Committee Requests and Committee Assignments: Do Members Get What They Want
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A Cross-state Analysis of Legislative Committee Request Success Rates
Ronald D. Hedlund, Northeastern University Claudia Larson, Northeastern University Rob A. DeLeo, Bentley University David P. Hedlund, Florida State University
A Paper Presented at the 12th Annual State Politics and Policy Conference, Rice University, Houston TX February 16-19, 2012
Not for quotation without the authors permission A Cross-state Analysis of Legislative Committee Request Success Rates
Abstract
The committee request/assignment process and the behavior of legislators associated with it has long been a topic of interest for political scientists. Previous state-level research (Hedlund 1989, 1992, and Hedlund and Patterson 1992) has demonstrated that variation exists in the degree to which members acquire the requests they make. More recent findings (Hedlund, DeLeo and Hedlund 2011and Hedlund, DeLeo, Hedlund and Larson 2011) using longitudinal data from one state, indicate that contextual/organizational/session factors (such as party stasis in chamber control and proportion of first-time party members) affect committee request success, but in nuanced and complex ways. More importantly, previous research also shows that personal behavior related to “risk-taking” surrounding the requests made (proximate factors) has greater impact on the legislators’ success at gaining membership to committees they requested than contextual/ organizational/session elements. In addition, the research evinces that the effects of risk-taking and contextual factors impact new legislators differently than experienced legislators, with risk-taking being especially important in experienced legislators’ committee request/assignment success.
Our previous research—state committee request/assignment data for 12 sessions of Democratic members from the Iowa House—provides the base for a new, four state, cross time analysis. Our interest here is in assessing how these contextual/organizational/session traits, as well as personal, factors affect request/assignment success; but, we add an additional level of predictor variables associated with different political settings—state-based characteristics. Do the prior conclusions apply when performing similar analyses on data from multiple legislative sessions for the Iowa, Maine, Pennsylvania and Wisconsin legislatures; and do state-based characteristics impact committee assignment success. We examine whether or not the different political conditions associated with states impact the effects of these other predictor variables on committee request success. To do so, we explore the interactive effects of individuals’ personal, risk-taking, contextual/organizational/ session variables and states’ political setting on committee request outcomes using 3-Level Hierarchical Linear Modeling (HLM).
2 A Cross-state Analysis of Legislative Committee Request
Success Rates 1
Fenno’s conclusion that “[C]ommittees matter.” has become the assumption guiding much subsequent research on legislative institutions in the U.S. (Fenno 1973, xiii). In this work it is widely recognized that committees have become essential to legislative operations at all levels of the U.S. government in policy formulation, revision and adoption. By enabling a division of labor, issue specialization and expertise development in legislatures, committees foster a process that makes possible effectual policymaking (Davidson and Oleszek 2004; Deering and Smith 1997; Francis 1989; Shepsle and
Weingast 1987; Francis and Riddlesperger 1982; Rosenthal 1981; Rosenthal 1974; Fenno 1973; Sokolow &
Brandsma 1971). Committees have also been found to affect the careers of individual legislators by allowing members to develop policy skill and knowledge, satisfy the policy expectations of their constituencies and strengthen their internal reputation, position and influence. Serving on the most advantageous committees also has been connected to positioning one’s self in the party, acquiring leadership positions and facilitating a member’s post-legislative career (e.g. lobbying) (Freeman 1995; Shepsle and Weingast 1987; Francis 1985;
Forina 1977; Shepsle 1975; Clapp 1963). For all of these reasons, political scientists have built an extensive body of literature and theory about virtually all aspects of legislative committee organization.
One set of concerns in prior political science research addresses how committees are established and formed. Hence, the process through which legislators request and are subsequently assigned to committees has been an ongoing topic of research interest. Inquiry about the committee request and assignment process seeks to understand the relative success legislators experience in receiving appointment
1 This paper is a major expansion of papers delivered at the 11th Annual State Politics and Policy Conference and the 2011 Northeast Political Science Annual Meeting. The authors express their thanks to several unnamed persons who made this research possible by providing the information used herein. We also wish to acknowledge the assistance of a number of people who aided in the information identification, data entry and consistency checking of these data, especially Taylore Karpa, Colleen Kelley, Jessica Headd and Michael Schiano. We also want to recognize the significant assistance of Justin Theodore Backal-Balik, Christopher Federici, Patrick Giusti, Abigail Reese, and Karen Marie Hedlund. The authors also owe a great debt of gratitude to Professors Alan Clayton-Matthews and Betsy J. Becker for their guidance regarding HLM, its use and interpretation. Finally, we want to acknowledge the financial assistance of Northeastern University and especially the Department of Political Science.
3 to requested committees. To this end, scholars have posited a number of explanations, including leaders’ desire to accommodate members’ requests (Hedlund 1989; Bullock 1985; Smith and Ray 1983; Shepsle
1978; Gertzog 1976; Westefield 1974), members’ reelection concerns (Masters 1961) and even the organizations’/institutions’ rules, norms and practices (Bullock 1985, 789; Hinckley 1978; Shepsle 1978;
Asher 1974; Swanson 1969; Masters 1961).
More recent research views the committee request and assignment process as an individualistic, strategic task for the member making committee requests. A complex interrelationship of individual goals, organizational and environmental constraints and perceptions about the strategic positions of other members
(who are also competing for coveted committee positions) are assumed to influence legislators' requests (Lee
2008; Amegashie 2003). By and large, this literature applies game theoretic models and implicitly views the committee request/assignment process as a form of risk taking: individuals expending varying degrees of personal energy and “political capital” to best position themselves to obtain assignment to their requested committees through a process in which they are competing with others for scarce political resources— assignment to the “right” legislative committees. (Lee 2008).
Although risk taking is inherent to game theoretic models of committee requests/assignments, research has yet to test for the impact of member risk taking behavior on the committee assignment process.
This paper seeks explicitly to begin filling the gap left by the absence of such work by examining the relative effect of risk taking behavior on legislator success during the committee request/assignment process. Here, we define risk as “the product of the likelihood of some event and the impact, value or utility of its outcome”
(Maule 2004, 19). As such, risk becomes the level of “chance, uncertainty or jeopardy” a person is willing to assume when initiating some action for which the outcome is in doubt. In our case the “outcome” is a member’s success in receiving their desired committees and the “value” or “utility is the members’ subsequent individual legislative standing and policy effectiveness in the legislative process. Because risk taking is presumed to be affected by the decision making environment surrounding this action, this study also examines a number of contextual/institutional/ organizational or “session level” traits. These variables seek to account for a number of “setting” influences that are unique to and highly pertinent to the legislative
4 milieu: variation in party control, leadership changes, the number of new members, etc. In other words, we assume that a legislator’s request making for committee assignments, as well as the risk taking a member decides to use, takes place within a specific organizational and political context. We believe that the organizational/situational/contextual changes from session to session affect members differently and also influence the member’s request-making behavior and, in turn, their success in obtaining desired committee appointments. In addition, this research recognizes that legislator behavior takes place within larger political systems that vary with regard to the political and environmental circumstances associated with each state in which also affects committee request behavior. Thus, our research question asks about the degree to which personal attributes as well as risk taking behavior, contextual/institutional/organizational traits and state environmental characteristics affect a legislator’s success in obtaining requested committee assignments.
This study analyzes longitudinal data (from 3 to 12 sessions) collected for Democratic members in the lower chamber (House or Assembly) of four states (Iowa, Maine, Pennsylvania and Wisconsin). No claim is made that these constitute a random sample of sessions or states, but the data do reflect diversity across a wide range of contextual/organizational/session traits. Since the multi-level data to be used are hierarchical and nested (individual legislators within legislative sessions within states), and because we wish to analyze the impact of all three levels of variables on member committee request outcomes, we use
Hierarchical Linear Modeling (HLM) as our primary data analysis strategy. HLM analyzes such ordered data by separating the analysis by “Levels” and treating individual-level variables (Level 1) and group-level variables (Level 2 and perhaps Level 3) as distinct yet interconnected factors affecting outcome variables.
Prior Research
A very useful array of explanatory frameworks and theories has appeared in political science for understanding the committee request/assignment process. While much of both the theoretical and empirical development of this literature is based on Congress, it has provided a foundation for research at a variety of levels. In one of the most influential early frameworks, Shepsle proposed an interest-advocacy- accommodation syndrome wherein individual members publicize their committee preferences in order to
5 inform party leaders (the decision makers) of their desired committee appointment(s) (Shepsle 1978). To foster and advance party loyalty and harmony, leaders endeavor to accommodate member requests (Hedlund
1989, 597; Bullock 1985, 789; Smith and Ray 1983, 219; Shepsle 1978; Gertzog 1976, 693; Shepsle 1975;
Westefield 1974, 1503). Taking a different approach, Frisch and Kelly (2006) presented a committee assignment politics (CAP) framework in which members pursue a complex political calculus when requesting committees. According to this framework, member requests are “conditioned by” their perceptions of the accessibility for membership on various committees, the role of party leaders in the assignment process and the process of committee selection itself (Frisch and Kelly 2006).
