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Personal Bankruptcy, Asset Risk, and
Entrepreneurship: Evidence from Tenancy by the
Entirety Laws∗
Je�rey Traczynski†
November 20th, 2014
Abstract
Personal bankruptcy law a�ects entrepreneurship decisions and credit markets for
small businesses. I show that personal bankruptcy law impacts �rm debt and equity
sources, indicating that personal bankruptcy law is immediately salient to small business
owners. I show that levels of personal asset protection a�ect small business decisions
by exploiting variation in tenancy by the entirety laws, a form of bankruptcy exemp-
tion available only to married people, to create within-state variation in bankruptcy
exemptions. I �nd that owners value unlimited asset protection more than the mean
level provided by homestead exemptions at more than $16,000 per year. I also �nd
that owners reduce labor supply between 3 and 6 hours per week compared to mean
exemptions. However, I do not �nd evidence of a statistically signi�cant impact of
tenancy by the entirety laws on spending on risky projects.
∗Preliminary and incomplete. Do not cite. Certain data included herein are derived from the Kau�manFirm Survey restricted access data �le. Any opinions, �ndings, and conclusions or recommendations ex-pressed in this material are those of the author and do not necessarily re�ect the views of the Ewing MarionKau�man Foundation.†Department of Economics, University of Hawaii at Manoa, Honolulu, HI 96822; jtraczyn@hawaii.edu.
1 Introduction
Small businesses are a large part of the U.S. economy, accounting for approximately 43%
of total payroll and 50% of employment in the U.S. and generating 65% of net new jobs
in the U.S. over the past 17 years (U.S. Small Business Administration, 2011). Small busi-
nesses also have very high turnover rates: in 2009, an estimated 552,600 new �rms opened
and 660,900 �rms closed. This turnover led to 60,847 business bankruptcies �led in 2009 as
�rm owners attempted to discharge debts accrued to their businesses. However, the num-
ber of bankruptcies due to business closures is likely underreported because �rm debts are
frequently personal liabilities of the �rm's owners for non-corporate �rms, so owners often
choose to �le for personal bankruptcy instead to eliminate both business and personal debts.1
Lenders may require that the owner guarantee business loans even for small corporate �rms,
making personal bankruptcy law relevant to a wide range of small businesses.2
The bankruptcy system may also lead to moral hazard problems in the operation of small
businesses. In personal bankruptcy, exemptions allow the debtor to keep some property as
part of the debtor's post-bankruptcy �fresh start.� These exemptions provide wealth insur-
ance to individuals and o�er protection against negative personal and business asset shocks
for small business owners. The insurance e�ect of exemptions may in�uence an owner's
decisions outside of the bankruptcy system, as the level of exemptions a�ects the amount of
risk an owner faces from negative shocks. Under generous bankruptcy exemptions, owners
may choose to engage in more risky investments, such as expensive research and develop-
ment projects with uncertain returns, or spend less time and energy on work knowing that
the bankruptcy system will cushion a business failure. To the extent that the bankruptcy
system distorts a �rm owner's decisions, the ine�ciencies created may be large. This pa-
per quanti�es the value of personal bankruptcy exemptions to �rm owners and documents
the e�ects on the operation of small businesses, particularly the labor supply decisions of
1Sullivan et al. (1999) and Lawless and Warren (2005) estimate that approximately 20% of all personalbankruptcy �lings involve the discharge of business debts.
2See Berkowitz and White (2004) for further discussion.
1
entrepreneurs.
Previous research analyzes the moral hazard problems posed by the bankruptcy system in
several ways. One strand focuses on an individual's decision to start a business, showing that
high state bankruptcy exemptions encourage self-employment through higher insurance and
cause interest rates for entrepreneurs to rise, with the positive insurance e�ects empirically
dominant (Fan and White, 2003; Berkowitz and White, 2004; Jia, 2010). In contrast, this
paper studies the decisions made after the creation of the business to see how the personal
bankruptcy system a�ects the amount of e�ort exerted by the entrepreneurs. Another strand
of research analyzes the e�ects of wage garnishments on individual labor supply after �ling
for bankruptcy theoretically at either the individual (Wang and White, 2000; White, 2005)
or macroeconomic level, with implications for the design of bankruptcy policy.3 Among
empirical papers, Han and Li (2007), Chen (2011), and Dobbie and Song (2013) estimate
the impact of bankruptcy on post-�ling labor supply. This paper presents empirical estimates
of the e�ects of the bankruptcy system's implicit wealth insurance on small business owners
regardless of whether they actually �le for bankruptcy. To the best of my knowledge, this
paper is the �rst to examine the labor supply e�ects of bankruptcy law on all small businesses.
A key challenge in assessing the importance of personal asset protections to entrepreneurs
is that the ability to exempt assets in bankruptcy is always available to debtors except in cases
of fraud. Similarly, �rm owners may become more interested in bankruptcy protections when
economic conditions are poor, making it di�cult to �nd e�ects of bankruptcy law through
the life of the �rm. Since business owners do not need to take any speci�c actions to use
exemptions when �ling for personal bankruptcy, it can be di�cult to determine if owners'
awareness of bankruptcy law is the cause of di�erent business operation decisions or how
valuable bankruptcy protections are to entrepreneurs. This problem explains the focus in
the prior literature on the one time decision to start a business rather than decisions that
can change over time, such as labor supply or investment decisions.
3See Athreya (2005) for a survey of macroeconomic equilibrium models of personal bankruptcy.
2
To address these issues, I use variation in tenancy by the entirety (TBE) laws across
states. TBE laws allow debtors in some states to exempt property owned jointly by a
husband and wife from the debts of only one spouse. A married entrepreneur can enjoy
the exemption o�ered by TBE laws only if the spouse has no role in �nancing the business.
Thus, loans must be made in the owner's name only and business assets must not be jointly
owned by husband and wife for TBE laws to apply. E�ectively, TBE laws create bankruptcy
exemptions that a debtor can choose to contract around through the types of debts the
owner acquires at any point in the life of the �rm, unlike all other bankruptcy exemptions.
A married small business owner has the opportunity to accumulate substantial individual
debts, which TBE property cannot be used to repay. Coupled with the high turnover rate
of small businesses, owners have both strong incentives and a clear opportunity to use TBE
laws to shield assets from creditors. This paper documents evidence of owners changing
the �nancing of their businesses to take advantage of bankruptcy exemptions through TBE
ownership, showing that bankruptcy laws a�ect the behavior of �rm owners throughout the
�rm's existence.
I investigate the e�ect of personal asset protections on small business decisions using
several complementary sources of individual and �rm level data. The empirical analysis uses
a di�erence-in-di�erence approach, exploiting cross-state variations in exemption levels and
TBE laws. My results show that �rm owners are aware of the protections o�ered by TBE
laws and arrange the �nancing of their businesses to maximize the protection of personal
assets. This e�ect is mildly stronger in states with stronger TBE laws and weaker in states
with large bankruptcy homestead exemptions, indicating that TBE laws and homestead
exemptions are substitutes. These results establish that �rm owners consider the level of
personal asset risk when making business decisions throughout the life of the �rm, not only
when closing the �rm or considering �ling for bankruptcy. Firm owners have lower revenues
when utilizing TBE laws, indicating that �rm owners are willing to surrender over $16,000
per year in pro�ts to obtain these asset protections.
3
I �nd that when �rm owners face less personal asset risk from business failure, they
devote less e�ort to their business by working fewer hours. This moral hazard e�ect is large:
a married �rm owner in a TBE state works between 3 and 6 fewer hours per week than
a married �rm owner in an average exemption state, a decrease in labor supply of 8-14%.
However, I �nd no statistically signi�cant e�ects on the probability of a �rm engaging in and
spending on research and development projects with uncertain returns. This suggests that
labor supply is a primary channel for the moral hazard created by bankruptcy law to a�ect
small businesses.
2 Background
2.1 Bankruptcy
When �ling for personal bankruptcy, debtors have a choice between Chapter 7 and Chapter
13 bankruptcy. Chapter 7 bankruptcy o�ers a complete discharge of debts, allowing the
debtor to keep only assets that can be held exempt from creditors. Chapter 13 requires
debtors repay some debts before receiving a discharge, but allows debtors to keep more
of their property. Businesses may �le a Chapter 11 reorganization bankruptcy that allows
the business to restructure contracts and retain assets while paying o� creditors, though
high �ling costs and long negotiations with creditors make Chapter 11 unattractive to small
business owners relative to Chapters 7 and 13.4
The Bankruptcy Abuse Prevention and Consumer Protection Act of 2005 (BAPCPA)
made many changes to the personal bankruptcy system in the United States. Most signi�-
cantly, BAPCPA introduced a means test whereby debtors with su�ciently high income are
under a �presumption of abuse� if they �le for Chapter 7 bankruptcy.5 The means test makes
it harder to �le Chapter 7, pushing �lers towards Chapter 13 and repaying some of their
4See Levin and Ranney-Marinelli (2005) for a discussion of changes to Chapter 11 as part of BAPCPAthat make Chapter 11 more di�cult for businesses.
