Financial Vulnerability of Small Business Owner-Manager Households
DISSERTATION
Presented in Partial Fulfillment of the
Requirements for the Degree of Doctor of
Philosophy in the Graduate School of The Ohio
State University
By
HoJun Ji, CPA, MBA, MS
*****
The Ohio State University
2012
Dissertation Committee:
Dr. Sherman D. Hanna, Advisor
Dr. Kathryn Stafford
Dr. Robert L. Scharff
Copyright by
HoJun Ji
2012
ABSTRACT
This dissertation provides insights into definitional issues of small business
owner-managers and which factors affect their financial vulnerability. In particular, the
primary purposes of this dissertation are to: (1) provide a thorough review of the
extensive literature on definitional issues of small businesses and their owners; (2)
suggest more complete classifications of small businesses and new measures of financial
vulnerability that are suitable for households who own or manage a small business; and
(3) examine factors affecting the level of financial vulnerability of small business ownermanager households, based on three major aspects of household financial status: assets,
income, and financial burden.
This dissertation analyzes the factors affecting financial vulnerability of small
business owner-managers using the 1992 to 2007 Surveys of Consumer Finances. The
financial vulnerability of the owner-managers is measured by three financial ratios: the
business asset ratio (BAR), the business income ratio (BIR), and the financial obligations
ratio (FOR). This dissertation defines the business asset ratio (BAR) as the ratio of
business assets to total household assets, the business income ratio (BIR) as the ratio of
business income to total household income, and the financial obligations ratio (FOR) as
the ratio of monthly household financial obligations to monthly household pre-tax
income. These three ratios provide a measure of the level of diversification in assets, a
measure of the level of diversification in household income, and a measure of the level of
financial burdens, respectively. The factors to be tested are divided into three subii
categories: social-demographic factors, economic status factors, and
attitudinal/expectation factors.
Using ordinary least square (OLS) regression models and a logistic regression
(Logit) model, this dissertation tests the effects of those factors on financial vulnerability
and finds that several common factors such as household type, education attainment, selfemployment status, homeownership, income and net worth, and health insurance
coverage are significantly related to financial vulnerability. In particular, household types,
self-employment status, and health insurance coverage have statistically significant and
have consistent effects on financial vulnerability in all three regression models.
Compared to married households, single headed small business owner-managers are
more likely to be financially vulnerable. Small business owner-managers with selfemployment status are also more likely to be financially vulnerable, compared to those
with other types of employment status such as salary earners, retirees, and unemployed.
On the other hand, uninsured small business owner-managers tend to be less financially
vulnerable.
iii
DEDICATION
Dedicated to my one and only love, Se-Eun
iv
ACKNOWLEDGMENTS
As a deeply flawed human being, I relied more on my Lord, Jesus Christ, my
family, and my friends in finishing my dissertation than most others. So, I would like to
acknowledge my gratitude to everyone who has supported me in this study and made my
years at The Ohio State University memorable and rewarding.
I would like to thank, first and foremost, Dr. Sherman Hanna, my advisor, for his
tireless guidance and patient demeanor. He deserves endless praises for his countless
hours spent working on this study and for the constant support he provided to me
throughout my graduate years. You’re the best, Dr. Hanna!
I also appreciate the members of my dissertation committee, Dr. Kathryn
Stafford, Dr. Robert Scharff, and Dr. Sharon Seiling who provided guidance and
recommendations in the completion of this dissertation. I would like to thank Dr.
Suzanne Lindamood for giving me many helpful comments and suggestions on my
dissertation.
Finally, my heartfelt appreciation goes to my parents and my brother (WooJun)
and great thankfulness to my wife Se-Eun and my kids Hannah (YeJin) and Daniel
(TaeJin) for their care, support, and love.
v
VITA
2003…………………………….…………..
B.B.A., Business Administration, Yonsei
University, The Republic of Korea
2006………………………….……………..
M.B.A., Finance and Accounting,
Cleveland State University, Cleveland,
Ohio, USA.
M.S., Family Resource Management,
Consumer Sciences, The Ohio State
University, Columbus, Ohio, USA.
2011………………………….……………..
2009 – 2011…….…………….………….…
Research and Teaching Assistant,
Department of Consumer Sciences, The
Ohio State University, Columbus, Ohio,
USA.
2011 – 2012…….………….…………….…
Instructor, Department of Consumer
Sciences, The Ohio State University,
Columbus, Ohio, USA.
2007-2009………………………………….. Senior Tax Accountant, JC TaxPros
Independence, Ohio, USA.
2009-Present………………………………..
Partner, JC TaxPros Group, Inc. Dublin,
Ohio, USA.
2012 – Present….….…………………….…
Lecturer, Department of Consumer
Sciences, The Ohio State University,
Columbus, Ohio, USA.
PUBLICATIONS & AWARDS
Ji, H. & Hanna, S. D. Factors Related to Financial Vulnerability of Small Business
Owner-Managers. Journal of Personal Finance. (In Press).
Hanna, S. D., Ji, H., Lee, J., Son, J., Letkiewicz, J., Lim, H., & Zhang, L. (2011).
Content analysis of Financial Services Review. Financial Services Review.
, 20 (3), 237-251.
vi
Ji, H., Hanna, S. D., Lawrence, F.C., & Miller, R. K. (2010). Two decades of the
Journal of Financial Counseling and Planning. Journal of Financial Counseling
and Planning, 21(1), 4-14.
Ji, H. & Hanna, S. D. (2011). Factors Related to Financial Vulnerability of Small
Business Owner-Manager Households. Proceedings of the Academy of Financial
Services.
Hanna, S. D., Ji, H., Lee, J., Son, J., Letkiewicz, J., Lim, H., & Zhang, L. (2010).
Content analysis of Financial Services Review. Proceedings of the Academy of
Financial Services.
Graduate Research Award, Department of Consumer Sciences, The Ohio State University
(May 2012)
University Fellowship, Graduate School, The Ohio State University (2009-2010)
FIELDS OF STUDY
Major Field:
Area of Emphasis:
Concentration Fields:
Consumer Sciences
Family Resource Management
Taxation, Finance, and Small Business
vii
TABLE OF CONTENTS
ABSTRACT ................................................................................................................... ii
DEDICATION ............................................................................................................... iv
ACKNOWLEDGMENTS ............................................................................................... v
VITA .............................................................................................................................. vi
LIST OF TABLES ........................................................................................................xii
LIST OF FIGURES ..................................................................................................... xiii
1 INTRODUCTION ........................................................................................................ 1
1.1 Background and Motivation of Research ................................................................1
1.1.1 Definitional Issues of Small Business Owner-Managers .................................. 1
1.1.2 Financial Vulnerability of Small Business Owner-Managers ............................ 3
1.2 Goals and Objectives of Research...........................................................................3
1.3 Contributions of Research ......................................................................................5
1.5 Organization Flows of Research .............................................................................6
2 LITERATURE REVIEW ............................................................................................. 8
2.1 Definitional Issues of Small Business Owner-Managers .........................................8
2.1.1 Operational Definitions of Small Business Owner-Managers ........................... 9
2.1.1.1 Business Motivation: Entrepreneurs vs. Small Business Owner-Managers
........................................................................................................................... 10
2.1.1.2 Business Formation: Different Types of Business Structure ..................... 13
2.1.1.3 Family Involvement: Non-family Business vs. Family Business .............. 15
2.1.1.4 Firm Size: Small Business vs. Large Business ......................................... 19
2.1.2 Occupational Definition of Small Business Owner-Managers ........................ 21
viii
2.1.2.1 Occupational Choices: Self-employment vs. Paid-employment ................ 23
2.2 Research on Financial Vulnerability of Small Business-Owner Managers ............ 26
2.2.1 Research on Household Portfolio of Small Business Owner-Managers .......... 26
2.2.1.1 Research on Household Portfolio of Business Owner-Managers .............. 26
2.2.1.2 Research on Household Portfolio of Small Business Owner-Manager ..... 30
2.3 Concluding Remarks ................................................................................................ 36
3 THEORETICAL BACKGROUND ............................................................................ 37
3.1 Financial Vulnerability of Small Business Owner-Managers ................................ 37
3.1.1 Modern Portfolio Theory & Diversification of Small Business OwnerManagers................................................................................................................ 37
3.1.2 Life Cycle Savings (LCS) Model ................................................................... 42
3.1.3 Ratio Analysis for Financial Vulnerability ..................................................... 45
3.2 Conceptual Models and Research Hypotheses ...................................................... 47
3.3.1 Conceptual Models ........................................................................................ 47
3.3.2 Research Hypotheses ..................................................................................... 51
4 METHOD................................................................................................................... 63
4.1 DATA .................................................................................................................. 63
4.1.1 Data Sets for Small Business Owner-Managers.............................................. 63
4.1.2 Survey of Consumer Finances ........................................................................ 66
4.1.2.1 Advantages of the SCF ............................................................................ 66
4.1.2.2 Limitations and Caveats of the SCF ......................................................... 67
4.1.2.3 Analytical Sample of Research ................................................................ 69
4.2 Variable Identification .......................................................................................... 71
4.2.1 Dependent Variables ...................................................................................... 71
4.2.1.1 Business Asset Ratio (BAR) .................................................................... 72
ix
4.2.1.2 Business Income Ratio (BIR) .................................................................. 72
4.2.1.3 Financial Obligations Ratio (FOR) .......................................................... 74
4.2.2 Independent Variables ................................................................................... 75
4.2.2.1 Socio-demographic Factors ..................................................................... 76
4.2.2.2 Economic Status Factors.......................................................................... 78
4.2.2.3 Attitudinal/Expectation Factors ............................................................... 78
4.2.2.4 Other Controlling Variables ..................................................................... 80
4.3 Statistical Method................................................................................................. 81
4.3.1 Ordinary Least Square (OLS) Regressions Analysis ...................................... 81
4.3.2 Logistic (Logit) Regression Analysis ............................................................. 83
4.3.3 Repeated Implication Inference (RII) Procedures ........................................... 85
5 RESULTS .................................................................................................................. 86
5.1 Descriptive Results of Research ........................................................................... 86
5.2 Multivariate Results of Research .......................................................................... 99
5.2.1 The Business Asset Ratio: OLS Regression Model ........................................ 99
5.2.2 The Business Income Ratio: OLS Regression Model ................................... 106
5.2.3 The Financial Obligations Ratio: Logistic Regression Model ....................... 112
5.3 Summary............................................................................................................ 118
6 CONCLUSIONS ...................................................................................................... 122
6.1 Conclusions ........................................................................................................ 122
6.2 Implications ....................................................................................................... 129
6.2.1 Implication for Financial Professionals/Institutions ...................................... 129
6.2.2 Implication for Policy Makers ...................................................................... 132
6.3 Suggestions for Future Research......................................................................... 134
REFERENCES ............................................................................................................ 136
x
APPENDIX ................................................................................................................. 152
xi
LIST OF TABLES
Table
Page
3.1
Hypotheses and/or Theoretical Justifications
59
4.1
Selected Data Sets of Small Businesses in U.S.
66
4.2
Descriptive Analyses: Composition of Analytical Sample
71
5.1
Descriptive Analyses: Selected Characteristics of Small Business Ownermanager Households by Survey Year
91
5.2
Distributions of Three Financial Vulnerability Ratios
92
5.3(a)
RII Means Test by Number of Employee
93
5.3(b)
SCF Sample Distributions: Self-employment vs. Business Owner-managers
94
5.4
RII Means Tests: Three Ratios by Independent Variables
95
5.5
Multivariate Analyses: OLS Regression Models for the Business Asset
Ratio (BAR)
104
5.6
Multivariate Analyses: OLS Regression Models for the Business Income
Ratio (BIR)
110
5.7
Multivariate Analyses: Logistic Regression Models for the Financial
Obligations Ratio (FOR)
116
5.8
Hypothesized Effects of Variables on the Financial Vulnerability and
Empirical Results
119
xii
LIST OF FIGURES
Figure
Page
2.1
Taxonomies of Small Business Owner
9
2.2
Family Business vs. Small Business
16
3.1
Conceptual Model of the Research
49
3.2
Selected Variables Setting of the Research
50
xiii
CHAPTER 1
1 INTRODUCTION
1.1 Background and Motivation of Research
1.1.1 Definitional Issues of Small Business Owner-Managers
Small businesses have been playing a critical role in our economy, and previous
literature shows an almost unanimous consensus with respect to the importance of small
business owner-managers and the self-employed. The 2011 Small Business Economy
Report by the U.S. Small Business Administration (SBA) reports that almost all of
employer firms (99.7%), referring to the firms having any payrolls during their fiscal or
calendar year, were small businesses. Such small businesses created more than 65% of
net new jobs over the past two decades and their payrolls amounted to about 40% of the
total U.S. private sector payroll (U.S. Small Business Administration, 2012). One of the
many indicators of the importance of small businesses is the Small Business Jobs Act of
2010, which established a $30 billion government fund for small businesses to create or
retain more than 650,000 new jobs (U.S. Senate Committee on Finance, 2010). Of the
aggregate employment figures in the U.S. economy, the self-employed are not a trivial
proportion. According to the estimates of the U.S. Bureau of Labor Statistics (2010),
15.3 million individuals were self-employed in 2009, including both those who had
1
incorporated their businesses and those who had not, accounting for 10.9% of total
employment (Hipple, 2010). Using the Survey of Consumer Finances, Cagetti and De
Nardi (2006) estimated that business owners having self-employment status hold about
33% of the U.S. economy's total net worth. On the other hand, their important role may
also negatively impact the economy. For instance, during the recent economic downturn,
the 2011 Small Business Economy Report shows that small businesses accounted for
almost 60% of net job losses. Due to their crucial role in contributing to economic
growth, small businesses in the U.S. have been actively studied by many interested
parties such as economists, social scientists, financial professionals, and policy makers.
“There is no single, uniformly acceptable definition of a small firm. This is
because a small firm in, say, the petrochemical industry is likely to have much higher
levels of capitalization, sales, possibly employment, than a small firm in the car repair
trades.” (Storey, 1994)
Small businesses are important but also very diverse. As noted by Storey (1994),
there are no universally accepted definitions of a small business and its owner-managers
mainly due to the wide diversity of business characteristics and those of their owners or
managers. However, most previous studies and government reports on this topic have
failed to thoroughly review definitional issues of a small business and used terms such as
small business owner-managers, business owners, and entrepreneurs interchangeably,
which resulted in more confusion and disagreement among researchers and practitioners.
Therefore, it is important to more precisely define a small business and its ownermanager.
2
1.1.2 Financial Vulnerability of Small Business Owner-Managers
Again, small businesses are important for economic growth. As much attention
has been given to various factors affecting small business operations such as management
and financing, academic interest in characteristics of small businesses owner-managers in
the U.S. has also grown rapidly in recent decades. However, relatively little empirical
research on the financial conditions of households who own or manage a small business
has been conducted. In particular, only a few studies such as Gutter and Saleem (2005)
have provided further empirical evidence on factors that contribute to the financial
vulnerability of small business owner-managers. In order to examine the financial status
of small businesses, it is necessary to analyze their owners or managers, as they are
generally privately owned and operated and the financial health of a small business is
closely related to the financial, physical, and even mental status of their owners or
managers. Therefore, this dissertation assumes that investigating the factors that affect
the financial vulnerability of households who own or manage small businesses can
provide insights into a better understanding of appropriate financial advice, financial
education, and government policies for small businesses as well as for their owners or
managers.
1.2 Goals and Objectives of Research
This dissertation first reviews the literature on definitional issues of small
business owner-managers by proposing five classifications of small businesses and their
owners or managers, based on their primary characteristics such as business motivation,
types of business formation, involvement of family members, business size, and
3
occupational choices. The previous literature and discussions for each category are
presented in Chapter 2.
This dissertation also aims to analyze factors affecting the financial vulnerability
of small business owner-managers using the 1992 to 2007 Surveys of Consumer Finances.
Their financial vulnerability is measured by three financial ratios: the business asset ratio
(BAR), the business income ratio (BIR), and the financial obligations ratio (FOR). In this
study, the business asset ratio (BAR) is defined as the ratio of business assets to total
household assets, the business income ratio (BIR) is defined as the ratio of proportion of
business income to total household income, and the financial obligations ratio (FOR) is
defined as the ratio of monthly household financial obligations to monthly household pretax income. These three ratios provide a measure of the level of diversification in assets,
a measure of the level of diversification in household income, and a measure for the level
of financial burdens, respectively. The factors to be tested are divided into three subcategories: social-demographic factors, economic status factors, and
attitudinal/expectation factors. Social-demographic factors include age, race/ethnicity,
marital status, presence of a child under 19, educational attainment, employment status,
legal business type, and the number of years in business. Economic status factors include
homeownership, household income (log), household net worth (log), and the availability
of emergency fund from friends or relatives. Attitudinal and expectation factors include
subjective risk tolerance, expectation of future income, expectation of inheritance, and
current income level relative to a normal year.
Therefore, the primary purposes of this dissertation are to: (1) provide a thorough
review of the extensive literature on definitional issues of small businesses and their
4
owners; (2) suggest more complete classifications of small businesses and new
measures of financial vulnerability that are suitable for households who own or manage a
small business; and (3) examine factors affecting the level of financial vulnerability of
small business owner-manager households, based on three major aspects of household
financial status: assets, income, and financial burden.
1.3 Contributions of Research
Having a more precise definition of a small business owner-manager is crucial for
many reasons. Firstly, establishing a clearer definition of a small business ownermanager could improve empirical sampling procedures so that in-depth research on
households owning or managing a small business will be possible for future researchers.
Secondly, it would also help financial institutions and professionals, policy makers,
future researchers, and educators develop better financial services, more effective policies,
more precise empirical analyses, and a better financial education. For example, banks or
lenders will benefit from the use of properly defined classifications as reference tools
when determining whether the applicants satisfy their loan requirements or when
launching new financial products or services. One reason is that characteristics of small
business owner-managers are typically different from those of other populations that
require different tools, different standards, and different references. In assuming the
importance of defining a small business owner-manager, this dissertation therefore
departs from discussions of definitional issues that previous studies failed to point out.
5
Lastly, determining the underlying factors in financial vulnerability of small
business owner-managers can also help policy makers and regulators when assessing the
potential impact of their new policies or proposals. For instance, the Small Business Jobs
Act of 2010 created several programs to help small businesses during the recent recession
by establishing a $30 billion government grant. This bill was intended to boost business
lending to small businesses through commercial banks. Although each individual lending
institution has its own underwriting procedures, in order to implement the new bill more
effectively, underwriting guidelines that are suitably designed for small businesses and
their owners or managers should be applied. This study proposes comprehensive
measures that can evaluate both business aspects and household aspects. The findings and
methodologies presented in this dissertation can be used as reference tools for
underwriting purposes as well as for evaluating the potential policy ramifications of the
new bill and the grant program.
1.5 Organization Flows of Research
This dissertation is organized into six chapters. In Chapter 2, a comprehensive
review of the previous literature related to definitional issues of small business ownermangers and research on their household finances is undertaken. Chapter 3 summarizes
and discusses the existing theoretical models dealing with household portfolio and the
financial vulnerability of small business owner-managers and formulates the research
hypotheses of this dissertation to be tested. In Chapter 4, the data sets and the statistical
methodologies employed in this dissertation are then presented. The definitions and
measurements of variables are also introduced in this chapter. Next, Chapter 5 provides
6
the statistical results of this study from both descriptive analyses and multivariate
analyses. Based on the statistical findings from Chapter 5, Chapter 6 further discusses the
importance of those findings and the implications for small business owner-managers,
financial institutions/professionals, and policy makers, and suggestions for future
research.
7
CHAPTER 2
2 LITERATURE REVIEW
2.1 Definitional Issues of Small Business Owner-Managers
Although the previous literature based on various theories and taxonomies
attempted to define the term “small business”, there have been many conflicting and
overlapping definitions such as small business owner-managers, entrepreneurs, the selfemployed, family business owners, etc. These terms have been used interchangeably,
resulting in unproductive debates and inconsistency issues in the field. Therefore, in order
to solve the definitional problems, the following classifications are proposed, based on
five primary characteristics of a small business and its owners or managers: (1) business
motivation, (2) types of business formation, (3) involvement of family members, (4)
business size, and (5) occupational choices. It is also expected that the following
discussions for each classification can provide an overview of the small business ownermanager definitions that have been used in the previous studies. As shown in Figure 2.1,
the previous literature relevant to the definitions of a small business owner-manager falls
into five major categories. Small business owner-managers on the upper level of
classification are first divided into operational and occupational definitions. Then, under
the operational definitions, four sub-criteria including motivation, formation,
8
involvement and firm size are applied to compare and contrast the existing definitions of
small business owner-managers.
Small Business Owner-Managers
Operational Definition
Motivation
Formation
Involvement
Firm Size
Occupational Definition
Small Business Orientation
vs.
Entrepreneurial Orientation
Incorporated
vs.
Non-incorporated
Small Business
vs.
Family Business
Small Business
vs.
Large Business
Employment
Paid-employment
vs.
Self-employment
Figure 2.1 Taxonomies of Small Business Owner-Managers
The first section is the literature review on the difference between small business
owner-managers and entrepreneurs. The second section is a literature review on business
owners based on different types of business entities defined in the legal and tax system.
The third section is a discussion of the previous literature examining the relationship
between small business owner-managers and family business owner-managers. The forth
section of the relevant literature focuses on comparing the owner-managers of a small
business to those of a large business, based on the most widely-used firm size standards,
as defined by the Small Business Administration (SBA). Finally, the fifth section deals
with the literature on the occupational choice of small business owner-managers.
