NEW INSIGHTS IN THE DETERMINANTS OF REGIONAL …€¦ · 10,000 Americans. These alarming increases in bankruptcy filing rates over the last decade were largely the impetus for the

NEW INSIGHTS IN THE DETERMINANTS OF REGIONAL VARIATION IN BANKRUPTCY FILING RATES *

Kelly D. Edmiston† Senior Economist Community Affairs Department Federal Reserve Bank of Kansas City 925 Grand Boulevard Kansas City, MO 64198-0001 USA November, 2005 Abstract Nonbusiness bankruptcy filing rates have increased very rapidly over the last couple of decades. In 1980, roughly 15 of every 10,000 Americans filed for bankruptcy protection. By 2004, that number had reached 54 of every 10,000 Americans. These alarming increases in bankruptcy filing rates over the last decade were largely the impetus for the Bankruptcy Abuse Prevention and Consumer Protection Act, which went into effect in October, 2005. A substantial literature already exists that seeks to determine the causes of personal bankruptcy, but critical holes in the literature remain. In particular, existing studies offer only weak inferences about the role of stigma in explaining the decision to file for bankruptcy or in explaining regional variation in bankruptcy filing rates. I enhance the existing literature by using innovative approaches to measuring the effects of age and geography, traditional proxies for stigma, and by utilizing a novel proxy for stigma, namely, religious adherence. There is also a lack of consensus on the effects of gambling on bankruptcy, with most research finding no statistically significant relationships. I utilize a unique measure of proximity to gambling establishments and subsequently find more definitive results. The existing literature lacks consensus on the effects of homestead exemptions as well, with some finding positive effects, some finding negative effects, and still others finding no effects. I assert that the explanation of these inconsistent results may lie in endogeneity, and therefore I estimate two-stage models that effectively instrument for homestead exemptions. In addition, I explore the effects on bankruptcy filing rates of factors generally left out of existing studies, including small business and self-employment, a full distribution of age and income, more narrow demographic definitions, disability, lack of health insurance, public assistance, housing and vehicle choices, and additional information on debts and debt service. JEL Codes: D1, R2

* I would like to thank Kate Fisher for extremely valuable research assistance. The views expressed in this paper are those of the author and do not necessarily reflect the views of the Federal Reserve Bank of Kansas City or the Federal Reserve System. † Tel: (816) 881-2004; Fax: (816) 881-2704; E-Mail: [email protected].

NEW INSIGHTS IN THE DETERMINANTS OF REGIONAL VARIATION IN BANKRUPTCY FILING RATES

1. Introduction

Nonbusiness bankruptcy filing rates have increased very rapidly over the last couple of

decades. In 1980, roughly 15 of every 10,000 Americans filed for bankruptcy protection (Figure

1).1 By 2004, that number had reached 54 of every 10,000 Americans, for a compound annual

growth rate of 5.6 percent. These alarming increases in bankruptcy filing rates over the last

decade were largely the impetus for the Bankruptcy Abuse Prevention and Consumer Protection

Act, which went into effect in October, 2005.

From 1960 – 1979, nonbusiness bankruptcy filing rates increased at a much slower 2.3

percent annual pace. Bankruptcy reform legislation passed in 1978 often is given the blame for

the acceleration in growth of filing rates in recent decades. However, passage of the legislation

should have lead to a one-time jump in the bankruptcy filing rate (a change in intercept in Figure

1) rather than an increase in the growth rate of bankruptcy filings (a change in slope in Figure 1).

More likely is that a combination of several factors is responsible for accelerating growth in

bankruptcy filing rates in the last couple of decades. Indeed, Congress expressed its concerns

about the underlying causes of bankruptcy in developing the 2005 law.

While bankruptcy filing rates have climbed consistently in the aggregate over time, there

is considerable variation across regions (Figure 2). In 2000, for example, non-business

bankruptcy filing rates in counties with over 50,000 population ranged from only 0.2 per 10,000

people in Bibb County, GA (Macon) to 155 per 10,000 people in Shelby County, TN (Memphis).

The lack of uniformity in bankruptcy filing rates across counties (and states) suggests that

regional factors likely play a key role in the determination of filing rates.

1 Data on bankruptcy filing rates were available from two sources that are unfortunately inconsistent with each other. Both series are presented in Figure 1. I consider the data from the Administrative Office of the U.S. Courts (with Census Bureau population data) to be more reliable and therefore focus on these data when possible.

DRAFT. Please do not distribute without permission from the author.

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A substantial literature already exists that seeks to determine the causes of personal

bankruptcy, but critical holes in the literature remain. In particular, existing studies offer only

weak inferences about the role of stigma in explaining the decision to file for bankruptcy or in

explaining regional variation in bankruptcy filing rates. I enhance the existing literature by using

innovative approaches to measuring the effects of age and geography, traditional proxies for

stigma, and by utilizing a novel proxy for stigma, namely, religious adherence. There is also a

lack of consensus on the effects of gambling on bankruptcy, with most research finding no

statistically significant relationships. I utilize a unique measure of proximity to gambling

establishments and subsequently find more definitive results. The existing literature lacks

consensus on the effects of homestead exemptions as well, with some finding positive effects,

some finding negative effects, and still others finding no effects. I assert that the explanation of

these inconsistent results may lie in endogeneity, and therefore I estimate two-stage models that

effectively instrument for homestead exemptions. In addition, I explore the effects on

bankruptcy filing rates of factors generally left out of existing studies, including small business

and self-employment, a full distribution of age and income, more narrow demographic

definitions, disability, lack of health insurance, public assistance, housing and vehicle choices,

and additional information on debts and debt service.

Many of the existing studies use data on individuals in an effort to examine the individual

decision to file for bankruptcy. Others utilize geographically aggregated data, such as county-

level or state-level data, to explore the reasons why bankruptcy filing rates differ across the

country. This study falls in the latter category. Specifically, I use a cross-section of all counties

in the United States for the year 2000. Panel data on bankruptcy filings are available through

2003; however, many of the variables included in this analysis are available only for 2000 or are

available only every ten years (including 2000). The goal here is to be as comprehensive as

possible in choosing independent regressors, and hence the benefits of using a large number of

regressors, which is possible only in a cross-section, were deemed to exceed the added benefit of

time-series variation and additional degrees of freedom.


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2. Model and Data

The basic model employed in this study is one of bankruptcy filing rates per 10,000

households. The units of observation are U.S. counties and data are from the year 2000.

Numerous potential determinants are included in the model and are described below. The

emphasis of the study is on the effects on bankruptcy filing rates of asset exemptions, wage

garnishment laws, proximity to gambling establishments, and proxies for social stigma, but

numerous other regressors are included in the model in an effort to make it comprehensive. Data

descriptions, sources, and simple statistics are presented in Table 1.

Exemption Levels

Each state has established a homestead exemption (EH, which in some cases is zero),

which limits the amount of home equity that must be used to pay unsecured debt under

bankruptcy.2 For a house valued at V, the total amount of home equity to be allocated to pay

unsecured debt would then be V – EH. In the case of a mortgage foreclosure, proceeds from the

sale of the house would be allocated (until exhausted) first to the transactions costs associated

with the foreclosure, then to the first mortgage, then to the second mortgage. Of any remainder,

only the amount in excess of EH would be used to pay unsecured debt.

Homestead exemptions allow bankruptcy filers to have higher post-bankruptcy

consumption than would be the case in the absence of exemptions. Because non-exempt assets

(such as deposit accounts) are easily converted to home equity (e.g., by selling assets and paying

off mortgage debt), high homestead exemptions offer protection for all kinds of wealth, not just

actual homesteads. Empirical evidence suggests that some high asset households move to high

(homestead) exemption states to make such a conversion of assets (Brinig and Buckley, 1996; 2 The homestead exemption generally would apply in a Chapter 7 bankruptcy. However, if there is such significant equity in the house that the creditors of the Debtor would be better off if the Debtor filed a Chapter 7 rather than a Chapter 13, the Debtor could argue that because of his entitlement to a homestead exemption, the equity is of inconsequential value. Further, if the Debtor has equity in his home, sells the home during the case, and the proceeds are required to be paid into his Chapter 13 plan, the Debtor would be entitled to assert his homestead exemption in the proceeds ahead of the Chapter 13 estate's interest.


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Elul and Subramanian, 2002). Homestead exemptions lower the cost of filing for bankruptcy,

and therefore higher exemption levels should be expected to lead to higher filing rates, all else

equal.

Most states include numerous other asset exemptions besides homestead exemptions.

These are impossible to value in any uniform way across states because the exemptions generally

are for specific items, such as wedding rings, livestock, and clothing. Some states provide both

specific property exemptions and dollar value limits for these items, however, and Dawsey and

Ausubel (2001) take advantage of this information in an attempt to get around the personal

property exemption valuation problem. They use the value limits provided in a limited number

of states to value personal property exemptions in all states. Despite this clever approach,

Dawsey and Ausubel are unable to get “consistent results” for personal property exemptions and

therefore “follow previous studies and focus on the homestead exemption” (17). Personal

property exemptions are not included in the present model because of a lack of confidence in the

valuation. Further, there is some tendency for personal property exemptions and real property

exemptions to move together as one surveys the states. The correlation coefficient in the

Dawsey and Ausubel data is 0.241.