A number of more explicit explanatory theories fit within the above frameworks for understanding committee requests/assignments. For example, the Masters-Clapp model sees committee assignment as a means for leadership to maximize the reelection prospects of individual members (Masters 1961, 354). A second theory holds that legislatures are marked by various norms, rules and practices that impact and stipulate the request and assignment process, thereby structuring individual behavior. Examples of such structuring elements include rules governing the selection process, norms of conformity and norms of seniority (Bullock 1985, 789; Hinckley 1978; Shepsle 1978; Asher 1974; Swanson 1969, 740; Masters 1961,
345).
Rational choice models of committee organization offer yet another, differing theoretical perspective on the committee request/assignment process. Three models of committee organization have dominated rational choice approaches: distributive, informational and partisan. The distributive model holds that committees consist of members who are willing to relinquish “control” over issues of less importance to their constituents in return for control over issues that are more important to them (Shepsle and Weingast 1981,
503; Shepsle 1979, 27). Members do this because special interests among the committee members’ constituencies exchange electoral support for members in return for favorable policy accomplishments by committee members in areas of high constituency interest. In this model, the appointment process reflects leaders’ calculus regarding what appointments maximize constituency interests because, by helping their members gain electoral support, leaders generate continued support for their own partisan influence. Another
6 rational choice variant, the informational model, holds that committees serve the median policy interest of the controlling party in the chamber by providing quality knowledge pertaining to policy issues (Krehbiel
1990, 149). Hence, in the assignment process, members seek assignments and leaders appoint members to committees based on members’ information/expertise in specific policy areas. Finally, the partisan model holds that party leaders seek to appoint committees compatible with their party’s position on issues, expressed as the median policy position of the party caucus (Cox and McCubbins 1993). Loyal party members provide the party with procedural control of the legislature. With this procedural control, legislators use committee positions to secure policy benefits for constituents, with these benefiting constituents then providing the members, through their parties, with electoral support.
More recent research has proposed an “all-pay auction” theory for explaining committee requests/ assignments. This game theoretic approach has also been employed to explain other political and economic phenomena (Amegashie 2003, 79). One of this theory’s key elements is the existence of both sincere and revealed preferences of assignments among persons involved in this “auction” of committee appointments.
Lee (2008) illustrated the relationship between sincere and revealed preferences, demonstrating “how a strong revealed preference does not necessary mean that the legislator sincerely values that assignment highly, and vice versa” (Lee 2008, 251). Instead, legislators, when pursuing committee requests, engage in a strategic calculus regarding tactics and likely outcomes and exert varying degrees of effort to achieve their desired outcome thereby reflecting their “political capital” and their perceptions regarding the strategic positions of other members “competing” in the assignment process. Thus, in his analysis, Lee accounts for a number of situational or temporal characteristics (seniority, party loyalty, committee transfers, effort constraints), as well as the interaction effect of “each competing member’s actions on the final probability of winning the assignment” (Lee 2008, 238). Earlier decision theoretic models, most notably Shepsle’s (1978), only account for situational factors.
Despite the appearance of game theoretic models and the proposed all-pay auction approach, most contemporary committee request/assignment research has not explicitly examined the impact of risk taking on committee assignment outcomes. Further, existing research is largely devoid of empirical measures of risk
7 taking behavior. It is within this context that the present research includes member risk taking as a predictor of committee assignment success. Risk taking is a prospective exercise, in that the individual risk taker knowingly forgoes an immediate level of “safety” or personal stasis and takes some level of chance in order to achieve a desired committee appointment (Jia, Dyer, and Butler, 1999). Regarding the committee request process, legislators demonstrate higher levels of risk anytime they request a committee for which (a) there is substantial competition among members for a limited number of committee appointments, or (b) when they seek appointment to different committees than they have held in the past. To this end, we operationalize two measures of risk taking below.
We also assume that risk taking is affected by the organizational context in which the behavior occurs—the level of risk taking is, in part, conditioned by the nature of the legislative organizational setting
(largely political) in which the decision maker is acting (Lee 2008). This is consistent with other studies of risk taking which recognize the influence of environmental factors, such as organizational and institutional opportunities and constraints, on individual decision making regarding risk. Thus, this research includes a number of exogenous traits that account for the specific institutional and political setting within which requests are being made. All of these variables account for differences that occur across legislative sessions, such as variation in party leadership, changes in party control, the number of new party members and the length of time a legislative leader has occupied his/her position.
Finally, this research assumes that the broader, political system/environment, of which this legislative chamber is a component, also has an effect on individuals and their risk taking in their committee request behavior. Although there has been no research regarding this possibility, we believe that the setting created by a state’s political system—e.g., its political culture, restrictions on the legislature, level of liberalism, degree of leaders’ institutional power and party competition—also affects this committee request/assignment process.
Variable Structure
8 This paper explores possible explanations for member-level results (committee assignment success outcomes) in the committee request/assignment process for Democratic members of four state legislatures for sessions between 1975 and 2010. Our analysis involves the use of three categories of explanatory variables – individual-level attributes, including risk taking behavior, organizational/situational/contextual traits and the nature of the states’ political system. The outcomes to be explained are various measures of member success in acquiring the committees they requested. Since many of the variables representing these three sets of explanatory factors are included in more than one theoretical model described above, we do not view our research as a test of any single theoretic model; rather we see this paper as theory-based exploratory research intended to examine the nature of the relationships among these traits and members’ committee assignment success. Further, little prior research has considered the effects of political system traits such as those associated with different states on individual-level activities, so a cautious approach is being taken here.
Previous state-level research on committee assignment success found that certain personal characteristics—namely gender, education and prior legislative service—each have a statistically significant impact in some instances on members’ success in the request/assignment process (Hedlund 1992, Hedlund and Patterson 1992). Further, preliminary analysis of the data used here found differences in the committee assignment process between legislators serving in their first session versus legislators who served and had assignments in the previous session. Thus, three individual-level, personal traits—Gender, Education, and prior Legislative Experience—are included as personal, individual-level explanatory variables here. (A listing of all the variables used in this analysis is found in Appendix A.)
Two distinct yet related measures of individual-level risk taking within the context of the committee request/assignment process have also been identified: 1) specific strategies pursued regarding the number of requests made, and 2) the liabilities associated with the quest for high-demand, highly sought-after committees. For the purposes of this analysis, risk taking is conceptualized in terms of the degree of “peril,”
“hazard” or “uncertainty” associated with pursuing a set of committee requests. It reflects choosing to take certain actions that involve a greater likelihood of failure to obtain desired committee assignments. A risk
9 taking strategy, then, can range from “safe” (making non-adventurous requests, which are likely to be obtained) to “improbable” (making somewhat precarious requests, which are much less likely to be obtained).
Our first risk taking variable, Requests Not Made, considers the proportion of committee requests that a member chose not to make relative to the number of requests she could have made. In discussing member request behavior, Rohde and Shepsle noted “ . . . that the number of requests that a member makes depends on certain strategic considerations, and thus we implicitly argued that a member would think that his probability of getting some requested committee depends (at least in part) on the number of requests made”
(1973, 897). Members are typically asked by leadership to provide a specific number of requests. A member providing a greater number (a more complete “complement”) of request options gives the appointing authority greater opportunity and, thus, greater latitude in accommodating member’s requests. Therefore making more requests should increase the probability that a member will receive some requested assignments. Making fewer requests than asked for imposes greater restrictions on the appointing authority’s ability to satisfy a member’s requests. 1 By offering leaders a smaller number of requests from which to choose, a member may jeopardize achieving a moderate level of individual success among an array of different levels of choices in order to achieve an especially high level of success among only top committee choices through undertaking this higher risk strategy.
The “risk” associated with choosing certain actions is also reflected in the relative attractiveness or competing demand associated with each of the committees requested. We capture this concept with our second risk taking variable, Request Popularity. Within any given session, certain committees are simply more coveted or desired and sought out than others. While the perceived desirability of a committee may be an outgrowth of an individual member’s interests (e.g., area of policy expertise/interest or constituency), certain committees (especially the finance-oriented ones) will have a greater appeal for all members. The greater the number of requests for a finite number of committee positions, the less likely it is that a member
10 will receive such a request. 2 Thus, when members request a higher-demand, more sought-after committee, they are taking a greater risk.
In addition to these two sets of individual-level characteristics affecting a member’s request strategy and ultimately his/her success in the committee requests/assignments process (personal attributes and risk taking are Level 1 variables in our HLM model), there are a number of context, institution-based features, associated with the legislative setting for every session, that are likely to have an independent impact on member behavior and assignment outcome success through the opportunities and constraints they provide.
Interviews with leaders in the states indicated that there were no major disruptions or changes in the roles of the House Democratic leadership regarding the appointment process during this period (when they were the minority party, Democrats’ recommendations for committee membership were almost entirely followed by the majority party), so those aspects were not included. Institutional traits associated with the
House/Assembly legislative organization were not the same across sessions and were consequently incorporated in our Level 2 variable set. For example, variation was found across sessions for the time length of party control of the chamber (Sessions of Party Control), Democratic party size in the chamber
(Proportion Democrats), if there was a Change in Party Control of the chamber, minority/majority party
Status for Democrats, size of change in the number of Democratic seats in the chamber (Democratic Change) and alterations in the proportion of Democrats who did not serve in the previous session (New Democrats).
