three hypotheses that come from the most recent study on Rules Committee rule assignment (Dion and Huber 1996). Fourth, I advance a model that tests two of these hypotheses on rule assignment data from the 94th to 106th Congresses. Finally, I examine the results of my statistical tests and make two conclusions: 1) we should expect more restrictive rules when a substantive committee and the Rules committee are on the same side of the Floor; and 2) committees that are further from the Floor are no more likely to receive restrictive rules than committees that are closer to the Floor. Distributive, Informational and Party-Based Theories: Distributive theories start by assuming that the ideal points of legislators’ preferences originate with their electoral incentives (Mayhew 1974). Further, Mayhew (1974) assumes that the primary goal of individual members of Congress (MCs) is reelection. Consequently, MCs have designed that institution to maximize the reelection chances of each individual member (Mayhew 1974). This design, by itself, is not enough to maximize each MC’s probability of reelection. Accordingly, MCs must perform credit claiming, position-taking and advertising to maximize their individual chances of reelection (Mayhew 1974). Individual committees allow members to perform these functions by providing particularistic benefits to their constituents while spreading the costs of these benefits over all House districts (Mayhew 1974, Weingast and Marshall 1988). Fenno (1978) formalizes Mayhew’s assumption about MCs’ primary goals in three ways. First, he argues that MCs want to be elected to the House to advance good public policy. His argument adds to Mayhew’s (1974) assumption in that MCs must be reelected to advance such 3 policy. Second, MCs want to be elected (and reelected) because they want to be influential in Congress (Fenno 1978). Third, MCs goal of good public policy suggests that members are motivated to make such policy, while influence suggests that members have a genuine stake in public policy outcomes. Smith and Deering (1983) interviewed first-term members of the 97th Congress to discover why each individual member preferred the committees he or she did. They discover that individual MCs preferred certain committees over others for three reasons: 1) district concerns; 2) policy concerns; and 3) because one committee was more prestigious than another. Further, Smith and Deering (1983) interacted these reasons and found that the plurality of members cited district and policy concerns when they decided their preferred committee assignment. More broadly, Smith and Deering’s (1983) study suggests that the decentralization of Congress in the 1970s allowed members to select committees and subcommittees that provide them opportunities to provide particularistic benefits, thereby ensuring electoral security. Assuring this security builds upon Mayhew’s (1974) reelection assumption in that allowing MCs to select committees and subcommittees provides the institutional structure through which individual MCs will pursue reelection. Weingast and Marshall (1988) test whether participating in credit claiming, position-taking and advertising (Mayhew 1974) leads to a situation in which a number of committees are composed of high demanders. High demanders are MCs who possess a greater than average interest in the committee’s policy jurisdiction (Weingast and Marshall 1988). To assess whether high demanders comprise House committees, Weingast and Marshall (1988) assume three things about individual MCs. First, “Congressmen represent the (politically responsive) interests located within their district” (Weingast and Marshall 1988, 136). Second, parties do not constrain 4 the behavior of individual representatives (Weingast and Marshall 1988, 137). Finally, majority rule constrains how individual MCs act (Weingast and Marshall 1988, 137). After creating and empirically testing their formal model, Weingast and Marshall (1988) make four conclusions about the composition and behavior of House Committees. First, committees are composed of high demanders. Second, the committee assignment mechanism operates as a bidding mechanism which assigns individuals to those committees they value most highly. Third, committee members gain a disproportionate share of the benefits from their policy area. Finally, if the interests represented on the committee change, then policy will change toward that new interest, with the interests of non-committee members held constant. Adler and Lapinski (1997) theorize that committees in the House will be composed of representatives who come from congressional districts with higher demand for the policy benefits that each committee controls. To test their theory, they gather census data about every House district from 1943 to 1994 and look at the membership of 13 standing House committees during this time. They find that several committees are composed of members representing districts whose characteristics indicate that they have high levels of “need” for the policy benefits under the jurisdiction of that committee. Distributive theories suffer from four problems. While their theory effectively explains how constituency service committees behave, it does not explain how non-constituency service committees act (Krehbiel 1991, Cox and McCubbins 1993). Second, House members have goals beyond reelection (e.g. Fenno 1978) and do not necessarily pursue those goals via reelection. Third, distributive theories treat policies and outcomes as the same thing (Krehbiel 1991). Finally, distributive theories underestimate how political parties affect committee behavior (Cox and McCubbins 1993). 