Empirical evidence on the relationship between homestead exemption levels and

bankruptcy filing rates is somewhat limited and mixed in its conclusions.

Peterson and Aoki (1984) created dummy variables based on the generosity of state

exemptions in 1980, representing the following categories: very high, high, low, and very low.

“Low” states had exemptions that were more restrictive than federal exemptions (under the 1978

law), while “high” states had exemptions that were more generous than federal exemptions. The

left out category was for states that did not have their own exemptions, but rather relied on

federal exemption standards.

Surprisingly, Peterson and Aoki found positive coefficients for all of the dummy

variables, three of which were significant, meaning that having state exemption rules increased

bankruptcy filing rates in 1980, regardless of whether the state exemptions were more or less


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restrictive than the federal standards. The authors suggest that this result may reflect a reverse

causality in that the over-riding of the federal law with more restrictive exemptions was likely a

response to especially elevated bankruptcy filing rates, a notion that is supported by the data.

That is, states with higher bankruptcy filing rates were more likely to impose more restrictive

exemption rules. The positive coefficient on high exemptions can be explained by would-be

filers responding to reduced costs of filing.

Apilado et al. (1978) and Shiers and Williamson (1987) both produce the unusual finding

that higher exemption levels lead to fewer bankruptcies. The explanation given by Shiers and

Williamson is that the amount of credit issued to high risk borrowers is lower in states with high

exemptions. In point of fact, Gropp et al. (1997) find that the size of a state’s bankruptcy

homestead exemption has a (statistically and economically) significant positive effect on the

probability that potential borrowers in the state will be denied credit. Of course, if the results of

Apilado et al. and Shiers and Williamson are reflective of the true relationship between asset

exemptions and bankruptcy filings, the effect of restrictive credit practices would have to more

than offset the stimulus to bankruptcies of lower filing costs. Another possible explanation for a

negative relationship between the level of asset exemptions and bankruptcy filing rates is that the

direction of causality goes the other way. It may well be that states with relatively high

bankruptcy filing rates keep exemptions low to keep the incentives to file for bankruptcy low and

that states with relatively low filing rates are more inclined to be generous with exemptions.

White (1987) finds a positive relationship between homestead exemption levels and

Chapter 7 filing rates, but a negative effect of homestead exemptions on Chapter 13 filings.

Using individual data on credit card holders, Agarwal et al. (2003) find a positive relationship

between homestead exemptions and the decision to file for bankruptcy. In some states,

homestead and personal property exemptions apply uniformly in and outside of formal

bankruptcy (see Woodward, 1982), which may diminish the relationship between homestead

exemptions and bankruptcy.


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Lin and White (2001) argue that since few states change their exemption levels each year

(11 over the period 1992 – 1997), “individual states’ bankruptcy exemptions can be treated as

exogenous in the model” (151). Supporting this view, in cross-sectional regressions using 1996

data, Posner et al. 2001 were able to explain 23 percent to 68 percent of the variation in

exemption laws across states, depending on the specification of the dependent variable. But the

only variable of any significance in the regressions was “history,” defined as the homestead

exemption in that state in 1920. This result changed little using pooled regressions over the

period 1975 – 1996. Nevertheless, given the potential duality in the direction of causality, it may

be important to instrument for exemptions, which I accomplish with two-stage models that

utilize both county and state-level data.

Information on homestead exemptions (for a married couple) were collected from a

search of the bankruptcy codes for all 50 states and the District of Columbia relevant for the year

2000. A variable was then created for each county to reflect the homestead exemption in its state

(or its predicted value) as a fraction of the median house value in the county. Six states (Florida,

Iowa, Kansas, North Dakota, South Dakota, and Texas) had exemptions that were unlimited in

value (but limited in acreage, e.g., the exemption might be for 40 acres and improvements). The

homestead exemptions in these states are given a value of $1,000,000, which should cover most

homesteads given that bankruptcy filers come predominantly from the bottom half of the income

distribution in their states (White, 2005). Further, a dummy variable (HSD) is created to

represent these states, taking on a value of unity for states with unlimited exemptions and zero

otherwise. For other states, the value of the homestead exemption in 2000 (the data year for this

study) ranged from $2,500 (Maryland) to $200,000 (Minnesota), with the mean exemption of

states with limited exemptions of $43,967.3 In 2005, the average exemption, excluding states

with unlimited exemptions, is $63,527 and ranges from $5,000 (Alabama, Kentucky, and

Maryland) to $500,000 (Massachusetts).

3 This average includes the value of the Federal homestead exemption ($15,000 in 2000) for states where the state exemption is less than the federal exemption, but petitioners have the option of taking the federal exemption.


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Social Stigma

Declining social stigma often has been put forth as a reason why bankruptcies have

increased at such a rapid rate in the United States over the last several years. In testimony before

Congress, for example, Federal Reserve Chairman Alan Greenspan argued that “personal

bankruptcies are soaring because Americans have lost their sense of shame.”4 The effects of

social stigma on bankruptcy filing propensities are probably impossible to estimate empirically

in any direct way because stigma cannot be directly measured, but the effects often can be

inferred from other empirical results.

Livshits et al. (2005) use an applied general equilibrium to get around the measurement

problem, incorporating a utility cost to bankruptcy in their model. They find that greater stigma

reduces the number of filings, but increases borrowing ex ante and generates “much more

desperate bankrupts” (28). Gross and Souleles (2002) use a panel of individual credit card

accounts to investigate credit card delinquency and the decision to file for bankruptcy. They find

that a credit card holder in 1997 was almost one percentage point more likely to file for

bankruptcy than an individual with similar characteristics in 1995, a result they compare to the

entire population of card holders becoming one standard deviation riskier. Gross and Souleles

suggest that the increased demand for bankruptcy may be attributable to a decline in social

stigma, but they give equal validity to the idea that information costs may have declined.

To the extent that social stigma varies regionally, geographic variables will at least partly

pick up the differences. This is essentially the approach used in Fay et al. (2002). The study

utilizes data on individuals from the PSID, and the authors suggest that the lagged bankruptcy

rate in the state in which a household resides may be an inverse proxy for stigma experienced by

that household. Their results suggest that the probability of filing for bankruptcy decreases with

the degree of social stigma. The authors also note that localization of bankruptcy filing might

4 Cited in Julie Kosterlitz, 1997 (May 3), “'Over the Edge,”' National Journal, 29(18), 871.


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reflect information flow: “[t]heir friends and relatives will probably tell them that filing for

bankruptcy is quick and easy” (710).

In the present study a number of measures of stigma are used with the hope that the

combination of results will lead to a more definitive answer to the stigma question.

Geographic variables, or more generally, neighborhood effects, are used to explain

regional patterns in bankruptcy filing rates, which at least in part should pick up the effects of

stigma. The simplest way this is accomplished is by including a set of dummy variables for the

eight regions defined by the Bureau of Economic Analysis. A second way is by including a

spatial autoregressive term, a procedure explained in some detail below. As shown in Figure 2,

there are strong patterns in bankruptcy filing rates across counties throughout the United States,

with especially high concentrations in the Southeast and lower Midwest.

Another way I pick up stigma is by including a set of variables reflecting adherence to

various religious groups within each county, under the assumption that different religions have

different views on the acceptability of defaulting on debt payments and filing for bankruptcy.

For example, the repayment of debts is a paramount moral obligation in Islam.5 Further, Islam is

commonly thought to prohibit the charging or paying of interest (see El-Gamal, 2001), which

likely results in less borrowing, and hence fewer bankruptcies. The repayment of debts is a

virtue in the Judeo-Christian tradition as well,6 but at the same time, perhaps the most important

tenet of the Christian religion is forgiveness.7 Similarly, Jewish tradition, as outlined by the Old

Testament, insists on the forgiveness of debts every seven years (Deuteronomy 15:1-2). This

suggests that bankruptcy, while subject to some stigma in the Judeo-Christian traditions, may not

receive the same condemnation by Jews and Christians as by Muslims. A central theme in

many Eastern religions is the avoidance of injury to others. Thus, there is likely to be stigma

attached to any injurious action, including bankruptcy and defaulting on debts. Further, in most 5 Consider the following verses, for example: “O you who believe! Fulfill (your) obligations” (Al-Maa’idah 5:1) and “Verily! Allah commands that you should render back the trusts to whom they are due . . . ” (An-Nisaa 4:58). 6 Consider, for example, the passage from Psalms 37:21 (KJV): “The wicked borroweth, and payeth not again.” 7 The Lord’s Prayer entreats God to “forgive us our debts, as we forgive our debtors” (Matthew 6:12, KJV, emphasis added).


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Eastern religions, karma has a critical place: all deeds have consequences, or “you reap what you

sew.” A default on debts would likely generate bad karma that would have to be paid in either

this life or a future life. The model includes as one measure of stigma the share of the population

in each county that adheres to Catholicism, an Eastern religion such as Hinduism or Buddhism,

Islam, Judaism, Mormonism (LDS), Orthodox Christianity, Unitarian Universalism, and

Protestantism.

