What Do We Mean By Loyalty and Competence in

Presidential Appointments?

by

Yu Ouyang

University of Kentucky

Evan Haglund

Vanderbilt University

and

Richard W. Waterman

University of Kentucky

Paper presented at the annual meeting of the International Political Science Association,

2014, Montreal, Canada.

1

Scholars, journalists and political pundits commonly assume that presidents

promote loyalty over competence when they make appointments. For instance, in his

discussion of George W. Bush’s appointment strategy, journalist Peter Baker (2014, 86)

writes, “Perhaps the most important lesson was the insistence on complete loyalty...”

Moynihan and Roberts (2010) also argue that the second Bush in the White House did

indeed promote loyalty over competence, while Aberbach and Rockman (2000) note that

the trend toward appointing loyalists to key administration positions is but one aspect in a

broader trend toward the politicization of the bureaucracy.

While the terms loyalty and competence are commonly employed in the political

appointments literature, a key question remains: What do we mean by loyalty and

competence in presidential appointments? Empirically speaking, how does one define

loyalty? Are there specific personal background characteristics that are most important

when presidents seek out loyalists for their administrations? Likewise, what is

competence? Are there specific qualities that help a president identify a potentially

competent appointee? If presidents and their personnel management teams do not have

well-defined answers to these questions when they implement a particular personnel

strategy, they likely will not be successful in recruiting and appointing appointees with

the desired characteristics of either loyalty or competence. It makes sense, then, that

presidents and their personnel advisers have certain criteria for selecting appointees; but

what are these criteria and how are they applied?

In this paper we address these questions by analyzing a unique data set of 3,366

resumes of individuals appointed by Presidents George W. Bush and Barack Obama

across fifty-one different federal institutions, including departments, commissions,

2

government corporations and other bureaucratic units. The resumes describe the

background characteristics of each appointee, including their training, work experience,

and political experience. We examine the qualifications of presidentially-appointed

Senate-confirmed (PAS) appointees, the focus of most previous studies of presidential

appointments (e.g., Krause and O’Connell 2011).1 As well as appointees who do not

require Senate confirmation (Schedule C, Senior Executive Service and Executive Office

of the President appointments). This broad dataset across appointment types thus allows

us to make more generalizeable inferences applicable beyond PAS appointments.

Using resumes we analyze a variety of different possible measures of loyalty and

competence and whether there are indeed identifiable personal characteristics that can be

attributed to each quality. We also discuss whether there are different types of loyalty

and competence or whether these are strictly dichotomous characteristics, as they

generally are presented in the presidential appointment literature. We also take a first run

at identifying the most important characteristics related to each appointment approach.2

1While Krause and O’Connell only examine PAS appointments, a major advantage of

their work over ours is that they examine presidential appointments over a much longer

time frame. Because of the limitations of the FOIA process, we were only able to acquire

data on Presidents G. W. Bush and B. Obama. Once an appointee retires or is fired, their

resume is sent to a holding facility where it is unavailable for a FOIA request. Hence, it

was not possible for us to delve deeper into the past for our analysis.

2The determination of what constitutes loyalty and competence is important. In our

larger research agenda we intend to determine if the chief executive uses appointments

strategically by appointing loyalists to agencies that ideologically are further away from

the president’s preferred policy position and whether they are more likely to appoint

competent individuals to agencies that share the president’s ideology.

3

Literature Review

One of the main foci of the appointment literature is whether presidents appoint

individuals on the basis of loyalty or competence (Edwards 2001; Moe 1985; Weko

1995; Moynihan and Roberts 2010) and how they seek to identify appointees with those

characteristics. Scholars long have collected data on the background characteristics of

executive personnel (Herring 1936; Macmahon and Millett 1939; Stanley, Mann, and

Doig 1967; Cohen 1988; Nixon 2004; Krause and O’Connell 2011), while others use

personal interviews and surveys to measure appointee attributes (Aberbach and Rockman

1976; Aberbach, Putman and Rockman 1981; Fisher 1987; Maranto 1993; Maranto and

Hult 2004; Michaels 1997).3 These studies suggest some possible characteristics that may

be related to loyalty, such as past work in an administration or for a campaign. They also

suggest that competence may be related to educational attainment and past task

experience in a particular field. But with the exception of Krause and O’Connell (2011),

there has been no systematic attempt to empirically identify the characteristics of loyalty

and competence. While Krause and O’Connell’s (2011) work is valuable, they examine

only PAS appointments. As Light (1995) argues, an examination of SES and Schedule C

is warranted because presidents are more likely to use these types of appointments for

political purposes, such as promoting loyalty. Likewise, Lewis (2008: 97) notes,

“Focusing on PAS positions also ignores the broader universe of appointed positions,

which is where politicization usually occurs” (see also Lewis and Waterman 2013). In

3One of the most impressive is a survey by Mackenzie and Light of PAS appointees

who served from November 1964 through December 1984 (ICPSR Study Number 8458,

Spring 1987), though to protect the identity of each appointment their data set is split,

thus separating the background information from the identity of the appointee.

4

addition to SES and Schedule C appointments, we also examine EOP personnel who are

a distinct category of appointments (PA) not requiring Senate confirmation but not SES

or Schedule C.

There is a second reason why it is important to empirically examine the nature of

loyalty and competence. Many of the works in the appointment literature are of a

prescriptive nature. For example, Hess (1976) recommends that presidents should

appoint individuals with prior federal governmental experience to the Cabinet as a means

of promoting competence over loyalty, while Nathan (1975; 1983) urges presidents to

reward loyalty. Contrarily, Waterman (1989) warns that a reliance on loyalty alone can

undermine presidential influence in the long run.

Such prescriptive studies are useful in framing the potential costs and benefits of a

loyalty or competence strategy, but they do not empirically identify what is meant by

loyalty and competence. To illustrate this question, while Hess (1976) recommends that

prior Washington experience is important, to which characteristic does this contribute?

One can argue that prior federal experience provides the basis for an appointee to learn

how Washington works and thus promotes competence. Yet, as Nathan (1975)

recommends, presidents are best served by promoting loyalists from within. He argues

that prior Washington experience is a testing ground to determine whether an appointee is

loyal to the president. Consequently, prior Washington experience appears to cut both

ways, offering presidents multiple cues. If so, then loyalty and competence are not

mutually exclusive constructs. Empirically speaking, then, we need to be able to identify

the characteristics of loyalty and competence. We therefore take the identification of the

characteristics of these loyalty and competence as our starting point.