5 Informational theories agree with distributive theorists by presuming that individual legislators are utility maximizers (Krehbiel 1991). Unlike distributive theories, Krehbiel (1991) argues that outcomes, not policies, determine an individual legislator’s utility. By distinguishing between outcomes and policies, he formulates a theory that remedies a major shortcoming of distributive theories. This distinction is important because a researcher cannot determine whether collectively choosing a policy indicates a legislator’s utility (Krehbiel 1991, 67). Krehbiel (1991) advances and solves a legislative signaling game to determine the implications of his theory as it applied to House committees. He discovers that committees whose policy specialists are more extreme as compared with nonspecialists in the rest of the legislature are less informative (Krehbiel 1991, 81). Krehbiel argues that this discovery is important because the Floor is more likely to place restrictive procedures on bills that less informative committees report (Krehbiel 1991, 90). Using such procedures is a way by which the Floor can force substantive committees to act in a manner consistent with the median House member’s preferences (Krehbiel 1991). As a consequence, committee preferences usually reflect the preferences of the Floor’s members. Krehbiel (1990) asks whether preference outliers comprise House committees. To answer this question, he uses interest group ratings of members from the 96th to 99th Congresses on numerous committees. He runs a series of difference in means tests to examine his hypothesis. He discovers that very few House committees are composed of preference outliers (Krehbiel 1990). He reasons this is the case because the Floor will punish preference outlying committees (Krehbiel 1990, 1991). Unfortunately, two problems exist with Krehbiel’s (1990) piece. First, he may have committed a Type II. Error by using interest group ratings that bias his results against attaining statistical significance (Kennedy 1998). Second, Krehbiel (1990) does not examine alternative theories that may better explain his results. His explanation is also consistent with 6 partisan theories in the sense that party leaders could punish preference outlying committees, not the Floor (Cox and McCubbins 1993). Krehbiel (1993) builds upon his earlier work (1991) by assessing the degree to which significant party behavior happens when standing committees are formed and MCs are appointed to Conference Committees. He (1993) generates five literature-based hypotheses that specify the conditions under which researchers could observe political parties in action. First, preferences notwithstanding, legislators in the majority parties are more likely to obtain their desired committee seats (Krehbiel 1993, 246). Second, independent of party, high demanders within a given issue area are more likely to obtain a given committee seat (Krehbiel 1993, 246). Third, majority parties respond to extreme preferences by rewarding high demanders (Krehbiel 1993, 246). Fourth, minority parties punish high demanders to respond to extreme preferences (Krehbiel 1993, 246).1 Finally, members of the majority party are more likely to be assigned to a conference committee, holding preferences and other influences equal (Krehbiel 1993, 254). He finds little evidence to support hypothesis one as majority party House members are not any more likely to receive a given committee seat (Krehbiel 1993, 251). Moreover, he discovers that the House Armed Services committee is the only committee composed of preference outliers (Krehbiel 1993, 252). Third, he only finds that the Agriculture, Education and Labor and Interior and Insular Affairs committees support the partisan counter-stacking hypothesis. Fourth, majority party members are no more likely to be assigned to a conference committee. Therefore, Krehbiel (1993) concludes that political parties play a minor role in the House. Krehbiel (1998) examines the conditions under which the Floor will influence legislative outcomes. He finds that the pivotal voter in a unidimensional policy space determines a 1 Krehbiel’s third and fourth hypotheses are also known as partisan counter-stacking hypothesis. 