The Direct and Indirect Effect of Personality Traits on Political
Attitudes. The mediating role of public sector affiliation
Paper prepared for the IPSA Meeting, Madrid July 8-12 2012
RC29 Psycho-Politics
Prof. Dr. Bernhard Kittel
Department of Industrial Sociology
University of Vienna
Brünner Straße 72 A-1210 Vienna (AUT)
[email protected]
Jun. Prof. Dr. Markus Tepe
(corresponding author)
Positive Political Theory / Political Economy
University of Oldenburg
Ammerländer Heerstr. 114-118, 26111 Oldenburg (GER)
[email protected]
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Abstract
Previous research has shown that government employees’ political attitudes and behavior
differ from their private sector counterparts, but stops short of questions about the origin
of these differences. This study presents a conceptual framework that refines the bureau
voting model (BVM) by focusing on the interplay between personality traits and
occupational experiences in shaping government employees’ political orientation. The
conceptual framework is tested by fitting a path model to General Social Survey (GSS)
data. Whereas government employment in service and production jobs is linked to higher
turnout and leftwing attitudes, only two personality traits – extraversion and openness –
exert substantially small effects on sector affiliation. On the basis of these findings the
bureaucratic personality concept ought to be classified as an outdated myth. Rather than
the distorted bureaucratic personality portrayed in some popular accounts, this study
stresses the role of occupational experiences for government employees’ political
preference formation.
Keywords: government employees, personality traits, sector cleavage voting
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INTRODUCTION
Government employees have a reputation for being different, both in their personality and their
political attitudes. Overconformity to rules, personal insecurities and effort-aversion are some
characteristics frequently used to portray what has been labeled a “bureaucratic” personality
(DeHart-Davis 2007). Concerning government employees’ political behavior, rational-choice
accounts do not present a more sympathetic description. Government employees are considered
as a homogenous interest group, which uses its electoral clout to protect their work privileges
and to push for extensive public spending (Bush and Danzau 1977; Garand, Parkhurst and Seoud
1991). In times of growing fiscal constraints it is not surprising that politicians from all political
camps utilize these ascriptions to create public support for the retrenchment of government
employment (e.g. The Economist 2010, 9-10).
Previous research has shown that government employees’ political attitudes and behavior
differ from their private sector counterparts, but stops short of questions about the origin of these
differences. This paper seeks to advance our understanding of government employees’ political
attitudes and behavior by shifting attention towards the interplay between government
employees’ personality traits and occupational experiences in shaping their political preferences.
The central question is: do government employees’ political attitudes and behavior significantly
differ from the rest of the population, and, if so, to which extent can these differences be
explained by a distinct “bureaucratic” personality?
In conceptualizing the nexus between government employment, personality traits and
political attitudes and behavior, we consider four integrated mechanisms: First, sector cleavage
voting suggests that government employees hold left-leaning state interventionist political views
as they expect to benefit from left-wing governments in terms of salaries and job security (e.g.
Garand, Parkhurst and Seoud 1991). Second, personality traits based voting presumes that
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political attitudes and behavior result from inborn tempers and dispositional traits (e.g. Gerber,
Huber, Doherty and Dowling 2011). Third, taking into account that government employees’
recruitment rules and career paths can differ rather substantially from those applying to the
private sector, certain bureaucratic personalities might have a predisposition towards working
for the government (e.g. DeHart-Davis 2007). Fourth, in this setup sector cleavage voting and
personality traits based voting are not considered as mutually exclusive explanations for political
attitudes and behavior. Instead, sector affiliation is expected to mediate the effect of personality
traits on political attitudes and behavior. Thus, personality traits might impact on government
employees’ political attitudes and behavior through their prior effect on sector affiliation. This
set of nested hypothesis is tested by applying path analysis to General Social Survey (GSS) data
from 2006.
THEORETICAL FRAMEWORK
Sector Cleavage Voting
Rational choice accounts on bureaucratic behavior start from the assumption of self-interested
rational actors who are not primarily interested in the goals of the organization they work for, but
in maximizing their personal benefits (Niskanen 1971; Sears and Citrin 1982). In any
organization, public or private, growth increases the likelihood of its employees to rise to a
position where wages are higher (Bennett and Orzechowski 1983, 272). In the private sector,
growth is determined by aggregate consumer demand, whereas public sector growth also
depends on political majorities. This provides government employees with the opportunity to use
their electoral clout to push for more public spending, which is supposed to increase their salaries
and improve their working conditions (Bennett and Orzechowski 1983, 272). In order to translate
their fiscal preferences into policy outcomes, government employees must outweigh contrary
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preferences, e.g. held by self-employed and private sector employees, and they must bring a
political party into government that actually raises public spending. Right-leaning parties may
present an implicit or explicit threat to downsize government, whereas left-leaning parties are
presumed to be more likely to maintain the status quo or to increase spending (Jensen et al. 2009,
712). Assuming political parties to hold identifiable partisan positions on public expenditure
(Tufte 1978), government employees are predicted to vote for left-leaning parties. These are the
micro-level foundations of what is known as the Bureau Voting Model (henceforth BVM). The
primary goal of the BVM is to show that the size of public budgets is a consequence of
government employees’ voting behavior (Garand 1988). This study focuses on the BVM’s
individual level assumptions. In line with Garand et al. (1991, 179), we analyze government
employees’ electoral turnout and support for left parties.
(H1)
Compared to private sector citizens, government employees are i) more likely to
participate in elections and they are ii) more left-leaning in their political orientation.
Prior studies tend to confirm that government employees are more likely to vote (Bennett and
Orzechowski 1983; Borcherding, Bush and Spann 1977; Corey and Garand 2002; Frey and
Pommerehne 1982; Jensen et al. 2009), even though in substantive terms the differences in
turnout probabilities are rather small (Jensen et al. 2009; Jaarsma, Schram and Van Winden
1986). Empirical evidence on fiscal preferences and partisan orientation among government
employees is more mixed. Knutsen’s (2005) descriptive analysis of public sector employees’
voting behavior across EU countries indicates that public sector employment correlates with leftwing voting, albeit the strength of this relationship varies across national contexts. For the US,
Garand et al. (1991) find that turnout, pro-public-spending attitudes and left-wing voting
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increases the likelihood of being a public servant. Employing international election study data
from 18 countries, Jensen et al. (2009) show that public employees are more likely to consider
themselves as left-leaning, to be more likely to participate in elections and to vote for left-wing
parties. However, if the BVM requires that both predictions – higher turnout and support for leftwing parties – need to be confirmed within countries (Garand et al. 1991, 180), findings are
rather weak (Jensen et al. 2009, 727).
This study suggests two refinements to explain government employees’ political
preference formation: First, by exclusively focusing on whether an individuals’ source of income
is located in the private or public sector, the BVM neglects the possibility that individuals’
political preferences are shaped by their daily experiences in the occupational world (Tepe
2012). Different occupations expose government employees to different forms of client
interaction and communicative experiences. Such experiences are expected to be more intense
where employees deal with the development of human individuality as in counseling or social
work (Kitschelt 1994, 17). Occupational experiences could therefore be important for employees
to develop common political views and to vote as a single constituency (Kitschelt 1994, 15ff).
According to these considerations it appears reasonable to move beyond a simple public/private
sector dichotomy and to distinguish at least two large groups of government employees: highskilled employees in management and professional jobs and employees working in service and
production jobs. If these two groups show different political attitudes and behavior, we can
conclude that the incentives that stem from sector affiliation (public vs. private) are trumped by
political views derived from government employees’ occupational experiences (high-skilled
administrative tasks vs. service provision).
The second refinement suggests that government employees’ political orientation might
be already grounded in their personality. According to this argument, certain personalities select
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themselves into government employment which indirectly affects their political behavior. Before
we continue to conceptualize the link between personality traits and sector affiliation, we thus
introduce the personality traits concept and discuss the direct relationship between personality
traits and political attitudes and behavior.
