HOW DOES INDIVIDUAL RISK PREFERENCE AFFECT EMPLOYMENT AND
INNOVATIVENESS?
By
HSIANG-KAI DONG
(Under the Direction of Barry Bozeman)
ABSTRACT
The primary goal of this dissertation is to find answers to the big question: Why are
public employees commonly criticized for being less innovative, more risk-averse, and more
unwilling to change? Prior research has indicated that public sector employees are indeed
different from their private peers. For example, previous researchers have found that public
employees are more concerned with job security and less concerned with higher pay. In addition,
they found that public sector is more change resistant than private sector. However, little is
known about the source of such differences. While the differences may arise from various
constraints inherent in the private vs. public employment settings, they may also reflect
underlying personality or preference dissimilarities. One likely source of preference
heterogeneity involves individual’s risk preferences. Since public employment is considered
relatively secure in comparison with employment in other sectors, this study hypothesizes that
more risk-averse individuals may self-select into the public sector. Then, if risk preference is
associated with other characteristics that may lead to criticism, such as more unwilling to change,
findings may explain why public employees are less innovative and more risk-averse.
This study uses the “relative risk tolerance” as a quantitative proxy for people’s risk
preferences. To minimize the potential for endogeneity bias, this study uses individuals’ earlystage risk tolerance responses to predict their future sector choices. Using data from the NLSY79,
this study finds that higher levels of risk aversion are predictive of greater propensity to pursue
careers in the public sector. An individual whose level of risk tolerance is one standard deviation
below the mean is 11% more likely to seek employment in the government relative to someone
with average risk tolerance. Individuals do have tendencies to “self-select” into different sectors
according to their risk preferences. In addition, this study finds a positive relationship between
risk preference and innovativeness. However, such a relationship is only significant for the nonextreme risk-seeking subgroup. For risk-seeking individuals, the relationship between risk
preference and innovativeness is simply undetermined. Several important policy and managerial
implications are discussed in this dissertation.
INDEX WORDS: Risk preference, Relative risk tolerance, Employment choices, Innovativeness
HOW DOES INDIVIDUAL RISK PREFERENCE AFFECT EMPLOYMENT AND
INNOVATIVENESS?
By
HSIANG-KAI DONG
B.A., National Cheng-Chi University, Taiwan, 2003
M.P.A., Texas A&M University, 2009
A Dissertation Submitted to the Graduate Faculty of the University of Georgia in Partial
Fulfillment of the Requirements for the Degree
DOCTOR OF PHILOSOPHY
ATHENS, GEORGIA
2013
© 2013
Hsiang-Kai Dong
All Rights Reserved
HOW DOES INDIVIDUAL RISK PREFERENCE AFFECT EMPLOYMENT AND
INNOVATIVENESS?
By
HSIANG-KAI DONG
Major Professor:
Committee:
Electronic Version Approved:
Maureen Grasso
Dean of the Graduate School
The University of Georgia
May 2013
Barry Bozeman
W. David Bradford
Hal G. Rainey
Vicky M. Wilkins
ACKNOWLEDGEMENTS
This dissertation would not have been possible without the support of many people. First
and foremost, I would like to express my sincere gratitude to my supervisor, Dr. Barry Bozeman,
for his valuable opinions and his willingness to give me the opportunity to work in the National
Administrative Studies Project – Decision Making (NASP-DM). This research project provides
important information used to complete this dissertation. I would also like to sincerely thank Dr.
David Bradford for his insightful discussions and guidance at the early stages of the formation of
this dissertation. He provided valuable advice on the methods used for this dissertation. In
addition, I would like to thank Derrick Anderson, Justin Bullock, Justin Stritch, and those who
devoted to the NASP-DM. This project would not have been possible without their great efforts
and contributions. Last but not least, I would like to thank my wife, Yen-Lin Lee, for her
understanding and encouragement during my studies. She has been taking good care of our
family including our newborn son, Brian. Her wholehearted and endless support allowed me to
focus on my Ph.D. and this dissertation. I want to express my love and special gratitude to her
and to my families including my beloved mother, grandmother, uncle Charly, and aunt. Without
the help and support of the particular people that mentioned above, I would not have been able to
finish my degree and this dissertation.
iv
TABLE OF CONTENTS
ACKNOWLEDGEMENTS ........................................................................................................... iv
LIST OF TABLES ........................................................................................................................ vii
LIST OF FIGURES .................................................................................................................... viii
CHAPTER 1 – INTRODUCTION ..................................................................................................1
CHAPTER 2 – LITERATURE REVIEW ON RISK PREFERENCE ............................................7
Individual Risk Preference .......................................................................................................7
Previously Used Measures of Risk Preference .......................................................................10
CHAPTER 3 – RESEARCH QUESTIONS AND HYPOTHESES ..............................................26
Choices of Jobs in Different Sectors ......................................................................................26
Relationship between Risk Preference and Innovativeness ...................................................31
CHAPTER 4 – DATA, VARIABLES, AND METHODS ...........................................................38
Data.........................................................................................................................................38
Criteria of Choosing the Most Appropriate Measure for This Study .....................................49
Variables .................................................................................................................................50
Methods ..................................................................................................................................59
CHAPTER 5 – ANALYSIS AND FINDINGS .............................................................................61
v
Relationship between Risk Preference and Sector Choice .....................................................61
Comparison between the NLSY79 and the NASP-DM .........................................................68
Relationship between Risk Preference and Innovativeness ...................................................69
CHAPTER 6 – CONCLUSION AND DISCUSSION ..................................................................75
Policy and Managerial Implication ........................................................................................77
Redesign the Reward and the Merit Systems ........................................................................79
Research Limits ......................................................................................................................81
Future Research Agenda.........................................................................................................83
REFERENCES ..............................................................................................................................87
APPENDICES .............................................................................................................................103
A Department Distribution of the Total Collected Samples in Nevada ..............................103
B Department Distribution of the Total Collected Samples in Indiana...............................104
C Revised Income Gamble Questions Used in the NASP-DM ...........................................106
D Summary Statistics of the NLSY79 (Characteristics in 1993) .......................................107
E Summary Statistics of Sector Changes from 1994 to 2006 .............................................108
F The Marginal Effects of Individual Risk Preference on Innovativeness (from the Ordered
Logit Model) ..................................................................................................................109
G Marginal Effects from the Negative Binomial Model on Sector Switches (between 1994
and 2006) .......................................................................................................................110
H Summary Statistics of the NASP-DM .............................................................................111
vi
LIST OF TABLES
Table 1: Summary of the Different Measures of Risk Preference .................................................11
Table 2: Lower and Upper Bounds of Risk Tolerance for Each Category ....................................23
Table 3: Comparison between the American Style and the Westminster Style Reforms ..............32
Table 4: Total Collected Samples and Samples Used for Survey Delivery ..................................41
Table 5: Managerial Status of the Indiana Sample ........................................................................41
Table 6: Departmental Distribution of the Nevada Sample ...........................................................42
Table 7: Departmental Distribution of the Indiana Sample ...........................................................43
Table 8: Survey Delivery Dates and Responses ............................................................................45
Table 9: Summary of Responses....................................................................................................48
Table 10: Percentage of Each Risk Tolerance Category in 1993, 2002, 2004, and 2006..............51
Table 11: Interval Regression Estimates of Risk Tolerance (1993) .............................................52
Table 12: Responses to the Innovativeness Questions (Original Responses) ...............................58
Table 13: Odds Ratio from the Multinomial Logit Model (Private Sector as the Base Group) ...62
Table 14: Odds Ratio from the Multinomial Logit Model (Nonprofit Sector as the Base Group)
........................................................................................................................................................65
Table 15: Comparison between Public Employees in the NLSY79 and NASP-DM Datasets ......69
Table 16: The Effects of Individual Risk Preference on Innovativeness .......................................70
vii
LIST OF FIGURES
Figure 1: Average Risk Tolerance for Different Job Categories (1993) ......................................54
Figure 2: Trends of the Average Risk Tolerance in Different Job Categories ..............................55
Figure 3: Relationship between Risk Tolerance and Innovativeness (Individuals with Low Risk
Tolerance, θ < 1) ............................................................................................................72
Figure 4: Relationship between Risk Tolerance and Innovativeness (Individuals with High Risk
Tolerance, θ ≥ 1) ............................................................................................................72
viii
CHAPTER 1 – INTRODUCTION
Public employees are commonly criticized for being less innovative, more risk-averse,
and more unwilling to change. However, are these fair judgments? If they are not fair judgments,
why do they occur? If they are, what factors may lead to these criticisms? In addition to the
different work settings between public and other sectors, are public employees essentially
different from non-public employees? The primary goal of this dissertation is to find answers to
these questions. Specifically, this study would like to examine whether public employees are
more risk averse than private and nonprofit peers. If so, does this phenomenon occur because
risk-averse individuals tend to sort themselves into the public sector because public employment
is more attractive to them? Then if being risk-averse is associated with other characteristics that
may lead to criticism, such as being more unwilling to change, the above questions could be
answered.
The study of differences between public, private, and nonprofit employees is not an
uncharted territory; it has long been a topic of research interest in public management. However,
this study aims to see this topic with new eyes. Prior research indicates that public employees are
indeed different from their private or nonprofit counterparts on several major dimensions such as
work motivation, job security, and salary (Buelens & Broeck, 2007; Cho & Lee, 2001; Karl &
Sutton, 1998). Specifically, public employees are generally found to have lower job satisfaction
(Buchanan, 1974; Khojasteh, 1993; Rainey, 1983), job involvement (Buchanan, 1975), and
organizational commitment (Boyne, 2002; Buchanan, 1974) than their private peers. In addition,
1
other public-private studies (Cacioppe & Mock, 1984; Karl & Sutton, 1998) found that private
employees are more likely to place greater value on economic rewards and incentives, while
government employees are generally more job-security oriented. Lyons, Duxbury, and Higgins
(2006) investigated employees in both the public and private sector and found that government
employees are more concerned with job security but less concerned with higher pay than their
private sector counterparts.
Similarly, ever since the rise of nonprofit studies, researchers have shown constant
interest in finding the differences between nonprofit employees and people who work in other
sectors as well. For example, some scholars found that intrinsic rewards work better for public
and nonprofit employees than for private employees (Khojasteh, 1993; Lewis & Frank, 2002;
Wright, 2001). Others argued that this phenomenon may come from the fact that public and
nonprofit workers are similar in terms of Public Service Motivation (PSM) and civic
participation (Houston, 2006; Lyons et al., 2006). Generally, it is believed that there exist some
fundamental variations among different groups, and the causes that may lead to such variations
are worth investigating, yet little is known about the source of such differences.
Although the differences may arise from various constraints inherent in the employment
settings, attitudes toward job security and salary may also reflect underlying preference
dissimilarities. Preference is a broad concept which consists of several different dimensions. One
of the dimensions, attitudes toward risk, explains why people behave differently under
uncertainties. Theories of decision making are discussed extensively by public management
scholars, but there have been few attempts to link them with individual traits. In other words, we
simply do not know who will make what decisions under different levels of risks and
uncertainties. The absence of personal risk tendency in the public organizational literature could
2
be the bridge to our understanding of the decision making outcomes. This study tries to fill the
gap by studying the impacts of risk preference on individuals’ employment and their willingness
to change.
Although exceptions exist, public employment is generally not designed to allow for
unbridled behaviors from the government employees or huge fluctuations in payment. Such a
design is one of the main differences that distinguish public employment from private
employment. If such difference is perceived by the general public, then a self-selection
mechanism may take place when people make employment decisions (Baldwin, 1991; Bellante
& Link, 1981).
In addition, the probability of becoming unemployed in the public sector is generally
lower than that in other sectors (Bloch & Smith, 1979; Hall, Gordon, & Holt, 1972). According
to the Bureau of Labor Statistics (2011), the unemployment rate in the public sector was 4.7% in
2011 while 9%, almost doubled, in the private sector. Such a phenomenon is consistent every
year. That being said, government jobs could be more attractive to individuals who prefer stable
lifestyles because research has already suggested that when an individual chooses his career in a
certain workplace, he is choosing the characteristics of that job (Bellante & Link, 1981).
Therefore, although the nature of different jobs is determined by the work environment and
external controls, people do have tendencies to choose jobs that match their preferences. We
cannot ignore the possibilities that people self-select into different sectors. The first interest of
this study is to discover such a mechanism.
The traditional organizational concepts of production factors regard land, labor, capital,
and entrepreneurship as the four major elements. However, researchers have recently
distinguished “human capital” from “labor” in the sense that the former is a stock of knowledge,
3
competence, and personality of the employees, while the latter simply refers to their ability to
work (Benhabib & Spiegel, 1994). Bray (1994) pointed out the importance of putting more
emphasis on the employees:
Those who make up an organization’s human resources have characteristics
important to their contributions and potential contributions. These include
knowledge and skills, aptitudes, motives, interests, values and attitudes, and
aspects of personality. At any particular point in work lives these characteristics
are a combination of what employees brought with them and their experiences
since they were hired (p.153).
Since this combination has played a decisive role in this person’s performance as well as in the
managerial practices of the organizations, if we want to improve the performance of organization
through introducing new operations or new ideas, employees’ willingness to cooperate is one of
the most critical tasks.
However, we cannot change the system or structure until we change the people (Howard,
1994). Research suggests that the most-often-cited barrier to change is not the poor design of the
plans or insufficient funding resources but employee resistance (Rainey, 1999; Stewart, 1994).
As a result, it seems that the change of human thoughts and attitudes is always the first step of
changing something. Any organizational change initiatives will work only when the employees
accept the initiatives and show their willingness to replace existing operations with new but
relatively uncertain ones. To the interests of this study, individual personality in terms of risk
attitude could be one of the most critical elements in determining and explaining such behaviors.
In general, risk-seeking individuals are more likely to show characteristics of being innovative
and more willing to accept new ideas. On the other hand, risk-averse people are usually more
likely to show greater resistance to change.
The second interest of this study is to discover the relationship between individual risk
preference and innovativeness. To achieve this goal, a focus on the employees’ personal traits is
4
critical. The managers’ abilities and organizations’ capacities of facilitating change has long
been a topic in public management (Abramson & Lawrence, 2001; Burke, 2002; Fernandez &
Rainey, 2006; Yukl, 2002). Nevertheless, the critique of the public sector being inactive or
unable to adapt to innovation has also challenged PA researchers for a long time. Osborne and
Gaebler (1992) quoted Drucker’s words and argue,
Almost anyone can be an entrepreneur, if the organization is structured to
encourage entrepreneurship. Conversely, almost any entrepreneur can turn into a
bureaucrat, if the organization is structured to encourage bureaucratic behavior.
“The most entrepreneurial, innovative people behave like the worst time-serving
bureaucrat or power-hungry politician six months after they have taken over the
management of a public-service institution.” (Osborne & Gaebler, 1992, p.xx-xxi)
In order to capture the sector differences, Chen and Bozeman (2012) examined
employees in both public and nonprofit sectors and found that public employees generally
perceive a more risk-averse culture than their private and nonprofit peers. However, what leads
to such differences? Can we conclude that the public employees are less innovative because they
are indeed more risk-averse than employees in other sectors? What is the true relationship
between risk preference and innovativeness? Are public employees less likely to be innovative
than their peers in other sectors because they have chosen the jobs which reflect their preferences
on risks and uncertainties?
Then the question becomes: “should public employees be blamed for not being
innovative or not willing to accept changes?” What if the fundamental problem is that the
characteristics of public employment is designed to be attractive to those who do not prefer high
levels of risks, and thereby the public sector ends up with more unwilling-to-change employees?
Since innovation is risky and uncertain in nature, risk-averse individuals will inevitably resist
innovative initiatives. Should any efforts be made to redesign the public employment in order to
recruit employees with different characteristics? This research also intends to examine the
5
relationship between individual risk preference and attitudes toward innovativeness. The process
of examining such a relationship involves two primary tasks: First, this study quantifies people’s
risk preference using a systematic method of calculating individuals’ risk preferences based on
economic theories (Ahn, 2010; Kimball, Sahm & Shapiro, 2008). Then it develops an index to
measure the concept of innovativeness (Hurt, Joseph, & Cook, 1977).
This study is organized as follows. First, Chapter 2 begins with a review of relevant
literature that discusses the concept of individual risk preference and some previously-used
measures of individual risk preference. Then Chapter 3 introduces the source of job security in
the public sector in order to explain why risk-averse individuals may prefer public jobs over
other types of employment. Also, it discusses the relationships between risk preference and
innovativeness. Two main research questions and hypotheses are presented. Chapter 4 describes
the data sets, major variables, and methodologies used to test the research hypotheses. Chapter 5
presents the reports of estimates of risk preference as well as other statistical analyses and
findings. Finally, Chapter 6 describes some research limits of this study and concludes with
policy and managerial implications that may be beneficial for future organizational
improvements.
6
Chapter 2 – Literature Review on Risk Preference
This study attempts to evaluate whether individuals’ risk preferences have self-selection
effects on people’s sector choices. In addition, it examines the assumption that more risk-tolerant
individuals are more likely to show characteristics of being innovative and more willing to
change. If these two assumptions are proved to be valid, then findings may contribute to the PA
literature and provide explanation on why public employees generally show greater resistance to
changes than their others. This chapter first introduces the concept of individual risk preference
and followed by the discussion of the measures that have been used to assess individual risk
preference.
Individual Risk Preference
The study of risk has been of interest to researchers and practitioners for several decades
(Bernstein, 1996), and the concept of risk preference is important to decision-making in both
individual behaviors and organizational management (Roszkowski & Snelbecher, 1990). For an
individual to make decisions, the expected utility theory says that he needs to consider two things:
the payoffs and the probability of getting the payoffs. With these two elements, he can then
calculate the “expected utility” and make correspondent decisions. However, the probability of
getting the payoffs (success) is uncertain. In order to let a person to make certain decisions,
therefore, one critical factor must be considered – his willingness to accept different levels of
risk. If that person is unwilling to accept a small probability of not getting the payoffs (failure),
7
he may choose to reject that option even though the expected utility is enormous. For instance, in
the management studies, scholars have found that a majority of the innovators share a similar
characteristic, which is “they are more willing to take risk” than the others (Rogers, 2003). For
innovators, there are no challenges left, no chances to make differences, and no chances of
accomplishing great things if they don’t take chances or risks. As a result, individual’s risk
preference is an important factor that should be considered when we study individual and
organizational decision-making.