A final measure of stigma included in the model is the age distribution. VISA (1996)

argues that younger age cohorts may view bankruptcy with less stigma than older groups, and

they find a statistically significant positive relationship between bankruptcy filing rates and the

share of the population that is 25 – 44, which supports this view. Flynn and Bermant (2003)

show 25 – 44 to be the “peak years” for filing for bankruptcy as well, and Domowitz and Eovaldi

(1993) also find a positive association between bankruptcy filing rates and the proportion of the

population that is 25 – 44. I include the entire age distribution of each county in the analysis in

part to pick up the effects of stigma.

Of course, there are other ways that age distribution might influence bankruptcy filing

rates as well, including by reflecting income and consumption patterns (VISA, 1996). Although

traditionally younger people have made the bulk of the bankruptcy petitions, the pool is

becoming increasingly older, and middle-aged people make up a significant part of the increase

in filings over the last couple of decades (Figure 3). Fay et al. (2002) find that the probability of

filing for bankruptcy increases with the age of the head-of-household but decreases with squared

age. They suggest that bankruptcy filings first pick up with age because access to credit

increases with age, but the effect of age reverses course later in life when households often have

accumulated wealth and do not need as much access to credit. White (1987) did not find a

significant relationship between bankruptcy and the proportion of the population that is elderly.

Wage Garnishments

When an individual or married couple files for bankruptcy protection, future earnings are

protected from garnishment. The idea behind this exemption is to give bankruptcy filers a “fresh


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start” and to provide proper work incentives for filers following discharge. Because of this,

wage garnishment often spurs bankruptcy filings. Naturally, the lower the amount of an

individual’s wages that can be legally garnished, the less is his incentive to file for bankruptcy

protection. Hence, bankruptcy filing rates, all else equal, are expected to be negatively related to

the proportion of wages protected from garnished. In this study, we account for wage

garnishment by including in the model the fraction of state household median income protected

from garnishment, keeping in mind that all but the wealthiest working-age individuals derive

virtually all of their income from wages and salaries (Burman and Kobes, 2003).8

Federal law sets a floor on monies collected via wage garnishment. In all states, the

District of Columbia, and U.S. territories, an employer may not garnish anymore than the lesser

of 25 percent of an employee’s earnings or the amount by which an employee’s earnings exceed

30 times the federal minimum wage, which in 2000 was $5.15 per hour. Some states provide for

even greater protection of employee’s wages. For example, Texas, North Carolina, and South

Carolina exempt all wages from garnishment, while Delaware, Illinois, New York, Pennsylvania,

and West Virginia allow individuals to protect 80 percent or more of their disposable income

from garnishment.

Surprisingly, Ararwal et al. (2003) find that the level of garnishment protection has a

negative effect on the probability of a credit card holder becoming delinquent, but they do not

find a statistically significant relationship between garnishment levels and the probability of

formally filing for bankruptcy. Apilado, et al. ( 1978), Sullivan and Worden (1990), and Dawsey

and Ausubel (2001), however, all find a negative association between personal bankruptcies and

the share of income protected from garnishment.

8 In 2000, the year of data used in this study, when capital gains were at an all time high during the late 1990s stock market boom, those making less than $100,000 earned 70 – 80 percent of their income from wages and salaries, with the bulk of the remainder coming from IRA distributions and social security. Thus for working age people in that income class, virtually all income comes in the form of wages and salaries.


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Legal Gambling

Legalized gambling has grown rapidly in the United States over the last two decades,

spurred largely by the introduction of gaming on Indian reservations in the 1980s. State law at

the time severely restricted most types of gambling, although exemptions were often granted for

state-sponsored gambling (i.e., lotteries) and charitable events (like “casino nights”). Indian

tribes believed that gambling could not be restricted or regulated on their reservations because

state laws generally do not apply to Indian tribes or Indian people within their reservations. The

exception is when the applicability of state laws is expressly authorization by Congress, and

Public Law 280 (1953) allows for state criminal laws to be enforced on reservations in some

states. There was some initial debate about the applicability of state gambling laws under Public

Law 280. A U.S. Supreme Court ruling on the issue (Cabazon v. California, 1987) sided largely

with Indian tribes and subsequently opened the door to widespread Indian gaming.9 Today there

are over 300 Indian casinos across the United States.

Commercial gaming expanded at about the same time as Indian gaming. South Dakota

legalized casino gambling in the historic town of Deadwood in 1989, and the State of Iowa

quickly followed with the first riverboat casinos. The following year, riverboat gambling was

legalized in Illinois and Mississippi. Today eleven states offer either land-based (6) or “floating”

(6) commercial casino gaming.

Casino gaming in some form (Indian or commercial) is allowed in 33 states. In 1999,

approximately $769 billion was wagered in the United States, encompassing all forms of

commercial gaming, leading to gambling losses to consumers (or revenues to the gaming

establishments) of roughly $59.4 billion, or about 0.64 percent of gross domestic product

9 The ruling essentially stated that if states allow any type of gaming, they must allow similar types of gaming on Indian reservations. My interpretation of the basic argument is that in cases where gaming is allowed in some form in a state, restrictions on Indian gaming amount to regulation, while in cases where all types of gaming are illegal, restrictions on Indian gaming are a matter of enforcing state criminal laws, in which case Public Law 280 would seem to apply. The current regulatory structure for Indian gaming is largely embodied in the Indian Gaming Regulatory Act (P.L. 100-497, codified at 25 U.S.C. 2701).


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(GDP).10 By 2003, consumer gambling losses increased to $72.9 billion, suggesting that the total

wager in that year was around $962 billion,11 an increase of over 25 percent in four years.

As shown in Figure 4, there is substantial variation in the geographic pattern and

concentration of private and Indian casinos across the United States. The heaviest concentrations

of Indian casinos are, unsurprisingly, in the upper Midwest, on the West coast, and in the State of

Oklahoma. The largest concentrations of private casinos are in Atlantic City, Deadwood, SD,

central Colorado, throughout the State of Nevada, and along the Mississippi river. Very little

gaming of any type is found in the eastern third of the country.

The mechanism whereby gambling would lead to a greater likelihood of filing for

bankruptcy is by raising debt levels relative to income. In one of the first formal studies to look

at the relationship between bankruptcy and gambling, Hira (1998) found that average annual net

income is about a third lower for gamblers than for non-gamblers and that the mean total debt of

gamblers is about 19 percent higher than the mean total debt of non-gamblers.

Problem gambling has been shown to increase with the availability of gambling

opportunities. The National Opinion Research Center (NORC) (1999), in a combined patron and

telephone survey, found that the prevalence of “problem and pathological gambling” roughly

doubles within 50 miles of a casino (vs. within 50 and 250 miles) (see also Goodman, 1995)

(ix).12 If bankruptcy is associated with problem gambling, therefore, bankruptcy also should be

associated with proximity to gambling establishments. The NORC report revealed that 19.2

percent of “pathological gamblers” have filed for bankruptcy at least once, compared to 5.5

percent for “low-risk gamblers” and 4.2 percent for non-gamblers. Granted, pathological

gamblers have personal characteristics that make them more likely to file for bankruptcy than

non-gamblers even ignoring their gambling problem, but given those characteristics, the

10 Christiansen Capital Advisors, LLC, “The Gross Annual Wager of the United States 2003.” 11 The total wager value for 2003 was estimated by the author from the 2003 value for consumer gambling losses provided by Christiansen Capital Advisors, LLC. 12 The use of the terms “problem gambling” and “pathological gambling” reflect specifically the criteria set by the American Psychiatric Association. For criteria, see http://www.ncpgambling.org/about_problem/ about_problem_timeline.asp.


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expected share who have ever filed for bankruptcy was only 10.8 percent, significantly less than

the actual 19.2 percent figure for pathological gamblers. In a study of gambling treatment in the

State of Minnesota, Stinchfield and Winters (1996) found that 21 percent of 944 gamblers

admitted for treatment had filed for bankruptcy.

Empirical results from the existing literature on the relationship between gambling and

personal bankruptcy are mixed. Results from state-level time series regressions for New Jersey,

Mississippi, and Nevada conducted by the U.S. Department of the Treasury (1999) suggest that

“gambling has no measurable impact on statewide bankruptcy rates” (54). The Treasury study

also found no impact when estimating panel regression using county-level data . This result was

echoed in the work of de la Viña and Bernstein (2002), who in a study examining 100 counties in

36 states find no statistically significant evidence that bankruptcy filing rates are higher in casino

and/or pari-mutual betting counties vis-à-vis non-gambling states. Using a sample of 398

counties from the casino states of Illinois, Iowa, Missouri, and Mississippi over a period of eight

years, Thalheimer and Ali (2004) also fail to show a significant relationship between access to

gambling establishments and personal bankruptcy filings. Their measure of access to gambling

for any given county was given by a gravity model: )031.0exp( d−=access , where d is the

distance from the geographic centroid of the county to the relevant gaming establishment, and

the decay parameter is based on the observation from another study (Illinois Gaming Board,

1997) that 21 percent of riverboat casino visits are made by those who live at least 50 miles

away.13

A limited number of studies have found a positive association between gambling and

personal bankruptcy filings. Nichols et al. (2000) compare bankruptcy filing rates in eight

casino counties in Illinois, Iowa, Mississippi, and Missouri with corresponding counties with

similar characteristics (by means of a cluster analysis) and find that the introduction of casino

gambling was associated with a significant increase in bankruptcy rates in five of the counties

13 The authors argue that this implies that accessibility declines from 100 percent to 21 percent as distance increases from 0 to 50 miles, yielding a 3.1 percent compound rate of decline.


