Household Debt Service Burden Outlook: an Exploration on the Effect of Credit Constraints

HOUSEHOLD DEBT SERVICE BURDEN OUTLOOK: AN EXPLORATION ON THE EFFECT OF CREDIT CONSTRAINTS

DISSERTATION

Presented in Partial Fulfillment of the Requirements for

The Degree Doctor of Philosophy in the Graduate

School of The Ohio State University

Jing Zhao, M.A.S.

* * * * *

The Ohio State University 2003

Dissertation Committee: Approved by Professor Sherman D. Hanna, Advisor ______Professor Catherine P. Montalto, Co-advisor Advisor Professor Sharon Seiling ______Professor Jonathan Fox Co-advisor Family Resource Management

ABSTRACT

The Permanent-Income and Lifecycle Hypothesis, which prescribes the household saving and consumption behavior in a simplified life scenario, was challenged by later researchers with more assumptions. One of these assumptions is the factor of liquidity constraints, or as appeared in this dissertation, credit constraints. Credit constraints refer to the borrowing ceiling a household could obtain from a lender. Banking credit supply theory asserts that the imposition of credit constraints is due to the adverse selection and moral hazard. This dissertation intended to analyze the household debt level of the United

States by using the debt service burden measure, which represents the relative portion of debt repayments to household income. Guidelines on the service burden were discussed with the variation in the component of the ratio calculation. Differences in the threshold choices were also noted across personal finance educators, practitioners and the lending industry. According to selective guidelines, the household debt service burden was categorized into zero, low, moderate and high tiers. The data source came from the 1998

Survey of Consumer Finances, a nationwide survey sponsored by the Federal Reserve

Board. Ordinal logit analysis was conducted to model the probability of a household falling into a higher versus a lower burden tier under the PI-LCH framework, accounting

for credit constraints. Models were fitted individually on consumer debt service burden and total debt service burden. The findings revealed that credit-constrained families were more likely to be in a higher burden tier. Ceteris paribus, households headed by a college attendee, married couples, working individuals, those with mortgage payments, lower income households, those with a low liquidity ratio, and households holding many credit cards had a larger probability to be highly burdened. However, variables such as the head's age and expectation proxy were not found significantly predictive. The life cycle model implies that the expectation of income growth should have an effect on a consumer's demand for credit, so the lack of an effect of that variable is particularly surprising. Credit-constrained families may have to pay higher interest rates, which leads to a larger repayment burden compared to their non-constrained counterparts.

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Dedicated to my parents

ACKNOWLEDGMENTS

I wish to thank my adviser, Dr. Sherman Hanna, for intellectual support, continuous encouragement, and patient advising in each step of the composition of this dissertation, especially for his understanding and consideration in making all this happen to the final end.

I am grateful to Dr. Catherine Montalto, who provided expertise and suggestions in the understanding of the datasets, without which the essential analysis is questionable.

I am also indebted to those who helped me in many ways, Bai Han, Scott Twaro, Guowen Tan.

VITA

January 3, 1976 ..…………….. Born – Shanghai, People’s Republic of China

1997 …………………………. B. S. Economics, Shanghai Institute of Foreign Trade

1997 – 1998 ………………….. Shanghai Education Center for Administrators; China (Shanghai) Schindler Elevator and Escalator Co., Ltd.

1998 – 2001 ………………….. Research Assistant, The Ohio State University

2001 …………………………. Master, Applied Statistics, The Ohio State University

FIELDS OF STUDY

Major Field: Family Resource Management

Minor Field: Statistics

TABLE OF CONTENTS

page Abstract …………………………………………………………………………. iii

Dedication ………………………………………………………………………. iv

Acknowledgments ………………………………………………………………. v

Vita ……………………………………………………………………………… vi

List of Tables ……………………………………………………………………. x

List of Figures …………………………………………………………………… xii

Chapters:

1. Introduction ………………………………………………………………. 1

1.1 Background ……………………………………………………. 1 1.2 Relevant literature …………………………………………….. 3 1.3 Research objective …………………………………………….. 5 1.4 Organization flows ……………………………………………. 6

2. Literature Review ……………………………………………………….. 8

2.1 Starting point: Permanent Income – Life Cycle Hypothesis…… 8 2.2 Justification of liquidity constraints in household debt ……….. 9 2.2.1 Liquidity constraints introduced …………………… 9 2.2.2 Credit supply theory of banking …………………… 11 2.2.3 Review of early frameworks on liquidity constraints.. 14 2.3 Household acquisition of credit ………………………………… 26 2.4 The coexistence of PI-LCH and credit supply theories …………. 28 2.5 Criteria of debt service burden ……………………………….. 31 2.5.1 Debt burden (debt ratio) guidelines: personal finance Perspective …………………………………………. 31 2.5.2 Debt service burden and home purchase: norms by lenders …………….……………………………. 35

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2.6 Recent household debt burden outlook ………………………… 37 2.7 Independent variables………………………………………….. 41 2.7.1 Overview of studies on household debt ……………. 41 2.7.2 Theoretical implication …………………………….. 42 2.7.3 Empirical works …………………………………… 44

3. Theoretical framework…………………………………………………… 58

3.1 Economics theories on household dissaving ………………….. 58 3.1.1 Permanent Income-Lifecycle Consumption Hypothesis 59 3.1.2 Models of credit constraint and future expectation .. 60 3.2 Credit supply function estimation difficulty .…………………… 65 3.3 Model predictors and hypotheses ………………………………. 67

4. Methodology ……………………………………………………………… 80

4.1 Data source …………………………………………………….. 80 4.2 Statistical model ……………………………………………….. 81 4.3 Multiple imputation ……………………………………………. 86 4.4 Variable construction …………………………………………… 87 4.4.1 Dependent variable …………………………………. 87 4.4.2 Independent variables ………………………………. 91

5. Results …………………………………………………………………… 98

5.1 Household debt holdings and debt payment …………………… 98 5.2 Sample household demographics ……………………………… 101 5.3 Credit-constrained households …………………………………. 105 5.4 Households in debt burden tiers ………………………………. 113 5.5 Households debt burden and credit constraints ……………….. 119 5.5.1 Total debt service burden and credit constraints ….. 119 5.5.2 Consumer debt service burden and credit constraints 120 5.6 Bivariate analysis results ……………………………………… 120 5.6.1 Consumer debt burden tiers by independent variables 121 5.6.2 Total debt burden tiers by independent variables …. 127 5.7 Multivariate analysis results …………………………………… 130 5.7.1 Consumer debt burden model ……………………… 130 5.7.2 Total debt burden model …………………………… 139

6. Conclusion and Implications …………………………………………….. 178

6.1 Overview on household debt service burden …..……………….. 178 6.2 Conclusion on credit constraints ………………………………. 180 6.3 Discussion on multivariate models …………………………….. 182

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6.4 Implications ……………………………………………………. 190 6.5 Limitations……………………………………………………… 197

Appendix A ……………………………………………………………………… 199

Appendix B ……………………………………………………………………… 201

Bibliography ……………………………………………………………………. 203

LIST OF TABLES

Table Page

2.1 Summary chart of literature review on the Permanent-Income/Lifecycle Hypothesis model and liquidity constraints factor…………………….. 55

5.1 Descriptive statistics of total debt outstanding by types of loans (all households) …………………………………….…… 144

5.2 Descriptive statistics of total debt repayment by types of loans (all households) ………………………………………… 145

5.3 Descriptive statistics of total debt outstanding by types of loans (mortgagers only) ……………………………………… 146

5.4 Descriptive statistics of total debt repayment by types of loans (mortgagers only) ……………………………………… 147

5.5 Descriptive statistics of consumer debt outstanding by types of loans (mortgagers only) ……………………………………… 148

5.6 Descriptive statistics of consumer debt repayment by types of loans (mortgagers only) ……………………………………… 149

5.7 Descriptive statistics of consumer debt outstanding by types of loans (debtors only) … ……………………………………… 150

5.8 Descriptive statistics of consumer debt repayment by types of loans (debtors only) …..………………………………………. 151

5.9 Distribution statistics (continuous variables) of all the sample households, Distribution statistics (categorical variables) of all the sample households.. 152

5.10 Distribution statistics (continuous variables) of the sample households with mortgage obligations, Distribution statistics (categorical variables) of the sample households with mortgage obligations… ………………… 155

5.11 Descriptive statistics of credit constrained and non-constrained households with T-test and non-parametric test. ……………...…………….…. …. 158

5.12 Distribution statistics of independent variables between credit constrained (yes) and non-constrained (no) households …………………………… 160

5.13 Descriptive statistics of credit constrained and non-constrained households with T-test and non-parametric test (home mortgagers only)………….. 162

5.14 Cosstabulation of total debt service burden tiers by credit constraints (households with mortgage obligations only)………………………..…. 164

5.15 Crosstabulation of consumer debt service burden tiers by credit constraints (all households) ……………………………………………………….… 165

5.16 Distribution statistics of independent variables (continuous) by consumer debt service tiers, Distribution statistics of independent variables (categorical) by consumer debt service tiers (row percentages) ………… 166

5.17 Distribution statistics of independent variables (continuous) by total debt service tiers (home mortgagers only), Distribution statistics of independent variables (categorical) by total debt service tiers (row percentage; home mortgagers only) ……………………………………………………….. 170

5.18 Ordinal logit model results on consumer debt service burden tiers (all households) ………………………..……….………………………. 173

5.19 Ordinal logit model results on total debt service burden tiers (home mortgagers only) ……………………………………………….. 176

6.1 Comparison summary on variable effect on household debt burden level between hypothesis and results …………………..…………………… 188

LIST OF FIGURES

Figure Page

5.1 Cumulative distribution of total debt service burden (PTI ratio) (all households) …………………………………………………………. 117

5.2 Cumulative distribution of consumer debt service burden (PTI ratio) (home mortgagers) ……………………………………………………….. 118

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CHAPTER 1

INTRODUCTION

1.1 Background

Over the past decades, a variety of financial innovations have provided consumers broad options for distribution of their short-term intakes to help them realize their lifetime goals. Consumer credit, which can be traced back centuries in the United States, has developed its own family of variations as well. Credit cards, home purchase mortgages, installment loans, home equity lines, etc. have undoubtedly become more and more familiar to ordinary households as ways to fulfill desired living goals or other purposes by acquiring external resources. Given the fact that the size of household debt has surpassed that of the U.S. government debt and constitutes one-fourth of total credit market debt outstanding, Santomero (2001) emphasized the significance of developing theoretical models and conducting empirical analysis of the consumer-borrowing phenomenon.

The United States experienced its longest unprecedented economic boom in the past decade since the end of the last recession (1990-1991); during this period, the level

of consumer credit/household debt has maintained an increasing trend. Federal Reserve aggregate statistics showed that as of January 2002, consumer credit outstanding has mounted to $1.66 trillion: $693 billion in revolving debt and $968 billion in non- revolving debt (Federal Reserve Board System website, 2002a). Needless to say, the credit system has contributed enormously to the U.S. macro-economy, and has been acknowledged, as the tool that has facilitated “the American dream”, advancing individual consumption that otherwise would not have occurred.

Nevertheless, debt obligation imposes a financial burden on household welfare for a considerable amount of time (typically determined by the terms of the utilization of credit) through required regular payback to offset the debt outstanding. Conventionally, households schedule the payment contribution on a monthly basis, largely out of the regular income intake. When households juggle between consumption, saving and paying off debt, debt payment becomes a deprivation in terms of the household’s economic flexibility. Since the regular payment is a fixed outgoing expense, a family’s capability to maintain the obligation depends upon 1) robust total resources and 2) a reasonable allocation among various outlets. While a loan payment plan between a creditor and a borrower is likely to be reached in favor of the borrower to accommodate his/her financial situation, changes in the either of the two factors mentioned above in family economics could still result in a serious debt problem. This has been witnessed as a widely spread phenomenon in the recent economic sluggish period. The percentage of homeowners who were 30 days or more delinquent exceeded 10 percent for the first time ever at the end of 2000, a figure greater than that in the recessions of the early 1990s and

1980s (Leonhardt, 2001). Even families who were sound in their finances were troubled by the staggering debt that would have been justified in a booming economy (Hilsenrath

& Higgins, 2002).

1.2 Relevant Literature

Historically, researchers in economics, family and consumer fields have studied borrowing behavior under the extensive household saving and consumption theories.

Permanent-Income and Life Cycle Hypotheses comprised the mainstream of the theoretical works that largely inspired and motivated scholars to explore household inter- temporal consumption and saving, in conjunction with factors including but not limited to: 1) economic variables, such as interest rate, personal income; 2) demographic variables, such as age, family cycle; 3) expectation and attitudinal variables, such as uncertainty (e.g., Hall, 1988; Dardanoni, 1991; Hayashi, 1985; Antzoulatos, 1994). In the traditional form of abstract modeling in economics, saving and consumption were deemed the two major components in a simplified scenario of household economic activities, within the framework that economic agents maximize utility throughout their lifetime. Even though borrowing or dissaving was reckoned as a crucial component in household resource allocation, it was, more or less, mentioned as an antonym to saving.

Traditionally, there has been a paucity of works on the household debt/consumer credit area in contrast to overwhelming emphasis on saving. Beyond that, for the limited volume of existing studies, or to be specific, the empirical studies, the features of these studies remained either in the exploratory stage or restricted to one or two categories in

the area of household debt, for example, credit card debt (for relevant discussion, refer to

Chapter 2). Unfortunately, little literature has attempted to incorporate the dissaving behavior into the framework of the Permanent Income-Life Cycle Hypothesis (hereafter

PI-LCH1), extensively analyzed the acquisition and payback performance of debt in households, and accordingly tested the effects of potential factors on the incidence of debt acquisition.

A prominent breakthrough in household consumption/saving structure, which on the other hand, involves a great deal of borrowing, is the factor of borrowing constraints or liquid constraints as defined by James Tobin (Hayashi, 1985, p183). Early pioneers in economics found that the existence of liquidity constraints was the vital element affecting household consumption/saving behavior, which contrasts with the description in the simple Permanent-Income or Life Cycle Hypothesis (e.g., Hall, 1978; Hayashi, 1982).

Thereafter, further analysis supported such an innovative general proposition through the examination of the extent of liquidity-constrained households, which is not a negligible

1 Literally, Modigliani first proposed the Life Cycle consumption hypothesis, and Friedman was the economist who did innovative work on the Permanent Income theory. These two theories stand as the fundamental framework for studying a consumer’s behavior in consumption between the present and the future. As Hall (1978) stated, consumers form estimates of their ability to consume in the long run and then set current consumption to the appropriate fraction of that estimate. The estimate is stated in the form of wealth, following Modigliani, in which case the fraction is the annuity value of wealth, or as permanent income, following Friedman, in which case the fraction should be very close to one. Therefore, these two core theories are often mentioned as a synthesis in economic literature.

portion of the U.S. population (e.g., Hall & Mishkin, 1982; Mariger, 1987; Cox &

Jappelli, 1993).

1.3 Research Objectives

The focus of this dissertation is on household debt burden, a critical component determining an ordinary family’s survival, sustenance and betterment. This research has attempted to justify the study on household debt in a theory-guided perspective, that is, relying on the Permanent-Income/Life Cycle Hypothesis and taking into account liquidity constraints. Meanwhile, effort has been spent on the choice of a representative measure of debt burden and the criteria of burden appropriateness. By so doing, the research has avoided the conventional but less informative absolute measure of debt holding or incidence of possession or absence of possession. Instead, ratio analysis, a technique that has recently found a new application in personal finance after years of contribution in business, will be employed. Debt payment to income ratio, alternatively referred as debt service burden, becomes the final measurement of household debt burden, reflecting the relative allocation of household resources in the borrowing commitment. The ratio calculation is derived on an annual basis, which allows for the examination of household debt burden within a fixed time period of one year. While realistically debt repayment occurs against family earning power more often than assets, the rationale also turns out to be sustainable in the theoretical derivation (for details, see Chapter 3).

Instead of looking at the burden in an absolute scale, or its linear relationship to all pertinent factors, the research question is in fact centered on the stepwise severity of

the debt burden, and the tendency of households to fall into one of the debt levels. A system of guidelines that were advocated by early researchers was reviewed, and the selected guidelines became the criteria for distinguishing the families’ degree of burden.

This dissertation adopts the liquidity constraints assumption that households (or some households) are bounded in generating the desired debt for any purpose, thus tending to form different consumption-saving-borrowing patterns. Liquidity constraints, from a lender’s perspective, are the outcome of the credit supply theory of banking.

Essentially, the research interest of debt burden is formed under the two overarching economics conditions: demand and supply (of credit)2.

1.4 Organization flows

It is the goal of this dissertation to examine the extent and severity of American household debt holdings, in particular, debt service burden; to explore the association of groups of factors that determine the level of debt payment to income ratio; and to project the likelihood of a household edging above the dangerous burden level by this ratio measure. The contents are organized as follows. Chapter two provides a detailed literature review that pertains to PI-LCH umbrella theories concerning debt/borrowing. In chapter three, core models are established and illustrated to lead to the consequent technical analysis. In chapter four, modeling approaches are presented to suit the

2 The demand element could be considered as in traditional Permanent-Income/LCH framework, a function of permanent income and life cycle variables.

corresponding dependent variables, with details on the datasets. After highlights and interpretation of the findings are presented in chapter five, the dissertation will conclude with summary, discussion, implication and certain limitation in chapter six.

CHAPTER 2

LITERATURE REVIEW

2.1 Starting Point: Permanent Income – Life Cycle Hypothesis

The Permanent Income hypothesis developed by Friedman (1957) and the Life

Cycle hypothesis proposed by Modigaliani and Brumberg (1954) in the early 1950s have become the fundamental theoretical framework in studying household consumption and saving behaviors. These theories have spawned extensions, such as the introduction of uncertainty with the purpose of making the models more realistic and sensible. It should be emphasized that the PI-LCH is essentially a consumption theory, with saving being the residual between income and current consumption (Browning & Lusardi, 1996).

Undeniably, the PI-LCH has helped to shed light on the optimal household consumption and saving path in lifetime utility maximization. However, researchers have challenged its validity and accuracy in describing households living in the real world.

Additional theoretical components have emerged to enrich the PI-LCH theory over the decades. These include introduction of the constant elasticity of substitution (Constant

Relative Risk Aversion) utility function (e.g., Zeldes, 1989a; Antzoulatos, 1994a, 1994b;

Xu, 1995), which is commonly used in inter-temporal models, consideration of

uncertainty (in the forms of income, life events, etc.) (e.g., Levhari & Srinivasan, 1969;

Dardanoni, 1991); and the effect of liquidity constraints (see relevant references in the following sections).

2.2 Justification of Liquidity Constraints in Household Debt

2.2.1 Liquidity Constraints Introduced

The PI-LCH framework has been extended to incorporate liquidity constraints3. A good deal of previous literature on inter-temporal optimization under uncertainty failed to consider borrowing limits that are inevitably imposed on most households. Instead, they simply considered two extremes: free borrowing, when only the lifetime budget constraint sets out the boundary, or no borrowing, when this ubiquitous activity in households is totally ignored (Antzoulatos, 1994a). Under the situation of free borrowing, the assumption is that the household can borrow or lend as much as it desires at a fixed interest rate, i.e., capital markets are perfect (Hayashi, 1985, p. 186).

Scholars have probed the impact of liquidity constraints in contesting the traditional PI-LC hypothesis. Some of the researchers have attributed the rejection of the

PI-LCH to the prevalence of liquidity-constrained households, e.g., Flavin (1981),

Hayashi (1985). In the explanation of the departure of tested consumption pattern from the PI-LCH principle, Hall (1978) suggested that consumers were unable to smooth out consumption over the long-term income streams as described in traditional PI-LCH; all

3 Hayashi (195) used the words “liquidity constraints” and “borrowing constraints” interchangeably and acknowledged the original source of “liquidity constraints” to be James Tobin.

was due to the imposition of liquidity constraints and other practical considerations. A similar supposition of liquidity constraints in households was also taken into account in

Hayashi (1982). His empirical work confirmed that a considerable proportion of the households were liquidity constrained. In all, liquidity constraints are deemed the likely reason that actual consumption follows periodic income closely, rather than sustaining an optimal smooth path as predicated from the PI-LCH model. Economists define the phenomenon that consumption is highly related to current income as excess sensitivity of consumption (e.g., Flavin, 1981; Hall & Mishkin, 1982; Xu, 1995; Garcia, Lusardi & Ng,

1997).

There are several variations in their conceptualization of liquidity constraints. An early one is tied to the existence of an imperfect capital market, where borrowing rates are substantially higher than lending rates, from a household’s standpoint (Hayashi, 1982, p. 912). Besides such a scenario for differential lending and borrowing rates, liquidity constraints were granted additional definitions: the cash-in-advance constraints and credit rationing as generalized by Hayashi (1987). Throughout the years, even as researchers attempted to develop their own representations of liquidity constraints, the fundamental conceptualization remained centered on those definitions from earlier studies4. To clarify

4 For example, Scott (2000) attempted to reach a “tractable model” in borrowing imperfections for the optimal consumption model. Note that his definition of imperfect capital market extends to “an upward sloping interest rate schedule”; in other words, “consumers rarely face binding credit constraints but are able to borrow funds at ever higher interest rates”. More illustration is presented later in the chapter.

In their notion of liquidity constraints, Hubbard & Judd (1986) emphasized nonnegativity constraints on net worth. As they hypothesized, consumers are not permitted to borrow against income that is to be received

at the beginning, in the context of this dissertation, liquidity constraints mean the credit limit imposed on households by lenders, which is sometimes referred as credit constraints5.

2.2.2 Credit Supply Theory of Banking

In the review of post-Keynesian theory of banks, Rochon (1999) stated that uncertainty affects the decision of a bank to extend a loan, with regard to interest rate on loans and the quantity of credit that they will supply to firms6. Uncertainty is rendered from two sources, the first being borrowers’ creditworthiness. Having been confronted with asymmetric information, banks reply heavily upon the past and present facts about a firm’s production and evaluate its potential for profit and/or success in the future, and thus the expected return of credit. The second source is the banks’ own uncertainty about

in the future and current consumption is limited by current resources (p5). The authors acknowledged that such a narrow concept of liquidity constraints was more often adopted in analysis og tax policy, and does not reflect problems of imperfect information in loan markets (p6). This dissertation does not intend to give large details about such notation for two reasons: (1) the focus is rather on borrowing against lifetime resources (permanent income) than borrowing against current (one-period) wealth; (2) as far as consumer debt is concerned, the context of an imperfect loan market is desirable and relevant.

5 Credit rationing, in the author’s understanding, reflects the standpoint of lenders who implement rationing decisions in loan processing. When individuals or households become the subject, the term liquidity constraints or credit constraints is more standard in the literature. There has been scarce endeavor to entail the borrowing and lending rate difference in the formal modeling of liquidity constraints. One challenge is the technical implausibility in mathematical derivation with the existence of two distinct interest rates.

6 Notice that conventional bank theory mainly deals with firms as borrowers.

the future, the whole economy. Because each firm has its own unique operation, banks’ uncertainty on borrowers will vary with the firm’s strength in credit whereas the banks’ own uncertainty is interwoven with the macro-economic environment (e.g., business cycle), which might further spread its impact to other economic entities, like firms. In short, the risks are respectively, “borrower’s and lender’s risks”, as put in post-Keynesian language (p. 281). As a shield to fight uncertainty, the banks rely on creditworthiness ratings and the rate of interest they charge on the loans. Banks will establish rules or criteria to assess the applicants. The components in the criteria may include but are not limited to the type of loan, the reputation of the borrower, the relationship with the bank, and the profitability of projects. The minimum set of criteria is also dynamic; in other words, changes are necessary to accommodate a larger uncertainty or some decisive shift in perspective. Once it is determined that a credit line will be extended, the rate of interest is determined by the robustness of this creditworthiness, such as how much collateral the firm has, its sales volume/revenue, and its capital value. Within a pre-determined range, an interest rate is charged to accommodate the risk level that banks are willing to accept, taking into account reasonable profit.

By the same token, banks respond to the credit-seeking needs of households in a similar way. As consumer loans remain the subject of this dissertation, the following discussion on banks’ lending decisions will be extended to households. Only this time, the borrowings are not additional funding for producers’ manufacture or profit, but instead, for the sake of household survival, maintenance or upscale. With the existence of asymmetric information, that is, the banks possess insufficient knowledge about

households’ creditworthiness; adverse selection and moral hazard are unfortunately common outcomes in the credit demand and supply settings (see Jappelli & Pagano,

1994; Xu, 1995). When lenders decide to grant a loan with a high interest rate, trustworthy debtors are likely to be discouraged from seeking such a deal in light of the high interest charges, whereas those high-risk individuals are likely to pursue the loan in spite of the possibility of default. To mitigate the negative effect created thereby, lenders react as though there were two hypothetical groups of potential debtors out there, one with good credit and the other with bad credit. To identify the potential trustworthy borrowers, for the past decades, lenders have executed a systematic evaluation methodology, credit scoring being one dominant ingredient. Credit bureaus assign a credit score to each individual to gauge quantitatively his/her creditworthiness. Based usually on sophisticated statistical modeling, the score is computed depending upon the individual’s credit acquisition and payment history, residence, employment status and other relevant financial information. According to the Credit Score Online Information

Center (no date), a credit score is counted for an individual’s payment history, amount owed, length of credit history, tendency to take on more credit and type of credit in use.

The credit bureau (also known as FICO®)7 score condenses the applicant’s risk- assessment into one numerical value, ranging from 1 to 800. A higher value typically attaches to individuals with sound credit. During the loan application procedures, lenders

7 This is the lending industry’s acronym in calling for credit bureau scores (e.g., Equifax, Experian, Trans-

Union). The name comes from the prestigious pioneer in developing scoring models, Fair, Isaac &

Company, Inc, CA. More information can be found at the company website: www.fairisaac.com.

inquire about the applicant’s bureau score, cross-referenced with internal scoring methods, if any, to determine whether or not the applicant is a reliable debtor. Thus, credit scoring becomes an automatic and efficient process in screening applicants. On the other hand, human judgment is sometimes necessary to take into account other pertinent information that is not reflected in the credit score but crucial in evaluation of potential risk.

2.2.3 Review of Early Frameworks on Liquidity Constraints

Noticeably, research studies on the effect of liquidity constraints on household consumption have adopted different approaches in both theoretical formulation and empirical studies. In the following, several major works that have helped to shed light on the two aspects will be discussed. An overall summary of the following cited economic theories or empirical works can be found in Table 2.1.

Theoretical Representation of Liquidity Constraints: Consumption-driven

In testing the “quantitative importance” of liquidity constraints, Hayashi (1985) posited a test between a null hypothesis of the conventional PI-LCH model without liquidity constraints and an alternative hypothesis of a general model with liquidity constraints. The latter was set up so that consumption c cannot exceed some upper bound exogenously given to the household:

≤ = ct+i kt+i (i 0, 1, 2, …) where t represents the current year.

When the upper bound k is sufficiently large, the hypothesis with liquidity constraints was in fact reduced to the PI-LCH model, that is, no boundary at all. To implement the test, an ideal step is to compare the consumption function from each hypothesis. However, such a theoretically reasonable idea was challenged by reality in practice. First, households’ expectation about future income is not observable. Second, it is technically difficult to derive a closed-form solution for the optimal consumption if future labor income is uncertain or stochastic, let alone when liquidity constraints were present (Hayashi, 1985, p. 188).

The theoretical incentive is centered in comparing the optimal consumption ct and

* the desired consumption ct . If they are different, it is strong evidence to conclude that the household is currently liquidity constrained. The hypothesis testing was then transformed into two different estimates of reduced-form equations for desired consumption between a Tobit model on high-saving (presumably those non-liquidity constrained) households and an OLS model on all households. Hayashi (1985) found a significant difference between the coefficients of the two models and concluded that there was an undisputable impact of liquidity constraints on consumption, which explicitly led to the rejection of the PI hypothesis.

In his clarification of the concept of liquidity constraints to distinguish from other forms, Zeldes (1989a) specifically confined liquidity constraints as a simple quantity constraint: a floor on the total end-of-period net stock of traded assets. In other words, borrowing is not allowed against future non-traded assets (mainly labor income), or debt

cannot exceed the total current value of traded assets (p. 311)8. He believed that the constraints would be binding for those who chose not to build up their wealth in earlier periods or those who received exceptionally bad draws of income or portfolio returns. By using a CRRA form utility function, the first order condition (i.e., the Euler equation) of consumption with liquidity constraints was written up as:

′ U (C + ;θ + )(1+ r ) U ′(C ;θ ) = E  i,t 1 i,t 1 it  + λ′′ it ct t + δ it  1 i 

λ′′ where it is the Lagrange multiplier (known at time t) associated with the constraints for time t and equal to the increase in expected lifetime utility that would result if the current constraints were relaxed by one unit. In plain words, it is the extra utility that would result from borrowing an extra dollar, consuming the proceeds, and reducing consumption by the appropriate amount in the next period. If it is assumed that economic

λ′′ agents are constrained from borrowing more, but not from saving more, it enters the above equation with a positive sign.

Hence, being confronted with a similar technical problem as previous scholars, i.e., the inability to derive a closed-form solution of consumption under constraints,

Zeldes (1989a) set out to test whether the currently binding borrowing constraints would lead to a violation of the (unconstrained) Euler equation. The question of binding liquidity constraints in one period was further rephrased into a question of the sign of the

λ′′ Lagrange multiplier ( it ) being positive or not. Even though Zeldes admitted to the usage of imperfect data and measurement, the results were evident enough to verify that

8 This assumption poses a quite similar stand to that of Hubbard & Judd (1986). 16

borrowing constraints affected consumption in the United States. The Euler equation is violated for observations with constraints and not violated for those without; the estimated average Lagrange multiplier associated with borrowing constraints is positive.

Deaton (1991) partially attributed the excess sensitivity of household consumption to borrowing constraints. His formulation of liquidity constraints turned out

≥ to be simply At 0 , current real assets being positive. His analytical results revealed that when borrowing is limited, saving and assets accumulation is quite sensitive to what consumers believe about the stochastic income process (p. 1223). Deaton performed analysis on time-series aggregate data, which is typical in consumption-related research, with various scenarios of income process, i.e., stationary, positive or negatively serially related. He found a seemingly contradictory result to the “procyclical saving” behavior: saving is contra-cyclical, rising at the onset of the slump, when incomes are falling, and falling at the onset of the boom, when incomes are rising. Such opposite phenomena were yet perceived as “rational” in his reasoning that when income is expected to rise more rapidly than its unconditional average growth rate, consumers have no motive to save.

But they may be prevented from borrowing because of borrowing restrictions. When income fall is expected in the future, saving is to “ameliorate the effects of the slump” by means of the still high income (p. 1240). One important conclusion he reached was that macro-level data can not be the source to model a typical liquidity constrained consumer.

As a matter of fact, consumption growth rate has exactly the opposite sign in the regression coefficient on lagged income growth for aggregate (positive) and micro data

(negative).