511 U.S.C. �707(b)(2)(B), 2005. 11 U.S.C. �707(b)(2)(A) contains details on the means test.
4
debts. However, the means test for Chapter 7 bankruptcy applies only to a debtor �whose
debts are primarily consumer debts,� which has been interpreted by courts to mean that
debtors with primarily business debts are not subject to the means test.6 As a result, the
BAPCPA changes in �ling Chapter 7 bankruptcy were smaller for business owners than for
consumers, and business owners retained most of the protection of personal bankruptcy law
that they had before 2005. Paik (2013) �nds that BAPCPA did not change the relationship
between bankruptcy laws and entrepreneurship.
Both states and the federal government o�er debtors a variety of exemptions to use
in bankruptcy. The homestead exemption is designed to shield housing equity and has the
greatest dollar value in most states. Personal property, such as cars, tools of trade, furniture,
and jewelry are covered by smaller exemptions. Some states o�er wildcard exemptions that
debtors can use for any type of property up to a certain dollar amount. Wildcard exemptions
are sometimes available to non-homeowners to use in place of the homestead exemption,
though not of the same dollar value.
Previous work investigates the link between personal bankruptcy law and small business.
Fan and White (2003) show that individuals are more likely to choose self-employment in
states with high personal bankruptcy exemptions, while Berkowitz and White (2004) �nd
that small businesses in states with high personal bankruptcy exemptions are more likely to
be denied credit, receive smaller loans, and pay higher interest rates. These papers focus on
�rm entry and access to credit, while this study examines the e�ect of the insurance o�ered
by exemptions on the business decisions of �rm owners.
On bankruptcy and labor supply, Wang and White (2000) calculate optimal rates for
wage garnishment of a debtor's post-bankruptcy labor income through simulations. White
(2005) develops a theoretical model that considers the relationship between labor supply
and bankruptcy �ling decisions in the context of optimal bankruptcy policy, including both
611 U.S.C. �707(b)(1), 2005. For interpretation of the clause, see Wedo� (2005) and In re Kinnee, CaseNo. 06-21356 (Bankr. E.D. Wis, 2006) (unpublished decision available at http://www.wieb.uscourts.
gov/opinions/files/pdfs/In_Re_Kinnee,_06-21356.pdf). In re Kinnee asserts that debt is primarilyconsumer if more than 50% of the amount is consumer debt.
5
exemption levels and wage garnishments. Han and Li (2007) �nd no e�ect of bankruptcy
�ling on post-bankruptcy labor supply using PSID data, while Chen (2011) �nds a positive
e�ect using NLSY79 data. Dobbie and Song (2013) exploit random assignment of �rst time
Chapter 13 �lers to bankruptcy judges and �nd that �ling increases earnings. In contrast,
this paper focuses on the e�ect that exemption levels have on labor supply regardless of �ling
status by providing wealth insurance.
2.2 Tenancy by the Entirety
Tenancy by the entirety is a form of joint ownership in which a husband and wife both own
the undivided whole of a piece of property. This concept of ownership re�ects the idea that
a husband and wife are a single entity. An individual spouse cannot unilaterally give away,
partition, or sell his or her interest in TBE property, as the property is owned by the union
of husband and wife rather than either of the individuals.
When an individual debtor �les for bankruptcy, the bankruptcy estate must include �all
legal or equitable interests of the debtor in property.�7 Property owned by an entirety is not
property of an individual debtor and is thus exempt from creditors with no dollar limit on
the value of the TBE property. A married debtor is therefore able to exempt property held
as TBE from creditors with claims only against the debtor. If a debtor has joint debts with
a spouse such as a home equity loan on property that both partners own, or if a married
couple �les for bankruptcy jointly, then property held as TBE is part of the bankruptcy
estate and may be sold to pay creditors.8 The protections of TBE are maximized when a
married individual can accumulate debt in the individual's name only.
An important feature of TBE laws is that married �rm owners can choose whether or not
to use the protection of TBE laws through the debt structure of the �rm and may choose
to forgo the protection of TBE laws by incurring a joint debt or allowing both spouses to
711 U.S.C. �541(a)(1), 20058After United States vs. Craft, 535 U.S. 274 (2002), TBE property can be subject to federal tax liens
against an individual spouse. Since all data used in this paper are from after 2002, this decision does notdirectly a�ect my empirical �ndings.
6
have an equity interest in the �rm at any time. This is di�erent from all other bankruptcy
exemptions, which a debtor cannot agree to give up. Since avoiding joint debts precludes
the use of common funding sources for small businesses, most notably home equity for a
married couple that jointly owns their home, qualifying for TBE protections requires careful
planning. I therefore test whether bankruptcy exemptions in�uence small business operation
decisions by evaluating if married entrepreneurs structure the debts and ownership of the
business so as to shelter assets under TBE laws.
Tenancy by the entirety as a form of ownership �rst appeared in England in the 1200s
(Carrozzo, 2001). Under the doctrine of coverture in English common law, TBE gave the
husband complete control of all property owned by a married couple, as the wife's right to
own property was minimal. Phipps (1951, p. 24) describes this early form of TBE existing
into the 19th century as �man and wife were one and the one was male . . . marriage
amounted to an absolute gift of all the wife's personal property to the husband.� In the
United States, Married Women's Property Acts passed by states in the mid-1800's allowed
wives to own and control property separately from their husbands, creating a con�ict with
traditional TBE.
State court interpretations of the relationship between the Property Acts and TBE cre-
ated variation in the strength of TBE laws across states. While some states eliminated the
protections of TBE and some kept it largely intact, other states opted for a middle ground
where some TBE property could be held to satisfy debts, subject to various rights of the
non-debtor spouse. Franke (2009) provides a description of TBE laws across the 25 states
and District of Columbia that recognize TBE in some form. Franke (2009) also categorizes
states as either �full� or �modi�ed� TBE, where full TBE states do not allow creditors of an
individual spouse to make any claims against TBE property.9 Table 1 summarizes TBE laws
across states in 2009. Though TBE is regarded in both the economics and law literatures
as a powerful form of asset protection due to its unlimited dollar value, this paper is the
9Older categorizations of TBE laws may be found in Phipps (1951) and Sawada v. Endo, 561 P.2d 1291,1294�95 (Haw. 1977).
7
�rst empirical evaluation of the e�ects of TBE laws on businesses and the �rst to exploit
variation in TBE across states.10
3 Empirical Model and Data
To estimate the e�ect of the exemptions on small business operation decisions, I use a
di�erence-in-di�erence model to compare decisions made by married business owners and
single business owners in states with and without TBE laws. As a baseline, I estimate the
statistical model
Yis = α + β1 ·marriedi + β2 · TBEs + β3 ·marriedi · TBEs + π ·Xis + εis (1)
where Yis is the outcome of interest, TBEs is a dummy variable indicating whether state s
recognizes TBE ownership in any form, marriedi indicates if the owner of �rm i is married,
and Xis are other control variables. When Yis is a binary variable, the regression model is
a logistic speci�cation. Xis consists of the �rm owner's years of work experience, age and
age squared, as well as dummy variables for the owner's education level, race, ethnicity, and
gender. I also include dummy variables for the legal status of the �rm and the 2-digit NAICS
code for the �rm's industry. For �rms with multiple owners, I de�ne the primary owner as
the owner who holds the largest percentage of the �rm. If two or more owners hold the same
percentage, this tie is broken in favor of the owner with a greater number of hours worked,
level of education, age, and years of work experience in order, following Robb and Robinson
(2013).
In equation (1), β3 is the estimate of how the di�erence between married and single �rm
owners in the outcome variable di�ers across states with and without TBE laws. Married
individuals in TBE states receive the treatment of an unlimited bankruptcy exemption for
10See Kalevitch (1986), Concannon (1990), Dickerson (1998), Carrozzo (2001), Hynes et al. (2004), Hynes(2004), and White (2007). Hynes et al. (2004) use TBE laws as an outcome variable in studying determinantsof property exemptions across states.
8
TBE property against the debts of only one spouse, while single people do not. Unmarried
�rm owners are exposed to many of the same regulations and economics conditions in each
state as married �rm owners, making them a plausible control group that is una�ected by
TBE laws. Identi�cation of the e�ect of TBE laws relies on the assumption that there are
no other di�erences between TBE and non-TBE states that a�ect the relative outcomes of
married and single �rm owners. I examine some supporting evidence for this assumption
below.