2.1.1 Operational Definitions of Small Business Owner-Managers
9
2.1.1.1 Business Motivation: Entrepreneurs vs. Small Business Owner-Managers
Differentiating small business owner-managers from entrepreneurs is critical
since erroneous and undefined descriptions of small business owner-managers and
entrepreneurs might cause confusion among researchers and practitioners and inhibit their
investigations (Carland, Hoy, Boulton, & Carland, 1984; Shane & Venkataraman, 2000;
Parker, 2004). Unfortunately, previous studies tended to neglect the inconsistency issue
of how to define the two terms and failed to distinguish adequately between them.
Defining ‘entrepreneurs’ is not a trivial issue and has been one of the most difficult tasks
faced by researchers in the field (Parker, 2002; Parker, 2004; Kim, 2008). This
dissertation assumes that reviewing the entire history of entrepreneur definitions would
not be particularly insightful nor necessarily reflect the primary focus of this study.
Considering the business motivation as a differentiator, Carland et al. (1984) first
proposed typologies of business owner-managers. Based on the well-known Schumpeter
(1934) definition of entrepreneurs, they suggested that business owners can be divided
into two broad types: those having small business orientation (SBO) and those having
entrepreneurial orientation (EO). According to the authors, small business ownermanagers are “those who operate a business producing family income as an extension of
his or her personality to further personal goals” whereas entrepreneurs are “those who
capitalize on innovative combinations of resources for the principal purposes of profit
and growth, using strategic management practices”. This distinction between SBO and
EO has been the subject of many previous studies, and many scholars have provided
empirical evidence that EO and SBO are distinct orientations (Stewart, Watson, Carland,
& Carland, 1998; Runyan, Droge, & Swinney, 2008). Using a survey of 767 small
10
business owner-managers and corporate managers from 20 states Stewart et al. (1998),
for example, confirmed that small business owners are more likely to have more of a
small business orientation (SBO) than an entrepreneurial orientation (EO).
Based on the concepts of SBO and EO, subsequent research has also attempted to
identify the relevant characteristics of small business owner-managers that are different
from those of entrepreneurs (Stewart et al, 1998; Miles, Covin, & Heeley, 2000; Runyan
et al., 2008). Stewart and Roth (2001), who referred to entrepreneurs as “growth oriented
entrepreneurs” and small business owner-managers as “income-oriented entrepreneurs”,
examined the relative risk-taking propensities of entrepreneurs and small business ownermanagers. Their findings reported that entrepreneurs have a higher level of risk
propensity than small business owner-managers. Using structural equation modeling
(SEM), Runyan et al. (2008) more carefully examined the impact of entrepreneurial
orientation (EO) and small business orientation (SBO) on business performance. They
proposed and tested measurement models to determine the extent of EO and SBO among
small business owner-managers. They found that beyond the differences in each goal
orientation, the preference for innovation is a determinant of being small business ownermanagers or being entrepreneurs, reflecting that small business owner-managers tend to
have less of a preference for innovation than entrepreneurs. Another finding of the
authors was that for the younger groups, both entrepreneurial orientation (EO) and small
business orientation (SBO) can predict their business performance while for the older
group, only small business orientation (SBO) can predict their business performance.
Another stream of research adopting the classification of Carland et al. (1984)
focused more on entrepreneurial orientation rather than both. In particular, the effects of
11
the three dimensions of entrepreneurial orientation (EO) including innovativeness,
proactiveness, and risk taking on small business performance were empirically tested
(Stewart, Carland, Carland, Watson, & Sweo, 2003; Wiklund & Shepherd, 2005). Using
a sample of 413 Swedish firms, Wiklund and Shepherd (2005) found that entrepreneurial
orientation positively influences small business performance. They also reported that as
entrepreneurial orientation increases, business owner-managers would have the ability to
find new opportunities that can differentiate their business from others, thus potentially
creating a competitive advantage. Stewart et al., (2003) also discussed different
entrepreneurial tendencies toward the three dimensions, based on the business ownermanager samples from the U.S. and Russia. In addition to the discussions of the three
aspects of entrepreneurial orientation, Kreiser, Marino, and Weaver (2002) proposed that
entrepreneurial orientation should be treated as a multi-dimensional construct, not a unidimensional one. While a uni-dimensional construct approach considers the three
components of EO not to be related, a multi- dimensional construct approach assumes
that each component of EO can be fully explained in accordance with other components.
However, using these motivation-based classifications might have some issues
since the psychological traits of business owner-managers such as their personality, goals,
and entrepreneurial psychology are typically not easy to measure and it is often difficult
even to find relevant variables from most national data sets on small businesses.
Although past research on small business owner-managers and entrepreneurs focused
mainly on their psychological attributes, these two definitions are still useful and
effective when explaining the differences between small business owners and
entrepreneurs. As encouraged by previous researchers such as Carland et al. (1988) and
12
Stewart and Roth (2001), understanding the differences between EO and SBO and using
these two approaches would help to avoid further confusion on the definition of a small
business owner-manager and an entrepreneur.
2.1.1.2 Business Formation: Different Types of Business Structure
The second operational definition is based on types of business formations,
especially different types of legal structures. Previous studies often assumed that small
business owner-managers are individuals who do not earn a wage or salary but who
derive their income by exercising their profession or business (DeNardi, Doctor, & Krane,
2007; Herranz, Krasa, & Villamil, 2009). However, there are also small business ownermangers who are employed by their own incorporated firm. It might be another big
challenge to classify small business owner-managers in terms of business formation since
two different and complex levels of classifications should also be considered: a
state/local-level classification based on business laws governed and enforced by each
state and a federal-level classification based on federal income tax filing purposes.
Many state or local governments require a business to have a business license, e.g.
an Ohio Vendor’s License, and/or a business permit, but this does not always mean that a
business must be incorporated in its state. In general, incorporated businesses providing
legal and tax benefits for business owners include corporations, limited liability
companies, and nonprofit organizations. On the other hand, non-incorporated businesses
include sole proprietorships and partnerships. Sole proprietorships are the most common
and simplest form of business organization, and the owner of the business (or sole
proprietor) is personally liable for all debts and obligations relating to the business.
Partnerships are different from sole proprietorships in terms of profit/loss/interest sharing.
13
They are business entities with shared ownership and usually involve legal agreements
that document how decisions such as dividing profits and resolving disputes will be made.
Similar to sole proprietorships, however, the personal property of general partners is not
generally protected, and they are personally liable for all obligations from their business.
There are three primary types of business entities for federal income tax purposes:
sole proprietorships, pass-through entities such as partnerships and S-corporations, and
C-corporations. Pass-through entities are not subject to federal corporate income tax, but
the shareholders or members of the business should be responsible for federal income tax
related to the profit transferred from their business. For C-corporations, however, both
businesses and their shareholders are subject to federal corporate tax and federal income
tax respectively, which is referred to as the “double taxation”. Many previous studies
tended to simply consider those who filed tax returns with a sole proprietorship or an Scorporation as a small business.
These different types of legal forms were included in a number of previous
studies that employed one or multiple government surveys, such as the Current
Population Survey (CPS), the Survey of Consumer Finances (SCF), the Survey of Income
and Program Participation (SIPP), the Survey of Small Business Finance (SSBF), and the
Panel Study of Entrepreneurial Dynamics (PSED). Many empirical studies on small
businesses included both state-level and federal-level business forms in their descriptive
analyses or often used them as explanatory variables. Using the SSBF, for example,
Glover and Short (2010) examined how the incorporation of business owners and their
bankruptcy affect their business performance. They found that incorporation can provide
a valuable insurance feature against adverse shocks, reporting that small business owners
14
who incorporated their firms are more likely to have better financial positions, such as
higher incomes, higher wealth, and pay lower interest rates on debt obligations than those
with unincorporated firms. Herranz et al. (2009) also used data from the Survey of Small
Business Finances (SSBF), but focused on the federal-level entities such as Scorporations, partnerships, and C-corporations. They found that a substantial number of
business owners chose to run small firms without any liability protection.
However, tax and legal-based classifications alone might not be the best way to
define a small business and its owner-manager since the business formation approach
itself cannot provide sufficient information such as the size of the business, the financial
status of the business, etc (Kirchhoff, 1996; Parker, 2004). Some problems might occur
if one employs tax and legal definitions without carefully considering other criteria. For
example, according to the current immigration laws (U.S. Department of State, 2011) and
U.S. tax law (U.S. Treasury, 2011), small foreign firms holding the Treaty Trade visa (E2) should file their tax returns either with a C-corporation using Form1120 or with a
partnership using Form 1065 regardless of the size of their firm. So, those small foreign
firms may be incorrectly classified as a large business. Therefore, it is necessary for
researchers or practitioner to understand the advantages and disadvantages of each legal
entity and how it affects other household characteristics since each type of legal form has
unique characteristics that might affect other important household factors such as
earnings, tax liability, and financial and business risks.
2.1.1.3 Family Involvement: Non-family Business vs. Family Business
One of the most challenging ‘grey areas’ in classifying a small business ownermanager has been between a small business and a family business since small businesses
15
cannot be viewed as entirely separate from their owner-managers (Olson, Zuiker, Danes,
Stafford, Heck, & Duncan, 2003; Yilmazer & Schrank, 2010). Two possible reasons are
that the financial resources of small businesses are often intertwined with those of the
household, and that business resources can hardly be distinguished from household
resources (Yilmazer & Schrank, 2010). Many attempts have made by researchers and
professionals, to distinguish between a family business and a non-family business
(Chrisman, Kellermanns, Chan, & Liano, 2010). They often assumed that a family
business creates reciprocal economic and non-economic values by combining the family
and business systems. However, no satisfactory distinction has been proposed yet.
Habbershon, Williams, and MacMillan (2006) explained this ‘grey area’ by using two or
three overlapping circles to form a model that is also useful in describing the complex
relationship between family members and owner-managers. Borrowing from this circles
model, this study presents a diagram that can address better definitional relationships
among a family business, a small business, and a non-family business (Figure 2.2).
16
Small Business
Non-family
Business
Family Business
Figure 2.2 Family Business vs. Small Business
Again, the terms small business and family business often appear to be used
interchangeably, but it is true that there are many small businesses that do not
demonstrate much family business, such as partnerships operated by non-family member
partners. On the other hand, not all family businesses are also small businesses. For
example, there are some closely held firms in which a few family members control the
operating and managerial policies of the firm as shareholders or partners, but the firms
fall into a large business category under certain authoritative standards such as the SBA
standard.
There have been two main conceptual streams in previous literature related to the
definition of a family business. Assuming that the outcomes of a family business might
substantially differ from those of a non-family business, one stream aimed at conducting
comparative studies of a family business and a non-family business, based on the degree
17
of family involvement (Chua, Chrisman, & Sharma, 1999; Astrachan & Shanker, 2003;
Sharma, 2004). Despite the difficulties in determining whether family members are
involved and how they contribute to their business, the level of family involvement in
identifying a family business or a non-family business has been actively examined by
previous scholars. For example, one of the most influential studies in the field, Chua et al.
(1999) reviewed over 250 papers in family business literature and the authors compiled
the various existing definitions of a family business. They also contributed to the
literature by providing an alternative approach to differentiating between a family
business from a non-family business. In addition to traditional approaches emphasizing
only family’s involvement, they suggested that a theoretically based definition must deal
with the essence of family influence such as the visions held by a family. Based on the
family involvement approach, Astrachan et al. (2003) suggested three definitions of a
family business from broad and inclusive definitions to a narrow and more exclusive one.
They essentially considered the degree of family voting control over the strategic
direction of a firm as the most important criterion. First, their broad definition is the most
inclusive and requires only that there be some family participation in the business, and
some control over the strategic direction of a business. Second, the mid-range definition
includes direct family involvement in the day-to-day operations. Lastly, their narrow
definition classifies a family business as a business in which the family members “retain
voting control” of the business and “multiple generations” of the family are involved in
the day-to-day operations of the business.
The other stream of research argued that these definitions related to family
involvement lack a theoretical basis for explaining how family involvement affects
18
business performance. Scholars such as Chrisman, Chua, and Litz (2003), Chrisman,
Chua, and Sharma (2005), and Habbershon et al. (2006) contributed to the literature by
further documenting the different components of family involvement and by extending
the essence of family influence with more aspects of family essence. For example, the
extended family essence of Habbershon et al. (2006) included his or her intentions to
maintain family control; family resources and capabilities; a family vision; and the
pursuance of a family vision. The authors asserted that family involvement is a necessary
but not sufficient condition to define a family business. Thus, even though two businesses
may have the same level of family involvement, they may not be family businesses due to
a lack of vision, resource problems, or behavioral issues.
Although there is no single agreed-upon definition of a family business, two
broad agreements concerning its definition have been adopted: (1) defining a family
business as a business owned and managed by ‘a nuclear family’ and (2) the role of
‘family involvement’ (Chua et al., 1999; Graves & Thomas, 2008; Abdellatif, Amann &
Jaussaud, 2010). This dissertation also assumes that the definition of a small business is
not synonymous with that of a family business and the key determinant is family
involvement.
2.1.1.4 Firm Size: Small Business vs. Large Business
The last operational definition is based on the firm-size standards. Due to the
ambiguity of a small business, many previous studies employing the conventional method
of using the terms “entrepreneur”, “small business”, and “the self-employed”
interchangeably followed legislative definitions developed by government authorities
such as the U.S. Small Business Administration (SBA). Most legislative definitions adopt
19
firm-size standards (Bridge, O’Neill, & Cromie, 2003). For instance, the European Union
(EU) divides small businesses into four sub-categories, based on the number of
employees: self-employed (one person in the business), micro-business (up to 19
employees), small business (20 to 99 employees), and medium-sized business (100 to 499
employees). On the other hand, in the U.S., one of the most commonly used definitions in
empirical studies for small businesses is drawn from the SBA firm-size standards (Berger
& Udel, 1998; Haynes, 2007; Gutter & Saleem, 2005; Ortiz-Molina & Penas, 2008;
Haynes & Brown, 2009; Ou & Williams, 2009; Haynes, 2010).
According to the statistical methodologies of the SBA firm-size standards, in
order to establish the size standards, the U.S. Small Business Administration first
assesses industry differences and the overall degree of competitiveness of each industry
(U.S. Small Business Administration, 2011a). They then examine the structural
characteristics of an industry by analyzing five primary factors such as average firm size,
the degree of competition within an industry, start up costs and entry barriers, distribution
of firms by size, and the small business share in Federal contracts. Other secondary
factors including legislative background, technological change, industry growth trends,
and the impact on SBA programs are also taken into account. Using advanced statistical
procedures that combine all of these factors, the SBA establishes the following standards
to define a small business, with some exceptions for certain industries such as wholesale
trade, manufacturing, and construction (U.S. Small Business Administration, 2011b).

Having fewer than 500 employees for most industries (100 employees for
wholesale trade industries), and
20

$7 million in average annual receipts or less for most non-manufacturing
industries.
This dissertation adopts the SBA firm-size standards and defines a small business
owner-manager household as a household who own or manage a business having fewer
than 500 employees and average annual gross receipts of $7 million or less. More
detailed discussions regarding how these firm-size definitions are used in the SCF data
sets are presented in Chapter 4.
The major advantage of the SBA definition is that using the number of employee
and annual gross sales receipts does not vary noticeably from one business to another.
However, there are also some caveats to the use when employing the SBA firm-size
definition. Firstly, the SBA definition is somewhat broad as it embraces all businesses by
only using employment and revenue standards. Thus, researchers borrowing the SBA
firm-size definition might need to tailor or adjust their definition of a small business,
depending on their particular target and the purpose of their research (Storey, 1994). For
example, even though Acs and Müller (2008) followed the SBA firm-size definition of a
small business, they divided their sample, the Metropolitan Statistical Areas (MSAs), into
two groups: small businesses with less than 20 employees and small businesses with 20
to 499 employees. The authors assumed that the adjustment might help them to better
understand the relationship between business dynamics and employment effects
2.1.2 Occupational Definition of Small Business Owner-Managers
21
While there is extensive literature on the occupational choices of U.S. households,
interestingly much less research has empirically analyzed the effect of self-employment
on the characteristics for the subset of households who own and manage a small business.
Again, one of main reasons is that the term self-employment has been used
interchangeably with other terms such as small business, entrepreneur, and family
business. However, are all small business owner-managers self-employed? Small
business owner-managers are not necessarily self-employed. And, self-employed
individuals are not necessarily small business owner-managers either. The empirical
finding of this study confirms that business ownership and self-employment are not
synonymous. This is expanded upon in Chapter 5.
The final definitional issue of a small business owner, the self-employed and their
traits are discussed in this section. Although numerous attempts have been made by
previous researchers who reviewed its existing definitions, defining “the self-employed”
has been one of the most difficult and intractable tasks in the field, partly due to different
definitions and sampling frames used in different data sets (Parker, 2004; Kirchhoff, 1996)
For this reason, previous empirical studies often employed a conventional method that
uses “small business owners” and “the self-employed” interchangeably (Carree & Thurik,
2003; Parker, 2004; Kim, Aldrich, & Keister, 2006; Praag & Versloot, 2007). Apparently,
no uniform framework and definition have been accepted yet, in terms of this operational
definition approach. However, most previous studies seem to revolve around the
important role of the self-employed and their businesses in the economy. And, they also
had a tentative consensus on considering self-employment as one’s occupational choice
22
and used different types of econometric models to analyze the differences between selfemployment and paid-employment (Parker, 2004).
Building on this consensus, this dissertation suggests that the operational
definitions and occupational definitions should be separately considered, expecting that
self-employment status might be an important determinant of financial vulnerability of
small business owner-managers. So far, four different operational definitions of a small
business owner have been discussed. Now, occupational definition, the other upper level
definition of a small business owner-manager, is discussed. The main focus of this
section is to differentiate the self-employed from other operational definitions discussed
above and to discuss the self-employment definition that can be distinguished from paidemployment in terms of occupational choices.
2.1.2.1 Occupational Choices: Self-employment vs. Paid-employment
Among the previous theories on self-employment decision, one of the most
frequently used approaches was the “Push-Pull” theory (Brooksbank, 2000). With this
theory, some negative external forces such as unemployment and lack of full-time jobs in
the market can be considered as ‘push’ factors that can actually ‘push’ people into selfemployment. On the other hand, pull factors may include flexibility of working hours,
independence, expectations of higher income, and government support programs to
attract people to self-employment. Based on the “push-pull” theory, one major line of
research attempted to examine an individual’s decision to enter into self-employment and
the influence of household background on the likelihood of becoming self-employed.
Using individual-level longitudinal data, for example, Henley (2004) found a positive
relationship between initial household conditions and the likelihood of being self23
employed. The author asserted that once an individual has been previously pulled or
pushed in to self-employment, he or she is more likely to continue to choose a selfemployment job. Dawson, Henley, and Latreille (2009) who also borrowed the push-pull
theory sought to gain an understanding of the reasons as to why individuals might choose
to become self-employed. Although no significant evidence that an individual is ‘pushed’
into self-employment was found, their results reported that multiple motivations towards
the choice of self-employment play an important role.
The expected utility theory is one of the most widely-used theories to support
individuals’ occupational choices. Based on the expected utility theory, the decision
maker chooses between risky or uncertain prospects by comparing his or her expected
utility values by adding the weighted utility values of outcomes. This theory has also
proved useful in explaining the occupational choices between self-employment and paidemployment as people make an employment choice by comparing the expected utility of
each case. In other words, individuals or households choose to be self-employed if the
total expected utility derived from being self-employed is greater than the expected utility
from their other employment options (Douglas & Shepherd, 2002).
The self-employed can also be distinguished from paid-employees by their mode
of income (Park, 2004). Rather than receiving a fixed wage or salary, the self-employed
earn returns on their practice, labor, or capital. Considering self-employment as a
working status of a business owner-manager, many empirical studies examined the
earnings differentials between self-employment and paid employment (Parker, 1999;
Hamilton, 2000; Papantheodorou, 2000; Falter, 2006; Torrini, 2006; Albarran, Carrasco,
and Martinez-Granado, 2007; Praag and Versloot, 2007; Benz and Frey, 2008). They
24
found that the self-employed and paid-employees should be studied separately due to
different types of income generation. Using the 1984 Survey of Income Program and
Participation (SIPP), for example, Hamilton (2000) conducted a thorough study of selfemployment income and the income equality between the self-employed and paidemployees. He compared the earnings distributions of the self-employed with those of
paid employees and tested different theories that can explain the earnings differential
between the two groups. Interestingly, his findings reported that the earnings of the selfemployed are lower than those of paid-employees at the 25th and 50th percentiles of
income distributions and that despite substantially lower returns than those with paidemployment, many people are willing to enter and remain in self-employment. Hamilton
also explained why people make this choice for employment, claiming that people enter
into self-employment, not because of the pecuniary rewards which are primarily driven
by income maximization, but rather other lifestyle considerations. In other words, certain
non-economic benefits of self-employment might explain the earnings differential,
including utility from being one’s own boss, or over-optimism.
When conducting empirical research using this employment status classification,
one should consider and discuss at least three important points including (1) ways of
treating workers (or independent subcontractors) who have multiple jobs with both selfemployment and paid-employment, (2) the control for those who receive a salary from
their own business, and (3) the employment status that is self-assessed by the respondents
in several government and empirical surveys. In particular, the share of small business
owner-managers who receive a salary from their own business in the U.S economy has
increased since 1989 (Hipple, 2010). In 2009, the incorporated self-employed who
25
receive a salary from their own business was about 3.9% of total U.S. employment. This
is mainly because of traditional benefits of the corporate structure such as limited liability
and tax considerations. The definition of self-employment used in this dissertation
attempts to incorporate these three points. Assuming that the self-employed are those
who hold a job where they are self-employed, individuals or households are considered to
be self-employed if they report self-employment as their employment status. Since the
effect on financial vulnerability of having someone who is self-employed in a household
is of primary interest for this dissertation, the following conventions for couple
households and those who have both self-employment and paid-employment are used.
