Consumer Bankruptcy and Adverse Effects: a Panel Data Analysis

Transcription

1 Consumer Bankruptcy and Adverse Effects: a Panel Data Analysis Shervin Dadbin Word Count: 5958 Student ID: Supervisor: Abigail Barr Module: L13520 Econometrics Project Date: April 2013 This project is presented in part fulfilment of the requirements for completion of an undergraduate degree in the School of Economics, University of Nottingham 0

2 CONTENTS INTRODUCTION...2 LITERATURE REVIEW...3 THEORETICAL FRAMEWORK...4 DATA...6 METHODOLOGY...8 EMPIRICAL RESULTS...12 CONCLUSION...18 BIBLIOGRAPHY...20 APPENDIX

3 INTRODUCTION In the four years following the global financial crisis there have been almost 5.5 million consumer bankruptcies in the United States. However, even in the decade leading up to the financial crisis, consumer bankruptcy filings averaged over 1.35 million a year. Thus, although the current recession has brought the issue of bankruptcy to the public eye, it had long been a problem. With such alarming figures, it is not surprising to know that the banking industry had spent $100 million lobbying for a change in bankruptcy legislation before a fundamental change was eventually made in 2005 with the introduction of the Bankruptcy Abuse Prevention and Consumer Protection Act (BAPCPA) (Egan, 2005). This change led to a more stringent set of rules and explains why bankruptcy peaked in 2005 and then dramatically fell by 71% in the following year in Figure 1 below. 2,500,000 Figure 1 2,000,000 1,500,000 1,000,000 Total Consumer Bankruptcy Chapter 7 Chapter ,000 0 Data extracted from the Administrative Office of the U.S. Courts. The chart shows overall total consumer bankruptcy filings, total consumer bankruptcy filings under Chapter 7 and total consumer bankruptcy filings under Chapter 13 for the years 1993 to Over the turn of the century, many celebrities such as Mike Tyson and Kim Basinger had filed for bankruptcy in order to clear their debts (HowToSaveMoney, 2011). With such cases in the media limelight, it seemed to mark a watershed in the perception of bankruptcy from a shameful option of last resort to a socially accepted alternative to paying off debts. Thus, understanding why exactly people file for bankruptcy in the 21 st century will have important policy implications and will offer us guidance on how to further tackle the issue. A vast array of interlinked factors explains why someone files for bankruptcy. Hence, it is sensible to assume that there may be more than one reason explaining each individual case and that not one factor inevitably leads to bankruptcy. This study aims to discover to what extent adverse effects cause consumer bankruptcy in the US. Although adverse effects is defined rather expansively as any unforeseen incident which affects an individual s ability to meet his/her financial obligations, in this study we focus specifically on three adverse effects: job loss, divorce and unexpected medical costs. 2

4 LITERATURE REVIEW There exists a vast amount of literature exploring the various determinants of US consumer bankruptcy. Such determinants range from the role of revolving credit card debt to the effects of casino gambling. Interestingly, academics are often divided over the importance of adverse effects in explaining US consumer bankruptcy. Sullivan, Warren and Westbrook (2000) took an extended look into unemployment, sickness and divorce among other factors leading to consumer bankruptcy by primarily focusing on a questionnaire taken from a sample of consumer bankruptcy filers from They found that 21.4% of filers were unemployed compared to the national unemployment rate of 6.7% and that 68% of filers attributed their insolvency to a job related problem. Furthermore, they found that almost a fifth of filers pointed out medical reasons for their bankruptcy and that over 15% of filers claimed that the collapse of their marriage led directly to their financial turmoil. Moreover, they compared the demographics of filers with the general population and concluded that bankruptcy is twice as likely to occur among divorced individuals. Himmelstein, Warren, Thorne et al. (2005) believe that illness and injury are pivotal in explaining consumer bankruptcy. They surveyed 1,771 consumer bankruptcy filers from 2001 and found that 28.3% of filings were accountable to illness or injury and that 54.5% could be attributed to some medical cause. Meanwhile, Gross and Souleles (2002) looked at credit card accounts and found a significant association between having no health insurance and having higher delinquent debt. It was also found that defaults on credit card debt increases with unemployment. Furthermore, Domowitz and Sartain (1999) used a multivariate nested logit regression to see how certain factors would change the conditional probability of bankruptcy. Their study shows that households with high medical debt (defined as exceeding two percent of income) have a probability of filing for bankruptcy that is over twenty eight times greater than the baseline probability. Divorce was found to be statistically insignificant, however, and it was shown that employment influences the type of bankruptcy filed. White (2007) points out that the findings of Sullivan et al (2000) have been scrutinised for over counting job loss as a reason for bankruptcy as they also counted those who immediately found new jobs after being made redundant. She also rebuts the results of Himmelstein et al (2005), arguing that health care expenditure as a reason for bankruptcy was over exaggerated. Instead, White highlights the data from the 1996 Panel Study of Income Dynamics (PSID) to try and play down the emphasis placed on adverse effects. The PSID found that only 21% of filers gave job loss as a primary reason for filing and only 16% gave illness, injury or medical costs as a primary reason. White concluded that revolving credit card debt was to blame for bankruptcies and not adverse effects. In a paper essentially focused on seeing how the financial gain from filing influences the decision to file, Fay, Hurst and White (2002) also tested the conjecture of adverse effects influencing the household decision to file. They used dummy variables in their regression to represent whether or not the household head had been unemployed or divorced or had suffered from a health problem in the past year. Although the coefficients of these dummy variables were all positive as expected, only 3

5 the divorce dummy was found to be statistically significant, albeit only at the 10% significance level. However, this study only used data from 254 bankruptcy filers which is arguably too small a sample. Other causes of bankruptcy explored by academics include revolving credit card debt, gambling, the existence of hyperbolic discounters and social stigma, to mention a few. Looking at 91 districts across America, Daraban and Thies (2010) attributed 2.3% of bankruptcy filings to casino gambling and 1.8% to lottery gambling. Laibson, Repetto and Tobacman (2003) used models and simulations of the consumption function to find that hyperbolic discounters (those with time inconsistent discount rates) borrow over three times as much as exponential discounters even if they face higher interest rates. Clearly, there is an abundance of literature trying to uncover the causes of consumer bankruptcy in America. It is important to note, however, that most of the previous studies on the role of adverse effects have been based on consumer surveys. As a result, it could be argued that these studies have made inferences from data which often proves problematic in its interpretation. For instance, in one survey it may be the case that a gambling addiction and depression due to bereavement would both be classed under the umbrella of a medical reason to file for bankruptcy while in another survey bereavement and addictions may have their own categories completely separate from medical reasons. Thus, there is a level of ambiguity over the magnitude of certain effects and a lack of consistency over the definition of such effects when using surveys. Moreover, a study based on a survey may fail to perform the multivariate statistical analysis necessary to determine the magnitude of the causal relationship or to rule out other factors (Dranove & Millenson, 2006, p.75). However, some studies have used multivariate regressions from their survey results but their analysis remains on an individual level. Consequently, this study differs from previous studies analysing the significance of adverse effects as it is not based on a consumer survey but instead on panel data from 48 US states and one federal district. Thus, it should allow us to avoid the usual complications of survey data and to isolate and analyse the importance of each adverse effect individually on a state level. THEORETICAL FRAMEWORK In order to understand why people file for bankruptcy, we must first understand the bankruptcy procedure and the different bankruptcy options available. Thus, a brief background on the litigation surrounding consumer bankruptcy and the types of consumer bankruptcy will be provided in this section. We must also acknowledge that a consumer is only classed as bankrupt once a bankruptcy petition 1 is filed. A consumer can file for bankruptcy under Chapter 7, Chapter 11 and Chapter 13. In this study, we do not specifically focus on Chapter 11 filings as they account for less than one in a thousand 2 of the total consumer bankruptcy filings looked at in this study. Filing under Chapter 7 involves the liquidation of the debtor s assets who is then discharged of his/her debt: the proceeds of which are 1 The document filed by the debtor (in a voluntary case) or by creditors (in an involuntary case) by which opens the bankruptcy case. (There are official forms for bankruptcy petitions.) The Administrative Office of the U.S. Courts [ 2 6,441 Chapter 11 filings out of 10,452,529 total filings. 4

