Three Essays in Consumer Finance: Debt Stress, Payments, and Student Loans DISSERTATION

Transcription

1 Three Essays in Consumer Finance: Debt Stress, Payments, and Student Loans DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Hyounjin Yi, M.A. Graduate Program in Economics The Ohio State University 2010 Dissertation Committee: Professor Lucia F. Dunn, Advisor Professor Stephen R. Cosslett Professor Gene E. Mumy

2 Copyright by Hyounjin Yi 2010

3 Abstract My dissertation consists of three essays. The first essay examines the relationship between household debt and consumption growth. I construct a household Debt Index based on nationally representative household level data. The Debt Index consists of four components which include debt-income ratio, debt-asset ratio, number of missed payments and required monthly payment-income ratio. I test whether the Debt Index has predictive power for consumption growth and find that it does not show predictive power. The second essay investigates the choice of payment method. Since credit card debt comes with a high interest rate, those who carry revolving credit on their credit cards face higher costs when they pay with a credit card than when they pay using other payment methods. This essay examines whether or not cash, check, and debit card use correlate with interest rates on credit cards applied to credit card revolvers. The findings indicate that among three non-credit card payment methods, only check use shows positive correlation, while cash and debit card use do not show any correlation with the interest rates on credit cards. This can be interpreted to mean that check users are more sensitive to pecuniary costs in payment than cash and debit card users. The last essay considers how education loans impact the early labor market behavior. Among several aspects of early labor market behavior, I investigate two issues. ii

4 First, I investigate the transition time from school to work of young college graduates. Unlike a standard job search model, I follow Danforth s (1979) framework which assumes utility maximization through consumption. The prediction from his model is that people who have less wealth take a shorter time to find their job than those who have more wealth. Since education loan is negatively correlated with asset, education loan is used instead of asset level. Estimation results from Cox proportional hazard model show that those who have positive amounts of education loan take less transition time from school to work than those who do not have an education loan. When I examine the impact of an education loan on each gender, both genders show shorter transition time from college to work if they carry an education loan. However, the magnitude of the impact of an education loan on the transition time to one s first job shows a gender difference. For women, the difference in the time-to-first-job between those who have a positive amount of education loan and those who do not have an education loan is large. The difference in the time-to-first-job between people with an education loan and people without one is also observed in men, although the difference is smaller than that in women. Second, I examine the impact of educational loans on the tenure of the first job of college graduates. Using the survival analysis method, I find that the impact of education loans on the tenure of one s first job shows gender difference. Female workers with a positive amount of education loans show shorter tenure than female workers without education loans. Contrary to female workers, male workers do not show any difference in their tenure at their first job by education loans. iii

5 Dedicated to my parents iv

6 Acknowledgments First and foremost, I wish to express my appreciation to my advisor, Professor Lucia Dunn. After reading her papers on Consumer Finance, I became interested in Applied Microeconomics. Without her support, I could not have even started my research in Applied Microeconomics. She helped me become familiar with household level data. Her insights and comments guided me as I made progress in my research, particularly when I was stuck. I also would like to express my special thanks to Professor Stephen Cosslett for his advice on some problems that I had with Econometrics. When I took his class on Econometrics Theory, I became interested in Econometrics methods, and used them in this research. In addition, he made valuable suggestions whenever I asked for help, even though I asked pretty naïve questions. In addition, I want to extend my appreciation to Professor Gene Mumy. As one of my dissertation committee members, he gave me useful feedback that helped to refine my research. I also wish to thank Professor Randy Olsen. Owing to him, I was able to easily access the data sets, CFM and NLSY79, and learned a lot about data management. v

7 My special thanks go to my parents and my husband. They encouraged me when I was not sure whether I could complete my studies toward a Ph.D. Without them, I could not have continued in the doctoral program for the last five years. I am grateful for their patience and love. vi

8 Vita December Born Incheon, Korea B.A. Economics, Korea University M.A. Economics, The Ohio State University Fields of Study Major Field: Economics vii

9 Table of Contents Abstract... ii Acknowledgments... v Vita... vii Table of Contents... viii List of Tables... xii List of Figures... xiv Chapter 1: Introduction... 1 Chapter 2: The Effect of Household Debt on Consumption Introduction Previous Research Overview of Debt Measures and Description of the CFM Construction of the Debt Index Empirical Results Conclusion Chapter 3: Credit Card Balance and a Consumer s Payment Choice viii

10 3.1. Introduction Related Literature Data Description and Summary Statistics The Empirical Model Estimation Results Estimation Results from only Demographic Variables Estimation Results from Convenience Users Estimation Results from Credit Card Revolvers Limitations of the Study Conclusion Tables Chapter 4: The Effects of Education Loans on the First Job Market Behavior of College Graduates Introduction Data Description Part A: The Effect of Education Loans on the Transition Time from College to Work Previous Research Descriptive Statistics ix