Personality Traits Based Voting
Personality traits are considered as consistent patterns of thought, feelings and actions (McCrae
and Costa 1990, 23). In contrast to values, which refer to “what a person considers as important”,
personality traits describe “what a person is like” (Roccas et al. 2002). Building on the idea that
socially relevant and salient personality characteristics are encoded in natural language,
personality traits have been described by the BFI (John and Srivastava 1999, 103). The concept
asserts that human personality can be measured on five dimensions: neuroticism, openness to
experience, extraversion, agreeableness, and conscientiousness. Roccas, Sagiv, Schwartz and
Knafo (2002, 792) describe the five traits as follows:
“Individuals who score high on extraversion tend to be sociable, talkative, assertive, and active; those who
score low tend to be retiring, reserved and cautious. Individuals who score high on agreeableness tend to be
good-natured, compliant, modest, gentle and cooperative. Individuals who score low on this dimension tend
to be irritable, ruthless, suspicious, and inflexible. Individuals who score high on openness tend to be
intellectual, imaginative, sensitive and open-minded. Those who score low tend to be down-to-earth,
insensitive, and conventional. Individuals scoring high on conscientiousness tend to be careful, thorough,
responsible, organized and scrupulous. Those who score low on this dimension tend to be irresponsible,
disorganized and unscrupulous. Individuals scoring high on neuroticism tend to be anxious, depressed,
angry and insecure. Those scoring low on neuroticism tend to be calm, poised, and emotionally stable.”
Political scientists have shown growing interest in personality traits as an explanation for
political attitudes and behavior (see Gerber, Huber, Doherty and Dowling (2011) for review).
Concerning political ideology the most consistent results have been found for openness (Gerber
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et al. 2011, 271). People scoring high on openness are more likely to favor government
involvement in the economy and are more likely to support liberal parties. People scoring high
on conscientiousness tend to be attracted by compliance with social norms and are more
supportive towards conservative parties (Gerber et al. 2011, 269). Gerber et al. (2010) find a
twofold effect for agreeableness as respondents scoring high in this dimension are more likely to
respond sympathetically to individuals in economic need, but also show higher levels of social
conservatism (Gerber et al. 2011, 271). Others have associated agreeableness with a communal
orientation and support for liberal parties (Barbaranelli, Caprara, Vecchione and Fraley 2007,
Caprara, Barbaranelli and Zimbardo 1999, Mondak and Halperin 2008, Schoen and Schumann
2007).
Research on the relationship between personality traits and political participation has
yielded less consistent findings (Gerber at al. 2011, 274, Vecchione and Caprara 2009, Mondak
2010). Extraversion and openness have been associated with higher rates of participation, even
though the relationships do not always reach conventional levels of statistical significance
(Gerber 2011, 274). People scoring high on extraversion are presumed to enjoy advocating their
preferences (Gerber et. al. 2011, 274), whereas respondents scoring high on openness are
presumed to respond favorably to new experiences. Since conscientiousness is linked to an
emphasis on personal duty and responsibility, Mondak and Halperin (2008, 343) expect that
conscientiousness should be consistent with higher levels of political participation, while Gerber
et al. (2011, 274) find that conscientiousness is associated with lower levels of participation.
In contrast to sector cleavage voting, which comes with a directional hypothesis, the link
between personality traits and political attitudes and behavior is less conclusive. Gerber et al.
(2011, 271) point out that this research is still in its infancy and has not yet established a
coherent theoretical framework to link personality traits to political attitudes and behavior.
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Gerber et al. (2011, 269) argue that personality traits do not have a singular effect on political
attitudes and behavior but mediate responses to the full range of stimuli that people encounter. In
this respect, personality traits capture the basic dispositions of individuals to engage with their
environments (Gerber et al. 2011, 269). This broad conceptualization of personality traits does
not attribute every single trait to a specific political attitude or behavior. Following Mondak and
Halperin (2008, 342ff.) and Schoen and Schumann (2007: 478ff.) we expect the following
associations between personality traits and political behavior:
(H2)
i) Extraversion, openness and conscientiousness are positively associated with
participation in elections. ii) Agreeableness and openness are positively associated with
left-leaning political attitudes whereas conscientiousness is negatively associated with
these attitudes.
Bureaucratic Personality
The third mechanism refers to the link between personality traits and sector affiliation. Given the
specific recruitment rules, career paths and working conditions that tend to characterize
government employment (Lewis and Frank 2002), these jobs could systematically attract certain
types of personalities. The central question is: which personalities are attracted by government
employment?
Since Weber (1978) there have been attempts to describe “bureaucratic” personalities.1
The most persistent image of a “bureaucratic” personality in public opinion may be that of an
overconforming (Merton 1940) and insecure (Thompson 1961) character. Merton (1940) regards
“bureaucratic” personalities as persons who have an inherent need for constraints and regulation
(see Cohen (1970) for a critical response). Elaborating on this idea, Thompson (1961) presents a
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theory of “bureaupathology”, in which bureaucrats are conceptualized as alienated individuals
who rely on conformity and rule abidance to overcome personal feelings of powerlessness and
insecurity (DeHart-Davis 2007, 892). These attributes of the “bureaucratic” personality have
been used to explain why leading government employees seek to exert an exorbitant amount of
control over public policies. In Thompson’s (1961) account “bureaucratic” behavior becomes a
pathological distortion of the Weberian bureaucracy (Bozeman and Rainey 1998, 167). In terms
of dispositional personality traits, overconformity to rules and personal insecurity should
correspond with high levels of conscientiousness and neuroticism. According to Thompson
(1961) we should expect the following relationship:
(H3i) Neuroticism and conscientiousness are positively associated with government
employment.
Despite the popularity of portraying bureaucrats as neurotic rule sticklers, there is remarkably
little systematic research on the relationship between personality and government employment.
Previous research on the “bureaucratic” personality focusing on rule-conformity does not support
the dominance of rule-bound “bureaucratic” personalities in public organizations (Bozeman and
Rainey 1998; Allinson 1984; Foster 1990). Bozeman and Rainey (1998) find that managers in
private organizations are more likely to prefer more rules than managers in public agencies.
DeHart-Davis (2007) presents the “unbureaucratic” personality as the mirror image of
Thompson’s (1961) “pathological bureaucratic” personality. Unbureaucratic government
employees are identified by their willingness to bend rules in order to improve citizen services.
Exploring the empirical validity of the “unbureaucratic” personality on a sample of municipal
employees in four US cities, DeHart-Davis (2007) finds that nonconformity and risk taking
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increase unbureaucratic attitudes. Hence, in contrast to the “pathological” personality depicted in
H3i, extraversion and openness rather than neuroticism and conscientiousness could be distinct
traits of government employees’ personality.
(H3ii) Extraversion and openness are positively associated with government employment.
Confronted with these conflicting hypotheses on the relationship between personality traits and
sector affiliation we could furthermore speculate whether the two depicted “bureaucratic
personalities” apply to different types of government employees. In fact, Thompson’s (1961)
conceptualization of a pathologic bureaucratic character focuses on administrative government
employees, whereas DeHart-Davis (2007) tends to take a broader view on types of government
employees. In terms of occupational differences within the group of government employees we
could therefore speculate that H3i) applies more to government employees occupied with
management and professional tasks, whereas H3ii) applies to government employees in service
and production jobs.
- Figure 1 -
Sector Mediation – An Integrated Framework
The conceptual model represented in Figure 1 suggests four relationships: The direct effect of
government employment on political attitudes and behavior (H1), the direct effect of personality
traits on political attitudes and behavior (H2) and the direct effect of personality traits on sector
affiliation (H3). All three relationships have been presented as self-contained theoretical
approaches.
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In line with Mondak and Halperin (2008, 339), however, we expect that personality may
not only correspond directly to variance in political attitudes and sectoral employment, but may
also produce indirect effects. H1 and H3 are not mutually independent but can be put into a
logical sequence. Personality traits affect political attitudes and behavior via its previous effect
on sector affiliation. The mediation effect from personality traits via sector affiliation on political
attitudes and behavior is our fourth hypothesis. It predicts that government employees’ political
attitudes and behavior can be decomposed into two parts; one that is based on government
employees’ occupational experiences and another which is based on “bureaucratic” personality
traits. The sector mediation hypothesis seeks to moves beyond a mere description of sectorspecific voting patterns towards an explanation of these patterns.
Given the already conflicting predictions on the relationship between personality traits
and sector affiliation (H3i/ii), it is even harder to derive directional hypotheses on the mediation
path from personality traits via sector affiliation towards political attitudes and behavior. Another
issue that needs to be left to empirical exploration rather than a priori theorization, concerns the
extent to which political attitudes and behavior are fully or partially mediated by sector
affiliation. As represented in Figure 1 we start from the assumption of partial mediation. What
already speaks in favor of partial mediation is the fact that not everybody whose dispositional
traits ‘fit’ with government employment actually finds a job in the public sector.
(H4)
The effect of personality traits on government employees’ political attitudes and behavior
is partially mediated by sector affiliation.
Finally, the conceptual model assumes a direct relationship between socio-demographic control
measures and personality traits. While there is growing evidence that the Big Five personality
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traits have some genetic basis (e.g., Bouchard 1997) and are quite stable through the life cycle
(Caspi, Roberts, and Shiner 2005), other scholars report systematic associations between
demographic variables and personality traits (Goldberg, Sweeney, Merenda and Hughes 1998).
