Racial/Ethnic Disparities in Household Debt Repayment
DISSERTATION
Presented in Partial Fulfillment of the Requirements for
the Degree of Doctor of Philosophy in the Graduate
School of The Ohio State University
By
Jonghee Lee, B.S., M.S.
*****
The Ohio State University
2009
Dissertation Committee :
Approved by
Dr. Sherman D. Hanna, Advisor
Dr. Jinkook Lee
Dr. Lucia Dunn
Advisor
College of Education and Human Ecology
Abstract
This study proposes to gain insight into which key factors influence household
debt repayment; whether these key factors differ across racial/ethnic groups; and
whether these factors result in racial/ethnic variance. In particular, this study aims first
to: (1) account for household characteristics related to repayment delinquency, (2)
examine whether race and ethnicity are related to debt payment problems, even
controlling financial events that might cause a decrease or disruption in the flow of
periodic household income, financial buffers available in emergency and any other
demographic variable and (3) examine how the effects of key factors differ across
race and ethnicity.
This study analyzes factors related to getting behind or missing payments on
household debt by two months or more using the 1992 to 2007 Survey of Consumer
Finances. This study defines payment delinquency according to two SCF questions
asking (1) whether payments on any loans were sometimes late or missed, and (2)
whether the respondent was ever behind in his or her payments by two or more
months. Following this, this study will investigate reasons the propensity toward
payment delinquency on household debt might differ by race and ethnicity. Finally,
this study aims to address the sample selection bias that can affect predictors of
delinquency risk in a given population of applicants. Therefore, this study tests for
whether the coefficient of the selection effect is significantly different from zero.
ii
Finally, the results of this study have implications for financial education programs
targeted at different racial/ethnic groups.
A logit selection model is used, with the first stage being whether the
household had any debt, and the second stage being whether payments on any of the
debt were made late or missed by two months or more. This study tests the effect of
consumers‘ financially adverse events, financial buffers and household debt burden
on household debt repayment delinquencies. This study expects that Black and
Hispanic households with debt would have the same delinquency rates as whites,
conditional on any other demographic and economic characteristic.
A probit analysis of having household debt shows that blacks, Hispanics, and
Asians/others are less likely to have household debt than whites. There is a significant
selection effect. The logit analysis of being delinquent shows that blacks are
significantly more likely to be delinquent than Whites, but Hispanics are significantly
less likely to be delinquent than Whites. Asians/others are not significantly different
from otherwise similar whites. This study shows that there exist racial/ethnic
differences in repayment delinquency behavior not only between white and minority
households but between blacks and Hispanics.
iii
Dedicated to God and my family
iv
ACKNOWLEDGMENTS
Firstly, I would like to thank my advisor, Dr. Sherman Hanna for his
continuous help and tremendous encouragement during the process of my dissertation
writing. During my PhD program, he was always supportive in my research and helps
me develop my research methodology, with lots of comments.
I wish to extend my sincere appreciation to my dissertation committee
members, Dr. Lucia Dunn and Dr. Jinkook Lee for their encouragement during the
process of my dissertation writing and for their advice, suggestions, and comments.
Without those many meetings and inspiring talks with my dissertation committee, it
would be hard for me to finish it on time.
I would like to express special thanks and gratitude to the Department of
Consumer Sciences for financial support during my Ph.D. study, especially Dr.
Sharon Seiling. Dr. Sharon Seiling has given me invaluable research experience in her
research projects. I really enjoyed three years working experience with her. I would
like to thank Dr.Gong-soog Hong. She has been supportive and provided helpful
comments to me.
Special thanks are expressed to my parents and my best friend Seoungbum for
their love, support and encouragement while I struggled to complete this dissertation.
v
VITA
February, 1977……………………………….…
Born, Seoul, South Korea
1999…………………………………………….. B.S., Education of Home
Economics, Korea University,
Seoul, South Korea
2001…………………………………………….. M.S., Home Economics, Korea
University, Seoul, South Korea
2005 – Current……………………………….…
vi
Research and Teaching Assistant,
Department of Consumer Sciences,
The Ohio State University,
Columbus, OHIO, USA.
PUBLICATIONS
Lee, J. & S. Yang.(2008). Debt Decision and Repayment of US Young Adults.
International Journal of Human Ecology, 10(1), 1-16.
Lee, J. (2008). Psychological Aspects of Household Debt Decision: The Use of the
Heckman‘s procedure. International Journal of Human Ecology, 9(1), 8195.
Lee, J. & S. D. Hanna. (2008). Racial/Ethnic Patterns in Credit Problems. Consumer
Interests Annual. 54. 127.
Lee, J. & S. D. Hanna. (2008). Delinquency Patterns by Racial/Ethnic Status: A
Selection Model. Proceedings of the Academy of Financial Services.
Lee, J. & S. Cho.(2008). The Effect of Credit on Spending Decisions: The Role of
the Credit Limit and Self Control. Proceedings of the Academy of Financial
Services.
Lee, J. & S. D. Hanna. (2007). Changes in Credit Attitudes among U.S. Consumers:
1992-2004. International Journal of Human Ecology, 8(1), 79-94.
Lee, J. & S. D. Hanna. (2007). Attitudes toward Using Credit for Loss of Income.
Consumer Interests Annual, 53, 59-72.
Loibl, C., J. Lee, J., Fox. & E. M. Gaeta. (2007). Women‘s High-Consequence
Decision Making: Choice Processes for Mutual Fund Investments.‖
Financial Counseling and Planning, 18(2), 35-47.
Lee, J. & S. D. Hanna. (2006). Factors Related to Consumer Credit Attitudes.
Proceedings of the Academy of Financial Services.
Lee, J. & Y. Lee. (2002). The Effect of Married Employees‘ Workweek on Leisure
and their Usage. Journal of Korean Home Management Association, 20(4),
165-178.
vii
FIELDS OF STUDY
Major Field:
Area of Emphasis:
Minor Field:
Consumer Sciences
Family Resource Management
Statistics
viii
TABLE OF CONTENTS
Page
ABSTRACT……………………………………………………………………….…..ii
DEDICATION………………………………………………………………..………iv
ACKNOWLEDGMENTS……………………………………………………….……v
VITA………………………………………………………………………………….vi
LIST OF TABLES…………………………………………………………..………xiv
LIST OF FIGURES……………………………………………………...………….xvi
CHAPTERS:
1
INTRODUCTION ................................................................................................ 17
1.1 Background ...................................................................................................17
1.2 Problem Statement ........................................................................................21
1.2.1
Theoretical Issues ...........................................................................21
1.2.2
Racial/Ethnic Classification ...........................................................22
1.3 Goals and Objectives .....................................................................................24
1.4 Contributions .................................................................................................25
1.5 Organization Flows .......................................................................................26
2
THEORETICAL BACKGROUND ..................................................................... 27
2.1 The Economics of Household Debt Acquisition ...........................................27
2.1.1
Credit Demand ................................................................................27
ix
2.1.1.1 Life Cycle-Permanent income Hypothesis ............................27
2.1.1.2 Extension of Basic Model .....................................................31
2.1.2
Credit Supply ..................................................................................34
2.1.3
Interaction of Credit Demand and Credit Supply ...........................37
2.2 Repayment of Household Debt .....................................................................38
2.2.1
The Optimization Model of Consumer Choice ..............................38
2.2.2
Empirical Models ...........................................................................41
2.2.2.1 The Equity Theory ................................................................41
2.2.2.2 The Cash Flow Theory ..........................................................42
2.2.3
Measurement of Household Debt Repayment ................................44
2.2.3.1 Delinquency ..........................................................................44
2.2.3.2 Default ...................................................................................46
2.2.4
Summary.........................................................................................47
2.3 Conceptual Models and Research Hypotheses..............................................49
2.3.1
Conceptual Models .........................................................................49
2.3.2
Research Hypotheses ......................................................................49
2.3.2.1 Acquisition of Household Debt .............................................49
2.3.2.2 Repayment of Household Debt .............................................60
3
LITERATURE REVIEW ..................................................................................... 66
3.1 Acquisition of Household Debt .....................................................................66
3.1.1
Demand of Household Debt ...........................................................66
3.1.1.1 Selected Variables related to Demand of Household Debt ...66
3.2 Repayment of Household Debt .....................................................................71
x
3.2.1
Selected Variables related to Repayment Delinquency ..................71
3.2.1.1 Demographic Characteristics ................................................71
3.2.1.2 Economic Characteristics ......................................................74
3.2.1.3 Financial Events ....................................................................76
3.2.1.4 Financial Buffers ...................................................................80
3.2.1.5 Household Debt Burden ........................................................81
3.3 Racial/Ethnic Disparities in Household Variables ........................................84
3.3.1
Household Acquisition of Credit ....................................................84
3.3.2
Repayment of Household Debt.......................................................90
3.3.3
Selected Other Characteristics ........................................................96
3.3.3.1 Psychological Characteristics ................................................96
3.3.3.2 Economic Characteristics ......................................................98
4
METHOD ........................................................................................................... 102
4.1 DATA ..........................................................................................................102
4.1.1
Administrative Data and Survey Data ..........................................102
4.1.2
Survey of Consumer Finances ......................................................104
4.1.3
Analytical Sample.........................................................................108
4.2 Variable Identification.................................................................................109
4.2.1
Dependent Variables.....................................................................109
4.2.1.1 Holding Household Debt .....................................................109
4.2.1.2 Repayment of Household Debt ...........................................110
4.2.2
Independent Variables ..................................................................111
4.2.2.1 Race/Ethnicity .....................................................................111
xi
4.2.2.2 Financially Adverse Events.................................................112
4.2.2.3 Financial Buffers .................................................................114
4.2.2.4 Household Debt Burden ......................................................115
4.3 Statistical Method........................................................................................116
4.3.1
Single Equation Analysis: Logistic Regression Analysis.............117
4.3.2
Two equations analysis:................................................................120
4.3.2.1 Sample Selection Bias Issue ................................................121
4.3.2.2 Heckman‘s Bivariate Two-Stage Model .............................123
4.3.2.3 Modified Heckman‘s Two - Stage Model ...........................124
4.3.2.4 Interaction Effects ...............................................................128
4.3.3
5
Summary.......................................................................................128
RESULTS ........................................................................................................... 131
5.1 Descriptive Result .......................................................................................131
5.1.1
Overall Household Characteristics ...............................................131
5.1.2
Descriptive Results across Racial/Ethnic Groups ........................132
5.1.3
Descriptive Results by Holding Household Debt .........................135
5.1.4
Descriptive Results by Repayment Delinquency .........................136
5.2 Multivariate Result ......................................................................................139
5.2.1
Sample Selection Model using all Racial/Ethnic Groups .............139
5.2.1.1 Holding Household Debt .....................................................139
5.2.1.2 Repayment Delinquency of Household Debt ......................141
5.2.2
Sample Selection Model using Separate Race/Ethnicity ..............145
5.2.2.1 Whites..................................................................................145
xii
5.2.2.2 Blacks ..................................................................................147
5.2.2.3 Hispanics .............................................................................150
5.2.2.4 Asians/others .......................................................................152
5.2.3
6
Interaction Effects.........................................................................154
CONCLUSIONS ................................................................................................ 196
6.1 Conclusions ..................................................................................................196
6.1.1. Holding Household Debt ..................................................................199
6.1.2 Repayment Delinquency of Household Debt ....................................201
6.2 Implication....................................................................................................208
6.2.1 Implication for Education ..........................................................................208
6.2.2 Future Research .................................................................................209
BIBLIOGRAPHY ….……………..……..………………..…..…....……………...213
APPENDIX: …………………………………….………..………...……………...226
xiii
LISTS OF TABLES
Table
Page
Table 5.1: Number of Respondents of 1992 -2007 Survey of Finances. Weighted
analysis of Survey of Consumer Finances, all 5 implicate. ....................................... 159
Table 5.2: Frequency of Respondents‘ Selected Variables of 1992 -2007 the Survey of
Consumer Finances. Weighted analysis of Survey of Consumer Finances. (Continue)
.................................................................................................................................... 160
Table 5.3 : Frequency of Selected Household characteristics, by Racial/ethnic
difference/Ethnic Category of 1992 -2007. Weighted analysis of Survey of Consumer
Finances. (Continued) ................................................................................................ 162
Table 5.4 : Proportion of Respondents‘ Selected Variables by holding household debt
of 1992 -2007. Weighted analysis of Survey of Consumer Finances.(Continued) ... 164
Table 5.5: Proportion of Repayment Delinquency across Selected Variables (Debtors
only) (Continued) ....................................................................................................... 166
Table 5.6: Probit Regression of Probability of Holding Household Debt ................. 168
Table 5.7: Logistic Regression of Probability of Being Delinquent on Household Debt
by Two Months or More (Continued)........................................................................ 169
Table 5.8 : Probit Analysis of Holding Household Debt for Whites ......................... 171
Table 5.9: Logistic Analysis for Probability of Being Delinquent for whites.
(Continued) ................................................................................................................ 172
Table 5.10: Probit Analysis of Holding Household Debt for blacks ......................... 174
Table 5.11: Logistic Analysis of Being Delinquent on Household Debt for blacks
(Continued) ................................................................................................................ 175
Table 5.12: Probit Analysis of Holding Household Debt for Hispanics.................... 177
Table 5.13: Logistic Analysis of Being Delinquent on Household Debt for Hispanics
(Continued) ................................................................................................................ 178
xiv
Table 5.14 : Probit Analysis of Holding Household Debt for Asians/others............. 180
Table 5.15: Logistic Analysis of Being Delinquent for Asians/others (Continued) .. 181
Table 5.16 : Logistic Analysis of Being Delinquent (Reference group= whites)
(Continued) ................................................................................................................ 183
Table 5.17 : Logistic Analysis of Being Delinquent (Reference group= blacks)
(Continued) ................................................................................................................ 185
Table 5.18 : Logistic Analysis of Being Delinquent (Reference group= Hispanics)
(Continued) ................................................................................................................ 187
Table 5.19 : Logistic Analysis of Being Delinquent (Reference group= Asians/others)
(Continued) ................................................................................................................ 189
Table 5.20 : Multiplicative Factor for the reference Group on the Moderating Variable
(Net worth) ................................................................................................................. 191
Table 5.21 : Multiplicative Factor for the reference Group on the Moderating Variable
(Health Insurance) ...................................................................................................... 192
Table 5.22 : Multiplicative Factor for the reference Group on the Moderating Variable
(Negative Transitory Income) .................................................................................... 193
Table 5.23 : Multiplicative Factor for the reference Group on the Moderating Variable
(Poor Health Status) ................................................................................................... 194
Table 5.24 : Multiplicative Factor for the reference Group on the Moderating Variable
(Household Debt Burden) .......................................................................................... 195
xv
LISTS OF FIGURES
Figure
Page
Figure 2.1 : Friedland‘s (1993) Framework of Credit Scoring Schemes ..................... 36
xvi
CHAPTER 1
1
1.1
INTRODUCTION
Background
Substantial households in the United States hold debt during substantive
portions of their lives. The life-cycle hypothesis from economics argues that
consumers should intertemporally reallocate their incomes over their lives to
maximize lifetime utility (Soman & Cheema, 2002). One form of intertemporal
allocation is to use past income (in the form of savings). A second form is the use of
future income for present consumption. Consumption can only be done if consumers
have access to a pool of money that they can draw from and replenish in the future--a
function enabled by consumer credit. The Federal Reserve‘s aggregate statistics
showed that as of February 2009, outstanding consumer credit has mounted to $2.56
trillion, with $ 955 billion in revolving debt and $ 1608 billion in nonrevolving debt
(Federal Reserve Board, 2009).
Many households will be able to successfully use debt to shift expenditures
from one period of life to another (Betti, Dourmashkin, Rossi, Verma, & Yin, 2001),
while other households might find a debt repayment plan unsustainable, putting them
at high-risk for payment problems. Empirical studies show that debt repayment
problems have increased simultaneously with household debt. With this increase in
17
household credit, increasing rates of debt problems have been observed. The number
of personal bankruptcy filings in the U.S. increase from about 300,000 in 1980 to
over 1.5 million in each of the years from 2001 to 2005. It reflects that personal
bankruptcy filings change quintupled between 1980 and 2005 (White, 2008).
According to The American Bankruptcy Institute (2008), loan delinquency and
charge-off rates have increased since 1990 and filings by individuals or households
with consumer debt increased 28.8 % to 503,749 for June in 2008 from the 2007 firsthalf total of 391,105.
Past research has found that racial/ethnic minority households have
significantly more credit payment problems than white households. In particular, the
higher rate of delinquency by racial/ethnic minorities constitutes the most consistent
finding across published studies on debt repayment (Sullivan & Fisher, 1988; Canner,
Gabriel, & Wolley, 1991; Berkovec & Gabriel, 1995; Godwin, 1999; Getter, 2003;
Lyons, 2004). Berkovec and Gabriel (1995), using behavioral data from more than
200,000 mortgage holders from the Federal Housing Administration (FHA),
examined the differences in average default rates by race. In particular, this study
showed that African American borrowers have higher overall default rates compared
those of white households by 2 percentage points. More recently, the subprime
mortgage crisis was found to cause a greater loss of wealth among racial/ethnic
minorities. In a report titled, "Foreclosed: State of the Dream 2008," United for a Fair
Economy demonstrated that African-Americans and Latinos have suffered
excessively in the subprime crisis. This report estimated that all subprime borrowers
of racial/ethnic minorities have lost between $164 billion and $213 billion for loans
taken since 2000. African-American borrowers lost between $72 billion and $93
18
billion and Latino borrowers lost between $76 billion and $98 billion for the same
period (Rivera, Cotto-Escalera, Desai, Huezo, & Muhammad, 2008).
Only a few studies provide further empirical evidences on factors which
contribute to the racial/ethnic gap in payment delinquency. Volkwein, Szelest,
Cabrera, and Napierski-Prancl (1998) pointed out that African-American and
Hispanic defaulters tend to be unemployed and to have personal problems that
interfere with repayment. More recently, Ross and Yinger (2003) attributed higher
default rates among minorities to the fact that minority borrowers tend to have larger
debt burdens, higher loan-to-value ratios, and poorer credit histories than their white
counterparts. Additionally, they speculated that unexpected economic downturns may
have particularly harsh effects on racial/ethnic minorities in light of discrimination in
labor markets and these effects might hamper minority borrowers‘ ability to pay
household debt. However, only a few studies have provided a detailed explanation of
racial/ethnic variance in payment performance of household debt.
In general, credit payment delinquency influences household finances
negatively. Late payments on household debt may have a number of serious economic
consequences for borrowers. The most immediate of consequences is accrued late fees.
Since 1993, late fees have risen by 194% (Cardweb, 2005). Annual interest rates on
many delinquent accounts rise rapidly to above 30%, remaining there for as long as
the creditor chooses (Killian, 2006). Debt repayment delinquency is recorded on
consumer credit reports, often resulting in lower credit scores. These credit reports are
then used to determine the level of risk associated with most loans or insurance. In
addition, delinquent borrowers with low credit scores may become targets of the
controversial ‗universal default‘ policies employed by many financial institutions.
19
Under such policies, delinquency on one credit card can trigger penalty rates on others,
even though others have been paid as scheduled (Sahadi, 2005). It is evident that even
a single delinquent payment has the potential to negatively affect an entire consumer
credit report. A significant number of banks issuing credit cards employ this universal
default policy. For example, a survey of 45 banks issuing 144 credit cards in 2005
reported that 44% of the banks employed a universal default clause (Loeb, 2007).
Therefore, credit applicants in this position may be obligated to accept higher interest
rates or additional fees on mortgages or loans, or even find themselves denied access
to these mortgages or loans as well. In addition, prospective employers may use them
in choosing between job candidates (Manning, 2001; Scott, 2005).
Researchers fail to reach consensus on whether and to what extent borrowing
leads to bankruptcy. However, aggregate data indicates that a higher fraction of
consumers are delinquent on their credit card loans than on consumer loans in general.
Also, the rate of credit card delinquency and charge-off are observed to be closely
related to bankruptcy rates over time (Stavins, 2000). Therefore, it is important that a
consumer‘s late payment be analyzed, as such a performance could be a signal of
bankruptcy. In the midst of a global recession, racial/ethnic minorities without the
economic cushion provided by savings and investments are expected to face severe
economic hardships. Therefore, given high credit payment problems among racial/
ethnic minorities, understanding the underlying factors related to racial/ethnic
disparities in debt repayment problems may be crucial to increasing their
homeownership rates without excessive risks and then achieving substantial growth in
financial wealth.
20
1.2
1.2.1
Problem Statement
Theoretical Issues
The Life Cycle-Permanent Income Hypothesis (LC-PIH) plays a large role in
motivating scholars to examine household intertemporal consumption, savings, and
borrowing. This hypothesis suggests that households are rational agents. They are
expected to form expectations about future income and wealth holdings. If income is
expected to be higher in the future, it is rational to borrow against future income in
order to maintain consumption over the course of their life cycles. This approach
assumes that the observed amount of debt at any point in time must be interpreted as
the household‘s utility-maximizing debt level (Bryant, 1990). In other words, when
households‘ incomes do not match their level of utility-maximizing consumption,
they either borrow from projected future income or draw on savings from past income
in order to finance consumption at the time. Having debt represents a household‘s use
of future income to pay for current consumption. It can be concluded that utilitymaximizing households are tasked with choosing an optimal level of present and
future consumption with the consideration of the constraints of present and future
income (Godwin, 1999). Accordingly, households are expected to borrow against
future earnings during early life stages when income is low, and then save more
during the most productive period. Finally, accumulated assets are projected to be
consumed during retirement.
LC-PIH does not leave room for any miscalculation that households might
make or uncertain knowledge that they might have (Bryant, 1990). For instance, some
borrowers might miscalculate when they make decisions on debt acquisition and debt
repayment based on reasons other than those suggested by LC-PIH. Some households
21
might inevitably face some uncertainties about future income and spending, and,
thereby, about the affordability of household debt. This means that unforeseen events
such as job loss, health problems, or divorce may place people in positions where they
are no longer able to meet debt obligations. As mentioned earlier, this has been
evidenced by a large number of credit payment problems.
This dissertation adopts the Cash Flow Theory of Default in order to explain
payment delinquency on household debt across racial/ethnic groups. Bankruptcy rates
do not tend to forecast general economic conditions since they can be significantly
influenced by changes in laws and lender practices over time (The Credit Union
National Association, 2004). Therefore, bankruptcy rates might not be a reliable
measure of the overall economic health of narrow set of households. In contrast to
bankruptcy rates, delinquency rates may be better measure of the overall health of the
household finance. Also, delinquency rates can reflect not only the financial health of a
household sectors but changes in underwriting and collection practices (The Credit Union
National Association, 2004).
1.2.2
Racial/Ethnic Classification
While the consumer credit literature has long suggested that there exist racial/
ethnic disparities in debt repayment performance, a rigorous analysis of payment
performance with attention to the different races and ethnicities of respondents is
lacking. Many studies on household debt payment include race and ethnicity along
with other main explanatory variables, but the racial/ethnic classification is
inconsistent across the literature. Most studies (Godwin, 1999; Canner, Gabriel, &
Wolley, 1991; Getter, 2003; May & Tudela, 2005) divided borrowers into white and
22
non-white (minority). They demonstrated higher likelihood of debt payment
delinquency for those households headed by black or minority individuals. These
studies compared households headed by whites or white individuals with household
headed by minorities, conflating several racial/ethnic groups rather than
distinguishing between data on black Americans, Hispanic Americans, Asian, and
those individuals choosing other racial/ethnic groups.
The reason that these studies compared aggregated minority groups with the
white group is partly because there are few households in particular racial/ethnic
minority groups in any one dataset. However, empirical results reported in aggregated
terms could cause credit or debt suppliers to consider debt applicants from racial/
ethnic minorities as a homogeneous undifferentiated whole. That is, the overall
characteristics of the minority group might be disproportionately factors of the
individual characteristics that a particular minority might have, when he/she applies
for credit. Since aggregate statistics about minority groups are often negative, as
compared to the majority groups‘ aggregate statistics, some borrowers from
racial/ethnic minorities could experience adverse treatment when they apply for credit.
The employment of such a broad grouping may not prove the best strategy in
examining racial/ethnic variances in household debt payment data. In this dissertation,
I delineated four race and ethnicity categories: white, black, Hispanic, and others. I
created dummy variables for each subset, with the largest category, white, serving as
the reference group. The aforementioned ‗others‘ encompasses ―American Indians,
Native Alaskans, Native Hawaiians, and other minority ethnic groups not mentioned
above.‖ Based on Census reports (Hanna & Lindamood, 2008) it is likely that a
23
majority of respondents choosing the "other" categories are of Asians or Pacific
Islander ancestry.
1.3
Goals and Objectives
This study proposes to gain insight into which key factors influence household
debt repayment; whether these key factors differs across racial/ethnic lines; and
whether these factors result in race and ethnicity variance. In particular, this study
aims first to: (1) account for household characteristics related to repayment
delinquency, (2) examine whether race and ethnicity are related to debt payment
problems, even controlling financial events that might cause a decrease or disruption
in the flow of periodic household income, financial buffers available in emergency
and any other demographic variable, and (3) ascertain how the effects of key factors
differ across race and ethnicity.
To some extent, this study expects that examining effects of all these factors
on the payment delinquency in terms of household debt is also a plausible test, not
only of the ability of the households to accumulate financial resources available to
meet the debt payment obligations when adverse financial events occur, but also on
the performance of the credit scoring that lenders are implementing in their credit
decision-making. If credit-scoring models in credit card approval, mortgage loans,
and consumer loans (Mester, 1997) eliminate individuals at risk of poor payment
performance efficiently, there should be no difference in debt repayment across
race/ethnicity. This study will then define payment delinquency according to two SCF
questions asking (1) whether payments on any loans were sometimes late or missed,
and (2) whether the respondent was ever behind in his or her payments by two or
24
more months. Following this, this study will investigate reasons the propensity toward
payment delinquency on household debt might differ by race and ethnicity. Finally,
this study aims to address the sample selection bias that can affect predictors of
delinquency risk in a given population of applicants. Therefore, this study tests for
whether the coefficient of the selection effect is significantly different from zero.
Finally, this study enumerates specific financial education agenda of borrowers with
payment difficulties across racial/ethnic lines.
1.4
Contributions
A study of the underlying factors in household debt payment difficulties could
help policymakers in evaluating the potential effects of such proposals. Aggregate
payment delinquency is of importance to financial institutions. All lending poses a
credit risk since any type of lending have the possibility of economic loss due to
repayment failure. Any given loan portfolio—that is, any group of loans defined by
the issuer—includes a certain percentage of borrowers who are not able or willing to
fulfill their obligations (Board of Governors of the Federal Reserve System, 2006).
For example, an increase in delinquency of total consumer credit repayment would,
ceteris paribus, diminishes banking sector profits, and thereby potentially cause an
increase in interest rate margins as compensation for increased risk. An excessive
increase in repayment delinquencies may cause lenders with low capital adequacy
ratios to become insolvent, resulting in widespread failures (Crook & Banasik, 2005).
Because it is impossible to know who will fail to make repayments with certainty,
lending institutions strive to manage consumer credit risk by estimating the scale and
probability of economic losses for each portfolio. Lenders could then employ this
25
information to make better lending decisions that would reduce the probability of
economic losses due to consumers‘ repayment failures. With improved knowledge
about risk factors, financial educators could disseminate information in efforts to help
households increase awareness of credit risks and the importance of building a good
credit history and perhaps avoid credit management difficulties. They could target
households at higher-risk for maximum efficacy.
1.5
Organization Flows
This dissertation begins by reviewing literature related to acquisition and
repayment of household debt and empirical evidence of racial/ethnic disparity in
credit markets in Chapter 2. The existing theoretical models dealing with obtaining
household and repaying household debt is discussed in Chapter 3. Also, Chapter 3
formulates the hypotheses to be tested. Next, Chapter 4 describes the data set utilized
in the analysis and the statistical methodologies employed in univariate and
multivariate analyses. The definition and measurements of variables are also
introduced in this chapter. Chapter 5 provides the statistic results of this study. Their
linkage with previous literature and hypotheses are discussed as well. In light of
statistical findings from this study, Chapter 6 further discusses the importance of
those findings based on the comparison with previous empirical analyses.
Improvements and extensions made in this research have been summarized. Finally,
implications for future research, practitioners, financial educators, and policymakers
are discussed.
26
CHAPTER 2
2
2.1
2.1.1
THEORETICAL BACKGROUND
The Economics of Household Debt Acquisition
Credit Demand
2.1.1.1 Life Cycle-Permanent income Hypothesis
The foundation of understanding of household debt begins with the Life
Cycle-Permanent Income (LC-PI) model. Since Modigliani (1966) and Friedman
(1957), LC-PI model has become the dominant conceptual framework for
understanding the nature of the consumption/ saving/borrowing of the representative
household under certain circumstances and the aggregate
consumption/saving/borrowing behavior in the economy (Betto, Dourmashkin, Rossi,
& Yin, 2007)
This theory provides insight on rationale behind debt acquisition of
households with budget constraints. In theory, household income and wealth
invariably change over the life stages of households. These changes are caused by
systematic variations in both income and needs, which occur as a result of maturing,
retirement, and other life events that generate changes in family composition
(Modigliani, 1986). Their incomes increase during periods of employment and
27
decrease at retirement. Households are expected to maximize their utility by
smoothing their marginal utility over their lifetimes. Although the age of the
household head is not the only factor correlated with consumption patterns, it is
expected to significantly influence a given household‘s debt-related decisions
(Yilmazer & DeVaney, 2005). For example, households are more likely to borrow
when the wage-earners are younger, to save during middle age, and to decrease
spending during retirement. Households will borrow to finance current consumption
in a period of low income, then households will repay in a period of high income.
This theory assumes that households are rational and far-sighted planner.
Therefore, households can form expectations about their future earnings and wealth
holdings and borrow against those expectations. Therefore, they are projected to
borrow more against future earnings during early stages of life when income is low,
save more during their most productive period, and, finally, dissave accumulated
assets after retirement.
This theory posits that there is unrestricted access to consumer credit (Betto,
Dourmashkin, Rossi, & Yin, 2007). Households are expected to choose optimal levels
of consumption, hence savings or borrowing, in each period, subject to an intertemporal budget constraint, in order to manage budget volatility (Bertola, Disney, &
Grant, 2006). The level of debt that households incur in the present and in the future is
expected to be the optimal level needed to maintain a balanced position and so to
stabilize consumption over the life cycle. Therefore, transitory changes in income are
expected to have little effect on household spending behavior. In addition, borrowers
are expected to fully repay all debt that they incur.
28
Bertola, Disney and Grant (2006) have a standard presentation of the modern
economic model of consumer behavior. This dissertation presents some mathematical
notations used by Bertola, Disney and Grant (2006).The household chooses the level
of consumption (c) in each period so as to maximize its life time utility with the intertemporal budget constraint. The lifetime utility of household is defined as a
discounted sum of period utility functions u ( ) in the following form
T
(Equation 1)
Max Et   j u (Ct  j )
j 0
T is the time horizon of individual planning. Et represents household
expectations based on information available at t. β = 1 / (1+δ) is the household‘s
discount factor where δ is the subjective discount rate. Maximization of (Equation 1)
is subject to the following asset evolution equation (Equation 2).
(Equation 2)
At+1 = (1 + rt+1 )(At + yt - Ct)
A is the level of assets. yt is labor income at time t. Interest rate on assets and
liabilities, rt , is determined in the credit market. This equation represent that asset in
any period (At+1 ) must be as same as assets in the previous period (At ) plus income
including labor income (yt) and the return on assets (At ·(1+rt+1 )) less the level of
consumption in that period (Ct). The optimal solution of this problem satisfies Euler
equations in the following form:
(Equation 3)
u(ct )  Et u(Ct 1 )[(1  rt 1 ) /(1   )]
Where there is a marginal effect u () is a decreasing function of consumption
if consumption fluctuations are welfare-decreasing. Therefore, optimization implies
that marginal utility at time t+1 is uniquely determined by preference and interest
rates and is not determined by anything that is predictable at time t or earlier.
29
If marginal utility is approximately linear in consumption, consumption
growth depends on the relative magnitudes of r and  . However, changes in
consumption are independent of predictable income changes, which are controlled by
access to the credit market. Linearity of marginal utility makes it possible to combine
the optimal condition and the inter-temporal budget constraint to obtain a relationship
between saving, income, and consumption,
(Equation 4) s t 
rAt
 y t  Ct
1  rt
And between savings and the evolution of income over time,

(Equation 5) st   (1  r )  j Et ( yt  j  yt  j 1 ).
j 0
When the present value of household income is expected to increase in the future, it is
rational to decrease savings. This household will draw from assets or borrow money
if its assets are not enough to meet its desired consumption level. On the contrary, if a
household expects its income to decrease in the future, it will save.
