BUREAUCRACY AND DEMOCRACY: BUREAUCRATIC PROFILES OF OECD
NATIONS AND CITIZENS’ ATTITUDES TOWARD GOVERNMENT
By
Seo Youn Choi

A DISSERTATION

Submitted to
Michigan State University
In partial fulfillment of the requirements
For the degree of
Political Science—Doctor of Philosophy
2014

ABSTRACT
BUREAUCRACY AND DEMOCRACY: BUREAUCRATIC PROFILES OF OECD NATIONS
AND CITIZENS’ ATTITUDES TOWARD GOVERNMENT
By
Seo Youn Choi

This dissertation examines how bureaucracies in democratic nations vary across a set of
key bureaucratic traits and how these traits relate to citizens’ attitudes toward government. The
bureaucracy is an important government institution for exercising public authority, but it has
received less scholarly attention relative to other political institutions. There is no consensus on
what attributes of public agencies are important in democratic societies. Moreover, previous
studies emphasizing the role of bureaucracies often rely on cross-national indicators without
rigorous conceptualization. How bureaucracies are similar and different cross-nationally also has
not been systematically examined. This dissertation aims to fill this lacuna.
To better understand and compare the administrative apparatus of the state, I propose
examining how a set of specific traits are presented jointly in a nation’s bureaucracy. For the
empirical analysis, I explore the bureaucracies of the member countries of the Organization for
Economic Co-operation and Development (OCED). I employ a nonmetric internal analysis of
preference data which is called multidimensional preference scaling (MDPREF). The underlying
structure of bureaucratic traits and how these traits are presented in national bureaucracies are
investigated. The results indicate that a set of key traits—independence from politics,
representativeness, impartiality, competency, and career-based system—are emphasized
variously in different nations. The findings suggest that bureaucratic characteristics are not

unidimensional and that relationships between traits, as well as the relative importance attached
to them, vary across nations.
Based on estimations from the MDPREF model, I develop a new measure of
Bureaucratic Profiles. A bureaucratic profile represents the relative levels of the key
bureaucratic traits presented in a national bureaucracy. Substantively, in general, higher values of
this measure indicate that the relative level of impartiality in a country’s bureaucracy increases,
and the relative level of competency decreases, compared to other traits. Using this new measure,
I examine if there is a systematic relationship between bureaucratic profiles and levels of
political support. Individual-level survey data from the 2004 International Social Survey
Programmes is merged with a country-level dataset—including Bureaucratic Profiles—to
investigate this association in a multilevel analysis. The findings suggest that the emphasis on
impartiality compared to other bureaucratic traits is important for maintaining public support for
government bureaucracy and regime performance. The linkages between political support and
the relative levels of other traits are also discussed.
This dissertation makes important contributions to previous studies on comparative
public bureaucracies by providing systematic evidence on the similarities and differences of
bureaucracies across nations. Further, it makes a fruitful addition to a recent debate on how best
to measure characteristics of bureaucracies cross-nationally. This project broadens our
understanding of the role of bureaucracies in democratic societies by showing what specific set
of bureaucratic traits jointly influence citizens’ attitudes toward government in these societies.

Copyright by
SEO YOUN CHOI
2014

To Mom and Dad:
Thanks for all your love, support, and encouragement.

v

ACKNOWLEDGEMENTS

I would like to express my sincere thanks to faculty members and colleagues at Michigan
State University. This dissertation would not have been possible without guidance and support
from them. I would like to convey my utmost gratitude to Saundra K. Schneider, the chair of my
doctoral dissertation committee, for her ceaseless support and encouragement. Her invaluable
guidance helped me to overcome many obstacles, grow as a researcher and political scientist, and
ultimately, complete my dissertation. I would like to thank William G. Jacoby for his teaching on
scaling methods (along with other statistical methods) and public opinion, and for his valuable
comments on my work and considerable support over the years. I would like to thank Eric C.C.
Chang for his comments and suggestions on my research, and for his warm encouragement over
the years. I would also like to thank Sandy Marquart-Pyatt for her time and assistance with my
work.
I would also like to thank my great friends and fellow graduate students who I met at
Michigan State University. Most of them have now graduated and are all over the country, but
they are always with me in spirit. My special thanks to Danielle Carter (at St. Mary’s College of
Maryland), Nicholas N. Kerr (at University of Alabama), Helen Lee, and Robert N. Lupton, who
provided lots of encouragement and great feedback on drafts of my dissertation. I also owe many
many thanks to several fellow graduate students, including Shih-Hao Huang, Dominique Lewis,
Niccole M. Pamphilis (at University of Edinburgh), Hsin-Hsin Pan, Johann Park (at Mississippi
University), and Wen-Chin Wu (at Asiabarometer). Without these people, my life at Michigan
State would have been tougher.

vi

I am also grateful to my teachers at Ewha Womans University in Korea, Tong Hee Park,
Sook Yeon Won, and Chung-Lae Cho, who continuously encouraged me to study abroad and
pursue doctoral studies in the United States.
Finally, I would like to express my deepest thanks to my family. I have been blessed with
endless love, warmness, understanding, resources, and encouragement from my family during
my studies. I was able to complete my dissertation because of warm and consistent support from
my parents, my husband, my brother, my grandmother, my aunts and uncles, and my parents-inlaws. I am very grateful for their support and prayers. Especially, to my parents, SangDae Choi
and MeaJa Oh, thank you so much for your support, encouragement, and countless love and
sacrifices during my long journey to the Ph.D degree. Without you, I would not have been able
to even begin my study abroad. I am so happy that I can finally finish my dissertation and
dedicate it to both of you. And to my lovely husband, Joo S. Lee, thank you so much for your
love, support, and patience. Without your consistent push, understanding, and supportive
presence, I would not have been able to end this journey. Finally, thank you, God, for guiding
and helping me through this academic journey.

East Lansing, February 2014
Seo Youn Choi

vii

TABLE OF CONTENTS

LIST OF TABLES ......................................................................................................... xi
LIST OF FIGURES ...................................................................................................... xii
CHAPTER 1
INTRODUCTION .......................................................................................................... 1
1.1 Background ......................................................................................................... 1
1.2 Why Study Bureaucracy? .................................................................................... 3
1.3 How to Study Bureaucracy? ................................................................................ 6
1.4 The Scope of the Study ........................................................................................ 8
CHAPTER 2
TRAITS OF BUREAUCRACIES IN DEMOCRATIC SOCIETIES.............................. 11
2.1 Bureaucratic Traits Required in Democratic Societies........................................ 11
2.1.1 Overview .................................................................................................. 11
2.1.2 Political Independence of Bureaucracy ...................................................... 14
2.1.3 Representativeness of Bureaucracy ........................................................... 18
2.1.4 Impartiality of Bureaucracy....................................................................... 20
2.1.5 Competency of Bureaucracy ..................................................................... 24
2.2 Relationships between Bureaucratic Traits ......................................................... 25
2.3 Measuring Bureaucratic Traits ........................................................................... 28
2.3.1 Cross-national Indicators of Bureaucracies ................................................ 29
2.3.2 Critiques of Cross-national Indicators of Bureaucracies ............................ 30
2.3.1 A Need for an Alternative Way of Measuring Bureaucracies..................... 34
2.4 Conclusion ........................................................................................................ 36
CHAPTER 3
BUREAUCRATIC PROFILES OF OECD NATIONS: A MDPREF ANALYSIS......... 37
3.1 Bureaucratic Profiles ......................................................................................... 37
3.2 Method: A Multidimensional Preference Scaling (MDPREF) Analysis .............. 40
3.3 Data and Measurements ..................................................................................... 44
3.3.1 Data Sources and Measurements of Key Traits.......................................... 44
3.3.2 Dataset ...................................................................................................... 50
3.4 Analysis ............................................................................................................ 52
3.5 Findings: Configuration of the Traits ................................................................. 54
3.6 Findings: Nation Vectors ................................................................................... 58
3.6.1 Interpreting Nation Vectors ....................................................................... 60
3.6.2 Interpreting the Orientations of the Nation Vectors ................................... 61
3.6.2.1 Nation Vectors between “1:00” and “4:00” .................................... 63
3.6.2.2 Nation Vectors between“5:00” and “8:00”..................................... 64
3.6.2.3 Nation Vectors between “9:00” and “10:00” and between
“10:00” to “12:00” ........................................................................ 67
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3.6.3 Comparing Specific Bureaucratic Profiles ................................................. 69
3.7 Conclusion ........................................................................................................ 73
CHAPTER 4
RELATIONSHIP BETWEEN BUREAUCRATIC PROFILES AND POLITICAL
SUPPORT..................................................................................................................... 76
4.1 Political Support for Regime Performance and Regime Institutions ................... 76
4.2 Existing Explanations for Political Support ...................................................... 79
4.2.1 Economic Factors ..................................................................................... 79
4.2.2 Social Factors ........................................................................................... 81
4.2.3 Political Factors ........................................................................................ 83
4.3 Bureaucracies and Political Support ................................................................... 85
4.4 Bureaucratic Profiles as a Measure of Bureaucratic Traits.................................. 88
4.4.1 New Measure of Bureaucratic Profiles ...................................................... 88
4.4.2 Bureaucratic Profiles and Political Support ............................................... 95
4.5 Method: Multilevel Model Analysis ................................................................ 100
4.6 Data and Measurements ................................................................................... 103
4.6.1 Dependent Variable: Public Attitudes toward Bureaucracies and
Satisfaction with Democracy................................................................... 103
4.6.2 Key Independent Variable: Bureaucratic Profiles .................................... 106
4.6.3 Control Variables .................................................................................... 106
4.6.3.1 Individual-level Control Variables ............................................... 106
4.6.3.2 Country-level Control Variables .................................................. 108
4.7 Empirical Results and Discussion: Citizens’ Attitudes toward Bureaucracies
Models ............................................................................................................ 111
4.7.1 Random Intercept Models: Attitudes toward Bureaucracies ..................... 111
4.7.2 The Relationship between Bureaucratic Profiles and Attitudes toward
Bureaucracies ......................................................................................... 117
4.7.3 Random Slope Models: Attitudes toward Bureaucracies.......................... 123
4.8 Empirical Results and Discussion: Citizens’ Satisfaction with Democracy
Models .......................................................................................................... 130
4.8.1 Random Intercept Models: Satisfaction with Democracy ........................ 130
4.8.2 The Relationship between Bureaucratic Profiles and Satisfaction with
Democracy.............................................................................................. 136
4.8.3 Random Slope Models: Satisfaction with Democracy ............................. 141
4.9 Conclusion ...................................................................................................... 146
CHAPTER 5
CONCLUSION ........................................................................................................... 148
5.1 General Findings and Contributions ................................................................. 148
5.2 Extensions and Implications for Future Studies ................................................ 156
5.3 Conclusion ...................................................................................................... 160
APPENDICES ............................................................................................................ 162
Appendix A. Description of Variables Used in Chapter 3 ............................................ 163
Appendix B. Supplementary Analysis for Chapter 3: Cluster Analysis ........................ 170
ix

Appendix C. Supplementary Analysis for Chapter 4 .................................................... 171
Appendix D. Residual Diagnostics for Chapter 4......................................................... 176
REFERENCES ........................................................................................................... 180

x

LIST OF TABLES

Table 3.1. Hypothetical Example of the Dataset ............................................................ 51
Table 4.1. Bureaucratic Profiles .................................................................................... 92
Table 4.2. Citizens’ Positive Attitudes toward Bureaucracies and Satisfaction with
Democracy (mean) ..................................................................................... 105
Table 4.3. Random-Intercept Multilevel Models of Citizens’ Attitudes toward
Bureaucracies, with Individual-level Controls ............................................ 112
Table 4.4. Random-Intercept Multilevel Models of Citizens’ Attitudes toward
Bureaucracies, with Country-level Controls ............................................... 116
Table 4.5. Random-Slope Multilevel Models of Citizens’ Attitudes toward
Bureaucracies ............................................................................................ 124
Table 4.6. Random-Intercept Multilevel Models of Citizens’ Satisfaction with
Democracy, with Individual-level Controls ................................................ 131
Table 4.7. Random-Intercept Multilevel Models of Citizens’ Satisfaction with
Democracy, with Country-level Controls ................................................... 134
Table 4.8. Random-Slope Multilevel Models of Citizens’ Satisfaction with
Democracy ............................................................................................... 142
Table C.1. Descriptive Statistics .................................................................................. 171
Table C.2. Correlation Matrix ..................................................................................... 171
Table C.3. Multilevel Model of Citizens’ Attitudes toward Bureaucracies: Withinversus Between-country Effects ................................................................. 172
Table C.4. Multilevel Models of Citizens’ Attitudes toward Bureaucracies ................. 173
Table C.5. Multilevel Model of Citizens’ Satisfaction with Democracy: Withinversus Between-country Effects ................................................................. 174
Table C.6. Multilevel Models of Citizens’ Satisfaction with Democracy ..................... 175

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LIST OF FIGURES

Figure 3.1. Hypothetical example of the vector representation ....................................... 43
Figure 3.2. The configuration of variable points ............................................................ 55
Figure 3.3. Full model of nations’ vector terminal points and variable points, with five
bureaucratic trait points and the mean direction vector ................................. 59
Figure 3.4. Full model, with the vector terminal points and the five trait points. ............ 62
Figure 3.5. Full model, with the vectors for the United States and Germany .................. 70
Figure 4.1. Full MDPREF model, with the mean direction vector as a reference vector
for a new measure, Bureaucratic Profiles..................................................... 89
Figure 4.2. Predictions and approximate 95% confidence intervals versus ranking of
citizens’ attitudes toward bureaucracies based on Model 4 in Table 4.4...... 118
Figure 4.3. Full MDPREF model, with the vectors for New Zealand and Poland ......... 120
Figure 4.4. Cross-level interaction effect between generalized trust and social trust on
citizens’ attitudes toward bureaucracies ..................................................... 126
Figure 4.5. Predictions and approximate 95% confidence intervals versus ranking of
citizens’ attitudes toward bureaucracies based on Model 5 in Table 4.5...... 129
Figure 4.6. Predictions and approximate 95% confidence intervals versus ranking of
citizens’ satisfaction with democracy based on Model 10 in Table 4.7 ....... 137
Figure 4.7. Full MDPREF model, with the vectors for Denmark and Slovakia ............ 138
Figure 4.8. Cross-level interaction effect between generalized trust and social trust on
citizens’ satisfaction with democracy ......................................................... 144
Figure 4.9. Predictions and approximate 95% confidence intervals versus ranking of
citizens’ satisfaction with democracy based on Model 11 in Table 4.8 ....... 145
Figure B.1. Tree diagram for the twenty-one variable points ....................................... 170
Figure D.1. Histograms of standardized level-1residuals and standardized level-2
residuals, for the random-intercept model of citizens’ attitudes toward
bureaucracies (Model 4)............................................................................. 176

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Figure D.2. Histogram of standardized level-1residuals, for the random-slope model
of citizens’ attitudes toward bureaucracies (Model 5) ................................. 176
Figure D.3. Histograms of random slopes, for the random-slope model of citizens’
attitudes toward bureaucracies (Model 5) ................................................... 177
Figure D.4. Histograms of standardized level-1residuals and standardized level-2
residuals, for the random-intercept model of citizens’ satisfaction with
democracy (Model 10) ............................................................................... 178
Figure D.5. Histogram of standardized level-1residuals, for the random-slope model
of citizens’ satisfaction with democracy (Model 11) .................................. 178
Figure D.6. Histograms of random slopes, for the random-slope model of citizens’
satisfaction with democracy (Model 11) ..................................................... 179

xiii

CHAPTER 1
INTRODUCTION

1.1 Background

What do people want bureaucracies to be in democratic societies? Indeed, bureaucracybashing has been prevalent since the late 1970s; media and politicians have described
bureaucracies as wasteful, unproductive, inefficient, unresponsive and antidemocratic form of
administration (Suleiman 2003; Wilson 1989). As the role of bureaucracy increases in modern
society, bureaucracies are often criticized as a problem to be solved, rather than an instrument
that can provide essential goods and services to citizens. Moreover, governmental bureaucracy is
often blamed for policy failures and framed as obsolete—or, more colorfully, as an
“organizational dinosaur” (Olsen 2005, 1)—that must be minimized and reformed (Pierson 1996;
Peters 1993). Does the prevalent disdain for bureaucracies indicate that we need to abandon
bureaucracies and diminish their role in the society? I argue that the answer is no, and that the
challenges posed instead call for our further attention to broadening and deepening our
understanding or what “quality” bureaucracies look like in contemporary societies.
A closer look at the attacks on bureaucracies reveals that such criticisms are often led by
misinformation and a lack of concrete evidence; and that politicians frequently target
bureaucracies as scapegoats for unpopular policy makings (Hill 1992; Pierson 1996). Moreover,
there is no consensus established in terms of the required characteristics and traits of
governmental bureaucracies in democratic societies. Instead, it is often the case that different
aspects of bureaucracies are variously identified as an object for attacks (Olsen 2010). For

1

example, as the role of bureaucracies and their discretion in the policy process increases,
politicians often criticize an ineffectiveness and unresponsiveness of bureaucracies. And, along
with this, the quest for politicization has increased as a way to secure control over bureaucracies
(Peters and Pierre 2004). On the other hand, the very politicization is criticized when it does not
yield the expected improvements in bureaucratic performance (Lewis 2008). And, instead, the
demand for impartiality and expertise has increases. 1
Indiscreet bureaucracy-bashing is not constructive and does not lead to a “better”
bureaucracy. If the reason for criticizing bureaucracies is to improve them, then critics should
pause to consider what the criteria are for “good” bureaucracies and whether these criteria are
being met. Given the significant role of bureaucracies in the policy process and in governance
(Kettl 2006; Pierre and Peters 2000), we need to “take bureaucracy seriously” (Hill 1992) and
“rediscover bureaucracy” (Olsen 2005). Otherwise, we may end up with the “dismantled state,” a
vicious cycle of distrust in the government and a malfunctioning state apparatus (Suleiman 2003).
Despite its importance, bureaucracy has received relatively little scholarly attention
relative to other political institutions (Hill 1992; Holmberg, Rothstein, and Nasiritousi 2009;
Rothstein and Teorell 2008a). For example, comparative studies examining the role of
bureaucracies emphasize their importance in democratic consolidation and economic
development (e.g., Bratton and Chang 2006; Diamond 1999; Evans and Rauch 1999; Knack
1

For example, politicization has been advocated in many industrialized countries as a way to
increase the political responsiveness of bureaucracies (Peters and Pierre 2004). However, in the
United States, the increasing number of political appointees was criticized as one of the reasons
that the Federal Emergency Management Agency failed to effectively respond to Hurricane
Katrina in 2005 (Lewis 2008); the quest for impartiality increases any time impartial treatment of
taxpayers by the Internal Revenue Service is suspected (Janofsky 2004; Stevenson 1997;
Weisman 2013). For another example, in Britain, they have long been debated allowing ministers
to choose their permanent secretaries—the senior officials; advocates emphasize that bureaucrats
are unresponsive and ineffectiveness in the current system, while critics are concerned that it will
risk impartiality (Economist 2012; BBC News 2012, 2013).
2

2003; Kohli 2004). However, most such studies do not pay sufficient attention to the specific
traits these bureaucracies have and how similarly or differently the properties of bureaucracies
vary across nations. Scholars instead seem to assume that bureaucratic characteristics are unidimensional and easily measured by simple cross-national indicators of “bureaucratic quality.”
Moreover, the discussion of bureaucracies is often conflated with an abstract conceptualization
of the state; thus, the specifics of governmental bureaucracies are little studied in the
comparative politics literature. Therefore, this dissertation aims at providing more specific
understandings of the public bureaucracy in democratic societies.

1.2 Why Study Bureaucracy?

Why is it worth studying the bureaucracy? In other words, is the bureaucracy an
important institution in the first place? Given negative images of governmental bureaucracies,
one may argue that bureaucracies are incompatible in democratic societies and should be
minimized to the greatest extent possible. However, governmental bureaucracies play an
inevitable role in preparing, developing and implementing public policies in democratic societies
(Heclo 1974; Meier and Bothe 2007; Schneider and Jacoby 1996). Bureaucracies are involved in
our daily lives in various ways. For example, food and drug safety and water quality are
regulated by governmental bureaucracies. Various types of public goods and social services are
delivered by public agencies. And, governmental power and authority are exerted through
bureaucracies and public policies signed into law are executed by bureaucracies. In this sense,
bureaucracies are essential institutions related to the “output” of the political system, which is
different from “input” institutions that channel various public demands to the government, such

3

as party systems and electoral rules (Kettl 2006; Rohrschneider 2005; Rothstein and Teorell
2008a).
Public bureaucracies, thus, influence ordinary citizens’ quality of life (Coggburn and
Schneider 2003; Whiteley et al. 2010). If governmental bureaucracies are incapable of delivering
public goods and services to citizens, or if, for example, clean and safe food and housing are
available only to a small group of people in power, then citizens rarely will be satisfied with their
lives. In addition, how people view and have confidence in their government is closely related to
how bureaucracies work. Particularly, bad experiences with public bureaucrats are likely to
engender negative feelings about the government (Hill 1992; Pierson 1996). Given that
providing citizens with a high quality of life and preserving the popular belief that government is
doing the right thing is important in democratic societies, deepening our understandings of
bureaucracies is a critical task.
Indeed, as Hill (1991) highlights in his “bureaucratic-centered view of governance,”
bureaucracy plays a non-negligible role in the policy process. Moreover, as government’s
coordinating power is required in a complex and globalized world, how national governmental
bureaucracies perform becomes increasingly important (Kettl 2000; Pierre and Peters 2000).
Indeed, the public bureaucracy is one of the critical actors in the policymaking process, which is
not only influenced by resources availability and various external constraints, but also impacts
the other actors in the decision making process (Hill 1991; Schneider and Jacoby 1996;
Schneider, Jacoby, and Coggburn 1997). Bureaucracy is not only a “tool for executing the
commands of elected leaders,” but also an “institution with a raison d’être and organizational and
normative principles of its own” (Olsen 2005, 3). In other words, bureaucrats participate in
policy process by applying their own expertise and knowledge with a degree of autonomy and

4

discretion that extends well beyond merely following what is written and directed by their
political masters (Hill 1992; Olsen 2005).
Comparative studies adopting state-centric approaches also reveal that bureaucracies are
important actors in political and social processes (Skocpol 1985). First, a “usable bureaucracy”
(Linz and Stepan 1996, 20) is an important element for a strong state (Migdal 1988). Studies of
economic development in less-developed countries emphasize that professional and powerful
bureaucracies contribute to economic development by securing property rights for individuals
and exerting coercive power when necessary (e.g., Evans and Rauch 1999; Keefer and Knack
1997). Kohli (2004), for example, argues that for rapid industrialization, it is crucial that the state
has a competent, “public-spirited” bureaucracy, through which the state pursues and implements
cohesive and effective economic policy. The role of bureaucracies, conceptualized as a structure
of the modern state, is also emphasized in democratic consolidations (e.g., Bratton and Chang
2006; Diamond 1999; Linz and Stepan 1996; Rose and Shin 2001). That is, a new democratic
government needs a bureaucracy at its own disposal that has “the effective capacity to command,
to regulate, and to extract tax revenues” (Linz and Stepan 1996, 21).
The critical role of bureaucracies is emphasized not only within new democracies, but
also within advanced industrialized countries. The state-centered approach to welfare state
development in Western European countries and in the United States demonstrates that
professional and trustworthy bureaucracies have played an important role in social policy making.
Heclo (1974) shows that in Sweden and Britain civil service public administrators consistently
exert influences on the development of unemployment insurance and old-age pension programs.
The autonomous bureaucracy is also important in Skocpol’s (1995) discussion of the
development of the American welfare state: The lack of a centralized and professional

5

bureaucracy was one reason that the United States failed in the 1940s to nationalize benefits
defined by the Social Security Act. Studies also show that national bureaucracies contribute to
welfare state expansion in industrialized countries (Rothstein, Samanni, and Teorell 2012;
Schneider and Ingraham 1984).
Since the revival of interest in the state since the 1970s (Skocpol 1985), bureaucracies are
considered to be an independent variable in several comparative politics literatures. However,
our specific understanding of the cross-national variation in bureaucracies has been advanced
little. Case studies have examined the characteristics of bureaucracies in a few countries, but they
have resulted in parallel descriptions and do not provide a systematic analysis of the similarities
and differences of bureaucracies. Moreover, it is unclear what particular attributes of
bureaucracies are relevant for cross-national comparison. It is often assumed, especially in largeN studies, that most advanced industrialized countries have “effective” bureaucracies and that
there is no significant variation among them. However, as Olsen (2005) and Holmberg et al.
(2009) note, it is possible that we have different patterns of bureaucracies, as there are various
forms of political institutions in democratic societies. 2 To explore this possibility and to provide
better measures for comparing bureaucracies, more studies on bureaucracies are necessary.

1.3 How to Study Bureaucracy?

With what criteria can we compare bureaucracies? As mentioned earlier, previous
comparative studies of bureaucracies often assume that there is a “good bureaucracy” that all
nations strives to establish, but the meaning of a “good” or a “quality” bureaucracy is not
2

For example, nations have different electoral systems, party systems, and forms of government
(i.e., executive-legislative relations).
6

specified. On the other hand, studies of comparative public administration stress the great
diversity of bureaucracies across nations (e.g., Painter and Peters 2010; Pierre 1995; Pollitt and
Bouckaert 201l; Rose 1985). The approach in these studies is very different from that in a largeN study where the role of bureaucracies is examined using a single indicator. Why are the two
approaches so different? Given these differences, how can we compare bureaucracies
meaningfully and systematically? Is there a single-best-definition of a “good bureaucracy”? How
can variations in public administrative structures and cultures be reconciled with a “good
bureaucracy”?
To address these inquires, I separate the question of “how national bureaucracies differ
from each other” from the question of “which characteristics of bureaucracy are viewed more
positively by citizens.” Instead of directly defining a “good bureaucracy,” I suggest first
specifying a list of possible key bureaucratic traits—all of which have been discussed previously
as important in democratic societies—and using them as the criteria for a comparison. In
comparing bureaucracies based upon the degree to which they possess these attributes, the first
question can be answered. Thus, Chapter 2 reviews and specifies key traits; Chapter 3
systematically compares bureaucracies cross-nationally by focusing on specific bureaucratic
attributes.
After identifying the key differences and similarities of bureaucracies across nations, the
second question can be examined empirically by testing the link between bureaucratic
characteristics and popular attitudes toward government. If a nation’s bureaucracy possesses
important attributes for democratic governance that can reconcile the institution’s inherent
tension with democracy, then it should be linked to higher levels of public support. Therefore,
this analysis will provide some suggestions for the meaning of “good bureaucracies,” which

7

refers to bureaucracies that are good for democratic societies from the citizens’ perspective.
Chapter 4 investigates how the similarities and differences in the configuration of the key
bureaucratic traits are associated with citizens’ attitudes toward their government.
In addition to the lack of consensus on the criteria necessary to compare and evaluate
bureaucracies, limited data availability is another difficulty involved in studying bureaucracy.
Indeed, the lack of available and reliable data is a primary hindrance to the development of
comparative public bureaucracy research (Brans 2003; Dahlstrӧm, Lapuente, and Teorell 2010;
Van de Walle 2006). Even the number of employees in the governmental bureaucracy—which
would seem to be measured straightforwardly—is debatable, because exact counts that are
comparable do not exist (OECD 1997). For this reason, many studies turn to existing crossnational indicators of bureaucracy provided by international organizations and commercial
groups, despite their acknowledged limitations. These existing indicators of bureaucracies are
briefly discussed in Chapter 2. Rather than relying on these indicators, I collect data that
measures various aspects of bureaucracies, especially the key traits specified in Chapter 2.
Furthermore, I will focus particularly on the relative importance of these key traits in each
nation’s bureaucracy. The data and method that ultimately will be used to compare bureaucracies
are discussed in Chapter 3.

1.4 The Scope of the Study

This dissertation examines bureaucracies in the thirty-four member nations of the
Organisation for Economic Co-operation and Development (OECD) (as of 2012). Focusing on
OECD member countries has some merits. First, although the organization obviously does not

8

cover all nations on the globe, countries from different regions are included. With its
enlargement since 2007, the OECD now includes not only advanced economies, but also some
less-developed countries in underdeveloped regions. Thus, by looking at OECD member nations,
bureaucracies are compared not only for Western European countries and the United States, but
also Asian, Latin American, and Eastern European countries. Second, more practically, data
availability is less problematic for OECD member nations (e.g., Demmke and Moilanen 2010;
Dahlström, Lapuente, and Teorell 2010; OECD 2009b). Therefore, limiting the scope of the
study to OECD member countries enables me to analyze as many national bureaucracies as
possible using existing data.
National governmental bureaucracies are the focus of this dissertation. Following Hill
(1992), I use the term bureaucracy for the institution despite the fact some scholars choose not to
use this term because of its negative connotations. The term bureaucracy emphasizes a “political
form of organization (emphasis added)” which exercises government authority and is not an
“agent… merely acting on behalf of others” (Hill 1992, 3). Bureaucracy is a political institution
that exercises public authority. Governmental bureaucracy is used as a collective term for
bureaucrats, which broadly include civil servants and public employees in various branches of
public administration and at different levels of government.
Moreover, importantly, bureaucracy is different from concepts of the state and
governance. It is an important element of the state, but the two terms are not interchangeable.
Bureaucracy is an administrative apparatus of the state, which provides capacity for the state to
“penetrate society, regulate social relationship, extract resources, and appropriate or use
resources in determined ways” (Migdal 1988, 4). Bureaucracy also is different from governance,
although it plays an important role in governance. Whereas governance is a broader topic

9

emphasizing the networks between various actors involved in the system, bureaucracy is the
“instrument through which all governments exercise their authority” (Suleiman 2003, 7) or the
“institutional arrangements used for implementation in a governance system” (Meier and
O’Toole 2006, 14).

10

CHAPTER 2
TRAITS OF BUREAUCRACIES IN DEMOCRATIC SOCIETIES

What are the criteria for comparing and evaluating bureaucracies? Specifying key
bureaucratic attributes is the first step toward better understanding and comparing bureaucracies
across nations. Previous literature on comparative bureaucracies has yielded no consensus on
which bureaucratic traits are important and required in democratic societies. This chapter
reviews these previous studies and highlights four traits—political independence,
representativeness, impartiality, and competency—as being the most important. I argue that this
set of key traits, rather than any single trait, need to be considered jointly in order for researchers
to make systematic and meaningful cross-national comparisons of bureaucracies, as the traits are
inter-related. Additionally, this chapter discusses existing cross-national indicators of
bureaucracies. The important limitations of prior research identified in this chapter further speak
to the need for creating an alternative measure of bureaucracies.

2.1 Bureaucratic Traits Required in Democratic Societies
2.1.1 Overview

What are the key bureaucratic traits considered to be important, or desirable, in
democratic societies? Normative democratic theories do not provide specific guidelines about the
role of a public bureaucracy in democracies (Olsen 2010). But, the relationship between
bureaucracies and democracies is often described as a dilemma or a paradox (Etzioni-Halevy
1983; Meier and O’Toole 2006; Pierre and Peters 2000; Thomson 1983). That is, a strong and

11

competent bureaucracy is necessary to allocate resources and deliver public services, but such a
bureaucracy should not be too strong as to threaten democratic values and democratic
development. Balancing the two traits is difficult, and often seems impossible. As a result, prior
studies have not reached a consensus on what traits a bureaucracy in democratic societies should
possess. Indeed, “[b]ureaucracy is not an unambiguous complement to democracy” (Suleiman
2003, 33). Moreover, due in part to this dilemma faced by democratic governments, views on the
proper role of bureaucracies are diverse: some view it as a necessary element and others assume
it as inherently ill-suited to democracy. And, these contrasting views have led researchers in
different traditions to identify different bureaucratic attributes as important.
On the one hand, bureaucracies are understood as essential institutions for democratic
government that provide information and expertise for policy making. Thus, capabilities of
developing and delivering policies are required for bureaucracies. From this perspective,
professional competence and political insulation of bureaucracies are stressed, as Etzioni-Halevy
(1983, 91-92) state that “only a full-fledged, politically independent bureaucracy can safeguard
full-fledged democratic procedures” because the democratic state must have “an organization
that will not only allocate the resources but will do so by non-partisan criteria.”
On the other hand, there are concerns that strong bureaucracies may serve as tools for
state repression and/or can exempt themselves from the control of elected politicians (EtzioniHalevy 1983; Kohli 2004). Moreover, as the role of bureaucracies increases, it is worried that an
independent and powerful bureaucracy could abuse its discretionary power and that its
hierarchical structure and rigid operating procedures make a bureaucracy undemocratic. From
this view, bureaucracies are considered as a potential challenge to democracy (Meier and
O’Toole 2006; Thomson 1983). And, various ways to reconcile the tension between

12

bureaucracies and democracy have been suggested. Some scholars emphasize ‘internal’
bureaucratic traits, such as professionalism (Friedrich 1940; Miller 2000) and impartiality and
fairness (Rothstein and Teorell 2008a), while others argue for ‘external’ mechanisms to control
bureaucratic behaviors, including politicization.
Therefore, I focus in this project on the bureaucratic traits that are suggested most
frequently as important and desirable in previous studies—that is, political independence,
representativeness, impartiality, and competency. 3 In the next section, I provide detailed
discussion of the importance of each trait separately, following previous studies. Then, because
my hypothesis underscores the interrelationships of these traits, I examine some possible
relationships between them.

3

Some may argue that there are other traits emphasized as a way to reconcile the tension
between democracies and bureaucracies. For example, as bureaucracies are attacked for their
unresponsiveness and inefficiency, many reforms in the public sector, including downsizing,
privatization and emulating a private sector’s managerial practice, were proposed under the name
of the New Public Management (NPM) movement. Under this influence, one may argue that
“business-likeness” or “managerialism” is an important trait of bureaucracies. However, I do not
include these ideas here because, first, they are based on a different assumption about the role of
the state bureaucracy in democratic societies. The proponents of business-like bureaucracy
assume that a bureaucracy has the same goals and motivations as private sector organizations and
that citizens are akin to customers. It is also assumed that emulating business organizations will
make governmental bureaucracies compatible with democracy. However, these assumptions are
criticized: citizens are not only customers, but also clients who want professional services from
the government bureaucracy (Mintzberg 1996; Suleiman 2003). Also, scholars worry that the
emphasis on managerial skills and the purchaser-provider relationship can demoralize public
bureaucrats who are committed to the public interest (Campbell 2004; Suleiman 2003). Second, I
think that it will be more reasonable to discuss how differently nations have responded to those
reform ideas, rather than examining how differently they are presented in nations’ bureaucracies
as key traits.
13

2.1.2 Political Independence of Bureaucracy

One of the important attributes of bureaucracies is an independence from political
influence. Since the late nineteenth century, there has been an effort to eliminate political
patronage in hiring and firing public employees. Public bureaucracies in Western European
countries, for example, were traditionally designed to “reduce … the risk of too much political
influence, corruption, misconduct, the exercise of private interests, and instability of government
(Demmke and Moilanen 2010, 25).” In the United States, too, civil service reform was aimed at
developing a merit system based on three fundamental principles: “competitive examinations for
entrance into the public service, … relative security of tenure for [those] employees [and] the
[political] neutrality of the civil service” (Van Riper 1958, 98-100).
In democratic societies, it is important to allocate resources and deliver public services
without bias and favoritism; for this, bureaucracy’s insulation from political pressure is required
(Etzioni-Halevy 1983). When bureaucracies fall under political influence—for example, if their
employment is totally dependent upon the whims of elected officials—it is possible that the
provision of public goods is biased. Incumbents could easily direct bureaucrats to allocate
resources in favor of a particular group in exchanging for past or future support from the group.
This possibility opens the door to widespread corruption and bribery. Moreover, if elected
politicians can freely recruit and dismiss government bureaucrats, then it is possible that public
sector jobs will be offered as a reward for political supporters. Thus, bureaucracies that are not
independent from political influence are likely to lead to unequal allocation of, and unfair access
to, state resources and public services.