For married households, the households are categorized as self-employed if either one of
the spouses report being self-employed, regardless of the status of the other spouse. For
the same reason, other households including singles who have a second self-employment
job are also considered to be self-employed even though they reported their paidemployment as their primary job. More detailed discussions of this are presented in
Chapter 4.
2.2 Research on Financial Vulnerability of Small Business-Owner Managers
2.2.1 Research on Household Portfolio of Small Business Owner-Managers
2.2.1.1 Research on Household Portfolio of Business Owner-Managers
In terms of studying the household portfolios of business owner-mangers, it is
critical to develop appropriate models to measure the risks of their business, despite it
being very challenging, particularly for those businesses that have not listed their
26
securities on a public market. This problem arises mainly from a lack of marketability of
business assets and the immeasurability of its risks. Valorizing the fair market value of a
non-publicly traded business has been always one of the most challenging tasks for
financial professionals. In addition, measuring financial risks on business assets and its
expected returns is more difficult than measuring those on other investment assets such as
stocks of public companies and mutual funds. These issues get worse when taking a
small business into consideration. In attempting to solve the issues, a number of
researchers have conducted many different theoretical and empirical studies.
As one of most influential studies on the investment returns of a business owner
to their business, Moskowitz and Vissing-Jørgensen (2002) documented the prevalence
of diversification of business owners, using four Surveys of Consumer Finances (SCF)
between 1989 and 1998. They compared the diversification level of private equity
investors to that of public equity investors, focusing more on ownership in publiclytraded corporations. They found that investment in private equity such as business assets
is extremely concentrated, reporting that households with entrepreneurial equity invest
more than 70% of all private equity holdings in a single private company where they
have an active management interest. Interestingly, their results showed that although
private equity investors are dramatically less diversified than public equity investors,
returns on private equity are on average not higher than those on publicly-traded equity.
According to modern portfolio theory, since potential return rises with the increase in
risk, return on investing in a single private company should be higher than that of
investing in public equity. Possible explanations for why they are entering into
entrepreneurship, despite the poor risk–return trade-off for their business, might include
27
non-economic factors such as their high risk tolerance, non-pecuniary benefits, overoptimism, and a misperceived level of risk.
Based on the findings of Moskowitz and Vissing-Jørgensen (2002) that the
average return to business assets is low and that the diversification of business owners is
poor, subsequent studies have developed models that may explain returns on private
businesses and reported empirical findings, using various data sets of business ownermanagers. For example, Bitler, Moskowitz, and Vissing-Jørgensen (2005) attempted to
explain why business owners choose to invest in private equity even though there does
not seem to be a compensation for idiosyncratic risk (or unsystematic risk). However,
they were unable to explain the initial motivations of becoming a business owner, since
the expected utility function of business owners cannot be computed explicitly. So, they
concluded that the decision to become a business owner-manager remains puzzling.
Other studies provided some evidence on this puzzling risk-return trade off,
asserting that business owners who invested in the business they own or manage are
aware of and concerned about the idiosyncratic risk. For example, using both the SCF
and the SSBF data sets, Müller (2011) investigated whether business owners require
compensation for their risk exposure, assuming that business owners should suffer from
high idiosyncratic risk if they are under-diversified. She found that with investing an
additional 10% of net worth in private equity, thereby increasing investment risk levels,
there is an increase of 15.7% in the expected return on the total invested portfolio. The
result of her study also reported that owners of riskier firms are more likely to take a
lower ownership share on their business. Her findings were in contrast to those of the
possible explanations suggested by Moskowitz and Vissing-Jørgensen (2002) that
28
business owners do not sufficiently understand the idiosyncratic risk of their business.
However, she was also unable to explain the decision on entry into business ownership.
No empirical research to date has provided a promising answer to the puzzling question,
“why do individuals invest in private equity given the low average level of returns?”
The previous literature has also provided a great deal of evidence on many areas
of the household portfolios of business owner-managers. However, recent research has
been focusing mostly on the impact of business ownership on general household financial
aspects. For example, a research by Gentry and Hubbard (2004) focused on the saving
and investment decisions of business owners. They found that business owners
accumulate more wealth than non-business owners partly due to a precautionary demand
for business financing. Their finding based on the 1989 SCF data set also stressed the
importance of interdependence between investment of business owners whose businesses
have a total market value of at least $5,000 and their saving decisions.
Using the Panel of Individual Tax Returns and the three Surveys of Consumer
Finances (SCF) from 1989 to 1995, Heaton and Lucas (2000) found that households
having substantial ownership of their employer’s stock tended to hold smaller fractions of
their liquid financial assets, and households with high and variable business income held
less wealth in stocks than other wealthy households. Their study was solely focused on
all business owners, defining a business owner as an individual who actively owns and
manages a business. Shum and Faig (2006), who extended the sample by adding another
SCF data set, also found that the decision to participate in the stock market is negatively
correlated with holdings of alternative risky investments such as investments in private
businesses. They claimed that economic stability or financial decisions of households
29
might differ not only in terms of business ownership but also by their level of risk
tolerance. The relationship between business owners and their risk tolerance level was
also investigated. Using the 1992, 1995, 1998, 2001, and 2004 SCF data sets, Wang and
Hanna (2007) investigated risk tolerance differences among three types of households:
non-business owner-managers, business owners-managers, and business owners but not
managers. They found that business owner-managers are more risk tolerant than nonbusiness owners, but they are less likely to hold stock investments than non-business
owners.
Gutter and Saleem (2005) examined whether households who own or manage a
business are more likely to have greater financial vulnerability than non-business ownermanager households. Using financial ratios obtained from the 1998 SCF, they found that
due to the lack of diversification in assets and income, business owner-manager
households are more likely to be financially vulnerable and suggested that business
owners should increase their allocation in other liquid assets or other financial assets in
order to insure against business risk. However, they used only the 1998 SCF survey
whose sample size for business owner-managers is relatively small. Gutter and Saleem
(2005) reported that they used the 2001 SCF, but based on their results it can be
concluded that they used the 1998 SCF. Another issue of their work was that although
they stated that their research focused on small business owners from the SCF, their study
was actually based on all households who own or manage a business, not a small business.
2.2.1.2 Research on Household Portfolio of Small Business Owner-Manager
30
Household Portfolio of Small Business Owner-Managers
Compared to research on the household portfolios of business owner-managers,
relatively little is known about the empirical analyses of the portfolios of households who
own or manage a ‘small business’. Most previous studies have failed to include careful
classifications or definitions of a small business owner-manager. However, a few studies
explicitly included discussions about their classifications of a small business ownermanager used for their empirical analyses.
Using multiple survey years of the SCF from 1989 to 1998, Haynes and Ou (2002)
documented U.S. business ownership by comparing the demographic and economic
characteristics of small business owner-managers to those of the non-business owner
managers. They employed the tax and legal-based classifications so that small business
owners defined in their sample were sole proprietorships, partnerships, S corporations,
and non-public C corporations. Unlike other studies, they categorized small business
owners into several groups by the number of employees and the number of businesses
owned by households. Business owners were classified into seven definitions: selfemployed business owners, small business managers, angel investors, other angel
investors, owner-managers, and multiple-business owners. They found that although
gains in both real net income and net worth have been substantially realized by
businesses between 1989 and 1998, the gains of their owners were lower than the gains
realized by non-business owners.
The impact of demographic characteristics of small business owner-managers on
their financial well-being was explored by some past researchers. For example, using the
31
1992 Characteristics of Business Owners (CBO) survey, Fairlie and Robb (2007)
examined the racial difference between African American–owned small businesses and
White American-owned small businesses in terms of sales, profits, employment, and
survival. The Characteristics of Business Owners (CBO) survey conducted by the U.S.
Bureau of the Census is a national data set that provides economic, demographic, and
sociological data on business owners and their business activities. This data set adopts the
tax-based definitions. In other words, the survey is collected from business owners who
filed an IRS form 1040 with Schedule C (sole proprietorship or self-employed person),
form 1065 (partnership), or form 1120S (S corporation). The authors found that due to
the lack of prior work experience in a small business, Black business owners are more
likely to be less successful than White business owners. A similar study by the authors
explored the gender effect on business performance (Fairlie and Robb, 2009). Their
findings reported that businesses owned by females are less successful than those owned
by males, mainly due to three reasons including: lower initial financial investment, less
business experience acquired through prior work in a similar business, and less prior
work experience in a small business. However, these two studies used the term small
business owners interchangeably with other definitions such as business owners and/or
sole proprietors.
Some recent research, for example Headd (2009), Haynes (2010), and Müller
(2011), reported empirical findings with more precise discussions about the definition of
a small business owner used in their studies. Headd (2009) adopting the SBA firm-size
definition examined the growth distribution of new firms, focusing on the changes in
employment growth distribution of existing firms over time. Using the Statistics of U.S.
32
Businesses (SUSB), the author found that while 95% of employers started with fewer
than 20 employees in 1992, about 90% of employers had fewer than 20 employees in
2006, which indicates that only a few start-ups have grown by more than a few
employees. Statistics of U.S. Businesses (SUSB) is a longitudinal data set examining the
entire population of small businesses and their employees since 1988. While this data set
provides business performance data by enterprise size, geographic area, and industry,
mainly covering employment activity for U.S. small businesses, very limited information
about their owners are available. The authors explicitly stated that they adopted the SBA
firm-size definition of a small business.
Using the multiple Surveys of Consumer Finances, Haynes (2010) examined
whether households owning or managing small businesses have improved their financial
status between 1998 and 2007, and analyzed the changes in the income and wealth of
small business owner-managers during that period. He also adopted the SBA firm-size
definition and found that households who own or manage multiple small businesses are
more likely to have accumulated wealth at faster rates than owner-managers of a single
small business. One interesting finding of his study was that small business ownermanagers have a higher likelihood of having high income and high wealth, compared to
those who do not own or manage a small business.
As briefly discussed earlier, Müller (2011) provided evidence that returns to
business equity are higher if business owners have a higher exposure to idiosyncratic risk.
She found that when returns to business equity are measured either as the earnings rate or
as capital gains, there might be a positive relationship between the returns to business
equity and idiosyncratic risk. However, her findings were somewhat unclear in terms of
33
target sample groups that she aimed for. To support her findings, she employed two data
sets, the Survey of Consumer Finances (SCF) and the Survey of Small Business Finances
(SSBF). The SSBF data sets conducted by the Board of Governors of the Federal Reserve
System provide information about small businesses that have fewer than 500 employees
from the non-agricultural and non-financial sectors, which is identical to the SBA
definition. The SSBF was obviously a data set for small businesses, and some important
features of the SSBF such as the firm-size standard are included in the discussions of her
paper. However, she did not adequately discuss how she handled small business ownermanagers in the SCF data sets, and actually conducted her research with all households
who own or manage a business, not necessarily small business owner-managers. This
issue can also be found in the study by Xaio, Alhabeeb, Hong, and Haynes (2001). The
authors examined the risky asset proportion of total household assets, and reported that
family business owner-managers are willing to assume more risk and hold more risky
portfolios of assets than households who do not own a business. However, they also
failed to clarify their definition among business owner-managers, family business owners,
and small business owner-managers. They used them interchangeably without a careful
discussion of each term.
This review of previous literature shows that there has been a lack of awareness of
definitional precision for a small business owner-manager that has resulted in the further
confusion of research findings (Shane & Venkataraman, 2000; Parker, 2004). This is why
previous researchers such as Carland et al. (1988) and Stewart & Roth (2001) stressed the
importance of understanding definitional issues, particularly for empirical research.
34
Financial Vulnerability of Small Business Owner-Managers
A crucial, though relatively neglected, dimension of research on small business
owner-managers has been in the area of their financial vulnerability and its association
with their household and business characteristics. In order to understand the financial
vulnerability of small business owner-managers and the key determinants in their
household portfolio, firstly it is essential to include both systematic risks and
unsystematic risk (or idiosyncratic risks) factors in the research models while considering
their business as part of their household investment portfolio. However, previous studies
have not fully addressed these risk factors of the small business owner-managers. This is
partly due to the immeasurability of the risks and the availability of proper data to
calculate the variability in business assets.
In addition to considering the risk factors, both household and business
characteristics should be taken into account since small businesses and their owners
cannot be separately viewed. For example, although Müller (2011) made the first attempt
to empirically examine the relationship between the idiosyncratic risk of small business
owner-managers relative to their net worth and average returns, she did not consider their
household characteristics that may affect their financial health. Yilmazer and Schrank
(2010) also supported this approach by stating that “the risk of using business resources
for the household may be magnified in small businesses where the temptation and
opportunity to utilize business assets and resources by the owner-managers may be
greater than in non-family businesses.” Therefore, it is important to investigate the
differences in the diversification of their household portfolios among small business
35
owner-managers having similar household and business characteristics and financial
resources.
2.3 Concluding Remarks
This chapter has reviewed previous literature on various definitional issues of a
small business and its owner-manager and the empirical literature on their financial
vulnerability. The literature has partially addressed the issues about financial
vulnerability of small business owner-managers with an emphasis either on their business
aspects or on household portfolios. However, most previous studies did not thoroughly
discuss their classifications of a small business owner-manager. More interestingly, none
of these studies comprehensively explored the factors leading to being financially
vulnerable and the measurements of financial vulnerability suitable for households who
own or manage a small business. Building on the literature and the issues discussed in
this chapter, a conceptual framework and hypotheses of this dissertation are presented in
the next chapter, based on modern portfolio theory (MPT) and the extended life cycle
savings model (LCS).
36
CHAPTER 3
3 THEORETICAL BACKGROUND
3.1 Financial Vulnerability of Small Business Owner-Managers
This chapter presents the Modern Portfolio Theory (MPT) and the extended Life
Cycle Savings (LCS) model that are used for the theoretical foundations of this
dissertation. Based on the key concepts and framework from these theories, the
conceptual framework and research hypotheses for this dissertation are proposed in the
later section of this chapter.
3.1.1 Modern Portfolio Theory & Diversification of Small Business OwnerManagers
Modern Portfolio Theory
Diversification is the cornerstone of MPT pioneered by Markowit (1952). MPT is
based on the mean-variance framework where investors maximize portfolio expected
return levels for a given amount of portfolio risk by selecting the optimal proportions of
various assets:
=∑
+∑
∙∑
,
37
(Equation 3.1)
where:
is the variance of return on the portfolio,
,
is the variance of return on asset i,
is the covariance between the return on assets i and j, and
and
are the
proportions invested in asset i and j, respectively.
As shown in Equation 3.1, in order to measure portfolio risks associated with
component assets, it is necessary to examine some important variables, including
expected rates of return, price volatilities of those assets, and correlations with other
assets. The variance of the portfolio is the sum of the contributions of the component
asset variances plus a term that involves the correlation coefficient between the returns on
the component assets. The correlations of the returns on the assets might be one of the
most practical contributions of the MPT, which also constituted the portfolio effect
(Moses, Singleton, & Marshall III, 2004). The portfolio effect depends on the correlation
between the returns of the assets in the portfolio, which can range from a value of -1 to
+1. A correlation coefficient of -1 indicates a perfect negative correlation, whereas a
correlation coefficient of +1 indicates a perfect positive correlation. A correlation
coefficient of zero indicates that the returns on the assets in the portfolio are unrelated to
each other. Mathematically, a perfectly positive correlation is the only case where
investors cannot benefit from diversification. Whenever the portfolio standard deviation
(σ) is less than 1, there are benefits to having diversification. So, how many assets are
required to produce a satisfactory level of risk reduction for the portfolio by comparing
the variance of each asset to the variance of portfolios with more assets? Typically, as the
number of assets held in the portfolio increases, the risk of each individual asset
decreases, depending on the level of covariance relationship of each asset in the portfolio.
Therefore, investors could reduce or sometimes eliminate their risk of the overall
38
portfolio by increasing the number of assets in their portfolio, in other words, by
diversifying their assets.
Modern Portfolio Theory & Application to Small Businesses
Previous researchers asserted that this diversification principle can also be applied
to risky non-financial assets such as small businesses, real estate investment, and equity
investments (Moskowitz & Vissing-Jørgensen, 2002; Gutter & Saleem, 2005; Müller,
2011). Golier (2001) and Hanna, Wang, and Lindamood (2008) suggested that it is
plausible that investments in risky non-financial assets such as a privately-owned
businesses could be substitute for investments in publicly-traded stocks to optimize
returns on household portfolios. However, very little work has been done on measuring
the non-financial risks for those who own or manage a small business. This measurement
issue can be presumably explained by another type of risk associated with non-financial
assets: the so-called background risk. A background risk is defined as a non-financial
market risk that is different from the general financial risk that investors face in financial
markets (Heaton & Lucas, 2000; Campbell, 2006; Hanna et al., 2008). Previous academic
studies, for example, Heaton & Lucas (2000), Gollier (2001), Campbell (2006), and
Hanna et al. (2008) reported theoretical models and empirical findings related to
background risk and its impact on household portfolios and investment choices.
Empirical studies found that household portfolios of those who own or manage a
small business have been, historically, highly undiversified (Gutter & Saleem, 2005;
Yilmazer & Schrank, 2010; Müller, 2011). According to theoretical and empirical
perspectives, it is quite obvious that diversification is more important for households who
39
own or manage a small business since business failure might destroy not only their
business assets, but also their business-derived income streams. This means that the real
risk of small business ownership is even greater than what is shown in the share of
business assets in total net worth (Carroll, 2002). MPT might provide some insights into
the diversification issues of small business owner-managers. Despite the difficulties of
obtaining a precise measure of the mean return to a small business, the estimated realized
returns to investment in small business are similar to those in the public market, and are
highly correlated with public equity returns (Moskowitz and Vissing-Jørgensen, 2002).
Therefore, viewing their business as a component in their overall household portfolio, it
is expected that small business owner-managers can increase their chances of financial
success and improve their financial vulnerability by properly diversifying their assets and
earnings.
However, the reasons for diversification for a small business and its ownermanager might be different from those of other groups, depending on their conservative
operating leverage, ability to prevent further losses, business motivations, and different
levels of desire for organizational growth (Lynn & Reinsch, 1990; Everett & Watson,
1998). Possible reasons may also include the low survival rates of small businesses,
relatively wide distribution of returns across small businesses, and the potential shortage
of management skills. Watson and Everett (1996) reported that approximately two thirds
of the underlying causes of small business failures are related to internal factors such as a
lack of appropriate management skills and inadequate financial resources. If business
owner-managers hold most of their household portfolio assets in the same business where
they work, the probability of business failure would substantially increase. Empirical
40
findings from the previous studies support this idea by reporting that households who
own or manage a business tend to rely too much on business income and assets, which
can lead to a greater vulnerability of the household and to a business failure (Moskowitz
& Vissing-Jørgensen, 2002; Gutter & Saleem, 2005; Müller, 2011). This becomes
apparent when the economy faces a recession. A recent study conducted by the U.S.
Small Business Administration (2011c) reported that only a quarter of new small
businesses last 15 years or more while 30% do not survive for more than two years.
When MPT is applied outside of traditional financial portfolios, some adjusted
approaches should also be taken into account and more theoretical and empirical work
should be included (Moskowitz & Vissing-Jørgensen, 2002). Hanna, Wang, and
Lindamood (2008) also noted that the relationship between risky non-financial and risky
financial asset investments is ambiguous because of the complexities that risky nonfinancial assets have. Therefore, for a more realistic approach, an adequate compromise
between the complexity of actual portfolios and the simplicity of theory might be needed.
Now, one might ask another important question: “how many assets and how many
different sources of income are required for a well-diversified portfolio of small business
owner-managers?” Although the number of assets in a portfolio might not be the sole
determinant of the level of diversification, industry rules-of-thumb on this question often
state that 15 to 30 stocks in a portfolio would be adequate (Statman, 1987). These rulesof-thumb are based on MPT where diversified portfolios reduce risk exposure by holding
combinations of multiple assets that are not perfectly correlated. In theory, a financial
risk, defined as the probability that the actual return will be different from the expected
41
return, exists when possible return is expected from an investment. Certain financial risks
could be mitigated by diversifying financial resources that have different correlation
coefficients although all financial risks cannot be completely avoided as systematic risk
in the market always remains. Extending this fundamental concept of MPT, several
empirical studies reported that a greater reduction in portfolio risk would be achieved by
increasing the number of assets in the portfolio (Domian, Louton, & Racine, 2003;
Shawky & Smith, 2005; Domian, Louton, & Racine, 2007). In other words, small
business owner-managers can optimize their household portfolio by diversifying nonfinancial asset investments that have a background risk.
One of the few instances in which a scholar explicitly dealt with the level of
diversification associated with small businesses and their owner-managers can be seen in
Müller’s (2011) work. She employed the share of net worth invested in the business as
proxy for exposure to idiosyncratic risk (or unsystematic risk) While her study
represented an interesting empirical contribution, the underlying model focuses only on
wealth and does not incorporate other household characteristics of small business ownermanagers. This dissertation capitalizes on the basic idea of Müller (2011) and presents an
extended research model that can capture the level of diversification as well as the effect
of other factors of small business owner-managers on their financial vulnerability.
3.1.2 Life Cycle Savings (LCS) Model
Since Modigliani and Brumberg (1954), the life cycle savings (LCS) model has
become a theoretical foundation to explain consumption behaviors in which individuals
42
and households maximize their expected lifetime utility. The initial life cycle model
assumed that a household faces a zero real interest rate, no uncertainty, and no
discounting of the utility of future income. However, this simple life cycle savings model
has been developed with many modified versions to analyze more complex consumption
behaviors (Yuh & Hanna, 2010). For example, Freyland (2004) and Yuh and Hanna
(2010) presented the extended life cycle savings model as their economic framework,
optimizing lifetime utility with non-zero interest rates and discounting the utility of future
consumption. This dissertation borrows some mathematical notations used by Freyland
(2004) and Yuh and Hanna (2010).