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and a significant decrease in one county. There was no statistically significant relationship

between bankruptcy filing rates and the introduction of casino gambling in the other two

counties, but the relationship was positive. A study by Barron et al. (2002) suggests that the

elimination of casino gambling would have reduced bankruptcy filings by five percent in casino

counties in 1998 and would have reduced the national filing rate by about one percent.

The examination of the relationship between gambling and bankruptcy in this study

improves on the existing literature firstly by including a full set of Indian gaming establishments

while also including all counties in the United States. Thalheimer and Ali (2004) did include

Indian gaming locations in their analysis of 398 counties, but many Indian gaming

establishments were likely missed. For example, Figure 4 shows 44 Indian establishments

spread throughout Oklahoma in 2000, whereas Thalheimer and Ali show 0 for 1997.

Information from the Oklahoma Indian Affairs Commission suggests there were at least 10

compacts with Indian tribes by 1996, the earliest being executed in 1992.14 Further, the National

Indian Gaming Commission asserts there were 36 Indian gambling establishments in Oklahoma

in 1997.15 Thalheimer and Ali likely were considering only class III casinos, which provide all

types of gambling and were not legalized in Oklahoma until 2005. However, the establishments

existing in 1997 (and 2000) provided “fast-style class II gaming,” in which substantial amounts

of money can be wagered.16 If all available gambling establishments are not included in the

analysis, the estimate of the relationship between gambling and bankruptcy rates would be biased

towards zero.

A second improvement upon the existing literature is an accounting for all forms of

gambling. In addition to Indian and commercial casinos, dummy variables are included to reflect

the legality of card rooms and race tracks and the presence of a state lottery.

14 http://www.state.ok.us/~oiac/gaming.htm 15 Personal correspondence on September 14, 2005. 16 Ibid.


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Finally, a novel formulation of accessibility to casino gambling is included in the study.

Specifically, the variable used to pick up proximity to casinos is for each county the minimum

distance from the geographic center of that county to the geographic center of a casino-

containing zip code. This distance is determined by calculating the distance from the population

center of each county to the center of each zip code that contains at least one casino, and then

taking the minimum of all of those distances for each county. This provides a measure that

allows accessibility to change as distance varies, and the assumption is that the relationship

decays in a linear fashion as distance increases. Given mixed results in the past, it is important to

bring another formulation to bear on the issue.

Entrepreneurship and Small Business

About 70 – 80 percent of new businesses fail in the first year, and of the remainder, half

fail within five years. Given these high failure rates, one might expect that entrepreneurs have

relatively high bankruptcy rates. In calendar year 2004, the latest date for which complete data

are available, only 34,317 of approximately 1.6 million bankruptcy filings, or less than 2.2

percent, were deemed to be business filings by the U.S. Bankruptcy Court.17 However, Lawless

and Warren (2005) find that these official reports significantly understate the actual number of

business filings because self-employed people, small business owners, and independent

contractors are likely to file for personal bankruptcy protection if they have a failed business

undertaking. Official reports put business filings at 2.3 percent of total filings in 2003. In

reality, as much as 18.6 percent of all bankruptcy filings in 2003 may have been due to business

failures (the actual figure is more likely around 13.5 percent). Thus, a significant number of

personal bankruptcy filings (as much as 19 percent) are business filings, and an examination of

the determinants of personal bankruptcy filing rates should account for this.

Fay et al. (2002) find that an individual owning his own business is not any more or less

likely to file for bankruptcy than individuals who do not own their own businesses. As less

17 Data is available at http://www.uscourts.gov/bnkrpctystats/bankrupt_f2table_dec2004.pdf (accessed August 23, 2005).


16

direct evidence of a relationship between entrepreneurship and personal bankruptcy filing rates,

Mathur (2005) finds that the decision to start a new business is positively related to own-state

homestead exemptions and negatively related to neighboring states’ homestead exemptions,

while the decision to close a small business is negatively related to own-state homestead

exemptions and positively related to neighboring states’ homestead exemptions (see also Fan and

White, 2003). The findings suggest that homestead exemptions are an important consideration in

the business start-up/closure decision, and hence also suggest that bankruptcy may be a factor.

I include in the model the fraction of total employment that is made up of self-employed

people. If the hypothesis above is correct, higher self-employment rates, should be associated

with higher bankruptcy filing rates. I also include net firm births, which is total firm births less

total firm deaths, per 10,000 residents as a measure of the environment for entrepreneurship in

local areas. A high number of firm births relative to firm deaths is indicative of an environment

conducive to entrepreneurship and small business and should be associated with lower

bankruptcy filing rates. Net firm births may also reflect the general business climate.

Economic and Social Factors and Healthcare

VISA (1996) finds employment growth rates to be “the single most powerful coefficient

explaining bankruptcy behavior” (13), although the unemployment rate does not have much

predictive value. White (1987) finds that a one percent increase in the county unemployment

rate leads to an increase in Chapter 7 bankruptcy filings of approximately one-half of one

percent, while higher unemployment rates, surprisingly, seem to lower the rate of Chapter 13

filings. In general results seem to be reversed in Chapter 7 and 13 filings in White’s study,

which suggests that the variables affect chapter choice in addition to the decision to file for

bankruptcy. Numerous others have found a positive relationship between nonbusiness

bankruptcy filing rates and the unemployment rate (see, e.g., Domowitz and Eovaldi,1993;

Dawsey and Ausubel, 2001; Agarwal et al., 2003).

Income has been shown to be a critical determinant of bankruptcy in a number of studies,

but the effects are mixed and depend on how income enters the model. Agarwal et al. (2003) and


17

Fay et al. (2002) find a negative relationship between income and bankruptcy, while Dawsey and

Ausubel (2001) find a positive relationship. Domowitz and Sartain (1999) find income not to be

a significant determinant of the probability of filing for bankruptcy, except when combined with

home ownership.

In 1995, for example, 32 percent of U.S. households in the lowest income decile had

credit cards (Yoo, 1998). That number increased steadily to 99 percent for the highest income

decile. The percentage for the U.S. as a whole was 75 percent. Likewise, average credit card

balances (for households with credit cards) increased steadily from $1,358 in the lowest income

decile to $2,551 (roughly 90 percent higher) for the highest income decile. Of course, given that

average income in the top decile is ten times higher than average income in the bottom decile,

lower income people, on average, have larger credit card balances relative to income.

Another potentially important economic factor is the share of the population receiving

public assistance. People receiving public assistance are likely to have much less access to credit

than others, which should be reflected in lower bankruptcy filing rates. Further, the existence of

financial support during times of hardship may reduce bankruptcy filings on the margin.

Domowitz and Eovaldi (1993) find a statistically significant negative relationship between per

capita transfer payments and bankruptcy filing rates.

Hospitals and doctors provide about $45 billion in uncompensated care each year, much

of which is bad debt.18 HCA, the largest hospital operator in the U.S., for example, reserves 12

percent of revenues for bad debt. Tenet Healthcare Corp., the second largest operator, reserves

11 percent. Unsurprisingly, these bad debts often arise from bankruptcy filings.

Warren et al. (2000) evaluate responses from 1,974 survey participants and find that one

out of four debtors identified injury or illness as a reason for filing for bankruptcy. One third (in

total) stated that they had substantial medical bills, defined as $1,000 or more in medical bills not

18 See “More Americans May Have Lost Medical Coverage for Fourth Year,” Bloomberg.com, August 30, 2005. Accessed September 30, 2005 at http://www.bloomberg.com/apps/news?pid=10000103&sid= aQNqUDzLGkRY&refer=us#.


18

covered by insurance. A more recent study by Himmelstein et al. (2005) has very similar

findings. Like the Warren et al. study, the Himmelstein et al. study finds that about one-quarter

of the 1,771 personal bankruptcy filers surveyed named injury or illness as a specific reason for

bankruptcy. An additional quarter were found to have uncovered medical bills of at least $1,000.

Domowitz and Sartain (1999) find “high medical debt” to have the “greatest single impact of any

household condition variables in raising the conditional probability of bankruptcy” (413).

Specifically, they find that households with high medical debt have a probability of filing for

bankruptcy greater than 28 times that of the baseline household.

In an effort to account for the role of medical costs in bankruptcy filing rates, I include in

the model the share of the county population without health insurance coverage. Agarwal et al.

(2003) found a positive association between the lack of health insurance coverage and the

probability of an individual filing for bankruptcy. Also included in the model is the share of the

population aged 21 – 64 that is disabled.

Credit and Debt

Clearly debt levels and access to credit are critical determinants of bankruptcy filing, as

bankruptcy implies liabilities in excess of assets. The proportion of debt that is unsecured is

significantly higher among bankruptcy filers than among the population in general (U.S.