14

In addition, under the spoils system—in which bureaucrats are replaced whenever the
incumbent party changes—governmental bureaucracies become ineffective and inefficient. If
there is a significant turnover at each election, new personnel need to be professionalized too
often, reducing bureaucratic effectiveness and morale. On the other hand, when political
independence is guaranteed, it contributes to continuity in public administration and the
accumulation of professional experience for individual bureaucrats. Also, as decision-makings
are not influenced by political pressure, administration of policies will be free from drastic
changes.
Moreover, bureaucrats who are independent from political influence can be a signal of
politicians’ credible commitment to pre-electoral promises (Levi and Sherman 1997). Knott and
Miller (2006, 2008) and Miller (2000, 2005) argue that hiring a “trustee-type” bureaucracy—
whose key characteristic is political independence—indicates politicians’ attempt to commit
themselves to their pre-electoral promises.4 This outcome occurs because a trustee-type
bureaucracy is supposed to enforce policies as previously promised even if political masters’
own interests change after an election. However, if politicians hire an “agent-type” bureaucracy,
which is not politically independent, but rather responsive to political masters’ short-term
interests, citizens’ perception of elected officials’ credibility may be destroyed.

4

They apply Thomas Schelling’s idea of credible commitment to develop their Trustee theory.
Schelling-style delegation can be illustrated by the case of a wealthy mother who hires a trustee
in order to protect her child from a kidnapper (Knott and Miller 2008). If the mother delegates
her fortune to a trustee, then he will be concerned only with her long-term interest, the shrewd
management of her wealth. If she delegates to an agent, however, the agent will respond to her
short-term interest, which of course is paying a ransom to the kidnapper in the event her child is
taken. Therefore, the kidnapper will not attempt to kidnap her son if, and only if, the mother
hires a trustee, as hiring a trustee signals her credible commitment not to pay a ransom in the
event of a kidnapping.
15

The emphasis since the late 1970s on politicization and political control of bureaucracies
stands in stark contrasts to an earlier focus on the establishment of political independence. 5 This
more recent emphasis has led to an increasing number of political appointees in the public sector
(Peters and Pierre 2004). Politicization efforts are justified as a way of preventing arbitrary
bureaucratic behaviors and ensuring political responsiveness and democratic accountability (Moe
1985; Peters and Pierre 2004). However, as Suleiman (2003) states, politicization was not
previously proposed as a way to improve bureaucracies. Instead, the need for political control
has been stressed only in response to observations of the increased number of political
appointees. In other words, the purported merits of politicization are used to justify the reality.
Moreover, the effectiveness of politicization remains in question.6 Contrary to the expectation of
politicization advocates, the consequences of politicization are often viewed negatively (Peters
and Pierre 2004). Scholars argue that increasing politicization may harm the bureaucrats’
expertise (Lewis 2008) and destroy their morale (Plowden 1984; Suleiman 2003). Instead, the

5

Politicization can be practiced in various ways, but “at the most basic level” it can be defined as
“the substitution of political criteria for merit-based criteria in the selection, retention, promotion,
rewards, and disciplining of members of the public service” (Peters and Pierre 2004, 2).
6
Previous studies have surveyed the practice of political appointments in a single country (e.g.,
Page and Wright 2007; Peters and Pierre 2004), but there is no empirical study that
systematically examines the effect of increasing political control over bureaucracies, or,
importantly, its consequences for citizens. One reason for this shortcoming is that these studies
are based on a principal-agent theory and its assumptions (Eisenhardt 1989; Weingast 1984) and
focus only on the relationship between bureaucracies and politicians. These studies assume that
bureaucrats are self-interest maximizers and the legitimacy of their behaviors is “automatically
suspect,” whereas their principals—elected politicians—provide an accurate representation of the
public’s preferences and their decisions are “necessarily legitimate” (Meier and O’Toole 2006,
12). However, other scholars argue that a bureaucracy’s accountability to politicians does not
necessarily guarantee good outcomes in the eyes of the public. Studies also suggest that
politicians do not always represent the public perfectly; a moral hazard problem exists in their
relationships with citizens (Knott and Miller 2008; Miller 2000, 2005; Powell 2004). Their
assumptions about bureaucrats also are empirically challenged: for example, some evidence
shows that bureaucrats tend to be public-minded rather than self-interested (e.g., Brewer 2003;
Campbell 2004; Suleiman 2003).
16

empirical studies provide some supportive evidence for the merits of politically independent
bureaucracies. They find its role in promoting economic growth (Keefer and Knack 1997; Evans
and Rauch 1999; Rauch and Evans 2000), in consolidating new, transitional democratic states
(Kohli 2004; Weimer 2005) and in curbing corruption (Dahlstrӧm, Lapuente, and Teorell 2012).7
Therefore, I specify in this study political independence, and not politicization, as one of
the essential traits of bureaucracies. A politically independent bureaucracy is defined as being
insulated from political pressures in the decision-making process, as well as in the recruitment
and dismissal of bureaucrats. Independent bureaucrats are able to provide policymakers with
objective advice and implement public policies, without favoring a particular political ideology.
It is important to note that a bureaucracy free from political pressure does not imply the total
separation between politics and administration, the so-called “politics-administration dichotomy”
(Hill 1992). That is, the trait does not indicate that bureaucrats do not make value-laden
decisions or that they implement policies without being personally involved in policymaking
process. It also does not necessarily indicate that bureaucrats’ political activities are completely
prohibited.8

7

Note that several empirical studies show that the political independence of bureaucracies is
highly correlated with a meritocratic recruitment. And, Rauch and Evans (2000), in their study of
less developed countries, find some support for the relationship. Dahlstrӧm, Lapuente, and
Teorell (2012) find similar results in their study of fifty-two, developed and developing countries.
8
A similar argument is made elsewhere. For example, Asmern and Reis (1996, 8-9: quoted from
Svara (2001)) note that “[N]eutrality does not mean that top-level civil servants cannot or should
not be involved in the articulation of public policy. Indeed, senior officials are professionally and
morally obliged to provide their political leaders with the best policy alternatives based on sound
argument. The expectation that they will render these services from a non-partisan position is the
crux of the matter.”
17

2.1.3 Representativeness of Bureaucracy

Descriptive representation in the bureaucracy is argued to reconcile the tension between
bureaucracies and democracy (Meier and O’Toole 2006; Selden, Brudney, and Kellough 1998).
When an “upper-class-dominated bureaucracy” was replaced with “performance-oriented
recruitment” in Britain and France in the eighteenth and nineteenth centuries, for example, the
expectation was developed that greater representation of the working class in the administrative
apparatus of the state makes civil servants more responsible (Subramaniam 1967, 1011). In the
United States, the idea of recruiting ordinary citizens was used in the nineteenth century as a
method for ensuring that the a merit-based civil service system would be truly democratic and
fair (Schultz and Maranto 1998; Van Riper 1958).
A representative bureaucracy is argued to reconcile tensions with democracy and
promote democratic values for several reasons. First, it is claimed to reduce arbitrariness in
administrative organizations and to prevent them from violating the norms of democratic
governance (Keiser et al. 2002; Meier and Nigro 1976; Meier and O’Toole 2006; Mosher 1982;
Selden 1997). That is, descriptive representation can prevent bureaucratic abuse of discretion
because a civil servant who shares the same demographic characteristics of the population—
regarding gender, race, religion, and class—will share citizens’ values, which will constrain
bureaucrats’ behavior. Second, a representative bureaucracy is believed to increase social
equality. Recruiting bureaucrats that represent the social and economic structure of society has
inherent value: it fosters equal access to bureaucratic positions for all citizens. It also has a
symbolic meaning by contributing to the elimination of societal prejudices and promoting

18

economic advancement of minority groups (Peters 2001; Rosenbloom 1974), as the interests of
diverse groups will be represented in the policy formulation and implementation process.
Prior studies of representative bureaucracies examine the effect of ‘passive’
representation—that is, whether ‘passive’ representation is translated into ‘active’
representation—in the context of the United States.9 They explore racial or gender compositions
of bureaucratic organizations and the effect of representativeness on outcomes, especially in
education (Keiser et al. 2002; Meier and O’Toole 2006; Meier, Wrinkle, and Polinard 1999; Pitts
2005), housing loan decisions (Selden, Brudney, and Kellough 1998; Sowa and Selden 2003)
and child support enforcement (Wilkins and Keiser 2006). The findings generally indicate that
minority and female representation in bureaucracies produces policy outcomes representing
minority and female interests, respectively. It is assumed that greater benefits distributed to
minority groups contribute to economic equity, implying that a representative bureaucracy is
compatible with democratic values (Keiser et al. 2002; Meier, Wrinkle, and Polinard 1999).
The theory is criticized on grounds that the practice of descriptive representation may
cause “partiality” (Lim 2006; Subramaniam 1967), or that the benefits for minority groups are
achieved at the expense of non-minority groups. If these criticisms are true, then a representative
bureaucracy may not be compatible with democratic values; however, other evidence exist
refuting these critics. Meier et al. (1999) find that nonminority students, as well as minority
students, perform better in the presence of a representative bureaucracy. Moreover, Nicholson-

9

Note that earlier studies focus on whether and to what extent a national bureaucracy is
(demographically) representative, with a normative assumption about a representative
bureaucracy. Comparisons across a few number of countries are discussed in Subramaniam
(1967) and Aberbach, Putnam and Rockman (1981, Chapter 3), for example. Also, Meier (1975)
shows that U.S. Civil Servants better reflect the social characteristics of the American public, in
their education and occupation, compared to other countries, but this descriptive
representativeness decreases in higher positions in the U.S. Civil Service.
19

Crotty, Grissom, and Nicholson-Crotty (2011) show that even in the case of the minority group
benefiting at the expense of a nonminority group, the favoritism happens only until the program
benefits are distributed equally across both groups. These studies provide evidence to support the
idea that a representative bureaucracy is compatible with democratic values.
It is also worth noting that managing diversity in the public sector workforce and
providing equal opportunities are important aspects of bureaucracies in developed nations (Auer,
Demmke, and Polet 1996; OECD 2009a). In particular, many countries have made efforts to
increase women’s participation in the public sector. The percentage of women in the public
sector workforce varies considerably across nations and across the level of positions within a
country (OECD 2009a). Descriptive representation based on religion or race and ethnicity are
also important, but they are very difficult to compare cross-nationally because of the degree of
heterogeneity in coding and reporting these statistics. The percentage of female representation in
the public sector workforce can be much more easily compared across countries. Therefore, this
dissertation focuses on gender representativeness in the public bureaucracy as one of the key
bureaucratic traits in democratic societies.

2.1.4 Impartiality of Bureaucracy

Procedural fairness or bureaucratic impartiality also has been argued as an important
element in democratic societies. Levi (1998) and Levi and Sherman (1997), for example,
maintain that voluntary public compliance is achieved through an impartial and professional
bureaucracy that implements public policies fairly and predictably. Studies stress that
impartiality is an especially important principle for “output” institutions, including bureaucracies

20

(Rohrschneider 2005; Rothstein and Teorell 2008a). Rohrschneider (2005, 853) argues that
impartial and fair implementation by output institutions of a regime is perceived by citizens as
“the capacity of the entire regime to account for their interests” and, thus, is relevant to citizens’
perceptions of representation.
Similarly, some studies emphasizing the role that “quality of government” plays in
societies, especially by scholars at the Quality of Government (QoG) Institute at the University
of Gothenburg, argue that impartiality is a basic, essential principle for government institutions
that exercise public authority (Holmberg and Rothstein 2012; Rothstein and Teorell 2008a;
Teorell 2009).10 Rothstein and Teorell (2008a, 170) define bureaucrats as being impartial when
they do “not take into consideration anything about the citizen/case that is not beforehand
stipulated in the policy or the law” and are not influenced by their own interests in exercising
authority. There are two things to stress in this conceptualization. First, it is different from “an
old-style Weberian rigid rule following, personal detachment, or the lack of creativity and
flexibility by the people working in the public sector” (178). That is, policies are not designed
with detailed rules that bureaucrats follow, but rather allow “rooms for flexibility and
professional judgment” where impartiality is the basic bureaucratic norm (Rothstein and Teorell
2008b, 203). Second, impartiality is emphasized here as a procedural norm. Thus, it is relevant to
how public authority is exercised rather than the contents of policies.11

10

Although their discussion focuses on governance or the “quality of government,” which is
broader than the bureaucracy, the core of the output institution—the institution that exercises
public authority—is bureaucracies and civil servants. Therefore, their discussion of “quality of
government as impartiality” implies that impartiality is one of the key traits of bureaucracies.
11
For example, Rothstein and Teorell (2008a, 170) state, “the enactment of [social policies that
provide supports for the poor families with children] would not break the principle of
impartiality, while denying such allowances for families from to a certain ethnic groups or
parents with a certain sexual orientation when implementing the policy would.”
21

The importance of procedural fairness and bureaucratic impartiality has been examined
cross-nationally. For example, Rohrschneider (2005) uses the procedural quality of bureaucracy
to explain citizens’ perceptions of the representation of parliaments and governments, and he
finds a positive relationship between the two in advanced democracies. With more sophisticated
indicator of impartiality, using the QoG Expert Survey, 12 Teorell (2009) examines several
theoretical arguments regarding the impact of impartiality on a variety of governmental
outcomes. He finds positive associations between impartiality and economic growth, institutional
trust and citizens’ personal happiness.
At the individual-level, the impact of perceptions of procedural fairness on citizens’
attitudes toward government is investigated in prior studies. Levi and Sacks (2009), in their study
of sub-Saharan African countries, find a positive association between citizens’ perceptions of
procedural fairness and their willingness to comply with governments’ tax decisions. In the
context of the United States, Hibbing and Theiss-Morse (2001) argue that procedural fairness
plays an important role in shaping citizens’ orientation toward government; Tyler (1990)
discusses its relationship with law compliance. Moreover, Kumlin and Rothstein (2005) argue
that perceptions of fairness have an important influence on building social capital in their
analysis of Swedish survey data.
One of the critiques of impartiality is that it is impractical, and undesirable, to specify all
circumstances of policies in advance (Wilson 2008). However, as noted earlier, impartiality is
different from mere rule-following or the formalistic and impersonal execution of assigned tasks;
it indicates equal respect and concerns for participants in the policy process (Rothstein and
12

The QoG Expert Survey is web-based survey answered by public administration scholars. The
data from the QoG Expert Survey will be used for the empirical analysis in this dissertation and
more information about the survey data, including the measure of impartiality, will be discussed
in Chapter 3.
22

Teorell 2008a, 2008b). Thus, impartiality does not require very detailed policies and rules for all
situations. Another critique is that impartiality may be a necessary but not a sufficient condition
for bureaucracies (Longo 2008; Stensӧta 2012). I will address this critique later, as I agree with it.
And, indeed, impartiality will be considered as one of the traits of a bureaucracy in democratic
societies, rather than the only one.
An impartial bureaucracy is different from “political impartiality” (Christoph 1957),
which indicates prohibiting bureaucrats’ political activity. It is worth mentioning that political
independence and impartiality can be related, but they are distinct traits. Political independence
may be required for impartiality because non-partisan decisions are difficult to make if
bureaucrats are under political pressures. But, insulation from political influence does not
guarantee impartiality because such bureaucrats may behave arbitrarily and favor a certain
interests over others. Another distinction is that the relationship between politicians and
bureaucrats is the key concern in the discussion of political independence, whereas it is the
relationship between bureaucrats and citizens for impartiality.
Impartiality can be implanted in bureaucracies through professionalism, a merit-based
recruitment and a security of tenure (Rothstein and Teorell 2008a). If bureaucrats are employed
based on political connections, instead of skills and merit, then it is difficult to expect impartial
bureaucratic behavior. Moreover, impartiality will be assured when it is ingrained into the ethos
of public service (Rothstein and Teorell 2008a).

23

2.1.5 Competency of Bureaucracy

Competency is another key bureaucratic trait because it enables democratic governments
to work effectively in developing and implementing public policies. For bureaucracies to
produce and deliver public goods and services, a skillful and competent workforce is required
(Etzioni-Halevy 1983; Peters 2001). Also, when discretion is authorized, bureaucrats’ decision
making is expected to be based on their expert knowledge and professional standards. Although
studies often do not explicitly discuss competency, it is taken for granted as an important
characteristic of bureaucracies.13 It would be of no use for democratic government if
bureaucracies are incompetent in the first place (Aberbach and Rockman 2000; Heclo 1977).
There has been a concern that bureaucracies staffed with experts can be too strong to be
under popular control and, thus, are incompatible with democratic societies. That is,
‘professionals’ or ‘experts’ are difficult to control because they may believe they know what is
best (Kearney and Sinha 1988). If an elite group of people who attain exceptionally high
education and professional training work in the governmental bureaucracy, they may not listen to
citizens’ opinions. Thus, there is a possibility that such bureaucrats abuse their authority.
However, Kearney and Sinha (1988) maintain that competent bureaucrats do not necessarily

13

Basically each country wants to attract and retain intellectually competent and qualified
personnel, although the methods of recruitment can vary across nations. Some nations, such as
Britain, advocate generalists and select bureaucrats based on their intellectual abilities, whereas
other nations, such as the United States, emphasize specialist expertise and training (Peters 2001).
Although different types of expertise and training are sought, the recruitment of bureaucrats in
most of nations basically requires post-secondary education in order to select skillful and
competent bureaucrats. Bonuses and higher salaries are also provided for better performance to
keep competent bureaucrats (Peters 2001).
24

threaten democratic government, especially when they are professional. That is, professionalism
can provide its own constraints on bureaucrats (Knott and Miller 2008).
The importance of competency and expertise is implicitly suggested in previous studies.
For example, critiques of politicization disclose its importance. Politicization arguments
overlook the fact that political control is valuable for democratic government only if bureaucrats
are competent and professional. In practice, tradeoffs between politicization and competency
have been observed, which the original argument of politicization does not consider. That is,
politicization increases the rate of executive turnover, reduces the incentives for career
professionals to develop expertise and also decreases the attractiveness of a public sector career
for qualified applicants (Lewis 2008; Suleiman 2003). Furthermore, when there is a small pool of
qualified people, it becomes difficult to find a person who has both political loyalty and
competency and expertise (Peters and Pierre 2004). These unexpected consequences of
politicization reveal the problems governments may face when bureaucratic competency is not
secured.

2.2 Relationships between Bureaucratic Traits

The four traits outlined in the previous section are analyzed separately in previous studies.
However, in order to better understand bureaucracies, we should consider all four traits together.
For example, a politically independent but totally incompetent or partial bureaucracy is
undesirable; and, a representative but partial bureaucracy is not the one we expect. It is critical to
examine how these traits are represented in a bureaucracy simultaneously, because they are not
mutually exclusive. Let us look at some of the ways in which these traits might be related.

25

The possible relationships are complex, and there is no one, predetermined relationship
that I would expect to uncover. Some traits are closely related, as illustrated earlier in the
discussion of political independence and impartiality. Others are potentially in conflict to each
other. For example, let us think about representativeness (here, gender representativeness) and
impartiality. On the one hand, the representative bureaucracy theory posits that bureaucrats will
share the values with the sub-population with whom they share demographic origins. If this
shared value is different than impartiality, then there is a possibility that the two traits will be at
odds. On the other hand, the potential tension between them will be lessened if females’ values
are similar to impartiality. For instance, many studies examining behavioral differences across
genders have argued that women are more likely to be fair and honest, and less likely to be
individualistic and opportunistic, than men (Dollar, Fisman, and Gatti 2001). If this theory holds,
then a bureaucracy with more female workers can also be more impartial. 14
For another example, let us think about the relationship between representativeness and
competency. Given that bureaucracies in many European countries are male-dominated (Auer,
Demmke, and Polet 1996), it is expected that a strict application of a formal entrance exam and
career-based promotions may be a barrier to establishing a representative bureaucracy. Many
nations have introduced various measures—including Affirmative Action for Women and gender
quotas—designed to increase the number of females in the public sector workforce (Auer,
Demmke, and Polet 1996). Thus, it is possible that these means to achieve greater

14

Similarly, studies show that a representative bureaucracy with respect to race is constrained by
other traits such as professional norms and distributional equity (Nicholson-Crotty, Grissom and
Nicholson-Crotty 2011; Watkins-Hayes 2011) and, thus, does not necessarily conflict with
impartiality.
26

representativeness lead to a loosening of the requirements in personnel management systems and
less emphasis on competency.
The relationship between representativeness and competency is not always understood as
conflicting, though. Eliminating prejudices against minority communities through the
establishment of a representative bureaucracy is considered components of (or part of the
broader meaning) of bureaucratic competence (Peters 2001). Further, as women’s educational
opportunities and achievements increase, it may not always be the case that a bureaucracy with
more female workers has less expertise and competency than a male-dominated one
What about the relationship between competency and other traits? It seems that
bureaucratic competency does not always comport with political independence. In order to
attract more skilled and competent personnel, some nations have introduced a position-based
recruitment system, which is the direct opposite to a career-based one, and open the positions
(especially senior positions) to the private sector workers. In a position-based system, the
position can be filled with external recruitment to find the best candidate for that position (Auer,
Demmke, and Polet 1996). And, in many incidences, this decreases the level of political
independence because it could be used as an opportunity to attract loyal partisans to government
bureaucracies (Suleiman 2003). Thus, this may make a bureaucracy vulnerable to political
influence and favoritism (Suleiman 2003). In addition, bureaucratic competency does not always
guarantee impartiality, although the traits are related theoretically. For example, bureaucrats who
are recruited through a competitive exam and have university degrees do not necessarily behave
impartially.
Given the complexity of these possible relationships among traits, which can be
compatible or conflicting, and also can depend upon context, I argue that examining

27

bureaucracies in terms of how they reflect these four key traits simultaneously will provide a
more comprehensive understanding of bureaucracies. How do we measure and compare the
characteristics of bureaucracies we have discussed thus far cross-nationally? The next section
reviews the measure of bureaucracies used in prior research and discusses their limitations.

2.3 Measuring Bureaucratic Traits

Empirical work analyzing bureaucracy as an independent variable and in the context of a
large-N studies usually rely on cross-national indicators of bureaucracies developed by
international organizations or commercial groups.15 Indeed, advancement of international
indicators for the “quality” of public administration, as a part of state capacity, has allowed
researchers to rank bureaucracies across nations and also enabled cross-national studies to take
bureaucracies more seriously (Van de Walle 2006). However, despite the attractiveness of these
cross-national indicators, they are criticized due to some of their important limitations.
Particularly, it is questionable if they appropriately reflect the key traits of bureaucracies in
democratic societies and capture the important cross-national variation of bureaucracies. Let us
briefly review these existing indicators of bureaucracies. 16

15

Exceptions include studies analyzing the “Weberianness Scale” developed by James Rauch
and Peter Evans (Evans and Rauch 1999; Rauch and Evans 2000), which focus on the
bureaucratic structure of economic agencies in the less-developed countries, and the study of
good government (where “good” is defined as “good-for-economic development”) by La Porta
et al.’s (1999). More recently, researchers in the Quality of Government Institute in University of
Gothenburg, Sweden, have studied the dimensions of bureaucracy and focused on impartiality as
a key principle for good government (e.g., Holmberg and Rothstein 2012).
16
Note that although this chapter focuses on two indicators, the general critiques are applied to
other indicators on the quality and performance of bureaucracies. Other measures include the
“Government Efficiency” indicator in the International Institute for Management Development
(IMD)’s World Competitiveness Yearbook and the “Bureaucratic Delays” indicator from the
28

2.3.1 Cross-national Indicators of Bureaucracies

The Government Effectiveness indicator from the World Bank’s Governance Indicators
(WGI) is one of the most popularly used measurement of bureaucracies.17 Public accessibility
and a wide coverage—about 200 countries are covered over the period of 1996 to 2011—make
this indicator attractive to both researchers and practitioners alike. The Government Effectiveness
indicator summarizes and “combine[s] responses on the quality of public service provision, the
quality of the bureaucracy, the competence of civil servants, the independence of the civil service
from political pressures, and the credibility of the government’s commitment to policies”
(Kaufmann, Kraay, and Mastruzzi 2005, 130). It is an estimated value based on an aggregation
of various variables from individual data sources that are relevant to government effectiveness,
and ranges from -2.5 (weak) to 2.5 (strong). About thirty individual indicators are aggregated to
estimate the values of the WGI measures; these individual items are collected from a number of
data sources, from expert evaluations to public opinion surveys (Kaufmann, Kraay, and
Mastruzzi 2005, 2008). The aggregation is conducted using a complex model called an
‘unobserved components model,’ where the estimated value is the weighted average of rescaled
scores for various data sources (Kaufmann, Kraay, and Mastruzzi 2010).
Business Environmental Risk Intelligence’s (BERI) Operation Risk Index. However, those
indicators focus on measuring levels of red tape and corruption, and do not provide information
about how, exactly, government bureaucracies are characterized. Van de Walle (2006) critically
accesses these existing indicators and concludes that their common problems are that they are not
clear about what is actually measured and that they provide partial, and often biased, views of
bureaucracies.
17
The Government Effectiveness indicator, among other indicators of WGI, specifically
measures the “management” dimension, which is relevant to bureaucracies (Pollitt and
Bouckaert 2011; Van de Walle 2006). Thus, I focus on this indicator. But, an index, comprised
of three or five indicators from WGI, is used most often in previous studies. For example, such
an index is used as a measure of “quality of (output) institutions” (Rohrschneider 2005), “general
governance” (Gilley 2006) or “stateness” (Bratton and Chang 2006).
29

The Bureaucracy Quality indicator from the International Country Risk Guide (ICRG),
conducted by Political Risk Services (PRS) Group, is another frequently used indicator. The use
of this indicator is advocated in the economic growth literature as a measure of a state’s capacity
to protect property and contractual rights (e.g., Keefer and Knack 1997; Knack and Keefer 1995).
It is also used in studies that try to capture the general administrative capacity of the state (e.g.,
Bäck and Hadenius 2008; Charron and Lapuente 2010; Dahlstrӧm, Lindvall, and Rothstein 2012;
Rothstein, Samanni, and Teorell 2012). The ICRG’s Bureaucracy Quality indicator is one of the
political risk components that the PRS Group uses to assess the political stability of countries
over time, and is available for the time period 1984 to 2012. This indicator ranges from zero (the
lowest quality) to four (the highest quality) and the points are assigned by PRS editors based on a
series of pre-set questions (PRS). According to their methodology report, Bureaucracy Quality
measures “institutional strength and quality of the civil service, [which is] strength and expertise
to govern without drastic changes in policy or interruptions in government services.” Specifically,
the “quality” of bureaucracy is rated according to an overall evaluation based on a criterion that
combines the autonomy, expertise, political independence, and mechanism for recruitment and
training (PRS). These elements are assumed as key traits that determine the “quality” of
bureaucracy.

2.3.2 Critiques of Cross-national Indicators of Bureaucracies

Despite the popular usage of these indicators, there are some important limitations. First,
the lack of clear conceptualization and operationalization of “government effectiveness” or the
“quality of bureaucracy” is one of the fundamental problems. This issue is more serious in the

30

WGI’s Government Effectiveness indicator (Dahlstrӧm, Lapuente, and Teorell 2010; Kurtz and
Schrank 2007a; Pollitt and Bouckaert 201l; Thomas 2010). The concept of “government
effectiveness” is not developed based on or associated with established theoretical discussions of
bureaucratic quality or effectiveness. Although Kaufmann et al. (2007a, 555) argue that their
concept of governance is closely related to the “work of Douglas North: the norms of limited
government that protect private property from predation by the state,” it is less clear how this is
relevant to their conceptualization of “government effectiveness” (see Kurtz and Schrnak 2007a,
2007b for this critique). In addition, “government effectiveness” is operationalized as “the
competence of the bureaucracy and the quality of public service delivery” (Kaufmann, Kraay,
and Mastruzzi 2005, 5), but it is vague what “competence” and “quality” of bureaucracy/ public
services indicate.
Second, related to the first point, the specific properties of bureaucracies are overlooked
and the overall evaluation of them is focused based on the assumption that all the relevant
characteristics of bureaucracies always go hand in hand. As a result, what these indicators
actually measure is obscured. In the case of the WGI’s, a variety of individual variables are
aggregated to provide an overall rating. But, these individual variables used seem to be related to
a broader concept of “governance” (Langbein and Knack 2010), rather than specific and relevant
characteristics of bureaucracies. Also, it is not explicitly discussed whether the variable tries to
measure particular bureaucratic characteristics or the performance of bureaucracies. For example,
the authors include the quality of general infrastructure and business elites’ ratings of the quality
of bureaucracy, as well as the consistency of policy direction and whether or not bureaucracy
hinders business activity. In addition, the individual items cover not only how the bureaucracy is

31

working in a country, but also how the public feels about it.18 They are too inclusive and it is
unclear why these metrics are chosen in the first place. In spite that there is no coherent logic
tying these various items together, it is assumed that all of these individual variables are
correlated.
The ICRG’s Bureaucracy Quality indicator provides a clearer and narrower
operationalization for the concept than the WGI’s does. But, this indicator also relies on overall
assessment, without providing any theory or logic behind their overall assessment of
“bureaucratic quality.” It simply premises that a “bureaucracy quality” is attained when all the
elements—autonomy, expertise, political independence, and mechanism for recruitment and
training—are sufficiently represented. However, what if, for example, one country with a higher
level of autonomy does not have a comparatively high level of expertise? Is this nation’s
bureaucracy rated higher overall than a nation whose bureaucracy has higher levels of expertise
but lower levels of autonomy? Or does the former nation receive a lower score than the
comparison country? These possible questions are not clearly addressed in their methodology
report. The measure wrongly assume that all four elements of “bureaucratic quality” necessarily
covary, and, does not provide information about how these individual attributes are presented in
bureaucracies.
The third limitation is that there is a little variation within advanced democracies
according to these summary ratings based upon cross-national indicators. Because the WGI’s
indicator is based on an aggregated estimate, the authors recommend considering the “margins of
18

Particularly, this makes it difficult to examine the relationship between the bureaucratic
characteristics and related concepts, using the Government Effectiveness indicator. That is, a
bureaucracy that meets the expectation in democratic societies will contribute to a higher level of
citizen satisfaction with government services. But, this hypothesis cannot be tested using
Government Effectiveness, because both a measure of characteristics of bureaucracies and public
satisfaction with public service delivery are included in this single aggregate indicator.
32

error” or 90 percent confidence intervals when making comparisons (Kaufmann, Kraay, and
Mastruzzi 2010).19 Interpreting their ratings according to this standard implies that bureaucracies
in most advanced industrialized nations are all effective and of high quality. For example, the
score for 2011 OECD member countries ranges from .32 to 2.25. But, the top fifteen countries,
whose scores range from 1.53 to 2.25, are actually not significantly different from each other.20
In turn, it makes difficult to discern any variations in bureaucracies within advanced democratic
countries.
A similar problem exists with the ICRG’s indicator. OECD member nations are scored
between three or four, with more than half of them receiving four points.21 Bureaucracies for all
nations democratized before 1975 were assigned a score of four, except for France, which
receives three points. Therefore, this indicator may be useful in comparing bureaucratic quality
between developed versus developing, or between consolidated versus transitional democratic
countries. However, it has a limited leverage when it compares bureaucracies within the tier of
advanced nations who are members of the OECD.

19

Indeed, comparing the exact values of the Government Effectiveness scores is not
recommended (Thomas 2010). The various individual data sources used to calculate this
aggregate score vary by country and year. Thus the estimated score for each country does not
rely on the same individual variables, even in the same year. Also, for each country, the
individual indicators used are not always the same across time. Although the authors argue that
their estimation techniques take this fact into account, this inconsistency of base data sources can
be problematic given that the choice of those individual variables is not based on a clear
conceptualization. Thus, the comparability of this country score may be put in question.
20
They are: Australia, Austria, Belgium, Canada, Denmark, Finland, Germany, Iceland,
Luxembourg, Netherlands, New Zealand, Norway, Sweden, Switzerland, and United Kingdom.
21
Nineteen countries, out of thirty four, receive 4 points and they are Australia, Austria, Belgium,
Canada, Denmark, Finland, Germany, Iceland, Ireland, Israel, Japan, Luxembourg, Netherlands,
New Zealand, Norway, Sweden, Switzerland, United Kingdom, and United States.
33

2.3.3 A Need for an Alternative Way of Measuring Bureaucracies

The cross-national indicators used in prior research are based on an abstract concept of
the one best way to measure “good bureaucracy.” These indicators fail to adequately capture the
key bureaucratic characteristics proposed in the literature. In assuming that all relevant attributes
are compatible, they do not consider the complex relationships that might exist among the traits.
Moreover, there is little variation within OECD member nations when these indicators are used
to measure bureaucratic characteristics. These limitations speak to a need for an alternative
measurement of bureaucratic traits. In addition, the basic assumption that a single description of
“good bureaucracy” exists seems to be problematic, and it suggests that we need to approach
measuring and comparing bureaucracies in a different way. With the cross-national indicators
reviewed here, bureaucratic characteristics are assumed to be one-dimensional. That is, the “best
bureaucracy” exists at one extreme of the continuum, and bureaucracies in different nations all
pursue the same traits. However, as Andrews (2010) states, “the good governance version of
good or effective government is a hollow one imposing a false one-best-way model (7-8).”
Some comparative public administration researches have argued for cross-national
differences in organizational and administrative cultures, as well as in the politico-administrative
relations. In examination of the differences and similarities between politicians and bureaucrats
(especially higher civil servants) in Britain, Germany, Italy, Sweden, the Netherlands, France,
and the United States, Aberbach, Putnam and Rockman (1981) find that American higher civil
servants are very different from other European counterparts—the former are more politicallyoriented and have more direct contacts with politicians and clienteles than the latter. Whereas
Aberbach et al. (1981) focus on the relationship between bureaucrats and political elites and

34

emphasize how the American case is different, Pierre (1995) examines administrative systems
and shows a greater variation among them than is assumed in one-best-way models. In
comparing bureaucracies in Britain, Germany, Sweden, France, the United States, and Japan,
Pierre (1995, 8) begins with a notion that “[m]ost public administrative systems seem to be
guided either by the Rechtsstaat model or by the Anglo-Saxon notion of the ‘public interest’.”
Based on case studies of each nation, he concludes that public administration in these states may
be categorized differently: they have different types of political and administrative career
patterns and different organizational structure.
Efforts to understand the variation of public bureaucracies are ongoing (Brans 2003); a
recent study further identifies various administrative cultures and traditions. Painter and Peters
(2010) propose that there are nine groups or families of administrative traditions in the world,
including Anglo-American, Napoleonic, Germanic, Scandinavian, Latin American, Postcolonial
South Asian and African, East Asian, Soviet and Islamic. 22 Although some hybrids can be
developed, they argue, administrative cultures are persistent and have “legacy effects.” As a
result, nations emphasize different characteristics of administrative systems and react differently
to the same problems. This implies that a nation may choose to implement different bureaucratic
traits in pursuit of better administrative performance. If this holds true, then it challenges the
assumption that the various characteristics purported to constitute “good bureaucracies” always
go together.

22

But they only illustrate the differences in various administrative traditions. A rigorous,
systematic examination of public administrations in different nations is still lacking.
35

2.4 Conclusion

In this chapter, I have discussed four key bureaucratic traits claimed to be important for
the administrative apparatus of the state in democratic societies. Each of these characteristics is
advocated individually in prior studies as a way to make bureaucracies effective and compatible
with democratic values. In order to better understand and compare bureaucracies across nations,
I argue that we need to consider all four traits—political independence, representativeness,
impartiality, and competency—simultaneously. In the last part of the chapter, the most common
existing indicators of bureaucracies were reviewed and assessed according to whether or not they
provide adequate tools to measure and compare bureaucracies. It seems that their utility is
limited: they do not provide sufficient information about specific attributes of bureaucracies and
do not successfully capture the possible variations in the presence of key bureaucratic traits
across nations. Given the theoretical and methodological limitations of these indicators, there is a
need for a new measure of bureaucracies.
How, then, can we measure bureaucracies according to the degree to which they
simultaneously possess the four traits discussed earlier? Examining the relationships among
these four traits is not straightforward given that these traits are not mutually exclusive, but
rather closely related to each other. In this study, I will adopt an approach that examines whether
and how bureaucracies in different nations possess a set of traits with different weights. This is
the task to which I turn in the subsequent chapter.

36

CHAPTER 3
BUREAUCRATIC PROFILES OF OECD NATIONS: A MDPREF ANALYSIS

This chapter proposes to consider the four bureaucratic traits discussed in Chapter 2 by
examining the relative levels of each trait presented in OECD member countries. A “bureaucratic
profile” for each country is created based upon results from a nonmetric internal analysis of
preference data which is often called as the MDPREF model or the vector model (Carroll 1972).
The results from the MDPREF model will be discussed in terms of both the clusters of traits
underlying bureaucracies and the variations in the configurations of bureaucratic traits across
nations.

