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

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.

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.

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Dedicated to God and my family

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.

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……………………………….… 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), 81- 95. 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.

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FIELDS OF STUDY

Major Field: Consumer Sciences Area of Emphasis: Family Resource Management Minor Field: Statistics

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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

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

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

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 Summary ...... 128

5 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

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5.2.2.2 Blacks ...... 147

5.2.2.3 Hispanics ...... 150

5.2.2.4 Asians/others ...... 152

5.2.3 Interaction Effects ...... 154

6 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

LISTS OF FIGURES

Figure Page

Figure 2.1 : Friedland‘s (1993) Framework of Credit Scoring Schemes ...... 36

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CHAPTER 1

1 INTRODUCTION

1.1 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

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 first- half 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

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.

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.

1.2 Problem Statement

1.2.1 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 utility- maximizing 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

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

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

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

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

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.

CHAPTER 2

2 THEORETICAL BACKGROUND

2.1 The Economics of Household Debt Acquisition

2.1.1 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

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.

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 j (Equation 1) Max Et  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 )  Etu(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.

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,

rAt (Equation 4) st   yt Ct 1 rt

And between savings and the evolution of income over time,

  j (Equation 5) st  (1 r) 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.

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.

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

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

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

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.

Family status /Living arrangement

Employment

Application Personal Information 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

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) CD , is : * t 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) CSt   t t (t  Kt )

where  t is household level variables.

Some or all of 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.

Then a household is credit constrained if

(Equation 9) CDt – CSt > 0

( t  yy  At1(1 rt1)  t -> 0 Hence P =  (wt )  t

where wt (Kt  X t ) and P is the probability that a household is credit constrained.

2.2 Repayment of Household Debt

2.2.1 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

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 :

n (Equation 11) P( S X ) =1 and S represent the ith choice variable of the i1 i i 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.

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).

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-to- value 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).

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

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

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‖,

―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.

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,

―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

(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

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 Conceptual Models and Research Hypotheses

2.3.1 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

begin a slow decline, and decrease sharply from the time that he/she retires. The LC-

PIH 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).

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

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).

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

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

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

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.

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

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

Hispanic. Bertola, Disney and Grant (2006) showed households whose head is non- white 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.

(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

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.

(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

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.

(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

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.

CHAPTER 3

3 LITERATURE REVIEW

3.1 Acquisition of Household Debt

3.1.1 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

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

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 upward- sloping 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

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.

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.

3.2 Repayment of Household Debt

3.2.1 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.

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

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.

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.

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

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.

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 six- year 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

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.

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

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

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

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-payments- to-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,

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

study showed that the ratio of total debt to income negatively related to the probability of slow or missed debt payments.

3.3 Racial/Ethnic Disparities in Household Variables

3.3.1 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

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.

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

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.

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 white- owned 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.

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

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

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,

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

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

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

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.

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

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

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

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 employer- sponsored 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

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 METHOD

4.1 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

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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

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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

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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, Starr-

McCluer, & 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

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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.

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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

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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

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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.

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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 non-

linear 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

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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-to- monthly 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

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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

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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.

 P  In  = β0 + β1 X1+ β2 X2+β3 X3+β4X4+ ε (4.2) 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,

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* it =1 if it  0 and 0 otherwise,

So the probability of interest is

P = Prob [ =1│  ] (4.3) it it

Assuming that εit is normally distributed with mean 0 and variance 1, one obtains the delinquency probability

(4.4) Prob [ =1 │ ] = Prob [ > 0 │ ] = Prob [ε ≤ ' it │ ]=

 ( ' it )

Where (•) is the standard normal CDF. The classification rule is

(4.5) = 1 if ( ) > 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:

1 if a respondent sometimes got behind or missed payments and he /she was ever behind (4.6) Payment in his/her payments Delinquency = 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) Creditworthiness = 0 + 1X +  i

1 if Creditworthiness ≥ min (4.8) Credit Acquisition = (Creditworthiness) 0 if Creditworthiness < min (Creditworthiness)

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 ( y1i ≥ 0)

(4.9) = y1i 0 if the respondent did not incur household debt ( < 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

* household debt ( y2i ≥ 0) (4.10) y2i = 0 if the respondent was not delinquent on repayment of

household debt ( < 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

Y1i = 1 if Y1i * > 0 Y1i = 0 if Y1i * ≤ 0

(4.12) 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

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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 notdelinquent nocard

= N N N y1i y2i y1i (1 y2i ) 1 y1i  {(Zi1 )F(Zi2 )}  {(Zi1 )(1 F(Zi2 )}  {1 (Zi1 )} i1 i1 i1

N  y1i y2i [In{(zi1 )  InF(zi2 )] (4.16) Ln L(α1, α2) = 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 >0, and 0 else.

y2i and X2i are only observed if = 1

and X1i are observed for all applicants.

Outcome equation

Repayment Delinquency equation: y2i = ·X2i + ε2i

* (4.18) y2i =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 [ = 1 │ = 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 ( y2i =1) or he/she was not ( = 0). The problem

is to build a satisfactory model of Prob [ y2i = 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

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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.

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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 RESULTS

5.1 Descriptive Result

5.1.1 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,

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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

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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

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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.

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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 Multivariate Result

5.2.1 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

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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 self- employed, 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

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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

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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

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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.

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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.

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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

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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.

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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.

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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

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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.

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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

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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.

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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

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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

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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.

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Year of Survey 1992 1995 1998 2001 2004 2007 total All 3,906 4,299 4,305 4,442 4,519 4,418 25,889 Respondents

Respondents with Different Racial/Ethnic 16 15 7 13 13 6 67 Identification

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.