Risk Preference as Personality
The question of whether risk preference should be considered as one dimension of
personality has raised a good amount of discussion in both psychology and sociology. Some
studies ignored the domain related to individuals’ risk preference when developing the categories
of personality, while others suggested that this dimension should be added since it is one critical
characteristic of human beings (Paunonen & Jackson, 2000; Saucier & Goldberg, 1998).
Personality can be conceptualized through a wide range of approaches. One most
commonly referred to model of personality is the Big Five personality traits model. This model
argues that the traits of personality can be categorized into five major factors– Extraversion,
Agreeableness, Conscientiousness, Neuroticism, and Openness to experience (McCrae & Costa,
1987). But among the five major factors, risk-related characteristics can hardly be found or
categorized. If we have to find a factor that at least contains the “concept” or the “notion” of risk
preference, the factor of “openness to experience” can be inferred to have some relationships
with risk preference. Since individuals with a high level of openness tend to have more
unconventional and untraditional preferences, they may be more open to changes or new things.
8
And since changes or new things are risky in nature, these people generally have a more riskseeking propensity. Also, since complex and subtle situations do not bother people with a high
level of openness as much as they bother those with a low level of openness, we can, to a certain
degree, argue that the openness factor contains a small portion of the concept of risk preference.
However, one of the major criticisms of the Big Five model is that this model does not
explain all dimensions of human personalities. Some psychologists found that the traditional Big
Five factors are not comprehensive enough to cover every domain of a human being. That being
said, the true concepts of risk preference are not fully explained by the Big Five model. For
instance, Saucier and Goldberg (1996) systematically developed 53 clusters of personality
“adjectives” (the descriptions of personalities and individual traits) and computed a multiple
correlation between the clusters and the traditional five factors. Their goal was to re-examine the
comprehensiveness of the Big Five factors in terms of covering an individual’s complex-andmultidimensional personality. They found that many of the personality adjectives cannot be
closely aligned with the traditional Big Five factors. Among those adjectives, individuals’ riskrelated characteristics, such as risk-taking or risk-averse, are not correlated with any of the five
factors. Literature also points out the dilemma that many scholars have difficulty categorizing
individual’s risk preference in the traditional Big Five. In order to solve this problem, individuals’
tendency to take risks is proposed to be added as one additional factor of personality (Paunonen
& Jackson, 2000).
The argument that people’s traits related to risk-taking (or thrill-seeking) should be
considered as one dimension of personality is supported by several psychologists. Nicholson et al.
(2005) argued that personality is the major drive for risky behaviors. They believe that some
people may have more risk-seeking propensities while others may have risk-averse ones. And
9
such traits are imbedded in individuals’ personalities. Farley (1986) called this risk-driven
propensity the “Type-T” personality, where T stands for “thrills.” The Type-T personality was
used to describe a personality which is more stimulation seeking, excitement seeking, thrill
seeking, arousal seeking, and risk-taking (Farley, 1986). This particular type of personality has
been studied to be related to a wide range of human behaviors, both positive and negative, such
as creativity, aesthetics, drinking, crime, and drug using (Horvath & Zuckerman, 1993;
Morehouse, Farley, & Youngquist, 1990). The Type-T personality can be further divided into
three subtypes – T-mental, T-physical, and T-balanced. The T-mental type represents people
whose stimulation seeking is primarily cognitive or psychological, while the T-physical type
represents people whose stimulation seeking is primarily physical. And if people have a balanced
stimulation seeking between mental and physical, they belong to the T-balanced type (Farley,
1986). And the Risk Taking Scale of Jackson Personality Inventory (JPI) was particularly
developed on the basis of treating risk taking as a personality dimension (Jackson, Hourany &
Vidmar, 1972). Therefore, according to the aforementioned definition and classification,
individual risk preference should be regarded as one critical dimension of personality.
Previously Used Measures of Risk Preference
The concept and notion of risk preference has been measured by psychologists,
sociologists, managers, and economists through various ways. However, although some
researchers have come up with “proxies” and “determinants” for risk preference (Hallahan, Faff,
& McKenzie, 2004), many argue that the lack of a widely accepted risk-assessment instrument
has been an ongoing problem in the study of risk preference for decades (Droms, 1988; Grable &
10
Lytton, 1999). The following section provides a summary of these “determinants” and “proxies”
of risk preference. The strength and weakness of each approach are discussed.
Based on the existing literature, risk preference has been measured through the following
approaches which can be sorted into five categories: (1) measure of investment choices –
assessing personal or household investment portfolio or allocation of property and money; (2)
measure of behaviors – assessing individual risky behaviors or consumption decisions on riskrelated commodities; (3) choice on hypothetical scenarios – evaluating individual responses to
hypothetical scenarios on job or investment choices; (4) measure of risk tolerance – assessing
individuals’ level of risk tolerance based on economic models; and (5) mixed measures – a
combination of the aforementioned approaches (Barsky, Kimball, Juster, & Shapiro, 1997; Hey,
1999; Schooley & Worden, 1996). Table 1 provides a summary of different measures for risk
preference.
Table 1 – Summary of the Different Measures of Risk Preference
Type
Name of
Measure
Ratio of Risky
Assets to Total
Wealth
Literature
Variable
Methods
Wang &
Hanna
(1997)
The 19831989
Survey of
Consumer
Finances
(SCF)
Continuous
number between
0 and 1 (1
represents the
highest level of
risk tolerance)
Risk tolerance is measured by the
ratio of risky assets to total
wealth. Risky assets in this study
are defined as assets that provide
an uncertain nominal cash flow,
such as mutual funds, corporate
stocks, and precious metals.
Pålsson
(1996)
Swedish crosssectional data
with 7,000
households
Calculated
continuous
variable (higher
value represents
more riskaverse)
Measure of
Investment
Choices
Relative Risk
Aversion
Coefficient
Data
11
The coefficient was calculated
using information on actual
amounts invested in various
assets such as real estate, mutual
funds, and bonds.
Sundén &
Surette
(1998)
The 1992 and
1995 Survey
of Consumer
Finances
(SCF)
Discrete choices
with different
expected riskreturn profiles
Choice of
Investment
Plans
KoganWallach
Choice
Dilemmas
Questionnaire
(CDQ)
CDQ (revised
version)
Choice on
hypothetical
scenarios
Youth
DecisionMaking
Questionnaire
Risk Taking
scale of the
Jackson
Personality
Inventory (JPI)
Financial Risk
Tolerance
Investment choice on defined
contribution plans. The categories
include (1) invest mostly in
stocks; (2) invest mostly in
interest earning assets (hereafter
“bonds”); and (3) investments
split between stocks and interestearning assets (hereafter
“diversified”).
Discrete choices
with different
expected riskreturn profiles
A variety of investment plans of
the retirement fund that reflect
different risk-return profiles: (1)
Cash; (2) Capital Stable; (3)
Conservative Balanced; (4)
Balanced; (5) Growth; (6)
Socially Responsible Shares; (7)
Shares
A score between
12 and 120 (12
represents being
conservative in
risk taking
situations)
The questionnaire is made up of
12 scenarios which portray
individuals who face a decision
that involves a risky activity.
Ordered variable
from 10
(maximum risk)
to 100
(maximum
caution)
The new Kogan-Wallach
Questionnaire contains 10 items.
Eight of the items were modified
from the original CDQ items, and
the other two were new items.
The average
responses
between 1 to 4
across five
hypothetical
risky decisions
(4 indicates a
higher risktaking tendency
The risky decisions included
decisions about allowing friends
to bring drugs into one’s home,
stealing a car, cheating on an
exam, shoplifting, and skipping
work without an excuse, all of
which adolescents, college
undergraduates, and adults
potentially could have done. And
the negative consequences might
result if the risky course of action
were taken.
Selfadministered
(college
students)
Ordered variable
This measure was designed to
assess the personality traits of risk
preferences using 20 hypothetical
questions
1992 Survey
of Consumer
Ordered variable
When you save or make
investment, would you take
Watson &
McNaught
on (2007)
The
superannuation
fund data,
UniSuper,
from 1 July
1997 to 30
June 2003
Brockhaus,
(1980)
Selfadministered
(employees in
the private
sector)
Stewart &
Roth
(2001)
Meta-analysis
using multiple
databases
Kogan &
Dorros
(1978)
Selfadministered
(college
students)
Ford,
Wentzel,
Wood,
Stevens, &
Siesfeld
(1989)
Selfadministered
(high school
students)
Gardner &
Steinberg
(2005)
Selfadministered
(adolescents
b/t 13 to 16,
youths b/t 18
to 22, and
adults aged 24
or more )
Jackson,
Hourany,
& Vidmar
(1972)
Sung &
Hanna
12
Question
(1996)
Risk
Preference
Index
Hsee &
Weber
(1999)
Financial Risk
Tolerance
Scores
Hallahan,
Faff, &
McKenzie,
(2004)
Risk
Preference
Scale
Gardner &
Steinberg
(2005)
Barsky,
Juster,
Kimball, &
Shapiro,
(1997)
Assessing
Risk
Tolerance
Based on
Economic
Models
Income
Gamble
Questions
Hariharan,
Chapman,
& Domian
(2000)
Hanna,
Gutter, &
Fan (2001)
Hanna &
Lindamood
(2004)
“substantial” financial risks
expecting to earn “substantial”
returns. (The word “substantial”
will be replaced by “aboveaverage”, “average”, and “no”.)
Finances
(SCF)
Selfadministered
(college
students)
The ProQuest
database over
the period
from May
1999 to
February 2002
Ordered variable
from 1 to 8 (8
represents most
risk-seeking)
Continuous
number between
1 and 100 (100
represents the
highest level of
risk tolerance)
Selfadministered
(adolescents
b/t 13 to 16,
youths b/t 18
to 22, and
adults aged 24
or more )
The average
responses
between 1 to 4
across five
scenarios (1
represents risks
are much greater
than benefits)
These scenarios included having
sex without a condom, riding in a
car driven by someone who has
been drinking, trying a new drug
that one does not know anything
about, breaking into a store at
night and stealing something that
one really wants, and driving over
90 mph on the highway at night.
Health and
Retirement
Study (HRS)
Four categorical
variables with
order in nature
(category I
represents most
risk-averse, VI
represents least
risk-averse)
I: Reject both one-third and onefifth thresholds
II: Reject one-third but accept
one-fifth thresholds
III: Accept one-third but reject
one-half thresholds
VI: Accept both one-third and
one-half thresholds
Health and
Retirement
Survey (HRS)
Ordered variable
from 0 (least
risk-tolerant) to 3
(most risktolerant)
The authors use linear regression
techniques to relate risk tolerance
to an individual’s (1) share of
risk-free assets among all assets
and (2) share of bonds among
risky assets.
Selfadministered
(college
students)
Selfadministered
(college
students)
Ordered
categorical
variable from
extremely low
risk tolerance to
extremely high
risk tolerance
Revised income gamble questions
borrowed from the SCF
Investment Risk questions (more
risk thresholds than Barsky et al.,
1997)
Predicted
relative risk
tolerance (the
larger the
number is the
higher the level
of risk tolerance)
The predicted relative risk
tolerance was calculated by
interval regression including
several demographics.
Kimball,
Sahm &
Shapiro
(2008)
Health and
Retirement
Study (HRS)
Ahn (2010)
The 1979
National
Longitudinal
13
The Risk Preference (RP) Index
was built by adding respondents’
choices on a set of 8 questions.
Psychometric attitude test
comprising 25 questions that
generate a standardized Risk
Tolerance Score (RTS).
Survey of
Youth
(NLSY79)
Ordered number
from 0 to 9 (9
represents the
highest level of
risk aversion)
This index is formulated based on
people’s answers to questions
involving the condition and
insurance of automobiles owned,
the use of seat belts, the head’s
extent of medical coverage, and
the head’s smoking and drinking
habits.
Selfadministered
data
(undergraduate
s)
Scores from
factor analyses
Measure of 30 risky activities,
such as crime, smoking, social
violation, and risky sexual
behaviors.
Gullone &
Moore
(2000)
Selfadministered
(younger
adolescents b/t
11 to 14 and
older
adolescents b/t
15 to 18)
Ordered variable
from 0 to 292
(292 represents
the strongest risk
Judgment and
highest level of
participation in
risky activity)
This measure collects information
relating to risk judgments and
behaviors in four areas: thrillseeking risk behaviors, reckless
risks, rebellious risks and
antisocial risks.
Nicholson,
Soane,
FentonO'Creevy,
& Willman
(2005)
Selfadministered
data
(undergraduate
s)
Ordered variable
from 5 to 30 (30
represents most
risk-seeking)
Index of Innate
Risk Aversion
Bellante &
Link
(1981)
General Risk
Appraisal
Scale
Horvath &
Zuckerman
(1993)
Adolescent
Risk
Questionnaire
Risk Taking
Index
Measure of
behaviors
Risk Taking
Behavior
Measure
Gardner &
Steinberg
(2005)
Maximum
Acceptable
Risk
Johnson,
Ozdemir,
Mansfield,
Hass,
Siegel, &
Sands
(2009)
Panel Study of
Income
Dynamics,
PSID
Selfadministered
(adolescents
b/t 13 to 16,
youths b/t 18
to 22, and
adults aged 24
or more )
Selfadministered
(patients over
the age of 18
and parents of
children under
the age of 18)
14
Decisions on
stopping or not,
and when to stop
the car on video
game
Continuous
variable (higher
value represents
more riskaverse)
A scale that assesses the overall
risk propensity in terms of
reported frequency of risk
behaviors in six domains:
recreation, health, career, finance,
safety, and social.
Risk taking was assessed with a
video game called “Chicken”
Chicken is a video game which
requires participants to make
decisions about whether to stop a
car that is moving across the
screen once a traffic light turns
from green to yellow.
This measure is calculated based
on subjects’ answers to a series of
trade-off questions. It is the levels
of risk in exchange for symptom
relief.
Measure of Investment Choices
The measure of investment choices is one of the most commonly used approaches to
assess individual risk preference in financial and economic literature. Wang and Hanna (1997)
assessed individual risk tolerance by calculating the ratio of risky assets to total wealth. The risky
assets include, for example, the market value of real estate held for investment purposes, mutual
funds, stocks, and precious metals. A higher value on the ratio represents a higher level of risk
tolerance. Similarly, another way of assessing individuals’ risk preferences is to observe their
investment choices or retirement packages. For instance, some researchers (Watson &
McNaughton, 2007) use individuals’ choices of investment plans to measure their risk
preferences. Each investment plan has its unique performance objectives, expected risk-return
profiles, and investment strategies. Based on the assets allocation and investment strategies, the
investment plans can be ranked from high risk/high returns to low risk/low returns. Also, others
(Sundén & Surette, 1998) use individuals’ investment choice on pension coverage and the
allocation of assets in defined contribution (DC) as a proxy for risk preference. Individuals who
prefer investing mostly in stocks are consider more risk-prone than others who choose to invest
mostly in bonds.
Relative to the other measures, using individuals’ investment choices is believed to be a
more objective approach since it evaluates individuals’ actual investment behaviors. However,
there are some disadvantages of using this measure. Firstly, the market value of investments on
risky assets is highly subject to the economic situations when the survey or investigation was
conducted. Given the fact that economic situations fluctuate on a daily basis, assessing the
market value of investments on risky assets may not be a reliable measure since risky assets
fluctuate even more rapidly and wildly than other stable investment tools. Such phenomena may
15
raise more concerns if the researchers cannot capture the patterns of people’s habits of
reallocating their investment portfolios according to the financial changes. An investment in
risky assets for a longer period of time is essentially different from the investment for a short
term, but it is quite challenging to tell which pattern is more risky than the other given the
complexity of the globe economic situations.
Secondly, such a measure is sometimes criticized for its reflection of individuals’
“abilities” rather than their preferences. For the majority, having a high level of liquid assets may
not be possible even though they wish to have one (Hanna, Gutter, & Fan, 2001). Therefore, they
either do not always have meaningfully different levels of risky assets, or do not have investment
portfolios that can authentically reflect their actual levels of risk preference.
Last but not least, findings from this approach may be “contaminated” by other factors.
For example, Grable and Lytton (1999) found that although older investors are less risky than
younger investors in terms of investment portfolio, such investment decisions may actually
means that they were “advised” to invest less in risky products. Therefore, although these
objective measures have the potential to effectively measure individual risk preference (Schooley
& Worden, 1996), such measures will be a good instrument only if the researchers can rule out
the impacts from the environment. That is, these measures will be valid only if we know the
decisions such as asset allocations or investment choices are the results of pure personal choices,
rather than a mutual agreement between that person and the financial advisors (Grable & Lytton,
1999).
16
Measure of Behaviors
Many researchers believe that assessing individuals’ risky behaviors including
consumption decisions on risk-related commodities is another reliable way to measure individual
risk preference. For example, Bellante and Link (1981) created the “Index of Innate Risk
Aversion” as a proxy for individual risk attitude. They used the Panel Study of Income Dynamics
(PSID) and built the index by adding up people’s answers to a set of consumer’s behavioral
questions – household head’s smoking and drinking habits, condition and insurance of
automobiles owned, and use of seat belts. They believe that the combination of these behaviors
and decisions can be used to represent individuals’ risk preferences. Indeed, such an approach
can be seen as an overall assessment of a person’s lifestyle and living philosophy which to a
certain degree reflects their attitudes toward risks.
However, the behavioral measure can be criticized because the index may also only
reflect individuals’ “abilities” rather than their preferences since the index was built based on
several revealed consumer behaviors. Another concern of building an index based on behavioral
questions is that these behaviors themselves are not only the outcomes of many individuals’ traits
and other environmental impacts, but are also imprecise and controversial in nature (Arcidiacono,
Sieg, & Sloan, 2007; Dawson, 1998). For instance, risky drinking habits may not be clearly
defined. Although heavy alcohol consumption is regarded as a type of impulsive behavior that
brings immediate benefits yet potentially unfavorable costs later in life (Critchfield, & Kollins,
2001; Vuchinich & Simpson, 1998), research also shows that moderate drinking is beneficial to
health (Ogborne & Smart, 2001). Therefore, since reasons for drinking vary and are likely to be
influenced by culture (Guise & Gill, 2007), how to define a “risky behavior” and to find a
“threshold” to separate risky behaviors from more conservative ones has challenged researchers
17
and practitioners for decades. Given the aforementioned concerns, finding an appropriate cutting
point is challenging and controversial enough, let alone if we simply use “drinking or not” as a
dummy variable to indicate whether an individual is risk-seeking or not.