In order to account for these associations we will start the empirical analysis with the assumption
of a direct relationship between socio-demographic measures and personality traits.
DATA AND METHOD
The model is tested on data from the General Social Survey (GSS) for the year 2006. The GSS is
a general population survey of residents of the United States conducted by the National Opinion
Research Center at the University of Chicago. The sample covers the voting age population
(18+). Since students, retirees and soldiers cannot be affiliated to any of the four sectors of
occupational activity, they have been excluded from the sample.
Endogenous Variables
Electoral participation is measured with a binary variable whether or not the respondent voted in
the last election. With respect to political attitudes, respondents have were asked which national
political party they prefer; answer categories range from (1) “Strong republican” to (7) “Strong
democrat”. Sector affiliation is constructed from two items: First, respondents were asked
whether they regard themselves as working for (1) the government, (2) a private firm or whether
(3) they are self-employed. In order to account for occupational differences within the group of
government employees, we use the International Standard Classification of Occupations (ISCO88). We distinguish two large occupational groups: Government employees whose occupation
falls into the first major ISCO-88 group (legislators, senior officials and managers) or the second
(professionals) are labeled government employees in management and professional jobs.
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Government employees whose occupation falls into any of the other major ISCO-88 groups
(technicians, clerks, service workers, machine operators etc.) are labeled government employees
in service and production jobs.2
The ISCO-88 is based on two concepts, the kind of tasks executed and the skills required
to carry out these tasks. Hence, the two groups of government employees reflect differences in
the occupational experiences (primarily high-skilled administrative tasks vs. service provision)
as well as differences in the skill level of government employees. Eight out of ten ISCO-88
major groups have been related to four broad skill levels defined in terms of the International
Standard Classification of Education (ISCED). Whereas the first ISCO-88 major group is not
affiliated to any skill-level, professionals are coded to possess the highest skill level (ILO 2004).
For example, government employment in management and professional jobs comprises
occupations such as senior government officials, production and operations department managers
or secondary education teaching professionals. Government employment in service and
production jobs comprises occupations such as electronics fitters, secretaries or child-care
workers. The resulting nominal variable capturing sector affiliation has four categories: (1)
Government employees in management and professional jobs, (2) government employees in
public service and production jobs, (3) private sector employees and (4) self-employed. As we
are interested in whether government employees ought to be considered as a homogenous group,
both, in terms of personality traits and political interests, private sector employees will serve as
the reference group.
Exogenous Variables
The psychological literature has suggested various measures for the Big Five traits. Costa and
McRae’s (1992) 240-item battery can be considered as the “gold standard” in measuring
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personality traits (Muck, Hell and Gosling 2007, 166). Since this battery is too lengthy for
general population surveys, researchers have suggested to measure personality traits with 60items (Costa and McRae 1992), 44-items (John and Srivastava 1999), 10-items (e.g. Gosling et
al. 2003) and even 5-items (Woods and Hampson 2005). The GSS 2006 includes the 10-item
battery constructed by Rammstedt and John (2007), the so-called BFI-10. In contrast to Gosling
et al.’s (2003) Ten Item Personality Inventory (TIPI), which introduces new adjectival items
based on a review of the existing Big Five instruments, Rammstedt and John (2007) selected two
items for each dimension from the BFI-44 battery by Costa and McRae (1992). The 10-items are
rated on a five-step scale ranging from “1= disagree strongly” to “5= agree strongly”. After
recoding the reversed items (see Technical Appendix Table 1) the pairs of items measuring the
same personality trait are summarized in five additive indices.
We choose to explore the empirical validity of our explanatory framework on an US
sample, since the direct effect of personality traits on political attitudes and behavior has already
been tested on US samples using longer (e.g. Mondak et al. 2010) and shorter versions (e.g.
Gerber et al. 2010) of the BFI. Thus, if we are able to reproduce these findings using the BFI-10
(Rammstedt and John 2007) from the GSS, we can be more confident in the instruments’ content
validity.
Furthermore, the path model includes two sets of control variables: age, gender,
educational attainment and employment status, which are standard predictors in the individuallevel analysis of survey data. A second set of control measures is taken from the literature on
sector cleavage voting (Jensen et al. 2009; Blais et al. 1990; Garand et al. 1991). Social class,
religious attendance and urban background, which capture alternative political cleavages, might
provide more meaningful explanations for individuals’ political attitudes and behavior than
sector affiliation. Before the exogenous variables were entered into the path model they have
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been tested for multicollinearity using the Variance Inflation Factor (VIF). The mean VIF for the
full set of variables is 1.10 with a minimum of 1.02 and a maximum of 1.17, all of which are
considered by Stine (1995, 55) and others (e.g. O’Brien 2007) as unproblematic values.
Method
The conceptual model presented in Figure 1 is examined with path analysis. Path analysis
provides a method to test the interrelated hypotheses positioned in the conceptual model without
conducting multiple estimations (Kline 2011, 103ff; Kaplan 2000). The operationalized model
contains binary endogenous variables (sector dummies and turnout) and one ordinal endogenous
variable (support for democrats). We thus use a probit model. The robust weighted least squares
mean and variance adjusted (WLSMV) estimator implemented in Mplus 6 fits path models with
any combination of dichotomous, ordinal or continuous outcome variables. WLSMV has two
advantages over weighted least squares (WLS): First, it can deal with non-positive definite
matrices by using a diagonal of the weight matrix and, second, it estimates robust standard errors
(Muthén, du Toit and Spisic 1997; Muthén 2004, 19ff). Simulation studies by Flora and Curran
(2004) report a generally positive performance of robust WLS estimation methods (also see
Kline 2011, 181). Due to the adoption of the non-respondent, sub-sampling design, the GSS
2006 explicitly requires the use of weights (GSS 2011, 2110). The weight that is used in the path
analysis (WTSS) takes into consideration the sub-sampling of non-respondents, the number of
adults in the household, and essentially maintains the original sample size.
The evaluation of the overall fit of the path model is based on three indicators (Kline
2011, 204ff.): the Root Mean Square Error of Approximation (RMSEA), the Comparative Fit
Index (CFI), and the Weighted Root Mean Squared Residual (WRMR). Hu and Bentler (1999, 1)
suggest that for the maximum likelihood method, a cutoff value equal or below 0.06 for the
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RMSEA and a cutoff value equal or above 0.95 for the CFI are needed “before we can conclude
that there is a relatively good fit between the hypothesized model and the data”. Yu and Muthén
(2002) suggest that these heuristics are also reasonable with categorical outcomes with sample
sizes above 250. According to Yu and Muthén (2002), the WRMR is better suited to assess the
overall model fit if the path model is estimated by WLSMV (also see Muthén 2004, 23ff). They
suggest that values for WRMR below 0.90 indicate good fit (Muthén 2004, 24, also see Yu 2002,
154ff).
The fitted path model is represented in two path diagrams. Following Kline (2011, 161),
the path diagrams report the unstandardized and the standardized path coefficients, which are
interpreted just as regression coefficients in multiple regression (Kline 2011, 103). Once we have
an appropriate overall model fit we apply a series of Chi-Square Difference Tests for model
trimming (Muthén and Muthén 2010, 553). This is done by comparing the unconstrained model
with a constrained model in which at least one path previously estimated freely is now
constrained to equal zero (Kline 2011, 214). The Chi-Square Difference Test implemented in
Mplus 6 is used to test the statistical significance of the decrement in overall fit as free
parameters are eliminated. Missing data are excluded by listwise deletion.3
RESULTS
Bivariate relationships
Most respondents (68.9 percent, 779 obs.) say that they work in the private sector. Government
employment is the second largest group. 8.6 percent (97 obs.) of the sample work for the
government in management and professional occupations and 9.3 percent (105 obs.) work for the
government in service and production jobs. The proportion of self-employed is 13.3 percent (150
obs.). Private sector workers serve as the reference category.4
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- Table 1 -
The bivariate associations between personality traits, sector affiliation and political attitudes and
behavior are summarized in Table 1. The top section of Table 1 presents average turnout rates
and support for the Democratic Party measured in terms of deviations from turnout rates and
support for democrats among private sector citizens. For the bivariate analysis the variable
measuring party support has been dichotomized (support and strong support for democrats = 1,
otherwise zero). Compared to their private sector counterparts, government employees in
management and professional jobs show the highest plus in the turnout rate (plus 28.6 percentage
points). Turnout rates for the other two groups (government employees in service and production
jobs and self-employed) are just slightly higher than the average turnout rate for private sector
employees (plus 15.3 to 16.2 percentage points).