6 then distributed to the respective creditors. Not all of the debtor s assets are liquefied. As bankruptcy law is designed to help debtors restart their lives, some assets such as the residence of the filer are exempt from liquefaction. However, the range of assets which are exempt and their thresholds vary across different states. 3 (USLegal, 2007) On the other hand, those filing under Chapter 13 keep their assets and are instead obliged to follow a repayment plan. Before the implementation of BAPCPA 4 on 17 th October 2005, consumers were allowed to propose their own repayment plan as long as they did not propose to repay less than the value of their non-exempt assets. Moreover, filers also had the privilege of choosing whether to file under Chapter 7 or Chapter 13. Thus, it is not surprising that the majority of consumers filed under Chapter 7. Clearly, the bankruptcy system prior to BAPCPA was susceptible to exploitation as even those who could afford to pay off all of their debts with a repayment plan would maximise their own financial gain by opting to file under Chapter 7. Furthermore, many filers would move their assets to other states in order to benefit from more exemptions. (White, 2007) After BAPCPA, consumers lost the right to choose between Chapter 7 and Chapter 13. Consumers are now only eligible to file under Chapter 7 if their relative household income is sufficiently lower than the median income of their state. Furthermore, the repayment plan is no longer proposed by the consumer but instead by an impartial bankruptcy court who takes into account the financial situation of the consumer. There are now also more stringent barriers on moving assets across states before filing. (White, 2007) It is because of these technicalities that, in this study, regressions will be run on not only aggregate consumer bankruptcy filings but also on Chapter 7 and Chapter 13 filings. The hypotheses that this study would like to investigate are outlined below. Hypothesis 1 We assume that, when two individuals enter a marriage, they assess their combined wealth and expected future incomes when making financial decisions. Therefore, after the unforeseen occurrence of a divorce, a divorced individual will have to meet financial obligations which he/she had committed to prior to the divorce. Meeting such obligations is often not feasible due to the change to the individual s finances caused by the divorce. Hence, it is hypothesised that divorce is a major cause of bankruptcy and so it is predicted that an increase in the divorce rate will increase the consumer bankruptcy rate. Hypothesis 2 When determining how much debt is sustainable, an individual takes into account current and future income. However, if he/she loses his/her job unexpectedly; debt which was previously thought to be sustainable may no longer be so due to the fall in income. Consequently, it is hypothesised that layoffs are a major cause of consumer bankruptcy and that an increase in the layoff rate will lead to an increase in the consumer bankruptcy rate. In this study, the number of layoffs will be measured 3 A detailed list of each state s asset exemptions is available on LegalConsumer.Com [ 4 Bankruptcy Abuse Prevention and Consumer Protection Act 5

7 by the number of initial claimants 5. Moreover, given the likelihood that someone will first look for another job before resorting to filing for bankruptcy, the lagged effect of layoffs will also be examined by looking at the previous year s layoffs as well as the contemporaneous effect of the current year s layoffs. Hypothesis 3 A sudden medical condition which requires costly healthcare can lead to financial strain and make an individual unable to meet his/her liabilities. However, if the individual has health insurance, he/she should not run the risk of facing unexpected high medical costs. Therefore, it is hypothesised that inadvertent medical costs are a major cause of bankruptcy and so an increase in the percentage of people with health insurance should reduce the consumer bankruptcy rate. DATA This study uses data from 48 US states and one federal district over the nine years from 2001 to The two US states not included are California and Indiana as they have no data on divorce for any of the years we are concerned with. The federal district used in this study is the District of Columbia, more commonly known as Washington, D.C. As a federal district, the District of Columbia is neither part of a US state nor a state in its own right. However, given its status as the capital of the US and its relatively large 2012 population of 632,323 (greater than that of Vermont and Wyoming) 6, I decided to include it in my study in order to increase the number of observations. For simplicity, we will refer to the District of Columbia as another state even though it is not actually a state. The sources of which the data is gathered from and a description of each variable can be seen in Table 1 overleaf. Each variable is then summarised further in Table 2. As can be seen in Table 2, most the variables have all 441 observations (49states x 9years). Laynow has only one observation missing (Wyoming 2007) and laypre has only two observations missing (Wyoming 2001 and 2008). Although divorce has a total of 28 missing observations, only five states lack at least one of their observations for divorce. 7 The Centers for Disease Control & Prevention confirmed to me that, although money is provided to states to collect data on marriage and divorce, many states have opted not to do so because of the prohibitive cost. 5 Defined by the U.S. Bureau of Labor Statistics as a person who files any notice of unemployment to initiate a request either for a determination of entitlement to and eligibility for compensation, or for a subsequent period of unemployment within a benefit year or period of eligibility [ 6 Based on estimates from the U.S. Census Bureau [ 7 The following observations are missing for divorce: Georgia ( ), Hawaii ( ), Louisiana (2001, ), Minnesota ( ), Oklahoma ( ). 6