The implication of the LC-PI model for understanding household borrowing
decision is that it is optimal for a household to borrow money under certain
circumstances and at certain stages of the life cycle. In particular a household is more
likely to borrow in their earlier life stages. If there is no unexpected change to
resources available or expenditure desired and in the absence of any inter-generational
transfer mechanism, the consumer‘s current assets will be exactly optimized by the
present value of household debts over entire life cycles (Betti, Dourmashkin, Rossi, &
Yin, 2007). This intertemporal budget constraint will hold and the household will
borrow the optimal level of borrowing and repay all debt that a household incurs.
30
2.1.1.2 Extension of Basic Model
The LC-PI model implies that borrowing money allows a household to
redistribute spending from periods in which income is relatively high to the periods in
which income is relatively low. A typical household has a hump-shaped profile of
earnings over a lifetime. In other words, earnings start low, increase until the
individual is in his or her prime age, then begin a slow decline, and decrease sharply
from the time that he/she retires. Therefore, it is expected that people borrow more
when they are young or later in their life and they save when they are in their prime
age. Throughout this consumption maintenance, an individual is expected to
maximize the present value of his or her expected utility with budget constraints.
This hypothesis makes several assumptions, some of which have been
contradicted by empirical evidence. First, these theories assume that current
consumption is independent of current income. However, empirical studies have
shown that household consumption is dependent on income over the life cycle. For
example, some people might not be able to borrow at all or as much as they ideally
prefer. Another possibility is that the rate at which they borrow money can be
extremely high. If a household has difficulty borrowing money and subsequently
redistributing spending from period when income is high and to period when income
is low, the household is able to consume no more than its current income and
accumulated wealth. Therefore, consumption of a household facing credit-constraint
would be more highly correlated with current disposable income than would the
consumption by a household who does not experience credit constraints.
31
Second, the LC-PIH does not leave room for imperfect expectation regarding
future information or miscalculation that a household can make (Bryant, 1990).
However, empirical studies have shown that people sometimes hold imperfect
expectations about their future and these imperfections influence households‘ holding
of debt and repayment of debt. For example, unforeseen events such as health
problems or job loss may place people in a position where they are no longer able to
meet their debt obligations. In these cases, borrowers are unable to repay their current
debts. They may end up with unable to repay debt and, in the most extreme cases,
filing for bankruptcy.
Third, the LC-PIH ignores the possibility that a household chooses not to
repay all debt that a household incurs. The possibility of default deviates from life
cycle consumption, which is explained by the standard consumption model (Lawrence,
1995). The possibility of default during periods when income is low provides
borrowers with Pareto-improving insurance, which then allows borrowers to borrow
against an uncertain future. Lawrence (1995) questioned how a default option can
change the standard interpretation of household consumption. In order to address this
question, he allowed greater flexibility to the assumption that people will fully repay
all household debts. Also, using a simple and two-period model, he allowed for
default when income is low in the second period. He demonstrated how
borrower/lending rate differentials and, in some cases, quantity restrictions on debt
arise endogenously. Income in the second period is expected to be uncertain with an
exogenous probability q that income will be zero. The borrower could have a loan in
order to increase his or her first period consumption by X1 and payment on this loan is
due in the second period in the amount of X2, where X2 is X1(1+R) and R is the
32
interest rate. In this model, the no-default restriction in the life cycle model becomes
less rigid because banks can have claims to all the resources held by the household
legally. In such a case, the household would not borrow due to the possibility of
earning zero income in the second period, thus leading to zero consumption. The
household would then maximize expected lifetime utility and yield savings of zero.
Lawrence (1995) instead assumes that the risk-averse household chooses to
borrow a positive amount in the first period. In the second period, if the household
faces an unexpected loss of income, the household will choose to default on the loan
in order to maintain a higher level of consumption. Lawrence (1995) concluded that
the households with high incomes show lower marginal utility on additional
consumption and therefore those households will choose to repay the loans. On the
contrary, the households with low incomes choose to default in order to maintain their
minimum consumption levels.
Fourth, many expositions of the Life Cycle Theory and Permanent Income
Hypothesis assume that all households can borrow at the same interest rate. However,
empirical studies have shown that so recent improvements in modern credit scoring in
credit markets allow lenders to distinguish between high and low credit quality
borrowers (Cutts, Van Order, & Zorn, 2000). These improvements allow more credit
applicants to access the credit market since lenders can segment credit applicants into
different markets, thus charging riskier groups higher rates to compensate for the
possibility of poor payment performance. Under this framework, different treatment
of borrowers in terms of credit availability and interest rates charged is considered
evidence of well-functioning credit markets. Hogarth and Hilgert (2002) examined
who paid mortgages with very high rates of interest using the 1995 and 1998 Survey
33
of Consumer Finances. They found that the older, lower income earners, the less
educated, and racial minorities were more likely to hold high–interest rates for home
loans. Many of these people incurred high-rate loans for debt consolidation. Getter
(2006) argued that high-risk households can access credit markets if they are willing
to pay higher rates to compensate lenders for the additional risks rather than face
rejection. More recently, Krinsman (2007) attributed widespread delinquencies in
subprime mortgage loans to higher interest rates and fees made to borrowers with
impaired or limited credit histories.
2.1.2
Credit Supply
Credit scoring is a tool used to help banks decide whether or not to grant credit
to individual consumers (Thomas, 2000). Many banks implement credit scoring
models in their credit decision-making. Credit scoring models are widely used in
deciding credit card approval, mortgage loans, and consumer loans as well as business
loan applications (Mester, 1997). Individuals‘ scores play a vital role in lenders‘
decisions on whether to extend their credit. According to Fair Isaac Company, over 90
percent of credit card lenders use credit scores when making their lending decisions
(EPIC, 2003). A low credit score may result in a denial of credit application. Credit
scoring models are developed by analyzing statistics and picking out characteristics
that are believed to relate to creditworthiness (EPIC, 2003).
Lyon (2001) illustrated the economics of credit supply in her dissertation.
Lenders try to establish a level of creditworthiness for each individual that applies for
a household debt. The level of creditworthiness is dependent upon four factors, such
as a household‘s current income, expected future income, wealth holdings, and credit
34
history. Using these factors, lenders are not only able to establish a measure of
creditworthiness for each individual, but they are able to determine which individuals
have a higher probability of defaulting on their debt. She assumed lenders reject debt
applications when a household‘s level of creditworthiness falls below a minimum
level. In this case, an individual‘s default risk is so high that the lender is not willing
to approve a debt under any circumstances, even if the borrower is willing to pay a
higher interest rate on the debt. The approval of a debt application and the rate of
interest that an individual should pay are based on the individual‘s level of
creditworthiness. Lenders grant a debt when an individual‘s creditworthiness is bigger
than the minimum level of creditworthiness. Also, lenders approve larger debt
amounts at lower rates of interest for the individuals whose creditworthiness is much
higher than the minimum level of creditworthiness. Otherwise, lenders charge higher
rates of interest and extend smaller amounts of credit to the individuals whose
creditworthiness is close or equal to the minimum level of creditworthiness.
It is optimal for lenders to levy the interest rate so low as to be attractive to
borrowers with high creditworthiness, and to control the risk of default occurrence by
borrowers with low creditworthiness by rationing credit to both high and low risk
borrowers (Bertola, Disney, & Grant, 2006). Stiglitz and Weiss (1981) suggested that
rationing arises because lenders set interest rates to obtain the optimal balance of
borrowers.
In a review of characteristics consistently considered in credit scoring schemes
internationally, Friedland (1993) suggested a framework comprised of five categories
of predictors: family status/ living arrangements, employment, personal information,
financial history, and credit bureau information.
35
Family status /Living
arrangement
Employment
Personal Information
Application
Scoring
Financial History
Credit Bureau
Information
Figure 2.1 : Friedland‘s (1993) Framework of Credit Scoring Schemes
An applicant is juxtaposed with successful recipients by constructing their
score and comparing it to the standard. The potential flaw in the model is that if there
were factors which enter into the acceptance decision but do not appear explicitly in
the rule and these same factors influence the response in the payment equation, then
the latter equation may produce biased predictions. Therefore, a predictor of
delinquency risk in a given population of applications can be systematically biased
because it is constructed from a nonrandom sample of past applicants, that is, those
whose applications were accepted.
One noteworthy issue is that the Equal Credit Opportunities Act (Federal
Reserve Board, 2003) and regulation B bans the use of certain characteristics in the
decision as to whether lenders grant credit to an applicant or not. This act prohibits
discrimination against an applicant for credit based on factors that are not considered
to be related to creditworthiness. These characteristics are gender, marital status, race,
whether an applicant receives welfare payment, color, religion, national origin and
age (Crook,1999). This does not imply that this act provides all credit applicants with
36
credit, but that it requires that creditors apply the same standards of creditworthiness
equally to all applicants. Under this act, discrimination on ―the basis of sex, marital
status, race, color, religion, national origin, age, or income from public assistance‖ is
illegal.
2.1.3
Interaction of Credit Demand and Credit Supply
A number of empirical studies (Jappelli, 1990; Cox and Jappelli, 1993; Crook,
2001) investigated the characteristics of people who are more likely to experience
credit constraints with the joint consideration of credit demand and credit supply.
These studies assumed a household solves its standard inter-temporal utility
maximization problem under LC-PIH. Let C* be the level of consumption in period of
which household solves this problem. Assume that
(Equation 6) Ci*    t   t (  t  X t )
where  t is household level variables. In any period, t, the consumer‘s desired
volume of credit, (Equation 7) CDt , is : CDt  Ct*  [ yt  At 1 (1  rt 1 )],
where yt is labor income in period t, At-1 is net assets at the end of period t-1 , and  t 1
is the interest rate between period t-1 and t. Assume that the supply of credit to a
household can be modeled as:
(Equation 8) CS t    t  t (  t  K t )
where  t is household level variables.
Some or all of  t can be a subset of the X. Also, it is expected that the maximum
interest rate, related to a household debt for which a household may apply, is
exogenously determined.
37
Then a household is credit constrained if
(Equation 9) CDt – CSt > 0
(   t  y y  At 1 (1  rt 1 )    t -> 0 Hence P =  (wt )   t
where wt  ( K t  X t ) and P is the probability that a household is credit constrained.
2.2
2.2.1
Repayment of Household Debt
The Optimization Model of Consumer Choice
The task of utility-maximizing requires consumers‘ to optimally combine
present and future consumption within their constraints of present and future income
(Godwin, 1999). This task is correlated with the costs and benefits that households
need to consider and can be applied to assess consumers‘ debt repayment decision.
Missing a debt repayment can increase expected utility by offering the household
money for current consumption that would otherwise be tied up in repayments.
Therefore, a household might neglect debt obligations when the expected utility from
withholding those payments is bigger than the expected utility from repaying the debt.
However, the full cost of neglecting debt repayment can be enormous, as discussed in
Chapter 1. Poor payment performance can result in late fees and lowered credit scores.
Filing bankruptcy can be recorded on individual‘s credit report up for to 10 years
(Lyons & Fisher, 2006).
Jackson and Kasserman (1980) used the optimization model of consumer
choice in order to analyze default decision-making processes of consumers. They
tested two competing hypotheses: the ―net equity‖ approach, based on the
optimization model, and the ―ability to pay‖ theory. Data obtained from the FHA file
of individual loans insured under the Section 203(b) program were used to examine
38
these hypotheses. In the optimization model, borrowers were expected to make
default decisions by rational comparisons of the financial costs and benefits. They
expected that if, after all costs and benefits are considered, home equity comes out to
be negative, borrowers would choose to default on loan repayment. This study
supported the maximization model of default.
Campbell and Dietrich (1983) extended Jackson and Kasserman‘s work and
presented empirical evidence on the determinants of defaults for insured residential
mortgages. They employed an optimization model of consumer choice to examine
mortgage default. During each period in the life of the mortgage, the borrower must
decide the status of that mortgage. The decision is made to maximize a utility function
defined over a vector of mutually exclusive qualitative choice, S, and a vector of
exogenous state variables, X. The utility maximizing choice is represented as a
probability function of the state variables,
(Equation 10) P( Si ┃ X )=fi ( X )
where the sum of the probabilities of all n elements in S for a given X is equal to
unity :
(Equation 11)

n
i 1
P( Si
X ) =1 and Si represent the ith choice variable of the
representative borrower. The qualitative choice challenge is to specify the set of
choices, S. The exogenous state variables, X, determine the utility maximizing choice
and restrict the form of the function, fi ( X ). Campbell and Dietrich (1983) divided
borrowers‘ status of that mortgage into four (1) continuing with the mortgage as
planned, (2) prepaying the mortgage or canceling the insurance, (3) delinquency, and
(4) default.
39
Based on the optimization model of consumer choice, it is assumed that
rational borrowers who become unable to meet payment obligations will choose to
default only when equity values have deteriorated to the point that default becomes
the least-cost resulting action. The cost incurred through default includes any direct
loss on the property and increased cost of future credit due to decrease in credit rating.
Therefore, they hypothesized that default decision-making had two principal
determinants: the loan-to-value ratio, showing the borrower‘s current equity position
in the mortgaged property, and the payment-to-income ratio, which illustrates the size
of the borrower‘s mortgage payment obligation relative to disposable income. This
study provided important findings about determinants of defaults for insured
residential mortgages. First, this study found a relationship between incidences of
default and a majority of the economic variables identified, subsequently implying
that those mortgages with significant potential for negative amortization also carry a
substantial risk of default. Also, the defaulting experience has been significantly
influenced by income variability, captured by the proxy measure of regional
unemployment rates.
Additionally, the Strategic Default Hypothesis assumes that when a loan is
used to purchase a real asset (for example, a house) and the capital market is perfect
with no transaction costs or reputation effects (such as social stigma or humiliation), a
borrower will increase his or her wealth by defaulting on that loan when its value
exceeds the value of the asset it was used to purchase. More realistically, if borrowers
should pay transaction costs and consequently have lower chance of approval for
future loans when they utilize default option, the default option will not be used until
the debt is considerably greater than the asset value (Kau et al 1994).
40
2.2.2
Empirical Models
Two apparently competing theories on home mortgage default behavior have
been argued in mortgage payment literature (Jackson & Kasserman, 1980). One is the
―Equity Theory‖ and the other is the ―Cash Flow Theory,‖ also known as the ―Ability
to Pay Theory of Debt Default.‖
2.2.2.1 The Equity Theory
The Equity theory assumes that borrowers make their debt payment decisions
based on their rational comparison of the financial costs and benefits involved in
continuing or discontinuing the periodic payments on the mortgage loan (Jackson &
Kasserman, 1980). Borrowers are expected to seek maximal the financial gain and
minimal financial loss. In other words, this theory propounds that borrowers refrain
from default to preserve sufficiently positive housing equity (Weagley, 1988). Under
the equity theory, the equity position of the borrowers is the most important factor in
accounting for borrowers‘ default decisions. This theory implies a rigid optimizing
behavior of mortgage borrowers when borrowers decide to pay their debt obligations.
For mortgage payment delinquency, the ‗equity‘ approach would suggest that
debt-holders would consider their relative financial position, measured by loan-tovalue ratios of new mortgages or the level of undrawn equity (house prices minus
mortgage debt). Consequently, ‗negative equity‘ could be expected to increase
mortgage repayment delinquency since debt holders may decide that keeping repaying
debt is no longer the rational option (Raven, 2004).
41
2.2.2.2 The Cash Flow Theory
The Cash Flow Theory presumes that borrowers do not default, provided that
their income flow remains sufficient to meet the periodic payment without undue
financial burden (Weagley, 1988). The ability to pay will matter only for liquidity
constrained households. If liquidity constraints are not binding, the household could
borrow further to maintain their income flow and alleviate any mortgage payment
problems. In other words, households are expected to refrain from default for as long
as his or her income is sufficient to meet periodic debt obligations without causing
undue financial burden. It also argues that lower monthly payments retard the
decision to default (Weagley, 1988). Under the Cash Flow Theory, the repayment
capability of the borrower, which is measured by the monthly repayment obligations
as a percentage of current monthly income, plays a critical role in accounting for
defaults. This is a unique application of the ‗equity‘ approach where borrowers are
prevented from behaving ‗rationally‘ by credit constraints, associated with imperfect
credit markets. Faced with an unexpected fall in income or rise in interest rates, they
may be unable to extend or renegotiate credit to finance their existing debt service
payments.
Under this approach, Jackson and Kasserman (1980) tested the idea that
borrowers default if their income flow becomes insufficient to satisfy periodic
payment requirements without imposing an excessive financial burden on them. They
found that, under this model, loan-to-value ratios and mortgage interest rates are
positively correlated with payment default. Barth and Yezer (1983) pointed to trigger
events as a common reason for default. This view is characterized by the ―Cash Flow
42
Theory,‖ which assumes that default will occur whenever an individual‘s current
income, less expenditures, falls below the total of mortgage principal plus interest.
Volkwein et al. (1998) employed the Cash Flow Theory ( or the ―ability to pay
theory‖) in examining the similarities and differences in student loan defaults among
whites, African Americans, and Hispanics. They assumed that the income levels of
borrowers and their families have a substantial impact on loan repayment behavior.
They suggested that this perspective prompts researchers to focus attention not only
on a borrower's earnings, marital status, and family size, but also on parental income,
as some individuals who find themselves in financial difficulty may be able to rely on
their parents for financial assistance. Volkwein et al. (1998) projected that inability to
pay would be the most obvious reason of default. This projection was confirmed by
the large number of defaulters indicating that the primary causes were unemployment
(58.9%) and low wages (49.1%), while ignorance and misinformation were not found
to be significant determinants.
Clauretie and Sirmans (2003) suggested that research examining loan default
needs to consider borrowers‘ personal and familial dynamics, including family size,
source of income, number of dependents, and total family earnings. Quercia,
McCarthy and Stegman (1995) examined whether certain trigger events in life that
can strain resources influence the probability of foreclosure. The authors analyzed
data from annual surveys over a six year time span, tracking borrowers who received
assistance through the FMHA Section 502 program. They found that both change in
marital status and loss of a dependent household member were statistically significant
and positively correlated with the probability of foreclosure. They also found that
43
higher payment burdens were associated with higher incidences of foreclosure,
suggesting that reductions in borrowers‘ ability to pay were a factor in foreclosures.
2.2.3
Measurement of Household Debt Repayment
A number of previous studies provide taxonomies illustrating debt repayment
problems. Some studies employ more general terms of late payment or delinquency,
while others use the term of default. The status of debt payment is not as clear-cut
because the debt generally goes into default only after it becomes delinquent. All
delinquencies represent delayed payments which do not end in default (Campbell and
Dietrich, 1983). The definitions of Household Debt Repayment discussed in the
previous studies are reviewed in this section.
2.2.3.1 Delinquency
Avery et al. (1996) propounded that delinquency occurs when a borrower fails
to pay off a loan as scheduled. Since loan payments are typically scheduled on a
monthly basis, the lending industry customarily identifies payment delinquency of
loans as ―either 30, 60, 90, or 120 or more days late depending on the length of time
the oldest unpaid loan payment has been overdue.‖
Canner and Luckett (1990) examined characteristics associated with
deteriorating payment performance using 1983 Survey of Consumer Finances. One
question from the data was used to measure payment delinquency: ―Thinking of all
the various loan payments you made during the last year, were all the payments made
the way they were scheduled, or were payments on any of the loans sometimes made
later or missed?‖ The response choices were as follows: ―all paid as scheduled‖,
44
―sometimes got behind or missed payments‖, and ―payments not due/started yet.‖
Whether the respondent answered ―sometimes got behind or missed payments‖ was
used as the dependent variable. If the respondent answered ―all paid as scheduled‖ or
―payments not due/started yet,‖ he or she was assumed to have made payments as
scheduled.
This survey question was also used in Godwin (1999) and Getter (2003). In
both of these studies, another question (―Were you ever behind in your payments by
two months or more?‖) was used to identify a household‘s deteriorating payment
performance. Responses available were ―yes,‖ ―no,‖ and ―inappropriate.‖ Godwin
(1999) combined two questions into a single variable that was coded once if
respondents reported they had gotten behind or missed payments on either or both of
the questions. Thus, Getter (2003) could distinguish between occasional delinquencies
and more serious delinquencies that could signal possible default risk by using two
questions.
Lyons (2004) explored the factors that significantly affect the probability that
a college student is at risk of credit card mismanagement or misuse. Students are
considered to be financially at risk if they meet at least one following criteria: 1)
holding credit card balances of more than $1000, 2) being delinquent on their
repayments of credit card by two months or more, 3) having reached the limit on
credit cards, and 4) only paying off their credit card balances some of the time, or not
at all. This study suggested that those who were delinquent on their credit card
payments by two months or more were the most at risk, implying that they clearly had
greater problems in managing their credit and making payments.
45
2.2.3.2 Default
Campbell and Dietrich (1983) noted that a binary categorization between
default and delinquency is not accurately comprehensive because mortgages generally
go into default only after becoming delinquent. They defined delinquency as simply a
late payment, while default represents a choice by the borrower to allow the title to
the property to revert to the lender.
Avery, Bostic, Calem, and Canner (1996) suggested that ―a loan is in default
as soon as the borrower misses a scheduled payment.‖ They, however, use the term
‗‗default‘‘ for the following four situations. 1) A lender forces foreclose on a
mortgage to gain title to the property securing the loan, 2) a borrower chooses to give
the lender the title to the property in lieu of foreclosure, 3) a borrower sells the home
and makes less than the full payment on the mortgage obligation and 4) a lender
agrees to renegotiate or modify the terms of the loan and forgives some or all of the
delinquent principal and interest payments.
Anderson and VanderHoff (1999) defined that default occurs when a borrower
maximizes wealth by choosing to cease mortgage payments and ultimately relinquish
the property to the lender. Within this framework, default results from decisions by
lenders as well as borrowers. The authors upheld the definition used by Campbell and
Dietrich (1983), adding that default is not simply an extension of a mortgage being
delinquent in payment but is in fact more inclusive.
Kim (2000) and Staten (2002) construed default as occurring when a
consumer has not paid his or her credit balances at the end of a month. Specifically,
default is defined as the failure of a consumer or a household to pay at least the
minimum payment on a credit card balance. Scott (2005) used the survey question,
46
―In the past 6 months, how many time did you not at least pay off the minimum
amount due on any of your credit cards?‖ to explore why people default on credit card
debt. A positive response to the question was categorized as default. Cutts and Green
(2004) equated default with borrower failure to meet obligations as outlined in a
mortgage agreement. They created this broader definition of default to encompass a
range of borrowers, from those who have missed one payment and have a second
payment due to those in the throes of the foreclosure process.
Several studies set a specific period of delinquency in order to define default.
For example, Avery, Calem, and Canner (2004) tested the potential for improving the
predictive accuracy of credit history scoring models. The dependent variable was
measured by whether the account was delinquent for 60 days or more. This failure to
pay, for convenience, was termed default. Clauretie and Sirmens (2003) defined
default to occur when a homeowner is between 30 and 90 days late on a mortgage
payment.
2.2.4
Summary
A complete explanation of the factors leading to acquisition of household debt
would need to take into account the supply of credit by lenders as well as complex
decisions involving risk and uncertainty that might result in acquisition of household
debt.
This chapter began by presenting theories and findings associated with
household credit payment problems. Several studies have expanded the traditional
leading models. For example, Canner and Luckett (1990) permitted relaxed
assumptions, allowing for a variety of rationales behind default decisions. Lawrence
47
(1995) used a modified model allowing no default restriction in order to explain why
a consumer would choose to default when faced with unexpected income loss.
According to the literature presented above, a number of determinants can influence a
household‘s likelihood of making payment delinquency, namely the following: (1)
borrower related variables such as age, income, or race (2) trigger events such as
changes in income or health status, (3) financial buffers such as wealth or health
insurance, and (4) debt related variables such as ratio of debt payment to income or
the size of household debt.
Literature (Canner, Gabriel, and Woolley, 1991; Berkovec et al, 1994;
Anderson and VanderHoff, 1999; Robert and Hill, 2002; Ross and Yinger, 2003)
focusing on the relationship between race or ethnicity and debt payment has arisen out
of a spectrum of motivations. Regardless of differences in their theoretical
underpinnings, however, these authors arrive at the consensus that racial/ethnic
minority borrowers (encompassing both non-white borrowers and households headed
by non-white individuals) are more likely to miss debt payments or default than
otherwise equivalent white borrowers. However, few studies have offered
explanations for this discrepancy. Thus, further empirical evidence must be generated
to fully comprehend this inconsistency in repayment behavior. Specifically, an
assessment of the influences of demographic, economic, and other characteristics on
household debt repayment behavior across racial/ethnic distinctions would prove
particularly valuable.
This discussion of the relative importance of demographic characteristics,
economic characteristics, trigger events, financial buffers, and debt-related variables
as tied to race and ethnicity utilizes a contemporary perspective on some of the
48
previous arguments related to this topic. All of these variables, given due
consideration by the aforementioned literature, are taken into account in the model
presented within this current dissertation in an effort to comprehensively explore
racial/ethnic variance in payment delinquency. This chapter serves to justify the set
of variables included in the quantitative model I will present in Chapter 4.
2.3
2.3.1
Conceptual Models and Research Hypotheses
Conceptual Models
The framework of this research is built upon two parts. The first part analyzes
the acquisition of household debt. The second part focuses on the repayment of
household debt. The probability of acquisition of household debt is expected to
predict the joint outcome of demand and supply of household debt. The implications
of the first part are integrated into the second part. In the second part, the hypothesis
that racial/ethnic groups have different repayment patterns is proposed. Also, the
affect of financially adverse events, financial buffers and household debt burden are
considered.
2.3.2
Research Hypotheses
2.3.2.1 Acquisition of Household Debt
(1) Age
a. Demand effect
Age is one of the most important factors in elucidating the credit demand. A
typical household has a hump-shaped profile of earnings over a lifetime. In other
words, earnings start low, increase until the individual is in his or her prime age, then
49
begin a slow decline, and decrease sharply from the time that he/she retires. The LCPIH presumes that the amount of debt acquired tends to be larger in early stages of the
life cycle than in later ones. The gradual decline of household borrowing implies the
contemporary upward earnings pattern throughout one‘s lifetime. It is expected that
young people carry more debt in order to balance between desired living standards
and scarce resources, compared to other age groups. Therefore, it is expected that
people borrow more when they are young or later in their life, therefore allocating
majority of savings in their prime age.
b. Supply effect
The Equal Credit Opportunity Act (ECOA) prohibits discrimination against an
applicant for credit based on factors that have been not considered to be related to
creditworthiness (Lamb, 2005). This does not mean that this act grants all credit
applicants an automatic right to credit, but it requires that creditors apply the same
standards of creditworthiness equally to all applicants. Under this act, discrimination
against ―the basis of sex, marital status, race, color, religion, national origin, age, or
income from public assistance‖ is illegal. Given this presumption, the age of credit
applicants should not play any role in the creditor‘s decision.
However, young people tend to lack credit history and subsequently might
experience more credit constraints. Additionally, the debt ceiling tends to lower for
the younger age group compared to the other age groups (Jappelli, 1990).
50
c. Overall effect
As age increases, household income becomes high enough to support their
demand (Mali and Kechen, 2008). Young people tend to have a high demand for
household debt because of their expectation of higher income in the future compared
with their current low income. Therefore, young people are more likely to borrow
while older people are less likely to borrow because they have enough income to
support their demand. The age-debt profile will be concave throughout the life cycle.
With this upward-sloping age-earnings profile, it is expected that household debt
increase, reach a peak at the age at which earnings exceed desired consumption, and
decline afterward.
(2) Education
a. Demand effect
Individuals who obtained education of different academic levels could differ
from each other in their borrowing decision. Also, credit demand might be in
alignment with the applicant‘s knowledge and skills. For example, those with greater
education may be expected to manage their financial affairs more prudently, therefore
being less likely to have unexpected demands for credit which are rejected. Higher
educational attainment may be an indication of higher future income and greater job
security. The earnings of college-educated household heads increase with their age by
less than more poorly educated households (Grant, 2003). Therefore, people with
higher education level will expect a higher return in earnings that will allow them to
repay. In addition, educational loans are a typical type of debt sought by highly
educated individuals. College or graduate students who cannot afford education costs
51
are likely to incur debt to cover the cost. Therefore, higher-educated people tend to
borrow more than the less-educated group, in light of a steady and possibly increasing
income stream in the future as well as possibility of educational loans.
b. Supply effect
Lenders tend to grant more credit to highly educated individuals because of
both their higher income levels and greater job security. In general, lenders do not ask
for information about the educational attainment of loan applicants; rather, occupation
type is sometimes recorded, which is associated with job security. Therefore, the
effect of education on credit applicants is expected to be negligible when credit
suppliers make decisions. Higher education levels signal higher future income and
desired borrowing. If the higher education attainment by credit applicants is regarded
as a predictor of future earnings and ability to repay, the supply of credit rises.
c. Overall effect
In all, all these possible scenarios would result in greater debt acquisition of
household debt for people with higher education attainment than otherwise.
(3) Marital status
a. Demand effect
Married couples tend to have more expenditure as compared to single or
divorced people or widowed people since they are bound with more responsibilities.
Married couple tends to more likely to hold mortgages, which explains why the
overall debt is higher for them (Fabbri & Padula, 2003).
52
On the contrary, double income couples have an alternative source of income
if one spouse stops working compared to single-headed households. In addition,
married couple may have lower consumption because of economics of scale of the
consumption of durables (Jappelli, 1990). Under the same circumstances, the effect of
marital status on the credit demand of households is ambiguous.
Households with children under 18 have more financial needs due to the desire
to pave the way for their children‘s living and education. When households‘ financial
resources are insufficient, borrowing money allows them to achieve these goals.
Compared to households without young children at home, their demand for household
debt will be higher.
b. Supply effect
Married couples might be in an advantageous position in acquiring as many
loans of the maximum amount that they desire. This is due to creditors being more
willing to lend to married couples because they can underwrite the loans jointly
(Fabbri & Padula, 2003).
Married couples might be less risky to suppliers because they are less mobile
geographically, and move less often. Therefore married couples might be able to be
granted as much credit as they desire.
However, under the Equal Credit Opportunity Act, certain questions deemed
discretionary may not be asked during the credit application process for loans.
Creditors are prohibited from discriminating against credit applicants on the basis of
marital status. Therefore, it is expected that there is no supply effect of marital status
53
on the probability of debt acquisition of households. In addition, lenders normally do
not seek information on dependents that credit applicants have.
c. Overall effect
Overall, it is expected that the effect of married couple on the probability of
debt acquisition is not clear. The final effect of children under 18 in the household on
the probability of debt acquisition is positive.
(4) Household Income
a. Demand Effect
LI-PIH presumes that low-income households may desire greater borrowing in
order to balance the difference between desired consumption and earned income
throughout life cycle. Households with higher permanent income are more confident
in their job security and therefore have lower savings and higher borrowing (Duca and
Rosenthal, 1993). A rise in permanent income increases the desire to acquire assets
(Cox and Jappelli, 1993). Therefore, permanent income might boost current
consumption demand by increasing borrowing. However, the level of current
household income should not necessarily be related to the level of borrowing. No
apparent link is expected between current income level and credit debt after
controlling for any other variable, as a rational households would consume at a level
that is commensurate with their permanent income or lifetime resources, regardless of
their current income level.
Given that the majority of demand is for mortgage debt, the most likely
interpretation of Cox and Jappelli is that families with higher permanent income have
54
a greater demand for housing. Households that expect their income to be higher in the
future would be expected to consume more, and this includes consumption on housing
and other durable goods (Bertola, Disney, & Grant, 2006). Therefore, a household‘s
anticipated future income and credit demand might be correlated. Projected future
income raises desired consumption and desired borrowing relative to current
resources. If a household has current income is higher than that of normal year's
income, the household should be more likely to save. If a household has current
income is lower than that of normal year's income, the household should be more
likely to borrow. When the present value of household income is expected to increase
in the future, it is rational to decrease savings and increase borrowing. Households
that are not certain about their future income should be less likely to borrow than
those that are sure that income will grow faster than inflation.
b. Supply effect
Household income has been considered a significant predictor on default risk
in credit score models. Undoubtedly, households who have steady and affluent
income are perceived as less risky borrowers and are more likely to be granted as
much as they desire. On the contrary, low-income households might have some
difficulties obtaining debt.
c. Overall effect.
Household income affects debt acquisition positively are expected to be
related with credit demand and credit supply. When credit constraints are taken into
account, it is hypothesized that current income negatively affects the probability of
55
holding household debt. On the contrary, optimistic future income expectation will
increase the probability of holding debt.
(5) Net worth
a. Demand effect.
Borrowing is regarded as a means to reconcile the gap between desired
consumption and income available during one‘s life stage. If an individual has some
net wealth, which comes from the surplus between income and consumption over the
time, one could draw on this as another source for consumption instead of borrowing
money. With net worth, an individual can afford more desired consumption and may
not need to borrow. Net worth, which is the total assets minus total liabilities of an
individual, can be used for any expenditure or emergency need. Therefore, households
who have accumulated more net worth are less likely to apply for household debt,
while household who have not accumulated enough net worth are more likely to have
demand for household debt.
b. Supply Effect
Carrying more household wealth is considered by creditors to be a strong
indicator of the borrower‘s repayment ability. Crook (2006) found that the effect of
net worth is generally positive when the demand is primarily for mortgages. Also, he
explained that high net worth and high borrowing are correlated since household
assets would provide collateral for more borrowing, and high-asset households may
be granted more credit.