11 Theoretical Framework The Empirical Model Estimation results Tests of comparison of transition time from school to work The Kaplan-Meier Survival Curve The Hazard Curve Test of Cox Proportional Hazard Rate Assumption Cox Regression Results Baseline Hazard Estimates Ordinary Least Squares Estimation Summary Part B: The Effect of the Education Loan on the Length of First Job Background and Previous Research Summary Statistics Empirical Model Survival Estimates and Hazard Curves Test of the Cox Proportional Hazard Rate Assumption Accelerated Failure Time Model Estimation Results x

12 Choice among Three Parametric Models Estimation Results Baseline Hazard Estimates Summary Conclusion Figures and Tables Bibliography References I References II References III Appendix A: Survey Questions for Constructing the Debt Index Appendix B: Survey Questions for Bill Payment in Consumer Finance Monthly xi

13 List of Tables Table 1. Components of the Debt Index Table 2. Explanatory Power of Debt Index for Consumption ( R ) Table 3. Increased explanatory power of the Debt Index ( R ) Table 4. Summary statistics of the Consumer Finance Monthly Table 5. Variable definitions Table 6. Summary statistics Table 7. Payment for grocery shopping Table 8. Probit: Predicted use of payment among credit card owners I Table 9. Probit: Predicted use of payment among credit card owners II Table 10. Probit: Predicted use of payment among credit card owners I Table 11. Probit: Predicted use of payment among credit card owners II Table 12. Probit : Predicted use of payment among convenience users I Table 13. Probit: Predicted use of payment among convenience users II Table 14. Probit: Predicted use of payment among revolvers Table 15. Length of transition time from college to work Table 16. Time-to-First-Job in weeks Table 17. Job Tenure of first fulltime job xii 2 2

14 Table 18. Variable definitions Table 19. Summary statistics Table 20. Comparison on the Time-to-First-Job by education loan dummy Table 21. Cox regression result I Table 22. Cox regression result II Table 23. Cox regression result (Tenure of first job is longer than six months) I Table 24. Cox regression result (Tenure of first job is longer than six months) II Table 25. Cox regression result for males I Table 26. Cox regression result for males II Table 27. Cox regression result for females I Table 28. Cox regression result for females II Table 29. OLS regression I Table 30. OLS regression II Table 31. Summary statistics of tenure of first job for each sex Table 32. Comparison of three parametric models Table 33. Parametric survival regression result for male workers Table 34. Parametric survival regression result for female workers xiii

15 List of Figures Figure 1. The Debt Index Figure 2. Kaplan-Meier survival estimates for all samples Figure 3. Kaplan-Meier survival sstimated for each sex Figure 4. Kaplan-Meier survival estimates for all samples by education loans Figure 5. Kaplan-Meier survival estimates for men Figure 6. Kaplan-Meier Survival estimates for women Figure 7. Smoothed hazard estimates for all sample Figure 8. Smoothed hazard estimates for men Figure 9. Smoothed hazard estimates for women Figure 10. Smoothed baseline hazard estimates for men Figure 11. Smoothed baseline hazard estimates for women Figure 12. Kaplan-Meier estimates for men: Exit from the first job Figure 13. Kaplan-Meier estimates for women: Exit from the first job Figure 14. Baseline hazard estimates for male workers (Gamma distribution) Figure 15. Baseline hazard estimates for female workers (Gamma distribution): xiv

16 Chapter 1 Introduction Debt is very important in households in the United States. If credit is available to a household, a household can increase its consumption possibilities which would not have been available otherwise. Households may use credit to finance their purchase of houses and durable goods. Also, student loans allow young college students to finance their tuition. Although many young college students are financially constrained, they can finance their tuition through an education loan since these loans do not require a collateral and credit checkups. In addition, uncollateralized credit such as credit card debt is a useful tool to finance their consumption when their income drops temporarily. Since credit cards allow the card owner to carry a flexible amount of revolving credit card debt as long as he pays a required minimum payment, it acts as a convenient method to finance their consumption. Since debts became important for a consumer, this research investigates the impact of debt on several aspects of an individual s behavior. Chapter 2 examines the impact of debt on consumption. Aggregate household debt in the United States has been a rising trend and the ratio of debt to disposable income has increased from 95.9% in 2000 to 133.1% in Thus, there is a rising concern that increased debt burden may hinder 1