7 chamber’s choices because political parties roughly counterbalance one another. Political parties counterbalance such that final legislative outcomes do not significantly differ from a situation in which no parties existed at all (Krehbiel 1998). His finding implies that political parties do not matter in the House. Informational theories, like their distributive counterparts, suffer from several problems. First, Smith (2000) notes that Krehbiel (1991, 1993, 1998) does not explain why individual MCs spend so much time bothering with party leaders, procedures, rules and activities. If parties did not matter, then legislators would not spend so much time worrying about political parties. Second, operationalizing preferences like Krehbiel (1998) theorizes involves decomposing his theoretical constructs such that one can only focus on a selective component (Smith 2000). Third, party leaders can influence legislative outcomes through their influence on a few key MCs (Cox and McCubbins 1993, Rhode 1991, Smith 2000). Fourth, isolating the effects of preferences and parties on outcomes is nearly impossible given that parties pervade every step of the legislative process (Cox and McCubbins 1993, Rhode 1991, Smith 2000). Finally, Krehbiel (1998) assumes that a pivotal legislator exists in every case, which is only certain if researchers assume a unidimensional policy space. Partisan theories were borne as a criticism of distributive and informational theories. Party- based theories accept distributive theory’s assumption that legislators’ primary goal is reelection (Cox and McCubbins 1993, Mayhew 1974). That said, they criticize informational and distributive approaches as both approaches omit the role of political parties in maximizing individual legislators’ utilities (Cox and McCubbins 1993, Kiewiet and McCubbins 1985). Also, both approaches in that an individual MC’s chances of reelection also depend on the, “…collective characteristics of the member’s party” (Cox and McCubbins 1993, 123). 8 Kiewiet and McCubbins (1985) develop an “electoral connection” (Mayhew 1974) model to explain why House appropriations to bureaucratic agencies fluctuate over time. They discover that Congress spends more money during election years than nonelection years. Second, they find that higher unemployment leads to more spending, particularly for public works agencies. Third, they discover that political parties matter in the appropriations process in that a higher percentage of Democrats in the House yields more federal spending (Kiewiet and McCubbins 1985). They argue this is the case because party leaders’ goal in assigning MCs to committees is to maintain the distinction between the parties, especially in the area of government spending. Cox and McCubbins (1993) construct a partisan theory built upon Kiewiet and McCubbins’s (1985) piece. Cox and McCubbins (1993) argue that MCs will act in a manner consistent with their party leaders’ preferences because the party’s reputation is a public good for all members of the party. Party leaders reward loyal MCs with more preferred committee assignments (Cox and McCubbins 1993). Further, loyal party members are more likely to have their request to change to a more desirable committee granted than less loyal members (Cox and McCubbins 1993). Finally, they ask whether party members on each committee are representative of the party as a whole. They find that Democrats on uniform externality committees are usually representative of Democrats within the rest of the House (Cox and McCubbins 1993). Second, they discover that Democrats on mixed externality committees are more likely to be representative of Democrats within the rest of the House than targeted externality committees, but less likely than uniform externality committees. Finally, they find that Democrats on targeted externality committees are the least likely to be composed of Democrats representative of the rest of the House. Binder (1995) examines the conditions under which the House will enact more restrictive procedures from 1789 to 1823. She argues that more traditional explanations of institutional 9 change in the House, which state that an increase in the size and workload of the House lead to the adoption of more restrictive rules, understate the influence of parties in dictating procedural choice. She theorizes that majority parties will employ more restrictive procedures when both parties’ preferences are highly cohesive and polarized over public policy (Binder 1995, 1098; Binder 1997). She discovers that majority parties employ more constrictive procedures when the conditions her theory specifies are satisfied.2 Aldrich and Rhode (1997) want to know when majority party organizations and leaders are more important. They argue that House party organizations and leadership are more important when they meet Cooper and Brady’s (1981) condition.3 Aldrich and Rhode (1997) discover that the majority party offers inducements to median-range legislators through voluntary contributions from MCs to their party. These contributions to median-range legislators are an important method by which party