5

Data

Our main research objective is to identify and then to compare and contrast the

characteristics of presidential appointments over two presidencies – George W. Bush and

Barack Obama – to determine if different presidents use similar criteria (loyalty or

competence) when they make appointments or whether they adopt different appointment

strategies within different bureaucratic units.4 To empirically identify what is meant by

the constructs of loyalty and competence, we require data that allows us to examine the

background characteristics and training of each Bush and Obama appointee. To do so,

we employ 3,366 resumes, acquired through a series of Freedom of Information Act

(FOIA) requests, of appointees serving in various positions across the two presidencies.

Resumes provide a valuable source of information about the background and

training of presidential appointees at the PAS, SES, Schedule C, and EOP levels.5 To

illustrate why each appointment type should be evaluated, David Lewis (2008: 22-24)

describes the distribution of appointees across these types in 2004: 1,137 PAS position in

the executive branch, with approximately 945 of these in policymaking positions; 6,811

4Waterman, Bretting and Stewart (Forthcoming) argue that Presidents George H. W.

Bush and Bill Clinton used different appointment strategies when they appointed

ambassadors.

5Bradley Patterson (2008: 95) writes, “Schedule C positions are established by

departments and agencies, but each such post must first be certified by the director of the

Office of Personnel Management (OPM) as being ‘policymaking’ or ‘confidential.’ Once

a Schedule C job is thus authorized, the department or agency may appoint a person to

the post, but only if that person is approved by the director of the White House Office of

Presidential Personnel (OPP). “The Senior Executive Service (SES) is the corps of

professional federal managers just below the level of assistant secretary. By law, only 10

percent of the positions in the SES may be filled on a noncareer basis. A department or

agency head may propose a political candidate to be appointed to such a position, but the

appointment must be cleared with the OPP director. Once White House approval has

been signaled, the OPM grants ‘noncareer appointing authority’ to the agency for the

placement.

6

SES officials, with 674 of these being non-career presidential appointments; and 1,596

Schedule C appointments. In another count of presidential appointments, Bradley

Patterson (2008: 93-94) writes, during the Presidency of George W. Bush there were

“four categories of noncareer positions – and the White House controls all selections to

all of them.”

1. Full-time positions, almost all established by statute, that are filled by personal

presidential appointment:

a. Presidential appointees requiring Senate confirmation (PAS) 1,177

This subcategory includes 189 ambassadors, 94 district attorneys, 94 U.S.

marshals, 15 in international organizations, and 4 in the legislative branch.

b. Presidential appointees not requiring Senate confirmation (PA) 21

c. Federal judges to be appointed 400

2. Full-time nonpresidential noncareer positions

All of these appointments are approved by the White House Presidential

Personnel Office

a. Noncareer positions in the Senior Executive Service (SES)… 796

b. Schedule C positions

3. Part-time presidential appointees, established in statute

a. PAS (requiring Senate confirmation) 579

b. PA (not requiring Senate confirmation) 2,509

4. White House Staff positions 944

As both Lewis’s (2008) and Patterson’s (2008) data demonstrate, if we examine

only PAS appointments we overlook many of the appointments that presidents make.

With regard to each type of appointment, resumes provide information on the

previous training, work experience (both inside and outside of government), and the

political activities of each individual. As such, they allow us to directly and empirically

measure each individual appointee on various measures of loyalty and competence. This

is an important contribution since many past studies generally discuss loyalty and

competence only in qualitative terms.

7

As for the method in which the resumes were submitted and evaluated, during the

Bush administration “all resumes and applications had to be sent via the Internet,

electronically instead of through the mail by the bushel. In August, three months before

the election, the team set up a website and developed the software… Some 90,000

applications arrived within a few weeks” (Patterson 2008: 97). The resumes and the

candidates for all appointment positions were then evaluated through the White House

Office of Presidential Personnel Office (OPP), thus ensuring that all appointments at

different levels served the president’s purposes (Patterson 2008). Under Obama, what is

now the Office of Performance and Personnel Management (OPPM) performs a similar

function. The centralization of the appointment process through a single White House

office is important, for it provides the institutional capability for a presidential

administration to identify and evaluate the loyalty or competence of a wide range of

potential job candidates. If appointments were handled in a variety of different

institutional settings then we would expect wider variations in a president’s appointment

strategy. Centralization means that presidents who desire loyalty (or alternatively

competence) have a greater capacity to appoint loyalists at different levels throughout the

bureaucracy.

We would have preferred to compare those who applied and did not get jobs with

those who did. Unfortunately, the Freedom of Information Act does not allow us to

access the resumes of those who were not appointed. We therefore cannot make

comparisons between the qualifications of those appointed and not selected. Still, with

3,366 resumes, we have access to a large data set of presidential appointments.

Furthermore, it is a data set that allows us to delve deep into the bowels of the

8

bureaucracy, rather than just examining the appointees at the top of various federal

agencies.

Our data also provide the basis for a comprehensive examination of presidential

appointments across 51 different federal agencies. Table 1 presents a breakdown of the

agencies included in our analysis, as well as the number of resumes obtained from each

agency. The agencies include 14 cabinet departments, 8 commissions, 3 government

corporations, and various other executive branch units. We also have 226 resumes from

personnel serving in the Executive Office of the President (EOP). Some of these

appointments required Senate confirmation (OMB appointments), while the others

represent appointments that presidents can make without Senate confirmation. That

category also includes SES and Schedule C appointments. In acquiring data, we did not

make a random selection of agencies, but rather include those agencies that responded to

our FIOA request. Still, the agencies in our data set perform a wide variety of different

tasks including national defense, homeland security, economic issues, health care,

transportation, farm credits, space exploration, and postal regulation. The 51 agencies

represent a wide swath of federal responsibilities and functions. They also represent

different organizational types and agency functions across the Executive Branch, a point

that will become more relevant in our continuing research. In sum, we have detailed

individual level data on appointee characteristics over a broad spectrum of the federal

bureaucracy. With these data our first task is to operationalize the concepts of loyalty

and competence.

- Table 1 About Here -

9

Loyalty versus Competence

Patterson (2008: 97) writes, “To the Bush personnel team, the first question was

not who, but what. ‘What do we want the person in the job to accomplish in the next two,

three, or four year that we will be here?’” George W. Bush’s first director of presidential

personnel recruitment, Clay Johnson, comments, “This is not a beauty contest. The goal

is to pick the person who has the greatest chance of accomplishing what the principal

wants done… After the strongest candidate(s) has been identified, assess the political

wisdom of the selection, and adjust accordingly” (quoted in Lewis 2008: 27).

