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).
1
A Cross-state Analysis of Legislative Committee Request
Success Rates *
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
*
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.
2
assignment process seeks to understand the relative success legislators experience in receiving appointment
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
3
to account for a number of “setting” influences that are unique to and highly pertinent to the legislative
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-
4
accommodation syndrome wherein individual members publicize their committee preferences in order to
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
5
members gain electoral support, leaders generate continued support for their own partisan influence. Another
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
6
on committee assignment outcomes. Further, existing research is largely devoid of empirical measures of risk
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.
7
Variable Structure
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
8
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
will receive such a request. 2 Thus, when members request a higher-demand, more sought-after committee,
they are taking a greater risk.
9
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.
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:
10
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 individuallevel 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 [MartoranoMiller, 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
(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.
11
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.
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
12
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
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
13
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
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.
14
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/
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
15
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 groupbased 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
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
16
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 Tratio 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
17
parsimonious 2-Level model, we added our Level 3, state-based variables and proceeded to remove nonstatistically 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
π1jk = β10k + β11kAVCOMA1jk
π2jk = β20k + β21kAVCOMA2jk
For the first Level 2 model:
18
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;
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:
19
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
___________________________
20
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
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,
21
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.
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
22
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
differences among mean scores exist across legislators with different levels of Legislative Experience and
23
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,
experienced Democrat members achieve lower levels of success in Assignments Requested and Received.
24
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
relationship (Y231 = 17.2320310, 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 (Y232
= 35.3413011, 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
25
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
26
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
27
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
28
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;
29






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.
30
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
SESSION
MAINE
1975-1976
PENNSYLVANIA
0
0
WISCONSIN
56
IOWA
Total
0
56
3.5%
1979-1980
0
0
49
0
49
1981-1982
0
0
47
0
3.1%
47
1985-1986
0
0
46
58
3.0%
104
1987-1988
75
63
52
56
6.6%
246
1989-1990
82
78
53
59
15.6%
272
1991-1992
82
86
54
53
17.2%
275
1993-1994
0
81
50
48
17.4%
179
1995-1996
0
0
0
35
11.3%
35
1999-2000
0
0
0
43
2.2%
43
2001-2002
0
0
0
43
2.7%
43
2003-2004
0
0
0
43
2.7%
43
2005-2006
0
0
38
46
2.7%
84
2007-2008
0
0
0
51
5.3%
51
2009-2010
0
0
0
54
3.2%
54
Total N
% of Total
239
308
445
589
3.4%
1581
15.1%
19.5%
28.1%
37.3%
100.0%
31
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
AVCOMA, Y010
-.03703
.38869
.03734
.08620
-.992
4.509***
Gender
Intercept, Y100
POLICYLIB, Y101
AVCOMA, Y110
.10550
.29386
-.48577
.04905
.10439
.12480
2.151*
2.815**
-3.892***