leaders influence individual MCs. Additionally, House party organizations and leaders are significant because majority parties enjoy important institutional advantages as majority rule gives them the ability to shape the rules in their members’ interest, constrained only by the few procedures found in the Constitution. Party-based theories, like distributive and informational theories, also suffer significant problems. First, these theories do not clarify why legislators should only contribute utiles to party organizations when they only care about the spatial location of the policy outcome (Smith 2000). In other words, party-based theories do not make it clear why MCs should deal with party 2 Future versions of this paper will include Binder’s (1995, 1997) workload and party hypotheses. Both hypotheses bear directly on the central question of this paper. At the very least, I would like to deal with both hypotheses in a refined version of my empirical test. Ideally, I would like to incorporate them into my formal model, though, I am not sure how well this will work. 3 Cooper and Brady (1981) argue that the majority party will control the House’s agenda, offer inducements to median-range legislators, and otherwise pull policy choices toward the median preference of the majority party when political parties are polarized. 10 organizations at all in light of their overriding reelection motivation. Second, the party-ness of their model comes in the form of an exogenous procedural privilege granted to a particular majority party player to make the first proposal and the right of the minority player to offer an amendment. The formal models underlying these theories do not allow for situations in which the minority party has proposal power (Smith 2000). Applying the Theories to the House Rules Committee: Fortunately, researchers have tested some of the predictions of distributive, informational and partisan theories on the conditions under which the Rules Committee will employ restrictive procedures. Huxtable (1994) incorporates the House Rules Committee into Ferejohn and Shipan’s (1990) formal model of public policymaking by administrative agencies. He finds that an autonomous Rules Committee keeps agency policies consistent with the House median member’s preference. In addition, policy outcomes move closer to the House median member’s ideal point as the Rules Committee’s ideal point moves away from the House (Huxtable 1994). On face value, Huxtable’s (1994) second conclusion suggests support for informational theories. However, Huxtable (1994) points out that this finding also may support the predictions of partisan theories as party leaders use the Rules Committee to control the committee system (Cox and McCubbins 1993). Unfortunately, Huxtable (1994) did not empirically test any of his discoveries to determine whether informational or party-based theories explain what is behind his conclusions. Dion and Huber (1996) advance a two-stage game in which they model the conditions under which the House Rules Committee behaves. They abstract this committee’s behavior through a series of assumptions (Morton 1999). First, they assume that all players in the game have perfect information (Dion and Huber 1996). Their assumption means that committees know what all 11 other actors (e.g. the Rules Committee and Floor) will do at each stage of the game. Second, all players know the structure of the game (Dion and Huber 1996). Third, the game is noncooperative (Dion and Huber 1996). Fourth, all players in the game are rational utility maximizers (Dion and Huber 1996). Finally, players will not play weakly dominant strategies (Dion and Huber 1996).4 They decompose their two-stage game into the proposal and procedural stages. In the proposal (first) stage, the Rules Committee has two options: 1) gatekeep5; or 2) make some other proposal along a left-right, unidimensional policy space (Dion and Huber 1996). The game ends if the Rules Committee chooses not to place a rule on a bill. In the procedural (second) stage, the Committee chooses between an open rule, restrictive rule, or to deny a rule (Dion and Huber 1996). Dion and Huber (1996) solve their game and derive three testable predictions that entail the conditions under which the Rules Committee assigns a restrictive rule. First, if the Rules Committee median and the median member of the substantive committee from which the bill came are on the same side as the Floor median, then the probability that a bill from that substantive committee will receive a restrictive procedure will increase (Dion and Huber 1996).6 For example, if the Rules Committee median and the Agriculture committee median are on the same side as the Floor median, then a bill from the Agriculture committee faces an increased probability of having a restrictive rule placed on it. 4 They make this assumption so that they are able to solve their game and generate testable hypotheses regarding the conditions under which the House Rules Committee will assign restrictive rules. For more information about their final assumption, please refer to their paper (1996), which I reference at the end of this paper). 