8 Table 1: Variable Descriptions and Sources Name Description Source bankruptcy Number of aggregate consumer bankruptcy filings per Administrative Office of 1000 people. the U.S. Courts ch7 Number of Chapter 7 consumer bankruptcy filings per Administrative Office of 1000 people. the U.S. Courts ch13 Number of Chapter 13 consumer bankruptcy filings per Administrative Office of 1000 people. the U.S. Courts divorce Number of divorces (and annulments) per 1000 people. CDC 8 /NCHS 9 laynow Number of initial claimants per 1000 people of current U.S. Bureau of Labor year. Statistics laypre Number of initial claimants per 1000 people of previous U.S. Bureau of Labor year. Statistics medical Percentage of population covered by either private or U.S. Census Bureau government health insurance. median Median household income in 2011 CPI-U-RS 10 adjusted U.S. Census Bureau dollars. auto Auto debt 11 balance per capita in 2002 CPI-U 12 adjusted FRBNY 13 Consumer Credit dollars. Panel autod Percentage of auto debt balance that is 90+ days FRBNY Consumer Credit delinquent. Panel credit Credit card debt balance per capita in 2002 CPI-U FRBNY Consumer Credit adjusted dollars. Panel creditd Percentage of credit card debt balance that is 90+ days FRBNY Consumer Credit delinquent. Panel mortgage Mortgage debt balance per capita in 2002 CPI-U FRBNY Consumer Credit adjusted dollars. Panel mortgaged Percentage of mortgage debt balance that is 90+ days FRBNY Consumer Credit delinquent. Panel Note that the data extracted from the Administrative Office of the U.S. Courts for the variables bankruptcy, ch7 and ch13 were given originally as total filings. I converted the data so that it became the number of flings per 1000 people by using intercensal population estimates from the US Census Bureau. Furthermore, the variables auto, credit and mortgage were given by the FRBNY Consumer Credit Panel in nominal terms before being adjusted for inflation by using data from the US Inflation Calculator 14. One must point out that some academics doubt the accuracy of the bankruptcy data collected by the Administrative Office of the U.S. Courts. The Administrative Office is responsible for recording the official data on both business bankruptcy filings and consumer bankruptcy filings. From 1985 to 2005, business bankruptcies as a percentage of total bankruptcies (business and consumer) monotonically decreased from 18.3% to 2.0%. 15 Lawless and Warren (2005) investigated this trend 8 Centers for Disease Control and Prevention 9 National Center for Health Statistics 10 CPI Research Series Using Current Methods 11 Debt from an automobile loan (a personal loan used to purchase an automobile). 12 CPI for All Urban Consumers 13 Federal Reserve Bank of New York 14 [ to-2008/] 15 See Appendix 7

9 by comparing the Administrative Office s data on business bankruptcies against other measures of business failures. The data on business failures from Dun and Bradstreet 16 were positively correlated with the Administrative Office s data on business bankruptcies up until 1985 but then, from 1986 to 1998, there existed a negative correlation between the two measures. Data from the Small Business Administration 17 also suggested that the Administrative Office s data was undercounting business bankruptcies. Lawless and Warren concluded that since the mid-eighties, many business bankruptcies were being filed as consumer bankruptcies instead. It is claimed that gray areas are often part and parcel of smaller business bankruptcies (Frasier, 1996, p.314) and so there is a prevalent error in identifying bankruptcy cases as business or consumer. Nevertheless, one must accept that the data collected from the Administrative Office is the official count of consumer bankruptcy filings and the best data we have. However, it is clear that improvements can be made to the ways in which consumer bankruptcy data is collected and processed in order to improve the accuracy and reliability of the data. Table 2: Summary Statistics of Variables Variable Observations Mean Std. Dev. Minimum Maximum bankruptcy ch ch divorce laynow laypre medical median auto autod credit creditd mortgage mortgaged METHODOLOGY Pooled ordinary least squares (OLS) estimation, fixed effects estimation and random effects estimation will all be used to test the hypotheses made previously in the Theoretical Framework section. It will then be decided as to which of these three econometric techniques gives the most reliable inferences. POOLED OLS For the pooled OLS estimation, we will focus on the following model: 16 Dun & Bradstreet is a credit-reporting and business information firm. More details about the firm can be found on their website [ 17 The Small Business Administration is a U.S. government agency that assists small businesses. More details about the agency can be found on their website [ 8

10 Notice that the dependent variable and the four independent variables we are interested in are logged. This is done so that our inferences can be made in percentage terms rather than absolute terms. Thus, it will enable us to see how a one percent change in the divorce rate, layoff rate and medical insurance rate will affect the percentage change in the bankruptcy rate. One important assumption of this model is that for any given values of the independent variables, the expected error term is equal to zero. In other words, the error term must be uncorrelated with each and every independent variable in the model (Barr, 2012). If there is a variable omitted from the model which is correlated with both the dependent variable and one or more of the independent variables then this assumption is violated and hence our inferences will no longer be reliable. Thus, although we are only really interested in the coefficients of lndivorce, lnlaynow, lnlaypre and lnmedical, seven other independent variables have been included in the model in order to mitigate omitted variable bias. We would expect a negative correlation between bankruptcy and the median household income as we would expect a higher income would increase the likelihood of being able to repay debts. Moreover, it is also highly likely that the amount of auto, credit card and mortgage debt affects someone s decision to file for bankruptcy. The proportion of auto, credit card and mortgage debt that is delinquent will also be likely to be correlated with bankruptcy. Thus, the seven additional regressors are all suspected to be correlated with the dependent variable. If they are also correlated with at least one of lndivorce, lnlaynow, lnlaypre or lnmedical then they should be included in the model. Divorces are often caused by financial strain and all of the seven additional variables have an influence on the financial stability of a household. We would also expect that current and previous layoffs would be correlated with delinquent debt due to the loss of income from losing one s job. Furthermore, a higher income is likely to be positively correlated with being able to afford medical insurance. This provides us with good reasoning to include median, auto, autod, credit, creditd, mortgage and mortgaged in our model so that we deal with the problem of endogeneity. The 9

11 suspected correlations have all been tested and the inclusion of all the independent variables in the model is justified. 18 One major problem with using pooled OLS estimation with a panel dataset is that it is highly likely that the model suffers from serial correlation. In other words, it is very likely that the error term in one year is correlated to the error term in another year and so the estimated standard errors will not reflect the true standard errors and this could lead to incorrect inferences. FIXED EFFECTS Fixed effects estimation is based on a model of time-demeaned variables. First, consider the following equation: Notice that in this equation the error term has been decomposed into two parts: the time-invariant error term that is common to all of the observations relating to a particular state; the idiosyncratic error term that varies across each state and across time even within a state (Barr, 2012). Next, note that in order to average a variable over time we apply the formula: Now, consider the following equation of variables averaged over time: A time-demeaned variable is given by the formula: Thus, subtracting equation (2) from equation (1) will give us the fixed effects transformation of the model: We can see that the time-invariant error term is no longer included in the model after the fixed effects transformation. Thus, in fixed effects estimation it does not matter if an independent 18 See Appendix 10

12 variable is correlated with the time-invariant error. One important assumption, however, is that for any given values of the independent variables and the time-invariant error term, the expected idiosyncratic error term must equal zero for each time period. Furthermore, there should be no perfect linear relationship between the independent variables. The last two assumptions are required (but not sufficient) to get unbiased estimates. We also require the idiosyncratic error term to be homoscedastic. (Wooldridge, 2009) A flaw of fixed effects estimation is that any time-invariant independent variable will not be included in the model as time-demeaning it will make it equal to zero. Fortunately, all our independent variables vary over time. 19 As previously mentioned, our data on divorce has missing observations. However, for each state we have observations from at least two years which makes fixed effects estimation feasible. Also, if the reason we have missing data for some i is not correlated with the idiosyncratic errors, the unbalanced panels cause no problems (Wooldridge, 2009, p.488). As there is no reason to believe that a state s ability to afford data collection on divorce is correlated with our idiosyncratic error term, the missing observations should cause no harm. RANDOM EFFECTS The third econometric technique used in this study is random effects estimation. The composite error term (the aggregate of the idiosyncratic error term and the time-invariant error term) is used in the model for random effects. As a result of the inclusion of the time-invariant error term in the model, serial correlation will remain a problem. Random effects estimation overcomes this problem by using generalised least squares (GLS) as opposed to the OLS used in the other two techniques (Wooldridge, 2009). The GLS transformation of the model which is used in random effects estimation is: Each variable in the model has been partially-demeaned: enough to deal with the problem of serial correlation but without throwing away too much variation. In this sense, random effects estimation is more efficient than fixed effects estimation and thus leads to better inferences. However, unlike fixed effects estimation, random effects estimation requires that the time-invariant error is uncorrelated with all the independent variables and that it is homoscedastic. The value of λ in the model is greater than zero and no larger than one. It can be shown that if λ were to equal zero, the model would be the same as the pooled OLS model and if λ were to equal one, the model would be the same as the fixed effects model. (Barr, 2009) 19 See Appendix 11