Another set of session factors is associated with how the committee system is formulated and configured for that session. If the committee system is constructed and fashioned to facilitate member involvement and utilization of committees, are legislators more likely to have different success levels in obtaining desired committee memberships? Specifically, the committee system’s design might impact the prospects/opportunities provided to members for achieving desired committee appointments. Two such features were identified as important: the number of committees created (Number Committees), and the average number of committee appointments per member (Appointments per Member). Both components create “opportunities” for members in their quest for committee assignments.
11 Finally, as noted above, variation in the party leadership might also affect committee request/ assignment success. Since the party leadership is the key element in the assignment of members to committees, its stature and standing are critical elements. Two features are identifies as important aspects: the nature of the Democratic Leadership Continuity (and succession) and the number of consecutive Sessions as Democratic Leader.
All of these traits can be viewed as features of the legislative chamber (the institution/organization/ context created) affecting and constraining what individual members can do, as well as how they can do things. Since these features are associated with the differing legislative sessions and they varied considerably across states and time, examining them permits analysis of their likely impact independent of the individual- level factors. In performing an analysis of these organizational, risk taking and outcome success variables, the existence of great variation across the range of values for the variables as well as across the states required us to use standardized values for these continuous variables rather than the “raw scores.” Thus, the values used in our analysis for these variables are the standardized scores for these variables based upon the state which makes interpretation of these values challenging.
Also important, but rarely studied in a comparative fashion, is the nature of the state’s political system and its effects on committee assignment success. One reason for the absence of such analysis is the limited availability of cross-state data regarding committee requests/appointments. 3 In this current research we are able to examine the effects of a number of different characteristics measuring the nature of the state political system and environmental setting. Included here are Elazar’s (1966) three political culture
(Traditional, Moralistic, Individualistic dummy) variables, the number of House Seats, the level of state liberalism (Liberal Opinion and Policy Liberalism [Wright, Erikson and McIver 1987]), the level of constitutional/statutory limitations on the legislature (Constitutional Limits and Statutory Limits [Martorano-
Miller, Hedlund and Hamm, 2010]), the existence of the Initiative for statutes, the level of professionalization of that state’s legislature (Professionalization [Squire 1992 a & b]), the Governor’s institutional power level (Governor Power [Beyle 1988]), the amount of institution-based power residing in the Speaker’s position (Speaker Power [Clucas 2001]) and the level of interparty competition in the state
12 (Interparty Competition [Ranney 1965]). Together, these characteristics tap different aspects of a state’s political environment likely to affect legislative operations and activities including committee appointments.
Our outcome variables account for the relative success of members in obtaining their desired
(revealed) requests, for which we have developed three indicators. Our primary measure of success is the proportion of committee requests a member is assigned–Requests Assigned. This indicator is based on the appointments one received in relation to what one sought (their revealed requests) and measures the relative degree to which what a member requested was in fact assigned. One might think of this as similar to a baseball player’s batting average—success in terms of attempts. (A hypothetical example of how this and other outcome measurements are derived is seen in Appendix B.)
From our interviews with legislators, we frequently found members describing their success in terms of the appointments received rather than requests made. Thus, we developed a second quantitative indicator of success in terms of the proportion of committee assignments that were requested and received –
Assignments Requested and Received. This indicator focuses on the nature of one’s current assignments
(successes in this process), specifically on the degree to which one’s current array of committee memberships includes assignments that were sought. Because assignments to committees that were requested are presumed to be more desired by the requestor than assignments to committees that were not requested, receipt of a higher proportion of committee requests in a member’s total array of committee assignments is another indicator of success. This indicator is quite different from Requests Assigned because it measures success in terms of consequences or results—the assignments actually received—not the attempts—requests.
A third indicator of committee request outcome focuses on the nature of the assignments one has received—how the assignments “measure-up” in terms of the level of demand for committee assignments that session. We measure this with our Assignment Popularity variable, which focuses on the relative demand level among all members for the committees to which a member is assigned. The higher this value, the greater the demand for the committees to which a member has been appointed. While this indicator does not involve any consideration of a member’s request behavior, it does provide insights about the characteristics of the committee assignments received based on their respective degree of popularity/demand.
13 Simply put, if a committee is a more desired committee (relative to that session’s other committees), being assigned to this higher demand committee indicates that the member assigned to this committee received a highly sought-after position. The value for this variable is based on the weighting for committees discussed above (and in endnote 2) regarding the Request Popularity, but here, the weighting is “averaged” for the committees to which one is assigned. This permits evaluation of the degree to which a member obtained membership on highly sought committees.
The inter-correlation among these three measures of outcome success for all respondents is very modest across all states as well as within each state thus indicating that these variables measure different types of success. (See Appendix C.) We include all three indicators in this analysis since they appear to represent different aspects of committee assignment outcome success. Their values were also standardized by state for use in the analysis.
Data and Methodology
In the House or Assembly for each of these four states, leaders for each political party solicited information from their members regarding the members’ committee preferences soon after the November general election via mail or email. Political parties conducted this process independent of one another, and each party had substantial, if not complete, input regarding their members’ committee appointments.4 There were also informal discussions between members and leadership regarding committee assignments. While these informal discussions are important to the assignment process in that they provide opportunities for members to explain to leadership their rationale for a request, these communications are not reflected in this study. Interviews with both leaders and legislators indicated that these meetings took place after a member had responded to the leaders solicitations and did not involve the leaders revealing their decisions about committee assignments.
The data utilized in this study are based on the revealed committee requests of the House Democrats in these four states, spanning sessions between 1975 and 2010. Table 1 displays the distribution of individual-level cases across the states and sessions, illustrating the differences noted above regarding
14 respondents. Iowa provides over one-third of all legislators and data for 12 of the 15 sessions being studied.
This is in contrast with Maine which provides about 15% of legislators and for only three sessions. Very ample, across-session data are available for Iowa and Wisconsin and more limited for Maine and
Pennsylvania.
______
Table 1 About Here ______
The information was provided to the authors through the “good offices” of a number of persons.
Hence, this paper presents an examination of 12 sets of Iowa House Democratic committee requests reported between the years 1985 and 2009; nine sessions in Wisconsin, 1975-2006; four sessions in Pennsylvania,
1987-94; and three in Maine, 1987-92. Access to such data is extremely rare, and thus the authors were limited to the information made available, plus general interviews conducted in these states with leaders and members for other purposes. Conversations with legislative leaders and staff confirm that the sets of information provided are a valid representation of the requests made by individual members.
Two notes of caution must be made at this point regarding the findings. First, since in HLM all respondents are analyzed together, the differences in the number of respondents as well as the number of sessions across the states means that the relationships among Iowa and Wisconsin respondents and associated sessions are likely to have a greater impact on the overall findings. Secondly, the use of only four states limits greatly the variation across values for these variables and affects the degree to which the analysis is able to identify fully the effects of state-level factors.
The data take the form of members’ committee requests – the individual preferences provided by members to their party leaders for their forthcoming committee assignments. Generally, members were instructed to list their top six or so choices for committee assignments, but members could decide how many requests to make. For each legislator for whom data were available, it was possible to make a one to N preference listing for committee requests based on the question wording. 5 The committee request data were
15 then merged with actual committee assignments (current as well as those from the previous session), personal background data, session level traits (organizational/institutional) and state-level characteristics.
As noted above, we employ hierarchical linear modeling (HLM) as our primary analysis strategy.
HLM is particularly well suited for analyzing multi-level data such as what is used here (Kelleher and Wolak
2007, Wells and Krieckhaus 2006, Steenbergen and Jones 2002, Bryk and Raudenbush 1992). It permits us to examine the possible effects of various different levels of contributory factors—individual as well as aggregate—while assessing the independent effects of each. Hence, this research will investigate the impact of 1) individual-level personal attributes (e.g., gender and amount of legislative service) and individual-level risk taking behavior (e.g., seeking more sought after assignments) when requesting committee assignments;
2) organizational/ situational/contextual traits (e.g., majority/minority party status, size of the Democrats’ presence in the chamber, proportion of “freshmen” Democrats and the status of the Democratic leaders); and
3) general political system characteristics (e.g. availability of the initiative, and the type of political culture) on our outcome variables—member committee request success outcome. Specifically, we assume that individual legislators (as well as their accompanying traits) are nested within and affected by the organizational conditions within differing legislative sessions with these legislators and session being nested within states, whose general characteristics also affect the impact of member behavior as well as organizational traits and committee assignment outcomes.