So what did the Bush administration want from its appointees? Joshua Bolten,

Chief of Staff under George W. Bush, notes (Patterson 2008: 43-44),

It wasn’t just the experience – the resumes of those individuals. I think it

was also the fact that the team that participated in the president’s

campaign was in many respects transplanted into comparable government

roles. The president very clearly said to me when I first arrived in March

of 1999 as a policy director at his campaign – almost two years before the

election; “I want to campaign the way I intend to govern.” So, in

structuring the work of the [White House] policy staff, I tried to do it in a

way that would literally make it possible to say, “Okay, tomorrow we’re

no longer campaigning: we’re actually governing.”

The administration’s emphasis on governing the way George W. Bush

campaigned suggests that loyalty was a primary objective of the younger Bush’s

presidency. Such an approach was not unique to the George W. Bush presidency.

The personnel director to George H. W. Bush, Chase Untermeyer, noted that a

Cabinet secretary asked, “Do you mean to tell me that just because some people

worked in a Bush campaign that I have to hire them in my department?” The

Cabinet secretary was told, “Had it not been for these people, and a lot of other

people, George [H. W.] Bush would not have been elected president and you

10

would not be the secretary of this department. That’s the only way it can be.”

(quoted in Patterson 2008: 100).

There is much anecdotal evidence to support the idea that presidents promote

loyalty over competence. Yet whether they do so or not is still an empirical question that

can only be addressed if we have reliable measures of loyalty and competence. So what

do loyalty and competence entail? By examining the resumes of the various appointees

we can gauge not only the qualifications of individual appointees, but more importantly

we can determine whether various qualifications are related to each other in a systematic

fashion. This in turn helps us to understand what the George W. Bush and Barack

Obama White Houses wanted from these appointees. And once we know what loyalty

and competence entail, we can then interpret these variables as direct measures of the

intent of the administration to promote either loyalty, competence or to a combination of

the two.

What then do we mean by loyalty and competence? Are they mutually exclusive?

Is there a tradeoff between the two constructs, as most scholars contend, with presidents

sacrificing competence at the altar of loyalty, or are the two concepts compatible such

that presidents select appointees who represent a blend of loyalty and competence?

Presidents would certainly have incentives to reward both characteristics, since loyalty

brings them fealty to their policy objectives and competence brings them the ability to

achieve their policy goals. As a result, there is no a priori reason to believe that

presidents necessarily select one criterion over the other unless there is a shortage of

either loyal or competent individuals to fill a particular position. Consequently, we treat

as empirical questions some of the basic assumptions of the past literature.

11

Again, resumes are an excellent source for examining whether loyalty and

competence exist as distinct characteristics since job applicants describe the

qualifications that they hope will secure a job. From the resumes we have identified the

following basic characteristics of job applicants. Table 2 provides a breakdown of the

various characteristics individuals identified as evidence of their qualification for a

presidential appointment. We also provide a breakdown by president.

- Table 2 About Here -

The background characteristic most often identified by applicants was “prior task

experience: (68.81 percent), followed by “their last job was of a political nature” (56.33),

“subject area expertise” (41.24), “worked for a campaign” (29.95) and “worked for a

member of Congress” (29.00). In past research scholars identify task and subject area

expertise as evidence of competence, with political experience in the last job, working for

a campaign or a member of Congress as evidence of loyalty. Consequently, the

frequencies demonstrate that we have a substantial number of responses to categories

previously categorized as either competence or loyalty. That is, the results from our

resumes do not skew the results toward either loyalty or competence. The data in Table 2

also demonstrate that large numbers of individuals identified a variety of other

background characteristics, with the smallest being “held prior elected office” at 1.72

percent and “worked on the transition” (5.11) or “worked in the White House” (5.82).

While these numbers are small they are not inconsequential.

Table 2 also demonstrates that there are differences in the background

characteristics of the Bush and Obama appointees. Obama was more likely to appoint

individuals with prior executive branch experience. There are also statistically significant

12

differences between the two presidents on the following characteristics: (1) previous

agency experience; (2) subject area expertise; (3) task area expertise; (4) worked in the

White House; (5) worked in Congress; (6) public management experience; (7) state-level

experience; (8) non-profit management experience; (9) previously an appointee; (10)

worked on the president’s campaign; (11) worked on the transition team; and (12)

worked for party. The aggregate numbers provide evidence that there was indeed a

difference between the appointment approaches of George W. Bush and Barack Obama.

These differences, however, may be due to an emphasis on particular background

characteristics and do not yet demonstrate that each president adopted a different overall

strategic approach: i.e., promoting loyalty over competence.

Before we can address that question we need to determine which of these

background characteristics can best be identified as loyalty or competence or a

combination of the two constructs. We also need to determine if presidents rely on

certain characteristics to reward both loyalty and competence. In other words, of the 18

background characteristics derived from the resumes noted in Table 2, we need a strategy

that can reveal their underlying relationships.

Figure 1 presents the results of the variables clustering, using a recently

developed extension of the standard cluster analysis, Variable Cluster Analysis.6 This

strategy allows us to arrange sets of variables into homogeneous clusters, which then

6Typically, the researcher performs cluster analysis on the observations themselves,

where the goal is to find clusters (or groups) of similar observations. For our approach the

goal is less on the observations themselves, but more on the variables. More specifically,

the aim is to find partitions with a larger dataset such that the variables within a single

cluster are strongly related to each other.

13

allow us to obtain meaningful information about the structures of a large dataset as well

as the underlying relationships between variables.

- Figure 1 About Here -

Results provided in the cluster analysis dendrogram indicate the existence of two

main structures in the appointee characteristics dataset, with seven variables representing

the left main cluster and ten variables comprising the right main cluster.7 To interpret this

figure, the lowest part of the tree-like structure is a leaf; each leaf represents one of the

variables used in the analysis. As we move further up the tree, some of the leaves begin

to fuse into branches, indicating that the variables (as represented by the leaves) are

similar to each other. The lower in the tree (the earlier) that these fusions occur suggests

greater similarity among the groups of variables in the leaves. In short, the vertical height

of the tree indicates the similarities (or, differences) among variables.8

Interestingly, the variables partition within each main structure follows neatly

with what one might intuit as denoting loyalty and competence. For instance, the seven

characteristics variables in the left main cluster likely indicate appointee loyalty. That is,

when presidents (or their personnel managers in the White House) look for someone who

will be loyal to the president’s policy goals, they will likely prioritize a candidate that, as

an example, previously worked on the president’s electoral campaign. Likewise, someone

with previous work experience in the executive branch should be a good candidate for

7 A dendrogram is a tree-like visual representation of data, typically used to

graphically summarize the results of a cluster analysis.