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Variables Categories Percentage Demographic Variables Race/Ethnicity White 75.76 Black 12.75 Hispanics 7.83 Asians/others 3.66 Education Level Less than High school 11.85 High School 30.79 Some College 19.89 Bachelor 37.48 Family Composition Married w/o children 26.60 Married w/ children 25.04 Single w/o children 36.60 Single w/ children 11.76 Children under 18 Yes 43.66 No 56.34 Employment Status Self Employed 10.18 Salary Earner 56.05 Not Working 16.50 Retired 17.27 Homeownership Yes 66.80 No 33.20 Expected Income Growth Sure the Same 32.01 Sure Increase 13.70 Sure Decrease 21.83 Not Sure 32.46 Age Less than 30 14.82 30~39 20.45 40~49 21.74 50~60 16.29 60 + 26.69 Income Income < $22,256 25.03 $22,256 ≤Income <$44,301 24.93 $44,301≤Income<$78,748 25.06 $78,748≤ Income 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)

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Table 5.2 Continued

Variables Categories Percentage

Net worth NW< $13,368.4 25.01 $13,368.4≤NW<$92,164.1 24.99 $92,164.1≤NW< $285,732 25.00 $285,732≤NW 25.00

Financially Adverse Events Transitory Income Higher than Normal Income 9.23 Low than Normal Income 17.46 Same as Normal Income 73.32 Health Status Excellent 21.57 Good 45.77 Fair 23.50 Poor 9.16 Financial Buffers Health Insurance Coverage Yes 80.89 No 19.11 Environmental Variable Year of Survey 1992 15.05 1995 16.61 1998 16.65 2001 17.16 2004 17.45 2007 17.08

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Race / Ethnicity Variables Categories Asians Whites Blacks Hispanics /others

Demographic Variables Age Less than 30 13.16 17.56 24.77 18.31 30~39 18.88 24.31 28.15 23.15 40~49 21.44 21.98 23.46 23.49 50~60 16.60 15.68 12.91 19.32 60 + 29.92 20.46 10.71 15.73 Education Less than high school 9.01 16.44 35.11 8.10 High School 39.88 33.75 30.00 20.72 Some College 19.66 22.88 16.93 20.67 Bachelor 40.44 26.93 17.97 50.50 Family Composition Married w/o children 29.89 13.01 17.37 25.53 Married w/ children 25.18 15.11 36.36 32.48 Single w/o children 36.65 43.92 26.48 31.63 Single w/ children 8.27 27.95 19.79 10.36 Employment Status Self Employed 11.39 4.42 7.44 11.15 Salary Earner 54.43 58.58 63.63 64.44 Not working 14.70 23.46 23.01 15.62 Retired 19.48 13.55 5.91 8.79 Homeownership Yes 72.97 46.47 45.20 56.11 No 27.03 53.53 54.80 43.89 Income Income < $22,256 20.91 42.74 35.89 25.41 $22,256 ≤Income 24.38 25.91 31.18 19.40 <$44,301 $44,301≤Income<$78, 26.31 19.64 21.85 24.77 748 $78,748≤ Income 28.40 11.72 11.09 30.42 Expected Income Sure the Same 33.89 24.96 26.81 28.72 Growth Sure Increase 13.69 11.97 14.08 19.03 Sure Decrease 24.07 16.06 11.32 18.04 Not sure 29.35 47.00 47.79 34.21 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)

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Table 5.3: Continued

Race / Ethnicity Variables Categories Asians Whites Blacks Hispanics /others

Financially Adverse Events Transitory Higher than Normal 9.45 7.49 10.11 8.82 Income Lower than Normal 16.05 21.96 23.25 18.48 Same as Normal 74.50 70.55 66.64 72.70 Health Status Excellent 22.11 20.77 15.91 24.40 Good 46.25 41.87 46.68 46.44 Fair 22.53 27.16 28.12 21.94 Poor 9.10 10.08 9.29 7.13

Financial Buffers Health Insurance Yes 84.77 73.70 55.46 79.95 No 15.23 26.30 44.54 20.05 Net worth NW< $13,368.4 18.94 45.86 48.04 28.62 $13,368.4≤NW<$92,164.1 23.89 30.36 26.87 25.24 $92,164.1≤NW< $285,732 27.53 16.76 16.49 19.53 $285,732≤NW 29.64 7.02 8.60 26.61

Household Debt Burden Debt to Income Debt to Income Ratio<10% 46.02 53.11 51.86 49.92 Ratio 10%≤ Debt to Income 30.28 22.34 20.96 25.54 Ratio<25% 25%≤ Debt to Income 14.94 12.82 14.40 13.44 Ratio<40% 40%≤Debt to Income Ratio 8.76 11.73 12.77 11.10

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Having Debt Variables Categories Yes Means Test Demographic Variables Race/Ethnicity White 73.07 Black 66.22 <0.0001 Hispanics 67.08 <0.0001 Asians/others 70.06 <0.0001 Education Less than High school 49.20 High School 69.52 <0.0001 Some College 76.91 <0.0001 Bachelor 81.67 <0.0001 Family Composition Married w/o children 71.25 Married w/ children 90.24 <0.0001 Single w/o children 59.03 <0.0001 Single w/ children 71.96 0.1215 Age Less than 30 73.25 30~39 82.07 <0.0001 40~49 84.62 <0.0001 50~60 81.32 <0.0001 60 + 49.02 <0.0001 Economic Variables Employment Status Self Employed 81.82 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)

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Table 5.4: Continued

Having Debt Variables Categories Yes Means Test

Net worth NW< $13,368.4 59.73 $13,368.4≤NW<$92,164.1 77.41 <0.0001 $92,164.1≤NW< $285,732 75.15 <0.0001 $285,732≤NW 74.18 <0.0001

Environmental Variable

Year of Survey 1992 58.27 1995 64.53 <0.0001 1998 74.36 <0.0001 2001 76.01 <0.0001 2004 77.20 <0.0001 2007 77.48 <0.0001 Total 71.62

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Delinquency Means Variables Categories by two months Test or more

Demographic Variables Race/Ethnicity White 6.58 Black 14.33 <0.0001 Hispanics 10.24 <0.0001 Asians/others 7.90 0.03234 Education Less than High School 5.89 High School 6.04 0.5122 Some College 7.44 <0.0001 Bachelor 4.04 <0.0001 Age Less than 30 8.11 30~39 8.52 <0.0001 40~49 6.27 <0.0001 50~60 5.48 <0.0001 60 + 1.89 <0.0001 Family Composition Married w/o Children 3.50 Married w/ Children 7.30 <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)