Another way to measure “individual behaviors” is to conduct experiments either on
computers or in laboratories. Proponents of this measure believe that they can observe people’s
true behaviors through manipulating different scenarios. This approach has been used by several
researchers. For instance, Gardner and Steinberg (2005) measured individuals’ risk-related
behaviors by asking the participants to play a video game called “Chicken” in which they make
decisions on whether to stop their cars at a traffic light turning from green to yellow (with the
impending appearance of a red). The participants were informed that they might crash if they
decided not to stop the car. The further they drove without crashing, the more points they got. So
the goal was to get as many points as they could. Such an approach has the advantage of
assessing individuals’ risk-taking tendencies under pressure and under limited time for
consideration. Also the authors argued that this approach is different from the hypothetical
questions because the participants had chances to act as if they were making “actual decisions”
in risky circumstances, instead of simply reporting what they would do on the papers (Gardner &
Steinberg, 2005).
However, this type of measure has its limits as well. First, although experiments allow for
some real decisions, making decisions in the laboratory or on a video game does not necessarily
reflect a person’s real-world behaviors since consequences are still disconnected from decisions.
If individuals do not have to bear the costs of taking risks, their decisions and behaviors may not
be consistent with what they would do if they face similar situations in real life. Therefore, this
approach may not be as effective as it claims.
18
Choices on Hypothetical Questions or Scenarios
Another commonly used approach to assess individual risk preference is to use
hypothetical questions. Mainly, two different types of questions have been developed. First, the
hypothetical questions involve possible scenarios that everyone might encounter in daily life,
such as riding in a car driven by someone who has been drinking, trying a new unknown drug, or
skipping work without an excuse. Several well-known questionnaire and scales were developed
based on this method. For example, the Kogan-Wallach Choice Dilemmas Questionnaire has
been repeatedly used by researchers (Brockhaus, 1980; Stewart & Roth, 2001; Wallach & Kogan,
1959). The questionnaire contains 12 scenarios which ask participants imagine facing decisions
that involve various risky activities. Participants need to choose between safer alternatives with
acceptable outcomes and risky ones with better outcomes. Such questions can be constructed and
described either in the first person or in the third person. Generally, responses observed from the
first person are more conservative than from the third person.
In addition, Gardner & Steinberg (2005) assessed individual risk preference through a set
of hypothetical questions and scenarios involving risky decisions. For instance, they built a Risk
Preference Scale using a modified version of the Benthin Risk Perception Measure (Benthin,
Slovic, & Severson, 1993) which contains five hypothetical scenarios involving risky behaviors.
In addition, they adopted the Youth Decision-Making Questionnaire (Ford, Wentzel, Wood,
Stevens, & Siesfeld, 1989) to understand individuals’ risky decision-making. The JPI Risk
Taking Scale was also built primarily based on decisions and behaviors on hypothetical
questions, and has been tested to be a valid measure of risk preference (Jackson, 1976; Paunonen
& Jackson, 1996; Stewart & Roth, 2001).
19
The second type of question primarily focuses on individuals’ investment choices or
attitudes toward winning and losing money in hypothetical situations. For instance, Sung and
Hanna (1996) analyzed the 1992 Survey of Consumer Finances (SCF) and found several
questions that can be used to measure individual “financial risk tolerance.” These questions ask
people to choose among investment options and strategies with different levels of risks but
corresponding returns. Similarly, Hsee and Weber (1999) developed a set of questions to assess
individuals’ attitudes toward winning and losing. One of the classic questions is to offer different
amounts of secure money in exchange for risky but larger payoffs.
The advantages of adopting the second type of question includes, firstly that this measure
has the flexibility of creating the scenarios. Researchers have more freedom to build whatever
they think is the most appropriate scenarios for their studies. Secondly, unlike the hypothetical
questions on assessing behaviors, researchers are able to identify the upper and lower bounds of
risk preferences using questions about investment choices and attitudes toward winning and
losing because those bounds can be calculated and presented in actual numbers.
However, this approach is challenged by some scholars because people may not reveal
authentic risk preferences in hypothetical questions. The validity of such measures was limited
by reliance on decisions under unreal circumstances. It is possible that participants’ real-world
decisions may not be consistent with their hypothetical ones. According to what Hallahan and his
colleage (2004) found, the majority do not have consistent answers between their self-reported
risk tolerance and their real preferences in terms of risks. More than 50% of the respondents tend
to underestimate their risk tolerance.
20
Assessing Individuals’ Risk Tolerance based on Economic Models
Although the aforementioned measures have been frequently used to measure individual
risk preference, none of the above approaches are rigorously linked to economic theory. As a
result, some economists introduce the concept of expected utility theory and use the utility
function to analyze individuals’ risk tolerance (θ). Then the risk tolerance can be used as a proxy
for risk preference. One of the most commonly seen assumptions of the utility function for the
risk tolerance is the “constant relative risk aversion” utility function (Hanna & Chen, 1997;
Pålsson, 1996). This concept was first developed by Pratt (1964) and Arrow (1971). They
suggested that the assumption of relative risk aversion is “constant” because relative risk
aversion generally does not change with wealth, but the absolute risk aversion does change with
individuals’ wealth (Merton, 1969). Such an assumption has been tested using individual
consumption data and household assets allocation information (Hanna & Chen, 1997). Results
suggest that the constant relative risk aversion utility function is a valid assumption. Accordingly,
several recent empirical studies have borrowed such an idea and choose to use the Income
Gamble Questions to measure individuals’ relative risk tolerance (Ahn, 2010; Barsky et al., 1997;
Hanna & Lindamood, 2004; Kimball et al., 2008).
Specifically, the first income gamble question asks individuals to respond to a
hypothetical situation:
Suppose that you are the only income earner in the family, and you
have a good job guaranteed to give you your current (family) income
every year for life. You are given the opportunity to take a new and
equally good job, with a 50-50 chance that it will double your (family)
income and a 50-50 chance that it will cut your (family) income by
a third. Would you take the new job?
21
Individuals who accepted the new, risky job (answered Yes to the first question) were
asked a follow-up question with a more risky option:
Suppose the chances were 50-50 that it would double your (family)
income and 50-50 that it would cut it in half. Would you still take the
new job?
Those who initially declined the new, risky job (answered No to the first question) were
asked another follow-up question with a less risky option:
Suppose the chances were 50-50 that it would double your (family)
income and 50-50 that it would cut it by 20 percent. Would you take
the new job?
According to Kimball and his colleague (2008), the relative risk tolerance can be
estimated based on the expected utility theory. Faced with the 50-50 income gamble situation, an
individual’s lifetime income could be doubled or cut by various fractions π. Then it is assumed
that he will accept the new, risky job when the expected utility exceeds the utility from the
current job. The expected utility formula is described as follows:
. 5U(2W) + .5U((1 − π)W) ≥ U(W)
(1)
where W represents the individual’s current income; π represents the fraction that individual may
lose when taking the new risky job.
In order to calculate individuals’ utility over lifetime income, a constant relative risk
aversion (CRRA) function is assumed (Chiappori & Paiella, 2011; Kimball et al., 2008; Pratt,
1964):
U(W) =
W1−1⁄θ
1 − 1⁄θ
(2)
22
where θ represents the coefficient of relative risk tolerance which may differ across individuals;
W represents each individual’s current income.
Like the other hypothetical questions on investment choices and attitudes toward winning
and losing money, the responses to the income gamble questions can also be used to construct
the bounds for risk preference (Ahn, 2010; Kimball et al., 2008). For example, the bounds for
individuals who belong to category 2 are .27 and .50 (Barsky et al., 1997). Table 2 reports the
bounds for each response category.
Table 2 – Lower and Upper Bounds of Risk Tolerance for Each Category
Lower
Upper
Accepted Rejected
Bound θ
Bound θ
none
1/5
0
0.27
Category 2
1/5
1/3
0.27
0.50
Category 3
1/3
1/2
0.50
1.00
Category 4 (most risk-tolerant)
1/2
None
1.00
∞
Category 1 (least risk-tolerant)
Source: The table for lower and upper bounds of risk tolerance can be found in: Ahn
2010, Barsky et al. 1997, and Kimball et al. 2008.
For some researchers (Hariharan, Chapman, & Domian, 2000), these four categories were
used as an ordered variable ranging from 0 (least risk-tolerant) to 3 (most risk-tolerant) to
estimate individuals’ propensity of purchasing risky assets. Also, they were treated as a set of
four dummy variables to predict individual risky behaviors. However, these approaches may not
be the most effective way of using this information. The major problem with these approaches is
23
that these methods only allow a less accurate estimate on individual risk preference because each
of the categories represents a “range” rather than a “point estimate” of risk preference.
Therefore, since many demographics and personal characteristics have been proved to be
correlated with individual risk preference, such as age (Gardner & Steinberg, 2005), gender
(Croson & Gneezy, 2009; Eckel & Grossman, 2008; Kogan & Dorros, 1978), ethnicity (Sung &
Hanna, 1996), religion (Diaz, 2000; Edmondson, 1986), wealth (Kennickell, Starr-McCluer, &
Sunden, 1997), marital status (Ahn, 2010; Grable & Lytton, 2001), and education (Belzil &
Leonardi, 2007; Grable & Lytton, 1998), treating the responses of the income gamble questions
as an ordered variable or a set of dummy variables may yield less variance with a relatively
limited explanation power. For example, when a 27-year-old single white male and a 58-year-old
married black female both ended up being in Category 2, using the ordered or dummy variable
approach meant that these two people had “exactly the same” level of risk tolerance. However,
this may not be the case. Obtaining more variance would be better under this circumstance. As a
result, an alternative way of manipulating these four categories is to get a “point estimate” for
each individual.
The first step is to estimate individual’s risk tolerance using the four different thresholds.
At this point, what can be observed from the survey responses are the lower and upper bounds
rather than their actual risk tolerance.
−∞ ≤ 𝜃𝑖∗ ≤ 0.27
0.27 ≤ 𝜃𝑖∗ ≤ 0.5
0.5 ≤ 𝜃𝑖∗ ≤ 1.0
1.0 ≤ 𝜃𝑖∗ ≤ ∞
where 𝜃𝑖∗ is individual’s observed risk tolerance.
24
As such, θ∗i is a latent variable which can be described base on the lower and upper
bounds. In order to get the estimated risk tolerance, we need to calculate the probabilities of
being in each of the four groups based on the following function:
0.27−𝑋𝑖 𝛽
)
𝜎
𝑃𝑟 [−∞ ≤ 𝜃𝑖∗ ≤ 0.27] = 𝐹 (
0.5−𝑋𝑖 𝛽
)−
𝜎
𝑃𝑟 [0.27 ≤ 𝜃𝑖∗ ≤ 0.5] = 𝐹 (
1.0−𝑋𝑖 𝛽
)−
𝜎
𝑃𝑟 [0.5 ≤ 𝜃𝑖∗ ≤ 1.0] = 𝐹 (
0.27−𝑋𝑖 𝛽
)
𝜎
𝐹 (
0.5−𝑋𝑖 𝛽
)
𝜎
𝐹 (
1.0−𝑋𝑖 𝛽
)
𝜎
𝑃𝑟 [1.0 ≤ 𝜃𝑖∗ ≤ ∞] = 1 − 𝐹 (
𝜃𝐿,𝐻 −𝑋𝑖 𝛽
where 𝐹 (
𝜎
) represents the standard normal cumulative density function with various
lower and upper bounds of risk tolerance.
Then the interval regression needs to be applied to calculate an estimated risk tolerance
for each individual based on their responses to the income gamble questions as well as other
related personal characteristics. Using the upper and lower bounds of risk tolerance, a predicted
risk tolerance (θ) is estimated for each individual.
This approach has its advantages of being more accurate than simply using categorical or
dummy variables for risk preference since it allows more variations across different individuals.
In addition, this measure is more feasible and workable than some other measures, such as the
experimental and investment-decision approaches, since these standardized questions can be
effectively and efficiently implemented in different surveys to collect information that provides
high reliability and opportunities for comparison purposes.
25
Chapter 3 – Research Questions and Hypotheses
Choices of Jobs in Different Sectors
Holland (1997) introduced the term “vocational interests” to express individuals’
tendencies to pursue careers in different vocations. He points out that the choice of a job is an
expression of personality and preference, and that vocational choice may have important
psychological and sociological meanings. He argues:
Just as we judge people by their friends, dress, and actions, so we
judge them by their vocations. Our everyday experience has generated
a sometimes inaccurate but apparently useful knowledge of what
people in various occupations are like. Thus we believe that carpenters
are handy, lawyers aggressive, actors self-centered, salespeople
persuasive, accountants precise, scientists unsociable, and the like
(Holland, 1997, p9).
He believed that members in a given vocation share similar personalities and preferences.
And since they have similar personalities and preferences, they tend to respond to many
situations in similar ways and create an environment for those specific characteristics. For
instance, Chen and Bozeman (2012) compared the differences between public and nonprofit
employees and find that public managers are, on average, more likely to perceive a higher level
of organizational risk aversion than their nonprofit peers. And such a phenomenon is still true
after they ruled out the mediation effects such as managerial trust and formalized personnel
26
constraints. Their findings suggest that members in the same workplace indeed share similar
traits, but workers in different sectors may possess different characteristics.
Differences between Public Employment and Other Types of Employment
Previous research has argued that one of the major differences between public
employment and other types of employment is the job stability (Bloch & Smith, 1979; Fogel &
Lewin, 1974). The relative job stability comes from two major characteristics of public
employment – a more stable income and a lower probability of becoming unemployed. And such
characteristics exist primarily because the merit system prohibits public employees from being
punished or laid off easily.
Historically, several civil reforms bolstered the principle of relative security of tenure.
For example, the Pendleton Act of 1883 is the foundation for the merit system. Security of tenure
was first established based on the assumption that such protection is needed to ensure the
political neutrality within the government workplace (Kellough & Nigro, 2006). The Act
regulated that government jobs should be rewarded on the basis of merit, instead of political
reasons. The tenure practices in the merit system have been providing considerable security for
public employees since its implementation (Fogel & Lewin, 1974). It is one of the most
important sources of job security in the public sector. Among the three principles of the merit
system,1 relative security of tenure distinguishes the public sector from the other sectors in terms
1
The merit-based employment systems are composed of three interrelated principles: 1) open competitive
examinations as the basis for selection, 2) political neutrality by employees, and 3) relative security of tenure
(Kellough & Nigro, 2006).
27
of employment risks because this principle places restrictions on the reasons for terminating the
employees. Government employees can be removed only for reasons that have to do with
malfeasance in the office. In other words, this act made it illegal to fire or to demote government
employees for political reasons (Pendleton Civil Service Reform Act, 1883).
However, the protection for public employees was not solidified until the well-known
Supreme Court case, the Cleveland Board of Education v. Loudermill, 470 U.S. 532 (1985) was
made. This case made a clear connection between property rights obtained from the merit system
and the due process of law in the U.S. Constitution. In other words, the Cleveland case
established a legal protection for public employees in dismissals by requiring the due process
(Rosenbloom & Bailey, 2006). And such requirements are coercive because once a state offers
its employees the due process protection, the government loses the right to determine how much
process is due; and therefore, the government needs to provide the required procedural due
process (Kuykendall & Facer, 2002).
The U.S. Constitution is the fundamental source of job security under this requirement.
The Fifth and Fourteenth Amendments 2 say that due process is crucial to the protection of
individual rights. The due process clauses in these amendments prohibit government from
denying individuals’ life, liberty, or property without due process of law. The due process of law
is applicable if one is being deprived of a property to which he has a right (Woodard, 2005). That
is, if an individual is legally a classified civil servant, he should have property interests in his
continuous employment, and thus should be protected under the merit system and entitled to due
2
Specifically, the 5th Amendment reads, “…nor shall any person… be deprived of life, liberty, or property, without
due process of law;…”; and the 14th Amendment reads, “…nor shall any State deprive any person of life, liberty, or
property, without due process of law;…”.
28
process.3 Since government employment makes the promise on the relative security of tenure
under the merit system, public employees own the property rights to their continued employment
(U.S. Supreme Court, 1985; Woodard, 2005). Accordingly, public employees generally face a
lower probability of becoming unemployed then their private counterparts. 4
On the other hand, however, the merit system does not restrict employees who work in
the private and nonprofit sectors or individuals who are self-employed. Non-government
employees overall are mostly directed by at-will employment and thus receive less security. Atwill employment is a generally applied policy to ensure productive efficiency and managerial
flexibility in private companies. The at-will doctrine suggests that an employment contract of
duration can be terminated by either party at any time for any reason, which means that an
employee can quit at any time and the employer can fire him without giving warning or incurring
any post-employment obligations (Blackburn, 1980; Stone, 2007).
Furthermore, the 5th and 14th Amendments do not impose any limitations upon nongovernmental organizations because employees do not own property rights to their continued
employment (Abernathy, 1957). The chance of becoming unemployed in the public sector should
be much smaller than that in other sectors, including both for-profit and non-profit sectors.
3
The due process of law says that any procedure that is due should provide a pre-termination opportunity for the
employees to respond, coupled with post-termination administrative procedures (Cleveland Board of Education v.
Loudermill, 470 U.S. 532, 1985).
4
A few states such as Texas, Georgia, and Florida have initiated civil service reforms on their personnel system and
turned themselves into the “at-will” states. As a result, the alleged stability and security of public employment may
have changed in recent years in these states. This study is fully aware of these changes. Possible impacts and future
research directions because of these changes are discussed in Chapter 6 (p.86).
29
Research Question and Hypothesis – I
Based on the aforementioned discussion on employment differences, the first section of
this study asks the following question: do public employees and employees in other sectors have
different levels of risk preference because they chose to work in different sectors? Specifically,
this research examines whether people “self-select” into different sectors where the job-related
characteristics resemble their risk preferences. Accordingly, this study develops the following
hypothesis:
Hypothesis 1: Since employment in the public sector is more secure and stable
than that in the other sectors (private, nonprofit, and self-employment), and the
financial risks in the public sector are generally lower, individuals who have
lower levels of risk preference are more likely to choose to work in the public
sector.