The main dataset for analysis is the Kau�man Firm Survey (KFS), a longitudinal �rm
level survey of companies founded in 2004.11 The KFS collects data from �rm owners on
characteristics ranging from basic demographics and hours worked to types of debts and
equity investments yearly. I use the KFS data over the period 2004-2009, so the sample con-
sists of �rms that have remained in business for at least 5 years. The di�erence-in-di�erence
analysis uses the 2009 data for demographics and �rm characteristics, while previous years
reveal if a �rm ever used a particular source of either debt or equity funding. The KFS was
created from a random sample of Dun & Bradstreet's 2004 listing of new businesses and
oversamples �rms from industries with a high industry-wide level of employees performing
research and development. All results presented from the KFS data use the provided sample
weights due to this sampling structure. I use the restricted access version of the data to
obtain information on the state in which each �rm is located. I report summary statistics
in Table 2. Nearly 47% of �rms in this sample are in states with some form of TBE law,
making the asset protections of TBE laws relevant to a large fraction of �rm owners across
the U.S.
To obtain a broader sample of �rms, I supplement this analysis with the 2007 Survey of
Business Owners Public Use Microdata (SBO) from the U.S. Census Bureau.12 The data
include all nonfarm businesses in the U.S. �ling IRS tax forms with receipts of $1,000 or
11More information about the KFS can be found at http://www.kauffman.org/kfs/About-the-KFS.
aspx.12More information about the SBO can be found at http://www.census.gov/econ/sbo/about.html.
9
more in a tax year and are weighted to be nationally representative. All results presented
use the provided sample weights. The SBO provides the state in which each �rm is located,
though some states are grouped together for disclosure purposes. I keep data from these
grouped states only if all states in a group have the same TBE laws. Summary statistics are
reported in Table 2. The �rm owners in the SBO sample have lower levels of education than
those in the KFS sample, but the samples appear otherwise similar.
I use the state identi�ers in both the KFS and SBO to match �rms' locations to data
on TBE laws from Franke (2009) summarized in Table 1. Since TBE laws o�er unlimited
exemptions, I also use data on state bankruptcy exemptions to determine the e�ect of TBE
laws beyond the regularly available state exemption levels. Intuitively, TBE laws do not o�er
much additional protection in bankruptcy if the state's bankruptcy exemptions are already
high or unlimited, while they may have a much greater e�ect on decision making of �rm
owners in states with very low exemption levels. Since TBE laws apply to property jointly
owned by a husband and wife, the marital home is likely the most valuable jointly owned
asset and thus the homestead exemption is the most relevant comparison for the extent to
which TBE laws provide additional wealth insurance.
Table 1 lists the available homestead exemptions for married couples in all states in
2009. For states with a de�ned homestead exemption, the correlation between the home-
stead exemption level and whether or not the state recognizes any form of TBE ownership
is -0.254, indicating that states with TBE laws tend to have lower homestead exemptions.
This correlation suggests that TBE laws may serve as a substitute for generous bankruptcy
exemptions. This negative relationship between homestead exemption size and TBE laws
also holds true when states with unlimited homestead exemptions are added to the sample.
I assign a value of $550,000 to the homestead exemption in states with unlimited exemp-
tions, matching the largest de�ned homestead exemption.13 After including the unlimited
13This is consistent with Berkowitz and Hynes (1999) and Traczynski (2011), both of which use a value of$500,000 for states with unlimited homestead exemptions. Both papers, however, use older samples whereno state had a de�ned homestead exemption larger than $500,000. I therefore increase the value assignedto unlimited exemption states so that unlimited exemptions remain the largest exemptions available. The
10
exemption states, the correlation between the homestead exemption and recognition of TBE
laws is -0.147. Interestingly, this negative relationship appears to be driven by states with
full TBE laws rather than modi�ed TBE laws. The correlation between the homestead ex-
emption size and having a modi�ed TBE law is 0.024 among states with de�ned exemptions
and 0.021 when including all states, while the correlation between homestead exemption size
and having full TBE protections is -0.303 across de�ned exemption states and -0.188 across
all states. The di�erential negative relationship between homestead exemption levels and
types of TBE laws motivates the heterogeneity analysis below.
Table 3 compares the observable characteristics of �rm owners in states with and without
TBE laws. The only statistically signi�cant di�erence in means at conventional levels across
these characteristics of �rms and owners is that non-TBE states appear to have a slightly
higher percentage of Hispanic �rm owners. This suggests that �rm owners in states with
and without TBE laws are similar, so there is no general sorting across states correlated
with TBE status. Since TBE laws a�ect only married people, Table 4 looks speci�cally at
whether married entrepreneurs in TBE states or their �rms di�er in their observables. Each
regression in Table 4 uses the observables variable at top as the dependent variable, with
only the Married, TBE, and married ·TBE dummies as explanatory variables.14 The results
show that married individuals in TBE states are more likely to be Asian than married �rm
owners in non-TBE states, a result statistically signi�cant at the 10% level. However, this
is the only observable di�erence between married �rms in TBE and non-TBE states, and
�nding only one of these 16 regressions to have an interaction term statistically signi�cant at
the 10% level is consistent with expected rates of Type I error. Overall, it appears that there
is little evidence of di�erences in observables across TBE and non-TBE states for all �rm
owners and for married �rm owners, supporting the claim that TBE laws are not correlated
results presented below are not sensitive to the exemption value chosen for unlimited homestead exemptionstates.
14�Other Legal Form� is excluded as a �rm characteristic because of the small number of �rms in the datawith this form, none of which are owned by a married entrepreneur in a TBE state. Columns (1) and (2) ofTable 4 report coe�cients from weighted least squares regressions, while all other columns report marginale�ects from logistic regressions.
11
with other observable di�erences that may confound estimates.
4 Results
4.1 Do Owners Take Advantage of TBE Protections?
As discussed above, �rm owners may only exempt property owned as TBE from the claims
of creditors of one spouse. If a creditor has a claim against both spouses, then the TBE
property may be used to satisfy the debt. Married �rm owners who value the protection
o�ered by TBE laws should therefore not use home equity loans to �nance the business,
as both spouses approving the mortgage would make the house vulnerable to seizure in
bankruptcy. There should also be less joint ownership of �rms by married partners in TBE
states, as business debts for which both spouses are liable similarly expose any TBE property
to collection. I test the e�ect of TBE laws using data on personal debts of �rm owners in
the KFS data using equation (1). I de�ne a binary variable equal to 1 if a �rm owner has
ever used a particular source of credit for business purposes and present results in Table 5.
Summary statistics for these variables are in Table 2. All standard errors are clustered at
the state level.
Columns (1)-(4) report logistic regression results for whether or not a �rm owner has ever
used personal loans from a bank including home equity loans or mortgages, business credit
cards issued in the owner's name, personal credit cards, or personal loans from family or
friends, respectively. The results show that married �rm owners in TBE states are less likely
to use mortgages or home equity loans and more likely to use business credit cards issued in
an owner's name than married �rm owners in non-TBE states. Business credit cards in the
owner's name separate business and personal debts, keeping debt in the name of only one
individual. By contrast, married �rm owners' personal credit cards may be held jointly with
a spouse, creating joint debts that expose TBE property, or may be held in only the �rm
owner's name. The data do not reveal if a personal credit card is joint, so the insigni�cant
12
result here is not surprising. Personal loans from friends and family are less likely than the
other three sources of credit to go through a formal market or use a contract and therefore
serve as a falsi�cation test. Since providing collateral and the ability to seize assets in
bankruptcy is likely less of a factor in obtaining loans from family and friends than in formal
credit markets, TBE laws should not a�ect the use of this source of credit. Column (4) shows
that TBE laws do not have a statistically signi�cant impact on the prevalence of personal
loans from friends and family. Also, no regression reveals a statistically signi�cant di�erence
in the level of the dependent variable for single individuals across TBE and non-TBE states,
another falsi�cation exercise as TBE laws should not impact single �rm owners.
The e�ects on home equity loans, mortgages, and business credit card use are also eco-
nomically signi�cant. The di�erence between married and single �rm owners using home
equity loans or mortgage debt to �nance a business is 10.9 percentage points smaller in TBE
states. Since 32.7% of �rms report ever using these loans, this e�ect represents a decrease in
the use of home equity loans and mortgages as a means of business �nance of approximately
one-third. For business credit cards, the 7.26 percentage point increase in usage corresponds
to a 10.7% increase in the use of this form of �nancing.
To determine if TBE laws a�ect sources of equity investments in the business, I de�ne a
binary variable equal to 1 if a �rm has ever received an equity investment from that source.