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Table 5.5: Continued

Delinquency Means Variables Categories by two months Test or more Financially Adverse Events Expected Future Sure the Same 5.32 Income Sure Increase 7.24 <0.0001 Sure Decrease 6.67 <0.0001 Not sure 11.56 <0.0001 Transitory Income Higher than Normal 6.79 Lower than Normal 15.14 <0.0001 Same as Normal 6.21 <0.0001 Health Status Excellent 3.80 <0.0001 Good 5.24 <0.0001 Fair 7.17 0.7637 Poor 7.08 Financial Buffers Health Insurance Yes 5.92 No 16.31 <0.0001 Net worth NW< $13,368.4 19.09 $13,368.4≤NW<$92,164.1 8.81 <0.0001 $92,164.1≤NW< $285,732 4.20 <0.0001 $285,732≤NW 1.34 <0.0001 Environmental Variable Year of Survey 1992 7.63 1995 8.19 <0.0001 1998 7.97 <0.0001 2001 7.02 <0.0001 2004 8.94 <0.0001 2007 7.11 <0.0001 Household Debt Burden Debt to Income Ratio Debt to Income Ratio<10% 7.29 10%≤Debt to Income Ratio<25% 6.47 <0.0001 25%≤Debt to Income Ratio<40% 6.58 <0.0001 40%≤Debt to Income Ratio 11.79 <0.0001

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Parameter Categories Estimate S.E. P value Year of Survey 1995 0.2252 0.0139 <.0001 1998 0.5508 0.0144 <.0001 2001 0.5589 0.0143 <.0001 2004 0.5594 0.0143 <.0001 2007 0.6046 0.0145 <.0001 Race/Ethnicity Blacks -0.0821 0.0145 <.0001 Hispanics -0.1895 0.0178 <.0001 Asians/others -0.1055 0.0218 <.0001 Age Age 0.0409 0.0016 <.0001 Age squared -0.0006 0.0000 <.0001 Education Less than High 0.2153 0.0142 <.0001 Less than Bachelor 0.4022 0.0159 <.0001 Some College 0.3354 0.0145 <.0001 Family Composition Married w/child 0.2396 0.0129 <.0001 Single w/o child -0.1701 0.0110 <.0001 Single w/ child -0.1634 0.0168 <.0001 Homeowner Yes 0.8831 0.0122 <.0001 Income Log of Income -0.0094 0.0024 <.0001 Net worth Log of Net worth -0.0311 0.0011 <.0001 Expected Income Growth Sure the Same 0.0011 0.0122 0.9299 Sure Increase 0.0369 0.0145 0.0109 Not Sure -0.1111 0.0122 <.0001 Employment Self Employed -0.0775 0.0121 <.0001 Not working -0.4748 0.0122 <.0001 Retired -0.4928 0.0149 <.0001 Intercept -0.3118 0.0484 <.0001 Percent Concordant 80.3 Table 5.6: Probit Regression of Probability of Holding Household Debt

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Odds Parameter Categories Estimate S.E. P-value Ratio Demographics Race /Ethnicity Blacks 0.3290 0.0406 <.0001 1.390 Hispanics -0.2210 0.0554 <.0001 0.802 Asians/others -0.1245 0.0800 0.1196 0.883 White (Reference) Age Age 0.1156 0.0078 <.0001 1.123 Age squared -0.0014 0.0001 <.0001 0.999 Education Level High School 0.1265 0.0502 0.0118 1.135 Some College 0.3228 0.0558 <.0001 1.381 Bachelor -0.1883 0.0557 0.0007 0.828 Less than High (Reference) Family Composition Married w/child 0.6963 0.0511 <.0001 2.006 Single w/o child 0.3073 0.0520 <.0001 1.360 Single w/ child 0.6792 0.0569 <.0001 1.972 Married w/o child (Reference) Current Income Log of Income -0.0448 0.0081 <.0001 0.956 Expected Income Sure the Same -0.2841 0.0462 <.0001 0.753 Growth Sure Increase -0.1006 0.0518 0.0521 0.904 Not Sure -0.0278 0.0428 0.5160 0.973 Surely Decrease (Reference) Employment Self Employed -0.3181 0.0479 <.0001 0.728 Not working -0.2070 0.0452 <.0001 0.813 Retired -0.8034 0.1050 <.0001 0.448 Salary Earner (Reference) Financial Buffers Net worth Log of Net worth -0.0992 0.0024 <.0001 0.906 Health Insurance Yes -0.6105 0.0333 <.0001 0.543 No (Reference) Financially Adverse Events Health Status Poor Health 0.5260 0.0537 <.0001 1.692 Excellent, Fair or Good (Reference) Transitory Income Low Income 0.5576 0.0337 <.0001 1.747 Higher or same Income (Reference) Table 5.7: Logistic Regression of Probability of Being Delinquent on Household Debt by Two Months or More (Continued)

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Table 5.7: Continued

Odds Parameter Categories Estimate S.E. P-value Ratio Household Debt Burden Monthly Debt to Income 0.6424 0.0706 <.0001 1.901 ratio

Environmental Variable Year of Survey 1995 0.3856 0.0602 <.0001 1.471 1998 0.4437 0.0601 <.0001 1.558 2001 0.5765 0.0587 <.0001 1.78 2004 0.3593 0.0619 <.0001 1.432 2007 0.3856 0.0602 <.0001 1.471 1992 (Reference) Mills Ratio 1.6446 0.1017 <.0001 5.179 Intercept -4.5547 0.2233 <.0001 Percent Concordant 82.5

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Variables Categories Estimate S.E. P-Value

Year 1995 0.2367 0.0155 <.0001 1998 0.5684 0.0163 <.0001 2001 0.5662 0.0162 <.0001 2004 0.5701 0.0163 <.0001 2007 0.6139 0.0164 <.0001 1992 (Reference) 0.0381 0.0019 <.0001 Age Age -0.0006 0.0000 <.0001 Age squared 0.2080 0.0176 <.0001 Education High School 0.3574 0.0193 <.0001 Some College 0.3170 0.0175 <.0001 Bachelor Degree 0.2367 0.0155 <.0001 Less than High (Reference) Family Composition Married w/child 0.1901 0.0143 <.0001 Single w/o child -0.2320 0.0121 <.0001 Single w/ child -0.1380 0.0216 <.0001 Married w/o child (Reference) Homeowner Yes 0.8398 0.0144 <.0001 No (Reference) Income Log of Income -0.0118 0.0028 <.0001 Net worth Log of Net worth -0.0451 0.0015 <.0001 Employment Self Employed -0.0285 0.0133 0.0314 Not working -0.4550 0.0146 <.0001 Retired -0.4723 0.0160 <.0001 Salary earner (Reference) Expected Income Sure the Same -0.0121 0.0133 0.3633 Growth Sure Increase 0.0424 0.0160 0.0080 Not Sure -0.0767 0.0137 <.0001 Surely Decrease (Reference) Intercept 0.0346 0.0566 0.5408 Percent Concordant 80.7 Table 5.8 : Probit Analysis of Holding Household Debt for Whites