Department of the Treasury, 1999; Sullivan et al., 1989). Revolving debt in particular,

especially credit card debt, seems to be positively correlated with bankruptcy filing (Dawsey and

Ausubel, 2001; Domowitz and Sartain, 1999). Existing research indicates that account balances

and installment debt relative to income also are positively associated with income (Agarwal et

al., 2003; VISA, 1996), although Domowitz and Eovaldi (1993) do not find a statistically

significant relationship between bankruptcy and debt relative to income.

Increased access to credit also has been found to be correlated with bankruptcy filing.

VISA (1996) finds that bankruptcy filing is increasing in the number of credit card accounts, but

they caution that the number of accounts per adult creates a redundancy with revolving debt as a

percent of consumer installment credit, which is also included in the model. On the other hand,


19

Agarwal at al. (2001) finds that bankruptcy filings are decreasing in the overall credit limit.

They also produce the expected result that bankruptcy filings are negatively associated with

credit scores.

Homes and Autos

Conversations with attorneys suggest that excessive expenditures on autos are common

among bankruptcy filings.19 I include variables reflecting the average number of vehicles in a

household and the proportion of total debt that is auto debt, on average, in the county.

Domowitz and Sartain (1999) find that a debtor who is not a home owner is about seven

times more likely to file for bankruptcy than the average homeowner. White (1987) finds a

positive relationship between home ownership and Chapter 13 bankruptcy filings, but does not

reveal a statistically significant relationship between Chapter 7 filings and home ownership.

Mortgage debt may be used to finance consumer spending by refinancing and cashing out

equity. Consumers, if they choose the right lender, can in some cases finance 125 percent of the

value of their home. According to the Freddie Mac website, in the second quarter of 2005, the

latest date for which data are available, “74 percent of Freddie Mac-owned loans that were

refinanced resulted in new mortgages with loan amounts that were at least five percent higher

than the original mortgage balances.”20 Controlling for mortgage debt, as done in this study,

median home value serves as a good proxy for home equity, which should be negatively related

to bankruptcy filing rates, given that home equity can be tapped for emergency spending and that

negative equity often spurs bankruptcy. VISA (1996) finds such a negative relationship

empirically between median home prices and bankruptcy filing rates, but surprisingly, Domowitz

and Eovaldi (1993) do not find a statistically significant relationship between homeowners’

equity and bankruptcy filings.

19 As are excessive expenditures on furniture, but data on furniture expenditures or related debt are not available at the county, or even state levels. 20 Accessed September 13, 2005 at http://www.freddiemac.com/ news/archives/rates/2005/2qupb05.html.


20

In this study we include not only median house value and mortgage debt (as a share of

total debt, which is also included in the model), but also owner-occupancy rate, the vacancy rate

of owner-occupied housing, and the share of owner-occupied dwellings carrying a mortgage. As

a measure of living expenses, we include median rent and median costs of owner-occupied

housing.

Demographics and Other Variables

As in most studies of bankruptcy, demographics variables are included in the models here

as additional controls. Existing literature suggests that these controls are important. White

(1987) finds a positive association between the proportion of the population that is black and

“Spanish” and Chapter 13 filings, but finds no relationship between percentage black and

Chapter 7 filings and a negative relationship among the share that is Spanish and Chapter 7

filings. In the White study it is difficult to distinguish effects of race and ethnicity on bankruptcy

filing and chapter choice; however, Domowitz et al. (1993) find a positive relationship between

bankruptcy filings and the proportion of the population that is nonwhite. I include a range of

race and ethnicity variables in my models to account for potential effects on bankruptcy filings.

Divorce often causes a substantial, immediate, and unanticipated reduction in income.

This is true for women in particular, who are estimated to suffer a 30 percent decline in

economic status in the first year following a divorce (Hoffman and Duncan, 1988).21 Numerous

studies of bankruptcy have found a statistically significant positive relationship between divorce

and bankruptcy (White, 1987; Shiers and Williamson, 1987; Fay et al., 2002). Domowitz and

Eovaldi (1993) found a positive but statistically insignificant relationship between divorce rates

and bankruptcy filing rates. Domowitz and Sartain (1999) find that people who are unmarried

have a higher probability of filing for bankruptcy than people who are married. I include in the

model the shares of the population in each county that are married, separated, divorced, and

widowed, with never married being the left out variable.

21 Economic status is defined as the ratio of average income relative to needs.


21

3. Methodology and Empirical Results

Let bv

be a vector of bankruptcy filing rates, X represent the matrix of explanatory

variables discussed in Section 2, and θv

be a vector of parameters. The basic model (Model 1) is

then given by

(1) εθ vvv+= Xb

where ( ) Ω2σεε =′vvE . The model is solved using least squares and White’s (1980)

heteroscedasticity consistent estimator.

Exemptions

In Model 1, the coefficient on homestead exemption is statistically insignificant, which

suggests that homestead exemptions have no impact on bankruptcy filing rates (Table 2).

Alternatively, the zero coefficient may indicate either a dual causality or countervailing effects.

The presumption underlying the empirical model is that homestead exemptions increase

bankruptcy filing rates by reducing the cost of bankruptcy, which theoretically should produce a

positive coefficient. But it is possible that states with relatively high bankruptcy filing rates

respond by lowering the homestead exemption. If this is the case, the coefficient theoretically

should be negative, which means the two forces may cancel each other out. Further, since

creditors tend to restrict lending to risky individuals in states with generous exemptions (Gropp

et al., 1997; Grant, 2001; Berkowitz and White, 2004), high exemptions may lead to tighter

credit restrictions and hence less borrowing and fewer bankruptcies. In an effort to account for

this potential endogeneity, a two-stage model was also estimated.

The two-stage model begins with an estimation of homestead exemptions in the 50 states

and the District of Columbia. The dependent variable is the level of the homestead exemption in

2000, in dollars. Virtually all of the independent variables included in the model are statistically

significant at the 90 percent confidence level or better (Table 3). Poorer states, as measured by

median household income, the poverty rate, and the share of households receiving some form of

public assistance, tend to have lower homestead exemptions. States with populations that are


22

older and states with larger families and greater shares of males and minorities tend to have more

generous exemptions. Surprisingly, both higher owner occupancy rates and home values tend to

lower exemption levels. The political tendencies of the state do not seem to have a significant

effect on the levels of homestead exemptions. These variables in total explained about 21

percent of the total variation in homestead exemption levels across states. Of course, as noted

above, states do not change exemption levels very often, and the most important determinant of

exemption levels in the Posner et al. (2001) study was the exemption level in 1920.

The predicted values for state homestead exemptions generated in the first stage were

utilized in the second stage. Specifically, for each county, the homestead exemption variable is

equal to the predicted value of the homestead exemption in the state in which it resides divided

by the actual median home value in the county. The values are restricted to be nonzero.

Because a generated regressor is used in the second stage, t-statistics are calculated using

bootstrapped standard errors, following Efron (1979) and as described in Greene (1993). Least

squares estimation of (1) results in an estimated parameter vector θ and a residual vector ε) :

(2) εˆ += θXb

A sample of B = 100 bootstrap estimates Bθθθ ~,,~,~21 L are then obtained by sampling n

observations from X, with replacement, and reestimating the model B times. The estimated

asymptotic covariance is then

(3) [ ][ ]∑ =

′−−

B

b bbB 1ˆ~ˆ~1 θθθθ

The two-stage model is presented in Table 2 as Model 2.

For most variables the results in Models 1 and 2 are virtually identical. For homestead

exemptions, however, the coefficient is now positive and significant at the 90 percent confidence

level. The value of 0.68 suggests that for every one percentage point increase in the share of the

median house value covered by the homestead exemption, bankruptcy filing rates increase by

0.68 per 10,000 households in the county. An exemption of 100 percent of median house value

would thus yield 35 additional bankruptcies per 10,000 households in that county relative to a


23

county in which the state exemption is only 50 percent of the median house value. The average

county had roughly 100 personal bankruptcies per 10,000 households in 2000 (the median had

91), so this would represent a 35 percent increase in the bankruptcy filing rate.

Social Stigma

I attempt to get at the role of stigma in explaining variation in bankruptcy filing rates in

multiple ways. The first way is by accounting for regional tastes for bankruptcy. The inclusion

of a set of dummy variables representing the eight BEA regions reveals a marked regional

pattern (Table 2, Model 3).22 Relative to counties in New England, which is the left-out region

in the model, counties in the Mid-Atlantic, Great Lakes, Southeast, and Rocky Mountain regions

have significantly higher bankruptcy filing rates, all else equal. The bankruptcy filing rates in

counties in the Plains, Southwest, and West regions are not significantly different from those

with similar characteristics in the Northeast. This result suggests that bankruptcy filers may face

greater stigma in these regions and in the Northeast than in the rest of the country.

An additional way I account for geographic differences is with a spatial autoregressive

model. Let D be an NxN matrix where

(4) ( ) 2,/000,000,1, jidjiD = ,

D(i,j) is the row i column j element of D, N is the total number of counties, and di,j is the arc

distance between the geographic centroid of county i and the geographic centroid of county j.

Letting ρ be a scalar, the spatial autoregressive model is given by

(5) ( )IDX 2,0~, σεερθvvvvvv

Nbb ++=

(5) is solved for θv

and ρ by the method of maximum likelihood.