56
However, realistically, information on household wealth is very limited.
Lenders may inquire about certain financial information, such as saving/checking
accounts, in the credit application (reference ***). For property-secured loans,
property appraised value is desired to determine the loan amount. Examples are home
mortgages, home equity lines of credit, and auto loans, where the value of the house
or vehicle is one vital piece of customer information. For unsecured lines, such as
revolving bankcard, information on household assets is seldom required. Liquid assets
are rarely requested upon application. Therefore, the supply effect is expected to be
minimal.
c. Overall Effect
The supply effect is indeterminate and the demand effect is negative. Therefore, it is
expected that the net effect of net worth is negative.
(6) Employment
a. Demand Effect
Employed consumers have the capability and desire to borrow more to finance
consumption or investment in advance than those out of the labor force. If a consumer
is currently working for pay, all things considered, the expected future income would
be higher than if a consumer is not currently working (Crook, 1996). People who are
unemployed are pessimistic about their expected future income prospects (Crook,
2006). Also, the LC-PI hypothesis presumes that those who expect their income to
increase will intend to incur debt while those who expect their income to decrease will
57
refrain from borrowing. Therefore, being employed would increase the demand for
credit while being unemployed would decrease the demand for credit.
b. Supply Effect
Lenders consider a credit applicant‘s employment status to project their future
ability to pay. For example, lenders collect information about ‗how long s/he has been
on the job?‘ to anticipate job stability, and hence the borrower‘s long term income
stream (reference,****). If employment status of credit applicants is regarded as a
predictor for future earnings and ability to repay, being unemployed decrease future
expected income and the debt ceiling (Japelli, ***).
c. Overall Effect
With all other factors equal, an individual is currently working for pay are
expected to be more probability of debt acquisition.
(7) Race and Ethnicity
a. Demand
Crook (1996) identifies characteristics of households who have been rejected
or discouraged from applying for credit by examining untransformed household
characteristics and their principal components. As a result, White reduces the
probability that a household will be discouraged from applying for credit. Crook
(1999) identified households who are discouraged from applying for credit from
certain lenders and confirmed his previous study (Crook, 1996). He demonstrated that
the probability of being discouraged is positively related to being black or to being
58
Hispanic. Bertola, Disney and Grant (2006) showed households whose head is nonwhite appear to have lower demand for consumer credit than those with a white head
of households. Therefore, the demand of credit by racial/ethnic minorities is expected
to be lower than that of white people.
b. Supply
Under the Equal Credit Opportunity Act, lenders cannot discriminate when
they make decisions on granting debt. Both federal and state anti-discrimination laws
include extensive provisions covering various types of discrimination related to real
property. In addition, the US Supreme Court has maintained that enforcement of
racially restrictive covenants is unconstitutional. Therefore, certain questions deemed
discriminatory may not be asked in the loan application process. Also, a creditor may
not discriminate on the basis of race, religion or national origin under the Consumer
Credit Protection Act in any credit transaction in evaluating the applicant
(Lawyers.com, 2009). Therefore, it is expected that there is no supply effect of race
on the probability of debt acquisition.
c. Overall
Overall, the probability of debt acquisition is lower for racial/ethnic minorities and
higher for whites.
59
(8) Summary
The probability of debt acquisition is determined jointly by credit demand and
credit supply. The demographic variables and economics variables of a household are
essential components in accounting for credit demand. From a lender‘s perspective,
most of the demographic variables such as sex, race, color, religion, national origin,
marital status, age, source of income are difficult to consider when the lender decides
to grant credit. Therefore, the probability of debt acquisition is expected to depend
primarily on the demand for credit
2.3.2.2 Repayment of Household Debt
Based on the theoretical discussions and empirical findings of previous related
research, my hypotheses as to payment delinquency of household debt are proposed
broadly and in three parts: (1) What are the significant factors that influence
household debt payment delinquency? (2) Does the probability of debt payment
delinquency differ in accordance with the race or ethnicity of borrowers? (3) If any,
which of these factors influence household debt payment delinquency with respect to
race and ethnicity?
(1) Race/ethnicity
Race and ethnicity might affect the likelihood of payment delinquency of
household debt due to racial/ethnic differences in the probability of income changes,
and the level of other financial resources available that may be utilized in financially
adverse events. Therefore, this study assumes there will be no difference between
racial/ethnic groups when controlling for all other variables related to demographic
60
characteristics, financially adverse events, financial buffers and household debt
burden.
(2)Age
It is often expected that young borrowers tend to have fewer savings and less
well-established credit histories. Therefore, these financial characteristics might make
them riskier than older borrowers with well-established credit histories and greater
savings or assets. Therefore, it is expected that younger homeowners are more likely
to experience debt repayment problems. On the contrary, often the younger may have
a higher probability of faster reemployment after job loss, which may enhance their
reinstatement rates compared with older homeowners.
Young people have little experience, but undiminished cognitive skills. Their
lack of experience makes them more prone to making mistakes. Over time, these
young people gain the experience that influences them to use both familiar and new
products with low cost. As people get older, the marginal value of experience lessens,
while diminishing cognitive skills begins to erode some knowledge and skills they
have attained earlier in life (Agarwal, Driscoll, Gabaix, & Laibson, 2006).
Therefore, this study expects that payment delinquency would fall into an
inverted u-shape with age. In other words, both younger and older individuals might
seem to have low-probability of payment delinquency. Therefore, this study expects
that as borrower‘s age increases to a maximum age level, his or her expected risk of
delinquency increases. Upon reaching that maximum age level, however, the expected
risk of delinquency begins to decrease.
61
(3)Income
Assume that a household‘s payment delinquency decisions depend on an
analysis of the relative benefits like waived liabilities versus the costs, such as
restricted future access to credit or social stigma. The level of the household‘s income
profile might heavily affect the balance between the relative cost and benefit. More
specifically, for people with low income, neither the possibility of restricted access to
credit nor social stigma is so important as to refrain from payment delinquency. On
the contrary, for people with higher levels of income, the consequences of restricted
access to credit or social stigma might be sufficiently crucial enough to deter payment
delinquency. Therefore, the income levels of borrowers exert substantial influence on
debt repayment behavior. Income will be negatively related to repayment difficulties
of household debt.
(4)Education
The effect of education on the debt repayment behavior can be discussed
based on the balance between the relative cost and benefits of neglecting debt
repayment. The balance might be affected by the borrower‘s education level. More
specifically, for people with low educational attainment, neither the possibility of
restricted access to credit nor social stigma is so important as to avoid payment
delinquency. On the contrary, for people with higher education levels, the cost caused
by poor payment performance might be sufficiently crucial enough to deter payment
delinquency. In addition, more educated people are not only more knowledgeable
about financial instruments but may also have considerably greater self-discipline
through longer durations of higher education programs. Therefore, the higher the
62
respondent‘s level of education, the less likely he or she is to be delinquent on
household debt.
(5)Trigger Events
Even though borrowers are assumed to have every intention of repaying debt
obligations at the time they are incurred, borrowers facing these types of adverse
circumstances may find them unable to do so. In other words, repayment problems
may result from unanticipated declines in household wealth and income or an
unexpected increase in household expenditures. For example, an income change
caused by employment shift from being full-time employed to being unemployed or
unanticipated increased spending related to medical care might disrupt a stable pattern
of household debt repayments. The end result for these borrowers may be payment
delinquency, and in the most extreme cases, bankruptcy. The coefficients for
unemployment, lower than normal income over the previous year, and poor health
status are expected to be positive. These negative disruptions to wealth and future
income increase a borrower‘s risk of delinquency, compounded by the fact that
unexpected economic downturns may have particularly harsh effects for minorities.
Of course, that can be attributed to racial/ethnic discrimination in the labor market.
Therefore, it is expected that, assuming other factors remain consistent, having
household members with poor health in the household increases the likelihood of
payment delinquency. Also, it is expected that having lower income compared to an
average year increases the likelihood of payment delinquency.
63
(6)Financial Buffers
Factors that reduce the repercussions of financially adverse events are
expected to reduce the likelihood of payment delinquency. Household wealth can
reduce the perceived risks due to variations in income and consumption brought about
by unanticipated life disruptions such as job loss and health problems.
The occurrence of trigger events can impact a household in a number of
different ways. In some cases, borrowers will successfully adapt to unexpected events
and continue to make regular credit card or loan payments by relying on a financial
buffer. For example, a lapse in employment may result in only a minor disruption for
a given household, provided that household can use their precautionary wealth.
Similarly, health expenditures related to poor health status may be mitigated by
current health insurance coverage. On the contrary, if a household does not have
health insurance or is unable to draw on household wealth, the impact of a drop in
income or the presence of health problems may be significantly more devastating. Of
the coefficients for holding net worth, health insurance is expected to be negative
presumably because this type of supplementary assistance can be a financial buffer
against negative events.
Therefore, family members being covered by health insurance will decrease
repayment difficulties of household debt. Also, net worth of households is expected to
decrease repayment difficulties of household debt.
(7)Debt Burden
This study expects that households become delinquent on debt payments if
their income becomes insufficient to meet the periodic payments without imposing
64
undue financial burden. Thus, it can be assumed that households consequently have
difficulty in repaying their debt because they excessively spend and became
overburdened. In this case, large household debt burden will be positively related to
payment delinquency. However, it can be assumed that a ratio of payment-to-income
that reflects the level of borrower‘s creditworthiness is related to the borrower‘s
payment performance. In this case, a higher ratio of debt burden is negatively related
to borrower‘s payment delinquency.
On the contrary, carrying outstanding household debt or large monthly
obligations might not be related to future debt repayment difficulties because those
creditors might be able to observe a household‘s monthly payments during the credit
approval process. Therefore, the effect of household debt burden will be varied.
65
CHAPTER 3
3
3.1
3.1.1
LITERATURE REVIEW
Acquisition of Household Debt
Demand of Household Debt
3.1.1.1 Selected Variables related to Demand of Household Debt
Many economists have explored the implications of departures from the
permanent income/life cycle hypothesis (PIH/LCH) of consumer behavior (Cox &
Jappelli, 1993). The mixed empirical findings of neoclassical models of consumption
have led to a surge of applied research directed at showing the existence of
consumers‘ credit constraints. They are linked to rationale behind acquisition of
household debt.
Income and Employment
Cox and Jappelli (1993) examined whether there exists a gap between desired
debt and observed debt and speculated that a rise in permanent income increases the
gap between current and future resources. Therefore, they expected that permanent
income will boost current consumption demand by creating an increase in borrowing.
They found that holding debt is significantly related to both permanent earnings and
current income. More specifically, current income and permanent income exerted
66
opposing effects on the demand for debt. These opposing income effects were
consistent with borrowing that is motivated by a desire to finance current
consumption. Families with higher permanent income are more confident in their job
security and therefore have less savings and higher borrowing because of
precautionary motives (Duca and Rosenthal, 1993). Additionally, Crook (1995) and
Duca and Rosenthal (1983) found a positive but nonlinear effect of income on debt,
regardless of the type of debt. Given that the majority of demand is for mortgage debt,
the most likely interpretation of Cox and Jappelli is that families with higher
permanent income have a greater demand for housing.
Crook (1996) assumed that if a head of household were currently working, the
expected future income would be higher than if the head were not currently working.
This would increase the demand for credit, but would also increase its supply to a
household. Crook (2006) showed that households whose head is unemployed demand
less debt in the US. He attributed this result to the fact that the unemployed are
pessimistic about their prospects for future income. Therefore, those people were
expected to have less demand for debt.
Net worth
The effect of the net worth that a household holds on the probability of
acquisition of household debt is uncertain. In general, an individual with a huge net
worth can afford more desired consumption and may not need to borrow. On the other
hand, Crook (2006) demonstrated that the effect of net worth was generally positive
when the demand is mostly for mortgages. High assets and high borrowing were
correlated since household assets would provide collateral for more borrowing, and
67
high-asset households may be granted more credit. On the supply side, net worth is
considered as a good indicator of the borrower‘s repayment ability. The higher the net
worth is, the higher the probability of obtaining a loan (Crook, 2006). Therefore, the
effect of net worth is indefinite.
Age
Cox and Jappelli (1993) included several age categories in their research in
order to understand desired debt throughout the life cycle: younger than 25, from 25
to 34, from 35 to 44, from 45 to 54, from 55 to 64, and older than 64. They found that
the probability of holding debt increases with age early on, remains flat between ages
25 and 54 and then declines sharply. They explained this result in that, with upwardsloping age-earnings profiles, household debt increase, reach a peak at the age at
which earnings exceed desired consumption and decline afterward. The pace of debt
dissimulation slowed significantly from age 55 to age 64. The age-debt profile was
concave throughout the life cycle. Crook (2001) also found that a household demands
less debt when the head of household reaches an age over 55 years old.
Young households are likely to have a high demand for credit because of their
expectation of higher income and higher consumption in the future compared with
their current low income. As their age increases, their income becomes higher and
they have enough income to support their demand. Therefore, older people are less
likely to borrow because they have sufficient incomes to support their demands (Mali
& Kechen, 2008).
On the contrary, Jappelli (1990) discussed the supply side in terms of adverse
selection. He suggested that the debt ceiling is likely to be lower for younger
68
consumers than for the rest of the population. If future income is considered illiquid
and risky, lack of credit history leads to a higher likelihood of credit constraints for
the young.
In sum, earning profiles tend to be upward sloping, and young people expect
higher future income and optimal consumption rises relative to current resources.
Therefore, young people are more likely to have a higher demand for credit. However,
the young with lack of credit history are more likely to experience credit constraints.
Education
The higher the level of education attained by debt applicants, the greater the
demand for debt. Therefore, it is consistent with the Life Cycle Theory. Crook (1996)
showed that more years of schooling by a household head increase both the future
income and the household's demand for credit. Also, more years of schooling
influences the credit supply since having received more education enables a potential
borrower to be more able to anticipate his or her repayment ability, aiding the lender
in their decision. Higher educational attainment may be an indication of higher future
income and greater job security. Therefore, the greater the number of years of a
person's schooling, the lower the probability that the person's demand will increase
more than their supply. And those with greater education may be expected to manage
their financial affairs more prudently, therefore they would be less likely to have
unexpected demands for credit, which are rejected. In addition, Grant (2003)
confirmed Crook (1996) by showing that the earnings of college-educated household
heads increase with their age by less than more poorly educated households.
69
Overall, it would be expected that years of schooling would be positively
related to the probability of acquisition of household debt.
Marital status and Family Size
Jappelli (1990) argued that demand and supply effects are likely to work
toward relaxing the constraints for married couples. In considering the demand,
married couple may have lower consumption because of economics scale in the
consumption of durables. In terms of supply, they might be less risky to suppliers
because they move geographically less often and therefore they can be granted more
credit because they are less mobile and because loans may be jointly underwritten.
In examining the impact of family size, Jappelli (1990) found that if desired
consumption increased at the time that family size is large, the constraint becomes
tighter. Crook et al (1992) supported his result by evidence that the probability of
default increases with the number of children. And households with large families are
more likely to have a very high rate of time preference, so they wish to have higher
level of optimal consumption than those with small families. Individuals in a large
family are more likely to borrow than those in a smaller family, as the larger family is
more likely to have a higher dependency ratio.
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3.2
3.2.1
Repayment of Household Debt
Selected Variables related to Repayment Delinquency
3.2.1.1 Demographic Characteristics
Age
Inconsistency exists about the effect of age on the probability of debt
repayment problems. Some studies found that the age of household heads was
negatively related to debt repayment difficulties. Morgan and Toll (1997), Stavins
(2000), Kim (2000), and Bertaut and Haliassos (2002) presumed that age of borrowers
could be a significant factor in credit default, and they demonstrated that borrower‘s
increased age decreased the likelihood of default. Anderson and VanderHoff (1999)
confirmed that younger borrowers have a higher default probability than older
borrowers. In addition, Martin and Hill (2005) included age variables and age squared
and found that age was positively significant and age squared was negatively
significant. They interpreted this finding to mean that as a respondent‘s age increases
up to a certain age, his or her expected number of defaults on credit card debt
increases, and then the expected number of defaults begins to decrease after reaching
that maximum age level.
Contrary to these findings, Hakim and Haddad (1999) found that age is
insignificant in all regions except the Southwest, where default appears strongly
associated with older borrowers. The authors suggested that declining property values
in Texas and surrounding areas might affect older borrowers, a problem caused by the
oil glut and the subsequent severe depression that followed during the time period of
this study.
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Ambrose and Capone (1998) justified the inconsistency of age effect. They
explained that it is often expected that young borrowers tend to have less savings and
less established credit histories, thus making them riskier than older borrowers with
well-established credit histories and greater savings or assets. Therefore, they
expected that younger homeowners are more likely to experience default or
foreclosure. On the contrary, often the younger may have a higher probability of
faster reemployment after job loss, which may enhance their reinstatement rates
compared with older homeowners. Therefore, Ambrose and Capone (1998) showed
that the anticipated age effect on reinstatement might remain unclear.
Family Composition
Previous studies have shown that marital status bears some influence on
repayment behavior in contradictory ways. First, the presence of a spouse may help
control credit spending, if spending by one spouse must be justified to the other or
remain consistent with an overall household plan. For example, Canner and Luckett
(1991) found that divorced or separated heads of household were more likely to report
debt payment problems than were married heads of households. Similarly, Stavins
(2000) and Kim (2000) found that married households were less likely to default.
Sullivan and Fisher (1988) conducted an analysis of the relationship between
the gender of the borrower and payment difficulties that incorporated theories of the
family life-cycle. They discussed the probability of having debt repayment problems
while focusing on several household types. For example, they found that women
borrowers were more likely to experience payment difficulties than men debtors in
almost stages of the life cycle. The one exception was unmarried young women with
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no children. In that group, unmarried young men are more likely to have credit
payment problems than women. Also, unmarried people with children are almost
twice as likely to have payment difficulties as the average rate, regardless of the
gender of the household head.
Exceptionally, a study by Martin and Hill (2005) included married, divorced,
widowed and single borrowers and disputed the significance of marital status
altogether.
Education
Education is frequently cited as a significant variable in projecting debt
payment difficulties. Sullivan and Fisher (1988) showed that credit payment was
highly correlated with the level of education obtained by borrowers. Households
where the head has less than a high school education showed higher percentages of
slower repayment than households where the head possessed more than high school
education and was a college graduate. Also, heads with less than a high school
education were 1.5 times more likely to have debt repayment difficulties than average.
Bertaut and Haliassos (2002) supported Sullivan and Fisher (1988) by demonstrating
that more educated people are also more knowledgeable about financial instruments.
Also, Stavins (2000) found that the higher the respondent‘s level of education, the less
likely he or she was to default on credit card debt. Bertaut and Haliassos (2002)
suggested that more educated people demonstrated considerable self-discipline over
the course of college degree programs.
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3.2.1.2 Economic Characteristics
Income
Studies on the relationship between household income and debt repayment
difficulties have also been yielded divided results.
Some studies (Sullivan & Fisher, 1988; Stavins, 2000; Bertaut & Haliassos,
2002; Martin & Hill, 2005) found that as the respondent‘s household income
increases, his or her debt delinquency decreases, and vice versa. Specifically, Sullivan
and Fisher (1988) found a significant correlation between household income and the
risk of debt repayment difficulty. Among the lowest income group, 37% of
households reported repayment delinquency, while only 7% of households in the
highest income group experienced this problem.
Berkovec, Canner, Gabriel and Hannan (1994) included the borrower's income
and squared income in order to test for nonlinearities in the relationship between a
borrower's income and the performance of his or her loan. They found that the
likelihood of default declines as the income of the borrower rises, but that this
relationship becomes less pronounced as income increases. Martin and Hill (2005)
examined income in thousand dollar increments and found that the respondent‘s
expected number of defaults decreases by 0.0016 with every $1,000 increase in their
income.
On the contrary, Canner and Luckett (1990), Godwin (1999), and Jacobson
and Roszbach (2003) found no relationship between income and the probability of
debt repayment difficulty. Additionally, Kim (2000) analyzed the effect of income in
his study and found no statistical significance, even though it had a negative sign.
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Home Ownership
Borrowers‘ residential stability has been studied as an indicator of credit
quality in the previous research. However, previous studies have shown inconclusive
results regarding the effect of home ownership, since home ownership might
influence people‘s repayment performance in two conflicting ways. For example, the
relationship between home ownership and repayment problems might be negative
because ownership itself reflects their good credit and steady income, otherwise he or
she would have been unable to acquire the house. Sullivan and Fisher (1988)
separated respondents into homeowners and renters and found that renters were
almost twice as likely to have debt repayment difficulties as homeowners. Martin and
Hill (2000) and Getter (2003) confirmed Sullivan and Fisher‘s findings by
demonstrating the negative effect of homeownership on debt repayment delinquency.
Conversely, home renters are not burdened with mortgage payments, whereas
mortgage payments are a considerable portion of debt repayment obligations of their
home-owning counterparts. Additionally, Stavins (2000), Kim (2000) and Martin and
Hill (2005) found this variable to be of no significance.
Employment status
A borrower‘s employment status can generate general predictions for their
debt repayment difficulties. Stavins (2000) found that the unemployed respondent was
more likely to experience repayment problems. Martin and Hill (2005) demonstrated
that any fluctuation in employment significantly increased the probability of credit
card default since many Americans live paycheck-to-paycheck. Reeder (2004)
reviewed studies that used aggregate measures of unemployment rate and concluded
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that the unemployment rates have been fairly consistent across studies. It was found
that higher rates of unemployment were associated with higher rates of mortgage
default and lower rates of prepayment. These results concur with the expectation that
unemployment would increase the likelihood of default and would also make it more
problematic for households to either refinance their mortgage or to absorb the
transaction costs of moving.
Two other studies showed the contradictory explanation about the
employment effect. In Getter‘s study (2003), unemployment was not found to be
statistically significant. He interpreted this result to indicate that, after controlling for
income drops, there might be no residual impact of being unemployed on repayment
problems. In fact, unemployed people whose income was not lower than their
expected income might reflect that they have sufficient wealth so as to not have to
earn. Reeder (2004) attributed this result to the lack of an indication of whether the
unemployment spell is voluntary.
3.2.1.3 Financial Events
Recent research has attempted to incorporate financially adverse events
influence on default behavior (Riddiough, 1991). In the extensive literature on debt
repayment, adverse events in a borrower‘s life circumstances are commonly
recognized as playing important roles in triggering repayment delinquency or default.
Generally, financially adverse events have been defined in previous studies as
disruptions like job losses, changes in income, health problems, and marital
breakdowns.
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For example, Coles (1992) surveyed mortgage lenders and found that 20-25%
of arrears and repossessions were associated with relationship failure. Another 40%
were associated with other income shocks such as unemployment and business failure.
Canner, Gabriel, and Woolley (1991) suggested that many credit problems
emerge from events that are difficult to predict even though a number of financial and
nonfinancial characteristics related to borrowers‘ creditworthiness are evaluated when
they are granted credit. Therefore, they argued that predicting future loan delinquency
from the creditor‘s perspective has large, unexplained, random components. They
argued that that trigger events played an important role in accounting for this
unexpected random components of mortgage defaults.
Gardner and Mills (1989) tried to predict which cases of serious delinquency
would end in foreclosure by reviewing a portfolio of over 5,000 loans from a single
lender. They used a sample of 713 loans that originated during the 1970s and became
seriously delinquent between 1979 and 1985. The data used in this study allowed
them to identify whether borrowers had experienced any changes in marital status,
health status, employment or income since the origination of the loan. Using this data,
they found that divorce, illness, loss of job, and reduced income were all related to
debt repayment delinquency.
Quercia, McCarthy and Stegman (1995) examined the default decisions of
low-income, subsidized rural borrowers by using data from annual surveys over a sixyear period. This data included borrowers assisted by the Farmers Home
Administration Section 502 program. It also contained annual payment-to-income
ratios as well as information on trigger events, such as the loss of a spouse or a
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dependent household member. The authors found that a change in marital status and
the loss of a dependent household member increased the probability of foreclosure.
Godwin (1999) examined the effect of a series of variables reflecting events
and changes that had occurred between 1983 and 1989 on the households' probability
of debt repayment difficulty in 1989. Specifically, employment and health changes,
support received or given, events related to housing and real estate and major
financial transactions that occurred in the period between 1983 and 1989 were
examined. The presence and extent of financial distress was measured by questions
asked in the SCF regarding whether the previous year‘s income had been about what
the respondent had anticipated, and, if not, whether it was higher or lower than
expected. The responses to this question were then used to identify whether a
household had experienced unforeseen financial stressors that lowered income.
Among these variables reflecting events and changes, three proxies were significantly
related. If households had received financial support from relatives or friends, their
odds of debt repayment difficulties were higher than if they had not. Among various
housing and real estate related events, making major improvements in the households‘
primary residences was most frequently reported.
Getter (2003) defined a trigger event as an unexpected rise or fall in income
that occurred after a household was granted credit. This study emphasized the
relevance of trigger events to repayment problems. Trigger events were defined as a
change in marital status, such as being divorced or separated, and an unexpected
income drop. He found that a household‘s risk of delinquency was significantly
correlated with the trigger events. Race was found to be significant when this
evaluation was applied to those who experienced negative income change.
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Avery, Calem and Canner (2004) tested the potential value of situational
factors in credit performance. They tested the relationship between local economic
circumstances, measured by county-level unemployment rates, and payment
performance on new accounts, measured by whether the account became delinquent
60 days or more. Past payment problems isolated in time were treated as temporary
adverse trigger events. In this test, it was assumed that a temporary adverse trigger
event creates a pattern in which all payment delinquencies occur at a single time, in
contrast to the more dispersed pattern expected of an individual with habitual
financial problems.
This study allowed for several possibilities with respect to the role of personal
trigger events because the authors used a spectrum of marital status characteristics.
Specifically, the model employed categorical variables distinguishing four groups of
individuals: those who were married, those who were recently married, those who
were married prior to July 1995, and those who were never married. They found that
single and married individuals had different inherent propensities for adverse trigger
events. Those individuals categorized as either newly divorced or separated exhibited
the highest estimated likelihood of default on new accounts, and these individuals also
had higher likelihoods of default than those in the ‗‗never married‘‘ group.
The likelihood of default increases substantially with the percentage of
minority population as defined by the census tract, yielding results consistent with the
notion that minority households may be more vulnerable to trigger events.
Additionally, Herbert (2004) reviewed the literature on trigger events to date
and noted that the difficult nature of obtaining quality data on both trigger events and
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financial instruments like mortgages impedes research and called for further study on
the topic.
3.2.1.4 Financial Buffers
Depending upon the magnitude of the impact of financially negative events as
well as how a household deals with such an event, a variety of repayment
performance behaviors may occur. As Black and Morgan‘s (1999) study demonstrates,
large holdings of stocks and bonds reduced the risk of delinquency even though the
impact of these holdings is small and statistically insignificant. They showed that
holdings of liquid assets lowered the risk of delinquency more dramatically, and they
interpreted this result to show that liquid assets are a superior buffer against
unexpected changes in income.
Getter (2003) found financial wealth and home ownership to be important
factors that may mitigate the impacts of financial disruptions and therefore decrease
the probability of being delinquent. By contrast, his study lent no support to any
association between health insurance and incidence of delinquency. This result proves
interesting given that Elmer and Seelig (1998) find that the share of persons without
health insurance seems to track fairly closely with foreclosure rates.
Reeder (2004) attributed Getter‘s findings of little to no association between
health insurance and incidence of delinquency to a high correlation between wealth
and access to health insurance. This study concluded that households can minimize
the probability of insolvency by diversifying precautionary wealth. Reeder (2004)
added that household characteristics and housing market circumstances are also
important factors in filtering the impacts of trigger events. This study demonstrated
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that the negative impact of trigger events can be mitigated by household savings,
insurance, or the borrower‘s ability to utilize a support network such as family,
friends or supportive services of governmental agencies and non-profit organizations.
Godwin (1999) found that having received financial support from relatives or friends
had a particularly significant effect on borrowers‘ debt repayment ability during 1989.
In summation, previous studies provided empirical evidence regarding factors
that may mitigate the impact of financial shocks on payment delinquency. Some
borrowers will successfully respond to trigger events and continue with their regular
payment schedules. For example, those borrowers may respond to economic
downturn or income loss by relying on financial buffers such as household wealth,
health insurance, and assistance from relatives or friends. Conversely, other
households may not be able to successfully negotiate trigger events. Unexpected
occurrences may prevent those with inadequate financial resources from being able to
repay debts, even though they may have every intention of fulfilling these obligations.
Therefore, it is important to examine whether available financial resources decrease
the probability of payment delinquency.
3.2.1.5 Household Debt Burden
Several relevant studies examining repayment delinquency have included debt
related variables in their models. The impact of burdensome debt that a household
bears on their debt repayment is inconclusive.
In general, the burden of debt might increase the likelihood of household
repayment problems. Sullivan and Fisher (1988) showed when the debt burden ratio
was constructed to include only outstanding consumer debt , the probability that the
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borrower would miss a repayment or make a late repayment increased with the ratio
of consumer debt to income. Berkovec, Canner, Gabriel and Hannan (1994) found
that loan-to-value ratio (LTV) was positively related to the likelihood of default.
Results indicated that lower levels of borrower equity in the home, as reflected in
loans with higher initial loan-to-value ratios, imply higher likelihoods of default.
Godwin (1999) assumed that households end up having difficulty repaying
their debt because they became overburdened. Therefore, he included in his model
households‘ debt portfolios, measured by whether households had each of six types of
debt (credit card debt, mortgage loans, automobile loans, durable goods loans, home
improvement debt, and other debt) as well as the amount of the outstanding balance of
each of these types of debt. Three such types of debt -- mortgage debt, automobile
debt, and durable goods debt -- were found to be significantly related to the likelihood
of experiencing debt repayment difficulty.
In Getter‘s (2003) study, the ratio of monthly payments to monthly income
was used because it is considered a more precise measurement of household payment
burden compared to the ratio of total outstanding debt to total annual income, a
measure commonly used in earlier studies. Getter suggested that a monthly-paymentsto-monthly-income ratio resulted in a more accurate comparison of the immediate
financial stress that households experience, while the total-debt-to-total-income ratio
was deceiving because the length of maturity and interest rates for these loans differ.
As Black and Morgan‘s (1999) study demonstrates, when borrowers are heavily
indebted, even small losses in income can trigger financial distress.
May and Tudela (2005) explored the determinants of mortgage payment
problems using data from the British Household Panel Survey (BHPS). With this data,
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they examined how both macroeconomic factors, such as interest rates and house
prices, and household-level factors, such as employment status, saving behavior,
changes in income and previous payment problems, affect the probability that a
household will meet mortgage commitments. They found that debt related variables
such as high loan-to-value ratios, past repayment problems, and carrying a
burdensome amount of unsecured debt increased the probability of mortgage payment
difficulties.
On the contrary, carrying outstanding credit card debt or large monthly
obligations might not increase future debt repayment difficulties. Lyon (2001)
reviewed factors affecting a households‘ ability to repay its debts and suggested a
different perspective regarding higher debt to disposable household income ratios and
higher ratios of debt payments to income. For example, she argued that the effect of
debt burden on household repayment problems depended substantially upon whether
a household has taken on additional debt and the distribution of that debt. It may be
that a substantial fraction of the households with large ratio of debt to household
income might hold large assets. In this case, household debt burden may not be a
significant determinant of the debt repayment problems after controlling asset
holdings.
In this vein, many lenders use borrowers‘ stock of debts relative to the flow of
household income to evaluate borrowers‘ ability to repay during the credit approval
process (Sullivan and Fisher, 1988). Sullivan and Fisher (1988) suggested that credit
quality might improve with the ratio of total debt to income since the bulk of total
debt held by households is mortgage debt and homeowners have good credit. Their
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study showed that the ratio of total debt to income negatively related to the probability
of slow or missed debt payments.
3.3
3.3.1
Racial/Ethnic Disparities in Household Variables
Household Acquisition of Credit
Probability of acquisition of household debt has been determined jointly by
credit supply and credit demand. Even though the racial/ethnic component in
household acquisition of credit has proved inconsistent, the racial/ethnic disparity in
the credit market can be elucidated by credit supply and credit demand.
Some studies attribute racial /ethnic difference in repayment performance of
household debt to issues of credit supply. Also, these studies are differentiated from
studies focusing on racial/ ethnic discrimination that might exist in the credit market
and from studies focusing on the genuine factors that creditors might consider when
they decide to grant debt to certain credit applicants. According to Canner, Gabriel
and Woolley (1991), congressional and media attention was more accorded to
redlining and racial preference in mortgage lending since the mid-1970s. Although
that controversy on racial / ethnic disparities in household acquisition of credit dates
back at least to the 1970s when a wave of Fair Lending legislation was enacted, the
debate was especially strong with the release of mortgage application data that was
collected as part of the Home Mortgage Disclosure Act (HMDA) (Canner & Smith,
1991). In particular, an initial report based on that data indicated that in 1990,
mortgage applicators from black households across the United States were denied at a
rate 2.4 times higher than applications from white households with similar incomes.