Dealing with Endogeneity
Some scholars (Baldwin, 1991; Bellante & Link, 1981) argued that having a risk-averse
personality may cause an individual to choose public sector jobs as his career because of the
merit system protection. However, although risk attitudes could be the major distinction between
public employees and employees in other sectors, prior research has not provided reliable
evidence for the existence of a self-selection mechanism. In other words, the causal relationship
has not been clearly identified because some studies have shown that risk preference may also be
affected by organizational and environmental factors (Bozeman & Kingsley, 1998). That is, a
person’s risk preference could be the “outcome” instead of the “cause.”
One standard response to this problem is to use an instrumental variable; however, no
literature has used or found reliable instrumental variable to solve such a problem. Alternatively,
this study chose to use individuals’ past risk preferences to predict their future sector choices in
order to ensure a “temporal precedence” on the cause (individual risk preference). The advantage
30
of using a panel data is that researchers are able to including lagged (multiple periods) measures
of the independent variable as a predictor of the dependent variable. Besides, although people’s
risk tolerance tends to decrease with age, the probabilities of changing their career paths also
decrease with age because people generally lose their interests and energy to find new jobs when
they get older. Therefore, using variables in people’s younger years to analyze the relationship
between risk preference and future job choices would be more appropriate than using variables in
their later lives. This study realizes that such an approach may not be a “perfect” way to
eliminate all possible endogeneity bias nor be able to provide uncontroversial and conclusive
evidence, but this is by far the best way to “minimize” the bias and to analyze a large sample of
employees in different sectors.
Relationship between Risk Preference and Innovativeness
The second interest of this study is the relationship between individual risk preference
and innovativeness. As we know, governments all over the world have implemented several
administrative reforms such as the American-style movements on reinventing governments in the
U.S. and the Westminster-style movements in New Zealand, Australia, and the U.K. Although
different countries had different causes, strategies and implementations of government reforms,
the primary goals generally include reducing costs of government, encouraging better
performance of the public sector, and introducing management ideas from the private sector. The
American style seeks to transform and reinvent the U.S. federal government in a short period of
time, while Westminster relies on privatization and other market-type mechanisms to reshape
government operations. Table 3 presents the comparisons between the American style reforms
and the Westminster style reforms.
31
Table 3 – Comparison between the American Style and the Westminster Style Reforms
 Reinventing government
 Seeks cheaper, more effective
government without shrinking
the scope of government
activities (works better, costs
less)
Westminster Style (New
Zealand, Australian, and U.K.)
 New Public Management
 Shrinks the government size
 Reduces costs & improves
performance
 Connects economic theories
with management reform ideas
from private sector
 Redefines what government
should do
Model
Business process improvement
New economics
Focus of Reform
Reform of operations
Transformation of structure
Role of Leadership
Relatively weak
Relatively strong
Goals
Fuzzy (organic changes)
Precise (top-down changes)
Role of Legislature
Relatively weak
Relatively strong
Results Measured
Outcomes
Outputs
Accountability
Political, through existing
systems
Managerial, through contracts
Risks
Low stakes
High stakes
American Style (U.S.A.)
Aims
Source: Kettl, D. F. (2005). The Global Public Management Revolution: A Report on the
Transformation of Governance.
Like private and nonprofit organizations constantly develop and initiate new strategies to
improve the quality of services and to survive, these government reforms involved innovative
32
approaches and new practices at work in the public sector. However, any innovative approaches
or practices will work only if the employees are willing to accept them and further enhance
necessary changes among themselves. Therefore, attitude toward innovation is considered one of
the most important factors in determining the success of government reforms and new programs
(Rainey, 1999).
Innovation is generally defined as “an idea, practice, or object that is perceived as new to
an individual or another unit of adoption” (Rogers, 1995). Feaster (1968) indicated that
innovativeness is an awareness of the need to innovate and a positive attitude toward change.
Rogers (2003) further refined his definition of innovativeness in his book, Diffusion of
Innovations, as “the degree to which an individual or other unit of adoption is relatively earlier in
adopting new ideas than other members of a system” (p22). On the basis of such a definition,
anything that may require a person to change his behaviors or ways of doing things could be seen
as innovation.
Researchers have developed a variety of measures for innovation. For example, the
Interest in Innovation Scale developed by Patchen, Pelz, and Allen (1965) and the Administrative
Creativity Scale developed by West and Berman (1997) are two ways to assess innovation at the
“organizational level” in terms of managerial and workplace support. Alternatively,
innovativeness has been seen as the “individual level” of innovation, and “willingness to change”
is useful for the development of an operational definition of individual innovation (Hurt et al.,
1977). It is not only an intuitive way of capturing the essence of innovativeness, but also a
generalized personal characteristic which has dramatic impacts on many of the decision-making
processes of human beings. Therefore, although the true meaning of innovativeness is broader
33
and can be defined differently across disciplines, this research uses the word “innovativeness” to
describe the changes faced by individuals and people’s openness when they confront changes.
One of the most prominent characteristics of innovation is that consequences are difficult
to measure since the payoffs of adapting innovativeness sometimes, if not always, come much
later than the costs. In addition, consequences of adopting innovation are often compounded with
risks and uncertainties which imply a lack of predictability, structure, or information regarding
rational decision-making (Rogers, 1995). Therefore, people are often not fully aware of all the
consequences of the innovativeness adoption. Promise of the success of any specific innovative
change is rare, which makes individual risk preference an important predictor of attitudes toward
changes.
The relationship between risk preference and innovativeness can be categorized as
follows: firstly, risk preference and innovativeness are both considered important elements of
“entrepreneurship” (Kearney, Hisrich, & Roche, 2010; Seo & Chung, 2012; Stevenson & Jarillo,
1990). Although some disagreements exist, literature generally suggests that the typical
entrepreneurial characteristics include the following elements: innovativeness, risk-taking, proactiveness, profit-seeking, network governance, and flexibility. Among these elements,
innovativeness and risk-taking are the traits that have been most frequently mentioned. Many
researchers found that the success of organizations and individuals highly depends on these two
characteristics. For instance, Howell and Higgins (1990) investigated the relationship between
leadership behaviors and personality traits and find that champions demonstrate higher levels of
risk taking and innovativeness. Also, Stewart and Roth (2001) found that entrepreneurs tend to
have a higher level of risk preference than middle managers. Accordingly, being risk-seeking
and innovative are two fundamental characteristics of entrepreneurship.
34
In addition to simply being two important elements of entrepreneurship, literature further
suggests that attitudes towards risks and attitudes toward innovativeness are positively correlated.
Sikora and Nybakk (2012) conducted a study using both quantitative and qualitative methods to
examine the impacts of risk preference on innovativeness. They found that individuals with
lower levels of risk aversion displayed higher levels on innovativeness. That is, a positive
attitude toward risks was positively related to attitudes toward new things and ideas. Farley
(1991) and his colleagues conducted a study on the relationship between risk-seeking tendency
and creativity. They asked a group of teachers to rank their students based on creativity without
knowing the students’ risk-related personality. Then they developed a questionnaire to measure
the students’ risk-seeking scores. They found that students who have revealed a higher level of
creativity were also more risk-seeking.
Also, innovativeness may lead to a higher level of risk tolerance. In Kirton’s well-known
paper, “Adaptors and Innovators: A Description and Measure,” he made a clear distinction
between adaptors and innovators according to their personalities and behaviors. Adaptors tend to
prefer existing methods while innovators are more likely to search for innovative or new ideas.
Kirton (1976) believed that since innovative and new things are more uncertain and risky in
nature, innovators will inevitably be more risk prone than adaptors so that they can explore more
chances. Later, the adaption-innovation relationship is further verified by other researchers
(Goldsmith & Matherly, 1987; Keller & Holland; 1978; Kirton, 2003; Singer, 1990); they all
confirmed that innovative attitudes are significantly correlated with individuals’ tolerance of
ambiguity and risk.
However, some people believe that entrepreneurs do not seek risk; they seek
opportunities (Osborne & Gaebler, 1993). Although entrepreneurs are perceived to be more risk
35
prone than others, some studies found that such a claim may not be valid all the time. Macko and
Tyszka (2009) conducted a laboratory study on college students and alumni to find the
relationship between risk preference and entrepreneurship. The authors found that when
entrepreneurs had the abilities and skills to control or influence the outcomes, they were more
risk prone than the non-entrepreneurs. But if the outcomes of taking risk depended purely on
chances, entrepreneurs and students who showed entrepreneurial characteristics were not more
risk seeking than the others. The authors found that when entrepreneurs and non-entrepreneurs
both confronted with well-defined outcomes and probabilities, they did not behave differently
from each other. For example, when confronted with lottery questions with well-defined
probability of winning certain amount of payoffs, entrepreneurs and non-entrepreneurs had
similar levels of risk preference. On the other hand, when they were given certain “scenarios”
where they have to make decisions based on vague or incomplete information, entrepreneurs are
more risky than non-entrepreneurs. Therefore, there is no conclusive evidence to show that
individual risk preference always has a positive relationship with entrepreneurship and
innovativeness.
In addition, answers to the question of whether public, private, and nonprofit employees
have significant dissimilarities in terms of innovativeness are not conclusive either. Rainey (1983)
compared public and private managers’ responses to an “interest in innovation” questionnaire
scale and found no significant differences between the two groups. Several other researchers also
provide evidence to support such an argument (Golembiewski, 1985; Robertson & Seneviratne,
1995). Research aimed at finding potential antecedents for the differences could be beneficial
(Damampour & Schneider, 2008; Tidd, Besant, & Pavitt, 2001). Accordingly, this study
proposes that decisions on whether to accept changes are subjective to individual characteristics.
36
Among all the characteristics, individuals’ abilities to tolerate risks and uncertainties could be
one of the most important traits.
Research Question and Hypothesis – II
Chen and Bozeman (2012) pointed out a constructive direction for future research
possibilities on risk preference – looking for a measure on risk-averse personalities and
associating this measure with sector differences to explore possible antecedents of risk-taking
behaviors. One of the purposes of this study is to find the antecedents that relate individual risk
preference to individual innovativeness under the organizational context. Together with the
finding of a possible self-selection mechanism, a risk-averse personality should provide another
perspective to answer the question, “Why are public employees less innovative than employees
in other sectors?” However, in light of the aforementioned competing effects of risk preference
associated with innovativeness, this study proposes that individual risk preference is correlated to
innovativeness, though the exact impacts may vary according to different subgroups in terms of
their levels of risk preference.
Hypothesis 2: Individual risk preference is positively associated with
innovativeness; specifically, less risk-averse individuals are more
likely to accept change initiatives. But such impacts are not identical
to everyone since people with different levels of risk tolerance view
things in various ways.
37
Chapter 4 – Data, Variables, and Methods
Data
This research uses information from two different sources. First, the 1979 National
Longitudinal Survey of Youth (NLSY79) is used to examine the relationship between individual
risk preferences and sector choices. Second, the National Administrative Studies Project –
Decision-Making (NASP-DM) is used for two purposes: First, the standardized income gamble
questions in the NASP-DM are beneficial to compare the differences between public employees
and the general population in terms of risk preferences. Second, the innovativeness questions are
beneficial to examine the relationship between individual risk preference and innovative attitudes
of the public employees.
The 1979 National Longitudinal Survey of Youth (NLSY79)
The NLSY79 is a nationally representative sample of more than twelve thousand young
adults aged 14 to 22 years old when they were first surveyed in 1979 (Bureau of Labor Statistics,
2003). These individuals were interviewed annually through 1994, and were interviewed on a
biennial basis from 1994 to the present. The NLSY79 contains data on individuals’ demographic
information, attitudes and expectations, and various labor force activities. The advantage of this
dataset is that it follows the same cohort in each survey wave, which allows me to examine the
trend of people’s risk preference and to use individuals’ past risk preference to predict future
sector choice in order to minimize the potential for endogeneity bias in the model.
38
The subjects of analysis are individuals who have successfully completed the “income
gamble questions” in the 1993 wave. Their employment choices and other control variables in
different waves were merged based on the unique identification code of each individual
(CASEID). Since the outcome variable in this study is individual’s employment choice which is
discrete and non-ordered in nature, this study applied the multinomial logit model to examine the
effects of risk tolerance on the probability of pursuing a career in different sectors. The
multinomial logit allows the parameters to vary across each employment choice and requires us
to choose one employment type as the base category. This study first presents the results using
the private sector as the base group and then compares if there is any changes using the nonprofit
sector as the base group.
The National Administrative Studies Project – Decision-Making (NASP-DM)
The NASP project was first conducted in 1992 as a product of Dr. Bozeman’s doctoral
seminar. Due to the success of NASP-I and its contribution to the PA field, two additional waves
have been launched. NASP- II was developed in 2003 with many of the same themes focused on
public organizations. NASP-III was developed in 2006 and aimed at collecting data from public
and nonprofit managers in Georgia and Illinois.
The current iteration of NASP is titled NASP-DM. It also began with a doctoral seminar
taught by Dr. Bozeman. It aims to increase the empirical knowledge of public management and
public administration. This study asked state government employees a range of questions related
to their work attitudes, behaviors, and decision-making. Targets of this project are employees
selected from public agencies and organizations in two states – Indiana and Nevada. This project
has the potential to address a variety of analyses such as examining variations across states and
across different job positions. It also has the potential to examine the impacts of individual
39
attitudes and preferences on managerial strategies and decision-making. The overall purpose is to
enhance understanding of workplace dynamics.
This study relies primarily on web-based survey responses from a collected sample of
14,824 government employees. Their contact information was obtained from the State Employee
Directory
(Indiana:
http://www.in.gov/core/find_person.html; Nevada: http://ned.nv.gov/)
Individuals listed on the Indiana and Nevada government directories were identified for inclusion
in this study. Table 4 shows the number of samples in different states. Nevada’s sample is
approximately twice the size of Indiana’s sample. According to our pretest results, we found that
managers are significantly more likely to respond to our survey.5 Also, giving the purpose of this
project – to study individuals’ decision-making, we intentionally oversampled employees who
had managerial-related titles in Indiana. 6 Table 5 shows the managerial status of the Indiana
samples used for the final survey delivery.
5
We conducted 6 pretests (with various orders of the survey questions) using the Indiana samples in June, 2012. We
randomly selected 100 participants for each pretest and found that managers were about twice more likely to
respond to the survey. Note: those who were selected in the pretests were excluded from the final sample pool. No
participants will take both the pretest and the full survey in this project.
6
Managerial-related titles include: directors, managers, supervisors, leaders, and executives. In our sample base,
employees with managerial titles account for 12.55% of all Indiana public employees. However, managerial
information of the Nevada employees is not available on the directory, so we did not oversample managers in
Nevada. But in order to capture the managerial status for all respondents, the NASP-DM included two managerialrelated questions: “Do you have employees whom you supervise?” and “If so, how many do you supervise?” In the
final dataset, employees who supervised at least one person account for 45.37% of all Indiana responses and 51.57%
of all Nevada responses. Such findings provide evidence for the fact that managers are more likely to respond to
surveys than non-managers.
40
Table 4 – Total Collected Samples and Samples Used for Survey Delivery
State
Total Collected Samples
Sample Used for Survey Delivery
Indiana
4,951
2,000 (40%)
Nevada
10,376
6,000 (60%)*
Total
15,327
8,000
*We used email testing software to pretest all of the email addresses before sending the survey.
This approach may ensure successful delivery of the survey. However, about 64% of the Nevada
email domains were either “grey listed” or “unable to be specified, “ which means we were not
sure if the survey can be successfully delivered. Therefore, in order to get reasonable and
comparable responses, we oversampled the Nevada employees.
Table 5 – Managerial Status of the Indiana Sample
N
%
Manager
838
41.9
Professional
1162
58.1
Total
2000
100
The public employees in both states came from agencies and departments of numerous
functions, among which Health & Human Services, Family & Social Services, Child Services,
and Transportation Services were generally the largest (see Appendix A and B for the
distribution of the total collected sample in Nevada and Indiana). Our randomly selected samples
used for survey delivery were aligned with the original samples. The aforementioned
departments and agencies were still the largest. Table 6 and Table 7 show the departmental
distribution of the Nevada and Indiana samples, respectively.