17 consumption which accounts for two thirds of GDP. Along this concern, this paper explores how debt burden is associated with consumption growth. The debt burden is measured with the Debt Index which is obtained from the household level survey. Chapter 3 examines how payment choice is affected by credit card debt. Using a credit card is a convenient payment method and it provides easily accessible credit. However, since credit card debt is charged with a high interest rate, those who carry revolving credit on their credit cards face higher payment costs when they pay with a credit card than when they pay using other payment methods. Therefore, people with a positive amount of credit card debt may show different patterns in choosing their method of payment from those who do not carry any credit card debt. This paper explores how the method of payment for those who owe credit card debt is associated with interest rate on a credit card. Chapter 4 examines how the education loan has an impact on the labor market behavior of young workers in their early careers. Student loans are offered without collateral or credit check. Also, these loans allow deferment options and extended repayment plans. Thus, education loans have been a readily accessible financial source for college students. Although education loans allow prospective college students to finance their higher education, there is growing concern that students are burdened with excessive debt. Among several aspects of education loans, Chapter 4 explores the impact of an education loan on the transition time from school to work and the job tenure of one s first job. 2

18 Several types of credits for different purposes have different kinds of impact on an individual s behavior consumption, payment choice, and labor market behavior. Although the Debt Index which is constructed from the household level data does not show predictive power for growth of consumption, debts have an influence on an individual s consumption behavior. A credit card user who carries positive credit card debt is more likely to use a check, an alternative method of payment, as the interest rate on his/her credit card is higher. For young workers, those who have a positive amount of education loans are more likely to start working sooner than those who do not have any education loans. The impact of an education loan on the tenure of one s first job shows a gender difference. Job tenure of female workers is affected by whether they have education loans or not, while the job tenure of male workers is not affected by education loans. This research investigates the impact of debt on individual behavior in a manner not previously examined in the previous research. Findings from this research show that an individual s behavior is affected by various types of debts. Consumers are affected by credit card debt in their payment choice and young college graduates are affected by education loans in their early labor market behavior. This finding about the impact of education loans on labor market behavior suggests that a move from grants to loans has an unexpected result on the labor market outcome. 3

19 Chapter 2 The Effect of Household Debt on Consumption 2.1 Introduction Aggregate household debt has shown a rising trend over the past several years. Outstanding household debt has increased from 7,011.4 billion dollars in 2000 to 13,839 billion dollars in Likewise, the ratio of debt to disposable personal income increased from 95.9% in 2000 to 133.1% in Some analysts claim that the level of debt does not influence household spending because most households have enough of a buffer to stabilize their consumption (Benito et al., 2007). Others argue that the rising household debt is a long-run trend that comes from changing attitudes toward debt and financial innovations, and they show that rising household debt is an international trend in advanced countries (Mote & Nolle, 2005; Crook, 2003). Since household spending makes up over two-thirds of the GDP, however, some analysts are concerned that high debt levels may curtail future household consumption and lenders may reduce available credit in response to high debt levels. For example, Debelle (2004) claims that the greater household debt burden will cause households to be 1 Federal Reserve Statistical Release Z.1. Flow of Funds Accounts of the United States, June 5, Bureau of Economic Analysis, Table 2.9 Personal Income and Its Disposition. 4

20 more tuned into changes in interest rate, income and asset prices. Even though many researchers have analyzed the effect of household debt on consumption, the results have not yet reached an agreement. While most literature about household debt and consumption is based on aggregate data in the research, I use individual and household level data. Several measures of household financial characteristics are integrated into an index, which is tested for its predictive power for future consumption growth. Most widely used measures of aggregated household debt come from the data series of the Federal Reserve Board (FRB). However, due to their highly aggregated nature, they sometimes show some discrepancy with the true financial situation of a household. For instance, Johnson (2007) points out that the consumer credit data are somewhat inflated, since not only is true credit card debt aggregated to the series, but transaction demand is also included in the series. Moreover, she shows that the inference about the relationship between consumer credit and consumption is very sensitive to whether or not the transaction demand is included in the series. At a micro level, the Survey of Consumer Finance (SCF) is widely used for research on household debt. Since the SCF is intended to provide information on the financial characteristics of a household, it contains detailed data about household assets and liabilities. Even though the SCF provides comprehensive information on a household s finances, however, the survey is only conducted every three years. Thus it cannot provide information about the short-run trend of household indebtedness. Unlike other papers, this paper is based on household level data which provide indepth information about household balance sheets. The remainder of this paper is 5

21 organized as follows. The next section provides an overview of prior related literature. Then, the data are described, and a Debt Index is suggested. After that, I test whether the Debt Index adds predictive power when other control variables are present. I conclude that the Debt Index does not show predictive power over the growth of consumption. 2.2 Previous Research The Permanent Income Hypothesis (PIH) assumes that households choose an optimal consumption path in a world without borrowing constraints. The PIH implies that changes in future consumption are not predictable since the consumption growth follows a random walk (Hall, 1978). However, in the real world households may face borrowing constraints and may not obtain optimal consumption. Also, households borrow with higher interest rates on their debt than they can earn from their assets. Thus, debt burdens from the interest payment can influence consumption. Much empirical evidence shows that some variables are useful in predicting the growth of consumption. These variables are as follows: Predictable changes in income (Campbell & Mankiw, 1990), employment expectation (Carroll & Dunn, 1997), Consumer Sentiment Index (Carroll, Fuhrer & Wilcox, 1994; Souleles, 2002), predictable changes in consumer credit (Bachetta & Gerlach 1997; Ludvigson, 1999), credit constraints (Japelli & Pagano, 1989) and debt service payment (Murphy, 2000). The findings regarding the relationship between household debt and consumption are diverse. Garner (1996) examines the effect of debt on consumption, using debt-toincome, debt-to-asset, and delinquency rates. The results show that the various debt 6