The parameter ρ is a measure of the degree that the bankruptcy filing rate in any county i

depends on the bankruptcy filing rates of all other counties, where the strength of the relationship

between filing rates in any two counties declines quadratic ally with the distance between them.

The spatial autoregressive term estimated in Model 4 (Table 2) is positive and statistically

22 Models 2 – 4 are all two-stage models.


24

significant at the 99 percent confidence level, which is indicative of stigma effects: a given

county’s bankruptcy filing rate is higher the higher is its neighbors’ bankruptcy filing rates.

It has been suggested that relatively high bankruptcy filing rates among younger cohorts

is indicative of declining stigma, the assumption being that social mores are declining over time.

My results show that bankruptcy filing rates are lower the greater the proportion of the

population that is aged 55 – 64, which is consistent with this view. Inconsistent with this view,

however, is that bankruptcy filing rates increase with the share of the population that is aged 75 –

84. These results actually are more consistent with stigma being a generational phenomenon

rather than chronological, which is also consistent with the pattern shown in Figure 3.

Perhaps the best proxy for stigma is the data on religious adherence. My results (Models

1 – 4) suggest that bankruptcy filing rates are lower the greater the concentrations of Muslims,

Eastern religion adherents, and Unitarian Universalists, and bankruptcy filing rates are higher the

greater the concentrations of Jews and Christians, relative to the non-religious share of the

population. These results are entirely consistent with the emphasis on repayment of debts in

Islam, avoidance of injury to others in the Eastern religions, and forgiveness in the Christian

religions, as well as with the Jubilee concept in Judaism.

While clearly not a direct measure of stigma, the confluence of effects of geographic

variables, the age distribution, and data on religious adherence is highly suggestive of a strong

role for stigma in the determination of bankruptcy filing rates.

In the remainder of the section, results from Model 3 are utilized in the discussion,

keeping in mind that for any given variable, coefficient values may differ across specification.

For most variables, these differences in values are negligible.

Wage Garnishment

As expected, the greater the protection given to wages, the lower the bankruptcy filing

rate. Specifically, the number of bankruptcies per 10,000 households decreases roughly one-to-

one for every one percentage point increase in the proportion of state household median income

protected from garnishment.


25

Legal Gambling

Minimum distance to a casino has a statistically significant negative effect on non-

business bankruptcy filing rates, meaning that the further a county centroid is from the nearest

(legal) casino, the lower is the filing rate. Specifically, an additional 100-mile distance from a

casino results in 4.3 fewer bankruptcy filings per 10,000 households, or given that the average

number of bankruptcies per county is 100, roughly four percent of filings in the average county.

Counties in states where card rooms are legal on average have 11 more bankruptcy filings than

counties in states where card rooms are illegal. Surprisingly, the availability of race track

gambling and a state lottery reduced filing rates compared to counties in states without race

tracks and state lotteries. Race track betting is legal in 42 states, while 33 states have state

lotteries, so there was little variation in the data, especially given that these are state-level

variables.

Entrepreneurship and Small Business

Both the self-employment rate and net firm births (firm births less firm deaths) are

negatively related to bankruptcy filing rates. A one percentage point higher self-employment

rate is associated with 4.4 fewer bankruptcy filings per 10,000 households, on average, which

conflicts with the expectation that higher self-employment rates would be associated with

increased bankruptcy filing rates. 10 additional net firm births are associated with roughly 2.6

additional bankruptcy filings per 10,000 households, suggesting, as expected, that greater

business success engenders lower bankruptcy filing rates.

Economic and Social Factors and Health Care

The employment rate is negatively related to bankruptcy filing rates, as expected. A one

percentage point difference in the employment rate is associated with about 0.8 less bankruptcy

filings per 10,000 households, a change of less than one percent for the average county.

Although this effect seems small on the surface, employment rates across counties varied from

23.8 percent to 84.6 percent in 2000. The mean employment rate was 57.4 percent and the

standard deviation was 7.5 percent, which means that a county one standard deviation up from


26

the mean would incur roughly 6 fewer bankruptcies than the county with the mean employment

rate.

Previous studies generally have included the level of income in individual studies or the

level of median or average income in aggregate studies, with a quadratic term in some cases, and

the results have been mixed. I include the entire distribution of income, and the results suggest

that bankruptcy filing rates likely rise with income initially (from very low income), drop

slightly in the middle income range, then spike at the $50,000 – $75,000 range before declining

steadily with higher levels of income (Figure 5). Very low bankruptcy filing rates would be

expected in the lowest income ranges because there is little access to credit, and therefore less

borrowing. At higher income levels, bankruptcy filing rates increase with much better access to

credit but relatively moderate incomes. At some point, at the $50,000 – $75,000 range in this

model, bankruptcy filing rates begin to decline with a reduced need to borrow relative to income.

Of course, these results reflect proportions of the population that are in the various

income ranges. The assumption is that these proportions mirror closely the distribution of filers.

In fact, Rodríguez et al. (2002), in a study using the 1998 Survey of Consumer Finances, show

that the income quintile with the highest bankruptcy filing rate is the third quintile, which in

2000 included households with income in the range of $33,006 to $52,272,23 which is consistent

with this view. Nevertheless, county income distributions may reflect other phenomena besides

the income distribution of bankruptcy filings. For example, any individual low income person

may be more likely to file for bankruptcy if he lives in a high income area than if he lives in a

low income area if there is pressure to “keep up with the Joneses.”

Bankruptcy filing rates are higher the greater the proportion of the population receiving

public assistance. This result is surprising in that public support in times of financial crisis

would presumably forestall bankruptcy filings that might otherwise occur. On the other hand,

controls for income are included in the model, and the receipt of public assistance may for some

23 Data on limits of income quintiles for 2000 are from the U.S. Census Bureau, Historical Income Tables – Households, Table H-1, accessed November 2, 2005 at http://www.census.gov/hhes/income/histinc/h01.html.


27

people (but certainly not all) reflect poor financial decision making and little capacity for

climbing out of financial messes. Moreover, a large proportion of the population receiving

public assistance may be indicative of underlying economic crises in the county.

As expected, bankruptcy filing rates are higher the greater the share of the population

without health insurance and the larger the share that is disabled. Specifically, a one percentage

point higher share of the population without health insurance is associated with 0.9 more

bankruptcies per 10,000 households. These shares ranged from 7.4 percent to 24.2 percent in

2000. A one percentage increase in the share of the 21 – 64 population that is disabled, which

ranged from 5.7 percent to 45.6 percent in 2000, is associated with 1.5 additional bankruptcies

per 10,000 households.

Credit and Debt

Results suggest that debt loads are an important factor in explaining bankruptcy filing

rates, but measures of access to credit, such as the number of new accounts and credit cards per

capita are not statistically significant explanatory factors. Total debt to income is positively

related to bankruptcy filing rates, but the magnitude is quite small at less than 0.2 filings per

10,000 households for every additional point in the debt to income ratio. The coefficient on the

past due index is positive and fairly substantial. A one unit higher past due index, which ranges

from 0.7 to 48.9, is associated with 1.6 additional bankruptcies per 10,000 households.

Homes and Autos

As expected, significant spending on autos is associated with higher bankruptcy filing

rates. A one percentage point higher share of the population with more than two vehicles is

associated with 1.4 additional bankruptcies per 10,000 households, while greater ratios of auto

debt to total debt are related to higher bankruptcy filing rates in roughly one-to-one fashion.

As in existing literature I find that higher rates of home ownership generate lower

bankruptcy filing rates. Specifically, a county with a one percentage point higher owner

occupancy rate has, on average, 0.8 fewer bankruptcy per 10,000 households. However, owner

occupancy rates across counties are highly concentrated around the mean of 74 percent (Table


28

1). Also supporting the literature is that my results suggest that home equity is associated with

lower bankruptcy filing rates. Specifically, median house value has a statistically significant

positive coefficient, while the share of owner-occupied homes with mortgages, controlling for

the owner occupancy rate, is positively correlated with bankruptcy filing rates. The vacancy rate

of owner-occupied dwellings does not appear to affect bankruptcy filing rates. Higher costs of

home ownership are associated with higher bankruptcy rates, but median rent does not seem to

affect bankruptcy filing rates.

Demographics and Other Variables

In terms of race, relative to the share of the population that is white, Black and

Hawaiian/Pacific Islander shares of the population are positively correlated with bankruptcy

filing rates, while proportions that are American Indian, Asian, or “Other Race” are negatively

correlated with bankruptcy filing rates. The results on black share of the population are

consistent with existing literature, which has little to say about other races.

As expected, the share of the population that is divorced is positively correlated with

bankruptcy filing rates. Specifically, a one percentage point higher share of the population that

is divorced is associated with 7.8 more bankruptcies per 10,000 households, which is substantial

even though county divorce rates are highly concentrated around their mean value (Table 1).