41
Table 6 – Departmental Distribution of the Nevada Sample
Department/Agency
N
%
Administration
101
1.68
Agriculture
38
0.63
Attorney General's Office
2
0.04
Business & Industry
260
4.34
Colorado River Commission
21
0.35
Conservation & Natural Resources
290
4.84
Controller's Office
3
0.05
Corrections
133
2.21
Cultural Affairs
79
1.32
Economic Development Commission
12
0.20
Education
59
0.99
Employment, Training & Rehabilitation
208
3.47
Gaming Control Board
15
0.25
Governor's Office
15
0.25
Health & Human Services
2499
41.65
Information Technology
66
1.10
Legislative Counsel Bureau
1
0.02
Lieutenant Governor's Office
1
0.02
Military
12
0.20
Mineral Resources Commission
4
0.07
Motor Vehicles
218
3.64
Peace Officers Standards & Training Commission
12
0.20
Personnel
32
0.53
Public Employees Benefits Program
1
0.02
Public Employees Retirement System
2
0.03
Public Safety
9
0.15
Public Utilities Commission
49
0.82
Secretary Of State
61
1.02
Taxation
172
2.87
Transportation
975
16.25
Treasurer's Office
22
0.37
Veteran's Services
9
0.15
Wildlife
36
0.60
Unknown
251
4.18
Total
6000
100
42
Table 7 – Departmental Distribution of the Indiana Sample
Department/Agency
N
%
Accounts, State Board of
1
0.05
Adjutant Generals Office
1
0.05
Administration, Indiana Department of
5
0.25
Agriculture, Indiana State Department of
3
0.15
Alcohol & Tobacco Commission
3
0.15
Animal Health, Board of
5
0.25
Attorney General's Office
18
0.9
Auditor of State
6
0.3
Budget Agency, State
8
0.4
Child Services, Department of
705
35.25
Civil Rights Commission
2
0.1
Community & Rural Affairs, Indiana Office of
5
0.25
Correction, Indiana Department of
107
5.35
Criminal Justice Institute
5
0.25
Economic Development Corporation, Indiana
5
0.25
Energy & Defense Development, Office of
1
0.05
Environmental Adjudication, Office of
1
0.05
Environmental Management, Indiana Department of
45
2.25
Family & Social Services Administration
240
12
Finance Authority, Indiana
2
0.1
Financial Institutions, Department of
6
0.3
Gaming Commission, Indiana
13
0.65
Health, Indiana State Department of
93
4.65
Higher Education, Commission for
1
0.05
Homeland Security, Department of
7
0.35
Hoosier Lottery
6
0.3
Horse Racing Commission
1
0.05
Housing & Community Development Authority,
3
0.15
Indiana
Inspector General
1
0.05
Insurance, Indiana Department of
15
0.75
Intelligence Fusion Center, Indiana
3
0.15
Labor, Department of
5
0.25
Law Enforcement Academy, Indiana
3
0.15
Library, Indiana State
2
0.1
Lieutenant Governor
2
0.1
Local Government Finance, Department of
9
0.45
Motor Vehicles, Bureau of
77
3.85
Natural Resources, Department of
56
2.8
Personnel Department, State
5
0.25
Professional Licensing Agency
6
0.3
43
Prosecuting Attorneys Council
Protection & Advocacy Services, Indiana
Public Records, Commission on
Regional Development Authority
Revenue, Department of
Secretary of State
State Board of Accounts
State Museum, Indiana
State Personnel Department
State Police, Indiana
State Student Assistance Commission of Indiana
Student Assistance Commission of Indiana, State
Tax Review, Indiana Board of
Technology, Indiana Office of
Transportation, Indiana Department of
Treasurer of State
Utility Consumer Counselor, Office of
Utility Regulatory Commission
Veteran's Affairs, Department of
Veterans Home, Indiana
Workforce Development, Department of
Unknown
Total
4
4
3
1
82
2
2
5
1
11
2
1
1
53
201
1
18
12
4
5
102
3
2000
0.2
0.2
0.15
0.05
4.1
0.1
0.1
0.25
0.05
0.55
0.1
0.05
0.05
2.65
10.05
0.05
0.9
0.6
0.2
0.25
5.1
0.15
100
Survey Delivery
We implemented a two-staged initial invitation process which contains one prenotification and one initial invitation (Andrews, Nonnecke, & Preece, 2003). The pre-notification
was sent out four days before the initial invitation (see Table 7). For the Indiana participants, the
pre-notification was delivered by email on Thursday, October 18, 2012 and the initial invitation
was delivered on Monday, October 22, 2012. The pre-notification included an internet link for
people who were interested in taking the survey early. Reminder emails were delivered on
October 30 and November 5. Pre-notification, invitation, and reminder emails all included a web
link for an opt-out request.
44
Then we administered a phone call reminder between November 13 and 16. We received
some participants’ responses of willingness to take the survey while some others requested to opt
out. After Thanksgiving, we included one additional reminder email on December 3 before
delivering the “final reminder” on December 17. We indicated that the survey would be
deactivated on December 20 on the final reminder.
For the Nevada participants, the pre-notification was delivered by email on Tuesday,
October 30, 2012 and the initial invitation was delivered on Friday, November 2, 2012. Similar
contents were included in these invitations. Five follow-up reminders were distributed before we
delivered the “final reminder” on December 17. The survey was also deactivated on December
20. Table 8 shows the dates of email contacts and associated number of responses and the final
distribution in each state.
Table 8 – Survey Delivery Dates and Responses
Wave
Date
Responses
Indiana (October-December 2012)
Contact 1: Pre-notification
October 18, 2012
39
Contact 2: Initial invitation
October 22, 2012
72
Contact 3: Follow-up
October 30, 2012
33
Contact 4: Follow-up
November 05, 2012
22
Contact 5: Phone reminder
November 14-16, 2012
8
Contact 6: Follow-up
December 03, 2012
13
Contact 7: Final reminder
December 17, 2012
28
Total: 215
Nevada (October-December 2012)
Contact 1: Pre-notification
October 30, 2012
45
142
Contact 2: Initial invitation
November 02, 2012
84
Contact 3: Follow-up
November 05, 2012
80
Contact 4: Follow-up
November 13, 2012
82
Contact 5: Follow-up
November 19, 2012
99
Contact 6: Follow-up
November 28, 2012
55
Contact 7: Follow-up
December 06, 2012
34
Contact 8: Final reminder
December 17, 2012
42
Total: 618
New Hampshire (October, 2012)
Contact 1: Pre-notification
October 18, 2012
32
Contact 2: Initial invitation
October 22, 2012
26
Stopped b/c reported spam
Total: 58
We used Qualtrics (www.qualtrics.com) to host our survey but not to distribute it. Rather,
we created a personal email address for our project manager ([email protected]) from which
to distribute personalized email invitations. We anticipated that a personal email address from a
university email domain would be less likely to be automatically spam filtered. This was also the
email address to which general questions and failed delivery messages were delivered. This
account was monitored regularly during the distribution and any concerns or questions from
respondents were swiftly answered.
Report of Response Rates
In the original email database, there was a total population of 4,951 and 10,376 email
addresses in Indiana and Nevada, respectively. We randomly selected 2,000 Indiana emails and
46
6,000 Nevada emails for the final sample pool. In order to maximize our response rates, we
administered personalized contacts with carefully crafted message to encourage the respondent
to complete the survey (Dillman, 1999). For example, reminders sent before and after
Thanksgiving and Christmas were tailored by holiday greetings and regards. Survey participants
were also given the opportunities to opt out the survey in three different ways: (1) Qualtrics –
respondents can click on a link at the bottom of each of the email invitations or reminders.
Qualtrics will record their intention of opting out of the survey. (2) Phone – we left our contact
information on the signature section of each email notification. Some of the respondents chose to
call us asking questions about the survey or indicated that they preferred not to receive any
further reminders. (3) Email – Some of the respondents chose to send emails indicating that they
wanted to opt out. The email account was monitored regularly every day by the project
managers. Once we received emails from the respondents, we took appropriate actions and
replied to the respondents immediately.
During the survey period, there were 428 Indiana respondents and 298 Nevada
respondents who opted out using one of these three options. Since we implemented one phone
call reminder in Indiana, more people opted out by phone in Indiana than in Nevada. Also, we
received an email from the manager of Department of Child Services asking us to stop sending
her department any emails after our initial invitation, so employees who work in the Department
of Child Services were all opted out per her request. During certain survey periods, we received
“temporarily out of office” automatic replies from several email addresses indicating that they
were (or would be) away from their office. We checked their away periods and if their away
periods overlapped with three or more of our email reminder periods, we counted them as invalid
participants. Among all the randomly selected email addresses, some of them were undeliverable
47
or had unspecified domains. In Nevada, a total of 2,734 returned/undelivered emails were found,
which reduced the sample size by 45.6%. Some previous studies regarding online surveys ethics
argued that unless participants say it is all right to contact them by e-mail, the online survey
should not be distributed (Bannan, 2003). However, since asking permission from all the
participants may not be practically feasible for the online surveys, participants should at least be
given the opportunity to opt out from the surveys (Evans & Mathur, 2005). Based on this logic,
people who opted out should be seen as unwilling to participate in the surveys. Their names
should not be on the contact list in the first place. Therefore, they should not be counted as nonrespondents. For the number of total opt-outs, please see Table 9.
Table 9 – Summary of Responses
Indiana
Nevada
Initial Sample
2000
6000
Opt out (by Qualtrics)
154
231
Opt out (by phone)
239*
9
Opt out (by email)
35
58
Opt out (Dpt. of Child Services)
705
0
Out of office (if missed 3 or more email notifications)
37
76
Returned/Undelivered
108
2734
Completed and Near completed (numerator)
215
618
Non-respondent
710
2333
Denominator (completed + Non-respondent)
925
2951
Response Rate
23.24%
21.37%
Total Response Rate
21.49%
*We implemented a phone reminder for all the Indianan respondents, so more people opted out by
phone in Indiana than in Nevada.
48
We received complete responses from 873 individuals, among them 209 were from
Indiana, 609 from Nevada, and 55 from New Hampshire. Also, we received near-completed
responses from 18 individuals, among them 6 were from Indiana, 9 from Nevada, and 3 from
New Hampshire. But since survey distributed to New Hampshire was reported spam, the 55
complete responses and 3 near-completed responses were not included in the final dataset. The
majority of those who nearly completed the survey had more than 70% of the survey completed.
There were another 518 individuals (196 in Indiana, 274 in Nevada, and 48 in New Hampshire)
who started the survey but did not finish more than 70% of the questions. These “started but not
completed” individuals were counted as non-respondents when calculating the response rates.
The response rates in Indiana and Nevada were 23.24% and 21.37%, respectively. The total
response rate was 21.49%.
Criteria of Choosing the Most Appropriate Measure for This Study
In order to examine the relationship between risk preference and sector choice, an
appropriate and reliable method of measuring individual risk preference is necessary. Given the
purpose of this study, this measure needs to meet the following criteria: First, this measure needs
to be effective and meaningful to each individual. Second, information of this measure needs to
be available in people’s early lives so that prediction on sector choices in later lives would be
possible. Last but not least, this measure needs to be replicable in the other surveys in order to
test the relationship between risk preference and other concepts such as innovativeness
(willingness to change) in this study.
49
Based on the aforementioned criteria, several commonly-used measures of risk
preference are not suitable for this study. For example, the measure of financial decisions and
investment strategies is not an appropriate approach for this study since the majority of the
younger population generally do not have significant and meaningful amount of liquid assets for
investment purposes. The small variation of such a measure may impede any meaningful results.
Also, conducting experiments to understand individuals’ attitudes may not be a feasible way for
a large and public-employee oriented sample. Given the fact that one of the major distinctions
between public and private employment is job security and income stability, assessing
individuals’ risk preferences according to their choice on hypothetical scenarios focusing on
individuals’ attitudes toward income and risks will be the most appropriate approach to evaluate
people’s risk preferences and to predict their sector choices. As a result, this study chooses to
assess individuals’ risk preference by estimating the risk tolerance (θ) based on economic models.
Variables
Main Independent Variable: Individual Risk Preference
The major independent variable in this study is individual risk preference. As mentioned
in Chapter 2, a wide variety of measures can be used as proxies or determinants for risk
preference. However, in order to predict individuals’ sector choices, to obtain a consistent
measure over different periods of time, and to get a measure which is based on economic theory,
this study uses the estimated relative risk tolerance as a quantitative proxy for risk preference
(Kimball et al., 2008). The relative risk tolerance is calculated based upon individuals’ responses
to the income gamble questions in the NLSY79. The income gamble questions can be found in
50
the 1993, 2002, 2004, and 2006 waves. These questions were also included in the NASP-DM
(see Appendix C for a revised format used in NASP-DM).
Based on the responses, individuals can be categorized into four different groups –
Category 1 (rejected the 20% risk threshold), Category 2 (accepted the 20% risk threshold but
rejected the 33% risk threshold), Category 3 (accepted the 33% risk threshold but rejected the
50% risk threshold), and Category 4 (accepted the 50% threshold). Table 10 shows the
distribution of risk tolerance categories in 1993, 2002, 2004, and 2006. In all four waves,
category 1 is always the largest group in the sample (ranging from 46.5% in 1993 to 55.9% in
2006), followed by category 4 (ranging from 17% in 2004 to 25.2% in 1993).
Table 10 – Percentage of Each Risk Tolerance Category in 1993, 2002, 2004, and 2006
Accepted
Rejected
1993
(n=8945)
2002
(n=7485)
2004
(n=7176)
2006
(n=7292)
Category 1
none
1/5
46.5%
55.2%
53.4%
55.9%
Category 2
1/5
1/3
11.5%
10.2%
15.8%
10.7%
Category 3
1/3
1/2
16.8%
15.7%
13.8%
14.9%
Category 4
1/2
None
25.2%
18.9%
17.0%
18.5%
Report of the Predicted Relative Risk Tolerance (Point Estimate for Each Individual)
Individual risk tolerance (θ) is estimated using data from the 1993 wave of the NLSY79:
̂∗ 𝑖93 = 𝑋𝑖93 ̂𝛽
𝜃
51
where Xi93 is the vector of individual characteristics in 1993, including age, gender, race, raised
religion, education, marital status, health condition, and family income.
I estimate the parameters using a sample of 6,889 valid income gamble responses. The
average predicted risk tolerance for 1993 is 0.52 (see Appendix D). Table 11 presents the
parameters of the interval regression. Consistent with expectations, age, health status, being
female, and having one or more children are negatively correlated with individuals’ risk
tolerance. Income also has a mildly negative effect on risk tolerance. However, inconsistent with
some literature (Sung & Hanna, 1996; McInish, Ramaswami, & Srivastava, 1993), results in the
analysis show that risk tolerance decreases with education. For example, an individual with a
high school diploma is more likely to have a lower level of risk tolerance than a person who does
not complete high school. Although having some college and having a graduate or a postgraduate degree do not show significant results, the joint test of all the educational dummy
variables indicates that education overall is a significant factor.
Table 11 – Interval Regression Estimates of Risk Tolerance (1993)
Age
Coefficient
-0.005*
S.E.
(0.003)
Female
-0.075***
(0.012)
Hispanic
-0.011
(0.018)
Black
0.017
(0.016)
Family Income
-0.004*
(0.002)
Married
-0.099***
(0.017)
Separated
-0.057**
(0.028)
Divorced
-0.015
(0.022)
52
Widowed
-0.088
(0.079)
High School
-0.049***
(0.019)
Some College
-0.027
(0.021)
College or More
-0.014
(0.023)
Health Limitation
-0.053**
(0.022)
Have One of More Kid(s)
-0.034**
(0.015)
Catholicism
0.044***
(0.016)
Jewish
0.154**
(0.069)
Other Religion
0.030
(0.021)
No Religion
-0.003
(0.030)
Constant
0.869***
(0.086)
Observations
6,889
Base group for different variables: non-Black and non-Hispanic, single, less than
high school, and Protestant
*** p<0.01, ** p<0.05, * p<0.1
In addition, after ruling out the effects of age, income, education, and other personal
characteristics, this study found that being an African American or a Hispanic does not increase
or decrease an individual’s risk tolerance. Therefore, findings indicate that race is not an
important predictor in estimating individuals’ risk preferences. 7 However, taken together,
religion and marital status both show significant results.
Figure 1 demonstrates the dissimilarities among employees in four different job
categories. The overall average risk tolerance is 0.52. For public employees, their level of risk
tolerance is the lowest with an average of 0.47, followed by people who work in the nonprofit
sector (θ = 0.49). On the other hand, individuals who seek self-employment (θ = 0.57) and
7
The joint test for Hispanic and Black provides a p-value equals to 0.22.
53
private employment (θ = 0.53) have relatively higher levels of risk tolerance, which is consistent
with the previous findings that risk tolerance is a key characteristic of entrepreneurship (Knight,
1921; Ahn, 2010). The average risk tolerance of a self-employed individual is 21.3% and 16.3%
higher than that of a public employee and a nonprofit employee, respectively.
0.6
0.55
0.52
0.5
0.57
0.53
0.47
0.49
0.45
0.4
Figure 1 - Average Risk Tolerance for Different Job Categories (1993)
Figure 2 shows the longitudinal trends of risk tolerance from 2002 to 2006. Consistent
with our findings in the 1993 wave, the relative rankings of the four job categories are the same
in different survey waves. Public employees are always the ones who have the lowest level of
risk tolerance followed by nonprofit employees. Self-employed individuals have the highest level
of risk tolerance throughout the three waves.
54
In addition, the longitudinal trends may provide some evidence to a controversial issue –
the effects of age on individuals’ risk tolerance. Previous research has mixed findings on this
topic. Some studies found that risk tolerance decreases with age (Grable & Lytton, 1998; Morin
& Suarez, 1983; Yao, Hanna, & Lindamood, 2004), whereas other studies found that risk
tolerance either increases with age (Wang & Hanna, 1997) or has no significant relationship with
age (Hawley & Fujii, 1993; Sung & Hanna, 1996). Since the NLSY79 follows the same cohort in
every wave, the decreasing trends shown in Figure 2 support the argument that age has a
negative effect on individuals’ willingness to take risks.
0.48
0.46
0.44
Self-employment
Private
0.42
Nonprofit
0.4
Public
0.38
0.36
2002
2004
2006
Figure 2 – Trends of the Average Risk Tolerance in Different Job Categories
55
Dependent Variables
In order to prove the argument is true that the less innovative attitudes of the public
employees may come from, among impacts of many other environmental factors, a self-selection
mechanism that had sorted people with specific characteristics into the public sector, two models
of analysis were needed. In the first model, the primary goal was to prove that a self-selection
mechanism exists. For this purpose, two main dependent variables were used. First, this study
selected individuals’ sector choice in 2006 as one dependent variable. The sector choices
included public, nonprofit, private, and self-employment. Since this variable was categorical, the
multinomial logit model was used to predict individuals’ future sector choices using their past
risk preferences. However, one concern of such a dependent variable needed to be considered.
That is, people’s sector choice in 2006 could be a coincidence of certain events. For instance, a
person could have been working in the nonprofit sector for several years before he switched into
the public sector in 2006. Also, it is possible that a person might have changed sectors more than
once between 1993 to 2006, 8 which would make his 2006 sector choice not representative
enough for his primary sector preference.
In order to minimize the impacts of such possibilities, this research took advantage of the
longitudinal data, and identified the sector in which each individual had stayed for the longest
period of time between 1994 and 2006. And that information was used as a second variable to
reinforce the arguments of this study. Since this variable was also categorical including four
8
This study calculated the total number of times that a person changed his/her sectors between 1994 and 2006.
Since the NLSY79 was surveyed on a biannual basis since 1994, a person could switch his/her sectors between zero
to a maximum six (6) times. There were 2,478 (35.76%) individuals who never switched sectors between 1994 and
2006, while 459 (6.63%) people switched their sectors more than 4 times during that period. The overall average
times of sector switches was 1.3 times. Please see Appendix E for details.
56
types of sector choices – public, nonprofit, private, and self-employment, the multinomial logit
model was applied to allow different parameters for each category.