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Odds Variables Categories Estimate S.E. P-value Ratio

Demographics Age Age 0.1328 0.0100 <.0001 1.142 Age squared -0.0017 0.0001 <.0001 0.998 Education Bachelor Degree -0.0032 0.0647 0.9606 0.997 High School 0.1199 0.0703 0.0878 1.127 Some College -0.4118 0.0695 <.0001 0.662 Bachelor Degree 0.1328 0.0100 <.0001 1.142 Less than High (Reference) Family Married W/child 0.5790 0.0587 <.0001 1.784 Composition Single w/o child 0.1036 0.0615 0.0921 1.109 Single w/ child 0.6505 0.0677 <.0001 1.917 Married w/o child (Reference) Current Income Log of Income -0.0214 0.0104 0.0389 0.979 Expected Income Sure the Same -0.2530 0.0550 <.0001 0.776 Growth Sure Increase -0.1607 0.0640 0.0120 0.852 Not Sure 0.1231 0.0511 0.0159 1.131 Surely Decrease (Reference) Employment Self Employed -0.3173 0.0553 <.0001 0.728 Not working -0.2522 0.0555 <.0001 0.777 Retired -0.7912 0.1279 <.0001 0.453 Salary earner (Reference)

Financial Buffers Net worth Log of Net worth -0.1170 0.0032 <.0001 0.89 Health Insurance Yes -0.6806 0.0414 <.0001 0.506 No (Reference)

Financially Adverse Events Health Status Poor 0.6698 0.0645 <.0001 1.954 Excellent, fair, or good Transitory Income Low income 0.5981 0.0415 <.0001 1.819 Same or High income (Reference) Table 5.9: Logistic Analysis for Probability of Being Delinquent for whites. (Continued)

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Table 5.9: Continued

Odds Variables Categories Estimate S.E. P-value Ratio

Household Debt Burden 0.8505 0.0863 <.0001 2.341 Debt to Income Ratio

Environmental Variable 0.3934 0.0696 <.0001 1.482 Year of Survey 1995 0.5161 0.0746 <.0001 1.676 1998 0.5736 0.0749 <.0001 1.775 2001 0.7093 0.0738 <.0001 2.032 2004 0.6001 0.0764 <.0001 1.822 2007 1992 (Reference) Mills Ratio 1.5708 0.1328 <.0001 4.811 Intercept -4.8368 0.2717 <.0001 -4.8368 Percent Concordant 83.2

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Variables Categories Estimate S.E. P-value year 1995 0.2581 0.0468 <.0001 1998 0.5953 0.0468 <.0001 2001 0.7118 0.0467 <.0001 2004 0.6020 0.0455 <.0001 2007 0.7044 0.0479 <.0001 1992 (Reference) Age Age 0.0225 0.0047 <.0001 Age squared -0.0003 0.0001 <.0001 Education Bachelor Degree 0.1958 0.0358 <.0001 High School 0.4802 0.0422 <.0001 Some College 0.7005 0.0463 <.0001 Less than High (Reference) Family Composition Married w/child 0.5381 0.0590 <.0001 Single w/o child -0.0565 0.0442 0.2014 Single w/ child -0.0621 0.0495 0.2094 Married w/o child (Reference) Homeowner Yes 1.1023 0.0367 <.0001 No (Reference) Log of Income 0.0718 0.0078 <.0001 Net worth Log of Net worth -0.0184 0.0025 <.0001 Employment Self Employed 0.1338 0.0443 0.0025 Not working 0.2201 0.0541 <.0001 Retired -0.0564 0.0398 0.1571 Salary earner (Reference) Expected Sure the Same -0.3267 0.0624 <.0001 Income Growth Sure Increase -0.5203 0.0332 <.0001 Not Sure -0.7248 0.0575 <.0001 Surely Decrease (Reference) Intercept -1.5353 0.1488 <.0001 Percent Concordant 82.1 Table 5.10: Probit Analysis of Holding Household Debt for blacks

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Odds Variables Categories Estimate S.E. P-value ratio Demographics Age Age 0.0598 0.0150 <.0001 1.062 Age squared -0.0005 0.0002 0.0020 0.999 Education High School 0.2829 0.1113 0.0110 1.327 Some College 0.5983 0.1271 <.0001 1.819 Bachelor 0.5204 0.1377 0.0002 1.683 Less than High

(Reference) Family Composition Married w/child 0.8246 0.1570 <.0001 2.281 Single w/o child 0.7172 0.1435 <.0001 2.049 Single w/ child 0.8974 0.1489 <.0001 2.453 Married w/o child (Reference) Current Income Log of Income -0.0021 0.0249 0.9343 0.998 Expected Income Sure the Same -0.4006 0.1143 0.0005 0.67 Growth Sure Increase 0.2450 0.1182 0.0382 1.278 Not Sure -0.2127 0.0992 0.0319 0.808 Surely Decrease (Reference) Employment Self Employed -0.2168 0.1571 0.1676 0.805 Not working 0.0781 0.0991 0.4307 1.081 Retired -0.7984 0.2141 0.0002 0.45 Salary earner (Reference)

Financial Buffers Net worth Log of Net worth -0.0777 0.0051 <.0001 0.925 Health Insurance Yes -0.5207 0.0728 <.0001 0.594 No (Reference)

Financially Adverse Events Health Status Poor 0.1609 0.1258 0.2012 1.175 Excellent, fair or good Transitory Income Low income 0.4265 0.0776 <.0001 1.532 Same or High income (Reference)

Table 5.11: Logistic Analysis of Being Delinquent on Household Debt for blacks (Continued)