Antzoulatos (1994a) considered the same issue in examination of consumption growth as a function of expected income. The condition of liquidity constraints is presented by consumption in period t , not exceeding the individual’s total assets, plus a

≤ + finite ceiling on borrowing, i.e., ct xt L . The consumption function is derived as follows:

∆ = µ + µ + λ ∆ + ε ln ct +1 0 1rt+1 Et ln yt+1 t +1

where rt+1 is the real interest rate, and Et yt+k denotes expected income for the period t + k, k ≥ 1.

In a comparatively fully developed version of this study, Antzoulatos (1994b) implied that optimal consumption is weakly increasing with the debt ceiling, whereas optimal savings and the welfare losses caused by borrowing constraints are weakly decreasing. Moreover, the debt ceiling could be determined by short-run income expectations and uncertainty, real interest rate, length of the planning horizon, the assets and the form of the utility function. From his standpoint, the optimal consumption function, without taking into account the debt ceiling, is deemed inappropriate. With the availability of credit, the consumption and saving behavior of a rational individual may not strictly follow the traditional PI-LCH path. For example, the PI-LCH would suggest that saving rate could be expected to be positively associated with income uncertainty for the sake of precautionary saving. On the contrary, Antzoulatos explained the apparent contradiction between low saving and high-income uncertainty of farmers as documented in Skinner (1988) as a result of encouraging borrowing opportunity.

Jappelli and Pagano (1994) examined the effects of liquidity constrains in the context of capital accumulation, aggregate saving rate and productivity growth rate at the national level. They resorted to a simple “overlapping-generation model” where individuals are assumed to live for three periods: first, Individuals borrow when young; second, they repay the loan and save for retirement; last, they consume the savings accumulated in the second period. The formulation of the model is:

= + β + β 2 Max. u(ct,t ,ct,t+1 ,ct,t+2 ) ln ct,t ln ct ,t +1 ln ct,t+2 (1)

c + c + e + t,t 1 + t,t 2 ≤ t +1 s.t. ct,t , (2a) Rt +1 Rt+1Rt+2 Rt +1

e ≤ φ t+1 ct,t (2b) Rt+1

β where is the discount factor, et+1 is real labor earnings at time t+1, and Rt+1 is the real interest factor between time t and t+1. Equation (2a) is the intertemporal budget constraint and equation (2b) is a liquidity constraint.

By incorporating a model showing that productivity is an exogenous process increasing with time, Jappelli and Pagano (1994) demonstrated that liquidity constraints raise savings and the effect of growth of saving is stronger in economics with liquidity constraints. When incorporating a model of productivity endogenous on the aggregate level of capital, they concluded that the higher saving rate induced by liquidity constraints also translated into faster growth. The differences in credit access to households were found to be dramatic across countries, and in turn contribute to the variation in national saving and growth rate among them. Through empirical examination

of nineteen countries worldwide, the authors confirmed that the more stringent the loan restrictions one country has, the higher the saving rate the country has. In addition, liquidity constraints had a positive impact on growth of saving and productivity, which is endogenous on aggregate capital. Furthermore, the authors stated their belief that financial deregulation helped to explain the decline of national saving, which coincided with the discussion in Antzoulatos (1994b).

Xu (1995) utilized a decomposition technique to segregate precautionary saving under liquidity constraints into two elements: the conventional precautionary saving made against income uncertainty (PS1) and the saving caused by liquidity constraints

(PS2). He established two optimal models of household consumption: 1. a standard stochastic life-cycle model; and 2. a model with limited borrowing imposed in each time period. Then, he derived the quantitative implications for saving under liquidity constraints (PS2): with borrowing constraints, consumers may naturally have to borrow less, and save more than they would otherwise in the life-cycle model; nevertheless, they may be willingly to do so even without borrowing constraints. The argument is that consumers might wish to save extra and accumulate an asset stock to loosen future liquidity constraints. Therefore, the demand for PS2 falls in response to less-tight constraints, coupled with increasing wealth. Intuitively, the implicit prediction for young people in contrast to that for age groups would be: saving is justified when liquidity constraints are serious and wealth is insufficient.

The Measurement Issue in Empirical Works: Variety of Choices

Hayashi (1985) initiated an unconventional approach to test the alternative hypothesis that PI is unjustifiable due to liquidity constraints. The assumption was made that current consumption (CON) was bounded at

k ≥ YD∗ + 0.2 * LIQ where YD* is disposable income minus contractual saving (payments on mortgages and installment debts), LIQ is the amount of liquid assets (including demand deposits, saving accounts, bonds and common stocks), and 0.2 stands as an arbitrary factor. The author incorporated a limited dependent variable,

Y = CON if CON

= U otherwise, where CON is measured consumption, dependent upon a vector of variables such as disposable income, assets, and the age of household head, and

U = 0.85*(YD + 0.2 * LIQ) functions as a threshold9. The test finally resided in a comparison between the estimates generated from the ordinary least square regression on all households and those from a Tobit procedure on high-saving households. It is necessary to emphasize that in the actual estimation of the two equations, the dependent variable “consumption” was replaced by the ratio of consumption toYD* to lessen the heteroskedasticity. The result of a significant deviance between the two sets of coefficient

9 Hayashi (1985) explained that the reason for the multiplier of 0.85 is to reduce the probability of measurement error for saving when calculating the measured consumption by liquidity-constrained households who satisfied the sample separation rule CON

estimators supported the alternative hypothesis, i.e., the presence of liquidity constraints affected the household consumption. Moreover, as expected, the effect of liquidity constraints was most salient for young households; their predicted consumption and measured consumption had the largest gap, although this is a disturbing reality.

The method in Hayashi (1985) has avoided the problem of deriving a closed-form solution for optimal consumption when future labor income is uncertain, cited by many as a technically inoperable problem cited by many (e.g., Zeldes, 1989a). Hayashi’s work is also one of the few studies that have relied upon the household survey data, instead of the frequently used aggregate or time-series data. Though the argument may linger that his criterion in separating the whole sample into high and low saving groups is arbitrary, his work does provide insight on the significance of liquidity constraints.

Zeldes’ (1989a) empirical analysis inherited the methodology of Hayashi (1985) -

-- splitting the sample into constrained and non-constrained groups, according to a ratio of wealth (financial assets) to disposable income, along with extra splitting criteria. The hypothesis that liquidity constraints did affect the household consumption level was confirmed; nonetheless, the author realized that the results were mixed and certain attention to the possible effects of technique details, e.g., true specification of the variables and the sampling criteria rule, was called for.

Flavin (1981) found that the estimate of the marginal propensity to consume is affected dramatically by the inclusion of proxies for liquidity constraints. She used the aggregate unemployment rate as a proxy for liquidity constraints and tested “myopia” and liquidity constraints in explaining the excess sensitivity findings. She reported that the

estimated marginal propensity to consume out of transitory income was explained almost entirely by proxies for liquidity constraints (see Hubbard & Judd, 1986, p. 5).

To gauge household access to credit markets in international settings, Jappelli and

Pagano (1994) chose the ratio of consumer credit to national income and maximum LTV

(loan to value) ratio for home purchase as indictors of the seriousness of liquidity constraints. Consumer credit is defined as the amount of household indebtedness that finances current consumption and the purchase of durable goods (p. 91). A third proxy of households’ ability to borrow, the beginning-of-period stock of consumer credit (divided by net national product), was tested as equivalently predictable in their empirical models.

Similar results of the estimated regression coefficients using the three proxies demonstrated a strong correlation among the three measurements of household access to credit markets.

Empirical Studies on Liquidity Constrained Households: Certain Proportion

As Deaton acknowledged, not every household is bound by liquidity constraints and there have been empirical explorations on the extent of liquidity-constrained households in the U.S. population. Hall and Mishkin (1982) claimed that 80% of the covariation between income and consumption followed the PI-LC path and another 20% showed simple proportionality between income and consumption. By their rationale, it is equivalent to say that twenty-percent of the consumption was due to the “constrained consumption behavior” of U.S. families (p. 480). A similar percentage figure was shown in Mariger (1987) --- liquidity constraints existed in 19.4% of the sampled population.

These households were identified by their shortened planning horizons10 in a combination of the records from the same group of households from two consecutive surveys (the

1963 Survey of Financial Characteristics of Consumers and the 1964 Survey of Changes in Financial Characteristics of Consumers). By repeating the sample-split method in

Zeldes (1989a), Mayfield (1989) estimated that 22 percent of the households participated in Consumer Expenditure Survey (CES) were constrained (see Cox & Jappelli, 1993, p.

199). Abstract and vague as the concept of liquidity constraints is, the aforementioned works have employed different techniques in solving the question as to whether or not

U.S. households are liquidity constrained and, if so, what percentage is constrained.

However, they all share one feature in their means to identify those constrained families: use of proxy characteristics of the household, i.e., relying upon “indirect evidence” (see

Jappelli, 1990, p. 219). In particular, Jappelli (1990) criticized the bias, inconsistency and lack of power in the sample-splitting techniques to segregate liquidity-constrained households. On the other hand, he utilized the self-reported information of respondents in the 1983 Survey of Consumer Finances (hereafter called SCF) with regard to a consumer’s personal encounter with financial intermediaries in credit application.

Households who were rejected in their loan application were considered “credit- constrained”. According to his definition, 12.5% of the sampled households were credit

10 Mariger inherited the concept of shortened planning horizon from Yaari (1965), Tobin (1972), etc. The argument is that the liquidity-constrained family necessarily consumes all of its available resources, which corresponds to a one-period horizon and further that the short horizon is more prevalent in young and

constrained. When including those ‘discouraged borrowers’ who simply did not go through the process in concern for the potential rejection, the proportion of all constrained families increased to 19% of the whole, a figure consistent with the largely quoted 20% in the previous studies.

Furthermore, researchers have looked beyond the numerical proportion into the next question --- who might be in this small but influential portion of the population? The probability of liquidity constraints is found to be negatively related to current income, wealth, age and positively to household size (Jappelli, 1990; Cox & Jappelli, 1993;

Garcia et al., 1997; Crook, 2001)11. Married couples are less restricted in borrowing than singles. Interestingly, business cycle, a non-household characteristic factor was once posited to have played a role in the possibility of household credit constraints (Note: it is not clear what direction the authors has proposed for this effect). Crook (2001) implied that some of the determinants may echo those in modeling payment default.

retired families. Young families, particularly those with children, may even have a short multi-period horizon under the circumstances. The horizon length varies with the presence of children in the household.

11 The original series of the Jappelli (1990) hypothesis on the sign of the predictors is not as straightforward as the results indicated. Among all predictors, future expected income, age, gender and race are the ones that could have mixed effects on the probability of liquidity constraints. The reason for such ambiguity is rooted in the simultaneous impact of credit supply and demand. Moreover, the estimated probability model

(logit) contained higher order independent variables; thus, the probability predicted is not a strict linear function of the predictors.

2.3 Household Acquisition of Credit

Nearly half a century ago, consumer credit was applauded as a stimulus for economic growth and stability (Smith, 1957, p. 967). Even more, the availability of the credit system has encouraged and popularized pride in property ownership. According to the implications of the PI-LCH model, borrowing is necessary when the average lifetime consumption surpasses the current income. This inter-temporal choice of external funding helps consumers optimize their resource allocation and sustain their overall living standards throughout their lifetime.

U.S. Household Debt During the Past Decade

Along with the unprecedented economic boom since the end of the last recession

(1990-1991), household debt in the United States has exhibited a simultaneous climbing pattern. Consumer credit outstanding (seasonally adjusted) recorded in January 2002 had mounted to $1.66 trillion: $693 billion in revolving debt and $968 billion in non- revolving debt (Federal Reserve Board website, 2002a)12. Federal Reserve aggregate

12 The aggregate data on the debt of the household sector (that is, all individuals in the economy) derive mainly from the reports of commercial banks and other depository institutions and finance companies.

These data are published regularly in statistical releases and as part of the Federal Reserve’s flow of funds account. Mortgage debt secured by one- to four- family homes is attributed to the household sector. Such mortgages include both primary home-purchase mortgages and all junior-lien debt, such as borrowing against home equity lines of credit. Consumer debt has two components: consumer installment credit, which covers most non-mortgage loans to consumers repayable in two or more payments, including automobile loans, credit card debt, personal cash loans, and sales finance contract; and non-installment

statistics showed that as of the third quarter in 1999, household mortgage debt had mounted to the level of $4.4 trillion, and consumer credit to $1.4 trillion, for a total figure of $5.8 trillion, surging up from $1.3 trillion at the end of 1980 and $3.4 trillion in late

1990 (Maki, 2000; Canner & Luckett, 1991). The rate of growth in total household debt outpaced that of income during the last two decades; the once record high of aggregate debt outstanding relative to disposable personal income at 78% was surpassed by 81% at the end of 1994 (Canner, Kennickell, & Luckett, 1995). In 2001, an average U.S. household carried a credit card outstanding balance of $4400, 123% up from a decade before, whereas personal income rose about 72% during the same period (Lim &

Benjamin, 2001). The median value of debt holdings reported in the 1998 SCF was

$33,000 (Kennickell, Starr-McCluer & Surette, 2000).

A high level of debt may curtail future consumer spending, aggregate demand, and real economic activity since consumers may be subject to liquidity constraints, and it may increase consumer default and bankruptcy rates (Paquette, 1986; Maki, 2000).

2.4 The Coexistence of the PI-LCH and Credit Supply Theories

Scholars have analyzed an extensive range of topics associated with household debt: debt holdings, debt payment, and payment difficulties (e.g., Dunkelberg & Stafford,

1971; Livingstone & Lunt, 1992; Godwin, 1999; Canner & Luckett, 1991). In these studies, household debt or payment phenomena are conventionally regarded as a function

consumer credit, mostly very short term credit such as bridge loans sometimes used to facilitate real estate or other transaction (Canner et al., 1995).

of wealth (human and nonhuman) and demographic, socioeconomic and psychological characteristics of households.

Nonetheless, there have been studies, though scattered, on household debt that have encompassed the full picture, seen from both demand and supply sides of household debt acquisition in conjunction with the PI-LCH framework. Over time, a significant improvement has been seen in research differentiating between liquidity constrained and non-constrained households in identifying the debt demand function (e.g., Duca &

Rosenthal, 1991; Cox & Jappelli, 1993; Crook; 2001), which suitably justifies the lender’s role in household debt incidence. As Cox and Jappelli (1993) observed, the restriction on debt is a function of lender and household characteristics. Jappelli and

Pagano (1994) asserted that household debt may reflect not only credit market imperfections, but also differences in the demand for loans, induced by factors as disparate as tax incentives, demographics, and preferences. Jappelli (1990) proposed that the fraction of credit-constrained consumers depends on consumers’ and lenders’ behaviors and varies with changes in current and future resources, demographic variables, and characteristics of financial intermediaries.

Perraudin and Sorensen (1992) were among the first who attempted to estimate the exogeniety of demographic characteristics in the likelihood of credit supply from banks and the debt demand from households by using the 1983 SCF13. In addition, a

13 The authors commented on the inability of aggregate data to explain the determination of credit constrains by demographic factors. There is substantial difference between their works and the defined

“consumption-based” tests for credit constraints that were typical in time series data analysis, as in Flavin

prediction model of application cost incurred during the interaction between lenders and credit applicants was established with certain demographic and geographic factors as predictors. For consumer debt only, the authors reported that being healthy, married, white, a house-owner, and from a suburban or rural part of the north central U.S., with a large current disposable income and a managerial or administrative job, made access to credit easier. It is difficult, on the other hand, for those who live nearer to a large city to obtain credit. The results demonstrated that loan demand highly correlates to demographic effects, e.g., race, marital status, and household size. A typical household headed by a Black male, unmarried and unemployed individual with small family size has less demand for credit. An application cost disutility function was estimated with household demographics, as well. The results indicated that a westerner without a college degree in rural areas (or inner cities) with a large family headed by a female is likely to be discouraged from initial application.

Duca and Roesenthal (1991) stressed the stratification of loan applicants by lenders, where different debt ceilings are granted based on observable indicators of credit risk. They argued that given various factors, such as government legislative intervention and cost-effectiveness considerations, lenders would choose to justify the credit risk by securing different sizes of loans, instead of charging borrowers different loan interest rates. Their empirical model further demonstrated this assumption, that is, by focusing on

(1981), Hayashi (1985). Meanwhile, they claimed that the time series data was a source of poor statistical performance, and that panel data has limited power, e.g., in the work of Hall & Mishikin (1982) and Zeldes

(1989a).

the debt ceilings that can be predicted from household characteristics under similar loan rates. The findings from the 1983 SCF evidenced that the amount of debt lenders are willing to extend increased with borrowers’ income and wealth, as well as with increased job and income security. The authors analyzed the actual and predicted preferred debt holdings for credit-constrained families, and asserted that if it were not for the borrowing limit lenders imposed, 30% of the sampled young (<35 years-old) households would hold substantially more debt and half of the constrained households would hold at least

$12,000 (1982 dollars) more debt.

Cox and Jappelli (1993) estimated that desired debt would have been 75% higher than actual debt if the households were not constrained. Thus, removing the liquidity constraints would have increased household liabilities in the 1983 SCF by 9% overall.

Counter to the saving/dissaving pattern to accommodate smooth consumption in traditional life-cycle theory, they found that desired debt exhibited a pronounced life- cycle pattern; in other words, borrowing increases until the age of the household head reaches the mid-thirties, then declines. Therefore, they postulated that young individuals would benefit most from loosened credit constraints. Furthermore, their selection model implied that the debt households demand has a positive impact on the probability of their being credit constrained and increases with the likelihood of holding debt, all through unobserved factors.

By adopting a bivariate probit model on a two-stage least square section model similar to the model of Cox and Jappelli (1993), Crook (2001) investigated the demand for debt in households conditioned on certain factors, which meanwhile could lead to a

household’s being rejected in applying for credit. He concluded that significant predictors related to credit constraints were consistent with the factors suggested in the credit scoring model with regard to the applicant’s default risk; however, not all of the predictors appeared explanatory in the demand equation.

The goal of this dissertation is to fill the gap in research related to household debt holdings as a result of household demand and lender extension, both of which are determined largely by characteristics of the household. In terminology, the effect of liquidity constraints will be accounted for in the PI-LCH model. However, a unique approach is determined in representing the debt under the name of debt burden, rather than the absolute amount of debt holdings or other specifications appearing before. In other words, throughout the paper, the degree and extent of household debt is gauged by this debt burden measure14.

2.5 Criteria of Debt Service Burden

2.5.1 Debt Burden (Debt Ratio) Guidelines: Personal Finance Perspective

In a pioneering attempt to use financial ratios as assessment tools in assessing personal financial status outside the conventional business arena, Griffith (1985) proposed several ratio calculations to investigate the debt burden of a household:

1) liquid assets/total debt;

14 Godwin (1996) acknowledged that no consensus was and may well ever appear on THE best operational measure on families’ debt. Rich branches of debt measures can indeed help to capture a snapshot of household debt, if used systematically with other financial indicators.

2) liquid and other financial assets/total debt;

3) liquid assets/non mortgage debt;

4) liquid assets/short-term debt plus 12 months payments on other debt

5) liquid and other financial assets/short-term debt plus 12 months payments on

other debt;

6) total debt/net worth

7) non-mortgage debt/net worth.

The various forms of the ratios with different components of numerator and denominator as illustrated above provide investigators with a flexible focus on particular areas in family finance in relation to the debt situation. Moreover, to be more practical, for each ratio, he recommended safety ranges for debt obligations to be financially affordable for a household. For example, of the level of ratio 4) exceeding 50% was considered good, and a maximum of 30 to 40 percent for ratio 7) would be reasonable.

Mason and Griffith (1988) assigned formal names to some of the earlier ratios and added new contents, such as

financial flexibility ratio = short-term debt plus 12 months payments on other debt total realized increase in net worth

debt/income ratio = total debt______total realized increase in net worth .

No suggestions were given, however, on the acceptable values of these ratios in the family balance sheet.

While ratio analysis became widespread in the consumer and personal financial management literature, the prevalence of a variety of debt ratios differing in the

conceptualization of debt and its relative measurement led Langrehr and Langrehr (1989) to seek a comparatively accurate and representative candidate to reflect household debt capacity. They refuted the usage of total debt in ratios like debt to financial assets as inappropriate because it implies that total debt should be repaid within one year, which is fairly unlikely; in addition, realistically, consumers tend to pay off debt from income flows instead of liquidity (non-financial) assets. Hence, based on the almost-similar form of debt service to take-home pay, they did some adjustment in the details of numerator and denominator. The ratio was constructed as below:

Total debt payment (mortgage + lease) (monthly) Disposable income – expenses + debt repayment , as a “residual income” ratio which they defined. They argued that lease portion should be included because a lease payment is similar to a home mortgage, and both match the features of installment debt. The deduction of living expenses is meant to justify the heterogeneousness in this aspect among households. An unusual suggestion in calculating debt payment was the idea that rents could be considered to even the difference between renters and homeowners (p. 400). In a sense, this ratio essentially shifted from a measure of strictly defined debt burden to one that gauges the payoff obligation capability.

Unfortunately, no specific ratio guidelines were mentioned in the paper to establish its prescriptive position.

Lytton, Garman and Porter (1991) streamlined appropriate debt ratios and postulated corresponding ratio norms in the hope of signaling financial danger.

Depending upon the nature of debt (consumer debt versus mortgage debt) and tax

consideration (pre- versus after-tax income), the debt repayment to income ratio they articulated has the following variations, as indicated by the assigned names:

Consumer debt-service ratio = Consumer debt repayments (monthly) Disposable income (monthly)

Debt-service ratio = Consumer and mortgage debt repayment (annually) Disposable income (annually)

Debt-to-income ratio = Gross annual debt repayment (annually) Gross annual income .

A safe debt limit for the first ratio is 10% or less; a ratio of 11-15% is accompanied by reduced financial flexibility; and 16-20% should be a sign that the household is fully extended. After home mortgage appears in the numerator as in the second ratio, the standard understandably shifts up: a value of 30% is considered a safe line. The distinction between the second and third ratio is the pre-tax or after-tax income the total debt payment is drawn against. If the denominator is replaced by gross income as in the third ratio, a range between 30-35% of debt-to-income ratio is regarded as realistically moderate.

Garman and Forgue (1991) stated that a debt service to income ratio (i.e., debt repayments to gross income) under 36% leaves reasonable leeway for other household expenses. The responses from financial planners and educators surveyed by Greninger,

Hampton, Kitt and Achacoso (1996) to identify financial ratio benchmarks in household portfolios revealed that 10% or less (a median value in the survey) would be an acceptable level for a non-mortgage debt payment to after-tax income ratio (i.e., formally termed debt safety ratio); 20% would be considered a danger-point. If mortgage debt payment was accounted for in the ratio (equivalently debt service ratio), the thresholds

were lifted respectively to 35% and 45% (both median). A similar recommendation was summarized by DeVaney (2000), who agreed that for the debt safety ratio, 20% should be an alarming point. On the other hand, maintaining a 5-point interval between 10% and

15% is strongly suggested. Meanwhile, an appropriate total debt payment to income ratio should not exceed 30-35% when using gross income or 40% when using disposable income. Noticeably, there was slight disagreement between planners and educators in the bearable debt burden level; educators seemed to be conservative in specifying the debt burden ceiling in contrast to planners.

2.5.2 Debt Service Burden and Home Purchase: Norms by Lenders

Greninger et al. (1992) observed that financial planners who answered their survey preferred the non-mortgage debt payment to the total debt payment ratio. Such

‘preference’, in fact, reflects the conventional practice that prevails in the lending industry.

Assessment of debt payment ratio is usually of interest when individuals apply for home mortgages. According to the American Banker Association’s (1996) recommendation, the approximate maximum fixed mortgage that an individual can afford should be based on the following two general guidelines:

House payments of principal, interest, taxes, and homeowner's insurance (PITI)

should not exceed 28% of one’s gross monthly pay.

This monthly housing payment (PITI) plus current monthly debt payments should

not exceed 36% of one’s gross monthly pay.

Ginne Mae (no date) claims that they use “debt-to-income ratio”, a ratio of a borrower's monthly payment obligations to his/her net effective income (FHA or VA loans) or gross monthly income (conventional loans), to screen potential reliable borrowers. The Mortgage Banker Association of America (1999) suggests that the potential home purchaser keep monthly housing costs (including mortgage payment, taxes, insurance and other fixed expenses) between 25 and 28 percent of the total monthly income. Monthly housing costs plus other long-term debt (expenses extending more than 10 months into the future) such as car or other installment loans should not exceed more than 36% of the gross monthly income.

U.S. Housing and Urban Development (2001b) indicates that generally a lender considers an individual’s monthly mortgage payment to total no more than 29% of his/her monthly gross income, which is monthly income before taxes and other paycheck deductions. The ‘debt-to-income ratio’ compares one’s gross (pre-tax) income to housing and non-housing expenses. Non-housing expenses include such long-term debts as car or student loan payments, alimony, or child support. Lenders also consider cash available for down payment and closing costs, credit history, etc. when determining maximum loan amount. According to the same government agency (2001a), to qualify for a conventional loan, the total monthly obligations that will extend more than 11 months into the future, including mortgage payments, property taxes, and insurance, as well as other debt payments, should not exceed 33 to 36% of gross income. A higher repayment ratio could

leave households with less financial flexibility and with limited paying-off power, and thus be likely to reduce their opportunity to become homeowners. Forty-eight percent of the households in the Census Bureau’s Survey of Income and Program Participation

(SIPP) could not be granted a home loan due to both excessive debt and insufficient income (Savage, 1995).

Lenders’ norms in gauging the individual’s debt payment ratio were accepted by

Canner et al. (1995), economists with the Federal Reserve Board. They examined household burden of debt by separating the SCF surveyed population into three categories: 1) households having no debt; 2) households having a debt payments-to- income ratio less than 10%; 3) households having a ratio more than 30% for consumer debt and 40% plus for total debt. They stressed that such classification corresponds to industry guidelines for extending credit (p. 333).

2.6 Recent Household Debt Burden Status

In the evolution of choices to quantify a household’s financial profile, ratio measurement has gained favorable agreement. Debt payment to income ratio has emerged in the family of yardsticks for household debt15. To clearly distinguish it from other

15 No single debt ratio should be posited as superior to another one. As a matter of fact, they share limitations and advantages when compared to each other. Rather, the variety of debt ratios supplies researchers with handy tools to look into household financial health from multiple angles. For example, total debt to assets, a value that tells the relative size of debt amount to assets, is widely utilized to capture household solvency status. But, actually, the choice of debt ratio relies upon the particular question the study intends to answer.

generally cited debt-to-income ratios, debt payment ratio has a twin name, debt service burden. From the name, it is self-explanatory that debt payment ratio is the ratio of scheduled payments to income and captures the household capability to repay debt obligations in proportion to their earnings on a routine basis. It is a measure of “flow to flow” (in resources).

Debt service burden made its debut in research in the late 1970s. Palash (1979) examined the 1960-70 exceptional consumer borrowing by means of debt outstanding and repayment to income ratio. Ever since, debt service burden measurement appealed to many economists in reporting the indebtedness in the household sector, in particular, those in the Federal Reserve System (e.g., Paquette, 1986; Canner et al., 1991; Canner et al., 1995; Edelberg & Fisher, 1997; Zandi & Chen, 1998; Garner, 1996; Black & Morgan,

1999; Maki, 2000).

Traditionally, due to the lack of microeconomic data, Federal Reserve economists relied upon aggregate data16. During the past decade, household debt service burden has

16 The following content illustrates the derivation of debt service burden on the Federal Reserve website.

“To create the measure of burden, payments are calculated separately for revolving debt and for each type of closed-end debt, and the sum of these payments is divided by disposable personal income as reported in the national income and product accounts. For revolving debt, the assumed required minimum payment is

2-1/2 percent of the balance per month. This estimate is based on the January 1999 Senior Loan Officer

Opinion Survey, in which most banks indicated that required monthly minimum payments on credit cards ranged between 2 percent and 3 percent and had not changed substantially over the previous decade.

Payments on closed-end loans, which are calculated for each major category of closed-end loan, are derived

climbed up since 1993 despite of a decline in the early 1990s (see chart 3 in Maki, 2000).

In the third quarter of 1999, it stayed at mid-point between 13% and 14%. Separately, consumer debt burden repeated the overall trend and exceeded the burden level of mortgage, while burden on mortgage stayed consistently under the 6%17.

from the loan amount outstanding, the average interest rate, and the remaining maturity on the stock of outstanding debt.

Estimates of the amount of mortgage debt are taken from the Federal Reserve Board's flow of funds accounts, and estimates of outstanding consumer debt are taken from the Federal Reserve's G.19 statistical release. For consumer debt, a more detailed breakdown by type of closed-end loan is obtained using internal Federal Reserve estimates and data from the installment credit publications of the American

Bankers Association.

Interest rates on closed-end consumer loans are obtained from the Federal Reserve's G.19 and G.20 statistical releases, with the exception of student loan rates, which are obtained from the Student Loan

Marketing Association (Sallie Mae). An estimate of the interest rate on the stock of outstanding debt is obtained by weighting the recent history of interest rates using information on the age of outstanding loans in the Federal Reserve Board's Survey of Consumer Finances. The interest rate on the stock of outstanding mortgage debt is an estimate provided by the U.S. Department of Commerce, Bureau of Economic

Analysis.

Maturity series for consumer debt are taken from the G.19 release and from the American Bankers

Association's installment credit publications. Maturity series for mortgage debt are obtained from Credit

Suisse First Boston.” (Federal Reserve Board System website, 2002b)

17 Make (2000) explained the difference to the shorter maturities on consumer debt.

It is acknowledged by the Federal Reserve that ideal data is data that provides required payments on every loan held by every household in the United States. Until the availability of survey (household) data such as SCF, the limitation of aggregate statistics veiling the characteristics within households is resolved. Two different approaches in gauging household debt service burden were seen in the SCF series summary (Kennickell et al., 2000). One is the median debt payments to income ratio for households with debt only. Such a measure, to the greatest extent, renders a more precise figure on debt burden among those who do have accumulated debt. The median ratios in four consecutive surveys (1989, 1992, 1995, 1998) are, respectively, 15.9%, 16.1%, 16.1% and 17.6%. A second debt burden calculation appears to be an alternative presentation of household debt burden. It is the ratio of total debt payments of all sampled families to total income of all. Obviously, this pseudo ‘aggregate ratio’ scores lower than the debtors-only method, with values in the four surveys being 12.7%, 14.1%, 13.6% and 14.5% from earliest to most recent. Interestingly, in spite of the sharp increase in total debt outstanding over the years, debt burden somehow stayed fairly stable within a small range.

Debt Service Burden and Financial Robustness

Debt service burden has been verified as a strong predictor in household financial trouble that is associated with excessive possession of debt. In their survey on household financial satisfaction, Lown and Ju (1992) found that the percentage of income to debt repayment was negatively associated with financial satisfaction, though the effect is small

(r = -.1812). Households with a high debt burden were four times as likely to be late or to miss payment than those with low burden (Canner & Luckett, 1991), which could be a prior sign for future default. Households that met the annual consumer debt payment to disposable income ratio guideline (a value not larger than 15%) in the 1983 SCF had about one-third of the likelihood to suffer insolvency18 three years later (as in the 1986

SCF) than those who did not meet (DeVaney, 1994). Zandi and Chen (1998) even asserted that an expected 30 basis point decline in debt service burden (aggregate measure) would ultimately result in 60,000 fewer bankruptcy filings by year-end 1999.