5 Gatekeeping happens when the Rules Committee chooses to deny a rule. 6 The Rules Committee places a restrictive procedure on a bill when it places any other rule on it than an open rule. 12 Second, if the substantive committee and Floor median members are not identical, then the Rules Committee is more likely to place a restrictive rule on a bill from that substantive committee (Dion and Huber 1996). For instance, bills sent to the Rules Committee from the Agriculture Committee face a greater chance of having a restrictive rule placed on them by the Rules Committee when the Agriculture Committee’s median member is not identical to the Floor median member. Finally, the Rules Committee is more likely to place restrictive rules on bills from committees whose median members are further from the Rules Committee median. Dion and Huber (1996) test their hypotheses and find that the Rules Committee will place more restrictive rules on bills coming from substantive committees whose median member and the Rules Committee median are on the same side as the chamber median. Also, they discover no evidence to support their second hypothesis and find little evidence that supports their third prediction. When combined, their findings are evidence against distributive theories in that they found that the Rules Committee does more than merely use restrictive rules to enforce cross- jurisdictional bargains between committees, like distributive theorists predict (Dion and Huber 1996, 26). Moreover, their discoveries are evidence against informational theories in two ways. First, their results contradict what informational theories (Krehbiel 1990, 1991) expect in that the Rules Committee will not place more restrictive rules on bills referred from preference outlying committees as a way to steer policy in those committees toward the Floor’s median member. Second, the Rules Committee is not any more likely to place restrictive rules on bills that originate in committees whose median members are further from the Rules Committee median. Informational theories predict the opposite result in that using restrictive rules in this manner in another method by which the Rules Committee will steer policy in substantive committees toward the chamber median’s preferences. Finally, their findings are consistent with partisan 13 theories in that they discover, with many qualifications, that their first hypothesis supports the idea that party leaders will use the Rules Committee to enforce their preferred legislative outcomes. Krehbiel (1997a) criticizes Dion and Huber’s (1996) article because Dion and Huber (1996) test their first hypothesis using a variable that only tests one of many predictions from distributive and informational theories. To correct this shortcoming, Krehbiel (1997a) tests other predictions from these theories alongside Dion and Huber’s (1996) legislative profile variable. He finds little evidence that supports Dion and Huber’s (1996) first hypothesis. Consistent with his prior research and informational theories, he discovers that restrictive rules encourage committees to specialize and inform the Floor (Krehbiel 1990, 1991, 1997a). Finally, restrictive rules do not facilitate noncentrist policy outcomes at the Floor median voter’s expense (Krehbiel 1997a). Dion and Huber (1997) advance a multi-point response to Krehbiel’s critique (1997a). First, they reanalyze Krehbiel’s (1997a) results and find that bills referred to the Rules Committee from substantive committees which are on the same side as the Floor median are twice as likely to receive restrictive procedures (Dion and Huber 1997). Second, they use alternative versions of his model based on his prior research (Krehbiel 1991) and uncover significant empirical support for their first hypothesis (Dion and Huber 1996, 1997). Finally, they check whether support for hypothesis one uncovers any support informational theories and find no evidence that it does. Regrettably, Dion and Huber (1996, 1997) only tested their theory on data from the 94th to 98th Congresses. One cannot totally fault them for doing so in that this was the only data available to them at the time they wrote their article (see Bach and Smith 1988). That said, I recently acquired data from the Rules Committee which describes what bills received open and 14 restrictive rules from the 99th to 106th Congresses (House of Representatives 1987, 1989, 1991, 1993, 1995, 1997, 1999 and 2001). Since the 98th Congress, Figure One shows that the proportion of bills (regardless of committee) that receive restrictive rules has increased drastically from 34.375% in the 98th Congress to 62.23% in the 106th Congress. [FIGURE ONE ABOUT HERE] My data on restrictive rules is significant because it allows me to: 1) provide a more detailed and expansive test of Dion and Huber’s (1996, 1997) and other predictions than was previously possible7; and 2) see what has happened since the Republicans became the majority party in the House in 1994. Utilizing an expanded dataset also allows me to employ a better empirical test than Dion and Huber’s (1996). Their empirical test uses feasible Generalized Least Squares to correct for problems inherent in time series, cross sectional data. Unfortunately, using this method produces artificially low standard errors, which bias any interpretation of their statistical results in favor of attaining statistical significance (Beck and Katz 1995). 