Clustering or nesting of the data refers to the fact that the values for some of our analytical variables have a constant value for several individual legislators under study. State environmental charteristics have the same values for all legislators from that state. Similarly, traits of a legislative session in which members seek committee assignments have a constant value for all members in that session. This situation violates the
“independence” assumption specified by most estimation methods. Studies regarding violation s of this assumption indicate that even relatively small amounts of non-independence can produce biased parameter estimates (Bliese 1998; Ostroff, 1993) Thus, for example, all Democratic members of the Wisconsin legislature experienced the same environmental factors associated with the state for all sessions. Further, the
1995-6 Wisconsin Assembly members were affected by the same, identical set of organizational/situational/
16 contextual factors associated with that session. This is not to indicate that the effects of these state-based, environmental characteristics and organizational/situational/contextual traits have an identical effect on all members, only that all members within a given state and a specified session experienced the same environmental and organizational factors, with a potential for similar effects. Adjusting for these group- based effects is one major reason for using HLM.
These environmental and organizational/ situational/contextual factors are expected to interact with various individual traits of members in their effect on committee request/assignment outcomes. Further, these environmental and organizational/situational/ contextual session variables were not randomly assigned, but reflect the nature of the political environment, the character of the legislative organization after the biennial election of members, the previous nature of the chamber Democrats and Republicans and the nature of the legislative context as created by the leaders/members of each state/session under study (Kelleher and
Wolak 2007; Wells and Krieckhaus 2006; Steenbergen and Jones 2002; Bryk and Raudenbush 1992).
Findings
The initial step usually taken in HLM analysis is to assess whether or not there are statistically significant differences in the outcome variables based on the cluster factors. In this research these cluster factors are state and legislative session. Analysis of Variance is often used in HLM analyses for this purpose, and that was the strategy used here. We evaluated differences in the three committee outcome success variables by state and session using Two-Way ANOVA. Added to this evaluation were the two risk taking variables because an absence of significant variation for these traits by session and state suggests potential challenges to further investigation. All outcome and risk taking variables have a discrete level of measurement and were examined via a two-way ANOVA, with the session being one grouping variable and state being the other (See Appendix D for this analysis).
For each Level 1 risk taking and outcome variable, an F-test was performed, indicating statistically significant differences for each of these outcome and risk taking variables across sessions—a finding that
17 supports moving forward with the HLM analysis. Regarding state-level variables, only one of the three outcome variables has a statistically significant F-ratio, while both of the risk-taking variables show statistically significant differences across the states; however, statistically significant interaction between state and session at the .05 level is identified for all outcome and risk variables. Taken together, these findings indicate that while different patterns occur for our two grouping factors (with session definitely indicating important differences regarding effects on committee assignment success), the effects of both state and session should be investigated further. We believe that the nature of the session in terms of its political composition, leadership and organization is the reason for these across-session differences, and this conclusion supports using an HLM analysis to identify which contextual factors representing state and session have an impact on our outcome variables.
Our objective is to determine the effects of contextual/organizational/ situational traits as well as state environmental chaRACTERISTICS on legislator risk taking as well as personal variables and, in turn, on committee assignment success outcomes. Due to sizeable variation in the range of values for our variables and our desire to compare across states having quite different means and scores for these variables, we, AS NOTED ABOVE, created standardized scores (z-scores) for all discrete individual-level variables for each state. As a result, each variable had a mean of 0.000 and a standard deviation of 1.000. In HLM, standardized scores are often used and known as “standardized models” with standardized effect sizes
(Raudenbush and Liu, 2000; Cohen, 1988). Using standardized models and scores allows us to simplify the interpretation of the results, as any derived score above or below 0.000 indicates that the variable’s score is above or below the average score for the sample. In addition to using standardized scores, all scores were centered on the grand-mean of the sample.
In constructing our multilevel models, the primary goals were both parsimony and explanatory power regarding the direction and statistical significance of relationships, as opposed to solely drawing comparisons based on model fit.6 To this end, our approach initially included all individual 7 and session explanatory variables in a single 2-Level model for each outcome variable. We gradually – one variable at a time – removed indicators from our model that failed to demonstrate statistical significance based on a T-
18 ratio test.8 In each subsequent run, we removed the variable that was least significant and continued until only variables showing statistically significant effects at the .05 level or better remained. After identifying a parsimonious 2-Level model, we added our Level 3, state-based variables and proceeded to remove non- statistically significant variables, regardless of their level using the same process as noted above. Although this is not the only way to build a multilevel model, little consensus seems to exist regarding the “best” way to add or drop a variable from a model, and the approach used here is a widely employed method. Thus, all of the independent variables included in the models presented below constitute statistically significant explanatory variables alone or through interaction.
To illustrate a typical hierarchical model, consider the formula for the initial outcome variable
“Requests Assigned,” after the removal of non-statistically significant variables (See Table 2).
The Level 1 model with Y representing Requests Assigned can be expressed as:
Yijk = π0jk + π1jkGENDERijk + π2jkRPCOMREQijk + eijk
For the Level 1 model, Yijk represents the proportion of committee Requests Assigned to legislator “i” for session “j” in state “k”;
π0jk is the intercept (e.g., the standardized mean/average score) for the proportion of Requests Assigned to a legislator for session “j” in state “k”;
π1jk is the standardized slope for GENDER (male-female) and Requests Assigned to legislators for session “j” in state “k” (e.g., the direction and strength of association between GENDER and Requests Assigned for session “j” in state “k”);
π2jk is the standardized slope for RPCOMREQ (the proportion of requests not made that could have been made, our Requests Not Made risk taking variable) and Requests Assigned for session “j” in state “k” (e.g., the direction and strength of association between RPCOMREQ and Requests Assigned for session “j” in state “k”); and eijk is the standardized residual of the Level 1 model that contains all other unobserved factors (e.g., the random effect represents the deviation of individual legislator “i” for session “j” in state “k” from the predicted score based on the Level 1 model).
At Level 2, with the introduction of the situational/contextual/organizational factors, three models are created. The models can be expressed as:
π0jk = β00k + β01kAVCOMA0jk + r0jk
19 π1jk = β10k + β11kAVCOMA1jk π2jk = β20k + β21kAVCOMA2jk
For the first Level 2 model:
β00k is the intercept (e.g., the standardized mean/average score) for state “k” when modeling the effect of the legislative session on the proportion of Requests Assigned;
β01k is the standardized slope for AVCOMA (Assignments per Member which is the average number of committee assignments for a Democratic member, an organizational variable) and an average legislator’s proportion of Requests Assigned (e.g., the direction and strength of association between AVCOMA and an average legislator’s proportion of Requests Assigned); r0jk is the standardized residual of the Level 2 model that contains all other unobserved factors (e.g., the random effect represents the deviation of requests made for session “j” in state “k” from the predicted score based on the Level 2 model).
For the second Level 2 model:
β10k is the intercept (e.g., the standardized mean/average score) for state “k” when modeling the effect of GENDER on Requests Assigned;
β11k is the standardized slope for AVCOMA and GENDER (e.g., the direction and strength of association between AVCOMA and GENDER);
For the third Level 2 model:
β20k is the intercept (e.g., the standardized mean/average score) for state “k” when modeling the effect of RPCOMREQ on Requests Assigned;
β21k is the standardized slope for AVCOMA and RPCOMREQ (e.g., the direction and strength of association between AVCOMA and RPCOMREQ);
At Level 3, with the introduction of the state-level factors, six models are created. The models can be expressed as:
β00k = Y000 + u00k β01k = Y010 β10k = Y100 + Y101POLLIB10k β11k = Y110 β20k = Y200 + Y201SPKRPOW20k β21k = Y210
For the first Level 3 model:
Y000 is the intercept (e.g., the standardized mean/average score) for the state-level model when modeling the effect of the proportion of Requests Assigned;
20 u00k is the standardized residual of the Level 3 model that contains all other unobserved factors (e.g., the random effect that represents the deviation of Requests Assigned in state “k” from the predicted score based on the Level 3 model).