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Table 5.11 : Continued

Odds Variables Categories Estimate S.E. P-value ratio Household Debt Burden Debt to income ratio 0.0200 0.1711 0.9070 1.02

Environmental Variable Year of Survey 1995 0.0697 0.1368 0.6104 1.072 1998 0.3398 0.1325 0.0103 1.405 2001 0.0351 0.1395 0.8012 1.036 2004 0.5178 0.1275 <.0001 1.678 2007 -0.1573 0.1413 0.2656 0.854 1992 (Reference) Mills Ratio 0.9411 0.1855 <.0001 2.563 Intercept -3.9990 0.5582 <.0001 Percent Concordant 74.7

176

Variables Categories Estimate S.E P-value Year of Survey 1995 0.0036 0.0641 0.9557 1998 0.4504 0.0597 <.0001 2001 0.4851 0.0586 <.0001 2004 0.4431 0.0563 <.0001 2007 0.4388 0.0576 <.0001 1992 (Reference) Age Age 0.0236 0.0067 0.0005 Age squared -0.0003 0.0001 <.0001 Education High School 0.3043 0.0401 <.0001 Some College 0.6372 0.0571 <.0001 Bachelor 0.5885 0.0558 <.0001 Less than High (Reference) Family Married W/child 0.3402 0.0526 <.0001 Composition Single w/o child -0.0195 0.0530 0.7134 Single w/ child -0.0067 0.0588 0.9090 Married w/o child (Reference) Homeowner Yes 1.0850 0.0445 <.0001 No (Reference) Income Log of Income 0.0786 0.0117 <.0001 Net worth Log of Net worth 0.0035 0.0032 0.2778 Employment Self Employed -0.0895 0.0607 0.1404 Not working -0.2472 0.0415 <.0001 Retired -0.4935 0.0968 <.0001 Salary earner (Reference) Expected Income Sure the Same 0.0541 0.0620 0.3832 Growth Sure Increase 0.0738 0.0700 0.2917 Not Sure -0.0959 0.0575 0.0952 Surely Decrease (Reference) Intercept -1.7146 0.1959 <.0001 Percent Concordant 82.0 Table 5.12: Probit Analysis of Holding Household Debt for Hispanics

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Odds Parameter Categories Estimate S.E. P- value Ratio

Demographics Age Age 0.0598 0.0278 0.0314 1.062 Age squared -0.0007 0.0003 0.0540 0.999 Education High School 0.2325 0.1358 0.0869 1.262 Some College 0.7427 0.1650 <.0001 2.102 Bachelor 0.3059 0.1787 0.0870 1.358 Less than High (Reference) Family Married w/child 0.7944 0.1869 <.0001 2.213 Composition Single w/o child 0.2780 0.1976 0.1593 1.321 Single w/ child 1.0286 0.1960 <.0001 2.797 Married w/o child (Reference) Current Income Log of Income -0.0542 0.0337 0.1074 0.947 Expected Income Sure the Same -0.4454 0.1665 0.0075 0.641 Growth Sure Increase -0.3959 0.1849 0.0322 0.673 Not Sure -0.3477 0.1518 0.0220 0.706 Surely Decrease (Reference) Employment Self Employed -0.2388 0.1657 0.1495 0.788 Not working -0.5092 0.1435 0.0004 0.601 Retired -1.9466 0.7557 0.0100 0.143 Salary earner (Reference)

Financial Buffers Log of Net Net worth -0.0501 0.0090 <.0001 0.951 worth Health Insurance Yes -0.2711 0.1031 0.0086 0.763 No (Reference)

Financially Adverse Events Health Status Poor 0.2242 0.1964 0.2537 1.251 Excellent, Fair, or Good (Reference) Transitory Income Lower than Normal 0.4335 0.1084 <.0001 1.543 Same or Higher than Normal(Reference)

Table 5.13: Logistic Analysis of Being Delinquent on Household Debt for Hispanics (Continued)

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Table 5.13 : (Continued)

Odds Parameter Categories Estimate S.E. P- value Ratio

Household Debt Burden Debt to Income Ratio 0.7672 0.2301 0.0009 2.154

Environmental Variable Year of Survey 1995 0.0887 0.1871 0.6353 1.093 1998 -0.5434 0.2039 0.0077 0.581 2001 0.1800 0.1735 0.2996 1.197 2004 -0.3709 0.1743 0.0334 0.69 2007 -0.2589 0.1766 0.1428 0.772 1992 (Reference)

Mills Ratio 73.6000 73.6000 73.6000 73.6000 Intercept -3.3936 0.8438 <.0001 Percent Concordant 73.6

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Variables Categories Estimate S.E. P value Year of Survey 1995 0.3038 0.0663 <.0001 1998 0.8262 0.0774 <.0001 2001 0.5434 0.0765 <.0001 2004 0.6958 0.0726 <.0001 2007 0.8748 0.0739 <.0001 1992 (Reference) Age Age 0.0342 0.0089 0.0001 Age squared -0.0005 0.0001 <.0001 Education High School 0.2080 0.0850 0.0144 Some College 0.6954 0.0943 <.0001 Bachelor 0.4362 0.0826 <.0001 Less than High (Reference) Family Married w/child 0.3405 0.0604 <.0001 Composition Single w/o child -0.0213 0.0591 0.7185 Single w/ child 0.5344 0.0944 <.0001 Married w/o child (Reference) Homeownership Yes 1.0996 0.0603 <.0001 No (Reference) Income Log of Income -0.0008 0.0119 0.9464 Net worth Log of Net worth -0.0377 0.0057 <.0001 Employment Self Employed 0.1981 0.0621 0.0014 Not working -0.4934 0.0637 <.0001 Retired -0.3792 0.0937 <.0001 Salary earner (Reference) Expected Sure the Same 0.0206 0.0700 0.7680 Income Growth Sure Increase 0.0849 0.0750 0.2576 Not Sure -0.2383 0.0671 0.0004 Surely Decrease (Reference) Intercept -0.8796 0.2526 0.0005 Percent Concordant 80.0 Table 5.14 : Probit Analysis of Holding Household Debt for Asians/others