8 To correct this problem, I will use Beck and Katz’s (1995) corrections for time-series, cross sectional data. I employ this method because Ordinary Least Squares combined with Panel Corrected Standard Errors (PCSEs) most effectively rectifies these concerns (Beck and Katz 1995). Their method works because, “The correction for contemporaneous correlation of the errors is only possible because we have 7 For this paper, I will only test two of Dion and Huber’s three hypotheses. I will test Dion and Huber’s other hypothesis and two hypotheses from Binder (1995, 1997) at a minimum in future versions of this paper. I did not test Dion and Huber’s second hypothesis because I could not get STATA to perform a Wilcoxon Difference in Medians test required to test this hypothesis. 8 The reason for this is that feasible Generalized Least Squares assumes that researchers know the process by which they generate their error terms. Analysts never know the process by which the error term is generated. Usually, this is not a problem, except with time-series, cross sectional datasets because, “…the error process has a large number of parameters” (Beck and Katz 1995, 634). Consequently, researchers will generate underestimated standard errors, which bias their results toward achieving statistically significant coefficients (Beck and Katz 1995). 15 repeated information on the contemporaneous correlation of the errors…” (Beck and Katz 1995, 638). This means that analysts know how and why the errors are correlated with one another when they deal with cross sectional, time-series data. Before using this method, I will specify Dion and Huber’s (1996) hypotheses that I intend to test in this version of my paper. Hypotheses: Hypothesis One: If the Rules Committee median and the median member of the substantive committee from which the bill came are on the same side as the chamber median, then the Rules Committee will place a higher proportion of restrictive rules on bills from that committee. Hypothesis Two: As a substantive committee’s median member diverges further from the Rules Committee’s median, the Rules Committee will place a higher percentage of restrictive rules on bills from that substantive committee. Modeling my Hypotheses: I will use OLS combined with Beck and Katz’s (1995) corrections to test my hypotheses. Toward this end, I will employ the following model: PCTREST*=b0+b1LEGPROF+b2CMTEDIST+biCONGRESSj+e My dependent variable (PCTREST) represents the percentage of bills from each substantive committee that received restrictive rules from the Rules Committee during the 94th to 106th Congresses. I gathered my data from two sources. First, I acquired this data for the 94th to 98th Congresses from table 5-1 of Bach and Smith’s book (Bach and Smith 1988, 116-117). Their table presents, “…the total number of bills that receive restrictive rules for each committee, as well as the proportion of these bills that receive restrictive rules” (Dion and Huber 1996, 35). Second, I gathered data for the 99th to 106th Congresses from a series of reports from the House of Representatives that list the type of rule that each bill received (House of Representatives 16 1987, 1989, 1991, 1993, 1995, 1997, 1999 and 2001). From here, I visited the Library of Congress’s Congressional information website (at http://thomas.loc.gov) to discover which committee referred each bill to the Rules Committee. Afterward, I computed the proportion of each committee’s bills that received a restrictive rule from the Rules Committee. My first independent variable (LEGPROF) represents the legislative profile that every substantive committee faces in each Congress. This variable directly tests Dion and Huber’s (1996) first prediction that open rules are much less likely when a substantive committee and the Rules Committee are on the same side as the Floor median (Dion and Huber 1996, 34). I gathered DW-NOMINATE scores from the 94th to 106th Congresses (Poole and Rosenthal; Rosenthal, Poole and McCarty 1997). Second, I computed each substantive committee’s median along with the chamber medians for each of these Congresses. Consistent with Dion and Huber (1996), I coded LEGPROF one if the House Rules Committee median and the median member of each substantive committee was on the same side as the chamber median. Many will argue with why I used DW-NOMINATE scores to measure each House member’s preferences. First, Hall and Grofman (1990) show