For the second Level 3 model:
Y010 is the intercept (e.g., the standardized mean/average score) for the state-level model when modeling the effect of AVCOMA;
For the third Level 3 model:
Y100 is the intercept (e.g., the standardized mean/average score) for the state-level model when modeling the effect of GENDER;
Y101 is the standardized slope for GENDER and POLLIB (e.g., the direction and strength of association between GENDER and POLLIB, with POLLIB being the Policy Leberal stat-level variable);
For the fourth Level 3 model:
Y110 is the intercept (e.g., the standardized mean/average score) for the state-level model when modeling the effect of GENDER and AVCOMA;
For the fifth Level 3 model:
Y200 is the intercept (e.g., the standardized mean/average score) for the state-level model when modeling the effect of RPCOMREQ;
Y201 is the standardized slope for RPCOMREQ and SPKRPOW (e.g., the direction and strength of association between RPCOMREQ and SPKRPOW, with SPKRPOW being the state’s level of institutional-based power residing in the Speaker position);
For the sixth and final Level 3 model:
Y210 is the intercept (e.g., the standardized mean/average score) for the state-level model when modeling the effect of RPCOMREQ and AVCOMA;
After combining all of the models together, the full model for Requests Assigned can be expressed as:
Requests Assigned = Y000 + Y010*AVCOMA + Y100*GENDER + Y101*POLLIB*GENDER + Y110*AVCOMA*GENDER + Y200*RPCOMREQ + Y201*SPKRPOW*RPCOMREQ + Y210*AVCOMA*RPCOMREQ + e + r + u
______
Table 2 About Here ______
21 Requests Assigned 9
Table 2 shows the results of our HLM analysis for the estimated model of our first committee success outcome variable, Requests Assigned. It shows that of the three personal, individual-level explanatory variables (Education, Legislative Experience and Gender) and the two risk-taking variables
(Requests Not Made and Request Popularity) only Gender (Y100 = .10550, t = 2.151) and Requests Not Made
(Y200 = 0.58257, t = 27.077) had significant relationships. Women exhibit higher success in obtaining their committee requests than do men. Regarding Requests Not Made, our results show that members who requested fewer committee assignments than asked for by the leadership (a high level of Requests Not Made
—a higher risk taking strategy) actually enjoyed a higher proportion of Requests Assigned. Being more limited, selective and restrained in the number of committee requests submitted (a higher level of risk taking) is actually associated with greater success outcome as measured by Requests Assigned. Thus, taking a risk in terms of not making all of the requests possible may actually help a member in terms of success for the level of Requests Assigned.
These relationships become more complex and nuanced when we account for our Level 2
(organizational/situational/contextual) variables. Broadly speaking, the relationships between the Level 1 and
Level 2 variables indicate that legislators, based on Gender and Requests Not Made, tend to adjust
(consciously or unconsciously) their risk-taking behavior to the nature of the committee system (specifically with the opportunities afforded for committee assignments by the committee system) associated with that session (i.e., the Assignments per Member that session, AVCOMA). The interaction of this Level 2 variable with both Level 1 variables is statistically significant (p < .001) in the negative direction with both Gender
(Y110 = -.48577, t = -3.892) and Requests Not Made (Y210 = -.23144, t = -4.132). The nature of the opportunities for committee appointments (less in number versus more) afforded to members for each session affects members by gender and their risk taking when making their committee requests for that
22 session. Women in sessions where fewer committee appointments per member are made (a more constraining opportunity structure), achieve greater success levels in their Requests Assigned. In addition, members making fewer requests for appointments in sessions where there is a lower average of committee appointments per member also achieve higher success. Thus, our analysis demonstrates that the nature of the organization in terms of the opportunities created by the committee system has an impact with Gender and
Requests Not Made on the Requests Assigned outcome variables. Notable by its absence from this model is any organizational influence from the partisan aspects of the session (e.g., proportion of Democrats, Party
Status or Change in Party Control) or the leadership (e.g., Leader Continuity or Sessions as Leader).
When Level 3, state-based variables, are added to the model, two variables (Policy Liberalism (Y101
= .29386, t = 2.815) and Speaker Power (Y201 = -.09920, t = -5.789)) have a statistically significant interaction with Gender and Requests Not Made respectively. In a state with more liberal policies, and a legislative committee system providing fewer opportunities for committee membership, women are more likely to experience greater success in being appointed to committees they request. Similarly, in a state with less formal power vested in the Speaker and during sessions when the committee opportunity structure holds fewer opportunities for committee membership, members who take greater risk by not requesting a full complement of committee requests actually achieve better success in the Requests Assigned outcome variable
Overall for the model of Requests Assigned, the results tell us that both personal and risk taking variables (Level 1) interact with the nature of the organization (the committee opportunities provided) and have a statistically significant effect on the existence of a relationship with the success outcome factor,
Requests Assigned. Level 3, state-based factors such as Policy Liberalism and Speaker Power, also have an interaction-based impact on Requests Assigned. While variables exist at all three levels that significantly impact the outcome variable, the most important finding based on its impact across all three Level 2 models, is the nature of the committee system opportunity structure and its influence on Requests Assigned. This affirms the importance of the committee system in explaining the outcome in addition to the individual characteristics.
23 As a final note on this model, as shown in Table 2, the percentage of variance attributable to each level is reported. The calculations show that the vast majority of the variance (96.133%) is attributed to the individual (Level 1) characteristics. These results suggest that explaining variation in Requests Assigned is more likely to be found at the individual level. In addition, these estimates are based on the total number of cases in each level (expressed as degrees of freedom (df)), and due to there being only 4 states, 28 sessions and more than 1500 cases, these substantial differences also skew the amount of variance found at each level.
Thus, this potential for skewed variance suggests that additional validation of these results (i.e., with more sessions and states) should be explored before making any concrete conclusions.
Assignments Requested and Received
The HLM model constructed for the second outcome variable (Assignments Requested and
Received, Table 3) is quite different than that found for Requests Assigned (Table 2), particularly with regard to: 1) the array of statistically significant Level 1 factors; 2) the impact of Level 2 and Level 3 explanatory factors; and, 3) the nature of the relationships concerning interactions among all three levels. At
Level 1, all three personal variables and both risk-taking variables are statistically significant or have statistically significant interactions with level 2 or level 3 variables in their impact on Assignments
Requested and Received. The pattern noted in Table 3 whereby the intercept term for a Level 1 variable
(e.g., Education and Gender) is not statistically significant, but, when Level 2 and/or Level 3 variables are added, a significant interaction appears requires explanation. This situation indicates the existence of significant interaction between the Level 1 and the Level 2 and/or Level 3 variables in their impact on the outcome variable; however, no significant variation exists between “average” legislators across sessions and states alone for Education and Gender with the outcome variable. Hence, Education and Gender only affect
Assignments Requested and Received as a result of interaction with level 2 and/or Level 3 variables. In contrast, Legislative Experience and Requests Not Made both have statistically significant intercept terms and significant effects both singularly and in concert with Level 2 and Level 3 variables when added. Despite the standardized nature of the data, these findings indicate that independent, statistically significant
24 differences among mean scores exist across legislators with different levels of Legislative Experience and
Requests Not Made across sessions and states alone and with an interaction among other factors in terms of the outcome variable—Assignments Requested and Received.
______
Table 3 About Here ______
While for the individual level Education variable there is no statistically significant independent relationship the outcome variable (Assignments Requested and Received), when formal Speaker Power (a
Level 3 variable) and the level of change in the Democratic proportion in the chamber (Change in Democrats
—CHNGDEM) are added to the model, statistically significant relationships are now present. (Speaker
Power, Y101 = - .04306, t = -2.260, and Change in Democrats, Y110 = - .76992, t = - 2.258.) These findings indicate the interactive effect of organization- and state-based factors on individual legislators in their committee assignment success.
Legislative Experience has a very complex and extensive set of relationship in terms of Levels 2 and
3 variables. For the Legislative Experience variable, the intercept term is positive and significant (Y200 = .
35123, t = 4.917) indicating that for legislators in different sessions and different states with different levels of experience, there are significant differences in the proportion of their Assignments Requested and
Received. Legislators with experience in the last session do significantly better in receiving the assignments they requested. For a legislator with “average” experience, lower levels of Governor Power and Speaker
Power are associated with a higher proportion of Assignments Requested and Received. In other words, experienced legislators receive higher levels of Assignments Requested and Received when the governor and speaker positions have lower levels of institutional power.
Legislative Experience shows a negative relationship with three organizational variables representing the nature of party control in the legislature. Specifically, for Legislative Experience and the number of sessions that the current majority party has been in control (PRVSES), there is a negative and significant relationship (Y210 = -.13414, t = -3.643). This indicates that when either party is in control longer,
25 experienced Democrat members achieve lower levels of success in Assignments Requested and Received.
For Legislative Experience and the majority/minority Status of the Democrats, there is a negative and significant relationship (Y220 = -.57691, t = -2.717), showing that when the Democrats are in the minority, experienced members are likely to get higher levels of success in Assignments Requested and Received. For
Legislative Experience and the proportion of New Democrats, there is a negative and significant relationship
(Y230 = -3.34729, t = -2.623) demonstrating that when there are fewer “new” Democrats in the House, experienced Democrats achieve higher levels of success in Assignments Requested and Received.
For Legislative Experience, proportion of New Democrats and the level of Policy Liberalism in the state (i.e., an interaction among the Level 1, 2, and 3 variables), there is an overall positive and significant
10 relationship (Y231 = 17.23203 , t = 3.308) with Assignments Received and Requested. In a state where there is high policy liberalism and fewer new Democratic members in the session, members with more legislative experience have higher levels of Assignments Received and Requested. Similarly when there is a high level of institutional Governor Power in the state, and the session has a lower number of new Democrats, there is an overall positive and significant relationship between Legislative Experience and Assignment success
11 (Y232 = 35.34130 , t = 2.396). This suggests that as the level of institutional Governor power increases in a state and the proportion of new Democrats decreases, members with legislative experience in the previous session will achieve greater success in Assignments Requested and Received. Finally Legislative
Experience and the committee opportunity structure—higher Mean Committee Assignments—have an overall positive and significant relationship with assignment success (Y240 = .55988, t = 3.376). When the committee opportunity structure offers more opportunities for members to have committee assignments, experienced members will have greater success in Assignments Requested and Received.