Requests Not Made
Intercept, Y200
SPKRPOW, Y201
AVCOMA, Y210
.58257
-.09920
-.23144
.02152
.01713
.05602
27.077***
-5.789***
-4.132***
Variance/%
( 96.133%)
( 3.864%)
( .003%)
df
Chi-Square
23
3
89.780***
.281
Random Effects
Level 1 (e)
Level 2 (r)
Level 3 (u)
.64956
.02611
..00002
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
32
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
Model for Assignments Requested/
Received
Intercept, Y000
AVCOMA, Y010
-.05110
-.39054
.04233
.09738
-1.207
-4.010**
Education
Intercept, Y100
SPKRPOW, Y 101
CHNGDEM, Y 110
.01191
-.04306
-.76992
.02584
.01906
.34094
-0.461
-2.260*
-2.258*
.35123
-1.98960
-.42100
-.13414
-.57691
-3.34729
17.23203
35.34130
.55988
.07144
.64059
.06917
.03682
.21236
1.21618
5.20883
14.75096
.16586
Gender
Intercept, Y300
AVCOMA, Y310
.06055
-.49855
.05707
.14246
1.061
-3.500**
Request Popularity
Intercept, Y400
-.18444
.02609
-7.070***
Requests Not Made
Intercept, Y500
CHNGDEMS, Y510
NUCMASS, Y520
-.06204
-.96018
-.02595
.02590
.34964
.00704
-2.396*
-2.746**
-3.687***
Legislative Experience
Intercept, Y200
GOVPOW, Y201
SPKRPOW, Y202
PRVSES, Y210
STATUS, Y220
NEWDEM, Y230
POLICYLIB, Y231
GOVPOW, Y232
AVCOMA, Y240
Random Effects
Level 1 (e)
Level 2 (r)
Level 3 (u)
Variance/%
.84128 (96.329%)
.03204 ( 3.668%)
.00002 ( .002%)
t Ratio
4.917***
-3.106**
-6.086***
-3.643**
-2.717**
-2.623**
3.308**
2.396*
3.376**
df
Chi-Square
23
3
83.287***
.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
33
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
SPKRPOW, Y101
.20121
-.26696
.05355
.04249
3.758***
-6.282***
Request Popularity
Intercept, Y200
SPKPOW, Y201
PRVSES, Y210
CHNGDEM, Y220
.55072
.05091
-.03933
.56285
.02117
.01706
.01074
.26937
26.009***
2.984**
-3.661***
2.087*
Requests Not Made
Intercept, Y300
.06452
.02135
3.022**
Random Effects
Level 1 (e)
Level 2 (r)
Level 3 (u)
.60461
.01398
.00001
Variance
(97.738%)
( 2.259%)
( .002%)
df
Chi-Square
24
3
62.571***
.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
34
Appendix A
Variable Key, Names and Content
VARIABLE
LABEL
VARIABLE
NAME
VARIABLE
DESCRIPTION
VARIABLE
CODE
Committee Outcome Success
COM
Requests Assigned
Committee Requests that were Assigned
PARRCD
Assignments
Requested/Received
Committee Assignments Have that Requested and Received
AVPOPASS
Assignment Popularity
Mean Popularity Score all Committees Assigned
Proportion of Requests
Made that Assigned
Proportion of
Assignments That
Requested & Received
Each Committee given a
popularity score
(proportion of requests
for that committee of all
requests made). This is
the Mean of all
Committees Assigned a
Member
Personal Background Traits
EDUCAT
Education Level
Education obtained
Higher Score is Higher
Level of Education
LEGEXP
Legislative Experience
Legislative Experience
GENDER
Gender
Gender
0=No Legislative
Service in Previous
Session;
1=Served in Previous
Legislative Session
1=Male; 2=Female
VARIABLE
LABEL
VARIABLE
NAME
VARIABLE
DESCRIPTION
Risk Taking Behavior
VARIABLE
CODE
RPCOMREQ
Requests Not Made
Proportion of “No Committee Requests” Made This Session
Proportion of no
requests/Total Could
Make
Each Committee given a
popularity score
(proportion of requests
for that committee of all
requests made). This is
mean for all committees
a member requested
AVPOPREQ
Request Popularity
Mean of Popularity Score for All Requests Made This Session
Organization/Situation/Context Factors
PRVSES
PRODEM
Time of Control
Democrats
Previous Sessions Current Majority Party Controlled Chamber
Proportion Democrats in Chamber this Session
CHANGDEM
Change in Democrats
Change in Democratic Proportion From Last Session
STATUS
CHANGCON
Party Status
Change Party Control
Party Status in Chamber, Minority/Majority
Change Party Control of Chamber
CONTLDR
Leader Continuity
Continuity of Democratic Leadership Succession for this
Session (Speaker/Minority Position)
Number of Sessions
Proportion OF Chamber
Size
+/- Number Democrats
as Proportion Last
Session Size
1=Minority; 2=Majority
0=No Change in
Control; 1=Change Party
Control
1=Leader from outside
leadership;
2=Leader Rank member
imp committee
3=Leader Whip/Asst
Leader Previous Session
4=Same Leader as
Previous Session
1
VARIABLE
LABEL
VARIABLE
NAME
VARIABLE
DESCRIPTION
VARIABLE
CODE
PRVSESLDR
Sessions as Leader
Continuous Sessions Present Leader Served in this position
NEWDEM
New Democratic Members
Proportion of “New” Democrats in this session
AVCOMA
Assignments per Member
NUCMAPP
Number of Committees
Average number of committee assignments for a
Democratic Member
Number of Standing Committees Appointed in Chamber
Number Consecutive
Sessions as Leader
Proportion of Democrats
in this Session that did
Not Serve in Last
Session
Higher Value=More
Assignments
Higher Value=More
Committees
CONLIM