Concerning support for the democrats, government employees in service and production
jobs show the strongest deviation (plus 13.9 percentage points). A negative deviation can be
observed for the self-employed (minus 4.16 percentage points). Political attitudes of government
employees in management and professional jobs hardly deviate from the average position of
private sector employees (plus 2.02 percentage points). Apparently, the descriptive results
support H1ii, but with an important refinement. It is government employees in management and
professional jobs that show higher turnout rates, whereas it is government employees in service
and production jobs that more strongly support the Democratic Party.
The second section of Table 1 shows the relationship between personality traits, turnout
and political orientation (H2). Cell entries represent the differences between voters and nonvoters (respectively supporters of the democrats and none supporters) in percentage points for
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each of the five personality traits. Turnout is positively associated with higher levels of
extraversion and negatively associated with higher levels of neuroticism. Support for democratic
candidates is positively associated with higher levels of agreeableness and negatively associated
with conscientiousness. However, none of these relationships is statistically significant and in
substantive terms the corresponding values for Cramer’s V indicate weak associations.
The bottom section of Table 1 reports the bivariate relationship between personality traits
and sector affiliation (H3). For each of the three sectors, cell entries represent the differences in
percentage points from the average personality traits score for private sector workers.
Extraversion is particularly low among government employees in service and production jobs,
whereas government employees in management und professional jobs show particularly low
levels of agreeableness. The other three personality traits take extreme values for the selfemployed. These respondents combine higher levels of conscientiousness and particularly higher
levels of openness with low levels of neuroticism. Again, none of the associations reaches
statistical significance and the corresponding values for Cramer’s V are low.
Fitting and Trimming of the Path Model
The empirical model has been developed by applying the following steps: First, we fitted a path
model that has the same structure as suggested by the conceptual framework (see Figure 1). The
three measure of overall model fit, however, miss the suggested cutoff values: the RMSEA is
0.12 (90 Percent C.I. 0.10 to 0.13), the CFI is 0.67, and the WRMR is 1.32., indicating that the fit
between the hypothesized model and the data can be substantially improved. Excluding the paths
from the socio-demographic control variables to personality traits leads to a significant
improvement in the overall model fit (RMSEA 0.04, CFI 0.9, WRMR 0.30). The second step of
the trimming process involved the elimination of paths from social class, urbanization and
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religious to the sector affiliation. The Chi-Square Test for Difference Testing remains
insignificant (Chi-square (9) = 11.03, p = 0.27) if these paths are constrained to equal zero,
suggesting to eliminate these paths. The third step of the trimming process concerns the question
whether the effect of personality traits on political attitudes and behavior is partially or fully
mediated by sector affiliation. In order to explore this question we also constrained the direct
paths from personality traits to political attitudes and behavior to equal zero. This time, the ChiSquare Test for Difference Testing is statistically significant at the 5 percent level indicating that
the additional elimination of these paths causes a significant decline in the overall model fit (ChiSquare (19) = 31.21, p = 0.04). From that we conclude that partial mediation provides a more
appropriate description of the dependencies among the set of variables than full mediation.
Figure 2 presents the unstandardized and standardized path coefficients for the fitted path
model on turnout and political orientation. In order to keep the graphical representation of the
path diagram manageable the full estimation results including disturbances are summarized in
Appendix Table 3. The Chi-Square Test of Model Fit for the Baseline Model, which compares
the final path model to the null model, is statistically at the highest margin (Chi-Square (70) =
395.86, p = 0.00). The three indicators accounting for the overall model fit indicate an acceptable
model fit. The trimmed path model has a RMSEA of 0.02 (90 Percent C.I. 0.00 to 0.04) a CFI of
0.98 and a WRMR of 0.47.
- Figure 2 -
Direct effects
Before we discuss findings on H1, H2 and H3 we briefly describe the direct effects of the control
variables (see Appendix Table 3). The elderly, people reporting higher level of religious
- 19 -
attendance, and higher educated respondents are more likely to vote, whereas respondents in
urban regions are less likely to vote. With respect to support for the Democratic Party the path
coefficients can be summarized as follows: Respondents reporting high levels of religious
attendance are less likely to support the democrats, whereas the elderly are more likely to do so.
These patterns are in line with prior research on electoral participation and are consistent with
prior research on the individual determinants of political ideology (e.g. Blais et al. 1990; Jensen
et al. 2009).
Sector-cleavage voting (H1): In line with the bivariate findings government employees in
management and professional jobs are more likely to vote but they are not more likely to support
the democrats, as compared to private sector employees. Government employees in service and
production jobs are more likely to vote than private sector employees, and they are also more
likely to support the Democratic Party. This pattern refines prior evidence on sector cleavage
voting (e.g. Jensen et al. 2009; Garand et. al. 1991). In contrast to the BVM, which assumes
government employees to behave as a homogenous interest group, the path model suggests that
occupation-specific experiences matter for government employees’ political attitudes.
Moreover, from the cleavage voting literature we would expect that government
employees and self-employed hold diametrically opposed political views. Even though selfemployment has no effect on turnout, the negative and statistically significant association
between self-employment and support for democrats tends to support this expectation.
Personality traits based voting (H2): None of the five traits exerts a statistically
significant effect on turnout. The negative effect of conscientiousness on turnout (p-value 0.11)
and, to a lesser extent, on support for democrats (p-value 0.15), however, only marginally fails
the conventional threshold. These weak but stable findings are in line previous studies. Gerber et
al. (2011, 220) also find a significant negative association between conscientiousness and both
- 20 -
turnout as well as support for democratic candidates. Mondak et al. (2010, 23) also report a
negative and significant association between conscientiousness and turnout. Mondak et al. (2008,
352) and Gerber et al. (2010, 120) report a negative and significant association between
conscientiousness and liberal political ideology. The positive and statistically significant effect of
openness on support for the Democratic Party also supports prior finding on personality traits
based voting (Mondak et al. 2008, 352; Gerber et al. 2010, 120, Gerber et al. 2011, 270) and
lends tentative support towards H2ii.
Bureaucratic Personality (H3): With respect to government employees in management
and professional jobs, none of the five variables measuring personality traits exert a statistically
significant effect. This non-finding can be theoretically insightful as it suggests that the
personality profile of high-skilled government employees does not significantly differ from the
average personality profile of private sector citizens. Hence, for this group of government
employees we find no evidence in favor of a distinct “bureaucratic” personality.
This picture slightly changes with respect to government employees in service and
production jobs. Higher levels of extraversion are negatively associated with government
employees in service and production jobs. Even though the association is statistically significant
at the lowest conventional level, the standardized path coefficient indicates a reasonable impact
compared to the impact of control measures (see Appendix Table 3). Extraversion has been
linked to the pursuit of excitement and higher willingness to take risks (Roccas et al. 2002). In
this respect the negative association between extraversion and government employees in service
and production jobs tilts tentative evidence in the direction of a reclusive public employee. This
observation, however, needs to be interpreted carefully. Among the set of regressions nested in
the path model, the model explaining government employment in service and production jobs
has the lowest model fit (see Appendix Table 3).
- 21 -
An interesting side finding is the impact of personality traits on self-employment.
Compared to private sector employees, self-employment is associated with higher levels of
openness. Openness has been related to nonconformity and innovation (Roccas et al. 2002).
These qualities might be particularly helpful for entrepreneurial activities. Even though it would
go far beyond the scope of this preliminary analysis to conclude that openness constitute
entrepreneur personalities, it seems plausible that this trait could be beneficial for entrepreneurs
in competitive market economies.
- Table 2 -
Indirect effects
Our fourth hypothesis predicts that personality traits affect political attitudes and behavior via
their prior effect on sector affiliation (H4). In terms of path analysis it presumes an indirect effect
from personality traits on political behavior and attitudes via sector affiliation. Since Figure 2
reveals that only two personality traits – extraversion and openness – have significant effects on
sector affiliation, we explore the indirect effects of these traits on political attitudes and behavior
in further detail (see Table 2).
Indirect effects are calculated as the product of the direct effects, either standardized or
unstandardized (Kline 2011, 164). The interpretation of the resulting mediation path coefficients
is the same as for the direct path coefficients. The path from extraversion to vote is mediated by
government employment in service and production jobs. The specific standardized indirect effect
is -0.12 x 0.11 = -0.01. The result -0.01 says that turnout is expected to decrease by about 0.01
standard deviations for every increase in extraversion of one full standard deviation via its prior
effect on sector affiliation. Since the coefficients for indirect effects can have complex
- 22 -
distributions it can be difficult to estimate their standard errors. The Sobel Test provides
approximated standard errors (Kline 2011, 165). MacKinnon (2008) and MacKinnon,
Lockwood, Hoffman, West and Sheets (2002) suggest to use bootstrapping methods to obtain
standard errors for indirect effects. Estimating bootstrapped standard errors with 2500 repetitions
indicates that the indirect effect from extraversion to vote via government employment in service
and production jobs is statistically significant at the 10 percent level.