Turning to Gender, Table 3 indicates that in the average session and state, there is no statistically significant relationship between gender and the proportion of Assignments Requested and Received—men and women do not differ in their outcome success score; however, when the Committee Assignments per
Member session trait is added into the analysis, this relationship changes. Now, there is an overall negative
26 and significant relationship with the level of Assignments Requested and Received (Y310 = -.49855, t =
-3.500). Again, there is significant interaction noted.
Finally regarding risk taking by individual members, Request Popularity has a straightforward relationship with assignment success—Request Popularity has no significant interactions with organizational and state-level factors—while Requests Not Made shows interaction with Level 2 variables. In the first case, as members seek assignment to more sought after and popular committees, this higher risk strategy has a negative effect on assignment success (Y000 = -.18444, t = -7.070). Pursuing assignment to higher demand committees in and of itself and without the influence of the session or state produces lower levels of
Assignment Requested and Received. A negative relationship for a risk taking independent variable is also seen when we analyze Requests Not Made. Across legislators in sessions and states, there is an overall negative and significant relationship (Y500 = -.06204, t = -2.396) with assignment success, thereby indicating that when a legislator does not make a full complement of requests (i.e., a higher level of Requests Not
Made), their proportion of Assignments Requested and Received is lower. With this risk taking factor, there also are significant interaction effects. The size of the change in the Democratic contingent in the chamber for that session—Change in Democrats—has an overall negative and significant relationship (Y510 = -.96018, t = -2.746) with this risk variable and, in turn, with assignment success. When the proportion of change in
Democrats has a larger decrease, and there are lower Requests Not Made, legislators have higher success in
Assignments Requested and Received. A negative relationship is also seen between this risk taking and the nature of the committee system. When the committee structure offers a more limited number of committees
(i.e., smaller number of committees), there is an overall negative and significant relationship (Y520 = -.02595, t = -3.687) between Requests Not Made and assignment success. In sessions where the committee structure offers fewer committees for members to belong, legislators having lower levels of Requests Not Made and have higher levels of Assignments Requested and Received.
As a final note on the second model, as shown in Table 3, the percentage of variance attributable to all three levels is reported. The results show that once again, the vast majority of the variance (96.329%) is
27 attributed to the individual (Level 1) characteristics. These results suggest that explaining variation in the
Assignments Requested and Received is more likely to be found at the individual level.
Assignment Popularity
Table 4 presents the HLM analysis for our third outcome variable—Assignment Popularity—and reflects the degree to which the assignments obtained by the member are on popular, highly sought-after, greater in-demand committees. The final model for this outcome variable is more simple and straightforward than the model found for Assignments Requested and Received. Legislative Experience has a positive and statistically significant effect on Assignment Popularity (Y100 = .20121, t = 3.758) showing that a legislator across sessions and states who has legislative experience is more likely to be successful in receiving assignment to high demand committees. This relationship is affected by the nature of the state such that when legislators are in a state having a lower level of institutional Speaker Power, members with legislative experience are more likely to receive assignments to more popular committees (Y101 = -.26696, t = -6.282).
______
Table 4 About Here ______
When the level 1 risk taking variables are added, Request Popularity shows a positive relationship with Assignment Popularity (Y200 = .55072, t = 26.009) indicating that if a legislator across sessions and states requests more popular committees, that legislator is significantly more likely to receive their requests.
The positive relationship between Request Popularity and Assignment Popularity is expected because, if a legislator does not request highly popular committees, the chances of getting assigned to more popular committees are reduced significantly. Table 4 also shows that the level of institutional Speaker Power in a state has a significant and positive impact on Request Popularity and, in turn, on Assignment Popularity
(Y201 = .05091, t = 2.984). In other words, in a state with a high level of Speaker Power and legislators making more requests for popular committees, members are, in turn, likely to receive assignments to the
28 more popular committees. Two organizational-session factors also appear in the final model showing their effects on Request Popularity and, in turn, on Assignment Popularity. When the current majority party (either
Republican or Democrat) is in control of the chamber for shorter lengths of time (i.e., lower levels for Time of Control) (Y210 = -.03933, t = -3.661), individual members request more highly popular committees and legislators are more likely to be assigned such popular committees. Further, when the Democratic party experiences a sizeable increase in membership in the last election (as opposed to a sizeable decrease
(CHNGDEM, Y220 = .56285, t = 2.087), members making more popular committee requests are likely to experience more success in landing membership on high-demand committees. Overall, the significant relationships in the model of Assignment Popularity suggest that the nature of the legislative session and the state’s characteristics influence individual risking taking behaviors and Request Popularity. Table 4 also shows that the other risk taking individual-level factor—Requests Not Made—has a positive and statistically significant relationship across sessions and states on Assignment Popularity (Y300 = .06452, t = 3.022).
Greater risk taking in terms of higher levels of Requests Not Made is associated with higher levels of
Assignment Popularity for a members committee appointments regardless of the nature of the session’s organization or state’s characteristics in which a legislator makes her requests.
Finally, as shown in Table 4 and similar to the previous two models, the percentage of variance attributable to all three levels is reported. The results show that the vast majority of the variance (97.738%) is attributed to the individual (level 1) characteristics. These results suggest that explaining variation in
Assignment Popularity is more likely to be found at the individual level.
Conclusions
This analysis of 28 sessions of committee requests among Democrats in the lower chambers of four state legislatures between 1975 and 2010 using multilevel modeling techniques (HLM) has provided models to account for three separate measures of member committee assignment outcome success. While these data provide important insights regarding how committee assignments are affected by different types of
29 individual-, organization- and state-based factors, the limited number of states analyzed plus the uneven number of legislators across the states limit somewhat generalizing from these findings; however, several important new insights are discovered in this research.
The approach used assumed that, while personal characteristics (ranging from individual traits, like gender and education, to risk taking behaviors) are no doubt important, these individual variables are, at least in part, conditioned by both the various organizational/contextual/situational factors that constrain what a member can expect in terms of committee assignments for a given session as well as by the state setting in which this behavior takes place. This present analysis substantiates this contention and reveals some important findings. First, the risk taking variables are by far the most important predictors of assignment success. Risk taking factors—Request Popularity and Requests Not Made—are the most frequently occurring explanatory variables for all of our outcome success variables. In addition, personal factors like prior Legislative Experience, Gender and Education Level also affect a member’s success in obtaining desired committees, but to a lesser extent than the risk taking variables.
Furthermore, this analysis also demonstrates the statistically significant interaction effects of the organizational/situational/contextual (political/organizational and leadership-based) traits associated with a given session and, to a lesser extent, the nature of the state’s political setting. A comparison of the three final models of Requests Assigned, Assignments Requested and Received, and Assignment Popularity indicates that:
All of the three models include some set of personal factors that affect outcome success— Legislative Experience and Gender in two models and Education Level in one model; Women and members with Legislative Experience have greater levels of committee assignment outcome success; All models include an array of risk taking variables that demonstrate a statistically significant effect on our assignment success variables. Thus, risk taking is an important element of assignment outcome success; Among the two risk taking variables, Request Not Made appears in all three models and Request Popularity in two; In all three models, when Level 2 organizational/situational/contextual variables are added to the Level 1 models, a statistically significant interaction becomes apparent. Therefore, Level 2 organizational/situational/contextual traits interact with personal attributes and risk taking and, in turn, affect assignment outcome success;
30 The effects of organizational/situational/contextual traits vary considerably and are more intermittent in their effects than the individual variables; The nature of the committee opportunity structure is the most frequently occurring of these organizational traits followed by various aspects related to the nature of Democratic party control (i.e., proportion of membership, extent of membership change, length as minority or majority party and minority/majority party status); Characteristics of the Democratic leadership are not found in any of the final models indicating that such session traits have less impact on member committee assignment success; State political characteristics included in all three final models relate to the nature of the institutional power vested in the House/Assembly Speaker and the Governor and the level of policy liberalism found in a state; Generally, lower levels of institutional power are associated with higher success scores; and, In order to understand the committee assignment process and its outcomes, attention needs to be given to individual (personal as well as risk taking), organizational/situational/ contextual as well as state level factors.