Maki (2000) summarized that lagged debt service burden is a statistically significant predictor of current delinquencies and changes in personal bankruptcies.

2.7 Independent Variables

2.7.1 Overview of Studies on Household Debt

The PI-LCH framework states that the intended balance between contemporary consumption and earnings basically determines household desired borrowing. Depending upon the earnings and consumption pattern in one particular life stage, borrowing needs may be explained as the gap between earnings and consumption. Demographic characteristics of the household, to some extent, help to identify the life cycle stage of the household and, thus, the borrowing behavior. Scholars once had attributed the increased

18 Insolvency of a household refers to the scenario where households’ liabilities exceed the market value of assets. DeVaney (1994) defined a household being insolvent if the household has net worth less than one month’s income.

indebtedness of households in the 1970s to a broader percentage of ownership of indebtedness, a growing number of families with both spouses working, and increased usage of credit cards and tax incentives (Palash, 1979). Since the 1980s, there have been numerous research studies completed on the topics concerning household debt portfolio, captured by the amount of household debt outstanding, debt payment, payment difficulty or debt ratio (e.g., Mueller & Hira, 1984; Sullivan, Warren & Westbrook, 1989; Canner

& Luckett, 1991; Duca & Rosenthal, 1991; Lytton et al., 1991; Fan, Chang & Hanna,

1993; Livingston & Lunt, 1992; Liao, 1994; Canner et al., 1995; Godwin, 1996;

DeVaney, 2000; Crook, 2001). In these studies, the relationship between some of the aforementioned debt forms and household demographics was examined from a theoretical standpoint or by empirical analysis, along with other factors of interest.

2.7.2 Theoretical Implication

The traditional PI-LCH model would predict that borrowing decreases with an individual’s age until prime-age, then possibly increases afterward. The major force that leads to this position behind the scenes is the climbing shape of one’s earning profile, from young to middle age, and decline at a peak time before retirement. In the meantime, it is assumed that the consumption needs are held constant over the life span; therefore, it is expected that borrowing to occur by a larger amount when the individual has a larger discrepancy between earning and consumption, particularly, in young age. Household characteristics that would be indicative of their earning and consumption pattern appear in theories as major contributors to the household debt obligation. For example, with

everything else the same, young families or low-income families are likely to be heavy borrowers.

Dunkelberg and Stafford (1971) relied on a ‘stock adjustment model’ and attempted to predict the net change in outstanding credit obligations (excluding the home mortgage) one-year apart. Among a group of estimations, they estimated that desired installment debt depended on longer run income (i.e., permanent income), variations in income, and family life cycle (a composite of age, marital status, age and presence of children and labor force status) in a non-linear fashion (p. 602).

The issue of expectation for the future on household borrowing behavior was examined by Fan, Chang and Hanna (1993) in a normative way. They constructed a two- period intertemporal consumption model of optimal credit use under an uncertain income scenario. From the framework, they derived a function of the optimal borrowing as relative to income that would be an adequate burden for households, a function of interest rate, time preference, expected income growth and risk aversion. Ceteris paribus, the amount of borrowing was formulated to associate positively with income growth rate and the probability that future income would increase.

When it comes to debt payment-to-income ratio, Lytton et al. (1991) and

DeVaney (2000) generalized that as an individual or family ages, the earning profile is likely to go upward, and the value of household debt payment-to-income ratio should decrease.

2.7.3 Empirical Works

Most of the previous research investigating the household debt topic explored multi- dimensional variables that could explain borrowing propensity, debt holdings, debt payment or payment difficulty.

A pioneering work in studying household dissaving behavior was attributed to

George Katona. In his 1949 work, he proposed the effects of multi-dimensional factors on household borrowing. In his unprecedented empirical study on the 1946-48 SCF,

Katona reported that dissaving did not turn out to be the reverse of saving and was not found to occur more in stereotyped low-income families. Instead, the middle-income group borrowed the most. Dissaving, as he defined it, including means of borrowing and reducing liquid asset holdings, did increase with income. Additional factors that could result in higher borrowing were hypothesized and tested; these included unusual medical or emergency expenses, unemployment/retirement, temporary income decline

(subjectively), other income decline (evidently), large durable goods purchase, and liquidity assets. Each of the factors helps to explain household dissaving behavior; but unfortunately, these incidences happened more frequently in low-income families. On the other hand, when mixed with income inequality, big dissavers are not necessarily big earners. In identifying the incentives for borrowing, based on his psychology expertise,

Katona suggested three circumstances (or combinations of circumstances): inability to meet ‘necessary expense’ out of income, unwillingness to meet ‘necessary expense out of income’, and willingness to make unusual expenditures (p. 684). Among these, willingness to spend more than income was seen as a result of income increases and

optimistic expectation. He even speculated that dissaving goes in the same direction as overall economic environment.

Variables in a broader scope were later added in the studies on household debt within the PI-LCH system. Household, or to be specific, householder’s demographics, was inevitably the first group of factors to proxy individual or household life cycle stages. Heck (1987) found that age, being a homeowner with mortgage debt, and having two salary earners in the family have positive effects on the use of credit cards. The number of family members showed a positive effect on the use of credit card, too (Hira,

1990). White, married families, the less educated, those who oppose credit use, and those who had experienced past income increase were more likely to use bank credit cards

(Steidle, 1994). When it comes to the usage of the store type of credit card, being young, less educated, a male-headed family, being White, married families who had past income increased and proponents to credit use were positively associated with the probability of store credit card use (Steidle, 1994). Households with higher total income, pension income, headed by a male, a married couple, and inclined to finance for luxury/durables were reported to have positive debt holdings (Duca & Rosenthal, 1991).

The findings of the impact from the same variables were not always echoed by other researchers. Income, age and gender of householder were not found to be significant independent variables to predict a household’s use of bank credit cards

(Steidle, 1994), whereas age was shown to pose a curvilinear relationship to the likelihood that households seek debt (Cox & Jappelli, 1993) and the amount they seek

(Liao, 1994). The probability of holding debt increased with early age, remained smooth

from 25 until 54 and plummeted afterwards (Cox & Jappelli, 1993). Cox and Jappelli

(1993) contended that household borrowing follows what is implied from the life cycle hypothesis, even though Liao (1994) found that younger and older families used less in consumer loans than middle-aged families. Family income, on the other hand, was hypothesized and tested significantly as a non-linear (curvilinear) predictor on the odds of a household having more debt (Godwin, 1999), as well as the desired household debt (in log scale) (Crook, 2001). The coefficients in Godwin’s model demonstrated that low or high-income families were less likely than middle-income families to be in a higher quintile of indebted families one year later. Liao (1994) discussed that there was a similarly inverted U shape of consumer loan dependent upon family income. Families in the two ends of income distribution (poor and wealthy) generated less consumer debt than those in the middle, holding other things equal. Crook (2001) calculated a maximum amount of desired debt at current income of $151,461, which verified an upside down U shaped relationship between desired debt and current income.

According to Cox and Japelli (1993), large suburban households headed by women are more likely to hold debt. Furthermore, the level of household debt holdings in the 1983 SCF were found to be positively associated with permanent earnings and net worth, but negatively associated with current income. The authors estimated that a one- dollar increase in permanent earnings raises desired debt by 42 cents whereas a one- dollar increase in current income lowers desired debt by 29 cents. They attributed the opposite directional effect of increases in permanent versus current income to explain the tendency of consumers financing for current consumption rather than borrowing to

acquire assets. The amount of borrowing was found to be associated with the increment of age until retirement age. Crook (2001) applied a similar technique to that of Cox and

Jappelli, but on a more recent dataset --- the 1995 SCF, on household debt demand. The findings revealed that desired debt (i.e., credit constraint is supposed to be absent) negatively changed with ages within 55-64 and 65+ and for risky decisions, but changed positively with net worth, family size, foreseen expense and being homeowners or workers. Meanwhile, other demographic characteristics, such as education, racial profile, marital status and expectation factors did not return hypothesized effects on the debt amount in either study19.

19 One controversial topic in the loan market is race discrimination. On one hand, studies showed a disparity in loan acceptance rate between White and minority families. According to Alicia H. Munnell,

Lynn E. Browne, James McEneaney, and Geoffrey M.B. Tootell, (authors of "Mortgage Lending in

Boston: Interpreting HMDA Data," Federal Reserve Bank of Boston, Working Paper WP-92-7, October

1992), if minorities in the Boston area had the same characteristics as whites, they would be denied loans

17 percent of the time, compared to only 11 percent for whites. Perraudin and Sorensen (1992) reported that after other household demographics are controlled, the effect of race on the probability of getting credit conditioned on application is about 10%. That is, White families scored nearly 0.1 higher in the probability than non-White families. The results held for two typical types of families differing by ethnical background. They refuted the theory that the rejection of more non-White families is due to a higher rate of application; in fact, non-White was found to be slightly less prone to want credit. On the other hand, under the governance of the Fair Credit Lending Act, no discriminative conduct based on race should occur to lenders. Critics, thus, questioned the validity of the above studies, on the representativeness of data, omission of variables and misspecification of the model. Along with the theoretical standpoint of credit supply, lenders should maximize profit and/or minimize potential loss by assessing borrowers’ default risk

Livingstone and Lunt (1992) approached consumer debt from the perspective of psychological association and provided a good review on the related literature. They integrated social and psychological factors into a comprehensive framework in addition to household economic characteristics and looked into the phenomenon of why individuals get into debt, by how much, and how much they repay in UK families. The following highlights were extracted from their study: variables in their discriminant function equation that enable a differentiation of debtors versus non-debtors were age, number of children, credit usage attitude, attitude on money management, feelings about control and coping factors. In the prediction of personal debt amount, they found that social class, disposable income, and number of debts contributed positively whereas mixed feeling about credit, disagreement on “keeping up with the Jones’s”, willingness to use credit, and number of bank accounts significantly reduced the debt acquired. Not surprisingly, disposable income and lines of debt were the important covariates in the amount of regular debt repayment, along with pro-credit attitudes (positive sign) and habits of paying off credit card balance (negative sign). Livingstone and Lunt commented that debtors in Britain were significantly younger than non-debtors. The reason is due to the generational differences in the attitudes towards debt rather than economic demands as a function of one’s stage in the life cycle. Changes in attitudes toward credit from

‘borrowing is a shame’ to a more favorable acceptance propel the large credit

rather than any of their personal differences, e.g., ethnicity. Duca and Rosenthal (1991) reasoned that race might be correlated with other (unobservable) variables that proxy default risk, e.g., discrimination in labor markets that affects job and income security, which in turn influences lender decisions.

occurrences. The authors reckoned that psychological effects were equivalently important to economic variables in the explanation of indebtedness. A positive linkage between consumer sentiment and debt repayment was verified in their study. Those who repaid more were more favorable inclined toward credit use and relatively more in control.

Rather, borrowing plus repaying for them was a budgeting strategy, which allowed for one’s immediate consumption.

Liao (1994) put together several categories of variables in the study of consumer loans --- expectation for the future, attitudes towards credit and financial risk, socio- demographics and financial factors --- to represent the comprehensive universe where the household’s borrowing decisions are made. Contradictory to the hypothesis, expectation for future economy, personal finances and interest rate turned out to be negative factors relative to the amount of consumer loans. Other variables had a desirable effect as assumed. Consumers who felt more positive toward credit and financial risk would borrow more consumer types of loans. Married or two wage-earner families held more consumer loans than those who were not. Households who were in the middle age range or moderate-income group borrowed more consumer loans than their counterparts in other categories. Households sought fewer consumer loans if they were credit constrained or possessed sufficient liquidity assets. Though household size and householder’s education were observed to have a positive sign in the estimation, they were not found to be statistically significant. It is worthwhile to point out that Liao emphasized the strong impact of credit constraints and called for future study on the influence of credit supply on household debt demand.

There seems to be a paradox between theory and reality on the expectational factor. Heck (1987) confirmed that an optimistic view on future economy and personal income had positive correlation to an individual’s use of credit cards. Another work that was in line with Heck’s was a study of Luckett and August (1985), in which they found an almost identical impact on willingness to borrow from expectation on economy and finances. Nonetheless, Liao (1994) and Steidle (1994) both reported opposite results to the generalization the theory predicts --- expectation for future economic condition or higher household income is negatively related to the amount of consumer (installment) loans or to his/her being a credit card revolver. As one possible reason, the decision as to whether or not to extend a loan and by how much from the lender’s side, rather than according to the consumers’ self-optimism may play a counter role in the actual loan acquisition among households.

For households who have borrowed, debt payment problems signal an excessive burden of debt or debt repayment in the household, which often leads to payment default or delinquency 20. Among newly wed couples, young age, low income, few sources of income, and little financial management training became features of those who were troubled by debt repayment (Godwin, 1996). Black and Morgan (1999) presented disagreement with the previous two studies in some aspects. They found that the ratio of

20 Note that default risk is the incentive of the credit scoring system prevailing in the lending industry. The very essential difference between industrial practice and the following studies is that lenders gauge credit applicants’ default risk up front from conventional and supposedly powerful predictors, whereas academic research works backwards, from the existing fact.

debt payment to income and log income positively associates with household payment default. Payment difficulty was more likely to be seen in the young, fewer years of the job, less educated households with small liquid assets. A third type of result was reported by Canner and Luckett (1990), who concluded that controlling for age, race, number of children, marital status, and relative liquid assets to debt, income and debt payment to income ratio had no effect on the probability of default. Not surprisingly, expectation of future income growth may have encouraged more borrowing, and thus a payment problem in households.

Throughout the above illustration and discussion, household borrowing is dependent upon certain individual characteristics that reflect earning patterns and consumption needs during life stages. It is also noticed that psychological factors

(attitude and behavior) enhance the understanding of household dissaving beyond economic or financial variables. Moreover, a proportion of the general public could not fulfill their borrowing demand because of the potential default risk (from the consumer side) or credit risk (from the lender side). In light of the interwoven association of all possible explanatory variables, a sophisticated model is then proposed for the examination of U.S. household debt burden, with influential factors summarized in the following categories:

1). Liquidity constraints.

The presence of credit constraints has presumably altered the consumption pattern for a portion of households, and thus their optimal resource management. Therefore, the PI-

LCH hypothesis on borrowing behavior in households is likely to be flawed. Taking

liquidity constraints into account in the household debt analysis ensures a more sensible explanation of such a subject.

2). Demographic characteristics.

Household life-cycle stage is typically represented by household demographics, which in turn become the conventional factors studied in research on consumer borrowing. The list includes age, education level, marital status, family size, home ownership and more. Take age as an example. The PI-LCH suggests that young people tend to borrow more when their contemporary earnings are short of the ‘smoothed-out’ lifetime consumption. On the other hand, as mentioned above, they are the same people who are least likely to borrow freely due to the concern of lenders (Duca & Rosenthal, 1991). The effect of age in debt obligation may have some mixed outcomes when the demand and supply sides are taken into account simultaneously; mixed effects are observed in other variables as well. It will be meaningful to explore those variables that have brought about controversial results in previous research.

3). Economic/financial factors.

Needless to say, household (permanent) income stands out as the widely studied and crucial determinant to household borrowing. Though there have been attempts to operationalize permanent income (Cox & Jappelli, 1993), current earnings have appeared frequently as a convenient and practical proxy with variations in pre- or post-tax household income. Meanwhile, household income poses a significant weight in the lenders’ criteria of a potential debtor. Other financial assets are joined with regular earnings in justifying extra needs in borrowing for households, as well as providing the

assurance that future payoff is guaranteed from the lenders’ standpoint. However, low- income households’ borrowing needs will most likely be hindered by the credit limit lenders will allow. Employment and entrepreneur status are additional evidence of the overall household capability to produce steady resources. These profiles of a household remain significant in the evaluation matrix of lenders.

4). Psychological variables.

Previous works indicated that an individual with a positive attitude towards credit usage would be more prone to be exposed to debt compared to those with a negative attitude.

The level of debt acquired goes in the same direction as the fondness of credit use. Unless someone has self-control or a particular coping strategy, the household will gradually accumulate debt for various reasons, like convenience. Easy access to “easy money” would increase the probability of the household being overextended. The large number of credit cards a person possesses could be a result of such subjective pursuit and objective availability. If mind drives behavior, then these consciousness variables are worthy of study.

5). Expectational/event factors.

Expectation on future economy or personal welfare enriches the concept of a forward- looking human being, with contact to the external economic universe. Intuitively, an individual who foresees a rosy future would act reasonably to incur debt for current consumption or investment, and rely on resources in the future to repay the obligation.

However, lenders may not share the consumers’ optimism. In other words, because of the joint effect in debt demand and supply, the true effect of expectation on household

indebtedness remains unclear. Additionally, under certain circumstances, households who encounter unexpected events within the households, such as medical expense, or outside, such as layoff in economic downturn, may worsen their borrowing burden.

continue continue Major Conclusion Conclusion Major liquidity of Existence constraints in some low- saving households Borrowing constraints the in consumption affect U.S.

Approach Approach and high-saving Split low-saving households compare Tobit and estimates on high-saving OLS versus households estimates on all households liquidity- Split group versus constrained non-constrained by ratio to income wealth the examined and violation of Euler two between equation groups

Sample Data Data Sample of 1963-1964 Survey Financial of Characteristics conducted Consumer the Board of by the of Governors system Reserve Federal Study 1968-1982 Panel of Income Dynamics Survey the by (PSID) CenterResearch at Michigan of University - -

Purpose Test between Permanent and hypothesis Income of hypothesis alternative constraints liquidity Test between Permanent and hypothesis Income of hypothesis alternative constraints liquidity factor Article Article Hayashi (1985) Zeldes (1989a) liquidity and model Hypothesis on the Permanent-Income/Lifecycle chart literature review of 2.1: Summary Table constraints

Continue Continue Major Conclusion Conclusion Major liquidity with Model for constraints are capable disparate explanation saving, in phenomenon both in assets accumulation microeconomic and data. macroeconomic of significance The including debt ceiling in optimal consumption- saving function, along with assets income and expectation. Optimal weakly consumption increase in the debt ceiling; optimal saving. Debt ceiling dynamically affect the consumption/saving behavior.

Approach Approach derivation Theoretical and simulation of consumption, income and on variations with assets, correlation, income serial growth aggregate and process thatincome mimics the time-series data U.S. of households. derivation Theoretical and simulation experiments with variations in economic environment variables and debt ceiling Sample Data Data Sample

Purpose the saving, Examine assets accumulation pattern households of constraints liquidity with the discrepancy and and micro between aggregate behavior. rational a on Study consumer’s consumption/saving behavior taking into account for borrowing constraints income and uncertainty Table 2.1 Continued 2.1 Continued Table Article Deaton (1991) Antzoulatos (1994b)

Major Conclusion Conclusion Major factors have Demographics on credit demand. effect Other factors have impact supply. on bank on constrains Liquidity the nation raise household saving rate, strengthen the saving, on growth of effect foster productivity and growth. plays constraints Liquidity a significant role in consumption and saving behavior both in micro- U.S. macro- dataand in

t. t.

Approach Approach Probability model on the decision supply lenders’ the consumers’ and demand. national on Regressions growth and with saving factors, as liquidity such constraints, to loan value rate, growth ratio, percentage consumer of produc tocredit national Simulation with estimates from previous works.

Sample Data Data Sample of 1983 Survey Consumer Finances by Board Reserve Federal country International financial statistics

Purpose Estimate credit demand on model supply and credit-constrained households of the effect Examine on constraints liquidity saving rate, productivity their growth, interrelationship and level. at national welfare to decompose Attempt under saving precautionary into constraints liquidity income from PS1 two: PS2 under and uncertainty constraints liquidity n Table 2.1 Continued 2.1 Continued Table Article Perraudin & Sorense (1992) Jappelli & Pagano (1994) Xu (1995)

CHAPTER 3

THEORECTIAL FRAMEWORK

3.1 Economics Theories on Household Dissaving

The literature review in the previous chapter revealed that a majority of the studies were initiated from an investigation on the relative portion of current consumption to permanent income (e.g., current income as one proxy), to reach the conclusion that credit constraints have a significant impact on the life-cycle consumption. This dissertation, however, is not intended only to add to these so-called “consumption-based” works. Rather, the focus is on the outcome between consumption and income--- dissaving. A disturbing phenomenon in consumer borrowing research so far is that most studies have been solely concerned with consumer borrowing demand from a household’s perspective, ignoring the credit supply from a lender’s standpoint. The bias and inaccuracy is obvious when it is widely acknowledged that credit constraints significantly reduce consumption. The goal here is to integrate the credit supply from financial institutions, e.g., banks, based on borrowers’ default risk, into the conventional life-cycle framework in regard to individual desired borrowing needs, so as to understand the household debt portfolio in a fuller context.

3.1.1 Permanent Income-Life Cycle Consumption Hypothesis

The standard life-cycle consumption/saving model under uncertainty formulated in Hall (1978) is as follows: (other versions appearing elsewhere may bear with different representation or notation of parameters)

T −t −τ Max. E ∑ (1+ δ ) u(c +τ ) t t=0 t

T −t −τ s.t. ∑ (1+ r) (c +τ − w +τ ) = A t=0 t t t

where

= Et mathematical expectation conditional on all information available in t ;

/ UDWH RI VXEMHFWLYH WLPH SUHIHUHQFH

r = real rate of interest ( r ≥ δ ), assumed constant over time;

T = length of economic life;

U (.) = one-period utility function, strictly concave;

= ct consumption;

= wt earnings;

= At assets apart from human capital.

The theoretical presentation implies several principles concerning how a rational

“economic person” should behave. An individual maximizes his/her expected utility derived from aggregate consumption throughout the life horizon. S/he is expected to consume all the lifetime resources by the end, that is, no inheritance should be left upon death. S/he is supposed to rely on all possible information when making his/her consumption/saving decisions, instead of deciding “by instinct or gut”. The assumption

of one single real interest rate helps to simplify the computation of future value of total assets. Periodic consumption does not have any direct relationship with contemporary earnings; rather, it is subject to the utility function and the accumulated assets during the length of one’s life. In addition, the real interest rate and rate of subjective time preference characterize the inter-temporal nature of the model, namely, the discounted value in multiple time streams.

3.1.2 Models of Credit Constraint and Future Expectation

In the assessment of studies that empirically tested credit constraints in multiple- period inter-temporal consumption models similar to Hall (1978)’s, Perraudin and

Sorensen (1992) noted the limited success of reliance upon time-series data (e.g., Hall,

1978; Flavin, 1981) and panel data (e.g., Hayashi, 1985; Zeldes, 1989a). They argued that the aggregate dataset prohibited the examination of the substantial dependence of preferences on individual demographic factors; and although panel data did include a smaller range of personal characteristics, measurement specification problems lingered.

Not only did they decide to conduct analysis on an alternative cross-sectional type of data, but also they supported the analysis with approaches different from the earlier ones.

Perraudin and Sorensen (1992) adopted a two-period model to accommodate their usage of a cross-sectional dataset, the 1983 Survey of Consumer Finances, in studying credit supply and demand in loan market accounting for credit constraints. They contended that in deciding whether or not to apply for a loan, consumers are influenced by two considerations. One involves concerns about lenders: the probability of a

consumer being granted a loan is a function of the applicant’s demographic characteristics (including income and net financial worth). Secondly, there is a direct loss

of utility ( dα1 ) from the consumer side when going through the application process. Such disutility was observed in the dataset as evidenced by households who desired credit but did not go through the procedures, and this cost may also be associated with individual demographic characteristics.

A quadratic utility function in the form as below was assumed21:

≡ λ − 2 U (Ct ) [ α1Ct Ct ]

λ where α1 indicates that the parameter potentially depends on the demographic characteristics of the individual, in particular, on the demand side. Given the assumptions, for a consumer who is rational to borrow, his/her optimal consumption

∗ C0 needs to achieve the maximum of the equation

21 Perraudin and Sorensen articulated their reason for the choice of such a utility function. “…human wealth figures had to be constructed by forecasting agents’ income using a simple linear forecasting rule.

This procedure unavoidably entails quite serious measurement error, which is generally quite intractable in nonlinear settings. One way to resolve this difficulty is to assume preferences for which the decision rules are linear in human wealth since the measurement errors then enter the model linearly. One may thus derive a discrete-choice model based on closed-form inequalities. The problem with this approach is that quadratic utility functions have several well-known drawbacks. They decrease outside a certain range and they imply increasing absolute risk aversion and zero prudence. Since other commonly applied utility functions, such as constant relative risk aversion (CRRA) functions, would not allow one to cope with the measurement error problem, we regarded these drawbacks of the quadratic utility function as a lesser evil.” (p. 183)

1 U (C ) + V ((W − C )(1+ r)) 0 (1+ δ ) 0

and to be larger than the amount of disposable resources Y0 , where V(.) is the value function, and W is lifetime wealth. Some other notations can be referred to as in Hall’s previous (1978) equation. The expected utility, for a consumer who both wants and applies for credit, is translated into

 ∗ 1 ∗   1  Vα = Pα U (C ) + V ((W − C )(1+ r)) + (1− Pα )U (Y ) + V ((W − Y )(1+ r)) − dα  0 (1+ δ ) 0   0 (1+ δ ) 0  1

whereas the value function of a consumer who decides not to apply is

1 V = U (Y ) + V ((W − Y )(1+ r)) . n 0 (1+ δ ) 0

≥ In the above equations, d α1 0 is the disutility the consumer suffers when applying for

credit, and Pα is the probability of being accepted by the bank. Essentially, this probability is the probability of a consumer’s going to default as predicted by individual demographic characteristics, income and employment history in the credit scoring model.

After mathematical manipulation, it is then shown that

2 + ∗ d > (1 r) − 2 > α1 Vα Vn iff (C0 Y0 ) . (1+ δ ) Pα

Hence, in the acquisition behavior of personal loans, consumers are classified into groups ---

∗ − ≤ Category (1): C0 Y0 0 , i.e., individuals do not seek credit;

1/ 2 ∗ ∗  d (1+ δ )  − > − ≤ α1 Category (2): C0 Y0 0, C0 Y0  2  , i.e., individuals who desire credit  Pα (1+ r)  but are discouraged from applying due to the cost of application;

1/ 2 ∗ ∗  d (1+ δ )  − > − > α1 Category (3): C0 Y0 0, C0 Y0  2  , i.e., individuals who apply for  Pα (1+ r)  credit regardless of the prospect of success or failure in results.

∗  1+ δ  After substituting C ≡ λ + 1− W ( λ is an expression of λα ,δ , r ), the above 0  (1+ r)2  1

inequalities are reduced into a simpler “deterministic function of Y0 ,W ”.

In the end, the authors suggested that one of the three outcomes in consumer borrowing decision can be estimated by a “non-linear, ordered logit” model.

Fan, Chang and Hanna (1993) utilized a different approach in theorizing a two- period inter-temporal consumption model to investigate optimal household debt, within a specific context of real income growth. They derived an optimal household credit level as a function of expected growth rate of real income, variance of future income, a consumer’s utility function (depending upon risk aversion), real interest rate, and consumer’s personal discount rate (p. 47). In the theoretical framework, they defined the universal optimization constraints problem into:

P *U (C ) + (1− P) *U (C ) Max. T = U (C ) + 2 2a 1 1+ ρ

= − subject to C1 I S

= + + + C2 (1 g) * I (1 r)* S

= + + C2a I (1 r) * S

where

T = Total two period utility

I = Year 1 income

= C1 Consumption in year 1

S = amount of savings in year 1 (negative value means borrowing)

g = growth rate in real income

r = real interest rate

P = probability that real income increases

ρ = personal discount factor, and

U (.) is a constant elasticity utility function or constant relative risk aversion (CRRA)

C1−x function, U = . It exists as an alternative to the quadratic function mentioned earlier 1− x and appears in most inter-temporal consumption works. The elasticity of marginal utility with respect to consumption is − x , where, mathematically, x is the same as the relative risk aversion coefficient in choice under uncertainty models.

When assuming perfect information in life, for a two-period scenario, a relationship between saving (dissaving) and income is generalized as below after complex mathematical derivation:

1+ r ( )1/ x − (1+ g) + ρ S = 1 + + I 1 r 1/ x 1 r ( ) + ( ) 1+ ρ 1+ ρ

where r, ρ and x all take positive values. When certain combinations of the parameters on the right-hand side of the equation result in a negative value, it translates into a dissaving position in the household, given a nonnegative value of income (I). Note that the choices of parameters should be within a reasonable range that realistically represents consumer preferences and constraints. For example, the relative risk aversion coefficient, x, is specified as a positive number, and a value between 2 and 12 is considered representative of most Americans (c.f. Hanna, Gutter & Fisher, 2003). Ceteris paribus, a larger amount of dissaving would be justified by the life cycle consumption theory for a household foreseeing an increase in future income by g (income growth rate).

3.2 Credit Supply Function Estimation Difficulty

From the theoretical model and early literature review, it becomes even clearer that a work merely focusing on the level of household debt holdings, and ignoring the consumers’ realistic capability to generate or banks’ willingness to grant the loan (or maybe, a portion of it)22, can lead to inevitable bias in gauging the seriousness of household debt. Too many papers on the subject of consumer/household debt have looked into only one side of the story (demand) and attempted to explain debt seeking from a life-cycle perspective. The picture of a consumer acquiring or possessing debt, and by how much, will only be made complete once a lender’s role is simultaneously

22 Sometimes banks may choose a means of counter-offer with the loan applicant due to the projected credit risk. That is, banks may approve only a lower amount of loan than the individual initially applied for instead of rejecting the whole deal.

accounted for. Recall that a lender endeavors to eliminate the default risk in the decision of credit supply by utilizing a predictive (presumably) quantitative assignment system --- credit scoring. Industrial practices disclose that the scope of the attributes by which a bank judges the creditability of the potential borrower are basically apart from what the life-cycle theory would deduce23. At the same time, academic scholars, such as Perraudin and Sorensen (1992), estimated separate models on the bank (lending) and the consumer

(borrowing) to be in line with the industrial trend.

Supposedly, a demand equation and a supply equation could be established, likely with different sets of parameters. One has the life stages factors to model household self- perceived borrowing needs; and the other has the household’s credit characteristics to model a lender’s loan decisioning. Then, the joint outcome of these two equations can be examined, in the relation of the independent variables in either model. However, there is a problem in estimating the supply equation because most of the significant factors that a lender evaluates on a borrower are not available in the analyzed data (the 1998 SCF).

Take an obvious example, the credit bureau score. Moreover, even if there were some proxy variables to use in the dataset, they should be those that were at the time of the loan application; otherwise, it would not be scientific to make a prediction based on a current snapshot. In all, this dissertation adopted the observed incidence reported by the

23 It is understandable that banks weigh predominantly factors associated with household payment capability, in the past, current and future. Thus, attributes such as monthly income, records of up to 2-3 months delinquency, employment history, residential status (rent or own), etc. are upfront concerns in the evaluation checklist.

household, whether credit constrained or not, and directly integrated it as a post facto factor in the model setting. It is equivalent to say that efforts to mimic credit-scoring models or to estimate the likelihood of credit constraints in the household are avoided in this context24.