I again estimate the e�ect of TBE laws using equation (1) and present results in columns
(5)-(7) of Table 5. I focus on two types of potential equity holders, government agencies and
spouses who are not also owners. Government agencies refers to Small Business Investment
Companies (SBICs), privately owned companies backed by the U.S. Small Business Admin-
istration that can make equity or debt investments in small businesses.15 SBICs receive
guarantees on loans up to a certain dollar amount from the Small Business Administration,
so these groups should show little sensitivity to risk. The results in column (5) of Table 5
con�rm this intuition, as married �rm owners in TBE states show no statistically signi�cant
15See http://www.sba.gov/content/sbic-program-0 for more information on the program.
13
di�erence in the likelihood of equity investment from government agencies. I also �nd no
di�erence in the likelihood of equity investments for single �rm owners across TBE and non-
TBE states. When limiting the sample to married �rm owners in column (6), I �nd that the
�rm owner's spouse is less likely to own an equity stake the �rm in TBE states. Since the
KFS does not indicate the relationships between �rm owners, I turn to the SBO data to see
if there are fewer instances of spouses jointly owning a business in TBE states. Column (7)
of Table 5 shows that the percentage of businesses jointly owned by married couples is lower
in TBE states. The results in columns (6) and (7) provide further evidence of �rm owners
attempting to maximize the protections of TBE laws by excluding a spouse from owning any
share of the business.
4.2 Heterogeneity in TBE Laws
I investigate whether �rm owners are more likely to change their loan types or receive di�erent
equity investors when TBE laws o�er stronger protections. I replace the dummy for TBE
laws in equation (1) with separate dummies for states with modi�ed and full TBE laws,
following the categorization of variation in TBE laws given by Franke (2009). In states with
full TBE laws, the protections for TBE property against creditors of an individual spouse
are very strong, while states with modi�ed TBE laws may allow creditors to attach liens to
TBE property or make other claims against it, subject to a variety of conditions.16
In Table 6, I estimate the e�ects of these di�erent types of TBE laws using the same
dependent variables as in Table 5. In columns (1)-(4) of Table 6, the di�erence between the
coe�cients on the interaction terms Full TBE ·Married and Modified TBE ·Married
is never statistically signi�cant, suggesting that the modi�cations made to TBE laws have
not led �rm owners to take out di�erent types of loans. Despite the di�erences in TBE laws
across states, �rm owners still try to take advantage of TBE protections by using business
credit cards and not using home equity loans or mortgages to �nance their business spending
16Franke (2009) contains a thorough discussion of the di�erent types of modi�cations made across states.
14
in all TBE states. Among types of equity investors, only column (6) shows a di�erential e�ect
of types of TBE laws, with full TBE states showing larger negative e�ect on the probability
of a �rm having an equity investment from an owner's spouse. As a whole, these results
show that the type of TBE law does not have a large e�ect on loan types or the identity of
�rm equity investors, though what di�erence exists suggests that there is a larger behavioral
response when TBE laws o�er stronger protection from creditors.
4.3 TBE Laws and Bankruptcy Exemptions
To the extent that TBE laws function as large bankruptcy exemptions for married people,
married �rm owners in TBE states that also have high homestead exemptions may gain
minimal additional asset protection from TBE laws. I interact a state's homestead exemption
for married couples (in $10,000s) with TBE laws, yielding
Yis = α + β1 ·marriedi + β2 · TBEs + β3 · exempts + β4 · TBEs · exempts
+β5 · TBEs ·marriedi + β6 ·marriedi · exempts
+β7 · TBEs ·married · exempts + π ·Xis + εis
(2)
where exempts is the state's homestead exemption. I focus on the homestead exemption
because housing equity is likely to be a married couple's largest jointly owned asset and
is therefore potentially a�ected by both TBE laws and a state's homestead exemption. I
show results in Table 7 for the full sample as well as a sample of states with a de�ned
homestead exemption to explore the sensitivity of the results to the $550,000 exemption
amount assigned to states with an unlimited homestead exemption. Speci�cations for the
sample of all states also include a dummy variable for whether a state has an unlimited
homestead exemption to capture any e�ects of an unlimited homestead exemption beyond
the dollar amount assigned.17
17For states that allow the use of federal exemptions, I replace the state homestead exemption with thefederal homestead exemption amount if the federal exemption is greater.
15
The results in Table 7 con�rm the intuition that TBE laws are substitutes for the wealth
insurance o�ered by homestead exemptions. For loans, the di�erence-in-di�erence estimates
of Table 5 show that TBE laws reduce the probability of using a mortgage or home equity
loan to �nance a small business and increase the likelihood of using a business credit card.
Columns (1) and (2) of Table 7 have a positive coe�cient on the triple interaction term,
indicating that married �rm owners in TBE states are more likely to use a mortgage or
home equity loan when exemption levels are high. This result shows that when exemptions
are high, �rm owners are less likely to take steps to preserve the protections of TBE laws.
Columns (3) and (4) repeat this analysis for the probability of using business credit cards
and show that the triple interaction term has the expected negative sign, though the term
is statistically insigni�cant. In non-TBE states with larger homestead exemptions, married
�rm owners are more likely to use business credit cards. However, this is not true in TBE
states, where there is no signi�cant di�erence in business credit card usage for married �rm
owners when the homestead exemption is larger. Finally, columns (5) and (6) show the
same pattern for spousal equity investment. Though TBE laws lower the probability of
a spouse having an equity investment in a �rm, the probability rises in TBE states with
higher homestead exemption levels as the asset protection of the high homestead exemption
replaces that of the TBE laws. Overall, these results show that TBE laws and bankruptcy
homestead exemptions function as substitutes, with �rm owners less likely to structure a
�rm's debts and ownership to take advantage of TBE asset protections when a state already
provides generous bankruptcy exemptions.
4.4 Owner Heterogeneity
Taking full advantage of the protections of TBE laws requires the owner to be aware of
TBE laws and plan out the debt structure of the �rm accordingly. More experienced or
sophisticated owners may be more likely to use TBE laws to shelter assets. To investigate
this, I interact various owner characteristics with TBE laws in equation (1) and present
16
results in Table 8. I use the owner's years of experience as a �rm owner, whether the
owner owns another �rm in the same industry, whether the owner owns any other �rm,
and whether the owner has completed a 4 year college or graduate degree as proxies for the
owner's likelihood of knowing about TBE laws and how to use them. I focus on the �rm
owner's decision to take out a bank loan or mortgage due to the consistent responsiveness
of this variable to TBE laws in previous results and because the need to avoid joint debts
with a spouse is a central feature of TBE laws.
Table 8 shows that in all regressions, the triple interaction term is negative, indicating
that a married owner in a TBE state with more work experience, ownership of other �rms,
or higher education is less likely to use a personal bank loan as part of business �nancing.
However, this di�erential e�ect is only statistically signi�cant for years of work experience.
Column (1) shows that each additional year of experience for the �rm owner reduces the
probability of ever taking out a personal bank loan by 0.76 percentage points. As 32.7% of
�rm owners report using a personal bank loan, this result implies that one additional year of
work experience decreases the use of personal bank loans by approximately 2%. The results
in Table 8 o�er some evidence that owners with more experience are more likely to take
advantage of TBE protections, a sensible result given the requirements of TBE laws.
4.5 E�ects of Asset Protections on Business Operations
The above results establish that �rm owners make �nancial decisions for the �rm to take
advantage of the personal asset protections o�ered by TBE laws. I now turn to how these
asset protections a�ect �rm outcomes and other business operation decisions such as labor
supply and spending on risky projects.
Firm pro�ts may be a�ected through several channels. If �rms are credit constrained
because owners gained asset protection at the cost of losing access to housing equity as capital
for business use, then pro�ts may fall because the business is smaller. The magnitude of a
fall in pro�ts provides a measure of how much �rm owners are willing to give up in exchange
17
for asset protections. I estimate e�ects using equation (2) to control for the potentially
important e�ect of the homestead exemption level on credit markets.
Columns (1) and (2) of Table 9 show that �rm revenues are lower for married owners in
TBE states, but there is no statistically signi�cant e�ect on �rm expenditures.18 Firms in
states with larger homestead exemptions see less of a negative e�ect of TBE laws on revenues
and expenditures, further supporting the substitutability of TBE laws for exemption levels as
shown in Table 7. These results are economically large: in a state with the mean homestead
exemption level, the estimates from column (1) imply that revenues for married �rm owner
in a TBE state are 20.1% lower while column (2) shows that expenditures are 8.9% lower.