31 Table 1
Number of Respondents by Session and State (Entries are the number of respondents with percentages given for the proportion of respondents by state and by session)
STATE Total SESSION MAINE PENNSYLVANIA WISCONSIN IOWA 1975-1976 0 0 56 0 56 3.5% 1979-1980 0 0 49 0 49 3.1% 1981-1982 0 0 47 0 47 3.0% 1985-1986 0 0 46 58 104 6.6% 1987-1988 75 63 52 56 246 15.6% 1989-1990 82 78 53 59 272 17.2% 1991-1992 82 86 54 53 275 17.4% 1993-1994 0 81 50 48 179 11.3% 1995-1996 0 0 0 35 35 2.2% 1999-2000 0 0 0 43 43 2.7% 2001-2002 0 0 0 43 43 2.7% 2003-2004 0 0 0 43 43 2.7% 2005-2006 0 0 38 46 84 5.3% 2007-2008 0 0 0 51 51 3.2% 2009-2010 0 0 0 54 54 3.4% Total N 239 308 445 589 1581 % of Total 15.1% 19.5% 28.1% 37.3% 100.0%
32 Table 2: Requests (Proportion of) Assigned All Legislators, Four States
(Standardized scores Level 1, Risk Taking/Outcome variables and Transformed scores Level 2 (Organization Variables) and Level 3 (State)
Fixed Effect Estimates SE t Ratio Model for Requests Assigned Intercept, Y000 -.03703 .03734 -.992 AVCOMA, Y010 .38869 .08620 4.509***
Gender Intercept, Y100 .10550 .04905 2.151* POLICYLIB, Y101 .29386 .10439 2.815** AVCOMA, Y110 -.48577 .12480 -3.892***
Requests Not Made Intercept, Y200 .58257 .02152 27.077*** SPKRPOW, Y201 -.09920 .01713 -5.789*** AVCOMA, Y210 -.23144 .05602 -4.132***
Random Effects Variance/% df Chi-Square Level 1 (e) .64956 ( 96.133%) Level 2 (r) .02611 ( 3.864%) 23 89.780*** Level 3 (u) ..00002 ( .003%) 3 .281
Note: AVCOMA = Average Number of Committee Assignments Per Member; POLICYLIB = Policy Liberalism Level in State; and SPKRPOW = Speaker’s Level of Formal Power. *p< .05; **p< .01; ***p< .001
33 Table 3: Assignments (Proportion of) Requested and Received All Legislators, Four States (Standardized scores Level 1, Risk Taking/Outcome variables and Transformed scores Level 2 (Organization Variables) and Level 3 (State) Fixed Effect Estimates SE t Ratio Model for Assignments Requested/ Received Intercept, Y000 -.05110 .04233 -1.207 AVCOMA, Y010 -.39054 .09738 -4.010**
Education Intercept, Y100 .01191 .02584 -0.461 SPKRPOW, Y 101 -.04306 .01906 -2.260* CHNGDEM, Y 110 -.76992 .34094 -2.258*
Legislative Experience Intercept, Y200 .35123 .07144 4.917*** GOVPOW, Y201 -1.98960 .64059 -3.106** SPKRPOW, Y202 -.42100 .06917 -6.086*** PRVSES, Y210 -.13414 .03682 -3.643** STATUS, Y220 -.57691 .21236 -2.717** NEWDEM, Y230 -3.34729 1.21618 -2.623** POLICYLIB, Y231 17.23203 5.20883 3.308** GOVPOW, Y232 35.34130 14.75096 2.396* AVCOMA, Y240 .55988 .16586 3.376**
Gender Intercept, Y300 .06055 .05707 1.061 AVCOMA, Y310 -.49855 .14246 -3.500** Request Popularity Intercept, Y400 -.18444 .02609 -7.070***
Requests Not Made Intercept, Y500 -.06204 .02590 -2.396* CHNGDEMS, Y510 -.96018 .34964 -2.746** NUCMASS, Y520 -.02595 .00704 -3.687***
Random Effects Variance/% df Chi-Square Level 1 (e) .84128 (96.329%) Level 2 (r) .03204 ( 3.668%) 23 83.287*** Level 3 (u) .00002 ( .002%) 3 .252
Note: CHNGDEM = Proportion change in the number of Democrats from last session; NEWDEM = Proportion of new Democrats; CHNGCNTR = Change in party control this session (0 = No change; 1 = Change in party controlling Chamber; STATUS = Democratic Party Status in Chamber (0 = minority; 1 = Majority; PRVSES = Number of Previous Sessions Controlling Party had Majority; GOVPOW = Governor’s Institutional Power; POLICYLIB = Policy Liberalism Level in State; SPKRPOW = Speaker’s Level of Formal Power; AVCOMA = Average Number of Committee Assignments Per Member; and, NUCMASS = Number of Committees Appointed this Session. *p< .05; **p< .01; ***p< .001
34 Table 4: Popularity of Assignments (Mean) All Legislators, Four States
(Standardized scores Level 1, Risk Taking/Outcome variables and Transformed scores Level 2 (Organization Variables) and Level 3 (State)
Fixed Effect Estimates SE t Ratio Model for Popularity of Assignments Intercept, Y000 -.00130 .03009 -.043
Legislative Experience Intercept, Y100 .20121 .05355 3.758*** SPKRPOW, Y101 -.26696 .04249 -6.282***
Request Popularity Intercept, Y200 .55072 .02117 26.009*** SPKPOW, Y201 .05091 .01706 2.984** PRVSES, Y210 -.03933 .01074 -3.661*** CHNGDEM, Y220 .56285 .26937 2.087*
Requests Not Made Intercept, Y300 .06452 .02135 3.022**
Random Effects Variance df Chi-Square Level 1 (e) .60461 (97.738%) Level 2 (r) .01398 ( 2.259%) 24 62.571*** Level 3 (u) .00001 ( .002%) 3 .347
Note: SPKRPOW = Speaker’s Level of Formal Power; PRVSES = Number of Previous Sessions Controlling Party had Majority; CHNGDEM = Proportion change in the number of Democrats from last session; and, NEWDEM = Proportion of new Democrats. *p< .05; **p< .01; ***p< .001
Appendix A Variable Key, Names and Content
VARIABLE VARIABLE VARIABLE
35 LABEL NAME DESCRIPTION
Committee Outcome Success COM Requests Assigned Committee Requests that were Assigned Proportion of Requests
PARRCD Assignments Committee Assignments Have that Requested and Received Requested/Received Requested & Received AVPOPASS Assignment Popularity Mean Popularity Score all Committees Assigned Each Committee given a
(proportion of requests for that committee of all requests made). This is
Committees Assigned a
Personal Background Traits EDUCAT Education Level Education obtained Higher Score is Higher Level of Education LEGEXP Legislative Experience Legislative Experience
1=Served in Previous
GENDER Gender Gender VARIABLE VARIABLE VARIABLE LABEL NAME DESCRIPTION Risk Taking Behavior RPCOMREQ Requests Not Made Proportion of “No Committee Requests” Made This Session requests/Total Could
AVPOPREQ Request Popularity Mean of Popularity Score for All Requests Made This Session Each Committee given a
(proportion of requests for that committee of all requests made). This is mean for all committees
Organization/Situation/Context Factors PRVSES Time of Control Previous Sessions Current Majority Party Controlled Chamber PRODEM Democrats Proportion Democrats in Chamber this Session Proportion OF Chamber
36 CHANGDEM Change in Democrats Change in Democratic Proportion From Last Session +/- Number Democrats
STATUS Party Status Party Status in Chamber, Minority/Majority 1=Minority; 2=Majority CHANGCON Change Party Control Change Party Control of Chamber Control; 1=Change Party
CONTLDR Leader Continuity Continuity of Democratic Leadership Succession for this 1=Leader from outside Session (Speaker/Minority Position) 2=Leader Rank member
3=Leader Whip/Asst Leader Previous Session
VARIABLE VARIABLE VARIABLE LABEL NAME DESCRIPTION
PRVSESLDR Sessions as Leader Continuous Sessions Present Leader Served in this position Number Consecutive
NEWDEM New Democratic Members Proportion of “New” Democrats in this session Proportion of Democrats in this Session that did
AVCOMA Assignments per Member Average number of committee assignments for a Democratic Member NUCMAPP Number of Committees Number of Standing Committees Appointed in Chamber
State Environment CONLIM Constitutional Limits Constitutional Limits Placed on Legislature Limits in Constitution SIZEHOU Size of House Number of Seats in House
CULTRAD Traditional Political Culture State have Traditional Political Culture (Elazar)
CULTMOR Moral Political Culture State have Moral Political Culture (Elazar) 0=No Moral; 1=Moral CULTIND Individual Political Culture State have Individual Political Culture (Elazar)
INITYN Initiative Exists State has Initiative to Originate and Pass Statutes
LIBOPIN Liberal State Opinion State’s Level of Opinion Liberalism (Wright, Erikson, McIver) POLIB Liberal State Policy State’s Level of Policy Liberalism (Wright, Erikson, McIver) GOVPOW Governor’s Power Governor’s Institutional Power (Beyle) High Score=More Power in Governor’s Position
37 SPKRPOW Speaker Power Speaker’s Formal Power (Clucas) High Score=More Power in Speaker’s Position VARIABLE VARIABLE VARIABLE LABEL NAME DESCRIPTION
PRORANK Professionalization Ranking Professionalization Score (Squire)
COMPIND Interparty Competition Interparty Competition Index—(Ranney) Competition between
COMTYPE Interparty Competition Type Interparty Competition Type ( Republican Domination
38 Appendix B
VARIABLE MAP OF HYPOTHETICAL COMMITTEE REQUESTS, ASSIGNMENTS AND COMMITTEES HELD LAST SESSION AND VARIABLE VALUE CALCULATION FOR ONE LEGISLATOR
Committees in Committee Committee Had Last Chamber Requests Assignments Session 1 X X X 2 -- X -- 3 -- -- X 4 ------5 X X -- 6 X -- -- 7 ------8 ------9 ------10 ------11 X -- -- 12 ------13 X -- X 14 -- X X 15 X -- -- 16 ------DISCUSSION: In this scenario, for “this session” there are 15 committees with up to 6 requests possible. One legislator makes 6 requests for committees, receives 4 assignments and had 4 assignments in the last session. In this example, the member did not make 0 of the requests possible (Requests Not Made = 0/6=.000) the member received 2 of their 6 requests in this session (Requests Assigned =.333); requested and received 2 of their 4 assignments for this session (Assignments Requested and Received =.500); of their 6 requests for this session, how many were for committees not held last session, 4 (Request Different Committees =4/6==.667); and, of theitr4 current assignments, how many were held in the last session 2 (Assignments Held Last =2/4=.500).