22 measures are not useful in predicting consumption growth. McCarthy (1997), Maki (2002), and Schmitt (2000) discuss the issue via Debt Service Ratio (DSR), delinquency, and consumer credit, respectively. They find some evidence that the consumer credit is useful in predicting future consumption growth, but that the other two measures are not significant. Johnson (2007) finds a negligible effect of consumer credit on total expenditures. Based on a different time period, Murphy (2000) observes that the DSR is an important predictor of spending on durable goods and services. His finding suggests a negative effect of the DSR on household spending. Some researchers use household-level data instead of aggregate data. The results from micro data are as follows. Johnson and Li (2007) use the household level measure of DSR. They estimate the debt service burden from the interview data of the CEX. Based on their estimated debt service ratio, they analyze the change of the distribution of the DSR. However, since they rely on several strong assumptions on monthly debt payments, their measure may not reflect the debt burden of households. Dunn et al. (2006) examine the effects of credit card debt on consumption growth. They use household level data and incorporate several components of credit card-related behavior into an index. They find a negative effect of credit card debt on durable consumption growth. This paper is based on data from a household level survey about debt, income, asset, missed payments and monthly required payments. The following section discusses the data used. 7

23 2.3. Overview of Debt Measures and Description of the CFM Different measures of debt are used in research on household debt and consumption. Measures on household debt are categorized into four types. The first measure is the amount of outstanding debt. The most commonly used measures for outstanding debt come from the Flow of Funds (FOF) accounts and from the Federal Reserve G.19 statistical release. The second measure is the debt payment. An easily accessible debt payment measure is the Debt Service Ratio and Financial Obligations Ratio (FOR). 3 Both ratios are provided by the Federal Reserve Board. The DSR is intended to capture the share of household after-tax income obligated to debt payment. It has been released quarterly since FOR was introduced in 2003 and includes more than simple contractual debt. For example, FOR includes consumer leases, rent payment, homeowner s insurance, and real estate taxes. The third measure is delinquencies and bankruptcies. Delinquencies generally rely on the data reported by the American Bankers Association and refer to the percent delinquent on closed-end loans. Also, personal bankruptcies showed a sharp climb until the Bankruptcy Abuse Prevention and Consumer Protection Act (BAPCPA) was adopted in Since the number of bankruptcy filing and delinquent rates can capture households in financial stress, they are used to investigate the relationship between household debt and consumption. The last measure is based on household level surveys, such as the SCF, the Consumer Expenditure Survey (CEX), and others. The measure 3 FOF, Consumer Credit Series (G.19), FOR, DSR and delinquency are available from the FRB. Aggregate bankruptcy measure is available from the U.S. Bankruptcy Court. 8

24 based on household surveys can examine the debt burden of each socio-demographic group. This research is based on a household-level survey, the Consumer Finance Monthly (CFM). The CFM is collected by the Center for Human Resource Research (CHRR) at the Ohio State University. The survey is a simple random sampling telephone survey which is conducted monthly in households across the nation. It has collected around 300 observations on household balances and other financial behavior every month since February Since the CFM provides financial information for households, it is comparable to SCF. The two surveys show very similar descriptive statistics such as age, income, asset, and debt. While the SCF provides more detailed information about the financial situations of households, the CFM offers in-depth data about the behavioral aspects of a household. For example, the CFM provides rich data about credit card usage, credit card balance switching, bill payment, and financial knowledge. In addition, it makes current data available in a more timely fashion, while other surveys are released with a considerable lag. The CFM contains 14,179 household observations from February 2005 to December The data for Debt Index is restricted to the observations with completed income data, and the data period is restricted from April 2006 to December Therefore, 11,047 observations with completed income information are used for the Debt Index. I use the weighted data to better represent the financial situation of the U.S. population. 9

25 2.4. Construction of the Debt Index The Debt Index is constructed based on questions about debt, assets, missed payments, and required monthly payments. All components have a positive effect on the index. The components of the index are given in Table 1. The debt is calculated from the survey question, How much do you owe after the most recent payment? Eight types of debt are summed to arrive at the total amount of debt. The types of debt include primary mortgages, Home Equity Lines of Credit, student loans, installment loans, bank loans, payday loans, credit card debt, and other debts. Components 1, 2, and 4 are calculated as averages of each household. I drop the highest 5% of observations in each component in computing these ratios. Component 3 is the percentage of households that missed any payments over the sample. The households with any missed payment are identified by three questions: In the last 12 months, have you been late on house payments by more than 60 days? In the past six months, how many times were minimum payments on any of your credit cards late or missed? In the past six months, how many times were minimum payments on any of your loans other than credit cards late or missed? Table 1. Components of the Debt Index 1. Debt to Income Component Name Component Description Total amount of debt taken as a percent of annual household income Continued 10