3.1 Bureaucratic Profiles

How do we compare bureaucracies in terms of the presence of a set of key bureaucratic
traits? One possible way to answer this question is to focus on the one trait that is the most
important. For example, if a consensus shows that competency is the most fundamental and
essential characteristic of a governmental bureaucracy, then we could compare bureaucracies
across nations according to the degree to which they possess this specific attribute. However,
using only one key trait as a yardstick for comparison and overlooking how other traits are
simultaneously represented will allow for only a partial understanding of what bureaucracies
look like. Thus, instead, I argue that we need to consider several key bureaucratic traits jointly.
As discussed in Chapter 2, the four traits are not exclusive to each other, but rather are
related in various ways. The complex relationships among traits also suggest a possibility that

37

these traits do not always increase or decrease together, even though they are all important
attributes. That is, possessing a high level of one trait does not guarantee exhibiting a high level
of another trait. For example, representativeness in a national bureaucracy does not necessarily
lead to political independence, and vice versa. They are appreciated for different reasons:
representativeness is emphasized as a way to reduce arbitrariness in administration and to
increase responsiveness to the public (e.g., Keiser et al. 2002; Selden, Brudney, and Kellough
1998), whereas political independence is appreciated as a mechanism to deter bureaucrats’
involvement in corruption (e.g., Dahlstrӧm, Lapuente, and Teorell 2012; Rauch and Evans 2000).
In order to study bureaucratic traits that are interrelated but not always consonantly, I
argue for scholars to look at how similarly and/or differently a set of traits are exhibited in each
country. I assume that all bureaucracies possess a common set of key traits, although each
national bureaucracy may present various levels of each trait.23 Imagine that we have data
containing the information about each nation’s scores on a set of traits, where scores reflect the
relative levels of each trait presented in each governmental bureaucracy. Then, within each
country, we can order the traits in terms of their scores on this measure, and this rank order
represents a nation’s “bureaucratic profile.” With the assumption that bureaucracies in all OECD
member nations possess some degrees of the same set of traits, we can then compare this
“bureaucratic profile” across nations. Bureaucratic profiles thus allow us to examine how several
key attributes are collectively and simultaneously presented in governmental bureaucracies
cross-nationally.

23

In other words, there will be a common underlying structure of bureaucratic traits in each
country. But, the configuration of traits is not given as a priori in the following analysis. Instead,
the results from the analysis in this chapter will provide information about their underlying
structure.
38

Three things are noteworthy regarding the concept of bureaucratic profiles. First, each of
bureaucratic traits is exhibited in degrees (e.g., from lower levels to higher levels). Hence, a
nation’s bureaucratic profile is not about which traits are present and which are absent. Rather,
the profile is based on the information about the extent to which each trait is exhibited. Second,
the profile is based on information about relative levels of a trait, compared to other traits within
a bureaucracy, but not necessarily about the absolute level of each trait. In other words, a
bureaucratic profile identifies which trait is exhibited in the least extent among all of the key
traits. But, the measure does not necessarily indicate that this bureaucracy, compared to other
nations’ bureaucracies, is defective or faulty with respect to that trait. It only suggests that this
specific trait is emphasized less than other traits in this nation’s bureaucracy. 24 Third, different
bureaucratic profiles simply suggest differences across nations’ bureaucracies, but do not imply
that one profile is better than another. Thus, instead of considering bureaucratic profiles as
ranking governmental bureaucracies cross-nationally, the profiles should be discussed in terms of
the similarities and differences in the degree to which key traits are presented in each country.
Bureaucratic profiles can be determined by various things, such as administrative legacy
or tradition (Painter and Peters 2010; Pierre 1995), public preference, and configurations of
political institutions.25 And, bureaucratic profiles reflect the relative importance of bureaucratic
traits emphasized in each national bureaucracy. Thus, although the specific bureaucratic profile
24

For example, let say that one trait is presented the least in nation A’s bureaucracy, whereas this
trait is presented the most in another nation B’s bureaucracy. That is, this trait will come in the
first place in B’s bureaucratic profile, while it is in the last place in A’s profile. In this case, it
does not necessarily indicate that the absolute level of this trait in country A is lower than it in
country B. If we have data comparing the level of this trait across national bureaucracies, and
compare it between A and B, then it is possible that the absolute level of this trait exhibited in A
is greater than that in B. But the important focus in the bureaucratic profile is the relative levels
of this trait in each country.
25
This study does not address the discussion regarding the determinants of the bureaucratic
profiles, although it will be an important topic for a future study.
39

present in each country is not exactly a “choice,” the term “nation’s choice” will be used
interchangeably with “bureaucratic profile” throughout this chapter for the convenience.

3.2 Method: A Multidimensional Preference Scaling (MDPREF) Analysis

In order to examine and compare the bureaucratic profiles of OECD member countries,
this chapter conducts a nonmetric internal analysis of preference data, which is often called as a
“multidimensional preference scaling” or MDPREF (Carroll 1972; Davison 1983; Weller and
Romney 1990). MDPREF is a special case of the “unfolding” method or a distance model for
preference data, which contain the proximity relation between “subjects” and “stimuli” (Carroll
1972; Davison 1983). Both subjects and stimuli are geometrically represented in the same spatial
configuration (Weller and Romney 1990, 45). This space where the stimuli and subjects are
represented together is called a ‘joint space’ (Jacoby 1991; Weller and Romney 1990).26
MDPREF is also called the vector model because subjects are represented by vectors or directed
line segments (Carroll 1972). It is one of the models proposed by psychologists and
psychometricians to understand the structure of individuals’ different perceptions of the stimuli,
with an assumption that underlying the stimuli is a set of dimensions common to all subjects
26

The MDPREF model analyzes the original variables of the preference data, and it examines
the structures of both “the row and column variables” together and presents both in the “jointspace” (Weller and Romney 1990, 45). This is a difference between MDPREF and factor
analysis. Factor analysis uses a cross-product matrix—correlation or covariance matrix—to find
a basic structure of either “the row variables or the column variables in relation to the latent
vectors” (Weller and Romney 1990, 27-45). I conduct the MDPREF and not factor analysis for
two main reasons. First, factor analysis focuses on the linear structure of the data. But, I argue
that the traits presented in each national bureaucracy do not always go up and down together—
they are not linearly related. Therefore, if factor analysis is used to capture such complex
relationships between traits, it will produce a less parsimonious scale. Second, and more
importantly, I focus on a “bureaucratic profile” and want to differentiate the configuration of
traits across nations. The MDPREF model fits better for this purpose.
40

(Carroll 1972). This model has been used, for example, to examine the variations in national role
conceptions of twenty-nine foreign policy decision makers (Wish 1980) and to analyze the
structure of individual value choices in the American public (Jacoby 2011a).
The data typically used for this model are preferential choice data which contain two
different sets of objects—subjects (which here are nations27) and stimuli (which here are
bureaucratic traits)—and the cell entries indicate subjects’ preference judgment for the stimuli
(Jacoby 1991; Weller and Romney 1990). 28 But, MDPREF is equally applicable to data in
which each cell entry includes the extent to which a subject (i.e., nation) possesses a stimulus
(i.e., bureaucratic trait), or the level of a trait exhibited in a nation. 29 And, the Eckart-Young
procedure, a strategy called “alternating least-squares, optimal scaling” or ALSOS (Carroll 1972,
Jacoby 1999), is used for the model estimation.
Within a common space, the trait points and the nation vectors are arranged, as much as
possible, in a way that the trait a nation’s bureaucracy exhibits to a greater degree comes closer
to the terminal points of this nation’s vector.30 As a result, the analysis produces two separate

27

Precisely, “subjects” here are national bureaucracies that possess the specified bureaucratic
traits. Since bureaucracies are compared across nations, I will use “nations” or “countries” to
represent national bureaucracies in this chapter.
28
This is a difference from a multidimensional scaling analysis, which usually uses similarities
data and examines the relationship among stimuli. Both similarities data and preferential choice
data contain information about the relationship between points in a pair (i.e., proximity relation).
But similarities data compare pairs of points from the same set, whereas preferential choice data
compare pairs of points from different sets (Jacoby 1991).
29
Indeed, a data theory can facilitate utilization of scaling methods—here, the vector model
(Jacoby 1991). According to a data theory, as Jacoby (1991, 4) states, a researcher observes
information in the real world and these empirical observations are transformed to the data, which
contain only “the information that [researchers] choose to analyze.” As such, I derive
“preferential choice” data from the empirical observations containing information about the
extent to which each country possesses various bureaucratic traits.
30
Specifically, the distance between the terminal point of the nation vector and the trait point’s
perpendicular projection onto that nation’s vector reflects the relative levels of that trait
presented in this nation. The points and vectors are arranged so that the order of trait points’
41

matrices: One for the coordinates of trait points and another for nations’ vector orientations,
which provide information of nations’ locations in relations to trait points (Weller and Romney
1990). Here, the bureaucratic traits are represented in the “center” of the configuration, and
countries are shown with their vector terminal points in the “outside” of the space. The vectors
originate from the centroid, or from the origin of the space; the extended line for the vectors can
also be drawn to facilitate interpretations of bureaucratic profiles.
The dimensionality of this space contains the “important sources of variability underlying
a set of objects” (Jacoby 1991, 28) and is a choice made by the researcher. One can determine
the number of dimensions empirically, after considering a fit measure, such as STRESS formula
two, and also interpretability and parsimoniousness (Davison 1983; Jacoby 1991). The
dimensionality is a minimum number of important sources of variability, and two-dimensional or
three-dimensional spaces are preferred because one can visualize the estimations from the model
(Jacoby 1991).
An illustration of simple hypothetical data will help understand how the trait points and
nation vectors are geometrically represented when MDPRF is used (Jacoby 2011a). Let us
assume that we observe the profiles of scores for three countries (labeled “1”, “2”, and “3”)
across three bureaucratic traits (labeled “A”, “B” and “C”). That is, the order of the relative
levels of traits presented in a nation’s bureaucracy is: A > B > C in county 1, C > A > B in
country 2 and B > C > A in country 3. Part 1 of Figure 3.1 shows how these three profiles are
represented in a two-dimensional joint space, with the three trait points and the three nation
vectors. The distance from the terminal point of a vector to trait points’ perpendicular projections
onto that vector reflects the relative levels of traits exhibited by that country. Specifically, as a

projections onto a nation vector monotonically reflects the order of the relative level of each trait
presented in that nation.
42

Figure 3.1. Hypothetical example of the vector representation
Part 1. Geometric representation of hypothetical data for three nations (labeled “1”, “2” and “3”)
across three bureaucratic traits (labeled “A”, “B” and “C”), where the order of the relative levels
of traits presented in a nation’s bureaucracy is: A > B > C in county 1, C > A > B in country 2
and B > C > A in country 3.

B

Country 1

A
Country 3

C

Country 2

Part 2. Perpendicular projections from the three traits onto the vector of country 1. The distance
between the terminal point of the vector and each projected point (“A1”, “B1” and “C1”) reflects
the order of the relative level of a trait presented in country 1. The smaller distance indicates the
more emphasized trait in country 1.

B

Country 1

A
A1

Country 3
B1
C1
C

Country 2

43

trait point’s projection onto a vector is closer to the terminal point of that nation vector, this trait
is presented more in that nation than other traits that are further from the terminal point of the
nation vector.
Part 2 of Figure 3.1 shows the perpendicular projections from the three points of
bureaucratic traits, A, B and C, onto the vector of country 1 (labeled “A1” “B2” and “C1,”
respectively, and marked with an x). The distance from the vector terminus to each of the
projected points, A1, B1 and C1, indicates the ordering of three traits exhibited in country 1: The
closer the trait is to the vector terminus, the more it is presented in that nation’s bureaucracy. For
this country, the projection from the point of trait A onto the country 1’s vector (i.e., A1) is
nearer to the vector terminus than the other two projected points, B1 and C1. And, C1 is the least
proximal to the terminal point of country 1’s vector. This represents that the country 1 has the
highest score on A, the second highest score on B and the lowest score on C.

3.3 Data and Measurements
3.3.1 Data Sources and Measurements of Key Traits

In order to measure bureaucratic traits, several data sets are used.31 They include the
Quality of Government (QoG) Expert Survey data, national statistics from the OECD and the
International Labour Office (ILO) database, the OECD’s Governance at a Glance 2009 dataset,
and survey data from the International Social Survey Programmes (ISSP).32 Among others, the

31

Because there is no one concrete measure for each bureaucratic trait, I instead employ several
indicators to measure the traits. Specifically, twenty-one variables are used capture bureaucratic
traits for thirty-four total countries
32
All data are publicly available. For access to the data and details on collection methodologies,
see http://www.qog.pol.gu.se/ (Teorell, Dahlström, and Dahlberg 2011); http://stats.oecd.org/;
44

QoG Expert Survey data provide ratings on various specific characteristics of public
administration across countries, which are not available in other data sources. The data are
collected by the QoG institute—using a web-based survey of public administration scholars in
each country. The respondents of the survey were sampled from “a list of persons registered with
four international networks for public administration scholars … and a small snowballing
component” (Dahlstrӧm, Lapuente, and Teorell 2010, 20). On average, twelve experts responded
in each country in consideration. The survey was conducted in 2008-9 and in 2010, and I assume
no significant changes in bureaucratic characteristics since 2004. The survey questionnaires are
designed to ask how experts think about their country’s public sector employees 33 on a scale
ranging from 1 to 7. The mean estimates for a country, provided in the QoG Country-Level
Survey Data, are used. According to Dahlström et al.’s (2010) cross-source validation and
assessment of respondent perception bias, they seem to well represent actual workings in a
certain country.
Other national statistics are obtained through the OECD and the ILO website. Moreover,
the OECD’s Governance at a Glance 2009 dataset provides various indicators of the public
sector for OECD member countries, which were developed based on statistical data collected
through the survey of senior officials in central government personnel department in each

http://laborsta.ilo.org/; http://www.oecd.org/gov/indicatorsofgoodgovernment.htm (OECD
2009b); and http://www.issp.org/data, respectively.
33
To define public sector employees, the survey states the following in the beginning of the
questionnaire: “All questions in this questionnaire pertain to the public sector employees of a
specific country of your choice…When asking about public sector employees in this survey, we
would like you to think about a typical person employed by the public sector in your country,
excluding the military. If you think there are large discrepancies between branches of the public
sector, between the national/federal and subnational/state level, or between the core
bureaucracy and employees working with public service delivery, please try to average them out
before stating your response.” (Dahlstrӧm, Lapuente, and Teorell 2010, 42).
45

country in 2005 (OECD 2009b). In addition, aggregated data from the ISSP survey data from
2000 to 200434 are used to collect the information on education level of bureaucrats.
From these various data sources, twenty-one variables are selected for this study. The
degree to which each nation exhibits a trait is observed across OECD member countries. And,
the observations are recoded so that higher values indicate that a country exhibits that trait to a
greater extent. The exact survey question wordings and further details on the data sources are
described in Appendix A. Let us look at the individual variables.
First, three variables from the QoG Expert Survey are selected to measure political
independence. One variable is used as an overall rating of political independence. The survey
question asks respondents the degree to which public sector employees behave to fulfill the
political ideology of the incumbent party. This variable inversely represents the degree to which
a bureaucracy is independent from politics. 35 Two other variables measure the recruitment
system of senior officials—the extent to which senior officials are recruited based on their career,

34

The ISSP surveys are conducted annually and include a uniform set of background questions.
Trained professional interviewers interview at least 1,000 randomly selected respondents in each
country with the language selected by the respondents. The ISSP surveys are conducted on “a
probability-based, nation-wide sample of adults” (ISSP 2009).
35
There is a possibility of different interpretations for what ‘fulfilling political ideology of an
incumbent party’ means—that is, partisanships or policy directives. But, I believe this variable
captures the partisanship aspect, and comparisons with responses to another question in the same
survey provide some support for my interpretation. The question compared asks the extent to
which “public sector employees strive to implement the policies decided upon by the top
political leadership,” which seems closer to the meaning of ‘following policy directives’ by
bureaucrats. The correlation between the two variables (with the country-level, aggregated data)
is -0.216 and not statistically significant (p-value = .227). Also, a t-test for the mean difference
between responses of these two questions for each nation shows that the mean responses are
significantly different in twenty-three nations (one-tailed test, at the .05 significance level).
Moreover, it is reasonable to expect that in a highly politicized bureaucracy following policy
directives would not be differentiated from fulfilling partisan expectations. And, the eleven
countries where the means are not statistically different include Eastern European and Latin
counties, which are known for a lack of political independence. Therefore, it seems reasonable to
assume that the survey question I used captures the degree of political independence.
46

and not influenced by political partisanship. These variables are included because appointing
senior officials for political affinity reduces political independence. Higher values on these three
variables indicate a greater level of political independence.36
To capture female representativeness in bureaucracies, four variables are selected. The
QoG Expert Survey provides experts’ overall evaluations on women’s representation in the
public sector. In addition, statistics from the OECD Government at a Glance data provide
detailed information about female representativeness. They are the percentage of females in three
different positions in the public sector—in the central government, in senior positions, and in
administrative positions in 2005. Because males have dominated public sectors and females are
more slowly promoted than males, the number of female workers in the public sector is
especially small in senior positions and relatively large at the administrative positions (Auer,
Demmke, and Polet 1996; European Commission 2010; OECD 2009a, 2009b). Therefore,
measuring the proportion of female workers at different positions in the public sector will help
better capture the degree of female representativeness.
To measure bureaucratic impartiality, five variables from the QoG Expert Survey are
used. The five items are those created by Rothstein and Teorell (2008a) and Teorell (2009),
where impartiality is defined as the following: “When implementing laws and policies,
government officials shall not take into consideration anything about the citizen/case that is not
beforehand stipulated in the policy or the law” (Rothstein and Teorell 2008a, 170). The first
question asks experts to rate their bureaucracy in terms of this theoretical definition of
impartiality. The next question asks about bureaucrats’ behavior in distributing money in a
hypothetical case involving a cash transfer program to the “needy poor.” Then, three questions
36

The proportion of political appointees in the bureaucracy can be another indicator, but such a
measure, which is comparable across nations, is not available.
47

ask about the degree to which public employees are not partial and biased in several incidents—
public procurement decision-making, implementation of policies, and granting licenses to
business startups.
In addition, five variables to capture the idea of a merit-based recruitment system and a
career-based system are included to operationalize the mechanisms for political independence
and impartiality. 37 These variables are collected from two data sources. The four variables from
the QoG Expert survey are: The degree to which the public sector employees have a lifelong
tenure, enter through formal exams, are recruited based on merits, and are not hired based on
political connections. In addition to these variables, a rating on a career-based versus positionbased system, which is provided by the OECD Government at a Glance 2009 data, is included. A
career-based system is one in which positions are usually filled within civil service system and
based on the career experience of candidates. The opposite recruitment system is a positionbased system, where external recruitment is exercised because finding candidates who best fit a
particular position is the main focus (Auer, Demmke, and Polet 1996).
Competency would be measured ideally if detailed information about the education and
professional training of public bureaucrats are available. But, such direct indicators are not
available. Therefore, I instead rely on the ISSP survey data to calculate the proportion of public
bureaucrats with a high level of education. 38 To discern a “high level of education,” I decide to
look at the proportion of people in the public sector holding university degrees compared to that
in the general public. Thus, the average ratio of public sector employees with a bachelor’s degree
37

As noted in Chapter 2, although impartiality and political independence are not the same, they
may share some common mechanisms to keep bureaucrats impartial and independent.
38
This education attainment measurement has some limitations but is the only available measure
for OECD countries over time (Kahn 2008). Following Kahn (2008), I assume that the statistics
for the respondents who are employed by the government reflect the statistics for public
bureaucrats in general.
48

or more from 2000 to 2004, compared to that of the general public, is used as a proxy. Further
explanation of this variable is included in Appendix A.
In addition, two more variables are used as proxies for bureaucratic competency—
government employees’ wages and compensation as a percentage of the wages and
compensation in the total economy, respectively. These indicators reflect the proportion of the
total wages and compensation in the economy used to remunerate government employees. For
more detailed descriptions of these two variables, see Appendix A. The data are gathered from
the OECD statistics (i.e., Structural Analysis Database in OECD.stat), and the average values
between 2000 and 2004 are used in this analysis. I assume that higher wages and compensation
will make highly-skilled workers more likely to work in the public bureaucracy. Thus, higher
levels of average wage and compensation are assumed to reflect a higher level of bureaucratic
competency.
Related to competency, I add a variable measuring the size of the government workforce.
If higher wages and compensation attract skilled workers to the public sector, then one
possibility is the government workers who are well-remunerated enjoy a prestigious status,
which should be associated with a lager government labor force. On the contrary, if a nation
pursues a smaller government and fewer public sector employees, then the choice often will be
associated with reducing benefits for government workers. This, in turn, will render public sector
work unattractive for high-skilled workers; this could lead to a loss of competent, qualified
employees (Suleiman 2003). To measure this variable, the number of government employees as
an average percentage of total employment between 2000 and 2004 is used. The data are
collected from ILO statistics.

49

Note that missing data are filled using a single imputation method in order to facilitate
the analysis. The imputation was performed by specifying a multiple regression for each of
twelve variables that have missing values in order to obtain the “best” predicted value for each
missing value. Regression equations producing the largest possible adjusted-R2 are used, where
each variable with missing data is regressed on a subset of available variables.39 Then, the
predicted value from each regression model is imputed for the missing value. When the predicted
value is out of the theoretical range of the variable, the value is recoded. That is, a missing value
is imputed with the theoretical minimum value when the predicted value is smaller than this;
with the theoretical maximum when the predicted value exceeds it.40

3.3.2 Dataset

From the empirical observations described above, I standardize the values for each
variable across countries. These standardized scores are used to gain the information on the
relative level of the specified traits presented in a bureaucracy. The standardized scores provide
us with a dataset including two objects, thirty-four nations and twenty-one bureaucratic traits,
which will be analyzed using a MDPREF model.

39

In the final regression equations I found for twelve variables with missing values, the adjusted
R ranges from .6398 to .9908. The predictor variables are selected from all available variables
from the data sources used, and not limited to the twenty-one variables used in the analysis. This
was done in order to minimize a potential problem that the results may be biased if the imputed
values falsely increase the relationship between variables.
40
For example, a variable of career-based system from the OECD Government at a Glance
theoretically may range from 0 to 1. And, the predicted value of this variable for Israel is -0.017.
Therefore, the value for Israel on this variable is imputed with 0, a theoretical minimum, instead
of -0.017, which is theoretically impossible.
2

50

Table 3.1. Hypothetical Example of the Dataset
Part 1. Cell entries are the ratings for three bureaucratic traits across three hypothetical countries.
Countries are rated on traits and a higher value on a trait indicates that a country exhibits that
trait to a greater extent.
Trait A
Trait B
Trait C
6
60
60
Country 1
2
50
130
Country 2
1
80
90
Country 3
Part 2. The data in Part 1 are standardized. This format of the dataset is used for the analysis.
Trait A
Trait B
Trait C
1.13
-.22
-.95
Country 1
-.38
-.87
1.04
Country 2
-.76
1.09
-.09
Country 3
Part 3. The relative level of traits exhibited in countries, or “preference ordering” information,
can be obtained from the dataset in Part 2.
Trait A
Trait B
Trait C
3
2
1
Country 1
2
1
3
Country 2
1
3
2
Country 3

Part 1 in Table 3.1 shows the hypothetical values for three nations. In the original
observation, each bureaucratic trait is rated across countries; a larger value on a bureaucratic trait
indicates that a country exhibits that trait to a greater extent. Then, the values are standardized, as
shown in Part 2 of Table 3.1. It shows that each nation has a profile of scores on a set of traits.
From the standardized values illustrated in Part 2, we can obtain information about the
order of the level of traits exhibited in each country’s bureaucracy—the data, as shown in Part 3
of Table 3.1. For country 1, for example, trait A receives the largest value, 1.13, and trait C
receives the smallest value, -.95. From this, we can interpret that country 1exhibits trait A to the
greatest degree and trait C to the lowest degree. Likewise, we can interpret that trait C is
exhibited to the greatest extent and trait B to the lowest extent in country 2, while country 3

51

emphasizes trait B the most and trait A the least. The information about the (order of) relative
levels of traits will be analyzed as the data.

3.4 Analysis

A nonmetric internal analysis of preference data, or a nonmetric MDPREF analysis, is
conducted on these data.41 A nonmetric analysis is employed because the level of measurement
for the variables was assumed to be ordinal. And, for the mode of analysis, this chapter employs
an internal analysis, in which a stimulus space is not given as a priori. That is, the data are used
entirely to estimate both dimensions of bureaucratic traits and the location of nations. A twodimensional model is used; the R2 is 0.719, where the R2 is calculated by comparing the input
data and the optimally-scaled predicted values from the model. 42
The analysis produces estimates of the coordinates for trait points and estimates of the
orientations for vectors representing different nations in a two-dimensional joint-space. The
results are interpreted in two ways: Identifying substantively important groupings of trait points
and interpreting the orientations of the nation vectors.43 Section 3.5 discusses the configuration

41

An R-function written by William G. Jacoby, together with R-package, optiscale is used in this
portion of the analysis (Jacoby 2011b).
42
This shows an acceptable level of model fit. The analysis also indicates that a two-dimensional
model is appropriate for the data.
43
Since clustering of the points seems more substantively interesting than the dimensions
themselves in the results, I will focus on interpreting the groupings of the points, as well as the
nation vectors. Nevertheless, to understand the “joint-space” within which points and vectors are
arranged, it is worth exploring the possible meaning of the dimensions. And, it seems that there
are two underling dimensions, a ‘structural-versus-behavioral’ dimension (horizontal dimension)
and a ‘traditional-versus-reformative’ one (vertical dimension). That is, some bureaucratic traits
are related to the system of recruitment and retention, while others are more about bureaucrats’
behaviors and actions in their relations to politicians and the public. And, if nations have
different choices among the traits, there will be nations emphasizing one side over the other. This
52

of the trait points that provides information on the relationship among the twenty-one variables
measuring bureaucratic attributes. The locations of the points and their groupings can be
interpreted as the “similarities” between the variables. This is because variables exhibited
similarly (in terms of their relative levels) across countries will be located closer to each other in
the joint space, whereas variables presented differently across nations will be positioned farther
away from each other. As a consequence, variables used to capture the same trait are expected to
be located near each other. Let us consider the variables designed to measure female
representativeness as an example. I expect that the relative level of female representativeness in
administrative positions will be similar to that in the central government, compared to the level
of other traits presented in a country, if both variables truly capture female representativeness.
The results also will enable a test of my argument that bureaucratic traits are not always
present together with the same degrees. That is, if my expectation holds, several clusters of
variable points will be produced. The clusters will indicate that bureaucracies possess a set of
distinct bureaucratic traits in which their relative importance varies across countries. If, however,
we see only one cluster of variables in the joint space, then my argument is not supported and the
result implies that key bureaucratic traits all go together. If this is the case, then previous
measures assigning a single overall rating to national bureaucracies would be considered
reasonable because a high level of one bureaucratic attribute always leads to that of another trait.

dimension is similar to Painter and Peters’ (2010) distinction between the legalistic and
management administration traditions. In addition, in the late 20 th century various models of
public management reforms have been proposed to transform an “old” to a “new” one, but their
influence has been varying across nations (Pollitt and Bouckaert 2011). And, some bureaucratic
traits considered in the analysis are closer to a “new” bureaucracy, while others are the core
attributes of an “old” one. Reflecting this, another dimension in the space seems to represent a
deviation from, or an adherence to, a traditional, “ideal-type rational/legal bureaucracy”
(Demmke and Moilanen 2010; Gualmini 2008; Pollitt and Bouckaert 2011).
53

After discussing the configuration of the traits, Section 3.6 discusses the orientation of
vectors representing nations’ bureaucracies. Each vector reflects a nation’s bureaucratic profile. I
discuss whether OECD member nations share similar profiles (i.e., similar patterns for the
relative levels of bureaucratic traits) or have distinct profiles. If all nations have similar
bureaucratic profiles, then the nation vectors all will be oriented in a single direction. If there are
various profiles, however, the nation vectors will be spread out.

3.5 Findings: Configuration of the Traits

Let us interpret the configuration of trait points. The estimates of the coordinates for trait
points are represented in a joint-space, as shown in Figure 3.2. The configuration allows us to
understand the similarity and dissimilarity among variables measuring bureaucratic traits. The
twenty-one variable points appear to form five clusters, which indicate five different bureaucratic
traits.44 The variables whose points can be grouped together indicate that these variables measure
the same construct. That is, the ranking of these variables in terms of their relative levels
presented in a bureaucracy are similar cross-nationally. The four key traits I originally
considered as being important to the structure of OECD member countries’ governmental
bureaucracies are found by the results. But, different from my expectations, one more cluster is
found; the results suggest that there are five distinct attributes underlying national bureaucracies
of OECD member nations.
The first grouping of variables can be found in the middle, right-hand side of the figure.
Seven variables are grouped together and labeled with one of bureaucratic traits, “impartiality.”
44

The five groups of variables are also supported by the results from the cluster analysis. See
Figure B.1 in Appendix B.
54

Figure 3.2. The configuration of variable points

female_senior

Female
Representativeness

female_central
female_administration
women_represnt

Impartiality
Competency

fair_licenses
distribute_needy

compensation
fair_implement
impartial

wage
higher_edu

fair_procurement
recruit_merit
recruit_no_polital

size_government

senior_no_political
no_fulfill_ideology
senior_career

Independence
from Politics

career_based
tenure
recruit_exam

Career-based
System

Note: The twenty-one variable points are shown with closed circles and their names. The points
are located in a two-dimensional space based on the estimates from MDPREF. The description
of the variables and the data sources can be found in the text and Appendix A. The circles around
points (in a dotted line) represent my interpretation of five clusters of points—the five traits—
and they are labeled accordingly (in bold).

Recall in Chapter 2 I discussed that an impartial bureaucracy is one that does not take into
account partisan, personal or a particular group’s interests in decision making. The seven
variables include the five variables designed to measure the theoretical construct of impartiality
55

(as Rothstein and Teorell (2008a) suggest), plus two variables capturing merit-based recruitment.
That is, the results show that recruitment based on merits and skills, and not on political
connections, is related to the degree to which bureaucrats are impartial in the policy process.
This finding is reasonable given that impartiality is difficult to preserve if bureaucrats’
employment is determined by politicians. Note that I expected not only merit-based recruitment,
but also formal exams, tenure, and a career-based system to be associated with an impartial
bureaucracy. But, only merit-based recruitment seems to be associated with impartiality. And, as
will be discussed below, other variables intended to capture the common mechanism for
impartiality and political independence form a distinct cluster.
Second, three variables in the bottom, right-hand side of the figure (in the lower-right
area) appear to form a group representing political independence. The proximity of these
variables is as expected. Also, as I argue that impartiality and political independence are distinct
but closely related characteristics, the first cluster (impartiality) and the second cluster
(independence from politics) being located relatively near each other confirms my expectation on
these traits’ relationship. This second cluster provides some supportive evidence that recruiting
and promoting senior officials based on their career reflects less politicization and more effort to
guarantee independence from political influence. Also, it implies that as bureaucracies’
independence from partisan politics is emphasized, senior officials are more likely to be recruited
within in the ranks of the public sector and not based on their partisanship.
The third cluster, in the bottom, left-hand side corner, includes three variables
representing a career-based system. The three variables are the degree to which public sector
employees are recruited through formal exams, promoted based on career, and work with a
secured tenure. It is different from my expectation that variables measuring career-based system

56

form a distinct group, separated from variables capturing political independence or impartiality.
In the sense that they are related to human resource management, the variables in the third
cluster (career-based system) are related to the variables forming the second cluster
(independence from politics). But, the distinction between the two also makes sense: Variables in
the second cluster are especially related to public sector employees’ behavior in their relations to
politics, whereas the variables in the third cluster concern the structural and organizational
institutions involved with recruiting and retaining public sector employees. 45
Fourth, the four variables in the middle, left-hand side of the figure appear to form a
cluster labeled ‘competency,’ as they relate to the retention of talented bureaucrats. The four
variables include compensation, wage, education levels of government workers, and the size of
the government workforce. Higher compensation and wages allow a public bureaucracy to attract
and maintain more competent workers. If the proportion of people with higher education is
greater in governmental bureaucracies compared to that in the public, then it indicates that more
talented people are recruited and working in the public bureaucracy. One may argue that the size
of the governmental workforce is not related to the other three variables within this cluster.
However, I think that this clustering is sensible given that downsizing in the public sector is
often argued to have a demoralizing and de-professionalizing consequence, which may make

45

This is an interesting finding. With the QoG Expert Survey data, Teorell (2009) develops and
measures impartiality, and Dahlström et al. (2010) differentiate “closedness (as opposed to
openness)” and “professionalism (as opposed to politicization)” of bureaucratic structure.
Although they are conceptually distinct, these three have not been clearly differentiated
empirically. When I conduct factor analysis, professionalism and impartiality are not
distinguished. On the other hand, the vector model employed here provides more supportive
evidence for their theoretical distinctions. It shows that impartiality and political independence
(which is similar to Dahlström et al.’s (2010) professionalism) are related to each other, but can
be differentiated.
57

bureaucratic jobs less attractive. Thus, as a country focuses more on attracting and retaining
talented employees, the size of public sector labor force will tend to increase.
Finally, four variables in the upper, left-hand side of the figure can be clustered as female
representativeness, consistent with my expectation. The cluster of these four variables is
meaningful because it captures the proportion of female bureaucrats using various measures. It is
interesting that the four variables are less tightly clustered than one might expect. This reflects
that female representativeness is not the same for all levels of positions in the public sector in all
nations. For example, for many nations, more female workers in lower level positions may not
always guarantee a similar proportion of female workers in more senior positions.

3.6 Findings: Nation Vectors

How do national bureaucracies exhibit these five traits? Do the OECD member nations
share similar profiles or do they have distinct profiles regarding the five bureaucratic traits?
These questions will be addressed by interpreting nation vectors and the relative locations of the
traits in the joint space. The terminal points for the nation vectors are presented in Figure 3.3,
with the variable points in the same space. The vectors are adjusted to a unit length, so that the
vector terminal points fall along a circle (Jacoby 2011a). The variable points are shown with
closed circles and the terminal points of nation vectors are marked with open circles. In order to
foster easier interpretation, I also present the mean points for each of five groups of variable
points—trait points—with open squares and corresponding labels.

58

Figure 3.3. Full model of nations’ vector terminal points and variable points, with five
bureaucratic trait points and the mean direction vector

EE

IS

CL

SK
DK
US
AU
NZ

HU
female_representativeness
SI

UK

PL
CZ
GR
PT

SE
NO
FI
CA
AT
NL
CH

impartiality
competency

MX

independence_from_politics

IT
career_based_system

LU
TR
ES

JP
IL
BE

DE

IE

KR

FR

Note: The nations’ vector terminal points (open circles) and the bureaucratic trait points (solid
circles) are shown in the joint space. The points are estimated from MDPREF. The mean points
for the five groups of variable points are presented with open squares and labeled. The arrow
oriented about 56 degree below the horizontal dimension represents the mean direction vector,
whose length is .079. The thirty-four countries are: AU (Australia), AT (Austria), BE (Belgium),
CA (Canada), CL (Chile), CZ (Czech Republic), DK (Denmark), EE (Estonia), FI (Finland), FR
(France), DE (Germany), GR (Greece), HU (Hungary), IS (Iceland), IE (Ireland), IL (Israel), IT
(Italy), JP (Japan), KR (Korea), LU (Luxembourg), MX (Mexico), NL (Netherlands), NZ (New
Zealand), NO (Norway), PL (Poland), PT (Portugal), SK (Slovakia), SI (Slovenia), ES (Spain),
SE (Sweden), CH (Switzerland), TR (Turkey), UK (United Kingdom) and US (United States).
59

3.6.1 Interpreting Nation Vectors

In order to interpret both the similarities and differences among bureaucratic profiles, we
focus on the angular separation between the nation vectors. In fact, the correlation between the
predicted bureaucratic profiles of any two countries is equal to the cosine of the angle between
the two vectors for those nations. Thus, the smaller is the angle between two vectors, the similar
the nations’ choice of bureaucratic traits, and vice versa. For instance, the angle between the
vector of Australia and that of New Zealand is very small (i.e., 1.24 degrees); this indicates that
the two countries have very similar bureaucratic profiles. Two nations are very different and
have an opposite order of relative trait importance if the angle between the vectors for them is
180 degrees. For example, the angle between the vector for the United States and that for Italy is
almost 180 degrees (precisely, 175.50 degrees). This means that the bureaucratic profiles are
almost opposite in these two nations.
Another way to look at the distribution of nation vectors is by taking their value average
and calculating the mean direction vector. The arrow shown in Figure 3.3 represents the mean
vector of all nation vectors. The length of this mean direction vector, which is called the “mean
resultant length” (Gill and Hangartner 2010), is relatively short in Figure 3.3. The mean resultant
length theoretically ranges from zero to one, where a smaller number indicates greater variation
in the full set of vectors. If there is no variation in vector orientations, then the mean resultant
length will be one (Gill and Hangartner 2010; Jacoby 2011a). Therefore, the short length of the
mean direction vector in Figure 3.3 indicates that bureaucratic profiles are very diverse across
OECD member countries.46

46

The mean resultant length in the result is .079.
60

Overall, the results show that the vectors are spread out around the entire circle. There is
a small concentration of vector terminal points in the middle right quadrant, around “3:00”
position. However, rather than concentrating only on this position, the nation vectors are
dispersed into several different groups. For example, nations are located closely at around the
“2:00” position, “5:00” position and “9:00" position. These groupings speak to the diversity of
bureaucratic profiles. To understand the profiles in detail, the next section interprets nations’
locations in relations to the trait points. To simplify interpretation, I focus on five trait points,
which refer to the means of the variable points within each of five clusters (shown with opensquares in Figure 3.3).