Firstly, the standard LCS model is restricted by the following lifetime budget
constraint:
∑
where
/( + ) ≤
is real rate of interest,
+∑
/( + ) =
is initial net assets,
is the present value of future income, and
+
=
(Equation 3.2)
is future household income,
is total lifetime wealth.
And, the lifetime utility of the household is defined as a discounted sum of utility
functions, ( ), in the following form:
max{
where
…
)
∑
( )/( + )
(Equation 3.3)
is the time horizon, of interest and
is the subjective discount rate.
43
In order to optimize consumption growth, Frelyland (2004) and Yuh and Hanna (2010)
also presented the following solution to the problem relating the marginal utility of
consumption in two periods to the interest rate and the subjective discount rate:
′(
′( )
)
=
(Equation 3.4)
This extended LCS model posits that in order to smooth their consumption,
individuals or households would annuitize their income throughout their expected
lifetimes by saving during their most productive working years and by dissaving during
retirement years. In other words, when facing income fluctuations, individuals or
households maintain the level of marginal utility of consumption as a constant over time
by either saving or dissaving. While the optimal consumption level depends only on their
total lifetime resources in a world of perfect certainty, it varies over their lifetime in the
real world with other lifecycle factors such as age, income, gender, marital status,
children, education, family size, net worth, home ownership, working status, and current
income level relative to past and future income (Wells & Gubar, 1966; Chen & Finke,
1996; Yuh & Hanna, 2010; Hanna, Yuh, & Chatterjee, 2012).
Although it is limited when uncertainty is concerned, the extended LCS model
might be the most rigorous model that provides a normative framework for analyzing
business ratios and financial obligation ratios of small business owner-managers. For
example, a high financial obligations ratio may result in unexpected decreases in
household income during the current period, leading to higher savings levels from their
current income (Browning & Lusardi, 1996; Yuh & Hanna, 2010; Hanna et al., 2012).
44
Therefore, maintaining a low financial obligations ratio can cover unforeseen events or
financial crises that might cause household income to be insufficient to meet the
household consumption level that maximizes lifetime utility. Based on the concept of
precautionary savings and the extended LCS model, research hypotheses and conceptual
models are developed.
3.1.3 Ratio Analysis for Financial Vulnerability
Financial statement analysis and ratio analysis have been frequently used by
investors, creditors, security analysts, governmental agencies, and many other parties
who rely on financial data for making economic decisions (Moon, Yuh, & Hanna, 2002;
Fabozzi & Peterson, 2003; Baek & DeVaney, 2004; Garman & Forgue, 2012). In
particular, financial ratio analysis is useful when analyzing how effectively the financial
resources of a business have been managed to achieve its goals, or whether its current
financial situation is sound enough to meet its planned insolvency levels. Financial
statement analysis and ratio analysis are also effective in measuring the level of financial
vulnerability of households owning or managing a small business. These analyses can
evaluate their current financial status as well as estimate future risks and potential profits
that they are expected to have. In general, ratios can be constructed by combining
elements appearing on financial statements such as balance sheets, income statements,
and statements of cash flow, depending on their financial characteristics. As noted by
Greninger, Hampton, Kitt, and Achacoso (1996), financial ratios can be utilized not only
in corporate finance but also in household finance such as investment, liquidity, savings,
asset allocation, inflation protection, tax burden, and insolvency.
45
Assuming that financial ratios are useful and efficient indicators in evaluating the
financial health of a household and a business, this dissertation proposes three financial
ratios that can capture the level of financial vulnerability of small business ownermanager households. The ratios include the ratio of business assets to total household
assets (Business Asset Ratio, BAR), the ratio of business income to total household
income (Business Income Ratio, BIR), and the ratio of financial obligations to household
income (Financial Obligations Ratio, FOR). It is expected that small business ownermanager households that have higher ratios might have a greater likelihood of being
financially vulnerable. These three measures of financial vulnerability are defined and
discussed in Chapter 4.
There are some important issues to relying on financial ratio analysis that
potential users should be aware of. Firstly, a single ratio might not provide a
comprehensive picture of the financial status of a household or a business (DeVaney,
2000; Fabozzi & Peterson, 2003; Baek & DeVaney, 2004). Thus, it should be used in
conjunction with other financial ratios to have a clearer measure of a household’s or
business’ financial strengths and weaknesses. Baek and DeVaney (2004) noted that in
order to be a better measure of financial wellness of individuals or households, at least
four aspects of their objective financial status, including liquidity level, solvency,
financial obligations, and savings level, should be evaluated. Fabozzi and Peterson (2003)
suggested that users should consider several financial characteristics at the same time to
maximize the benefits of using financial ratios.
Another issue with using financial ratio analysis is its limited information.
Fabozzi and Peterson (2003) noted that financial ratios should be interpreted and used
46
with care as these ratios may not be telling the whole story about the financial health of a
household or a business. For example, ratios may not capture all of the important
information such as the quality of a business and the experiences or its owner-managers.
This type of information may not be derived directly from financial ratios although they
can give an indication. However, advanced statistical models that can incorporate several
factors not measurable with financial ratio analysis might provide more meaningful
insights into financial status, which is the aim of this dissertation.
Making reasonable thresholds of financial ratios is another challenging issue for
using financial ratio analysis. Financial ratio analysis might be more meaningful with
reasonable comparisons. To better interpret the results from a financial ratio analysis, the
financial ratios being used should be either benchmarked to those of high performing
businesses in a similar industry or compared with a guideline ratio recommended by
experts, financial institutions, and sometimes governments (Fabozzi & Peterson, 2003).
To determine the financial vulnerability of small business owner-managers, this
dissertation also uses a guideline for the financial obligations ratio recommended by the
government and financial professionals. The ratio guideline for the financial obligations
ratio is also presented in Chapter 4.
3.2 Conceptual Models and Research Hypotheses
3.3.1 Conceptual Models
The primary goal of measuring the level of financial vulnerability is to maintain
the current financial status, such as the total value of the household portfolio, income
47
levels, and consumption levels when unexpected events happen. This is consistent with
the primary goal of the LCS model in maintaining a stable consumption level throughout
his or her lifetime as well as of the MPT model in minimizing his or her exposure to
investment risk. Therefore, the basic conceptual approach of this dissertation is to
examine the functional relationship between the level of financial vulnerability of small
business owner-mangers and possible determinants of it. More specifically, a conceptual
model that consists of explanatory variables such as socio-demographic characteristics,
financial resource availability, and attitudinal/expectation factors are developed. As
shown in Figure 3.1, the framework of this dissertation is to analyze the factors affecting
the financial vulnerability of small business owner-managers. To measure the level of
financial vulnerability, three ratios, the business asset ratio, the business income ratio,
and the financial obligations ratio, are employed. These ratios should allow for a
comprehensive evaluation of aspects of the financial vulnerability of small business
owner-managers or the financial aspects of small business owner-managers. More
detailed explanations and justifications for these ratios are described in the next chapter.
48
Explanatory Variables
Response Variables
Socio-demographic Factors
Business Asset
Ratio
Economic Status Factors
Business Income
Ratio
Attitudinal/Expectation
Factors
Financial
Obligations Ratio
Other Controlling Factors
Figure 3.1 Conceptual Model of the Research
49
Measurement of Financial Vulnerability
Age
Race/Ethnicity
Marital Status
Education
Ratio Analysis
(OLS regressions)
Self-employed
Threshold
Analysis (Logit
regression)
Business Entity
Business
Asset Ratio
(BAR)
Business
Income Ratio
(BIR)
Year of Business
Operated
Financial
Obligations
Ratio (FOR)
Economic
Factors
Risk Tolerance
Inheritance
Expected
Expectation of
Future Income
Figure 3.2 Independent and Dependent Variables of the Research
50
3.3.2 Research Hypotheses
Small business owner-mangers with different characteristics such as sociodemographic factors, economic status factors, and attitudinal/expectation factors might
behave differently in the diversification of their financial sources and in handling their
financial burdens. It is important to analyze whether those characteristics affect the level
of their financial vulnerability, even after controlling other factors including survey years,
health status, and health insurance coverage.
Based on the previous literature and theoretical and conceptual models, the
following hypotheses are proposed for the empirical analysis of this study (See Table 3.1
and Figure 3.2). To test the hypotheses, three multivariate analyses are used with the
same explanatory variables. For the business asset ratio and the income asset ratio,
ordinary least squares regressions are used. For the financial obligations ratio, a logistic
regression model with a dichotomous dependent variable having a ratio over a certain
threshold level is used. More detailed discussions about the research methodologies are
also presented in the next chapter.
Socio-demographic Factors
(1) Age
The life cycle savings model implies that in order to smooth consumption over the
life cycle, households with a typical income pattern will acquire debt up to middle age,
and then pay off the debt and accumulate assets until retirement in order to maintain
consumption in retirement. Previous researchers studying the relationship between
household investment behavior and age reported that allocations to risky investment are
51
related to age, although the pattern depends on whether human capital is included in total
wealth calculation (Morin & Suarez, 1983; Schooley & Worden, 1996; Wang & Hanna,
1997; Grable & Lytton, 1999; Cocco, 2001). Their empirical findings showed that
individual investment behavior varied with age and that older households tended to be
less risk-tolerant than younger households. Therefore, it is reasonable to assume that
younger small business owner-managers will tend to take greater risks so that their
financial vulnerability will increase.
Hypothesis 1: As age increases, a small business owner-manager tends to take
fewer risks. Thus, financial vulnerability will decrease.
(2) Race/Ethnicity
Based on the LCS model, when controlling for all important variables, no
significant differences should be found in multivariate analyses (Yuh & Hanna, 2010).
Thus, it is assumed that race and ethnicity might not affect financial vulnerability if
holding all other variables constant. As noted by Yuh & Hanna (2010), however, there
may be some racial/ethnic differences related to cultural differences and past
discrimination.
Hypothesis 2: Based on the life-cycle savings modes, there should be no
difference when controlling for all other variables.
(3) Marital Status
Financial vulnerability might depend on household types or marital status.
Married households tend to have an alternative source of income when they face
unexpected events such as job loss, health problems, divorce, or death of his or her
52
spouse. Therefore, it is expected that married small business owner-managers will be less
likely to be financially vulnerable.
Hypothesis 3: Since a married household has an alternative source of income, a
married small business owner-manager will less likely to be financial vulnerable.
(4) Education
Education might also be another factor affecting the level of financial
vulnerability as the knowledge of small business owner-managers is strongly associated
with their business and their financial status. Since more-educated small business ownermanagers tend to have more access to financial information and to have a better
understanding of financial diversification and management, it is assumed that educational
level would negatively be related to financial vulnerability.
Hypothesis 4: As education level increases, cognitive ability and knowledge will
tend to increase. Thus, financial vulnerability will decrease.
(5) Self-employed
As discussed in Chapter 2, this research assumes that self-employment is viewed
as one of the occupational categories, not the operational categories. More specifically,
only small business owner-managers who have self-employment status are considered.
Since examining the effect of self-employment status on financial vulnerability is a
primary focus of this study, it is assumed that for couple households, the employment
status of the household is categorized as “the self-employed” if either spouse is selfemployed. The self-employed tend to face more income stream risks (Browning and
Lusardi, 1996; Yuh & Hanna, 2010) and they will have more risk than one with the head
(and spouse/partner if present) with an income from a salary. Therefore, it is expected
53
that self-employment will be positively related to the level of financial vulnerability,
reflecting the fact that small business owner-managers who reported self-employment
status would be more financially vulnerable.
Hypothesis 5: As a self-employed small business owner-manager tends to have
riskier income streams, their financial vulnerability will increase.
(6) Sole Proprietorship
As the simplest form of business entity, a sole proprietorship has some
operational advantages, including ease of tax preparation and money management and
minimal legal costs for start-up required. However, if one sets up his or her business as a
sole proprietorship (except sole proprietors who set up an LLC for tax filing purposes),
all of the personal and business assets of the sole owner are at greater risk than other
types of business entities. A sole proprietor is personally liable for all business-related
obligations and activities, which means that if a business defaults on a debt or loses a
lawsuit, its creditors can have a legal right on the personal properties of the business
owner. Therefore, it is assumed that in order to reduce their potential risk, small business
owner-managers who chose a sole proprietorship as their business structure will be less
likely to be financially vulnerable when holding other things constant.
Hypothesis 6: Holding other things constant, a small business owner-manager
having a sole proprietorship as their business legal structure will tend to be less
financially vulnerable.
(7) Years of Business Operated
54
More experienced small business owner-managers are expected to have more
knowledge of financial diversification, business operations, and management. Thus,
financial vulnerability will decrease as the number of years in business increases.
Hypothesis 7: As years of business operation increase, managerial and
operational experiences tend to be improved. Thus, financial vulnerability will
decrease.
Economic Status Factors
(8) Homeownership
Homeownership may have both negative and positive effects on financial
vulnerability, depending on its measures. As homeownership increases, total household
assets (the denominator of the business asset ratio), the business asset ratio will decrease.
Therefore, it is expected that homeowners among small business owner-managers are less
likely to be financially vulnerable with regard to the business ratios. On the other hand, as
homeowners usually have higher housing-related expenses such as mortgage payments
and property taxes, their financial obligations ratio will increase.
Hypothesis 8(a): As homeownership increases total household assets, and the
business asset ratio tends to decrease. Thus, financial vulnerability will decrease.
Hypothesis 8(b): As homeowners have higher financial obligations, the financial
obligations ratio tends to increase. Thus, financial vulnerability will increase.
(9) Availability of Emergency Fund
Small business owner-managers who think they can get an emergency fund from
their friend or relative tend to take more risks. Thus, the availability of an emergency
55
fund might be positively related to financial vulnerability. Thus, it is expected that those
who think they can get $3,000 from a friend or relative for emergency are more likely to
be financially vulnerable, relying more on their business income and assets. The level of
their financial vulnerability is more likely to be higher.
Hypothesis 9: Based on the precautionary savings model, the availability of
emergency fund might be positively related to financial vulnerability.
(10) Income and Net Worth
According to the precautionary savings perspectives, income and net worth
should be negatively related to financial vulnerability, particularly to the financial
obligations ratio. Holding other things constant, therefore, it is expected that small
business owner-managers with higher income and net worth are less likely to be
financially vulnerable.
Hypothesis 10: Since a small business owner-manager having higher income and
net worth tends to have sufficient income resources and assets, financial
vulnerability will decrease.
Attitudinal/Expectation Factors
(11) Risk Tolerance
Previous research found that many demographic and financial factors are related
to a household’s risk tolerance level (Sung & Hanna, 1996; Wang & Hanna, 1997; Yao,
Gutter, & Hanna, 2005; Wang & Hanna, 2007). Due to the willingness to take additional
risks for additional expected rewards, small business owner-managers having high risk
56
tolerance might have higher financial ratios and less diversified household portfolios than
those with low risk tolerance. In other words, more risk tolerant small business ownermanagers are willing to take greater risks if they expect to earn higher returns. Thus, it is
hypothesized that the financial vulnerability of small business owner-managers would be
positively related to their subjective risk tolerance levels.
Hypothesis 11: Since a more risk tolerant small business owner-manager is
willing to take greater risks and expect higher returns, financial vulnerability will
increase.
(12) Expectation of Inheritance
Small business owner-managers who expect an inheritance tend to take greater
risks so that they are more likely to invest more in their risky assets such as their private
business, which leads to an increase in the level of financial vulnerability.
Hypothesis 12: As a small business owner-manager expecting an inheritance
tends to invest more in the current business, financial vulnerability will increase.
(13) Expectation on Future Income
Again, as small business owner-managers who expect their real income to grow in
the future, they tend to be more future oriented and take greater risks during the current
period. Therefore, their financial vulnerability is likely to be higher than those who do not
expect real income growth.
57
Hypothesis 13: As a small business owner-manager expecting real income growth
tends to take greater risks, financial vulnerability will increase.
(14) Current Income Relative to Normal Income
If current income is lower than that of a normal year's income, small business
owner-managers might be willing to take riskier positions in their business and income to
maintain their household consumption level. This results in having higher financial
vulnerability ratios and a higher financial obligations ratio. Thus, it is expected that
having current income levels lower than normal income will be positively related to
financial vulnerability.
Hypothesis 14(a): If current income is lower than normal, a small business
owner-manager will be more financially vulnerable than one with current income
at a normal level.
Hypothesis 14(b): If current income is higher than normal, a small business
owner-manager will be less financially vulnerable than one with current income
at a normal level.
58
Independent Variables
Category
Expected
Effect
Hypothesis/
Theoretical Justification
Socio-demographic Factors
59
Age of respondent
N/A
−
Racial/ethnic status
White
Black
Hispanic
Other
?
?
?
Married
Partner
Single(Male)
Single(Female)
−
−
−
Marital status
Having child < age 19
Respondent’s education
Self-employed
No
Yes
+
<High school degree
High school degree
Some college
College and above
−
−
−
No
Yes
Table 3.1 Hypotheses and/or Theoretical Justifications
As age increases, a small business owner-manager
will tend to take fewer risks. Thus, financial
vulnerability will decrease.
There should be no difference when controlling for
all other variables.
Since a married household has alternative source of
income, a married small business owner-manager
will less likely to be financial vulnerable.
Having a child under 19 will be positively related to
financial vulnerability.
As educational levels increase, cognitive ability and
skills will tend to increase. Thus, financial
vulnerability will decrease.
As a self-employed small business owner-manager
tends to have riskier income streams, their financial
vulnerability will increase.
+
59
Continued
Table 3.1 continued
Independent Variables
Business entity
Years of business operated
Category
Expected
Effect
Hypothesis/
Theoretical Justification
Holding other things constant, an SBOM having a
sole proprietorship as their business legal structure
will tend to be less financially vulnerable.
Other entities
Sole Proprietorship
−
N/A
−
Financial vulnerability will decrease as the years the
business has operated for increases.
+/−
As homeownership increases total household assets,
the BAR will decrease while as homeowners have
higher financial obligations, the FOR will increase.
Economic Status Factors
Homeownership
No (Renters)
Yes (Home owners)
60
Income(Log)
Net Worth(Log)
In emergency, could get
$3,000 or more from friend or
relative?
N/A
−
As a higher income SBOM is more likely to have
sufficient resources, financial vulnerability will
decrease.
−
As a higher net worth SBOM is more likely to have
sufficient resources, financial vulnerability will
decrease.
N/A
No
Yes
+
Availability of an emergency fund will increase
financial vulnerability.
+
Risk tolerance level will be positively related to
financial vulnerability.
Attitudinal/Expectation Factors
Subjective risk tolerance
No risk
Average
60
Continued
Table 3.1 continued
Independent Variables
Category
Above average
Substantial
Expect inheritance
Future income expectation
Expected
Effect
+
+
Hypothesis/
Theoretical Justification
As an SBOM expecting inheritance tends to invest
more in the current business, financial vulnerability
will increase.
+
Sure grow
Sure same
Sure low
−
−
As an SBOM expecting real income growth tends to
take greater risks, financial vulnerability will
increase.
−
+
If current income is lower than that of a normal
year's income, an SBOM will be more likely to be
financially vulnerable.
61
Not expect
Expect inheritance
Current income relative to
normal
Normal
Higher than normal
Lower than normal
Other Controlling Variables
Survey year
Health status
Year 1992
Year 1995
Year 1998
Year 2001
Year 2004
Year 2007
−/+
−/+
−/+
−/+
−/+
The trends of the BAR and the FOR will follow
those of housing prices as the BAR will be
negatively related to housing prices while FOR will
be positively related to them. (The S&P Case-Shiller
Home Price Indices are used)
Excellent
Fair
Good
−
−
Those with worse health are less likely to be
financially vulnerable today as they will take fewer
risks.
61
Continued
Table 3.1 continued
Independent Variables
Category
Poor
All in household covered by
health insurance
Expected
Effect
−
Hypothesis/
Theoretical Justification
An SBOM covered by health insurance tends to take
−
fewer risks. Thus, health insurance coverage will be
negatively related to financial vulnerability.
Note + is positive effect, − is negative effect, and ?is neutral. The reference category is indicated in bold face.
No
Yes
Table 3.1 Hypotheses and/or Theoretical Justifications
62
62
CHAPTER 4
4 METHOD
Developing or finding reliable data sets that are also suitable for the purposes of
research has been one of the main concerns for researchers. This chapter begins with
discussions about major data sets that can be employed for the studies dealing with small
businesses and their owner-managers, followed by explanations of the variables
examined by this dissertation.
4.1 DATA
As discussed in Chapter 2, defining a small business owner-manager has been one
of the most challenging tasks in the field. Different research purposes and sampling
frames used in different data sets might be one of the main reasons. In other words,
various groups interested in small businesses such as professionals, researchers, policy
makers, and educators investigate different issues, using different kinds of data (Ou,
2010). For example, some need detailed information to answer a variety of questions
concerning the characteristics of the owner-managers while others look only for the
characteristics of their business.
4.1.1 Data Sets for Small Business Owner-Managers
There are several publicly available data sources used in previous research on
small businesses. The data sets providing various aspects of small businesses as well
63
details about their owner-managers include cross-sectional data sets such as the Survey of
Consumer Finances (SCF), the Survey of Small Business Finance (SSBF), the Survey of
Business Owners (SBO), the Survey of Income and Program Participation (SIPP), the
American Community Survey (ACC), and the Current Population Survey (CPS). There
are also longitudinal data sets such as the Panel Study of Entrepreneurial Dynamic
(PSED), the Kauffman Firm Survey (KFS), and the National Family Business Survey
(NFBS). Again, the definitions of a small business owner-manager adopted by the
previous studies using these data sources were not identical and were frequently
intertwined with other terms such as entrepreneurs, family business, and other types of
owner-operated business entities. Although there are several data sets available on small
businesses in the U.S., five major data sets are selected and compared to the SCF that this
dissertation employs, based on the level of availability of data sources for small
businesses and their owner-managers (Table 4.1).