26 Table 1. continued 2. Debt to Asset 3. Missed Payment 4. Monthly Required Payment to Income Total amount of debt taken as a percent of household assets Percentage of sample that has missed payments at least once Monthly required minimum payment on debt as a percent of monthly income Component 4 is intended to capture the household debt burden. It is comparable to the DSR from the FRB. The DSR is computed as a ratio of two aggregate variables: aggregate required debt payments and aggregate after-tax income. To capture the debt payment burden, the DSR includes the minimum required payment, rather than the actual payment. Also, the DSR is estimated by several assumptions about average interest rates and average remaining maturity. Payments on a mortgage are obtained from the outstanding balance, remaining maturity, and average interest rate. The information on mortgage payments is obtained from three different sources. The outstanding balance is taken from FOF, the average interest rate is taken from the Bureau of Economic Analysis (BEA), and the remaining average maturity is taken from mortgage lenders. Payments on non-revolving loans are calculated by the consumer credit series, and average interest rates and remaining maturity are estimated from the distribution of loans in the SCF. Required payment on revolving credit card debt is estimated on the assumption of 2.5% on the outstanding of 11

27 revolving credit. The 2.5% is based on a Senior Loan Officer Opinion Survey in However, if more credit card users pay off their entire balance in every billing cycle rather than carry credit card debt, then the estimates from 2.5% of the ending balance of each month may overestimate the required payment. Also, in 2006, banks increased the minimum monthly payment from about 2% to 4%. Since the minimum required payment is estimated from various data series and assumptions, it may not reflect the true debt burden of households. In the construction of the Debt Index in this research, the respondents are asked directly about their minimum required payment. The survey question is, What is your monthly required payment? For each type of debt, the respondents are asked the amount of their monthly required payment. For credit card minimum payment, the question is, For your most recent credit card statement, what was the total minimum payment? The minimum required payment in the Debt Index is the sum of these eight categories of required payment. The ratio of the minimum required payment to income is computed for each household and the ratio is averaged across households. Since the ratio is intended to capture a household s payment requirement, it is similar to the DSR. However it is not explicitly comparable to the DSR. The DSR from FRB is based on the after-tax disposable income, while I use before-tax income. In addition, I use the average ratio of the minimum required payment to income across households while the DSR is based on two aggregate variables. In what follows, I specify how the Debt Index is constructed. 12

28 In computing the Debt Index 4, each observation is denoted by yi, j, t where i stands for respondents, j refers to four components of the index, and t refers to month. For the continuous three components, y jt, is computed as the sample mean of observations in component j in month t. The number of observations of component j in month t is denoted by n t. Debt-to-income, debt-to-asset, and required minimum payment to income ratio are calculated in this way. The example is given below: y n 1 t j, t yi, j, t nt i 1 (2.1) For the component of missed payments, y jt, is computed as the percentage of the sample with yi, j, t over the number of observations of component j in time t. That is, if a respondent missed any payments, then yi, j, t is given dummy variable 1 and otherwise yi, j, t is zero. y n 1 t 100 y (2.2) j, t i, j, t nt i 1 Then, each component is standardized by its standard deviation over time. This standardization allows each component to contribute differently to the index. If a component shows high standard deviation, then its contribution is smaller than other 4 I followed the index computation method as in Dunn et al. (2006). 13

29 components. After that, four standardized components are averaged to obtain the preliminary Debt Index. D y jt, jt,, j Preliminary Debt Index t = j 1 D jt, (2.3) I chose January 2007 as the base month. In January, debt levels are generally high, since it follows the end of the year shopping season. Considering seasonal consumption patterns, I set January 2007 as the reference month. The preliminary Debt Index is adjusted to the base month and multiplied by 100 to fix the reference index to a scale of 100, as follows. Debt Index t Preliminary Debt Indext Preliminary Debt Index in January, 2007 *100 (2.4) Figure 1 shows the Debt Index up to December The Debt Index presents a well-known seasonal movement. It peaks in January, following the major holiday shopping season at the end of each year. The seasonal trend can be interpreted as a rational behavior to adjust the timing of consumption (Dunn et al., 2006). 14

30 80 Debt Index Figure 1. The Debt Index m1 2006m1 2007m1 2008m1 2009m1 Month Empirical Results In the empirical estimation, I used a simple consumption model following Carroll, Fuhrer and Wilcox (1994), who test whether or not the Consumer Sentiment Index is useful in predicting consumption growth. The model is given as follows: log( C ) D (2.5) t 1 0 i t i t i 1 15