8 It is important to note that we cannot draw conclusions based on their proximity on

the horizontal axis. For example, based on their proximity on the horizontal axis, one

may conclude that serving on the president’s inauguration team and having previous

agency experience are similar. However, this is incorrect. The organization in the tree

structure suggests that these two variables, in fact, belong to two very distinct clusters

(Gareth et al 2013, 397).

14

those presidents prioritizing competence. In sum, the variables in the loyalty factor are:

(1) worked in Congress; (2) last job political; (3) worked on the president’s transition

team; (4) worked in the White House; (5) worked for the party; (6) worked for the

president’s campaign; and (7) worked on the president’s inauguration team. The variables

for the competence factor are: (1) previous agency experience; (2) previous experience in

the executive branch; (3) was previously an appointee; (4) elected office experience; (5)

public management experience; (6) state-level experience; (7) private management

experience; (8) non-profit management experience; (9) task expertise; and (10) subject

area expertise. In addition, the variable clustering also reveals the possibility of sub-level

clusters within the main loyalty and competence factors.9 Here we focus on the

characteristics in the main categories: loyalty and competence.

Bayesian Structural Equation Modeling

To analyze appointee ratings pertaining to measures of loyalty and competence,

we utilize a Bayesian Structural Equation Modeling (BSEM) framework. The BSEM

approach offers a number of advantages. First, a Bayesian approach to structural equation

modeling allows for, among others, greater flexibility in modeling complex data

structures and the incorporation of prior knowledge.10 Second, by allowing each observed

item to have its own, unique variance, not only do we isolate what the observed items

have in common, we also can assess the underlying relationships between the unique

variances of each individual observed item. Third, by isolating the shared variance of the

9 We will examine these possible sub-level data structure in later analyses.

10 For greater details on Bayesian Structural Equation Models, see Lee (2007),

Muthén and Asparouhov (2012), and Song and Lee (2012).

15

items from the unique variances of each individual item, we can better account for

measurement error in the data. This is particularly important if the latent variable is used

in subsequent analyses.

More specifically, we utilize the measurement model component of BSEM to

model appointee loyalty and competence. This method is a generalization of confirmatory

factor models widely used in political science. Let 𝒚𝒊 be a p × 1 observed random vector,

the measurement model is defined as:

𝒚𝒊 = 𝚲𝝎𝒊 + 𝝐𝒊,

where 𝚲 is a p × q factor loading matrix, 𝝎𝒊 is a q × 1 vector of factor scores, and

𝝐𝒊 is a p × 1 vector of error terms which is independent of 𝝎𝒊. 𝝐𝒊 follows a normal

distribution with mean 0 and variance 𝚿𝝐, which is a diagonal covariance matrix of

measurement errors. 𝝎𝒊 follows a normal distribution with mean 0 and variance 𝚽, which

is a positive definite covariance matrix of latent variables.

Let 𝒀 = (𝒚𝟏, … , 𝒚𝒏) be the observed data matrix, 𝛀 = (𝝎𝟏, … , 𝝎𝒏) of latent

factor scores, and 𝜽 be the structural parameter vector contains the unknown elements of

Λ, Φ, and 𝚿𝝐 in the model. To identify this model, we set some appropriate elements in

Λ to some fixed known values. We specify a binomial distribution for the appointee

characteristics data, estimating the probability that an appointee holds the given

characteristic given their value on the latent variable. We estimate the models using R

and JAGS, with diffuse, conjugate priors on all free parameters.11

11 All Bayesian results presented below are the posterior medians drawn from a

minimum of 5,000 simulated observations after parameters reached convergence. For an

excellent exposition on Bayesian data analysis, see Gelman et al (2014)

16

Assessing Appointee Loyalty and Competence

Table 3 shows the results of Model 1, a two-factor Bayesian confirmatory model.

In each of the two factors, one characteristic variable is set as the scaling variable to help

identify the model (the coefficient fixed to 1). In the loyalty factor, the coefficient for

having worked for the presidential campaign is defined as fixed. The coefficient for

having subject area expertise is set as fixed in the competence dimension. The reported

coefficients are posterior medians and the stars indicate that the 95% Bayesian credible

interval for that parameter does not include zero.

- Table 3 About Here -

For the loyalty factor, all variables load positively with the underlying latent

construct, with the exception of the characteristic having worked on the president’s

inauguration team.12 The positive coefficients associated with the remaining five

observed binary indicators of loyalty suggest that higher levels of the latent variable –

Loyalty – translate into higher probabilities that the appointee possesses that

characteristic. As an example, the more loyal that an appointee is, the more likely it is

that he or she will have once worked for the political party. In short, Model 1 suggests

that when presidents seek loyalist candidates for executive appointments, they will do

well to prioritize those who previously worked for the party, worked in the White House,

worked on the president’s transition team, held a political job, and worked in Congress.

For the competence factor, the 95% Bayesian credible interval for all variables

does not include zero, indicating that all variables correspond with the underlying

construct. One surprising result, however, is that having task experience loads negatively

12We will return to this discussion later when we examine sub-dimensions within the

main loyalty factor.

17

on the factor, which suggests that higher levels of competence are associated with a lower

probability that the appointee has task expertise. With this exception, all other variables

associated with the competence factor loads positively, indicating that higher levels of

competence mean that the appointee is more likely to possess that particular qualification.

Examining the Sub-dimensions of Loyalty and Competence

In the appointment literature, scholars generally discuss the value of loyalty

versus competence. Given the large number of variables that our analysis includes, we

demonstrate the specific characteristics that are associated with these two concepts. Our

models suggest then that simple dichotomy between loyalty and competence may be too

simplistic. After all, when examining a resume a personnel manager is more likely to pay

attention to certain measures of loyalty or competence. If so, which measures provide the

strongest evidence that a particular individual is either loyal or competent? Prior

experience such as subject area expertise may provide some guidance for an

administrator looking for competence, but so too may task or previous agency

experience. Likewise, what is a more reliable measure of loyalty: campaign experience

or working for a member of Congress? Furthermore, when we speak of loyalty and

competence, is there but one type of loyalty and one type of competence, or are they

multi-dimensional concepts? If so, then a personnel manager may be interested in one

type of competence rather than another. To answer these questions we need to delve

deeper into these two basic concepts. One way to answer these questions is to examine

the results from the resumes of presidential appointees, to determine if identifiable

patterns exist within the data. We do so first for competence.

18

Figure 2 represents the dendrogram for our various measures of competence, as

derived from our earlier analysis.13 When we do so we find three sub-clusters of

variables within the competence dimension. The first cluster consists of people who have

previous experience in the agency to which they have been appointed, previously served

as an executive appointee, or once worked in the federal bureaucracy. All three of these

variables are related to federal government experience. The second sub-cluster includes

appointees who previously held a public office, have management experience, or have

work experience at the state level. This second cluster represents previous experience

holding some type of public office. These two clusters combined represent what we call

managerial experience.