13 To summarise, pooled OLS estimation includes all of the time-invariant error in the model, random effects estimation partially includes the time-invariant error in the model and fixed effects does not include any of the time-invariant error in the model. Thus, it is useful to use all three techniques and compare the three sets of estimates in order to analyse the bias caused by the inclusion of the timeinvariant error. Even if the time-invariant error term is uncorrelated with all the independent variables in all the time periods, pooled OLS estimation still tends to suffer from serial correlation in its error terms. Hence, fixed effects and random effects estimations give us more valid inferences. The latter is preferred to the former as it is more efficient but only if the time-invariant errors are uncorrelated with the independent variables. In order to choose which estimation to use, a Hausman test will be carried out which looks at the sampling variation of the fixed effects estimates and also compares the coefficients of the fixed effects and random effects estimates. (Wooldridge, 2009) EMPIRICAL RESULTS Table 4 gives an overview of all the regression results. STATA, the software used in this study, has built-in features to control for heteroscedasticity in all three econometric techniques used. 20 However, even with homoscedasticity, pooled OLS estimation still suffers from serial correlation in its error terms. Thus, our focus turns to fixed effects and random effects estimation and a Hausman test is used to determine which of these estimations we will make our inferences from. The random effects model is the more efficient of the two models and the fixed effects model the more consistent. The Hausman test checks that the more efficient random effects model gives consistent estimates by comparing it against the more consistent fixed effects model (Princeton University DSS, 2003). The Hausman test was run for each of the three dependent variables and the results can be seen in Table 3. Table 3: Hausman Tests Bankruptcy Chapter 7 Chapter 13 Use fixed effects estimation Use random effects estimation Use fixed effects estimation Interestingly, the Hausman test suggests using fixed effects estimation for bankruptcy and Chapter 13 but not Chapter 7. This suggests that there is some omitted time-invariant variable which is correlated with Chapter 13 but not Chapter 7. Because of this omitted variable, random effects estimation for bankruptcy and Chapter 13 will suffer from endogeneity. It can be speculated as to what this time-invariant omitted variable is. Prior to BAPCPA, the consumer could choose between Chapter 7 and Chapter 13 and so anything which would affect the consumer s decision to file for one chapter would inevitably affect his/her decision to file under the other chapter. Hence, it is most likely that this omitted variable was 20 See Appendix 12

14 correlated with Chapter 13 and not Chapter 7 only after BAPCPA when consumers lost the privilege to choose between the chapters: so a variable which affected the consumer s decision to file for one chapter no longer necessarily affected their decision to file for the other. As Chapter 13 was forced on those who had incomes exceeding a certain threshold, this omitted variable should only affect those with incomes above the threshold and not those below as then the omitted variable would also influence Chapter 7 filings. One supposition is that only those with relatively high incomes are affected by the stigma associated with bankruptcy due to their social class and that this stigma varies across states but not over time. Another theory is that recent college graduates have large student debts which makes them more likely to file for bankruptcy. Since college graduates earn relatively high incomes, they will only be eligible to file under Chapter 13 and not Chapter 7. However, for this conjecture to hold, the number of recent college graduates per thousand will have to be time invariant which may not be the case. Nevertheless, we will use the results from the Hausman test to select the results we will make our inferences from as summarised in Table 5. 13

15 Table 4: Pooled OLS, Fixed Effects and Random Effects Regression Results POOLED OLS FIXED EFFECTS RANDOM EFFECTS Bankruptcy Chapter 7 Chapter 13 Bankruptcy Chapter 7 Chapter 13 Bankruptcy Chapter 7 Chapter 13 lndivorce (0.0817) [8.20]*** (0.0822) [10.13]*** (0.1814) [2.57]** (0.1156) [-0.22] (0.1196) [-0.26] (0.2011) [1.87]* (0.1114) [1.58] (0.1285) [1.68]* (0.2109) [2.16]** lnlaynow (0.0480) [-0.08] (0.0431) [-0.34] (0.1103) [0.35] (0.0248) [-2.43]** (0.0266) [-2.03]** (0.0436) [-1.55] (0.0247) [-2.12]** (0.0257) [-2.04]** (0.0430) [-1.13] lnlaypre (0.0460) [1.49] ( ) [1.34] (0.1063) [1.59] (0.0219) [2.46]** (0.0234) [2.95]*** (0.0397) [0.99] (0.0218) [2.98]*** (0.0220) [3.30]*** (0.0401) [1.55] lnmedical (0.5315) [1.15] (0.4621) [-0.05] (1.2263) [1.10] (0.7031) [0.36] (0.6867) [-0.03] (1.0567) [1.71]* (0.6750) [0.44] (0.6591) [0.21] (1.0592) [1.45] median () [1.33] () [0.74] () [3.19]*** - () [-2.56]** - () [-2.44]** () [1.82]* - () [-2.90]*** - () [-2.73]*** () [1.08] auto (0.0001) [-2.07]** () [-5.68]*** (0.0001) [1.76]* (0.0001) [-2.54]** (0.0001) [-2.72]*** (0.0002) [-1.32] (0.0001) [-2.43]** (0.0001) [-3.04]*** (0.0001) [-0.93] autod (0.0331) [0.50] (0.0341) [-2.09]** (0.0597) [4.12]*** (0.0167) [-0.98] (0.0163) [-2.09]** (0.0417) [-0.65] (0.0213) [-0.42] (0.0219) [-1.88]* (0.0404) [0.19] credit (0.0001) [-7.90]*** (0.0001) [-6.13]*** (0.0001) [-5.45]*** (0.0001) [5.09]*** (0.0001) [4.29]*** (0.0002) [4.32]*** (0.0001) [3.19]*** (0.0001) [3.17]*** (0.0002) [3.41]*** creditd (0.0114) [2.85]*** (0.0119) [-1.76]* (0.0283) [7.29]*** (0.0151) [2.54]** (0.0173) [2.64]** (0.0344) [0.57] (0.0152) [3.20]*** (0.0181) [2.26]** (0.0307) [1.86]* mortgage () [3.91]*** () [4.05]*** () [2.89]*** - () [-4.07]*** - () [-3.33]*** - () [-3.74]*** - () [-3.42]*** - () [-2.94]*** - () [-3.65]*** mortgaged (0.0196) [2.63]*** (0.0204) [4.80]*** (0.0335) [-2.31]** (0.0125) [0.96] (0.0139) [1.21] (0.0267) [0.14] (0.0125) [1.00] (0.0150) [1.74]* (0.0231) [-0.57] 14