that using interest group ratings to measure preferences generates policy-related biases such that most statistical results obtained by using these scores are unreliable. Second, Londregan and Snyder (1994) advance a heterogeneous preference model that uses NOMINATE and ADA scores to estimate the most preferred policy position of each House member. Unfortunately, their data is only available through the 98th Congress. Using a slightly different model, Snyder and Groseclose (2000) update Londregan and Snyder’s scores through the 105th Congress. Regrettably, Snyder and Groseclose’s (2000) measures create another set of problems while they attempt to rectify problems with using 17 NOMINATE and interest group ratings. First, they assume that political parties do not try to influence individual MCs decisions on lopsided roll call votes (Snyder and Groseclose 2000, 193). Their assumption is unreasonable as political parties pervade the way MCs vote. Rejecting their assumption means that we cannot construct consistent estimates of each legislator’s true preferences independent of party influences (McCarty, Poole and Rosenthal 2001). Second, they do not control for observational interdependence when they construct their measures. Many roll call votes are not independent of one another because MCs want to be consistent in how they vote (Fenno 1978, Morton 1999). Third, many lopsided votes exist only on procedural, not policy matters. This fact is important because one can ask whether procedural votes indicate a MC’s policy preferences. Finally, McCarty, Poole and Rosenthal (2001) show that using Groseclose and Snyder scores (2001) biases a researcher’s results toward exaggerating party effects. Because all other measures of preferences are just as problematic (and in some cases, more) as using DW-NOMINATE scores, I will these scores to measure each individual MCs policy preferences. My second independent variable (CMTEDIST) represents the distance between every substantive committee median and the Floor median in each House. Like the first independent variable, I used DW-NOMINATE scores to construct this variable. I took the absolute value of the difference between the committee and chamber median’s positions. My final independent variable (CONGRESSj) denotes a series of dummies that control for House-specific effects that invariably affect rule assignment. First, I generated a separate Congress variable for each House. Second, I coded each variable one for the Congress to which it corresponded and zero otherwise. For example, I coded the Congress variable for the 94th Congress one for all committees in that Congress and zero otherwise. Finally, I should note that 18 b0, b1, b2 and bi denote parameters that I will estimate. The “e” in my equation signifies the error term. [TABLE ONE ABOUT HERE] Table One illustrates four different models. Model One examines how legislative profile affects the dependent variable while controlling for Congress-specific effects. Model Two examines how committee distance affects my response variable while controlling for Congress- specific effects. Model Three looks at how legislative profile and committee distance together affect the dependent variable. Model Four only includes committee distance apart from my control variables. I include this model to perform a partial Chi-Square test that determines whether knowing committee distance is useful in predicting the value of the dependent variable. Models one and three support hypothesis one because legislative profile is positively signed and statistically significant at the .001-level. When the Rules Committee and substantive committee medians are on the same side as the Floor median, model one suggests that we should expect to see 22.543% more restrictive rules on bills from that substantive committee. Further, when the Rules and substantive committee medians are on the same side as the chamber median, model three suggests that we should expect 20.71% more restrictive rules on bills from that substantive committee. However, model two supports my second hypothesis, while model three does not support that hypothesis. Model two supports hypothesis two because committee distance carries a positive sign and is statistically significant. This model suggests that an increase of .1 in committee distance yields an approximately 9.1% increase in the proportion of restrictive rules placed on that committee’s bills. Model three did not support hypothesis two because the coefficient on committee distance, while appropriately signed, was not statistically significant. 