- Figure 2 About Here -

While managerial experience is important, there is another dimension of

competence. The third cluster in the dendrogram involves people with private

management experience, experience working for a non-profit, task related expertise, and

subject related expertise. For the analysis we present here we did not include a measure

of education in our model because almost all of our respondents have an advanced

degree, but when we ran that model separately, education was related to this third

dimension of competence, which we call policy-related expertise.

13 A careful reader will notice that the dendrogram presented in Figure 2 looks similar

to the competence component in the results of the full Variable Cluster Analysis

presented in Figure 1. The fact that these results are similar underscore the robustness of

the variables clustering. As Hastie, Tibshirani, and Friedman (2009) note, minor changes

in the data can lead to different representations of the dendrograms (521). Given this, the

fact that the dendrogram for the set of competence variables is similar to that above,

despite excluding all of the variables denoting loyalty, is striking.

19

In sum, when a personnel manager is examining a resume, there are specific

measures that best relate to managerial experience, while others provide information on

an individual’s policy-related experience. What is particularly interesting is that these

variables cluster in an intuitive manner. There are clear distinctions in our results

between private and public work experience, for example. How then do the various

measures of loyalty cluster? We turn to an examination of that in Figure 3.

- Figure 3 About Here -

For loyalty there are two identifiable sub-clusters. The first dimension includes

appointees who worked in Congress, usually for a specific member of Congress, or

whose last job was in politics. The second dimension includes individuals who worked

on a campaign, worked for the party, worked on the inauguration team, served on the

transition team, or worked in the White House. The first sub-cluster represents what we

call outsider loyalty, while the second represents personal loyalty to the president. This

distinction is important because presidents may be more likely to appoint individuals who

exhibit personal loyalty to more sensitive or important political positions within the

bureaucracy, a point that we will address in our continuing research. Here, however, we

ask a more fundamental question. Which measures are more strongly associated with the

sub-clusters or sub-dimensions of loyalty and competence? In other words, which of the

five factors of personal loyalty are most important? We cannot answer that question with

the dendrograms. SEM or structural equation models, however, can provide insights into

this question. When we look at the SEM models for each sub-cluster, what do they tell

us about the importance of each variable?

Competence Sub-dimensions

20

Table 4 presents the results of the sub-levels analyses on the competence factor.

We estimate three separate models. The first column, Model 2 is a single factor Bayesian

confirmatory model of appointee competence. The scaling variable is subject area

expertise. The second column of Table 4 presents Model 3, which is a two-factor model

with subject area expertise and prior executive branch experience as the respective fixed

variables. The third column (Model 4) is the most complex model of appointee

competence. The fixed variables for the three underlying latent competence sub-

dimensions are: (1) subject area expertise; (2) public management experience; and (3)

prior executive branch experience.

- Table 4 About Here -

With the exception of task experience, all other variables load positively on the

competence factor(s), suggesting that higher levels of competence are associated with a

higher probability that the appointee will possess that characteristic. For example, higher

levels of competence indicate a higher probability that the appointee will have some sort

of management experience, whether at the non-profit, public, or private level. Again,

most surprising of these results is the negative posterior median for having task

experience. This is interpreted as meaning that the more competence an appointee is, the

less likely that he or she will have task experience.

To compare models, we provide two separate measures of model fit. Deviance

compares the fit of the model to the original data. Lower deviance indicates better model

fit. The Deviance Information Criteria (DIC) adds a penalty to deviance, which adjusts

21

for model complexity.14 Similar to deviance, lower values of the DIC indicate the better

performing model.

By both the deviance measure and the DIC, the three-factor Bayesian

confirmatory model (Model 4) is the best fitting model. Compared to the next best model

(Model 3), the value of the deviance measure is 223 lower for Model 4 (20074.78) than

for Model 3 (20297.9). Similarly, the Deviance Information Criteria also indicates that

the most complex model (Model 4) is the best model of appointee competence. Even

after adjusting for greater model complexity, Model 4 still outperforms lesser complex

models (Model 2 and 3). The DIC for the three-factor solution is 25840. In comparison,

the DIC values for Model 2 and Model 3 are 25962 and 26025, respectively. The

difference between the DIC values for the three-factors and the one and two-factors

solutions is 122 and 185, respectively, not an insignificant reduction in DIC.

Unequivocally, then, the three-factor Bayesian confirmatory solution (Model 4) is the

best model of appointee competence.

As important as which is the best model for appointee competence is the relative

importance of each variable in defining competence. Put differently, which variables best

discriminate between candidates for appointment? One metric to use in assessing this

question is the value of the coefficients in the respective models.15 Generally speaking, if

14By the formula common in most Bayesian textbooks, the DIC is calculated as the

sum of the deviance measure and a measure of model complexity, typically one-half the

variance of the deviance measure. While the deviance and the DIC are common measures

of model fit in Bayesian analysis, it remains a debate regarding the “best” model fit

statistic for Bayesian research. For the continuing debate concerning the current state of

the literature on the uncertainty, the validity, and the limitations of the DIC, see Plummer

(2012) and Gelman (2011).

15As Brown (2006) notes, factors loadings in a confirmatory factor model are

analogous to the item discrimination parameter in an item response theory (IRT) model.

22

presidents emphasize competence, they should seek out a candidate that has previously

held elected office (Coefficient = 13.01). The second defining characteristic of strong

competence is prior experience in the agency to which the candidate will be appointed

(Coefficient = 6.84). While previously held elected office and prior agency experience

are the two strongest indicators of competence presented in Model 2, we must also

consider the relative short supply of such appointment candidates for office. Of the 3,366

Bush and Obama appointees in our dataset, only 1.7% and 9.7% of all appointees

previously held elected office and previously worked in the agency to which they were

appointed, respectively.

Further, the results in Model 4 suggest that the story of the relative importance of

the competence variables is more nuanced. As noted earlier, prior elected office

experience is indicative of competence related to public office, while prior agency

experience signifies federal government experience. Lastly, related to policy-related

expertise, the results in Model 4 suggest that the best indicator of this particular

component of competence is non-profit management experience (Coefficient = 5.86).