According to The HMDA, black and Hispanic applicants showed substantially higher
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denial rates than their white counterparts. Minority applicants were two to three times
more likely to be denied mortgage loans than white applicants. Even high-income
minorities in Boston were more likely to be turned down for loans than low-income
whites (Anderson & VanderHoff, 1999).
However, the HMDA data does not include household credit history or wealth
as well as other important variables that appear on loan application forms. Therefore,
many other individuals in government, the banking industry and academia have
questioned whether the HMDA data implies that lenders discriminate against
applications from racial / ethnic minorities.
Partly in response to that debate, the Boston Federal Reserve Bank conducted
a study of mortgage application denial rates in Boston (Munnell et al., 1992) using a
much wider range of loan applicant characteristics than had been previously analyzed.
An important finding of the study is that allowing for differences in wealth and credit
history of loan applicants reduces but does not eliminate race related differences in
mortgage denial rates.
Although these results provide a new perspective on the Boston mortgage
market, Jappelli (1990) argued that this study still has limitations. In particular, the
decision to apply for a loan is treated as exogenous. If instead minority applicants are
disproportionately discouraged from applying for mortgages, then the Boston study
may understate the effects of discrimination. On the other hand, if households are able
to substitute different forms of debt to offset the constraints on access to a given type
of loan, then the Boston study could overstate the effect of discrimination on the
ability of minority households to obtain credit.
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For decades public policy goals have included increasing racial / ethnic
minorities‘ access to owner occupied housing by eliminating racial discrimination in
mortgage lending (Anderson & VanderHoff, 1999) Such allegations spurred congress
to pass several laws designed to eliminate discrimination (e.g., Fair Housing
Act,1968; Equal Credit Opportunity Act, 1974; Home Mortgage Disclosure Act,
1975).
Gary Becker‘s The Economics of Discrimination (1971) provided insight for
those looking to understand discrimination in the marketplace (Blanchflower, Levine
and Zimmerman, 1998). Becker translated the notion of discrimination into financial
terms. Becker (1971) defined two types of discrimination in the marketplace:
statistical discrimination (or information-based discrimination) and prejudicial
discrimination (or preference-based discrimination). He demonstrated that statistical
discrimination occurs when lenders impose particularly strict underwriting criteria on
racial / ethnic minorities, basing these decisions on a perceived relationship between
racial / ethnic minority status and financial risk characteristics that are unobservable at
the individual level. Additionally, he presumed that prejudicial discrimination occurs
when lenders reject some loan applications from members of a minority group,
despite the expectation that these loans would have yielded positive profits. In this
case, loans granted to minorities would be more profitable, on average, than they
would be in the absence of prejudicial discrimination. Thus, Becker suggested that a
demonstrated higher profit margin on loans to minority applicants can be construed as
evidence of prejudicial discrimination in the credit market.
Cox and Jappelli (1993) found that desired debt of Blacks is not significantly
atypical or unlike that of the rest of the population. They argued that blacks receive
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discriminatory treatment from the credit market, as evidenced by their results. Also,
Yao, Gutter, and Hanna (2005) noted that race and ethnicity an indicative of hindered
access to financial markets.
Munnel, Browne, McEneaney and Tootell (1996) analyzed mortgage
application data collected by the Federal Reserve Bank in Boston. They found that
10% of white applicants‘ loan applications were rejected compared to 28% of black
applicants‘ loan applications being rejected. Even after accounting for a large number
of variables related to the creditworthiness of the borrowers such as amount of debt,
ratio of debt to income, credit history or loan characteristics, black applicants were
still 7% less likely to be granted loans. Therefore, they argued that racial / ethnic
discrimination is prevalent in the mortgage market.
Blanchflower et al. (1998) compared the success rates of credit applications
from white and non-white owners of small businesses. Using data from the 1993
National Survey of Small Business Finances, the authors examined whether the loan
requests made by minority-owned firms were more likely to be denied, taking into
consideration any differences between white-owned and minority-owned businesses.
Race and ethnicity were divided into four groups: white, black, Hispanic and other.
The authors found strong statistical evidence of racial / ethnic differences in the
market for small business credit. Even after accounting for the obvious differences in
creditworthiness, supplementary characteristics of firms, and educational
qualifications of the owners, black-owned firms remained more likely to have loan
requests denied than their white counterparts. Similiarly Cavalluzo et al. (2002) also
found higher rates of rejection among minority-owned businesses looking to borrow,
as compared with otherwise equivalent white-owned businesses.
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Bostic and Lampani (1999) explored whether the effect of small businesses‘
local geography had been inappropriately omitted from earlier analyses of variation in
the credit experiences of white-owned and minority-owned firms. Minority-owned
firms, in their sample, were approved for loans less frequently than white-owned
firms. However, the data indicated that these firms also had fewer assets, poorer credit
histories, and other qualities that made them appear more risky to prospective lenders.
After controlling for firm, owner, loan and banking market characteristics, Bostic and
Lampani found a statistically significant disparity between the approval rates of
white-owned and black-owned firms, but none between the approval rates of whiteowned firms and firms owned by Asians and Hispanics. More importantly, these
results suggest that the economic and demographic characteristics of a firm‘s local
geography must be considered in pursuit of a more accurate assessment of racial
disparities and complex understanding of the underlying causes.
On the other hand, Bostic and Lampani (1999) attributed racial disparities in
loan approval to the genuine creditworthiness of applicants, rather than racial
discrimination.
In sum, racial / ethnic minorities are found to be significantly less likely to
incur household debt, even after controlling for education, income and financial
wealth (Jappelli, 1990; Cox & Jappelli, 1993; Crook, 1996). This may be founded in
the fact that there is more limited targeting of credit to racial / ethnic minorities by
credit issuers, which may be the predominant factor behind this result in the supply
needs(Carol & Michael, 2006). Or it might be due to the possible racial / ethnic
discrimination in the credit market.
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On the contrary, other studies attributed racial / ethnic difference in repayment
performance of household debt to credit demand.
Jappelli (1990) defined credit-constrained individuals as those who had
requests for credit rejected by financial institutions. Jappelli assumed that non-white
individuals have a lower level of desired borrowing, owing to a lower level of
expected future income. On the other hand, it is assumed that non-white individuals
are more severely rationed by lenders due to discriminatory lending practices, and so
the net impact of expected future income on credit constraint is difficult to discern.
Ultimately, Jappelli found that non-white credit applicants demanded lower amounts
of credit than comparable white applicants. He interpreted this as indicative of extant
discrimination in credit market.
Crook (1996) assumed that individuals from different racial backgrounds may
have different propensities to consume and may be discriminated against in the credit
market. Crook found that the probability that a household is credit-rationed increases
if the head of household is Black or American Indian/Eskimo/Aleut/Asian rather than
white. The effect of being Hispanic rather than white was not statistically significant.
However, Crook (1996) did not interpret these results as indicative that credit
suppliers are rejecting or discouraging applicants just because of the applicants‘ race.
This stems from the fact that the model relates to the probability that a household's
demand for credit exceeds its supply, not just the probability that the household will
be supplied. Therefore, Crook expected that the findings result from certain racial /
ethnic groups having a greater demand for credit, relative to their ability to repay (as
determined according to factors other than race), compared to other racial / ethnic
groups. An alternative explanation is that lenders are rejecting or discouraging credit
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applicants, not on the basis of race and ethnicity, but on the basis of variables
correlated with race and ethnicity. Such an alternative explanation was not supported
because the linear correlation coefficient between any race indicator and any other
non-race indicator was not significant.
In summation, studies provide conflicting interpretation of the existence of
racial / ethnic differences in the access to credit. A number of studies (Jappelli, 1990;
Munnel et.al., 1996; Blanchflower et al., 1998; Cavalluzo et al., 2002) concluded that
the evidenced difference would traditionally be attributed to discrimination. These
authors argued that racial / ethnic minority borrowers, more so than their white
counterparts, face unwarranted obstacles in obtaining credit or loans.
Conversely, Jappelli (1990) and Crook (1996) focused on racial and ethnic
disparities in the credit demand in order to explain the parallel racial and ethnic
disparities in acquisition of household debt. If future prospects for racial and ethnic
minorities were pessimistic, this would tend to discourage racial and ethnic
minorities‘ current spending and influence assumption that they will repay their debt
later.
3.3.2
Repayment of Household Debt
Canner, Gabriel and Woolley (1991) stated that analysis focusing only on
rejection rates of credit access cannot draw definitive conclusions about the existence
of racial and ethnic discrimination, even though differences in neighborhood lending
patterns may be an indicative of racial and ethnic bias in mortgage lending. They
defined a minority as a household head who is black or Hispanic. They suggested a
model of mortgage loan allocation based on default risk and tested any remaining
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effects associated with race and ethnicity or racial and ethnic composition of
neighborhood. Using a bivariate logistic model of household loan delinquency, they
found that loan delinquency was more likely for households headed by minority
individuals. Additionally, they found that unemployed households, households
receiving government assistance, separated or divorced households, and households
with many children were more likely to be delinquent on loan repayment.
Most studies showed the racial / ethnic minority group had higher risk of
payment problems than otherwise comparable whites. Anderson and VanderHoff
(1999) pointed out that the HMDA data did not include information on credit histories,
loan-to-value ratios or other factors frequently considered in mortgage decisions.
Therefore, they suggested that much of the literature instead focuses on default rates
among approved borrowers and argued that the presence of racial / ethnic
discrimination can be evidenced by lower default rates for minorities.
Martin and Hill (2000) also argued that the empirical question of statistical
basis for racial discrimination cannot be adequately answered by loan approval
studies. Rather, they suggested evaluating the loan repayment performance in the
period after loan approval.
Berkovec, Canner, Gabriel and Hannan (1994) tested a default probability
model using logistic regression and estimated the proportional relevance of the loan
characteristics, neighborhood characteristics, and borrower characteristics to the
likelihood of default. Dummy variables indicating that the borrower is African
American, American Indian or Alaskan Native, Asian or Hispanic were included, with
whites representing the reference group. In particular, they found black borrowers
exhibited a higher likelihood of default than did white borrowers. Aside from blacks,
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no other racial or ethnic group was found to significantly differ from whites in their
default likelihood, even controlling other factors. They concluded that racial and
ethnic minority borrower loans tend to be less profitable.
Anderson and VanderHoff (1999) created a mortgage default model using
national data on conventional mortgages that were current from 1986 and 1992. They
included those measures of default options and borrower characteristics that were
expected to affect default rates and transaction costs. Their default probability model
illustrated that the marginal default rate for black households was significantly higher
than that of white households, even when controlling for equity, borrowers‘ age,
borrowers‘ education level and the total number of dependents in the household.
On the contrary, a few exceptional studies showed that racial / ethnic minority
groups had lower risk of payment problems. Quercian, McCarthy and Stegman (1995)
examined the effect of borrower related factors, equity, and cash flow on the default
decision among low income rural borrowers using panel data from annual surveys
over a six-year period. They compared minorities including African American,
Hispanics or other nonwhite groups with white borrowers. They found that racial and
ethnic minorities showed a lower risk of default than nonminority borrowers. They
interpreted this result to imply that racial / ethnic minority applicants are subjected to
stricter standards than their nonminority counterparts at the time of loan approval
process due to mortgage lending discrimination.
Ambrose and Capone (1998) examined the likelihood of various default
resolution options so as to gain a deeper appreciation for the processes underlying
mortgage foreclosure using data on FHA-insured mortgages. They found that
minorities have higher probabilities of reinstatement and lower probabilities of
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foreclosure than whites. They interpreted their results to indicate that minority
borrowers may view their current mortgage as having greater value than white
borrowers due to perceived costs of obtaining new credit. Thus, they suggested that
trigger-event-induced minority borrowers might have a greater incentive to reinstate
their mortgage than white borrowers. Further, they attributed these results to the fact
that minorities have difficulty obtaining credit and, therefore, work harder to maintain
it once it is acquired. Perhaps it may also be true that minority defaulters with high
loan-to-value loans are more likely to be trigger-event defaulters than are similarly
situated white borrowers.
Only a few studies discussed the possible cause of racial / ethnic difference in
debt repayment. Two exceptional studies were conducted by Fredericks et al (1998)
and Ross and Yinger (2003). Fredericks et al (1998) examined the apparent causes of
default and repayment behavior among white and minority populations. They
conducted bivariate analyses for each of five populations: African Americans, Native
Americans, Asians, Hispanics and Whites. They found that the dominant factors
contributing to loan default among white and minority populations differed more in
degree than in type, although default rates varied widely among racial / ethnic groups.
In other words, the variables that influenced loan defaults held constant across white
and minority populations, but their extent of these variables‘ influence differed in
accordance with race and ethnicity. In their study, gender and marital status were
consistently correlated with default behavior, but being female and being married
lowered the default rate more dramatically for non-white borrowers than for white
borrowers. Similarly, having a parent who attended college, having completed a
degree, being married, and not having dependent children were all factors that
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lowered the likelihood of default and increased the likelihood of repayment. These
factors had the strongest impact on African-American borrowers. Blacks and
Hispanics in this study, as compared with whites, demonstrated lower levels of degree
attainment, lower levels of academic achievement, almost twice the number of
dependent children, and almost twice the rate of separation and divorce.
More recently, Ross and Yinger (2003) expected that unobserved variables
could lead to higher default rates among minorities, even after accounting for these
observed variables. For example, they concluded that minorities may have higher
average default rates and they suggested that unexpected economic downturns may
have harsher effects on minorities, owing to discrimination in labor markets. Also,
they attributed these results to the fact that minority applicants are more likely to have
larger debt burdens, higher loan-to-value ratios, and poorer credit histories than white
applicants on average. In part, Getter supported Ross and Yinger (2003) in attributing
higher delinquency risks for black and Hispanic borrowers to the negative income
stressors that were reported by a large percentage of surveyed participants.
In sum, previous literature has long suggested that there exist racial / ethnic
disparities in debt repayment performance. They have operated on the assumption of
higher likelihoods of debt payment delinquency for those households headed by black
or minority individuals. However, a rigorous analysis of payment performance with
attention to the different races and ethnicities of respondents is lacking. Many studies
on household debt payment include race and ethnicity along with other key
explanatory variables, but the racial / ethnic classification is inconsistent across the
literature. These studies compared households headed by whites or white individuals
with household headed by minorities, conflating several racial / ethnic groups rather
94
than distinguishing between data on black Americans, Hispanic Americans, Asian and
those individuals choosing other racial / ethnic groups.
These studies compared aggregated minority groups with the white group
partly because in the same datasets there are few households in the same minority
categories. However, empirical results reported in aggregated terms could cause credit
or debt suppliers to consider debt applicants from racial / ethnic minorities as a
homogeneous, undifferentiated whole. That is, the overall characteristics of the
minority group might be disproportionately factors of the individual characteristics
that a particular minority might have, when he/she applies for credit/debt. Since
collective statistics about minority groups are often negative, as compared to the
majority groups‘ aggregate statistics, some borrowers from racial / ethnic minorities
could experience adverse treatment when they apply for credit.
The employment of such a broad grouping may not prove the most accurate
strategy in examining racial / ethnic variances in household debt payment data. Yao,
Gutter, and Hanna (2005), examining the effect of race and ethnicity on financial risk
tolerance, suggest that race and ethnicity may be not only representative of cultural
background but also an indicator of hindered access to financial markets.
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3.3.3
Selected Other Characteristics
The discussion below suggests culture may play a significant role in the
attitudes toward money and financial practices.
3.3.3.1 Psychological Characteristics
This chapter reviews the literature on family financial management in the two
largest minority groups in the US – African Americans and Hispanics. Findings in this
literature review suggest that there are differences in the way selected racial and
ethnic groups manage money.
A review of the existing literature suggests that there is little documentation of
the financial management behaviors of Americans who are members of the two
largest current minority groups (i.e., Hispanics and African Americans). However,
since 1991, there has been a noticeable increase in the literature on ethnic and racial
minority groups (Bowen and Lago, 1997). Although few in numbers, these studies
provide preliminary evidence that there are differences in the way various ethnic and
racial groups handle money. Families with low incomes were more likely than middle
income families to have difficulty saving, to discuss family finances openly in their
children's presence, not to use formal banking services, and not to search for product
information before buying. Among the ethnic and racial groups, differences were
noted in savings patterns, investment practices, use of credit, family money
management and the financial socialization of children.
The United States is a multicultural country where the population of ethnic
minorities, particularly Hispanics, is becoming more visible (Xiao, 2008). The
majority of studies regarding the effect of traditional socioeconomic, psychological
96
factors and consequences of financial management have focused on the dominant U.S.
racial population (e.g., Bowen & Lago, 1997; Medina & Chau, 1998; Zhou & Su,
2000). Therefore, research on the financial behavior of racial/ethnic minorities has
been limited.
The Hispanic group is the largest and most rapidly growing ethnic minority in
the United States. This group has promising levels of spending power that can
influence the U.S economy (U.S. Census Bureau, 2000). In terms of Hispanics‘
financial practices, Xiao (2008) suggested to consider Hispanics‘ overall lower level
of education and their low educational attainment and their reluctance to engage in
long-term financial management are important consideration.
Barajas (2003) demonstrated compelling evidence of significant differences in
financial attitudes of Mexican American individuals compared to Anglo individuals.
This study identifies ten barriers which are related to the cultural beliefs and an
ingrained philosophy about money. These ten barriers are ―Depending on others to
take care of you,‖ ―Storing rather than investing money,‖ ―Consulting experts,‖ ―The
trap of informality,‖ ―More ego can mean less money,‖ ―Scarcity and Abundance,‖
―A Divine Excuse for Doing Nothing,‖ ―Getting Something for Nothing,‖ ―The Pain
of Procrastination,‖ and ―Conflicting Beliefs and Attitudes about Money.‖ These
barriers often place constraints on individual financial success within the minority
groups. Literature on the financial attitudes and habit of Latinos, specifically those of
Mexican American heritage supports several barriers. Acknowledging that the sample
of Mexican American participants in their study would perhaps not correspond
exactly the entire Mexican American population, Medina, Saegert and Gresham
(1996) argued that Mexican American participants‘ money practices can be explained
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by the barrier reflecting ―The Pain of Procrastination.‖ From their results, the authors
suggest Mexican Americans are more likely to credit cards and personal debt for their
immediate needs at the expense of long term planning. The authors assert Mexican
Americans are less likely to engage in behaviors related to medium to long-term
personal gain such as ―savings, investing and speculating with money at the expense
of present consumption.‖ Consistent with findings by Barajas (2003), Medina et al.
(1996) also argued that Mexican Americans are less likely to engage in long-term
financial planning.
3.3.3.2 Economic Characteristics
Segments of the population likely to experience financial problems are
households headed by single female parents. Female-headed households is low on
average since there is only one income earner and women‘s earnings in the labor
market is relatively low (Tickamyer & Bokemeier 1988; Ellwood, 2000; Blank, 2002).
Family structure, especially the proportion of female headed families, has been
highlighted as an important factor that contributes to higher poverty rates among
racial and ethnic minority groups (Eggebeen & Lichter 1991). Snyder and
McLaughlin (2004) showed that black and Hispanic female-headed households with
children are 2.4 and 1.7 times more likely to be poor than their white female-headed
households even after controlling for individual characteristics. Households headed by
single female parents are more common in selected racial and ethnic groups. In the
US, the percentage of female-headed households was higher among Blacks and
Hispanics than among Asians (Myers, 1991). Snyder, McLaughlin and Findeis
(2006) examine race of female-headed households with children and how household
98
composition is associated with family economic well-being using data from the 2000
5% Public Use Micro data Sample of the U.S. Census. They found that poverty rate
is highest among racial/ethnic minority groups and for female-headed households
with children in non-metro areas than central cities and suburban areas.
McLaughlin and Sachs (1988) and Snyder and McLaughlin (2004) conclude
that these differences suggest the effect of race/ethnicity on economic well-being. A
sizable percentage of all households hold financial assets, but the amount of financial
assets differs widely across racial/ethnic groups. Several studies on household wealth
have reported that financial wealth is more heavily concentrated in white households
than in racial/ethnic minority groups in the U.S (Wolff, 2000)
Working-age adults who are Hispanics or blacks in the United States are at
greater risk of experiencing gaps in insurance coverage. In addition, these groups
often lack access to health care and face more medical debt than white working-age
adults, according to The Nation's Health report (2006). In 2005, Hispanics had the
highest rate of uninsured people among all working-age adults. Nearly two-thirds
(62%) of Hispanic adults, approximately 15 million, were uninsured at some point
during 2005. Approximately 50% of Hispanics reported they were currently uninsured,
while another 14 percent said they were insured but experienced a gap in their
coverage during the year (Doty & Holmgren, 2005). The high rate of uninsured
people among Hispanic adults is partly due to relatively low rates of employersponsored and public insurance (Carillo, Trevino, Betancourt, & Coustasse, 2001).
Only 34 percent of working-age Hispanics had employer-sponsored coverage in 2005,
even though two-thirds have either full or part time jobs. Employment rates among
Hispanic adults approach those of whites. However, Hispanic workers are more
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likely to be employed by companies which do not provide health benefits (Quinn,
2000).
Mahon (2006) demonstrates Hispanic and African-American Adults Face
Healthcare Gap in the report titled ―Health Care Disconnect: Gaps in Coverage and
Care for Minority Adults.‖ Some of findings from the report is presented below.In
general low-income people have high uninsured rates, but rates are especially high
among low-income Hispanics. Approximately 76% of Hispanics with income under
200 percent of the Federal poverty level, which is twice the poverty level is $38,700
for a family of four, lacked insurance coverage during the year. This estimate is
compared with 46 % of low-income white adults. One reason for the high uninsured
rate for low-income Hispanics is that, as a group, they are less likely to be covered by
public insurance such as Medicaid/ Medicare. Only 15 % of low-income Hispanic
adults surveyed had government provided medicare, whereas 21 percent of white
adults did. But even at higher income levels, Hispanic adults have significantly higher
uninsured rates than white or African American adults. Among adults with income
equal to or exceeding twice the poverty level, 40 % of Hispanics were uninsured
during 2005. This estimate can be compared with 23 % of African Americans and
12 % of whites.
African American adults also have significantly higher uninsured rates than
white adults. Approximately 33% of African American adults—more than 6 million
people—reported they were uninsured in 2005, compared with 20% white adults. The
high uninsured rate among African Americans can be partly explained by their low
rates of employer-sponsored coverage relative to whites. Only 53 % of working-age
African Americans has health insurance coverage through their own employer or that
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of a family member. This estimate is lowered than that of white working-age adults
(71%). In comparing low-income African Americans and their white counterparts,
uninsured rates are nearly the same, however. Among adults with income below
200 % of the poverty level, 44 % of low income African Americans and 46 % of low
income white adults lacked coverage during the year. Although a smaller proportion
of low income African Americans have employer-sponsored insurance coverage,
public insurance coverage among African Americans is significantly higher than it is
for white adults.
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CHAPTER 4
4
4.1
METHOD
DATA
This section identifies and describes existing data about household debt. First,
this section compares those specific data structures, administrative data and survey
data, which have been used in previous household credit studies. The strengths and
weaknesses of both structures for the purposes of testing the hypotheses are discussed.
Finally, the Survey of Consumer Finances (SCF) used in this study will be described
in detail.
4.1.1
Administrative Data and Survey Data
Data on household credit can be obtained from both the supply and the
demand information. Supply data, obtained from credit suppliers or credit bureaus,
contains the relevant information needed by the supplier in order to make an approval
or rejection decision with respect to a credit application (Bertola & Hochguertel,
2005). Such data can provide information as to whether credit applications are denied;
if not, what amount of credit is issued; and whether the debt is repaid. Since these
databases contain millions of observations, the cross-sectional size of this data allows
accurate statistical inference (Bertola & Hochguertel, 2005). Therefore, administrative
102
data could serve as a statistically precise source for studying credit issues in
terms of tracking credit histories and credit repayment behavior.
Two broadly used sources for administrative data are the Consumer Credit
Delinquency Bulletin and the Visa Survey. In Lawrence‘s (1997) article, these two
data sets are described. Each quarter since the first quarter of 1973, the American
Bankers Association has conducted a survey of over 500 banks nationwide in which
the banks report the percentage of bank card accounts with positive outstanding
balances that are past due thirty or more days at the end of each month. The
Association regularly publishes these numbers in the Consumer Credit Delinquency
Bulletin (Lawrence, 1997). In a related initiative, the Visa Survey gathers information
regarding the major components of revenues and costs associated with bank card
operations (Lawrence, 1997).
Both of these sources have numerous uses, but it must be noted that
administrative data can be lacking when it comes to testing the particular hypothesis
in this study. First, administrative data can prove insufficient in providing insight into
the personal circumstances of credit applicants. Another element missing from
existing administrative data is the complete balance sheets of individuals. For
example, credit-scoring databases may contain information about the level of
outstanding debt held by credit card holders, whereas assets remain unobserved
(Bertola & Hochguertel, 2005). Therefore, it might be difficult to capture an accurate
understanding of the complete financial burden households carry, as well as how
households respond to adverse events that affect household finances. Also,
information on household demographics may be inaccurate in administrative data,
especially when an applicant may believe there is benefit in supplying biased
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information (Crook, 2005). Finally, the data obtained from lenders are lacking in
information on race and ethnicity, because it is illegal to include such information in
their data. It is the most significant shortcoming of administrative data for the current
study.
On the other hand, appropriately structured surveys can offer a variety of
covariates and make useful information available to empirical researchers (Bertola &
Hochguertel, 2005). Such data provides personal characteristics that correlate with the
demand for and availability of credit. For example, household characteristics such as
family arrangement or the number of dependents in the household can be examined.
Whether collected for research or official statistical purposes, survey data will offer
useful covariates for testing the aforementioned hypotheses. Next, one survey data
source, the Survey of consumer Finances (SCF) is described, in detail.
4.1.2
Survey of Consumer Finances
One source for survey data on household credit that is widely used is the US
Survey of Consumer Finances (SCF). This is a cross-sectional survey sponsored every
three years by the Board of Governors of the Federal Reserve System; it provides
detailed information on the finances of American families (Bucks, Kennickell, Mach,
& Moore, 2009). The Survey of Consumer Finances contains information on credit
and debit use combined with rich detail on household characteristics and financial
attitudes (Zinman, 2004), as well as reportage on the demand for credit and
transactions. Overall, it is most effective and appropriate for testing my hypothesis.
The SCF allows for the examining of household credit issues by drawing from
its extensive reporting of both household balance sheets and households‘ financial
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services usage. This has been the case since the publication of the 1983 SCF, when
most information about delinquent credit card payment came from lenders (Bertaut &
Haliassos, 2005). The use of the household-level data, such as SCF, offers several
important advantages for studying the role of race in debt repayment difficulties. In
this particular case, it can illuminate the stability of the payment delinquency model,
and SCF data proves especially useful in testing the hypotheses in a number of
distinct ways.
First, the SCF contains a broad array of household characteristics. Because
this study attempts to test whether a given household will be more likely to become
delinquent on household debt repayment if the survey respondent is non-White, the
model in this study must be precise in its measurement of race and ethnicity.
Second, the survey providies crucial information related to household debt.
Each wave of the SCF provides detailed information on the sources, terms, and uses
of a wide range of consumer credit options (Bertaut & Haliassos, 2005). It might seem
that a more ideal data set for assessing consumers‘ credit payment reliability and
access to credit should include scores from the credit bureau. Fortunately, although it
does not contain actual credit score data, the SCF does offer most of the important
predictors of credit scores (Edelberg, 2007). Specifically, the SCF determines levels
of indebtedness and information on credit payment performance. For example, it
provides information on individuals‘ characteristics such as income, liabilities, and
assets through complete balance sheets. The SCF asks respondents to detail their
household credit payment performance, a question that allows me to measure
respondents‘ payment delinquency and estimate the degree to which race affects the
likelihood its occurrence. The SCF allows one to test whether or not all the
105
respondents‘ loan or mortgage payments were made as scheduled and whether or not
respondents were ever behind in their payments by two months or more. The answers
to these questions can then be used as proxies for a debtor‘s payment performance
(Charles et al., 2006).
Third, as discussed earlier, unexpected changes in individuals‘ circumstances
are widely recognized as contributing significantly to a negative triggering effect on
household debt payment (Canner & Luckett, 1990; Springer & Waller, 1993; Quercia,
McCarthy, & Stegman, 1995; Gross & Souleles, 2002; Dominy & Kempson, 2003;
Getter, 2003; Reeder, 2004; Lyons, 2004; Avery, Calem, & Canner, 2004). It is
noteworthy that the studies using cross-sectional data have been criticized for being
conducted in static or comparative static contexts (OXERA, 2004). Also, a ‗snapshot‘
from a particular point in time cannot adequately address debt issues that may arise
over an extended period (Bridges & Disney, 2004). However, a key measure of
financial shock occurrence is the SCF question asking whether or not last year‘s
income was approximately what the respondent had anticipated it to be, and, if not,
whether it was higher or lower than expected. This question allows respondents to be
grouped according to whether or not their particular income level was unusually low.
In Getter‘s study (2003), this question is found to be the best available from the SCF
for creating a comparative static study that captures the effects of a negative shock on
income. Additionally, the demographic variables represented in the SCF, such as
employment and marital status, are useful in that several studies on consumer credit
proxy for trigger events such as divorce and unemployment (Cappoza, Kazarian, &
Thomas, 1997, 1998). In the current study, financially negative trigger events, such as
106
unexpected decreases in annual income or poor health status, are assumed to increase
the probability of payment delinquency.
Finally, even though several studies (Godwin, 1999; Lyons, 2004, Getter,
2003, Dunn & Kim, 1999) comprehensively examine the relationship of household
indebtedness to payment delinquency, they show mixed findings due to inconsistent
measures of indebtedness. For example, in the studies undertaken by Godwin (1999)
and Getter (2003), the level of indebtedness does not appear to be significantly related
to future debt repayment difficulties. However, in Lyons‘ (2004) research, those with
debt in excess of $1,000 are more likely to be delinquent on credit card payments.
Fortunately, the SCF allows for the computation of the three ratios most commonly
used in previous studies on credit payment: the debt ratio, the debt payment ratio, and
the mortgage debt service ratio (Bajtelsmit, 2006). These ratios might be good proxies
of household indebtedness when analyzing credit card payment behavior.
In sum, the SCF is particularly well suited for testing the hypothesis in this
study. This data set proves superior to those utilized for analysis based on lender data
or administrative data. The Survey of Consumer Finances (SCF), then, is the best
choice when analyzing the effect of race, as well as other household characteristics
(trigger events, financial buffers, debt-related variables), on the likelihood of
household debt payment delinquency. In addition, as Yao, Gutter, and Hanna (2005)
did, this study uses multiple years of the Survey of Consumer Finances (SCF) in order
to increase the sample size of the different racial and ethnic groups, allowing for a
stronger assessment of the effects of race and ethnicity on household debt payment.
It is noteworthy that only a few studies have identified racial/ethnic status as a
predictor of credit held. This is partially due to a lack of data on both direct measures
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of credit constraints and on potentially explanatory variables. Credit grantors are not
allowed under the Equal Credit Opportunities Acts 1974 and 1976 to ask questions
regarding age, race and certain other characteristics.
4.1.3
Analytical Sample
The five SCFs from 1992 to 2007 are combined in this study to obtain an
adequate sample size because the number of debt delinquencies for any single survey
year is relatively low for our econometric estimates. This study expects that
combining four datasets permits one to obtain meaningful estimates on the
determinants of payment delinquency of household debt and increase the reliability of
descriptive and multivariate tests.
All analyses are in reference to a household during 1992 to 2007. 25,889
respondents who were interviewed from 1992 to 2007 are used in this study. For the
1992 survey, 3,906 families were interviewed (Kennickell, Starr-McCluer, & Sunden,,
1997). In the 1995 survey, 4,299 families were interviewed (Kennickell, StarrMcCluer, & Surette, 2000). In the 1998 survey, 4,305 families were interviewed, and
in the 2001 survey, 4,449 were interviewed (Bucks, Kennickell, & Moore, 2006). In
the 2004 survey, 4,519 were interviewed and in the 2007 survey, 4,418 families were
interviewed (Bucks, Kennickell, Mach, & Moore, 2009). The SCF coded some
respondents as different racial/ ethnic identification in different implicates (Hanna &
Lindamood, 2008). Since the racial and ethnic identifications are critical in this study,
this study deleted from the sample those households that did not have the same racial
and ethnic categories in all five implicates.