19 Consistent with Moffett (2003), I perform a partial chi-square test to determine whether there is anything to hypothesis two. To execute this test, researchers should run the full model with all variables (Model Three) and another model with the variables that one would like to examine to determine their usefulness in predicting the dependent variable (Model Four). After this, one subtracts the chi-square values to determine whether the difference in chi-square values is statistically significant. If the difference is statistically significant, then knowing the values of the independent variables is useful in predicting the value of the dependent variable (Moffett 2003). I employed this test and discovered that the difference in chi-square values is statistically significant at the .001-level. This means that knowing the absolute value of the distance between each committee and the Floor is quite useful in predicting the percentage of restrictive rules that the Rules Committee applies to each committee in each Congress. Unfortunately, for informational theorists, knowing committee distance is not horribly useful because the R2 for the reduced model is .0237. This means model four only explains 2.37% of the variation in the dependent variable. Finally, models one through three explain between 26.41 and 31.84 percent of the variation in the response variable. Also, these models are useful in predicting the value of the dependent variable as the probability that these models are completely useless in predicting the response variable is less than .0001. Discussion of Results: Models one and three confirmed hypothesis one, which is consistent with previous work as Dion and Huber (1996, 1997) found the same thing. Therefore, we should expect 20-22% more open rules when a substantive committee and the Rules Committee are on the same side as the Floor median (Dion and Huber 1996, 34). Like Dion and Huber (1996, 1997) find, my result is 20 consistent with party-based predictions of Rules Committee rule assignment at the expense of distributive and informational theories. However, model three did not confirm hypothesis two. My finding coincides with some prior studies on the subject (Dion and Huber 1996, 1997), but is inconsistent with others (Krehbiel 1997). While my finding is evidence against informational theory’s preference outlier prediction, one would do well to regard it with some caution in that my finding could be the result of misspecifying my model as I did not test Binder’s (1995, 1997) or Schickler’s (2000) partisan explanations and one of Dion and Huber’s hypotheses. On the other hand, my finding could be due to the possibility that committee distance does not affect the proportion of restrictive rules assigned to a substantive committee’s bills in each Congress. 21 References: Adler, E. Scott and John S. Lapinski 1997. “Demand-Side Theory and Congressional Committee Composition: A Constituency Characteristics Approach.” American Journal of Political Science 41: 895-918. Aldrich, John H. and David Rhode 1997. “Balance of Power: Republican Party Leadership and the Committee System in the 104th House.” Presented at he Annual Meeting of the Midwest Political Science Association, Chicago. Bach, Stanley and Steven S. Smith 1988. 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Marshall 1988. “The Industrial Organization of Congress; or, Why Legislatures, Like Firms, Are Not Organized as Markets.” The Journal of Political Economy 96: 132-163. 25 Table One: Ordinary Least Squares Analysis on the Effects of Legislative Profile and Committee Distance on the Percentage of Restricted Rules, N=238 Percentage of Bills Receiving Restricted Rules from the House Rules Committee Independent Variables Model One Model Two Model Three Model Four Legislative Profile 22.543*** (3.797) - 20.710*** (3.263) - Committee Distance - 91.011* (35.271) 45.111 (30.134) 94.900** (33.930) 94th Congress - -40.730*** (7.173) - - 95th Congress 2.463 (7.780) -35.880*** (6.594) 3.402 (7.723) - 96th Congress 12.174 (6.637) -24.835*** (5.436) 13.065* (6.534) - 97th Congress 14.855* (6.556) -23.813*** (5.551) 15.114* (6.584) - 98th Congress 24.349*** (6.007) -13.461** (4.749) 24.670*** (6.038) - 99th Congress -4.908 (7.126) -41.285*** (5.576) -4.551 (7.122) - 100th Congress 15.308* (5.990) -19.584*** (4.788) 16.141** (5.946) - 101st Congress 26.078** (7.782) -11.934 (6.778) 26.662** (7.718) - 102nd Congress 56.188*** (6.414) 14.725** (5.251) 56.837*** (6.373) - 103rd Congress 55.616*** (6.630) 17.343** (5.428) 56.371*** (6.565) - 104th Congress 39.182*** (7.502) 2.405 (6.733) 39.493*** (7.547) - 105th Congress 34.827*** (7.549) - 35.555*** (7.543) - 106th Congress 44.613*** (6.179) 8.326 (5.195) 43.977*** (6.447) - Constant 5.130 (6.669) 49.350*** (6.043) 1.914 (6.694) 36.165*** (3.479) N 238 238 238 238 R-Squared .3137 .2641 .3184 .0237 Wald Chi-Squared 691.13 839.52 942.76 7.82 Prob>Chi-Squared <.0001 <.0001 <.0001 .0052 Notes: The values in parentheses are panel corrected standard errors. Second, * denotes p<.05, ** denotes p<.01, *** denotes p<.001; all two-tailed tests. Finally, the 94th Congress in Models One and Three and 105th Congress in Model Two were dropped due to collinearity. 26 Figure One: Percentage of Bills from the 94th to 106th Congresses Receiving Restricted Rules 0 10 20 30 40 50 60 70 94 95 96 97 98 99 100 101 102 103 104 105 106 Congress PercentofBillswithRestrictedRules