Index of Innovativeness
Rogers and Shoemaker (1971) conceptualized innovativeness as the relative degree to
which an individual adopts innovations earlier than others in the social system. To develop the
innovativeness index, they generated an initial pool of 53 items. These items were developed
based on the characteristics of the five innovativeness categories: innovator, early adopter, early
majority, late majority, and laggards/traditionalist. This study adopts the operational concepts of
innovativeness and definitions from Hurt and his colleague (1977) in order to examine the
relationship between risk preference and innovativeness. They developed a 10-item scale as an
alternative shorter version of Rogers and Shoemaker’s original scale. According to Hurt’s
analysis, the 10-item version had a great internal reliability (Nunnally’s r = .89) and its
correlation with the original scale is .92. Given the space of incorporating questions into the
NASP-DM, the 10-item scale was used as the primary measure of innovativeness.
Table 12 presents the complete list of the items of innovativeness. Since all the questions
were asked in a negative way, responses to these questions were reversely coded so that a higher
value on the innovativeness index represents a more innovative attitude. Also, in order to
determine whether there was a large principal component in these questions, this study
conducted a component factor analysis. The results showed that two factors exist (with
eigenvalues of 3.11 and 1.30). Question #1, #2, #3, #4, #6, #8, and #10 belong to one factor, and
#5, #7, and #9 belong to the other. Then the Kaiser criterion was adopted to determine how many
factors were meaningful. Results showed that both factors were meaningful and worth keeping.
57
Table 12 – Responses to the Innovativeness Questions (Original Responses)
Question
Mean*
S. D.
1. I am generally cautious about accepting new ideas
2.64
1.02
2.00
0.76
1.82
0.80
2.05
0.87
5. I find it stimulating to be original in my thinking and behavior
3.99
0.84
6. I tend to feel that the old way of living and doing things
is the best way
2.33
0.86
7. I am challenged by ambiguities and unsolved problems
3.69
1.01
8. I must see other people using new innovations before
I will consider them
2.03
0.75
9. I am challenged by unanswered questions
3.73
0.95
10. I often find myself skeptical of new ideas
2.43
0.92
2. I rarely trust new ideas until I can see whether the
vast majority of people around me accept them
3. I am aware that I am usually one of the last people
in my group to accept something new
4. I am reluctant about adopting new ways of doing things
until I see them working for people around me
* Five-point scale with 1 being strongly disagree and 5 being strongly agree.
For each item, an individual can score from a minimum of 1 (strongly disagree) to a
maximum of 5 (strongly agree) on each item, yielding an index ranging from 10 to 50 by
summating all the responses. Theoretically, responses from survey questions are inherently
ordered (Greene, 2008), and thus an index developed from multiple survey responses should be
considered an ordered variable and therefore should use the ordered logit model for analyses.
However, practically, researchers have treated an index which as a relatively larger range as an
interval variable and used the ordinary least square (OLS) for analyses. Such an approach can
also generate reliable results. This research conducted both regression analyses for the impacts of
58
risk preferences on individuals’ willingness to change, and found similar results.
The
coefficients from the OLS are presented and discussed in Chapter 5 and the marginal effects
from the ordered logit model are presented in Appendix F.
Methods
Multinomial Logit Model for Sector Choice
To explore the impacts of risk preference on the probability of choosing employment in a
certain sector, this study modeled a multinomial logit equation based on individual’s relative risk
tolerance and other demographic information. Multinomial logit assumes the following
probability functions:
𝑃𝑟 [𝑌𝑖06 = 𝑗| ̂
𝜃 ∗ 𝑖93 , 𝑋𝑖93 , 𝛼] =
̂∗ 𝑖93 𝛼0 +𝑋𝑖93 𝛼
𝑒𝜃
𝑗=𝐽
𝜃 𝑖93 𝛼0 +𝑋𝑖93 𝛼
1 + ∑𝑗=1 𝑒 ̂
𝑃𝑟 [𝑌𝑖93~06 = 𝑗| ̂
𝜃 ∗ 𝑖93 , 𝑋𝑖93 , 𝛼] =
∗
̂∗
𝑒 𝜃 𝑖93 𝛼0 +𝑋𝑖93 𝛼
̂∗
𝑗=𝐽
1+∑𝑗=1 𝑒 𝜃 𝑖93 𝛼0 +𝑋𝑖93 𝛼
where 𝑌𝑖06 represents the first outcome variable: individual i’s sector choice in 2006, including
four categories: j = public employment, nonprofit employment, private employment, and selfemployment; 𝑌𝑖93~06 represents the second outcome variable: the sector in which individual i had
stayed for the longest time between 1993 and 2006, including the aforementioned four categories
̂∗ 𝑖93 represents individual i’s predicted risk tolerance in 1993; 𝑋𝑖93 is the vector of
as well; 𝜃
individuals’ characteristics in 1993, which includes age, gender, race, education, family income,
marital status, number of children, and health condition; e represents the exponential distribution
function; α represents the parameters of the independent variables. This study first provides
results using private employment as the base group, followed by using nonprofit employment as
59
the base group. The parameters on the chosen base group were normalized to 0 when the
corresponding parameters for the other categories were estimated.
Ordinary Least Square for Index of Innovativeness
This study modeled the OLS equation based on individual’s relative risk tolerance and
other demographic information as follows:
𝑌𝑖 = 𝛽0 + 𝛽1 𝑅𝑖 + 𝛽2 𝑋𝑖 + 𝜀𝑖
where 𝑌𝑖 represents the outcome variables: individual i’s overall attitudes toward changes,
ranging from 19 to 50 with an average of 35; 𝑅𝑖 represents individual i’s estimated risk tolerance;
𝑋𝑖 is the vector of individuals’ characteristics, which includes age, gender, race, education,
marital status, and number of children; 𝜀𝑖 represents the error term; 𝛽1 and 𝛽2 represent the
parameters of the independent variable.
60
Chapter 5 – Analysis and Findings
Relationship between Risk Preference and Sector Choice
The results support the hypothesis that individuals with different levels of risk
preferences will self-select into different sectors. Table 13 presents the odds ratio from the
multinomial logit model with private sector as the base group. Columns with odd numbers (1, 3,
5, and 7) show the results using individuals’ sector choice in 2006 as the dependent variable,
while columns with even numbers (2, 4, 6, and 8) show the results using individuals’ sector
choice (served for the longest) between 1993 and 2006 as the dependent variable. Consistent
with expectation, individuals’ risk preference is an important factor in predicting employment
types. The smaller-than-one odds ratio (0.684) on column (1) supports the hypothesis that more
risk-averse individuals are more likely to work in the public sector. If an individual’s relative risk
tolerance increases by one unit, he is 31.6% less likely to work in the public sector. However,
one unit increase in risk tolerance means turning an extremely risk-averse individual into an
excessive risk-seeking person, which is not a widely acceptable way of interpreting reality.
Alternatively, a more reasonable way of describing the impacts of risk preferences on sector
choices is that an individual whose level of risk tolerance is one standard deviation below the
mean is 11% more likely to work in the government than a person with average risk tolerance.
And such impacts are statistically significant at 1% level.
61
Table 13 – Odds Ratio from the Multinomial Logit Model (Private Sector as the Base Group)
Public Sector
Nonprofit Sector
Private Sector
Self-Employed
(1)
at 2006
(2)
b/t
1994-2006
θ (in 1993)
0.684*** 0.418***
(0.094)
(0.055)
Age
1.050**
1.059***
(0.021)
(0.020)
Female
1.376*** 1.491***
(0.128)
(0.128)
Hispanic
1.893*** 1.863***
(0.228)
(0.207)
Black
1.669*** 1.925***
(0.184)
(0.195)
0.967*
0.947***
Family
Income
(0.018)
(0.016)
Married
1.138
1.304**
(0.146)
(0.154)
Separated
1.026
0.990
(0.219)
(0.195)
Divorced
1.132
1.131
(0.191)
(0.173)
Widowed
2.436*
2.038
(1.161)
(0.975)
Kids or Not
1.173
1.117
(0.138)
(0.119)
High School 2.339*** 2.280***
(0.435)
(0.376)
3.914*** 3.853***
Some
College
(0.761)
(0.667)
7.591*** 8.191***
College or
More
(1.570)
(1.514)
0.652*
0.633**
Health
Limitation
(0.145)
(0.118)
Constant
0.015*** 0.011***
(0.010)
(0.007)
Observations 3,601
4,830
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
(3)
at 2006
0.915
(0.177)
1.029
(0.030)
2.495***
(0.355)
1.136
(0.204)
1.137
(0.178)
0.893***
(0.028)
1.346
(0.249)
0.793
(0.262)
0.861
(0.215)
0.752
(0.795)
0.919
(0.151)
2.384***
(0.709)
5.366***
(1.621)
7.742***
(2.480)
0.807
(0.232)
0.012***
(0.011)
3,601
(4)
b/t
1994-2006
0.937
(0.165)
1.031
(0.028)
2.308***
(0.301)
1.106
(0.188)
1.300*
(0.188)
0.919***
(0.024)
1.181
(0.195)
0.956
(0.264)
0.565**
(0.140)
0.659
(0.690)
0.953
(0.142)
3.134***
(0.913)
6.529***
(1.934)
10.815***
(3.359)
0.760
(0.192)
0.006***
(0.006)
4,830
62
(5)
at 2006
(6)
b/t
1994-2006
Base Group
3,601 4,830
(7)
at 2006
1.460**
(0.227)
1.038
(0.025)
0.840
(0.093)
0.974
(0.143)
0.896
(0.120)
1.041**
(0.020)
0.863
(0.130)
1.650**
(0.368)
1.073
(0.210)
0.000
(0.001)
1.030
(0.142)
0.928
(0.154)
1.152
(0.213)
1.271
(0.263)
1.650**
(0.327)
0.044***
(0.035)
3,601
(8)
b/t
1994-2006
1.628***
(0.238)
1.042*
(0.024)
0.899
(0.094)
0.805
(0.113)
0.662***
(0.089)
1.034*
(0.019)
0.971
(0.141)
1.425
(0.311)
0.743
(0.150)
0.983
(0.747)
1.171
(0.156)
0.890
(0.134)
1.120
(0.190)
1.114
(0.217)
1.228
(0.230)
0.028***
(0.021)
4,830
On the other hand, the larger-than-one odds ratio (1.46) on column (7) suggests that
individuals with higher levels of risk tolerance are more likely to be self-employed. If an
individual whose level of risk tolerance increases by one unit, he is 46% more likely to be selfemployed. Or, a one-standard-deviation increase in risk tolerance results in a 15.7% increase in
the probability of choosing a self-employment career. Nevertheless, although the odds ratio on
column (3) does not equal to 1 (0.915), it is statistically insignificant. That is, the differences
between nonprofit and private sector are not significant. Therefore, the levels of individual risk
tolerance do not determine whether a person will be working in the private sector or the
nonprofit sector.
Similar conclusions can be drawn from the analyses on the second dependent variable –
the sector in which an individual has worked for the longest period of time between 1994 and
2006. The odds ratio of 0.418 in column (2) reinforces the validity of the argument that riskaverse individuals tend to choose a public employment career. For interpretative purposes, the
odds ratio means that a one-unit increase in individual risk tolerance results in a 58.2% decrease
in the probability of pursuing a public career. For a person with one-standard-deviation higher on
risk tolerance, he is 19.8% less likely to work in the public sector. On the contrary, a higher level
of risk tolerance results in a higher probability of staying self-employed for a longer period.
Column (8), like column (7), also gives a larger-than-one odds ratio (1.628). This ratio is even
larger than the ratio in column (7), which further proves that higher levels of risk tolerance lead
to higher probabilities to choose a self-employment career. Meanwhile, the differences between
nonprofit and private are still insignificant even though people’s sector choices between 1993
and 2006 were used as an alternative dependent variable.
63
In order to show the differences and similarities between nonprofit and private sectors,
this study also presents the results from the multinomial logit model using nonprofit employment
as the base group. Column (9) in Table 14 shows the odds ratio of choosing public employment
against choosing nonprofit employment. Again, the argument that risk-averse individuals are
more likely to choose to work in the public sector is approved. The 0.748 odds ratio suggests that
a one-unit increase in individual risk tolerance leads to a 25.2% decrease in probability of
choosing public employment. Only the effect of risk tolerance is significant at 90% level in this
model. But when the second dependent variable was used (column 10), the magnitude of the
effects of risk tolerance increased dramatically and became significant at 99% level. The odds
ratio suggests that an individual whose risk tolerance is one standard deviation below the mean is
18.9% more likely to work in the public sector.
Contrarily, the odds ratios of choosing self-employment against choosing nonprofit
employment (column 15 and 16) are both significant at 95% level. The larger-than-one odds
ratios suggest that less risk-averse individuals are more likely to choose a self-employed career.
Once more, Table 12 shows that the differences between private and nonprofit sectors are
insignificant. Such results are consistent with my expectation in terms of job security. Literature
suggests that private employment and nonprofit employment generally do not receive as much
protection as public employment. In order to test whether jobs in the private and nonprofit sector
indeed share similar characteristics, this study further conducted a logit analysis on these two
categories (a binary variable: 1, nonprofit jobs; 0, private jobs) and tested if risk tolerance has a
zero coefficient (Kennedy 2008). Results showed that the Z-score for coefficient of the risk
tolerance variable is -0.06 (P-value = 0.948). Such findings reinforce that private and nonprofit
sectors do not differ from each other in terms of individuals’ perceived level of risks.
64
Table 14 – Odds Ratio from the Multinomial Logit Model (Nonprofit Sector as the Base Group)
Public Sector
Nonprofit Sector
Private Sector
Self-Employed
(9)
at 2006
θ (in 1993)
(10)
(11)
(12)
b/t
at 2006
b/t
1994-2006
1994-2006
0.748*
0.446***
(0.161)
(0.090)
Age
1.020
1.027
(0.033)
(0.031)
Female
0.551*** 0.646***
(0.086)
(0.094)
Hispanic
1.666*** 1.684***
(0.326)
(0.315)
Black
1.468**
1.481**
(0.253)
(0.238)
1.083**
1.031
Family
Income
(0.037)
(0.030)
Married
0.845
1.105
(0.172)
(0.205)
Separated
1.293
1.035
(0.465)
(0.324)
Divorced
1.314
2.002**
(0.359)
(0.544)
Widowed
3.238
3.095
(3.489)
(3.350)
Kids or Not
1.277
1.173
(0.233)
(0.196)
High School 0.981
0.728
(0.331)
(0.237)
0.729
0.590
Some
College
(0.249)
(0.195)
0.981
0.757
College or
More
(0.352)
(0.261)
0.808
0.833
Health
Limitation
(0.270)
(0.244)
Constant
1.306
1.653
(1.403)
(1.668)
Observations 3,601
4,830
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Base Group
3,601
4,830
65
(13)
at 2006
(14)
b/t
1994-2006
(15)
at 2006
(16)
b/t
1994-2006
1.093
(0.212)
0.972
(0.029)
0.401***
(0.057)
0.880
(0.158)
0.880
(0.138)
1.120***
(0.036)
0.743
(0.137)
1.261
(0.416)
1.161
(0.289)
1.329
(1.405)
1.088
(0.179)
0.419***
(0.125)
0.186***
(0.056)
0.129***
(0.041)
1.238
(0.356)
86.844***
(84.637)
3,601
1.067
(0.187)
0.970
(0.026)
0.433***
(0.056)
0.904
(0.153)
0.769*
(0.111)
1.088***
(0.028)
0.847
(0.140)
1.046
(0.289)
1.770**
(0.439)
1.519
(1.590)
1.050
(0.156)
0.319***
(0.093)
0.153***
(0.045)
0.092***
(0.029)
1.315
(0.332)
156.095***
(140.945)
4,830
1.596**
(0.370)
1.009
(0.036)
0.337***
(0.057)
0.857
(0.186)
0.788
(0.152)
1.166***
(0.041)
0.641**
(0.143)
2.079**
(0.773)
1.246
(0.369)
0.000
(0.001)
1.121
(0.224)
0.389***
(0.128)
0.215***
(0.073)
0.164***
(0.060)
2.044**
(0.663)
3.845
(4.493)
3,601
1.738**
(0.376)
1.010
(0.034)
0.390***
(0.062)
0.728
(0.152)
0.509***
(0.095)
1.125***
(0.034)
0.823
(0.171)
1.491
(0.497)
1.314
(0.404)
1.492
(1.858)
1.229
(0.232)
0.284***
(0.091)
0.172***
(0.057)
0.103***
(0.036)
1.615
(0.484)
4.315
(4.791)
4,830
As far as the other variables, age is an important factor in choosing government jobs but
not in the other models. Older people are more likely to pursue careers in the public sector than
young individuals. Compared with the non-Hispanic and the non-African American group, being
of Hispanic or African American origin is predictive of choosing public employment. Although
only a few of the marital-related dummy variables show significant results, marital status in
general is a significant and important predictor of sector choices.9
Also, it appears that gender demonstrates different impacts when it comes to future job
choices. Based on the results on Table 13 (private sector as the base group), women are less
likely to enter the private sector or to be self-employed, while they are more likely to be working
in the public and nonprofit sectors. In addition, results on Table 14 (nonprofit sector as the base
group) further show that the odds ratios of choosing other kinds of employment against nonprofit
employment are all significantly smaller than one, which suggests that women have the highest
probabilities of working in the nonprofit sector.
Results of the socioeconomic status (family income and education) variables both meet
my expectation. Individuals from rich families are less likely to pursue a public career, but more
likely to choose to work either in the private sector or to be self-employed. The education-related
dummy variables are all significant. Based on the results from Table 13 and 14, higher educated
individuals are more likely to dedicate themselves to public and nonprofit works. Such impacts
also increase when people’s educational levels increase. For example, compared with the leasteducated group (less than high school), the probability of working in the public sector is about
twice as large as that of working in the private sector for a person with a high school diploma,
9
The joint test for all marital-related dummy variables provides a chi2 value equal to 22.86 with the p-value
smaller than 0.029.
66
while about three times larger if he has some college education. In general, a higher level of
education is predictive of choosing employment in the public and nonprofit sector. Further,
results of health limitation are consistent with my expectation. Individuals with some health
problems are less likely to work in the public sector, but more likely to be in the selfemployment category. However, no significant relationships were found between having kids
and working in certain sectors.