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Odds Parameter Categories Estimate S.E P-value Ratio Demographics Age Age 0.0786 0.0438 0.0729 1.0820 Age squared -0.0008 0.0005 0.1002 0.9990 Education High School -0.8426 0.2872 0.0033 0.4310 Some College -0.1459 0.3251 0.6535 0.8640 Bachelor -1.1075 0.2878 0.0001 0.3300 Less than High

(Reference) Family Married W/child 1.5818 0.3317 <.0001 4.8640 Composition Single w/o child 1.2120 0.3286 0.0002 3.3600 Single w/ child 1.1957 0.3695 0.0012 3.3060 Married w/o child (Reference) Current Income Log of Income -0.1628 0.0324 <.0001 0.8500 Expected Sure the Same -0.1232 0.2977 0.6790 0.8840 Income Growth Sure Increase -0.0525 0.3031 0.8626 0.9490 Not Sure 0.0825 0.2683 0.7584 1.0860 Surely Decrease (Reference) Employment Self Employed 0.1592 0.2554 0.5331 1.1730 Not working 0.1991 0.2319 0.3906 1.2200 Retired -1.5678 0.7261 0.0308 0.2080 Salary Earner (Reference)

Financial Buffers Net worth Log of Net worth -0.0779 0.0143 <.0001 0.9250 Health Insurance Yes -0.5113 0.1949 0.0087 0.6000 No (Reference)

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)

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Table 5.15 : Continued

Odds Parameter Categories Estimate S.E. P-value Ratio

Household Debt Burden Debt to Income Ratio -1.4902 0.4207 0.0004 9.6190

Environmental Variable Year of Survey 1995 0.3862 0.3074 0.2091 1.4710 1998 0.5766 0.3515 0.1009 1.7800 2001 1.0795 0.3132 0.0006 2.9430 2004 0.3482 0.3136 0.2669 1.4170 2007 -0.0713 0.3731 0.8484 0.9310 1992 (Reference) Mills Ratio 2.2638 0.4353 <.0001 Intercept -3.4649 1.1795 0.0033 -1.4710 Percent Concordant 87.9

182

Exponent Variables Categories Logistic of S.E p-value Coefficient Coefficient

Age Age 0.1144 1.1212 0.0078 <.0001 Age squared -0.0014 0.9986 0.0001 <.0001 Education High School 0.0965 1.1013 0.0501 0.0541 Some College 0.2963 1.3449 0.0556 <.0001 Bachelor -0.1967 0.8214 0.0555 0.0004 Less than High

(Reference) Family Composition Married w/child 0.2963 1.3449 0.0556 <.0001 Single w/o child -0.1967 0.8214 0.0555 0.0004 Single w/ child 0.7049 2.0236 0.0513 <.0001 Current Income Log of Income 0.3022 1.3528 0.0521 <.0001 Expected Income Sure the Same -0.2921 0.7467 0.0464 <.0001 Growth Sure Increase -0.0975 0.9071 0.0520 0.0608 Not Sure -0.0322 0.9683 0.0428 0.4519 Employment Self Employed -0.2990 0.7416 0.0483 <.0001 Not working -0.1973 0.8209 0.0454 <.0001 Retired -0.7916 0.4531 0.1051 <.0001 Financial Buffers Net worth Log of Net worth -0.1086 0.8971 0.0029 <.0001 Health Insurance Yes -0.7432 0.4756 0.0406 <.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

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)

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Table 5.16: Continued

Exponent Variables Categories p-value Logistic of S.E Coefficient Coefficient

Interaction Effects

Financial Buffers Log of Net worth *blacks 0.0264 1.0268 0.0054 <.0001 *Hispanics 0.0369 1.0376 0.0077 <.0001 *Asians/others 0.0110 1.0111 0.0113 0.3303 Health Insurance *blacks 0.3151 1.3704 0.0806 <.0001 *Hispanics 0.6528 1.9209 0.1036 <.0001 *Asians/others 0.1786 1.1955 0.1798 0.3206

Financially Adverse Events

Health Status *blacks -0.5656 0.5680 0.1379 <.0001 *Hispanics -0.6422 0.5261 0.1998 0.0013 *Asians/others -0.5054 0.6033 0.3021 0.0943 Transitory Negative *blacks -0.2227 0.8004 0.0858 0.0094 Income *Hispanics -0.2031 0.8162 0.1119 0.0696 *Asians/others 0.4279 1.5340 0.1696 0.0116

Household Debt Burden Debt to income ratio *blacks -0.5130 0.5987 0.1573 0.0011 *Hispanics 0.1661 1.1807 0.2026 0.4123 *Asians/others -1.5310 0.2163 0.3225 <.0001 Mills 1.5937 4.9219 0.1018 <.0001 Intercept -4.4155 0.0121 0.2240 <.0001

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Logistic Exponent Variables Categories Coefficient of S.E p-value Coefficient

Environmental Variables Year of Survey 1995 0.3045 1.3559 0.0574 <.0001 1998 0.3755 1.4557 0.0602 <.0001 2001 0.4245 1.5288 0.0602 <.0001 2004 0.5624 1.7548 0.0587 <.0001 2007 0.3464 1.4139 0.0619 <.0001

Demographics Race/Ethnicity white -0.2016 0.8171 0.0832 0.0154 Hispanic -0.9358 0.3922 0.1221 <.0001 Asian/others -0.2712 0.7624 0.1953 0.1649 Age Age 0.1144 1.1212 0.0078 <.0001 Age squared -0.0014 0.9986 0.0001 <.0001 Education Bachelor Degree 0.0965 1.1013 0.0501 0.0541 Family Composition Married W/child 0.2963 1.3448 0.0556 <.0001 Single w/o child -0.1967 0.8214 0.0555 0.0004 Single w/ child 0.7049 2.0236 0.0513 <.0001 Current Income Log of Income -0.0435 0.9574 0.0083 <.0001 Expected Income Surely Same -0.2921 0.7466 0.0464 <.0001 Growth Surely Increase -0.0975 0.9071 0.0520 0.0608 Not Sure -0.0322 0.9683 0.0428 0.4519 Employment Self Employed -0.2990 0.7415 0.0483 <.0001 Not working -0.1973 0.8209 0.0454 <.0001 Retired -0.7916 0.4531 0.1051 <.0001

Financial Buffers Net worth Log of Net worth -0.0822 0.9210 0.0048 <.0001 Health Insurance Yes -0.4281 0.6517 0.0704 <.0001