The total indirect effect of extraversion on support for the Democratic Party (second
section of Table 2) is substantively small and statically significant at the lowest margin. The total
indirect effect, however, appears to be driven by the specific indirect effect from extraversion via
government employment in service and production jobs on support for democrats. The
standardized mediation coefficient for that path says that support for the Democratic Party is
expected to decrease by about 0.02 standard deviations for every increase in extraversion of one
full standard deviation via its prior effect on sector affiliation. Hence, if government employees
in service and production jobs were not characterized by low levels of extraversion, the positive
association between this group of employees and support for the Democratic Party would have
been even slightly stronger.
The second personality trait that matters for sector affiliation is openness, which has a
positive direct effect on self-employment. The path decomposition shows that openness has no
direct and no indirect effect on turnout; all mediation path coefficients are statistically
insignificant (third section of Table 2). However, there is some evidence that self-employment
mediates the effect of openness on support for the Democratic Party. The total indirect effect of
openness on support for democrats is driven by the specific indirect path via self-employment
(fourth section of Table 2). The standardized mediation coefficient for the specific indirect path
says that support for the Democratic Party is expected to decrease by about 0.03 standard
- 23 -
deviations for every increase in extraversion of one full standard deviation via its prior effect on
self-employment. Hence, the negative association between self-employment and support for the
democrats would have been stronger if self-employees were not characterized by high levels of
openness.
DISCUSSION
In order to explain why government employees’ political attitudes and behavior differs from
private sector citizens, this study presents a theoretical framework that focuses on the interplay
between government employees’ personality traits and occupational experiences in shaping their
political preferences. Findings from the path model advance our understanding of government
employees’ political preference formation in four ways:
First, instead of considering government employees as homogenous bloc of voters this
study points out the relevance of occupational experiences for government employees to develop
common political interests. A simple public/private dichotomy conceals the variance of political
attitudes within the government workforce (H1i/ii). Disaggregating government employees by
their skill level and occupation reveals that the positive association between government
employment and support for left-wing parties, which has been reported early (e.g. Jensen et al.
2009; Wise and Szücs 1996; Blais et al. 1990; Garand et al. 1991), stems from government
employees in service and production jobs, whereas government employees occupied with
management and professional tasks do not differ in their political attitudes from the rest of the
population.
Second, even though findings on personality traits based voting are mixed with respect to
the theoretical expectations put forward by Mondak and Halperin (2008, 342ff.) and Schoen and
Schumann (2007: 478ff.), the path model has been able to replicate findings from prior studies
- 24 -
which relied on alternative item batteries for measuring the Big Five personality traits (Mondak
et al. 2008, 352; Gerber et al. 2010, 120). Overall, these findings support the assumption that the
personality concept can provide a unique explanatory factor in political behavior and individual
voting patterns.
Third, the direct relationship between personality traits and sector affiliation shows that
we can hardly assume a homogenous “bureaucratic” personality neither in the spirit of Merton’s
(1940) and Thompson’s (1961) overconforming and timid bureaucrat nor in the spirit of an proactive government employee (DeHart-Davis 2007). In this respect, the concept of a unified
bureaucratic personality needs to be qualified as an outdated myth. If anything, the observation
that government employees in service and production jobs show slightly lower levels of
extraversion than private sector citizens constitutes some weak evidence in favor of the
proposition of a reclusive public employee. Findings on openness suggest that there could be a
selection bias in government recruitment, as this personality trait, which has been associated with
entrepreneurial attitudes, is underrepresented in the government workforce.
Fourth, there is equally weak evidence for an indirect effect from personality traits on
government employees’ political orientation. Even though personality traits affect political
attitudes and behavior via its previous effect on sector affiliation – namely self-employment and
government employees in service and production jobs – the specific indirect effects are rather
small (H4). This observation corroborates the conclusion that government employees’
experiences in the occupational world are more influential determinants of their political
attitudes and behavior than inherited personality traits.
The empirical analysis is certainly limited in at least two ways. First, the four
employment groups are of very different sizes. Such dummy-coded predictors with unequal
group sizes reduce the power of significance tests and may partially explain the weak statistical
- 25 -
associations. Second, as shown by Rammsted and John (2007, 203), we would probably detect
stronger relationships between personality traits, sector affiliation and political orientation with a
larger instrument than the BFI-10. Both aspects, which are due to the limited availability of
detailed BFI and sector employment measures in population surveys, need to be considered in
putting the path model into perspective.
The conceptual model presented in this study would certainly benefit from further
rigorous theoretical and empirical exploration. First, tasks of the public sector, administrative
cultures, and employment conditions therein vary considerably across countries (Painter and
Peters 2010, Tepe, Gottschall and Kittel 2010). Further research might focus on how these
national contexts condition the interplay between personality traits, government employment and
political orientation. Second, this study uses the BVM, which stems from the rational choice
literature, as the theoretical reference point to conceptualize a refined model of government
employees’ political preference formation. The Public Service Motivation framework (e.g.
Wright 2001), departs from the assumption of economically self-interested agents. These
scholars consider government employees to be characterized by “attitudes that go beyond selfinterest and organizational interest, that concern the interest of a larger political entity and that
motivate individuals to act accordingly whenever appropriate” (Vandenabeele 2007, 549).
Further research might therefore address whether such pro-social and communal orientations
exist among government employees and whether these orientations can be traced back to certain
personality traits. We expect that all these extensions will make it even more difficult to identify
a homogenous and internally consistent “bureaucratic personality”.
- 26 -
ENDNOTES
1
Weber (1978) treats impersonality as a distinctive feature of the recruitment process in the civil
service. Downs (1967) provides a whole typology of bureaucratic characters (distinguishing
climbers, conservers, zealots, advocates and statesmen), which Dunleavy (1991, 168)
condemns as “venturing into amateur social psychology”.
2
The Erikson-Goldthorpe (EGP) class scheme might have been an even more adequate measure
to capture occupational experiences. The use of this scheme, however, resulted in very low
numbers of observation per class and has therefore not been considered in this analysis.
3
The GSS (2006) dataset, the Stata command files and Mplus command files are available for
replication purposes.
4
The GSS contributes to the International Social Survey Program (ISSP). The ISSP 2006 is a
cross-national research collaboration to conduct social surveys. The BFI-10 is only available
for the GSS’s subsample that contributes to the ISSP. Hence our sample has just one third of
the original size of the GSS in 2006. Further descriptive statistics are reported in Appendix
Table 2.
- 27 -
REFERENCES
Allinson, C. W. 1984. Bureaucratic Personality and Organisation Structure. Aldershot: Gower.
Barbaranelli C., G.V. Caprara, M. Vecchione, and C.R. Fraley. 2007. Voters’ personality traits in
presidential elections. Personality and Individual Differences 42: 1199-1208.
Bennett, J. T., and W.P. Orzechowski. 1983. The voting behavior of bureaucrats: Some empirical
evidence. Public Choice 41(2): 271-283.
Blais, A., S. Dion, and D. Blake, D. 1990. The Public/Private Sector Cleavage in North America.
Comparative Political Studies 23(3): 381-403.
Borcherding, T.E., W.C. Bush, and R.M. Spann. 1977. The Effects on Public Spending of the
Divisibility of Public Outputs in Consumption, Bureaucratic Power, and the Size of the
Tax-Sharing Group. in T. Borcherding (ed.), Budgets and Bureaucrats: The Sources of
Government Growth. Durham: Duke University Press. pp. 211-228.
Bouchard, T. 1997. The Genetics of Personality. In Handbook of Psychiatric Genetics, eds.
Kenneth Blum and Ernest P. Noble. Boca Raton, FL: CRC Press, 273–96.
Bozeman, B. and H. Rainey. 1998. Organizational Rules and the “Bureaucratic Personality”.
American Journal of Political Science 42(1): 163-189.
Bush, W., and A. Danzau. 1977. The voting behavior of bureaucrats and public sector growth. in
T. Borcherding (ed.), Budgets and Bureaucrats. Durham: Duke University Press, pp. 9099.
Caprara, G. V., C. Barbaranelli, and P.G. Zimbardo. 1999. Personality profiles and political
parties. Political Psychology 20: 175-197.
