final report

DOES THE PROPORTION OF CHAPTER SEVEN TO

CHAPTER THIRTEEN BANKRUPTCY FILINGS

INFLUENCE CONSUMPTION LEVELS IN THE USA?

By

603934 – Edward Little

604690 – Edwin Moses

610814 – Anthony Nwaorgu

611357 – Edward Broomhall

615719 – George Barratt

Achim Hauck

Independent Study Unit

Due: 23rd March 2015

Word Count: 5960

Contents……………………………………………………….…..I

List of Tables………………………………………………………………..II

List of Figures…………………………………………….…………………II

List of Abbreviations………………………………………..………………II

Abstract………………………………………………………….…………III

Main Report

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

II. Economic Framework……………………………………………..3

III. Literature Review………………………………………….………5

IV. Methodology ……………………………………………...………7

V. Data Analysis & Results…………………………………………13

VI. Limitations…………………………………………….…………19

VII. Conclusion……………………………………………….………20

VIII. Appendix…………………………………………………………21

IX. Bibliography……………………………………………..………40

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List of Tables

Table Number Table Title Page No.

1 Ordinary Least Squares Regression Results: Proportion of Filings

11

2 Ordinary Least Squares Regression: State Exemption Levels

12

3 Johansen Cointegration Test: Trace Test 134 Johansen Cointegration Test: Max-Eigenvalues 145 Granger Causality Test 156 Forecast Error Variance Decomposition 16

List of Figures

Figure Number Figure Description Page No.

1 Initial Ordinary Least Squares Regression Model: Proportion of Filings

8

2 Revised Ordinary Least Squares Regression Model: Proportion of Filings

9

3 Revised Ordinary Least Squares Regression Model: State Exemption Levels

9

List of Abbreviations

Abbreviation Abbreviation Description

BAPCPA Bankruptcy Abuse Prevention and Consumer Protection Act

GDP Gross Domestic Product

OLS Ordinary Least Squares

VAR Vector Autoregression

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FINANCIAL INTERMEDIATION ISU GROUP REPORT

Abstract

Bankruptcy laws have been under increasing scrutiny in recent years. This study empirically examines to what extent a State’s proportion of Chapter Seven to Chapter Thirteen filings for bankruptcy have an effect on the respective State’s Consumption value, in order to add some new perspective to the present debate on consumer bankruptcy laws. We undertake this with the assistance of a panel data set for all 52 States over a sixteen-year period from 1997 – 2012. We find a significant positive correlation between a State’s Proportion of Filings and its respective Consumption per Capita value.

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I. Introduction

The US Bankruptcy code has been in place since 1978 and plays an integral role within the economy (White, 1987). If it is regulated correctly then it can help stimulate investment within the economy leading to economic growth. This is due to the fact that individual consumers are encouraged to borrow and spend because it is possible for them to be relieved of their debts, should they run into financial difficulties. However, if a large number of bankruptcies occurred on mass, then this can lead to economic issues such as low productivity and a subsequent recession. A recession will increase the likelihood of further bankruptcies as the economy spirals downwards, unless policy makers introduce macro-economic techniques to stabilise the economy; such as cutting interest rates.

There are two main types of bankruptcy filings that individuals have access to: Chapter Seven filings and Chapter Thirteen filings.

Under Chapter Seven an individual has exempt assets and non-exempt assets. Once the non-exempt assets have been used to pay off outstanding creditor debts, all of the individual’s other debts are written off. The State the individual resides in has local laws which constitute what will be an exempt asset and what is non-exempt. Under Chapter Thirteen an individual must submit and have a payment plan approved where they commit to repaying their debts over three to five years (Cornwell & Xu, 2014).

Prior to 2005, a number of individuals would strategically file for bankruptcy under Chapter Seven as it meant that they could free themselves of any debts they had acquired. However, in 2005 The Bankruptcy Abuse Prevention and Consumer Protection Act (BAPCPA) was introduced to target these individuals and instead try to force them to file under Chapter Thirteen so their debts were reorganised.

This legislation was generally believed to be an improvement on the bankruptcy system, due to it offering greater protection to creditors, and in 2006, the difference between Chapter Seven and Chapter Thirteen filings fell by 85% (Cornwell & Xu, 2014).