3.6.2 Interpreting the Orientations of the Nation Vectors

The terminal points of nation vectors for the thirty-four OECD member countries and the
mean points for each of the five bureaucratic traits (i.e., the means of the variable points within
each of the five clusters) are presented in Figure 3.4, with open circles and closed circles,
respectively. Recall that bureaucracies in nations whose vectors are located close together should
actually share similarities; countries located farther away from each other exhibit different
bureaucratic profiles. These comparisons are what I focus on in the following discussion. 47

47

The exact predicted profiles are presented and discussed in Chapter 4, where a new measure is
developed based on the results interpreted here.
61

Figure 3.4. Full model, with the vector terminal points and the five trait points

EE

IS

CL

SK
DK
US
AU
NZ

HU
female_representativeness
SI

UK

PL
CZ
GR
PT

SE
NO
FI
CA
AT
NL
CH

impartiality
competency

MX

independence_from_politics

IT
career_based_system

LU
TR
ES

JP
IL
BE

DE

IE

KR

FR

Note: Open circles represent the nations’ vector terminal points. The solid circles indicate the
five traits, which are the mean points for each of the five groups of variable points. The
individual variable points are not shown. All the points are located based on the results from
MDPREF. See Note in Figure 3.3 for the full names of the thirty-four countries.

62

3.6.2.1 Nation Vectors between “1:00” and “4:00”

Nations positioned between “1:00” and “4:00” include Nordic countries and AngloAmerican countries. In these nations, impartiality is exhibited to a greater degree than other traits.
Specifically, in countries positioned around “2:00,” female representativeness is the second most
exhibited characteristic and career-recruitment system is displayed to the least extent. The United
States and Australia are examples of countries fitting this profile. These countries have some
commonalities: For instance, they are advocates for New Public Management (NPM) movements
and also much influenced by the NPM ideas (Halligan 2010; Pollitt and Bouckaert 2011). Thus,
business management skills were introduced and personnel powers were decentralized here. This
could explain why the point of career-based system is the furthest away from the terminal points
of the vectors for these countries. Along with benchmarking business management, politicization
was also emphasized as a way to make bureaucracy more responsive (Pollitt and Bouckaert 2011;
Suleiman 2003). And, this seems to be reflected in the bureaucratic profile, which shows that the
level of political independence presented is less than that of impartiality and female
representativeness in these nations.
In nations located around “3:00” position impartiality is again the trait most exhibited,
but independence from politics is predicted to be second. And, competency is exhibited to the
least extent, relative to other traits. Sweden and Norway are examples of countries fitting this
profile, which again are substantively similar. It is reported that there were some reform attempts
based on the idea of NPM, but dramatic reforms were not observed in Nordic countries

63

compared to Anglo-Australian countries (Pierre 2010; Pollitt and Bouckaert 2011).48 This could
explain why, compared to the United States or Australia, political independence is still relatively
salient (in fact, the second important trait) in these countries. In Northern European countries,
including Sweden, Finland and the Netherlands, the “anti-government theme” (Pollitt and
Bouckaert 2011,166) or the “rhetoric of reforming an old (bad) bureaucracy to a new (good)
bureaucracy” (Demmke and Moilanen 2010, 1) has not been as strong as in Anglo-Australian
countries, and the political control over public management is less emphasized (Pollitt and
Bouckaert 2011). Thus, in nations located around the “3:00” position, it is more likely that career
civil servants and political careers are separated and that a non-partisan culture exists, despite the
increasing number of political appointees (Pollitt and Bouckaert 2011). Plus, most of these
nations’ civil service systems are described as position-based system in which recruitment and
promotions are not based on a specific career (Auer, Demmke, and Polet 1996). This fact is
consistent with the vector model results that the point of career-based system being located
farther away from these nations’ vector terminus.

3.6.2.2 Nation Vectors between“5:00” and “8:00”

Vector terminal points in the bottom of the figure, between the “5:00” and “8:00”
positions, represent very different bureaucratic profiles from those mentioned above. For these
bureaucracies, career-based system is displayed at a higher level than other traits. The nations in
this position include Japan, Germany, France, Belgium, and Spain. These nations’ bureaucracies
48

Compared to other Nordic countries, Denmark is described as being most influenced by the
NPM ideas (Greve 2006; Jensen 1998). And, the vector model results are consistent with this
observation, in that Denmark is located near the Anglo-Australian countries at the “2:00”
position, rather than the “3:00” position with other Nordic countries.
64

are often characterized by legal and formal administrative culture (e.g., Painter and Peters
2010).49 Nations located in this area, however, have different profiles regarding the other four
bureaucratic traits.
The nations whose vector terminal points are around the “5:00” position are often
described as having a career-based system (Auer, Demmke, and Polet 1996; Demmke and
Moilanen 2010), which is reflected in the results. The results show that the bureaucracy’s
independence from politics is highly emphasized, as is career-based system; female
representativeness is the least displayed bureaucratic trait. Nation vectors in this position include
those for Germany and Japan, for example. These nations seem to share some commonalities.
For example, bureaucracies in Korea and Japan are influenced by German-type bureaucracies
(Nakamura 2003; Paik 2003; Painter 2010) and civil service systems in Ireland and Germany are
considered as career-based and closed-systems (Auer, Demmke, and Polet 1996). The German
model of public bureaucracy is understood as a Rechtsstaat-type, where public administrative
systems are based on public law and a very ‘traditional bureaucracy’ (Pierre 1995; Pollitt and
Bouckaert 2011). In addition, the public sector workforce in these nations is dominated by males;
the gap between men and women is still large (Auer, Demmke, and Polet 1996; OECD 2009a). It
is reported that although these countries have de jure equal opportunity, women still enter
bureaucracy at lower rates than men and move up the ranks more slowly (European Commission
2010; UNPAN 2006).

49

Also note that the terminal points of the vectors for Austria, the Netherlands, and Switzerland
are around the “3:00” position, but they are at lower position (thus, closer to the vector terminal
points that are around the “5:00” position). It seems that career-based system is emphasized more
in these nations than in those around the “3:00” position. This reflects that they have Germanic
tradition (Painter and Peters 2010), although these countries have some similarities to Nordic
countries.
65

Nations whose vector terminal points are at the bottom of the figure, the “6:00” position,
are a little different than countries like Germany, although there are many similarities between
them. France and Belgium are the examples of a French-type or a Napoleonic-type bureaucracy
(Ongaro 2008, 2010; Painter and Peters 2010). Similar to nations located at the “5:00” position,
it appears that these countries assign the highest importance to career-based system, while female
representativeness is not highly ranked (Auer, Demmke, and Polet 1996; European Commission
2010; OECD 2009). Moreover, these countries, as well as Germany, are described as being
resistant to reforms based on NPM ideas (Auer, Demmke, and Polet 1996; Pollitt and Bouckaert
2011). The main characteristics of a Napoleonic-type bureaucracy are legalism and formalism
characterized by a competitive exam and centralized state structure (Demmke and Moilanen
2010; Ongaro 2008, 2010; Painter and Peters 2010).
Countries positioned around the lower-left quadrant in the figure, between the “7:00 and
8:00” positions, are Southern European counties, including Greece, Italy, Portugal, and Spain.
Bureaucracies in these countries are characterized with a high degree of legal formalism, similar
to a French-type bureaucracy (Ongaro 2008, 2010). However, different from it, patronage and
politicization are prevalent in these Southern European countries (Painter and Peters 2010).
Indeed, increasing political patronage and the existence of the spoils system are serious problems
facing the public bureaucracies in Italy and Greece (Demmke and Moilanen 2010; Painter and
Peters 2010; Pollitt and Bouckaert 2011). Moreover, difficulties combating these problems have
increased as several administrative reforms, largely influenced by the NPM, were introduced
(Ongaro 2008, 2010). Studies show that such reforms have been more influential in Southern
European counties than in Continental European countries. These characteristics explain that the

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bureaucratic profiles of nations positioned at between “7:00 and 8:00” emphasize both political
independence and impartiality less than other traits.

3.6.2.3 Nation Vectors between “9:00” and “10:00” and between “10:00” to “12:00”

As we move further in a clockwise direction, the nations whose vectors are oriented
between the “9:00” and “10:00” position include mostly Central and Eastern European
countries.50 Bureaucracies in these nations, such as Hungary and Slovenia, share some
similarities. The creation of a professional civil service, and transforming the old, politicized
state administration, are among the important goals (Verheijin and Rabrenovic 2007). Although
the public management systems in these nations were influenced by Continental European
countries, the career-based system is not as strong as in those nations. This is due to the unstable
legal basis for civil service, weak job security and no centralized competitive exams in Central
and Eastern European countries (Demmke and Moilanen 2010; Meyer-Sahling 2010; Verheijin
and Rabrenovic 2007). And, these countries confront a difficult task that they attempt to establish
transparent and open recruitment systems, and at the same time create an impartial system
(Meyer-Sahling 2010; Verheijin and Rabrenovic 2007).51

50

The terminal points of vectors representing Mexico and Chile are also around this position.
The results seem to reflect that Latin American systems are influenced by Spain’s and Portugal’s
system, especially regarding surface level legalism and formalism, but also prevalent
particularism and clientelism in practice (Painter and Peters 2010).
51
Note that these countries usually face double tasks, to develop democracy and to establish a
professional bureaucracy. Different from Western European countries, the state bureaucracies
have not been consolidated before democratic politics was introduced. And, the lack of an
established legal basis for bureaucracies tend to make these countries vulnerable to patronage
politics, especially when the party system is not institutionalized (O’Dwyer 2004).
67

The bureaucratic profiles of these nations reflect these observations. The trait point that is
the farthest from the vector terminus of these countries is impartiality. Also, as severe
politicization is one of their problems, it is reasonable that political independence is less
exhibited than other traits. In addition, their bureaucratic profiles show that competency is
presented to a greater extent than other bureaucratic traits. This suggests a bureaucrat in these
countries may be considered a prestigious occupation, as more highly educated people work for
the government and a greater proportion of workers are in the public sector, which pays well.
Finally, four nations are spread out in the upper-left quadrant to the top of the figure,
between the “11:00” to “12:00” positions. One of them, Estonia is described as having a very
open and highly politicized bureaucracy (Randma 2001). This is reflected in the results that
career-based system and independence from politics are farther away from the terminal point of
the vector representing Estonia than other traits. One interesting common characteristic of these
four countries is that they have a relatively large percentage of women in the senior positions of
the central government (European Commission, 2010). In their bureaucratic profiles, female
representativeness is the trait present to the greatest extent. But, more importantly, the
bureaucratic profiles for these nations show that other traits are exhibited to a lesser degree than
is female representativeness.
Moreover, the vectors for these nations show that career-based system is the least
demonstrated trait. These bureaucratic profiles suggest a possibility that female
representativeness conflicts with career-based system. In other words, they may have been able
to achieve greater female representativeness because they put less emphasis on a career-based
system. A position-based system, which is the opposite of career-based one, could contribute to
employing more females who may face some barriers in the entrance to, and promotions in,

68

bureaucracies. Although less obvious, in nations whose vectors located between “1:00” and
“2:00,” female representativeness also is presented at a higher levels than other traits, except for
impartiality; and career-based system is the least emphasized trait. This result also suggests
possible conflicts between female representativeness and career-based system.

3.6.3 Comparing Specific Bureaucratic Profiles

Beyond general interpretations of the locations of nation vectors in relation to trait points,
let us take a look at specific nation vectors. This example illustrates how a particular nation’s
bureaucratic profile can be interpreted and compared with that of another nation. Figure 3.5
shows the vectors for two countries, the United States and Germany, along with the terminal
points of the nation vectors (with open circles), and the points for five bureaucratic traits (with
closed circles). The vector pointing toward the upper-right represents the United States and the
one directed toward the bottom-right is for Germany. If you draw perpendicular projections from
the five points onto the vector for the United States, for example, impartiality is shown to be the
trait most emphasized, followed by female representativeness, independence from politics, and
competency, with career-based system as the least emphasized bureaucratic attribute. The
bureaucratic profile looks different for Germany. Career-based system receives the greatest level
of importance in Germany, followed by independence from politics, impartiality, and
competency; female representativeness is the least emphasized bureaucratic characteristic.
The nation vectors seem to reflect the American and German bureaucracies relatively
well. For example, in the United States, a merit-based civil service was created by the Pendleton
Act of 1883, but a large number of political appointees continue to be employed compared to

69

Figure 3.5. Full model, with the vectors for the United States and Germany

Note: All the points are located based on the results from MDPREF. Open circles represent the
terminal points of nation vectors and the closed circles indicate the five traits. The vector for the
United States is shown with an arrow pointing toward the upper-right in the joint space. The
vector orienting toward the bottom-right is for Germany. The vectors are originated from the
centroid as shown with solid, directed line segments. The extended lines are drawn for vectors as
shown with dashed lines to foster interpretations of bureaucratic profiles. The angle between the
two vectors is 90.49 degrees. It is estimated that the bureaucratic traits are presented in the
United States in the order of impartiality, female representativeness, independence from politics,
competency, and career-based system; in Germany in the order of career-based system,
independence from politics, impartiality, competency, and female representativeness. See Note in
Figure 3.3 for the full names of the thirty-four countries.
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other industrialized countries to this day (Pierre 1995; Van Slyke and Riccucci 2003). This could
explain why the relative level of political independence presented in the U.S. bureaucracy is in
the third place among five traits. Another characteristic of the U.S. bureaucracy is that
bureaucrats are recruited not only for the entry level positions, but also for top positions (the
Senior Executive Services). Relatedly, career distinctiveness is relatively weak in the United
Sates because workers frequently move between the public and private sector (Peters 2001).
Hence, it is expected that the relative level of career-based system is the lowest among the five
traits in the American bureaucracy. In addition, female representativeness is the second closest
point to the vector terminus; it reflects that the U.S. is one of the countries with a relatively large
percentage of women in the civil service (Peters 2001).
Regarding the German bureaucracy, its vector seems to reflect that there exists a strong
norm that bureaucrats should be neutral and independent from political influence; that there is a
lower degree of movements between politics and public administrative positions; and that
bureaucratic expertise is considered as an important attribute, as opposed to party loyalty (Pierre
1995). Particularly, the historical development of the civil service explains why career-based
system and political independence are the highest valued traits in German bureaucracy. In
Germany, a professional civil service was “institutionalized” in 1794 by the Prussian civil
service code specifying their role as “public (and no longer personal) servants” and additional
features, such as guaranteeing lifelong tenure and requiring university training, were added in the
1820s (Derlien 2003, 103). This Prussian civil service code and the “traditional principles” have
been the basis for the civil service law in the German republics (Derlien 2003). The
constitutional status of civil service provides a “strong safeguard against basis reforms” (Derlien

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2003, 104, italics are original), and thus the structure and privileged status of the German
bureaucracy is hardly changed.
Perhaps a more interesting point is implied when we compare the two nation vectors in
the joint space, which provides information that the bureaucratic profiles for the United States
and Germany are different from each other. The angle between the two vectors is close to 90
degrees (precisely, 90.49 degrees), meaning a close-to-zero correlation between the two profiles.
Given that the German and American models of public administration—the Rechtsstaat model
and the Anglo-Saxon notion of the ‘public interest’ model, respectively, in previous studies (e.g.,
Pierre 1995; Pollitt and Bouckaert 2011)—are often described as very different, no correlation
between the two bureaucratic profiles is entirely expected.
Moreover, as we compare the two bureaucratic profiles, the relationships between some
of the bureaucratic traits provide interesting insights. Recall that in Chapter 2, some complex
relationships between bureaucratic attributes were discussed. The expectations on cross-national
differences in the different potential relationships among these traits were realized empirically in
the vector model. For example, three traits in Germany’s bureaucratic profile—impartiality,
political independence, and career-based system—are located very near to each other. This may
suggest that in Germany, a career-based system is perceived as a trait contributing to, rather than
hindering, impartiality and political independence. And, this result is reasonable given that in
Germany career-based system is advocated for promoting professionalism, which functions as an
internal constraint on bureaucrats’ behaviors (Derlien 2003; Peters 2001). Similarly,
professionalism contributes to impartiality.
The relationship between the three traits looks quite different in the United States. The
relative location of trait points to the vector for the United States shows that impartiality and

72

career-based system are positioned on opposite sides of the joint space, with political
independence in the middle. This suggests that, first, career-based system is not linked to
impartial bureaucracies in the U.S., different from Germany. Instead, as a way of improving
impartiality, the U.S. seems to focus on a position-based system, which has been advocated as a
way to make bureaucracies more “democratic” and to improve public responsiveness (Van Slyke
and Riccucci 2003). Second, the different location of political independence in the U.S. also
implies that it is not perceived as being linked to impartiality, as in Germany. In sum, the results
show variations in perspectives on the consequences of a career-based system and in the
relationship between impartiality and political independence, in the two countries. 52

3.7 Conclusion

In this chapter, the key bureaucratic traits were empirically examined and compared
across OECD member countries. Following discussions in the previous chapter, I proposed to
examine “bureaucratic profiles” which provided an opportunity to systematically examine the

52

This difference may be due to the different historical development of bureaucracy in each
country. For example, Shefter (1977, 423-433) interprets the development of bureaucracy in
Germany as the survival of a “constituency for bureaucratic autonomy,” which emerged in the
pre-democratic era, throughout the regime changes that Germany experienced. That is, patronage
practices and any political interference have been prevented and bureaucratic autonomy has been
protected because a professional bureaucracy—where civil service exams are instituted and a
lifelong tenure is guaranteed—was established and institutionalized in the process of statebuilding, before the creation of a mass electorate and the development of political parties. On the
other hand, the sequence of development was reversed in the United States, as Shefter (1977, 447)
notes: “the creation of a mass electorate in the Jacksonian period preceded by half a century the
emergence of American’s constituency for bureaucratic autonomy during the Progressive era.” A
bureaucracy that institutes formal exams and whose recruitment and dismissal are protected from
outside interference was not established before the political parties and candidates competed with
each other. And, thus, for the bureaucracy, being democratic and not threatening democratic
values is more important (Van Slyke and Riccucci 2003) than being competent and autonomous.
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presence of various important traits simultaneously and cross-nationally. The analysis using the
MDPREF model showed that bureaucracies in OECD nations share a common underlying
structure—characterized by a greater or lesser degrees of political independence,
representativeness, impartiality, competency, and career-based system—but, more importantly,
have very diverse bureaucratic profiles. That is, national bureaucracies present varying degrees
of these five traits; each nation seems to emphasize different bureaucratic traits.
This finding is important because it suggests that previous measures assigning an overall
rating to public bureaucracies, combining all of the important traits, potentially provide a
misleading portrait of the degree to which each trait is presented. Indeed, five clusters of traits,
instead of four I hypothesized, were found in the two-dimensional joint space. It suggests a
complex relationship between impartiality, political independence, merit-based system, and
career-based system. That is, in some nations a career-based system is considered very
differently from other three traits, whereas all of them are treated as compatible traits in other
nations. The complexities of these relationships, and their variation cross-nationally, are
precisely what previous, and more normative, assessments of governmental bureaucracies miss.
The wide variety in bureaucratic profiles is the most interesting finding in this chapter.
There is not a single pattern of relative levels of bureaucratic traits exhibited by all of the OECD
countries. This provides empirical support for the various types of “administrative traditions”
argued by comparative public administration researches (Brans 2003; Painter and Peters 2010;
Pierre 1995). Examining various bureaucratic profiles systematically shows similarities and
differences among national bureaucracies. Moreover, the findings speak to the possibilities, as
Andrews (2010) and Holmberg et al. (2009) posit, that countries have various institutional
configurations of bureaucracy. Do these various bureaucracies lead to different consequences in

74

a society? Particularly, in this dissertation project, I ask whether or not these different
bureaucratic profiles are linked to different public attitudes toward government. This will be
examined empirically in Chapter 4.

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CHAPTER 4
RELATIONSHIP BETWEEN BUREAUCRATIC PROFILES AND POLITICAL
SUPPORT
This chapter examines the linkage between various bureaucratic profiles and citizens’
political support. In order to explore what citizens expect from governmental bureaucracies, I
focus on political support as the dependent variable. If the bureaucratic profiles reflect what traits
citizens want bureaucracies to possess in democratic societies, and if the relative importance of
such traits matches citizens’ expectations, then we should find higher levels of political support
in such nations. Thus, using a new measure of bureaucratic traits, Bureaucratic Profiles, which is
constructed based on the results from the previous chapter, I will investigate if there is a
systematic relationship between a particular configuration of bureaucratic traits and levels of
political support. Studying the effect of bureaucratic profiles on political support will provide an
additional explanation for cross-national differences in levels of political support that has not
been fully accounted for in previous studies.

4.1 Political Support for Regime Performance and Regime Institutions

Citizens’ political support is an essential element of democratic consolidation (Andrain
and Smith 2006; Chu et al. 2008; Easton 1975; Hetherington 1998), and advanced democratic
countries strive to enhance levels of political support (Dalton 2004; Nye, Zelikow, and King
1997; Norris 1999c, 2011). Political support is a multidimensional phenomenon that can be
differentiated according to “objects” of political support (Dalton 2004; Easton 1975; Norris

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1999c; Suleiman 2003).53 In this chapter, I focus on two specific dimensions of political support,
political support for regime performance (i.e., satisfaction with operation of democracy) and
regime institutions (i.e., attitudes toward bureaucracies, especially).
Political support for regime performance is defined as “evaluations of the way the regime
works, and particularly satisfaction with the way the democratic process functions in practice”
(Norris 1999c, 17). This is different from political support for regime principles which refers to
support for democratic values or commitment to democracy. Political support for regime
performance is often measured with the survey question that asks how much respondents are
satisfied with ‘the way democracy is functioning.’ Studies have shown that levels of citizens’
(dis)satisfaction with regime performance are not the same as their levels of (dis)approval of a
democratic regime over other regime types (Dalton 1999; Klingemann 1999).54
Whereas political support for regime performance tries to capture a “middle level of
support,” political support for regime institutions focuses more on a “realistic view of democracy”
(Norris 1999c, 11). Different from a general evaluation of how democracy is functioning in
practice, political support for regime institutions focuses on specific institutions of the state, such
as parliaments, the legal system, the state bureaucracy, and political parties. Political support for
regime institutions is defined as citizens’ evaluations of “the formal structure, not the specific
53

For example, Norris (1999c), in Critical Citizens, differentiates the five objects of political
support: political community, regime principles, regime performance, regime institutions, and
political actors. This classification is expanded from the distinctions made by David Easton.
Studies also provide empirical evidence that citizens can make a distinction between these
objects, and that this distinction is theoretically and practically important (Dalton 1999;
Klingemann 1999; McAllister 1999; Suleiman 2003).
54
As some studies note, it may be possible that this question captures other aspects of people’s
satisfaction with democracy—democracy as a process, regime principles or performance of
incumbents. However, this survey question is generally used in previous studies to gauge
individuals’ support for system performance (Dalton 2004; Anderson and Tverdova 2003). For
example, studies show that this question measures public attitudes that are different from their
trust in politicians or support for regime principles (Dalton 2004; Klingemann 1999).
77

incumbents or office-holders” (Norris 1999c, 19). Some studies focus on public confidence in a
particular institution such as parliament and the civil service (e.g., Anderson and Tverdova 2003;
McAllister 1999; Van der Meer 2010), while others examine a combined index of citizens’
evaluations of several political institutions (e.g., Chang and Chu 2006; Norris 1999b; Mishler
and Rose 2001). In this chapter, I particularly examine citizens’ attitudes toward government
bureaucracies.55
Prior studies have observed cross-national variations in levels of the two dimensions of
political support examined here (Dalton 1999; McAllister 1999). Klingemann (1999), for
example, finds that cross-national differences are greater in these two objects of political
support—regime performance and institutions, whereas levels of political support for community
(i.e., national pride) and for regime principles (i.e., support for democratic values) are
consistently high in most of the countries examined. On average, satisfaction with democratic
performance is higher in Western European countries than Eastern European or Latin American
countries. Specifically, Denmark and Norway demonstrate relatively higher levels of satisfaction,
while Greece and Italy show lower levels. Although studies have examined various factors to

55

Focusing on an individual institution, rather than creating an index of several institutions, is
favorable because it is possible that people develop different attitudes toward different
institutions. For example, Rothstein and Stolle (2008) argue that popular attitudes are different
for the institutions on the “representational” side (i.e., the input institutions, such as parliaments)
than for the “implementational” side institutions (i.e., the output institutions, such as
bureaucracies). Suleiman (2003) also shows that the level of confidence in the civil service is
different (i.e., more favorable) from other political institutions. These studies suggest that the
expectations people have of bureaucracies may be different from those they have of other
political institutions.
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explain cross-national differences in levels of political support,56 there remains room for further
investigation (Anderson and Tverdova 2003; McAllister 1999; Norris 1999b).

4.2 Existing Explanations for Political Support

What are the explanations for cross-national differences in levels of political support?
Some studies look at micro-level explanations for political support, such as socioeconomic
variables, national attachment, and perceived economic situations (e.g., Andrain and Smith 2006;
Berg 2007; Van de Walle 2007; Vigoda-Gadot, Shoham, and Vashdi 2010). These studies
examine whether or not the key micro-level factors have consistent effects across nations. On the
other hand, other studies examine the effect of macro-level variables, such as economic
performance, political institutions, and cultural factors (e.g., McAllister 1999; Miller and
Listhaug 1999; Mishler and Rose 2001; Newton 2001; Norris 1999b). Recent studies, moreover,
consider both individual- and country-level variables to explain variation in political support
(e.g., Anderson and Tverdova 2003; Van der Meer 2010). Let us examine in more detail the
determinants of political support which are investigated in previous studies.

4.2.1 Economic Factors

First, economic factors are discussed as one of the key explanations for citizens’ political
support. This argument stems from the micro-level account that an individual is more likely to

56

In this chapter I use this term, political support, in a broader sense than it has conventionally
been used to refer to both popular satisfaction with democratic regime performance and public
attitudes toward bureaucracies.
79

have positive attitudes toward the government when she is more economically well-off or when
she is satisfied with the national economy (McAllister 1999). The positive relationship between
economic satisfaction (whether sociotropic or egocentric) and political support is empirically
supported in various studies (e.g., Andrain and Smith 2006; Chang and Chu 2006; Dalton 2004;
McAllister 1999; Mishler and Rose 2001).
At the country-level, the relationship between economic factors and political support is
more complicated. The rationale for the macro-level relationship between a nation’s economic
performance and levels of political support is weak, and the empirical evidence is mixed. For
example, McAllister (1999) uses the 1990-1 World Value Survey (WVS) data on 24 countries to
examine the relationship between economic performance and public confidence in parliaments
and in the civil service. While he finds supportive evidence that an individual’s satisfaction with
the nation’s economic performance positively influences her confidence in both political
institutions, a macroeconomic condition (measured with a nation’s GDP per capita) has a
negative relationship with levels of institutional confidence. He suggests that a more affluent
country shows less confidence in political institutions because people in old and affluent
democratic countries have higher expectations of democratic institutions.
However, this negative association between economic performance and political support
has not always been confirmed in other studies. For example, Norris (1999b) uses the 1990-3
WVS data on 25 nations and finds that GNP per capita is positively related to institutional
confidence. Moreover, the results are mixed in studies that use different economic indicators to
capture the health of the economy, such as economic growth and the inflation rate. Although it is
argued that the public will feel that government is doing its job properly when the nation’s
economic situation is healthy (e.g., a higher growth rate or a lower inflation rate), this argument

80

is not consistently supported in empirical studies.57 Given the limitations of an economicallydriven explanation of political support, other studies have proposed alternate factors that possibly
have an impact on levels of political support (Norris 1999b).

4.2.2 Social Factors

Another frequently mentioned explanation for political support is social factors. Beyond
an individual’s socioeconomic status and demographics such as age, gender and education levels
(Andrain and Smith 2006; Van de Walle 2007), previous studies examine the relationship
between social trust and political support. The argument is that with generalized trust, people are
more likely to participate in organizations and politics and to exert a collective influence on the
political process. This, in turn, results in a more responsive and accountable government
(Fukuyama 1995; Putnam 1993). Therefore, social trust is expected to lead to a higher level of
political support. This relationship has been examined at both the individual- and the countrylevel, yielding some supportive evidence in both instances.
In previous studies, survey data about interpersonal trust has largely been used to capture
the level of social trust. Social trust or generalized interpersonal trust is considered a core
concept of social capital (Freitag and Buhlmann 2009; Newton 1999, 2001). At the individual-

57

For example, Anderson and Tverdova (2003) find a significant positive relationship between
public attitudes toward government and the economic growth rate, using the 1996 International
Social Survey Program (ISSP) data on 16 nations. But, Van der Meer (2010) finds no significant
effect of GDP per capita and economic growth on trust in parliament, using three waves of
European Social Survey data on 26 countries. Rohrschneider (2005) presents mixed results
regarding this relationship. He finds a negative relationship when he examines people’s
representational judgment on national parliament and government, using 1999 Eurobarometer
data on 13 nations; and a positive relationship with perceived political credibility of politicians,
using 1996 ISSP data on 13 countries.
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level, studies have found that as a person trusts people in general, she is more likely to have
higher levels of political confidence and satisfaction with democracy (e.g., Andrain and Smith
2006; Dalton 2004; Zmerli and Newton 2008). This is because a trusting individual is inclined to
trust government officials and view them in a more positive way than those who do not have
interpersonal trust. Especially when interpersonal trust is understood as one component of a
“civic culture” (Almond and Verba 1963; Inglehart 1988), it can be implied that a trusting person
will also trust political institutions (Mishler and Rose 2001). Another explanation is that a
trusting person shows higher levels of political support because she is more likely to participate
politically—such as by contacting government agencies—which, in turn, increases her levels of
political support (Andrain and Smith 2006; Dalton 2004). Although some studies find a weak or
non-significant relationship between interpersonal trust and political support at the individual
level (e.g., Mishler and Rose 2001; Newton 2001), Zmerli and Newton (2008) argue that the
relationship holds when better and sensitive measurements are employed, especially in European
countries and the United States.
This relationship also applies to the aggregated level. Newton (2001, 207), for example,
argues that social trust is a “societal [and] not an individual property.” Using the percentage of
trusting people in each country as a measure of social trust, he finds a positive association
between social trust and political confidence in his cross-national study based on the 1991-1995
WVS survey data on 42 countries. Although focusing on a single country, the United States,
Keele (2007) also argues that the decline of social capital has caused a declining level of political
trust in the long-run. Related to this, the relationship between social trust and government
performance is also examined. Knack (2002) finds that social trust is associated with better
government performance in the American states. Based on data of local government in Germany

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and the U.S., Tavits (2006) shows that social trust increases policy activism, that is, more people
actively participate in demanding public goods and services.
Although some scholars question the causal direction between social trust and political
support (e.g., Brehm and Rahn 1997), I argue that what generates social trust is not political
support itself, but trustworthy state institutions (Levi 1998; Rothstein and Stolle 2008). In other
words, it is important to conceptually distinguish between trust-generating state institutions and
individuals’ attitudes toward these institutions (i.e., political support), to better understand the
relationship between social trust and political support. A government institution can provide
credible assurances for interpersonal trust, that is, it (informally) institutionalizes trust by
reducing the risks of trusting others (Levi 1998; Levi and Stoker 2000; Rothstein 2011).58
Studies have argued that certain state institutions—such as impartial and professional
bureaucracies (Kumlin and Rothstein 2005; Zucker 1986), incorruptible and nonpartisan
government institutions (Freitag and Buhlmann 2009), or non-suppressive state (Fukuyama
1995)—can help to generate higher levels of social trust. In turn, higher social trust will lead to
higher levels of political support.

4.2.3 Political Factors

The third explanation of political support concerns political institutions. Because political
institutions function at the national level, their relationship with political support is discussed
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For example, in a study of the production of trust from1840 to 1920, Zucker (1986, 89-94)
argues that the development of bureaucracy and the professionalization of it in the 1800s and the
early 1900s was a source of the production of process-based trust in the United States. Moreover,
she argues that government officials function as “intermediaries” between individuals and firms
who distrusted each other. That is, government was asked to create regulation and legislation
which would formalize the patterns of interactions so as to produce (institutionalized) trust.
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only at the country-level. Several studies argue that the effect of political institutions on political
support is important because individuals will develop attitudes about government through their
experience with political institutions (Anderson and Guillory 1997; Norris 1999b). One of the
most important political factors is the level of democratization. That is, when freedom and
political liberty are well protected in a nation, citizens will more positively evaluate regime
performance and regime institutions (Norris 1999b; Mishler and Rose 2001). But, empirical
findings are mixed: whereas Norris (1999b) shows that levels of democracy, measured with
Freedom House scores, have a statistically significant relationship with institutional trust,
Anderson and Tverdova (2003) finds no significant effects on system support.
In addition, the direct effect of various political institutions—particularly constitutional
arrangements—on political support is examined in prior studies. Lijphart (1999) argues that
consensual democracies with power-sharing political institutions perform “kinder and gentler”
than majoritarian democracies do. He also shows that consensual democracies, particularly on
the executive-parties dimension, enjoy higher levels of public satisfaction with democracy. But,
Anderson and Guillory (1997) argue that consensual democratic institutions do not necessarily
produce higher levels of system support at the aggregated-level than majoritarian institutions.
Rather, they are expected to narrow the winner-loser gap in satisfaction with democracy.
Because focusing on the type of democracy does not provide information on which
political institutions would enhance political support, Norris (1999b) further examines the effect
of individual political institutions. Using the 1990-3 WVS data on 25 nations, she finds that
nations with higher levels of democratization (according to the Freedom House score),
parliamentary systems, unitary systems, two-party systems, and majoritarian electoral systems
are more likely to have higher levels of confidence in institutions. The findings provide limited

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support for the positive effects of consensual democracies. Perhaps surprisingly, power-sharing
institutions, such as federal systems, multi-party systems, and proportional representation (PR)
electoral systems do not necessarily have higher levels of political support than their
counterparts.59 In other studies, the effects of political institutions are mixed. For example, Van
der Meer (2010) finds that level of trust in parliament is higher in PR electoral systems. But,
Rohrschneider (2005) finds very little support for this relationship between political institutions
(i.e., majoritarian versus proportional systems) and citizens’ evaluations of the representativeness
of their government, where he controls for the quality of arbitrating institutions, bureaucracies
and judiciaries.