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4.2
Variable Identification
4.2.1
Dependent Variables
4.2.1.1 Holding Household Debt
Two dependent variables are included in my model with the consideration of
sample selection effects (discussed below). In the first equation, the dependent
variable is whether or not a household has household debt. This study measures
holding household debt and repayment of household debt using the following two
SCF questions. Also of note is the fact that the research model operates under the
assumption that respondents understand the combined question to refer to all
payments on mortgage, home equity, installment, credit cards, and any other
outstanding loans. By doing so, this study aims to capture a comprehensive picture of
the overall payment difficulties of American households (Bucks, Kennickell, Mach, &
Moore, 2009). Therefore, this study uses the term ―household debt‖ rather than
―consumer debt,‖ which excludes home mortgage.
X3004 Now thinking of all the various loan or mortgage payments you made
during the last year, were all the payments made the way they were scheduled,
or were payments on any of the loans sometimes made later or missed?
1. All paid as scheduled or ahead of schedule
5. Sometimes got behind or missed payments
0. Inapplicable
X3005 Were you ever behind in your payments by two months or more?
1. Yes
5. No
0. Inapplicable
109
If a respondent answered, ―All paid as scheduled or ahead of schedule,‖ or,
―Sometimes got behind or missed payments‖ to X3004, he/she was considered to
have household debt. If a respondent answered ―Inapplicable‖ both under X3004
question and X3005 question, he/she was considered to have no household debt
incurred.
4.2.1.2 Repayment of Household Debt
If a respondent answered, ―All paid as scheduled or ahead of schedule,‖ under
X3004 question and ―Inapplicable‖ under X3005 question, he/she was considered to
have made payments as scheduled. If a respondent answered, ―Sometimes got behind
or missed payments‖ under X3004 question and ―YES‖ under X3005 question, he/she
was considered to make his/her payment late by two months or more (e.g. more than
60 days). If a respondent answered, ―Sometimes got behind or missed payments‖
under X3004 question and ―NO‖ under X3005 question, he/she was considered to
make his/her payment late by less than one month.
These two questions allowed for distinction between those who experienced
serious repayment delinquency by two months and those who only missed a payment
occasionally due to temporary financial mishaps or forgetting to mail a payment. To
adhere to the proposed statistical model, each household was grouped into one of the
four payment performance tiers, such as scheduled repayment, delinquent repayment
less than two months, severely delinquent payment more than two months, and no
household debt.
110
4.2.2
Independent Variables
This study selected variables on the basis of the theoretical relevance to the
research literature discussed above. Independent variables were chosen based on the
Life Cycle Theory and Cash Flow theory, as well as previously gathered empirical
results. Unless otherwise stated, variables were constructed from corresponding
questions asked in the 1992-2007 Survey of Consumer Finances. The independent
variables used in this model are comprised of demographic variables, economic
variables, financially adversities, financial buffers, and household debt burden
highlighted in the debt repayment literature. Other variables are described in Table 4.1.
4.2.2.1 Race/Ethnicity
A number of studies (Godwin, 1999; Canner, Gabriel, & Wolley, 1991; May
& Tudela, 2005)
have come to the conclusion that, controlling for other variables,
racial and ethnic minorities are more likely to experience difficulty with debt
repayment than otherwise similar whites.
Only a few studies have come up with more complex models that used diverse
racial and ethnic categories. For example, Martin and Hill (2000) divided borrowers
into white (non-Hispanic), white Hispanic, and minority. Getter (2003) divided racial
and ethnic categories into white, black, and Hispanic. In keeping with earlier research,
this study demonstrated that households with black or Hispanic family heads were
more likely to have late or missed payments. Lyons (2004), who categorized race into
white, black, Hispanic, and Asian, found that black and Hispanic students are
significantly more likely to be delinquent on payments than white students, with black
individuals significantly more likely to be delinquent on credit card payments by two
111
months or more. The relationship of race and ethnicity to debt payment performance
is relatively consistent across studies, though methods of racial and ethnic
classification differ. Most studies have arrived at this conclusion by separating
borrowers into white and non-white (or minority) categories. In light of this, it can be
assumed that the utilization of more detailed racial/ethnic categories might yield
additional insight.
This study used the same classification system of racial/ethnic categories as
the SCF. Each respondent was asked, ―Which of these categories do you feel best
describe you?‖ when shown a card listing racial/ethnic groups. Six racial/ethnic
categories were presented to respondents, but in the public dataset, the SCF combines
Asian, American Indian, Alaska Native, Native Hawaiian, Other Pacific Islander, and
Other into a single category ―others.‖ The current study, by contrast, delineated four
racial and ethnic categories: white, black, Hispanic, and others. For purposes of
analysis, this study created dummy variables for each, with the largest category, white,
serving as the reference group. The aforementioned ‗others‘ includes American
Indians, Alaskan Natives, Native Hawaiians, and members of other ethnic groups not
specified. The majority of such households are most likely to be Asians. Therefore,
this group is called ‗Asians/others.‘
4.2.2.2 Financially Adverse Events
In order to study the relationship between financially adverse events and debt
repayment, data including the incidence rate of these events among borrowers and
how households deal with these events is needed. As Reeder (2004) noted, most
studies of mortgage performance have relied on data from mortgage servicers that
112
only contain whether and when a default or prepayment occurred and the
characteristics of the borrower and the loan at origination. Also, cross-sectional data
lacks specific information regarding the time of the event. Therefore, the point at
which a financial event began might be obscured.
However, under this data limitation, Getter (2003) used 1998 SCF question,
“Is your 1997 income unusually high or low compared to what you would expect in a
normal year?‖ and showed that this survey question was the most effective for use in
performing a comparative static exercise to capture the effect of a negative shock on
income. Therefore, the current study has measured the incidence of financial shock
with a similar survey question.
Transitory Income
This study used the following question from the SCF, ―Is this income
unusually high or low compared to what you would expect in a "normal" year, or is it
normal?‖ This tactic allows grouping according to whether or not their income was
unusually low. The possible answers to this question were ―high,‖ ―low,‖ or ―normal.‖
If a respondent reported that this income was unusually high to what he/she would
expect in a normal year, the dichotomous variable unusually high was coded as 1, and
0 otherwise. If a respondent reported that this income was unusually low to what
he/she would expect in a normal year, the dichotomous variable unusually low was
coded as 1, and 0 otherwise. If a respondent reported that this income was normal, the
dichotomous variable normal was coded as 1, and 0 otherwise.
113
Health Condition
Health conditions were assessed using the SCF survey question, ―Would you
say your health is excellent, good, fair, or poor?‖ If a household head reported poor
health, the dichotomous variable poor was coded as 1. If a household head reported
excellent, good or fair, the variable of health conditions was coded as 0.
4.2.2.3 Financial Buffers
Financial buffers can lessen the impacts of financial shocks on borrowers‘
repayment of their household debt. Some borrowers will successfully respond to
unexpected financial shocks and continue to keep their regular repayment schedules
by relying on financial buffers such as net worth or health insurance. Conversely,
other households may not be able to successfully negotiate those financial events.
Net Worth
In this study, net worth was calculated by the difference between families‘
gross assets and their liabilities. Here, the log of net worth was used to allow a nonlinear relationship between repayment delinquency and net worth since money-based
variables are more prone to give rise to nonlinear relationship than any other variable
(Cohen, et al., 2003).
Health Insurance Coverage
Health insurance coverage was measured by a SCF question asking whether
members in the households were able to receive benefits from government health
insurance programs, such as Medicare, Medicaid, CHIP (Children's Health Insurance
114
Program) Tri-Care, VA, or other military programs. If all members in a given
household were covered by such programs, the dichotomous variable equals 1. If not,
it equals 0.
4.2.2.4 Household Debt Burden
Debt Service Ratio
Controlling for other characteristics such as household income and wealth,
bigger loans are generally considered riskier than smaller ones. The model in this
study included debt service ratio, which is a primary measure employed by the
Federal Reserve to assess the level of indebtedness of American households and to
provide a view of the economic health of the overall household sector (The Credit
Union National Association, 2004). The debt service ratio measures the share of
income committed by households for paying interest and principal on their debts. The
high ratio reflects that households have less money available to purchase goods or
services, and households with high debt service ratio are more likely to make debt
repayment problems when they experience financial adversities such as employment
status problems or health problems.
Under the Cash Flow Theory, the repayment capability of the borrower, which
is measured by the monthly repayment obligations as a percentage of current monthly
income, plays a critical role in accounting for defaults. The monthly payments-tomonthly income ratio employed in this dissertation comprises all the monthly debt
obligations of the household including all mortgages, home equity, installments,
credit cards, personal loans, education, and any other monthly debt obligations.
As Getter (2003) noted, this debt service ratio captures only the payment
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obligations that can be obtained from the Survey Consumer Finances but not all of the
payment obligations a household may have can be observed. Nevertheless, it is
assumed that this variable is the best available measure of the approximation of the
ratio of payment obligations to income in this study.
The household debt burden is based on data from the Survey of Consumer
Finances. The ratio is calculated by total monthly payments on debt and monthly
household income. The total monthly payments on debt includes both interest and
principal payments on debt. The total monthly payments on debt and monthly
household income are readily available in the Survey of Consumer Finances public
use micro-data file available on the Federal Reserve Board Website. More detailed
information on total household payment and gross household income is shown in
Appendix.
4.3
Statistical Method
This section identifies two statistical approaches that could be used to test the
potential correlations between race and ethnicity and access to household debt as well
as race/ethnicity and repayment performance of household debt. These approaches
can be used to identify the statistically significant factors that affect a household‘s
probability of being delinquent on household debt. Also, this chapter identifies two
techniques. A binary logistic regression model characterizes the first approach. The
second focuses on the issue of sample selection bias.
If one attempts to test whether racial and ethnic minority borrowers are more
likely than white borrowers to become delinquent on their payments, a logistic
regression seems at first to be an adequate statistical method. This method, which
116
draws on a single equation election model, would be appropriate as long as no
systematic difference exists between those who incurred household debt and those
who did not. However, when the question of whether or not such systematic
differences exist cannot be adequately answered, a sample selection model is
preferable.
In the paragraph that follows, a logistic regression with a single equation will
be discussed first, and then the focus will turn to the type of sample selection bias that
might occur in studies on household debt repayment. Finally, this study uses a
modified Heckman‘s two-step procedure to estimate the two-equation system.
4.3.1
Single Equation Analysis: Logistic Regression Analysis
In assessing the likelihood of a household falling behind on payments, a
statistical analysis can be undertaken to ascertain which factors are related to
repayment delinquency of household debt. If one attempts to test whether a household
with particular characteristics is more likely to be delinquent on their payments than a
household with other characteristics, the standard logit or probit can be an adequate
statistical method.
Greene (1992) designed a model for consumer loan default and credit card
expenditure that was based on statistical models for discrete choice. He expects that
there is a definable probability that individuals will default on a loan. This
interpretation was applied to all individuals in a single population. The observed
outcome, default or no default, is derived from the characteristics of the individuals.
Also, the only reliable outcome that can be generated by the model is a measure of
probability. Greene‘s (1992) model for consumer loan default can be applied to the
117
payment delinquency model in this study. The dependent variable might be identified
with the propensity to delinquency.
(4.1)
it*   ' it   it
―An intuitively appealing interpretation‖ of y*it is as a quantitative measure of
―how much trouble the individuals is in.‖ The observed variable in my econometric
model, y*it is binary, taking the value one if the household i were behind their
payments by two months or more during the 12 months previous to the survey at
period t and 0 otherwise.
Where vector X includes independent variables, β is the vector of coefficients to be
estimated and εit is the error term. It is assumed that the probability of being
delinquent on their payments by two months or more is a function of four types of
independent variables: (1) demographics, (2) financially adverse events, (3) financial
buffers, and (4) household debt burden through the reduced form of a logistic
regression equation.
(4.2)
 P 
In
 = β0 + β1 X1+ β2 X2+β3 X3+β4X4+ ε
 1 P 
Where
X1 = Demographics
X2 = financially adverse events
X3 = Financial buffers
X4 = Household debt burden
If it* is sufficiently large in relation to the attributes -- that is, if the
individual is in severe trouble-- they are delinquent on their debt repayment. Formally,
118
it =1 if it*  0 and 0 otherwise,
So the probability of interest is
(4.3)
Pit = Prob [ it =1│  it ]
Assuming that εit is normally distributed with mean 0 and variance 1, one
obtains the delinquency probability
(4.4) Prob [ it =1 │  it ] = Prob [ it* > 0 │  it ] = Prob [ε ≤  '  it │  it ]=
 (  '  it )
Where  (•) is the standard normal CDF. The classification rule is
(4.5) it = 1 if  (  '  it ) > P*,
Where P*is a threshold value chosen by the researcher.
A probit or logistic binary regression is considered to be an adequate statistical
method here, as the dependent variable in this study is dichotomous. The variable can
be considered binary as long as it has a value of either 0 (making repayment of
household debt as scheduled) or 1 (being delinquent by two months or more).
Most of the previous studies on repayment of household debt (Canner &
Luckett, 1990; Getter, 2003; Canner, Gabriel, & Woolley, 1991; Godwin, 1999) have
used logistic regression analysis to examine the characteristics associated with poor
payment performance derived from household survey data. This has consistently been
the case, despite discrepancies in the model specifications. The logistic model has the
following general form:
(4.6) Payment
Delinquency =
1 if a respondent sometimes got behind or
missed payments and he /she was ever behind
in his/her payments
0 Otherwise (if a respondent made debt
repayment as scheduled)
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Payment delinquency of household debt measured in this study is a binary
response; either the individual becomes delinquent (Delinquency =1), or he/she does
not become delinquent on payment of household debt (Delinquency=0). A probability
close to 1 indicates a strong likelihood of delinquency, whereas a probability close to
0 indicates that payments are likely to be paid in a timely manner.
4.3.2
Two equations analysis:
When estimating the probability of payment delinquency of household debt,
this propensity cannot be observed if the individual does not have any household debt.
A general discrete choice model using only those who incur household debt would be
appropriate as long as no systematic difference exists between those who need to pay
off debt and those who do not. However, as discussed in theoretical framework, a
number of empirical studies on individuals‘ access to the credit or loan markets have
shown racial and ethnic disparities in loan application acceptance and rejection, even
after accounting for factors related to profit. When the question of whether or not such
systematic differences exist cannot be adequately answered, a sample selection model
is preferable. The important thing is how to build a satisfactory model of Prob
[ Delinquency=1 │ Holding household debt ] when certain factors that explain
repayment delinquency of household debt also factor into debt holding. Therefore,
this study requires a newly developed and specifically tailored approach to the issue
of sample selection problems.
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4.3.2.1 Sample Selection Bias Issue
This new approach must initially acknowledge that a sample selection bias
might have occurred in previous studies. In practice, Heckman (1979) described
possible three cases where sample selection bias might occur. First, sample selection
decisions made by researcher or data processors can operate in much the same fashion
as self-selection. In other words, data may be non-randomly selected because of
decisions made by researchers. Data procedures, when undertaken with fitted
regression functions, can confuse the behavioral parameters of interest with the
parameters of the function determining the probability of becoming the sample.
Second, there may be self-selection by the individuals or data units being investigated.
Third, in the regression model, selection bias can occurs if one or more regressors are
correlated with the residual term. Since the residual term captures the effects of all
omitted and imperfectly measured variables, any regressors that are correlated with
unmeasured or mismeasured variables can proxy for these variable.
Some studies (e.g. Godwin, 1999; Getter, 2003; Lyons, 2004) simply dropped
any households with no debt for their sample segmentation. Given that not all
individuals have debt, the set of those who have household debt is a selected sample.
Therefore, sample selection by a researcher might cause sample selection bias. This
would constitute a deviation from most previous studies on debt repayment, which
derive data from a sample of only those who have been granted a debt, with the
criteria by which applicants are rejected not being taken into account. If respondents
need to repay debt, they must first have debt. Therefore, holding household debt could
be an important source of sample segmentation. The selection bias stemming from
the first case needs to be addressed in this study.
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An applicant is juxtaposed against successful recipients by constructing their
credit score and comparing it to the average score. The underlying model that
produces the score can be viewed as a predictor of some responses (e.g. debt
repayment delinquency), conditional of the sampling rule -- in this case, on the
acceptance of household debt application. The potential flaw in the model is that if
there were factors that enter into equation related to the acceptance decision but do
not appear in the rules explicitly and these same factors influence the response in the
payment equation, then the latter equation may produce biased predictions. Therefore,
a predictor of delinquency risk in a given population of applications can be
systematically biased because it is constructed from a nonrandom sample of past
applicants (e.g. those whose applications were accepted).
Suppose that one builds a model of an economic response, denoted ‗y‘, (in this
case, delinquency), for the purpose of predicting the behavior of individuals in a
specific population denoted ‗A‘ (in this case, those having household debt). One
might represent the model generally as E [ y │A] = fA (x, β), where ‗x‘ denotes a set
of attributes assumed to explain the variation in E [ y │A]. Under normal conditions,
data would be drawn randomly from population A and plugged into the model, which
could then be used to make predictions. However, ―suppose that an individual's
presence in population A is determined by some process that is correlated with, if not
necessarily a function of y, itself. Then, the model-building process could be tainted
by this latent effort‖ (Greene, 1998).
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(4.7)
(4.8)
Creditworthiness =
 0 + 1 X +  i
1 if Creditworthiness ≥ min
(Creditworthiness)
0 if Creditworthiness < min
(Creditworthiness)
Credit Acquisition =
Where the Creditworthiness is the latent variable measuring the underlying
propensity toward having debt, X represents household characteristics. Credit
acquisition , then, constitutes a dichotomous variable (either one acquires credit or
not).
Those who have debt are not representative of the general household
population. It is evidenced by the fact that approximately 75 percent of all households
hold at least one credit card, and 58% of those holding a credit card carry a balance,
according to the 2004 Survey of Consumer Finances (Ekici & Dunn, 2006). Clearly,
not all applicants who are approved for debt have the means to pay them off.
Therefore, apparent inconsistencies in approval and denial practices might be due to
debt applicants‘ under-evaluated characteristics rather than individuals‘ economic
characteristics. As a result, some people with low creditworthiness might acquire debt
due to large error terms.
4.3.2.2 Heckman’s Bivariate Two-Stage Model
In an effort to correct the sample selection bias of the regression on the
payment delinquency, the Heckman procedure (1979) can be employed. The logistic
estimation described in the previous section provides the necessary information to test
and correct for potential bias.
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The Heckman selection model provides consistent estimates of the Ordinary
Least Squares equation by including an estimate of the expected value of the error
terms, the Inverse Mill's Ratio (Long, 1997). A probit regression model in the first
stage provide the expected values of the residuals truncated at the second stage. In the
first stage, the dependent variable will be coded 1 if the respondent has household
debt and 0 if otherwise. However, this model has limitations due to its specifications.
In the Heckman model, the dependent variable in the second equation should be
continuous, but the delinquency variable from SCF is a discrete variable rather than a
continuous one.
4.3.2.3 Modified Heckman’s Two - Stage Model
This study uses a modified version of Heckman‘s two-step procedure that
accommodates a probit and a logistic equation to determine the probability of
payment delinquency. This model takes into consideration the problem of sample
selection bias as a case of partial unobservability. As with the Heckman approach, the
underlying selection mechanism will be incorporated.
This study focuses primarily on the prediction of respondents‘ payment
delinquency, measured here as a binary response. Either the individual in question is
delinquent ( D=1 ), or not ( D=0 ). The problem at hand is to build a satisfactory
model of probability [ D=1 │individual incurred debt ] in light of other factors that
explain the delinquent payment performance of an individual and also factor into the
individual‘s holding of household debt. The binary choice variable y1i takes value 1 if
an individual incurred debt and 0 if he/she did not. Formally,
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1 if the respondent incurred household debt
(4.9)
y1i =
( y1i* ≥ 0)
0 if the respondent did not incur household debt
( y1i* < 0)
The second binary variable, y2i , takes the values 1 if a respondent becomes delinquent
on repayment of household debt and 0 if he/she does not become delinquent.
Formally,
1 if the respondent was delinquent on repayment of
(4.10)
y2i =
household debt ( y2i* ≥ 0)
0 if the respondent was not delinquent on repayment of
household debt ( y2i* < 0)
Use for data when self-selected is as follows
(4.11)
Y1i* =  x1i + 1i
Y2i* =  x 2i + 2i
but only observe Y such that
(4.12)
Y1i = 1 if Y1i * > 0
Y1i = 0 if Y1i * ≤ 0
Y2i = Y2i * if Y1i = 1
Y2i not observed if Y1i = 0
Since one only observes whether or not respondents become delinquent on
their debt repayments if they incurred household debt to begin with, there is not only a
censoring rule for (y1i , y2i), but also an observation rule. To illustrate, assume that we
have empirical household debt holding and repayment delinquency equations with
binary dependent variables
125
1 if a household incurs household debt
(4.13)
Y1=Zα1+ε1
Y1 =
0 if not,
1 if a household is delinquent on repayment
(4.14)
Y2=Zα2+ε2
Y2 =
of household debt
0 if not,
This allows three types of observations: no household debt, delinquent
repayment of household debt, and on-time repayment of household debt.
The
estimation of probability therefore takes the following form:
(4.15)
l=
 pr (delinquent )   pr (notdelinquent )   pr (nocredit )
delinquent
=
N

i 1
notdelinquent
N
N
i 1
i 1
{( Zi1 ) F ( Zi2 )} y1i y2i   {( Zi1 )(1  F ( Zi2 )} y1i (1 y2i )   {1  ( Zi1 )}1 y1i
N
(4.16)
nocard
Ln L(α1, α2) =

y1i y2i [ In{( zi1 )  InF ( zi 2 )]
i 1
N
+  y1i (1  y2i )[ In{( zi1 )  In(1  F ( zi 2 )]
i 1
N
+  (1  y1i ) In[1  ( zi1 )]
i 1
Where Φ (·) univariate standard normal c.d.f. F (·) logit transformation. The structure
equations are as follows.
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Choice equation
(4.17) Household Debt Acquisition Equation : y1i* = ·X1i + ε1i
y1i = 1 if and only if y1i* >0, and 0 else.
y 2i and X2i are only observed if y1i = 1
y1i and X1i are observed for all applicants.
Outcome equation
Repayment Delinquency equation: y 2i = ·X2i + ε2i
(4.18) y 2i =1 if and only if y2i* > 0, and 0 otherwise.
Selectivity [ε1i ε2i] ~ N2[0,0,1,1   1 2 ]
The vector of attributes,  , are the factors used in the Household Debt
Acquisition equation. The probability of interest in the current study is the probability
of repayment delinquency of given that a respondent acquired household debt, which
is
(4.19) Prob [ y 2i = 1 │ y1i = 1] =
 2  2i   , 1i   ,  
1i   
where  is the bivarite normal cumulative probability. If  equals 0, one can expect
that there is no selection bias and the unconditional model described earlier is
appropriate. This model can be developed.
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4.3.2.4 Interaction Effects
This dissertation adopts the Cash Flow Theory of Default in order to explain
payment delinquency on household debt across racial and ethnic groups. Under this
theory, financially adverse events, financial buffers available in the case of emergency,
and the amount of household debt burden are considered important factors to account
for people‘s repayment performance. Whether individuals are delinquent on their debt
repayment more than two months is the outcome variable. Even though financially
adverse events, financial buffers available in emergency, and debt burden are focal
independent variables, this study aims to examine whether the relationship between
these focal variables and payment delinquency was comparable across races or
ethnicities. Because race/ethnicity is a categorical variable, it is represented by three
dummy variables representing black, Hispanic and Others, with white being treated as
the reference group. Product terms are generated between each of these dummy
variables and focal independent variables.
4.3.3
Summary
This study employs a modified version of Heckman‘s two-step procedure that
accommodates a probit and logistic regression model. Household debt repayment
delinquency as measured here is a binary response: either the individual was
delinquent by two months or more ( y 2i =1) or he/she was not ( y 2i = 0). The problem
is to build a satisfactory model of Prob [ y 2i = 1 │ y1i = 1] when there are factors that
explain delinquency behavior which also affect the probability of an individual
acquired household debt. This study models holding household debt as a function of
128
demographics and economic variables. Payment delinquency is a function of
demographics, economic variables, financially adverse events, financial buffers
available, and debt burden. Results of the first probit analysis, which estimate the
probability of holding household debt, are used to compensate for sample selectivity.
Correction for the sample selection bias in the second equation occurs through the
transformation or residuals from the first step equation into an additional variable to
adjust for the correlation of the errors between two equations (Lee et al, 2004). Rho is
the coefficient of this variable, and one can infer that a significant Rho shows a
significant sample selection bias is present in the single equation estimates of the
second equation.
For the purposes of this study, three separate models were created. The first
model (presented in Tables 5.6-5.7) was an intermediate model that included the
indicator variable but omitted the interaction variables. The probability of
delinquency in household debt repayment was then estimated based on a vector of
individual household economic variables, trigger events, and debt-related variables as
well as indicator variables of race/ethnicity. This equation directly accounted for any
effect of household minority status on delinquency risk. If the race and ethnicity
variable were to appear as significant in the regression, this would indicate strong
variance in payment delinquency rates across different racial and ethnic groups, and it
would yield results as to which of those groups are at the highest risk of default.
The second model (presented in Tables 5.8-5.15) was a condensed model that
omitted the indicator variable of race/ethnicity and the set of interaction variables
between race/ethnicity and other independent variables and several independent
variables. These models are done on each group separately.
129
Finally, the third full model (presented in Tables 5.16-5.24) included the
indicator variable of race/ethnicity, the set of interaction variables between
race/ethnicity and other independent variables and several independent variables, thus
providing for racial/ethnic variance in the effects of the independent variables.
To identify each model, certain variables are included in one equation and
excluded from the other. In particular, I included specific variables such as financially
adverse events, financial buffers and household debt burden in the debt repayment
delinquency equation and excluded them form the debt holding equation, since these
factors do not affect whether an individual has debt demand. The first multivariate
procedure was a binary probit performed both to identify and determine the likelihood
of having debt repayment difficulties and to estimate the inverse Mills ratio. If it was
statistically significant, the inverse Mills ratio was then included as an explanatory
variable in binary logit regression model to estimate the probability of payment
delinquency.
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CHAPTER 5
5
5.1
5.1.1
RESULTS
Descriptive Result
Overall Household Characteristics
Table 5.1 lists the number of available observations for each survey year. As
discussed in Chapter 4, the Survey of Consumer Finances provides some respondents
with different racial and ethnic identification in different implicates (Hanna &
Lindamood, 2008). Since the racial and ethnic identification is critical in this study,
this study chose not to include individuals who did not have the same racial and
ethnic categories in all five implicates from the sample. The number of individuals
who did not have the same racial and ethnic categories in all five implicates are 16 (in
1992), 15 (in 1995), 7 (in 1998), 13(in 2001), 13 (in 2004) and 6 (in 2007). They were
deleted from the sample. The remaining respondents were 3,890 in 1992; 4,284 in
1995; 4,298 in 1998; 4,429 in 2001; 4,506 in 2003 and 4,412 in 2007. They were used
for this study.
A descriptive summary of respondents used in this study is presented in Table
5.2. Table 5.2 shows the frequency of selected variables. White and blacks comprise
of 75.8% and 12.8% of the study respectively. Hispanics and Asians/others compose
of 7.8% and 3.7% of respondents respectively. In terms of education attainment,
131
37.5% of household heads obtained a college degree, while 11.9% of household
heads obtained less than a high school degree. 66.8 % of respondents owned their
home.
In terms of family composition, those married with children under age 18
comprised 25.04% and the married without children under age 18 comprised 26.6%.
The single with children comprised 25.04% and the single without children comprised
36.6 %. In terms of self-reported health condition, 9.2% reported that either one of
couples has poor health status.
A majority of respondents (80.9%) answered that everybody in the household
was covered by private health insurance or government health insurance programs,
such as Medicare, Medicaid, CHIP (Children's Health Insurance Program) Tri-Care,
VA, or other military programs.
In terms of transitory income, 73.3% answered that their income was the same
as what they would expect in a normal year and 17.5% reported that there income was
usually lower than what they would expect in a normal year. Net worth and income
were divided into quartiles.
5.1.2
Descriptive Results across Racial/Ethnic Groups
Table 5.3 shows the crosstab result between racial and ethnic categories and
selected variables used in this study. It illustrates racial and ethnic discrepancies in
selected household characteristics, even though other independent variables are not
controlled. Whites and Asians/others have higher education attainment than Hispanics
and blacks. The percentage of those whose household head obtained a college degree
132
for white and Asians/others are 40.4% and 50.5% respectively, while the percentages
of black and Hispanics are 26.9% and 18.0% respectively.
In terms of family composition, racial and ethnic disparities exist. The
proportion of single people with children under age 18 is the highest for blacks
(28.0%) and Hispanics (19.8%). Relatively the proportions of white (8.3%) and
Asians/others (10.4%) are lower than those of blacks and Hispanics.
In terms of employment status, blacks composed the highest percentage
(23.5%) of those who are not working at the year of survey and Hispanics showed the
second highest percentage (23.0%). The percentage of retirees was higher for white
and black groups compared to Hispanics and Asians/others.
The percentage of those who reported that both of couples have excellent
health status is 22.1% for whites and 24.4% for Asians/others, while 20.1% of blacks
and 15.9% of Hispanic. 10.1% of blacks and 9.3% of Hispanics reported that the
respondent or spouses (partners) has poor health conditions, while 9.1.0% of whites
and 7.1% of Asians/others.
This table shows how many respondents reported that their income one year
prior to the survey year was unusually low compared to what they would expect in a
normal year. The percentage of individuals reporting lower income compared to a
normal year‘s income and the percentage of individuals with higher income compared
to normal year‘s income are higher for Hispanics than any other race and ethnicity.
Whites showed the second highest percentage (9.5%) of individuals with higher
income compared to normal year‘s income. Blacks showed the second highest
percentage (22.0%) of lower income compared to a normal year‘s income. On the
contrary, 74.5% of whites answered that their income one year prior to the survey
133
year was normal, and this percentage is higher than that of any other race and
ethnicity.
It also showed that the percentage of individuals reporting uncertainty about
future income is higher for blacks (47%) and Hispanics (47.8%). The percentage of
people who expected that their income would decrease was relatively high for whites
(24.1%) and Asians/others (18.0%) compared to blacks and Hispanics. Those who did
not have any idea of whether their family income for next year would adjust with
inflation were higher for blacks and Hispanics than whites (29.4%) and Asians/others
(34.2%), while those who expected that their total family income would maintain
comparably with inflation were higher for whites and Asians/others. Asians/others
showed the highest percentage (19.0%) of those who expected their total family
income would to increase more rapidly than prices and whites (13.7%) showed the
second highest percentage.
Hispanics showed the lowest percentage of homeownership (45.2%) and
blacks showed the second lowest percentage (46.5%). These percentages are only
62% and 64% of that of whites, respectively. Blacks and Hispanics were in the
weaker economic positions since the percentages of those who were in the lowest
income and net worth quartiles were higher than those whites and Asians/others.
However, black households have higher incomes, are older, are more educated,
and are more likely for everybody in the household to be covered by health insurance
than Hispanic households. Hispanic households are more likely to consist of married
couples with children than black households.
134
In sum, racial and ethnic groups differ in selected independent variables.
Therefore, it is important to control for some of these characteristics in a multivariate
analysis in order to estimate the independent effects of racial/ethnic differences.
5.1.3
Descriptive Results by Holding Household Debt
Table 5.4 shows crosstab relationships between whether a respondent holds
household debt and independent variables used in this study. Under the column
indicating whether the respondents incur household debt, the left column shows the
proportion of those who incur household debt in each demographic category and the
right column contains mean tests results comparing the rate of holding debt, for levels
of each group of independent variables, compared to the rates for the reference
categories.
For all households, the rate of Holding household debt is 71.6%, with 73.1%
for whites, 66.2% for blacks, 67.1% for Hispanics, and 70.1% for Asians/others.
Mean test results show that blacks, Hispanics and Asians/others are the less likely to
have household debt and that rates are significantly lower for them than for white
respondents. Blacks and Hispanics are less likely to have household debt than the
otherwise similar Asians/others (not shown in Table 5.4), while the percentages of
Hispanics and blacks are not significantly different from each other.
Those who are more educated are more likely to hold household debt. The
percentage of those holding household debt among people with college degrees is 1.7
times higher than the percentage among people whose education level is less than
high school.
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Those with homeownership are more likely to have household debt than those
who do not own their home. Those who are married with children under age 18 show
the highest percentage (90.2%) of having household debt. The percentage of those
who have household debt and are married with children is different from that of those
who are married without children. Those who are single without children under age
18 show the lowest percentage (59.0%) of having household debt.
Salary earners are more likely to have household debt while those who are
retired are less likely. The percentage of individuals holding debt among salary
earners is twice the ratio of that of retired people. People who expected that their
income would go up more than prices were more likely to have household debt, while
people who were not sure about their future income were less likely. Those who are in
higher income quartiles show the higher percentage of holding household debt.
According to this table, the percentage of respondents who held household debt
increased between 1992 and 2007 from 58.3% to 77.5%.