Survey of Small Business Finances (SSBF)
The Survey of Small Business Finances (SSBF), formerly referred to as the
National Survey of Small Business Finances, is one of the most comprehensive sources
of data available on small businesses' use of financial services and the suppliers of these
services. The SSBF provides financial information on small businesses with fewer than
500 employees in the United States. Similar to the SCF, information about business
characteristics of a small business and its primary owners can be obtained in this data set.
However, the definition of a small business owner-manager used in the SSBF is the
“principal shareholder” of the firm, who may or may not be a manager. In addition, while
the SCF contains very detailed information about the household financial information of
64
small business owner-managers such as investment portfolios, the SSBF contains
relatively limited data on the principal shareholder’s net worth and other household
characteristics.
Survey of Business Owners (SBO)
According to the U.S. Census Bureau, the Survey of Business Owners (SBO) is a
national survey of a very large, statistically-representative sample of small business
owners and their businesses. This data set provides a comprehensive source of
information on selected economic and demographic characteristics for businesses and
business owners by gender, ethnicity, race, and veteran status. The business
characteristics include the number of employer and non-employer firms, sales and
receipts, annual payroll, and employment. Compared to the SCF and SSBF, however the
SBO is not ideal for research on financial aspects of small businesses since it has very
limited financial information on businesses owners and their household finances,
focusing more on demographic variables of business owners such as race, ethnicity, ages,
etc.
Panel Study of Entrepreneurial Dynamics II (PSED II)
The Panel Study of Entrepreneurial Dynamics II (PSED II) is one of the major
longitudinal data sets for small business owners. The PSED II replicated by the PSED I
that had 685 individual samples who were involved in the business formation process in
1998 and 1999, was initiated in 2005 to collect more detailed information about start-up
businesses, their financing, and their owners. Despite the benefits of longitudinal data
that allows for the collection of start-up financing information during the long process of
65
start-up operation, the PSED II has a relatively small sample size and limited sources
about the household characteristics of small business owner-managers. However, this
data set might be ideal for those who are interested in analyzing the small business life
cycle.
Data Set
Sample unit
Data type
Time frame
Major variables
SCF
Households
Cross-sectional
Triennial
Household finance;
Business owners; Business
characteristics
SSBF
Businesses
Cross-sectional
Quinquennial
Business finance; Firm legal
entity; Owner information
SBO
Businesses &
owners
Cross-sectional
Quinquennial
Demographics of businesses
& owners; Financing
PSED
Nascent
business
owners
Longitudinal
One time (2005)
Firm legal entity; Start-ups;
Business finance; Owner
demographics
Table 4.1 Selected Data Sets of Small Businesses in U.S.
4.1.2 Survey of Consumer Finances
4.1.2.1 Advantages of the SCF
The Survey of Consumer Finances (SCF) data set sponsored by the U.S. Federal
Reserve Board and the U.S. Department of the Treasury is a triennial survey of the
economic wealth and other demographic characteristics of U.S. households. The SCF is
one of the best pubic survey data sets in terms of detail about household assets and debts,
consisting of a randomly selected, nationally representative, and area probability sample
selected from Internal Revenue Service data. Data collected includes various aspects of
household finances, for example sources of household income and expenses, holdings of
66
assets and debts, employment status, demographic characteristics, and risk-taking
attitudes.
It also provides useful information about the financial behavior and investment
activities of households who own or manage a business in the United States. Detailed
household financial activities such as investment, sources of financing, gross sales, net
sales, industry , how and when the business was acquired, and the number of employees
are included. The SCF data sets have more relevance to the financial activities of
business owner-managers than to those of the business they own or manage as the units
of analysis are the households in the U.S. Since the main focus of this dissertation is to
examine factors that influence the level of financial vulnerability of small business
owners-managers, it is assumed that the SCF is the best publicly available resource for
the research purposes of this study.
4.1.2.2 Limitations and Caveats of the SCF
Limitations
However, there are some limitations of using the 1992-2007 SCF. Firstly, the SCF
may not be ideal for the economic analysis of occupational choices between selfemployment and paid-employment since this data set does not provide sufficient
information about households’ movement into and out of self-employment. Generally,
those types of studies require longitudinal data sets or customized data sets that can link
employer perspectives and employee perspectives. Within the bounds of a cross-sectional
empirical approach, appropriate assumptions and implications restricting research
purposes and focuses might provide a solution to this issue. However, since the
67
behavioral analysis of occupational choices is not the main focus of this study, this
disadvantage might not substantially limit the empirical findings of this study.
Another disadvantage of using the SCF data sets is that although the survey
collects information about the businesses that households own or manage, the database
may not the best source of information on the small businesses in the United States. The
reason is that again the SCF provides somewhat limited information about the small
business itself, compared to their owner-managers’ information.
In addition, since the SCF consists of cross-sectional data whose primary focus is
to profile the structure and characteristics of respondents, it does not provide other
meaningful information such as business decisions, actions, and developments that occur
at different business life-cycle stages. Thus, research opportunities for time-series
analysis are not available in the 1992-2007 SCF data sets.
Caveats
According to the suggestions of Lindamood, Hanna, and Bi (2007), there are also
some caveats that researchers using the SCF data sets should be aware of, including: the
identification of respondent, weighting of analyses, and use of multiple implicates. Firstly,
some previous studies assumed that without a careful discussion about a respondent’s
identification most of the survey questions were answered by the household head for a
couple of households. However, the respondent is not necessarily the head of household
head, but the one who is the most financially-knowledgeable person in the household.
Carefully considering this identification issue is critical for certain types of variables in
the SCF such as risk tolerance attitude, race/ethnicity, and education since a mismatch of
68
variables and explanations may result in confusion and misleading interpretations of the
findings (Lindamood et al., 2007).
Another issue is related to the sampling procedures of the SCF. The SCF contains
over-represents higher net worth households, which requires its users to weight
descriptive analyses to obtain the results that are representative of the U.S. population as
a whole. For example, while the actual number of households in the combined data sets
owning and managing a business is 7,200 (27.8%), with the sampling weights, 3,181
households (12.3% of households) own or manage a business, including both small and
large businesses.
Since1989, the SCF has employed multiple imputation methods to provide the
best possible estimates for the missing data. As Montalto and Sung (1996) demonstrated,
significance tests for the results with RII procedure and the results with only one
implicate might be different. Therefore, in order to adequately handle missing data in the
SCF data sets, "repeated-imputation inference" (RII) techniques should be used
(Montalto & Sung, 1996; Lindamood et al., 2007; Kennickell, 2009). This procedure
would help to obtain better estimates of variances for descriptive and multivariate
analyses. For multivariate analyses, however, un-weighted data should be used to
minimize biased estimates. Following the suggestions by Lindamood, et al. (2007), this
study weights descriptive analyses but does not weight multivariate analyses as testing
the hypotheses is its primary goal.
4.1.2.3 Analytical Sample of Research
69
Attempting to obtain more robust estimates, this dissertation combines the crosssectional data sets of the Survey of Consumer Finances (SCF) from 1992 to 2007, with a
total of 25,889 households in the combined overall sample. From 1992 to 2007, the
number of total households interviewed each survey year was 3,906, 4,299, 4,305, 4,449,
4,519, and 4,418, respectively. Based on the classifications and definitions discussed in
Chapter 2, subsamples are selected among the business owner-manager households. This
subsample includes households who own or manage a small business having fewer than
500 employees and annual average gross sales of less than $ 7 million. For sub-sampling
purposes, this dissertation utilizes the following unique SCF survey questions: “Now I
would like to ask you about businesses you may own. Do you and your family living here
own or share ownership in any privately-held businesses, farms, professional practices,
limited partnerships or any other types of partnerships? Do not include corporations with
publicly-traded stock or any partnerships that have already been recorded earlier.” and
“Do you or anyone in your family living here have an active management role in any of
these businesses?” Households who answered yes to those two questions are defined as
small business owner-managers. Due to the weighting issue of the SCF, the actual
number of households in the SCF combined data sets owning and managing a small
business is 5,492 whereas with the sampling weights, the number of small business
owner-manager households appears to be 3,069 (11.85% of all households). More
detailed descriptive results are presented in the next chapter (Table 4.2).
In the SCF, a small number of households have negative values in assets and
income. Among the 5,492 small business owner-manager households, 0.11% of
households reported either negative total assets or negative business assets while 8.05%
70
of households reported either negative total income or negative business income, with a
total of 447 households. Since financial ratios are not well defined if these values are
negative, only households with positive and zero values are included in this empirical
analysis. Table 4.2 presents the compositions of the analytical sample for this study.
Based on the definitions discussed in the previous chapter, the final sample of this
dissertation consists of 5,045 small business owner-managers.
Small
%†
Total
Non-Business
Owner (B)
Business Owners (A)
(A+B)
Large
Actual
%†
Actual
%†
Actual
%†
Actual
Selfemployment
8.7
4,395
0.3
1,442
4.9
1,571
13.8
7,408
Other
employment
3.2
1,097
0.1
267
82.8
17,117
86.2
18,481
11.9
5,492
0.4
1,709
87.7
18,689
100.0
25,889
Column Total
† Percentages are based on the weighted numbers whereas actual numbers are not
weighted.
Table 4.2 Descriptive Analyses: Composition of Analytical Sample
4.2 Variable Identification
4.2.1 Dependent Variables
To measure the level of financial vulnerability, this dissertation employs three
financial ratios, including the business asset ratio, the business income ratio, and the
financial obligations ratio. Again, there are a small number of extreme values or outliers
in the distribution of the threes ratios. For the descriptive and multivariate analyses, those
71
with negative values in total household and business income and total household and
business assets are deleted, because having negative values causes ambiguous results.
(Hanna et al., 2012).
4.2.1.1 Business Asset Ratio (BAR)
The first measure considers the value of business assets and partly the household
debt status. The ratio of the business assets to the total household assets is used as a proxy
for exposure to business financial risk related to household assets. Total household assets
are defined as the sum of all assets, including retirement assets, investment assets, and
other non-financial assets. As shown in Equation 4.1, this variable is denoted as BAR, the
business asset ratio:
BAR =
(Equation 4.1)
In the appendices, this dissertation presents another way of interpreting the
business asset ratio by breaking the ratio down into two distinct elements: household debt
ratio and business asset coverage ratio.
4.2.1.2 Business Income Ratio (BIR)
Building on Hamilton (2000), many subsequent studies employed the following
simple accounting equation to address the concerns about the validity of business income
streams.
Net profit = Revenue − Expenses = Draw + Retained earnings
(Equation 4.3)
Net profit from running a business can be used as a measure of business income. As an
alternative measure, draw (the money drawn from the business on a regular basis by the
72
owner) can also represent business income. The SCF does have information on the righthand side variables in Equation 4.3. The SCF asks about the net income of the all the
active and non-actively managed businesses that the household has an interest in. In other
words, a household that owns or manages a small business is asked how much they
received from retained earnings and how much they received in salary or wages before
taxes. As shown in Equation 4.4, the second dependent variable is denoted as BIR, the
business income ratio:
BIR =
(Equation 4.4)
The business income ratio represents the proportion of business income to total
household income, reflecting the level of diversification in total household income. If
small business owner-managers earn most of their income from the business, then the
income ratio will be close to 1.0. So, small business owner-managers with a higher
business income ratio may suffer if the business income drops substantially due to a lack
of income diversification. This idea about the business income ratio is theoretically
similar to the emergency fund holding concept or the precautionary savings model where
a household with a low level of precautionary savings may experience serious financial
problems when its household income drops or there are unexpected, large expenses.
Business net income and business net cash flow are not identical. Business net
cash flow refers to the amount of cash that a business receives and spends, reflecting the
change in the cash and cash equivalents of a business in a particular period of time.
Although business net cash flow measures the changes in the cash inflows or cash
outflows of its business operations, it does not capture the profitability of a business or
73
the level of its financial leverage. On the other hand, business net income refers to the
profit that remains with a business or its owner-managers after all the business expenses
including depreciation expenses are deducted from the total business revenue. Business
net income reflects the change in the financial holdings of a business incurred from its
operations. For example, a positive business net income indicates that the financial
position of a business is improving, while a negative business net income (or business net
loss) indicates that the business operations resulted in its financial holdings decreasing or
worsening. Therefore, evaluation of a business operation or financial holding need to be
based on business net income, not business net cash flows. This study also assumes that
business net income can provide better insight into the financial status of a business and
employs it as one of components of the dependent variable.
4.2.1.3 Financial Obligations Ratio (FOR)
“Monthly financial obligations include rent, vehicle leases, debt payments, real
estate taxes on the household’s residence, and homeowners insurance. Rent payments
include the monthly rent on a home, site, or farm/ranch, and lease payments include all
monthly lease payments on vehicle. Debt payments are the total of monthly payments on
all types of loans including credit cards, mortgages, lines of credit, home improvement
loans, land contracts, other residential property, vehicle loans, student loans, installment
loans, margin loans, loans against insurance policies, pension loans, and other loans.”
(Hanna et al., 2012)
Borrowing the methodologies of Dynan, Johnson, & Pence (2003) and Hanna et
al. (2012), this dissertation uses the financial obligations ratio as the last dependent
74
variable, which is a dichotomous variable for whether the household has a heavy
financial burden:
FOR =
(Equation 4.5)
The financial obligations ratio is defined as the ratio of monthly household
financial obligations to monthly household pre-tax income and uses the 40% guideline
used for the analysis. This dissertation assumes that small business owner-managers
having a monthly financial obligations ratio of greater than 40% of pretax income are
considered as those with having a heavier financial burden. In other words, financial
burden is determined by whether a small business owner-manager has a financial
obligations ratio of more than 0.4. The variable is coded as 1 if the financial obligations
ratio exceeds 0.4 and 0 otherwise.
4.2.2 Independent Variables
Based on the extended LCS model and MPT, this dissertation selects a set of
independent variables that can be divided into three factors including: socio-demographic
variables, economic status variables, and attitudinal/expectation variables. These
variables are constructed from corresponding survey questions used in the SCF data sets
(See Figure 3.2). For groups of independent variables such as marital status, the
significance levels of effects are for comparisons with the reference category, e.g.,
married. In some cases, separate calculations to estimate other differences, e.g., single
female versus single male, are conducted. More detailed information on the independent
variables is shown in the appendices.
75
4.2.2.1 Socio-demographic Factors
(1) Age
A continuous variable is used to measure the relationship between the age of the
respondent and their financial vulnerability. In order to detect any nonlinear effects of age,
age squared is included in the regression models. In addition, for descriptive analysis
purposes, six age dummy variables are created: “<30”, “31-40”, “41-50”, “51-60”,
“61-70”, and “>70”.
(2) Race/Ethnicity
For the SCF classification of racial/ethnic categories, respondents were asked,
“Which of these categories do you feel best describe you?” Although six racial/ethnic
categories were presented to respondents, only four categories are available in the public
data sets as the SCF combines: Asian, American Indian, Alaska Native, Native Hawaiian,
Other Pacific Islander, and Other into another single category, “Others”. Thus, the
race/ethnicity of a respondent is measured with four dummy variables: “White, Black,
Hispanic, and Other”
(3) Household Type
Household type is measured with using four dummy variables: “Married, Living
with partner, Single male, and Single female”.
(4) Education
76
The four dummy variables used to measure the relationship between the
educational attainment of a respondent and their financial vulnerability are “Less than
high school, High school graduate, Some college, and Bachelor’s or above degree”
(5) Self-employed
The SCF data sets also have a question about employment status: “We are
interested in your present job status. Are you working now, temporarily laid off,
unemployed and looking for work, on sick leave, disabled and unable to work, retired, a
student, a homemaker, or what?” and “What is your current main job? Do you work for
someone else, self-employed, or other?” The effect of having someone being selfemployed in a household is of primary interest for this dissertation. Thus, for married
households, the households are categorized as being self-employed if either one or both
spouses answered yes to the self-employment question, regardless of the status of the
other spouse. As for those who have both self-employment and paid-employment statuses,
the following convention is employed. Households who have a second self-employment
job are considered to be self-employed even though they reported their paid-employment
as their primary job.
(6) Sole Proprietorship
Business structure is measured with a dichotomous variable that equals 1 if the
household runs a business as a sole proprietorship, and 0 otherwise. Other business
entities include corporations, partnerships, LLCs, LLPs, and foreign corporations. Sole
proprietors who set up an LLC or an LLP for federal income tax filing purposes are also
coded as 0.
77
(7) Years of Business Operated
The number of years that a business has operated for is measured by using a
continuous variable.
4.2.2.2 Economic Status Factors
(1) Homeownership
Homeownership is measured with a dichotomous variable that is coded as 1 if the
respondent in the SCF said that the household owns a house; otherwise it is coded as 0.
(2) Availability of Emergency Fund
In the SCF, respondents were asked,: “In an emergency could you or your
(husband/wife/partner) get financial assistance of $3,000 or more from any friends or
relatives who do not live with you?”. This dichotomous variable about their availability of
additional funds from friends or relatives is coded as 1 if they answered yes to the
question and 0 otherwise.
(3) Income and Net Worth
For multivariate analyses, total household income and total net worth are
controlled by continuous variables: “the natural log of income” and “the natural log of
net worth”.
4.2.2.3 Attitudinal/Expectation Factors
(1) Risk Tolerance
To measure the subjective risk tolerance level, the following SCF survey question
is used: “Which of the statements on this page comes closest to the amount of financial
78
risk that you are willing to take when you save or make investments?” The four answers
to this survey question creating four dummy variables include “Take substantial financial
risks expecting to earn substantial returns”, ”Take above average financial risks
expecting to earn above average returns”, “Take average financial risks expecting to
earn average returns”, and “Not willing to take any financial risks”
(2) Expectation of Inheritance
Expectation of inheritance is measured by the following SCF question: “Do you
expect to receive a substantial inheritance or transfer of assets in the future?” If
respondents answered “yes” to the question, it is coded as 1 and 0 otherwise.
(3) Expectation on Future Income
Expectation of future income is measured by using four dummy variables created
from two questions in the SCF: “Over the next year, do you expect your total income to
go up more than prices, less than prices, or about the same as prices?” and “At this time,
do you have a good idea of what your income for next year will be?”. Among the
households who had a good idea of next year’s income, small business owner-managers
are classified as four dummy variables: “Sure same” when they expected their total
income to go up about the same as prices, “Sure grow” when they expected their total
income to go up more than prices, “Sure low” when they expected their total income to
go up less than prices, and “Not sure” when they were unsure about their future income
in relation to prices.
(4) Current Income Relative to Normal Income
79
In terms of measuring the current income and financial vulnerability, three
dummy variables are created, using one SCF survey question: “Is this income unusually
high or low compared to what you would expect in a ‘normal’ year, or is it normal?” The
three answers to this survey question are “High, Low, or Normal”. For example, the
dichotomous variable, “Higher than normal”, is coded as 1 if respondents reported that
their current income is higher than what they would expect in a normal year, and 0
otherwise.
4.2.2.4 Other Controlling Variables
(1) Survey Year
Six dummy variables for survey years including “1992, 1995, 1998, 2001, 2004,
and 2007” are used to capture the changes in the level of financial vulnerability since
1992.
(2) Household Health Status
Respondents are asked a question about their household health status: “Would you
say your health is excellent, good, fair, or poor?” For couple households, the respondent
is also asked to evaluate the health of the spouse/partner. For non-couple households,
household health status is based on the respondent's answer about his or her health
status. For couple households, household health status is based on both the respondent's
answer about his or her own health status and the answer about the spouse or partner's
health status. If the perceived health status of one spouse/partner is worse than that of the
other, then the worse one is used as the household's health status, e.g., if one is excellent
and the other is good, then household health status is coded as good.
80
(3) Health Insurance Coverage
The SCF has a question about whether everyone in the household is covered by
government or private insurance: “Is everyone here covered by some type of government
or private health insurance?” This dichotomous variable is coded as1 if respondents
answered yes to this question. If not, it is coded as 0.
4.3 Statistical Method
To test the proposed hypotheses, two statistical techniques are selected, a linear
regression model using the Ordinary Least Square method (OLS) and a logistic
regression model (Logit). Pohlmann and Leitner (2003) suggested that although logistic
regression results are comparable to those of OLS in many respects, a logistic regression
should be employed for models using binary dependent variables. The reason is that one
of the assumptions for OLS is violated if the dependent variable is dichotomous, and Logit deals
with this issue. Discussions about these two statistical methods are presented in this
section.
4.3.1 Ordinary Least Square (OLS) Regressions Analysis
It is assumed that an OLS regression model is appropriate for the business asset
ratio and business income ratio. OLS regression is simply a fitting mechanism, based on
minimizing the sum of squared residuals or the residual sum of squares (RSS) and is
generally considered to be the most common linear model analysis in social science
research (Pohlmann & Leitner, 2003). OLS regression is used when dependent variables
81
are continuous, unbounded, and measured on an interval or ratio level and when
independent variables are either interval, ratio, or dichotomous in nature (Menard, 2002).
Assuming that the level of financial vulnerability is affected by various household
characteristics and some business characteristics, the following linear regression model
using the OLS technique is proposed:
=
∗
+ε
·
The dependent variables
(Equation 4.6)
are the ratio of business assets to total household assets, and
the ratio of business income to total household income. The set of the independent
variables (
) include socio-demographic variables, economic status variables, and
attitudinal/expectation variables.
∗
is the vector of the coefficients to be estimated, and
ε is the error term.
H0:
′
HA: Not all
=
∗
′
=
′
/
=0
=0
Test Statistic (the F-test):
=
where MSR = Mean Regression Sum of squares &
Although the most common measure of fit of the regression is
misleading since it is always possible to increase
variable. Thus, the adjusted
=
, this may be
by adding another independent
value sometimes provides a better measurement for fitting.