4.3 Bureaucracies and Political Support

As an explanation of political support, few previous studies have taken into account the
role of bureaucracy. However, it seems plausible that a bureaucracy influences how people view
their government because it plays a critical role in the policy process. Moreover, citizens come
into contact with bureaucrats more frequently than they meet with political representatives over
the course of their lives. Given the variety of bureaucratic profiles found in the previous chapter,
I further examine in this chapter whether countries with different bureaucratic characteristics
have different levels of political support. Before discussing this relationship, let us briefly

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These power-sharing institutions seem to have stronger effects on democratic consolidation
than on popular support for political institutions. Norris (2008) examines the effect of these
power-sharing institutions on levels of democracy and finds that countries with PR electoral
systems, parliamentary monarchies, and federalism are more likely to have consolidated
democracy.
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discuss limitations in prior studies concerning bureaucracies and its effect on popular attitudes
toward the government.
Although some studies suggest that bureaucracies influence how people view their
government (e.g., Rohrschneider 2005), no empirical studies have provided a comprehensive
analysis of the impact of specific bureaucratic characteristics. Studies examining this link
between bureaucracy and popular attitudes toward government often fail to specify bureaucratic
characteristics that are related to different levels of political support. For example, Gilley (2006)
finds a positive relationship between “general governance” and state legitimacy. Even when
scholars articulate specific properties of bureaucracies, they end up showing a relationship
between a broader concept of “governance” and public attitudes, due to the limited availability of
data on bureaucratic characteristics (Brans 2003). Rohrschneider (2005) argues that the
impartiality and procedural fairness of bureaucracies and judiciaries will inform citizens that the
regime has a capacity to meet their interests. But, as a measure of bureaucracy, he uses an index
of three World Bank’s Governance Indicators—rule of law, control of corruption, and
government effectiveness—which captures a broad and abstract concept, rather than specific
bureaucratic traits such as impartiality and fairness. The same indicator is used in Gilley’s (2006)
study. Thus, it is difficult to tell from these researches what characteristics of bureaucracies are
contributing to public evaluations of government.
Another line of studies focuses on the eroding effect of corruption (in the public sector)
on political support. Anderson and Tverdova (2003) argue that country-level corruption in the
public sector negatively influences citizens’ attitudes toward government. At the individual-level,
studies show that a person who perceives higher levels of corruption among public officials is
less likely to have confidence in institutions (Chang and Chu 2006; Mishler and Rose 2001).

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Given these studies, one could argue that levels of corruption characterize a bureaucracy and link
it to political support. However, as stated in Chapter 2, I would argue that the actual level of
corruption is not a fundamental bureaucratic trait, but a consequence of bureaucratic profiles. For
example, bureaucracies emphasizing political independence and merit-based recruitment will be
less involved in corruption (Dahlstrӧm, Lapuente, and Teorell 2012; Rauch and Evans 2000).60
Therefore, studies on the effect of corruption on political support suggest a link between
bureaucracies and political support, but do not provide sufficient explanation about what specific
bureaucratic characteristics are related to political support.
In addition, some studies focus on individuals’ evaluations of government performance to
predict levels of political support. For example, Kim (2010) finds that citizens’ evaluations of
government performance are associated with trust in government in Korea and in Japan.
However, this study does not speak to specific bureaucratic traits that could affect government
performance. Vigoda-Gadot et al. (2010) examine this link more specifically in six European
nations—Ireland, Israel, Lithuania, Norway, Slovakia, and Spain. They explore how citizens’
perceptions of various aspects of managerial excellence in public sector are associated with
public sector image, satisfaction with public service, and confidence in public administration.
Although they provide some insights about the relationship between specific bureaucratic
characteristics and public attitudes toward bureaucracy, they only focus on micro-level
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Moreover, I argue that perceived levels of corruption is not always the same as actual levels of
corruption. And, perception of corrupted bureaucracies is the attitudes people have about
bureaucracies. Thus, as will be discussed in Section 4.6.1, I consider an individual’s perception
about the prevalence of corruption in public service as one way to measure (unobserved) overall
attitudes toward bureaucracies. On the other hand, one may argue that the perceived levels of
corruption should be controlled in predicting levels of public satisfaction with democracy. To
account for this, I tested the model controlling for country-level perceived corruption (measured
with Corruption Perception Index from Transparency International, following Anderson and
Tverdova (2003)) and found a robust relationship between Bureaucratic Profiles and satisfaction
with democracy.
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relationships. Thus, country-level factors, including the different properties of national
bureaucracies, are not accounted for.
In sum, although prior studies suggest a possible link between a bureaucracy and political
support, they do not speak to specific bureaucratic traits and cross-national bureaucratic
differences that could be associated with different levels of political support. Therefore, in order
to empirically examine what specific configuration of bureaucratic traits contributes to higher
levels of political support, I propose a new measure of bureaucratic traits, Bureaucratic Profiles
and examine its relationship with political support. Next section describes how I construct this
new measure and, then, discusses how different bureaucratic profiles might be related to
different levels of political support.

4.4 Bureaucratic Profiles as a Measure of Bureaucratic Traits
4.4.1 New Measure of Bureaucratic Profiles

A new measure of bureaucratic traits, which is called “Bureaucratic Profiles,” is
developed based on the estimation of nation vectors obtained in the previous chapter. Recall that
a nation’s location in relation to trait points reflects the rank order of relative levels of
bureaucratic traits presented in each nation. To summarize the data, I look at the mean direction
vector, or a mean resultant length (see Section 3.6). This shows how a set of bureaucratic traits
are presented in a bureaucracy in OECD member nations, on average. See Figure 4.1 for the
location of the mean direction vector.
The trait points can be projected onto the (extended) mean direction vector. It shows that
the positions of perpendicular projections for three traits—political independence, career-based

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Figure 4.1. Full MDPREF model, with the mean direction vector as a reference vector for a
new measure, Bureaucratic Profiles

Note: Open circles and solid circles represent nations’ vector terminal points and the five traits,
respectively, which are estimated from MDPREF in Chapter 3. The arrow originated from the
centroid represents the mean direction vector and its extend line is drawn with a dotted line. This
mean direction vector is used as a reference vector in creating a new measure, Bureaucratic
Profiles. The numeric values are assigned for each country according to the angular separation
between each nation vector and the reference vector. See Chapter 3 for the details of trait points.
See Note in Figure 3.3 for the full names of the thirty-four countries.

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system, and impartiality—are closer to the terminal point of the mean direction vector. This
indicates that these three traits are presented more than the other two traits in OECD member
nations, on average. Competency and female representativeness are relatively farther away from
the terminal point of the mean direction vector. This means that these two bureaucratic traits are
relatively less presented, on average. Specifically, when the traits are ordered in terms of their
closeness to the terminal point of the vector (i.e., relative levels presented in the nation
represented by the vector), we see the following order: political independence, career-based
system, impartiality, competency, and female representativeness.
By setting this mean direction vector as a reference vector, I assign the numeric values to
create Bureaucratic Profiles, which reflects the positive and negative angular separations of each
nation vector from the reference vector in radians. Thus, a (hypothetical) nation represented by
the mean direction vector will be coded as zero (0). As a vector orientation moves in a
counterclockwise direction, the value for Bureaucratic Profiles increases from 0 to pi (π). For
example, a nation whose vector is one radian from the reference vector in a counterclockwise
direction will be coded as a positive one (1). And, the nation vector which is located 180 degrees
(i.e., π radians) from the mean direction vector in a counterclockwise direction is coded as pi (π).
In this nation the rank order of bureaucratic traits, in terms of their relative levels, is the opposite
of that for the reference vector because the angle between two vectors inversely reflects the
correlation between the two bureaucratic profiles. On the other hand, as a vector orientation
moves in a clockwise direction, the value of Bureaucratic Profiles decreases from 0 to negative
pi (-π). For example, a nation whose vector is one radian from the reference vector in a
clockwise direction will be coded as minus one (-1). And, the nation vector coded as a negative

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pi (-π) is located 180 degrees from the mean direction vector in a clockwise direction, and has
the opposite bureaucratic profile from the one for the reference vector.
Table 4.1 describes the values of Bureaucratic Profiles for each nation (see the second
column). It ranges from -2.87 (in Slovakia) to 3.09 (in Chile). To understand the substantive
meaning of this value, the third column lists the specific profiles for each nation, that is, the traits
in the order of relative levels presented in each bureaucracy. The trait exhibited to the greatest
degree, compared to other traits, will be in the first place and the one presented to the least extent
will be in the last place in the profile. For example, Korea has a value of 0.00, which is the
closest value to the mean direction vector. And, the specific bureaucratic profile for Korea
indicates that traits are exhibited in the following order: independence from politics (IND),
career-based system (CAR), impartiality (IMP), competency (COM), and female
representativeness (REP). This is what is described above as the mean direction vector’s profile.
I stated earlier that the values of Bureaucratic Profiles reflect each nation vector’s
deviation from the mean direction vector, in either counterclockwise or clockwise direction.
What does this specifically entail? Both Table 4.1 and Figure 4.1 help to facilitate a substantive
understanding of the bureaucratic profile. Using information in Table 4.1, let us explore how the
relative levels of bureaucratic traits change as the value of Bureaucratic Profiles changes. Note
that the descriptions of nation vectors on the 24-hour clock scale in the following refer to their
orientation in the joint space, as shown in Figure 4.1.
When closely examining the specific profile of each nation, the first noticeable thing is
that, in general, the relative level of IMP presented in a bureaucracy is the smallest when the
value of Bureaucratic Profiles is small, and it increases as the value gets larger. More
specifically, IMP is the least presented trait when Bureaucratic Profiles is between -1.31 and

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Table 4.1. Bureaucratic Profiles
Nation

Bureaucratic
The order of relative importance of traits
Profiles (radians)
Slovakia (SK)
-2.867
REP
COM
CAR
IND
IMP
Hungary (HU)
-2.586
REP
COM
CAR
IND
IMP
Slovenia (SI)
-2.447
COM
REP
CAR
IND
IMP
Poland (PL)
-2.335
COM
REP
CAR
IND
IMP
Czech Republic (CZ)
-2.328
COM
REP
CAR
IND
IMP
Greece (GR)
-2.316
COM
REP
CAR
IND
IMP
Portugal (PT)
-2.214
COM
CAR
REP
IND
IMP
Mexico (MX)
-2.095
COM
CAR
REP
IND
IMP
Italy (IT)
-1.773
CAR
COM
REP
IND
IMP
Luxembourg (LU)
-1.513
CAR
COM
IND
REP
IMP
Turkey (TR)
-1.351
CAR
COM
IND
REP
IMP
Spain (ES)
-1.309
CAR
COM
IND
REP
IMP
Israel (IL)
-1.128
CAR
COM
IND
IMP
REP
Belgium (BE)
-0.933
CAR
IND
COM
IMP
REP
France (FR)
-0.804
CAR
IND
COM
IMP
REP
Germany (DE)
-0.132
CAR
IND
IMP
COM
REP
Ireland (IE)
-0.068
CAR
IND
IMP
COM
REP
Korea, Rep. (KR)
-0.000
IND
CAR
IMP
COM
REP
Japan (JP)
0.070
IND
CAR
IMP
COM
REP
Switzerland (CH)
0.783
IMP
IND
CAR
REP
COM
Netherlands (NL)
0.844
IMP
IND
CAR
REP
COM
Austria (AT)
0.892
IMP
IND
CAR
REP
COM
Canada (CA)
0.917
IMP
IND
REP
CAR
COM
Finland (FI)
0.986
IMP
IND
REP
CAR
COM
Norway (NO)
1.011
IMP
IND
REP
CAR
COM
Sweden (SE)
1.063
IMP
IND
REP
CAR
COM
United Kingdom (UK)
1.169
IMP
IND
REP
CAR
COM
New Zealand (NZ)
1.403
IMP
IND
REP
COM
CAR
Australia (AU)
1.424
IMP
REP
IND
COM
CAR
United States (US)
1.447
IMP
REP
IND
COM
CAR
Denmark (DK)
1.631
IMP
REP
IND
COM
CAR
Iceland (IS)
2.412
REP
IMP
COM
IND
CAR
Estonia (EE)
2.524
REP
IMP
COM
IND
CAR
Chile (CL)
3.089
REP
COM
IMP
IND
CAR
Note: The third column presents the rank order of the relative levels of five traits presented in
each nation’s bureaucracy. For example, the trait which is emphasized the most importantly
comes in the first place. “IMP” represents impartiality, “IND” independence from politics, “CAR”
career-based system, “COM” competency, and “REP” female representativeness.
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-2.87 (i.e., when nation vectors are located between around the “7:00” and “10:00” positions). As
the value of Bureaucratic Profiles increases, the relative level of IMP increases; and IMP is
presented to a greater degree than other traits when Bureaucratic Profiles is between .78 and
1.63 (i.e., located between around the “2:00” and “4:00” positions). Note that the relative level of
IMP decreases in countries with the highest values (i.e., that are positioned around “12:00” and
“11:00”), which is due to the “circular” nature of the data (Gill and Hangartner 2010).61
The relative level of COM generally decreases as the value of Bureaucratic Profiles
increases, specifically when it changes from -2.45 to 1.17 (i.e., as a nation vector moves from the
“9:00” to the “2:00” position in a counterclockwise direction). Thus, COM is emphasized more
than other traits in nations positioned around “9:00” and “10:00.” And, it is the least exhibited
trait in countries located around the “3:00” position. But, also note that the relative level of COM
increases as the value of Bureaucratic Profiles increases from 1.17 to 3.09 (i.e., as a nation
vector moves from the “3:00” to the “12:00” position in a counterclockwise direction).
Next, let us focus on the order of REP and IND in bureaucratic profiles. REP is exhibited
with a greater degree than other traits in Slovakia and Hungary, two countries with the lowest

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That is, IMP is presented to a smaller degree in countries with the highest and lowest values of
Bureaucratic Profiles. To understand the “circular” nature of the data, let us look at Slovakia and
Chile, which have the lowest and the highest values of Bureaucratic Profiles, respectively.
Although they have very different scores on this measure, they are located close to one another
in the joint space (see Figure 4.1). This is because, Chile has a bureaucratic profile which is very
different from one for the mean direction vector—the angle between the two is 177.01 degrees in
the counterclockwise direction—and, thus, Chile has a higher value on this measure. On the
other hand, Slovakia has the lowest value on this measure because it too has a very different
profile than one for the mean direction vector, but in a clockwise direction (i.e., the angle
between the two is 164.27 degrees in a clockwise direction). Thus, when we compare the profiles
of these two countries, it shows that REP and COM are placed in the first and the second places
in both nations (which reflects that their profiles are almost opposite to the mean vector’s one)
but that the order of other three traits are different (which is because they are deviated in the
different directions). That is, these two countries’ profiles have some similarities even though
they have extreme values on this measure; which shows the circular nature of the data.
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values of Bureaucratic Profiles; and its relative level decreases as the value of Bureaucratic
Profiles increases to zero (i.e., as the vector moves from the “10:00” to the “5:00” position in a
counterclockwise direction). Thus, REP is the least exhibited trait in nations located around the
“5:00” and “6:00” positions. Then, its relative level increases as the value of Bureaucratic
Profiles increases from zero to 3.09. This reflects how nation vectors are different than the mean
direction vector. That is, the relative level of REP is the smallest for nations close to the
reference vector; and its relative level increases as the nation vector deviates from the reference
vector in either a counterclockwise or clockwise direction.
As opposed to this pattern, IND is exhibited to a greater degree than other traits in nations
with Bureaucratic Profiles of zero (i.e., when a nation vector locates close to the reference
vector). The relative level of IND decreases as a nation vector deviates in either a
counterclockwise or clockwise direction. Thus, IND is presented to a lesser degree than other
traits when Bureaucratic Profiles is between -1.77 and -2.87 (i.e., located between around the
“8:00” and “10:00” positions), or when it is between 2.41 and 3.09 (i.e., positioned around
“11:00” and “12:00”).
Finally, let us explore the changes in the relative level of CAR. In countries with the
lower values of Bureaucratic Profiles CAR is displayed more than IND and IMP, but less than
REP and COM. The relative level of CAR increases as Bureaucratic Profiles increases to -.07
(i.e., as a nation vector moves from the “10:00” to the “6:00” position in a counterclockwise
direction). Thus, CAR is exhibited to the greatest extent in nations positioned around between
“6:00” and “7:00.” Then, its relative level decreases as Bureaucratic Profiles increases from -.07
to 3.09. In nations located around the “2:00” position, CAR is exhibited to the least extent,
relative to other traits.

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4.4.2 Bureaucratic Profiles and Political Support

What is the relationship between Bureaucratic Profiles and levels of political support? I
have proposed to look at this relationship in order to determine which bureaucratic profile
reflects what people view as important and desirable in democratic societies. If we find that a
particular bureaucratic profile is related to higher levels of political support, it will imply that this
particular bureaucratic profile is the one that is desired in democratic societies from the
perspective of citizens. This assumes that people want bureaucracies to possess a set of traits in a
certain style (i.e., with a particular order of the relative levels of traits) in democratic societies
and that this desire is universal. An alternative possibility is that what people want from
bureaucracies in democratic societies varies across nations. If each country has designed and
developed their output institutions as the best-fit for their nation’s democratic government and to
reflect their own citizens’ preference, it is possible that nations with various bureaucratic profiles
all enjoy higher levels of political support. If this is the case, no systematic relationship between
Bureaucratic Profiles and levels of political support will be found. However, I hypothesize that
universally there is a particular bureaucratic profile that people find desirable in democratic
government.
Which bureaucratic profile will contribute to higher levels of political support? Although
all the traits considered to construct a measure of Bureaucratic Profiles are discussed as key
attributes for bureaucracies in democratic societies, can we think of differences in political
support depending on the configuration of the relative levels of these traits presented in a
bureaucracy? Before hypothesizing a pattern between Bureaucratic Profiles and political support,
let us think through the possible relationship between the level of each trait represented in a

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bureaucracy and political support based on discussions in Chapter 2. It will help to draw an
expectation about the systematic pattern between the two variables.
First, given previous studies, especially Rohrschneider (2005) and Rothstein and Teorell
(2008a), impartiality is expected to be the most important trait that impacts levels of political
support. If bureaucrats behave impartially—that is, if individuals are treated fairly by bureaucrats
and public goods are provided in an unbiased way—people’s experience with such a bureaucracy
will enhance how they evaluate their government (Hibbing and Theiss-Morse 2001; Levi 1998;
Levi and Sherman 1997; Rohrschneider 2005; Rothstein and Teorell 2008a). However, given
that the question of interest here is not about which single trait is most important in its relation to
political support, but about which bureaucratic profile is, I posit that levels of political support
will be higher for a nation where impartiality is exhibited to a greater degree than other traits in a
bureaucracy. Then, how will the relative levels of other traits matter?
Independence from politics is also an important trait that contributes to higher levels of
political support. But the relationship between its relative level and political support seems more
complicated. On the one hand, I expect a positive influence of political independence on political
support. This is because political independence is, itself, necessary for a bureaucracy to be
impartial. This is also implied by the result of a MDPREF analysis: As depicted in Table 4.1,
there is no case where the relative level of political independence is the lowest and that of
impartiality is the highest. In other words, when impartiality is presented with the greatest degree,
the level of political independence exhibited in that bureaucracy is also relatively high. And,
when it is low, independence from politics is also presented in a lower level, compared to other
traits. Moreover, as I have argued in Chapter 2, the politicization of bureaucracies, which can
undermine political independence, will not necessarily improve bureaucrats’ responsiveness to

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the public. This is because bureaucrats who follow elected leaders’ partisan directives may not
respond to the general public’s demands, but instead work for political masters’ short-term
interests. Therefore, in general, higher levels of political independence, as oppose to
politicization, will be associated with higher levels of political support.
On the other hand, if a politically independent bureaucracy becomes an omnipotent and
uncontrollable one, it will not contribute to perceptions that bureaucracies and democratic
government are performing well. Considering implications of high levels of political
independence, I posit that only when a certain level of political independence is presented in a
bureaucracy, and when it is accompanied by impartiality, will people have higher levels of
political support. That is, nations where independence from politics is presented in a greater
degree than other traits will not necessarily enjoy higher levels of political support, if they are
lacking in impartiality. For example, let us consider Korea and Japan. As shown in Table 4.1, the
relative level of impartiality in these countries is next to career-based system (i.e., independence
from politics is the most exhibited trait and impartiality is the third important one). Hence, I
expect that levels of political support in these countries will not be as high as in nations where
impartiality is displayed to a greater degree than independence from politics.
Next, let us think about female representativeness. A representative bureaucracy will
signal the government’s effort for inclusiveness of and responsiveness to the public. Also,
women may feel that their voices are equally heard as men’s when female bureaucrats are
present. In these ways, female representativeness can contribute to higher levels of political
support. However, there are contrasting views on the effect of representative bureaucracies as
discussed in Chapter 2. Some critics suggest that it can cause “partiality” (Lim 2006;
Subramaniam 1967), whereas others argue that it does not behave in a way that harms

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democratic values (Meier, Wrinkle, and Polinard 1999; Nicholson-Crotty, Grissom, and
Nicholson-Crotty 2011). Particularly with female representativeness, it is argued that more
female bureaucrats will be more impartial (Dollar, Fisman, and Gatti 2001). Therefore, I expect
that female representativeness will contribute to levels of political support only if it comes with
impartiality. That is, when a nation’s bureaucracy possesses representativeness to the greatest
extent, but presents lower levels of impartiality, this nation will not enjoy high levels of political
support.
For example, among five nations where female representativeness is in the first place in
the Bureaucratic Profiles (see Table 4.1), impartiality is the least emphasized trait in Slovakia
and Hungary. But, Iceland, Estonia, and Chile seem to emphasize impartiality, together with
female representativeness. Thus, I expect that the latter three nations will have higher levels of
political support than the two nations where impartiality is not well preserved. In these two
nations (and also in other nations where impartiality is much less represented than
representativeness), it is possible that a representative bureaucracy causes partiality and, in turn,
will lead to lower levels of political support.
What about the linkage between the relative level of competency and political support? I
argued in Chapter 2 that competency is a key bureaucratic trait that is usually taken for granted
and not explicitly discussed in prior studies. Does this imply that the relative level of competency
presented in a bureaucracy is not an important factor for people evaluating governments? Or will
people have higher levels of political support when competency is greatly emphasized? I argue
that higher relative levels of competency will lead to more favorable popular views of
democratic governance, but only if impartiality is also emphasized in a bureaucracy.

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The Bureaucratic Profiles presented in Table 4.1 reveals an interesting feature, which
makes directly testing such a hypothesis impossible. The bureaucratic profiles in OECD member
nations show that, in general, as the relative level of impartiality increases, levels of competency
decrease, and vice versa. Specifically, nations located between “7:00” and “10:00” seem to
prioritize competency over impartiality; while nations around “1:00” to “4:00” appear to choose
impartiality over competency (see Figure 4.1.). Thus, there is no case where the two traits are
exhibited to a greater degree than other three traits. Instead, there seems to exist tradeoffs
between emphasizing impartiality and competency. Hence, I posit that individuals will be less
satisfied with bureaucracies and democratic performance if competency is prioritized over
impartiality in their nation’s bureaucracy.
Finally, what is expected for the linkage between a nation’s emphasis on career-based
system and political support? First of all, the level of career-based system in a bureaucracy is not
an important factor in itself; its relationship with other traits will be more important in
determining its impact on political support. In Chapter 2, I argued that career-based system will
go together with impartiality and political independence. But, the analysis of the MDPREF
model shows that, as listed in Table 4.1, there are mixed relationships between them. Among the
nations with the greatest emphasis on career-based system (i.e., between the “5:00” and “8:00”
positions), impartiality and political independence are preserved only in some nations. For them,
I expect that levels of political support will be higher when career-based system is emphasized.
On the other hand, I posit that if lower levels of career-based system are accompanied by lower
levels of political independence, then levels of political support will be also low. This is because
position-based system (which is the opposite of career-based system) will not have an intended
effect—increasing effectiveness and efficiency of administration, by recruiting the best people

99

for the positions—if it is used as a way of politicization. In this case, a lack of emphasis on
career-based system will decrease the political independence that a national bureaucracy
possesses. In turn, levels of political support will decrease.
Considering all these expectations together, what do I hypothesize as a general pattern
between a measure of Bureaucratic Profiles and levels of political support? A positive and
significant relationship between them is expected. This is because as the value of Bureaucratic
Profiles increases, the relative level of impartiality increases; and as the value decreases, the
relative level of competency increases and that of impartiality decreases. Of course, individuals
within a country may feel differently about how bureaucracies are performing, and they may
have different attitudes toward government. However, I think that overall levels of political
support will be linked to different bureaucratic profiles.

4.5 Method: Multilevel Model Analysis

In order to examine the relationship between bureaucratic profiles and political support, a
multilevel analysis is used, where level-2 is the country and level-1 is the individual citizen. A
multilevel analysis is appropriate because the dependent variable, individuals’ political support,
has a clustered nature: Survey respondents are nested within countries. Therefore, it is highly
possible that any individual’s opinion is correlated with others within the same country. 62

62

The intra-class correlation (ICC) shows the correlation between the two randomly drawn level1 units (individuals) within the same, randomly drawn, level-2 unit (country). The ICC is .158
for the public attitudes toward bureaucracies model and .161 for the satisfaction with democracy
model. This indicates the possibility that the assumption of independence among units of
analysis is violated. The ICC value of .158 and .161 are not very small. Snijders and Bosker
(2011, 18) note that the ICC values range from .10 to .25 in a number of studies of educational
performance in American schools, where a multilevel modeling is used.
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In addition, the key independent variable is measured at the country-level. Thus, a
conventional OLS regression model at the individual-level could lead to erroneous conclusions.
If the effects of country-level variables are examined using an OLS regression model with
pooled data, all these variables will seem to be significant even when they are not (Snijders and
Bosker 2011; Steenbergen and Jones 2002; Weldon 2006). Another option would be a simple
country-level analysis based upon aggregated data. But, this would miss any information about
individual differences within a country—the effects of the individual-level characteristics are
overlooked in such a model (Luke 2004). Indeed, levels of political support vary across
individuals within a nation and we need to account this variability. 63 Therefore, we need to
control the factors to explain individual variations within a country, as well as country-level
variables. 64
In the following sections, I investigate two models, one for examining the relationship
between bureaucratic profiles and public attitudes toward bureaucracy; and another looking at
the association between bureaucratic profiles and citizens’ satisfaction with democratic
performance. The two models are treated separately, because these two dependent variables are

63

Public attitudes toward bureaucracies range from -1.96 to 2.09 (with the mean of 0 and the
standard deviation of .83) in pooled data. The standard deviation in each country ranges from .61
(in Poland) to .94 (in Chile). Citizens’ satisfaction with operation of democracy in their country
ranges from 0 to 10 (with the mean of 6.01 and the standard deviation of 2.25) in pooled data.
The standard deviation in each nation ranges from 1.70 (in Switzerland) to 2.45 (in Mexico).
64
A multilevel modeling is increasingly used in other similar studies examining the effect of
country level variables on public attitudes. Anderson and Singer (2008) look at the effect of
income inequality on public attitudes toward political institutions, using 2002-3 European Social
Survey (ESS) data; Anderson and Tverdova (2003) examine the effect of corruption on public
attitudes toward government, using 1996 International Social Survey Programme (ISSP); Van
der Meer (2010) conducts a three-level random-coefficient analysis to study citizens’ trust in
parliament using 2002-2006 ESS data.
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intended to capture distinct concepts as discussed in Section 4.1.65 For the multilevel models I
use a REML estimation because the number of country-level units is relatively small (RabeHesketh and Skrondal 2008; Snijders and Bosker 2011).66
For a better model specification, I employ two analytical strategies (see Snijders and
Bosker 2011). First, for each model, I begin with individual-level variables only and, then, I
include aggregated mean of individual-level variables to distinguish between within- versus
between-country effects of these variables. If only an individual level variable is included, then
its individual- and country-level effects are confounded, which causes “cluster-level confounding
or cluster-level omitted-variable bias” problem (Rabe-Hesketh and Skrondal 2008). Thus, it
should be tested if one variable’s between-county effect is different from its within-country
effect (Bartels 2008; Snijders and Bosker 2011). Therefore, I test the model with aggregated
mean of all individual-level variables, and only the statistically significant variables are included
in the final model for the consideration of parsimoniousness. 67
Second, I explore possibilities of a random slope, that is, a varying effect of an
individual-level variable across countries, following Snijders and Bosker’s (2011) suggestion. It
would be the best to choose between a fixed and a random coefficient based on theory, but
statistical considerations are also important. Snijders and Bosker (2011, 87) state that one may
easily decide not to consider random slopes when no theory suggests any clues; but “this implies

65

Examination of the relationship between these two concepts is beyond the scope of this
dissertation and left for future studies.
66
The number of country-level units is twenty-eight. Note that this number of units is not too
small to apply a multilevel modeling. According to Stegmueller’s (2013) results from a Monte
Carlo experiment, the relative bias of estimates is close to zero when the number of country is
greater than 25 (and the ICC is .10).
67
The non-significant, aggregated mean of individual-level variables are dropped because
including a number of unnecessary country-level factors can make the model estimation unstable
(Hoffman 2010; Snijders and Bosker 2011).
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a risk of invalid statistical tests, because if some variable [has] a random slope, then omitting this
feature from the model could affect the estimated standard errors of the other variables.”
Therefore, the possibilities of varying individual-level effects across nations will be explored.

4.6 Data and Measurements
4.6.1 Dependent Variable: Public Attitudes toward Bureaucracies and Satisfaction with
Democracy
To measure citizens’ attitudes toward bureaucracies and satisfaction with democracy, the
survey questions from the 2004 International Social Survey Programme (ISSP) are used. Due to
the data availability, only twenty-eight countries among OECD member nations are examined. 68
The first dependent variable gauges public attitudes toward bureaucracies. Based on the three
questions that ask how respondents think about public services, I create an index of attitudes
toward bureaucracies. The survey questions used to create this index are:69
Q54. Thinking of the public service in (COUNTRY), how committed is it to serve the
people? [very committed, somewhat, not very, not at all committed]
Q55. When the public service makes serious mistakes in (COUNTRY) how likely is it that
they will be corrected? [very likely, somewhat, not very, not at all likely]
Q56. How widespread do you think corruption is in the public service in (COUNTRY)?
[hardly anyone is involved, a small number, a moderate number, a lot, almost
everyone is involved]

68

Six countries that are not included in the ISSP survey data are Estonia, Greece, Iceland, Italy,
Luxembourg, and Turkey.
69
Note that in the survey, it tries to clarify the meaning of public service. Translation notes in the
codebook say the following: 54-56. Public service should be translated with the appropriate
term for government officials. Do not use the term “bureaucrat.”
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The three survey items are recoded so that higher values indicate more positive attitudes,
and then standardized. Cronbach's alpha for the three variables is .656 in pooled data. 70 The
index ranges from -1.96 to 2.09. Aggregated at the country-level, Poland has the least favorable
attitudes toward public bureaucracies (-.66), while New Zealand has the most favorable (.49).
The average value of aggregated mean of this index is .007 (std. = .33), where Hungary is the
nation that comes closest to this mean value, at .006 (see Table 4.2).
The second dependent variable captures political support for regime performance, which
is a level of respondents’ satisfaction with operation of democracy in their country. The survey
question asks: “Q57. On the whole, on a scale of 0 to 10 where 0 is very poorly and 10 is very
well, how well does democracy work in (COUNTRY) today?” Aggregated mean value at the

70

In each of individual countries, the smallest Cronbach's alpha is .286 in Israel and, then, it
ranges from .537 in Mexico to .711 in Slovakia. In Israel, the correlations between three
variables are relatively weak; especially, correlation between the first variable (i.e., perceived
commitment of bureaucracy) and the third one (i.e., perceived corruption in bureaucracy) is not
statistically significant. With this exceptional case of Israel (illustrated below) I check whether
the results change when Israel is excluded; the relationship between Bureaucratic Profiles and
political support holds for the models examined in this chapter. Also, after excluding Israel, I
calculate the factor scores for each country separately and conduct analyses; there are also no
substantial changes. In this chapter, I report the results when dependent variable is the factor
scores obtained from the pooled data (including Israel). Let me briefly explain Israeli case. A
weak relationship among the three variables can be due to diverged attitudes between Israeli
Jews and non-Jews (Vigoda-Gadot and Yuval 2004). In the 2004 ISSP data, Israeli Jews and
Israeli Arabs are not distinguished as in other years of the ISSP survey, but I am able to compare
the differences between these two groups based on respondents’ religious affiliation. For Israelis
who are not Jewish, I find that, the correlation between the two variables is the same as in other
nations—positive and significant—and the Cronbach’s alpha of the three items is .564. However,
for Israeli Jews the correlation between the two variables is negative and significant (i.e.,
Pearson’s correlation coefficient is -.068, with the p-value of .036). This may be because Israeli
Jews perceive bureaucrats’ involvement in corruption differently than Israeli Arabs; and think
corrupted bureaucrats are actually committed to serve people. Indeed, in Israel it is noted that
Arab citizens generally do not receive equal treatment from the public administrators (Yaroni
and Rosenbloom 2003). Moreover, Israeli bureaucracy is highly politicized and partisan politics
prevails in personnel management; some types of bureaucratic corruption, such as “petty bribery”
and “proteksia” (the use of political connections for favorable treatment by bureaucrats), are
condoned (Yaroni and Rosenbloom 2003; Werner 1983).
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country-level ranges from 4.17 (in Slovakia) to 7.77 (in Denmark). The average value of
aggregated mean of this dependent variable is 5.98 (std. = .89), where Spain has the value closest
to this mean value, at 6.13 (See Table 4.2).
Table 4.2. Citizens’ Positive Attitudes toward Bureaucracies and Satisfaction with
Democracy (mean)
Positive Attitudes toward Bureaucracies
Satisfaction with Democracy
New Zealand
.49
Denmark
7.77
Switzerland
.38
Austria
7.02
Austria
.38
New Zealand
6.91
Denmark
.34
Switzerland
6.88
Norway
.30
Australia
6.88
Ireland
.28
Canada
6.78
United Kingdom
.27
Finland
6.72
Canada
.26
Norway
6.67
Israel
.25
Ireland
6.65
Australia
.23
Netherlands
6.64
Finland
.22
United States
6.58
United States
.17
Sweden
6.38
Netherlands
.15
United Kingdom
6.13
Sweden
.11
Spain
6.13
Spain
.06
Israel
5.70
Germany
.05
Korea, South
5.68
Belgium
.05
Portugal
5.68
Hungary
.01
Germany
5.62
France
-.04
Chile
5.62
Korea, South
-.28
France
5.57
Slovenia
-.31
Japan
5.43
Chile
-.32
Belgium
5.40
Portugal
-.33
Czech Republic
5.30
Mexico
-.34
Slovenia
4.97
Slovakia
-.46
Hungary
4.95
Japan
-.49
Mexico
4.93
Czech Republic
-.55
Poland
4.22
Poland
-.66
Slovakia
4.17
Note: It describes cross-national differences in levels of political support in the twenty-eight
OECD member nations. It shows each country’s mean value of the two dependent variables. The
data are obtained from the 2004 ISSP and the survey questions are described in the text. The first
column presents each nation’s mean score on the index of attitudes toward bureaucracies. The
second column reports a country’s mean values on the question about satisfaction with
democracy, on a 11-point scale, from 0 (very poorly satisfied) to 10 (very well satisfied).

105

4.6.2 Key Independent Variable: Bureaucratic Profiles

A new measure of Bureaucratic Profiles, created as described in Section 4.4.1, is used to
examine which configuration of bureaucratic traits are relevant to higher levels of political
support and whether there is a systematic pattern between bureaucratic profiles and levels of
political support. Bureaucratic Profiles is a continuous variable which theoretically ranges from
(-π) to (π) radians. The larger the absolute value of Bureaucratic Profiles, the more different the
bureaucratic profile (i.e., the order of the relative levels of traits) than the one for the reference
vector. In the data, this variable ranges from -2.87 radians for Slovakia to 3.09 radians for Chile
(see Table 4.1. for the distribution of this variable).
In the results below, the coefficient of this variable will indicate the changes in the
dependent variable for each one radian change in Bureaucratic Profiles in a counterclockwise
direction. But, it will be more important to identify a systematic pattern between the rank order
of bureaucratic traits and levels of political support. Therefore, in interpreting the results, I will
discuss in more detail which bureaucratic profile is related to higher or lower levels of political
support, by looking at the predicted values of dependent variables and their associated
Bureaucratic Profiles.