Financially Adverse Events Health Status Poor 0.1182 1.1254 0.1238 0.3397 Lower than Transitory Income 0.3756 1.4558 0.0759 <.0001 Normal

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)

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Table 5.17 :Continued

Logistic Exponent Variables Categories Coefficient of S.E p-value Coefficient

Interaction Effects

Financial Buffers Log of Net worth *white -0.0264 0.9739 0.0054 <.0001 *Hispanics 0.0105 1.0106 0.0086 0.2233 *Asians/others -0.0154 0.9847 0.0120 0.1997 Health Insurance *white -0.3151 0.7297 0.0806 <.0001 *Hispanics 0.3377 1.4017 0.1187 0.0045 *Asians/others -0.1365 0.8724 0.1890 0.4704

Financially Adverse Events Health Status *white 0.5656 1.7605 0.1379 <.0001 *Hispanics -0.0767 0.9262 0.2265 0.7350 *Asians/others 0.0602 1.0620 0.3203 0.8510 Transitory Negative 0.2227 1.2494 0.0858 0.0094 Income *white *Hispanics 0.0196 1.0198 0.1290 0.8790 *Asians/others 0.6506 1.9167 0.1815 0.0003

Household Debt Burden Debt to income ratio *white 0.5130 1.6703 0.1573 0.0011 *Hispanics 0.6791 1.9721 0.2330 0.0036 *Asians/others -1.0180 0.3613 0.3427 0.0030 Mills 1.5937 4.9219 0.1018 <.0001 Intercept -4.2140 0.0148 0.2373 <.0001

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Exponent Variables Categories Logistic of S.E p-value Coefficient Coefficient

Environmental Variables Year of Survey 1995 0.3045 1.3559 0.0574 <.0001 1998 0.3755 1.4557 0.0602 <.0001 2001 0.4245 1.5288 0.0602 <.0001 2004 0.5624 1.7549 0.0587 <.0001 2007 0.3464 1.4140 0.0619 <.0001 Demographics Race/Ethnicity white 0.7342 2.0838 0.1086 <.0001 black 0.9358 2.5493 0.1221 <.0001 Asian/others 0.6646 1.9437 0.2073 0.0013 Age Age 0.1144 1.1212 0.0078 <.0001 Age squared -0.0014 0.9986 0.0001 <.0001 Education High School 0.0965 1.1013 0.0501 0.0541 Some College 0.2963 1.3449 0.0556 <.0001 Bachelor -0.1967 0.8214 0.0555 0.0004 Family Composition Married w/child 0.7049 2.0236 0.0513 <.0001 Single w/o child 0.3022 1.3528 0.0521 <.0001 Single w/ child 0.6825 1.9788 0.0570 <.0001 Current Income Log of Income -0.0435 0.9574 0.0083 <.0001 Expected Income Surely Same -0.2921 0.7467 0.0464 <.0001 Growth Surely Increase -0.0975 0.9071 0.0520 0.0608 Not Sure -0.0322 0.9683 0.0428 0.4519 Employment Self Employed -0.2990 0.7416 0.0483 <.0001 Not working -0.1973 0.8209 0.0454 <.0001 Retired -0.7916 0.4531 0.1051 <.0001 Financial Buffers Net worth Log of Net worth -0.0716 0.9309 0.0073 <.0001 Health Insurance Yes -0.0905 0.9135 0.0963 0.3474

Financially Adverse Events Health Status Poor 0.0415 1.0424 0.1903 0.8273 Transitory Income Lower than Normal 0.3952 1.4847 0.1046 0.0002

Household Debt Burden Debt to Income Ratio 0.9283 2.5302 0.1900 <.0001 Table 5.18 : Logistic Analysis of Being Delinquent (Reference group= Hispanics) (Continued)

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Table 5.18: Continued

Exponent Variables Categories Logistic of S.E p-value Coefficient Coefficient

Interaction Effects

Financial Buffers Log of Net worth *white -0.0369 0.9638 0.0077 <.0001 *black -0.0105 0.9896 0.0086 0.2233 *Asians/others -0.0259 0.9744 0.0132 0.0498 Health Insurance *white -0.6528 0.5206 0.1036 <.0001 *black -0.3377 0.7134 0.1187 0.0045 *Asians/others -0.4741 0.6224 0.1999 0.0177

Financially Adverse Events Poor Health Status *white 0.6422 1.9007 0.1998 0.0013 *black 0.0767 1.0797 0.2265 0.7350 *Asians/others 0.1369 1.1467 0.3515 0.6971 Transitory Negative *white 0.2031 1.2252 0.1119 0.0696 Income *Hispanics -0.0196 0.9806 0.1290 0.8790 *Asians/others 0.6309 1.8793 0.1952 0.0012

Household Debt Burden Debt to Income Ratio *white -0.1661 0.8470 0.2026 0.4123 *black -0.6791 0.5071 0.2330 0.0036 *Asians/others -1.6971 0.1832 0.3657 <.0001 Mills 1.5937 4.9219 0.1018 <.0001 Intercept -5.1498 0.0058 0.2458 <.0001

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Variables Categories Logistic Exponent of S.E p-value Coefficient Coefficient

Environmental Variables Year of Survey 1995 0.3045 1.3559 0.0574 <.0001 1998 0.3755 1.4557 0.0602 <.0001 2001 0.4245 1.5288 0.0602 <.0001 2004 0.5624 1.7549 0.0587 <.0001 2007 0.3464 1.4140 0.0619 <.0001 Demographics Race/Ethnicity white 0.0697 1.0722 0.1869 0.7093 black 0.2712 1.3115 0.1953 0.1649 Hispanic -0.6646 0.5145 0.2073 0.0013 Age Age 0.1144 1.1212 0.0078 <.0001 Age squared -0.0014 0.9986 0.0001 <.0001 Education High School 0.0965 1.1013 0.0501 0.0541 Some College 0.2963 1.3449 0.0556 <.0001 Bachelor -0.1967 0.8214 0.0555 0.0004 Family Composition Married W/child 0.7049 2.0236 0.0513 <.0001 Single w/o child 0.3022 1.3528 0.0521 <.0001 Single w/ child 0.6825 1.9788 0.0570 <.0001 Current Income Log of Income -0.0435 0.9574 0.0083 <.0001 Expected Income Surely Same -0.2921 0.7467 0.0464 <.0001 Growth Surely Increase -0.0975 0.9071 0.0520 0.0608 Not Sure -0.0322 0.9683 0.0428 0.4519 Employment Self Employed -0.2990 0.7416 0.0483 <.0001 Not working -0.1973 0.8209 0.0454 <.0001 Retired -0.7916 0.4531 0.1051 <.0001 Financial Buffers Net worth Log of Net worth -0.0975 0.9071 0.0111 <.0001 Health Insurance Yes -0.5646 0.5686 0.1755 0.0013