16 Table 4 Continued POOLED OLS FIXED EFFECTS RANDOM EFFECTS Bankruptcy Chapter 7 Chapter 13 Bankruptcy Chapter 7 Chapter 13 Bankruptcy Chapter 7 Chapter 13 d (0.0722) [1.08] (0.0642) [2.69]*** (0.1723) [-0.89] (0.0522) [-1.01] (0.0548) [-1.18] (0.0976) [0.18] (0.0469) [-1.11] (0.0514) [-0.69] (0.0830) [-0.40] d (0.0744) [1.79]* (0.0687) [4.16]*** (0.1755) [-1.28] (0.0745) [1.48] (0.0776) [1.36] (0.1404) [1.87]* (0.0648) [1.51] (0.0691) [1.99]** (0.1197) [1.45] d (0.0739) [1.16] (0.0696) [3.89]*** (0.1845) [-1.88]* (0.0755) [1.41] (0.0786) [1.39] (0.1579) [1.86]* (0.0663) [1.48] (0.0699) [2.13]** (0.1343) [1.50] d (0.0759) [4.97]*** (0.0709) [8.65]*** (0.1829) [-1.43] (0.0732) [6.82]*** (0.0769) [7.57]*** (0.1547) [2.67]** (0.0633) [7.66]*** (0.0670) [9.12]*** (0.1351) [2.43]** d (0.0833)*** [-12.36]*** (0.0763) [-12.59]*** (0.1822) [-6.40]*** (0.0990) [-8.27]*** (0.1048) [-9.46]*** (0.1876) [-0.33] (0.0895) [-9.55]*** (0.0944) [-10.20]*** (0.1586) [-1.31] d (0.0778)*** [-9.02]*** (0.0745) [-8.86]*** (0.1734) [-4.21]*** (0.0907) [-6.10]*** (0.0988) [-7.12]*** (0.1919) [0.72] (0.0787) [-7.10]*** (0.0850) [-7.77]*** (0.1601) [0.31] d (0.0790)*** [-7.07]*** (0.0779) [-6.12]*** (0.1747) [-3.83]*** (0.0797) [-4.39]*** (0.0835) [-5.26]*** (0.1810) [1.42] (0.0721) [-5.03]*** (0.0738) [-5.46]*** (0.1590) [1.01] d (0.0901) [-7.14]*** (0.0903) [-4.66]*** (0.1897) [-6.57]*** (0.0838) [-1.69]* (0.0852) [-2.39]** (0.2017) [2.43]** (0.0896) [-2.44]** (0.0939) [-2.09]** (0.1879) [1.41] _cons (2.4354) [-0.61] (2.1091) [0.79] (5.6578) [-1.64] (3.1104) [-0.02] (3.0442) [0.33] (4.5543) [-2.41]** (3.0182) [-0.08] (2.9792) [0.10] (4.5322) [-2.12]** Results are shown in the format: Coefficient, (Cluster Robust Standard Error), [t-statistic / z-statistic]. *Significant at the 10% level **Significant at the 5% level ***Significant at the 1% level Note that the coefficients and standard errors have been rounded to four decimal places and the t-statistics and z-statistics to two decimal places. 15

17 Table 5: Main Results Bankruptcy (fe) Chapter 7 (re) Chapter 13 (fe) lndivorce (0.1156) (0.1285) (0.2011) [-0.22] [1.68]* [1.87]* lnlaynow (0.0248) [-2.43]** lnlaypre (0.0219) [2.46]** lnmedical (0.7031) [0.36] median -7.15e-06 (2.80e-06) [-2.56]** auto (0.0001) [-2.54]** autod (0.0167) [-0.98] credit (0.0001) [5.09]*** creditd (0.0151) [2.54]** mortgage -1.56e-05 (3.84e-06) [-4.07]*** mortgaged (0.0125) [0.96] d (0.0522) [-1.01] d (0.0745) [1.48] d (0.0755) [1.41] d (0.0732) [6.82]*** d (0.0990) [-8.27]*** d (0.0907) [-6.10]*** d (0.0797) [-4.39]*** d (0.0838) [-1.69]* _cons (3.1104) [-0.02] (0.0257) [-2.04]** (0.0220) [3.30]*** (0.6591) [0.21] -7.30e-06 (2.67e-06) [-2.73]*** (0.0001) [-3.04]*** (0.0219) [-1.88]* (0.0001) [3.17]*** (0.0181) [2.26]** -1.29e-05 (4.38e-06) [-2.94]*** (0.0150) [1.74]* (0.0514) [-0.69] (0.0691) [1.99]** (0.0699) [2.13]** (0.0670) [9.12]*** (0.0944) [-10.20]*** (0.0850) [-7.77]*** (0.0738) [-5.46]*** (0.0939) [-2.09]** (2.9792) [0.10] (0.0436) [-1.55] (0.0397) [0.99] (1.0567) [1.71]* 1.23e-05 (6.74e-06) [1.82]* (0.0002) [-1.32] (0.0417) [-0.65] (0.0002) [4.32]*** (0.0344) [0.57] -2.73e-05 (7.31e-06) [-3.74]*** (0.0267) [0.14] (0.0976) [0.18] (0.1404) [1.87]* (0.1579) [1.86]* (0.1547) [2.67]** (0.1876) [-0.33] (0.1919) [0.72] (0.1810) [1.42] (0.2017) [2.43]** (4.5543) [-2.41]** Results are shown in the format: Coefficient, (Cluster Robust Standard Error), [t-statistic / z-statistic]. *Significant at the 10% level **Significant at the 5% level ***Significant at the 1% level Note that the t-statistics and z-statistics have been rounded to two decimal places and the coefficients and standard errors to four decimal places except in the case where this gives zero where it is instead rounded to three significant figures. 16

18 In regard to the first hypothesis, it seems that there is no significant relationship between the divorce rate and the aggregate bankruptcy rate. However, a positive relationship exists between the divorce rate and both the Chapter 7 rate and the Chapter 13 rate, albeit only at the 10% significance level. A 1% increase in the divorce rate is associated with a 0.22% increase in the Chapter 7 rate and a 0.38% increase in the Chapter 13 rate. Although these results are not overwhelming, they confirm that divorce does in fact influence bankruptcy. Significant results are also found regarding layoffs and their effect on the aggregate bankruptcy rate and the Chapter 7 rate but not the Chapter 13 rate. The number of layoffs in the same calendar year (laynow) measures the contemporaneous effect of being laid off and the number of layoffs in the previous calendar year (laypre) measures the delayed effect of being laid off. It makes sense that layoffs do not affect Chapter 13 bankruptcies as consumers would have been less likely to have a job when filing and so could not engage in a repayment plan. As expected, the relationship between lagged layoffs and bankruptcy is positive: a 1% increase in the previous year s layoff rate is associated with a 0.05% increase in the bankruptcy rate and a 0.07% increase in the Chapter 7 rate. On the other hand, the relationship between instant layoffs and bankruptcy was significant and surprisingly negative: a 1% increase in the current year s layoff rate is associated with a 0.06% fall in the bankruptcy rate and a 0.05% decrease in the Chapter 7 rate. After BAPCPA, the consumer s income was measured based on his/her average income over the six months prior to filing. This income would then determine whether he/she exceeded the threshold to file under the more favourable Chapter 7. Thus, it may have been that those who were laid off purposely delayed filing so that their measured income would be lower. Perhaps the most disappointing result with respect to the three hypotheses is on health insurance. The signs on the coefficients on health insurance are positive for bankruptcy, Chapter 7 and Chapter 13, though only significantly for the latter. A 1% increase in the proportion of the population with health insurance is associated with a 0.25% increase in the bankruptcy rate and a 1.81% rise in the Chapter 13 rate. This unanticipated result may be due to the percentage of people with health insurance being a poor indicator of the impact of facing unexpected medical costs. Firstly, the quality of health insurance may differ between insurance provided by employment, insurance purchased directly and insurance provided by the government. Furthermore, even those who are insured at the time of illness may still face additional out-of pocket costs (Himmelstein et al, 2005). In hindsight, a better variable to use would have been out of insurance medical costs; though such data is unavailable on a state-level. Most of the control variables turned out as expected. An increase in the real household income of $1,000 (in 2011 dollars) is associated with a 0.72% decrease in the bankruptcy rate, driven mainly by its negative impact on the Chapter 7 rate. A significant positive relationship holds between the bankruptcy rate and credit card debt and credit card delinquency: a real $100 increase (in 2002 dollars) increases the bankruptcy rate by 5%; a one percentage point increase in the percentage of credit card debt that is delinquent increases the bankruptcy rate by 3.84%. Mortgage and auto debt have a significant negative relationship with the bankruptcy rate, possibly because the largest loans go to those with the best credit rating. The percentage of mortgage debt and auto debt delinquent was only significant for Chapter 7 and only at the 10% significance level. However, for auto debt delinquency the coefficients are surprisingly negative. 17