To translate these �gures into an estimate of the e�ect on pro�ts, I �rst note that the
de�nition of total expenditures in the KFS data includes amounts spent on wages, salaries,
interest on loans, capital leases, and materials. This may not include all expenditures of
the �rm, partially explaining the di�erence between the reported values of total revenues,
total expenditures, and �rm pro�ts. I therefore calculate the reduction of the gap between
revenues and expenditures associated with TBE laws, and apply this percentage to the mean
level of pro�ts. Using the average values of revenues and expenditures, a 20.1% reduction in
revenues and a 8.9% reduction in expenditures means that the di�erence between revenues
and expenditures falls from approximately $332,000 to $209,000, reducing the gap to 63%
of its previous level. As the mean value of pro�ts is $14,505, such a reduction implies that
pro�ts would fall by $5383. The results in Table 5 imply that roughly one-third of �rm
owners take advantage of TBE laws by not taking out a loan against home equity. Scaling
up this estimate appropriately, I �nd that �rm owners are willing to give up approximately
$16,150 in yearly pro�ts in exchange for the additional asset protections of TBE laws above
what their states already protect through homestead exemptions.
Columns (3)-(6) explore some of the possible mechanisms behind the changes in revenues
and expenditures. Column (3) shows that there is no statistically signi�cant change in the
18A stacked regression shows that the di�erence between the TBE ·Married coe�cients in columns (1)and (2) is statistically signi�cant at the 10% level.
18
number of employees hired when asset protections are greater, suggesting that �rms do not
change in size by this measure. Instead, changes in behavior other than hiring must lead to
the observed declines in revenue. Columns (4) and (5) show that conditional on obtaining
personal or business loans, TBE laws do not have a statistically signi�cant e�ect on the total
size of loans, and column (6) con�rms this result for home equity loans and mortgages. This
result indicates that any e�ects of limited credit for married �rm owners in TBE states must
arise from not obtaining loans rather than the loan amounts. This is consistent with using
TBE laws as asset protection, as only the existence of a loan and not the loan amount is
relevant for making an asset vulnerable to seizure by creditors.
To further explore the mechanisms behind these observed changes in �rm pro�tability,
I examine whether changes in revenue are related to changes in owner e�ort, and whether
changes in costs can be explained by decisions to take on riskier projects. The question
of how wealth insurance a�ects labor supply is an open one in the literature.19 If greater
work e�ort decreases the probability of business failure and loss to the owner, then wealth
insurance and work hours are substitutes and may be negatively related. Similarly, greater
insurance may limit the downside risk of failed projects, encouraging �rm owners to have
more projects with uncertain returns. I again use equation (1) to determine if TBE laws
a�ect the hours worked by a �rm's owner and present results in Table 10.
In column (1), the di�erence-in-di�erence approach shows a statistically insigni�cant
negative e�ect of the asset protections of TBE laws on owner hours worked. This estimate
implies that at the mean homestead exemption level, the availability of TBE protections
causes a 14.7% reduction on the intensive margin of hours worked. In column (2), I control
for the possible confounding e�ects of state homestead exemptions by adding interactions as
in equation (2). I �nd that in non-TBE states, married �rm owners work 22.3% more hours
than their single counterparts, a di�erence of approximately 9 hours per week. However,
19See Krueger and Meyer (2002) for an overview of the e�ects of social insurance programs (particularlyincome maintenance programs) on labor supply and White (2005) for a theoretical model of labor supplyresponse to post-bankruptcy wage garnishments. Athreya and Simpson (2006) study the interaction betweenbankruptcy systems and unemployment insurance.
19
this di�erence disappears entirely in TBE states as the negative interaction e�ect on TBE ·
Married is -0.282. This result indicates that if a state has a homestead exemption for
married couples of $0, TBE laws cause hours worked to fall by 28.2%.
This intuition is further supported by the pattern of coe�cients on other interaction
terms in column (2). The positive coe�cient on Married ·Exemption indicates that higher
homestead exemptions are associated with fewer hours worked for married people, while the
positive coe�cient of very similar magnitude on TBE ·Married · Exemption shows that
higher homestead exemptions do not a�ect the labor supply of married �rm owners in TBE
states. The negative e�ect of the homestead exemption on the labor supply of married but
not single �rm owners is consistent with the higher homeownership rate of married individuals
relative to singles making the protections of the homestead exemption more relevant to the
married �rm owners. As a whole, these results show that �rm owners with signi�cant asset
protections reduce their labor supply.
While TBE laws appear to have a large e�ect on the labor supply of entrepreneurs,
workers that do not own the �rm do not enjoy any di�erential asset protection in TBE
states and therefore should �nd their work hours una�ected by bankruptcy law. The e�ect
of TBE laws on the work hours of non-owners therefore provides another falsi�cation test.
Since the KFS contains data only about �rm owners, I use the Current Population Survey
Outgoing Rotation Group for 2009. I de�ne a business owner to be a individual identi�ed
as having a business or farm and restrict the sample to individuals 16 or older, in the labor
force, and working at one job. Column (3) shows that the di�erence-in-di�erence approach
of equation (1) over the entire CPS sample yields a statistically signi�cant negative e�ect
on work hours, while columns (4) and (5) show that this e�ect comes entirely from �rm
owners and not wage workers. The estimates from column (4) indicate a decrease of 7.93%
in work hours for �rm owners, representing approximately 3 hours per week, while column (5)
shows that wage workers have a statistically insigni�cant and precisely estimated decrease of
only 0.99%. Column (6) reports estimates from a triple di�erence model, showing that the
20
di�erence of the e�ect of TBE laws on work hours between owners and workers is statistically
signi�cant. The point estimate in the CPS ORG of the e�ect on labor hours of �rm owners
is smaller than in the KFS sample in column (1), though each lies within the 95% con�dence
interval of the other.
Table 11 shows the e�ects of TBE laws on �rm spending on research and development.
Columns (1) and (2) report results for the extensive margin of R&D spending using a binary
variable equal to 1 if a �rm has any R&D expenditures, while columns (3) and (4) report
results for the intensive margin of R&D spending using the natural log of the cumulative
total of dollars spent over the period 2004-2009. While point estimates of the e�ect of TBE
laws are generally positive, indicating that owners are more likely to undertake and spend
more money on a risky project when personal asset protections are stronger, the results
are not statistically signi�cant. Thus, I �nd at most mild impacts of the personal asset
protections of TBE laws on �rm project selection.
5 Conclusion
Previous research has found a connection between bankruptcy exemptions and entrepreneur-
ship, generally through the channels of encouraging self-employment or credit market. This
paper o�ers evidence that �rm owners actively seek the protections of bankruptcy law by ar-
ranging business �nances to maximize exemptions available through tenancy by the entirety,
a form of property ownership available to married people in some states. I �nd that �rm
owners choose both debt and equity funding sources for their business operations that pre-
serve personal asset protections of TBE laws and that this e�ect diminishes as bankruptcy
homestead exemptions rise. This �nding suggests that TBE laws and bankruptcy exemp-
tion levels are substitutes as forms of wealth insurance for business owners and establishes
speci�c evidence of �rm owners changing behavior in response to personal asset protections.
I estimate that �rm owners value this additional asset protection at approximately $16,150
21
per year in lost pro�ts associated with not using housing equity to fund a business, an action
that would remove TBE protections.
For �rm owners, the protections a�orded by personal bankruptcy law may allow them to
take greater risks, knowing that the bankruptcy system will minimize their losses in case the
business fails. This additional risk may come in the form of not putting forth as much e�ort
towards the success of the business or engaging in more research and development spending
on projects with uncertain returns. I �nd that owner labor supply is signi�cantly negatively
a�ected by high personal asset protection. At mean homestead exemption levels, I �nd that
the unlimited exemption provided by TBE laws drives down married owner labor supply
by between 8 and 14% relative to singles, a reduction of between 3 and 6 hours per week.
However, I do not �nd an e�ect on project selection as the point estimates on �rm R&D
spending are imprecise.
In future work, I will use the March CPS from 2009 to include additional controls relevant
to the hours worked decision, such as spousal earnings. I will also add results showing
that using changes in exemption levels is insu�cient to identify these e�ects, as changes in
exemptions are correlated with changes in home values and therefore owner wealth post-
BAPCPA. Additionally, changes in exemption levels theoretically a�ect both the supply and
demand for credit unlike TBE laws, allowing estimation of only a total market e�ect rather
than the valuations of �rm owners.