3.3 Model Predictors and Hypotheses

Obviously, maintaining critical debt burden thresholds establishes a protection buffer to both borrowers and lenders. Borrowers can purport to have substantial evidence that the additional undertaking of debt will not negatively affect their normal financial activities. Lenders, on the other hand, can be assured partly (with other factors still at stake) that borrowers who do not surpass the threshold are monetarily sound, have sufficient leeway to maintain a desired life after payment and will be less likely to become delinquent.

Since the cutoff level of debt burden stands as a significant criterion in gauging a consumer’s or a household’s financial robustness in debt obligations, in this dissertation, the exploration of the household debt burden measure does not look into the burden itself,

24 From the author’s personal experiences working in a regional bank, it is noted that the lending industry usually follows a concurrent two-step evaluation procedure with all loan applicants. First, the applicants are assigned a score, indicating the creditworthiness of this individual. Next, after the automatic screening, a portion of the low score, high-risk files will be reviewed by a loan officer for human judgment. Decisions for large volume of the original applicants are determined immediately by the scorecard, a quantified tool driven by sophisticated statistical modeling. Significant indicators in the model include but are not limited to income, employment, residential status, and credit history.

but, into the degree of household debt severity against the classification guidelines. The consideration in not modeling the numerical value of debt burden (or other forms of variation) is two fold. First, the ratio itself did not form any nicely defined distribution that was straightforward to model upon. A graphic illustration is shown as Figure 5.1-2.

Had a non-liner regression model been utilized, such as log transformation or higher order estimation, the interpretation of the parameter effect on the dependent variable would not be unidirectional. It would not be just a matter of complication, but also of the meaningful ties back to the practical question the research tries to answer. Second, as discussed in the previous chapter, a variety of debt burden guidelines have been proposed by financial educators and widely used throughout the lending industry. These criteria have become an interesting study for both academia and business to ponder on. Hereafter, the purpose centers on a segmentation of households who remain in different debt burden tiers according to general ‘rules of thumb’. To capture a complete picture of household debt burden, two types of debt burden are under examination with their respective guidelines: total debt burden, or total debt payment to gross income ratio, and consumer debt burden, or consumer debt payment to income ratio.

Thresholds of 30% and 40% on total debt burden are chosen. The reason for such thresholds is that 30% appeared to be a lower end of a range of debt burden guidelines postulated by both financial educators and professionals (see Greninger et al., 1996), and

40% seemed to be a most tolerable burden ceiling when including the home mortgage loan. Accordingly, households are grouped into one of the categories: less than 30%, between 30 and 40%, or greater than 40%, from a less severe to more severe burden

scenario. Households who neither possess any debt nor distribute any obligation payment are grouped independently into one category. Thus, the defined categories for total debt burden are: no debt burden (zero debt or debt payment), low burden (<30%), moderate burden (30-40%), and high burden (40%+). In the same fashion, thresholds of 10% and

20% on consumer debt burden are applied based on the earlier literature review.

Households are categorized, too, in four tiers: zero consumer debt burden, low burden

(<10%), moderate burden (10%-20%) and high burden (20%+).

Furthermore, the research extends to examine the profile of households in one category and how it differs from that of another category, and the predicative factors that may help to explain the difference. Therefore, hypotheses are formed with respect to the comparison of the probability of a household’s being in one particular debt service burden tier against the thresholds. On the other hand, the differentiation process complies with the lenders’ criteria in loan decisioning based on the debt payment to income ratio.

To accommodate the joint occurrence of the simple life-cycle consumption hypothesis and credit constraints, each variable that may affect the outcome of a household being under or above thresholds is probed separately in the corresponding demand and supply context, and then systematically considered whenever applicable.

(1) Demographic variables.

Age (of household head).

a. Demand effect. The simple life-cycle model implies that the amount of debt

acquisition tends to be larger in scale in early stages than in later ones. The

gradual decline of consumer borrowing mirrors the contemporary upward

earnings pattern throughout one’s lifetime. It is not unusual to see young

households carry more debt in order to balance between desired living standards

and scarce resources, compared to other age groups. Even though the elder

households do not take in much income flow after retirement, they usually rely

upon their savings/wealth accumulated over time and would be less likely to make

large borrowing. b. No supply effect. The regulatory lending policy in banks or other financial

intermediaries is that discrimination in loan application by age is illegal. Given

such presumption, the age factor should not play any role in the bank’s decision.

However, as observed by Jappelli (1990), the young population is typically the

recipient of a smaller credit line than requested. c. Overall effect. Jointly, it is expected that a high burden occurs most likely to

young households. In this light, it is hypothesized that age has a negative effect on

the possibility of a higher debt burden category: the younger the household head,

greater the chance they are in a high burden group.

Education (of household head). a. Demand effect. Individuals who obtained education of different lengths or

academic levels could differ from each other in their debt seeking. With

everything else the same, 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. 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 generate an amount of debt to cover the cost and expect a higher return in

earnings after graduation that will allow them to repay. All these possible

scenarios would lead to more repayment in a household headed by a more highly

educated person than otherwise. b. Supply effect. In theory, lenders tend to grant more credit to highly-educated

individuals because of both their higher income and greater job security.

Normally, lenders do not ask for information about the educational attainment

from loan applicants; rather, occupation type is sometimes recorded, which is

linked to job security. In all, it is assumed that credit supply is in alignment with

the applicant’s knowledge and skills. c. Overall effect. It is hypothesized that education level has a positive effect on debt

burden; the higher the educational attainment of the householder, the more likely

it is that the household has a high debt burden. Having said so, the relationship

may not appear to be strictly linear. That is, householders in the highly educated

group may not have a higher debt burden than the intermediately educated.

Graduates with college or graduate/professional degree could be more

knowledgeable about money management and more cautious about the

appropriateness of debt position.

Marital status (of householder). a. Demand effect. Family-oriented, married couples tend to be bound with more

activities and responsibilities, and thus have more expenditure as compared to

single or divorced persons. In addition, double income couples have an alternative

source of income if one spouse leaves the work force as opposed to single-headed

households. Under the same circumstances, they are more likely to borrow to

finance their purchase. b. Supply effect. Lenders are more willing to lend to married couples because of the

joint responsibility in the account in case the deal goes bad. In other words,

married couples also have an advantageous position in receiving as much loan as

they desire. c. Overall effect. Provided there is a similar household profile, married couple

headed households would be more likely to be found in a high debt burden tier.

Household size. a. Demand effect. Big households have more needs in consumption because of a

large number of consumers internally. With everything else the same, larger

households may acquire more debt to satisfy the deficit between normal

consumption and intake than smaller households, and thus make larger repayment

out of available resources. b. No supply effect. Lenders seldom take into account whether the loan applicants

reside in a large household or not. It is not surprising that this variable is purely a

demand variable. c. Overall effect. After all, this variable is framed as positively affecting the

probability of household being in a high debt burden tier.

Presence of non-adult children.

a. Demand effect. Households with dependents, regardless of whether they are

toddlers, teenagers or college students, have to pave the way for the younger

generation’s living, growth and education. When a household’s own resources are

insufficient, credit usage assists them in attainment of these goals. Compared to

households without young child(ren), their debt burden is inclined to be high.

b. No supply effect. Lenders normally does not seek information of this kind.

c. Overall effect. The final effect of children living within the household on debt

burden is all from the demand side and is positive. Hence, it is expected that these

households tend to rank high in debt burden tier.

Summary: the demographics of a household as elaborated so far are essential components under the umbrella of the life-cycle concept. It is important to be aware that interaction between several of them is unavoidable. All these variables help to sketch a general profile of a household, in terms of its composition, relation, size, and resource intake.

Though, from a lender’s perspective, most of the variables are reviewed as trivially informative or not reviewed at all in their supply decision.

(2) Economic/financial variables.

Household Income.

a. Demand effect. Simple LCH would suggest that with the increase of income, the

size of borrowing is likely to be reduced. Low-income households may acquire

larger amount of loans to make up for the difference between desired

consumption and earned income, and thus they may suffer a hefty debt burden.

b. Supply effect. Household earning is a conventionally 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 a full amount of loan. On the contrary, low-income households could be

repelled from obtaining large loans and seemingly possess less debt. c. Overall effect. What has been reported in empirical studies that household income

affects debt holding positively may well be the joint outcome of demand and

supply. When credit constraints are taken into account, it is hypothesized that

current income negatively affects the probability of households being in a higher

debt burden tier.

Liquid assets. a. Demand effect. Borrowing is regarded as a means to make up the shortage

between periodic income and consumption in one’s life stage. On the other hand,

if an individual has some wealth, which is built from the surplus between income

and consumption over the time, s/he could use this as another source for

consumption instead of seeking external funds (which makes more sense than

paying considerable interest on credit). Liquid assets, by name, are particular

assets that are ready to be accessed at any time. These monetary instruments can

be converted easily and instantly into usable funds for any expenditure or

emergency need. With this in mind, households who have accumulated more

liquid assets are less likely to apply for credit, and vice versa.

b. Supply effect. Theoretically, households who hold more assets (liquid or non)

assure lenders of their robust financial position. However, in reality, information

on household wealth is very limited. Lenders may inquire about certain financial

information, such as saving/checking accounts in the credit application (but, not

dollar amount). For property-secured loans, property appraised value is desired to

determine the loan amount. Examples are home mortgage, home equity lines of

credit, and auto loan, 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. Even if they are reported by the applicant voluntarily,

lenders are reluctant to bet on the self-report value. c. Overall effect. The eventual outcome is naturally dominated by the demand side,

that is, liquid assets have a negative effect on household borrowing and debt

burden.

Homeownership. a. Demand effect. Nearly all homebuyers rely upon mortgage loans to realize their

homeownership. Until the day the loan is fully paid off, house dwellers are

undoubtedly indebted with the largest amount of loan in their life. In addition, the

possession of a house helps to build a huge amount of tangible assets for the

owners. Many innovative financial instruments, e.g., home equity loans, second

mortgage and home equity lines of credit, allow homebuyers to borrow against

the equity value. Meantime, the maintenance and improvement on the physical

estates are another major area of expense that may cause homeowners to incur

extra borrowing needs. All of these may leave homeowners with a higher debt

burden than renters. b. Supply effect. Residential status is a major decision rule in grating loans. Home

residents are considered more stable and less risky than renters or other living

styles. Being able to purchase a house is proof that the household has sufficient

payoff strength, which, on the other hand, must have been reckoned by the

mortgage lenders upon application. Meanwhile, owning a home increases the

probability of additional loan opportunities, since the house can be the collateral

for lines of credit (e.g., home equity lines of credit). c. Overall effect. Home purchase with mortgage has already left the households with

a considerable payment commitment in contrast to their live-on-rent peers. The

burden may be heavier if homeowners build on too much extra debt by other

means. Therefore, homeownership could be a factor that contributes to a high debt

burden.

Employment status. a. Demand effect. Employed workers have the capability and desire to borrow more

to finance consumption or investment in advance than those out of the labor force. b. Supply effect. Lenders weigh in an applicant’s employment history to forecast

future payment sources. For example, lenders collect information about ‘how long

s/he has been on the job?’ to project job stability, and hence the borrower’s long-

term income stream. As opposed to retired or non-working individuals, the

employed are expected to be able to borrow more.

c. Overall effect. Given that demand and supply work together ‘favorably’, a lower

burden grouping could only be less probable for workers.

Business entrepreneur.

a. Demand effect. Family owned businesses or a small business adds a business-

running function to the household in addition to its internal consumption and

household production. For these households, borrowing could be large because of

the financing for household business besides the living needs.

b. No supply effect. Usually, lenders put the applicant through a different evaluation

system for a consumer loan versus a small business loan. Unless the customer is

given special preference, loan approval should not differentiate between a

business owner and one who is not but may have a similar application profile.

c. Overall effect. In all, households that are engaged in entrepreneurship are

expected to exceed the burden threshold more easily than otherwise.

Summary: economic variables are a central piece of banks’ assessment on loan applicants’ risk. Personal income and employment status have been considered important predictors in credit scoring. They represent a stable (vs. unstable) and affordable (vs. scarce) resource of the household, from which financing is justified. For those who are not restricted in borrowing needs, favorable factors in lenders’ eyes about borrowers may bring about an unsafe debt burden to the households.

(3) Sentiment and behavioral variables.

Credit use.

a. Demand effect. The literature review in the previous chapter implies that people

who think positively about credit use are more likely to seek extended credit lines,

such as credit cards.

b. Supply effect. Lenders are sensitive about potential default that may result from

aggressive credit seeking. Applicants who have been inquired about by previous

lenders through the credit bureau will be assigned negative points in the credit

score. The argument is that lenders perceive it as a sign that these individuals may

‘desperately’ need extra resources to fill up the debt hole.

c. Overall effect. Having said so, it is still hypothesized that credit usage proponents

are more likely to be in an overly burdened tier.

Borrow orientation.

Early and wise planning of borrowing for long-term investment purposes, other

than for the sake of temporary consumption, misfortune or emergency, may put

households in a less stressful debt position, and reduce the household’s

probability of staying on the tip of the financially obligated iceberg.

Summary: dissaving behavior essentially exists as a unique decision made by a person, an action that could be driven by one’s unique preferences and cognition, apart from pure economical sense. Psychological and perceptional variables help to identify realistic human decision-making, a subtle yet unobservable ingredient beyond the quantitative economics mechanism.

(4) Future expectation and past experience.

Expectational factor.

The life-cycle consumption hypothesis asserts that household borrowing occurs as

a decision to consume in advance and compensate in the future. Individuals who

foresee an increase in future resources are more inclined to advance the

consumption in the current period by seeking credit, in hopes that the additional

earnings later can assist them in paying back the debt without impacting then-

consumption. Hence, it is hypothesized that people who expect raised future

resources are likely to be in the high debt burden tier.

Household experiences.

Households might have experienced a mishap in the past that left them in a big

hole of debt. Such events include but are not limited to an unexpected huge

expense (e.g., on medical treatment), household member’s layoff from current job

or other family tragedy. These misfortunes may profoundly change the

household’s financial outlook. Thus, for these households, it is assumed that their

debt burden surges beyond a safe level.

Summary: individuals live in a dynamic world, where past experiences can affect the behavior at present and in the future. Borrowing, as a means to reallocate household lifetime resources, its occurrences and frequencies are likely to be affected by the unexpected lifetime events when expenses are to be financed, therefore, leave payment back burden to individuals in the later period.

CHAPTER 4

METHODOLOGY

4.1 Data Source

Data analysis for this dissertation was conducted on a publicly available survey --- the 1998 Survey of Consumer Finances (SCF). The triennial survey was sponsored by the

Board of Governors of the Federal Reserve System and collected by the National Opinion

Research Center at the University of Chicago (NORC) between June and December of

1998. The collection of data was completed through in-person interviews with households that were chosen from nationwide sampling. The median interview length was about 1.25 hours. The survey is designed to provide detailed information on U.S. households’ balance sheets and their use of financial services, as well as on their pension, labor force participation, and demographic characteristics as of the time of the interview.

It also gathers information on households’ attitudes and preferences on financial decision-making and product choices.

The 1998 SCF started with a dual-frame sampling design, which is comprised of one standard, multistage area-probability sample (a geographically based random sample) and one supplemental sample to disproportionately include wealthy households. The second part of the sample came from a list of statistical records derived from tax data.

The overall response rate was about 35%, with a 70% response rate in the area- probability sample and 10% among the wealthiest households. Among the 4,309 completed interviews, 2,813 were from the area-probability sample and 1,496 from the list sample.

Two crucial issues in the data require particular attention. One is weighting.

Weights were computed for each case to represent the population households. Weights are also used to adjust for differential non-response to the survey due to either complete non-response to the survey or non-response to selected items within a survey. The other is multiple imputation. Multiple imputation was used so that missing information in each observation (household) was replaced by an imputed value and the imputation was repeated four times to reduce the variation. Therefore, the total size of the dataset is five times the actual number of survey participants, 21,525. The above description of the 1998

SCF was extracted from Kennickell et al. (2000).

4.2 Statistical Model

The relationship of credit constraints and various exogenous variables to household debt burden is modeled under a non-linear regression model, i.e., with the dependent variable being a categorical variable. The choice of a categorical dependent variable instead of a continuous one as in a linear regression model is due to three considerations. First, the preliminary univariate analysis on the debt payment-to-income

(PTI) ratio (both consumer debt and total debt) showed that the distributions of the ratios are by no means normally distributed; this violates the premise for a linear regression.

Rather, the ratios are positive and primarily clustered around small values. Because of the violation of the error term assumption, a linear function of the numerical ratio is inappropriate. Second, after a review of the literature on debt burden in the previous chapter, it appeared that the appropriateness or concern of household debt status heavily centered on the standards or thresholds of debt burden (or PTI ratio). Thus, the numerical debt burden is of less importance than the relative position of burden severity, i.e., zero- low-moderate-high rank. Last, but not least, McCullagh (1980) and Anderson (1984) justified the usage of ordinal models on a “grouped continuous” variable, where a continuous variable (e.g., income) is classified into groups according to a pre-specified range criterion and then treated as a categorical type of dependent variable in modeling.

Next, a decision was made concerning whether to apply an ordered or unordered model on the categorical dependent variable. The review on debt PTI ratio guidelines disclosed that the threshold helps to identify a low/heavy burden of household debt repayment commitment. A high debt burden implies a more serious financial problem in the household. Therefore, it is obvious there is an ordered context in the level of debt burden and the order is of major concern. The categorical format of both debt PTI ratios accommodates the analysis on the rank order of the burden level, from low to high, based on respective guidelines. Had an unordered model been used, the intrinsic ordering value in the dependent variable would have been lost.

The theoretical modeling on an ordinal variable, y, is typically formulated in association with a continuous, latent variable, y*. The observed y is related to y* according to the measurement model

= τ ≤ * ≤ τ yi m if m−1 yi m for m=1 to J where J represents the number of ordinal categories, and τ ’s are called thresholds or cutpoints (Long, 1997, p. 117). Back to this paper, J equals four, four levels of household debt burden, from zero to high. τ ’s are exactly the debt burden thresholds or guidelines to segregate the four categories.

In addition, the latent variable y* is assumed to form a linear regression on the

* = β + ε independent variables, yi xi i . Depending upon the underlying distribution of an error term, an ordered logit model (assuming a logistic distribution with

2 µ = 0,σ 2 = π ) or ordered probit model (assuming a normal distribution) is often used 3 in the regression25.

The probability of the observed value yi , for example, of value 1, can be modeled

* by yi :

= = τ ≤ * < τ P(yi 1| xi ) P( 0 yi 1 | xi ) .

β + ε Given the linear combination of xi i , the probability is further converted into

= = τ − β ≤ ε < τ − β P(yi 1| xi ) P( 0 xi i 1 xi | xi ) .

The probability that a random variable is between two values is the difference between the cumulative density function (cdf) evaluated at these values.

25 Other than the different assumptions in the underlying distribution, logit and probit models provide similar conclusions of parameter effect. By multiplying a probit estimate by π / 3 =1.814 , we can get

The generalization of the probability of any observed outcome y = m given x becomes

= = τ − β − τ − β P(yi m | xi ) F( m xi ) F( m−1 xi ) (Long, 1997, p. 121)

Because a logit model presents additional estimations on odds ratio, which are particularly of interest for pairwise category comparison, the ordinal dependent variables are modeled under the assumption of a logistic distribution in this research. Clogg and

Shihadeh (1994) listed several major types of ordinal regression models that deal with the contrasts between categories. Among them, the cumulative logit model was explained as a contrast of “at or above category m” versus “below m” for each possible value of the ordinal variable. If the ordinal variable has J values, there will be J-1 cumulative logits:

P(Y ≤ m) logit[P(Y ≤ m)] = log( ), where m=1,…, J-1. 1− P(Y ≤ m)

With a vector of predicator X, the estimation of an ordered logit model becomes

≤ = α + β logit[P(Y m)] m m X , where m=1,…, J-1.

β = β Furthermore, a simpler model can be evolved when a restriction m is imposed on the above model. When the parameter β does not have a subscript, the model assumes an identical effect of X for all J-1 collapsings of the response into binary outcomes. When this model fits well, it requires a single parameter rather than J-1 parameters to describe the effect of X (Agresti, 1996).

the corresponding logit estimate. However, when there is large concentration of observations in tails of the distribution, the logit model seems more appropriate (Amemiya, 1981).

This model is typically referred to as a proportional odds model (McCullagh,

1980; Long, 1997). The interpretation for this model refers to the odds ratio for the

collapsed response scale, for any fixed j (Agresti, 1996). For two values x1 and x2 of X, the odds ratio utilizes cumulative probabilities and their complements,

P(Y ≤ j | X = x ) / P(Y > j | X = x ) 2 2 . ≤ = > = P(Y j | X x1 ) / P(Y j | X x1 )

The log of this odds ratio is the difference between the cumulative logits at those two

β − values of x. This equals (x2 x1 ) , proportional to the distance between the x values.

The same proportionality constant ( β ) applies for each possible point j for the collapsing. Because of the common effect of β for j category, the logistic regression curves for a binary response with pair of outcomes (Y ≤ j) and (Y > j) for each category

α share the same shape. However, with different j , curves shift parallel by a certain amount. If β >0, the implication from the model is that when x increase, the response on

Y is more likely to fall at the low end of the ordinal scale, and vice versa.

In a sense, the proportion odds model is a simplified version of the cumulative logit model and allows a parsimonious interpretation of the effect of the predictor X on the J-1 cumulative logits. However, in the data analysis of this dissertation, the test of one single parameter or a proportional odds model revealed that the parallel assumption of cumulative logits was not valid with a Score test. The Score test was carried out when running PROC LOGISTIC in SAS© by specifying the particular output option. If a Score test is non-significant, the parallel assumption is considered appropriate; otherwise, it is not sensible to fit a proportional odds model. A simple description of the Score test can

be found in Appendix A, and more detailed samples can be referred to in Stokes, Davis and Koch (2000). Therefore, an alternative analysis on J-1 (here, 3) separate cumulative logit models was conducted (two similar examples were given by Clogg & Shihadeh

(1994) and DeMaris (1992) ). The three separate cumulative logit models essentially turn into three binary logistic models:

 Y ≤ 1 logit(Y ≤ 1) = log  = α + β x ;  Y > 1 1 1

 Y ≤ 2  logit(Y ≤ 2) = log  = α + β x ; and  Y > 2  2 2

 Y ≤ 3  logit(Y ≤ 3) = log  = α + β x  Y > 3 3 3

where 1,2,3 are the ordered groups divided up by debt burden thresholds, forming the zero, low, moderate and high burden groups. The research focus switched to a comparison of the different magnitude of effect from one same predictor on each cumulative odds ratio, contrasting a relatively lower group to a higher one in the debt burden level.

Statistical analytical software SAS© (Version 8) has provided a complete platform in all the consequent data analysis, most important, its capability in handling the desired ordered logit model through PROC LOGISTIC.

4.3 Multiple Imputation

The existence of five implicates in the 1998 SCF dataset was noted earlier. It is not sensible to treat the entire 21,525 records as one independent sample because the

extra four imputed records for each household are replicates. Neither is it simple to just select one implicate, because doing so ignores the purpose of multiple imputation and would be likely to result in biased estimation.

To handle multiple implicates in a univariate or multivariate analysis, the repeated-imputation inference (RII) technique was developed by Rubin (1987). Montalto and Sung (1996) and Montalto and Yuh (1998) have, in particular, applied the RII technique to the multiple imputed SCF data in fitting a simple linear regression model as well as non-linear multivariate model. Detailed theoretical basis and technical illustration can be found in Appendix B. A sophisticated SAS© program written under the RII approach is included to estimate the cumulative logits models. Each of the three binary logistic models based on debt burden thresholds went through the RII process. There are a total of three equations estimated on consumer debt burden and two equations on total debt burden. By so doing, the RII technique helps to accomplish efficient estimates and valid inference (Montalto & Yuh, 1998).

4.4 Variable Construction

4.4.1 Dependent Variable

As discussed in the previous chapter, the debt burden (i.e., debt PTI ratio) has been used in the personal finance literature and the lending industry to quantitatively measure a household’s debt servicing obligation relative to income. Variations exist in the computation of the debt payment due to the various types of loans that are acquired by households. In this research, the calculation on the debt repayment (annualized)

encompasses two types of loans. For an installment type of loan, such as home mortgage or automobile loan, debt repayment is derived by multiplying the scheduled one-period payment with the scheduled frequency of payment in a year. For a revolving type of loan, such as credit card, special handling is given as illustrated below. Household annual payment on all lines of obligations is included. By doing so, it is hoped that an accurate and comprehensive picture of an American households’ overall paying commitment pressure can be captured.

In the calculation of household debt burden, annual debt payment becomes the numerator. When it comes to what debt components are to be counted, it is decided to separate total debt from consumer debt. The reasons are three-fold. First, there is a salient difference between total debt and consumer debt: the former includes home mortgage and the latter excludes it. Maki (2000) observed that in spite of the fact that consumer debt is only about one-third the size of mortgage debt, the required payments on consumer debt are actually higher than those on mortgage debt because of the shorter maturities on consumer debt. The differentiation between the two types of household debt is often documented in government public release. The second consideration is due to the distinct thresholds recommended for the total debt PTI ratio and the consumer debt PTI ratio.

Further analysis with two separate guidelines was later conducted, and the results for the two debt PTI ratios were compared26. Another merit is from the data itself. The 1998 SCF

26 The comparison is limited to certain extent, however. In the estimation of logit model on total debt burden, the sample only includes households that have made mortgage payment. The reason is simple: home renters are not obligated with mortgage payment whereas mortgage payment is a considerable

has abundant information on a variety of loans that were reported by households --- credit card, loans for home mortgage, property purchase or home improvement, lines of credit, investment loans, vehicle loans, education loans, margin loans, pension loans, life insurance loans, and other consumer loans. Detailed lists of loans facilitate the choice of debt construction. Installment loans comprise the majority of household loans. The annual payment on these installment loans is computed by multiplying the scheduled payment amount to the frequency during a year.

Assumptions are then established then for revolving types of loans. Credit card payment calculation bears a strong assumption that a current card balance is supposed to be paid off within one year, for instance, in DeVaney’s (2000) proposed guidelines.

Annual payment on credit cards is the sum of the current balance and any interest charges, which will be assumed to be an annual percentage rate (APR) of 18%27. It is necessary to mention lease payments in particular. Even though scholars have seldom touched lease payments in their debt construction, it is believed that leasing and purchasing vehicles essentially differ concerning the title and the ownership, but not in terms of the means or the amount of regular payment. Therefore, the lease payment is included in our installment loan debt payment calculation.

portion of homeowners’ debt payment. Thus, total debt burden could be sharply different between the two subpopulations with residential status variation. On the other hand, the logit model on consumer debt burden is estimated based on the entire sample.

27 The sampled households were asked about the interest rate they paid on the credit card with the largest balance or the new credit card they had. After excluding those households who reported having no credit cards (33%), it was found that 18% is the modal percentage rate these credit cards holders paid.

The denominator, straightforwardly enough, is the gross annual household income in the year of 1997, the year preceding the time of the survey. In the preliminary data analysis, it is found that a small portion of the sampled households (1%) recorded non- positive household income during that year. These values have raised concern because mathematically a division by a zero denominator is infinite and a division by a negative value makes the interpretation tricky. To make this a legitimate and intact dataset, income for the 1% of households with non-positive income is replaced with $1 instead of throwing them out28. Thus far, the formulation of the dependent variables, the total debt

PTI ratio and the consumer debt PTI ratio, is basically completed. The difference between the former and the latter is that consumer debt payment excludes payments on home mortgage, home improvement or house-related loans. To suit the proposed statistical model, each household was identified into one of the four debt burden tiers by the two distinct ratio guidelines, ordered from zero, low, moderate to high level of the debt burden.

The total debt PTI ratio (Debt service ratio):

1) No burden (i.e., a ratio equal to zero);

2) Low burden (i.e., a ratio greater than zero but lower than 30%);

3) Moderate burden (i.e., a ratio greater than 30% but lower than 40%);

28 Conceptually, if a household has a non-positive income but has made debt payment throughout the year, it implies that their debt is far in excess of their earnings capability. Even though it might be the case that the payment is drawn from other assets, but as far as debt payment to income ratio is concerned, these families are still considered as overly extended in their debt.

4) High burden (i.e., a ratio greater than 40%).

The consumer debt PTI ratio (Debt safety ratio):

1) No burden (i.e., a ratio equal to zero);

2) Low burden (i.e., a ratio lower than 10%);

3) Moderate burden (i.e., a ratio higher than 10% but lower than 20%);

4) High burden (i.e., a ratio beyond 20%).

4.4.2 Independent Variables

Interesting predictors that have been researched in literature and wait to be tested as part of the model herein are organized as follows. Unless stated, variables are constructed from corresponding questions asked in the 1998 SCF.

First of all, credit constraints phenomenon: following the innovative idea of

Jappelli (1990), the factor of credit constraints is generated from two questions: 1) whether or not lenders/creditors rejected a credit application from the household or the household was not given as much credit as it applied for in the past five years; 2) whether or not members of the household thought of applying for credit but changed their mind because they thought it might be turned down. Households that answered ‘yes’ to the second question were defined as ‘discouraged borrowers’ by Jappelli; though he admitted that these households could be laxly regarded as part of the credit-constrained population.

Households who responded to either of the question with a ‘yes’ are identified as credit constrained in this paper, and non-constrained otherwise. Thus, the supply side of credit

was controlled entirely by this one proxy variable, a binary variable (‘yes’ or ‘no’ type of data).

Age of the householder: in order to preserve and capture the non-linear relationship of an age effect, the numerical age of the main respondent in the household upon interview was grouped into five categories: less than 35 years old, 35-44, 45-54, 55-

64 and more than 65 years old. Each category was set as a dummy variable. Four dummy variables were placed into the model with the category ‘less than 35’ as the reference group.

Education level of the householder: the variable comes from a combination of answers to the 1998 SCF questions about the grade of schooling and the highest degree the householder has earned. Individuals who finished less than grade 12 were defined as

‘high school dropout’; those who obtained a high school diploma or passed a high school equivalency test were categorized as ‘high school graduates’; those who earned higher than a Bachelor’s degree were marked as ‘graduate level’, those who obtained a college

Bachelor’s degree were ‘Bachelor’, and the remaining others were grouped into ‘college attendees’. Therefore, four categories of the education attainment variable appeared in the model as four dummy variables, with the lowest education level as the reference group.

The formation of such education variables places stress on the effect of the education level, not the years in school.

Marital status: this is the interviewee’s current legal marital status as identified by choices of ‘single’, ‘married’, ‘separated’, ‘divorced’, ‘widowed’, and ‘never married’.

To reduce the number of choices, ‘single’ and ‘never married’ were defined as ‘single’,

‘separated/divorced/widowed’ as ‘alone’, and the rest as ‘married’. Hence, this variable appeared in the model as two dummy variables with ‘alone’ being the reference level.

Household size: the number of people in the household according to the householder, excluding spouse/partner who does not usually live there and who is financially independent. Households who have more than 10 people were coded at the maximum of 10. This variable appeared in the model as a numerical value.

Presence of non-adult children: whether or not the household reported dependent child(ren) younger than eighteen in the household. This variable was created by looking at the relationship of each household member to the householder and designating the child(ren) who is(are) no more than age eighteen. This variable entered the model as a binary variable (yes/no).