19 As anticipated, the 2005 year dummy was positive and highly significant as, for the first ten months, consumers rushed to file for bankruptcy before the more stringent laws of BAPCPA were in place. In the years that followed, the bankruptcy rate declined as a result of the less debtor-friendly litigation. Table 6 shows the results of testing consecutive dummy variables against each other to confirm their significance. Table 6: Testing Dummy Variables test d04=d05 test d05=d06 test d06=d07 test d07=d08 test d08=d09 Bankruptcy Chapter 7 Chapter 13 Moreover, regressions were run to see if the impact of divorce, layoffs and medical insurance changed over time. 21 From , the positive relationship between the divorce rate and the Chapter 13 rate fell significantly. Given the timescale, this change was not due to BAPCPA and it is unclear as to why exactly this is the case. However, the relationship of the current and previous year s layoff rate with both the bankruptcy rate and the Chapter 7 rate became more positive in the three calendar years after BAPCPA. This may be because BAPCPA eliminated the impact of consumers filing tactically to maximise their financial gains which made the impact of adverse effects like layoffs more influential in dictating the bankruptcy rate. The relationship between health insurance and the bankruptcy rate also became significantly more positive from 2006 to 2009, supporting this hypothesis. CONCLUSION This study has found that the adverse effects of divorce and job loss are indeed determinants of bankruptcy. If the divorce rate rises by 1% then the Chapter 7 bankruptcy rate and Chapter 13 bankruptcy rate increase by 0.22% and 0.38% respectively. Moreover, the lagged effect of the layoff rate has a significant positive relationship with the rate of bankruptcy: a 1% increase in the former leads to a 0.05% rise to the latter. However, the contemporaneous effect of layoffs on bankruptcy was significantly negative, suggesting that some filers may have purposely chosen not to file for bankruptcy immediately after losing their jobs in order to lower their perceived income and become eligible for Chapter 7 filing. Furthermore, having health insurance was shown to not have any real impact on the bankruptcy rate which may suggest that the adverse effect of high medical costs does not influence bankruptcy. However, it may be that having health insurance does not stop one from facing unexpected medical costs and so the relationship between this adverse effect and bankruptcy may in fact exist. Hence, 21 See Appendix 18

20 this study could have been improved by using a better measure of unforeseen medical costs. Moreover, other variables could have been included in our models to deal with endogeneity such as the number of subprime mortgages, casino gambling expenditure, a measure of social stigma and a measure of the level of asset exemptions in each state. It must also be pointed out that the Administrative Office of the US Courts ought to rethink the way in which their data is collected as it is used to shape bankruptcy policy which not only affects the credit industry but millions of ordinary people every year. Such data inaccuracies can undermine the reliability of the academic studies which try to unravel the determinants of bankruptcy. 19

21 BIBLIOGRAPHY Academic Publications: Daraban, B. & Thies, C.F. (2010). Estimating the Effects of Casinos and of Lotteries on Bankruptcy: A Panel Data Set Approach, Journal of Gambling Studies, Vol. 27, pp Domowitz, I. & Sartain, R.L. (1999). Determinants of the Consumer Bankruptcy Decision, Journal of Finance, Vol. 54(1), pp Dranove, D. & Millenson, M.L. (2006). Medical Bankruptcy: Myth versus Fact, Health Affairs, Vol. 25(2), pp Fay, S., Hurst, E. & White, M.J. (2002). The Household Bankruptcy Decision, American Economic Review, Vol. 92(3), pp Frasier, J.C. (1996). Caught in a Cycle of Neglect: the Accuracy of Bankruptcy Statistics, 101 COM. L.J Gross, D.B. & Souleles, N.S. (2002). An Empirical Analysis of Personal Bankruptcy and Delinquency, The Review of Financial Studies, Vol. 15(1), pp Himmelstein, D.U., Warren, E., Thorne, D. & Woolhandler, S. (2005). Illness and Injury as Contributors to Bankruptcy, Health Affairs, Vol. 24, pp Laibson, D., Repetto, A. & Tobacman, J. (2000). A Debt Puzzle, NBER Working Paper No Lawless, R.M. & Warren, E. (2005). The Myth of the Disappearing Business Bankruptcy, California Law Review, Vol. 93(3), pp Sullivan, T., Elizabeth W. & Westbrook, J.L. (2000). "The Fragile Middle Class: Americans in Debt, Yale University Press White, M.J. (2007). Bankruptcy Reform and Credit Cards", NBER Working Paper No Lectures: Barr, A. (2012). Simple and Multiple Regression Models: A review Lecture 1, L13520 Econometrics Project, University of Nottingham Barr, A. (2012). Panel data analysis (continued) - Lecture 5, L13520 Econometrics Project, University of Nottingham Barr, A. (2012). Panel data analysis (continued) - Lecture 7, L13520 Econometrics Project, University of Nottingham 20

22 Texts: Wooldridge, J.M. (2009). Introductory Econometrics: A Modern Approach, 4 th edition, South- Western Cengage Learning Websites: Egan, T. (2005). Newly Bankrupt Raking in Piles of Credit Offers, The New York Times, [URL: Unknown author (2011), Top 20 Celebrities who have Filed Bankruptcy, How to Save Money, [URL: Unknown author (2007), Bankruptcy Law & Legal Definition, US Legal, [URL: Unknown author (2003), Panel Data, Princeton University: Data and Statistical Services, [URL: 21

23 APPENDIX DATA The monotonic decrease of business bankruptcies as a percentage of total bankruptcies (15 th footnote) 20% 18% 16% 14% 12% 10% 8% 6% 4% 2% 0% The data is extracted from the Administrative Office of the US courts. The chart shows business bankruptcies as a percentage of total bankruptcies from 1985 to 2005 for the twelve months ending June 30 th. METHODOLOGY Variable Correlations (18 th footnote) lnbankruptcy lnch7 lnch13 median auto autod credit creditd mortgage mortgaged lndivorce lnlaynow lnlaypre lnmedical median auto autod credit creditd mortgage mortgaged

