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 Do not cite.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