Household income: In the construction of the dependent variable, non-positive income is substituted by $1, and this remains the same for household income as the independent variable. There exists large variation and potential outliers in the absolute values of household income. In order to present the potential non-linear pattern from income, categories of the income variable were formed. In particular, the category grouping was based on household income deciles: less than $8,100, $8,100-13,999,

$14,000-19,999, $20,000-25,999, $26,000-32,999, $33,000-41,999, $42,000-52,999,

$53,000-66,999, $67,000-92,999 and $93,000+. The nine higher ranges of household income were in the model as dummy variables with the lowest income group as the reference category.

Liquid assets: the total worth of defined liquid assets reported by the household, including checking account, savings account, money market deposit and mutual funds, call account at brokerage, and Certificates of Deposit29. Instead of using the variable as is, a liquid assets to monthly income ratio was used. Four categories of the ratio were created, namely, <1, 1-3, 3-6 and 6+. Each of the three letter categories appeared in the model as a dummy variable, and the first category is treated as a reference group.

Homeownership: Based on the answer to the question “is there a mortgage or land contract on this (home/home and land/apartment/property)?”, households were classified into one of several dwelling styles: ‘partially own (still pay mortgage)’, ‘fully own (clean of mortgage)’, and ‘rent’. Two of the three variables entered the model as dummy, leaving the ‘renters’ as the reference group. As a reminder, in the consumer debt burden model, these variables were ignored.

Employment status: reflects the current job status of the household head. In addition to the supplied choices between ‘worker’, ‘retired’ and ‘unemployed or other’, a fourth category of employment type was identified as ‘self-employed’ for those who run their own business. Hence, three dummy variables of employment status entered the model, and any magnitude comparison will be against ‘unemployed’ individuals.

Business ownership: was determined by the answer to the question of whether or not the respondent and/or the family own or share ownership in any privately-held business, farms, professional practices, limited partnership or any other types of

29 Selection and calculation on liquid assets can be referred to in the 1998 SCF codebook. The definition here differs slightly from theirs in the addition of certificates of deposit. 95

partnerships. The variable took part in the model as a binary variable with a ‘yes’ vs. ‘no’ answer.

Credit use: was from the question how the interviewee perceives the usage of installment plan to make purchases. Options were given as follows, ‘Good idea’, ‘Good in some ways, bad in others’, and ‘Bad idea’. Two dummy variables were created in the model with the reference group, ‘Bad idea’.

Number of credit cards: the number of credit cards a household holds was another measure of a household’s favorable attitude on credit use; generally, there was a positive correlation between the propensity to use a credit card and the number of cards. This was defined as the aggregate credit cards the household had, regardless whether it is a bank card (such as Visa, Mastercard) or store card (e.g., Sears, Macy’s). This variable was a continuous variable in the model.

Borrowing orientation: originated from choices about when the respondent feels comfortable in borrowing to cover the expense of a vacation trip, to cover the living expenses when income is cut, to finance the purchase of a fur coat or jewelry, to finance the purchase of a car or to finance educational expense. Depending upon the planning horizon and the purpose, these six answers were collapsed into three scenarios: borrowing for luxury (vacation trip, fur coat or jewelry), borrowing for long-term utility

(education or car), and borrowing for unexpected event (when income is cut). Two dummy variables were placed in the model, with the reference category being ‘borrowing for luxury’.

Expected income growth: came from the answer to the question of how the household foresees their future household income: will it ‘go up more than prices’, ‘up less than prices’, or ‘about the same as prices’? Similarly, two dummy variables entered the model, leaving ‘up less’ as the reference level.

Expected to inherit: inheritance could increase one’s total resources and impact one’s money planning. It is hypothesized that the householder’s response to whether or not they expect to receive any substantial inheritance or transfer of assets in the future will capture the expectational factor in the household that could induce differences in their debt acquisition. This variable entered the model as a binary variable.

Unemployed experience: the interviewee was asked whether or not s/he was unemployed and looking for work during the past twelve months. This variable shows up in the model as a binary variable. Since no information on household spending on large medical expense or other unexpected costs was collected in the 1998 SCF, the past experience variable is limited to unemployment experience.

CHAPTER 5

RESULTS

5.1 Household Debt Holdings and Debt Payment

A descriptive summary of household total debt holdings and annual debt payment as of 1997 is presented in Table 5.1-8. The series of tables is intended to present household debt as total debt versus consumer debt only, as well as the entire sampled households versus households that had incurred home mortgages. Table 5.1 shows the minimum, maximum, mean, median and two quartiles on total debt outstanding in dollars by types of loan for all households. The mean and median total debt outstanding was, respectively, $48,450 and $13,800. Further classification by types of loans shows that home mortgage ($33,248) was the largest share of total debt an average household owed, followed by borrowing on other property ($3,744), on automobile loans ($3,537), and on credit cards ($1,817). The percentages of these four types of loans as a whole are, respectively, 68%, 8%, 7% and 4%. Basic statistics on the annual total debt payment are included in Table 5.2. The mean debt payment is $8,187, the median $4,080. The rank of the amount of debt payment on different loans was quite consistent with that of the amount outstanding: an average of $4,441 annual payment was towards home mortgage

(54%), $801 committed to other property (10%), $1,403 to automobile loans (17%), and

$303 to credit cards (4%).

Moreover, statistics of total debt outstanding and the debt payment were analyzed solely on households in the sample who owed mortgage loans or paid mortgages.

Approximately 42% of households fell into this category, which represented 42,982,828

U.S. population. A computation of total debt based on mortgage payers would make a better representation of the actual total debt situation for those who indeed had all types of debt. Such a move was verified as worthwhile when Table 5.3 and 5.4 showed that the amount of debt outstanding and annual payment on home mortgage loans were evidently far above the previous whole-sample average. On the average, households who purchased a home had borrowed an average of $103,288 (median $79,600). The mean measure of total debt outstanding was double the size of a household’s owing in the all-households scenario. Homeowners owed a mean balance of nearly $80,000 (a median of $63,000) in home purchase, $6,933 in other property purchases and $5,505 in vehicle loans. In Table

5.4, the annual payment on each type of loan was listed for these households. The annual debt payment of these households is $16,570 (mean) and $13,038 (median). On average, they devoted a mean of $10,596 (a median of $8,400) to mortgage obligations annually.

Other household-owned property and vehicle loans are the next two largest paybacks,

$1,347 and $2,164. After the comparison to an all-households scenario, it is noticed that home mortgagers had relatively more debt outstanding in pension, life insurance or margin loan than in credit cards. This may somehow imply the broad practice of households borrow against pension and life insurance to finance their residence purchase.

Meanwhile, it is easy to see that this group of households borrowed more on all other types of household loans than an average American household.

When only consumer debt is concerned, that is, after home related loans are removed from total debt30, the mean and median consumer debt outstanding naturally dropped sharply to, $15,202 and $2,800 (refer to Table 5.5). The four largest major types of consumer loans in dollar amount resided as loans for other property ($3,744), vehicle loans ($3,537), credit cards ($1,817), and pension/insurance backed loans ($1,754).

Though the size of the loan for other property, on the average, was the largest among the four, it is the payment for vehicle loans that championed all, $1,403 or $117 a month

(Table 5.6). The rank order of the amount of loans owed and repaid within a year was fairly similar to what was seen in the total debt calculation; households spent $801 in paying property loans, $303 in both credit cards and other consumer loans, $277 in vehicle lease payments, etc. Table 5.6 also showed that an average household made consumer debt repayment of $3,747 in the year of 1997.

In alignment with the same rationale of separating home mortgagers from non- mortgagers, consumer debt status was further explored on real borrowers, that is, those who had made a non-zero consumer loan borrowing. About 67% of the households (or

68,199,707 U.S. population) were found to be indebted by at least one type of consumer debt. Thus, a further examination of debt statistics based on those indebted households,

30 Home related loans in the 1998 SCF that are excluded in consumer debt calculation include standard home mortgage, loans of house purchase besides regular mortgages, and loans on home improvement.

However, home equity secured lines of credit is considered as consumer debt (see Lytton, et al., 1991).

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instead of the whole population is provided as supplementary information. Results on the debt outstanding and debt payment are respectively demonstrated in Table 5.7 and 5.8.

These households generated an average of $22,857 in consumer loans, including $5,629 for other property, $5,319 on automobiles, and $2,732 in credit cards. They spent an average of $5,495 a year to offset their consumer debt outstanding, with automobile installment payments being the largest, $2,109 or $176 a month (see Table 5.8).

Repayments on other property, credit cards and vehicle lease payments consisted of around 10-20% of the entire consumer debt repayment.

5.2 Sample Household Demographics

A preliminary examination of the sampled households revealed some typical characteristics of the households sampled in the 1998 SCF. Details are tabulated in Table

5.9-10; the two tables differ in the scope of the sample, one representing the entire sample and the second mortgagers only. On the average, the head of household was about

49 years old (a median of 46); the youngest household head was 17, the oldest 95. The mean of household income in 1997 was $52,295, median $33,000. Large variation existed in the range of household income, from a negative extreme31 ($-1,000,000) to a positive extreme ($176,890,000). On the average, households possessed $21,009 worth of liquid assets (a median of $3,000). Some of them did not have any liquid assets, and some had up to $34 million. The average number of individuals living in a household was

31 Negative income may occur under certain circumstances, such as capital loss in investment or business.

In the 1998 SCF, about 1.3% of the households reported a yearly earnings in 1997 of less than zero.

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around two, 2.59 as the mean, and 2 as the median. The biggest household in the dataset was composed of 11 individuals. On the average, the sampled households did not have a young child under the roof, though some had as many as eight children. They possessed an average of 3-3.5 credit cards, and one household even had 31 in total.

Once the householder’s age, household size and the number of credit cards variables were collapsed into categories, the distribution percentage became more salient across the categories. Householders who were younger than 34 or between 34 and 44 were the two major population subgroups: each comprised about 24% of the entire sampled population in the 1998 SCF. Householders aged 45-54 or 65 and above followed, with 20% for each age group, leaving those between 55 and 64 the smallest percentage of all. The majority of the households had 3-5 residents (38%), and one- or two-individual households are prevalent too (25% and 33%). As seen earlier, the holdings of credit cards vary considerably among all households. Close to half of the households had one credit card but no more than five, 27% had no credit card, and almost one-fourth claimed to hold six to ten or more credit cards.

In regard to educational attainment, the dominant percent of householders

(31.87%) were high school graduates or equivalent, seconded by householders with some college education at a percentage of one-fourth. Sixteen percent of the householders had schooling less than high school; lower percentage groups were those who earned a bachelor’s degree (15.6%) or a graduate degree (11%). Nearly 70% of the sampled households were homeowners, of which two-thirds were still under the mortgage obligation, i.e., 41% in all. The others were households who lived on a rental basis.

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Married households comprised half of the sample. About 18% of the householders were single and never married, and other marital status categories such as divorced, separated and widowed made up the remaining 30%. Thirty-five percent of all households had at least one child younger than eighteen. A small proportion (12.68%) of the sampled households actively managed their own business within the household. About half of the households held liquid assets of less than their one-month household income, 20% of all had liquid assets of between one- and three-month income, 10% held assets between three- and six-month income, and nearly 20% held assets of more than six-month worth of income. The householders who acknowledged the credit use as being both good and bad turned out to be the largest group, 37.51%, followed by the opponents (33.56%) and the proponents (28.93%). There was an overwhelming agreement (98%) among the households that financing is likely to be used for a long term plan, other than on luxury expenses or unplanned emergency. Slightly over 11% of the interviewees claimed to be out of the workforce during a year period. A fairly large portion (40.80%) of the households expected the same growth rate between personal income and commodity prices. Most households did not foresee a fortune from heritage in the near future

(86.75%). Up to 60% of the householders were employed workers, along with 11% self- employed, 10% unemployed and 19% retired individuals.

Interest had also been extended to the characteristics of the mortgagers in view of the focused analysis on the total debt burden later (Table 5.10). First, the average and median age of the mortgagers were 2-3 years younger than that of the whole sample. In addition, the age variable showed less variation in the range and quartiles than that of the

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whole sample. Home mortgagers’ average annual income, $73,737 (a median of a

$53,000), was higher than the national average. Their holdings of liquid assets were at a level equivalent to the whole sample. The mortgagers also scored trivially higher in household size, the number of children and the number of credit cards.

The phenomenon of a mortgager being older than a typical American householder was evidenced by the categorical age variable. Nearly one-third of the mortgagers were aged 35-44, and over one-quarter were between 45 and 54. Not surprisingly, few of the householders older than 60 (8%) still paid on mortgages. Recall that household income of all the households was evenly separated into ten groups, where mortgagers seemed to have larger percentages in the high-income tail and smaller percentages in the low- income tail. Each of the income groups that earn less than $32,000 a year comprised less than 10% of all the mortgagers. The three largest percentages were seen in mortgagers who had an annual income of $53,000-69,999 (16%), $67,000-92,999 (18%) and $93,000

(17%). Half of the mortgager households (52%) had 3-5 members, and 30% of them had two individuals. Mortgagers tended to acquire more credit cards: over half had more than one but less than five, 25% had more than six but less than ten, and 8% had ten plus cards.

Other profiles of the mortgagers were different to some extent from the national representative population. Mortgagers had comparatively more education than the whole sample. Up to 30% of the mortgagers were high school graduates, 50% were college attendees or graduates, 14% had earned a professional degree, and only 8% did not finish high school. About three-fourths (72%) of mortgagers were married couples. Mortgagers

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seemed to split equally when it came to having adolescents in the household or not, 48% versus 52%. A relatively large percentage of the mortgagers reported involvement in business. A majority of (75%) the mortgagers had up to three month’s worth of income in liquid assets, and slightly over 10% had 3-6 or 6 months’ income. Mortgagers did not depart from their peers either in their opinion of credit usage, or in their borrowing orientation. Only 8% of the mortgagers had experienced layoff during the previous one year. Compared to the national sample, mortgagers became quite neutral or slightly optimistic about the expected future income growth. Seventeen percent of the mortgagers expected a future windfall via inheritance. Within expectation, most of the mortgagers worked, either for someone else (70%) or for themselves (16%).

5.3 Credit-Constrained Households

Households who did not succeed in seeking loans to meet their full needs from lenders in the past five years were considered as credit-constrained. By such a definition,

21.82% of households in the 1998 SCF were classified into these households, which included households who were either disapproved of their loan application (19.28%) or granted a reduced loan amount (2.53%). To note, more than one-third of the sampled households admitted that they did not apply for additional loans, which leaves the rest,

40%, as strictly defined non-constrained households. In addition, there existed the scenario that a household did not take action to apply for loans, worrying that they might be rejected anyway. In all, 15% of the sample has gone through such concern. These households were included as credit-constrained households as well. Hence, households

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who either experienced loan disapproval/reduction in the requested loan amount, or retreated before acting were considered as credit-constrained. Overall, there were 28.51% of the households classified as constrained households32. These households represented

29,233,409 U.S. population.

The credit-constrained households differed from their non-constrained peers in debt acquisition, debt payment and general profiles (Table 5.11). Total debt owed by the constrained households was an average of $46,331, whereas the non-constrained households owed more debt, $49,625 (but with a lower median). Such mean difference of total debt outstanding between these two groups was not tested significant. Meanwhile, a non-parametric rank order test revealed that the difference in total debt outstanding between the two groups of households was only significant at 0.1 level33. Annual payment on all debt averaged at $8,593 in the constrained households, and $8,026 in the non-constrained households. The difference was tested insignificant, too, by both

32 The percentage is derived by adding up the percentages with either or both of the ‘yes’ to the questions as shown in the following crosstabulation.

Discouraged from applying Credit rejection/counteroffer yes no no apply 4.73 31.75 yes, reject 8.05 11.23 yes, not much 0.59 1.96 no 1.95 39.74 % of credit constrained = 28.51

33 The fact that the mean and median measurements for the total debt outstanding variable reside dramatically apart from each other may signal a non-standard normal distribution, and thus the inappropriateness of relying on a parametric test for average group difference. Because a parametric t-test assumes a well-defined distribution of the variables, in this case, it is not valid.

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parametric and non-parametric tests34. Excluding home mortgage, the constrained households, on the average, borrowed $17,639 in consumer loans, as opposed to the non- constrained households, at $14,230. The constrained households paid $4,707 a year to offset consumer debt balance, while the non-constrained households paid $3,246.

Consumer debt owed and annual payments made by households were significantly larger in the constrained households (by a non-parametric test).

The total debt burden among the credit-constrained households was much higher than that of the non-constrained households, with a mean and median for the former being 118.9 and 0.161, and for the latter 19.4 and 0.099. Again, the wide deviance between the two average statistics suggests that the distribution of this ratio was not a normal, bell-shaped curve, especially given the extremely large maximum value. In this perspective, the non-parametric method is reckoned more robust to compare the median difference of the total debt burden between the constrained and non-constrained households. The difference was then tested significant at 0.001. Under a similar rationale, the consumer debt burden of the constrained households was proved significantly larger than that of the non-constrained households: a mean of 66.2 versus 11.3, and a median of

0.081 versus 0.009.

Table 5.11, additionally, presented the variation of household characteristics as continuous variables between these two types of households. The head of the constrained

34 Credit-constrained families paid off more payment in total debt, even though they borrowed less on the whole. To some extent, such a puzzle could be explained by a higher interest rate a credit-constrained household might face.

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household was substantially younger than their non-constrained counterpart, 39 years old versus 56. The constrained households earned significantly less in annual income

($38,522) compared to the non-constrained households ($57,787) and possessed far less liquid assets as well ($6,824 versus $26,665). The constrained households tended to be a larger, with an average size of three, and having children compared to the non- constrained households. These households had fewer credit cards (3) than those otherwise

(3.7), suggesting that the constraints factor might be the reason they are not approved.

These mean (or median) differences were tested significant at 0.001.

Categorical classification of household characteristics in association with credit constraints was presented in Table 5.12. For all the sampled households, the younger the householder is, the more probable the household is constrained. Nearly half of the householders who were under 35 were credit constrained, and the percentage of constrained households in each age group decreased with the increment in age; and among those who were 65 or older, only 4% were constrained. Such a pattern also applies to the grouping of annual income. Generally speaking, a larger percentage of credit- constrained households exist among the lower-income households than among the higher-income households. Nearly all of the income groups earning an annual income under $53,000 had more than one-third of the households constrained whereas income groups between $53,000 and $93,000 had 20%, and $93,000+ had only 14% as constrained households. The lower the education level the householder achieved, the greater the chance the household to be constrained. Among those with a high school diploma or less, 30% was constrained. The percentage of constrained households

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decreased with the educational attainment: 26% for householders with a bachelor’s degree and 17% for householders with a graduate degree. However, householders with some college education seemed to be a group that was disproportionately but heavily constrained (34%). Residential status is a natural aspect that distinguishes the constrained households from the non-constrained. Nearly 45% of the renters were constrained, in contrast to 27% of the homeowners who were paying on mortgages or 10% of the homeowners who had cleared off mortgages. Single-person headed households (i.e.,

‘never married’) were more inclined to have constraints imposed than households headed by a married couple, or by an individual who was separated, divorced or widowed. Two- fifths of the households who did not have young child(ren) were reported as credit- constrained compared to one-fifth of those who had at least one child. Small business households had an identical likelihood (28%) of being credit constrained compared to other households. The percentage of the households who were credit constrained decreased dramatically with the values of the liquid assets-to-monthly-income ratio. Out of one hundred households, forty of them who had a ratio of less than one were credit constrained, twenty five who had a ratio 1-3, and fifteen who had a ratio 3-6 were constrained whereas merely six who had a ratio larger than six were credit constrained.

Meanwhile, credit constraints were more prevalent in the households (34%) where the householder thought favorably about credit use and probably had actively sought or relied upon loans. Earning capability is a crucial element in a bank’s decision matrix to extend credit. Not surprisingly, up to 50% of the unemployed households were constrained. A pattern appears in a householder’s perceptions about the future and the household’s

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experience of credit constraints: the more optimistic s/he feels, the more likely the household is to be bounded with credit constraints. It is evidenced that among householders who either expected income to grow more than price or expected to inherit, a higher percentage fell ‘victim’ to being credit constrained. Oddly enough, householders who were salary earners had a greater chance of being constrained (36%) than retirees and self-employed.

The prominence of credit constraints is examined secondly for only homeowners with mortgage payments for the total debt burden concern. Compared to the national sample, a similar percentage of households (24%) were rejected or granted credit lines less than requested. However, a small portion (14%) of the mortgage-paying households had not applied for loans during the last five years whereas a dominant portion of mortgagers (60%) had succeeded in borrowing without any compromise. Of all the mortgagers, 11% were discouraged from going forward on a loan application. In a way, the constraints were relatively less likely to have occurred to households who applied for a home loan and were currently paying off the mortgage. It turned out that close to 27% of the mortgager households were classified as credit-constrained households35.

Percentage-wise, this agrees with proportion of the entire sample; in number, it

35 The percentage is calculated based on another crosstabluation, as below, with the same rule:

Credit Discouraged from applying rejection/counteroffer yes no no apply 1.19 13.52 yes, reject 7.14 14.25 yes, not much 0.42 2.2 no 1.79 59.5 % of credit constrained = 26.99

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approximates 11,579,248 of the U.S. population.

Table 5.13 focuses on the variable differences between the constrained and non- constrained mortgagers. Total debt outstanding and annual debt repayments were not significantly different between these two types of households. The mean total debt balance in the constrained mortgager households was $96,979, and it was $106,889 for the non-constrained households. The median total debt was $78,070 for the former and

$80,000 for the latter. On the other hand, the median total debt PTI ratio of the constrained mortgagers was 0.291 (or 29.1%), which is statistically different from that of the non-constrained households, 0.226 (or 22.6%).

When it comes to the profiles of these households, variations by credit constraints were obvious. The constrained mortgagers were, on the average, five years younger than the non-constrained peers, 42 versus 47. The average annual income of the constrained mortgagers was $52,184, far below that of the other group, $81,700. Moreover, they held less than $10,000 in liquid assets while the other group had an average of $24,347. They had slightly more persons in the household, in which more children resided. However, they did not have as many credit cards (4) as their non-constrained counterparts (5).

The general pattern of the association between credit constraints and household characteristics as shown in the previous discussion on all-households case is repeated.

The older the householder was, the smaller the percentage was seen in that age group as credit constrained. The percentages of each age group are, respectively, 40% in those younger than 35, 29% among those aged 35-44, 25% among 45-54, 20% among 55-64 and 10% among 65 or older. The higher the annual household income or education level

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was, the less likely it was that these households were constrained. In particular, mortgagers who were not high school graduates had more than 40% chance of being constrained, a figure higher than the national comparative. The larger the household size was, the greater the chance of the mortgagers of being constrained. One-third of the households with young child(ren) were constrained in contrast to one-fifth without a child. Among the households with a smaller liquid assets-to-income ratio, a large percentage was constrained as compared to those with a larger ratio. Up to 28% of the working mortgagers were constrained, less than for the unemployed (44%), but more than for the retirees (9%). Half of the households who did not have any credit card reported being constrained. On the other hand, it was the households who had 6-10 credit cards that showed the smallest percentage of being constrained. Householders who expected a better future financial position, i.e., expected income growth to outpace living expenses and/or expected an inheritance in the future, were likely to be constrained. Marital status, business ownership and credit usage opinion did not help to differentiae the two types of households.

As stated in the previous chapter, banks base their lending decision upon factors in credit score, credit history, life stability and other factors. Some of the above characteristics have directly or indirectly affected credit factors, which eventually link to the outcome of credit constraint. The point is that the empirical observations elaborated above do not and should not necessarily imply the predictiveness of household profile to constraint possibility in the context of this research.

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5.4 Households in Debt Burden Tiers

The focus of this paper is to represent household debt through the debt payment- to-income (PTI) ratio. The statistics of debt PTI ratios are first presented in numerical values. The total debt PTI ratio for all households averaged at 47.78 (the median 0.12 )36; the total debt PTI ratio on mortgage payers averaged at 91.34 (the median 0.245) and the consumer debt PTI ratio averaged at 26.98 (the median 0.028). Undoubtedly, homebuyers had a relatively higher total debt burden than a typical household. There existed a drastic departure between the mean and median statistics on each measure. This, again, suggested an asymmetric distribution of the corresponding PTI ratio. It is noted that the distributions are all skewed towards the right-side tail (large ratio values); in other words, a large frequency appeared within the range of 0 to 1. Cumulative distribution graphs of the two debt ratios are plotted in figure 5.1-2. Under such circumstances, a focus on the median value ensures a robust estimation of the ratios. Put into words --- in general, U.S. households used 12% of their pre-tax income to pay off any type of debt and less than 3% on any type of consumer debt. Overall, mortgager households consumed over 24% of their gross income on any type of debt.

To accurately reflect the seriousness of consumer debt, ratios were re-calculated only on debtors for consumer types of debt. The reason for this is that the inclusion of households holding no debt has mitigated the actual debt status. To stress this point,

36 In all the following debt payment to income ratio calculation, if the denominator (gross family income) is less than zero, it is then replaced by $1 to avoid a resultant debt payment ratio being negative, and the awkwardness of assigning a practical meaning to such a negative value.

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66.5% of all the households were found to be debtors. These households had a mean consumer debt PTI ratio around 40, a median of 0.085. In summary, indebted U.S. households consumed 8.5% of their gross income to offset their borrowing excluding mortgages.

To recall, the total debt PTI ratio is averaged at 118.9 for the constrained households and at 19.4 for the non-constrained households; the difference is detected by a t-test as significant (see Table 5.11). The consumer debt PTI ratio significantly departed between the two groups of households, a mean of 66.3 (constrained) versus 11.3 (non- constrained). It is salient, though unexpected, to see that the constrained households had a larger debt burden (total or consumer debt) than those who were not. Earlier, it was mentioned that the overall distributions of the two ratios were highly skewed towards large values, and this remains the same when the constraints factor is accounted for.

Again, median measurements are regarded as more approximate to the true centroid of the ratio distribution. The median total debt PTI ratio was 0.161 for the constrained households and 0.099 for the non-constrained. The median consumer PTI ratio was 0.081 for the former and 0.009 for the latter. Thus far, the credit-constrained U.S. households annually repaid on total debt at 16%, or on consumer debt at 8% of their gross income.

Households who were not credit-constrained devoted, respectively, 9.9% and 0.9% of their gross income on annual total debt and consumer debt payment. A non-parametric test showed that the median values of both ratios were significantly different between households with and without credit constraints. Although, against the proposed ratio

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guidelines, none of the median values translates into a high debt burden that needs to be of concern.

According to the criteria adopted in chapter four, households were further categorized into hierarchies of debt burden, respectively, on the total debt burden and the consumer debt burden. On the total debt burden, households who did not pay on home mortgages were excluded from the calculation. Among all the home mortgagers, it was found that 64% of the non-zero burden households were under the lower threshold (30%), i.e., the ‘low burden’ group; over 15% of them were between the two thresholds, i.e., the

‘moderate burden’ group (Table 5.14). Therefore, a total of 79% of the households who had mortgage loans and made payments were able to keep a low or moderate debt burden relative to their household gross income by our criteria. On the other hand, that places the remaining 20% of the mortgagers into a financially unhealthy ‘high burden’ group, because their debt payment consumed at least 40% of their earnings in one year. These households could be on the verge of financial stress, especially in case of unexpected changes (setback) in their overall finances. Had these households attempted to purchase a home if they had not yet done so, such an outstanding debt burden might position them in a very disadvantageous situation.

With regard to the consumer debt burden, a large percentage (35%) of the households had a zero debt burden (see Table 5.15). To recall, for the consumer debt PTI ratio, the suggested cutoffs are, respectively, 10% and 20%. Over one-third of the households were qualified in the ‘low burden’ group where the consumer debt burden is lower than 10%. Up to 18% of the households was considered ‘moderately burdened’,

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with a consumer debt PTI ratio greater than 10% but less than 20%. Altogether, more than 80% of the households appeared to be financially sound concerning the consumer debt burden. The ‘high burden’ group comprised over 11% of the overall population, a proportion approximate to the similarly burdened group in the total debt burden case.

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100%

90%

80%

70%

60%

50%

40% Cumulative Distribution

30%

20% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Debt-Income Ratio

Figure 5.1 Cumulative distribution of consumer debt service burden (PTI ratio) (all households)

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100%

90%

80%

70%

60%

50%

40%

30%

Cumulative Distribution 20%

10%

0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Debt-Income Ratio

Figure 5.2 Cumulative distribution of total debt service burden (PTI ratio) (home mortgagers)

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5.5 Household Debt Burden and Credit Constraints

One of the objectives of this paper is to look into the household debt burden in association with the presence of credit constraints in that household, and to understand the impact this factor plays in the level of the burden. This question is examined as follows. The flow of the text is that the total debt burden is discussed first, and followed by the discussion of the consumer debt burden. The corresponding tables for each are

Table 5.14 and Table 5.15.

5.5.1 Total Debt Service Burden and Credit Constraints

A chi-square test indicated that the existence of credit constraint differed significantly across households in the different total debt burden tiers. Generally speaking, credit constraints appear to have a positive relationship with the level of the total debt burden, i.e., the percentage of the constrained households increases with the level of burden. On the other hand, the distribution of households by burden level differed between the credit-constrained and non-constrained households. For the constrained households, about 52% were ‘low burden’ households, 18% ‘moderate burden’ and 30% ‘high burden’. Households who were not constrained appeared to be lesser burdened with debt payment. The evidence was that a much larger percentage of these households (68%) were grouped as ‘low burden’, and a much smaller percent

(17%) were in ‘high burden’ than for constrained households. Such an observation, somehow, is surprising given the presumed hypothesis that the credit-constrained households are likely to have a lower debt burden.

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5.5.2 Consumer debt service burden and credit constraints

Within expectation, a quite similar trend was detected in the consumer debt burden in terms of a significant interdependence between credit constraints and the burden level. The percentage of households who were constrained increased, too, with the burden level. It was found that 15% of the debt-free households were credit constrained (Table 5.15). The incidence of credit constraints occurred in 31% of the ‘low burden’ households, 39% of the ‘moderate burden’ households and 47% in the ‘high burden’ households. It is equivalent to say that the proportion of the non-constrained households negatively correlates with their debt burden level. Compared to the non- constrained households, constrained households had a comparatively larger percentage in the ‘high burden’ tier (19% vs. 9%), but a smaller percentage in the ‘zero burden’ tier

(18% vs. 42%). About one-quarter of the constrained households had a moderate debt burden, while 15% of the non-constrained households were in the same tier. Apparently, severe debt burden occurred more frequently within households who were in fact restrained from debt acquisition.

5.6 Bivariate Analysis Results

Next, a summary is presented on the distribution of debt burden tiers in relation to the independent variables as framed in the demand side. The following bivariate analysis does not take into account the supply factor, i.e., credit constraints. Therefore, the interpretation of direct explanatory power should be taken with caution (Table 5.16 and

Table 5.17). The discussion starts with the consumer debt burden.