Financially Adverse Events Health Status Poor 0.1784 1.1953 0.2958 0.5466 Transitory Income Negative Income 1.0262 2.7904 0.1650 <.0001

Household Debt Burden Debt to income ratio -0.7688 0.4636 0.3160 0.0150

Table 5.19 : Logistic Analysis of Being Delinquent (Reference group= Asians/others) (Continued)

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Table 5.19: Continued

Logistic Exponent of Variables Categories Coefficient Coefficient S.E p-value

Interaction Effects

Financial Buffers

-0.0110 0.9891 0.0113 0.3303 Log of Net worth *white *black 0.0154 1.0155 0.0120 0.1997 *Hispanics 0.0259 1.0262 0.0132 0.0498 Health Insurance *white -0.1786 0.8364 0.1798 0.3206 *black 0.1365 1.1463 0.1890 0.4704 * Hispanics 0.4741 1.6066 0.1999 0.0177

Financially Adverse Events

Poor Health Status *white 0.5054 1.6576 0.3021 0.0943 *black -0.0602 0.9416 0.3203 0.8510 *Hispanics -0.1369 0.8721 0.3515 0.6971 Negative Transitory *white -0.4279 0.6519 0.1696 0.0116 Income * black -0.6506 0.5217 0.1815 0.0003 *Hispanics -0.6309 0.5321 0.1952 0.0012 Household Debt Burden Debt to income ratio *white 1.5310 4.6228 0.3225 <.0001 *black 1.0180 2.7677 0.3427 0.0030 *Hispanics 1.6971 5.4581 0.3657 <.0001 Mills 1.5937 4.9219 0.1018 <.0001 Intercept -4.4852 0.0113 0.2888 <.0001

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Multiplicative Factor Race/Ethnicity comparison to comparison to comparison to comparison to whites blacks Hispanics Asians/others whites 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 comparison to comparison to comparison to comparison to whites blacks Hispanics Asians/others whites 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)

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Multiplicative Factor Race/Ethnicity comparison to comparison to comparison to comparison to whites blacks Hispanics Asians/others whites 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 comparison to comparison to comparison to comparison to whites blacks Hispanics Asians/others whites 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 comparison to comparison to comparison to whites blacks Hispanics Asians/others whites 1.00 1.67 0.85 4.62 blacks 0.60 1.00 0.51 2.77 Hispanics 1.18 1.97 1.00 5.46 Asians/others 0.22 0.36 0.18 1.00

Table 5.24 : Multiplicative Factor for the reference Group on the Moderating Variable (Household Debt Burden)

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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 cross- sectional survey sponsored by the Board of Governors of the Federal Reserve System.

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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 non- white 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

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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).

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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

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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.

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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.

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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

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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.

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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

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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

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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).

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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

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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

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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.

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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 Description $44,301.7≤Income<$ $44,301.7 ≤ Household‘s annual income < $78,748.2 78,748 $78,748≤ Income $78,748.2 ≤ Household‘s annual income Home ownership Yes if the household owns a home, 0 otherwise No if the household does not own a home, 0 otherwise Expected Income Growth Surely Same 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 Surely Increase 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 Not Sure 1 if a respondent does not has a good idea of what family

income for next year will be, 0 otherwise Surely Decrease 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 Employment Self Employed 1 if household head is self employed, 0 otherwise Not working 1 if household head is not working, 0 otherwise Retired 1 if household head is retired, 0 otherwise Salary earner 1 if household head is a salary earner, 0 otherwise

Financial Buffers Net worth Log of household‘s net worth (if net worth ≤0, use Log of Net worth ln (0.01)) NW< $13,368.4 net worth < $13,368.4 $13,368.4≤NW<$92, $13,368.4 ≤ net worth < $92,164.1

164.1 $92,164.1≤NW< $92,164.1 ≤ net worth < $285,732.0

$285,732 $285,732≤NW $285,732 ≤ net worth Health Insurance Yes 1 if everyone in the respondent‘s household is covered by private health programs or some type of government, 0 otherwise No 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 Description

Financially Adverse Events Health Status Excellent, fair or good 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 poor or, for couples, one (or both) has poor health, 0

otherwise Transitory Income Lower than Normal 1 if income is unusually low compared to what he/she

Income would expect in a "normal" year, 0 otherwise Same or Higher than 1 if income is unusually high or same compared to Normal Income what he/she would expect in a "normal" year, 0 (Reference) otherwise

Household Debt Burden Debt to Income Ratio Ratio of total monthly payments on all debt to monthly (PIRTOTAL) household income if income > 0 then PIRTOTAL=max((TPAY/MAX((INCOME/12),1)),1); Total monthly payments on all debts include payments Total monthly payments on credit cards, mortgages, home equity loans, home (TRAY ) 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 Monthly household Household income / 12

income Debt to Income Ratio The ratio of monthly payments to monthly income

<10% <10% 10% ≤ Debt to Income 10% ≤ The ratio of monthly payments to monthly

Ratio <25% income <25% 25% ≤ Debt to Income 25% ≤ The ratio of monthly payments to monthly

Ratio <40% income < 40% 40% ≤ Debt to Income 40%≤ The ratio of monthly payments to monthly

Ratio income

Environmental Variable Year of Survey 1992 1 if year of survey is 1992, 0 otherwise (reference)

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Variables Description 1995 1 if year of survey is 1995, 0 otherwise 1998 1 if year of survey is 1998, 0 otherwise 2001 1 if year of survey is 2001, 0 otherwise 2004 1 if year of survey is 2004, 0 otherwise 2007 1 if year of survey is 2007, 0 otherwise

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