31 where D t denotes the Debt Index. This simple model tests whether or not the lagged Debt Index has predictive power for aggregate consumption growth on its own. I estimate the equation with three kinds of consumption measure and various lag specifications with monthly spending data. The results are shown in Table 2 and the adjusted-r 2 is provided. Table 2. Explanatory Power of Debt Index for Consumption ( R ) ( log( C ) D ) t 1 0 i t i t i 1 2 Dependent Variable 2 Lags (p-value) Total Spending 5 7.4% (0.12) Non-Durable Spending 3.3% (0.23) Durable Spending 2.6% (0.26) 3 Lags (p-value) 5.4% (0.22) 6.3% (0.06) -2.4% (0.52) 4 Lags (p-value) 0.0% (0.41) 0.8% (0.40) -6.9% (0.71) * The numbers in parentheses are P-values for the joint significance of the Debt Index and its lagged values. I found the lagged Debt Index was not significant in predicting three kinds of spending under various lag specifications. Then I tested whether or not the Debt Index adds explanatory power when other control variables exist. I used the lagged growth rate of real disposable income 6 and lagged dependent variables as control variables. The reduced form of consumption function had the following form. 5 Real personal consumption expenditure is from BEA. It is chained 2000 dollars and seasonally adjusted. 6 Disposable personal income per capita is from BEA. It is chained 2000 dollars and seasonally adjusted. 16

32 (2.6) log( C ) D Z t 1 0 i t i t i t i 0 i 0 where Zt idenotes control variables. I used two-four lags as control variables since the data were monthly. The Debt Index did not add any explanatory power for three kinds of consumption. Results are given in the Table 3. Table 3. Increased explanatory power of the Debt Index ( R ) ( log( C ) D Z ) t 1 0 i t i i t i t i 1 i 1 Dependent Variable 2 Lags 3 Lags 4 Lags (p-value) (p-value) (p-value) Total Spending -5.6% -10.5% -16.8% (-0.17) (-0.35) (-0.36) Non-Durable Spending 4.7% 29.2% 27.8% (0.16) (0.12) (-0.48) Durable Spending 8.0% -48.2% -59.4% (0.41) (-0.73) (-0.87) * Numbers in parentheses are P-values for the joint significance of the lagged Debt Index coefficient Conclusion The goal of this paper was to examine the relationship between household debt and consumption growth. I constructed a household Debt Index based on household level data. The Debt Index consisted of four components: debt-income ratio, debt-asset ratio, 17

33 number of missed payments, and required monthly payment-income ratio. I tested whether the Debt Index had predictive power for consumption growth. I found that the Debt Index did not show predictive power for the consumption growth of total, durable, and nondurable consumption. This unsatisfactory result may be due to the short period of data which covers 33 months. Since the household level data provides more information about each household s financial situation, further examination on the relationship between each credit variable and consumption is needed. 18

34 Table 4. Summary statistics of the Consumer Finance Monthly Variable Total Observations 11,047 2,572 2,918 3,087 2,470 Male White Age Education Household size Children Homeowner Employed Log income Income 64,331 59,784 64,630 66,960 65,425 Log networth Networth 251, , , , ,060 Bankrupt Debt to Income Debt to Asset DSR Miss any payment Debt Stress Index * Summary statistics are weighted 19

35 Chapter 3 Credit Card Balance and a Consumer s Payment Choice 3.1. Introduction Consumers purchase goods using various payment methods. The methods of payment which are commonly available to U.S. consumers are cash, checks, debit cards and credit cards. Cash is the oldest payment method. It is universally accepted as final payment and is easy to obtain from ATMs or bank offices. Checks are the oldest type among non-cash payment. In 1979, checks represented 85.7% of non-cash retail payments, but checks have been replaced by electronic payment, such as debit and credit cards. 7 The number of non-cash payments with checks was 37.3 billion, which accounted for 45.8% of the number of non-cash retail payments in 2003 and decreased to 30.5% in The credit card is one of the major forms of payment in the United States. Credit cards were used for 19.0 billion transactions in 2003, which accounted for 23.3% of noncash payments. The number of transactions with credit cards increased to 21.7 billion in 7 Gerdes and Walton (2002). 8 Gerdes (2008). 20