Loyalty Sub-dimensions

Table 5 presents the results of sub-level analyses on the loyalty factor. The

principal question here is should we model the loyalty dimension as a single, large factor,

or as two smaller factors identified in Figure 3. Accordingly, we estimate two separate

Bayesian confirmatory factor models on the variables associated with the loyalty

dimension. In the first column, Model 5 is a one-factor Bayesian confirmatory model

with the characteristic worked for the campaign as the fixed variable. In the second

As such “…items with relatively high…parameter values are more strongly related to the

latent variable” (Brown 2006, 398).

23

column (Model 6), we estimate a two-factor solution, with worked for the campaign and

the last job political as the fixed variables for their respective sub-dimensions.

- Table 5 About Here -

For both the one-factor and the two-factors solutions, all free parameters load

positively on the loyalty (sub)dimension. Again, we use the deviance and the DIC to

compare models. Lower values of each indicate the better model. Comparing the two

models, the deviance and the DIC offer mixed results concerning which is the best fitting

model. By the deviance measure, the two-factor solution – i.e., the more complex model

(Model 6) – outperforms the one-factor model of the loyalty dimension in terms of pure

model fit. The deviance for the one-factor Bayesian confirmatory model of appointee

loyalty is 18773. In comparison, the deviance for the two-factor solution is 14458.

While the deviance measure indicates that the more complex model (Model 6) fits

the data better, the DIC suggests the opposite result. The Deviance Information Criteria

(DIC), which penalizes for having a more complex model, indicates that the one-factor

model of appointee loyalty is the preferred model.16 The DIC for the one-factor appointee

loyalty model is 21960, while the DIC for the two-factor model is 28919.

Lastly, note that the 95% credible interval now does not encompass zero.

Previously in the initial model of loyalty and competence presented in Table 3, having

worked on the president’s inauguration team was not associated with appointee loyalty.

16It is interesting to note that most researchers rely on the DIC to arrive at the better

fitting model, the original authors of the Deviance Information Criteria assert that one

should only rely on the DIC for generating a set of alternative models, not a definite

indicator of the “best” model. As Speigelhalter, Best, and Carlin (1998) note, “we do not

recommend that DIC be used as a strict criterion for model choice …. We rather view

DIC as a method for screening alternative formulations in order to produce a list of

candidate models for further considerations” (3, emphasis in original). We follow this

advice and view both Model 5 and Model 6 as candidate models of appointee loyalty.

24

Here, however, the opposite is true. Higher levels of loyalty are associated with a higher

probability that the appointee will have once worked on the president’s inauguration

team.

Which variables best define loyalty? The three strongest indicators of loyalty in

general are worked in the White House, worked on the inauguration team, and worked on

the transition team (Model 5).17 That is, the more loyal appointees previously worked in

the White House, worked on the president’s inauguration team, and/or worked on the

president’s transition team. What if presidents employ a finer-grained assessment of

loyalty, or what we call outsider loyalty and personal loyalty? Results in Model 6

indicate that the best identifier for personal loyalty is previously working on the

president’s inauguration team (Coefficient = 5.39), followed closely by White House

experience (Coefficient = 5.25). In comparison, the best indicator for outsider loyalty is

previous experience working in Congress (Coefficient = 88.75).

Theoretically and empirically, identifying that presidents likely distinguish

personal loyalty from outsider loyalty is important because it allows us to make a finer

and more-nuanced explanation of presidential control of the bureaucracy. Results in

Table 5 suggests that not only is there more than one component of the loyalty

dimension, but also that presidents can strategically appoint more loyal appointees to

various parts of the federal bureaucracy by targeting specific appointee characteristics.

17 As with earlier, we note that these are relatively “rare” traits. Only 5.8%, 6.1%, and

5.1% of the 3,366 appointees had White House, inauguration team, and transition team

experiences, respectively.

25

Re-Assessing Appointee Loyalty and Competence

Given the results above that both the loyalty and the competence dimensions

contain sub-level components, one natural question to ask is how the results of the

revised models will differ from those presented in Table 3. In this section, we re-examine

appointee loyalty and competence by fitting different models with increasing model

complexity.

Table 6 reports the results of three additional Bayesian confirmatory factor

models. Model 7 is the exact model as reported in Table 3, shown here for comparison

purposes. Model 8 is a three-factor Bayesian confirmatory model on appointee loyalty

and competence. Loyalty is defined as a single factor, while competence is distinguished

into two separate factors. The fixed variables are: (1) worked on the president’s

campaign; (2) subject area expertise; and (3) prior executive branch experience. Model 9

is a four-factor solution; loyalty again as a single factor, and competence is defined as

three factors. The scaling variables are: (1) worked for the president’s campaign; (2)

subject area expertise; (3) public management experience; and (4) prior executive branch

experience. Model 10 is the most complex model; it is a five-factor model, with loyalty

broken into two factors and competence into three separate factors. The fixed variables

for Model 10 are: (1) worked on the president’s campaign; (2) last job was political; (3)

subject area expertise; (4) public management experience; and (5) prior executive branch

experience.

- Table 6 About Here -

Comparing the four different models, based on the deviance measure and the DIC,

the results are mixed with regard to which is the best model. Using the deviance measure,

26

the four-factor solution (Model 9) is the better model (Deviance = 39969.3), by a slight

margin over Model 10 (Deviance = 36679.6). Using the DIC, however, the two best

performing models are Model 8 (the three-factor solution) and Model 9 (the four-factor

solution). Numerically, the value of the DIC for Model 8 is 49672, about a 1096

reduction in DIC compared to Model 9 (DIC = 50768).

Regarding the factor loadings, two results stand out. The first is the robustness of

the negative coefficient for task experience in the competence dimension. Earlier, we

noted that it is a counterintuitive finding. A negative coefficient for task experience

indicates that the more competent an appointee is, the less likely it is that he or she will

have prior task experience at the time of his or her appointment to office. While this is

counterintuitive, this finding is robust across seven different model specifications.18 It

may suggest the paradox we introduced earlier in this paper. Presidents seeking

competence and loyalty may search for those with prior task experience. For a loyalist

seeking president, past performance on the job may be a solid indicator of that

appointee’s liberal or conservative credentials.

The second surprising finding from the models presented in Table 6 is the

negative coefficient for having worked in Congress in Model 10. Across the six different

model specifications,19 only in Model 10 is the coefficient negative, indicating that higher

levels of loyalty are associated with a lower probability that the appointee will have

18This includes the three models in Table 4 and the four models presented here in

Table 6.

19This includes the two models in Table 5 and the four models presented here in Table

6.