5.1.4
Descriptive Results by Repayment Delinquency
Table 5.5 shows crosstab results between whether a respondent is delinquent
on repayment of household debt and independent variables used in this study. The
respondents used in this crosstab are only those who incur household debt. Under the
column indicating whether the respondents were delinquent on repayment by two
months or more. The last column contains mean tests result comparing the rate of
holding debt for levels of each group of independent variables, compared to the rates
for the reference categories. This table indicates that the percentage of debt repayment
delinquency differs across racial/ethnic groups. The crosstab results show the racial
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and ethnic differences in percentage of repayment delinquency. Whites show the
lowest percentage of repayment delinquency (6.6%), while blacks show the highest
percentage (14.3%).
The percentage of repayment delinquency decreases with the level of
education attainment up to the level of a college degree. In other words, those who
obtained a college degree showed the lower percentage of delinquency than those who
some college degree and those who obtained a high school diploma showed a lower
percentage of delinquency than those who had an educational level below high school.
One exception is that those who had a college degree showed the higher percentage of
delinquency than those who obtained a high school diploma.
Among those who were not working at the year of survey, 13.7% were
delinquent on debt repayment, while among those who were retired, only 2.8% were
delinquent on their repayment. Singles with children under age 18 shows the highest
percentage of repayment delinquency, 16.9%, while married couples without children
show the lowest percentage, 3.5%.
Overall, the financial position of delinquent households is substantially weaker
when compared to those who are not delinquent. Those who are delinquent are in the
lower quartiles in income and net worth than those who are not.
These findings suggest that net worth and income play a crucial role in
determining whether a household will experience some level of difficulty in repaying
their household debt. In addition, the amount of income earned by delinquent
households is substantially lower compared to that held by the average households (in
2007 dollars).
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In terms of financially adverse events, 15.1% of those who reported that their
income was unusually low compared to normal year‘s income are delinquent on their
debt repayment by two months or more. This percentage is approximately 2.4 times
higher than that of those who reported that their income was the same as their normal
year‘s income. Among those who reported that the respondent or his or her spouses
(or partner) have his/her poor health status, 7.1% are delinquent on their repayment of
household debt by two months or more.
In terms of financial buffers, delinquent households are less likely to have
health insurance. The proportion of repayment delinquency for families who are not
covered by health insurance is almost 2.8 times higher that those whose families are
covered by health insurance.
Monthly repayment-to-income ratio differs between those who were
delinquent on their repayment of household debt and those who were not delinquent
on their repayment. Those in the highest category of the monthly repayment to
income ratio are more likely to be delinquent on their debt repayment than those in
the lowest category, though those in the middle categories are less likely to be
delinquent than those in the lowest category. This pattern may be related to rent
burdens not being counted in the debt to income ratio.
The percentage of delinquency in payments increased from 1992 to 1995,
decreased from 1995 to 2001, and then peaked in 2004. Since 2004, the percentage of
delinquencies has dropped considerably. The proportion of respondents who were
delinquent on any type of debt repayment by two months or more increased between
1992 to 1995, from 7.6% to 8.2%. Also, the proportion decreased between 1995and
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2001, from 8.2% to 7.0% and then peaked at 8.9% in 2004 and then fell down to 7.1%
after 2004.
Overall, this initial investigation of the data provides insight into the factors
that may explain why some borrowers are more likely to be delinquent on their debt
repayment than others. Those factors that are likely to be significant contributors to
whether borrowers are delinquent include demographics, economics variables,
financial buffers, financially adverse events and household debt burden. The next step
is to examine whether or not the regression results support these preliminary findings.
5.2
5.2.1
Multivariate Result
Sample Selection Model using all Racial/Ethnic Groups
5.2.1.1 Holding Household Debt
Table 5.6 illustrates that the coefficient, standard error and p-value of each
independent variable associated with the probability that respondents incurred
household debt by using the probit model. Table 5.7 shows that the statistics of each
independent variable associated with the probability of repayment delinquency of
household debt by two months or more by using the logistic model.
The probit regression analysis shows that the racial/ethnic status of the
respondent has a significant effect on the probability of having household debt, even
when controlling for all other explanatory variables. Blacks, Hispanics and
Asians/others are significantly less likely to incur household debt than otherwise
similar whites. After changing the reference group with blacks, the probit analysis
shows that Hispanics are less likely to incur household debt than otherwise similar
139
blacks, while Asians/others is not statistically different from blacks in the probability
of having household debt.
Statistically significant increases in the probability of having household debt
from 1992 to 2007 are observed. Households in 2007 show the highest likelihood of
holding household debt, while households in 1992 show the lowest likelihood. After
running the probit model with different reference year (not shown in Table 5.6), this
study finds that the year of 1992 shows the lowest probability of holding household
debt and that probability increases until 1998. Also, the probability remained the same
during 1998 and 2004, and then has increased continuously since 2004.
In the probit model, respondents‘ age and age squared are significant. These
results show that the respondents‘ age is curvilinearly related to the likelihood that
they incur household debt. At mean value of other variables, predicted probability of
having debt increase until age 34.1 and then decreases.
Married couples with children under age 18 are more likely to have household
debt than married couples without children, while single are less likely to have
household debt regardless of having children compared with married couples without
children. Additionally, being single with children and being single without children
are not significantly different (not shown in Table 5.6).
Salary earners are more likely to have household debt than those who are selfemployed, not working or retired. Homeownership is positively related to the
probability of having household debt.
Those who expect their total family income would go up more than the cost of
living are more likely to have household debt than those who expect their income to
drop below prices. Those who did not have any idea of what their family income for
140
next year would be are less likely to have household debt than those who expect their
income to grow less than prices. However, those who expect that their total family
incomes to maintain comparably with costs of living are not significantly different
from those who expect their total family income to grow less than prices.
This study shows the negative relationship between household income and the
likelihood of holding household debt and between net worth and the likelihood of
holding household debt.
As described above, this study use a probit analysis for explaining the
probability that respondents had any household debt during the prior year of the
survey. The Mills ratios calculated in this first stage are included in the second
logistic analysis for correcting possible sample selection bias.
5.2.1.2 Repayment Delinquency of Household Debt
Table 5.7 presents the coefficient, standard error, p-value and odds ratio from
the logistic regression for explaining the probability that that a respondent was
delinquent on repayment of household debt by two months or more.
Note that the effect of the Mills ratio is significant in the logistic regression
results. It supports that the probability that a respondent has household debt and the
probability that he/she is delinquent on household debt are correlated. This result
indicates that controlling for sample selection is critical to get unbiased estimates in
the repayment delinquency model. The logistic regression correctly predicts
delinquency for 82.5 % of the households.
It is quite apparent that the probability of repayment delinquency increased
between 1992 and 2007. Households in 1995, 1998, 2001 and 2004 are more likely to
141
be behind in their payments by two months or more than households in 1992.
Households in 2007 have nearly 1.4 times the odds of being delinquent as households
in 1992, and households in 2004 have nearly 1.8 times the odds of being delinquent as
households in 1992. After running separate logistic models with different reference
groups of years, this study finds that the probability increased from year 1992 to 1995,
remains approximately the same from 1995 to 2001, peaks at year 2004, and then
slightly decreased since then (not shown in Table 5.7).
Blacks have a significantly higher predicted probability of delinquency than
whites, with 1.4 times the odds of whites of being delinquent, even after controlling
for demographic and financial characteristics, as well as for the selection effect.
However, Hispanics have lower predicted probability of delinquency than whites,
with odds only 80% of the likelihood of whites. Asians/others are not significantly
different from the reference group, whites.
In the logistic regression analysis, the respondent‘s age is curvilinearly related
to the likelihood that households are delinquent on payment of household debt by two
month or more. At mean value of other variables, predicted probability of being
delinquent on payment increase until age 41.3 and then decrease.
Married couples without children exhibited the lowest likelihood of repayment
delinquency compared to all other family compositions, including married couples
with children, singles without children and singles with children. Married couples
with children have a significantly higher predicted probability of delinquency than
married couples without children, with twice the odds of married couples without
children of being delinquent, even after controlling for demographic and financial
characteristics. Singles with children have a significantly higher predicted probability
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of delinquency than married couples without children, with approximately twice the
odds of married couples without children of being delinquent. Separate logistic
regression models with different references of family composition show that married
couples and singles with children show the highest probability of delinquency, while
married couples without children show the lowest probability (not shown in Table
5.7). An obtained education level of a bachelor degree significantly decreased the
probability of repayment delinquency probability.
Household economic characteristics have demonstrated significant effects on
the probability of repayment delinquency. The log of household income is negatively
related to the probability of being delinquent. Salary earners show the highest
likelihood of repayment delinquency compared with all other employment statuses,
even when controlling for other independent variables. In other words, those who are
self-employed, not working or retired are less likely to be delinquent on household
debt repayment than those who are salary earners. Retired people have lowest
predicted probability of delinquency compared with salary earners, with odds only
45% of the odds of salary earners.
Financially adverse events that household experienced also generated
significant effects on the probability of repayment delinquency. Self-reported health
status and negative transitory income are found to have a positive effect on repayment
delinquency. People who reported poor health conditions are more likely to be
delinquent compared to those who reported their health as being in excellent, fair, or
good condition. People who reported poor health status have 1.7 times the odds of
being delinquent as those who reported their health status as excellent, good or fair.
Also, transitory income was captured by the proxy measure income changes
143
compared to a normal year. Adverse transitory income changes also appear to have a
similar impact on the probability of being delinquent. People with lower than normal
income last year have 1.7 times the odds of being delinquent as people with higher
than normal income or same as normal income last year.
Financial buffers decrease the probability of being delinquent on repayment.
The probability of being delinquent is lower for those whose family members were
able to receive benefits from government health insurance programs. Those who were
able to have health insurance had odds of being delinquent 54 % of the level of those
who were not. Log of net worth of households was negatively related to the
probability of being delinquent on household debt repayment.
In terms of expectation about future income, those who expect that their total
family income to increase at the same rate as prices are less likely to be delinquent on
household debt repayment than those who expect their income to grow less than
prices. The delinquency probability of those who expect that their total family income
would rise beyond prices and those who did not have any idea of what their family
income for next year would be were not significantly different from that of those who
expect their incomes to increase less than price increase.
This analysis revealed that monthly debt to income ratio indicating
households‘ debt burden is associated with increased delinquency likelihood when
holding other factors constant.
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5.2.2
Sample Selection Model using Separate Race/Ethnicity
5.2.2.1 Whites
Table 5.8 and Table 5.9 present the coefficient, standard error and p-value
from the two stage model for whites. The probit regression (Table 5.8) analysis shows
significant increases in the probability of holding household debt from 1992 to 2007.
Households in 2007 show the highest likelihood of holding household debt, while
households in 1992 show the lowest likelihood.
In the probit model, respondent‘s age and age squared are significant. This
result demonstrates that the respondents‘ age is curvilinearly related to the likelihood
that they have household debt. More specifically, predicted probability of holding
debt increase until age 31.8 and then decreases.
Married couples with children under age 18 are more likely to incur household
debt than married couples without children, while singles are less likely to have
household debt than married couples without children, regardless of whether the
singles have children. Salary earners are more likely to have household debt than
those who are self-employed, not working or retired. Homeownership increases the
probability of having household debt.
Those who expect their total family income to increase faster than prices are
more likely to have household debt than those who expect their income to fall lower
than prices. Those who did not have any idea of what their family income for the next
year would be like are less likely to incur household debt than those who expect their
income to fall under current price levels. Household income and net worth decrease
the likelihood of holding household debt.
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Table 5.9 presents the logistic analysis results of the probability of being
delinquent for whites. This logistic regression analysis shows that the overall effects
of financial events and financial buffers concur with expectations. With respect to
financially adverse events, lower income compared to normal years is associated with
payment delinquency of whites. Those who had lower income compared to a normal
year have 1.8 the odds of being delinquent as those who had the same income
compared to normal year or those who had higher income compared to normal. In
addition, those who reported poor health status had approximately twice the odds of
being delinquent as those who reported excellent, good or fair health status.
In terms of financial buffers, the levels of net worth and health coverage
influence the repayment delinquency for whites. Those whose family members are
covered by health insurance appear to be less likely to be delinquent than those whose
family members are not.
Those who are able to have health insurance had odds of being delinquent
51 % of the level of those who are not. As expected, net worth significantly decrease
the likelihood of repayment delinquency for whites.
The predicted probability of being delinquent on repayment of household debt
is higher for married couples with children and singles with children than the
reference group of married couples without children.
Singles with children have 1.9 the odds of being delinquent as married people
without children. Married couples with children have 1.8 the odds of being delinquent
as married people without children. However, the likelihood of delinquency in
singles without children is not significantly different from that of married couples
without children.
146
Those who have the highest educational attainment are less likely to be
delinquent compared to those who having any other level of educational attainment.
Those who had college degrees had odds of being delinquent 66 % of the level of
those who did not. Household economic characteristics confirm the presence of
significant effects on the probability of payment delinquency. Income of households
is negatively related to the probability of being delinquent for whites. Salary earners
show the highest likelihood of payment delinquency as compared with others,
controlling for any other independent variables.
White households in 1995, 1998, 2001, 2004 and 2007 are more likely to be
behind in their payments by two months or more than households in 1992.
Households in 2007 have 1.8 times the odds of being delinquent on payments
as households in 1992, and households in 2004 have twice the odds of being
delinquent as households in 1992. The household debt burden measured by monthly
the debt-to-income ratio is positively related to the probability of repayment
delinquency. The effect of the Mills ratio is significant in the logistic regression result
and it indicates that controlling for sample selection is critical to arriving at unbiased
estimates in the payment delinquency model.
5.2.2.2 Blacks
Table 5.10 and Table 5.11 present the coefficient, standard error, and p-value
from the two stage model for black households. The probit regression (Table 5.10)
analysis shows significant increase in probability of having household debt from 1992
to 2007. Households in 2007 show the highest likelihood of holding household debt,
while households in 1992 show the lowest likelihood.
147
In the probit model, respondents‘ ages and ages squared prove significant,
illustrating that the respondents‘ age is curvilinearly related to the likelihood that they
incur household debt. Predicted probability of having debt payments increases until
age 41.7 and then decreases. Married couples with children under age 18 are more
likely to incur household debt than married couples without children, while singles
are not significantly different from married couples without children. Salary earners
are more likely to incur household debt than those who are self-employed, not
working or retired. Homeownership is positively related to the probability of holding
household debt.
Those who expected that their total family income would increase faster than
prices are more likely to have household debt than those who expected their income to
grow less than prices. Income is positively related to the probability of having
household debt, while net worth is negatively related to the probability.
Table 5.11 shows the probability of blacks of being delinquent. The overall
effects of adverse economic events and financial buffers support expectations. With
respect to adverse economic events, those who experienced negative transitory
income are more likely to be delinquent on debt repayment. In addition, those who
reported poor health status are not significantly different from those who reported
excellent, fair or good health status.
In terms of financial buffers available, the level of net worth and health
coverage influence the payment delinquency for blacks. Those whose family
members are covered by health insurance appear to be less likely to be delinquent
than those whose family members are not. Those who are able to have health
148
insurance had odds of being delinquent 59 % of the level of those who are not able.
As expected, net worth decreases the probability of repayment delinquency for blacks.
It is worthwhile to address other demographic characteristics. The different
effect of age for blacks is quite apparent from the result for whites. At mean value of
other variables, predicted probability of being delinquent on payment increases until
age 59.8 and then decreases.
There is positive relationship between education and repayment delinquency is
found for this variable for blacks, unlike in the same model for whites where it has a
considerable negative effect.
The predicted probability of being delinquent on repayment of household debt
is the lowest for married couples without children. Married couples with children
have 2.3 times the odds of being delinquent as married couples without a child and
singles with children have 2.5 times the odds of being delinquent as married couples
without a child. This study finds that there is a weak relationship between income and
probability of repayment delinquency like in same model for whites.
Blacks in 2004 have 1.7 times the odds of being delinquent by two months or
more as blacks in 1992. After running separate logistic models with different year
reference group, this study finds that blacks in 2004 are the most likely to be
delinquent, while blacks in 2001 and 2007 show the lowest probability of being
delinquent.
For black households, the household debt burden does not have a significant
effect on the probability of repayment delinquency. Additionally, the effect of the
Mills ratio is significant in the logistic regression result.
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5.2.2.3 Hispanics
Table 5.12 shows the probit analysis for the probability of holding household
debt for Hispanics. In the probit model, respondent‘s age and age squared are
significant. The predicted probability of holding debt payments increases until age
39.3 and then decreases holding all other things constant.
Married couples with children under age 18 are more likely to have household
debt than married couples without children. However, single people are not
significantly different from married couples without children, regardless of the
existence of children. Salary earners are more likely to incur household debt than
those who are self-employed, not working or retired. Homeownership is positively
related to the probability of holding household debt.
This study illustrates the weak relationship between Hispanics‘ expectation
about their future income and their holding household debt. The probability of having
household debt increases as household income.
Table 5.13 shows the logistic analysis for the probability of repayment
delinquency for Hispanics. With respect to adverse economic events, income changes
are associated with repayment delinquency of Hispanic households. Those who
experienced negative transitory income are more likely to be delinquent on debt
repayment. Those who had lower income compared to normal year have 1.5 times the
odds of being delinquent on repayment as those who had the same income or higher
income compared to a normal year. Health status is not significant factor in explaining
Hispanics‘ repayment performance unlike in the same model for whites where it has
positive effects.
150
In terms of financial buffers available, the level of net worth and health
coverage influence the payment delinquency for Hispanic households. Those whose
family members are covered by health insurance appear to be less likely to make late
repayments than those whose family members are not.
Those who are able to have health insurance have odds of being delinquent
76 % of the level of those who are not. Net worth decreases the probability of
payment delinquency for Hispanic households.
For Hispanics, the likelihood of repayment delinquency increases as the debt
burden increases. The predicted probability of being delinquent on payment for
Hispanics increases until age 46 and then decreases.
The effect of household income is not significant for explaining the
probability of being delinquent on debt repayment for Hispanics.
The predicted probability of being delinquent on payment of household debt
is the lowest for married couples without children and single households without
children.
Married couples with children have 2.2 times the odds of being delinquent on
repayment as married couples without children. Singles with children have 2.8 times
the odds of being delinquent on repayments as the reference group. Salary earners
show the highest likelihood of payment delinquency as compared with people with
other employment status, holding for any other independent variable constant.
Hispanic households in 1998 show the lowest likelihood and Hispanic
households in 2001 showed the highest likelihood of repayment delinquency during
1992 and 2007.
151
Hispanic households in 1998 have odds of being delinquent 58 % of the level
of Hispanic households in 1992. After trying separate logistic models with difference
reference categories of year, this study finds that the probability for Hispanics in 1992,
1995, 2001 and 2007 are not significantly different. The effect of the Mills ratio is
significant in the logistic regression result.
5.2.2.4 Asians/others
Table 5.14 presents the probit analysis for the probability of holding
household debt for Asians/others. The probit regression analysis shows the probability
of holding household debt from 1995 to 2007 are significantly higher than the
probability in 1992. Households in 2007 show the highest likelihood of holding
household debt, while households in 1992 show the lowest likelihood.
In the probit model, respondents‘ ages and ages squared are significant. The
predicted probability of holding household debt increases until age 34.2 and then
decreases.
Married couples and singles with children under age 18 are more likely to
incur household debt than married couples without children.
Self-employed people are more likely to incur household debt than salary
earners, while retired people or people not working are less likely. Homeownership is
positively related to the probability of holding household debt.
In terms of expectation about future income, this study finds that those who
did not have any idea of what their family income for next year would be are less
likely to hold household debt than those who expected their income to grow less than
prices. However, whether people expected that total family income would go up more
152
than prices or would decrease to less than prices is not significantly related to the
probability of holding household debt. Net worth has a negative effect on the
likelihood of holding household debt. However, income has a non significant effect.
Table 5.15 shows the logistic analysis for the probability of being delinquent
on debt repayment for Asians/others. The overall effects of financial events, financial
buffers and household debt burden support expectations. With respect to adverse
economic events, Asians/others who experienced lower income compared to a normal
year have 2.5 times the odds of being delinquent on repayment as Asians/others who
did not. On the contrary, health status does not have a significant effect on the
probability of repayment delinquent for this group.
In terms of financial buffers, the level of net worth and health coverage
available influences the payment delinquency for this group. Those whose family
members are covered by health insurance appear to be less likely to be delinquent
than those who are not. Those who are able to have health insurance have odds of
being delinquent 60 % of the level of those who are not. As expected, log of net
worth decreases the probability of repayment delinquency for this group and with the
expected sign.
There is no statistically significant relationship between age and the repayment
delinquent for this group. The predicted probability of being delinquent on repayment
of household debt is the lowest for married couples without children. Married couples
with children have 4.9 times the odds of being delinquent by two months or more than
the reference group and singles with children have the 3.3 times the odds of being
delinquent as the reference group.
153
Those who obtained a college degree are less likely to be delinquent compared
to those who having lesser education attainment.
Those who had college degrees had odds of being delinquent 33 % of the
level of those who did not. Income of households is negatively related to the
probability of being delinquent. In terms of environmental variables, this study shows
that Asians/others in 2001 shows the highest probability of being behind in their
payments by two months or more since 1992. To the contrary, the likelihood of
repayment delinquency for this group in 2004 and 2007 show the lowest probability
of being behind in their payments by two months or more.
For Asian/other households, the likelihood of repayment delinquency
decreases as the debt burden increases. Additionally, the effect of the Mills ratio is
significant in the logistic regression result. These results indicate that controlling for
sample selection is critical in reaching unbiased estimates in the payment delinquency
model.
5.2.3
Interaction Effects
This section considers the case where the interaction effect of interest involves
a mixture of race/ethnicity and other selected variables. The qualitative variable such
as race/ethnicity is conceptualized as the moderator variable and the qualitative /
continuous variables are focal independent variables.
This study examines the relationship between variables relevant to consumers‘
cash flow and whether they are delinquent on their repayment of household debt by
two months or more. This study hypothesized that individuals with financially adverse
events would be more likely to be delinquent on their repayment; those who have
154
financial buffers would be less likely to delinquent on their repayment; those who
have more household debt burden are more likely to be delinquent.
This study also has interests in whether this relationship is comparable across
different racial and ethnic groups. Race and ethnicity have two possible effects. The
first could be a constant effect which is an overall or homogenous effect of race and
ethnicity on repayment delinquency. The second possible type of effect, a coefficient
effect, implies that there are specific determinants of repayment delinquency among
whites, blacks, Hispanics and Asians/others. These effects would have emerged if the
effects of any one explanatory variable are different among racial/ethnic groups. The
cause of such differences may have been explained by differences in financial buffers,
financially adverse events and household debt burden tied to these coefficient effects.
The significance of interaction terms indicates that there are racial and ethnic
differences in the determinants of repayment delinquency. This is achieved by
including interaction terms between each of the predictor variables and race/ethnicity,
thus indicating that race and ethnicity has a moderating effect on several independent
variables. This demonstrates that the effects of adverse events, financial buffers and
household debt on repayment performance differ among different racial/ethnic groups.
In this study whether someone is delinquent on debt repayment is the outcome
variable, financially adverse events. Financial buffers and household debt burden are
the focal independent variables. Race/ethnicity is the moderator variables. Because
race/ethnicity is a categorical variable, it is represented by three dummy variables.
Each race/ethnicity is treated as a reference group in turn. Product terms are generated
between each of these dummy variables and selected independent variables.
155
Table 5.16 ~ 5.19 presents the logistic coefficients, the exponents of the
coefficient, standard error and p-value. The hierarchical test of the interaction effect
shows a statistically significant effect. The probit analysis of holding household debt
is not shown.
Selected independent variables are part of the product term and therefore the
coefficients associated with them do not represent a ―main effect‖ but instead reflect a
conditional effect. In order to explore the relative importance of independent variables
among different racial/ethnic groups, this study derives the value of the multiplicative
factor for each selected variable for each of these four racial and ethnic categories.
As Jaccard (2001) suggested, this study conducts several logistic models with a
different racial and ethnic reference group. These separate logistic models yields the
multiplicative factor for the reference group on the moderator variables, whoever that
reference group may be.
This strategy is presented in Table 5.16 using whites as the reference group for
race/ethnicity, Table 5.17 using black as the reference group for race/ethnicity, Table
5.18 using Hispanics as the reference group and Table 5.19 using Asians/others as the
reference group. From the four analyses in Table 5.16 ~ 5.19, one can characterize the
multiplicative factor by which the predicted odds change for a 1 unit change increase
in selected independent variable for each of the four ethnic groups. These results are
presented in Table 5.20 ~ 5.24. If selected independent variables has the same effect
for all four racial/ethnic groups, in other words if there is no interaction effect, the
multiplying factor will be the equal in all four groups (except for sampling errors).
One can compare the multiplicative factor for blacks with that for whites by taking the
ratio of the two multiplicative factors. These ratio are presented in column indicating
156
multiplicative factors. If the two multiplicative factors is different among racial/ethnic
groups, the value of this ratio will diverge from 1.0.
In terms of net worth (Table 5.20), the multiplicative factor for blacks is about
1.03 times the magnitude of that for whites. This factor for Hispanics is about 1.04
times the magnitude of that for whites. However, the insignificance of interaction
terms between Asians/others and net worth shows that the multiplicative factor for
Asians/others is not statistically significant.
In terms of health insurance (Table 5.21), the multiplicative factor for blacks is
about 1.4 times the magnitude of that for whites; the multiplicative factor for
Hispanics is about 1.9 times the magnitude of that for whites; and the multiplicative
factor for Asians/others is about 1.2 times the magnitude of that for whites. However,
the insignificance of interaction terms between Asians/others and health insurance
that the multiplicative factor for Asians/others is not statistically significant. The
multiplicative factor for Hispanics is about 1.4 times the magnitude of that for blacks.
The multiplicative factor for Asians/others is about 1.2 times the magnitude of that for
whites. However, the multiplicative factor of health insurance for Asians/others is not
different from that for whites. In other words, the effect of health insurance on the
repayment delinquency differs among racial/ethnic groups. The effect is more severe
for blacks and Hispanics than for whites. Also, it is more severe for Hispanics than for
blacks and more severe for Hispanics than for Asians/others.
In terms of poor health status, the multiplicative factor for blacks is about
57 % the magnitude of that for whites; the multiplicative factor for Hispanics is about
53 % the magnitude of that for whites; and the multiplicative factor for Asians/others
is about 60 % the magnitude of that for whites. However, the insignificance of
157
interaction terms of Asians/others show that the multiplicative factor is not
statistically significant for this group. On the contrary, the multiplicative factor for
whites is about 1.9 times the magnitude of that for Hispanics. In other words, the
effect of poor health status on the repayment delinquency also differs among
racial/ethnic groups. The effect is more severe for whites than any other race/ethnicity.
In terms of negative transitory income, the multiplicative factor for blacks is
about 80 % the magnitude of that for whites. The multiplicative factor for whites is
about 1.3 times higher than the magnitude of that for blacks. The multiplicative factor
for Asians/others is about 1.9 times the magnitude of that for blacks. The
multiplicative factor of negative transitory income for Asians/others is about 1.9 times
the magnitude of that for Hispanics. In other words, the negative transitory income
influences the repayment delinquency influences consumers‘ repayment performance
among racial/ethnic groups in a different way. The effect is more severe for
Asians/others and whites than for blacks and Hispanics.
In terms of household debt burden, the multiplicative factor for whites is about
1.7 times the magnitude of that for blacks; the multiplicative factor for Hispanics is
about twice the magnitude of that for blacks. However, the multiplicative factor of
household debt burden for Hispanics is not significantly different from that for whites.
158
Year of Survey
All
Respondents
Respondents
with Different
Racial/Ethnic
Identification
1992
1995
1998
2001
2004
2007
total
3,906
4,299
4,305
4,442
4,519
4,418
25,889
16
15
7
13
13
6
67
Remaining
3,890
4,284
4,298
4,429
4,506
4,412
25,822
respondents
Table 5.1: Number of Respondents of 1992 -2007 Survey of Finances. Weighted analysis of Survey of
Consumer Finances, all 5 implicate.
159
Variables
Demographic Variables
Race/Ethnicity
Education Level
Family Composition
Children under 18
Employment Status
Homeownership
Expected Income Growth
Age
Income
Categories
Percentage
White
Black
Hispanics
Asians/others
Less than High school
High School
Some College
Bachelor
Married w/o children
75.76
12.75
7.83
3.66
11.85
30.79
19.89
37.48
26.60
Married w/ children
Single w/o children
25.04
36.60
Single w/ children
Yes
11.76
43.66
No
Self Employed
Salary Earner
Not Working
Retired
Yes
No
Sure the Same
Sure Increase
Sure Decrease
Not Sure
Less than 30
30~39
40~49
50~60
60 +
Income < $22,256
$22,256 ≤Income <$44,301
$44,301≤Income<$78,748
$78,748≤ Income
56.34
10.18
56.05
16.50
17.27
66.80
33.20
32.01
13.70
21.83
32.46
14.82
20.45
21.74
16.29
26.69
25.03
24.93
25.06
24.99
Table 5.2: Frequency of Respondents‘ Selected Variables of 1992 -2007 the
Survey of Consumer Finances. Weighted analysis of Survey of Consumer
Finances. (Continue)
160
Table 5.2 Continued
Variables
Categories
Net worth
NW< $13,368.4
$13,368.4≤NW<$92,164.1
25.01
24.99
$92,164.1≤NW< $285,732
$285,732≤NW
25.00
25.00
Higher than Normal Income
9.23
Financially Adverse Events
Transitory Income
Health Status
Financial Buffers
Health Insurance Coverage
Environmental Variable
Year of Survey
Percentage
Low than Normal Income
Same as Normal Income
Excellent
Good
Fair
Poor
17.46
73.32
21.57
45.77
23.50
9.16
Yes
80.89
No
19.11
1992
1995
15.05
16.61
1998
2001
2004
2007
16.65
17.16
17.45
17.08
161
Variables
Race / Ethnicity
Categories
Whites
Blacks
Hispanics
Asians
/others
13.16
17.56
24.77
18.31
30~39
40~49
18.88
21.44
24.31
21.98
28.15
23.46
23.15
23.49
50~60
60 +
16.60
29.92
15.68
20.46
12.91
10.71
19.32
15.73
Less than high school
High School
9.01
39.88
16.44
33.75
35.11
30.00
8.10
20.72
Some College
Bachelor
19.66
40.44
22.88
26.93
16.93
17.97
20.67
50.50
Married w/o children
Married w/ children
Single w/o children
Single w/ children
Self Employed
Salary Earner
Not working
Retired
29.89
25.18
36.65
8.27
11.39
54.43
14.70
19.48
13.01
15.11
43.92
27.95
4.42
58.58
23.46
13.55
17.37
36.36
26.48
19.79
7.44
63.63
23.01
5.91
25.53
32.48
31.63
10.36
11.15
64.44
15.62
8.79
Homeownership
Yes
No
72.97
27.03
46.47
53.53
45.20
54.80
56.11
43.89
Income
Income < $22,256
$22,256 ≤Income
<$44,301
$44,301≤Income<$78,
748
$78,748≤ Income
Sure the Same
Sure Increase
Sure Decrease
20.91
42.74
35.89
25.41
24.38
25.91
31.18
19.40
26.31
19.64
21.85
24.77
28.40
33.89
13.69
24.07
11.72
24.96
11.97
16.06
11.09
26.81
14.08
11.32
30.42
28.72
19.03
18.04
Demographic Variables
Age
Less than 30
Education
Family Composition
Employment Status
Expected Income
Growth
Not sure
29.35
47.00
47.79
Table 5.3 : Frequency of Selected Household characteristics, by Racial/ethnic
difference/Ethnic Category of 1992 -2007. Weighted analysis of Survey of Consumer
Finances. (Continued)
162
34.21
Table 5.3: Continued
Race / Ethnicity
Variables
Categories
Whites
Financially Adverse Events
Transitory
Higher than Normal
Income
Lower than Normal
Health Status
Same as Normal
Excellent
Good
Fair
Poor
Financial Buffers
Health Insurance Yes
No
Net worth
NW< $13,368.4
$13,368.4≤NW<$92,164.1
$92,164.1≤NW< $285,732
$285,732≤NW
Household Debt Burden
Debt to Income
Debt to Income Ratio<10%
Ratio
10%≤ Debt to Income
Ratio<25%
25%≤ Debt to Income
Ratio<40%
40%≤Debt to Income Ratio
163
Blacks
Hispanics
Asians
/others
9.45
16.05
7.49
21.96
10.11
23.25
8.82
18.48
74.50
22.11
46.25
22.53
9.10
70.55
20.77
41.87
27.16
10.08
66.64
15.91
46.68
28.12
9.29
72.70
24.40
46.44
21.94
7.13
84.77
15.23
18.94
23.89
27.53
29.64
73.70
26.30
45.86
30.36
16.76
7.02
55.46
44.54
48.04
26.87
16.49
8.60
79.95
20.05
28.62
25.24
19.53
26.61
46.02
53.11
51.86
49.92
30.28
22.34
20.96
25.54
14.94
12.82
14.40
13.44
8.76
11.73
12.77
11.10
Having Debt
Variables
Categories
Demographic Variables
Race/Ethnicity
White
73.07
Black
Hispanics
66.22
67.08
<0.0001
<0.0001
Asians/others
Less than High school
70.06
49.20
<0.0001
High School
Some College
69.52
76.91
<0.0001
<0.0001
Bachelor
Married w/o children
Married w/ children
Single w/o children
Single w/ children
Less than 30
30~39
40~49
50~60
60 +
81.67
71.25
90.24
59.03
71.96
73.25
82.07
84.62
81.32
49.02
<0.0001
Self Employed
81.82
Education
Family Composition
Age
Economic Variables
Employment Status
Yes
Means Test
<0.0001
<0.0001
0.1215
<0.0001
<0.0001
<0.0001
<0.0001
Salary Earner
83.08
<0.0001
Not Working
58.66
<0.0001
Retired
40.79
<0.0001
Homeownership
Yes
78.58
No
57.61
<0.0001
Expected Income Growth
Sure the Same
72.42
<0.0001
Sure Increase
82.63
<0.0001
Sure Decrease
70.18
Not sure
67.14
<0.0001
Income
Income < $22,256
45.68
$22,256 ≤Income
69.92
<0.0001
<$44,301
$44,301≤Income<$78,7
82.80
<0.0001
48
$78,748≤ Income
88.08
<0.0001
Table 5.4 : Proportion of Respondents‘ Selected Variables by holding household debt of
1992 -2007. Weighted analysis of Survey of Consumer Finances.(Continued)
164
Table 5.4: Continued
Having Debt
Variables
Categories
Net worth
NW< $13,368.4
$13,368.4≤NW<$92,164.1
$92,164.1≤NW< $285,732
$285,732≤NW
Yes
Means Test
59.73
77.41
75.15
74.18
<0.0001
<0.0001
<0.0001
1992
1995
58.27
64.53
<0.0001
1998
2001
2004
2007
74.36
76.01
77.20
77.48
71.62
Environmental Variable
Year of Survey
Total
165
<0.0001
<0.0001
<0.0001
<0.0001
Variables
Categories
Demographic Variables
Race/Ethnicity
White
Black
Hispanics
Asians/others
Education
Less than High School
Age
Family Composition
High School
Some College
Bachelor
Less than 30
30~39
40~49
50~60
60 +
Married w/o Children
Married w/ Children
Delinquency
by two months
or more
Means
Test
6.58
14.33
10.24
7.90
5.89
<0.0001
<0.0001
0.03234
6.04
7.44
4.04
8.11
8.52
6.27
5.48
1.89
3.50
7.30
0.5122
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
Single w/o Children
8.55
<0.0001
Single w/ Children
16.93
<0.0001
Employment Status
Self Employed
5.86
Salary Earner
7.70
<0.0001
Not working
13.67
<0.0001
Retired
2.80
<0.0001
Homeownership
Yes
5.14
No
15.14
<0.0001
Income
Income < $22,256
15.05
$22,256 ≤Income
11.14
<0.0001
<$44,301
$44,301≤Income<$78,
7.15
<0.0001
748
$78,748≤ Income
2.03
<0.0001
Table 5.5: Proportion of Repayment Delinquency across Selected Variables (Debtors only)
(Continued)
166
Table 5.5: Continued
Variables
Delinquency
by two months
or more
Categories
Financially Adverse Events
Expected Future
Sure the Same
Income
Sure Increase
Sure Decrease
Not sure
Transitory Income
Health Status
Financial Buffers
Health Insurance
Net worth
5.32
7.24
6.67
11.56
Higher than Normal
Lower than Normal
Same as Normal
Excellent
Good
Fair
Poor
6.79
15.14
6.21
3.80
5.24
7.17
7.08
Yes
No
NW< $13,368.4
$13,368.4≤NW<$92,164.1
$92,164.1≤NW< $285,732
$285,732≤NW
Environmental Variable
Year of Survey
1992
1995
1998
2001
2004
2007
Household Debt Burden
Debt to Income Ratio Debt to Income Ratio<10%
10%≤Debt to Income Ratio<25%
25%≤Debt to Income Ratio<40%
40%≤Debt to Income Ratio
167
5.92
16.31
19.09
8.81
4.20
1.34
Means
Test
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
0.7637
<0.0001
<0.0001
<0.0001
<0.0001
7.63
8.19
7.97
7.02
8.94
7.11
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
7.29
6.47
6.58
11.79
<0.0001
<0.0001
<0.0001
Parameter
Categories
Estimate
S.E.