24 All the independent variables are time variant (19 th footnote) lndivorce The year. Mean Std. Dev. Freq Total lnlaynow The year. Mean Std. Dev. Freq lnlaypre The year. Mean Std. Dev. Freq lnmedical The year. Mean Std. Dev. Freq

25 median The year. Mean Std. Dev. Freq auto The year. Mean Std. Dev. Freq autod The year. Mean Std. Dev. Freq credit The year. Mean Std. Dev. Freq

26 creditd The year. Mean Std. Dev. Freq mortgage The year. Mean Std. Dev. Freq mortgaged The year. Mean Std. Dev. Freq Total

27 lnbankruptcy as dependent variable EMPIRICAL RESULTS Heteroscedasticity of pooled OLS (20 th footnote) White's test for Ho: homoskedasticity against Ha: unrestricted heteroskedasticity chi2(173) = Prob > chi2 = Cameron & Trivedi's decomposition of IM-test Source chi2 df p Heteroskedasticity Skewness Kurtosis Total Fitted values resid2 Fitted values 26

28 lnch7 as dependent variable White's test for Ho: homoskedasticity against Ha: unrestricted heteroskedasticity chi2(173) = Prob > chi2 = Cameron & Trivedi's decomposition of IM-test Source chi2 df p Heteroskedasticity Skewness Kurtosis Total Fitted values resid2 Fitted values 27

29 lnch13 as dependent variable White's test for Ho: homoskedasticity against Ha: unrestricted heteroskedasticity chi2(173) = Prob > chi2 = Cameron & Trivedi's decomposition of IM-test Source chi2 df p Heteroskedasticity Skewness Kurtosis Total Fitted values resid2 Fitted values 28

30 Seeing how the relationship between the independent variables of interest and the dependent variables change over time (21 st footnote) lndivorce. xtreg lnbankruptcy lndivorce d02divorce d03divorce d04divorce d05divorce d06divorce d07divor > ce d08divorce d09divorce lnlaynow lnlaypre lnmedical median auto autod credit creditd mortga > ge mortgaged d02 d03 d04 d05 d06 d07 d08 d09, fe cluster(id) Fixed-effects (within) regression Number of obs = 410 Group variable: id Number of groups = 49 R-sq: within = Obs per group: min = 2 between = avg = 8.4 overall = max = 9 F(27,48) = corr(u_i, Xb) = Prob > F = (Std. Err. adjusted for 49 clusters in id) Robust lnbankruptcy Coef. Std. Err. t P> t [95% Conf. Interval] lndivorce d02divorce d03divorce d04divorce d05divorce d06divorce d07divorce d08divorce d09divorce lnlaynow lnlaypre lnmedical median -7.18e e e-06 auto e-06 autod credit creditd mortgage e e-06 mortgaged d d d d d d d d _cons sigma_u sigma_e rho (fraction of variance due to u_i) 29

31 . xtreg lnch7 lndivorce d02divorce d03divorce d04divorce d05divorce d06divorce d07divorce d08d > ivorce d09divorce lnlaynow lnlaypre lnmedical median auto autod credit creditd mortgage mort > gaged d02 d03 d04 d05 d06 d07 d08 d09, re cluster(id) Random-effects GLS regression Number of obs = 410 Group variable: id Number of groups = 49 R-sq: within = Obs per group: min = 2 between = avg = 8.4 overall = max = 9 Wald chi2(27) = corr(u_i, X) = 0 (assumed) Prob > chi2 = (Std. Err. adjusted for 49 clusters in id) Robust lnch7 Coef. Std. Err. z P> z [95% Conf. Interval] lndivorce d02divorce d03divorce d04divorce d05divorce d06divorce d07divorce d08divorce d09divorce lnlaynow lnlaypre lnmedical median -7.56e e e-06 auto autod credit creditd mortgage e e-06 mortgaged d d d d d d d d _cons sigma_u sigma_e rho (fraction of variance due to u_i) 30

32 . xtreg lnch13 lndivorce d02divorce d03divorce d04divorce d05divorce d06divorce d07divorce d08 > divorce d09divorce lnlaynow lnlaypre lnmedical median auto autod credit creditd mortgage mor > tgaged d02 d03 d04 d05 d06 d07 d08 d09, fe cluster(id) Fixed-effects (within) regression Number of obs = 410 Group variable: id Number of groups = 49 R-sq: within = Obs per group: min = 2 between = avg = 8.4 overall = max = 9 F(27,48) = corr(u_i, Xb) = Prob > F = (Std. Err. adjusted for 49 clusters in id) Robust lnch13 Coef. Std. Err. t P> t [95% Conf. Interval] lndivorce d02divorce d03divorce d04divorce d05divorce d06divorce d07divorce d08divorce d09divorce lnlaynow lnlaypre lnmedical median e e auto autod credit creditd mortgage e mortgaged d d d d d d d d _cons sigma_u sigma_e rho (fraction of variance due to u_i) 31

33 lnlaynow. xtreg lnbankruptcy lndivorce lnlaynow d02laynow d03laynow d04laynow d05laynow d06laynow d07l > aynow d08laynow d09laynow lnlaypre lnmedical median auto autod credit creditd mortgage mortg > aged d02 d03 d04 d05 d06 d07 d08 d09, fe cluster(id) Fixed-effects (within) regression Number of obs = 410 Group variable: id Number of groups = 49 R-sq: within = Obs per group: min = 2 between = avg = 8.4 overall = max = 9 F(27,48) = corr(u_i, Xb) = Prob > F = (Std. Err. adjusted for 49 clusters in id) Robust lnbankruptcy Coef. Std. Err. t P> t [95% Conf. Interval] lndivorce lnlaynow d02laynow d03laynow d04laynow d05laynow d06laynow d07laynow d08laynow d09laynow lnlaypre lnmedical median -6.61e e e-07 auto autod credit creditd mortgage e e-06 mortgaged d d d d d d d d _cons sigma_u sigma_e rho (fraction of variance due to u_i) 32

34 . xtreg lnch7 lndivorce lnlaynow d02laynow d03laynow d04laynow d05laynow d06laynow d07laynow d > 08laynow d09laynow lnlaypre lnmedical median auto autod credit creditd mortgage mortgaged d0 > 2 d03 d04 d05 d06 d07 d08 d09, re cluster(id) Random-effects GLS regression Number of obs = 410 Group variable: id Number of groups = 49 R-sq: within = Obs per group: min = 2 between = avg = 8.4 overall = max = 9 Wald chi2(27) = corr(u_i, X) = 0 (assumed) Prob > chi2 = (Std. Err. adjusted for 49 clusters in id) Robust lnch7 Coef. Std. Err. z P> z [95% Conf. Interval] lndivorce lnlaynow d02laynow d03laynow d04laynow d05laynow d06laynow d07laynow d08laynow d09laynow lnlaypre lnmedical median -6.78e e e-06 auto autod credit creditd mortgage e e-06 mortgaged d d d d d d d d _cons sigma_u sigma_e rho (fraction of variance due to u_i) 33