22
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25
Table 1: 2009 Homestead Exemptions and Tenancy by the Entirety Laws by State
State Homestead TBE State Homestead TBEAlabama 10000 Montana 500000Alaska 70200 Modi�ed Nebraska 60000Arizona 150000 Nevada 550000Arkansas unlimited Modi�ed New Hampshire 200000California 75000 New Jersey 0 Modi�edColorado 120000 New Mexico 120000
Connecticut 150000 New York 100000 Modi�edDelaware 0 Full North Carolina 37000 Full
District of Columbia unlimited Full North Dakota 100000Florida unlimited Full Ohio 40000 Modi�edGeorgia 20000 Oklahoma unlimited Modi�edHawaii 30000 Full Oregon 39600 Modi�edIdaho 100000 Pennsylvania 0 FullIllinois 30000 Modi�ed Rhode Island 300000 Modi�edIndiana 30000 Full South Carolina 100000Iowa unlimited South Dakota unlimited
Kansas unlimited Tennessee 7500 Modi�edKentucky 10000 Modi�ed Texas unlimitedLouisiana 25000 Utah 40000Maine 90000 Vermont 150000 Full
Maryland 0 Full Virginia 10000 FullMassachusetts 500000 Modi�ed Washington 125000
Michigan 34450 Full West Virginia 50000Minnesota 300000 Wisconsin 40000Mississippi 150000 Full Wyoming 20000 FullMissouri 15000 Full Federal 40400
Sources: State statutes for homestead exemptions and Franke (2009) for TBE laws. Homestead exemptions are as applicable to a married couplewith no age or disability modi�cations. �Full� and �Modi�ed� refer to the type of bar TBE provides against creditors in that state. �Full� meansthat creditors of an individual spouse cannot obtain an interest against TBE property. �Modi�ed� means that creditors of an individual spousemay obtain some interest in TBE property, though the exact nature varies by state. See Franke (2009) for further details.
26
Table 2: Summary Statistics
KFS SBO CPSVariable Obs. Mean Std. Dev. Obs. Mean Std. Dev. Obs. Mean Std. Dev.
Share Firms in full TBE states 2606 0.247 0.431 2,165,680 0.265 0.442Share Firms in mod. TBE states 2606 0.220 0.414 2,165,680 0.230 0.421
Work experience 2590 12.806 10.726Married 2606 0.667 0.471 173,534 0.578 0.494
Hours worked 2582 40.416 22.515 1,295,939 34.46 21.52 173,534 37.24 12.57Age 2585 48.943 10.655 1,296,083 50.28 12.52
Female 2587 0.313 0.464 2,165,680 0.332 0.471Some college 2606 0.359 0.480 2,165,680 0.156 0.364
College graduate 2606 0.309 0.462 2,165,680 0.133 0.340Graduate degree 2606 0.197 0.398 2,165,680 0.101 0.301
Hispanic 2606 0.052 0.221 2,165,680 0.090 0.287Ever used personal bank loan 2408 0.327 0.469Ever used business credit card 2408 0.682 0.466Ever used personal credit card 2408 0.707 0.455Ever used family/friends loan 2408 0.194 0.395
Ever equity investment from government 1722 0.018 0.131Ever equity investment from spouse 1168 0.070 0.256
Total revenues 1892 834,372.30 5,161,120Total expenditures 2514 502,507.50 4,078,269
Firm pro�ts 2498 14,505.36 756,588.90Ever spend on R&D 2408 0.376 0.484Total R&D spending 2408 14,235.70 180,751.3
Share Firms jointly owned by spouses 2,165,680 0.142 0.349Share Business owners 173,534 0.083 0.275
Sources: 2004-2009 Kau�man Firm Survey, 2007 U.S. Census Bureau Survey of Business Owners, and 2009 Current Population Survey OutgoingRotiation Group. All summary statistics weighted using provided sample weights. KFS data reports data for �rms in 2009 using previous yearsfor �rm histories. Data on debt and equity �nancing is a dummy variable indicating whether a �rm used the given source of funding at any timeover the period 2004-2009. Total expenditures includes expenses on wages, salaries, interest on loans, capital leases and materials. Hours workedand age computed in SBO data from the medians of ranges reported in data.
27
Table 3: Di�erence in Observables by TBE Laws
Non-TBE States TBE StatesObs. Mean SD Obs. Mean SD p-value
Married 1385 0.66 0.474 1221 0.676 0.468 0.472Owner Experience 1372 12.591 10.528 1218 13.05 10.946 0.3485
Age 1370 48.704 10.499 1215 49.214 10.828 0.2995Some College 1385 0.346 0.476 1221 0.372 0.484 0.2471
4-yr College Degree 1385 0.323 0.468 1221 0.293 0.456 0.1623Graduate Degree 1385 0.198 0.399 1221 0.196 0.397 0.8955
Black 1385 0.079 0.269 1221 0.09 0.287 0.3883Asian 1385 0.05 0.218 1221 0.043 0.203 0.5017
Hispanic 1385 0.068 0.251 1221 0.034 0.18 0.0012Other 1385 0.017 0.129 1221 0.02 0.14 0.6385Female 1371 0.298 0.457 1216 0.33 0.47 0.1499
Sole Proprietorship 1388 0.339 0.474 1219 0.35 0.477 0.6152Limited Liability Company 1388 0.33 0.47 1219 0.298 0.457 0.124
S-Corporation 1388 0.215 0.411 1219 0.246 0.431 0.1059C-Corporation 1388 0.067 0.25 1219 0.063 0.244 0.7667
General Partnership 1388 0.032 0.175 1219 0.027 0.162 0.5664Limited Partnership 1388 0.015 0.121 1219 0.014 0.119 0.9128Other Legal Form 1388 0.002 0.046 1219 0.001 0.028 0.4116
Source: 2009 Kau�man Firm Survey. All summary statistics weighted using provided sample weights.
28
Table4:
Di�erence
inObservablesbyTBELaw
sandMarital
Status
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Dep.Var.:
Owner
Experience
Age
Som
eCollege
4-yrCollege
Degree
GraduateDegree
Black
Asian
Hispanic
Married
1.247*
0.710
0.0342
0.0443*
-0.00611
-0.0526***
-0.0128*
-0.0200*
(0.722)
(0.881)
(0.0222)
(0.0257)
(0.0223)
(0.0148)
(0.00702)
(0.0106)
TBE
1.031
0.0868
0.0525*
-0.0122
0.00633
-0.00572
-0.0346
-0.0283
(0.836)
(0.949)
(0.0287)
(0.0320)
(0.0273)
(0.0294)
(0.0250)
(0.0261)
TBExMarried
-0.900
0.603
-0.0382
-0.0269
-0.0140
0.0311
0.0399*
-0.0111
(0.906)
(1.081)
(0.0369)
(0.0374)
(0.0362)
(0.0266)
(0.0218)
(0.0185)
Obs.
2589
2584
2603
2603
2603
2603
2603
2603
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
Dep.Var.:
Other
Fem
ale
SoleProprietorship
Lim
ited
LiabilityCom
pany
S-Corporation
C-Corporation
General
Partnership
Lim
ited
Partnership
Married
0.00905
-0.0698***
0.0582**
-0.0119
-0.00135
-0.0141
-0.0280***
0.00328
(0.00985)
(0.0261)
(0.0265)
(0.0284)
(0.0219)
(0.0110)
(0.00932)
(0.00698)
TBE
0.00386
0.0272
-0.000616
-0.00956
0.0219
-0.0191
0.00130
0.00514
(0.0115)
(0.0312)
(0.0476)
(0.0683)
(0.0438)
(0.0157)
(0.0131)
(0.0111)
TBExMarried
-0.00159
0.00995
0.0159
-0.0375
0.0148
0.0234
-0.00889
-0.00947
(0.0139)
(0.0349)
(0.0450)
(0.0421)
(0.0389)
(0.0196)
(0.0127)
(0.0118)
Obs.
2603
2586
2600
2600
2600
2600
2600
2600
Regressionsare
weightedestimatesofequation(1)usingsamplingweights
providedin
KFSdata.Columns(1)and(2)are
weighedleast
squaresregressions,whilecolumns(3)-(16)are
logitregressions
andreportedcoe�cients
are
averagemarginale�ects.Dependentvariable
islistedattopanddescribedin
text.
*,**,***denote
statisticalsigni�canceatthe10%,5%,and1%
levelsrespectively.
Standard
errors
inparenthesesare
Huber-Whiterobust
estimatesclusteredatthestate
level.
29
Table5:
TBE,LoanTypes,andEquityInvestors
Loans
Equity
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Dep.Var:
Pers.
bankloan
Bus.
creditcard
Pers.
creditcard
Pers.
familyloan
Govt.