36 2006, while the share among the non-cash payment has remained stable about 23.3% of non-cash payments. 9 At a household level, 72.3% of households have at least one bank credit card (2004 Survey of Consumer Finance). Credit cards are widely accepted by most merchants, and the benefit of their cashless transaction contributes to their popularity. Transactions with credit cards allow interest-free funds for days, as long as a customer pays off the entire balance in a billing cycle. Furthermore, by paying with a credit card, a consumer has the option to convert the balance into uncollateralized funds without any transaction costs. The debit card is a relatively recent payment medium. The volume of debit transactions was only 1.4 billion retail payments in The number of transactions with debit cards was 15.6 billion in 2003, which accounted for 19.2% of non-cash payments, and it has rapidly increased to 25.3 billion, which accounted for 27.1% of noncash payments in While PIN-debit transactions are aided by Electronic Funds Transfer (EFT) networks, transactions with signature debit are processed through Visa or Master Card systems. If a merchant has a contract with any EFT networks and installs a pin pad, the consumers can pay with debit by swiping the card in the terminal and entering a personal identification number (PIN). In addition, debit transactions can be authorized with a signature without keying in a PIN. If a consumer has a debit card of the Visa or MasterCard brands, then a merchant can make a signature debit transaction via 9 Gerdes (2008). 10 Gerdes and Walton (2002). 11 Gerdes (2008). 21

37 Visa or MasterCard credit card networks. Then, the transaction amount is debited from the customer s linked account within two days. Since the use of electronic payment increases rapidly, many researchers investigate the electronic payment method, such as debit and credit cards. In particular, debit and credit cards are analyzed from the market structure and the fee on payment card systems. Many of them investigate the nature of two sided markets which involve a cardholder and a merchant. They analyze the optimal fee structure for cardholders and a merchant. Much research about household payment choice is based on the survey data. Similar to other research on the household payment choice, the current research is based on the household survey data. Using the household level data which have been collected for , I analyze which factors influence the payment choice. In particular, I examine whether or not a payment choice of a revolver is affected by interest rates on a credit card. If a consumer carries a credit card balance, the consumer cannot benefit from the free float. Therefore, a revolver has an incentive to use other payment methods in order to avoid finance charges from using credit cards. Since the high interest rate on credit cards implies higher finance charges, I test whether the interest rate on a credit card balance has an effect on payment choice among cash, checks, and debit cards. I find that a revolver s check use is positively correlated with the interest rates on a credit card, while cash and debit card use do not show any correlation with the credit card interest rates. This result can be interpreted as meaning that the cash and debit card is chosen by one s behavioral motive such as self-control. More cost sensitive revolvers 22

38 are more likely to use checks. In the following section, I describe previous literature about payment choice. In the second part, I describe the household level data and show descriptive statistics. In the third part, an empirical model is described. Finally, I conclude and discuss the limitations of this work Related Literature Payment methods are analyzed from various perspectives. Some economists analyze the payment method from a viewpoint of money demand. For instance, Duca and Whitesell (1995) investigate the relationship between credit cards and transaction balances using, the 1983 SCF. They find that credit card ownership is negatively correlated with transaction deposits. Some researchers rely on aggregate data and examine the trend of payment method use. For example, Humphrey (2004) investigates the time-series data of payment methods over and finds that checks replaced cash in the 1970s, credit cards replaced checks in the 1980s, and debit cards replaced both cash and checks in the 1990s. Although the theoretical research on the payment method using the aggregate data is not much, the research using micro-level data is more diverse. In particular, credit cards have been analyzed by many researchers since their introduction in the 1950s. Also, debit cards have attracted much attention since their rapid adoptions. Ausubel (1991) examines attributes of the credit card market. The credit card market is unregulated and many new firms enter the market due to the lack of an entry barrier. In addition, the number of consumers is large, and credit card companies make 23

39 more profit than the banking industry. Even though the credit card market looks competitive, the industry has the characteristics of a noncompetitive market, such as high and sticky interest rates. Ausubel claims these features come from consumers irrationality, since they tend to underestimate the probability that they will let a credit card balance carry over, inducing a high interest rate. Brito and Hartly (1995) argue that even though credit card interest rates are high, carrying a credit card balance is a rational consumer behavior because a transaction cost of credit card debt is lower than that of other loans. In particular, credit card debt is a rational choice when the period of credit is short or unpredictable, since many other loans are costly to set up. Several researchers have examined the portfolio puzzle, the coexistence of credit card debt with high interest rates and liquid assets with low interest rates. Gross and Souleles (2002) find the portfolio puzzle is pervasive in all types of demographic groups in their proprietary data. They suggest that behavioral models can be an alternative explanation for the portfolio puzzle. Behavioral explanations are based on the assumption that credit cards defer payments from consumption, and that the separation between payment and consumption leads to excessive spending. Bertaut and Haliassos (2002) suggest an accountant-shopper model to explain the puzzle. In their model, an accountant is fully rational and controls the shopper s excessive spending by carrying credit card debt which reduces available credit. The accountant decides how much to pay off the credit card debt and the shopper faces uncertainty over the payment. Since the shopper faces uncertainty, he refrains from excessive spending. In the accountant-shopper model, credit card debt with a high 24