Constitutional Limits
Constitutional Limits Placed on Legislature
SIZEHOU
Size of House
Number of Seats in House
CULTRAD
Traditional Political Culture
State have Traditional Political Culture (Elazar)
CULTMOR
CULTIND
Moral Political Culture
Individual Political Culture
State have Moral Political Culture (Elazar)
State have Individual Political Culture (Elazar)
INITYN
Initiative Exists
State has Initiative to Originate and Pass Statutes
LIBOPIN
Liberal State Opinion
POLIB
Liberal State Policy
GOVPOW
Governor’s Power
State’s Level of Opinion Liberalism (Wright, Erikson,
McIver)
State’s Level of Policy Liberalism (Wright, Erikson,
McIver)
Governor’s Institutional Power (Beyle)
SPKRPOW
Speaker Power
Speaker’s Formal Power (Clucas)
State Environment
Higher Value=More
Limits in Constitution
Higher Value=More
Seats
0=No Traditional;
1=Traditional
0=No Moral; 1=Moral
0=No Individual;
1=Individual
0=No Initiative;
1=Initiative
Higher Value=More
Liberal
Higher Value=More
Liberal
High Score=More Power
in Governor’s Position
High Score=More Power
in Speaker’s Position
2
VARIABLE
LABEL
VARIABLE
NAME
VARIABLE
DESCRIPTION
VARIABLE
CODE
PRORANK
Professionalization Ranking
Professionalization Score (Squire)
COMPIND
Interparty Competition
Interparty Competition Index—(Ranney)
COMTYPE
Interparty Competition Type
Interparty Competition Type (
High Score=More
Professionalized
Higher Score=More
Competition between
Parties in State
Higher Score=More
Republican Domination
in State
3
Appendix B
VARIABLE MAP OF HYPOTHETICAL COMMITTEE
REQUESTS, ASSIGNMENTS AND COMMITTEES HELD LAST
SESSION AND VARIABLE VALUE CALCULATION FOR ONE
LEGISLATOR
Committees in
Chamber
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Committee
Requests
Committee
Assignments
Had Last
Session
X
X
X
--
X
--
--
--
X
--
--
--
X
X
--
X
--
--
--
--
--
--
--
--
--
--
--
--
--
--
X
--
--
--
--
--
X
--
X
--
X
X
X
--
--
--
--
--
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
Requested &
Received
Requests
Received
1
.366
.065
N
1580
1574
1573
Assignments
Requested &
Received
Pearson Correlation
.366
1
.372
N
1574
1574
1573
Assignment
Popularity
Pearson Correlation
.065
.372
1
N
1573
1573
1573
Requests
Received
Pearson Correlation
Assignment
Popularity
Correlation of Outcome Variables—Maine
Table C-2
Assignments
Requested &
Received
Requests
Received
1
.473
.031
N
239
239
239
Assignments
Requested &
Received
Pearson Correlation
.473
1
.212
N
239
239
239
Assignment
Popularity
Pearson Correlation
.031
.212
1
N
239
239
239
Requests
Received
Pearson Correlation
Assignment
Popularity
1
Correlation of Outcome Variables—Pennsylvania
Table C-3
Assignments
Requested
Assignment
& Received
Popularity
Requests
Received
Pearson Correlation
1
.536
.333
N
308
308
307
Assignments
Requested &
Received
Pearson Correlation
.536
1
.455
N
308
308
307
Assignment
Popularity
Pearson Correlation
.333
.455
1
N
307
307
307
Requests
Received
Correlation of Outcome Variables—Wisconsin
Table C-4
Assignments
Requested &
Received
Assignment
Popularity
1
.513
.089
N
445
445
445
Assignments
Requested &
Received
Pearson Correlation
.513
1
.199
N
445
445
445
Assignment
Popularity
Pearson Correlation
.089
.199
1
N
445
445
445
Requests
Received
Requests
Received
Pearson Correlation
2
Correlation of Outcome Variables—Iowa
Table C-5
Assignments
Requested &
Received
Assignment
Popularity
1
.401
.075
N
588
582
582
Assignments
Requested &
Received
Pearson Correlation
.401
1
.310
N
582
582
582
Assignment
Popularity
Pearson Correlation
.075
.310
1
N
582
582
582
Requests
Received
Pearson Correlation
Requests
Received
Correlation of Risk Variables—All States
Table C-6
Requests
Not Made
Requests Not
Made
Request
Popularity
Pearson Correlation
Request
Popularity
1
-.157
N
1580
1580
Pearson Correlation
-.157
1
N
1580
1580
3
Correlation of Risk Variables—Maine
Table C-7
Requests
Not Made
Requests Not
Made
Pearson Correlation
N
Pearson Correlation
Request
Popularity
N
Request
Popularity
1
-.021
239
239
-.021
1
239
239
Correlation of Risk Variables—Pennsylvania
Table C-8
Requests
Not Made
Requests Not
Made
Pearson Correlation
N
Request
Popularity
Pearson Correlation
N
Request
Popularity
1
-.159
308
308
-.159
1
308
308
4
Correlation of Risk Variables—Wisconsin
Table C-9
Requests
Not Made
Requests Not
Made
Request
Popularity
Pearson Correlation
Request
Popularity
1
.430
N
445
445
Pearson Correlation
.430
1
N
445
445
Correlation of Risk Variables—Iowa
Table C-10
Requests
Not Made
Requests Not
Made
Request
Popularity
Pearson Correlation
Request
Popularity
1
.162
N
588
588
Pearson Correlation
.162
1
N
588
588
5
APPENDIX D
TABLE D-1
2-Way ANOVA—Requests Received By
Session and State
Tests of Between-Subjects Effects
Source
Corrected Model
Type III Sum of Squares
65.826a
df
27
Mean Square
2.438
F
Sig.