Spouse
SpousalJointOwnership
Married
0.393***
0.003
-0.00185
0.00123
0.844
(0.140)
(0.129)
(0.152)
(0.161)
(0.860)
[0.0797]
[0.000591]
[-0.000374]
[0.000183]
[0.0214]
TBE
0.238
-0.206
0.00547
0.159
-0.0161
-0.782**
-0.247***
(0.178)
(0.167)
(0.199)
(0.184)
(0.796)
(0.339)
(0.086)
[0.0483]
[-0.0405]
[0.00111]
[0.0237]
[-0.00041]
[-0.0478]
[-0.0366]
TBExMarried
-0.538***
0.369**
0.0819
-0.325
-0.425
(0.180)
(0.178)
(0.197)
(0.230)
(1.194)
[-0.109]
[0.0726]
[0.0166]
[-0.0485]
[-0.0108]
Obs.
2375
2373
2375
2363
1184
1125
1,541,265
Data
KFS
KFS
KFS
KFS
KFS
KFS
SBO
Regressionsare
weightedestimatesofequation(1)usingsamplingweightsprovidedin
KFSdata.Allcolumnsare
logitregressions.
Estimatesreportedin
bracketsare
averagemarginale�ects.Dependent
variable
islistedattopanddescribedin
text.
*,**,***denote
statisticalsigni�canceatthe10%,5%,and1%
levelsrespectively.Standard
errors
inparenthesesare
Huber-Whiterobust
estimates
clusteredatthestate
level.
Controlvariablesin
columns(1)-(6)are
work
experience,age,age2,dummyvariablesforeducationlevel,race,ethnicity,andgenderofowner,aswellaslegalstatusand
2-digitNAICScodeof�rm
.Column(7)doesnotuse
�rm
legalstatusandincludesdummiesforagecategories,includingmissingage,dueto
variableavailabilityin
theSBOdata.
30
Table6:
LoanTypes
andEquityInvestorsbyTBEIntensity
Loans
Equity
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Dep.Var:
Pers.
bankloan
Bus.
creditcard
Pers.
creditcard
Pers.
familyloan
Govt.
Spouse
SpousalJointOwnership
Married
0.393***
0.00422
-0.000984
0.00118
0.809
(0.140)
(0.130)
(0.152)
(0.161)
(0.876)
[0.0796]
[0.000831]
[-0.000199]
[0.000176]
[0.0202]
FullTBE
0.183
-0.137
-0.162
0.153
0.151
-1.504***
-0.185***
(0.202)
(0.176)
(0.238)
(0.225)
(0.914)
(0.400)
(0.070)
[0.0371]
[-0.0269]
[-0.0328]
[0.0228]
[0.00377]
[-0.0911]
[-0.0273]
FullTBExMarried
-0.471**
0.232
0.166
-0.316
-1.779
(0.204)
(0.201)
(0.246)
(0.242)
(1.510)
[-0.0955]
[0.0457]
[0.0335]
[-0.0471]
[-0.0443]
Modi�ed
TBE
0.302
-0.284
0.218
0.167
-0.349
-0.192
-0.336***
(0.225)
(0.205)
(0.208)
(0.224)
(0.865)
(0.408)
(0.147)
[0.0611]
[-0.0559]
[0.0441]
[0.0249]
[-0.00868]
[-0.0116]
[-0.0497]
Modi�ed
TBExMarried
-0.615**
0.522**
-0.0289
-0.336
0.838
(0.247)
(0.258)
(0.186)
(0.343)
(1.305)
[-0.125]
[0.103]
[-0.00584]
[-0.0502]
[0.0209]
Obs.
2375
2373
2375
2363
1184
1125
1,514,164
Data
KFS
KFS
KFS
KFS
KFS
KFS
SBO
F-testP-value
0.590
0.325
0.393
0.955
0.146
0.011
0.315
Regressionsare
weightedestimatesofequation(1)usingsamplingweights
providedin
thedata
listed.Allcolumnsare
logitregressions.
Estimatesreportedin
brackets
are
averagemarginale�ects.
Dependentvariable
islistedattopanddescribedin
text.
*,**,***denote
statisticalsigni�canceatthe10%,5%,and1%
levelsrespectively.Standard
errors
inparenthesesare
Huber-Whiterobust
estimatesclusteredatthestate
level.Controlvariablesin
allregressionsare
work
experience,age,age2,dummyvariablesforeducationlevel,race,ethnicity,andgenderofowner,aswellaslegalstatus
and2-digitNAICScodeof�rm
.�F-test
P-value�isthep-valueofanF-test
forequality
ofthecoe�cients
ontheinteractionvariablesin
eachcolumn.
31
Table7:
TBEandHom
estead
Exem
ptions
Loans
Equity
(1)
(2)
(3)
(4)
(5)
(6)
Dep.Var:
Pers.
bankloan
Pers.
bankloan
Bus.
creditcard
Bus.
creditcard
Spouse
Spouse
Married
0.516**
0.511***
-0.301
-0.276
(0.204)
(0.194)
(0.256)
(0.244)
[0.103]
[0.103]
[-0.0584]
[-0.0539]
TBE
0.264
0.268
-0.368
-0.327
-1.548***
-1.440***
(0.249)
(0.248)
(0.264)
(0.258)
(0.455)
(0.429)
[0.0529]
[0.0542]
[-0.0715]
[-0.0639]
[-0.0966]
[-0.0847]
Hom
estead
Exem
ption
0.00615
0.00529
-0.00939
-0.00847
-0.00842
-0.00273
(0.0107)
(0.00962)
(0.00994)
(0.00972)
(0.0139)
(0.0149)
[0.00123]
[0.00107]
[-0.00182]
[-0.00165]
[-0.000526]
[-0.000160]
TBExExem
ption
0.00265
0.00250
0.00245
0.00222
0.0770***
0.0674***
(0.0126)
(0.0115)
(0.0128)
(0.0127)
(0.0187)
(0.0188)
[0.000532]
[0.000504]
[0.00476]
[0.00434]
[0.00481]
[0.00396]
TBExMarried
-0.637**
-0.631**
0.541*
0.520*
(0.261)
(0.254)
(0.303)
(0.299)
[-0.128]
[-0.127]
[0.105]
[0.102]
Married
xExem
ption
-0.0172*
-0.0163*
0.0359**
0.0353**
(0.00992)
(0.00884)
(0.0144)
(0.0144)
[-0.00345]
[-0.00329]
[0.00696]
[0.00690]
TBExMarried
xExem
ption
0.0216*
0.0193*
-0.0192
-0.0181
(0.0126)
(0.0116)
(0.0177)
(0.0176)
[0.00434]
[0.00390]
[-0.00372]
[-0.00353]
Obs.
1976
2375
1976
2373
915
1125
Data
KFS
KFS
KFS
KFS
KFS
KFS
Hom
estead
Exem
ption
De�ned
All
De�ned
All
De�ned
All
Regressionsare
weightedestimatesofequation(1)usingsamplingweights
providedin
thedata
listed.Allcolumnsare
logitregressions.
Estimatesreportedin
brackets
are
averagemarginale�ects.
Dependentvariable
islistedattopanddescribedin
text.
*,**,***denote
statisticalsigni�canceatthe10%,5%,and1%
levelsrespectively.Standard
errors
inparenthesesare
Huber-Whiterobust
estimatesclusteredatthestate
level.
Homesteadexemptionmeasuredin
unitsof$10,000.Controlvariablesare
work
experience,age,age2,dummyvariablesforeducationlevel,race,ethnicity,and
genderofowner,aswellaslegalstatusand2-digitNAICScodeof�rm
.Columnsusingallstatesalsoincludeadummyvariableforstateswithanunlimitedhomesteadexemption,whilecolumnsusing
only
stateswithade�nedhomesteadexemptionexcludeall�rm
sin
stateswithunlimitedhomesteadexemptions.
32
Table8:
Use
ofPersonal
BankLoansbyFirm
Owner
Characteristics
(1)
(2)
(3)
(4)
Dep.Var:
Pers.
bankloan
Pers.
bankloan
Pers.
bankloan
Pers.
bankloan
Married
0.257
0.350**
0.496**
0.0133
(0.209)
(0.166)
(0.195)
(0.157)
[0.0518]
[0.0708]
[0.0998]
[0.00269]
TBE
0.133
0.269
0.428*
-0.0106
(0.304)
(0.210)
(0.258)
(0.303)
[0.0268]
[0.0545]
[0.0863]
[-0.00214]
Owner
Characteristic
-0.0116
0.135
0.594**
-0.455
(0.0124)
(0.335)
(0.259)
(0.284)
[-0.00235]
[0.0272]
[0.120**]
[-0.0919]
TBExOwner
Characteristic
0.00792
-0.149
-0.387
0.515
(0.0191)
(0.508)
(0.362)
(0.490)
[0.00160]
[-0.0302]
[-0.0781]
[0.104]
TBExMarried
-0.0419
-0.476**
-0.522**
-0.316
(0.325)
(0.212)
(0.247)
(0.280)
[-0.00847]
[-0.0963]
[-0.105]
[-0.0638]
Married
xOwner
Characteristic
0.0105
0.292
-0.174
0.764**
(0.0125)
(0.290)
(0.277)
(0.300)
[0.00212]
[0.0590]
[-0.0351]
[0.154]
TBExMarried
xOwner
Characteristic
-0.0376*
-0.348
-0.0619
-0.434
(0.0209)
(0.537)
(0.449)
(0.478)
[-0.00760]
[-0.0705]
[-0.0125]
[-0.0876]
Obs.