To see whether the set of independent variables is useful in predicting dependent
82
variables, the null hypotheses are tested. If the null hypotheses are true, then the
dependent variable does not depend on the levels of the independent variables. This
means that there is no association between the dependent (response) variable and the set
of independent (explanatory) variables. On the other hand, if the null hypotheses are
rejected, one may conclude that there is a certain level of association among selfemployment, household and business characteristics of small business owners, and their
financial vulnerability.
4.3.2 Logistic (Logit) Regression Analysis
It is assumed that a logistic regression might be an adequate statistical method to
test whether small business owner-managers with particular characteristics are more
likely to be financially vulnerable than those with other characteristics. A logistic
regression is a statistical method that is useful when the response variable is dichotomous
and at least one of the explanatory variables is continuous (Pohlmann & Leitner, 2003).
The logistic regression model for this dissertation can be written as follows:
∗
=
′
ln
=
P( =1) =
∗
+ε
·
′
(Equation 4.7)
·
(Equation 4.8)
(β′ ·
)
′
(β ·
)
=
(β′·
)
(Equation 4.9)
, the binary observed variable in this regression model (Equation 4.7) takes 1 if
the financial obligations ratio of the household n is over 0.4 and 0 otherwise. Vector X
83
includes independent variables, β as the vector of coefficients to be estimated, and ε as
the error term. Three types of independent variables include socio-demographic variables,
economic status variables, and attitudinal/expectation variables in a logistic regression
(Equation 4.8). Unlike a simple linear regression model, there is no random disturbance
term in the equation for the logistic regression model (Allison, 1999).
Then, Equation 4.8 can be simplified further by dividing both the numerator and
denominator by the numerator itself. As shown in Equation 4.9, desired property will
always be a number between 0 and 1, regardless of the value of
not
′
are equal to zero is of main interest. If
′
and X . Whether or
= 0, then the probability of having a
heavy financial burden is independent of the level of x. If
having a heavy financial burden increases as
′
′
> 0, then the probability of
increases. Conversely, if
probability of having heavy financial burden decreases as
′
< 0, then the
increases. A hypothesis test
and test statistic are presented as follows:
H0:
′
=
HA: Not all
=
′
′
=
′
/
=0
=0
( )
According to Allision (1999), the odds ratio of an event is the ratio of the
expected number of times that an event will occur to the expected number of times it will
not occur. For example, an odds of 5 implies that one expects 5 times as many
occurrences as non-occurrences.
84
Odds ratio = (
)
=
(Equation 4.10)
Equation 4.10 implies that an odds ratio of less than 1 corresponds to probabilities
< 0.5, whereas an odds ratio greater than 1 corresponds to probabilities > 0.5. In the
variable settings of this dissertation, for instance, an odds ratio greater than 1 means that
the probability of being financially vulnerable is increasing as
increases, and an odds
ratio less than 1 means that the probability of being financially vulnerable is decreasing
as
increases. Sometimes the odds ratio is reported in papers, rather than the estimate, .
However, special attention should be paid when interpreting odds ratios since odds ratios
and the ratio of actual probabilities are synonymous.
4.3.3 Repeated Implication Inference (RII) Procedures
As suggested by Montalto and Yuh (1998), Repeated Imputation Inference (RII)
technique is used for the OLS regression models and the Logit regression model. The RII
procedures use data from all five implicates and incorporate estimates of error due to
missing data to produce adjusted estimates of variance. The RII technique also averages
the coefficients of each individual implicate of the SCF dataset.
85
CHAPTER 5
5 RESULTS
5.1 Descriptive Results of Research
The numbers of households who own or manage a small business for each survey
year from 1992 to 2007 are 856, 876, 844, 845, 817, and 807, respectively. As shown in
Table 5.1, the mean inflation-adjusted total income of households who own or manage a
small business has increased between 1992 and 2007, from $107,492 to $153,127 while
their mean inflation-adjusted total assets have increased from $851,626 in 1992 to
$1,584,475 in 2007. This is mainly due to the substantial increase in both retirement
assets, from $51,621 in 1992 to $145,671 in 2007, and business assets, from $299,156 in
1992 to $532,524 in 2007. The mean inflation-adjusted net worth has increased from
$755,066 in 1992 to $1,388,480 in 2007. The business income ratio has substantially
increased in the 1992 to 2007 period from 24% to 40% while the business asset ratio and
the financial obligations ratio have been relatively stable, staying in the 24% to 28%
range and 24% to 30% range, respectively.
The distributions of the three financial ratios are shown in Table 5.2. Some small
business owner-managers have very low values of the ratios. Over 10 % of small
business owner-managers have a zero for their business asset ratio while over 25% of
small business owner-managers have a zero income ratio, perhaps due to losses from the
86
business. On the other hands, some small business owner-managers have very high
ratios, with about 5% having a business asset ratio of 0.81 or higher, over 10% having a
business income ratio of 1.0 or higher and about 5% having a financial obligations ratio
of 0.82 or higher.
The sample distributions of the number of employees among small businesses in
the SCF data sets used for this study are presented in Table 5.3(a). This table also shows
the mean values of three financial ratios by the number of employees. As shown in the
table, small business owner-managers having 5 employees or less represent the majority,
with about 80% of the total small business sample, based upon the SBA firm-size
standard. Small business owner-managers with 51 to 100 employees have the highest
mean of the business asset ratio, those with more than 100 employees have the highest
mean of the business income ratio, and those with fewer than 6 employees have the
highest mean of the financial obligations ratio. Overall, small business owner-managers
having fewer than 6 employees have relatively lower financial ratios. In particular, the
business asset ratio of small business owner-managers having 51 to 100 employees is
significantly higher than that of small business owner-managers having fewer than 6
employees. In terms of the financial obligations ratio, small business owner-managers
with 5 or less employees have significantly higher ratios than other groups.
Table 5.4 presents the mean tests of the three financial ratios by categories of
independent variables used in the regression models. The third column of the table
provides sample compositions of each category. Significance tests with RII procedures
indicate whether the mean ratio of each category is significantly different from that of the
reference category.
87
Small business owner-managers younger than 30 years old have significantly
higher business asset and financial obligations ratios than other older small business
owner-managers while they have significantly lower business income ratios than other
older small business owner-managers. One plausible explanation for this result is that in
terms of household portfolio allocations, young business owner-managers tend to rely too
much on their business assets as they have not yet accumulated enough wealth to
diversify their household portfolio. Based on the LCS model, age is related to a saving
and borrowing behavior, with saving first increasing with age, then decreasing with age,
and with borrowing (or dissaving) showing the opposite pattern. Partly due to higher loan
payments on home mortgage, student loans, and business loans, the financial burdens of
younger small business owner-mangers might be heavier than those of the older groups.
For the racial/ethnic variables, White households among small business ownermanagers represent the majority, with about 90% of the sample. This result is consistent
with many previous studies about small businesses in the US. Hispanic households who
own or manage a small business have significantly higher mean values for both the
business asset and financial obligations ratios than White households, whereas Black
households have significantly lower mean values for the business income ratios than
white households.
Married households are also the majority with about 81% of the small business
owner-managers sample. Single male households have significantly higher mean values
in all three ratios than married households. Single female households have significantly
higher mean values of both the business income and the financial obligations ratios than
married households.
88
More than 56% of small business owner-managers have at least college degrees,
and relatively low financial vulnerability ratios. The mean business ratios of small
business owner-managers with higher educational attainment are significantly lower than
high school dropouts.
Table 5.3(b) presents the sample distributions of employment status and business
ownership in the SCF data sets, which reflects that business ownership and selfemployment are not identical. Small business owner-managers reporting selfemployment as their employment status is prevailing with more that 81% of the sample.
All of their mean financial ratios are significantly higher than those with other
employment statuses such as salary earners, retirees, or the unemployed. Small business
owner-managers with a sole proprietorship also have significantly higher mean values in
all three ratios than those with other types of legal business entities such as a corporation,
a partnership, or an LLC.
Most households owning or managing a small business also own a house. The
descriptive result shows that about 90% of small business owner-managers are
homeowners. However, homeownership has mixed effects on the ratios as homeowners
have a significantly lower mean business asset ratio than renters while they have a
significantly higher business income ratio than renters.
More than 47 % of small business owner-mangers reported that they are willing to
take an average financial risk. Small business owner-managers willing to take a
substantial risk have a significantly higher mean value in all three ratios than households
unwilling to take any risk. However, interestingly, a mean business income ratio of those
89
who are willing to take substantial risk is not significantly different from that of those
unwilling to take any risk.
The mean values of the ratios also differ by expectation of an inheritance. Small
business owner-managers who expect an inheritance have a significantly lower mean
business asset ratio than those not expecting an inheritance whereas those expecting an
inheritance have a significantly higher mean value of both the business income and
financial obligations ratios than those not expecting.
The majority of small business owner-managers, 68 % of the samples, have their
current year income lower than a normal year, but the mean value of their ratios are not
significantly different from those who have current income that is similar to a normal
year. On the other hand, the mean ratios of those having a current income that is higher
than a normal year are significantly higher than those having a current income that is
similar to a normal year.
Again, the mean business asset ratio of small business owner-managers has
declined from 28.8% in 1992 to 24.78% in 2007 whereas their mean business income
ratio has increased from 28.44% to 39.75% in the same time period. The financial
obligations ratio has also increased from 25.58% in 1992 to 30.76% in 2007.
Most small business owner-managers are covered by some type of private or
government health insurance including Medicaid and the Medicare program. The results
show that small business owner-managers having health insurance coverage have
significantly lower ratios than uninsured small business owner-managers.
90
Survey Year
1992
1995
1998
2001
2004
2007
45
46
47
48
49
48
107,492
106,295
122,875
156,642
136,969
153,127
40,592
40,715
48,793
52,082
42,468
57,810
Business asset
299,156
359,601
368,350
503,372
459,444
532,524
Total asset
851,626
920,824
1,082,565
1,512,274
1,422,766
1,584,475
35,299
38,491
38,365
50,076
53,245
48,357
200,449
238,342
354,222
509,514
365,637
431,679
Retirement asset
51,621
66,563
89,771
162,681
115,291
145,671
Total debt
96,559
92,892
115,151
131,012
161,508
195,995
Net worth
755,066
827,932
967,414
1,381,262
1,261,259
1,388,480
BAR (%)
28.76
26.95
27.38
26.92
25.79
24.78
BIR (%)
28.44
28.73
29.05
25.98
27.92
39.75
FOR (%)
25.58
24.63
26.99
26.35
27.73
30.76
Weighted % of Households*(%)
12.88
11.33
11.34
11.82
11.95
11.89
856
876
844
845
817
807
Age
Total income
Business income
Liquid asset
Financial asset
91
Sample size(n=5,045)†
† To generate valid estimates for the U.S. population, all results including weighted % of households are weighted except for the
actual number of households.
Table 5.1 Descriptive Analyses: Selected Characteristics of Small Business Owner-manager Households by Survey Year
91
Cumulative Distribution
BAR (%)
BIR (%)
92
0.00
0.00
5%
0.00
0.00
10%
3.31
0.00
25%
18.19
9.42
50%
44.78
56.67
75%
70.33
100.00
90%
81.20
100.00
95%
100.00
283.58
100%
Table 5.2 Distributions of Three Financial Vulnerability Ratios
92
FOR (%)
1.46
3.59
11.07
21.48
35.61
56.59
82.10
273.16
Number of Employee
Percentage
in Category
(%)
Frequency
BAR (%)†
(n = 5,045)
BIR (%)†
FOR (%)†
93
< 6±
23.26
28.66
28.03
79.3
3,022
38.96***
36.20***
24.56***
6-20
15.8
1,309
43.97***
28.47
20.42***
21-50
3.4
425
45.18***
32.80***
15.39***
51-100
1.1
170
37.76***
39.94***
16.54***
101-499
0.5
119
Note: ***p ≤ .01, **p ≤ .05, and *p ≤ .10
† All results for Mean Tests are based on weighted data except for the actual number of households.
±The reference category used in this table is for those who have fewer than 6 employees. Significance test is for mean difference from
reference category for each variable. RII procedure used to account for implicates structure of SCF data sets.
Table 5.3(a) RII Means Test by Number of Employee
93
%†
Actual
Non-business ownermanagers
†
%
Actual
Self-employment
8.98%
5,836
4.86%
1,571
Other employment
3.31%
1,364
82.85%
17,117
12.29%
7,200
87.71%
18,689
Business Owner-Managers
Column Total
† Percentages are based on the weighted numbers whereas actual numbers are not weighted.
Table 5.3(b) SCF Sample Distributions: Self-employment vs. Business Owner-managers
94
94
Independent Variables
Categories
Percentage
in Category
(n = 5,045)
BAR †
BIR †
FOR †
Socio-demographic Variables
Age of respondent (< 30)
95
Racial/ethnic status (White)
Marital status (Married)
Having child < age 19 (No)
Respondent’s education
(<High school degree)
< 30
34.87
0.3105
0.1982
0.3487
30-39
31.61
0.2907***
0.2775***
0.3161***
40-49
28.29
0.2638***
0.3209***
0.2829***
50-59
25.42
0.2406***
0.3175***
0.2542***
60-69
20.03
0.2497***
0.2890***
0.2003***
>69
11.92
0.2881***
0.3689***
0.1192***
White
90.27
0.2669
0.3023
0.2641
Black
2.83
0.2460**
0.2393***
0.3035***
Hispanic
2.79
0.3221***
0.3111
0.3358***
Other
4.11
0.2543
0.3026
0.2976***
Married
80.59
0.2583
0.2849
0.2566
Partner
4.02
0.2925***
0.2302***
0.2792***
Single(Male)
9.87
0.3281***
0.3528***
0.3151***
Single(Female)
5.53
0.2466
0.3997***
0.3225***
No
51.37
0.2702
0.3018
0.2485
Yes
48.63
0.2649***
0.2973***
0.2906***
<High school degree
4.86
0.3153
0.3740
0.2526
High school degree
22.27
0.3047
0.3096***
0.2834***
Some college
16.54
0.2880***
0.3042***
0.2982***
College and above
56.34
0.2267***
0.2786***
0.2525
Table 5.4 RII Means Tests: Three Ratios by Independent Variables
95
Table 5.4 continued
Independent Variables
Categories
Employment Status
(Others)
Others
Self-employed
Business entity
(Other entities)
Years of business operated (< 4)
Percentage
in Category
(n = 5,045)
18.79
BAR †
BIR †
FOR †
96
0.2281
0.1373
0.2404
81.21
0.2808***
0.3542***
0.2803***
Other Entities
54.42
0.3107
0.2672
0.2614
Sole Proprietorship
<4
45.58
0.2343***
0.3242***
0.2770***
27.73
0.2562
0.2169
0.3126
4-5
9.87
0.2585
0.2627***
0.2907***
6-10
19.79
0.2637
0.3509***
0.2729***
11-20
23.18
0.2710***
0.3397***
0.2374***
21-30
11.44
0.2650
0.3807***
0.2289***
31-40
5.58
0.3467***
0.4175***
0.1527***
>40
2.41
0.3975***
0.4339***
0.1251***
No (Renters)
9.74
0.3979
0.2788
0.3021
Economic Status Variables
Homeownership (No)
Yes (Home owners)
90.26
0.2429***
0.3034***
0.2643***
In emergency, could get $3,000
or more from friend or
relative?(No)
Attitudinal/Expectation Variables
No
58.20
0.2737
0.2913
0.2661
Yes
41.80
0.2590***
0.3107***
0.2758***
Subjective risk tolerance
(No risk)
No risk
15.72
0.2952
0.3281
0.2742
Average
47.27
0.2547***
0.2893***
0.2594***
Above average
29.58
0.2500***
0.2833***
0.2814
7.43
0.3319***
0.3318
0.2946***
Substantial
96
Continued
Table 5.4 continued
Independent Variables
Categories
Expect inheritance (Not expect)
Not expect
Future income expectation
(Sure grow)
Current income relative to normal
(Normal)
Percentage
in Category
(n = 5,045)
78.89
BAR †
BIR †
FOR †
0.2734
0.2933
0.2680
97
Expect inheritance
21.11
0.2454***
0.3226***
0.2786***
Sure grow
29.18
0.2559
0.2743
0.2423
Sure same
23.10
0.2683***
0.2861*
0.2809***
Sure low
13.44
0.2270***
0.2484***
0.2386
Not sure
34.28
0.2929***
0.3478***
0.2996***
Normal
14.79
0.2657
0.3043
0.1796
Higher than normal
17.21
0.2791***
0.3439***
0.3859***
Lower than normal
68.00
0.2642
0.2848
0.2515***
Year 1992
16.96
0.2876
0.2844
0.2558
Year 1995
17.37
0.2695***
0.2873
0.2463**
Year 1998
16.73
0.2738**
0.2905
0.2699***
Year 2001
16.76
0.2692***
0.2598***
0.2635
Year 2004
16.19
0.2579***
0.2792
0.2773***
Year 2007
16.00
0.2478***
0.3975***
0.3076***
Other Controlling Variables
Survey year (1992)
Health status (Poor)
All in household covered by
Poor
2.67
0.2969
0.2432
0.2470
Fair
13.21
0.3152
0.3319***
0.2802*
Good
47.89
0.2548***
0.2831***
0.2634***
Excellent
36.23
0.2600***
0.3155***
0.2787***
No
10.90
0.3361
0.4046
0.3593
97
Continued
Table 5.4 continued
Independent Variables
health insurance (No)
Categories
Yes
Percentage
in Category
(n = 5,045)
BAR †
BIR †
FOR †
89.10
0.2529***
0.2771***
0.2514***
† All results for Mean Tests are based on weighted data. The reference category used in each means test is indicated in parentheses.
Significance test is for mean difference from reference category for each variable. RII procedure used to account for implicates
structure of SCF data sets.
Note: ***p ≤ .01, **p ≤ .05, and *p ≤ .10
Table 5.4 RII Means Tests: Three Ratios by Independent Variables
98
98
5.2 Multivariate Results of Research
As discussed in Chapter 4, three dimensions of financial vulnerability including
the business asset ratio, the business income ratio, and the financial obligations ratio are
used to identify which types of households are most at risk in terms of asset and income
diversification as well as their financial obligations. OLS regression models test the
effects of household characteristic variables on the financial vulnerability ratios while a
Logistics regression model tests the effects of those variables on the financial burden
ratio. Table 5.5 and Table 5.6 present that the coefficient, standard error and p-value of
each independent variable associated with the level of financial vulnerability by using the
OLS regression models. Table 5.7 illustrates that the statistics of each independent
variable associated with the probability of having a heavy financial burden by using the
Logistic regression model with the coefficient, standard error, p-value and odds ratio. As
shown in the results tables for the multivariate analyses, many of the variables in the
regression models have significant effects on the three financial ratios.
5.2.1 The Business Asset Ratio: OLS Regression Model
Socio-demographic Factors
Table 5.5 reports that age and age squared variables are significantly related to the
business asset ratio. As previously discussed, the age squared variable is included in the
regression equation to see if age is non-linearly related to the business asset ratio. While
age is negatively related to the business asset ratio, age squared is positively related to the
99
business asset ratio. The result indicates a pattern of the business asset ratio decreasing
with age until age 86, and then the ratio increases with age at an increasing rate.
Small business owner-managers in the ‘Other' racial/ethnic category have a
lower business asset ratio than White small business owner-managers. Black and
Hispanic households are not significantly different from White households in terms of the
business asset ratio.
Compared with married households, single male households have a higher
business asset ratio, but partner and single female households are not significantly
different from married households in the level of business asset diversification. In order
to see a gender difference in the business asset ratio, this study also conducts a separate
regression model with a different reference group. After changing the reference group
with male households, the business asset ratio model shows that single male small
business owner-managers have a higher business asset ratio than single female small
business owner-managers. The presence of a child under age 19 is also not significantly
related to the business asset ratio.
The business asset ratio does not vary significantly with education for those with
less than a high school degree through some college, but households with a respondent
with a college degree have significantly lower ratios than those with less than a high
school degree. The regression model using households with a high school degree as the
reference group also shows that households with a respondent with a college degree have
significantly lower ratios than those with a high school degree.
100
Households with a self-employed head (or spouse/partner for couple households)
have a higher business asset ratio than those without self-employment status.
Households with a sole proprietorship have a significantly lower business asset
ratio than those with a different legal structure.
As the number of years in business increases, the business asset ratio increases.
Economic Status Factors
Small business owner-managers who own a house have a significantly lower
business asset ratio than renters. The result is consistent with previous studies such as Ji
and Hanna’s (2012) finding that among small business owner-managers, homeowners
tend to have a higher business asset ratio than renters.
Two continuous variables for income and net worth are transformed into by
calculating the natural logarithms, but using ln (.01) for zero or negative values. For the
business asset ratio, the coefficient for the log of income is significant and negative while
the coefficient for the log of net worth is significant and positive.
The multivariate result shows that the availability of an emergency fund from
friends or relatives is positively related to the business asset ratio. Small business ownermanagers who are able to borrow $3,000 from friends or relative in an emergency have a
higher business asset ratio than those who cannot.
Attitudinal/Expectation Factors
Small business owner-managers’ risk attitudes toward financial risks strongly
influence the business asset ratio. Table 5.5 shows that small business owner-managers
101
who are willing to take substantial financial risks have a significantly higher business
asset ratio, compared to those who are willing to take no risk. However, households
willing to take average or above-average risks have a significantly lower business asset
ratio than those taking no financial risk.
The expectation of a substantial inheritance has a significant and negative effect
on the business asset ratio. Small business owner-managers expecting to receive a
substantial inheritance have a lower business asset ratio than those who have no
expectation of a substantial inheritance.
In terms of future income expectations, small business owner-managers who
expect their next year’s income to increase less than price have a lower business asset
ratio than those who expect their next year’s income to increase more than price.