Caspi, A., Roberts, B. and Shiner, R. 2005. Personality Development: Stability and Change.
Annual Review of Psychology 56: 453–84.
Cohen , H. 1970. Bureaucratic Flexibility: Some Comments on Robert Merton’s “Bureaucratic
Structure and Personality. British Journal of Sociology 21(4): 390-399.
Corey, E. C., and J.C. Garand. 2002. Are Government Employees more Likely to Vote? An
Analysis of Turnout in the 1996 U.S. National Election. Public Choice 111(3-4): 259283.
Costa, P. and R. McCrae. 1992. NEOPI-R. Professional Manual. Odessa, FL: Psychological
Assessment Resources.
DeHart-Davis, L. 2007. The unbureaucratic personality. Public Administration Review 67: 892903.
Downs, A. 1967. Inside bureaucracy. New York: Little Brown Company.
Dunleavy, P. 1991. Democracy, bureaucracy and public choice: Economic explanations in
political science. New York: Prentice Hall.
Flora, D. B., and P.J. Curran. 2004. An empirical evaluation of alternative methods of estimation
for confirmatory factor analysis with ordinal data. Psychological Methods 9: 466-491.
Foster, J. L. 1990. Bureaucratic Rigidity Revisited. Social Science Quarterly 71(2): 223-249.
Frey, B. S., and W.W. Pommerehne. 1982. How powerful are public bureaucrats as voters?
Public Choice 38(3): 253-262.
Garand, J.C. 1988. Explaining Government Growth in the U.S. States. The American Political
Science Review 82(3): 837-849.
- 28 -
Garand, J.C., C.T. Parkhurst, and R.J. Seoud. 1991. Bureaucrats, Policy Attitudes, and Political
Behavior: Extension of the Bureau Voting Model of Government Growth. Journal of
Public Administration Research and Theory 1(2): 177-212.
General Social Survey (GSS) 2006. Cross-section only; Release 1.1, Feb. 2011.
http://www3.norc.org/GSS+Website/Download/STATA+v8.0+Format/ (accessed 15
March. 2012).
General Social Survey (GSS) 2011. Technical Appendix A: Sampling Design and Weighting
http://publicdata.norc.org:41000/gss/.%5CDocuments%5CCodebook%5CA.pdf
(accessed 15 March. 2012).
Gerber A., G.A. Huber, D. Doherty, C.M. Dowling. 2011. The Big Five Personality Traits in the
Political Arena. Annual Review of Political Science 14: 265-287.
Gerber, A., G.A. Huber, D. Doherty, C.M. Dowling, and S. Ha. 2010. Personality and Political
Attitudes: Relationships across Issue Domains and Political Contexts. American Political
Science Review 104(1): 111-133.
Goldberg, L., D. Sweeney, P. Merenda, and E. Hughes. 1998. Demographic variables and
personality: the effects of gender, age, education, and ethnic/racial status on selfdescriptions of personality attributes. Personality and Individual Differences 24(3) 393403.
Gosling, S. D. 2011. A Note on Alpha Reliability and Factor Structure in the TIPI
http://homepage.psy.utexas.edu/homepage/faculty/gosling/tipi_alpha_note.htm (accessed
15 March. 2012).
Gosling, S. D., P.R. Rentfrow, and W.B. Swann. 2003. A Very Brief Measure of the Big-Five
Personality Domains. Journal of Research in Personality 37: 504-528.
Hu, L. and P.M. Bentler. 1999. Cutoff criteria for fit indexes in covariance structure analysis:
conventional criteria versus new alternatives. Structural Equation Modeling 6(1) 1-55.
International Labour Organization (ILO) 2004. Conceptual framework.
http://www.ilo.org/public/english/bureau/stat/isco/isco88/publ2.htm (accessed 15 March.
2012).
International Social Survey Program (ISSP). 2006. Work Orientation III (ZA4350_F1.dta).
Cologne. GESIS.
Jaarsma, B., A. Schram, and F.V. Winden. 1986. On the voting participation of public
bureaucrats. Public Choice 48(2): 183-187.
Jensen, J, P. Sum, and D. Flynn. 2009. Political orientations and behavior of public employees:
A cross-national comparison. Journal of Public Administration Research and Theory 19:
709-730.
John, O.P. and S. Srivastava. 1999. The Big Five trait taxonomy: History, measurement, and
theoretical perspectives. in L.A. Pervin and O.P. John (eds.), Handbook of personality
theory and research, New York: Guilford Press. pp. 102-138.
Kaplan, D. 2000. Structural Equation Modeling: Foundations and Extensions. Thousand Oaks:
Sage.
Kitschelt, H. 1994. The Transformation of European Social Democracy, Cambridge: Cambridge
University Press.
Kline, R.B. 2011. Principles and practice of structural equation modeling (3nd ed.). New York:
Guilford.
- 29 -
Knutsen, O. 2005. The impact of sector employment on party choice: A comparative study of
eight West European countries. European Journal of Political Research 44(4): 593-621.
Lewis, G.B. and S.A. Frank. 2002. Who Wants to Work for Government? Public Administration
Review 62(4): 395-404.
MacKinnon, D.P. 2008. Introduction to Statistical Mediation Analysis. New York: Erlbaum.
MacKinnon, D.P., C.M. Lockwood, J.M. Hoffman, S.G. West, V. and Sheets. 2002. A
comparison of methods to test mediation and other intervening variable effects.
Psychological Methods 7(1) 83-104.
Marsh, H.W., K.T. Hau, J.R. Balla, and D. Grayson. 1998. Is More Ever Too Much? The
Number of Indicators per Factor in Confirmatory Factor Analysis. Multivariate
Behavioral Research 33(2): 181-220.
McCrae R.R., and P.T. Costa. 1990. Personality in adulthood. New York: Guilford.
Merton, R. 1940. Bureaucratic Structure and Personality. Social Forces 18: 560-668.
Mondak J.J. 2010. Personality and the Foundations of Political Behavior. Cambridge:
Cambridge University Press.
Mondak, J.J., M.V. Hibbing, D. Canache, M.A. Seligson, and M.R. Anderson. 2010. Personality
and Civic Engagement: An Integrative Framework for the Study of Trait Effects on
Political Behavior. American Political Science Review 104(1): 85-110.
Mondak, J.J. and K. Halperin. 2008. A Framework for the Study of Personality and Political
Behavior. British Journal of Political Science 38: 335-362.
Muck, P., B. Hell and S.D. Gosling. 2007. Construct Validation of a Short Five-FactorModel
Instrument. A Self-Peer Study on the German Adaptation of the Ten-ItemPersonality
Inventory (TIPI-G). European Journal of Psychological Assessment 23(3): 166–175.
Muthén, B.O. 2004, Mplus Technical Appendices 1998-2004 (Version 3, March 2004). Los
Angeles, CA: Muthén & Muthén.
Muthén, L.K. and B.O. Muthén. 2010. Mplus User’s Guide. Sixth Edition. Los Angeles: Muthén
& Muthén.
Muthén, B.O., S. du Toit, and D. Spisic. 1997. Robust inference using weighted least squares and
quadratic estimating equations in latent variable modeling with categorical and
continuous outcomes. Accepted for publication in Psychometrika. Available at:
http://www.gseis.ucla.edu/faculty/muthen/articles/Article_075.pdf (accessed 15 March.
2012).
Niskanen, W. 1971. Bureaucracy and representative government, Chicago: Aldine Atherton.
Nunnally, J. C. 1978. Psychometric theory (2nd ed.). New York: McGraw-Hill.
O’Brien, R. 2007. A Caution Regarding Rules of Thumb for Variance Inflation Factors. Quality
& Quantity 41: 673-690.
OECD. 2009. Government at a Glance 2009. Paris: OECD.
Painter, M. and Peters, B. G. 2010. Tradition and Public Administration. Hampshire: Palgrave
Macmillan.
Rammstedt, B. and O. John. 2007. Measuring personality in one minute or less: A 10-item short
version of the Big Five Inventory in English and German. Journal of Research in
Personality 41: 203–212.
Roccas, S., L. Sagiv, S. Schwartz, and K. Knafo. 2002. The Big Five Personality Factors and
Personal Values. Personality and Social Psychology Bulletin 28(6): 789-801.
- 30 -
Schoen, H., and S. Schumann. 2007. Personality Traits, Partisan Attitudes, and Voting Behavior:
Evidence from Germany. Political Psychology 28 (4): 471–98.
Sears, D. and J. Citrin. 1982. Tax Revolt: Something for Nothing in California. Cambridge:
Harvard University Press.