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5.6.1 Consumer Debt Burden Tiers by Independent Variables

Age of the householder

Householders within the zero debt burden tier were significantly older (an average age around 56) than those within any other burden tier, in which a typical householder is close to his/her mid-forties. After further grouping the age into 10-year intervals, the same conclusion is derived: nearly 70% of households with the householder older than 65 were free of debt, a percentage far exceeding that of any other age group. Age groups 35-

44 and 45-54 had the smallest proportion of households in the ‘zero burden’ tier (22% and 25%), and the most households in the low-moderate tiers (65% and 63%). In contrast, householders younger than 35 were more likely to fall into the moderate-high burden tiers (21% and 17%).

Household income

Households in the low burden tier were the wealthiest of all, earning an average annual income of $66,552 in 1997. Households within the zero and moderate burden tiers made, respectively, $48,588 and $47,024 a year. Households in the high burden tier made the lowest average income, $31,160. In conjunction with the average householder age, it appeared that the zero burden tier was seen more in older households with moderate income sources. Moreover, the lower the household income, the more probable the household is in either the zero burden tier or the high burden tier. Income groups earning less than $26,000 had the largest percentage of households that were in the zero burden tier (over 35%) and in the high burden tier (up to 20%). For households earning more than $26,000, the percentage of households that were in the lower burden tiers decreased

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with the increment of household income; in addition, the percentage in the higher tiers increased with income. Nonetheless, the wealthiest households ($93,000+) actually did not have the worst burden status, instead, an overall modest burden. About 28% of them were in the zero burden tier and 12% in the moderate burden tier. To some extent, the phenomenon is consistent with what the PI-LCH would imply: low-income households are likely to borrow more heavily to make ends meet.

Education level of the householder

Fewer of the higher educated individual headed households had no debt burden.

Among the households headed by someone who had earned a bachelor’s degree or attended a college, only 26% had a zero burden whereas 38% of the households headed by a high school graduate and 53% headed by someone with less than grade 12 had a zero burden. Noticeably, a high percentage (35%) of the highest education group, namely, graduate or professional degree earners, was also debt free. These households were also clustered within a low burden tier. Households headed by a college attendee or a college graduate were found to be concentrated in the moderate-high tiers. Therefore, the impact of the educational attainment showed a non-linear pattern when credit constraints are not counted.

Marital status of the householder

Relative to the single person headed households, households with married couples were less likely to be free of debt burden: only 28% were debt free as opposed to over

40% of single (including separated/divorced/widowed) person headed households.

Married couples were more likely to be found in the low-moderate burden tiers. There

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was a similar percentage of those high burden tiered households across any type of marital status (up to 12%). In all, borrowing is more popular among married couple households and their burden is relatively higher than for the single-headed households.

Household size

Households in the zero burden tier had a smaller size than those in other tiers, an average of 2 versus 2.7-2.9. In particular, households within the low burden tier had the biggest households of all, with an average of nearly three individuals in the households.

When household size was classified into sizes of ‘1’, ‘2’, ‘3-5’ and ‘6+’, it is observed that the smaller the household was, the less likely they were to be burdened with any or high debt payment. A majority of the households with one or two members were found within the zero burden tier whereas the largest percentage of the households with three or more members was found within the low burden tier.

Presence of non-adult child(ren)

Only 22% of the households with young child(ren) had a zero burden whereas

42% of the households without a child were in the same tier. On the other hand, across all the other debt burden tiers, there were larger percentages of the households with young child(ren) compared to their counterpart households without children.

Residential status

A large percentage (56%) of homeowners without mortgage obligation were burden free, which differed significantly from homeowners who still paid on mortgages, as well as home renters. Between the latter two subpopulations, renters were more likely to be within the zero or high burden tier, and mortgage payers were inclined to be within

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the low-moderate burden tiers. In a sense, the burden issue is more prevalent among homeowners obliged with mortgages than households with any other type of resident style.

Liquid assets-to-income ratio

The average amount of household liquid assets across the four debt burden tiers

(from zero to high) was $32,166, $17,393, $11,823 and $12,188. The moderate to high burden was more probable among the households who had a low liquid assets-to-income ratio, and the zero burden more probable among those who had a high ratio. Obviously, possessing a higher level of liquid assets relative to monthly income is associated with the likelihood of being in a high burden tier. For those who had borrowed and were making payment, the figures implied that the lesser-burdened households held more liquid assets than the higher burdened households. The observed relationship of these two variables to some extent echoed the PI-LCH hypothesis: liquid assets play the role of a supplementary source for borrowing.

Business ownership

The fact that a household runs its own business increased the probability of its having a higher debt burden. The percentages of business households in the four debt burden tiers were, respectively, 24.38%, 40.74%, 20.80% and 14.09% (from zero to high). To contrast, the percentages in the same order for the non-business households were 36.89%, 34.42%, 17.41% and 11.28%. It is not hard to conclude that business owner type of households were more likely to be debt burdened, from low to high.

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Employment status

Debt burden levels varied substantially with the householder’s employment status.

Households with the head not currently in the work force, e.g., retired and unemployed, had a majority (56% and 67%) in the zero burden tier whereas households with the head currently employed stayed dominantly in the low-moderate burden tiers (58% and 64%).

In addition, the self-employed and salary-earner households experienced a high debt burden to a similar extent (over 12%).

Credit general use

The percentage of households with a zero burden decreased with the degree to which the householder is fond of using credit. Twenty-six percent of the credit-lovers had no debt burden and 43% of the credit-dislikers were in the same tier. Conversely, the percentage of households within other burden tiers increased with the individual’s favorable attitudes towards financing. Comparatively, at the low-high burden tiers, larger proportions were seen in the households where there was a positive attitude towards credit than in the households with mixed feelings, and far exceeding the percentages of the households with negative feelings.

Number of credit cards

Not surprisingly, households within the zero burden tier had the smallest number of credit cards (2.27). Other burden tiered households all had an average of four credit cards. It was the low burdened households who held the most credit cards, 4.26 on the average, not the high burden tier as would be assumed. To recall, credit card balance ranks fourth among all types of household debt in terms of borrowing volume (see Table

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5.1), and the average number of credit cards in American households is 3.5 (Table 5.9).

Three-fifths of the no-card households had a zero burden while 28% were debt free among those who held one to five cards. Households within a zero burden tier were less common among the cardholders with six or more cards. The more credit cards the household held, the more likely they were to position themselves in a higher debt burden tier. It was found that over 17% of the households with ten credit cards or more were in the high burden tier, and the percentage of households in the same tier decreased with the decrease of credit cards.

Borrowing orientation

Households who believed that they would finance for luxury items, such as a vacation, fur or jewelry, were less likely to be debt burden free and more likely to end up with a higher debt burden as opposed to those who claimed that they would borrow for investment (automobile and education) or for unexpected events (income loss). There was not much quantitative deviance in the distribution of the debt burden tiers between households borrowing for investment and for unexpected events, though the latter seemed slightly more highly burdened.

Expectation of income growth

As hypothesized, to take on debt is a rational decision for individuals who expect growth in their future income. It was observed that the more optimistic households were about their future earning power, the more probable they were indebted. Less than one- fourth of the households who foresaw that their income would grow faster than commodity prices were in the zero burden tier, and 40% of the same tiered households

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were found among those who anticipated an income increase slower than the pace of commodity prices. The effect of optimism with regard to the future income was also demonstrated through the indebted households: relatively larger percentages were shown consistently in the ‘low-moderate-high’ burden tiers for households who felt confident in earning more in the future.

Expectation of future fortune

The debt burden seriousness varied significantly between households who considered themselves likely to receive an inheritance in the future and those who did not. Only 21% of the possible-inheritors were free from debt, in comparison to 37% of the non-inheritors. In each of the low to high burden tiers, the percentage of households expecting a fortune down the road surpassed that of households otherwise.

Income interruption

Whether or not the householder experienced layoff during the previous twelve months prior to the interview was used as a proxy for a recent income interruption. It did not demonstrate a significant difference across the debt burden tiers.

5.6.2 Total Debt Burden Tiers by Independent Variables

In studying the total household debt burden in association with the demand and supply factors, only households that made mortgage payments were included in the analysis. In other words, home renters or homeowners who were off the mortgage commitment were discarded. The reason for this is that a total debt burden only makes sense to those who generated any type of debt, home loans included. Only homeowners

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who made mortgage payments were truly debtors in a total debt burden concept.

Therefore, the model would be misleading if the dependent variable did not follow one identically independent distribution --- the total debt burden means one thing for mortgagers but another for renters.

Hence, total debt burden tiers were reduced into three categories, the low tier

(below 30%), the moderate tier (30-40%) and the high tier (40%+). Households within these three tiers differed from each other, but not as much as expected (Table 5.17). The average age of the householder in the three tiers was similar, around 45 years old.

Households in the lowest burden tier earned the highest gross annual income, $91,530, followed by the moderate burden tier, $54,126 and the high burden tier, $34,265.

Statistically, the differences in income among burden tiers were confirmed as significant.

Significant difference in liquid assets across the burden tiers was tested. From the low to high burden tier, households in each tier on the average possessed respectively $23,390,

$14,748 and $15,624. The low burdened households seemed to be larger in household size than the other tiered households. The high debt burdened households, however, were found to hold the least number of credit cards (4.35) compared to the low and moderately burdened households.

The householder’s age and household income were then categorized, and significance tests on the bivariate association were run. Householders aged 35-44 and 45-

54 maintained a very similar distribution in the debt burden tiers: a majority of the households were in the low burden. Households who were either younger or older than the above two age groups, on the other hand, had a smaller percentage in the low burden,

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but a larger percentage in the high burden. In a sense, this could suggest a non-linear relationship of the age factor to the debt burden tiers. A substantial impact of household income was also revealed. A considerable portion of the low-income mortgage homeowners had a high burden, and a reverse scenario was found among wealthy households. The percentage of the low burdened households (or high burdened) increased

(or decreased) with household income. Education level also affected the total debt burden significantly, and in a non-linear pattern. Married homeowners were more prevalent in the low burden tier whereas single homeowners were in the moderate-high burden tiers, especially, divorcee or separated individual-headed households. Working households were likely to be in the low burden tier and the retired or unemployed households more in a high burden. Opposite to the findings as in the consumer debt burden tiers, for households who had the largest liquid assets-to-income ratio (6+), a small percentage of households were found in the low burden tier and a large percentage of these households were found in the high tier, as compared to the households who had a ratio less than six.

The effect of credit card possession seemed to center on the contrast of the cardholders versus non-holders. Households having no credit cards were seen more in the high burden tier instead of the low tier as expected. Rather, households having at least one credit card had relatively greater probability of being in the low burden tier. In addition, if the householder reported unemployment, they were more likely to be in the high burden tier.

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5.7 Multivariate Analysis Results

5.7.1 Consumer Debt Burden Model

The results of the three separate cumulative logistic regressions are tabulated in

Table 5.18. The model parameter estimates, chi-square statistics and p-value are all derived after applying the RII technique. The overall significance statistics are computed from the chi-square statistics (-2 log likelihood for the contribution of the explanatory variables) in each implicate for the three models (the mathematical expression as shown in the Appendix B). The tests are significant (p<0.001) for all three cumulative binary models. Meanwhile, the Hosmer and Lemeshow goodness-of-fit test is proved to be supportive for all three of these models (a non-significance test implies a fitting model on the data, refer to Hosmer & Lemeshow, 1989). The first regression (k=1) has the cumulative logit(Y ≤ 1) as its dependent variable, and models the log odds of ‘being burdened’ (aggregating the low-moderate-high burden tiers) versus ‘not being burdened’

(the zero burden tier)37. In the same fashion, the second regression (k=2) depends upon the cumulative logit(Y ≤ 2) and models the log odds of ‘above the 10% threshold’

(aggregating the moderate-high burden tiers) versus ‘10% below’ (aggregating the zero- low burden tiers). The third regression (k=3), with the dependent variable being logit(Y ≤ 3) , models the log odds of ‘above the 20% threshold’ (the high burden tier) versus ‘20% below’ (aggregating the zero-low-moderate burden tiers). In a way, these

LOGISTIC’ syntax.

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three equations examine the likelihood of households being in a particular debt burden tier(s) versus its complement tier(s).

Earlier in the methodology section, it was mentioned that the assumption of parallel cumulative logits, which requires only one single parameter for independent variables, was rejected by a Score test. Such testing is confirmed after comparing the parameter estimates across the three logistic models. If the assumption were valid, the coefficients of all the independent variables would be close in the three models.

However, this is not the case. For example, the factor of credit constraints has an obviously different effect on the three cumulative logits, each being 0.836, 0.604, and

0.624. Some other parameter coefficients even have different signs in the three logistic models, e.g., household annual income.

The factor of credit constraints shows a significant and positive effect on the probability of a household falling into the low-moderate-high burden tiers, which can simply be referred to as ‘indebted’. The odds of households who were constrained yet to have generated debt (made repayments) are about 2.308 ( e0.836 ) times as high as those who were not, everything else being equal. Such a significant and positive effect can be drawn, too, from the other two regressions. The coefficient of credit constraints is 0.604 in the second regression and 0.624 in the third one. In plain words, the odds of households with credit constraints falling above the 10% burden threshold are 1.829

( e0.604 ) times as large as those who were not constrained (as opposed to the 10% below).

The similar odds of households with credit constraints falling into the high burden tier are

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1.866 ( e0.624 ) times as high as the non-constrained households (as opposed to all other tiers). The effect of credit constraints gets smaller from the first to the third model. In other words, credit constraints have a more profound role in differentiating those who are burdened and those who are not than in the differentiation between those who are heavily burdened and not. This proposition can also be implied from the decline of the relative contribution of the chi-square value of the constraints factor to the total chi-square statistics of the model across the three models (not shown).

In regard to the age variable, only the age group ‘65 or older’ appears significant in the first model (‘indebted’ versus ‘debt-free’), with a negative sign. In this model, age groups younger than 64 did not differ from each other in the likelihood of acquiring and paying on debt. Compared to individuals less than 35 years old, the odds of the oldest group (65+) seeking debt and making payments are only half (0.6), holding other factors constant. The odds of the same aged households being in moderate-high tiers versus zero- low tiers are nearly 0.7 compared to the same young households. Nevertheless, in the third model that contrasts the probability between ‘the high tier’ and ‘the zero-low- moderate tiers’, the factor of age shows no significant impact, with everything else controlled.

Household income imposes a different effect on each of the three logits comparison, in terms of the sign and the magnitude of the coefficient. Among the three models, it is apparent that household income plays a crucial role in distinguishing between a non-zero tier and a zero tier (k=1), and between a high tier versus others (k=3), even though the income variable turned out to be positive in the former and negative in

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the latter. Income groups that make an annual income of $14,000 or more all had significantly larger odds ratio (except for the highest income group) to be in an indebted position than the lowest income group, as opposed to not having debt. On the other hand, all the income groups above $20,000 had significantly lower odds ratio than the reference income group in a high debt burden tier (as opposed to the other tiers); the higher the household income, the lower the odds ratio. The odds of households within the income groups more than $14,000 being indebted ranged from two to three, compared to the lowest income group. Noticeably, the highest income did not differ from the lowest income group in their likelihood of having a debt burden. Income has a limited effect in the second model. Only households earning more than $42,000 were found to be significantly different from the baseline households and were less probable to be in the moderate-high tiers versus the zero-low tiers. The odds of households in the highest income group, that is, those who earned more than $93,000 a year, being in a high burden tier are less than one-twentieth; the odds of households making $33,000-53,000 annually are about one-fifth, and for those earning $20,000-33,000 about one-third, all in contrast to the lowest income households. Again, from the perspective of a chi-square contribution, the weights of the categorized income variables are exceptionally large in the third model.

Educational attainment of the householders does not impose a consistent effect on the three logits. In the first regression modeling ‘burdened’ versus ‘non-burdened’, education of the householder has a very limited effect. Basically, householders with a high school degree or equivalent, a bachelor’s degree or even a graduate/professional

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degree have no significant difference in being grouped in any of the burdened tiers (k=1) as compared to householders headed by high school dropouts. For those who obtained some college education, the odds of these households getting into a burdened position versus a non-burdened one are about 1.7 times as likely as that of their peers without a high school diploma. When the high burden tiered households are the focus as opposed to everyone else (k=3), college educated headed households turn out to be quite distinguished from the base household (less than high school). The odds of a householder who had some college education or a bachelor’s degree being in the high burden tier (as opposed to not) are nearly doubled that of the lowest education group.

Single status (particularly never-married) is the only significant marital status in the first model that predicts the likelihood of a non-zero burden. These households are less prone to seek debt obligation than other singles that were divorced, separated or widowed. The odds of a burdened single household are about 0.7 times as that of a divorced household. Marital status is not tested significant in the second model contrasting between above the 10% threshold and below. When the threshold soars to

20%, single status, again, shows a significant and negative effect. All three models indicate a lower probability of single households going beyond higher thresholds than those ‘alone’ types of households. Married couple headed households do not differ much from the ‘alone’ types of households in their burden severity.

Housing status was tested as a significant factor in the first model of positive burden. The odds of homebuyers with mortgage commitment are 1.5 times as likely as their renter counterparts to be burdened, and the odds of homebuyers without mortgage

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commitment are 0.8 times as renters. Only the variable of homeowners with mortgage payment is significant in the second model, and none of the residential choices is significant in the third model. In general, the magnitude and the direction of the effect are preserved. That is, with the existence of mortgage obligation, the inclination of households to borrow and repay (more) on consumer debt is higher, and without mortgage obligation, the inclination is smaller.

Business ownership has a significant and positive impact on households being above either the 10% or the 20% threshold versus being below, but not on ‘burdened’ versus debt free comparison. And the degree of the effect increases with the higher threshold. The odds of households managing business and exceeding the 10% threshold are 1.4 times as high as those who are non-business-owners and the odds of business households in the high tier are 1.7 times as those otherwise. In plain words, business entrepreneurs have larger odds of more serious consumer debt burden.

Households who had a liquid assets-to-income ratio greater than six had half the odds of those with a ratio less than one to be indebted, and those who had a ratio between three and six had 0.8 times the odds of the same reference population. When it comes to the other two more serious debt comparisons, a liquid assets-to-income ratio was not significant. Only households who had a ratio over six were found to be significantly different from the baseline household, with odds 0.7 times as large as that of the households who had a ratio below one.

The householder’s employment status exhibits substantial dominance in all of the three models. To some extent, the variable of self-employment affects the three

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cumulative logits in a similar way, evidenced by the comparable parameter estimates

(0.633, 0.625, and 0.883). Generally speaking, the odds of a self-employed householder being burdened (versus non-burdened), being above the 10% threshold (versus below the

10%), or being above the 20% threshold (versus below the 20%) double the corresponding odds of someone who is unemployed. The status of a second measure of employment, salary earner, has a homogeneous effect in each model, too. Householders who earn a regular salary are found to be significantly different from unemployed householders. The odds of a working (employed by others) household burdened with consumer debt (as opposed to not) are around two and a half the odds of an unemployed household, keeping other factors constant. With the increase of the burden severity, the odds comparison is lowered to two. The odds of a working household above the 10% threshold or above the 20% threshold (as opposed to below) are both twice as likely as for an unemployed household. In all, the status of staying in the labor force or not of the householder certainly helps to explain a lower versus a higher household debt burden.

The importance of employment status is also substantiated through the comparative chi- square statistics.

As expected, credit usage opinion explains the likelihood of household indebtedness, specifically, in the higher debt burdened scenario rather than the lower. A householder’s opinion on credit use stands as a significant predictor on the first logit

(burden versus no burden) model with a positive direction. The odds of households to have consumer debt burden (versus no) in those who encouraged credit usage are 1.7 times as likely as that of those who think negatively about credit. Households who hold

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mixed feelings about credit use turned out to be significant only in the first model, where smaller odds (1.2) are seen for them to be in the burden (versus no burden) tier compared to the credit averse. To some extent, the effect of a positive attitude towards credit use demonstrates a diminishing magnitude on the first two logits from the low to high seriousness of the burden comparison (i.e., k=1 to k=2).

Number of credit cards, as another proxy for credit usage behavior, was indeed a crucial determinant in all of the three logits models. The coefficient estimates for this factor bear resemblance to one other, if not strictly speaking, 0.092, 0.07, and 0.092.

Since the number of credit cards functions as a continuous variable, the interpretation of its effect on the logit (or odds) is different from that of dummy variables. To generalize, the odds of households above one certain threshold versus below are as 1.1 times for each additional credit card the households acquire38. Among all the independent variables, this variable could be claimed the second most influential of all, according to the chi-square statistics in all three logits models.

38 The marginal effect of a continuous independent variable x, changed by the amount of δ is formulated as

log ( , + δ ) it X xk = δ × β exp( k ) log it(X , xk ) where X is the vector of independent variables, beta is the coefficient estimates for the logit model. The δ δ × β interpretation is “for an increase of in xk , the odds of the logit are changed by the factor exp( k ) , holding all other variables constant”. The original reference with different notations can be reviewed in

Long (1997).

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The next variable that deserves attention is the variable designed to reflect the borrowing orientation. Compared to households who tended to finance for luxury items, such as a cruise or fur, those who borrowed for a long horizon utility, such as a car or education, have larger odds of being burdened (1.8), or being above the 10% threshold

(1.7), but not above the 20% threshold in a significant way. When it comes to the model of the high burden tier versus the other tiers, none of the borrowing tendency variables turns out to be significant. Households that are inclined towards long-term commitment may plan the repayment on a regular basis, which is captured in our numerator. For the temporary consumption type (luxury items), the annual repayment measure may not truly represent the actual burden.

The variable of layoff experience in the previous year only affected the odds of being in debt burden versus no burden, but not the other two odds comparisons.

Households who once experienced interruption in their income stream have larger odds of debt repayment burden, 1.4 times the odds of households otherwise.

The overall impression is that the presence of credit constraints in a household has a statistically and quantitatively key impact on the odds of households being (consumer) debt burdened (versus not), or maintaining a moderate-high debt burden (versus zero- low), or a high burden (versus otherwise) as defined. Household annual income holds a dominant weight in the third model, which discriminates between highly burdened households (above the 20% burden threshold) and other (below 20%) households. Simply put, low income or scarce resources remain the most salient identifier for the highly burdened households. The importance of possession of resources was further verified by

138

the employment variable: those who work and have income have the capability of financing, and also are likely to be overly extended. Householders’ opinion towards credit usage and their according behavior strongly explain the possibility of households going over or under one threshold. Households who support the general use of credit and rely heavily on credit cards have larger odds of falling into a relatively higher (as opposed to lower) burdened tier. The factor of age, on the other hand, does not impose much effect, as expected. In addition, it is observed that some variables have substantially different effects on the three cumulative logits, which suggests that the role of those variables differs among the three models. In other words, even though it is reckoned that ordering from the first to the third logits implies a contrast of more serious debt burden position, the effect of the variables does not follow a strictly increasing pattern; rather, the estimated parameters show a non-monotone impact on the logits. This, in turn, strengthens the rejection of the proportional odds assumption.

5.7.2 Total Debt Burden Model

The total debt burden model was built upon a portion of the 1998 SCF dataset which contained only the sample of home mortgagers. With the difference in mind, the number of cumulative models and independent variables turns out to be different from the consumer debt burden model. None of the mortgagers is free of debt, and all households have a positive total debt burden. Given the two thresholds on the total debt burden (30% and 40%), these households are divided into three burden tiers: the low, moderate and high tiers. Two individual cumulative logit models are therefore set up for

139

the exclusive pairwise comparison of the two logits. To note, when all the households were homeowners, those dummy variables identifying residential status were removed; this results in fewer variables than those in the logit models on the consumer debt burden tiers. All the other procedures follow the previous analysis.

In Table 5.19, the parameter estimates, chi-square, and p-value are listed after the

RII method is utilized. The two logit models are both tested to have appropriately fit the dataset, with non-significant Hosmer-Lemeshow tests and significant overall model statistics (not shown). The first equation (k=1) models the logit (or log odds) of above the

30% threshold versus below 30%, and the second one (k=2) models the logit of above the

40% threshold as opposed to below 40%.

The significant effect of several variables in the consumer debt burden scenario is also presented in the total debt burden model. Credit constraints comprise one of them.

Households who were constrained in borrowing have larger odds of being above the 30% threshold as well as above the 40% threshold (as opposed to below). These odds are nearly double the odds of households who were not constrained, if two households resemble each other in all other aspects.

The coefficient of household income has a negative sign in both models, which implies that all the income groups have smaller odds than their lowest income counterparts to be found in a more severe debt burden category (as opposed to a lesser one). Households with annual income between $20,000-32,999 that are in the moderate- high tiers (as opposed to the low tier) have odds of less than 0.2 as large as households with annual income less than $8,100. The odds of the income group between $42,000 and

140

$66,99 are as low as 0.06 times that of the baseline household to fall into a moderate-high tier. Such odds plummet to 0.03 and 0.01 for households with annual income $67,000-

92,999 and with annual income $93,000 or more, compared to the lowest income households. The respective odds comparisons between each of the income groups and the reference group are even dramatic in size for a higher threshold, 40%. The odds of households being in the high burden tier and making from $20,000 to $43,999 a year are up to 0.08 times the odds of as those making less than $8,100. The highest two income groups have no more than 0.01 the odds of being in the high tier (as opposed to the other tiers) compared to the reference group. In conclusion, households who earn more income have smaller odds of experiencing a severe debt burden. The weights of household income are fairly substantial. In the first logit model (k=1), if the chi-square values from each independent variable are added up, the chi-square of all the income group variables comprises nearly four-fifths of the total (not shown). An identical figure was found in the other logit model (k=2).

Education level of the householder demonstrates a significantly positive association with the log odds of a total debt burden over the 30% and 40% threshold

(versus below). In the first model (k=1), except for householders who had a high school diploma, all other households showed significant differences in the likelihood of being in a moderate-high tier in contrast to those less than 12th grade. Householders with a higher education level have up to 2.3 odds of being in the moderate-high burden tiers (as opposed to the low tier) compared to those without a high school diploma. The magnitude of the impact on the log odds increases with education level, until it peaks at

141

the level of a bachelor’s degree and then drops at the highest educated group

(graduate/professional degree earners). Graduate/professional degreed households within the moderate-high burden tiers have larger odds than those of households headed by high school graduates, but smaller odds than those of the college-educated group. In the second model (k=2), householders with different educational background separate themselves evidently from the least educated. The odds of households headed by a college attendee being in a high burden tier (as opposed to the low-moderate tiers) are 3.6 times as large; the odds of households headed by someone with a bachelor’s degree are

3.7 times as large; and the odds of households headed by someone with a graduate degree are 2.7 times as large, as those of households headed by someone who is not even a high school graduate. The finding that households with graduate/professional degrees have smaller odds than those who attended college but did not graduate is similar to the first logit model, but with a greater difference in exponential factors.

The effect of the liquid assets-to-income ratio showed an opposite sign in both models as hypothesized. Households with a ratio greater than six have odds of 1.8 to be in a moderate-high tier versus low tier, compared to those holding liquid assets less than one-month income. In the second model between the high tier and the low-moderate tier, households holding liquid assets greater than three-month worth of income have significantly larger odds than those of the baseline households, nearly double in size.

Psychological and behavioral inclination towards credit is another vital component in evaluating the seriousness of household debt burden. Only the households who are positive on credit use differ significantly from those who oppose in the first logit

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model (k=1), contrasting between the moderate-high tiers and the low tier. The odds of the supporting households in the moderate-high tiers are nearly 1.5 times as large as those of the credit averse. Such odds were repeated in the second model. The variable, number of credit cards, shares analogous coefficient estimates between the two cumulative logits,

0.049 and 0.051. The odds of households above either of the burden thresholds (as opposed to below) were about 1.1 times as large for each additional credit card households acquire, keeping everything else the same.

The majority of the explanatory power rests in the previously discussed variables.

None of the other listed variables, such as the householder’s age, his/her marital status, business ownership and employment status was found significant in either of the models.

The number of significant variables is much smaller than that in the consumer burden model. Somehow, this hints that the attributes of consumer debt and total debt (including home mortgage) may be inherently distinguishable from each other. The differentiation also applies to the predictors associated with having consumer debt versus total debt.

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Debt Outstanding ($) Type All Households Minimum Quartile 1 Mean Median Quartile 3 Maximum Credit Card 0 0 1,817 0 1,300 133,000 Home Mortgage 0 0 33,248 0 51,000 5,560,000 Lines of Credit (including HELOC) 0 0 993 0 0 11,800,000 Other Property (various purposes) 0 0 3,744 0 0 10,100,000 Car Loan 0 0 3,537 0 7,552,000 1

44 Vehicle Lease* 0 0 1,188 0 0 112,000 Education Loan 0 0 1,468 0 0 142,000 Pension, Life Insurance, and Margin Loan 0 0 1,754 0 0 27,000,000 Other Consumer Loan 0 0 701 0 0 4,590,000 Total 0 0 48,450 13,800 69,500 27,000,000 Note: Representative of 102,548,842 U.S. population, using weight x42001. * prevailing value of leased vehicle as of fall 1998.

Table 5.1: Descriptive statistics of total debt outstanding by types of loan (all households).

Debt Repayment $ Type All Households Minimum Quartile 1 Mean Median Quartile 3 Maximum Credit Card 0 0 303 0 217 22,166 Home Mortgage 0 0 4,441 0 7,440 780,000 Lines of Credit (including HELOC) 0 0 257 0 0 1,404,000 Other Property (various purposes) 0 0 801 0 0 17,496,000 Car Loan 0 0 1,403 0 0 1,860,000 Vehicle Lease* 0 0 277 0 0 54,000

1 Education Loan 0 0 218 0 0 108,000 45

Pension, Life Insurance, and Margin Loan 0 0 185 0 0 1,158,125 Other Consumer Loan 0 0 303 0 0 3,384,000 Total 0 0 8,187 4,080 11,880 17,496,073 Note: Representative of 102,548,842 U.S. population, using weight x42001. * prevailing value of leased vehicle as of fall 1998.

Table 5.2: Descriptive statistics of total debt repayment by types of loan (all households).

Debt Outstanding $ Type Mortgagers Only Minimum Quartile 1 Mean Median Quartile 3 Maximum Credit Card 0 0 2,796 300 3,000 133,000 Home Mortgage 90 30,000 79,273 63,000 100,000 5,560,000 Lines of Credit (including HELOC) 0 0 1,553 0 0 481,000 Other Property (various purposes) 0 0 6,933 0 0 3,091,000 Car Loan 0 0 5,505 0 8,500 7,552,000 Vehicle Lease* 0 0 1,896 0 0 112,000 1

46 Education Loan 0 0 1,594 0 0 100,000

Pension, Life Insurance, and Margin Loan 0 0 2,954 0 0 7,000,000 Other Consumer Loan 0 0 982 0 0 3,001,000 Total 100 42,500 103,288 79,600 127,400 9,694,200 Note: Representative of 42,982,828 U.S. population, using weight x42001. * prevailing value of leased vehicle as of fall 1998.