The result from the business asset ratio model suggests that small business ownermanagers who have unusually low current incomes have a lower business asset ratio than
those who have a current income that is similar to a normal year. However, small
business owner-managers with unusually high current incomes are not significantly
different from those with normal current incomes.
Other Controlling Factors
The predicted business asset ratio is higher in 1992 than in each of the other
survey years.
Household health status is significantly related to the business asset ratio, but it
has mixed effects. Small business owner-managers who reported poor or fair health
conditions have a lower business asset ratio, compared to those who reported their health
102
as being excellent. On the other hand, small business owner-managers who reported good
health conditions have a higher business asset ratio than those who reported their health
as being excellent.
Whether all people in the household who own or manage a small business are
covered by health insurance does make a significant difference in the business asset ratio,
but it is negatively related to the ratio, indicating that those whose household members
are covered by any types of health insurance tend to have a lower business asset ratio
than those without any health insurance.
103
Business Assets
Total Household Assets
Coefficient
s.e.
p-value*
0.5524
0.0234
<.0001
Age of respondent
-0.0069
0.0008
<.0001
Age of respondent (Squared)
0.00004
0.0765
<.0001
-0.0097
0.0071
0.1751
0.0003
0.0081
0.9708
-0.0286
0.0078
0.0003
Partner
0.0107
0.0070
0.1250
Single(Male)
0.0306
0.0053
<.0001
Single(Female)
-0.0041
0.0060
0.4957
Having child < age 19
0.0003
0.0035
0.9307
0.0120
0.0065
0.0674
Some college
-0.0101
0.0070
0.1518
College and above
-0.0631
0.0067
<.0001
0.0382
0.0037
<.0001
-0.0812
0.0032
<.0001
0.0031
0.0002
<.0001
Homeownership
-0.1992
0.0049
<.0001
Log (Income)
-0.0086
0.0012
<.0001
Log (Net worth)
0.0257
0.0007
<.0001
Emergency fund from friend/ relative
0.0218
0.0056
0.0001
Average
-0.0208
0.0041
<.0001
Above average
-0.0119
0.0048
0.0135
0.0405
0.0074
<.0001
Intercept
Socio-demographic Variables
Racial/ethnic status (White)
Black
Hispanic
Other
Marital status (Married)
Respondent’s education(<High school degree)
High school degree
Self-employed
Sole Proprietorship
Years of business operated
Economic Status Variables
Attitudinal/Expectation Variables
Subjective risk tolerance (No risk)
Substantial
104
Business Assets
Total Household Assets
Coefficient
s.e.
p-value*
-0.0277
0.0039
<.0001
Sure same
0.0001
0.0045
0.9850
Sure low
-0.0271
0.0053
<.0001
Not sure
0.0054
0.0044
0.2232
Higher than normal
0.0063
0.0048
0.1914
Lower than normal
-0.0120
0.0040
0.0029
Year 1995
-0.0188
0.0054
0.0005
Year 1998
-0.0160
0.0054
0.0033
Year 2001
-0.0355
0.0071
<.0001
Year 2004
-0.0437
0.0070
<.0001
Year 2007
-0.0565
0.0071
<.0001
Poor
-0.0368
0.0094
<.0001
Fair
-0.0384
0.0090
<.0001
Good
0.0206
0.0094
0.0273
-0.0450
0.0044
<.0001
Expect inheritance
Future income expectation (Sure grow)
Current income relative to (Normal)
Other Controlling Variables
Survey year (Year 1992)
Health status (Excellent)
All covered by health insurance
* The reference category is indicated in parentheses. Significance test (unweighted) is for
whether the coefficient is different from the reference category for each variable.
Table 5.5 Multivariate Analyses: OLS Regression Models for the Business Asset
Ratio (BAR)
105
5.2.2 The Business Income Ratio: OLS Regression Model
Socio-demographic Factors
The combined effect of effects for age and age squared (Table 5.6) imply that the
business income ratio increases with age until age 33.5, then decreases with age.
The racial/ethnic group chosen by the respondent is not significantly related to the
business income ratio, which is inconsistent with Ji & Hanna’s (2012) finding that Black
small business owner-managers have a significantly lower business income ratio than
White small business owner-managers.
The marital status of small business owner-managers represented by four dummy
variables with the reference group being married households affects the business income
ratio. Compared with married households, partner households have a lower business
income ratio whereas single headed households have a higher business income ratio.
While both male and female single households have a significantly higher business
income ratio, single female households do have a larger effect on the business income
ratio than single male households. Examining a gender difference in the business income
ratio, this study conducts a separate regression analysis with a different reference group.
After changing the reference group with male households, the business income ratio
model shows that single male small business owner-managers have a significantly lower
business income ratio than single female small business owner-managers. The presence
of a child under age 19 is also positively related to the business income ratio. Small
business owner-managers living with a child under 19 have a higher business income
ratio than those living without a child.
106
Household educational background makes a significant difference in the business
income ratio. Small business owner-managers who obtained a higher degree have a lower
business income ratio than those who did not get a high school diploma. Households who
obtained at least a college degree have a larger negative effect on the business asset ratio,
which shows that compared to high school dropouts that those with beyond a college
degree tend to have a lower business income ratio. The regression model using
households with a high school degree as the reference group also shows that households
with a respondent with a college degree have a significantly lower business income ratio
than those with a high school degree.
Small business owner-managers with a self-employed head (or spouse/partner for
couple households) have a significantly higher business income ratio than those without
self-employment status.
Small business owner-managers with a sole proprietorship have a significantly
higher business income ratio than those with other legal business structures.
Small business owner-managers have more years in business and have a
significantly higher business income ratio.
Economic Status Factors
The effect of homeownership on the business income ratio is different from that
found in the business asset ratio model. Compared to renters, small business ownermangers with homeownership have a higher business income ratio.
Again, to examine economic factors affecting the business income ratio of small
business owner-managers, both income and net worth variables are transformed into
107
logarithms with base 10. Both variables have significant and positive effects on the
business income ratio.
The availability of emergency funds from friends or relatives is also significantly
and positively related to the business income ratio. Small business owner-managers who
can borrow an emergency fund of $3,000 or more from friends or relatives have a higher
business income than those who cannot do so.
Attitudinal/Expectation Factors
Compared to small business owner-managers who take no financial risks, those
willing to take an average risk tolerance or an average above-risk tolerance have a lower
business income ratio. However, households willing to take substantial risks are not
significantly different from those not taking any risk.
Unlike the business asset ratio model, the expectation of a substantial inheritance
is significantly and positively relates to the business income ratio. Small business ownermanagers expecting a substantial inheritance tend to have a higher business income ratio
than those not expecting an inheritance.
Small business owner-managers expecting their next year’s income to increase
less than price or to increase the same as price have lower business income ratio,
compared to those expecting their next year’s income to increase more than price.
Current income relative to a normal year has somewhat ambiguous effects on the
business income ratio as both small business owner-managers with unusually low current
108
incomes and those with unusually high current incomes have a higher business income
ratio than those with current incomes that are similar to a normal year.
Other Controlling Factors
The predicted business income ratio is higher in 1995 and 1998 than in 1992, but
is lower in 1995, 1998, and 2007 than in 1992, and the effect for 2007 is higher than in
any other year.
The effect of health status is positively related to the business income ratio.
Small business owner-managers, who reported excellent health conditions have a lower
business income ratio, compared to those with worse health conditions.
Whether all in the household who own or manage a small business are covered by
health insurance has a great impact on the business income ratio. Small business ownermanagers whose household members are covered by any type of health insurance have a
significantly lower business income ratio than those without any health insurance.
109
Business Income
Total Household Income
Coefficient
s.e.
p-value*
-0.2940
0.0330
<.0001
0.0067
0.0011
<.0001
-0.0001
0.1079
<.0001
Black
-0.0179
0.0101
0.0756
Hispanic
-0.0110
0.0114
0.3349
Other
-0.0008
0.0110
0.9385
-0.0298
0.0098
0.0024
Single(Male)
0.0928
0.0075
<.0001
Single(Female)
0.1503
0.0085
<.0001
Having child < age 19
0.0200
0.0050
<.0001
High school degree
-0.0282
0.0092
0.0023
Some college
-0.0274
0.0099
0.0056
College and above
-0.0445
0.0094
<.0001
Self-employed
0.1848
0.0052
<.0001
Sole Proprietorship
0.0553
0.0045
<.0001
Years of business operated
0.0056
0.0003
<.0001
Homeownership
0.0223
0.0069
0.0013
Log (Income)
0.0093
0.0018
<.0001
Log (Net worth)
0.0089
0.0009
<.0001
Emergency fund from friend/ relative
0.0355
0.0079
<.0001
Average
-0.0120
0.0057
0.0352
Above average
-0.0171
0.0068
0.0118
0.0097
0.0104
0.3515
Intercept
Socio-demographic Variables
Age of respondent
Age of respondent (Squared)
Racial/ethnic status (White)
Marital status (Married)
Partner
Respondent’s education(<High school degree)
Economic Status Variables
Attitudinal/Expectation Variables
Subjective risk tolerance (No risk)
Substantial
110
Business Income
Total Household Income
Coefficient
s.e.
p-value*
0.0382
0.0055
<.0001
Sure same
-0.0260
0.0064
<.0001
Sure low
-0.0587
0.0075
<.0001
Not sure
0.0055
0.0063
0.3781
Higher than normal
0.0181
0.0068
0.0075
Lower than normal
0.0431
0.0057
<.0001
Year 1995
0.0151
0.0076
0.0468
Year 1998
0.0237
0.0077
0.0020
Year 2001
-0.0526
0.0100
<.0001
Year 2004
-0.0335
0.0099
0.0007
Year 2007
0.0821
0.0100
<.0001
Poor
0.0786
0.0132
<.0001
Fair
0.0465
0.0127
0.0002
Good
0.0758
0.0132
<.0001
-0.1067
0.0063
<.0001
Expect inheritance
Future income expectation (Sure grow)
Current income relative to (Normal)
Other Controlling Variables
Survey year (Year 1992)
Health status (Excellent)
All covered by health insurance
* The reference category is indicated in parentheses. Significance test (unweighted) is for
whether the coefficient is different from the reference category for each variable.
Table 5.6 Multivariate Analyses: OLS Regression Models for the Business Income
Ratio (BIR)
111
5.2.3 The Financial Obligations Ratio: Logistic Regression Model
Socio-demographic Factors
As shown in Table 5.7, in the Logistic regression model for the financial
obligations ratio, both the respondent‘s age and age squared are significantly related to
the likelihood of having a heavy financial burden. While age is positively related to the
likelihood of having a heavy burden, age squared is negatively related to it. The result
shows a pattern of likelihood of having heavy financial obligations that decrease with age
until 34.6 and then they increase.
No racial/ethnic differences between households with White respondents and
those with another race/ethnicity are significantly related to the probability of carrying a
heavy financial burden. This result is different from Hanna et al.’s (2012) finding that
Hispanic and Asian/other households have a significantly higher likelihood of carrying a
heavy financial burden than White households. However, they also found that households
with black respondents are not significantly different from those with White respondents.
Marital status has a significant effect on the financial obligations ratio. Compared
to married households, single female households have a higher likelihood of having a
heavy financial burden. However, other types of households such as partner and single
male-headed households are not significantly different from married households. To see a
gender difference in the financial obligations ratio, additional multivariate analyses with a
different reference group are conducted. After changing the reference group with male
households, the financial obligations ratio model reports that single male small business
112
owner-managers are significantly more likely to have a heavy financial burden than
single female small business owner-managers. The presence of related children under age
19 is not significantly related to the financial obligations ratio.
Compared to those with less than a high school diploma, households with a high
school educational level and/or some college degree are more likely to carry a heavy
financial burden. Whereas, a separate regression model using households with a high
school degree as the reference group shows that households with a respondent with a
college degree are less likely to have a heavy financial burden than those with a high
school degree.
Households with a self-employed head (or spouse/partner for couple households)
are more likely to carry a heavy financial burden than those without self-employment
status.
Sole proprietorship and the number of years in business are not significantly
related to the financial obligations ratio.
Economic Status Factors
Among small business owner-managers, homeowners are more likely to have a
higher financial obligations ratio than renters. The result is consistent with what Hanna et
al (2012) found in their study that homeowners tend to have a heavy financial burden.
The same logarithm transformations are used for both income and net worth
variables for the financial obligations ratio. Both variables are negatively related to the
likelihood of having a heavy financial burden.
113
The effect of the availability of an emergency fund of $3,000 or more from
friends or relatives on the likelihood of having a heavy financial burden is not found to be
significant.
Attitudinal/Expectation Factors
The effect of small business owner-managers’ risk tolerance levels on financial
vulnerability in terms of the financial obligations ratio model is not significantly different
from the reference group of those who are not willing to take any financial risks.
The expectation of a substantial inheritance is not significantly related to having a
heavy financial burden.
Future income expectations of small business owner-managers also have no
significant effect on the financial obligations ratio model.
The effects of current income relative to a normal year are significantly related to
the financial obligations ratio for both variables: an unusually high current income
relative to a normal year and an unusually low current income relative to a normal year.
Compared to those with current incomes that are about normal, small business ownermanagers with unusually high current incomes are less likely to have a heavy financial
burden whereas those with unusually low current incomes are more likely to have a
heavy financial burden.
Other Controlling Factors
114
The predicted financial obligations ratio is higher in 2004 and 2007 than in 1992, which
is similar to Hanna et al.’s (2012) findings where the calculated likelihood of having a
heavy burden in 2007 is 30.4%, twice as high as the 1992 calculated likelihood of 15.4%.
Similar to the results from the business ratios shown in Table 5.5 and Table 5.6,
whether all in the household who own or manage a small business are covered by health
insurance is negatively related to the likelihood of having a heavy burden. This result
shows that those whose household members are covered by any type of health insurance
are less likely to have a heavy financial burden than those without any health insurance.
However, no significant effects are found in the relationship between household health
status and the likelihood of having a heavy financial burden.
115
FOR > 40% of Income
Coefficient
p-value*
s.e.
-0.2504
0.7828
0.9084
0.0623
0.0393
0.0302
1.0640
-0.0009
0.0066
3.2298
<0.001
-0.1682
0.4736
0.2347
0.8450
Hispanic
0.4570
0.0602
0.2432
1.5790
Other
0.0120
0.9634
0.2609
1.0120
-0.0093
0.9682
0.2342
0.9910
Single(Male)
0.2812
0.1107
0.1763
1.3250
Single(Female)
0.7156
0.0001
0.1876
2.0450
Having child < age 19
0.1478
0.2183
0.1201
1.1590
Intercept
Odds Ratio
Socio-demographic Variables
Age of respondent
Age of respondent (Squared)
Racial/ethnic status (White)
Black
Marital status (Married)
Partner
Respondent’s education(<High school degree)
High school degree
0.6341
0.0061
0.2312
1.8850
Some college
0.4946
0.0448
0.2464
1.6400
College and above
0.2953
0.2203
0.2409
1.3430
0.4321
0.0012
0.1332
1.5400
Sole Proprietorship
-0.0411
0.7027
0.1078
0.9600
Years of business operated
-0.0107
0.1144
0.0068
0.9890
0.8284
<.0001
0.1714
2.2900
Log (Income)
-0.2577
<.0001
0.0567
0.7730
Log (Net worth)
-0.0555
0.0042
0.0194
0.9460
Emergency fund from friend/ relative
-0.0945
0.5953
0.1779
0.9100
Average
-0.0931
0.4854
0.1334
0.9110
Above average
-0.1422
0.3712
0.1590
0.8670
Substantial
-0.0210
0.9299
0.2384
0.9790
Self-employed
Economic Status Variables
Homeownership
Attitudinal/Expectation Variables
Subjective risk tolerance (No risk)
116
FOR > 40% of Income
Coefficient
p-value*
s.e.
-0.1173
0.3760
0.1326
0.8890
-0.2556
0.1069
0.1585
0.7740
Sure low
-0.0076
0.9667
0.1816
0.9920
Not sure
-0.1008
0.4917
0.1467
0.9040
Higher than normal
-0.8302
0.0001
0.2171
0.4360
Lower than normal
0.8283
<.0001
0.1211
2.2890
Year 1995
-0.0006
0.9975
0.1933
0.9990
Year 1998
0.3241
0.0870
0.1894
1.3830
Year 2001
0.4664
0.0487
0.2366
1.5940
Year 2004
0.5269
0.0217
0.2295
1.6940
Year 2007
0.8945
<.0001
0.2296
2.4460
Poor
0.1325
0.6943
0.3370
1.1420
Fair
-0.0780
0.8114
0.3269
0.9250
Good
0.1645
0.6259
0.3374
1.1790
-0.4250
0.0016
0.1347
0.6540
Expect inheritance
Future income expectation (Sure
grow)
Sure same
Odds Ratio
Current income relative to (Normal)
Other Controlling Variables
Survey year (Year 1992)
Health status (Excellent)
All covered by health insurance
* The reference category is indicated in parentheses. Significance test (weighted) is for
whether the coefficient is different from the reference category for each variable.
Table 5.7 Multivariate Analyses: Logistic Regression Models for the Financial
Obligations Ratio (FOR)
117
5.3 Summary
Table 5.8 summarizes the hypothesized effects and the empirical results. To
analyze several aspects of small business owner-managers, three ratios including the
business asset ratio, the business income ratio, and the financial obligations ratio are
used. These ratios may measure both the level of financial vulnerability and the
likelihood of carrying a heavy financial burden. Compared to those in business ratio
models, however, more life cycle variables in the financial obligations ratio model,
including age and the presence of children under age 19, do not have significant effects
on the likelihood of having a heavy financial burden.
Many common determinants of financial vulnerability such as household type,
education attainment, self-employment status, homeownership, income and net worth,
and health insurance coverage are found to be significant in all three models. In particular,
the effects of household types, self-employment status, and health insurance coverage on
financial vulnerability are statistically significant and have consistent effects on all three
models. Compared to those with couple households, single-headed small business ownermanagers are more likely to be financially vulnerable. The results also suggest that small
business owner-managers with self-employment statuses are more likely to be financially
vulnerable while those lacking health insurance coverage tend to be less financially
vulnerable. One plausible explanation for the health insurance coverage factor is that
small business owner-managers might experience more severe financial difficulties when
unexpected medical problems occur compared to those who have health insurance so that
they might not be willing to take risks in their assets and income. Therefore, their
financial vulnerability decreases.
118
Research Hypothesis
As age increases, a small business owner-manager
tends to take fewer risks. Thus, financial
vulnerability will decrease.
There should be no difference in race/ethnicity
when controlling for all other variables.
119
With an alternative source of income, a married
small business owner-manager will less likely to
be financial vulnerable.
Having a child under 19 will be positively related
to financial vulnerability.
Expected
effects
−
No effect
−
+
Empirical Results†
BAR
BIR
FOR
Effect negative up
to age 86*
[Min:85]
Effect positive up
to age 33.5, then
negative [Max:48]
Effect negative up
to age 34.6, then
positive*
[Min:36]
n/s
n/s
−*
−*
−*
[Single (M)]
[Single]
[Single]
n/s
+*
n/s
−*
−*
−
[Other]
As the education level increases, cognitive ability
and skills will tend to increase. Thus, financial
vulnerability will decrease.
−
As a self-employed SBOM tends to have riskier
income streams, their financial vulnerability will
increase.
+
+*
+*
+*
−
−*
+
n/s
−
+
+
n/s
Holding other things constant, an SBOM with a
sole proprietorship as their business legal
structure tends to be less financially vulnerable.
As the years of business operated increase,
financial vulnerability will decrease.
[>College]
Table 5.8 Hypothesized Effects of Variables on the Financial Vulnerability
and Empirical Results
119
+
[<College]
Continued
Table 5.8 continued
Empirical Results†
Expected
effects
BAR
BIR
FOR
As homeownership increases total household
assets, the BAR will decrease while as
homeowners have higher financial obligations,
the FOR tends to increase.
−/+
−*
+
+*
As a higher income an SBOM is more likely to
have sufficient resources, so financial
vulnerability will decrease.
−
−*
+
−*
−
+
+
−*
+
+*
+*
n/s
+
+*/−
−
[See Table 5.5]
[Average]
n/s
Research Hypothesis
120
As a higher net worth SBOM is more likely to
have sufficient resources, financial vulnerability
will decrease.
The availability of an emergency fund will
increase financial vulnerability.
The subjective risk tolerance level will be
positively related to financial vulnerability.
As an SBOM expecting an inheritance tends to
invest more in the current business, financial
vulnerability will increase.
+
−
+*
n/s
As an SBOM expecting real income growth tends
to take greater risks, financial vulnerability will
increase.
+
+*
+*
n/s
Continued
120
Table 5.8 continued
Empirical Results†
Expected
effects
BAR
If current income is lower than that of a normal
year’s income, an SBOM will be more likely to
be financially vulnerable.
+
−
Those with worse health are less likely to be
financially vulnerable today as they will take less
risk.
−
−*
Research Hypothesis
BIR
+*/−
[See Table 5.6]
+/−*
[See Table 5.6]
FOR
−
n/s
121
An SBOM covered by health insurance tends to
take fewer risks. Thus, health insurance coverage
−*
−*
−*
−
will be negatively related to financial
vulnerability.
† n/s: The effects are not significant. Category having a significant effect is indicated in parentheses. *with shaded cell indicates that
the empirical result is consistent with the hypothesized effect. For those with mixed effects, see also Table 5.5, Table 5.6, and Table
5.7.