P value
Year of Survey
1995
1998
2001
2004
2007
Blacks
Hispanics
Asians/others
0.2252
0.5508
0.5589
0.5594
0.6046
-0.0821
-0.1895
-0.1055
0.0139
0.0144
0.0143
0.0143
0.0145
0.0145
0.0178
0.0218
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
Age
Age
Age squared
0.0409
-0.0006
0.0016
0.0000
<.0001
<.0001
Education
Less than High
Less than Bachelor
Some College
Married w/child
Single w/o child
Single w/ child
Yes
Log of Income
0.2153
0.4022
0.3354
0.2396
-0.1701
-0.1634
0.8831
-0.0094
0.0142
0.0159
0.0145
0.0129
0.0110
0.0168
0.0122
0.0024
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
Log of Net worth
Sure the Same
Sure Increase
Not Sure
Self Employed
Not working
-0.0311
0.0011
0.0369
-0.1111
-0.0775
-0.4748
0.0011
0.0122
0.0145
0.0122
0.0121
0.0122
<.0001
0.9299
0.0109
<.0001
<.0001
<.0001
Retired
Intercept
-0.4928
-0.3118
0.0149
0.0484
<.0001
<.0001
Race/Ethnicity
Family Composition
Homeowner
Income
Net worth
Expected Income Growth
Employment
Percent Concordant
80.3
Table 5.6: Probit Regression of Probability of Holding Household Debt
168
Parameter
Demographics
Race /Ethnicity
Age
Education Level
Family Composition
Current Income
Expected Income
Growth
Employment
Financial Buffers
Net worth
Health Insurance
Estimate
S.E.
P-value
Odds
Ratio
0.3290
-0.2210
-0.1245
0.0406
0.0554
0.0800
<.0001
<.0001
0.1196
1.390
0.802
0.883
0.1156
-0.0014
0.1265
0.0078
0.0001
0.0502
<.0001
<.0001
0.0118
1.123
0.999
1.135
Some College
0.3228
Bachelor
-0.1883
Less than High (Reference)
Married w/child
0.6963
Single w/o child
0.3073
Single w/ child
0.6792
Married w/o child (Reference)
Log of Income
-0.0448
Sure the Same
-0.2841
Sure Increase
-0.1006
Not Sure
-0.0278
Surely Decrease (Reference)
Self Employed
-0.3181
Not working
-0.2070
Retired
-0.8034
Salary Earner (Reference)
0.0558
0.0557
<.0001
0.0007
1.381
0.828
0.0511
0.0520
0.0569
<.0001
<.0001
<.0001
2.006
1.360
1.972
0.0081
0.0462
0.0518
0.0428
<.0001
<.0001
0.0521
0.5160
0.956
0.753
0.904
0.973
0.0479
0.0452
0.1050
<.0001
<.0001
<.0001
0.728
0.813
0.448
Log of Net worth
Yes
No (Reference)
0.0024
0.0333
<.0001
<.0001
0.906
0.543
<.0001
1.692
<.0001
1.747
Categories
Blacks
Hispanics
Asians/others
White (Reference)
Age
Age squared
High School
-0.0992
-0.6105
Financially Adverse Events
Health Status
Poor Health
0.5260
0.0537
Excellent, Fair or Good (Reference)
Transitory Income
Low Income
0.5576
0.0337
Higher or same Income (Reference)
Table 5.7: Logistic Regression of Probability of Being Delinquent on Household Debt
by Two Months or More (Continued)
169
Table 5.7: Continued
Parameter
Categories
Household Debt Burden
Monthly Debt to Income
ratio
Environmental Variable
Year of Survey
1995
1998
2001
2004
2007
Estimate
S.E.
P-value
Odds
Ratio
0.6424
0.0706
<.0001
1.901
0.3856
0.4437
0.5765
0.3593
0.3856
0.0602
0.0601
0.0587
0.0619
0.0602
<.0001
<.0001
<.0001
<.0001
<.0001
1.471
1.558
1.78
1.432
1.471
1.6446
0.1017
<.0001
5.179
-4.5547
82.5
0.2233
<.0001
1992 (Reference)
Mills Ratio
Intercept
Percent Concordant
170
Variables
Categories
Year
1995
1998
2001
2004
2007
1992 (Reference)
Age
Age squared
High School
0.2367
0.5684
0.5662
0.5701
0.6139
0.0381
-0.0006
0.2080
0.3574
0.0155
0.0163
0.0162
0.0163
0.0164
0.0019
0.0000
0.0176
0.0193
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
Some College
Bachelor Degree
0.3170
0.2367
0.0175
0.0155
<.0001
<.0001
0.1901
-0.2320
-0.1380
0.0143
0.0121
0.0216
<.0001
<.0001
<.0001
0.8398
0.0144
<.0001
-0.0118
0.0028
<.0001
-0.0451
-0.0285
-0.4550
-0.4723
0.0015
0.0133
0.0146
0.0160
<.0001
0.0314
<.0001
<.0001
-0.0121
0.0424
-0.0767
0.0133
0.0160
0.0137
0.3633
0.0080
<.0001
Intercept
0.0346
Percent Concordant
80.7
Table 5.8 : Probit Analysis of Holding Household Debt for Whites
0.0566
0.5408
Age
Education
Family Composition
Homeowner
Income
Net worth
Employment
Estimate
Less than High (Reference)
Married w/child
Single w/o child
Single w/ child
Married w/o child (Reference)
Yes
No (Reference)
Log of Income
Log of Net worth
Self Employed
Not working
Retired
S.E.
P-Value
Salary earner (Reference)
Expected Income
Growth
Sure the Same
Sure Increase
Not Sure
Surely Decrease (Reference)
171
Variables
Demographics
Age
Education
Family
Composition
Current Income
Expected Income
Growth
Employment
Categories
Age
Age squared
Bachelor Degree
High School
Some College
Bachelor Degree
Less than High (Reference)
Married W/child
Single w/o child
Single w/ child
Married w/o child (Reference)
Log of Income
Sure the Same
Sure Increase
Not Sure
Surely Decrease (Reference)
Self Employed
Not working
Retired
Salary earner (Reference)
Financial Buffers
Net worth
Log of Net worth
Health Insurance Yes
No (Reference)
Estimate
S.E.
P-value
Odds
Ratio
0.1328
-0.0017
-0.0032
0.1199
-0.4118
0.1328
0.0100
0.0001
0.0647
0.0703
0.0695
0.0100
<.0001
<.0001
0.9606
0.0878
<.0001
<.0001
1.142
0.998
0.997
1.127
0.662
1.142
0.5790
0.1036
0.6505
0.0587
0.0615
0.0677
<.0001
0.0921
<.0001
1.784
1.109
1.917
-0.0214
-0.2530
-0.1607
0.1231
0.0104
0.0550
0.0640
0.0511
0.0389
<.0001
0.0120
0.0159
0.979
0.776
0.852
1.131
-0.3173
-0.2522
-0.7912
0.0553
0.0555
0.1279
<.0001
<.0001
<.0001
0.728
0.777
0.453
-0.1170
-0.6806
0.0032
0.0414
<.0001
<.0001
0.89
0.506
0.6698
0.0645
<.0001
1.954
0.5981
0.0415
<.0001
1.819
Financially Adverse Events
Health Status
Transitory Income
Poor
Excellent, fair, or good
Low income
Same or High income (Reference)
Table 5.9: Logistic Analysis for Probability of Being Delinquent for whites. (Continued)
172
Table 5.9: Continued
Variables
Categories
Estimate
S.E.
P-value
Odds
Ratio
Household Debt Burden
Debt to Income Ratio
0.8505
0.0863
<.0001
2.341
0.3934
0.0696
<.0001
1.482
0.5161
0.0746
<.0001
1.676
0.5736
0.0749
<.0001
1.775
0.7093
0.0738
<.0001
2.032
0.6001
0.0764
<.0001
1.822
1.5708
-4.8368
83.2
0.1328
0.2717
<.0001
<.0001
4.811
-4.8368
Environmental Variable
Year of Survey
1995
1998
2001
2004
2007
1992 (Reference)
Mills Ratio
Intercept
Percent Concordant
173
Variables
Categories
Estimate
S.E.
P-value
year
1995
0.2581
0.0468
<.0001
1998
2001
0.5953
0.7118
0.0468
0.0467
<.0001
<.0001
2004
2007
0.6020
0.7044
0.0455
0.0479
<.0001
<.0001
1992 (Reference)
Age
0.0225
0.0047
<.0001
-0.0003
0.1958
0.4802
0.7005
0.0001
0.0358
0.0422
0.0463
<.0001
<.0001
<.0001
<.0001
0.5381
-0.0565
-0.0621
0.0590
0.0442
0.0495
<.0001
0.2014
0.2094
1.1023
0.0367
<.0001
0.0718
-0.0184
0.1338
0.2201
-0.0564
0.0078
0.0025
0.0443
0.0541
0.0398
<.0001
<.0001
0.0025
<.0001
0.1571
-0.3267
-0.5203
-0.7248
0.0624
0.0332
0.0575
<.0001
<.0001
<.0001
Intercept
-1.5353
Percent Concordant
82.1
Table 5.10: Probit Analysis of Holding Household Debt for blacks
0.1488
<.0001
Age
Education
Family Composition
Homeowner
Net worth
Employment
Expected
Income Growth
Age squared
Bachelor Degree
High School
Some College
Less than High (Reference)
Married w/child
Single w/o child
Single w/ child
Married w/o child (Reference)
Yes
No (Reference)
Log of Income
Log of Net worth
Self Employed
Not working
Retired
Salary earner (Reference)
Sure the Same
Sure Increase
Not Sure
Surely Decrease (Reference)
174
Variables
Demographics
Age
Education
Family Composition
Current Income
Expected Income
Growth
Employment
Financial Buffers
Net worth
Health Insurance
Estimate
S.E.
P-value
Odds
ratio
0.0598
-0.0005
0.2829
0.5983
0.5204
0.0150
0.0002
0.1113
0.1271
0.1377
<.0001
0.0020
0.0110
<.0001
0.0002
1.062
0.999
1.327
1.819
1.683
0.8246
0.7172
0.8974
0.1570
0.1435
0.1489
<.0001
<.0001
<.0001
2.281
2.049
2.453
Married w/o child (Reference)
Log of Income
-0.0021
0.0249
0.9343
0.998
Sure the Same
-0.4006
Sure Increase
0.2450
Not Sure
-0.2127
Surely Decrease (Reference)
Self Employed
-0.2168
Not working
0.0781
0.1143
0.1182
0.0992
0.0005
0.0382
0.0319
0.67
1.278
0.808
0.1571
0.0991
0.1676
0.4307
0.805
1.081
Retired
-0.7984
Salary earner (Reference)
0.2141
0.0002
0.45
Log of Net worth
Yes
0.0051
0.0728
<.0001
<.0001
0.925
0.594
0.1258
0.2012
1.175
0.0776
<.0001
1.532
Categories
Age
Age squared
High School
Some College
Bachelor
Less than High
(Reference)
Married w/child
Single w/o child
Single w/ child
-0.0777
-0.5207
No (Reference)
Financially Adverse Events
Health Status
Poor
0.1609
Excellent, fair or good
Transitory Income
Low income
0.4265
Same or High income (Reference)
Table 5.11: Logistic Analysis of Being Delinquent on Household Debt for blacks (Continued)
175
Table 5.11 : Continued
Variables
Categories
Estimate
S.E.
P-value
Odds
ratio
Household Debt Burden
Debt to income ratio
0.0200
0.1711
0.9070
1.02
Environmental Variable
Year of Survey
1995
1998
2001
2004
0.0697
0.3398
0.0351
0.5178
0.1368
0.1325
0.1395
0.1275
0.6104
0.0103
0.8012
<.0001
1.072
1.405
1.036
1.678
-0.1573
0.1413
0.2656
0.854
0.9411
-3.9990
74.7
0.1855
0.5582
<.0001
<.0001
2.563
2007
1992 (Reference)
Mills Ratio
Intercept
Percent Concordant
176
Variables
Categories
Estimate
S.E
P-value
Year of Survey
1995
0.0036
0.0641
0.9557
1998
2001
0.4504
0.4851
0.0597
0.0586
<.0001
<.0001
2004
2007
0.4431
0.4388
0.0563
0.0576
<.0001
<.0001
Age
1992 (Reference)
Age
0.0236
0.0067
0.0005
Education
Age squared
High School
-0.0003
0.3043
0.0001
0.0401
<.0001
<.0001
0.6372
0.5885
0.0571
0.0558
<.0001
<.0001
0.3402
-0.0195
-0.0067
0.0526
0.0530
0.0588
<.0001
0.7134
0.9090
1.0850
0.0445
<.0001
0.0786
0.0117
<.0001
0.0035
-0.0895
-0.2472
-0.4935
0.0032
0.0607
0.0415
0.0968
0.2778
0.1404
<.0001
<.0001
0.0541
0.0738
-0.0959
0.0620
0.0700
0.0575
0.3832
0.2917
0.0952
-1.7146
0.1959
<.0001
Family
Composition
Homeowner
Income
Net worth
Employment
Expected Income
Growth
Some College
Bachelor
Less than High (Reference)
Married W/child
Single w/o child
Single w/ child
Married w/o child (Reference)
Yes
No (Reference)
Log of Income
Log of Net worth
Self Employed
Not working
Retired
Salary earner (Reference)
Sure the Same
Sure Increase
Not Sure
Surely Decrease (Reference)
Intercept
Percent Concordant
82.0
Table 5.12: Probit Analysis of Holding Household Debt for Hispanics
177
Estimate
S.E.
P- value
Odds
Ratio
Age
0.0598
Age squared
-0.0007
High School
0.2325
Some College
0.7427
Bachelor
0.3059
Less than High (Reference)
Married w/child
0.7944
0.0278
0.0003
0.1358
0.1650
0.1787
0.0314
0.0540
0.0869
<.0001
0.0870
1.062
0.999
1.262
2.102
1.358
0.1869
<.0001
2.213
Single w/o child
Single w/ child
0.2780
1.0286
0.1976
0.1960
0.1593
<.0001
1.321
2.797
Married w/o child (Reference)
Log of Income
-0.0542
0.0337
0.1074
0.947
0.1665
0.1849
0.1518
0.0075
0.0322
0.0220
0.641
0.673
0.706
0.1657
0.1435
0.7557
0.1495
0.0004
0.0100
0.788
0.601
0.143
-0.0501
0.0090
<.0001
0.951
-0.2711
0.1031
0.0086
0.763
Financially Adverse Events
Health Status
Poor
0.2242
Excellent, Fair, or Good (Reference)
0.1964
0.2537
1.251
Lower than Normal
0.4335
0.1084
Same or Higher than Normal(Reference)
<.0001
1.543
Parameter
Demographics
Age
Education
Family
Composition
Current Income
Expected Income
Growth
Employment
Categories
Sure the Same
-0.4454
Sure Increase
-0.3959
Not Sure
-0.3477
Surely Decrease (Reference)
Self Employed
-0.2388
Not working
-0.5092
Retired
-1.9466
Salary earner (Reference)
Financial Buffers
Net worth
Health Insurance
Transitory Income
Log of Net
worth
Yes
No (Reference)
Table 5.13: Logistic Analysis of Being Delinquent on Household Debt for Hispanics
(Continued)
178
Table 5.13 : (Continued)
Parameter
S.E.
P- value
Odds
Ratio
0.7672
0.2301
0.0009
2.154
1995
1998
0.0887
-0.5434
0.1871
0.2039
0.6353
0.0077
1.093
0.581
2001
2004
0.1800
-0.3709
0.1735
0.1743
0.2996
0.0334
1.197
0.69
2007
1992 (Reference)
-0.2589
0.1766
0.1428
0.772
73.6000
-3.3936
73.6
73.6000
0.8438
Categories
Estimate
Household Debt Burden
Debt to Income Ratio
Environmental Variable
Year of Survey
Mills Ratio
Intercept
Percent Concordant
179
73.6000 73.6000
<.0001
Variables
Categories
Estimate
S.E.
P value
Year of Survey
1995
0.3038
0.0663
<.0001
1998
2001
0.8262
0.5434
0.0774
0.0765
<.0001
<.0001
2004
2007
0.6958
0.8748
0.0726
0.0739
<.0001
<.0001
Age
1992 (Reference)
Age
0.0342
0.0089
0.0001
Education
Age squared
High School
-0.0005
0.2080
0.0001
0.0850
<.0001
0.0144
Some College
Bachelor
0.6954
0.4362
0.0943
0.0826
<.0001
<.0001
Less than High (Reference)
Married w/child
0.3405
0.0604
<.0001
0.0591
0.0944
0.7185
<.0001
0.0603
<.0001
Income
Single w/o child
-0.0213
Single w/ child
0.5344
Married w/o child (Reference)
Yes
1.0996
No (Reference)
Log of Income
-0.0008
0.0119
0.9464
Net worth
Employment
Log of Net worth
Self Employed
-0.0377
0.1981
0.0057
0.0621
<.0001
0.0014
Not working
Retired
-0.4934
-0.3792
0.0637
0.0937
<.0001
<.0001
0.0206
0.0700
0.7680
0.0849
-0.2383
0.0750
0.0671
0.2576
0.0004
Family
Composition
Homeownership
Expected
Income Growth
Salary earner (Reference)
Sure the Same
Sure Increase
Not Sure
Surely Decrease (Reference)
Intercept
-0.8796
0.2526
Percent Concordant
80.0
Table 5.14 : Probit Analysis of Holding Household Debt for Asians/others
180
0.0005
Parameter
Demographics
Age
Education
Family
Composition
Current Income
Expected
Income Growth
Employment
Categories
Estimate
S.E
P-value
Odds
Ratio
Age
Age squared
0.0786
-0.0008
0.0438
0.0005
0.0729 1.0820
0.1002 0.9990
High School
Some College
-0.8426
-0.1459
0.2872
0.3251
0.0033 0.4310
0.6535 0.8640
Bachelor
Less than High
(Reference)
Married W/child
-1.1075
0.2878
0.0001 0.3300
1.5818
0.3317
<.0001 4.8640
Single w/o child
Single w/ child
1.2120
1.1957
0.3286
0.3695
0.0002 3.3600
0.0012 3.3060
0.0324
0.2977
0.3031
0.2683
<.0001
0.6790
0.8626
0.7584
0.2554
0.2319
0.5331 1.1730
0.3906 1.2200
-1.5678
0.7261
0.0308 0.2080
-0.0779
-0.5113
0.0143
0.1949
<.0001 0.9250
0.0087 0.6000
Married w/o child (Reference)
Log of Income
-0.1628
Sure the Same
-0.1232
Sure Increase
-0.0525
Not Sure
0.0825
Surely Decrease (Reference)
Self Employed
0.1592
Not working
0.1991
Retired
Salary Earner (Reference)
Financial Buffers
Net worth
Log of Net worth
Health Insurance Yes
No (Reference)
0.8500
0.8840
0.9490
1.0860
Financially Adverse Events
Health Status
Poor
-0.0700
0.3253
0.8297 0.9320
Excellent, fair or good
Lower than Normal
0.8997
0.1874
<.0001 2.4590
Transitory
Same or Higher than Normal
Income
(Reference)
Table 5.15: Logistic Analysis of Being Delinquent for Asians/others (Continued)
181
Table 5.15 : Continued
Parameter
Categories
Estimate
Household Debt Burden
Debt to Income Ratio
Environmental Variable
Year of Survey
1995
1998
2001
2004
2007
1992 (Reference)
Mills Ratio
Intercept
Percent Concordant
182
S.E.
P-value
Odds
Ratio
-1.4902
0.4207
0.0004
9.6190
0.3862
0.5766
0.3074
0.3515
0.2091
0.1009
1.4710
1.7800
1.0795
0.3482
0.3132
0.3136
0.0006
0.2669
2.9430
1.4170
-0.0713
0.3731
0.8484
0.9310
2.2638
-3.4649
87.9
0.4353
1.1795
<.0001
0.0033
-1.4710
Variables
Logistic
Coefficient
Exponent
of
Coefficient
0.3045
0.3755
0.4245
0.5624
0.3464
1.3559
1.4557
1.5288
1.7549
1.4140
0.0574
0.0602
0.0602
0.0587
0.0619
<.0001
<.0001
<.0001
<.0001
<.0001
0.2015
-0.7342
-0.0697
1.2232
0.4799
0.9327
0.0832
0.1086
0.1869
0.0154
<.0001
0.7093
0.1144
1.1212
0.0078
<.0001
Age squared
-0.0014
0.9986
0.0001
<.0001
High School
Some College
Bachelor
Less than High
(Reference)
Married w/child
Single w/o child
Single w/ child
0.0965
0.2963
-0.1967
1.1013
1.3449
0.8214
0.0501
0.0556
0.0555
0.0541
<.0001
0.0004
0.2963
-0.1967
0.7049
1.3449
0.8214
2.0236
0.0556
0.0555
0.0513
<.0001
0.0004
<.0001
Categories
Environmental Variables
Year of Survey
1995
1998
2001
2004
2007
Demographics
Race/Ethnicity
Black
Hispanic
Asian/others
Age
Education
Family Composition
Age
S.E
p-value
Current Income
Log of Income
0.3022
1.3528
0.0521
<.0001
Expected Income
Growth
Sure the Same
Sure Increase
Not Sure
Self Employed
Not working
Retired
-0.2921
-0.0975
-0.0322
-0.2990
-0.1973
-0.7916
0.7467
0.9071
0.9683
0.7416
0.8209
0.4531
0.0464
0.0520
0.0428
0.0483
0.0454
0.1051
<.0001
0.0608
0.4519
<.0001
<.0001
<.0001
Log of Net worth
Yes
-0.1086
-0.7432
0.8971
0.4756
0.0029
0.0406
<.0001
<.0001
Financially Adverse Events
0.6838
1.9814
0.0633
<.0001
Health Status
0.6838
1.9814
0.0633
<.0001
Transitory Income
0.5983
1.8190
0.0410
<.0001
Employment
Financial Buffers
Net worth
Health Insurance
Household Debt Burden
Debt to Income Ratio
0.7622
2.1430 0.0833
<.0001
Table 5.16 : Logistic Analysis of Being Delinquent (Reference group= whites) (Continued)
183
Table 5.16: Continued
Variables
Categories
Logistic
Coefficient
Exponent
of
Coefficient
S.E
p-value
Interaction Effects
Financial Buffers
Log of Net worth
Health Insurance
*blacks
*Hispanics
*Asians/others
*blacks
*Hispanics
*Asians/others
0.0264
0.0369
0.0110
0.3151
0.6528
0.1786
1.0268
1.0376
1.0111
1.3704
1.9209
1.1955
0.0054
0.0077
0.0113
0.0806
0.1036
0.1798
<.0001
<.0001
0.3303
<.0001
<.0001
0.3206
-0.5656
-0.6422
-0.5054
-0.2227
-0.2031
0.4279
0.5680
0.5261
0.6033
0.8004
0.8162
1.5340
0.1379
0.1998
0.3021
0.0858
0.1119
0.1696
<.0001
0.0013
0.0943
0.0094
0.0696
0.0116
-0.5130
0.1661
-1.5310
1.5937
-4.4155
0.5987
1.1807
0.2163
4.9219
0.0121
0.1573
0.2026
0.3225
0.1018
0.2240
0.0011
0.4123
<.0001
<.0001
<.0001
Financially Adverse Events
Health Status
Transitory Negative
Income
Household Debt Burden
Debt to income ratio
*blacks
*Hispanics
*Asians/others
*blacks
*Hispanics
*Asians/others
*blacks
*Hispanics
*Asians/others
Mills
Intercept
184
Variables
Logistic
Coefficient
Exponent
of
Coefficient
0.3045
0.3755
0.4245
0.5624
0.3464
white
Hispanic
Asian/others
Age
Age squared
Bachelor Degree
Married W/child
Single w/o child
Single w/ child
Log of Income
Surely Same
Surely Increase
Not Sure
Self Employed
Not working
Retired
Log of Net worth
Yes
Categories
Environmental Variables
Year of Survey
1995
1998
2001
2004
2007
Demographics
Race/Ethnicity
Age
Education
Family Composition
Current Income
Expected Income
Growth
Employment
Financial Buffers
Net worth
Health Insurance
Financially Adverse Events
Health Status
Poor
Lower than
Transitory Income
Normal
S.E
p-value
1.3559
1.4557
1.5288
1.7548
1.4139
0.0574
0.0602
0.0602
0.0587
0.0619
<.0001
<.0001
<.0001
<.0001
<.0001
-0.2016
-0.9358
-0.2712
0.1144
-0.0014
0.0965
0.2963
-0.1967
0.7049
-0.0435
-0.2921
-0.0975
-0.0322
-0.2990
-0.1973
-0.7916
0.8171
0.3922
0.7624
1.1212
0.9986
1.1013
1.3448
0.8214
2.0236
0.9574
0.7466
0.9071
0.9683
0.7415
0.8209
0.4531
0.0832
0.1221
0.1953
0.0078
0.0001
0.0501
0.0556
0.0555
0.0513
0.0083
0.0464
0.0520
0.0428
0.0483
0.0454
0.1051
0.0154
<.0001
0.1649
<.0001
<.0001
0.0541
<.0001
0.0004
<.0001
<.0001
<.0001
0.0608
0.4519
<.0001
<.0001
<.0001
-0.0822
-0.4281
0.9210
0.6517
0.0048
0.0704
<.0001
<.0001
0.1182
1.1254
0.1238
0.3397
0.3756
1.4558
0.0759
<.0001
Household Debt Burden
Household Debt
Debt to Income
0.2492
1.2829 0.1435 0.0824
Burden
Ratio
Table 5.17 : Logistic Analysis of Being Delinquent (Reference group= blacks) (Continued)
185
Table 5.17 :Continued
Variables
Categories
Logistic
Coefficient
Exponent
of
Coefficient
-0.0264
S.E
p-value
0.9739
0.0054
<.0001
0.0105
1.0106
0.0086
0.2233
-0.0154
0.9847
0.0120
0.1997
-0.3151
0.3377
-0.1365
0.7297
1.4017
0.8724
0.0806
0.1187
0.1890
<.0001
0.0045
0.4704
0.5656
-0.0767
0.0602
1.7605
0.9262
1.0620
0.1379
0.2265
0.3203
<.0001
0.7350
0.8510
0.2227
1.2494
0.0858
0.0094
0.0196
0.6506
1.0198
1.9167
0.1290
0.1815
0.8790
0.0003
0.5130
0.6791
-1.0180
1.5937
-4.2140
1.6703
1.9721
0.3613
4.9219
0.0148
0.1573
0.2330
0.3427
0.1018
0.2373
0.0011
0.0036
0.0030
<.0001
<.0001
Interaction Effects
Financial Buffers
Log of Net worth
*white
*Hispanics
Health Insurance
*Asians/others
*white
*Hispanics
*Asians/others
Financially Adverse Events
Health Status
*white
*Hispanics
*Asians/others
Transitory Negative
Income
*white
*Hispanics
*Asians/others
Household Debt Burden
Debt to income ratio
*white
*Hispanics
*Asians/others
Mills
Intercept
186
Variables
Categories
Logistic
Coefficient
Exponent
of
Coefficient
0.3045
0.3755
0.4245
0.5624
0.3464
Environmental Variables
Year of Survey
1995
1998
2001
2004
2007
Demographics
Race/Ethnicity
white
black
Asian/others
Age
Age
Age squared
Education
High School
Some College
Bachelor
Family Composition
Married w/child
Single w/o child
Single w/ child
Current Income
Log of Income
Expected Income
Surely Same
Growth
Surely Increase
Not Sure
Employment
Self Employed
Not working
Retired
Financial Buffers
Net worth
Log of Net worth
Health Insurance
Yes
Financially Adverse Events
Health Status
Poor
Transitory Income
Lower than Normal
S.E
p-value
1.3559
1.4557
1.5288
1.7549
1.4140
0.0574
0.0602
0.0602
0.0587
0.0619
<.0001
<.0001
<.0001
<.0001
<.0001
0.7342
0.9358
0.6646
0.1144
-0.0014
0.0965
0.2963
-0.1967
0.7049
0.3022
0.6825
-0.0435
2.0838
2.5493
1.9437
1.1212
0.9986
1.1013
1.3449
0.8214
2.0236
1.3528
1.9788
0.9574
0.1086
0.1221
0.2073
0.0078
0.0001
0.0501
0.0556
0.0555
0.0513
0.0521
0.0570
0.0083
<.0001
<.0001
0.0013
<.0001
<.0001
0.0541
<.0001
0.0004
<.0001
<.0001
<.0001
<.0001
-0.2921
0.7467
0.0464
<.0001
-0.0975
-0.0322
-0.2990
-0.1973
-0.7916
0.9071
0.9683
0.7416
0.8209
0.4531
0.0520
0.0428
0.0483
0.0454
0.1051
0.0608
0.4519
<.0001
<.0001
<.0001
-0.0716
-0.0905
0.9309
0.9135
0.0073
0.0963
<.0001
0.3474
0.0415
0.3952
1.0424
1.4847
0.1903
0.1046
0.8273
0.0002
Household Debt Burden
Debt to Income Ratio
0.9283
2.5302 0.1900
Table 5.18 : Logistic Analysis of Being Delinquent (Reference group= Hispanics)
(Continued)
187
<.0001
Table 5.18: Continued
Logistic
Coefficient
Exponent
of
Coefficient
*white
*black
*Asians/others
*white
*black
*Asians/others
-0.0369
-0.0105
-0.0259
-0.6528
-0.3377
-0.4741
Financially Adverse Events
Poor Health Status
*white
*black
*Asians/others
Transitory Negative
*white
Income
*Hispanics
*Asians/others
Household Debt Burden
Debt to Income Ratio
*white
*black
*Asians/others
Mills
Intercept
Variables
Categories
S.E
p-value
0.9638
0.9896
0.9744
0.5206
0.7134
0.6224
0.0077
0.0086
0.0132
0.1036
0.1187
0.1999
<.0001
0.2233
0.0498
<.0001
0.0045
0.0177
0.6422
0.0767
0.1369
0.2031
-0.0196
0.6309
1.9007
1.0797
1.1467
1.2252
0.9806
1.8793
0.1998
0.2265
0.3515
0.1119
0.1290
0.1952
0.0013
0.7350
0.6971
0.0696
0.8790
0.0012
-0.1661
-0.6791
-1.6971
1.5937
-5.1498
0.8470
0.5071
0.1832
4.9219
0.0058
0.2026
0.2330
0.3657
0.1018
0.2458
0.4123
0.0036
<.0001
<.0001
<.0001
Interaction Effects
Financial Buffers
Log of Net worth
Health Insurance
188
Variables
Categories
Logistic
Coefficient
Exponent of
Coefficient
0.3045
0.3755
0.4245
0.5624
0.3464
Environmental Variables
Year of Survey
1995
1998
2001
2004
2007
Demographics
Race/Ethnicity
white
black
Hispanic
Age
Age
Age squared
Education
High School
Some College
Bachelor
Family Composition
Married W/child
Single w/o child
Single w/ child
Current Income
Log of Income
Expected Income
Surely Same
Growth
Surely Increase
Not Sure
Employment
Self Employed
Not working
Retired
Financial Buffers
Net worth
Log of Net worth
Health Insurance
Yes
Financially Adverse Events
Health Status
Poor
Transitory Income
Negative Income
Household Debt Burden
Debt to income ratio
S.E
p-value
1.3559
1.4557
1.5288
1.7549
1.4140
0.0574
0.0602
0.0602
0.0587
0.0619
<.0001
<.0001
<.0001
<.0001
<.0001
0.0697
0.2712
-0.6646
0.1144
-0.0014
0.0965
0.2963
-0.1967
0.7049
0.3022
0.6825
-0.0435
1.0722
1.3115
0.5145
1.1212
0.9986
1.1013
1.3449
0.8214
2.0236
1.3528
1.9788
0.9574
0.1869
0.1953
0.2073
0.0078
0.0001
0.0501
0.0556
0.0555
0.0513
0.0521
0.0570
0.0083
0.7093
0.1649
0.0013
<.0001
<.0001
0.0541
<.0001
0.0004
<.0001
<.0001
<.0001
<.0001
-0.2921
0.7467
0.0464
<.0001
-0.0975
-0.0322
-0.2990
-0.1973
-0.7916
0.9071
0.9683
0.7416
0.8209
0.4531
0.0520
0.0428
0.0483
0.0454
0.1051
0.0608
0.4519
<.0001
<.0001
<.0001
-0.0975
-0.5646
0.9071
0.5686
0.0111
0.1755
<.0001
0.0013
0.1784
1.0262
1.1953
2.7904
0.2958
0.1650
0.5466
<.0001
-0.7688
0.4636
0.3160
0.0150
Table 5.19 : Logistic Analysis of Being Delinquent (Reference group= Asians/others)
(Continued)
189
Table 5.19: Continued
Variables
Categories
Logistic
Coefficient
Exponent of
Coefficient
-0.0110
S.E
p-value
0.9891
0.0113
0.3303
0.0154
0.0259
-0.1786
0.1365
0.4741
1.0155
1.0262
0.8364
1.1463
1.6066
0.0120
0.0132
0.1798
0.1890
0.1999
0.1997
0.0498
0.3206
0.4704
0.0177
*white
*black
*Hispanics
*white
* black
*Hispanics
0.5054
-0.0602
-0.1369
-0.4279
-0.6506
-0.6309
1.6576
0.9416
0.8721
0.6519
0.5217
0.5321
0.3021
0.3203
0.3515
0.1696
0.1815
0.1952
0.0943
0.8510
0.6971
0.0116
0.0003
0.0012
Household Debt Burden
Debt to income ratio
*white
*black
*Hispanics
Mills
Intercept
1.5310
1.0180
1.6971
1.5937
-4.4852
4.6228
2.7677
5.4581
4.9219
0.0113
0.3225
0.3427
0.3657
0.1018
0.2888
<.0001
0.0030
<.0001
<.0001
<.0001
Interaction Effects
Financial Buffers
Log of Net worth
Health Insurance
*white
*black
*Hispanics
*white
*black
* Hispanics
Financially Adverse Events
Poor Health Status
Negative Transitory
Income
190
Multiplicative Factor
Race/Ethnicity
whites
comparison to
whites
comparison to
blacks
comparison to
Hispanics
comparison to
Asians/others
1.00
0.97
0.96
0.99
blacks
1.03
1.00
0.99
1.02
Hispanics
1.04
1.01
1.00
1.03
Asians/others
1.01
0.98
0.97
1.00
Table 5.20 : Multiplicative Factor for the reference Group on the Moderating Variable (Net worth)
191
Multiplicative Factor
Race/Ethnicity
whites
comparison to
whites
comparison to
blacks
comparison to
Hispanics
comparison to
Asians/others
1.00
0.73
0.52
0.84
blacks
1.37
1.00
0.71
1.15
Hispanics
1.92
1.40
1.00
1.61
Asians/others
1.20
0.87
0.62
1.00
Table 5.21 : Multiplicative Factor for the reference Group on the Moderating Variable (Health
Insurance)
192
Multiplicative Factor
Race/Ethnicity
whites
comparison to
whites
comparison to
blacks
comparison to
Hispanics
comparison to
Asians/others
1.00
1.25
1.23
0.65
blacks
0.80
1.00
0.98
0.52
Hispanics
0.82
1.02
1.00
0.53
Asians/others
1.53
1.92
1.88
1.00
Table 5.22 : Multiplicative Factor for the reference Group on the Moderating Variable (Negative
Transitory Income)
193
Multiplicative Factor
Race/Ethnicity
whites
comparison to
whites
comparison to
blacks
comparison to
Hispanics
comparison to
Asians/others
1.00
1.76
1.90
1.66
blacks
0.57
1.00
1.08
0.94
Hispanics
0.53
0.93
1.00
0.87
Asians/others
0.60
1.06
1.15
1.00
Table 5.23 : Multiplicative Factor for the reference Group on the Moderating Variable (Poor Health
Status)
194
Multiplicative Factor
Race/Ethnicity
comparison to
whites
comparison to
blacks
comparison to
Hispanics
comparison to
Asians/others
whites
1.00
1.67
0.85
4.62
blacks
Hispanics
Asians/others
0.60
1.18
0.22
1.00
1.97
0.36
0.51
1.00
0.18
2.77
5.46
1.00
Table 5.24 : Multiplicative Factor for the reference Group on the Moderating Variable (Household
Debt Burden)
195
CHAPTER 6
6
CONCLUSIONS
6.1 Conclusions
The Life Cycle-Permanent Income Hypothesis (LC-PIH) has had an important
role in economists' explanations of consumers‘ borrowing decisions. However, the
LC-PIH does not leave room for any miscalculation or imprecise assessment of
household information (Bryant, 1990). More realistically, some households inevitably
face uncertainties regarding their future incomes and expected spending habits.