4.6.3 Control Variables
4.6.3.1 Individual-level Control Variables

Other factors that have been argued to predict political support are also controlled at both
the individual- and the country-level. At the individual level, I control for demographic variables,

106

including age (modeled as a non-linear effect71), education, 72 sex (female), and working status
(unemployed). In addition, generalized trust73 and the level of political interest 74 are controlled.75
I hypothesize that the old, males, the employed, and those who attain a higher level of education
will be more likely to have higher levels of political support because they tend to be in a socially
and economically better condition than vulnerable groups (e.g., the young, females, the
unemployed, and the less-educated), as found in previous studies (e.g., Brehm and Rahn 1997;
Chang and Chu 2006; Dalton 2004; Hibbing and Theiss-Morse 2001).
I also expect that a person who is inclined to trust people in general will be more likely to
have favorable attitudes toward bureaucracies and democratic government, following Andrain
and Smith (2006). And, an individual with more interests in politics is expected to have higher
71

Age variable is modeled as a potentially non-linear effect, following Van der Meer’s (2010)
suggestion. Along with age, an additional variable, which is squared and divided by 100 (to
adjust extreme values), is included. Age is recoded so that the value of zero is meaningful: zero
indicates a 47-year-old person.
72
Education variable ranges from -3 (“No formal qualification”) to 2 (“University degree
completed”). The value of zero indicates “Higher secondary complete.”
73
The responses are dichotomized. The survey question asks “Generally speaking, would you
say that people can be trusted or that you can’t be too careful in dealing with people?”
Responses are recoded: “People can almost always be trusted” and “People can usually be
trusted” are coded as 1, and “You usually can’t be too careful in dealing with people” and “You
almost always can’t be too careful in dealing with people” are coded as 0.
74
Political interest variable is measured using the survey question, “How interested would you
say you personally are in politics?” This variable ranges from -1.5 (not at all interested) to 1.5
(very interested).
75
It should be noted that some variables are not controlled in this analysis due to the lack of data.
For example, as mentioned earlier, Anderson and Guillory (1997) argue that individuals’
political majority/minority status affects levels of satisfaction with democracy and this
relationship is mediated by political institutions. But, this political allegiance account is not
included in the analyses here because of a large number of missing values in the responses to a
party-identification question in the 2004 ISSP data. For example, 40% of respondents do not
answer this question in Austria and 53% in Poland. Another factor that cannot be controlled is
the perception of personal economic situation. The variable of ‘Top - Bottom self-placement,’
which is available in the 2004 ISSP data, can be used; but I do not include this because it is not
asked in the United Kingdom. Although no data are available to directly measure a personal
financial situation, the employment status (unemployed) can be considered as a proxy for this
variable (McAllister 1999).
107

levels of political support, as prior studies have found (e.g., Anderson and Guillory 1997;
Anderson and Tverdova 2003). A dummy variable for public sector workers is also included
because an individual who works for the government is more likely to have positive attitudes
toward government than one who does not.
Additionally, for the model predicting citizens’ satisfaction with democracy, I account for
an individual’s perceived level of fairness of election.76 This is because peoples’ view of
democracy is closely related to the free and fair elections (Chu et al. 2008). I posit that a
respondent is more likely to be satisfied with democracy as she perceives elections as fair.

4.6.3.2 Country-level Control Variables

The country mean of the individual-level variables are included in order to discern their
between- and within-country effects (see Bartels 2008; Enders and Tofighi 2007). Particularly,
following Newton (2001), I expect that the aggregated level of generalized trust in each country
(i.e., the percentage of trusting persons in a country) will have a positive association with public
attitudes toward bureaucracies and democratic performance. As mentioned earlier, there are
several mechanisms that would make this link possible. People in general more actively demand
what they want and participate in politics in a high-trust society; governments will perform better
due to reduced transaction costs in such a society (Fukuyama 1995; Newton 2001; Putnam 1993;
Tavits 2006). And, it needs to be tested empirically whether or not this aggregated level
relationship is different from the individual level association.

76

The perceived fairness of election is measured using the following survey question: “Thinking
of the last national election in (COUNTRY), how fair was it regarding the opportunities of the
candidates and parties to campaign?” The response ranges from -2 (very unfair) to 2(very fair).
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To account for alternative explanations for the cross-national differences in political
support, I consider three other country-level variables.77 First, I control for economic growth rate
using the annual percentage growth rate of GDP/per capita from the World Bank. Previous
studies show that economic performance could contribute to higher levels of support for
governments (Anderson and Tverdova 2003; McAllister 1999; Norris 1999c). Economic growth
should be positively related to political support because people link better national economic
performance with well-functioning bureaucracies and their democratic government.
The level of democracy is another explanation for cross-level variations in levels of
political support. For example, McAllister (1999) and Mishler and Rose (2001) suggest that
political support in post-Communist countries can be different from other Western European
nations because of their relatively new experience with democratic institutions. Also, as
individuals’ freedom and political rights are protected in a nation, people will have higher levels
of political support. Thus, the level of democracy can be an important factor to explain crossnational differences in levels of political support. To measure the level of democracy, I include
the Freedom House index (i.e., the average score of ‘political rights’ and ‘civil liberties’).78
Third, I control for the type of electoral system. The consensual democracy argument
implies that the government in countries with PR electoral systems will have higher levels of
political support because citizens’ preferences are better represented and public participation is
generally encouraged (Lijphart 1999; Norris 1999b; Norris 2008). However, following
Rohrschneider (2005), I do not expect that the type of electoral systems will explain the
77

They are measured at the year the ISSP survey was actually conducted in each country.
Country-level control variables (except for a dummy variable of PR electoral systems) are grandmean centered so that the value of zero is meaningful. See Table C.2, in Appendix C, for the
correlation matrix for these variables.
78
This variable is recoded from the original data so that it theoretically ranges from -3 (least free)
to 3 (most free). In the dataset, it ranges from 1.5 to 3.
109

variations of political support across nations—especially in terms of attitudes toward
bureaucracies and satisfaction with democracies. This is because majoritarian and PR electoral
systems target different properties of democracies—either accountability or representation—and,
thus, the PR system will not particularly contribute to higher levels of political support (Golder
and Stramski 2010). I measure PR electoral systems using the Database of Political Institutions
(DPI) (Beck et al. 2001; Teorell et al. 2011).
Note that the consensual democracy argument also suggests that a parliamentary system
and a federal system perform better than a presidential system and a unitary system, respectively
(Lijphart 1999; Norris 2008). Thus, one may argue that nations with such power-sharing
institutions will have higher levels of political support. However, I expect that all these political
institutions will not have direct effects on political support, as in the case of electoral systems,79
and thus they are not included in the current analyses. 80

79

Although it is plausible that such power-sharing institutions contribute to the better quality of
democracy (Lijphart 1999; Norris 2008), their impact on public attitudes is ambiguous. In fact,
studies have not focused on the consequences of these institutions on public attitudes. For
example, the merits of each form of government have been discussed (e.g., Cheibub 2006;
Gerring, Thacker, and Moreno 2005; Linz 1990), but it is not clear whether parliamentary
systems will lead to higher political support than presidential systems. Norris (1999) finds a very
small difference between parliamentary and presidential systems in the level of political support.
Moreover, Weaver and Rockman (1993) argue that the institutional influences on government
effectiveness are more relevant to the second tier institutions, including electoral rules. They also
show a greater variation of government effectiveness within both presidential and parliamentary
systems. Regarding federal versus unitary systems, the consensual democracy argument predicts
that federal systems represent public preference better than unitary systems, because the power is
shared between different levels of government and minority communities are provided with
autonomy (Lijphart 1999; Norris 2008). However, again, its consequence on public attitudes
toward government is unclear. In addition, I think that the forms of government (i.e., presidential
or parliamentary) or the systems of the state (i.e., federal or unitary) will influence the variations
in the characteristics of bureaucracies; thus, they may have an indirect effect on political support.
I tested the model controlling for the forms of government and the systems of the state, and the
results supported my expectations. The differences in the level of political support between
presidential and parliamentary systems (which is measured with a system variable from the DPI)
and between (constitutional) federal and unitary systems (which is measured using a fedtype
110

4.7 Empirical Results and Discussion: Citizens’ Attitudes toward Bureaucracies Models

Let us begin with the model that explores public attitudes toward bureaucracies.81 First,
because my main interest is to examine the relationship between Bureaucratic Profiles and
political support, I start with a simple random intercept model. The results of the random
intercept models are discussed in Section 4.7.1. Meanwhile, the relationship between
Bureaucratic Profiles and political support is further illustrated in Section 4.7.2. Section 4.7.3
presents random slope models, because it is important to explore the possible random
coefficients for a statistical consideration.

4.7.1 Random Intercept Models: Attitudes toward Bureaucracies

The results for random-intercept multilevel models for citizens’ attitudes toward
bureaucracies are presented in Table 4.3 and 4.4. I begin with the model where only individuallevel variables are included to explain public attitudes (Model 1) and compare it with the model
where my key independent variable, Bureaucratic Profiles is added (Model 2) in Table 4.3. The
models with other country-level controls are shown in Table 4.4. Let us begin with the model
where only individual-level variables are included.

variable from Norris (2008)) are not statistically significant, controlling for the individual-level
variables. The results are the same when Bureaucratic Profiles is controlled, while the
relationship between Bureaucratic Profiles and political support is statistically significant.
80
Descriptive statistics for all variables used in the analysis can be found in Table C.1 in
Appendix C.
81
All multilevel models in this chapter are estimated using xtmixed command in STATA
SE10.1. The residual diagnostics are discussed in Appendix D.
111

Table 4.3. Random-Intercept Multilevel Models of Citizens’ Attitudes toward
Bureaucracies, with Individual-level Controls
Only Level 1 Controls
(Model 1)

Bureaucratic Profiles with
Level 1 Controls
(Model 2)

Country Level
.108 (.028)*
Bureaucratic Profiles
GDP/capita growth rate
Democracy
(Freedom House)
Electoral system: PR
Country-level
generalized trust
Individual Level
.264 (.010)*
.264 (.010)*
Generalized Trust
*
-.001 (.000)
-.001 (.000)*
Age
.009 (.002)*
.009 (.002)*
Age2/100
*
-.021 (.010)
-.021 (.010)*
Female
.013 (.004)*
.013 (.004)*
Education
*
-.070 (.024)
-.070 (.024)*
Unemployed
.189 (.012)*
.189 (.012)*
Public Sector Worker
*
.066 (.006)
.066 (.006)*
Political Interest
*
-.173 (.055)
-.155 (.045)*
Constant
Variance Components
.082
.053
Country-level intercept
Individual-level
.555
.555
Residuals
Proportion Reduction in
Error:**
08.11%
12.22%
at Level 1
25.13%
51.19%
at Level 2
57348.854
57341.774
-2* Log-Likelihood
Note: The restricted maximum likelihood (REML) estimator is used. Standard errors are in
parentheses. Number of observations = 25409. Number of countries = 28.
*
Coefficient is statistically significant at .05 level, directional hypothesis test.
**
Proportion Reduction in Error is calculated at both levels, as suggested by Snijders and Bosker
(2011). See ft. 82.

As shown in Model 1, individual-level variables generally display the expected
associations with attitudes toward bureaucracies. Specifically, males and those who attain higher
education are more likely to have positive attitudes than their counterparts, on average,

112

everything being equal. This suggests that people who are in a socioeconomically better situation
have more positive views on bureaucracies, perhaps because their experience with government
agencies has been relatively good. In addition, ‘age’ variable has a non-linear effect: attitudes
toward bureaucracies are the most negative among the mid-fifties. It may imply that individuals
begin to view bureaucracies more negatively as they expose to more hardship and accumulate
social experiences, especially from their twenties to fifties. But, then, as they are retired and get
older, they become to have positive views. This non-linear relationship, alternatively, may reflect
the generational differences. That is, those who are in mid-fifties in 2004 have more negative
attitudes toward bureaucracies than other generations.
Other individual-level variables also have expected relationships. As hypothesized,
public sector workers have more positive attitudes than a person who does not work in the public
sector. Perhaps, this is because a bureaucrat views himself and his colleagues as being committed
to the service, and bureaucracies as doing a good job in general. Furthermore, an individual with
greater interest in politics is more likely to have positive attitudes toward bureaucracies. This is
perhaps because a person is more likely to have accurate information about how government
bureaucracies are performing as he gets interested in politics. An individual who is not interested
in politics may have a negative image of bureaucracies without concrete evidence.
Finally, an unemployed respondent shows less positive attitudes toward bureaucracies.
This seems to reflect two things. On the one hand, the unemployed are in an economically
vulnerable situation than the employed and, thus, they view governmental bureaucracies more
negatively. On the other hand, the unemployed may blame governments’ economic performance
for their employment status and, thus, have negative attitudes about bureaucracies.

113

When Bureaucratic Profiles is included, as shown in Model 2 of Table 4.3, the
proportion reduction in error (PRE) 82 at the level-2 increases from 25.13% to 51.19%. The
positive coefficient of this variable means that as the value of Bureaucratic Profiles increases by
one radian, public attitudes toward bureaucracies increase by .108 points, on average, when
individual-level variables are held constant. The effect of Bureaucratic Profiles when all controls
are included will be discussed in more detail later in Section 4.7.2.
As the next step of model-building, I test whether within- and between-country effects
are different for all individual-level variables. It turns out that only interpersonal trust has an
independent between-country effect (see Table C.3 in Appendix C). Model 3 in Table 4.4 shows
the results, when the aggregated-mean of interpersonal trust is included. Other aggregated mean
variables are dropped for the parsimoniousness of the model.
The t-ratio for the coefficients of this “Country-level generalized trust” variable (t
= .012/.003 = 4) indicates that a between-country effect of interpersonal trust is indeed different
than its within-country effect. That is, a trusting person is more likely to have positive attitudes
toward bureaucracies (by .263 points higher) than a person who does not have generalized
interpersonal trust, all things being equal. At the same time if her country has more trusting
82

There are several ways of calculating the variance explained by the model (Hoffman 2010). In
Table 4.3 through Table 4.8 (and in Appendix C), I report the proportional reduction of error
(PRE) at both level-1 and level-2, as suggested by Snijders and Bosker (1999). PRE for
predicting level-1 and level-2 outcomes are calculated as following, respectively:

Also note that for calculating PRE for random slope models, they suggest using the parameter
estimates obtained from the model with the same fixed part but omitting random slopes (i.e., the
random intercept model). Thus, PREs are calculated in this way for the following random slope
models, Models 5, 6, 11, and 12.
114

persons than other countries, it will contribute to more positive attitudes, independent from
whether or not she trusts people in general. Therefore, an individual in a country with a larger
proportion of trusting people (which can be interpreted as a country with higher levels of social
trust) is, on average, more likely to have more positive attitudes about bureaucracies than an
individual in a nation with lower levels of social trust, all things being equal. Specifically, the
between-country effect of interpersonal trust is .275 (=.263 + .012). This finding provides
support for the association between social trust and political support at both the individual- and
the country-level. Previous studies have debated about the level this relationship holds (e.g.,
Newton 2001), and this result suggests that both are possible. This leads to an interesting
question of whether the effect of an interpersonal trust on attitudes toward bureaucracies varies
depending on the level of social trust in society. And this will be discussed further with the
random slope models explored in Section 4.7.3.
As shown in Model 3 (in Table 4.4), the coefficient for Bureaucratic Profiles is positive
and statistically significant, when the aggregated-mean of interpersonal trust in included,
although the size of the effect decreases from .108 to .074. When other country-level variables
are controlled, the coefficient for Bureaucratic Profiles changes to .053, but is still positive and
statistically significant.83

83

One may suspect the multicollinearity between country-level variables or concern that the
inclusion of a number of country-level variables causes model instability due to the smaller
number of level-2 units. Therefore, I tested the models where the key independent variable,
Bureaucratic Profiles, and each one of three county-level control variables are included. The
results are presented in Table C.4 in Appendix C. The coefficient for each of county-level
control variables does not reach statistical significance, holding constant for Bureaucratic
Profiles and other individual-level variables (and aggregated level of generalized trust). But, the
coefficient for Bureaucratic Profiles is always statistically significant. These results provide
supportive evidence for the relationship between Bureaucratic Profiles and citizens’ attitudes
toward bureaucracies.
115

Table 4.4. Random-Intercept Multilevel Models of Citizens’ Attitudes toward
Bureaucracies, with Country-level Controls
With the Aggregated-Mean of
Level 1 Controls
(Model 3)

With Level 2 Controls
(Model 4)

Country Level
.074 (.025)*
.053 (.030)*
Bureaucratic Profiles
-.041 (.031)
GDP/capita growth rate
Democracy
.147 (.122)
(Freedom House)
-.048 (.115)
Electoral system: PR
Country-level
.009 (.003)*
.010 (.003)*
generalized trust
Individual Level
.263 (.010)*
.263 (.010)*
Generalized Trust
*
-.001 (.000)
-.001 (.000)*
Age
.009 (.002)*
.009 (.002)*
Age2/100
*
-.021 (.010)
-.021 (.010)*
Female
.013 (.004)*
.013 (.004)*
Education
*
-.070 (.024)
-.070 (.024)*
Unemployed
.189 (.012)*
.189 (.012)*
Public Sector Worker
*
.066 (.006)
.066 (.006)*
Political Interest
*
-.161 (.038)
-.120 (.099)
Constant
Variance Components
.038
.036
Country-level intercept
.555
Individual-level
.555
Residuals
Proportion Reduction in
Error:**
14.50%
14.71%
at Level 1
65.61%
66.95%
at Level 2
57341.816
57347.866
-2* Log-Likelihood
Note: The restricted maximum likelihood (REML) estimator is used. Standard errors are in
parentheses. Number of observations = 25409. Number of countries = 28.
*
Coefficient is statistically significant at .05 level, directional hypothesis test.

The results shown in Model 4 (in Table 4.4) indicate that as the value of Bureaucratic
Profiles increases by one radian, public attitudes toward bureaucracies increase by .053 points,
all things being equal. That is, let say there are two individuals who are the same but different
only in their countries’ Bureaucratic Profiles: One person’s country has the value of

116

Bureaucratic Profiles that is one radian greater than the country of another person. This person
will have, on average, .053 points more positive attitudes toward bureaucracies than another.
Because, in general, as the value of Bureaucratic Profiles increases, a bureaucracy presents
greater degree of impartiality and smaller level of competency, relative to other traits, this
positive relationship suggests that citizens are more likely to have favorable attitudes toward
bureaucracies when bureaucratic impartiality is emphasized more than other traits, especially
competency. I provide a more substantive interpretation of the relationship between Bureaucratic
Profiles and attitudes toward bureaucracies in the next section.
The results in Model 4 also indicate that, holding constant for Bureaucratic Profiles and
other individual-level variables (and aggregated level of generalized trust), economic growth,
levels of democracy, and PR electoral systems do not have statistically significant relationships
with public attitudes toward bureaucracies.84 Further explanations for these country-level control
variables are discussed in the random slope models (in Section 4.7.3).

4.7.2 The Relationship between Bureaucratic Profiles and Attitudes toward Bureaucracies

Let us look at in more detail the relationship between my key independent variable,
Bureaucratic Profiles, and public attitudes toward bureaucracies. The results reported in Model 4
(in Table 4.4) show that there is a positive and statistically significant relationship between
Bureaucratic Profiles and popular attitudes toward bureaucracies, as expected, when both
individual- and country-level variables are controlled. In order to interpret this relationship
substantively and in more depth, let us explore which bureaucratic profiles are associated with
84

The results are the same when different economic indicators—GDP per capita (logged) and
inflation rate—are used.
117

the highest and lowest predicted values of dependent variable, public attitudes toward
bureaucracy. Figure 4.2 shows the mean of the predicted values for each country, along with 95%
confidence intervals. The predicted values are calculated by considering linear predictors of the
fixed part and the contributions based on predicted random effects (which here is a random
intercept).

1

Figure 4.2. Predictions and approximate 95% confidence intervals versus ranking of
citizens’ attitudes toward bureaucracies based on Model 4 in Table 4.4

NZ

.5

CH AT

BE DE ES

US IL

DK

MXPT SI CL KR

0

Prediction

FRHU
FR
HU

SE NL

NO
AU FI CA UK IE

SK
JP

CZ

-1

-.5

PL

0

10

20

30

Rank

Note: The predicted values are calculated based on a random-intercept model for citizens’
attitudes toward bureaucracies, which are shown in Model 4 in Table 4.4. The predictions for
public attitudes toward bureaucracies are aggregated at the country-level. These values are
ranked and plotted with approximate 95% confidence intervals. Country names are shown on top
of confidence intervals. See Note in Figure 3.3 for the full names of the thirty-four countries.

118

Which countries have the highest and the lowest predicted values of attitudes toward
bureaucracies? As shown in Figure 4.2, New Zealand has the highest predicted value. The
bureaucratic profile of this country indicates that impartiality is emphasized the most and careerbased system is presented the least in a bureaucracy. The specific order of the relative levels of
bureaucratic traits exhibited in this nation is: Impartiality, independence from politics, female
representativeness, competency, and career-based system (see Table 4.1). That is, in a nation
with the most positive attitudes toward bureaucracies, political independence and female
representativeness are also emphasized to some extent, but less than impartiality. And, careerbased system and competency are the least presented attributes. On the other hand, Poland has
the lowest predicted value of public attitudes toward bureaucracy. And Poland’s bureaucratic
profile shows that bureaucratic traits are presented in the following order: Competency, female
representativeness, career-based system, political independence, and impartiality (see Table 4.1).
First, the comparison of these two nations’ bureaucratic profiles suggests that citizens are
more likely to view their bureaucracy positively when impartiality is presented to the greater
extent than other traits in a bureaucracy. This is because New Zealand emphasizes impartiality
more than other four traits, whereas impartiality is the least exhibited trait in Poland. The
comparison also suggests that when competency is emphasized more than other traits and, at the
same time, when impartiality is the least presented trait, citizens perceive a bureaucracy more
negatively.
To understand this visually, let us look at Figure 4.3. New Zealand is around the “2:00”
position and Poland is around the “9:00” position. And, the closest point to the vector terminus is
impartiality in case of New Zealand, whereas it is competency for Poland. Moreover, Figure 4.3
shows that as a nation vector moves in a counterclockwise direction from Poland to New

119

Figure 4.3. Full MDPREF model, with the vectors for New Zealand and Poland

Note: Open circles and closed circles represent the nation vector’s terminal points and the five
traits, respectively, that are estimated from the MDPREF analysis as discussed in Chapter 3. The
vectors for New Zealand and Poland, nations with the highest and the lowest predicted value of
citizens’ attitudes toward bureaucracies, respectively, are presented with arrows (in solid lines)
and their extended lines (in dashed lines). The vector for New Zealand is shown with an arrow
pointing upper-right in the space. It is estimated that the bureaucratic traits are presented in New
Zealand in the order of impartiality, independence from politics, female representativeness,
competency, and career-based system. The vector directing upper-left is for Poland, where the
bureaucratic traits are exhibited in the order of competency, female representativeness, careerbased system, independence from politics, and impartiality. See Note in Figure 3.3 for the full
names of the thirty-four countries.
120

Zealand,85 the relative importance of competency decreases and that of impartiality increases. In
turn, it indicates that individuals view bureaucracies more favorably when impartiality is
presented to a greater degree than competency in their national bureaucracies.
Second, the result suggests that when independence from politics is emphasized, in
addition to bureaucratic impartiality, citizens are more likely to have positive attitudes toward
bureaucracies. In New Zealand’s bureaucratic profile, independence from politics is the second
most important trait. But, it is the fourth one in Poland.
On the one hand, this may reflect that political independence generally present together
with impartiality in a bureaucracy. And, it provides some support that a sore emphasis on
politicization will not have a positive impact on popular attitudes toward bureaucracy. As
political independence is exhibited to a lesser degree than other traits, which may occur with
increasing politicization, citizens tend to have negative attitudes toward bureaucracies. On the
other hand, it does not necessarily mean that people view bureaucracies the most favorably when
political independence is emphasized more than other traits in a bureaucracy: nations around the
“5:00” position do not enjoy the most positive public attitudes of bureaucracies. Therefore, these
findings suggest that independence from politics is an important bureaucratic trait, but it should
not undermine impartiality in a bureaucracy.
Is the relative level of female representativeness also closely related to different levels of
public attitudes toward bureaucracies? It is the third most important trait in New Zealand and it is
the second most important one in Poland. The relative level of female representativeness itself
appears not to be a critical factor shaping popular view about bureaucracies. But, as closely
examining bureaucratic profiles, it shows that the rank order between impartiality and
85

Recall that as the value of Bureaucratic Profiles increases, a nation vector in the joint space
moves in a counterclockwise direction.
121

representativeness changes as a nation vector moves from Poland to New Zealand (in a
counterclockwise direction). That is, impartiality is exhibited less than representativeness in
Poland, and it is the opposite in New Zealand. It suggests that, therefore, bureaucracies are
viewed favorably by citizens when impartiality is emphasized over representativeness.
Finally, how is the relative level of career-system exhibited in a bureaucracy associated
with public attitudes toward bureaucracies? It is the least presented trait in New Zealand, while
the third most important trait in Poland. Does this mean that people view their bureaucracy more
positively when career-based system is displayed at a lower level than other traits? The
relationship does not seem to be straightforward. On the one hand, if both bureaucratic
impartiality and political independence are secured, as in New Zealand, then attitudes toward
bureaucracies become more positive as career-based system is exhibited less than other traits. On
the other hand, this relative emphasis on position-based system (i.e., lack of career-based system)
does not always have a positive effect on public attitudes: for nations where bureaucracies are
not impartial and under greater political influence, emphasis on position-based system seems to
be associated with more negative popular attitudes toward bureaucracies. This may reflect
problems faced by some countries that implemented public management reforms, while basic
levels of impartiality and political independence are not established, as in Central and Eastern
European countries (Raadschelders, Toonen, and Van der Meer 2007).
Taking all the interpretations discussed previously into consideration, I conclude that
citizens are more likely to view a bureaucracy positively when impartiality (together with
political independence) is presented more than other traits and, at the same time, competency is
relatively less emphasized than other attributes. This implies that people may want bureaucracies
prioritize impartiality over other traits.

122

In addition to this, more importantly, the findings provide information about how other
bureaucratic traits should be presented, relative to impartiality, in a national bureaucracy. In
other words, to be a bureaucracy that is suitable in democratic societies and, more specifically,
one that is publicly supported, some degree of independence from politics should be guaranteed.
An exclusive emphasis on political independence is not desirable, but lack of political
independence is also problematic. Moreover, prioritizing competency over other bureaucratic
traits is also not viewed positively by citizens. When a bureaucracy has high levels of
competency, but it is not impartial, and not free from political influence, people may think that
such a bureaucracy is not serving the public interest. Instead, this type of bureaucracy only
enjoys a predominant status but behaves arbitrarily. Finally, only when impartiality is
emphasized more than other traits, a higher level of female representativeness and a lower degree
of career-based system seem to be associated with positive popular attitudes toward
bureaucracies.

4.7.3 Random Slope Models: Attitudes toward Bureaucracies

This section examines the possibilities of varying individual-level coefficients across
nations, following Snijders and Bosker’s (2011) suggestion. The previous model (Model 4) is reexamined by allowing all individual-level variables to vary across nations. As done previously, I
only retain significant random slopes in the model, based on the deviance difference test. Model
5 in Table 4.5 shows the results for the random-slope model when Bureaucratic Profiles and
other country-level control variables are included.

123

Table 4.5. Random-Slope Multilevel Models of Citizens’ Attitudes toward Bureaucracies
Random Slopes,
with Level 2 Controls
(Model 5)

Cross-level Interaction:
Interpersonal Trust and
Social Trust (Model 6)

Country Level
.046 (.025)*
.070 (.024)*
Bureaucratic Profiles
.016 (.024)
GDP/capita growth rate
Democracy
.322 (.099)*
(Freedom House)
-.117 (.092)
Electoral system: PR
Country-level
.014 (.003)*
.007 (.003)*
generalized trust
Individual Level
.261 (.018)*
.259 (.017)*
Generalized Trust
-.000 (.001)
-.000 (.001)
Age
2
*
.009 (.002)
.009 (.002)*
Age /100
-.022 (.010)*
-.022 (.010)*
Female
*
.014 (.004)
.014 (.004)*
Education
-.069 (.023)*
-.069 (.023)*
Unemployed
*
.197 (.018)
.197 (.018)*
Public Sector Worker
.061 (.010)*
.062 (.010)*
Political Interest
Cross-level Interactions
Generalized Trust *
Country-level
.003 (.001)*
generalized trust
Generalized Trust *
Democracy (Freedom
House)
-.068 (.088)
-.162 (.043)*
Constant
Variance Components
.062
.048
Country-level intercept
Individual-level
.006
.005
Generalized Trust
.000
.000
Age
.004
.005
Public Sector Worker
.002
.002
Political Interest
.549
.549
Residuals
Proportion Reduction in
Error:
14.71%
14.32%
at Level 1
66.95%
64.20%
at Level 2
57193.39
57197.1
-2* Log-Likelihood
Note: The restricted maximum likelihood (REML) estimator is used. Standard errors are in
parentheses. Number of observations = 25409. Number of countries = 28.
*
Coefficient is statistically significant at .05 level, directional hypothesis test.
124

The results indicate that four variables have random slopes. The effects of interpersonal
trust, age, public sector workers, and political interest vary across nations. Whereas the reason
why these effects vary is another interesting topic to investigate in future studies, in this chapter I
will focus on the effect of interpersonal trust.
Indeed, this is one of interesting findings: interpersonal trust has both the within- and the
between-country effects, and the within-country effect varies across countries. This indicates that
a trusting person is more likely to have positive attitudes toward bureaucracies than a person who
does not have interpersonal trust, but the size of this effect (precisely, difference between the two)
is not the same for all nations. One possible explanation for the random slope of interpersonal
trust is levels of social trust in a country. A trusting person views bureaucracy more positively,
but this is more likely to happen when other people in her county also tend to trust others—
which means, higher levels of social trust in her country.
Recall that prior studies posit that interpersonal trust increases a person’s engagement in
social and political activities, and this makes the government more trustworthy (e.g., Fukuyama
1995; Putnam 1993; Tavits 2006). But, in order to make this happen, there should be a relatively
large number of people with generalized interpersonal trust. If a trusting person lives in a nation
with lower levels of social trust (i.e., if a smaller percentage of people have interpersonal trust in
her country), the impact of interpersonal trust on political support may be less recognizable.
In order to take this possibility into account, I test the model including a cross-level
interaction effect between interpersonal trust and the percentage of trusting persons in a nation.
In the model, I only include the main independent variable, Bureaucratic Profiles, and other
country-level independent variables are not included. This is because of a possible
multicollinearity problem and also because too many level-2 variables can cause model

125

instability, especially when cross-level interactions are considered (Hoffman 2010; Snijders and
Bosker 2011). The results are shown in Model 6 in Table 4.5.

.2

.4

.6

Figure 4.4. Cross-level interaction effect between generalized trust and social trust on
citizens’ attitudes toward bureaucracies

0

Trusting person

-.4

-.2

Untrusting person

0

20

40
60
Percentage of trusting persons

80

100

Note: It shows the changes in the difference between trusting and untrusting person in their
attitudes toward bureaucracies across different levels of social trust (i.e., percentage of trusting
persons in a nation). Two reference lines (i.e. dotted lines) indicate the range of observed values
of social trust. The results are based on Model 6 in Table 4.5.

When the multiplicative term is included in the model, the cross-level interaction effect is
statistically significant. This implies that the individual-level effect of interpersonal trust varies
across nations and the effect gets greater if an individual is living in a country with higher levels
of social trust. Specifically, a trusting person in a country where 47% of people have generalized
trust has more positive attitudes about bureaucracies than an untrusting person, by .259 higher
126

points, all other things held constant. But, when there are 10% more trusting people in her
country, everything being equal, she is more likely to view bureaucracies positively by .289
higher points (= .259 + .003*10), compared to an untrusting person. This suggests that the
individual-level effect of interpersonal trust becomes greater in a high-trust society.86 This
interaction effect is depicted in Figure 4.4.
For the effects of country-level independent variables, let us look at Model 5 in Table 4.4
again. In the random-slope model (Model 5), the relationship between Bureaucratic Profiles and
public attitudes toward bureaucracies holds as in the previous models, although the size of the
effect changes to .046. What about country-level control variables? Similar to the random
intercept model with the country-level controls (Model 4), economic growth does not have a
statistically significant relationship with attitudes toward bureaucracy. The sign of the coefficient
for economic growth is positive in the random slope model (Model 5), but it is not statistically
different than zero. Interestingly, the relationship is found to be negative in the random intercept
model (Model 4) and also in other models with a smaller number of country-level variables.87
When the economic condition is the only country-level independent variable, it has a
negative and statistically significant relationship with public attitudes. It could be a result of
higher expectation of people in nations with better economic conditions, as McAllister (1999)
argues. However, when Bureaucratic Profiles is included in the model, this relationship becomes
non-significant. This suggests the properties of bureaucracy may matter more for citizens than a
86

This interesting result and implication need to be explored further in future studies. Especially,
because ‘the percentage of trusting people’ is a very limited measurement for social trust, future
studies to reexamine this relationship using better indicators of social trust will be fruitful. For
example, additional data on the reciprocity norms and network organizations could improve the
measurement of social trust (Paraskevopoulos 2010).
87
This association is negative and not significant when two other country-level control variables
are excluded from Model 4 (see Appendix C, Table C.4). Moreover, it is the same when other
economic indicators, GDP per capita (logged) and inflation rate, are used.
127

nation’s economic situation because bureaucracies influence their daily lives and well-being
through the policy making and service delivery (Coggburn and Schneider 2003; Whiteley et al.
2010).
PR electoral systems have a negative effect—that is, people have more negative attitudes
when their nation adopts PR electoral systems—but, it is not statistically different than zero in
the random slop model (Model 5), the random intercept model (Model 4), and the model where
other country-level controls are not included (see Table C.4 in Appendix C). The absence of a
significant effect for PR systems lends support to my expectation. The negative relationship
could be observed because PR electoral systems are likely to produce a multiparty parliament
with no single chain of accountability (Norris 2004). But, it is not statistically significant effect.88
Different from the random intercept model (Model 4), the level of democracy now has a
statistically significant relationship with public attitudes toward bureaucracies. More democratic
nations enjoy higher levels of support for bureaucratic institutions, on average, if economic
conditions, electoral systems, levels of social trust, and a bureaucratic profile are held constant.
Specifically, compared to a citizen in a country with a Freedom House score of 2, a person in a
country with the score of 3, on a scale ranging from -3 (least free) to 3 (most free), is more likely
88

One may argue that electoral systems influence public attitudes indirectly through levels of
corruption in the government. Chang and Golden (2006) argue that corruption increases with
district magnitudes under open-list PR systems. Given this argument, it is possible that public
attitudes toward bureaucracies decrease when the country adopts open-list PR systems and has a
large number of district magnitudes. To account for this, I tested the model with three
variables—open-list PR systems, the average number of representatives elected by each electoral
district to the lower house, and their multiplicative term—instead of one variable, PR systems
(data are collected from DPI 2004 (Beck et al. 2001)). The multiplicative term is statistically
significant and negative in the random intercept model. It seems that people have more negative
attitudes about bureaucracies when district magnitudes increase, under open-list PR systems, but
not in closed-list PR systems. But, it is not significant in the random slope model (and also in the
model for satisfaction with democracy) and, thus, it will need a further investigation. Note that
the association between Bureaucratic Profiles and public attitudes holds in these model
specifications.
128

to have more positive attitudes toward bureaucracies by .322 points, all things being equal. When
freedom and civil liberty are less protected in a nation, individuals appear to view bureaucracies
as being more arbitrary and less committed to serve people.

1

Figure 4.5. Predictions and approximate 95% confidence intervals versus ranking of
citizens’ attitudes toward bureaucracies based on Model 5 in Table 4.5

FI

.5

SE
FR

Prediction

HU

NO DK
AUCA

NL US

BE DE ES

AT NZ
CH

UK IE

IL

0

CL
CZ SK

MX

PT

SI

KR

JP

-1

-.5

PL

0

10

20

30

Rank

Note: Based on a random-slope model for citizens’ attitudes toward bureaucracies, which are
shown in Model 5 in Table 4.5, the predicted values are calculated. The predictions for public
attitudes toward bureaucracies are aggregated at the country-level. These values are ranked and
plotted with approximate 95% confidence intervals. Country names are shown on top of
confidence intervals. See Note in Figure 3.3 for the full names of the thirty-four countries.

The comparison of the random-slope model with the random-intercept one provides
further support for the positive relationship between Bureaucratic Profiles and public attitudes
toward bureaucracies: it is robust when random slopes are considered. Figure 4.5 presents the

129

mean of the predicted values for each country, along with 95% confidence intervals, based on
Model 5 in Table 4.5. The ranking of the country’s mean predicted values is the same as that
obtained from the random-intercept model.

4.8 Empirical Results and Discussion: Citizens’ Satisfaction with Democracy Models

Let us turn to the next model that examines political support for the regime performance,
satisfaction with operation of democracy. The models are analyzed and discussed in the same
order as in the models for attitudes toward bureaucracies. Section 4.8.1 discusses random
intercept models; the findings of the relationship between Bureaucratic Profiles and satisfaction
with democracy are further explained in Section 4.8.2. The results from random slope models are
presented and discussed in Section 4.8.3. The cross-level interaction between interpersonal trust
and social trust is also considered. The findings are generally similar to the models for attitudes
toward bureaucracies; the similar results are discussed briefly.