Table 5.3: Descriptive statistics of total debt outstanding by types of loan (households with mortgage obligations only)

Debt Repayment $ Type Mortgagers Only Minimum Quartile 1 Mean Median Quartile 3 Maximum Credit Card 0 0 466 50 500 22,166 Home Mortgage 100 5,400 10,596 8,400 13,200 780,000 Lines of Credit (including HELOC) 0 0 397 0 0 276,000 Other Property (various purposes) 0 0 1,347 0 0 5,328,000 Car Loan 0 0 2,164 0 0 490,800 Vehicle Lease* 0 0 445 0 0 54,000 Education Loan 0 0 270 0 0 108,000 1

47 Pension, Life Insurance, and Margin Loan 0 0 239 0 0 540,459 Other Consumer Loan 0 0 495 0 0 1,228,000 Total 100 8,207 16,570 13,038 19,660 5,349,600 Note: Representative of 42,982,828 U.S. population, using weight x42001. * prevailing value of leased vehicle as of fall 1998.

Table 5.4: Descriptive statistics of total debt repayment by types of loan (households with mortgage obligations only).

Debt Outstanding ($) Type All Households Minimum Quartile 1 Mean Median Quartile 3 Maximum Credit Card 0 0 1,817 0 1,300 133,000 Lines of Credit (including HELOC) 0 0 993 0 0 11,800,000 Other Property (various purposes) 0 0 3,744 0 0 10,100,000 Car Loan 0 0 3,537 0 3,300 7,552,000 Vehicle Lease* 0 0 1,188 0 0 112,000 Education Loan 0 0 1,468 0 0 142,000 1

48 Pension, Life Insurance, and Margin Loan 0 0 1,754 0 0 27,000,000 Other Consumer Loan 0 0 701 0 0 4,590,000 Total 0 0 15,202 2,800 16,000 27,000,000 Note: Representative of 102,548,842 U.S. population, using weight x42001. * prevailing value of leased vehicle as of fall 1998.

Table 5.5: Descriptive statistics of consumer debt outstanding by types of loan (all households).

Debt Repayment $ Type All Households Minimum Quartile 1 Mean Median Quartile 3 Maximum Credit Card 0 0 303 0 217 22,166 Lines of Credit (including HELOC) 0 0 257 0 0 1,404,000 Other Property (various purposes) 0 0 801 0 0 17,496,000 Car Loan 0 0 1,403 0 0 1,860,000 Vehicle Lease* 0 0 277 0 0 54,000 Education Loan 0 0 218 0 0 108,000 1

49 Pension, Life Insurance,

and Margin Loan 0 0 185 0 0 1,158,125 Other Consumer Loan 0 0 303 0 0 3,384,000 Total 0 0 3,747 833 4,800 17,496,073 Note: Representative of 102,548,842 U.S. population, using weight x42001. * prevailing value of leased vehicle as of fall 1998.

Table 5.6: Descriptive statistics of consumer debt repayment by types of loan (all households).

Debt Outstanding $ Type Debtors Only Minimum Quartile 1 Mean Median Quartile 3 Maximum Credit Card 0 0 2,732 550 2,900 133,000 Lines of Credit (including HELOC) 0 0 1,492 0 0 11,800,000 Other Property (various purposes) 0 0 5,629 0 0 10,100,000 Car Loan 0 0 5,319 0 8,200 7,552,000 Vehicle Lease* 0 0 1,786 0 0 112,000 Education Loan 0 0 2,206 0 0 142,000

1 Pension, Life Insurance, 50

and Margin Loan 0 0 2,637 0 0 27,000,000 Other Consumer Loan 0 0 1,054 0 0 459,000 Total 1 2,900 22,857 10,000 24,000 27,000,000 Note: Representative of 68,199,707 U.S. population, using weight x42001. * prevailing value of leased vehicle as of fall 1998.

Table 5.7: Descriptive statistics of consumer debt outstanding by types of loan (debtors only).

Debt Repayment $ Type Debtors Only Minimum Quartile 1 Mean Median Quartile 3 Maximum Credit Card 0 0 455 92 483 22,166 Lines of Credit (including HELOC) 0 0 386 0 0 1,404,000 Other Property (various purposes) 0 0 1,065 0 0 17,496,000 Car Loan 0 0 2,109 0 0 1,860,000 Vehicle Lease* 0 0 417 0 0 54,000 Education Loan 0 0 328 0 0 108,000 Pension, Life Insurance,

1 and Margin Loan 0 0 280 0 0 1,158,125 51 Other Consumer Loan 0 0 455 0 0 3,384,000

Total 0 840 5,495 3,583 6,360 17,496,073 Note: Representative of 68,199,707 U.S. population, using weight x42001. * prevailing value of leased vehicle as of fall 1998.

Table 5.8: Descriptive statistics of consumer debt repayment by types of loan (debtors only).

Variable Minimum Quartile 1 Mean Median Quartile 3 Maximum (Continuous) Age (years old) 17 35 49 46 61 95 Family income ($) -1,000,000* 17,000 52,295 33,000 60,000 176,890,000 Liquid assets ($) 0 510 21,009 3,000 12,700 34,209,000 Household size 1 1 2.59 2 4 11 Number of children 0 0 0.67 0 1 8 Number of credit cards 0 0 3.5 3 5 31

152 Note: * 1.3% negative income Representative of 102,548,842 U.S. population, using weight x42001.

Continue

Table 5.9 (a): Distribution statistics (continuous variables) of all the sample households.

Table 5.9 Continued

(Categorical) % Credit rejection/reduction Yes 21.82 No 41.70 No application 36.48 Discouraged from applying Yes 15.32 No 84.68 Age of the householder 0-34 23.56 35-44 23.54 45-54 19.43 55-64 13.00 65+ 20.48 Education level of the householder Less than high school 16.46 High school or GED 31.87 Some college 24.63 Bachelor's degree 15.61 Graduate degree 11.43 Residental status Home owner w/ 41.20 Home owner w/o 25.05 Renter 33.74 Marital status of the householder Married 52.33 Separated 4.00 Divorced 15.00 Widowed 10.28 Never married 18.38 Household size 1 25.16 2 32.93 3-5 38.27 6-10 3.64 Presence of non-adult children Yes 35.40 No 65.60

continue

Table 5.9 (b): Distribution statistics (categorical variables) of all the sample households. 153

Table 5. 9 Continued

(Categorical) % Business ownership Yes 12.68 No 87.32 Liquid assets/monthly income ratio <1 50.93 1-3 21.75 3-6 9.36 6+ 17.95 Credit general use opinion Good idea 28.93 Mixed 37.51 Bad idea 33.56 Number of credit cards 0 27.48 1-5 48.12 6-10 18.59 10+ 5.80 Borrowing orientation Long term 97.69 Luxury 0.47 Unexpected 1.84 Unemployed in last 12 months Yes 11.82 No 88.18 Expectation on income growth compared to price Income up than price 23.53 Income less than price 28.67 The same 40.80 Expect to inherit Yes 13.25 No 86.75 Employment status Self-employed 11.26 Worker 59.19 Unemployed 10.59 Retired 18.93 Note: Representative of 102,548,842 U.S. population, using weight x42001.

154

Variable Minimum Quartile 1 Mean Median Quartile 3 Maximum (Continuous) Age (years old) 18 37 46 44 54 91 Family income ($) -70,000* 33,000 73,737 53,000 78,000 176,900,000 Liquid assets ($) 0 1,380 20,425 4,520 13,400 20,023,000 Household size 1 2 3.05 3 4 11 Number of children 0 0 0.94 0 2 7 Number of credit cards 0 2 4.76 4 7 31 Note: 155 * 0.56% negative income Representative of 42,982,828 U.S. population, using weight x42001. Continue

Table 5.10 (a): Distribution statistics (continuous variables) of the sample households with mortgage obligations.

Table 5.10 Continued

(Categorical) % Credit rejection/reduction Yes 24.00 No 61.29 No application 14.71 Discouraged from applying Yes 10.54 No 89.46 Credit constrained Yes 26.98 No 73.02 Family income $0-8,099 2.02 $8,100-13,999 2.98 $14,000-19,999 4.84 $20,000-25,999 5.60 $26,000-32,999 8.59 $33,000-41,999 11.59 $42,000-52,999 13.62 $53,000-66,999 15.99 $67,000-92,999 17.72 $93,000+ 17.06 Age of the householder 0-34 18.42 35-44 32.37 45-54 26.51 55-64 14.51 65+ 8.2 Education level of the householder Less than high school 8.00 High school or GED 29.25 Some college 28.08 Bachelor's degree 20.27 Graduate degree 14.40 Marital status of the householder Married 71.71 Separated 2.03 Divorced 14.06 Widowed 3.69 Never married 8.51 Continue

Table 5.10 (b) Distribution statistics (categorical variables) of the sample households with mortgage obligations 156

Table 5.10 Continued

(Categorical) % Household size 1 12.94 2 30.05 3-5 52.58 6-10 4.43 Presence of non-adult children Yes 48.49 No 51.51 Business ownership Yes 18.97 No 81.03 Liquid assets/monthly income ratio <1 51.09 1-3 25.91 3-6 11.12 6+ 11.88 Credit general use opinion Good idea 29.42 Mixed 36.77 Bad idea 33.81 Number of credit cards 0 11.86 1-5 53.93 6-10 25.29 10+ 8.92 Borrowing orientation Long term 98.49 Luxury 0.55 Unexpected 0.96 Unemployed in last 12 months Yes 8.43 No 91.57 Expectation on income growth compared to price Income up than price 24.51 Income less than price 25.43 The same 50.07 Expect to inherit Yes 17.08 No 82.92 Employment status Self-employed 16.33 Worker 70.27 Unemployed 5.58 Retired 7.83 Note: Representative of 42,982,828 U.S. population, using weight x42001. 157 Credit constrained*

Variable Minimum Q-1 Mean Median Q-3 Maximum

Total debt ($) 0 2,100 46,331 16,200 67,000 27,000,000 Annual total debt payment ($) 0 600 8,593 4,680 11,520 5,349,600

Consumer debt ($) 0 550 17,639 7,000 20,540 27,000,000 Annual consumer debt payment ($) 0 100 4,707 2,600 5,520 5,328,000 Total debt service burden 0 0.033 118.9 0.161 0.301 69,760 Consumer debt service burden 0 0.005 66.2 0.081 0.168 44,520 158 Age 18 29 39.0 38 47 85 Family income ($) -49,000 16,000 38,522 30,000 48,000 16,290,000 Liquid assets ($) 0 180 6,824 1,000 3,020 9,111,320 Household size 1 2 2.94 3 4 11 Number of children 0 0 0.99 1 2 8 Number of credit cards 0 0 3.00 2 5 25 Continue

Table 5.11: Descriptive statistics of credit constrained and non-constrained households with T-test and non-parametric test.

Non-constrained** T-test Non-parametric Variable Minimum Q-1 Mean Median Q-3 Maximum p-value test

Total debt ($) 0 0 49,625 11,900 70,000 15,290,000 NS*** 0.07 Annual total debt payment ($) 0 0 8,026 3,647 12,000 17,496,073 NS NS

Consumer debt ($) 0 0 14,230 1,300 14,210 15,290,000 NS <.0001 Annual consumer debt payment ($) 0 0 3,246 250 4,320 17,496,073 NS <.0001 Total debt service burden 0019.4 0.099 0.230 1,407,733 NS <.0001 Consumer debt service burden 0011.3 0.009 0.095 1,407,733 NS <.0001 159 Age 17 39 53 51 67 95 <.0001 <.0001 Family income ($) -1,000,000 17,000 57,787 36,000 65,000 176,900,000 0.02 <.0001 Liquid assets ($) 0 990 26,665 5,000 20,200 34,209,000 <.0001 <.0001 Household size 1 1 2.45 2 3 10 <.0001 <.0001 Number of children 0 0 0.54 0 1 7 <.0001 <.0001 Number of credit cards 01 3.73 3 6 31 <.0001 <.0001 Note: Overall, % of credit constrained households = 28.51; non-constrained households = 71.49. * Representative of 29,233,409 U.S. population, using weight x42001. ** Representative of 73,315,433 U.S. population, using weight x42001. *** NS = non-significant.

All Households Mortgagers (row percent) yes no yes no Age of householder+++/### % % % % Less than 35 48.99 51.01 40.15 59.85 35-44 36.34 63.66 29.44 70.56 45-54 28.33 71.67 24.60 75.40 55-64 17.33 82.67 19.91 80.06 65 and over 4.21 95.79 10.08 89.92 Family Income+++/ $0-8,099 36.14 65.84 32.61 67.39 $8,100-13,999 25.79 74.21 34.47 65.53 $14,000-19,999 31.09 68.91 39.59 60.41 $20,000-25,999 34.96 65.04 39.73 60.27 $26,000-32,999 33.77 66.23 39.06 60.94 $33,000-41,999 36.79 63.21 38.72 61.28 $42,000-52,999 31.88 68.12 30.52 69.48 $53,000-66,999 23.33 76.67 21.46 78.54 $67,000-92,999 18.95 81.05 17.88 82.72 $93,000+ 14.06 85.94 14.99 85.01 Education Level+++/### Less than high school 29.27 70.73 42.82 57.18 High school or GED 29.03 70.97 29.46 70.55 Some college 33.88 66.12 30.49 69.51 Bachelor's degree 26.37 73.63 22.60 77.40 Graduate degree 17.27 82.73 12.48 87.52 Residental status+++ Home owner w/ mortgage 27.22 72.78 NA NA Home owner w/o 10.17 89.83 NA NA Renter 43.68 56.32 NA NA Marital status+++ Married 24.97 75.03 25.87 74.13 Alone (Separated, divorced, widowed) 27.43 72.57 29.99 70.01 Single (Never married) 40.28 59.72 29.31 70.69 Household size+++/### 1 22.18 77.82 23.03 76.97 2 23.71 76.29 19.78 80.22 3-5 35.12 64.88 30.64 69.36 6+ 46.12 53.88 44.03 55.97 Presence of non-adult children+++/### yes 21.94 78.06 33.11 66.89 no 40.50 59.50 21.21 78.79 Continue

Table 5.12 Distribution statistics of independent variables between constrained (yes) and non-constrained (no) households.

160

Table 5.12 Continued

All Households Mortgagers (row percent) yes no yes no Business ownership yes 28.64 71.36 30.13 69.87 no 28.49 71.51 26.24 73.76 Liquid assets/monthly income ratio+++/### <1 40.29 59.71 36.64 63.36 1-3 23.90 76.10 20.08 79.92 3-6 14.74 85.26 14.96 85.04 6+ 6.91 93.09 11.75 88.25 Employment status+++/### Self-employed 26.12 73.88 27.63 72.37 Worker 35.16 64.84 27.46 72.54 Retired 5.70 94.30 8.96 91.04 Not working 34.47 65.53 44.33 55.67 Credit general use opinion+++ Good idea 34.13 65.87 26.30 73.70 Mixed 27.27 72.23 28.05 71.95 Bad idea 25.05 74.95 26.41 73.59 Number of credit card+++/### Zero 38.57 61.43 50.51 49.49 1-5 25.14 74.86 24.25 75.75 6-10 23.03 76.97 21.38 78.62 10+ 26.30 73.70 28.07 71.98 Borrowing Orientation+++ Borrow for Investment 22.59 77.41 27.60 72.40 Borrow for Luxury 38.26 61.74 25.86 74.14 Borrow for Unexpectation 35.36 64.64 26.67 73.33 Expectation on income growth compared to price+++/## Up more than price 40.58 59.42 32.84 67.16 Up less than price 22.41 77.56 23.58 76.42 the same 26.22 73.78 25.84 74.16 Expect to inherit++/### yes 33.36 66.64 45.66 54.34 no 27.77 72.23 25.26 74.74 Unemployed in last 12 months yes 47.88 52.12 26.18 73.82 no 25.91 74.09 27.15 72.85 Note: + significant at 0.05 level, ++ significant at 0.01,+++ significant at 0.001 (all households scenario). # significant at 0.05 level, ## significant at 0.01, ### significant at 0.001 (mortgagers scenario).

161

Credit constrained* Variable Minimum Q-1 Mean Median Q-3 Maximum

Total debt ($) 3,980 46,000 96,979 78,070 119,620 3,591,190 Total debt payment ($) 800 8,520 17,060 12,187 19,333 5,349,600 Total debt service burden 0.004 0.206 241.219 0.291 0.451 69,760

162 Age 19 33 42 41 49 79 Family income ($) -49,000 28,000 52,184 40,000 62,000 6,570,000 Liquid assets ($) 0 500 9,812 1,800 5,500 9,111,320 Household size 1 2 3.40 3 4 11 Number of children 0 0 1.23 1 2 6 Number of credit cards 0 1 4.12 3 6 24 Continue

Table 5.13: Descriptive statistics of credit constrained and non-constrained households with T-test and non-parametric test

(home mortgagers only).

Table 5.13 Continued

Non-constrained** T-test p- Non- Variable Minimum Q-1 Mean Median Q-3 Maximum value parametric test

Total debt ($) 100 40,130 106,889 80,000 130,000 9,694,200 NS*** NS Total debt payment ($) 100 8,040 16,390 13,200 19,800 1,480,574 NS NS Total debt service 1

63 burden 0.000 0.155 35.955 0.226 0.344 20,107 NS <.0001

Age 18 38 47 46 55 91 < .0001 <.0001 Family income ($) -70,000 37,000 81,700 59,000 84,000 176,900,000 < .0001 <.0001 Liquid assets ($) 0 2,170 24,347 6,300 17,330 20,023,000 < .0001 <.0001 Household size 1 2 2.93 3 4 9 < .0001 <.0001 Number of children 0 0 0.83 0 2 7 < .0001 <.0001 Number of credit cards 0 2 4.99 4 7 31 < .0001 <.0001 Note: * Representative of 11,597,248 U.S. population, using weight x42001. ** Representative of 31,385,580 U.S. population, using weight x42001. *** NS = non-significant

Total debt burden Credit constraints tier Yes No Total column % column % column % <30% 253 (14.0) 51.87 895 (49.6) 67.93 63.6 row % 22.01 77.99 30-40% 88 (4.88) 18.07 197 (10.9) 14.93 15.78 30.91 69.09 40%+ 146 (8.11) 30.05 226 (12.51) 17.14 20.62 39.32 60.68 Note: 164 Counts in cells, percentages in the parentheses. bivariate chi-square test p<0.0001 Representing 42,982,828 U.S. population, using weight x42001

Table 5.14: Crosstabulation of total debt service burden tiers by credit constraints (households with mortgage obligations only).

Consumer debt Credit constraints burden tier Yes No Total column % column % column % Zero 225 (2.22) 18.31 1295 (30.08) 42.08 35.3 row % 14.79 85.21 <10% 468 (10.88) 38.15 1048 (24.34) 34.05 35.22 30.88 69.12 10-20% 300 (6.97) 24.47 468 (10.87) 15.2 17.84 39.09 60.91 20%+ 234 (5.44) 19.07 267 (6.20) 8.67 11.64

1 46.72 53.28

65 Note: Counts in cells, percentages in the parentheses. bivariate chi-square test p<0.0001 Representing 102,548,842 U.S. population, using weight x42001

Table 5.15: Crosstabulation of consumer debt service burden tiers by credit constraints (all households).

Consumer debt burden tiers Variables Zero burden <10% 10-20% 20%+ % of total 35.30 35.22 17.84 11.64 (Continuous)1 Age of the householder 56 45 44 43 Family income ($) 47,024 66,553 48,588 31,160 Household size 2.17 2.90 2.74 2.69 Number of credit cards 2.27 4.26 4.10 4.16 Liquid assets ($) 32,166 17,393 11,823 12,188 Note: 1 Mean difference significance test at 0.001 level.

166 Continue

Table 5.16 (a): Distribution statistics of independent variables (continuous) by consumer debt service burden tiers.

Table 5.16 Continued

Consumer debt burden tiers Variables Zero burden <10% 10-20% 20%+ % of total 35.30 35.22 17.84 11.64 (Categorical) Age of the householder2 Less than 35 26.13 35.73 21.07 17.06 35-44 22.65 43.93 21.69 11.73 45-54 25.46 41.02 22.31 11.21 55-64 34.69 38.08 17.23 10.00 65 and over 69.12 17.38 6.37 7.13 Family Income2 $0-8,099 64.06 13.29 4.96 17.69 $8,100-13,999 55.87 21.70 5.10 17.34 $14,000-19,999 42.47 25.14 14.26 18.13 $20,000-25,999 37.82 29.60 19.32 13.26 $26,000-32,999 27.87 33.87 25.32 12.94 $33,000-41,999 27.67 35.52 25.85 10.97 $42,000-52,999 23.23 45.87 22.20 8.70 $53,000-66,999 23.76 40.03 26.45 9.76 $67,000-92,999 24.03 50.14 21.67 4.16 $93,000+ 28.05 55.30 12.39 4.26 Education Level of the householder2 Less than High school 52.96 24.16 13.40 9.48 High school diploma or GED 38.17 32.17 18.13 11.54 Some college 25.57 37.98 21.72 14.73 Bachelor’s degree 26.38 42.22 19.08 12.32 Graduate or professional degree 35.03 44.14 13.43 7.41 Marital Status of the householder2 Alone 45.66 27.69 14.24 12.40 Married 27.98 40.90 20.02 11.10 Single 39.64 31.04 17.39 11.93 Household size2 1 50.77 26.41 13.42 9.40 2 38.68 31.24 18.30 11.78 3-5 23.35 43.11 20.66 12.87 6+ 23.40 49.12 14.72 12.75 Continue

Table 5.16 (b): Distribution statistics of independent variables (categorical) by consumer debt service burden tiers (row percentages).

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Table 5.16 Continued

Consumer debt burden tiers Variables Zero burden <10% 10-20% 20%+ Presence of non-adult child(ren)2 Yes 22.32 43.40 21.17 13.11 No 42.42 30.74 16.02 10.83 Residential Status2 Owner with mortgage 19.93 44.68 23.38 12.01 Owner with no mortgage 56.37 25.34 10.96 7.33 Renter 38.43 31.01 16.19 14.37 Business ownership2 Yes 24.38 40.74 20.80 14.09 No 36.89 34.42 17.41 11.28 Liquid assets/monthly income ratio2 <1 29.48 36.09 21.05 13.39 1-3 25.91 43.53 18.71 11.85 3-6 33.21 40.15 17.25 9.39 6+ 64.11 20.25 7.88 7.49 Employment Status2 Self-employed 27.36 38.56 19.23 14.85 Salary earners 22.86 41.59 22.77 12.78 Retired 67.08 19.55 6.55 6.83 Unemployed 56.33 24.16 9.06 10.44 Credit general use opinion2 For 25.92 39.02 21.75 13.31 Mixed 35.78 35.32 17.59 11.31 Against 42.86 31.83 14.76 10.55 Number of credit card2 zero 60.94 18.33 11.39 9.34 1-5 27.18 41.22 20.03 11.57 6-10 23.23 43.49 19.99 13.29 10+ 19.94 38.96 23.37 17.74 Borrowing Orientation2 Borrow for Investment 35.39 35.92 18.93 9.76 Borrow for Luxury 20.02 42.30 22.53 15.14 Borrow for unexpectation 34.13 34.83 17.53 13.69 Continue

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Table 5.16 Continued

Consumer debt burden tiers Variables Zero burden <10% 10-20% 20%+ Expectation on income growth compared to price2 More 24.71 41.16 19.99 14.14 Same 36.42 47.94 17.74 10.52 Less 42.12 30.17 16.26 11.45 Expect to inherit2 Yes 21.24 44.27 21.08 13.41 No 37.45 33.84 17.35 11.36 Unemployed in last 12 months Yes 35.77 34.99 18.06 11.18 No 31.84 36.92 16.22 15.01 Note: 2 Chi-square test significant at 0.001 level. Representing 102,548,842 U.S. households, using weight x42001.

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Total debt burden tiers Variables <30% 30-40% 40%+ % of total 63.60 15.78 20.62 (Continuous) Age of the householder 46 45 47 Family income** ($) 91,530 54,126 34,265 Household size* 3.10 3.01 2.93 Number of credit cards** 4.85 4.92 4.35 Liquid assets ($) 23,390 14,748 15,624 Note: * chi-square test significant at 0.05 level. ** chi-square test significant at 0.01 level.

Continue

Table 5.17 (a): Distribution statistics of independent variables by total debt service burden tiers (row percentages; home mortgagers only)

170

Table 5.17 Continued

Total debt burden tiers Variables <30% 30-40% 40%+ % of total 63.60 15.78 20.62 Age of the householder** Less than 35 58.75 18.16 23.09 35-44 65.99 16.52 17.49 45-54 66.81 16.02 17.17 55-64 65.65 12.39 21.96 65 and over 49.83 12.61 37.56 Family Income** $0-8,099 13.26 3.87 82.87 $8,100-13,999 17.73 8.18 74.09 $14,000-19,999 24.33 15.66 60.01 $20,000-25,999 41.71 16.74 41.55 $26,000-32,999 44.11 19.81 36.09 $33,000-41,999 57.15 17.09 25.76 $42,000-52,999 63.26 21.47 15.27 $53,000-66,999 66.53 23.00 10.48 $67,000-92,999 81.34 13.77 4.90 $93,000+ 89.21 6.08 4.71 Education level of the householder** Less than High school 53.50 17.94 28.56 High school diploma or GE 64.25 15.99 19.75 Some college 59.00 15.66 25.34 Bachelor’s degree 66.24 14.69 19.07 Graduate or professional degree 73.14 15.90 10.96 Marital status of the householder** Alone 53.54 12.55 33.91 Married 67.79 15.62 16.59 Single 51.67 24.57 23.76 Household size 1 55.68 18.49 25.83 2 62.07 16.10 21.83 3-5 66.73 14.90 18.37 6+ 59.92 16.14 23.94 Continue

Table 5.17 (b): Distribution statistics of independent variables (categorical) by total debt service burden tiers (row percentages; home mortgagers only). 171

Table 5.17 Continued

Total debt burden tiers Variables <30% 30-40% 40%+ Presence of Non-adult child(ren) Yes 65.24 16.44 18.33 No 62.06 15.16 22.78 Business ownership Yes 61.89 16.52 21.59 No 64.00 15.60 20.40 Liquid assets/monthly income ratio** <1 63.21 17.17 19.62 1-3 69.91 14.40 17.70 3-6 66.77 11.48 22.75 6+ 52.90 16.81 30.28 Employment Status** Self-employed 60.06 14.90 25.08 Salary earners 67.54 16.57 15.89 Retired 51.30 10.41 38.30 Unemployed 41.69 15.89 42.42 Credit general use opinion For 59.56 17.13 23.31 Mixed 64.97 15.61 19.42 Against 65.62 14.78 19.60 Number of credit card* zero 56.17 13.54 30.29 1-5 64.53 16.04 19.43 6-10 65.56 15.29 19.15 10+ 62.29 18.55 19.15 Borrowing Orientation Borrow for Investment 63.61 16.00 23.38 Borrow for Luxury 54.34 14.66 1.00 Borrow for unexpectation 42.51 27.45 30.04 Expectation on income growth compared to price More 60.84 18.60 20.56 Same 65.87 15.15 18.98 Less 61.79 14.29 23.92 Expect to inherit Yes 66.86 15.55 17.59 No 62.93 15.82 21.25 Unemployed in last 12 months** Yes 54.30 13.84 31.86 No 64.46 15.96 19.59 Note: * chi-square test significant at 0.05 level. ** chi-square test significant at 0.01 level. Representing of 42,982,828 U.S. population, using weight x42001. 172

Non-zero burden vs. Zero Moderate-high burden vs. High burden vs. Zero-low- (k=1) Zero-low burden(k=2) moderate (k=3) Coefficient exponential Coefficient exponential Coefficient exponential Variables estimate factor estimate factor estimate factor Intercept1 -2.154*** - - Intercept2 - -2.060*** - Intercept3 - - -2.438*** 0.087 Credit constrained 0.836*** 2.308 0.604*** 1.829 0.624*** 1.866 Age of the householder (Reference group=Less than 35) 35-44 0.025 1.025 -0.134 0.874 -0.203 0.816 45-54 -0.008 0.992 -0.081 0.922 -0.206 0.814 55-64 -0.009 0.991 -0.179 0.836 -0.094 0.910 65 and over -0.502** 0.606 -0.410* 0.664 -0.200 0.819 Total annual household income (1997 dollars) (Reference group=Less than $8,100) $8,100-13,999 0.355 1.426 -0.097 0.907 -0.194 0.824

1 $14,000-19,999 0.679** 1.972 0.025 1.025 -0.536 0.585 73 $20,000-25,999 0.673** 1.960 -0.085 0.919 -1.068*** 0.344 $26,000-32,999 0.947*** 2.577 0.072 1.075 -1.117*** 0.327 $33,000-41,999 0.774*** 2.168 -0.159 0.853 -1.591*** 0.204 $42,000-52,999 1.021*** 2.776 -0.456* 0.634 -1.719*** 0.179 $53,000-66,999 0.708** 2.029 -0.476* 0.621 -1.93*** 0.145 $67,000-92,999 0.706** 2.027 -0.852*** 0.427 -2.629*** 0.072 $93,000+ 0.248 1.282 -1.617*** 0.198 -2.946*** 0.053 Continue

Table 5.18 Ordinal logit model results on consumer debt burden tiers (all households).

Table 5.18 Continued

Non-zero burden vs. Zero Moderate-high burden vs. High burden vs. Zero-low- (k=1) Zero-low burden(k=2) moderate (k=3) Coefficient exponential Coefficient exponential Coefficient exponential Variables estimate factor estimate factor estimate factor Educational attainment of the householder (Reference group=Less than high school) High school diploma or 0.116 1.123 0.136 1.146 0.239 1.270 Some college 0.513** 1.670 0.392** 1.481 0.654** 1.922 Bachelor’s degree 0.290 1.336 0.294 1.342 0.662** 1.938 Graduate or professional degree -0.047 0.954 -0.037 0.964 0.374 1.454 Marital status of the householder (Reference group = Divorced/ Separated) Married 0.090 1.094 0.201 1.222 0.145 1.156 Single -0.376** 0.687 -0.229 0.795 -0.448* 0.639

1 Household size 0.062 1.064 -0.062 0.940 -0.002 0.998 74 Presence of children under age 18 -0.113 0.894 -0.016 0.984 -0.060 0.942 Home ownership (Reference group=Renter and other) Owner with mortgage 0.423*** 1.527 0.335** 1.398 0.222 1.249 Owner with no mortgage -0.253* 0.777 -0.114 0.892 -0.234 0.791 Business ownership 0.093 1.098 0.301* 1.351 0.501** 1.650 Liquid assets/monthly income ratio (Reference group = <1) 1-3 -0.023 0.977 -0.143 0.867 -0.010 0.990 3-6 -0.278* 0.757 -0.196 0.822 0.001 1.001 6+ -0.797*** 0.451 -0.375** 0.687 0.034 1.035 Employment status of the householder (Reference group=Unemployed or Other) Self-employed 0.633*** 1.884 0.625** 1.868 0.883*** 2.417 Salary earners 0.911*** 2.487 0.720*** 2.055 0.715** 2.045 Retired 0.329 1.389 0.222 1.249 0.380 1.463 Continue

Table 5.18 Continued

1 Borrowing orientation (Reference group = Borrowing for luxury) 75 Borrow for unexpected -0.058 0.944 -0.012 0.988 0.040 1.041 Borrow for long-term 0.596*** 1.815 0.505** 1.657 0.197 1.218 Income growth expectation (Reference group = Up less) Up more 0.189 1.209 -0.093 0.911 -0.071 0.932 The same 0.088 1.092 -0.032 0.969 -0.087 0.917 Expectation to inherit 0.042 1.043 0.036 1.037 0.052 1.053 Income interruption 0.350* 1.419 0.037 1.037 0.070 1.073 Note: * p<0.05, ** p<0.01, *** p<0.001

Moderate-high burden vs. Low High burden vs. Low- burden (k=1) moderate burden (k=2) Coefficient exponential Coefficient exponential Variables estimate factor estimate factor Intercept1 1.201 3.323 - Intercept2 - 0.850 2.341 Credit constrained 0.664*** 1.943 0.805*** 2.237 Age of the householder (Reference group=Less than 35) 35-44 -0.045 0.956 -0.039 0.962 45-54 0.073 1.076 0.091 1.095 55-64 -0.108 0.897 0.026 1.027 65 and over 0.243 1.275 -0.031 0.970 Total annual household income (1997 dollars) (Reference group=Less than $8,100) $8,100-13,999 -0.206 0.814 -0.715 0.489 $14,000-19,999 -0.674 0.510 -1.272 0.280 $20,000-25,999 -1.764* 0.171 -2.548*** 0.078 $26,000-32,999 -1.647* 0.193 -2.617*** 0.073 1

76 $33,000-41,999 -2.265** 0.104 -3.212*** 0.040 $42,000-52,999 -2.618*** 0.073 -3.8*** 0.022 $53,000-66,999 -2.822*** 0.059 -4.221*** 0.015 $67,000-92,999 -3.529*** 0.029 -4.878*** 0.008 $93,000+ -4.724*** 0.009 -5.655*** 0.003 Educational attainment of the householder (Reference group=Less than high school) High school diploma or GED 0.400 1.492 0.688 1.989 Some college 0.831** 2.296 1.293*** 3.645 Bachelor’s degree 0.845** 2.328 1.328*** 3.773 Graduate or professional degree 0.736* 2.088 1.007** 2.738 Continue

Table 5.19: Ordinal logit model results on total debt burden tiers (home mortgagers only).