However, this report will analyse whether US States with a higher proportion of Chapter Seven to Chapter Thirteen filings for Bankruptcy also exhibit a higher Consumption per Capita value.

The aim of the report is to examine whether it is beneficial for a State to have a higher proportion of Chapter Seven bankruptcy filings, despite the intentions of the BAPCPA, due to our hypothesis that a high proportion will lead to a higher Consumption per Capita value. Consumption is recognised as the biggest driver of GDP, (Anbao and Danhua, 2011) so a higher proportion of Chapter Seven filings could have a knock-on effect from an increase in Consumption to overall GDP growth.

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The motivation and reasons for the research is to utilise the results produced from the investigation to examine if the US Bankruptcy procedure is set up in order to provide an optimal setting for the economy to grow. Whilst it is generally accepted that the BAPCPA has made the procedure fairer and now offers more protection for creditors, policy makers tend to make decisions in order to optimise economic variables. Therefore this report seeks to present data showing that a higher proportion of Chapter Seven Filings should be encouraged, in order to give weight to the argument that Bankruptcy Laws should again be altered, so that economic variables such as Consumption and GDP are optimised.

Our results show a significant positive correlation between a State’s Proportion of Filings and its respective Consumption per Capita value. Although we recognise further research is required, this provides evidence in support of the hypothesis that a higher proportion of Chapter Seven Filings is beneficial to a State’s Economy.

These results may be particularly useful to US Courts, who may decide given the benefits to the Economy, it may be practical to adjust the State Exemption Levels so a balance can be struck between protecting creditors while providing the best foundations for the Economy to grow.

The paper is organised as follows. The next Section gives a description of the underlying economic theory. In the following section, we examine previous literature relevant to the topic. Section Four discusses the Data, Empirical Model and Methodology used. In Section Five, the results are presented. In Section Six, the various limitations to our investigation are explored and considerations are made as to where the report could be improved. In the final section, concluding remarks are given.

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II. Economic Framework

If an individual files for Bankruptcy under Chapter Seven then they can immediately have all outstanding debts removed from them. Subsequently, the consumer will immediately be able to return to their normal pattern of spending, as they are no longer indebted to creditors and spending a large proportion of their income on repaying outstanding debts. In comparison, an individual who files under Chapter Thirteen must create a payment plan to structure the repayment of their debts over three to five years. It can be seen that this individual’s Consumption will be lower as they must apportion a share of their future income to the payment plan, rather than being spent on goods and services.

Due to this rationale, we expect to see US States with a higher proportion of Chapter Seven to Chapter Thirteen Filings exhibiting higher Consumption per Capita values, ceteris paribus. We expect our empirical methodology to show a positive correlation which is statistically significant.

As well as this, each State has a differing Exemption Level on what assets can be utilised in order to repay creditors. The State Exemption Levels are likely to be heavily correlated with the Proportion of Filings. This is because a high Exemption Level means individuals are less likely to meet the aforementioned Exemption Level and are more likely to qualify for Chapter Seven Filing. We have therefore produced a second model to test the effect of the differing States’ Exemption Levels and have substituted the Proportion of Filings for The States’ Exemption Level. Again, we expect to see a positive correlation between Consumption per Capita and the State’s Exemption Level which is statistically significant.

In addition to this we expect to see a time lag effect for the influence of the Proportion of Filings on Consumption. One reason for this is that bankruptcy proceedings take time, where it is uncommon for bankruptcy processes to be completed in under four months and most take six months to a year. (Mann & Porter, 2010). This means that despite the theory which supports individuals will immediately return to a normal pattern of spending, the administration of a bankruptcy process could inhibit this for up to a year.

Additionally, when an individual declares bankruptcy, it is unlikely these people will immediately begin consuming outside their means again. Instead it is more likely these individuals will act more conservatively so as not to file for bankruptcy a second time. If an individual declares bankruptcy under Chapter Seven, they are not allowed to file under Chapter Seven again for six years (Fay, Hurst & White, 2002). Therefore there is an argument to suggest that these individuals will not return to their normal pattern of consumption until they have the safety net of Chapter Seven filings in place again.