To conclude, results from this study show that individuals do choose to work in sectors
where the levels of job security match their risk preferences. 10 People with lower levels of risk
tolerance are more likely to select the public sector, while individuals with higher levels of risk
tolerance are more likely to be self-employed. The differences between choosing “public and
private” and “public and nonprofit” employees are significant. However, there are no significant
differences between choosing private and nonprofit sectors.
10
In addition, this study calculated the number of times that a person switched his sectors between 1994 and 2006,
and used this number as a supplemental dependent variable to examine whether risk tolerance is a predictor of sector
changes (please see Appendix E for the summary statistics of sector switches). Since this variable was a count
variable and the results from the over-dispersion test showed that the variance did not equal the mean (the associated
chi-squared value is 208.37), this study used the negative binomial model for analysis in order to relax the Poisson
assumption that the mean equals the variance and to get consistent and unbiased estimates. Results show a positive
relationship between risk preference and tendency of sector switches. Appendix G presents the marginal effects of
risk tolerance on number of times that an individual changed his sectors between 1994 and 2006. For an individual
whose risk tolerance is one standard deviation above the mean, he is 6% more likely to leave his current job and
switch to another sector than an individual with average risk tolerance.
67
Comparison between the NLSY79 and the NASP-DM
Table 15 presents the differences between public employees in the NLSY79 and in the
NASP-DM (see Appendix H for the summary statistics of the NASP-DM). For public employees
in the NLSY79, their levels of risk tolerance decreased throughout the years. The percentages of
the most risk-averse group (Category 1) have grown from 47.7% in 1993 to 56.3% in 2006,
which may suggest that people will be less tolerant of risk as they become older. Although
people who worked in the public sector in these four different periods of time were not
necessarily the same cohort, a decreasing tread of risk tolerance could still be observed. Overall,
the average risk tolerance has gradually decreased from 0.449 in 1993 to 0.374 in 2006. One
interesting phenomenon is the constant size of the most risk-seeking group (Category 4). The
percentages of that category do not, like the other categories, decrease or increase. A stable 15%
of the public employees showed high levels of risk tolerance in all four waves in the NLSY79.
As far as the NASP-DM, individuals’ information was collected in 2012, and that sample
was older than the sample in NLSY79. Similar to the NLSY79 samples, Category 1 has the
largest percentage among all four categories. More than half of the public employees are highly
risk-averse. However, the two data sets are different because of NLSY79’s relatively large size
in Category 2 and small size in Category 3 and 4. One possible explanation for such a
phenomenon goes to the relatively older survey respondents in the NASP data. That is,
individuals who had high (Category 3) or extremely high (Category 4) levels of risk tolerance
may become more risk-averse when they get older, thereby resulting in a larger percentage in
Category 2. Although a comprehensive comparison is not feasible given different samples in the
two datasets, the NASP-DM and NLSY79 do not seem to differ from each other to a large degree.
68
Table 15 – Comparison between Public Employees in the NLSY79 and NASP-DM Datasets
Accepted Rejected
NLSY79
NASP-DM
Wave
Survey Year
1993
2002
2004
2006
2012
Category 1
None
1/5
47.7%
47.1%
51.4%
56.3%
55.9%
Category 2
1/5
1/3
12.1%
13.6%
12.7%
8.8%
27.7%
Category 3
1/3
1/2
25.7%
22.8%
21.3%
20.3%
11.3%
Category 4
1/2
None
14.5%
16.5%
14.6%
14.6%
5.0%
Average Estimated Risk Tolerance
0.449
0.396
0.387
0.374
0.306
Average Age
32.1
41.0
43.4
44.8
51.0
Observations
898
739
793
803
802
Relationship between Risk Preference and Innovativeness
The effects of individuals’ risk preferences on their willingness to change are presented
in Table 16. 11 Column (1) shows the coefficients from the ordinary least square model for all
public employees in the NASP-DM. In this model, risk tolerance does not provide a significant
result (P=0.156). However, since the literature suggests that the relationship between individuals’
risk preferences and innovativeness is not clearly defined and may vary according to the subjects,
this study further conducted Chow tests to check if the coefficient estimates are different under
different circumstances or should be analyzed differently for different subgroups.
11
The marginal effects of risk tolerance on innovativeness from the ordered logit model are presented in Appendix F.
69
Table 16 - The Effects of Individual Risk Preference on Innovativeness (from OLS)
(1)
(2)
(3)
All
Low (θ <1)
High (θ ≥1)
Relative Risk Tolerance (θ)
1.192
3.617***
-9,084.824
(0.839)
(1.296)
(5,786.875)
Age
-0.005
-0.005
-0.115
(0.005)
(0.005)
(0.132)
Female
0.855**
0.845**
2.234
(0.347)
(0.359)
(2.277)
Supervisor
0.977***
1.028***
2.139
(0.338)
(0.342)
(2.409)
Hispanic
1.697*
1.718*
No obs
(0.923)
(0.916)
Black
1.798*
1.554
8.503
(1.005)
(1.045)
(6.157)
Asian
-0.359
-0.515
-2.959
(0.987)
(1.000)
(7.037)
Native American
-0.543
-0.061
-15.024**
(0.979)
(0.992)
(7.165)
Other Race
-0.619
-0.645
4.095
(0.853)
(0.886)
(5.054)
Married
0.136
0.162
-0.592
(0.392)
(0.400)
(2.118)
Have Kid(s)
-0.067
-0.241
2.042
(0.344)
(0.351)
(2.350)
Some College
0.819
0.760
-7.687
(0.814)
(0.828)
(5.330)
College
1.185
1.005
-7.384
(0.747)
(0.762)
(5.497)
Master Degree
1.566**
1.135
-2.258
(0.793)
(0.813)
(5.702)
Professional Degree
1.282
0.630
15.909
(1.149)
(1.204)
(12.083)
Ph. D.
0.794
0.496
No obs
(1.508)
(1.506)
Constant
32.938***
32.583***
9,130.835
(0.893)
(0.921)
(5,790.825)
Observations
782
745
37
R-squared
0.039
0.043
0.555
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
70
Results show that individuals with high risk preferences and individuals with low risk
preferences are essentially different, especially those who have extreme risk preferences. When
the respondents were separated by their risk tolerance at the threshold of θ=1, results from the
Chow tests show that people with low risk tolerance (smaller than 1) and people with high risk
tolerance (larger than 1) should be analyzed separately (Prob > F = 0.000) in order to obtain more
accurate estimates for each subgroup.12 Figure 3 shows the relationship between risk tolerance
and scores on the innovativeness index for individuals who have a lower level of risk tolerance
(θ<1). Figure 4 shows the relationship between risk tolerance and scores on the innovativeness
index for individuals who have a higher level of risk tolerance (θ≥1).
12
In order to test whether individuals with high and low risk tolerance should be modeled separately, this study
applied the Chow test for this purpose. First, the null hypothesis that “there is no difference between risk-averse and
risk-seeking individuals (the intercepts and all slopes are the same)” was developed. Second, the Chow test was used
to compute the F statistic: 𝐹 =
[𝑆𝑆𝑅𝑝 −(𝑆𝑆𝑅1 +𝑆𝑆𝑅2 )]
𝑆𝑆𝑅1 +𝑆𝑆𝑅2
∗
[𝑛−2(𝑘+1)]
𝑘+1
. Third, this study ran the restricted model for
individuals whose risk tolerance was higher than 1 to obtain 𝑆𝑆𝑅1 and for those whose risk tolerance was lower than
1 to get 𝑆𝑆𝑅2 . Then this study ran the model for all the respondents to get 𝑆𝑆𝑅𝑝 . According to the results, the F
statistic is 7.48 (Prob > F = 0.000). Findings rejected the null hypothesis. Thus, the coefficients are different for riskaverse and for risk-seeking individuals.
71
37
36
34
35
Scores on
Innovativeness
Index
.1
.2
.3
.4
E(y*|L_rt<y<H_rt)
95% CI
.5
.6
Fitted values
Figure 3 – Relationship between Risk Tolerance and Innovativeness (Individuals
25
30
Scores on
Innovativeness
Index
35
40
45
with Low Risk Tolerance, θ < 1)
1
1.001
1.002
E(y*|L_rt<y<H_rt)
95% CI
1.003
1.004
Fitted values
Figure 4 – Relationship between Risk Tolerance and Innovativeness (Individuals
with High Risk Tolerance, θ ≥ 1)
72
Column (2) on Table 16 shows the coefficients of the “low risk preferences” subgroup
(unwilling to risk 50% cut in income for a possible 100% increase in income). In this model, risk
tolerance is found to be significantly correlated with innovativeness. According to the results, a
one-unit increase in the risk tolerance leads to a 3.62 increase in the innovative index.
Alternatively, if a person’s relative risk tolerance is one-standard-deviation below the mean, his
scores on the innovativeness index will be 0.75 higher than a person with average risk tolerance.
As far as the other control variables, being female is significantly correlated with
innovativeness. The effect is significant at 99% level. In addition, being a supervisor is also
significantly correlated with individuals’ willingness to change. Managers generally show a
higher propensity of willingness to change than their non-manager peers do. Although some of
the demographic characteristics have significant coefficients, race and education in general are
not important predictors for innovativeness.13
However, risk tolerance is not a significant factor for people who have an extreme level
of risk preference (willing to risk more than 50% cut in income for a possible doubled income).
These individuals’ attitudes toward innovativeness cannot be predicted or explained by their risk
preferences. Also, when the other factors were further investigated, those significant control
variables in column (2) become insignificant. Although the insignificant coefficients could come
from the smaller sample size in model (3), such a phenomenon may also suggest that riskseeking individuals’ attitudes toward change are not as predictable as risk-averse individuals.
13
The joint test for race yielded an F-value of 1.25 (P=0.281) in model (1), 1.12 (P=0.352) in model (2), and 1.23
(P=0.330) in model (3). Also, the joint test for education yielded an F-value of 0.99 (P=0.428) in model (1), 0.59
(P=0.738) in model (2), and 2.03 (P=0.127) in model (3). Since none of the above is significant at any acceptable
level, race and education are not significant factors in predicting innovativeness.
73
In conclusion, an individual’s risk preference is positively and significantly associated
with his attitudes toward change. But such a correlation only exists if this individual does not
have an extremely high level of risk preference. Otherwise, a person’s willingness to change may
not be estimated or predicted by his risk preference. Such findings are quite interesting and may
provide policy and managerial implications which will be discussed in the next chapter.
74
Chapter 6 – Conclusion and Discussion
This study contributes to the existing literature by providing evidence for the impacts of
individuals’ risk preferences on sector choices. Findings from the NLSY79 data suggest
substantial differences between public employees, nonprofit employees, private employees, and
self-employed individuals in terms of risk preferences. In addition, a self-selection mechanism
exists when people made career decisions. Specifically, this study found that higher levels of risk
preferences measured using the ration of relative risk aversion – a measure derived directly from
economic theory – are predictive of greater propensity to pursue careers in the public sector.
Lower levels of risk aversion, on the other hand, are predictive of a greater propensity to enter
private sector and self-employment. An individual whose level of risk tolerance is one standard
deviation below the mean is 11% more likely to seek public employment relative to someone
with an average risk tolerance. Also, individuals with higher levels of risk tolerance are more
likely to be self-employed. A one-standard-deviation increase in risk tolerance results in a 15.7%
increase in the probability of choosing to be self-employed.
Findings further suggest that private and nonprofit employment share similar levels of
“perceived” job security. The impact of individual risk preference is not significant in
determining whether a person will choose to work in the private sector or will pursue a nonprofit
career. But it is a significant factor in predicting an individual’s choice between public and
private employment, as well as between public and nonprofit employment. The magnitudes of
the impacts of risk preference are consistent with the job security levels of different types of
75
employment. Such findings reiterate the fact that private and nonprofit jobs do not receive as
much legal protection as government jobs do because of the merit system. Individuals do have
tendencies to choose careers that match their preferences, especially their preferences regarding
risks.
Another contribution of this study is the discovery of dissimilar patterns in the
relationship between risk preference and innovativeness. For individuals who feel uncomfortable
to risk a 50% cut in income for a possible doubled income (risk tolerance, θ, smaller than 1),
their attitudes toward change is significantly correlated with their levels of risk tolerance. In
other words, more risk-averse individuals’ innovativeness can be predicted by their levels of risk
tolerance. However, for individuals who are willing to risk more than a 50% cut in income for a
possible doubled income (risk tolerance, θ, larger than 1), their attitudes toward change cannot be
predicted by their attitudes toward risk. The relationship between risk preference and
innovativeness is simply undetermined for the more risk-seeking subgroup. One possible
explanation for such findings is that, given the nature of being less risk-averse, innovative
initiatives must be attractive to these people to a certain degree because they may receive better
outcomes in return. But being an extreme thrill-seeking person may suggest a different
characteristic. Extreme risk seeking behaviors have been seen as a pathological addiction such as
gambling (Hollander, Buchalter, & DeCaria, 2000; Zuckerman, Meeland, & Krug, 1985). Such
an addition is proved to have negative impacts on people’s personal and social functioning.
Although this study is not able to conclude that people who are willing to risk more than a 50%
cut in income for a possible doubled income are addicted to gambling, they indeed behave
differently from the others. Given the above discussion, however, the NASP-DM data indicates
that only 5% of the respondents belong to the most risk-seeking subgroup (those who are willing
76
to take risk even when the expected utility of taking risks is smaller than not taking risks). Such
finding is consistent with several previous studies (Sundén & Surette, 1998; Sung & Hanna,
1996). In other words, attitudes toward innovativeness are still strongly correlated with risk
preferences for the majority of the public employees.
Policy and Managerial Implications
Several important policy and managerial implications can be drawn from the
aforementioned findings. First of all, since the level of relative job security may serve as a
natural mechanism of filtering employees, more risk-averse individuals tend to self-select into
the public sector, and therefore, this group of people is more likely to demonstrate similar
characteristics in the workplace. But what does having a group of risk-averse employees in the
public sector suggest? What disadvantages and advantages does this phenomenon bring to the
government and to the general public? The following paragraphs discuss the characteristics that
could be missing when the public employees are more risk-averse than employees in the other
sectors.
First of all, risk-taking is believed to be correlated with entrepreneurship (Dess &
Lumpkin, 2005; Frishammar & Horte, 2007) and innovativeness (Rogers, 2003). Findings of this
study reinforce such a relationship by specifically focusing on public employees. Being riskaverse is significantly and positively associated with being unwilling to change. Thus, having a
group of risk-averse individuals working together may suggest that these people may be less
innovative than others and therefore develop and construct a more risk-averse work environment.
In addition, decisions made by a more risk-averse public servant are not likely to be the same as
decisions made by his risk-seeking peers. With uncertainty and relative hazards, the risk-averse
77
person may prefer more cautious alternatives or incremental processes, while the risk-seeking
public employee may choose bolder and more innovative actions. Especially in a world where
changing is the only constant, when facing a situation which requires prompt reactions under
limited information and certain level of risks, a public servant could be criticized for his
irresponsiveness and inactiveness. In addition, one general perception argues that the diffusion of
innovation is slower and more difficult in the public sector (Albury, 2005; Borins, 2002). This
study also provides explanation for such phenomenon: since innovation requires certain levels of
risk-taking, it is not uncommon for risk-averse individuals to show resistant and conservative
attitudes toward things that are uncertain in nature.
However, there are also positive sides of having more risk-averse public servants. Being
risk-averse may suggest that the public employees are able to provide a good stewardship of
public resources and to perform consistent operations of daily activities. Wart (2003)
summarized the types, missions, and functions of leaders in the administrative context. To a
considerable degree, leadership in the public sector has been seen as stewardship or
administrative conservatorship (Terry, 1990; 1995). Such a perspective suggests that preserving
and maintaining are also important values to the public sector, and therefore excessive
entrepreneurship could be a threat, rather than a niche, under such context. As Wart (2003)
argued in his article, “Entrepreneurial behaviors cannot be blithely endorsed when public
administrators are entrusted with the authority of the state.” Indeed, public employees should not
embrace merely the principles of entrepreneurship. Empirically, Kearney and her colleague
(2010) found that many of the entrepreneurship characteristics such as innovation and
proactiveness may not always be applicable to public organizations.
78
A well-known example of public employees being too risky at work would be the Orange
County Bankruptcy event in 1994 (Will, Pontell, & Cheung, 1998). Although the treasurer’s
investment strategies had been providing considerable income for the county for years, such
risky strategies failed in 1994 when the interest rates increased and financiers for the county
required increased collateral from the county (Halstead, Hegde, & Klein, 2004). However, the
treasurer would not have encountered this problem if there were a sound check and balance
mechanism that could have stopped this event before the situations got worse. Therefore, after
the crisis, the flexibility of the local officials had been transferred to the state legislature. In
addition, the state government was asked to closely monitor the fiscal conditions of the local
governments (Baldassare, 1998). With all the aforementioned arguments, having a group of riskaverse public employees may not be a disadvantage since this group of people may possess
characteristics that are beneficial to the purpose of protecting public interests. To many people,
administrative conservatorship is still considered an important value of the existence of the
public sector. Without having a strong tendency to chase excitements and thrills, risk-averse
public employees could actually be the bolster of consistent government operations and the
preserver of critical traditions.
Redesign the Reward and the Merit Systems
Given the above discussion, then the question becomes, should public employees be
blamed for being less innovative and more risk-averse? Findings from this study suggest that we
need to be cautious when blaming public employees for being risk-averse because a self-select
mechanism exists. Such a selection may come from the fact that public employment is designed
79
to be more attractive to individuals with a lower level of risk tolerance, which makes the public
sector inevitably recruit more cautious employees.
Putting aside the controversies regarding whether this phenomenon is favorable or
unfavorable, what can the public sector do if it wants to recruit people with “different”
characteristics? What changes could the public sector make in order to attract less risk-averse
individuals? Three possible approaches can be considered. Firstly, the public organizations can
redesign the reward system so that public employees not only are rewarded for their success (the
outcome) but also are recognized for their willingness to try (the process). Govindarajan and
Trimble (2012) argued that organizations should hope for failure because it means that they are
stretching, and they should think how to combine failed ideas to form exciting new ones. Since
innovators may bring different viewpoints and solutions to administrative and organizational
problems (Kirton, 1976), if the public employees are already risk-averse and thus chose public
careers, the public personnel practices must allow a certain degree of failure in order to prevent
an overall risk-averse atmosphere from developing in the workplace. For example, the public
sector can remove regulations that prevent employees from trying new things and replace those
regulations with incentives that can recognize process rather than simply outcomes. Since
investments in innovations may not guarantee corresponding payoffs, these risk-averse public
employees may be more willing to move themselves from their “comfort zone” if their
willingness to change can be recognized.