Stine, R. 1995. Graphical Interpretation of Variance Inflation Factors. The American Statistician
49(1) 53-56.
Tepe, M., K. Gottschall, and B. Kittel. 2010. A Structural Fit of States and Markets? Public
Administration Regulation and Market Economy Models in the OECD. Socio Economic
Review 8(4): 653-684.
Tepe, M. 2012. The Public/Private Sector Cleavage Revisited. The impact of government
employment on political attitudes and behavior in eleven West European countries,
Public Administration 90(1): 230–261.
The Economist 2010. Stop! The size and power of the state is growing and discontent is on the
rise. The Economist January 23rd-28th: 9-10.
Thompson, V. 1961. Modern Organization. New York: Knopf.
Tufte, E. 1978. Political Control of the Economy, Princeton: Princeton University Press.
Vandenabeele, W. 2007. Toward a Theory of Public Service Motivation: an Institutional
Approach. Public Management Review 9(4): 545-556.
Vecchione, M. and G.V. Caprara. 2009. Personality Determinants of Political Participation: The
Contribution of Traits and Self-Efficacy Beliefs. Personality and Individual Differences
46: 487-492.
Wise, L.R., and S. Szücs. 1996. The Public/Private Cleavage in a Welfare State: Attitudes
Toward Public Management Reform. Governance 9(1): 43-70.
Weber, M. 1978. Economy and Society: An Outline of Interpretive Sociology. Edited by G. Roth
and C. Wittich. Los Angeles: University of California Press.
Woods, S.A. and S.E. Hampson. 2005. Measuring the Big Five with single items using a bipolar
response scale. European Journal of Personality, 19, 373–390.
Wright, B.E. 2001. Public-sector work motivation: A review of the current literature and a
revised conceptual model. Journal of Public Administration Research and Theory, 11(4):
559-586.
Yu, C.-Y. an B.O. Muthén 2002. Evaluation of model fit indices for latent variable models with
categorical and continuous outcomes. Technical report.
Yu, C.Y., 2002. Evaluating cutoff criteria of model fit indices for latent variable models with
binary and continuous outcomes (Doctoral dissertation, University of California, Los
Angeles, 2002). Retrieved March 11, 2008 from
http://www.statmodel.com/download/Yudissertation.pdf (accessed 15 March. 2012).
- 31 -
Figure 1. Conceptual framework
Bu
re
au
cr
(
at H3)
ic
Pe
rs
on
al
ity
v
1) e
(H vag
a
le
rc
to
c
Se
g
in
ot
Note: Dotted lines represent the effect of control variables.
Table 1. Bivariate relationship
Gov. Man. & Prof.
Gov. Serv. & Prod.
Self-employed
Cramers' V
(compared to private sector employees)
Turnout
28.6
15.3
16.2
0.19***
Left
2.02
13.9
-4.16
0.10**
Vote
Left
(compared to
(compared to
Cramers' V
Cramers' V
none-vote)
none-left)
Extraversion
2.15
0.09
-0.38
0.08
Agreeableness
1.79
0.06
1.25
0.08
Conscientiousness
-0.03
0.05
-1.47
0.07
Openness
0.82
0.09
0.07
0.09
Neuroticism
-2.36
0.09
0.61
0.07
Gov. Man. & Prof.
Gov. Serv. & Prod.
Self-employed
Cramers' V
(compared to private sector employees)
Extraversion
2.71
-3.40
1.25
0.09
Agreeableness
-2.02
1.74
0.72
0.09
Conscientiousness
0.38
1.46
2.72
0.07
Openness
3.26
-0.46
7.27
0.09
Neuroticism
2.05
-2.54
-4.14
0.10
Note: Cramer’s V range from 0-1, * p<0,10, ** p<0,05, *** p<0,01 Pearson Chi-Square Test, Turnout, left
and personality traits have been scales to range from 0 to 100. Values in cells represent differences to the
mean responses of private sector citizens.
- 32 -
Figure 2. Path model
-0
.0
7
*
0
0.0
0.0
4
0
-0.
0.
00
10
0.
3
00
-0
.0
2
0.
-0
.1
2
0
0.0
0.0
6
0
-0.
0.
00
09
0.
4
00
-0
.
04
0.
Note: Number of observations: 1131 (using sampling weights), Number of endogenous variables: 5,
Number of exogenous variables: 12, WLSMV estimator, regressions include control variables for age,
gender, highest level of education, subjective social class, urbanization, religious attendance and
employment status. Full estimation results including disturbance variances represented in Appendix Table
3., RMSEA = 0.02 (90% C.I. 0.00, 0.04), CFI = 0.98, WRMR = 0.47, * p<0,10, ** p<0,05, *** p<0,01
- 33 -
Table 2. Decomposition of indirect effects
Exogenous
Variable
Decomposition
Endogenous variable
Unst.
Vote
S.E.
St.
0.032
-0.005
0.036
0.027
0.010
0.028
0.045
-0.007
0.051
-0.009*
-0.012
Unst.
0.007
Left
S.E.
0.004
-0.013*
0.017
0.02
0.009
0.021
0.006
-0.022
0.028
-0.010**
-0.016
Unst.
0.006
Vote
S.E.
-0.017
0.008
-0.025
0.026
0.012
0.028
-0.023
0.011
-0.034
0.01
0.009
Left
S.E.
0.013
Extraversion
Total
Total indirect
Direct
Specific indirect
via Gov. service & production
St.
Extraversion
Total
Total indirect
Direct
Specific indirect
via Gov. service & production
St.
Openness
Total
Total indirect
Direct
Specific indirect
via Self-employed
Unst.
St.
Openness
Total
0.052**
0.019
0.085
Total indirect
-0.021**
0.010
-0.035
Direct
0.074***
0.021
0.119
Specific indirect
via Self-employed
-0.017***
0.008
-0.028
Note: Unst. = unstandardized, St. = standardized, Bootstrapped with 2500 repetitions, *p<0,10, ** p<0,05,
*** p<0,01.
- 34 -
Appendix Table 1. Definition and coding of variables
Name
Endogenous variables
Vote
Left
Definition and coding
Sector affiliation
Did you vote in last election? Yes=1, No=0
Party affiliation: (1) Strong republican (2) Not very strong republicans (3)
Independent, close to republicans (4) Independent (5) Independent, close to
democrats (6) Not very strong democrat (7) Strong democrat
Working for private versus public sector? (1) Government manager &
professionals [Government employees in ISCO88 Major Group 1 or 2, soldiers
excluded], (2) Government service & production [Government employees not
in ISCO88 Major Group 1 or 2, soldiers excluded], (3) Private sector
employment (4) Self-employed.
Personality traits (TIPI-G)
- Conscientiousness
- Extraversion
- Openness
- Agreeableness
- Neuroticism
I see myself as someone who … (1 to 5 scale)
... does a thorough job, ... tends to be lazy (R)
... is outgoing, sociable, … is reserved (R)
... has an active imagination, ... has few artistic interests (R)
... is generally trusting, ...tends to find fault with others (R)
... gets nervous easily, ... is relaxed, handles stress well (R)
Control variables
Age
Female
Education
Respondent’s age in years
Gender
Highest education level / degree from no formal qualification (1) to university
degree (6)
Social class
Top-bottom self-placement on a 10 point scale, measured as deviation from
the country mean response
Urbanization
Type of community from rural (1) to city (5)
Employment status
Unemployed and other non-working (housewife, home duties, helping family
member), Reference category: Employed (full- and part-time employed)
Religious attendance
Attendance of religious services: Ranges from (1) Never to (8) Several times a
week
Note: R = reversed; Age, Degree, Class, Urbanization and the BFI Personality traits have been zstandardized in order to compare within model effect sizes.
Dataset: General Social Survey (GSS) 2006, Dataset (ZA4350_F1.dta), Stata command files and Mplus
command files are available for replication purposes.
- 35 -
Appendix Table 2. Summary statistics
Variable
Political behavior and attitudes
Vote (Dummy)
Left
Production sector affiliation
Gov. Manager & prof. (Dummy)
Gov. Service & production (Dummy)
Private sector employee (Dummy)
Self-employed (Dummy)
Personality traits (Big5)
Extraversion
Agreeableness
Conscientiousness
Openness
Neuroticism
Individual control. measures
Age
Female (Dummy)
Highest edu. Degree
Subjective social class
Urbanization
None-working (Dummy)
Religious attendance
Note: Number of observation 1131.
Mean
Std. Dev.