Table 5.8 Hypothesized Effects of Variables on the Financial Vulnerability and Empirical Results
121
CHAPTER 6
6 CONCLUSIONS
6.1 Conclusions
As discussed in Chapter 2, many conflicting and overlapping definitions of a
small business owner-manager have been used interchangeably in previous literature,
which caused inconsistency issues. In order to solve this definitional problem, this
dissertation proposes five classifications, based on five primary characteristics of a small
business and its owner-manager that include: business motivation, types of business
formation, involvement of family members, business size, and occupational choices. This
dissertation finds that not only more precise definitions of small business ownermanagers are important, but more importantly, evaluating the factors affecting their
financial vulnerability are critical. Based on the extended Life Cycle Savings (LCS)
model and Modern Portfolio Theory (MPT), this dissertation aims to develop a
framework to evaluate the financial vulnerability of small business owner-managers and
to determine whether there are some common factors or household characteristics
associated with the level of their financial vulnerability.
122
Typically, households who own or manage a small business have a high share of
their total household assets invested in their single business and under-diversified and
unstable income streams, which exposes them to higher risks that may also affect their
overall financial well-being. Thus, the framework proposed in this dissertation
empirically measures the level of their financial vulnerability using three unique financial
ratios that capture major financial aspects of small business owner-managers. Based on
the multiple Surveys of Consumer Finances from 1992 to 2007, multivariate analyses
reveal that many factors of small business owner-managers including socio-demographic
factors, economic status factors, and attitudinal/expectation factors are important
determinants of their financial vulnerability. The major findings of this study are
presented in Table 5.8, which also compares the differences between the hypothesized
effects of factors and the empirical results of this study.
For the business asset ratio, the combined effect of age and age squared is that the
likelihood of being financially vulnerable decreases with age until age 86, then increases.
However, for the business income ratio, the combined effect of age and age squared is
that the likelihood of being financially vulnerable increases with age until age 33.50, then
decreases. The result of the financial obligations ratio also indicates that the respondent’s
age is curvilinearly-related to the level of financial vulnerability, with having the
likelihood of carrying a heavy financial burden, increasing until age 34.60 and then
decreasing.
Contrary to the hypothesis, small business owner-managers in the other
racial/ethnic group are significantly different from White small owner-managers in all
three models for financial vulnerability. One explanation could be that some other factors
123
whose direct measures are not included in the current models such as the types of
business funding and the way they intermingle their financial resources due to different
family cultures, may be correlated with their race or ethnicity.
Based on the descriptive results, married small business owner-managers are less
likely to be financially vulnerable than single headed small owner-managers. The results
from the separate regression analysis show that there is a gender difference in financial
vulnerability. Single female households tend to be more financially vulnerable than single
male households in terms of the business asset ratio, whereas single male households tend
to be financially vulnerable in terms of the business income ratio and the financial
obligations ratio.
Having a child under 19 is not a significant predictor in the business asset ratio
model and the financial obligations ratio. However, it shows a small positive impact in
the business income ratio.
It is hypothesized that small business owner-managers with less financial
knowledge might have less access to financial information or lack managerial ability to
perform or understand complex risk analysis to optimize their risk exposure. The results
show that educational attainment is a relatively strong predictor in the business ratio
models even after controlling for many other factors, and more educated households tend
to be less financially vulnerable.
Apparently, self-employment is a strong predictor of the financial vulnerability of
a small business owner-manager. The results from all three models show a positive effect
on financial vulnerability compared to those who have other employment statuses such as
wage and salary employed, and retired or non-employed, which also supports the
124
hypotheses about the effect of self-employment on financial vulnerability. Interestingly,
as shown in Table 5.3(b), there are a nontrivial number of non-business owner-managers
who reported self-employment status (4.86% of the sample). This may be due to
misunderstanding of respondents who are on the margin between paid-employment and
self-employment such as independent contractors or temporary worker who get a 1099
from their employee. Thus, a more precise definition of having a self-employment status
in the household should be considered when collecting relevant survey data samples.
Sole proprietorship status has mixed effects on financial vulnerability. The
coefficient for a sole proprietorship is significantly negative in the business asset ratio
model, reflecting that small business owner-managers running their business as a sole
proprietorship tend to be more financially-vulnerable than those having other types of
legal business structures such as a corporation, a partnership, an LLC, or an LLP.
Although it is negatively related to financial vulnerability in the business asset ratio
model, a sole proprietorship is positively-related in the business income ratio model.
Small business owner-managers with a sole proprietorship have a higher business income
ratio than those with other types of legal business structures. The result for the financial
obligations ratio does not yield significant differences between sole proprietorships and
other business legal entities.
Based on the results, how long the business has been operating is significantly and
positively related to both the business asset ratio and business income ratio,
demonstrating a pattern of increasing the level of financial vulnerability at an increasing
rate with the number of years that the business has operated for. As the number of years
their business operated increases, small business owner-managers tend to be more
125
financially vulnerable, which is different from the expected effect. However, the effect of
the number of years in business is not significantly related to the probability of having a
heavy financial burden.
Homeownership is negatively related to the business asset ratio while it is
positively related to the business income ration and the likelihood of having a heavy
financial burden. These results support the hypothesis that as homeownership increases
total household assets, the business asset ratio will decrease and as homeowners tend to
have higher housing expenses such as mortgage payments and property taxes, their
financial obligations ratio will increase.
Household income (log) is negatively related to the business asset ratio while the
Household net worth (log) is positively related to the business asset ratio. Both variables
in the business income ratio model have a positive effect on the level of financial
vulnerability. Unlike the results found in the business income ratio model, both variables
are negatively related to the likelihood of having a financial burden. As income and net
worth increase, the likelihood of carrying a financial burden decreases, in other words,
their financial vulnerability also decreases.
Being able to obtain $3,000 or more from outside the household is significantly
related to the business asset ratio and to the business income ratio, but it is not
significantly related to the financial obligations ratio. Considered as additional income
recourse or financing option, the availability of emergency funds from friends or relatives
might increase the willingness to take risks, which will lead to an increase in financial
vulnerability. This is partially supported in the models.
126
As shown in table 5.8, the hypothesis regarding the level of subjective risk
tolerance is also partially supported only in the business asset ratio model. Small business
owner-managers willing to take substantial risks are significantly more likely to have a
higher business asset ratio than those not willing to take any risk. In other words, as the
level of risk tolerance that a small business owner-manager household is willing to take
increases, the household is more likely to be financially vulnerable. The results provide
empirical evidence that can theoretically link the risk taking attitudes of a small business
owner-manager and his or her financial vulnerability. As small business owner-managers
are willing to take a riskier position in their financial investments, they might act more
aggressively about investing in non-financial assets such as a private business. If small
business owner-managers expect higher returns from their business, they might invest
more in their business so that they are more likely to rely more on their business in terms
of income resources and investment properties. This is supported in the model. However,
the estimates for the business income ratio suggest that the attitude toward financial risks
is negatively related to the financial vulnerability of small business owner-managers,
when controlling other factors. In the business income ratio model, small business ownermanagers who are willing to take average or above-average risks are less likely to be
financial vulnerable, compared to those who are not willing to take any risk, indicating
that financial vulnerability decreases. Unlike the business asset ratio and the business
income ratio, the effect of small business owner-managers’ risk tolerance levels on
financial vulnerability in the financial obligations ratio model is not significantly
different from the reference group of those who are not willing to take any financial risks.
127
Expectations about future income show a positive relationship with financial
vulnerability in both the business asset ratio model and the business income ratio model.
This supports the hypothesis implying that if small business owner-managers expect
income growth in the future, they tend to be more future-oriented and take greater risks in
the current period. Therefore, small business owner-managers who are optimistic about
their future income tend to be more financially vulnerable. However, the effect of
expectation about future income is not significantly related to the financial obligations
ratio.
Interestingly, the hypothesis about current income relative to normal year income
is not supported either in the business ratio model or in the financial obligations ratio
model. The effects of current income relative to normal year income are not consistent in
all three measures. One reason might be that the current income relative to normal year
income is not a perfect measure of financial vulnerability as other considerations such as
psychological factors and adjusted consumption behaviors may affect their financial
vulnerability. For example, small business owner-managers who had a lower income than
their normal year income are more likely to experience a substantial drop in their
business income and the value of their business assets. Due to financial hardships, they
may become more prudent in terms of their cash-flow management and income stream
risks, which will eventually decrease their financial vulnerability.
Consistent with the hypothesis that as housing prices increase, the financial
obligation ratio will also increase, increasing patterns are observed in the financial
obligations ratio regression model. Many reports related to U.S. national housing prices
show that there was a substantial increase during that period until the housing market
128
collapsed in 2007. For example, the S&P Case-Shiller Home Price Indices (2012), the
leading measure of U.S. home prices, show similar patterns that the index levels for the
U.S. National Home Price Indices have continuously increased from 1992 until late 2006
when housing prices began declining.
Health insurance coverage is one of the common determinants that are associated
with financial vulnerability in the three models of this dissertation. It shows a significant
and negative effect on financial vulnerability, which supports the hypothesis that as small
business owner-managers covered by health insurance tend to take fewer risks their
financial vulnerability will increase after controlling other factors.
6.2 Implications
6.2.1 Implication for Financial Professionals/Institutions
This dissertation contributes to literature on small business loans and the
implications of underwriting guidelines. As Parker (2003) noted, theoretically, if both
lenders and borrowers are perfectly-informed about all financial aspects of borrowers as
well as if the financial markets are flexible and competitive, then all borrowers having
positive net worth should be able to obtain their desired funding. In realty, however, this
ideal scenario rarely occurs, mainly due to imperfect information about the past and
current financial information of borrowers. For this reason, financial institutions and
underwriters evaluate several factors such as any involved financial or business risks, the
credit history of borrowers, and the value of collateral if any. After evaluating those
factors, lenders make a decision to accept the loan, to lower the loan amount, or to deny
the loan. From a lender’s perspective, how accurately they can evaluate the sustainability
129
of a borrower's financial status and whether they will be able to receive loan payments on
time from borrowers is always of concern. These concerns become more apparent in a
downturned economy. Lenders tend to review the borrower's household income, assets,
and outstanding debt more thoroughly to ensure his or her capability to repay the loan.
Due to stricter underwriting guidelines, certain borrowers such as small business ownermanagers who have unstable income streams or a substantial amount of intangible
business assets that are not identifiable are typically unable to obtain the funding that they
desire or any funding at all during an economic recession. Lenders might also lose their
profit potential if asymmetric information occurs, where lenders have limited or different
information about the quality of prospective borrowers in capital markets. Again, since
there are several characteristics that can affect the financial risks that lenders should
identify, it is critical for them to engage appropriate systems and expertise to properly
implement risk analysis for small businesses and their owner-managers. Considering
these issues related to debt financing for small businesses and their owner-managers, this
dissertation suggests that special attention should be paid to small business ownermanagers when determining whether or not they satisfy the loan requirements of lenders
and additional measurements for their level of financial vulnerability, in order to mitigate
problems related to imperfect information in capital markets.
As briefly discussed in Chapter 2, one of the research areas in personal finance
that is understudied is the intermingling issue of households who own or manage a small
business, particularly the diversification issue of their business income resources and
other income resources. Compared to the attention given to other general household
resource management issues, there has been significantly less attention to households
130
who own or manage a small business and the relationship between their financial
resources and their business ownership, perhaps due to a lack of useful data sets (Dew,
2008; Yilmazer & Schrank, 2010). Another reason could be that determining
intermingling issues between their household resources and business resources is
complex. Most previous intermingling research has addressed the flow directions of
household and business resources, either household resources to the business, or business
resources to the household. However, no studies to date fully address how the risks
caused by the financial interaction between the business and the owner would affect the
overall household financial well-being (Yilmazer & Schrank, 2010). In order to properly
examine the various intermingling issues of small business owner-managers related to
their financial health, it is first required for lenders or underwriters to determine the
factors affecting the intermingling level of their financial and non-financial resources, in
other words, the level of household resource allocation and diversification. Evaluating the
level of resource allocation and diversification should then be considered not only by
lenders or underwriters but also by small business owner-managers and the borrowers, in
order to identify income risks and stabilize their income streams. The reason for this is
that if small business owner-managers lose their business income, then their financial
stress would rise to the point where they would be unable to meet their financial
obligations. This risk of this business income loss might be heightened when they rely
too much on their business income.
Based on an extended life cycle model and the modern portfolio theory, this
dissertation also proposes a measurement of the level of income diversification for small
business owner-managers: the business income ratio. In addition to standard
131
measurement tools such as the debt to service and the financial obligations ratio, financial
institutions and financial experts should assess the level of household income portfolios
when providing financial advice or evaluating credit risks for their potential clients.
From the preceding discussions, it can be seen that identifying which small
business owner-managers are more financially vulnerable is also beneficial to various
parties such as financial service institutions, financial advisors, and educators. Certain
factors related to financial vulnerability of small business owner-managers should be
taken into account when developing new financial services products or financial
education programs. Given the findings of this study, for example, self employed and/or
single headed small business owners tend to be more financially vulnerable. Thus,
suggesting adequate diversification plans or cash flow managements that may prevent
small business owner-managers from detrimental results such as business failures or
financial crisis can be a solution to the issue of financial vulnerability among small
business owner-managers.
6.2.2 Implication for Policy Makers
This dissertation is intended to help policymakers develop solutions, policies, and
potential laws. Policy makers developing new legislation without considering the
definitional issues of a small business might lead to confusion and unproductive debates.
For example, President Obama signed into law the Affordable Care Act (ACA) on March
23, 2010. Although it will potentially create regulatory burdens and raise tax burdens, the
ACA is one of the most significant transformations of the U.S. health care system since
Medicare and Medicaid in 1965. According to the U.S. Department of Health and Human
Services (2012), beginning in 2014: “The ACA will require most Americans to buy
132
health insurance or pay fines of $95 per individual up to $285 per family or 1% of taxable
household income, whichever is greater.” It is expected that as a comprehensive health
care reform package, the ACA will provide opportunities to improve the U.S. health care
system and to expand health insurance coverage to people with lower income (U.S.
Department of Health and Human Services, 2012). Many previous studies reported that
health insurance coverage has been playing an important role in U.S household finances.
This dissertation provides additional evidence that small business owner-managers
covered by health insurance coverage are less likely to be financially vulnerable.
In light of the above, one driving question on the mind of researchers who are in
the small business field is: “How does this new bill affect small businesses and their
owner-managers?” Attempting to answer this question, this dissertation suggests that
additional studies that deal with the definitional issue of a small business are required.
With this new requirement, financial protection and an increase in health care access will
be provided for lower income taxpayers. Although the Affordable Care Act requires
employers to provide health insurance for their employees, small businesses with 50 or
more employees would be exempt from this new requirement. However, the bill is not
clear as to how the 50 full-time equivalent requirements are selected and what formula
should be used. Policy makers should develop more detailed instructions, exceptions, and
regulations with regard to how the ACA works. As presented in Table 5.3(a), the sample
distributions of five different categories based on the number of employees report that
small business owner-managers with fewer than 21 employees represent the majority,
with about 95% of the sample. Therefore, policy makers and regulators should first have
a more precise definition of small businesses and then categorize them into different
133
groups based on that definition. Different requirements and regulations should also be
applied to each group with increasing percentage rates, which is similar to a progressive
tax system. This might help government agencies and regulators implement the ACA
more effectively and efficiently, and eventually reduce the cost of government regulation.
6.3 Suggestions for Future Research
This dissertation determines the factors affecting financial vulnerability using
three financial ratio analyses. Future research and further investigations moving on from
this study should include more advanced financial ratio analyses with theoretically and
practically designed thresholds. As discussed earlier, relating financial ratio analysis to
reference points or standards provides better insights into the determinants of the
financial vulnerability of small business owner-managers. A plausible reference point of
a financial ratio might be obtained by establishing its trends from past years, by
comparing it to an industry standard, or by comparing it with a guideline required by
other interested parties such as governments or underwriters. One possible alternative to
develop a reference point is to evaluate the level of their financial vulnerability using
categorical variables such as four categories with lower than 25%, 25% to 50%, 50% to
75%, and higher than 75% rather than continuous variables. Ordered or cumulative
logistic regressions might be suitable for this approach, which may provide different
insights into the financial vulnerability of small business owner-managers.
Another possible area that might be utilized in future research surrounding this
topic is to examine the relationship between the financial health of business and the
financial health of its owner. As noted in the previous chapter, the SCF data sets have
134
limited information about businesses, which also limits the extent of this dissertation.
Therefore, having more business characteristics including use of household assets for
business, business liabilities, and debt structures available can improve the measurement
of financial vulnerability of households owning or managing a small business.
In addition, since the 1992-2007 SCF data sets used in this dissertation are not
panel data where each surveys are conducted in a single year, it is hard to compare the
level of financial vulnerability of small business owner-managers across the years. The
new panel Survey of Consumer Finances (SCF) between 2007 and 2009 could solve this
data limitation by containing the same small business owner-manager samples observed
over multiple time periods. The panel survey re-interviewed the panel of households that
had participated in the 2007 SCF (Bricker, Kennickell, Moore, & Sabelhaus, 2012). This
new panel survey might allow future researchers to examine more factors of small
business owner-managers and acquire more-detailed information on changes over the
period between 2007 and 2009.
135
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APPENDIX
1. Description of Explanatory Variables
Variables
Descriptions
Socio-demographic Variables
Age
Age of respondent
Age squared
Age squared of respondent
Racial/ethnic status
White
Black
Hispanic
Other
Marital status
Married
Partner
Single(Male)
Single(Female)
Having child < age 19
Respondent’s education
< High school degree
High school degree
Some college
College and above
Self-employment
Sole Proprietorship
Years of business
operated
1 if a respondent said he/she is White, 0 otherwise
1 if a respondent said he/she is Black, 0 otherwise
1 if a respondent said he/she is Hispanic, 0 otherwise
1 if a respondent said he/she chose a category other than
white, black, or Hispanic,0 otherwise
1 if a respondent is married, 0 otherwise
1 if a respondent is with partner, 0 otherwise
1 if a respondent is single male, 0 otherwise
1 if a respondent is single female, 0 otherwise
1 if the household has a child under 19, 0 otherwise
1 if respondent is a high school dropout, 0 otherwise
1 if respondent obtained high school degree, 0 otherwise
1 if respondent obtained some college degree, 0 otherwise
1 if respondent obtained college/ above degree, 0 otherwise
1 if the household reported self-employment status, 0
otherwise
1 if the household reported sole proprietorships, 0
otherwise
The number of years in business
Economic Status Variables
Homeownership
Log (Income)
1 if the household owns a house, 0 otherwise
Log of household‘s annual income
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Variables
Log (Net worth)
Emergency fund from
friend/ relative
Descriptions
Log of household‘s net worth
1 if the household can borrow emergency fund of $3,000
from friends/relatives, 0 otherwise
Attitudinal/Expectation Variables
Subjective risk tolerance
No risk
Average
Above average
Substantial
Expect inheritance
1 if not willing to take any risk, 0 otherwise
1 if not willing to take average risk, 0 otherwise
1 if not willing to take above average risk, 0 otherwise
1 if not willing to take substantial risk, 0 otherwise
1 if respondent said he or she is expecting substantial
inheritance, 0 otherwise
Future income expectation
Sure grow
Sure same
Sure low
Not sure
1 if a respondent has a good idea of what family income
for next year will be and expects total income to go up
more than prices, 0 otherwise
1 if a respondent has a good idea of what family income
for next year will be and expects total income to go about
the same as prices, 0 otherwise
1 if a respondent has a good idea of what family income
for next year will be and expects total income to go less
than prices, 0 otherwise
1 if a respondent does not has a good idea of what family
income for next year will be, 0 otherwise
Current income relative to
Normal
Higher than normal
Lower than normal
1 if a household current income is the same as normal year,
0 otherwise
1 if a household current income is higher than normal year,
0 otherwise
1 if a household current income is lower than normal year,
0 otherwise
Other Controlling Variables
Survey year
Year 1992
Year 1995
Year 1998
Year 2001
Year 2004
1 if year of survey is 1992, 0 otherwise
1 if year of survey is 1995, 0 otherwise
1 if year of survey is 1998, 0 otherwise
1 if year of survey is 2001, 0 otherwise
1 if year of survey is 2004, 0 otherwise
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Variables
Year 2007
Health status
Excellent
Poor
Fair
Good
All covered by health
insurance
Descriptions
1 if year of survey is 2007, 0 otherwise
1 if household health status is excellent, 0 otherwise
1 if household health status is poor, 0 otherwise.
1 if household health status is fair, 0 otherwise.
1 if household health status is good, 0 otherwise.
1 if household is covered by any health insurance, 0
otherwise
2. Additional Approach of the BAR
In order to provide further insight into the business asset ratio, an additional
explanation about the elements of the BAR is presented in Appendix by breaking the
ratio down into two distinct elements: household debt ratio and business asset coverage
ratio.
BAR =
x
The first element is a debt ratio, which is the proportion of household debt to total
household assets. Debt ratio indicates how much the household relies on debt to its total
assets, providing a quick measure of the amount of debt that the household has. Debt
ratio also measures financial ability of a household or a business to pay its current debts.
(Garman & Forgue, 2012, p77) Those with a higher debt ratio are more likely to have a
greater risk than those with a lower debt ratio. While a number of variants exist, experts
recommend that individuals or businesses maintain a maximum debt ratio of lesser than
0.5 to minimize chances of becoming insolvent.
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The other element of the business asset ratio is a business asset coverage ratio,
which is the proportion of business assets to total household debt. A coverage ratio
measures the ability of an individual or business to cover a particular financial obligation.
(Fabozzi & Peterson, 2003) A business asset coverage ratio that compares household
business assets to total household debt shows how well the business can cover the amount
of the household’s existing debts.
In order to keep their business as well as household less financially vulnerable,
households who own or manage a business are required to maintain a healthy business
asset coverage ratio. Experts agree that the resulting asset coverage ratio for most
industries should be at least 1.5, which ensures that the business cannot be financially
overextended or overuse household resources.
Future study may borrow this approach to analyze the level of financial
vulnerability or to develop a better measure of it.
Thank you God! I love you Lord!
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