The second approach is similar to the previous one but focuses on limiting the risks and
uncertainties when decisions need to be made in the public sector, instead of encouraging public
employees to be less risk-averse. As Simon (1955) suggested, people are administrative men
who make satisfactory decisions under limited information. Risks and uncertainties usually come
80
from the limited information that prevents people from making better decisions. Since some
researchers (Kim, 2010; Sikora & Nybakk, 2012) found that innovativeness can be encouraged
by limiting risks and uncertainties, governments should implement policies and managerial
strategies that reduce risks and uncertainties.
Thirdly, the dilemma that public employment may inevitably be more attractive to riskaverse people may be resolved through the reform of the personnel system. In the past few
decades, there is a growing tendency in the governments to replace tenure practices with at-will
policies (Battaglio, 2010). Texas was the first state that eliminated job security from public
employment through at-will policies (Coggburn, 2006). In 1996 and 2001, respectively, Georgia
and Florida followed this trend and initiated similar policies to replace the merit system.
Advantages of such reform include improving efficiency and inducing better performance in the
public sector (Kellough & Nigro, 2006). Most importantly, such a reform may inform people that
public employment is no longer very different from other types of employment in terms of job
security and income stability, and therefore minimize the possibility that risk-averse individuals
may choose to work in the public sector solely because their goal is to get secure jobs that offer
stable income. Such change may encourage innovative attitudes among employees and may
recruit people with less risk-averse attitudes through the self-selection mechanism.
Research Limits
Admittedly, there are some limits in this study. First, although the models include some
of the major factors that could affect people’s sector choices, this study cannot rule out the
possibility that omitted variables exist. For example, one commonly mentioned characteristic
that could drive people to seek public employment is the “public service motivation (PSM)”
81
(Perry, 1996; Perry & Wise, 1990). Perry (2000) argued that people with higher levels of PSM
are generally prepared to risk their personal loss on behalf of community interest and the public
good. That is to say, these people may be less risk-averse than others in terms of attitude toward
income stability. If so, then neglecting PSM may cause omitted variable bias to this study.
However, such an argument has not received sufficient empirical support from the literature. No
strong evidence has shown a positive relationship between risk preference and PSM; therefore,
lacking PSM in the model may not be a major problem. This study is aware of the importance of
the PSM variable, but whether it really leads to serious bias to the models needs further
investigation.
Second, this research used a scenario-based survey question, but may be extended into an
experimental study. If time and other resources are available, a longitudinal experiment could be
designed to further ensure the findings in this research. For instance, we could conduct a study
on college students to know their risk preferences by collecting their responses to similar
questions. Then we could assess the same students again after they find jobs and compare their
risk preferences and test whether the predication of their job choices are accurate. We could also
launch follow-up surveys to investigate the patterns of their career changes. Such a design not
only has the advantage of eliminating the self-response bias but also has the advantage of
enhancing the reliability of career predication by considering the changes of career patterns.
Third, although it is beneficial to know whether people are risk-averse or risk-seeking, a
determinative conclusion may not be possible because they may not always act consistently
across situations (Bazerman, 1986). An individual could be typically risk-averse under one
circumstance, while find other situations where they become risk-seeking (Grable & Lytton,
1999; Nicholson et al, 2005). For instance, Roszkowski (1998) and Rowland (1996) found that
82
an individual’s level of risk tolerance for physical activities is not consistent with their risk
tolerance for investment and financial decisions. Also, Slovic (1972) found that some people are
in favor of gambling with their money for a living but act conservatively in terms of their health
or making rules for their children. As a result, although this study uses the term “risk-averse” to
describe those respondents who were not willing to risk even a small portion of income for a
possible doubled income, such a term may not necessarily reflect their preferences on other
things. However, this study has considered such a problem and chosen the measure that best
represents the difference among various types of employment – the levels of income security.
Using a set of hypothetical questions regarding individuals’ attitudes toward income, and
evaluating the responses based on economic theories is the best approach according to the needs
of this study. To conclude, even though a perfect measure for risk preference does not exist,
researchers should always be careful on choosing the most appropriate “proxy” or “determinant”
of risk preference for their studies.
Future Research Agenda
Some of the research findings and limitations suggest avenues for further research. First,
as mentioned in Chapter 3, one of the major reasons that this study needs to deal with
endogeneity bias is because job-related settings and work environments may affect employees’
attitudes toward risks. However, such an impact points out several research possibilities for
future studies: Does work environment actually affect employees’ risk preference? If that is the
case, then how long does it take for such an impact to occur? Specifically, PA researchers may
be more interested in knowing whether public workplaces make their employees more
conservative and less likely to accept changes after staying in the work environment for certain
83
periods of time. And, is such an impact exogenous? That is, do work environments in different
sectors leads to various impacts on employees’ attitudes? In order to answer the aforementioned
questions, a longitudinal study focusing on the changes of individual risk preference is required.
Future study should to be designed to investigate the following information over multiple periods:
individual risk tolerance, individual innovativeness, sector choices, years of experience in each
sector, job-related variables such as amount of formal regulations, levels of hierarchy, span of
control, and practice of reward systems. Well designed research containing the above
information may facilitate the discovery of such impacts.
Second, although this study compares the differences between public and private
employees using two data sets, the relationship between risk preference and innovativeness was
examined by the public employee sample in the NASP-DM only. Such a concern may pose
threats to the generalizability of the findings. However, no empirical evidence has been found
that the relationship between risk preference and innovativeness is essentially different for public
and private employees. It would be interesting if risk preference and innovativeness are
correlated with each other for public employees but not for employees in other sectors. Future
research can consider expanding the survey and recruit individuals from the private sectors to
prove the validity of such an argument.
Third, this study has conducted a preliminary investigation on people’s sector-switching
behaviors. And findings suggest that less risk-averse individuals are more likely to switch sectors
along their career paths. Nevertheless, this study cannot provide conclusive evidence for the
“switching patterns.” This study found that some people changed their sectors back and forth in a
given period of time, which poses a challenge on identifying their true sector preferences. Also,
since people have various reasons to change their jobs or sectors, the true impacts of risk
84
preference on sector switches require not only further investigation with a more systematical way
of coding the switches but also more complete information on reasons for sector changes. This
study anticipates that risk-averse individuals are more likely to switch from the “non-public”
sectors to the public sector, but more complete information is needed to conclude with this
argument.
Fourth, findings from this study help the public sector to understand the differences
between managers and subordinates in terms of risk preference and innovativeness. Being a
supervisor is associated with being less risk-averse and having more innovative attitudes. In
other words, supervisors are more willing to take risks for better payoffs and are more willing to
accept innovative initiatives than their subordinates. However, such a finding does not provide
sufficient evidence to identify the causal relationship because of data limits. Using crosssectional data, this study is not able to examine whether working in a managerial position makes
a person more risk-seeking, or being more risk-seeking and more innovative leads to a promotion
for that person. Future research in this direction might offer additional insights on the
relationship between risk preference, innovativeness, and managerial status.
Another possible topic of future research would be finding whether the civil service
reforms lead to any substantial changes in personnel recruitment. Although these reforms have
taken place in Texas, Georgia, and Florida, most of the other states still follow the tenure
practices and guarantee a more secure employment choice to the people. The perception that
public employment is more secure than other types of employment may still embedded in
people’s minds. Even though it is believed that abolishing the tenure protection may improve
efficiency and innovation in the government, the true impacts on recruiting public employees
with different characteristics, especially attitudes toward risks, are undetermined. In addition,
85
even after the reforms, if the other sectors cannot provide a “more secure” employment than the
public sector, little change would occur in terms of attracting “the best and the brightest.” In the
NLSY79 data set, the state identifier is unavailable, and therefore prevents this study from
capturing the impacts regarding this issue. To the interests of this study, future research should
consider empirically testing the impacts of civil service reforms on recruiting different public
employees in terms of risk preference. Since attitudes toward risk are important to decisionmaking, finding evidence regarding the civil reforms would be extremely beneficial to the
development of public administration and public management.
86
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APPENDICES
Appendix A – Department Distribution of the Total Collected Samples in Nevada
Department
N
%
Administration
166
1.60
Agriculture
54
0.52
Attorney General's Office
212
2.04
Business & Industry
426
4.11
Colorado River Commission
33
0.32
Conservation & Natural Resources
486
4.68
Controller's Office
38
0.37
Corrections
600
5.78
Cultural Affairs
107
1.03
Economic Development Commission
26
0.25
Education
113
1.09
Employment, Training & Rehabilitation
833
8.03
Gaming Control Board
224
2.16
Governor's Office
21
0.20
Health & Human Services
3856
37.16
Information Technology
112
1.08
Judicial Discipline Commission
3
0.03
Legislative Counsel Bureau
264
2.54
Lieutenant Governor's Office
1
0.01
Military
42
0.40
Mineral Resources Commission
9
0.09
Motor Vehicles
289
2.79
Peace Officers Standards & Training Commission
14
0.13
Personnel
58
0.56
Public Employees Benefits Program
26
0.25
Public Employees Retirement System
25
0.24
Public Safety
12
0.12
Public Utilities Commission
84
0.81
Secretary Of State
96
0.93
Supreme Court
83
0.80
Taxation
262
2.53
Transportation
1173
11.30
Treasurer's Office
31
0.30
Veteran's Services
51
0.49
Wildlife
45
0.43
Unknown
501
4.83
Total
10376
100.00
103
Appendix B – Department Distribution of the Total Collected Samples in Indiana
Department
N
%
Accounts, State Board of
34
0.69
Adjutant Generals Office
1
0.02
Administration, Indiana Department of
20
0.40
Agriculture, Indiana State Department of
8
0.16
Alcohol & Tobacco Commission
14
0.28
Animal Health, Board of
22
0.44
Arts Commission, Indiana
1
0.02
Attorney General's Office
58
1.17
Auditor of State
14
0.28
Budget Agency, State
5
0.10
Child Services, Department of
508
10.26
Civil Rights Commission
4
0.08
Community & Rural Affairs, Indiana Office of
9
0.18
Correction, Indiana Department of
996
20.12
Criminal Justice Institute
10
0.20
Economic Development Corporation, Indiana
12
0.24
Education Employment Relations Board, Indiana
2
0.04
Education, Department of
48
0.97
Energy & Defense Development, Office of
1
0.02
Environmental Adjudication, Office of
1
0.02
Environmental Management, Indiana Department of
140
2.83
Faith-Based Community Initiatives, Office of
2
0.04
Family & Social Services Administration
890
17.98
Finance Authority, Indiana
9
0.18
Financial Institutions, Department of
9
0.18
Gaming Commission, Indiana
36
0.73
General Assembly
59
1.19
Governor's Office
3
0.06
Health, Indiana State Department of
186
3.76
Historical Bureau, Indiana
1
0.02
Homeland Security, Department of
46
0.93
Hoosier Lottery
33
0.67
Horse Racing Commission
1
0.02
Housing & Community Development Authority, Indiana
19
0.38
Inspector General
3
0.06
Insurance, Indiana Department of
18
0.36
Intelligence Fusion Center, Indiana
16
0.32
Labor, Department of
12
0.24
Law Enforcement Academy, Indiana
8
0.16
Library, Indiana State
11
0.22
Lieutenant Governor
5
0.10
104
Lobby Registration Commission, Indiana
Local Government Finance, Department of
Motor Vehicles, Bureau of
Natural Resources Commission
Natural Resources, Department of
Personnel Department, State
Professional Licensing Agency
Prosecuting Attorneys Council
Protection & Advocacy Services, Indiana
Public Records, Commission on
Public Retirement System, Indiana
Regional Development Authority
Revenue, Department of
Secretary of State
State Board of Accounts
State Museum, Indiana
State Personnel Department
State Police, Indiana
State Student Assistance Commission of Indiana
Student Assistance Commission of Indiana, State
Technology, Indiana Office of
Transportation, Indiana Department of
Treasurer of State
Utility Consumer Counselor, Office of
Utility Regulatory Commission
Veteran's Affairs, Department of
Veterans Home, Indiana
War Memorial, Indiana
White River State Park
Workers Compensation Board
Workforce Development, Department of
Unknown
Total
105
2
13
100
1
193
20
7
2
4
2
52
2
109
10
42
26
18
197
4
2
46
526
4
11
16
1
27
2
3
2
230
2
4951
0.04
0.26
2.02
0.02
3.90
0.40
0.14
0.04
0.08
0.04
1.05
0.04
2.20
0.20
0.85
0.53
0.36
3.98
0.08
0.04
0.93
10.62
0.08
0.22
0.32
0.02
0.55
0.04
0.06
0.04
4.65
0.04
100.00
Appendix C – Revised Income Gamble Questions Used in the NASP-DM
Directions:
Image that you have to choose between two possible jobs: Job A would guarantee your
current income. Job B is possibly better paying, but the income is also less certain. For each row,
please circle which job you would prefer in the last two columns.
Job A:
guarantee your
Job B:
a 50-50 chance to DOUBLE your income, BUT
current income
with a 50-50 chance to cut your income by 10%
A
B
current income
with a 50-50 chance to cut your income by 20%
A
B
current income
with a 50-50 chance to cut your income by 33%
A
B
current income
with a 50-50 chance to cut your income by 50%
A
B
current income
with a 50-50 chance to cut your income by 75%
A
B
106
Circle the Job you
would prefer:
Appendix D – Summary Statistics of the NLSY79 (Characteristics in 1993)
Mean
Relative Risk Tolerance (θ)
0.52 (min 0.18 – max 1.12)
Age
31.9
Family Income ($)
38,192
Number of Kids
1.5
n
Female
6283
Hispanic
2002
Black
3174
Non-Black and Non-Hispanic
7510
Protestantism
6401
Catholicism
4273
Jewish
117
Other Religions
1307
No Religion
532
Single
2521
Married
4917
Separated
524
Divorced
1002
Widowed
47
Less than High School
1303
High School Diploma
3916
Some College
2087
College or More
1704
Has Health Limitation
726
*Protestantism include: Protestant, Baptist, Episcopalian, Lutheran,
Presbyterian
107
SD
0.34
2.31
31,227
1.34
%
49.53
15.78
25.02
59.20
50.68
33.83
0.93
10.35
4.21
27.98
54.57
5.82
11.12
0.52
14.46
43.46
23.16
18.91
8.06
Methodist, and
Appendix E – Summary Statistics of Sector Changes from 1994 to 2006
Times of Sector Changes
n
%
0
2,478
35.76
1
1,707
24.64
2
1,462
21.1
3
823
11.88
4
350
5.05
5
103
1.49
6
6
0.09
Total
6,929
100
*Sector choices were measured every two years in the NLSY79. Therefore, the maximum
number that a person can switch his sectors is six (6).
108
Appendix F – The Marginal Effects of Individual Risk Preference on Innovativeness
(from Ordered Logit Model)
(1)
(2)
(3)
All
Low (θ<1)
High (θ≥1)
Risk Tolerance
0.498
1.445***
678.335
(0.333)
(0.493)
(672.995)
Age
-0.002
-0.002
-0.018
(0.002)
(0.002)
(0.021)
Female
0.315**
0.306**
0.745*
(0.132)
(0.138)
(0.394)
Supervisor
0.343***
0.357***
-0.126
(0.130)
(0.132)
(0.390)
Hispanic
0.668*
0.686**
No obs
(0.347)
(0.349)
Black
0.788**
0.651
0.653
(0.397)
(0.423)
(0.899)
Asian
-0.235
-0.317
-0.948
(0.376)
(0.390)
(1.011)
Native American
-0.256
-0.133
-2.549**
(0.368)
(0.371)
(1.117)
Other Race
-0.211
-0.190
-0.537
(0.309)
(0.330)
(0.640)
Married
0.023
0.020
0.155
(0.149)
(0.153)
(0.445)
Have Kid(s)
-0.002
-0.059
0.182
(0.130)
(0.133)
(0.407)
Some College
0.320
0.331
0.062
(0.319)
(0.322)
(0.891)
College
0.501*
0.485
-0.393
(0.295)
(0.298)
(0.833)
Master Degree
0.588*
0.457
0.993
(0.313)
(0.317)
(0.878)
Professional Degree
0.401
0.213
-0.542
(0.447)
(0.464)
(1.038)
Ph. D.
0.186
0.096
No obs
(0.577)
(0.584)
Observations
782
745
37
Pseudo R-squared
0.007
0.008
0.340
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
109
Appendix G - Marginal Effects from the Negative Binomial Model on Sector
Switches (between 1994 and 2006)
dy/dx
s.e.
Risk Tolerance (in 1993)
0.187***
(0.046)
Female
0.279***
(0.032)
Hispanic
0.065***
(0.044)
Black
Family Income
0.073***
-0.011***
(0.040)
(0.006)
Married
-0.107***
(0.045)
Separated
0.104***
(0.076)
Divorced
0.107***
(0.059)
Widowed
-0.271***
(0.173)
Kid(s) or not
High school
0.012***
-0.124***
(0.039)
(0.047)
Some College
-0.074***
(0.052)
College or More
-0.117***
(0.057)
Health Limitation
0.100***
(0.061)
Observations
6,929***
*** p<0.01, ** p<0.05, * p<0.1
110
Appendix H – Summary Statistics of the NASP-DM
Estimated Relative Risk Tolerance (θ)
Innovativeness Index
Age
Number of Kids
Supervisor
Female
White
Hispanic
Black
Black (not African American)
Asian
Native American
Pacific Islander
Other Race
Race (Refused)
Married
High School
Some College
College
Master
Professional Degree
Ph. D.
Other Degree
Mean
0.31 (min 0.09 – max 1.003)
35.23 (min 19 – max 50)
51
0.44
n
443
434
722
27
24
17
26
23
5
19
27
635
46
143
412
201
31
13
14
111
SD
0.21
4.63
33.26
0.48
%
51.15
50.76
80.94
3.03
2.80
1.91
2.91
2.58
0.56
2.24
3.03
74.36
5.45
16.59
47.91
23.32
3.60
1.51
1.62