Min
Max
0.67
4.24
0.47
1.89
0
1
1
7
0.09
0.09
0.69
0.13
0.28
0.29
0.46
0.34
0
0
0
0
1
1
1
1
6.65
7.49
8.55
7.27
4.90
1.72
1.45
1.19
1.64
1.79
2
2
4
2
2
10
10
10
10
10
43.46
0.52
4.61
4.48
4.30
0.19
4.27
12.87
0.50
1.16
1.85
0.79
0.39
2.40
18
0
1
1
3
0
1
89
1
6
10
5
1
8
- 36 -
Appendix Table 3. Path model (full estimation results)
Unst.
S.E.
p-values
St.
Dist. Var. est.
R squared
0.34
Vote
Gov. Man. & Prof.
0.14**
0.07
0.05
0.15
Gov. Serv. & Prod.
0.13*
0.07
0.09
0.11
Self-employed
0.10
0.07
0.17
0.09
Conscientiousness
-0.07
0.05
0.11
-0.07
Extraversion
0.04
0.03
0.22
0.05
Openness
-0.03
0.03
0.45
-0.03
Agreeableness
0.03
0.03
0.37
0.04
Neuroticism
-0.03
0.03
0.26
-0.05
Age
0.02***
0.00
0.00
0.23
Female
-0.04
0.11
0.68
-0.02
Education
0.36***
0.06
0.00
0.34
Social class
-0.03
0.03
0.26
-0.04
Urbanization
-0.10*
0.06
0.10
-0.07
None working
0.08
0.13
0.51
0.03
Religious
0.07***
0.02
0.00
0.13
Left
0.09
Gov. Man. & Prof.
0.00
0.06
0.98
0.00
Gov. Serv. & Prod.
0.14**
0.05
0.01
0.14
Self-employed
-0.18***
0.05
0.00
-0.19
Conscientiousness
-0.05
0.03
0.15
-0.06
Extraversion
0.02
0.02
0.44
0.03
Openness
0.07***
0.02
0.00
0.12
Agreeableness
0.02
0.03
0.52
0.02
Neuroticism
0.00
0.02
0.89
0.01
Age
0.01***
0.00
0.00
0.12
Female
0.06
0.08
0.45
0.03
Education
-0.06
0.05
0.25
-0.06
Social class
0.02
0.02
0.44
0.03
Urbanization
0.06
0.04
0.16
0.05
None working
0.05
0.11
0.63
0.02
Religious
-0.05***
0.02
0.00
-0.12
Vote
Left
0.00
0.05
0.93
0.00
Note: Unst. = unstandardized, St. = standardized, Dist. Var. = Disturbance variances represented as
estimated R-squares (Muthén 2004, 3), Table continues on next page.
- 37 -
Appendix Table 3 continued. Path model (full estimation results)
Unst.
S.E.
p-values
St.
Dist. Var. est.
R squared
0.35
Gov. Man. & Prof.
Conscientiousness
0.00
0.06
0.99
0.00
Extraversion
0.01
0.04
0.75
0.02
Openness
0.01
0.05
0.79
0.02
Agreeableness
-0.07
0.06
0.22
-0.08
Neuroticism
0.05
0.04
0.26
0.07
Age
0.00
0.01
0.95
0.00
Female
0.45***
0.16
0.00
0.18
Education
0.61***
0.06
0.00
0.55
None working
-0.12
0.19
0.54
-0.04
Gov. Serv. & Prod.
0.04
Conscientiousness
0.00
0.06
0.98
0.00
Extraversion
-0.07*
0.04
0.08
-0.12
Openness
-0.03
0.04
0.43
-0.05
Agreeableness
0.04
0.05
0.37
0.06
Neuroticism
-0.02
0.04
0.61
-0.04
Age
0.00
0.01
0.72
0.02
Female
0.05
0.13
0.72
0.02
Education
0.05
0.07
0.45
0.05
None working
-0.32*
0.18
0.07
-0.12
Self-employed
0.15
Conscientiousness
0.00
0.05
0.96
0.00
Extraversion
0.02
0.04
0.58
0.03
Openness
0.10***
0.03
0.00
0.15
Agreeableness
-0.01
0.04
0.87
-0.01
Neuroticism
-0.03
0.03
0.46
-0.04
Age
0.02***
0.01
0.00
0.28
Female
-0.20*
0.12
0.10
-0.09
Education
0.07
0.05
0.12
0.07
None working
-0.45**
0.16
0.01
-0.16
Note: Unst. = unstandardized, St. = standardized, Dist. Var. = Disturbance variances represented as
estimated R-squares (Muthén 2004, 3), Number of observations: 1131 (using sampling weights), RMSEA =
0.02 (90% C.I. 0.00, 0.04), CFI = 0.98, WRMR = 0.47, * p<0,10, ** p<0,05, *** p<0,01.
- 38 -
Technical Online Appendix on the BFI-10
This appendix provides further information on the BFI-10 (Rammsted and John 2007).
Table 1 shows that the pairwise correlation of those items measuring the same dimension
is positive and statistically significant in all five cases. Cronbachs Alphas are below that
cutoff heuristic for each trait dimension. Similar to Muck et al. (2007, 169), we
furthermore observe statistically significant but substantially low pairwise correlations
(average pairwise correlation = 0.14). Finally, we have run a Confirmatory Factor
Analysis (CFA) on the BFI-10, even though Kline (2011, 358) and others (e.g. Marsh,
Hau, Balla and Grayson 1998, 182) recommend at least three indicators per factor.
Models with an insufficient number of indicators are prone to estimation problems. This
presumption is confirmed with respect to the BFI-10. Running the CFA with five factors
(each factor represents one of the five personality traits) results in a Heywood case
(negative variance estimate; Kline 2011, 158).
The construct validation of the short BFI instruments has been the subject of indepth studies (e.g. Muck et al. 2007, Rammstedt and John 2007, Gerber et al. 2011).
According to these authors the usual procedures to measure construct validity (e.g.
Cronbach’s Alpha or Confirmatory Factor Analysis) are misleading with respect to short
BFI instruments. Short BFI instruments are designed with the purpose to measure very
broad domains with only two items per dimension, using items at both poles of the five
personality dimensions (Gosling 2011). The primary goal of the short BFI instruments is
to create a item battery that optimizes content validity (Gosling 2011). Thus, we cannot
expect that the BFI-10 fulfills the demands for construct validity we would apply to
larger instruments.
- 39 -
The content validity of the TIPI and BFI-10 has been explored by Muck et al.
(2007), Rammstedt and John (2007) and Gerber et al. (2011). Inter alia, content validity
requires convergence between personality trait scales measured by item batteries of
different length. Rammsted and John (2007, 206) show find that although the BFI-10
scales include less than 25 percent of the full 44-items BFI by John and Srivastava
(1999), they predict almost 70 percent of the variance of the full scales. Based on these
tests, Rammsted and John (2007, 203) conclude that the BFI-10 “yields effect sizes that
were lower than those for the full BFI-44 but still sufficient for research settings with
truly limited time constraints”.
Online Appendix Table 1. BFI-10 items and additive index correlations
Dimension
Item
Extraversion
Agreeableness
Conscientiousness
Neuroticism
Openness
Dimension
Extraversion
Agreeableness
Conscientiousness
Neuroticism
Openness
Mean
Std.
R is outgoing, socialable
R is reserved (re.)
R is generally trusting
R tends to fault with others (re.)
R tends to be lazy (re.)
R does a thorough job
R get nervous easily
R is relaxed (re.)
R has artistic interest
R has an active imagination
3.96
2.68
4.11
3.38
4.10
4.45
2.54
2.36
3.31
3.96
0.95
1.15
0.88
1.04
0.91
0.57
1.11
1.05
1.17
0.93
Average interitem
covariance
0.32
0.15
0.25
0.37
0.20
Number of
items in scale
2
2
2
2
2
Scale reliability
coefficient
0.49
0.26
0.40
0.54
0.34
Additive Indices
Pairw. correlation
Pairw. corr.
Pairw. corr.
Extraversion
Agreeableness
Conscientious.
Agreeableness
0.12
(0.00)
Conscientiousness
0.07
0.25
(0.01)
(0.00)
Neuroticism
-0.14
-0.20
-0.23
(0.00)
(0.00)
(0.00)
Openness
0.15
0.05
0.10
(0.00)
(0.13)
(0.00)
Note: R = Respondent, re. = Item reversed, p-values of pairwise correlation in parenthesis.
- 40 -
Pairwise
correlation
0.32
(0.00)
0.15
(0.00)
0.25
(0.00)
0.37
(0.00)
0.20
(0.00)
Pairw. corr.
Neuroticism
-0.13
(0.00)