27

worked in Congress. Most likely, this is due to model specification, given that no other

models return similar results.20

Conclusions

Arriving at a more precise definition of loyalty and competence is an important

first step to understanding the appointment strategies that presidents adopt. It also

provides an important contribution to the political appointments literature by specifying

the precise mechanisms presidents can use to when they select appointees for different

positions within the bureaucracy. As noted by various scholars in previous research, the

presidential politicization of the bureaucracy is often characterized by the selection of

loyal and/or competent appointees to federal agencies. In this paper, we explored

empirical definitions of loyalty and competence. We addressed the following questions:

What do we mean by loyalty and competence? Are there specific personal background

characteristics that help presidents identify loyal and competence appointees?

Using a unique data set consisting of 3,366 resumes of individuals appointed by

Presidents George W. Bush and Barack Obama across 51 different federal institutions,

we explore these questions using recently developed techniques in cluster analysis and

Bayesian structural equation modeling. In addition to PAS appointments (which require

Senate confirmation, our data also encompass Schedule C, Senior Executive Service, and

Executive Office of the President appointments. In short, our data are generalizable and

more comprehensive than many past studies of presidential appointments.

20This possibility is even more likely considering that the results in Table 5 offer

mixed evidence regarding whether loyalty should best be fitted as either a single or two

factor solution.

28

Results indicate that there are identifiable personal characteristics attributed to

measures of appointee loyalty and competence. For example, background characteristics

such as work experience in the White House and on the president’s inauguration team are

strong and consistent indicators of loyalty, while a variety of prior management

experience variables are excellent markers of competence. Further, we also show that

loyalty and competence, respectively, are not unidimensional constructs. Competence, for

instance, is best described as three distinct subfactors, consisting of competence resulting

from federal government experience, competence stemming to public office experience,

and competence deriving from policy-related expertise. These empirical findings

contribute and extend the existing presidential appointment literature, which generally

treats loyalty and competence as unidimensional concepts.

For us this is merely the first step in our examination of presidential

appointments. As part of a larger project on presidential appointments, understanding the

underlying properties of loyalty and competence allows us to better determine the

mechanisms of presidential appointments in relationship to a larger presidential

appointment strategy. For instance, do agency ideology and agency insularity impact a

president’s choice of loyal and competent appointees to such agencies? In the context of

our data, we posit that two presidents (George W. Bush and Barack Obama) strategically

appoint a combination of loyal and competence personnel to the federal bureaucracy,

with loyalist appointments more likely to agencies more ideologically distant from the

president’s policies.

A second extension relates to presidential success in Congress and the number of

presidential appointees that previously worked in Congress. Approximately three in 10

29

appointees (29%) in our data set had prior work experience in Congress, usually for a

representative or a senator. Does this indirect representation of Congress directly in the

president’s employ allow for greater presidential success legislatively? This is consistent

with Patterson’s (2008) accounts, which suggest that having personnel within the

executive branch familiar with the inter-workings of Congress allows for greater ease by

which the president can reach agreements with specific members of Congress.

Third, we will examine if the type of appointments that presidents make is related

to the performance of various federal bureaucratic units. Finally, we will examine

variations in how presidents use different types of appointments, SES, Schedule C, EOP,

and PAS. It may be that presidents strategically use some appointment types as a means

of promoting loyalty, more so than other types. Again, presidents may use different types

of appointments to promote different goals, loyalty or competence, in different agencies,

depending on that agencies fealty to the president’s political agenda. In sum, by

empirically identifying what we mean by loyalty and competence, we open the door for a

number of more detailed empirical examinations of the relationship between presidential

appointments and policy outcomes.

30

Table 1: Agencies and Resumes

N Percentage

African Development Foundation 7 0.21

Central Intelligence Agency 6 0.18

Commodity Futures Trading Commission 22 0.65

Corporation for National and Community Services 3 0.09

Defense Nuclear Facilities Safety Board 4 0.12

Department of Agriculture 5 0.15

Department of Commerce 8 0.24

Department of Defense 205 6.09

Department of Education 303 9.00

Department of Energy 757 22.49

Department of Health and Human Services 127 3.77

Department of Homeland Security 56 1.66

Department of Housing and Urban Development 3 0.09

Department of Interior 308 9.15

Department of Justice 74 2.20

Department of Labor 286 8.50

Department of State 4 0.12

Department of Transportation 230 6.83

Department of Treasury 128 3.80

Environmental Protection Agency 6 0.18

Equal Employment Opportunity Commission 3 0.09

Executive Office of the President 226 6.71

Export-Import Bank of the United States 11 0.33

Farm Credit Administration 8 0.24

Federal Aviation Administration 1 0.03

Federal Communications Commission 46 1.37

Federal Election Commission 10 0.30

Federal Labor Relations Authority 1 0.03

Federal Mediation and Conciliation Service 2 0.06

Federal Reserve System 3 0.09

General Services Administration 178 5.29

Millennium Challenge Corporation 1 0.03

National Aeronautics and Space Administration 57 1.69

National Credit Union Association 8 0.24

National Endowment for the Arts 5 0.15

National Endowment for the Humanities 38 1.13

National Labor Relations Board 4 0.12

National Mediation Board 3 0.09

Nuclear Regulatory Commission 5 0.15

Office of the Director of National Intelligence 1 0.03

31

Overseas Private Investment Corporation 44 1.31

Peace Corps 38 1.13

Pension Benefit guaranty Corporation 9 0.27

Postal Regulatory Commission 17 0.51

Securities and Exchange Commission 7 0.21

Small Business Administration 46 1.37

Social Security Administration 1 0.03

U.S. Agency for International Development 2 0.06

U.S. International Trade Commission 25 0.74

U.S. Office of Government Ethics 1 0.03

U.S. Office of Personnel Management 2 0.06

Unknown Agency Affiliation 21 0.62

Total 3,366 100.00

32

Table 2: Background Characteristics from Resumes

Bush Obama Total

Executive Branch* 384 (20.63) 429 (28.50) 813 (24.15)

Previous Agency* 112 (6.02) 214 (14.22) 326 (9.69)

Subject Area* 643 (34.55) 745 (49.50) 1,388 (41.24)

Task Area* 1,196 (64.27) 1,120 (74.42) 2,316 (68.81)

White House* 126 (.77) 70 (4.65) 196 (5.82)

Congress* 575 (30.90) 401 (26.64) 976 (29.00)

Public Management* 186 (9.99) 195 (12.96) 381 (11.32)

State-Level Experience* 299 (16.07) 191 (12.69) 490 (14.56)

Private Management 192 (10.32) 169 (11.23) 361 (10.72)

Non-Profit Management* 98 (5.27) 173 (11.50) 271 (8.05)

Was Prior Appointee* 178 (9.56) 265 (17.61) 443 (13.16)

Last Job Political 1,038 (55.78) 858 (57.01) 1,896 (56.33)

Worked on Campaigns* 461 (24.77) 547 (36.35) 1,008 (29.95)

Worked on Transition Team* 41 (2.20) 131 (8.70) 172 (5.11)

Worked on Inauguration Team 104 (5.59) 100 (6.64) 204 (6.06)

Worked for Party* 583 (31.33) 206 (13.69) 789 (23.44)

Held Prior Elected Office 37 (1.99) 21 (1.40) 56 (1.72)

This table provides a breakdown of appointees by their background characteristics. The

numbers are the number of appointees appointed who possesses that characteristic. The

percentage of the overall appointee holding that background trait is in parentheses. The

background trait variable is starred is there is a statistically significant difference between

the number of appointees by Obama holding that trait, compared to Bush appointees.