Married households also have higher filing rates than never married households. Domowitz and

Sartain (1999) find that people who are unmarried have a higher probability of filing for

bankruptcy than people who are married. My results are not inconsistent with this finding in the

sense that in the Domowitz and Sartain analysis, divorced people were included in the unmarried

category, while the comparative (and left out) category here is never married. Surprisingly, the

share of the population that is separated is negatively associated with bankruptcy filings, even

though separation is known to cause substantial financial strain. A possible explanation for this

unlikely finding is that people in the throes of a marital dissolution are unlikely to want to deal

with bankruptcy inconveniences at such a time in their lives, and those in severe financial


29

distress will put off filing as long as possible. The share of the population that is widowed does

not have a statistically significant impact on bankruptcy filing rates.

4. Conclusions

This paper analyzes a variety of potential determinants of bankruptcy filing rates.

Proximity to gambling establishments was found to be associated with higher bankruptcy filing

rates. The use of an innovative, and I believe much improved, measure of proximity helps to

shed light on a still unresolved issue. Homestead exemptions were found to be a substantial

determinant of bankruptcy filing rates, once controls for endogeneity were included in the model.

This finding suggests that the weak relationship between homestead exemptions and bankruptcy

filing rates found in much of the existing literature may be more of an econometric issue than a

substantive issue. Another important policy variable, the share of wages protected from

garnishment, was shown to be a significant factor in the determination of bankruptcy filing rates

as well, and the results support the conceptual view that wage garnishment spurs bankruptcy

filing. Data on religious adherence was incorporated in the model in an effort to account for the

role of stigma in explaining bankruptcy filing rates. I believe that the religion variables included

in this study are to date the closest proxy to stigma that have been used in bankruptcy studies.

Further, results from regional dummy variable and spatial autoregressive models bring new

evidence to bear on the stigma issue, as well as related spatial issues, and the inclusion of the

entire age distribution, rather than median age, provides more informative results on the role of

age in determining bankruptcy filing rates. Numerous other variables also were included to

provide results from the most comprehensive model of bankruptcy filing rates to date in terms of

the number of potential factors investigated.

The new bankruptcy law likely will have some effect on filing rates by curbing abuses

and forcing more petitioners into a reorganization plan that requires them to pay part of their

debts. This study suggests that exemption and garnishment laws also should be evaluated to

ensure that consumers are offered some protection and a fresh start without being given too


30

much incentive to engage in risky financial behavior that may lead to bankruptcy. But efforts to

change consumer behavior through education likely will have the greatest impact, as many of the

factors shown to affect bankruptcy filing rates, such as gambling, debt composition, home

ownership, and prompt payment of bills, are reflections of financial decisions, and good

decisions require substantial knowledge.


31

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Consumer Bankruptcies in Canada and the United States,” working paper, University of Western Ontario, March 9.

Mathur, Aparna (2005). “A Spatial Model of the Impact of State Bankruptcy Exemptions on

Entrepreneurship,” Office of Advocacy, U.S. Small Business Administration, July. National Opinion Research Center (1999). Gambling Impact and Behavior Study: Report to the

National Gambling Impact Study Commission, April. Nichols, Mark W., B. Grant Stitt, and David Giacopassi (2000). “Casino Gambling and

Bankruptcy in New United States Casino Jurisdictions,” Journal of Socio-Economics, 29(3), 247 – 261.

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33

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State Law Journal, 43(2), 335 – 376.


34

TABLES AND FIGURES Figure 1: Personal Bankruptcies in the U.S., 1980 – 2004

0.0

10.0

20.0

30.0

40.0

50.0

60.0

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

Year

Ban

krup

tcie

s/10,

000

Am

eric

ans

Treasury (1999)

Administrative Office of the U.S.Courts


35

Figure 2: Bankruptcy Filing Rates by County, United States, 2000


36

Figure 3: Age Distribution of Bankruptcy Filings, 1991 and 2001

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0Sh

are

of F

Iling

s

< 25 25-34 35-44 45-54 55-64 > 65

Age Group

1991 2001

Source: Consumer Bankruptcy Project, as cited in Suein Hwang, "Mr. Hester Takes a Mall Job," The Wall Street Journal, August 6, 2004.


37

Figure 4: Gambling in the United States, 2000


38

Figure 5: Results on Income Distribution

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

< 15

K

15K

- 25

K

25K

- 35

K

35K

- 50

K

50K

- 75

K

75K

- 10

0K

> 10

0K

Income Range

Coe

ffici

ent V

alue

All are significant at the 99 percentconfidence level.


39

Table 1: Data Sources Variable Source Religion Association of Statisticians of American Religious Bodies (ASARB), Religious

Congregations and Memberships in the United States: 2000, Nashville TN, Glenmary Research Center, 2002.

Education U.S. Census Bureau, Decennial Census Age Distribution U.S. Census Bureau, Decennial Census Race U.S. Census Bureau, Decennial Census Income U.S. Census Bureau, Decennial Census Social Households w/ Public Assistance Disabled Population, 21 – 64 No Health Insurance

U.S. Census Bureau, Decennial Census U.S. Census Bureau, Decennial Census U.S. Census Bureau, Current Population Survey, Annual Social and Economic

Supplement Marital Status U.S. Census Bureau, Current Population Reports Housing Median House Value Median Costs, OOCC Housing Median Rent Owner-Occupany Rate Vacancy Rate, OOCC Housing OOCC Housing, % w/ Mortgage Mortgage Debt / Total Debt

U.S. Census Bureau, Decennial Census U.S. Census Bureau, Decennial Census U.S. Census Bureau, Decennial Census U.S. Census Bureau, Decennial Census U.S. Census Bureau, Decennial Census U.S. Census Bureau, Decennial Census Trendata, TransUnion LLC

Vehicles Number in Households Auto Debt / Total Debt

U.S. Census Bureau, Decennial Census Trendata, TransUnion LLC

Economy Employment Rate Self-Employment Rate Net Firm Births

Annual Statistical Supplement, 2000, U.S. Social Security Administration

(www.ssa.gov/policy/docs/statcomps/supplement/2000, accessed July 1, 2005).

Unpublished data from the U.S. Census Bureau, 1989 – 2000 Business Information Tracking Series.

Debt Management Trendata, Transunion LLC Gaming Minimum Distance to Casino Lottery, Card Rooms, and Race

Tracks

Author’s calculation using data from the National Indian Gaming Commission

(www.nigc.gov), various tribal web sites, calls to tribal contacts, various state gaming agency compliance reports, and unpublished data provided by Casino City Press.

“Gaming Activities,” Internal Revenue Service (www.irs.gov/businesses/ small/industries/article/0,,id=99639,00.html, accessed on June 3, 2005).

State Law Legal codes of the 50 states and the District of Columbia


40

Table 2: Results, Bankruptcy Filing Rates

GLS OLS-IV† OLS-R-IV† SAR-IV† Variable / Model 1 2 3 4

Intercept - 128.1 (- 1.360)

- 145.3 (- 1.688)

- 194.2* (- 1.892)

- 173.3* (- 1.760)

Christian - Catholic 0.050 (0.734)

0.048 (0.624)

0.019 (0.260)

0.146* (1.822)

Eastern Religions - 13.79*** (- 3.609)

- 13.75*** (- 4.016)

- 13.72*** (- 3.498)

- 7.575** (- 2.018)

Islam - 4.308 (- 1.294)

- 4.243** (- 2.129)

- 4.809** (- 2.564)

- 6.106*** (- 3.052)

Jewish 2.420** (2.506)

2.369*** (3.100)

1.976** (2.527)

1.452** (2.106)

Christian - LDS 0.178 (1.505)

0.188 (1.613)

0.129 (0.972)

0.192* (1.945)

Christian - Orthodox 0.195 (0.127)

0.178 (0.126)

0.500 (0.356)

- 0.158 (- 0.109)

Unitarian Universalist - 44.19*** (- 5.390)

- 44.02*** (- 4.580)

- 38.42*** (- 4.231)

- 33.27***

(- 3.474)

REL

IGIO

N (%

)

Christian - Protestant 0.155** (2.341)

0.152*** (2.862)

0.141** (2.038)

0.053 (0.895)

< High School - 0.011 (- 0.044)

- 0.002 (- 0.009)

0.031 (0.124)

- 0.311 (- 1.232)

Some College / Associate Deg

- 0.849*** (- 3.686)

- 0.831*** (- 3.571)

- 0.507** (- 2.212)

- 0.182 (- 0.808)

EDU

C (%

)

Bachelor’s or Higher - 0.004 (- 1.365)

- 0.003 (- 1.176)

- 0.003 (- 1.225)

- 0.005* (- 1.691)

< 15 0.409 (0.413)

0.454 (0.386)

0.947 (0.968)

1.144 (1.049)

15 – 19 0.181 (0.191)

0.146 (0.142)

0.377 (0.332)

0.103 (0.105)

20 – 24 1.016 (0.788)

1.189 (0.905)

1.720 (1.331)

0.547 (0.388)

35 – 44 - 2.452 (- 1.560)

- 2.365 (- 1.582)

- 2.124 (- 1.374)

- 0.682 (- 0.450)

45 – 54 - 0.669 (- 0.586)

- 0.399 (- 0.365)

- 0.196 (- 0.177)

0.472 (0.423)

55 – 59 - 4.277* (- 1.691)

- 4.374* (- 1.960)

- 4.551* (- 1.738)

- 4.685** (- 2.069)

60 – 64 - 4.483* (- 1.671)

- 4.329** (- 2.049)

- 3.842 (- 1.366)