4.8.1 Random Intercept Models: Satisfaction with Democracy

Table 4.6 and Table 4.7 present four random-intercept models. Model 7 in Table 4.6
presents the model when only the individual-level controls are included; it is compared to Model
8, where Bureaucratic Profiles is added. In addition, I test the within- versus between-country
effect of individual level variables and include aggregated-mean of interpersonal trust in Model 9
in Table 4.7. The model when all country-level controls are included is shown in Model 10 in
Table 4.7. Let us begin with interpreting the effects of individual-level variables.

130

Table 4.6. Random-Intercept Multilevel Models of Citizens’ Satisfaction with Democracy,
with Individual-level Controls
Only Level 1 Controls
(Model 7)

Bureaucratic Profiles with
Level 1 Controls
(Model 8)

Country Level
.359 (.054)*
Bureaucratic Profiles
GDP/capita growth rate
Democracy
(Freedom House)
Electoral system: PR
Country-level
generalized trust
Individual Level
.525 (.027)*
.527 (.027)*
Generalized Trust
-.000 (.001)
-.000 (.001)
Age
.010 (.005)*
.010 (.005)*
Age2/100
*
-.116 (.025)
-.116 (.025)*
Female
.096 (.010)*
.095 (.010)*
Education
*
-.209 (.063)
-.208 (.063)*
Unemployed
.019 (.032)
.019 (.032)
Public Sector Worker
.155 (.016)*
.155 (.016)*
Political Interest
Perceived Election
.503 (.013)*
.502 (.013)*
Fairness
*
5.504 (.140)
5.561 (.090)*
Constant
Variance Components
.533
.204
Country-level intercept
Individual-level
3.783
3.783
Residuals
Proportion Reduction in
Error:
13.71%
20.28%
at Level 1
33.82%
74.63%
at Level 2
102369.03
102346.46
-2* Log-Likelihood
Note: The restricted maximum likelihood (REML) estimator is used. Standard errors are in
parentheses. Number of observations = 24513. Number of countries = 28.
*
Coefficient is statistically significant at .05 level, directional hypothesis test.

As shown in Model 7 in Table 4.6, individual-level variables mostly have expected
relationships with citizens’ satisfaction with democracy, except that there is no statistically
significant difference in levels of satisfaction between public sector workers and those who are

131

not. It shows that individuals’ opinion about the operation of democracy in their nation is not
influenced by the work place, especially whether they work for government or not. Perhaps, this
is because the ways that people participate in politics and their demands are channeled to the
government are not critically different between governmental and non-governmental workers.
In regards to other variables, males and those who attain higher-level education are more
likely to be satisfied with democratic performance than their counterparts. Age has a non-linear
effect: levels of satisfaction with democracy are the lowest among the late-forties. As in the
models for attitudes toward bureaucracies, it seems that people become less satisfied as they get
older from their twenties to late-forties. But the older generations (e.g., the sixties in the year of
the survey) generally have higher levels of satisfaction than the younger generations.
In addition, the unemployment status has a negative association, as expected. The
unemployed may blame the government for their personal financial situation or feel that the
government does not care about their interest. In turn, this will make them less satisfied with
democratic performance in their country. Levels of political interest have a positive relationship.
A person who is interested in politics will be better informed about how political process works
and also have more chance to input her demands. In turn, she will be more satisfied with
democratic performance in her country. Moreover, the more a person perceives the last elections
as fair, the more likely she is satisfied with democratic performance of her country. This linkage
is reasonable given that elections are important components of democracy.
As my key independent variable, Bureaucratic Profiles, is added (Model 8), it explains a
relatively large proportion of variance at level-2: PRE at level-2 increases from 33.82% in Model
7 to 74.63% in Model 8. Bureaucratic Profiles has a positive association with citizens’

132

satisfaction with democracy; this is statistically significant. The interpretation of this effect is
further discussed in Section 4.8.2.
When the between-country effects of individual-level variables are tested, only
interpersonal trust shows a country-level effect that is different from the within-country effect
(See Table C.5 in Appendix C). Thus Model 9 in Table 4.7 shows the results when only
aggregated-mean of interpersonal trust is included. The effect of interpersonal trust can be
interpreted as the following. A trusting person has higher levels of satisfaction with democracy
(by .522 points). Independent of this effect, a person living in a country with a greater number of
trusting people (i.e., greater social trust) has higher levels of satisfaction with democracy
compared to an individual from a low-trust country, all things being equal. The size of this
between-country effect is .556 (= .522 + .034). These findings, again, support the notion that the
relationship between social trust and political support exist at both the individual- and the
country-level.
Bureaucratic Profiles still has a positive and statistically significant association with
citizens’ satisfaction with democracy. As the aggregated-mean of interpersonal trust is included
in the model, only the size of the effect decreases from .359 in Model 8 to .274 in Model 9.
Further, the results in Model 10 in Table 4.7 show that this positive effect of Bureaucratic
Profiles holds when other country-level variables are included. 89

89

Similar to the models explaining public attitudes toward bureaucracies, I tested the models
where my key independent variable, Bureaucratic Profiles, and one of three county-level control
variables are included. The results are presented in Table C.6 in Appendix C. When one of
country-level controls is added to Model 9, the relationship between Bureaucratic Profiles and
satisfaction with democracy is always statistically significant. Holding constant for Bureaucratic
Profiles and other individual-level variables (and aggregated-mean of generalized trust), only
economic growth has a statistically significant effect. The coefficients for Freedom House score
and that for PR electoral systems are not statistically different from zero. These results provide
133

Table 4.7. Random-Intercept Multilevel Models of Citizens’ Satisfaction with Democracy,
with Country-level Controls
With the Aggregated-Mean of
Level 1 Controls
(Model 9)

With Level 2 Controls
(Model 10)

Country Level
.274 (.042)*
.251 (.042)*
Bureaucratic Profiles
-.149 (.043)*
GDP/capita growth rate
Democracy
.239 (.171)
(Freedom House)
.133 (.161)
Electoral system: PR
Country-level
.023 (.005)*
.019 (.005)*
generalized trust
Individual Level
.529 (.027)*
.523 (.027)*
Generalized Trust
-.000 (.001)
-.000 (.001)
Age
.010 (.005)*
.010 (.005)*
Age2/100
*
-.116 (.025)
-.115 (.025)*
Female
.095 (.010)*
.095 (.010)*
Education
*
-.207 (.063)
-.207 (.063)*
Unemployed
.017 (.032)
.017 (.032)
Public Sector Worker
.154 (.016)*
.153 (.016)*
Political Interest
Perceived Election
.502 (.013)*
.501 (.013)*
Fairness
*
5.547 (.067)
5.452 (.140)*
Constant
Variance Components
.103
.067
Country-level intercept
Individual-level
3.783
3.783
Residuals
Proportion Reduction in
Error:
22.31%
23.02%
at Level 1
87.23%
91.65%
at Level 2
102337.03
102331.97
-2* Log-Likelihood
Note: The restricted maximum likelihood (REML) estimator is used. Standard errors are in
parentheses. Number of observations = 24513. Number of countries = 28.
*
Coefficient is statistically significant at .05 level, directional hypothesis test.

The results in Model 10 indicate that as the value of Bureaucratic Profiles increases by
one radian, citizens’ satisfaction with operation of democracy increases by .251 points, on
support for robustness of the relationship between Bureaucratic Profiles and citizens’
satisfaction with democracy.
134

average, controlling for all individual- and country-level variables. Let say there are two persons
who are the same but different only in their countries’ bureaucratic profiles: One person’s
country has the value of Bureaucratic Profiles that is one radian greater than the country of
another person. This person will have, on average, .251 points higher levels of satisfaction than
another. And, this is statistically different than zero. I provide a more substantive interpretation
of this relationship in the next section.
Next, let us look at the effects of country-level control variables in Model 10 (in Table
4.7). Similar to the model explaining citizens’ attitudes toward bureaucracies, the level of
democracy and PR electoral systems do not have statistically significant relationships with levels
of satisfaction with democracy (see Section 4.8.3 for more discussion of these variables).
Contrary to my expectation, the results in Model 10 show that economic growth has a negative
relationship with levels of satisfaction with democracy. But, note that when different economic
indicators—GDP per capita (logged) or inflation rate—are used, no statistically significant effect
is observed. This is in line with previous studies finding mixed support for the effect of
economic performance (Rohrschneider 2005). Further studies will be needed to discern this
unexpected relationship.
One possible interpretation of this finding is that individuals do not link improved
national economic situation with better government performance. An individual’s personal
financial situation may influence her level of political support, as it is implied by the negative
effect of unemployment. But, an individual’s economic situation or satisfaction with national
economy can be different from how national economy actually does. Moreover, some other
factors may exist that undermine levels of political support in nations with higher economic
growth rates. For example, in the data, most of countries with higher economic growth rate in the

135

survey year are Eastern European and Latin American countries. 90 Thus, it suggests that Eastern
European and Latin American countries enjoy higher economic growth rates than other counties,
but this better economic performance does not translate into higher levels of political support. If
such a high economic growth rate has been achieved by sacrificing democratic values and
procedures, for instance, this could lead to lower levels of satisfaction with democracy in these
countries. Further studies should investigate this puzzling relationship between economic
performance and political support.

4.8.2 The Relationship between Bureaucratic Profiles and Satisfaction with Democracy

Let us interpret in more detail the relationship between Bureaucratic Profiles and
satisfaction with democracy. Examining bureaucratic profiles in the two countries with the
highest and lowest predicted values of dependent variable, satisfaction with democracy, will help
to interpret the positive relationship between the two variables. Figure 4.6 shows the mean of the
predicted values for each country, along with 95% confidence intervals.
Figure 4.6 shows that Denmark has the highest predicted value of satisfaction with
democracy and Slovakia has the lowest predicted value. In Denmark, impartiality is presented to
the greatest extent compared to other bureaucratic traits, and female representativeness is the
second most presented trait. And, career-based system is the least emphasized one. That being
said, in nations with the highest predicted level of satisfaction with democracy, the bureaucratic

90

For example, countries whose economic growth rate is greater than one standard deviation
from the mean (i.e., 4.7% of economic growth rate) include Czech Republic, Hungary, Israel,
Mexico, Chile, and Slovakia.
136

traits are exhibited in the following order: Impartiality, female representativeness, independence
from politics, competency, and career-based system (see Table 4.1).
On the other hand, Slovakia seems to emphasize female representativeness and
competency compared to other traits. And, political independence and impartiality are presented
to lesser degrees than other traits. The specific order of the relative levels of traits exhibited in
Slovakia is: Female representativeness, competency, career-based system, independence from
politics, and impartiality (see Table 4.1).

10

Figure 4.6. Predictions and approximate 95% confidence intervals versus ranking of
citizens’ satisfaction with democracy based on Model 10 in Table 4.7

ES UK SE
FR
JP

PT

KR

AU

AT

CL IL DE

6

SI

MXCZ
HU

BE

US IE FI NL NO CA NZ CH

PL

4

SK

2

Prediction

8

DK

0

10

20

30

Rank

Note: The predicted values are calculated based on a random-intercept model for citizens’
satisfaction with democracy, which are shown in Model 10 in Table 4.7. The predictions for
citizens’ satisfaction with democracy are aggregated at the country-level. These values are
ranked and plotted with approximate 95% confidence intervals. Country names are shown on top
of confidence intervals. See Note in Figure 3.3 for the full names of the thirty-four countries.
137

Figure 4.7. Full MDPREF model, with the vectors for Denmark and Slovakia

Note: Open circles and solid circles represent the nation vector’s terminal points and the five
traits, respectively, that are estimated from the MDPREF analysis as discussed in Chapter 3. The
vectors for Denmark and for Slovakia, nations with the highest and the lowest predicted value of
citizens’ satisfaction with democracy, respectively, are presented with arrows (in solid lines) and
their extended lines (in dashed lines). The vector pointing upper-right in the space represents
Denmark, where bureaucratic traits are presented in the order of impartiality, female
representativeness, independence from politics, competency, and career-based system. The
vector directing upper-left is for Slovakia, where the traits are exhibited in the order of female
representativeness, competency, career-based system, independence from politics, and
impartiality. See Note in Figure 3.3 for the full names of the thirty-four countries.

138

Let us look at Figure 4.7 to visually compare the Bureaucratic Profiles for Slovakia
(around the “10:00” position) and Denmark (around “2:00”). 91 First, the comparison between the
two profiles suggests that citizens are more likely to perceive democracy functioning
satisfactorily when impartiality is the most important feature of a country’s bureaucracy. If the
priority is on securing impartiality, then a nation’s bureaucracy, perhaps, provides public goods
and services in a fair and unbiased way. Such a bureaucracy would contribute to citizens’ equal
access to public goods and services (Rothstein and Teorell 2008a). In turn, these citizens are
more likely to be satisfied with the performance of democracy in their country.
These findings also suggest that individuals are less likely to be satisfied with the
function of democratic regimes, when competency is emphasized more than other traits. In such
a nation, bureaucrats perhaps have prestigious status. In other words, if a country put exclusive
emphasis on competency, people attaining higher-level education are likely to work in the
government and get paid relatively well. The size of government workforce will also be
relatively large. Thus, if these privileges of bureaucrats are not accompanied by their impartial
behaviors in the policy process, people may feel that their “competent” bureaucrats do not serve
citizens, but instead abuse their power without popular control. In turn, this will make people less
satisfied with their democratic system. In addition, political independence is also not much
presented in Slovakia, together with impartiality. That being said, it seems that bureaucracies
from countries with lower levels of democratic satisfaction emphasize competency without
securing impartiality and political independence. The greater level of competency compared to
other traits may be related to a lower degree of political independence in these countries. If this is
91

Recall that as the value of Bureaucratic Profiles increases, a nation vector in the joint space
moves in a counterclockwise direction. Figure 4.7 helps to visualize the changes in the relative
levels of traits presented in a national bureaucracy as a nation vector moves from Slovakia
(around the “10:00” position) to Denmark (around “2:00”) in a counterclockwise direction.
139

the case, then too much emphasis on competency is perhaps related to patronage, and this
decreases citizens’ satisfaction with democracy.
Career-based system is the third most displayed bureaucratic trait in Slovakia and the
least exhibited trait in Denmark. Does this indicate the negative impact of career-based system
on citizens’ satisfaction with democracy? Although the rank order of career-based system in a
bureaucratic profile decreases when comparing Bureaucratic Profiles for Slovakia and Denmark,
recall that the rank order of career-based system does not continuously decrease as the value of
Bureaucratic Profiles increases. It seems that, instead, which traits are emphasized together with
a career-based system in bureaucracies is more important factor for citizens’ democratic
satisfaction.
For example, let us look at nations whose vectors located between the “7:00” and the
“11:00” position in Figure 4.7. In these nations, competency is greatly emphasized and
impartiality is not presented as much as other traits in a bureaucracy. For them, having relatively
greater levels of career-based system seems to help enhance democratic satisfaction. On the other
hand, let us compare bureaucracies where nation vectors are positioned between “1:00” and
“5:00.” The priority is given to impartiality over competency in these nations—that is, levels of
impartiality increase and levels of competency decreases. Among these nations, relatively lower
levels of career-based systems contribute to greater satisfaction with democratic government.
These findings suggest that bureaucracies emphasizing position-based systems (i.e., less degree
of career-based system) are likely to contribute to democratic governance only when impartiality
is presented to a greater extent.
Finally, how is the relative level of female representativeness presented in a bureaucracy
associated with public satisfaction with democracy? Female representativeness is the second

140

most exhibited bureaucratic trait in Denmark, whereas it is presented to a greatest degree
compared to other traits in Slovakia. Why do these two countries have very different levels of
public satisfaction with democracy, while both emphasize female representativeness more than
other bureaucratic traits? The rank order between female representativeness and impartiality is in
stark contrasts between two nations’ profile. In Denmark, impartiality is presented to a greater
degree than female representativeness, whereas it is the opposite in Slovakia. Thus, the results
suggest that citizens are satisfied with democracy when female representativeness in a
bureaucracy is emphasized, but only when impartiality is prioritized over female
representativeness. If impartiality is not presented more than other traits in a bureaucracy, a
greater emphasis on female representativeness perhaps decreases public satisfaction with
democracy.

4.8.3 Random Slope Models: Satisfaction with Democracy

Next, the possibilities of random slopes are explored. Only significant random slopes are
retained in the model based on the deviance difference test. Model 11 reported in Table 4.8
shows the results for the random-slope model, when Bureaucratic Profiles and all country-level
control variables are included. The results show that three variables have the random slopes. That
is, the effects of interpersonal trust, age, and perceived fairness of elections on citizens’
satisfaction with operation of democracy vary across nations. Different from the results in the
model explaining public attitudes toward bureaucracies, the effect of political interest does not
vary across nations and public sector workers have no significant effect.

141

Table 4.8. Random-Slope Multilevel Models of Citizens’ Satisfaction with Democracy
Random Slopes,
with Level 2 Controls
(Model 11)

Cross-level Interaction:
Interpersonal Trust and
Social Trust (Model 12)

Country Level
.267 (.036)*
.275 (.040)*
Bureaucratic Profiles
*
-.158 (.037)
GDP/capita growth rate
Democracy
.341 (.149)*
(Freedom House)
.168 (.136)
Electoral system: PR
Country-level
.017 (.004)*
.020 (.005)*
generalized trust
Individual Level
.508 (.037)*
.505 (.037)*
Generalized Trust
-.000 (.002)
-.000 (.002)
Age
2
*
.008 (.005)
.008 (.005)*
Age /100
-.115 (.025)*
-.116 (.025)*
Female
*
.097 (.010)
.097 (.010)*
Education
-.220 (.063)*
-.220 (.063)*
Unemployed
.025 (.032)
.026 (.032)
Public Sector Worker
*
.148 (.016)
.150 (.016)*
Political Interest
Perceived Election
.529 (.032)*
.528 (.032)*
Fairness
Cross-level Interactions
Generalized Trust *
Country-level
.004 (.003)
generalized trust
*
5.408 (.129)
5.527 (.077)*
Constant
Variance Components
.113
.142
Country-level intercept
Individual-level
.018
.016
Generalized Trust
.000
.000
Age
Perceived Election
.024
.024
Fairness
3.737
3.737
Residuals
Proportion Reduction in
Error:
23.02%
22.29%
at Level 1
91.65%
87.03%
at Level 2
102131.62
102149.26
-2* Log-Likelihood
Note: The restricted maximum likelihood (REML) estimator is used. Standard errors are in
parentheses. Number of observations = 24513. Number of countries = 28.
*
Coefficient is statistically significant at .05 level, directional hypothesis test.

142

I explore one possible explanation for varying effect of interpersonal trust in this
chapter.92 It is possible that levels of social trust in a country influence the effect of an
individual’s interpersonal trust on satisfaction with democracy. In a low-trust society, a trusting
person’s participation in political and communal organizations may not be effectively translated
into a collective influence on the political process. Thus, the cross-level interaction effect
between interpersonal trust and social trust (i.e., the percentage of trusting people) is examined.
The results are reported in Model 12 in Table 4.8. It shows that a trusting person has on
average .505-point greater satisfaction with democracy than an untrusting person, all things
being equal, when their country has the 47% of trusting people. And, if that country has 10%
more trusting people, the difference between trusting and untrusting person in the level of
satisfaction with democracy becomes greater, which is .549-point (= .505 + .004*10). But, the
current data do not provide sufficient evidence that this increased difference is statistically
different than zero.93 This interaction effect is visualized in Figure 4.8.
Do the effects of country-level variables hold the same in the random slope models? Let
us look at Model 11 again. The effect of Bureaucratic Profiles and economic growth are the
same as in the previous random-intercept model (Model 10), although the size of their effects is

92

Similarly, future study should explore the reasons of varying effects of age and perceived
election-fairness.
93
In order to investigate the interactive relationship between individual’s interpersonal trust and
social trust in more detail, the improved measurement of on social trust will be needed (see ft 86).
Future studies should also consider alternative explanations for varying effects of generalized
trust. Perhaps, the effect of interpersonal trust depends on the existence of trust-generating
institutions. For example, Jamal and Nooruddin (2010) argue that the “democratic utility of
trust”—that is, whether generalized trust is linked to support for democratic principles—depends
on a country’s level of democracy. The mechanism is that people will be more likely to trust
each other when political institutions can secure their interest. Therefore, they argue that
interpersonal trust will be associated with support for democratic principles, but only when the
country enjoys higher levels of democracy.
143

slightly changed. And, PR electoral systems have no statistically significant effect on democratic
satisfaction, as expected.94

7

8

Figure 4.8. Cross-level interaction effect between generalized trust and social trust on
citizens’ satisfaction with democracy

6

Trusting person

4

5

Untrusting person

0

20

40
60
Percentage of trusting persons

80

100

Note: It shows the changes in the difference between trusting and untrusting person in their
satisfaction with democracy across different levels of social trust (i.e., percentage of trusting
persons in a nation). Two reference lines (i.e. dotted lines) indicate the range of observed values
of social trust. The results are based on Model 12 in Table 4.8.

94

Note that this relationship is positive and statistically significant when Bureaucratic Profiles is
not included in the model. The positive relationship may reflect that PR electoral systems are
designed to focus on representativeness of voters’ preferences (Norris 2004). In turn, citizens
could be more satisfied with functioning of democracy when their interests are represented in
politics. Alternatively, this may be because the winner-loser gap is smaller under PR systems
(Anderson and Tverdova 2003; Norris 2004). However, this relationship is not statistically
significant when Bureaucratic Profiles is included.
144

Interestingly, the relationship between the level of democracy and satisfaction with
democracy becomes statistically significant when random slopes are considered in the model
(Model 11). For example, a person in a country with the score of 3 (most free) is more likely to
be satisfied with democracy by .341 points when compared to a citizen in a country with a
Freedom House score of 2, all things being equal. It seems plausible because more democratic
nations will be able to preserve democratic values and, in turn, it will influence how citizens
view the performance of democratic government.

10

Figure 4.9. Predictions and approximate 95% confidence intervals versus ranking of
citizens’ satisfaction with democracy based on Model 11 in Table 4.8

DK

AU
SE

8

UK
BE

FI NL

NO CA NZ

AT
CH

DE ES

CL

IL

PT
KR

SI MX
HU
PL

4

6

SK

JP

IE

2

Prediction

CZ

FR

US

0

10

20

30

Rank

Note: Based on a random-slope model for citizens’ satisfaction with democracy, which are
shown in Model 11 in Table 4.8, the predicted values are calculated. The predictions for citizens’
satisfaction with democracy are aggregated at the country-level. These values are ranked and
plotted with approximate 95% confidence intervals. Country names are shown on top of
confidence intervals. See Note in Figure 3.3 for the full names of the thirty-four countries.
145

The comparison between the random-slope and the random-intercept model provides
further evidence for the positive relationship between Bureaucratic Profiles and public attitudes
toward bureaucracies. Figure 4.9 presents the mean of the predicted values for each country,
along with 95% confidence intervals, based on Model 11 in Table 4.8. The ranking of the
country’s mean predicted values is the same as those obtained from the random-intercept model,
except the ranking of Mexico and Hungary.

4.9 Conclusion

This chapter has explored the linkage between bureaucracies and citizens’ political
support, using a new measure of Bureaucratic Profiles created from the estimations obtained in
the previous chapter. Specifically, I have examined which bureaucratic profile, or what
combination of bureaucratic traits, contributes to public satisfaction with democracy and
attitudes toward bureaucracies, using a multilevel model. The results show that there are
particular profiles that are associated with higher/ lower levels of political support, which implies
that not all nations’ bureaucratic profiles perfectly represent what citizens want their
bureaucracies to possess in democratic societies.
I find that bureaucratic profiles generally have a similar relationship with citizens’
attitudes toward bureaucracies, and with citizens’ democratic satisfaction. In countries with
higher levels of political support, impartiality are importantly emphasized but relatively little
emphases are placed on competency and career-based system in their bureaucracies. As relative
levels of impartiality decrease, and, at the same time, as relative levels of competency increase,
levels of political support decrease. Thus, the empirical findings in this chapter suggest that a

146

bureaucracy that implements policies fairly and participates in policy making without favoritism
or bias is perhaps what citizens want.
In addition to this, more interestingly, the results suggest that different relationships
between traits, in terms of their relative levels presented in a national bureaucracy, are associated
with different levels of political support. The findings speak to the importance of balancing
tradeoffs of bureaucratic traits. For example, political independence is important, but a sore
emphasis on this trait does not seem to be desirable from the perspective of citizens: people view
bureaucracies more positively only when impartiality is presented more than political
independence. Less emphasis on career-based system in a bureaucracy leads to higher levels of
political support only when impartiality is preserved: for countries with lower degrees of
impartiality, it seems to have a negative effect on political support. For another example, female
representativeness is another important trait influencing public satisfaction with democracy; but
levels of public satisfaction with democracy decrease if female representativeness is emphasized
more importantly than impartiality.

147

CHAPTER 5
CONCLUSION

The aim of this dissertation has been twofold. First, it proposes an alternative way to
compare and understand bureaucracies in OECD member nations by developing a new measure
of Bureaucratic Profiles. I claim that bureaucratic characteristics are multi-dimensional and that
not all nations have the same attributes of bureaucracy. The empirical results show that
bureaucratic profiles vary across OECD member nations. Second, this study examines citizens’
attitudes about bureaucracies and democratic regime performance. The results demonstrate how
various bureaucratic profiles are linked to levels of political support, and also suggest the
importance of relatively high levels of impartiality in bureaucracies. This dissertation adds
insights to current debates on cross-national indicators of ‘governance’ and puts forth a better
measure of bureaucratic traits. In this concluding chapter, I summarize the general findings and
contributions that this dissertation makes. I also suggest directions for future study.

5.1 General Findings and Contributions

In this dissertation, I argue to examine a set of key bureaucratic traits simultaneously,
rather than focusing on a single aspect of bureaucracies as previous studies have suggested. In
prior studies, different characteristics of public agencies have emphasized to reconcile the
potential tensions between bureaucracy and democracy, but the complex relationships between
these traits have been overlooked. Without both establishing the criteria for judging “good”
bureaucracies and understating possible tradeoffs between different key traits, conventional

148

indicators of bureaucracies used in large-N studies have failed to compare national bureaucracies
in a meaningful way. Unlike previous studies, I do not assume that there is a single bureaucratic
attribute (or dimension) that is normatively “good” for all nations. Instead, I choose four key
bureaucratic traits, all of which have been (separately) proposed as an important bureaucratic
characteristic in previous studies. I then examine how these key attributes were presented
simultaneously in nations’ bureaucracies, using multidimensional preference scaling (MDPREF).
That is, I focus on the “bureaucratic profile” which shows the relative levels of each trait
presented in a national bureaucracy. Moreover, based on the estimations from the MDPREF
analysis, Bureaucratic Profiles, a new measure of bureaucratic traits, is developed, and its
linkage to popular attitudes toward bureaucracies and democratic performance is examined.
By investigating bureaucratic profiles across OECD member nations, I am able (1) to
examine and identify key bureaucratic traits and (2) to explore similarities and/or differences in
the relative levels of these key traits presented in national bureaucracies. I find five clusters of
trait variables—which I named impartiality, political independence, career-based system,
competency, and female representativeness—as an underlying structure of bureaucratic traits in
OECD member countries. Furthermore, and more importantly, the results show that different
nations have different configurations of a set of traits represented in a bureaucracy.
With these findings, this dissertation contributes to our broader and deeper understanding
of bureaucracies from a comparative perspective. The results are based on a new way of looking
at bureaucracies comparatively. Focusing on bureaucratic profiles, instead of a single
bureaucratic trait, allows us consider that bureaucratic attributes are interrelated and also
sometimes contradict to each other. That is, nations may make decisions to balance a tradeoff
between different traits (Peters 2001) and the relative importance, or weight, of each trait can

149

vary across nations. 95 The various bureaucratic profiles found in this dissertation demonstrate
that each nation chooses to emphasize different traits. Some nations seem to focus more on
impartiality and less on career-based system, while in other nations, the order of the relative
levels of traits is the opposite. For example, Anglo-Saxon countries seem to choose impartiality
over career-based system, while Southern European countries put more emphasis on careerbased system than impartiality. It also appears that bureaucracies in Anglo-Australian countries
share similarities with those in Nordic countries, in that they emphasize impartiality over
competency. This is the opposite in Central and Eastern European countries.
Moreover, this dissertation makes an important contribution to studies of comparative
public administration: It provides quantitative data on variation in bureaucracies. Case studies
illustrating bureaucracies across a small number of nations have suggested that there are various
“traditions of public administration” (e.g., Painter and Peters 2010; Pierre 1995; Tummala 2003).

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Related to this, it is worth mentioning some studies discussing that organizations choose
between and balance the tradeoffs of different bureaucratic characteristics. Herbert A. Simon, in
“The Proverbs of Administration” (1946), suggests that some “principles” that are widely
accepted for improving administrative efficiency—specialization, unity of command, and span of
control—are contradictory to each other, but are all necessary. He notes that, “[n]o single one of
these items [can be a sufficient] guiding principle” in designing administrative organization.
More importantly, he notes that “[m]utually incompatible advantages must be balanced against
each other” to maximize over-all efficiency, “just as an architect weighs the advantages of
additional closet space against the advantages of a larger living room” (62). Thus, he proposes
exploring alternative administrative arrangements that assign different weights to these principles.
Some empirical studies have shown such cases, although the organizations examined were not
governmental bureaucracies. For example, Hall (1963) compares ten organizations in the United
States in terms of the degree to which a series of dimensions of ‘bureaucracies’—hierarchy of
authority, division of labor, system of rules, impersonality, and technical competence—were
presented in organizations; he finds variations in the configuration of “dimensional magnitude”
across ten organizations. Reimann (1973), in studying nineteen business organizations in the
United States, finds various structural arrangements along three bureaucratic structural
dimensions—decentralization, specialization, and formalization. Similar to these studies, but
with a slightly different focus, I examine various configurations of a set of key traits presented in
a bureaucracy, which may reflect nations’ choices to balance the advantages and disadvantages
of several key traits.
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But, with descriptive accounts, it is difficult to compare similarities and differences of
bureaucratic characteristics across nations in a systematic way. The comparative bureaucracy
research does not provide the quantitative data necessary to compare bureaucracies across a large
number of nations (Brans 2003). And, the existing cross-national indicators of “bureaucratic
quality” or “effective bureaucracy” fail to capture various configurations of the machinery of
government that can produce the same, high-quality outcomes (Andrews 2008, 2010; Holmberg,
Rothstein, and Nasiritousi 2009). This dissertation, especially Chapter 3, fills this gap by
providing empirical evidence and quantitative data of wide variations in the configuration of
bureaucratic traits. By examining how a set of traits are represented within each bureaucracy
with various relative importance, this dissertation is able to systematically show the variety of
bureaucratic characteristics across nations and develops a single scale to represent such
information.
As the second part of the analysis, Bureaucratic Profiles, a new measure of bureaucratic
traits, is used to examine which bureaucratic profile is related to higher levels of political support,
specifically, citizens’ attitudes about bureaucracies and democratic performance. The results
from the multilevel models show that when the relative level of impartiality exhibited in a
bureaucracy is greater than those of other traits, citizens are more likely to have higher levels of
political support. The findings suggest that impartial behaviors of public agent in policymaking
processes are what people want for their bureaucracies in democratic political systems.
The results also show that citizens do not favorably view a government bureaucracy and
regime performance when competency is the main concern in national bureaucracies, but
bureaucratic impartiality and independence from politics are understated. It is important to note
that this does not necessarily indicate that people do not want competent public agencies. Recall

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that merit-based recruitment, which also contributes to having skillful and competent workers in
public agencies, is clustered together with other indicators of impartiality in all nations. Also,
given that competency in this dissertation is about the retention of skilled agents with higher
wages and compensation, the findings imply that people do not want bureaucracies that enjoy
privileged status in society but not care much about providing impartial services to citizens.
Moreover, this finding demonstrates the dilemma that democratic government confronts:
bureaucracies should be strong enough to effectively carry on public policies, but they should not
be too strong as to threaten democratic values and democratic development. That is, people may
want their national bureaucracies to be competent in delivering public goods and services, but
this should not be something that ignores other key traits.
Some interesting insights about other key traits are also demonstrated in the results.
Female representativeness seems to play an important role in shaping public attitudes toward
bureaucracies and regime performance only if it does not harm impartiality. In addition,
emphasizing career-based system enhances political support if a nation’s bureaucracy possesses
relatively lower levels of impartiality; while a relatively lower emphasis on career-based system
helps if impartiality is relatively well secured. This implies that changing a bureaucracy from a
career-based system to a position-based one will not always contribute to public satisfaction with
government. Without preserving impartiality, this could cause a problem. Thus establishing
career-based systems may work better to enhance political support, when the degree to which
impartiality is exhibited in a bureaucracy is smaller than other traits. Another interesting finding
is that a relatively greater emphasis on independence from politics does not seem to be a problem
unless impartiality is present along with this trait.

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These findings provide support for previous research criticizing politicization and
emphasizing others means of democratic control of bureaucracies. Indeed, one dominant view on
bureaucracies in political science focuses on the relationship between bureaucracies and their
political masters—whether they are responsive to the elected politicians and/or how much
discretion is given to bureaucracies. This line of studies has largely been influenced by the
rational choice approach and the principal-agent theory since Barry M. Mitnick’s (1980: cited in
Waterman and Meier 1998) and Barry Weingast’s classic (1984) study. Within this literature,
political responsiveness is considered as a desired characteristic of bureaucracies in democratic
societies. However, the assumption that bureaucracies are “agents” of politicians and an
institution to be tightly controlled leads to a very limited understanding of diverse bureaucratic
characteristics that may exist. Moreover, scholars have criticized this perspective on
bureaucracies, and have suggested different characteristics, such as a representativeness (e.g.,
Meier and O’Toole 2006) and impartiality and professionalism (e.g., Rothstein and Teorell
2008b; Suleiman 2003). And, the findings in this dissertation suggest that impartiality and
representativeness can be better tools for democratic control of bureaucracies. The emphasis on
politicization could harm political insulation of bureaucracies, which, in turn, destructs citizens’
evaluations on bureaucracies and regime performance. Generally these findings corroborate
Suleiman’s (2003) argument that politicization and business-emulating reforms have resulted in
deprofessionalization of bureaucracies, and this will lead to “dismantle” the state with lowering
trust in government.
From the analyses in this dissertation, we can see that different nations make different
choices about which traits to emphasize and that a set of bureaucratic traits have different
relationship with public support for bureaucracies and regime performance. With these

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discussions, this dissertation makes an important addition to a recent lively debate on measuring
“the quality of government”—sometimes termed as “the quality of state” or “governance.” There
have been increasing efforts to understand and measure ‘output institutions’ in a more precise
and substantive way (Fukuyama 2013; Rothstein and Teorell 2008b). Although some scholars
focus on a broader concept such as governance or state capacity, the bureaucracy is at the core of
these discussions. One very recent scholarly debate has been initiated by Francis Fukuyama’s
(2013) commentary, “What is Governance,” in Governance (See http://governancejournal.net/
category/commentary/ for scholars’ exchange of opinions on this commentary). Although the
title implies that it is about governance, he focuses on how to conceptualize and measure the
state apparatus, where he suggests autonomy and capacity as key components of the quality of
government bureaucracies.
This is not a new debate; there have been some scholarly attempts to develop a better
indicator of bureaucracy. For example, the QoG Institute produces the Expert Survey data
examining structural dimensions of bureaucracies and focusing on impartiality as a norm for
“quality of government.” Two dimensions of bureaucratic structure are suggested, that is,
professionalism and closedness (Dahlstrӧm, Lapuente, and Teorell 2010). Also, the OECD
Governance at a Glance provides statistical data on various aspects of government performance
in OECD member nations, including government revenues, government expenditures, public
employment, human resource management practices, budget practices and procedures, regulatory
management, and integrity (OECD 2009, 2011). For the 27 EU member nations, Demmke and
Moilanen’s (2010), which is updated from the study of Auer et al. (1996), provide detailed data
on specific features of civil service systems, based on the “Traditional – Post-bureaucracy

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continuum model,” which consists of legal status, career-structure, recruitment, salary system,
and tenure system.
This dissertation makes a contribution to this debate and the efforts to measure
bureaucracies, by utilizing, but also going beyond, the data provided by the QoG Institute and the
OECD Governance at a Glance. Consistent with other studies (e.g., Dahlstrӧm, Lapuente, and
Teorell 2010; Fukuyama 2013), I show that bureaucratic characteristics are better understood as
multi-dimensional concepts—that is, it is not only about the continuum of “bureaucraticness,”
but about capturing several important attributes. Moreover, this dissertation is different from the
studies of “good governance” and their measures, in that Bureaucratic Profiles focus specifically
on governmental bureaucracies and it does not assume that one profile is “better” than another in
priori. Instead of normatively defining “goodness” of the state administrative apparatus, on
which these debates focus, I examine bureaucratic characteristics that are associated with citizens’
positive attitudes toward public agencies.
Finally, this dissertation contributes to the study of political support, by explaining crossnational differences in levels of public support for bureaucracies and regime performance. It
broadens our understandings of the role of bureaucracies in democratic societies, especially from
the perspective of citizens. In the literature, cross-national variation in levels of political support
has not been fully explained, although economic, political, and social factors have been
suggested as key determinants. I argue that, particularly in Chapter 4, bureaucratic characteristics
in a nation can influence people’s attitudes toward government institutions and performance.
This is because a bureaucracy is an important institution that participates in the policy making
process and delivers public goods and services to the public (Rohrschneider 2005). Overall, the
findings suggest that the relative level of impartiality compared to other key bureaucratic traits

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represented in a national bureaucracy matters with regards to citizen’s attitudes about
governments. With Bureaucratic Profiles, a new measure of bureaucratic traits, I am able to
show that variation in bureaucratic characteristics explains levels of political support: various
configurations of a set of key bureaucratic traits have different effects on how people view their
government bureaucracies and their democratic system.
In addition to the relationship between bureaucracies and political support, I also find an
interesting association between interpersonal trust and political support. The findings suggest
that there are both individual- and country-level effects of generalized trust and also the
possibility of an interaction effect between them. That is, in a high-social trust society,
individuals are more likely to have higher levels of political support and the difference between a
trusting and an untrusting person is also amplified in this setting. Although the measurement of
social trust needs to be improved for further examination of this relationship, the findings
provide interesting insights for future studies of political support.