40 interest rate is a self-control device to suppress the shopper s impulsive spending. Prelec and Loewenstein (1998) suggest a mental accounting model which implies that people prefer advanced payment for spending and deferred payment until work is performed. Fusaro (2008) suggests a theoretical model, and he shows evidence of self-control motive to support his model in debit card use. He uses account level data, and claims that a household uses debit cards as the lowest cost instrument to manage expenditure. He argues that budgeting involves an opportunity cost of time, and a household with a high opportunity cost of time uses debit cards, rather than credit cards. He finds that debit users are more likely to pay off large credit card balances, and they withdraw smaller amounts of money. He interprets this pattern as evidence that a debit card is used as a tool of spending restraint. Also, using the 2004 SCF, Lee et al. (2007) find that debit card use is negatively correlated with household unsecured debt. They interpret the correlation as evidence that consumers who want to avoid debt accumulation intentionally pay with debit cards. Contrary to the mental accounting, or self-control model, Zinman (2009) finds evidence that economic motive is a determinant in the choice between credit and debit cards. He claims that a consumer chooses credit or debit cards in order to minimize payment costs. Using the SCF, he tested whether debit card use can be explained with the cost minimization motive or not. He found favorable evidence for the cost minimization motive for debit card use, but he could not reject the self-control motive for debit card use. 25

41 Other researchers have investigated a consumer s payment choice. Their approach can be classified broadly into two categories. The first group of payment researchers has examined the difference in payment use among various demographic groups. Using the 1998 SCF data, Stavins (2001) found age and education are important factors for payment choice. Using gasoline station data, Carow and Staten (1999) also found that younger and more educated people are more likely to use debit cards. Researchers in the second group analyzed the effects of the characteristics of payment. Hayashi and Klee (2003) found that transaction characteristics influence payment choice. Klee (2008) used grocery store scanner data to investigate how average transaction time is different among four methods of payment. Cash takes the least transaction time among cash, debit card, credit card, and checks. Debit and credit cards show similar transaction time and checks take the longest transaction time among these four methods of payment. She claims that short transaction time is a significant factor influencing the move from use of check to use of debit card. 3.3 Data Description and Summary Statistics This research is based on the Consumer Finance Monthly (CFM). The CFM has rich data about credit card usage, credit card balance switching, bill payment, and financial knowledge. In addition, it makes current data available in a more timely fashion, while the SCF is released with triennial intervals and considerable lags. In particular, the CFM has detailed information about what payment instrument has been used within the 26

42 last month for fourteen categories. The payment instrument covers cash, checks, credit cards, debit cards, electronic bills, direct payments, and others. The categories include house payments such as mortgage and rent, utilities, household furnishings, groceries, dining out, apparel, personal care, car payment, gasoline, medical services, entertainment, taxes, and insurance premiums. For this research, I use the CFM data because bill payment questions were included in I restrict the sample to households which have at least one general purpose credit card and drop observations with missing information regarding demographics, financial variables, and bill payment variables. I use observations with positive income and net wealth and exclude observations of the highest 1% of net worth. This process allows me to use 2,827 observations. Table 6 shows the socioeconomic characteristics of the sample. Three fourths of the sample uses credit cards at least once within the last month and 37.6% of the sample report use of a debit card. Of the sample, 39% carries a credit card balance, the average utilization ratio is 12.7%, and the average credit card balance is $2,693. Of the sample of households, 8% have filed bankruptcy and 13.3% have missed payment at least once for any type of loan or credit card within the last six months. Revolvers are younger and more likely to be employed than convenience users. Convenience users have higher income and net worth than revolvers. Credit card revolvers are more likely to have a credit card at credit limit and have missed a loan or credit card payment. Convenience users are more likely to have a reward credit card than revolvers. The average credit card 27

43 balance of revolvers is $6,905 and the average utilization ratio is 32.7%. The total credit limit of credit card revolvers is not much different from that of convenience users, while the number of credit cards of revolvers is greater than that of convenience users by 0.4. The charged amount on credit cards does not show difference between revolvers and convenience users, while the paid amount shows a difference. About 80% of convenience users pay with credit cards at least once, while 70% of revolvers use credit cards for any kinds of bill payments. Almost half of revolvers use debit cards, while only 29.3% of convenience users pay with debit cards. Interest rates on credit card balances are higher among convenience users than revolvers, which can be explained by the higher search effort of revolvers (Calem & Mester, 1995; Kim, Dunn & Mumy, 2005). In the following section, I describe a model for testing which factors determine the payment choice The Empirical Model If a consumer carries a credit card balance and the charged interest rate on the balance is high, the consumer purchases fewer goods with the credit card. Instead of buying goods with credit cards, the consumer is more likely to purchase goods with cash, checks, or debit cards. Based on the above consideration, I test whether or not the use of each payment method is correlated with interest rates on credit cards among credit card revolvers. Prediction from the simple demand function implies that convenience users payment 28