2375
2375
2375
2375
Data
KFS
KFS
KFS
KFS
Owner
Characteristic
Workexperience
Ownsanother
�rm
insameindustry
Ownsanyother
�rm
College
orgraduateeducation
Regressionsare
weightedestimatesofequation(1)usingsamplingweights
providedin
thedata
listed.Allcolumnsare
logitregressions.
Estimatesreportedin
brackets
are
averagemarginale�ects.
Dependentvariable
islistedattopanddescribedin
text.
*,**,***denote
statisticalsigni�canceatthe10%,5%,and1%
levelsrespectively.Standard
errors
inparenthesesare
Huber-Whiterobust
estimatesclusteredatthestate
level.Controlvariablesin
allregressionsare
work
experience,age,age2,dummyvariablesforeducationlevel,race,ethnicity,andgenderofowner,aswellaslegalstatus
and2-digitNAICScodeof�rm
.�OwnerCharacteristic�isthenumberofyears
ofwork
experienceoftheownerin
Column(1),andadummtindicatingwhetherthe�rm
ownerownsanother�rm
inthe
sameindustry,ownsanyother�rm
,orhasacollegedegreeorhigherlevelofeducationin
Columns(2),(3),and(4)respectively..
33
Table9:
E�ectof
TBELaw
son
Firm
Outcom
esandCredit
(1)
(2)
(3)
(4)
(5)
(6)
Dep.Var:
ln(Total
Revenues)
ln(Total
Expenditures)
ln(Employees)
ln(Pers.
Loans)
ln(B
us.
Loans)
ln(Pers.
BankLoan)
Married
0.343
0.193
0.129
0.245
0.406
-0.157
(0.217)
(0.184)
(0.130)
(0.276)
(0.337)
(0.303)
TBE
0.224
0.161
-0.0304
-0.0814
0.232
-0.493
(0.313)
(0.207)
(0.155)
(0.299)
(0.349)
(0.393)
Hom
estead
Exem
ption
0.00531
0.00478
0.00880
0.00896
0.0141
-0.00849
(0.0109)
(0.00737)
(0.00545)
(0.00858)
(0.00861)
(0.00979)
TBExExem
ption
-0.0216**
-0.0207**
-0.00870*
-0.00676
-0.00732
0.0136
(0.00813)
(0.00811)
(0.00477)
(0.00799)
(0.00878)
(0.0119)
TBExMarried
-0.586**
-0.375
-0.174
0.0615
-0.167
0.539
(0.268)
(0.253)
(0.174)
(0.339)
(0.467)
(0.434)
Married
xExem
ption
-0.00573
-0.00425
-0.00563
-0.0139*
-0.00363
0.00418
(0.00528)
(0.00708)
(0.00466)
(0.00797)
(0.00764)
(0.00886)
TBExMarried
xExem
ption
0.0234***
0.0174*
0.00567
0.00261
-0.00890
-0.0156
(0.00811)
(0.00966)
(0.00588)
(0.00911)
(0.0110)
(0.0123)
Obs.
1877
2371
1415
992
696
737
Data
KFS
KFS
KFS
KFS
KFS
KFS
Hom
estead
Exem
ption
All
All
All
All
All
All
Regressionsare
weightedestimatesofequation(2)usingsamplingweights
providedin
thedata
listed.Allcolumnsare
WLSregressions.
Dependentvariable
islistedattopanddescribedin
text.
*,
**,***denote
statisticalsigni�canceatthe10%,5%,and1%
levelsrespectively.Standard
errors
inparenthesesare
Huber-Whiterobust
estimatesclusteredatthestate
level.
Homesteadexemption
measuredin
unitsof$10,000.Controlvariablesare
work
experience,age,age2,dummyvariablesforeducationlevel,race,ethnicity,genderofowner,andwhetherastate
hasanunlimitedhomestad
exemption,aswellaslegalstatusand2-digitNAICScodeof�rm
.
34
Table10:Labor
Supply
E�ects
ofTBELaw
s
(1)
(2)
(3)
(4)
(5)
(6)
Dep.Var:
Ln(H
ours
Worked)
Ln(H
ours
Worked)
Ln(H
ours
Worked)
Ln(H
ours
Worked)
Ln(H
ours
Worked)
Ln(H
ours
Worked)
Married
0.0791
0.223**
0.0736***
0.122**
0.0410***
0.0410***
(0.0803)
(0.111)
(0.00758)
(0.0576)
(0.00806)
(0.00807)
TBE
0.119
0.191
0.00673
0.0576
0.00785
0.00785
(0.0926)
(0.131)
(0.00612)
(0.0382)
(0.00626)
(0.00626)
Hom
estead
Exem
ption
0.00350
(0.00398)
TBExExem
ption
-0.00469
(0.00381)
TBExMarried
-0.147
-0.282**
-0.0111*
-0.0793*
-0.00993
-0.00993
(0.0967)
(0.130)
(0.00621)
(0.0407)
(0.00611)
(0.00611)
Married
xExem
ption
-0.00834***
(0.00296)
TBExMarried
xExem
ption
0.00826*
(0.00416)
BusinessOwner
0.273
(0.264)
Married
xOwner
0.0809
(0.0598)
TBExOwner
0.0498
(0.0391)
TBExMarried
xOwner
-0.0694*
(0.0417)
Obs.
2528
2528
173,534
15,692
157,842
173,534
Data
KFS
KFS
CPS
CPS(O
wners)
CPS(W
orkers)
CPS
Hom
estead
Exem
ption
All
Regressionsare
weightedestimatesofequation(1)usingsamplingweights
providedin
thedata
listed.Allcolumnsare
WLSregressions.
Estimatesreportedin
brackets
are
averagemarginale�ects.
Dependentvariable
islistedattopanddescribedin
text.
*,**,***denote
statisticalsigni�canceatthe10%,5%,and1%
levelsrespectively.Standard
errors
inparenthesesare
Huber-Whiterobust
estimatesclusteredatthestate
level.Controlvariablesin
allregressionsare
work
experience,age,age2,dummyvariablesforeducationlevel,race,ethnicity,andgenderofowner.
KFSregressionsinclude
controlsforlegalstatusand2-digitNAICScodeof�rm
.CPSregressionsincludecontrolsforoccupationandindustry
usingprovidedcodingscheme,aswellasdummiesforfamilystructure,disability
status,andmonth
ofinterview.Columnsusinghomesteadexemptiondata
alsoincludeadummyvariableforstateswithanunlimitedhomesteadexemption.
35
Table 11: Other Business Operation E�ects of TBE Laws
(1) (2) (3) (4)Dep. Var: R&D R&D Ln(R&D Spending) Ln(R&D Spending)Married -0.450*** -0.473** -0.255 -0.576
(0.155) (0.227) (0.331) (0.364)[-0.0941] [-0.0983]
TBE -0.150 0.0292 0.302 0.0588(0.240) (0.319) (0.438) (0.514)[-0.0315] [0.00607]
Homestead Exemption 0.0168** -0.0279*(0.00772) (0.0146)[0.00349]
TBE x Exemption -0.00809 0.0127(0.00768) (0.0138)[-0.00168]
TBE x Married 0.130 -0.00566 0.0890 0.231(0.224) (0.291) (0.386) (0.426)[0.0273] [-0.00118]
Married x Exemption 0.00206 0.0187*(0.00593) (0.0103)[0.000428]
TBE x Married x Exemption 0.00968 -0.00759(0.00807) (0.0132)[0.00201]
Obs. 2379 2379 554 554Data KFS KFS KFS KFS
Homestead Exemption All All
Regressions are weighted estimates of equation (1) using sampling weights provided in the data listed. Columns (1) and (2) are logit regressionsand columns (3) and (4) are WLS regressions. Estimates reported in brackets are average marginal e�ects. Dependent variable is listed at topand described in text. *, **, *** denote statistical signi�cance at the 10%, 5%, and 1% levels respectively. Standard errors in parentheses areHuber-White robust estimates clustered at the state level. Control variables in all regressions are work experience, age, age2, dummy variables foreducation level, race, ethnicity, and gender of owner, as well as legal status and 2-digit NAICS code of �rm. Columns using homestead exemptiondata also include a dummy variable for states with an unlimited homestead exemption.
36
top related