5.2 Extensions and Implications for Future Studies

This dissertation provides a new measure, Bureaucratic Profiles, and shows a variety of
bureaucracies within OECD nations. Future studies will need to examine the determinants, or the
correlates, of variations in bureaucratic profiles. For example, why do some nations choose to
emphasize impartiality over competency, while others do the opposite? Why is political
independence emphasized along with impartiality in some bureaucracies but not in others? When
are female representativeness and career-based system presented in a way that they do not
decrease levels of impartiality in a bureaucracy? Such questions can be explored. Various

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administrative traditions (Painter and Peters 2010), special training within bureaucracies, and
demand from the public can be considered as potential explanations. Moreover, the configuration
of political institutions and veto points (Knott and Miller 2006; Tsebelis 2002), the competition
of political parties (Geddes 1991), and the sequence of state and party building relative to the
development of a bureaucracy (O’Dwyer 2004; Shefter 1977) may also be explored as
determinants of bureaucratic profiles.
This project examines popular attitudes toward government as a possible consequence of
various bureaucratic profiles. Studies can be extended to examine different outcomes. For
example, one may look at which profiles are related to lower levels of corruption. Some studies
argue that emphasizing merit-based recruitment systems (Dahlstrӧm, Lapuente, and Teorell 2012;
Rauch and Evans 2000) and impartiality (Rothstein and Teorell 2008b, Teorell 2009) are
important in curbing corruption. But, other studies have showed that increasing civil-service pay
can also be helpful (Van Rijckeghem and Weder 2001). If this is true, do countries that focus
more on increasing competency of bureaucracies by paying higher wages and compensation have
lower levels of corruption? Using Bureaucratic Profiles, one may probe which pattern of the
relative levels of a set of traits—especially between impartiality and competency—contributes to
lower levels of corruption.
Studying the relationship between Bureaucratic Profiles and the development of different
policies will be another interesting extension. One of the interesting findings was the
correspondence between variation in welfare-state regimes and variation in bureaucratic profiles.
Specifically, I find that Social-democratic welfare states share a similar bureaucratic profile, as
do Liberal and Conservative welfare states. In the comparative welfare state literature, as
mentioned in Chapter 1, the “state-centric” approach (Huber and Stephens 2001, 15) focuses on

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the capacity of the state apparatus to explain the development of welfare states. It suggests that
the existence of autonomous bureaucracies have contributed to introductions and expansions of
social policies that benefit the broader public. More recently, Dahlstrӧm, Lindvall, and Rothstein
(2012) argue that bureaucratic capacity, which is defined as the “competence and reliability” of
national bureaucracies, is related to the development of policies that entail a relatively large
amount of discretion in their implementation. They show that bureaucratic capacity has a
positive relationship with spending on active labor market policies (which require discretion),
but not with spending on parental leave benefits (which need less discretion in implementation)
in advanced democracies. The mechanism of this relationship is that people will approve the
expansion of spending on policies involving bureaucratic discretion only if their bureaucracies
have the capacity to implement programs in a reliable manner.96
These studies pose an interesting question, which is whether different bureaucratic
profiles are connected to nations’ differential focus on various policies. Given these studies, I
would argue that a bureaucracy that puts a higher priority on preserving political independence
and career-based recruitment systems will play an important role in such a policymaking process.
Based on studies of government spending priorities, 97 futures studies will investigate whether a
greater emphasis on the political independence of bureaucracies is associated with government’s

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Moreover, studies have shown that policy adoptions and expansions are influenced by
bureaucratic institutions (Schneider and Ingraham 1984; Schneider and Jacoby 1996; Schneider,
Jacoby, and Coggburn 1997). A different level of emphasis on female representativeness may
also influence policy implementation (Wilkins and Keiser 2006) and government spending
priorities (Dolan 2002).
97
For example, “government spending priorities” between policies targeting subsets of the
population and those providing broader collective goods can effectively capture government’s
differential emphasis on policies (Jacoby and Schneider 2001, 2009). The spending priority
scores developed for OECD member nations by Pamphilis (2012) will provide an opportunity to
explore the relationship between various bureaucratic profiles and different spending patterns.
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priority on policies for the general public compared to policies for targeted groups within the
OECD member nations.
In addition, future studies examining bureaucratic profiles in different sets of nations,
especially including developing countries will broaden our understanding of government
institutions. It would be an interesting first step to look at whether the underlying structure of
bureaucratic traits in developing countries is the same as what I have found with OECD member
nations. For example, Daslstrӧm et al. (2011) report that one of the dimensions of bureaucracy,
“closedness,” is not applicable to developing countries. If this is because bureaucratic
dimensions in these countries are indeed different from those in Western Europe, then it suggests
that the configuration of bureaucratic traits is different in the rest of the globe.
Another venue for extending this study of bureaucratic profiles is to measure and
compare them across different levels of government (e.g., bureaucracy in the central government
versus one in the local government) and across various departments (e.g., bureaucracies mainly
in charge of regulations versus ones deliver services). For example, previous studies suggest that
the configuration of relative levels of bureaucratic traits can be different in law-enforcing
agencies from that in service-delivery agencies. Gingerich (2012), in studying Bolivia, Brazil,
and Chile, argues for disaggregating bureaucracies to measure bureaucratic capacity because the
elements of capacity have a greater cross-agency variation within a country. Thus, it would be a
fruitful addition to compare bureaucratic profiles of different agencies within a nation, and also
compare them with an overall profile that this dissertation has presented.

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5.3 Conclusion

Bureaucracy is an important and complicated institution that influences citizens’ life.
Bureaucracy bashings without understanding key bureaucratic traits and their complex
relationships, as many political elites and media have done, are not fruitful ways to improve
bureaucracies to do what it is supposed to do in democratic societies. Instead, in order to enhance
bureaucratic capabilities in preparing and delivering public policies—with necessary discretion
but not threatening democratic values—it is important to acknowledge that there is no single,
best model for bureaucracy. There are different aspects of bureaucracies that are important for
democratic governing. Moreover, increases or decreases in one specific bureaucratic trait do not
always lead to the same change in another key trait. As the findings in this dissertation suggest,
different nations seem to make different choices to balance between various bureaucratic traits.
Different configurations of bureaucratic traits may lead to different policy developments
and different capacity in public service deliveries. I believe that this dissertation takes an
important step to understand the consequences of the variety of bureaucratic profiles by focusing
on the perspective of citizens. The analysis has shown that there is a particular pattern of
bureaucratic profile that is preferred by citizens. It points to the importance of impartiality, which
is in line with the findings of several other studies. As an output institution, impartiality and
fairness may be the most important trait for the bureaucracy in democratic societies. However,
more importantly, what this dissertation has further revealed is that it is not only impartiality, but
also its relative importance and its relationships with other key bureaucratic traits that influence
people’s views about democratic government. I hope future researches can provide further

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understandings of the causes and consequences of various bureaucratic profiles. This will
broaden our understanding of bureaucracies and its important role in democratic societies.

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APPENDICES

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Appendix A. Description of Variables Used in Chapter 3
This Appendix describes the variables used in Chapter 3. It would help to understand the
observations used, although the data used for the multidimensional preference scaling analysis is
the combination of these observations, as discussed in Section 3.3.2. Here, I discuss the values of
the original observations—before being imputed for missing values and recoded. These variables
are all standardized before being used for the analysis.
Independence from politics
In order to measure political independence, I include three variables from the QoG Expert survey.
All three variables’ response ranges from 1 (Not at all) to 7 (To a very large extent). The
responses are aggregated for each nation and I use the aggregated version of the survey data
provided by the QoG institute (i.e., the QoG Country-Level Survey Data). Note that this applies
to all variables collected from the QoG Expert survey.
One variable (no_fulfill_ideology_govt) is obtained from the question:
Q8. To what extent would you say the following applies today to the country you have
chosen to submit your answers for?
e. Public sector employees strive to fulfill the ideology of the party/parties in
government?
The mean score for each country ranges from 2.88 to 5, with an average value of 3.92 and a
standard deviation of .59 (where Korea and Poland are around this mean value). That is,
bureaucrats are the least likely to behave in a partisan manner (which may indicate the highest
level of independence from politics) in Japan (2.88), while a bureaucracy is the most likely to be
influenced by partisanship (possibly implying the least degree of independence from politics) in
Estonia (5). I assume that fulfilling the political ideology of incumbent parties indicate a lower
degree of political independence. Thus, this variable is recoded; a nation where political
independence is presented to a greater degree is scored high on this variable.
Two other variables (senior_career and senior_no_political) are about the recruitment of senior
officials. They are measured using the following questions, respectively:
Q2. Thinking about the country you have chosen, how often would you say the following
occurs today:
e. Senior public officials are recruited from within the ranks of the public sector?
d. The top political leadership hires and fires senior public officials?
For the first variable (senior_career), the mean score for each country ranges from 4.2 to 6.67
and the average value is 5.25 (with a standard deviation of .68), where Austria, Switzerland, and
Turkey are close to this mean value. It shows that senior officials are usually recruited based on
career in France (6.67), whereas it is the least likely in Poland (4.2). I assume that as senior
officials are recruited based on career and from within the ranks, a bureaucracy is more likely to
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be independent from political pressures. Thus, a higher score on this variable indicates a greater
degree of political independence exhibited in that nation. Next, the country’s mean score of the
second variable (senior_no_political) has an average value of 4.34, with a standard deviation of
1.25, where Israel and Korea have values close to the mean. It ranges from 1.56 to 6.14, which
indicates that recruitment of senior officials is mostly dependent on the top political leadership in
Mexico (6.14), whereas it is the least likely to happen in Ireland (1.56). I assume that as
recruitment of senior officials is dependent on the top political leadership, a bureaucracy is less
likely to be independent from political influence. Thus, this variable is recoded so that higher
values correspond to a greater degree of political independence.
Female Representativeness
To capture female representativeness, four variables are used. A nation achieving greater levels
of female representativeness receives a higher score on these variables. Three variables are from
the OECD Government at a Glance data and one variable is from the QoG Expert survey.
One variable from the QoG Expert survey (women_represnt) is from the following question:
Q8. To what extent would you say the following applies today to the country you have
chosen to submit your answers for?
i. Women are proportionally represented among public sector employees?
The response ranges from 1 (Not at all) to 7 (To a very large extent). The mean score for each
country ranges from 2.07 to 5.89. That is, female representativeness among public sector
workers is the highest in Estonia (5.89), whereas it is the lowest in Mexico (2.07). The average
value is 4.47 (and a standard deviation is .87), where Switzerland and Poland are around this
mean value.
Three other variables (female_central, female_senior and female_administration) are from the
OECD Government at a Glance data. The data provide the proportion of female workers in “the
core civil service in central government” in three different positions (OECD 2009b). The first
one is the percentage of females among central government employees. It ranges from 12% (in
Turkey) to 69% (in Poland) and the average value is 46.18, with a standard deviation of 14.47
(where Norway is close to the mean value). The figures are different for different positions. The
percentage of females in senior positions in central government ranges from 2% (in Japan) to 38%
(in Greece). Female representativeness in the senior positions is generally lower, compared to
that in the central government. The average value is 23.68, with a standard deviation of 11.07,
which is close to the percentage observed for Norway and Finland. Again, when it comes to the
percentage of women in administrative positions, it ranges from 23% (in Germany) to 83% (in
Portugal and in Norway) and its average value is 54.47%, with a standard deviation of 18.76
(where the percentage in Belgium is near this value).

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Impartiality
For measuring impartiality, five variables from the QoG Expert Survey are used. As stated in
Section 3.3.1, the five items are developed by Rothstein and Teorell (2008a) and Teorell (2009).
The response to the questions, except for the second one, ranges from 1 (Hardly ever) to 7
(Almost always); the response to the second question ranges from 0 to 100 (percentage).
The first variable (impartial) is the response to the following question:
Q4. By a common definition, impartiality implies that when implementing policies,
public sector employees should not take anything about the citizen/case into
consideration that is not stipulated in the policy. Generally speaking, how often would
you say that public sector employees today, in your chosen country, act impartially when
deciding how to implement a policy in an individual case?
This question is designed to ask respondents to rate their bureaucracy in terms of the theoretical
definition of impartiality (Rothstein and Teorell 2008a). The mean score for each country ranges
from 3.5 to 6.4, and its average value is 5.13, with a standard deviation of .80. Bureaucracies in
France, Germany, Luxembourg and Sweden receive a score close to the mean value. According
to this question, Australia (6.4) has the most impartial bureaucracy, whereas level of impartiality
is the lowest in Slovakia (3.5).
The second variable (distribute_needy_to_needy) measures a percentage reaching “the needy
poor” in the following question:
Q6. Hypothetically, let’s say that a typical public employee was given the task to
distribute an amount equivalent to 1000 USD per capita to the needy poor in your country.
According to your judgment, please state the percentage that would reach: (Six response
categories for which the respondents could fill in a number from 0 to 100 percent.)
Rothstein and Teorell (2008a) design this question assuming that the percentage of money
actually distributed to the targeted population, the needy poor, will reflect impartial behaviors of
bureaucrats. The other categories include “People with kinship ties to the public employee,”
“Middlemen/consultants,” “The public employee’s own pocket,” “The superiors of the public
employee” and “Others.” The percentage reaching “the needy poor” ranges from 30% (in
Luxembourg) to 92.44% (in Norway). The average value is 68.82, with a standard deviation of
16.34, where the percentages in Estonia and Germany are close to this average value.
The next three variables (not_favor_procurement, not_favor_implementation and
not_favor_licenses) are measured with the following questions, respectively:
Q2.Thinking about the country you have chosen, how often would you say the following
occurs today?
g. Firms that provide the most favorable kickbacks to senior officials are awarded public
procurement contracts in favor of firms making the lowest bid?

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h. When deciding how to implement policies in individual cases, public sector
employees treat some groups in society unfairly?
i. When granting licenses to start up private firms, public sector employees favor
applicants with which they have strong personal contacts?
The mean score of the first variable (not_favor_procurement) ranges from 1 to 5, with an
average value of 3.00 and a standard deviation of 1.25 (where Chile, France, and Korea are rated
closer to the mean value). It shows that the public procurement contracts are conducted the most
fairly and impartially in New Zealand (1), while it is done the least fairly in countries like Greece,
Luxembourg, and Mexico (5). The mean score for the second variable
(not_favor_implementation) ranges from 1.6 to 5 and has an average value of 3.10 (and a
standard deviation is .87), where Poland is rated closer to the mean value). This tells us that the
extent to which people are treated fairly and impartially in implementation of policies is the
highest in Switzerland (1.6), whereas it is the lowest in Luxembourg (5). The country’s mean
score on the final variable (not_favor_licenses) ranges from 1 to 5.54, and its average value is
2.97, with a standard deviation of 1.18 (where Finland and the United States are rated close to
the average value). It indicates that the degree of impartiality in issuing licenses for business
start-ups is the highest in Iceland (1), while it is the lowest in Mexico (5.54). In order for higher
values of each variable to correspond to a greater degree of impartiality, these three variables are
recoded.
Mechanisms for impartiality and political independence
In addition to the variables for impartiality and independence from politics, I include five
additional variables to capture the idea of a merit-based system and a career-based system that
are discussed as the mechanisms for political independence and impartiality. The response to the
first four questions ranges from 1 (Hardly ever) to 7 (Almost always); the fifth variable ranges
from 0 to 1.
The first four variables (recruit_merit, recruit_no_political, recruit_exam and tenure) are from
the response to the following questions in the QoG Expert survey data, respectively:
Q2. Thinking about the country you have chosen, how often would you say the following
occurs today:
a. When recruiting public sector employees, the skills and merits of the applicants decide
who gets the job?
b. When recruiting public sector employees, the political connections of the applicants
decide who gets the job?
c. Public sector employees are hired via a formal examination system?
f. Once one is recruited as a public sector employee, one stays a public sector employee
for the rest of one’s career?
The first variable’s (recruit_merit) mean score for a country ranges from 3 to 6.5, and its average
value is 5.07, with a standard deviation of 1.01 (where Israel is rated at this mean value). It
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indicates that the recruitment of bureaucrats in New Zealand (6.5) is based on merit system most
of time, whereas it is not always the case in Luxembourg or in Mexico (3). The country’s mean
score of the second variable (recruit_no_political) ranges from 1.67 to 6, with an average value
of 3.57 and a standard deviation of 1.24 (where France and Estonia are rated at this mean value).
The recruitment of bureaucrats in Luxembourg (6) most of time relies on the political
connections of the applicants, whereas recruitment is the least likely to be influenced by political
connection in Japan (1.67). This variable is recoded to make its higher values indicate that
recruitment in a nation’s bureaucracy is not likely to be based on political connection. The third
variable’s (recruit_exam) mean score has an average value of 4.65, with a standard deviation of
1.35, where the nations rated around this mean value is the United States. It ranges from 1.75 (in
New Zealand) to 7 (in Luxembourg), which indicates that a formal examination is usually
conducted to recruit bureaucrats in Luxembourg, whereas this is not always the case in New
Zealand. The country’s mean score of the fourth variable (tenure) ranges from 2.7 (in Estonia) to
6.48 (in Greece), with its average value is 4.91 and a standard deviation of .90 (where Sweden
and Slovenia are the nations rated around this mean value). It shows that bureaucrats in Greece
enjoye a guaranteed tenure most of time, whereas they are least likely to do so in Estonia.
The fifth variable (career_based_hrm) is obtained from the OECD Government at a Glance 2009
data. This variable measures the degree to which each nation has a career- versus position-based
system. It is developed based on policies for becoming a civil servant in general and for
recruiting senior civil servants, and systems for appointing entry-level positions, and for
allocating posts across departments (OECD 2009b). The original indicator ranges from 0 (careerbased) to 1 (position-based). The Netherlands’s system is the closest to an ideal position-based
system (.78), while the recruitment system in France is the closest to an ideal career-based one
(.05). The German system is around the average of the countries in consideration. And this
variable is recoded: Smaller value represents a system open to external recruitment and larger
values indicate a system where most of bureaucrats are recruited at lower levels and move
upward the rest of their career. That is, after being recoded, countries with a career-based system
are scored high on this variable.
Competency
For competency, four related variables are used. One variable measures bureaucrats with a
higher level of education; the next two variables are about the wages and compensation of
bureaucrats; and the fourth variable is the size of the government workforce.
The first variable (higher_edu_ratio) measures the ratio between the percentage of government
workers with higher education and the percentage of everyone in the country with higher
education, as an average between 2000 and 2004. Using the ISSP survey data and background
variables, I first create a variable, the percentage of government employees with a higher
education—bachelor’s degree or more. Based on the question about type of work place,
167

respondents who answer that they “work for government or public sector” are considered
government employees. Using the same criteria of higher education, the percentage of general
public with a higher education is also calculated. After that, the ratio between the two is
calculated for the five years from 2000 to 2004; then, the average ratio though 2000 to 2004 is
used for this analysis. This variable ranges from .93 (in Denmark) to 3.45 (in Chile). This
indicates that in Chile the percentage of people attaining a bachelor’s degree or more is much
greater in the government than in the general public. On the other hand, in Demark the
percentage of people attaining a bachelor’s degree or more is similar in the government and in
the public (precisely, a little smaller in the government than in the public). The average value is
2.08 (with a standard deviation of .66) and Czech Republic has the value closest to this.
The next two variables (compensation_to_total and wage_to_total) are obtained from the
OECD’s STAN database (Structural Analysis Database in OECD.stat), as an average between
2000 and 2004. The variables are measured based on the System of National Accounts (SNA),
which is “the international statistical standard for the national accounts, adopted by the United
Nations Statistical Commission” (United Nations 2009). I use two variables, “LABR” and
“WAGE,” provided by the STAN database, which are the compensation of employees and the
wages and salaries of employees paid by producers, respectively. Compensation of employees
(LABR) is defined as “the total remuneration, in cash or in kind, payable by an enterprise to an
employee in return for work done by the latter during the accounting period.” (United Nations
2009, 131). And wages and salaries (WAGE) are the remuneration payable by employers at
regular intervals in cash or in kind, excluding social insurance benefits paid by employers
(United Nations 2009, 140). They are measured for the total economy which can be grouped into
five mutually exclusive institutional units, including non-financial corporations, financial
corporations, government units, non-profit institutions serving households, and households
(United Nations 2009). Thus, I calculate the compensation of employees, and wages of
employees, in the government units as a percentage of that in the total economy. The government
units are defined as “unique kinds of legal entities established by political process that have
legislative, judicial or executive authority over the other institutional units within a given area”
(United Nations 2009, 62). And, based on the Untied Nation’s classifications registry of ISIC
Rev. 3, government units are operationalized as the “Division 75: public administration and
defence; compulsory social security” (UNSD).
In the data, the compensation variable (compensation_to_total) ranges from 6.38% (in Sweden)
to 16.98% (in Greece), with an average of 9.40% (where Slovakia and Estonia are around this
value) and a standard deviation of 2.21. It shows that, for example, in Sweden, out of the money
spent for compensation of employees in the total economy, 6.38% of it is consumed by
government units. Higher values indicate that in this country, compared to other countries,
government units constitute greater proportions in the final consumption of income in the total
economy, specifically in terms of compensation of employees. The wage of employees variable
168

(wage_to_tatal) ranges from 6.17% (in Sweden) to 15.8% (in Greece). It indicates that, for
example, in Greece, out of the money paid for wages and salaries in the total economy, 15.8% of
it is spent in government units. Higher values indicate the proportion of the government units to
the total economy in terms of wages and salaries paid is greater in this nation than other nations.
The average value is 9.13% (with a standard deviation of 2.02) and Slovakia (9.10%) is around
this value.
The fourth variable (size_govt_to_total_ilo) is obtained from the ILO database on labour
statistics, LABORSTAT. This is a ratio of the general government sector employment to the total
employment. In this data, the general government sector employment refers to “all of the
government units, social security funds and nonprofit, non-market public or private institutions
which are controlled and mainly financed by public authority” (Hammouya 1999, 3). It ranges
from 3.36% (in Korea) to 9.82% (in Belgium). It indicates that the proportion of government
workers in the total employment is the smallest in Korea and the largest in Belgium. The average
value is 6.05% (with a standard deviation of 1.50) and Estonia and Denmark (5.93%) are around
this value.

169

Appendix B. Supplementary Analysis for Chapter 3: Cluster Analysis
Figure B.1. Tree diagram for the twenty-one variable points
females in senior positions
females in central govt
females in administrative positions
women's representation
career-based senior officials
not strive to fulfill govt ideology
non-political senior officials
impartial in distribution
impartial in granting licenses
overall impartiality
impartial in implementation
non-political recruitment
impartial in procurement
merit based recruitment
tenure
career-based HRM
formal entrance exam
compensation
higher-education attained
size of government
wage
0

.1

.2
.3
Linakge Distance

.4

.5

Note: The dendrogram from the cluster analysis of twenty-one variable points shows the possible
five groupings. The average-link clustering is used.

170

Appendix C. Supplementary Analysis for Chapter 4
Table C.1. Descriptive Statistics
Variable
Attitudes toward Bureaucracy
Satisfaction with Democracy
Bureaucratic Profiles
Generalized trust
Age (0: 47-year-old)
Female
Education (0: Higher secondary complete)
Unemployed
Public sector worker
Political interest
GDP/capita growth rate
Democracy (Freedom House)
Electoral system: PR

Mean
0.00
6.01
-.13
.47
.28
.53
-.29
.05
.21
-.04
2.80
2.90
.83

Std.
Deviation
.83
2.25
1.57
.50
17.20
.50
1.49
.22
.41
.86
1.25
.30
.38

Minimum
-1.96
0.00
-2.87
0.00
-32.00
0.00
-3.00
0.00
0.00
-1.50
.97
1.50
0.00

(3)

(4)

Maximum
2.09
10.00
3.09
1.00
51.00
1.00
2.00
1.00
1.00
1.50
6.56
3.00
1.00

Table C.2. Correlation Matrix
Variables
(1)
(2)
(1) Bureaucratic Profiles
1.000
(2) Country-level generalized trust
.394**
1.000
*
(3) GDP/capita growth rate
-.323
-.257
(4) Democracy (Freedom House)
.232
.124
(5) Electoral system: PR
-.389**
.214
**
*
Note: significant at .05 level, significant at .10 level

171

1.000
-.171
.120

1.000
-.162

(5)

1.000

Table C.3. Multilevel Model of Citizens’ Attitudes toward Bureaucracies: Within- versus
Between-country Effects

Bureaucratic Profiles

Generalized Trust
Age
Age2/100
Female
Education
Unemployed
Public Sector Worker
Political Interest

Within- versus Between-country Effects
.074 (.041)*
Within Effect
.263 (.010)*
-.001 (.000)*
.009 (.002)*
-.021 (.010)*
.013 (.004)*
-.070 (.024)*
.189 (.012)*
.066 (.006)*

Between Effect
.009 ( .004)*
.006 ( .016)
-.359
-.039
-.000
-.134
.076

(1.333)
( .115)
( .032)
( .553)
( .263)

-.174 (.059)*
Constant
Variance Components
.048
Country-level intercept
.555
Individual-level Residuals
**
Proportion Reduction in Error:
12.93%
at Level 1
55.66%
at Level 2
57354.826
-2* Log-Likelihood
Note: The restricted maximum likelihood (REML) estimator is used. Standard errors are in
parentheses. Number of observations = 25409. Number of countries = 28.
*
Coefficient is statistically significant at .05 level, directional hypothesis test.

172

Table C.4. Multilevel Models of Citizens’ Attitudes toward Bureaucracies
Control:
Economic Growth

Control:
Democracy

Control:
PR system

Country Level
.064 (.025)*
.067 (.025)*
.064 (.030)*
Bureaucratic Profiles
-.046 (.030)
GDP/capita growth rate
Democracy
.168 (.121)
(Freedom House)
Electoral system:
-.073 (.118)
Proportional
Country-level
.009 (.003)*
.009 (.003)*
.010 (.003)*
generalized trust
Individual Level
.263 (.010)*
.263 (.010)*
.263 (.010)*
Generalized Trust
-.001 (.000)*
-.001 (.000)*
-.001 (.000)*
Age
2
*
*
.009 (.002)
.009 (.002)
.009 (.002)*
Age /100
-.021 (.010)*
-.021 (.010)*
-.021 (.010)*
Female
*
*
.013 (.004)
.013 (.004)
.013 (.004)*
Education
-.070 (.024)*
-.070 (.024)*
-.070 (.024)*
Unemployed
*
*
.189 (.012)
.189 (.012)
.189 (.012)*
Public Sector Worker
.066 (.006)*
.066 (.006)*
.066 (.006)*
Political Interest
*
*
-.158 (.037)
-.161 (.037)
-.103 (.102)
Constant
Variance Components
.036
.036
.039
Country-level intercept
Individual-level
.555
.555
.555
Residuals
Proportion Reduction in
Error:**
14.77%
14.70%
14.35%
at Level 1
66.88%
67.33%
64.71%
at Level 2
57344.734
57342.28
57343.882
-2* Log-Likelihood
Note: The restricted maximum likelihood (REML) estimator is used. Standard errors are in
parentheses. Number of observations = 25409. Number of countries = 28.
*
Coefficient is statistically significant at .05 level, directional hypothesis test.

173

Table C.5. Multilevel Model of Citizens’ Satisfaction with Democracy: Within- versus
Between-country Effects

Bureaucratic Profiles

Generalized Trust
Age
Age2/100
Female
Education
Unemployed
Public Sector Worker
Political Interest
Perceived Election Fairness

Within- versus Between-country Effects
.244 (.066)*
Within Effect
.522 (.027)*
-.000 (.001)
.010 (.005)*
-.115 (.025)*
.095 (.010)*
-.207 (.063)*
.018 (.032)
.155 (.016)*
.502 (.013)*

Between Effect
.023 ( .006)*
-.005 ( .026)
-.241
-.118
-.058
.144
-.500
.037

(2.153)
( .183)
( .052)
( .881)
( .407)
( .314)

5.574 (.223)*
Constant
Variance Components
.120
Country-level intercept
3.783
Individual-level Residuals
Proportion Reduction in Error:**
21.96%
at Level 1
85.09%
at Level 2
102341.65
-2* Log-Likelihood
Note: The restricted maximum likelihood (REML) estimator is used. Standard errors are in
parentheses. Number of observations = 24513. Number of countries = 28.
*
Coefficient is statistically significant at .05 level, directional hypothesis test.

174

Table C.6. Multilevel Models of Citizens’ Satisfaction with Democracy
Control:
Economic Growth

Control:
Democracy

Control:
PR system

Country Level
.242 (.036)*
.262 (.042)*
.283 (.051)*
Bureaucratic Profiles
-.153 (.043)*
GDP/capita growth rate
Democracy
.285 (.203)
(Freedom House)
Electoral system:
.072 (.199)
Proportional
Country-level
.021 (.004)*
.023 (.004)*
.022 (.005)*
generalized trust
Individual Level
.523 (.027)*
.523 (.027)*
.523 (.027)*
Generalized Trust
-.000 (.001)
-.000 (.001)
-.000 (.001)
Age
2
*
*
.010 (.005)
.010 (.005)
.010 (.005)*
Age /100
-.115 (.025)*
-.116 (.025)*
-.116 (.025)*
Female
*
*
.095 (.010)
.095 (.010)
.095 (.010)*
Education
-.206 (.063)*
-.208 (.063)*
-.207 (.063)*
Unemployed
.017 (.032)
.017 (.032)
.017 (.032)
Public Sector Worker
*
*
.152 (.016)
.154 (.016)
.153 (.016)*
Political Interest
Perceived Election
.501 (.013)*
.502 (.013)*
.502 (.013)*
Fairness
5.559 (.057)*
5.548 (.065)*
5.553 (.061)*
Constant
Variance Components
.068
.099
.106
Country-level intercept
Individual-level
3.783
3.783
3.783
Residuals
Proportion Reduction in
Error:**
22.99%
22.39%
22.23%
at Level 1
91.48%
87.73%
86.76%
at Level 2
102330.89
102336.42
102338.31
-2* Log-Likelihood
Note: The restricted maximum likelihood (REML) estimator is used. Standard errors are in
parentheses. Number of observations = 24513. Number of countries = 28.
*
Coefficient is statistically significant at .05 level, directional hypothesis test.

175

Appendix D. Residual Diagnostics for Chapter 4
Inspections of residuals are conducted to check whether level-1 and level-2 residuals are
normally distributed (Rabe-Hesketh and Skrondal 2008; Snijders and Bosker 2011). If the model
is true, estimated residuals at both levels have normal sampling distributions. Overall, it seems
residuals at both levels do not violate normality assumptions. I first estimate residuals for the
citizens’ attitudes toward bureaucracies model, the random-intercept model with country-level
controls (Model 4).98 Figure 1 shows the histograms of standardized level-1 and level-2 residuals,
respectively. The normality assumption for the residuals at both levels does not seem to be
violated severely.

0

0

.1

.2

.2

.4

.3

.6

.4

Figure D.1. Histograms of standardized level-1residuals and standardized level-2 residuals,
for the random-intercept model of citizens’ attitudes toward bureaucracies (Model 4)

-4

-2

0
Standardized level-1 residuals

2

4

-2

-1
0
Standardized level-2 residuals

1

0

.1

.2

.3

.4

Figure D.2. Histogram of standardized level-1residuals, for the random-slope model of
citizens’ attitudes toward bureaucracies (Model 5)

-4

-2
0
2
Predicted level-1 standardized residuals

4

98

To estimate residuals at both levels, gllapred command is used after estimating models using
gllamm command in STATA SE10.1.
176

In addition, level-1 residuals are estimated from the random-slope model (Model 5) of citizens’
attitudes toward bureaucracies.99 Figure 2 shows its histogram. In the random-slope models, it is
assumed that level-2 random coefficients are normally distributed. Figure 3 shows the
histograms for random slopes of interpersonal trust, age, public sector workers, and political
interest.

0

0

2

50

4

100

6

150

Figure D.3. Histograms of random slopes, for the random-slope model of citizens’ attitudes
toward bureaucracies (Model 5)

-.1
0
.1
Predicted random slope: Interpersonal Trust

.2

-.005

0
.005
Predicted random slope: Age

.01

0

0

2

4

5

6

8

10

10

-.2

-.1

-.05
0
.05
Predicted random slope: Public Sector Worker

.1

-.1

-.05
0
.05
Predicted random slope: Political Interest

.1

Next, residuals at both levels are estimated for the citizens’ satisfaction with democracy model,
the random-intercept model with country-level controls (Model 10). Figure 4 shows the
histograms of standardized level-1 and level-2 residuals, respectively. Both histograms look
normal and the normality assumption for the residuals at both levels does not seem to be violated
severely.

99

The level-2 residuals can only be estimated when the model is estimated with gllamm, and
not with xtmixed. But, the random slope model with four random slopes (Model 4) was not
estimated using gllamm. Therefore, I report the histogram of level-1 residuals for the random
slope model here.
177

.6
0

0

.1

.2

.2

.3

.4

.4

.5

Figure D.4. Histograms of standardized level-1residuals and standardized level-2 residuals,
for the random-intercept model of citizens’ satisfaction with democracy (Model 10)

-4

-2
0
Standardized level-1 residuals

2

4

-2

-1

0
Standardized level-2 residuals

1

2

In addition, level-1 residuals are estimated from the random-slope model (Model 11) of citizens’
attitudes toward bureaucracies. Figure 5 shows its histogram. In the random-slope models, it is
assumed that level-2 random coefficients are normally distributed. Figure 6 shows the
histograms for random slopes of interpersonal trust, age and perceived fairness of elections.

0

.1

.2

.3

.4

.5

Figure D.5. Histogram of standardized level-1residuals, for the random-slope model of
citizens’ satisfaction with democracy (Model 11)

-4

-2
0
2
Predicted level-1 standardized residuals

4

Finally, diagnostics for influential cases are conducted. To estimate diagnostics for influential
cases, MLT package (Möehring and Schmidt 2013) is used, after estimating models using
xtmixed command in STATA 12. Overall, the relationship between Bureaucratic Profiles and
political support (in both public attitudes toward bureaucracies and satisfaction with democracy)
still holds in the model where potential influential cases are excluded from the data. For a further
robustness check, I examine the models only with the twenty original member nations of the
OECD; the relationship holds.

178

0

0

1

20

2

40

3

60

4

5

80

Figure D.6. Histograms of random slopes, for the random-slope model of citizens’
satisfaction with democracy (Model 11)

-.1
0
Predicted random slope: Interpersonal Trust

.1

-.02

0

1

2

3

-.2

-.3

-.2
-.1
0
.1
Predicted random slope: Fairness of Elections

.2

179

-.01

0
Predicted random slope: Age

.01

.02

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180

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