33

Table 3: Assessing Loyalty and Competence, Model 1

Note: Two-factor Bayesian confirmatory model estimated. Worked on campaigns and

subject area expertise are defined as fixed for identification. Coefficients are posterior

medians and the stars indicate that the 95% credible interval does not include zero.

Loyalty

Worked on Campaign 1.00 (Fixed)

Worked on Inauguration 4.97

Worked for Party 1.30 *

Worked in White House 5.40 *

Worked on Transition 5.31 *

Last Job Political 0.05 *

Worked in Congress 0.77 *

Competence

Has Subject Area Expertise 1.00 (Fixed)

Has Task Experience -0.50 *

Has Non-Profit Management Experience 5.87 *

Has Private Management Experience 4.76 *

Has Public Management Experience 5.55 *

Has State-Level Work Experience 3.54 *

Previously Held Elected Office 23.15 *

Has Prior Executive Branch Experience 3.04 *

Has Prior Agency Experience 6.99 *

Was Previously an Appointee 5.53 *

N 3366

Deviance 42429.96 *

Deviance Information Criteria (DIC) 50938.10

2 Factors

(1)

34

Table 4: Assessing Competence Sub-dimensions

This table presents the results of the sub-level analyses on the competence factor. Variable association with

sub-clusters within the competence dimensions follows earlier results from Figure 2.

Competence

Has Subject Area Expertise 1.00 (Fixed) 1.00 (Fixed) 1.00 (Fixed)

Has Task Experience -0.37 * -0.48 * -0.43 *

Has Non-Profit Management Experience 5.14 * 6.05 * 5.86 *

Has Private Management Experience 4.08 * 4.87 * 4.56 *

Has Public Management Experience 5.08 * 5.51 * 1.00 (Fixed)

Has State-Level Work Experience 2.87 * 3.58 * 0.67 *

Previously Held Elected Office 13.01 * 109.94 * 73.26 *

Has Prior Executive Branch Experience 2.74 * 1.00 (Fixed) 1.00 (Fixed)

Has Prior Agency Experience 6.84 * 2.87 * 2.89 *

Was Previously an Appointee 5.20 * 2.16 * 2.16 *

N 3366 3366 3366

Deviance 22652.34 * 20297.90 * 20074.78 *

Deviance Information Criteria (DIC) 25962.00 26024.90 25839.70

1 Factor 2 Factors 3 Factors

(2) (3) (4)

35

Table 5: Assessing Loyalty Sub-dimensions

This table presents the results of the sub-level analyses on the loyalty factor. Variable association with sub-

clusters within the loyalty dimensions follows earlier results from Figure 3.

Loyalty

Worked on Campaign 1.00 (Fixed) 1.00 (Fixed)

Worked on Inauguration 7.81 * 5.39 *

Worked for Party 1.86 * 1.22 *

Worked in White House 8.39 * 5.25 *

Worked on Transition 6.75 * 4.40 *

Last Job Political 0.19 * 1.00 (Fixed)

Worked in Congress 1.05 * 88.75 *

N 3366 3366

Deviance 18773.44 * 14457.91 *

Deviance Information Criteria (DIC) 21959.90 28918.90

1 Factor 2 Factors

(5) (6)

36

Table 6: Appointee Loyalty and Competence Revisited

This table presents the results of four separate Bayesian confirmatory factor models, with increasing model

complexity.

Loyalty

Worked on Campaign 1.00 (Fixed) 1.00 (Fixed) 1.00 (Fixed) 1.00 (Fixed)

Worked on Inauguration 4.97 4.99 * 4.96 * 5.05 *

Worked for Party 1.30 * 1.31 * 1.31 * 1.29 *

Worked in White House 5.40 * 5.38 * 5.34 * 5.37 *

Worked on Transition 5.31 * 5.19 * 5.12 * 5.18 *

Last Job Political 0.05 * 0.05 * 0.06 * 1.00 (Fixed)

Worked in Congress 0.77 * 0.77 * 0.77 * -3.47 *

Competence

Has Subject Area Expertise 1.00 (Fixed) 1.00 (Fixed) 1.00 (Fixed) 1.00 (Fixed)

Has Task Experience -0.50 * -0.59 * -0.49 * -0.52 *

Has Non-Profit Management Experience 5.87 * 5.96 * 5.85 * 5.78 *

Has Private Management Experience 4.76 * 4.91 * 4.65 * 4.65 *

Has Public Management Experience 5.55 * 5.38 * 1.00 (Fixed) 1.00 (Fixed)

Has State-Level Work Experience 3.54 * 3.81 * 0.73 * 0.73 *

Previously Held Elected Office 23.15 * 105.40 * 77.03 * 82.11 *

Has Prior Executive Branch Experience 3.04 * 1.00 (Fixed) 1.00 (Fixed) 1.00 (Fixed)

Has Prior Agency Experience 6.99 * 2.61 * 2.54 * 2.60 *

Was Previously an Appointee 5.53 * 2.23 * 2.27 * 2.23 *

N 3366 3366 3366 3366

Deviance 42429.96 * 40213.47 * 39969.26 * 39979.63 *

Deviance Information Criteria (DIC) 50938.10 49672.40 50768.00 51066.99

2 Factors 3 Factors 4 Factors 5 Factors

(7) (8) (9) (10)

37

Figure 1: Variable Cluster Analysis – Loyalty and Competence

This figure presents the results of the variable cluster analysis. Results indicate

two main clusters, with possible subclusters within each.

38

Figure 2: Variable Cluster Analysis: Competence Subdimensions

This figure presents the results of the variable cluster analysis performed on only

variables associated with the competence dimension. Results indicate three possible

subclusters within the main competence dimension.

39

Figure 3: Variable Cluster Analysis: Loyalty Subdimensions

This figure presents the results of the variable cluster analysis performed on only

variables associated with the loyalty dimension. Results indicate two possible subclusters

within the main loyalty dimension.

40

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