35 . xtreg lnch13 lndivorce lnlaynow d02laynow d03laynow d04laynow d05laynow d06laynow d07laynow > d08laynow d09laynow lnlaypre lnmedical median auto autod credit creditd mortgage mortgaged d > 02 d03 d04 d05 d06 d07 d08 d09, fe cluster(id) Fixed-effects (within) regression Number of obs = 410 Group variable: id Number of groups = 49 R-sq: within = Obs per group: min = 2 between = avg = 8.4 overall = max = 9 F(27,48) = corr(u_i, Xb) = Prob > F = (Std. Err. adjusted for 49 clusters in id) Robust lnch13 Coef. Std. Err. t P> t [95% Conf. Interval] lndivorce lnlaynow d02laynow d03laynow d04laynow d05laynow d06laynow d07laynow d08laynow d09laynow lnlaypre lnmedical median e e auto autod credit creditd mortgage e mortgaged d d d d d d d d _cons sigma_u sigma_e rho (fraction of variance due to u_i) 34

36 lnlaypre. xtreg lnbankruptcy lndivorce lnlaynow lnlaypre d02laypre d03laypre d04laypre d05laypre d06la > ypre d07laypre d08laypre d09laypre lnmedical median auto autod credit creditd mortgage mortg > aged d02 d03 d04 d05 d06 d07 d08 d09, fe cluster(id) Fixed-effects (within) regression Number of obs = 410 Group variable: id Number of groups = 49 R-sq: within = Obs per group: min = 2 between = avg = 8.4 overall = max = 9 F(27,48) = corr(u_i, Xb) = Prob > F = (Std. Err. adjusted for 49 clusters in id) Robust lnbankruptcy Coef. Std. Err. t P> t [95% Conf. Interval] lndivorce lnlaynow lnlaypre d02laypre d03laypre d04laypre d05laypre d06laypre d07laypre d08laypre d09laypre lnmedical median -6.43e e e-08 auto autod credit creditd mortgage e e-06 mortgaged d d d d d d d d _cons sigma_u sigma_e rho (fraction of variance due to u_i) 35

37 . xtreg lnch7 lndivorce lnlaynow lnlaypre d02laypre d03laypre d04laypre d05laypre d06laypre d0 > 7laypre d08laypre d09laypre lnmedical median auto autod credit creditd mortgage mortgaged d0 > 2 d03 d04 d05 d06 d07 d08 d09, re cluster(id) Random-effects GLS regression Number of obs = 410 Group variable: id Number of groups = 49 R-sq: within = Obs per group: min = 2 between = avg = 8.4 overall = max = 9 Wald chi2(27) = corr(u_i, X) = 0 (assumed) Prob > chi2 = (Std. Err. adjusted for 49 clusters in id) Robust lnch7 Coef. Std. Err. z P> z [95% Conf. Interval] lndivorce lnlaynow lnlaypre d02laypre d03laypre d04laypre d05laypre d06laypre d07laypre d08laypre d09laypre lnmedical median -6.39e e e-06 auto autod credit creditd mortgage e e-06 mortgaged d d d d d d d d _cons sigma_u sigma_e rho (fraction of variance due to u_i) 36

38 . xtreg lnch13 lndivorce lnlaynow lnlaypre d02laypre d03laypre d04laypre d05laypre d06laypre d > 07laypre d08laypre d09laypre lnmedical median auto autod credit creditd mortgage mortgaged d > 02 d03 d04 d05 d06 d07 d08 d09, fe cluster(id) Fixed-effects (within) regression Number of obs = 410 Group variable: id Number of groups = 49 R-sq: within = Obs per group: min = 2 between = avg = 8.4 overall = max = 9 F(27,48) = corr(u_i, Xb) = Prob > F = (Std. Err. adjusted for 49 clusters in id) Robust lnch13 Coef. Std. Err. t P> t [95% Conf. Interval] lndivorce lnlaynow lnlaypre d02laypre d03laypre d04laypre d05laypre d06laypre d07laypre d08laypre d09laypre lnmedical median e e auto autod credit creditd mortgage e mortgaged d d d d d d d d _cons sigma_u sigma_e rho (fraction of variance due to u_i) 37

39 lnmedical. xtreg lnbankruptcy lndivorce lnlaynow lnlaypre lnmedical d02medical d03medical d04medical d0 > 5medical d06medical d07medical d08medical d09medical median auto autod credit creditd mortga > ge mortgaged d02 d03 d04 d05 d06 d07 d08 d09, fe cluster(id) Fixed-effects (within) regression Number of obs = 410 Group variable: id Number of groups = 49 R-sq: within = Obs per group: min = 2 between = avg = 8.4 overall = max = 9 F(27,48) = corr(u_i, Xb) = Prob > F = (Std. Err. adjusted for 49 clusters in id) Robust lnbankruptcy Coef. Std. Err. t P> t [95% Conf. Interval] lndivorce lnlaynow lnlaypre lnmedical d02medical d03medical d04medical d05medical d06medical d07medical d08medical d09medical median -6.77e e e-06 auto autod credit creditd mortgage e mortgaged d d d d d d d d _cons sigma_u sigma_e rho (fraction of variance due to u_i) 38

40 . xtreg lnch7 lndivorce lnlaynow lnlaypre lnmedical d02medical d03medical d04medical d05medica > l d06medical d07medical d08medical d09medical median auto autod credit creditd mortgage mort > gaged d02 d03 d04 d05 d06 d07 d08 d09, re cluster(id) Random-effects GLS regression Number of obs = 410 Group variable: id Number of groups = 49 R-sq: within = Obs per group: min = 2 between = avg = 8.4 overall = max = 9 Wald chi2(27) = corr(u_i, X) = 0 (assumed) Prob > chi2 = (Std. Err. adjusted for 49 clusters in id) Robust lnch7 Coef. Std. Err. z P> z [95% Conf. Interval] lndivorce lnlaynow lnlaypre lnmedical d02medical d03medical d04medical d05medical d06medical d07medical d08medical d09medical median -6.49e e e-06 auto e-06 autod credit creditd mortgage e e-06 mortgaged d d d d d d d d _cons sigma_u sigma_e rho (fraction of variance due to u_i) 39

41 . xtreg lnch13 lndivorce lnlaynow lnlaypre lnmedical d02medical d03medical d04medical d05medic > al d06medical d07medical d08medical d09medical median auto autod credit creditd mortgage mor > tgaged d02 d03 d04 d05 d06 d07 d08 d09, fe cluster(id) Fixed-effects (within) regression Number of obs = 410 Group variable: id Number of groups = 49 R-sq: within = Obs per group: min = 2 between = avg = 8.4 overall = max = 9 F(27,48) = corr(u_i, Xb) = Prob > F = (Std. Err. adjusted for 49 clusters in id) Robust lnch13 Coef. Std. Err. t P> t [95% Conf. Interval] lndivorce lnlaynow lnlaypre lnmedical d02medical d03medical d04medical d05medical d06medical d07medical d08medical d09medical median e e auto autod credit creditd mortgage e mortgaged d d d d d d d d _cons sigma_u sigma_e rho (fraction of variance due to u_i) 40