APPENDIX C Table C-1 Correlation of Outcome Variables—All States
Assignments Requests Requested & Assignment Received Received Popularity Requests Pearson Correlation 1 .366 .065 Received N 1580 1574 1573 Assignments Pearson Correlation .366 1 .372 Requested & Received N 1574 1574 1573 Assignment Pearson Correlation .065 .372 1 Popularity N 1573 1573 1573
Correlation of Outcome Variables—Maine Table C-2
Assignments Requests Requested & Assignment Received Received Popularity Requests Pearson Correlation 1 .473 .031 Received N 239 239 239 Assignments Pearson Correlation .473 1 .212 Requested & Received N 239 239 239 Assignment Pearson Correlation .031 .212 1 Popularity N 239 239 239
Correlation of Outcome Variables—Pennsylvania Table C-3 Assignments Requests Requested & Received Received Assignment Popularity Requests Pearson Correlation 1 .536 Received N 308 308 Assignments Pearson Correlation .536 1 Requested & Received N 308 308 Assignment Pearson Correlation .333 .455 Popularity N 307 307
Correlation of Outcome Variables—Wisconsin Table C-4
Assignments Requests Requested & Received Received Assignment Popularity Requests Pearson Correlation 1 .513 Received N 445 445 Assignments Pearson Correlation .513 1 Requested & Received N 445 445 Assignment Pearson Correlation .089 .199 Popularity N 445 445 Correlation of Outcome Variables—Iowa Table C-5
Assignments Requests Requested & Assignment Received Received Popularity Requests Pearson Correlation 1 .401 .075 Received N 588 582 582 Assignments Pearson Correlation .401 1 .310 Requested & Received N 582 582 582 Assignment Pearson Correlation .075 .310 1 Popularity N 582 582 582
Correlation of Risk Variables—All States Table C-6
Requests Not Made Request Popularity Requests Not Pearson Correlation 1 -.157 Made N 1580 1580 Request Pearson Correlation -.157 1 Popularity N 1580 1580 Correlation of Risk Variables—Maine Table C-7
Requests Not Made Request Popularity Requests Not Made Pearson Correlation 1 -.021
N 239 239 Request Popularity Pearson Correlation -.021 1
N 239 239
Correlation of Risk Variables—Pennsylvania Table C-8
Requests Not Made Request Popularity Requests Not Made Pearson Correlation 1 -.159
N 308 308 Request Popularity Pearson Correlation -.159 1
N 308 308 Correlation of Risk Variables—Wisconsin Table C-9
Requests Not Made Request Popularity Requests Not Made Pearson Correlation 1 .430
N 445 445 Request Popularity Pearson Correlation .430 1
N 445 445
Correlation of Risk Variables—Iowa Table C-10
Requests Not Made Request Popularity Requests Not Made Pearson Correlation 1 .162
N 588 588 Request Popularity Pearson Correlation .162 1
N 588 588 APPENDIX D
TABLE D-1
2-Way ANOVA—Requests Received By Session and State
Tests of Between-Subjects Effects
Source Type III Sum of Squares df Mean Square F Sig. Corrected Model 65.826a 27 2.438 2.503 .000 Intercept 4.174 1 4.174 4.285 .039 Session 44.752 14 3.197 3.282 .000 State 6.270 3 2.090 2.146 .093 Session * State 20.106 10 2.011 2.064 .024 Error 1511.661 1552 .974 Total 1577.486 1580 Corrected Total 1577.486 1579 a. R Squared = .042 (Adjusted R Squared = .025) TABLE D-2
2-Way ANOVA—Assignments That Were Requested & Received By Session and State
Tests of Between-Subjects Effects
Source Type III Sum of Squares df Mean Square F Sig. Corrected Model 109.604a 27 4.059 4.303 .000 Intercept .001 1 .001 .001 .977 Session 68.408 14 4.886 5.180 .000 State 2.243 3 .748 .793 .498 Session * State 42.717 10 4.272 4.528 .000 Error 1458.456 1546 .943 Total 1568.061 1574 Corrected Total 1568.060 1573 a. R Squared = .070 (Adjusted R Squared = .054) TABLE D-3
2-Way ANOVA—Assignment Popularity By Session and State
Tests of Between-Subjects Effects
Source Type III Sum of Squares df Mean Square F Sig. Corrected Model 189.907a 27 7.034 7.952 .000 Intercept .727 1 .727 .822 .365 Session 167.050 14 11.932 13.491 .000 State 42.117 3 14.039 15.873 .000 Session * State 29.262 10 2.926 3.308 .000 Error 1366.491 1545 .884 Total 1556.413 1573 Corrected Total 1556.398 1572 a. R Squared = .122 (Adjusted R Squared = .107) TABLE D-4
2-Way ANOVA—Risk Via Requests NOT Made By Session and State
Tests of Between-Subjects Effects
Source Type III Sum of Squares df Mean Square F Sig. Corrected Model 203.800a 27 7.548 8.439 .000 Intercept 1.335 1 1.335 1.492 .222 Session 152.655 14 10.904 12.190 .000 State 30.778 3 10.259 11.469 .000 Session * State 48.030 10 4.803 5.370 .000 Error 1388.243 1552 .894 Total 1592.054 1580 Corrected Total 1592.043 1579 a. R Squared = .128 (Adjusted R Squared = .113) TABLE D-5
2-Way ANOVA—Risk Via Request Popularity By Session and State
Tests of Between-Subjects Effects
Source Type III Sum of Squares df Mean Square F Sig. Corrected Model 342.610a 27 12.689 15.858 .000 Intercept .439 1 .439 .549 .459 Session 245.703 14 17.550 21.933 .000 State 38.741 3 12.914 16.139 .000 Session * State 105.123 10 10.512 13.138 .000 Error 1241.856 1552 .800 Total 1584.466 1580 Corrected Total 1584.466 1579 a. R Squared = .216 (Adjusted R Squared = .203) Bibliography
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2 In determining the popularity of a committee for assess demand for a committee, we determined the proportion of requests made for each committee across all requests made that session. This became a weight for that committee indicating the demands made for assignment to every committee. For calculating the request level for a member’s array of committee requests, we determined the mean of the demand weighting for all the committees a member requested at the beginning of the session.
3 Previous cross-state research (Hedlund 1992 and Hedlund and Patterson 1992) demonstrated, using OLS within states, that variation existed in the explanatory factors across states, but was unable to perform systematic analysis of a comparative nature.
4 The House Rules (2009-10) state the process that is leader-centered, as follows: “All committees shall be appointed by the speaker, unless otherwise especially directed by the house. Minority party members of a committee shall be appointed by the speaker upon recommendation of the minority leader” (Iowa House of Representatives, Rules-2009- 10, Rule 46).
5 Problems with the forms submitted for one Wisconsin session limits the applicability of this conclusion for a small number of members.
6 An alternative method of testing model significance can draw comparisons between the deviance scores of each model. By and large, this technique assumes that lower deviance scores are indicative of the strength of the model. We observed only marginal differences in our deviance scores across models.
7 Prior research has indicated that each of these individual level variables has statistically significant effects on committee assignment success and hence they were included in each initial model, 8 Often, this first-run revealed that a number of variables were not significant. In each subsequent run, we removed the variable that was least significant.
9 A note about interpreting the HLM final models created in our analyses is needed. Relationships with the intercept terms (Y000 etc.) referenced in the formula, represent values for the average—level 1: the average legislator, level 2: the average session, and level 3: the average state—and indicate an effect of these variables on the values for the outcome variable, not the slope—the direction and strength of the effect. This means that their effect is on the location where the regression line crosses the “y” axis and hence on the mean/average value of our outcome variable (proportion of Requests Assigned). While intercepts are an important aspect of our model and for understanding the outcome variable, it is not a primary focus of our research question and will not receive any in-depth treatment. In addition, it is also important to note that because the values for many of our variables are standardized, many intercept terms are not significantly different from zero and are not easily interpreted without conversion back to the original scores for these variables.
10 Policy Liberalism is not represented by a standardized value due to only having state-level data from four states 11 Governor Power is not represented by a standardized value due to only having state-level data from four states