- 4.038* (- 1.773)

65 – 74 - 1.768 (- 1.101

- 1.740 (- 1.106)

- 1.332 (- 0.872)

- 0.248 (- 0.162)

75 – 84 4.497** (2.117)

4.560** (2.492)

3.970* (1.764)

3.768* (1.836)

AG

E D

ISTR

IBU

TIO

N (%

)

> 84 - 2.049 (- 0.919)

- 2.096 (- 0.909)

0.235 (0.093)

- 0.410 (- 0.153)

t-statistics are in parentheses ***, **, and * indicates statistical significance at the 1, 5, and 10 percent levels, respectively. † indicates t-statistics are computed with bootstrapped standard errors. (Continued on next page)


41

Table 2 (Continued)


Black 1.134*** (7.708)

1.133*** (9.342)

1.152*** (9.624)

0.871*** (6.987)

American Indian - 0.689*** (- 4.269)

- 0.688*** (- 3.974)

- 0.733*** (- 4.526)

- 0.584*** (- 4.290)

Asian - 0.103 (- 0.190)

- 0.112 (- 0.204)

0.202 (0.358)

- 0.282 (- 0.447)

Hawaiian / Pacific Islander 9.402** (2.560)

9.070*** (2.657)

7.307* (1.810)

5.400 (1.542)

Other Race - 0.770*** (- 3.091)

- 0.760*** (- 3.127)

- 0.480* (- 1.818)

- 0.068 (- 0.275)

RA

CE

(%)

2 or More Races - 0.526 (- 0.495)

- 0.386 (- 0.345)

0.430 (0.384)

0.268 (0.269)

15K – 25K 2.217*** (3.654)

2.199*** (3.776)

1.944*** (3.502)

2.181*** (4.283)

25K – 35K 2.964*** (4.874)

2.989*** (4.707)

2.926*** (4.561)

2.354*** (4.027)

35K – 50K 1.831*** (3.140)

1.874*** (3.737)

1.872*** (3.240)

1.327** (2.318)

50K – 75K 2.873*** (5.312)

2.956*** (6.370)

3.144*** (6.962)

2.192*** (4.070)

75K – 100K 2.073** (2.436)

2.126** (2.510)

2.319*** (3.095)

0.996 (1.227)

INC

OM

E (%

)

> 100K 1.497** (2.355)

1.491** (2.457)

1.582*** (2.629)

0.135 (0.204)

Households w/ Public Assist.

0.922 (1.341)

0.992* (1.834)

1.671** (2.495)

0.785 (1.330)

Disabled Pop. 21-64 1.691***

(4.505) 1.765*** (5.176)

1.621*** (4.702)

1.460*** (4.340)

SOC

IAL

No Health Insurance 1.520*** (4.620)

1.533*** (5.252)

0.986** (2.345)

0.906*** (3.318)

Married 1.739*** (4.459)

1.778*** (4.660)

1.847*** (5.044)

0.902*** (2.692)

Divorced 7.272*** (9.127)

7.212*** (9.315)

7.824*** (11.063)

5.201*** (7.599)

Separated - 6.108*** (- 3.663)

- 5.988*** (- 3.724)

- 7.457*** (- 5.369)

- 5.418*** (- 3.517)

MA

RIT

AL

STA

T

Widowed 3.336*** (2.776)

3.425*** (3.653)

3.464*** (3.744)

1.078 (1.084)



42

Table 2 (Continued)


Median House Value ($) - 0.00025*** (- 3.677)

- 0.00024*** (- 5.470)

- 0.00020*** (- 3.935)

- 0.00022*** (- 5.760)

Median Costs, Owner-Housing

0.048***

(2.986) 0.047*** (3.261)

0.034** (2.424)

0.055*** (4.324)

Median Rent 0.010 (0.465)

0.009 (0.474)

0.004 (0.187)

0.029 (1.401)

Owner-Occup. Rate - 0.877*** (- 3.700)

- 0.878*** (- 4.082)

- 0.839*** (- 3.516)

- 0.389** (- 2.042)

Owner-Occup. Vacancy Rate

1.068 (1.620)

0.907 (1.219)

1.021 (1.374)

1.133* (1.660)

Owner-Occup. w/ Mortgage

1.078*** (6.397)

1.097*** (6.407)

1.021*** (5.846)

0.859*** (5.714)

HO

USI

NG

Mortgage Debt Share of Total

- 0.010 (- 0.884)

- 0.009 (- 0.593)

- 0.003 (- 0.219)

- 0.013 (- 0.912)

None 0.514 (1.052)

0.498 (1.199)

0.526 (1.387)

- 0.098 (- 0.242)

Two 0.150 (0.264)

0.124 (0.315)

0.057 (0.127)

- 0.248 (- 0.553)

More than Two 1.443*** (4.188)

1.448*** (5.441)

1.446*** (4.971)

0.402 (1.458) V

EHIC

LES

Auto Debt Share of Total 1.101*** (4.546)

1.028*** (4.396)

1.016*** (3.834)

0.828*** (3.351)

Employment Rate - 0.852*** (- 3.292)

- 0.894*** (- 3.682)

- 0.825*** (- 3.406)

- 0.722*** (- 2.972)

Self-Employ Rate - 4.450*** (- 5.429)

- 4.305*** (- 4.915)

- 4.402*** (- 4.261)

- 1.681** (- 2.448)

ECO

NO

MY

Net Firm Births - 0.242** (- 1.965)

- 0.236** (- 2.011)

- 0.257** (- 2.540)

- 0.322*** (- 2.911)

Total Debt to Income 0.190** (2.394)

0.201*** (2.647)

0.157** (2.065)

0.062 (0.787)

Revolving Debt Share of Total

- 0.481* (- 1.941)

- 0.492* (- 1.734)

- 0.593**

(- 2.249) - 0.398

(- 1.503)

Number of Bankcards - 7.691 (- 1.297)

- 8.281 (- 1.515)

- 4.655 (- 0.832)

1.697 (0.350)

Number of New Accounts - 159.5 (- 0.923)

- 161.2 (- 0.989)

- 82.25 (- 0.487)

- 139.88 (- 0.889)

DEB

T M

AN

AG

EMEN

T

Past Due Credit Bills Index

1.555*** (5.524)

1.550*** (6.032)

1.590*** (6.991)

1.272*** (5.531)

Min Distance to a Casino - 0.040*** (- 4.822)

- 0.040*** (- 5.917)

- 0.043*** (- 5.777)

- 0.017*** (- 2.703)

Lottery - 5.357* (- 1.768)

- 4.969** (- 2.054)

- 2.814 (- 1.195)

- 11.821*** (- 5.904)

Card Rooms 8.918** (2.542)

7.831* (1.706)

10.78*** (2.616)

4.885 (1.347) G

AM

ING

Race Tracks - 13.932*** (- 3.900)

- 14.26*** (- 4.846)

- 13.67*** (- 4.835)

- 3.554 (- 1.585)



43

Table 2 (Continued)


Homestead Exemption 0.119 (0.649)

0.680* (1.765)

0.939** (2.244)

0.253 (0.685)

Unlimited Homest. Exmp. - 3.589 (- 0.981)

- 5.096* (- 1.659)

- 2.354 (- 0.729)

- 4.706 (- 1.610)

STA

TE L

AW

Not Subj Wage Garnishment

- 1.072*** (- 9.962)

- 1.042*** (- 12.113)

- 1.038*** (- 12.200)

- 0.704*** (- 9.554)

Mid Atlantic 25.617*** (5.219)

Great Lakes 6.499 (1.328)

Plains 0.208 (0.044)

Southeast 17.28*** (4.017)

Southwest 6.918 (1.297)

Rocky Mountain 12.24** (2.140) R

EGIO

NA

L D

UM

MIE

S

West/Pacific 4.119 (1.096)

ρ 0.528*** (18.92)

Adj. R2 0.5169 0.5174 0.5244 0.5137 t-statistics are in parentheses ***, **, and * indicates statistical significance at the 1, 5, and 10 percent levels, respectively. † indicates t-statistics are computed with bootstrapped standard errors. (Continued on next page)


44

Table 3: Results, Homestead Exemptions

Variable Parameter Value (t-statistic) Variable Parameter Value

(t-statistic)

Intercept - 7,796,626* (- 1.733) Percent White - 5,853*

(- 1.844)

Poverty Rate - 140,452*** (- 2.891) Average Family Size 1,770,668***

(3.045)

Median House Value - 8.062***

(- 3.059) Household Median Income - 32.52*

(- 1.905) Wage Not Subject to Garnishment

- 4,577** (- 2.166)

Households with Public Assistance

- 238,177**

(- 2.644) Percent of Population that is Male

122,891* (1.811) Owner-Occupany Rate - 21,184*

(- 1.954)

Median Age 76,621*** (2.813) Percent GOP - 14,540

(- 1.406) Adjusted R2 0.2063 No. Observations 51 ***, **, and * indicates statistical significance at the 1, 5, and 10 percent levels, respectively.

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NEW INSIGHTS IN THE DETERMINANTS OF REGIONAL …€¦ · 10,000 Americans. These alarming increases in bankruptcy filing rates over the last decade were largely the impetus for the