2.503
.000
Intercept
4.174
1
4.174
4.285
.039
Session
44.752
14
3.197
3.282
.000
6.270
3
2.090
2.146
.093
20.106
10
2.011
2.064
.024
Error
1511.661
1552
.974
Total
1577.486
1580
Corrected Total
1577.486
1579
State
Session * State
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
Corrected Model
Type III Sum of Squares
109.604a
df
27
Mean Square
4.059
.001
1
68.408
F
Sig.
4.303
.000
.001
.001
.977
14
4.886
5.180
.000
2.243
3
.748
.793
.498
42.717
10
4.272
4.528
.000
Error
1458.456
1546
.943
Total
1568.061
1574
Corrected Total
1568.060
1573
Intercept
Session
State
Session * State
a. R Squared = .070 (Adjusted R Squared = .054)
1
TABLE D-3
2-Way ANOVA—Assignment Popularity By
Session and State
Tests of Between-Subjects Effects
Source
27
Mean Square
7.034
.727
1
167.050
State
Session * State
Corrected Model
Type III Sum of Squares
189.907a
df
F
Sig.
7.952
.000
.727
.822
.365
14
11.932
13.491
.000
42.117
3
14.039
15.873
.000
29.262
10
2.926
3.308
.000
Error
1366.491
1545
.884
Total
1556.413
1573
Corrected Total
1556.398
1572
Intercept
Session
a. R Squared = .122 (Adjusted R Squared = .107)
2
TABLE D-4
2-Way ANOVA—Risk Via Requests NOT Made By
Session and State
Tests of Between-Subjects Effects
Source
27
Mean Square
7.548
1.335
1
152.655
State
Session * State
Corrected Model
Type III Sum of Squares
203.800a
df
F
Sig.
8.439
.000
1.335
1.492
.222
14
10.904
12.190
.000
30.778
3
10.259
11.469
.000
48.030
10
4.803
5.370
.000
Error
1388.243
1552
.894
Total
1592.054
1580
Corrected Total
1592.043
1579
Intercept
Session
a. R Squared = .128 (Adjusted R Squared = .113)
3
TABLE D-5
2-Way ANOVA—Risk Via Request Popularity By
Session and State
Tests of Between-Subjects Effects
Source
27
Mean Square
12.689
F
15.858
.439
1
.439
.549
.459
245.703
14
17.550
21.933
.000
38.741
3
12.914
16.139
.000
105.123
10
10.512
13.138
.000
Error
1241.856
1552
.800
Total
1584.466
1580
Corrected Total
1584.466
1579
Corrected Model
Type III Sum of Squares
342.610a
Intercept
Session
State
Session * State
df
Sig.
.000
a. R Squared = .216 (Adjusted R Squared = .203)
4
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4
Endnotes
1
On the other hand, providing a greater number of requests may be seen as a foolhardy strategy, given the stated desire
on leaders to accommodate member wishes as well as if a member highly covets a highly desirable committee. Given
this proclivity for accommodation, members may be predisposed to limit their choices to only those requests that are
truly and highly coveted so that members can ensure that they will get the committee assignments they really want the
most as opposed to experiencing leadership’s accommodation in the form of being appointed to committees they
requested, but about which they care much less. Nonetheless, such a “limited request” strategy may backfire if the
leader is not willing or able to accommodate members’ requests.
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.
2
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.
3
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-200910, Rule 46).
4
Problems with the forms submitted for one Wisconsin session limits the applicability of this conclusion for a
small number of members.
5
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.
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,
7
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.
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
10
5