Therefore, they might be uncertain about the affordability of their household debt.
More specifically, unforeseen events such as job loss, health problems, or divorce
may place individuals in positions where they are no longer able to meet their debt
obligations. Consequently, some borrowers might become burdened with more debt
than they can afford, or they may face difficulty due to poor debt management. As
mentioned earlier, this has been demonstrated through the observation and analysis of
a large number of common credit payment problems.
This dissertation adopts the Cash Flow Theory of Default in order to explain
payment delinquency on household debt across racial and ethnic groups. According to
this theory, financially adverse events such as health
196
problems or negative transitory income, financial buffers such as net worth or health
insurance, and the amount of existing debt a household carries are considered
important factors in accounting for consumers‘ repayment performance. These factors
are significant in the model presented in this study. A number of studies regarding
consumer credit repayment (e.g. Godwin, 1999; Getter, 2003; Lyons, 2004) simply
elected not to include any households with no debt in their sample segmentation.
Given that not all individuals have debt, the set of those who do incur household debt
is a selected sample. Therefore, such sample selection as performed by these
researchers might result in sample selection bias. This would constitute a deviation
from most previous studies on debt repayment, which derive data from a sample of
only those who have been have debt. Factors related to having debt have not been
fully taken into account. In an effort to correct the sample selection bias of the
regression on the payment delinquency, the Heckman procedure (1979) can be
employed. Since the delinquency variable from the Survey of Consumer Finances is a
discrete variable rather than a continuous one, this study uses a modified version of
Heckman‘s two-step procedure that employs probit and logistic equations to
determine the probability of payment delinquency. It is noteworthy that the effect of
the Mills ratio is significant in the sample selection models. It reflects the correlation
between the probability that a respondent obtains household debt and the probability
that he/she is delinquent on household debt by two months. These findings
demonstrate how critical controlling for sample selection is in order to arrive at
unbiased estimates in repayment delinquency risk models.
This data uses the Survey of Consumer Finances (SCF). This is a crosssectional survey sponsored by the Board of Governors of the Federal Reserve System.
197
It contains detailed information on the finances of American families (Bucks,
Kennickell, Mach, & Moore, 2009), such as information on credit and debit use as
well as demographic variables. Therefore, the SCF is the most appropriate source to
employee when analyzing the effects of race and other characteristics (trigger events,
financial buffers, debt-related variables) on the likelihood of household debt payment
delinquency.
Previous studies that establish the foundation for examining racial and ethnic
disparities in credit issues have proved somewhat limited. First, only a few empirical
studies identified borrowers‘ race and ethnicity and evaluated such characteristics
while studying borrowers‘ repayment performance. This is partly due to a lack of
sufficient data on borrowers‘ racial/ethnic groups. Creditors are prohibited from
asking credit applicants about certain characteristics that have been considered
irrelevant to creditworthiness and decisions regarding whether to grant credit to an
applicant. Under the Equal Credit Opportunities Acts (1974, 1976), race/ethnicity is
one of the characteristics banned in the financial underwriting process.
Fortunately, it is possible to directly identify race and ethnicity of borrowers
through the data contained within the Federal Reserve Board‘s Survey of Consumer
Finances. Using this survey, several studies (Jappelli, 1990; Cox & Jappelli, 1993;
Crook, 1996) examined the racial and ethnic disparities in the credit market. These
studies came to the consensus that racial and ethnic minorities including both nonwhite borrowers and households headed by non-white individuals are less likely to
have household debt, even controlling for any other demographic variable.
This dissertation recognized and differentiated between four racial and ethnic
categories: white, black, Hispanic, and ‗Asian/other.‘ I created dummy variables for
198
each subset, with the largest category, white, serving as the reference group. A
number of previous studies compared households headed by whites with households
headed by minorities, combining several racial and ethnic groups rather than
distinguishing between data on black Americans, Hispanic Americans, Asian
Americans, and so forth. These studies broadly compared minority groups, which
were not separated according to race or ethnicity, to the white group. This was
attributable to the fact that these studies did not include a large subset of any one
racial or ethnic minority group. Therefore, this study uses multiple survey years
(1992-2007) of the Survey of Consumer Finances (SCF) in order to increase the
sample size of the different racial/ethnic groups and allow for a stronger assessment
of the effects of race/ethnicity on household debt payment.
Overall, the main goal of this study is to develop an understanding of whether
there exist racial/ethnic differences in debt repayment performance. If differences
appear present, this study will then identify the most important factors affecting
delinquency for each racial/ethnic that can be identified in the datasets. To accomplish
this objective, I first aimed to measure empirically whether racial and ethnic
differences exist in patterns of household incurrence of debt. Next, I tried to examine
the most important factors affecting delinquency among different racial and ethnic
groups. Lastly, this study examined the influences and correlations between
race/ethnicity and focal independent variables.
6.1.1. Holding Household Debt
Conditions affecting the supply of credit have changed dynamically and
consumer credit use has increased significantly for almost two decades (Getter, 2006).
199
Diverse innovations in consumer credit markets have allowed consumers to finance
their expenditures with credit during the late 1980s easily (Luckett, 1986; Canner,
Fergus, & Luckett, 1988). For example, more flexible underwriting standards and
automated credit scoring scheme encouraged greater competition among lenders to
supply credit during the 1990s. More recently, households have been offered lower
interest rates and smaller down payments to entice them and make credit more
accessible to a wider segment of the population (Lyons, 2003).
The findings presented in this paper illustrate how the holding household debt
has increased in US over the past 15 years. Therefore, this study supports that there is
an increasing trend of holding household debt. Also, this study finds that recent
changes to provide more affordable credit tend to have increased borrowing
opportunities for U.S. households, and especially those traditionally limited in their
borrowing options, such as racial and ethnic minorities (Lyon, 2003).
Based on my literature review, I arrived at the conclusion that racial/ethnic
disparities do exist in the rates of household debt acquisition. Consistent with the
previous evidence, this study confirms that the probability of holding household debt
increased from 1992 to 2007. These findings hold true across all households
regardless of race and ethnicity. The probit regression analysis identifies that the
probability of holding household debt differs among races, holding other elements
constant. As previous studies showed, racial/ethnic minorities are less likely to incur
household debt. In particular, blacks, Hispanics, and Asians/others are significantly
less likely to incur household debt than whites of similar demographic backgrounds.
In addition, this study observes that there exist disparities even among racial/ethnic
minorities. When the reference group was changed from whites to blacks, black
200
respondents could then be compared to other races/ethnicities. The probit analysis
shows that Hispanics are less likely to hold household debt than otherwise similar
blacks, while Asians/others are not statistically different from blacks regarding the
probability of holding household debt. Therefore, this result reflects that some
racial/ethnic minorities still experience credit constraints compared to whites. A
separate logistic regression model for each race and ethnicity shows that the
probability of having household debt increased from 1992 to 2007 across all
racial/ethnic groups.
6.1.2 Repayment Delinquency of Household Debt
Previous studies arrived at the consensus that racial/ethnic minority borrowers
are more likely to miss debt repayments or default than otherwise equivalent white
borrowers. However, little consensus has been reached on how to explain these
disparities or to address the existence of variation in repayment performance among
racial/ethnic minorities.
Some studies on debt repayment performance have considered race/ethnicity
as a proxy for economic or social attributes in explaining racial disparities in
repayment performance. Studies following this perspective tend to control for
socioeconomic status (as measured by income or education) instead of analyzing
class. Based on the finding that the influences of race/ethnicity on payment
performance tend to become less distinct after adjusting for demographic or economic
characteristics, the proponents of this perspective suggest that the gap in repayment
performance among racial/ethnic groups would close significantly after reducing
disparities in demographic or economic characteristics.
201
Other studies on debt repayment performance have considered race/ethnicity
as representing more than demographic or economic characteristics. The fact that the
relationship between race/ethnicity and repayment performance is pervasive even
after adjusting for socioeconomic status seems to indicate that racial/ethnic disparities
are strong indicators of a wide range of social conditions including not only social and
economic status, but also biased treatment toward different racial/ethnic minorities in
the credit market (e.g. racial discrimination). This study supports the second
consideration over the first, based on the discoveries illuminated throughout the
research.
This study projects that financially adverse events, financial buffers, or
household debt burden as well as racial/ethnic differences may be especially crucial in
explaining borrowers‘ repayment performance. This study hypothesized that
individuals who experienced financially adverse events would be more likely to be
delinquent on household debt than individuals who did not. Individuals who had more
financial buffers would be less likely to be delinquent on household debt repayment
than individuals without comparable financial buffers. Individuals with higher
amounts of household debt would be more likely to be delinquent than individuals
with lower household debt.
Descriptive statistics in this study demonstrate higher rates of payment
delinquency for each racial/ethnic minority group than for whites. The multivariate
analysis shows that black households were more likely to be behind on payments of
any loans or mortgage by two months or more than otherwise similar white
households. However, Hispanic households were less likely to be delinquent on their
payments of any type of household debt than otherwise similar white households.
202
Asians/other households were not significantly different from whites in terms of the
probability of repayment delinquency.
One possible explanation for the higher delinquency rates of blacks and
Hispanics in the descriptive analyses is a generally weaker economic position and
lower education attainment level compared to whites. In general, individuals with
lower income might be less able to afford the out-of-pocket costs of health care, even
if they have health insurance coverage. Less education may impair their familiarity
with complex financial terms, their ability to communicate with creditors, and to
understand credit providers‘ instructions. Therefore, these characteristics among racial
/ethnic minorities can hinder their ability to successfully cope with financially adverse
events and to manage their debt repayment successfully. Higher levels of educational
attainment help consumers make rational and better informed decisions. Gaining
knowledge from education is certainly one way to advance consumers‘ financial
knowledge and to help them manage their credit rationally. Consumers who have less
financial knowledge might lack the experience and technical ability to perform
complex computations on how they balance their finances between present and future
incomes and expenditures. Those who have lower educational attainment tend to have
difficulties in saving, using formal banking services, and searching for product
information before buying compared to middle income families. Therefore, lower
education attainment for blacks and Hispanics can be one factor to account for their
high repayment delinquency rate.
Even though black and Hispanics have share generally weaker economic
positions, Hispanics are less likely to be delinquent on household debt than otherwise
similar blacks. One possible explanation might be related to the differences in
203
Hispanics‘ immigrant status. For example, people with illegal immigrant status would
be reluctant to draw the attention of the government by breaking any rules
surrounding credit and repayment policies. Therefore, they might elect not to be
delinquent on repayment of household debt in order to avoid potential problems
regarding the legality of their residence. The Hispanic group is the largest and most
rapidly growing ethnic minority in the United States, but
Camarota (2007) concluded that " ... 5.6 million of the 10.3 million
immigrants in the March 2007 CPS who indicated that they arrived in 2000 or later
are illegal aliens.‖ Therefore, Hispanics might be more cautious about holding
repayment obligations and subsequent difficulties than otherwise similar blacks. It
may also be that American culture and marketing is more likely to lead African
American households to become over-extended, whereas Hispanic immigrants are
less likely to because of more limited exposure to such marketing. They have less
experience in using financial institutions and may additionally be less likely to
overuse credit. It is plausible that a lower amount of marketing that targets Hispanics
may significantly influence their tendency to have lower probabilities of delinquency.
This study supports the hypothesis that financially negative events have a
significant impact on a household‘s ability to repay debt in timely manner. Higher
probabilities of repayment delinquency were observed in those who reported that their
income in the previous year was unusually low compared to what they would expect
in a normal year and those who reported poor health status. The variables related to
financially adverse events were all significant across all racial/ethnic groups, with the
only exception being poor health status, which did not have a significant impact on
the delinquency risk of Hispanic households.
204
This study hypothesizes that financial buffers encourage consumers to
continue repaying their debt obligations according to schedule. The variables related
to financial buffers were all significant across racial/ethnic groups. Financial buffers
such as net worth and health insurance coverage for family members were also
important determinants in analyzing delinquency risks.
Net worth is strongly related inversely to delinquency, presumably because
households with more positive net worth can use resources to avoid delinquency.
Respondents lacking health insurance may experience even more severe difficulties in
repayment of household debt in the event of unexpected medical problems compared
to those who have health insurance.
Household debt burden was also an important predictor of delinquency risk.
The monthly payments-to-income ratio was positively related to the probability of
being delinquent. If the debt burden that households bear is high, they have less
money available to purchase goods or services. Therefore, consumers having higher
amounts of debt burden are more likely to have repayment problems when they
experience financial adversities such as job loss or illness. The findings on the effects
of household debt burden are somewhat perplexing. The multivariate results showed
that the effect of household debt burden was significant in predicting the likelihood of
household debt repayment delinquency when controlling for racial/ethnic differences.
However, separate multivariate models for each race and ethnicity illustrated that the
effects of household debt burden were not same across racial/ethnic groups.
The effect of the household debt payment burden on delinquency is reasonable
for white and Hispanic households, as an increasing burden increases the likelihood of
delinquency. With any type of household emergency, a household with a high debt
205
burden will face difficult choices and may have to skip debt payments. The lack of a
significant relationship between the debt burden and delinquency for black
households is puzzling, though it is possible that household disruptions unrelated to
the debt burden might affect delinquency independent of the debt burden. The debt
burden measure does not include rent payments, so for low income households
generally, being likely to be renting, there may be budget problems even with a low
debt burden. However, the fact that the debt burden has a significant effect for
Hispanic households but not for black households, when both have similar economic
characteristics, needs further investigation. The interaction models (e.g., Table 5.16)
provides some additional insight into the pattern for black households. The combined
effect of the debt burden variable by itself and the interaction term with black results
in a positive predicted effect for the debt burden variable on delinquency.
The pattern for Asian/other households is even more puzzling, as the
likelihood of delinquency decreases as the debt burden increases. It is possible that
use of debt for homeownership or business investments may increase the motivation
of these households to keep up with payments, perhaps receiving assistance from
relatives or community networks to maintain the payments.
The effect of expected future income on the probability of debt repayment also
differed across racial/ethnic groups. In particular, blacks and Hispanics who were not
certain about their future income were less likely to be delinquent than white, while
whites who were not certain about their future incomes were more likely to be
delinquent. One possible source for these results is the differences in beliefs or
expectations of unfair or discriminatory treatment against racial/ethnic minorities in
the market. Given historical discrimination against racial/ethnic minorities in
206
employment and financial markets, people from these groups may be more sensitive
to unexpected adversities. In other words, they may experience negative projections
of future income more pessimistically than whites, thus being more reluctant to incur
additional debt out of greater anxiety about future repayment abilities.
The findings presented in this study showed that the probability of repayment
delinquency increased from 1992 to 1995 and then again in 2001 and 2004 in general.
This result supports the reports that delinquency rates were on the rise throughout the
1990s, peaked in 2004, and dropped afterwards. These yearly trends are consistent
across racial/ethnic groups.
The report from the Board of Governors of the Federal Reserve System (2000)
showed that, with the rapid increase of household debt, the household debt service
burden increased to levels not seen since the late 1980s. Even though unemployment
ratio was relatively low and household net worth was high by historical standards, the
credit quality of the household appears to have been impaired, and delinquency rates
on home mortgages, credit cards, and auto loans peaked in 2000. More recently, The
American Bankers Association reported that the seasonally adjusted percentage of
credit card accounts at least 30 days overdue reached the highest level since 1973
(Chicago Tribune, 2005). Negative events during 2004 such as rising interest rates, a
decline in savings, increases in the cost of basic necessities such as education, housing
and health care and stagnant household income are plausible causes for increased
probability of repayment delinquency. In addition, gasoline prices are considered
partially responsible for increasing financial strife at this time (Chicago Tribune,
2005).
207
6.2 Implication
6.2.1 Implication for Education
Given that consumers‘ acquisition of household debt and their repayment
problems have increased, it is important to find ways to moderate their increasing
repayment problems and to help them make better financial management choices. The
credit counseling industry has undergone a dramatic change (Loonin, & Plunkett,
2003). Consumers‘ demand for credit counseling has increased, but funding to
agencies has been sharply reduced. Therefore, the efficacy of financial education
programs need to be reviewed and new educational agendas for credit counseling
agencies need to be developed.
Leppel (2002) suggests that financial programs should specifically focus on
students from ―educationally disadvantaged backgrounds‖ and specifically identifies
black women as a potential audience for such programs. Since the population of
ethnic minorities is becoming more visible, numerous programs need to be developed
with specific attention to and accommodation of a multicultural emphasis.
Traditional financial education programs have assumed a homogenous
audience and financial topics and need to be reconfigured or replaced with programs
that are developed with attention to cultural differences. Even though some racial
minorities were born in the US, they might have grown up with live with families
with limited familiarity with the US financial systems because of low income.
In terms of more specific educational agendas, building sufficient financial
buffers needs to be prioritized since it equips consumers with modest financial buffers
that can cushion financially adverse shocks. This, in turn, should help those who have
difficulties in repaying their payment obligations in a timely fashion, improve their
208
credit histories over time, and make them eligible for low-cost sources of credit. It
seems likely that regardless of the nature of the initial financially adverse events, the
cases that reach the point of serious delinquency are those in which borrowers are
unable to overcome the impact of these events or sell their property to resolve the
crisis or draw on their accumulated assets. Therefore, financial educators should
emphasize the value of holding financial buffers.
As demonstrated in Chapter 4, selected variables relevant to Cash Flow
Theory were significant across racial/ethnic groups. However, the extent to which
selected independent variables influence repayment delinquency differed between
racial/ethnic groups. For example, large household debt burden more severely
influence the repayment delinquency for Hispanics and blacks than whites and
Asians/others. Therefore, educational focus should be toward helping people from
these racial/ethnic groups to understand how to balance their current income and
future expected income as well as helping them to incur only the amount that they can
afford. Also, the amount of net worth influences the probability of repayment
delinquency more sharply among Hispanics and blacks than it does to whites‘
repayment delinquency. Therefore, for the Asians/others groups, building a financial
net worth can be one way to lessen their repayment problems.
6.2.2 Future Research
As discussed previously, this study observed that there exist differences in
repayment performance between black and Hispanic households, even though the two
groups have similar financial situations and levels of educational attainment. However,
the majority of studies regarding socioeconomic characteristics, psychological
characteristics, and consequences of financial management have focused on the
209
dominant U.S. racial population, i.e., whites. Research on racial/ethnic minorities has
been limited. Therefore, future research about cultural difference needs to be more in
depth. For instance, conducting a qualitative research such as a focus group study
allows for greater understanding of the causes of the differences in delinquency rates.
Future research surrounding this topic should include a larger sample.
Oversampling of racial/ethnic minority household may make more robust estimates of
patterns possible. The current analysis, due to a data limitation, did not contain a large
sample from Hispanics and Asians/others. As reviewed in Chapter 3, there are
differences in the way racial/ethnic groups manage family finances. Among the ethnic
and racial groups, differences were noted in not only family money management, such
as savings patterns, investment practices, use of credit, but also the financial
socialization of children and attitudes perpetuated toward financial management.
It is practically infeasible for consumers to calculate their expected income
stream for their entire lifetime and the accorded interest and discount rates with
certainty. Consumers tend to be bound to make their decisions under bounded
rationality. For the previous decade, numerous studies have shown that psychological
factors play a pivotal role on financial decision-making. Economic psychologists
examine how external stimuli influence consumer‘s general economic decisions by
examining variables such as motives, aspirations, and expectations (Katona, 1975).
However, few studies have explored the role of psychological factors in the
consumers‘ credit behavior. Given the importance of credit repayment problems in
household well-being, future studies must examine psychological factors that
influence people‘s credit decisions and their management of them.
210
211
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APPENDIX
Description of Independent Variables
Variables
Description
Demographics
Racial/ethnic self-identification of the respondent (based on variable X6809)
White
1 if a respondent said he/she is White, 0 otherwise
Black
1 if a respondent said he/she is Black, 0 otherwise
Hispanic
1 if a respondent said he/she is Hispanic, 0 otherwise
1 if a respondent said he/she chose a category other than
Asian/others
white, black, or Hispanic,0 otherwise
Age
Age
Age of household head
Age squared
Squared age of household head
Education
1 if household had respondent obtained less than high
Less than High
school degree, 0 otherwise
1 if household had respondent obtained high school
High
degree, 0 otherwise
1 if household had respondent obtained some college
Some College
degree, 0 otherwise
1 if household had respondent obtained a college degree,
College Degree
0 otherwise
Family Composition
1 if a respondent is married and has children under 18 , 0
Married w/child
otherwise
1 if a respondent is married and does not have children
Married w/o child
under 18 , 0 otherwise
1 if a respondent is a single and has children under 18 , 0
Single w/ child
otherwise
1 if a respondent is a single and does not have children
Single w/o child
under 18 , 0 otherwise
Current Income
Log of household‘s annual income (if income ≤0, use
Log of Income
ln (0.01))
Income < $22,256
Household‘s annual income < $22,256.4
$22,256 ≤Income
$22,256.4 ≤ Household‘s annual income < $44,301.7
<$44,301
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Variables
$44,301.7≤Income<$
78,748
$78,748≤ Income
Home ownership
Yes
No
Expected Income Growth
Surely Same
Surely Increase
Not Sure
Surely Decrease
Employment
Self Employed
Not working
Retired
Salary earner
Description
$44,301.7 ≤ Household‘s annual income < $78,748.2
$78,748.2 ≤ Household‘s annual income
if the household owns a home, 0 otherwise
if the household does not own a home, 0 otherwise
1 if a respondent has a good idea of what family income
for next year will be and expects total income to go about
the same as prices, 0 otherwise
1 if a respondent has a good idea of what family income
for next year will be and expects total income to go up
more than prices, 0 otherwise
1 if a respondent does not has a good idea of what family
income for next year will be, 0 otherwise
1 if a respondent has a good idea of what family income
for next year will be and expects total income to go less
than prices, 0 otherwise
1 if household head is self employed, 0 otherwise
1 if household head is not working, 0 otherwise
1 if household head is retired, 0 otherwise
1 if household head is a salary earner, 0 otherwise
Financial Buffers
Net worth
Log of Net worth
NW< $13,368.4
$13,368.4≤NW<$92,
164.1
$92,164.1≤NW<
$285,732
$285,732≤NW
Health Insurance
Yes
No
Log of household‘s net worth (if net worth ≤0, use
ln (0.01))
net worth < $13,368.4
$13,368.4 ≤ net worth < $92,164.1
$92,164.1 ≤ net worth < $285,732.0
$285,732 ≤ net worth
1 if everyone in the respondent‘s household is covered
by private health programs or some type of government,
0 otherwise
1 if everyone in the respondent‘s household is not
covered by private health programs or some type of
government, 0 otherwise
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Variables
Financially Adverse Events
Health Status
Excellent, fair or good
Poor
Transitory Income
Lower than Normal
Income
Same or Higher than
Normal Income
(Reference)
Household Debt Burden
Debt to Income Ratio
(PIRTOTAL)
Total monthly payments
(TRAY )
Monthly household
income
Debt to Income Ratio
<10%
10% ≤ Debt to Income
Ratio <25%
25% ≤ Debt to Income
Ratio <40%
40% ≤ Debt to Income
Ratio
Environmental Variable
Year of Survey
1992
Description
1 if respondent's self-perceived health condition is
excellent, fair or good or, for couples, neither has
poor health, 0 otherwise
1 if respondent's self-perceived health condition is
poor or, for couples, one (or both) has poor health, 0
otherwise
1 if income is unusually low compared to what he/she
would expect in a "normal" year, 0 otherwise
1 if income is unusually high or same compared to
what he/she would expect in a "normal" year, 0
otherwise
Ratio of total monthly payments on all debt to monthly
household income
if income > 0 then
PIRTOTAL=max((TPAY/MAX((INCOME/12),1)),1);
Total monthly payments on all debts include payments
on credit cards, mortgages, home equity loans, home
equity lines of credit, other home improvement loans,
loans for other residential real estate, education loans,
installment loans, vehicle loans, pension loans, margin
loans, loans against the cash value of life insurance
and other miscellaneous loans.
(Note) SCF code assumes payments on credit cards =
2.5% of the balances
Household income / 12
The ratio of monthly payments to monthly income
<10%
10% ≤ The ratio of monthly payments to monthly
income <25%
25% ≤ The ratio of monthly payments to monthly
income < 40%
40%≤ The ratio of monthly payments to monthly
income
1 if year of survey is 1992, 0 otherwise (reference)
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Variables
1995
1998
2001
2004
2007
Description
1 if year of survey is 1995, 0 otherwise
1 if year of survey is 1998, 0 otherwise
1 if year of survey is 2001, 0 otherwise
1 if year of survey is 2004, 0 otherwise
1 if year of survey is 2007, 0 otherwise
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