Table 5.19 Continued

Moderate-high burden vs. Low High burden vs. Low- burden (k=1) moderate burden (k=2) Coefficient exponential Coefficient exponential Variables estimate factor estimate factor Marital status of the householder (Reference group = Divorced/ Separated) Married 0.130 1.139 -0.093 0.911 Single 0.163 1.177 -0.504 0.604 Household size 0.040 1.041 0.100 1.105 Presence of children under age 18 0.033 1.034 -0.106 0.900 Business ownership 0.190 1.209 0.101 1.106 Liquid assets/monthly income ratio (Reference group = <1) 1-3 0.120 1.127 0.345 1.412 3-6 0.229 1.257 0.596* 1.815 6+ 0.546** 1.726 0.804*** 2.235 Employment status of the householder (Reference group=Unemployed or Other)

177 Self-employed -0.089 0.915 0.558 1.748 Salary earners -0.593 0.553 -0.445 0.641 Retired -0.312 0.732 0.266 1.305 Credit general use (Reference group = Against) For 0.395** 1.484 0.386* 1.470 In-between 0.074 1.077 0.078 1.081 Number of credit cards 0.049*** 1.050 0.051** 1.052 Borrowing orientation (Reference group = Borrowing for luxury) Borrow for unexpected 0.049 1.050 0.058 1.060 Borrow for long-term -0.200 0.819 -0.447 0.640 Income growth expectation (Reference group = Up less) Up more 0.249 1.283 -0.041 0.960 The same 0.019 1.019 -0.107 0.899 Expectation to inherit -0.148 0.862 -0.061 0.941 Income interruption -0.390 0.677 -0.435 0.647 Note: * p<0.05, ** p<0.01, p<0.001

CHAPTER 6

CONCLUSION AND IMPLICATIONS

6.1 Overview on Household Debt Service Burden

Throughout the paper, household debt status is gauged by utilizing a quantified household debt burden --- debt repayment to income ratio. This measurement, framed on a yearly basis, captures the repayment made during a year period on various loans as relative to the contemporary gross income. Resorting to the self-reported financial details on households that were included in the 1998 SCF data, complex calculation was conducted to cover all the possible loan types and to streamline the debt burden concept.

The household debt burden was examined with the total household debt and the consumer debt considered individually. Separation of the consumer debt from the total household debt was acknowledged due to the uniqueness of home mortgage as a dominant portion of the total debt, but only existing for a certain percentage of the whole population. Meanwhile, households with mortgage obligations were singled out for further analysis as well. Overall, reasonable statements can be made for the households sampled in the 1998 SCF data as follows. The median total debt service burden remained fairly moderate (0.12 or 12%) against the suggested guidelines, 30% and 40%. Home mortgagers alone maintained a much higher actual total debt service burden (0.245 or

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24.5%), a level that should not be considered as alarming according to the threshold. The consumer debt service burden for indebted households was approaching the lower guideline of 10% (0.085 or 8.5%). When including households who did not borrow, consumer debt service burden for all the households was naturally lowered to 0.028 or

2.8%, the lowest figure of all. It is important to emphasize, in light of the asymmetric and skewed distribution of various debt burden measures, that all the above statistics are reported at the median values.

Percentage-wise, looking at the distribution of debt service burden, there was some indication that the debt burden in U.S. households may not be of serious concern.

Using the suggested ratio thresholds as the cutoff, the distribution of debt service burdens was collapsed into tiers, representing the degree of the burden from zero, low, moderate to high. Taking the service burden for all types of consumer debt, the guidelines suggested a safe line at 10% and a less safe line at 20%. Thirty-five percent of the households were at the zero burden tier39, another 35% were below the 10% lower threshold, up to 18% of the households had a moderate service burden (10-20%), and approximately 12% were highly burdened. In the same fashion, when it comes to total debt service burden among home mortgagers, 64% were in the low burden tier (<30%),

16% in the moderate tier (30-40%) and the remaining 21% were in the high tier (>40%).

Nonetheless, when credit constraints were taken into account, the distribution of households within each service burden tier differed dramatically between constrained and

39 This is not equivalent to saying that these households were not indebted. Because the dataset only recorded a year-period snapshot, implications could only be made conditionally.

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non-constrained groups. A smaller percentage was found in the zero burden tier and larger percentages were found in the low to high burden tiers in credit-constrained households than those of non-constrained households. A similar pattern was found in home mortgagers, where a larger percentage of constrained households had a total debt service burden above 30% than did non-constrained households. To some extent, even though credit-constrained households comprised a small portion of the population, their debt service burden seemed to assume a large number.

6.2 Conclusion on Credit Constraints

The previous work has disclosed that credit constraints are sustained as a vital factor behind the household consumption and saving phenomenon. Given the presumed association between consumption and saving (or dissaving), this study of household debt acquisition behavior took into consideration the existence of credit constraints. Based upon the criteria of Jappelli (1990), the prevalence of credit constraints in American households was examined. Overall, credit-constrained households, those who had had requested loan applications denied, represented over 20% of the U.S. population. The percentage of the credit-constrained households among all American households was consistent with the findings in the previous research studies (e.g., Hall & Mishkin, 1982;

Mariger, 1987). If loosely defined, as in this paper, the scope of the credit-constrained households also includes those who did not go through the application process because of the concern for an unpleasant outcome. By that definition, a higher percentage, nearly

29%, in all, were credit constrained.

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When it comes to the debt level and payment amount, constrained families differed in some degree from their non-constrained counterparts. The two groups of households had incurred a similar amount of indebtedness. However, constrained households seemed to have paid out more in the annual payments than non-constrained households, both in total debt and consumer debt items. On the other hand, constrained households earned less annual gross income and possessed less liquid assets. The two components combined resulted in a higher debt burden (both total debt and consumer debt) for constrained households. Other demographic characteristics of the two groups also demonstrated variation between them. Credit constraints are likely to occur among households with one or more of the following characteristics: young individuals, large size, with adolescents living-in, headed by a less-educated individual, with a single, unemployed, and optimistic householder. These implications stand true among mortgagers as well.

After segregating the debt service burden into tiers (both consumer and total debt), constrained households were more likely to be found in a higher burden tier than a lower tier, whereas non-constrained households were less likely found in a higher burden tier. The relationship was significant at 0.001 level. To note, the bivariate association does not necessarily present the causal effect of the constraints factor on the debt tiering.

It could also be argued, to some extent, that households that sustained a higher burden could easily fall into the situation of being credit constrained. The question on credit constraints asked about the respondent’s encounter with credit constraints during the past five years, and the debt burden was computed based on the current debt position.

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Therefore, it is assumed that the credit constraints pre-existed at least in timing prior to the debt burden accumulation, according to the questions in the 1998 SCF data, from which the variable of credit constraints and the debt burden were derived. On the other hand, if the debt burden being high or low was treated as an indicator of being credit constrained or not, it should be the debt burden upon the credit application that is of interest to see the predictiveness, not the current level. In view of all these assumptions, credit constraints were used as a predictor in the multivariate model.

6.3 Discussion on Multivariate Models

To test the effect of all the hypothesized independent variables simultaneously, multivariate models were built and interpreted. A proportional odds model was initially proposed to model the categorical burden tiers in one simple equation, with the assumption that the independent variables impose a proportional increasing or decreasing effect on the relative odds. Unfortunately, the attempt to use such a model was rejected based on a Score test. Instead, multiple formulated equations were run and their respective goodness-of-fit was tested. Use of cumulative logistic function to predict the probability of one event as opposed to the combined other events was assumed. All the analytical procedures followed the repeated imputed inference (RII) technique to suit the multiple imputed SCF datasets.

In the models that focus on the consumer debt burden tiering, credit constraints were found to be a significantly positive force in the probability of households in the higher debt tier(s) versus the lower tier(s). The magnitude of the effect of credit

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constraints decreases from the modest contrast (burden vs. zero burden) to the serious contrast (high burden vs. others). That is, credit constraints had a more profound effect in differentiating those who were burdened and those who were not than in differentiating between those heavily burdened and otherwise, keeping everything else constant. Such a finding, in a way, deviates from what has been expected: due to credit constraints, these households are not successful in acquiring the desired loan, which could lead to a debt payment lower than it would otherwise be, thus resulting in an actual lower debt service burden. From what is observed, credit-constrained households, in fact, had larger odds of sustaining a higher debt burden than non-constrained households. Needless to say, credit- constrained households also had larger odds of sustaining a burdened position (versus not) than non-constrained ones. One possible way of exploring the apparent contradiction is from the numerator of the dependent variables, the annual debt payment. The effect of credit constraints on the several logits is seen after controlling for all the other independent variables. Hence, when household income is controlled, a high debt burden is driven purely by a large annual debt payment. The finding that larger odds occur to credit-constrained households in a high burden tier implies that these households faced a larger annual debt payment than the non-constrained households. Credit-constrained households may not succeed in borrowing as much as non-constrained households, but they make repayments larger than their counterparts. This presents an issue: constrained households may have paid on a higher price of the loans, that is, these households are charged with a higher interest rate than non-constrained households.

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Meanwhile, keeping other factors the same (including credit constraints), household income presents a non-linear relationship with the probability of a household’s falling into an indebted or high burden position. Higher income households have higher odds of owing debt, but lower odds of having higher burden, as opposed to lower income households. The magnitude (absolute value) of the negative parameter estimators of household income in the relative odds comparison increases with the seriousness of the burden tiers contrast. That is, household income had the largest negative coefficients in the model of a high burden versus a non-high burden among all three models. Setting aside the experience of being credit constrained, it is probable that high income provides the household with a solid payback resource and a relatively low debt burden level. The effect of household income is consistent with the hypothesis and some of the former researches (e.g., Godwin, 1999; Liao, 1994).

Education does not impose a strictly linear effect in the logit. Instead, it was the group of householders with some college experience who were significantly different from the other education groups in terms of the odds of falling into the relatively higher debt tiers (as opposed to the lower ones). Households headed by someone with a bachelor’s or a graduate/professional degree were in a similar debt burden position as those with or without a high school diploma, everything else being constant, though a head of household with a bachelor’s degree could have the largest odds of being in the worst burden level comparison (high burden vs. others). An expected positive relationship between education and the burden tier was not verified through the analysis.

The analysis does somewhat suggest the mixed impact of years of education. Apparently,

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households headed by a highly educated individual make different decisions in the amount of household debt acquisition or debt repayment relative to their household income. Single-person headed households had less propensity to be indebted or in a higher burden tier than the divorced/separated/widowed singles, if they have the same characteristics in other aspects. Being married does not necessarily lead to a high or a low debt burden compared to a single household if the households were quite similar in other ways. Not surprisingly, households with mortgage payments were inclined to have a higher (consumer) debt burden than households who rent. This is within expectation. On one hand, homeowners build up their assets from their house; on the other hand, expenses on the house, such as improvement, maintenance and equity loans, may result in the owner’s additional financing. None of this would be an issue for home renters. A household’s involvement in its own enterprise remains, as expected, a positive factor in its likelihood to be in a higher burden tier.

A household’s earning power and capability is again proved to be the vital element in household debt burden. Being employed, regardless of the employer being self or others, could become financial assurance to the household themselves, as well as to the lenders. Everything else being the same, workers with a constant income stream are more likely to seek more debt and to be granted it, thus falling into a higher repayment burden.

As hypothesized, households with relatively more liquid assets holdings to income have smaller odds to be highly burdened than households otherwise. In a way, liquid assets function as a buffer to support household consumption, thus mitigating the exposure to external financing. The inclination to use credit for consumption has a limited effect on

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household debt burden tiering. Households holding positive views about credit have larger odds of being in the relatively higher debt tiers (as opposed to the lower ones) compared to the credit averse. This psychological variable echoes findings in some of the early works. An individual’s psychological evaluation often influences his/her behavioral outcome. In this case, a positive opinion on credit encourages the means of borrowing for consumption, and is likely to lead to an excessive burden of debt repayment. In addition, another variable that captures the household’s acceptance of credit use, number of credit cards, emerges as a significantly predictive factor throughout all the relative tier odds comparisons, even though the magnitude of its positive effect is miniscule. A householder’s other borrowing preference turns out to be predictive as well. Households who tend to borrow for investment, e.g., for automobile or education, have larger odds of being in a higher burden tier (only in the consumer debt burden model) than those who would borrow for luxurious consumption, e.g., travel or expensive merchandise. The reason behind this is that unlike car loans or education loans, luxury items may be more prevalent among wealthy households, who are capable of the borrowing and the repayment, so that repayment does not consume a big portion of the household’s income.

Education and/or investment sometimes appear as an indispensable part of household consumption for an average household.

One variable expected to have a strong effect based on theory was not found significant. It is the expectation factor as proposed. The expectation variables relied upon the answers of the household head to the questions in the 1998 SCF data, such as “do you expect income to go up more than price, or up less, or the same next year?” The measure

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is a fairly subjective judgment. Presumably, households who expect income to grow in the next period should be rational to borrow in this period; however, lenders might not run the risk of trusting such an expectation and finding themselves to be wrong later.

Lenders neither care to collect this information from the applicant, nor evaluate it the same as other financial information. Hence, in the model, when credit supply (credit constraints) is controlled, debt severity does not depend heavily on the future expectation as described in the theoretical models. Additionally, the householder’s age, household size, the presence of non-adult child(ren), along with personal layoff encounter in the past year were not contributive to the models either. A summary comparison between the hypothesized variable effect and the multivariate analysis results is tabulated in Table 6.1.

Credit constraints take a considerable weight among all variables in each of the cumulative logit models, as measured by the relative chi-square value. However, with the increasing seriousness of burden tier comparisons (from k=1 to 3), the weight of credit constraints becomes weakened, as compared to variables such as household income and the number of credit cards. In other words, being credit constrained or not helps to differentiate a household’s being a borrower from being a non-borrower, or a moderately burdened borrower from a low burdened borrower, better than it flags an overly burdened borrower from other borrowers. In the latter case, household income and the number of credit cards seem to be more powerful indicators.

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Hypothesis Results debt consumer debt total debt Variable burden burden burden

Credit constraints - + +

Demographics: Age - or curvilinear ns ns Education + curvilinear curvilinear Married + ns ns Household size + ns ns With children + ns ns Economic: Family income - or curvilinear curvilinear - Liquid/income ratio - - + Homeownership + curvilinear na Employment status + + ns Business ownership + + ns Sentiment and behavior: Positive on credit use + + + Number of credit card + + + Borrow long-term + + ns Expectational factor: on future income growth + ns ns on future inheritance + ns ns Family Events Income interruption + ns ns Note: -: negative; +: positive; ns: non-significant; na: not applicable.

Table 6.1: Comparison summary on variable effect on household debt burden level between hypothesis and results

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In the models that concentrate on total debt burden, two cumulative logit models were run for households who reported house mortgage payments; one (k=1) models the logit of total debt burden over the 30% threshold versus below, and the other (k=2) models the logit of total debt burden over the 40% threshold versus below. As with the consumer debt burden models, a parallel relationship between the independent variables and the logits was rejected according to the Score test. Compared to the case of the consumer debt burden models, similarity was seen in terms of the significance tests and the coefficient signs in some variables, but large differences existed as well, especially in the number of insignificant variables.

Only credit constraints, household income, householder’s education, liquid assets to monthly income ratio and the number of credit cards turned out to be significant in either of the models. Most signs of the estimated coefficients are consistent with those of the consumer debt burden tier models. However, a positive effect of liquid ratio was found, opposite to the hypothesis and the findings in the consumer debt models. Among households with mortgage obligations, the more liquid assets relative to household income the household has, the more likely they are to be highly burdened. On one hand, households can borrow against the asset value that has accrued from their assets. On the other hand, regular mortgage payments may impose a non-trivial burden to them already.

With relatively small liquid assets as backup, households may become conservative in their debt acquisition.

The finding that there were fewer significant variables predictive to the household total debt tiering than those to the consumer debt tiering implies that homebuyers with

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mortgage obligation could be more homogenous in their characteristics and attributes than the entire sample as in the consumer debt burden models. Variables such as the amount of financial assets, behavioral tendency or expectational factors are not as saliently important as other variables, which matches the findings in the consumer debt burden models. Table 6.1, again, generalizes the consistency and inconsistency between the theory hypothesis and the final findings.

6.4 Implications

While the U.S. economy was going through unprecedented growth at the time the survey was conducted, the sharp accumulation of household debt and moderate payment problem were noted as well. By relying upon a measurement of debt commitment relative to the flow of resources (i.e., household income), the indebtedness of U.S. households has come to be seen with more clarity throughout the paper. The original research questions were established from both credit demand and supply perspectives, and more reflections are given back to the ‘two faces of the mirror’.

The factor of credit constraints significantly determines a household’s positioning against the proposed debt burden thresholds. On one hand, discreet credit granting mitigates the default risk for the lenders; on the other hand, it plays the role of protecting the households from potential overextension. From the credit supply standpoint, the credit decision is dependent upon the applicant’s creditworthiness, which is largely projected from past experiences. A rejected loan application (or a counter-offer) may arise from several major reasons: unsatisfactory credit history, including but are not

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limited to derogatory records or bankruptcy, insufficient income and/or insufficient customer experience40. Naturally, insufficient income is often experienced in households headed by young individuals, for example, students or those who have just started working. Insufficient customer experience sometimes could occur with immigrant households, who have not built up a credit history within the United States. As reported in the 1998 SCF, households may have a prior suspicion that they will be perceived as

‘bad’ debtors in the eye of lenders; therefore, they simply do not take the chance of facing a possibly unfavorable outcome. Nonetheless, in order to maintain a desired living condition, these literally constrained households who have had no luck with the mainstream lenders may well turn to other types of creditors in the market, such as sub- prime lenders or consumer finance companies.

The birth of sub-prime lenders suits the borrowing needs of these so-called ‘risky’ households. In some ways they even aggressively target such a niche in the lending market. Their tactics to deal with these rejects from the prime lenders are simply a risk- based pricing strategy, which basically translates into the matrix of ‘high (low) risk - high

(low) interest rate’. Sub-prime customers will be quoted at a higher interest rate (if not much higher) than from a prime lender when everything at stake is the same. By so doing, sub-prime lenders intend to offset any future loss due to a possible event of default or worse from their customer’s principal and interest payments. Constrained households

40 As an industry norm, lenders are required to provide the loan applicant with the reasons that his/her application is rejected or counter-offered. The maximum number of reasons is four. These reasons most likely come from the credit bureau upon checking, or the creditor himself.

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may be desperate for financing, but only find themselves being accepted by sub-prime lenders. With no other alternative solutions, constrained households may well be paying more than those who are not constrained for exactly the same amount of credit. Thereby, given two households with the identical amount of loans and household income, the constrained household could end up with a higher debt burden than the non-constrained household.

Predatory lending has lately been received much attention, because these lenders aggressively target low credit score, credit risky households. Households who either were rejected by the prime lenders or who assumed they might be rejected are welcome by these lenders. Even for some households who have a high credit score and who would have been accepted by a prime lender, predatory lenders will still charge the borrower an overpriced interest rate to make more profit. In this sense, a high debt burden for these households would be outrageous, and these households will have less left from the repayment to spend on other life consumption than they would otherwise. Even though the argument is that credit constraints are probably inevitable in society, it should not become a penalty to these households so that they are paying a higher price than those who are not constrained.

Earlier works reviewed in Chapter Two have identified some determinants of U.S. household credit constraints. Information from household profiles such as gross income and/or occupation contributes, in part, to the lenders’ judgment on the household’s earning power or repayment capability. Nevertheless, it is the individual’s credit performance that lenders set most weight on or, in short, the credit score that lenders

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evaluate. Supposedly, the score condenses a person’s former credit history quantitatively and becomes a predictor of the person’s future behavior. Therefore, in order to offset the negative impact of credit constraints to household debt burden, the awareness of credit score should be enhanced in terms of its elements, significance and role in consumer life, as well as cautions and steps to maintain reasonable credit, by all means.

Such educational efforts should involve commitments from the academia, the financial sector, government agencies and other profit (e.g., media) or non-profit organizations (e.g., credit counseling). An early start in such education would have a long-term impact, for example, starting classes in high school. Credit card issuers have been notoriously expanding their share among college students, most of whom have little knowledge about how to establish a sound credit history but indulge and swamped in the

‘easy money’ (of note, here, the interest rate for the students could be outrageously high).

Lenders, especially the prime lenders, need to be encouraged to disseminate information to their existing or prospective customers for the sake of both better servicing their customers and improving their portfolio performance. While the business’ gesture may seem like attracting clients, government agencies, such as the Fed and non-profit organizations would be the candid, reliable, neutral suppliers for consumer credit related topics. The media, on the other hand, is another important forum for public awareness and knowledge enhancement.

The next perspective is to set aside the credit supply, that is, to keep credit constraints constant and look at the credit demand in association with debt burden. Since the independent variables selected in this paper represent typical facets of a sophisticated

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household profile in the life cycle, inferences can only be derived within such a scope.

Obviously, household resources stand as the uttermost important factor for determining household debt burden level, coupled with employment status. Since household debt burden is a relative measure in conjunction to household income, if the household is confronted with a deprivation of their income, partially or entirely, the debt burden severity will be altered dramatically. This supposition has been witnessed lately in

America. Millions of households experienced financial difficulty when the nation’s economy headed into recession and workers experienced layoff. The overturn of the economy has brought households into a situation where they used to have affluent income and the confidence in a booming economy to justify their borrowing needs. Now, they have to juggle the suddenly lessened income (if their stock assets were not all gone due to the bubble burst) to service various debts. The mishap has hit every walk of life and the impact would be more serious to the middle-class households than to others, say, the low-income households. The last economic cycle indulged the middle-class households with many incentives for financing. Homeowners took out home equity loans, home equity lines of credit; many of them opened store/credit cards that were ‘pre- approved’. As the debt kept stacking up, however, suddenly they found themselves out of work or not earning as much as they used to, and the debt burden become overwhelming.

The New York Times Kilborn (2002) reported a historical high of (home) foreclosure rate of 0.4% across the states over the past thirty years. To conclude, the macro-economic cycle and trend that impacts the labor force, the stock market and the national economy is also an overarching determinant to a micro-household’s financial outlook. As

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unpredictable and uncontrollable as the external environment is, households need to take corrective measures when debt repayment becomes a concern. Households need to consider cutting back regular large expenses for a better allocation of resources, prioritizing their payments (in particular, the debt repayment), and possibly negotiating with creditors for a feasible plan to pay off the outstanding balance. Turning back to debt burden level, had households kept a low or moderate burden, they would feel less distress and more leeway in handling the repayment once hardship visited.

The same time debt service burden is examined as a relative yardstick on a household’s financial status, this leads to another relative concept in the household consumption-saving arena, overspending. In most overspending literature, spending/expenditures in comparison to income in a given time becomes the focal interest where spending can be financed from current income, by credit or by withdrawal of saving (Bae, Hanna & Lindamood, 1993, p. 14). Overspending occurs in households when the ratio of spending to income exceeds 100% (Lytton, et al., 1991; Bae, et al.,

1993). Routine debt payment is considered as part of spending, along with other expenses through income or assets, or both. If the ratio of debt payment to income should not exceed a certain level, e.g., a maximum of 40%, the remaining spending to income could only be as large as 60% in order not to be overspending. Our probe into debt payment as a percentage of gross income limited the grasp of overall household resource allocation, especially when expenditures/spending represent concurrent and competing outgoing monetary allocation. From this perspective, by understanding the interrelationship between the two major components of household spending as defined, households would

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be in a more controlling position with a dynamic sense of resource management. In financial planning, households would be better off to lay out their budget in advance in terms of the allocation of their resource intakes, to foresee the relative sizes of the spending or burden on different ventures. In the interim of repaying the loans, households should consider the fact that loan payments are scheduled and fixed in their distribution time and amount. If at one point, the debt service becomes a burden, households could adopt some possible remedies in their other spending: cut back their normal life expense if feasible, and/or forbid themselves from further credit seeking for consumption goods.

On the contrary, households who spend little or moderately in debt payment may not necessarily be free of trouble; instead, if their overall spending exceeds their income capacity, they are likely to face a different scenario of financial stress, draining their income or assets gradually, which would negatively affect their well-being in the event of an emergency or in the long run.

Moreover, as far as the analysis and techniques used in the dissertation are concerned, there are some issues that might be given additional attention. As pointed out in the early chapters, the factor of credit constraints entered the model as an observed incidence. No direct prediction on the likelihood a household’s of being credit constrained was conducted in light of the lack of predictive consumer credit characteristics in the SCF data. For example, the credit score of the householder, the creditor’s payment (default) history, etc. are conventionally dominant factors a lender use to gauge the creditability of a loan applicant in the lending industry. To model the credit constraints based simply on household demographics available in the SCF could capture

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a fraction of the whole picture a lender would assess. Nevertheless, in future research, attempts could be made to model the constraints factor with some proxy variables generated from the survey data and then bring it in as an independent variable, so to resolve the controversy of its being an exogenous versus an endogenous variable.

6.5 Limitations

One limitation in this paper exists in the calculation of annual debt repayment of the sampled households in the 1998 SCF, in particular, the annual payment on credit card balance. Assumption is made that the credit card outstanding balance is the full amount the household needs to repay within one year, plus an interest payment based on a typical credit card APR. This specification may have overstated the actual debt burden of those households, because households may pay off a modest portion of the balance at a time, leaving the interests to accrue over the years. By all means, the observed U.S. household debt burden status was not as serious as initially thought.

In the calculation of debt service payment for each household, repayments come from the reported figure of the regular payment and the frequency from households, which was the only information available in the 1998 SCF to be used to aggregate the annual payment. However, some households may pay at a premium interest rate, and thus make a large repayment due to their constrained situation. It would be beneficial to look at the interest rate at which the debt repayment is accrued. By doing so, more attention could be paid to the credit constrained households and the pattern of their payment with a lower/higher interest rate and less/more repayment than their non-constrained

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counterparts, though, the issue is not terribly serious because the variation of interest rates may have reflected the borrower’s characteristics. As far as the debt service burden is concerned, an independent variable of interest rate may be redundant to the other types of independent variables, which already comprehend the household’s characteristics.

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APPENDIX A

The following descriptions are taken from “score statistics and test”, the section on “PROC LOGISTIC” (SAS Institute Inc., 1999). A null hypothesis versus an alternative hypothesis testing of a proportional odds assumption in a logistic regression model is carried out through a Score test. In this test, the response variable is an ordinal variable, which normally has more than two values. Let Y be the response variable, taking values 1, …, k, k+1. Suppose there are s explanatory variables. The general cumulative model without making the parallel lines assumption is

≤ = ′ γ ≤ ≤ g(Pr(Y i | x)) (1, x ) * i ,1 i k

γ = α β β ′ where g(.) is the link function, and i ( i , i1,..., is ) is a vector of unknown parameters consisting of an intercept. Under the null hypothesis of parallelism

β = β = = β ≤ ≤ H 0 : 1m 2m ... km ,1 m s, there is a single common slope parameter for each of

β β the s explanatory variables. Let 1 ,..., s be the common slope parameters. Let

α α βˆ βˆ ˆ1 ,..., ˆ k and 1 ,..., s be the MLEs (maximum likelihood estimator) of the intercept

γ parameters and the common slope parameters. Then under H 0 , the MLE of is

γ = γ ′ γ ′ ′ γ = α βˆ βˆ ′ ≤ ≤ ˆ0 ( ˆ1 ,..., ˆk ) with ˆi ( ˆ i , 1 ,..., s ) ,1 i k

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′ γ −1 γ γ and the chi-squared score statistics U ( ˆ0 )I ( ˆ0 )U ( ˆ0 ) has an asymptotic chi-square distribution with s(k-1) degrees of freedom. This tests the parallel lines assumption by testing the quality of separate slope parameters simultaneously for all explanatory variables.

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APPENDIX B

The following are some formula of the statistics that appeared in this paper, some of which are discussed in chapter five. These mathematical equations are basically the

Appendix part in Montalto & Yuh (1998).

The best estimate of the non-linear regression coefficients is the average of the coefficients estimated from each of the five implicates (m=5) where Qi is a 1*k parameter vector (k is the number of independent variables excluding the intercept term).

− ∑Qt Q = t=1 (1) m m

The within imputation variance is the average of the variance-covariance matrices from the five implicates where Ui is a k*k matrix.

− ∑U t t=1 U m = (2) m

The between imputation variance is the sample variance in the estimates of Qi from the five implicates and is estimated by

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m − − − − ∑(Qt Q m )`(Qt Q m ) B = t=1 (3) m m −1

The transpose of the vector is indicated by `.

The total variance-covariance matrix is given by

− = + + −1 Tm U m (1 m )Bm . (4)

The familiar Wald statistics for testing individual coefficient equal to zero is computed by

− 2 2 (Q ) χ = m . (5) Tm

The test statistic for the model overall significance follows an F distribution with k and (k+1)v/2 degrees of freedom,

− d m −1 m − r ^ k m +1 m D m = (6) + 1 rm

− χ 2 where d m = average of the statistics from the models fitted for each implicate,

− −1 −1 (1+ m )Tr(B U m ) r = m (7) m k where Tr(.) is the sum of the diagonal elements of the k*k matrix. Then

= − + −1 2 v (m 1)(1 rm ) (8)

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