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There is a further consideration which the research encompasses in regards to the consumptions levels and bankruptcy volumes. A high consumption value for a State might mean a large volume of bankruptcies due to the fact people are spending lavishly beyond their means. If these States have high Exemption Levels, then they will also have a large proportion of Chapter Seven filings, so there is an argument to say the causality of a large proportion of Chapter Seven filings is a large consumption not the other way around. As a mitigating factor in response to this we have included the Granger Causality test to check for robustness.

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III. Literature Review

There is a limited availability of literature concerning the topic in question. To the best of our knowledge, this report is original in measuring the effect a State’s proportion of Chapter Seven Filings to Chapter Thirteen Filings for bankruptcy has on the respective State’s value of Consumption per Capita.

The most similar piece of literature that appears to be available is written by Filer and Fisher (2002) who measure the Consumption effects on filing for personal bankruptcy. They use a relatively very small sample to our own of just 137 bankruptcy filings and investigate to what extent filing for bankruptcy had an effect on these individuals’ subsequent consumptions. They found that the households who filed for Chapter Seven during the sample years of 1990-1995, were able to generate a consumption growth of 15%.

Whilst Filer and Fisher (2002) have tried to accurately measure how the decision to file for bankruptcy has affected the consumption of these individual households, their small sample size is a limitation to their research. 137 filings represents a tiny amount in comparison to the total number of filings each year and is therefore highly likely to not be representative of the entire country. They focus on the microeconomic effects filing for bankruptcy has on the utility of a small number of individual consumers, in comparison to the macroeconomic effects set out by this report, investigating if there is a true correlation between the Proportion of Filings and the Consumption per Capita.

Whilst Filer and Fisher (2002) conclude that on average, individuals who filed for bankruptcy are able to subsequently increase their consumption by 15%; Porter and Thorne (2006, pg.1) on the contrary report the “Failure of Bankruptcy’s Fresh Start.” Through their investigation they find that one year post bankruptcy, one in three individuals who file for consumer bankruptcy subsequently report that they are in a financial position similar or worse than when they originally filed for bankruptcy. Their results are surprising given the objective of the bankruptcy system is to relieve individuals of their burdening debts, providing them with a fresh start. However, their results imply that the majority, (two in three individuals), are in a better financial position a year after declaring bankrupt.

The results also show that the reason for some individuals being in a worse position, is due to them being committed to a number of costs they cannot support under their current income, as well as being affected by unexpected shocks such as illness, injury or unemployment. In this regard they find that income is a decisive factor in an individual’s well-being post-bankruptcy. Porter and Thorne (2006) also make their conclusions from qualitative data collected, in the form of questionnaire answers. This is in comparison to

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Filer and Fisher (2002) who use quantitative data to investigate how much individuals are consuming post-bankruptcy, albeit an increase in consumption does not necessarily equate to a successful fresh start after bankruptcy.

In addition to the effect filing for bankruptcy has on Consumption, our report also investigates to what extent differing Exemption Levels have an effect on Consumption. Fay, Hurst and White (2002, pg.1) use their report to investigate “The Household Bankruptcy Decision”, by estimating a model of household bankruptcy decisions, through the use of regressions of independent variables to test several hypotheses. They find that an increase of $1000 in the financial benefit from declaring bankruptcy, which is due to higher Exemption Levels, results in a 7% increase in the probability of an individual declaring bankruptcy. The hypothesis regarding financial benefit uses a similar variable to one of the dependent variables being tested in our study; Income Expectations. Both test the significance of future income in the filing decisions of consumers.

The conditions studied by Fay et. al (2002) are emphasised by the report titled “Consumption, Debt and Portfolio Choice. Testing the effect of Bankruptcy Law”, by Lehnert and Maki (2002, p.1). They also find that higher Exemption Levels are associated with a larger volume of bankruptcy filings as individuals look to take advantage of the opportunity to discharge more of their debts while keeping the assets that they own.

It is widely recognised that the bankruptcy law was initially very debtor friendly, in the sense that creditors had very little protection from debtors defaulting on their debts by declaring bankrupt under Chapter Seven. However in 2005, the BAPCPA was brought in to offer greater protection to Creditors. Cornwell and Xu (2014) used their paper to study if the BAPCPA had any effect on the proportion of Chapter Seven to Chapter Thirteen types of bankruptcy declared and they found that between 1998 and 2008 the number of Chapter Seven filings exceeded that of Chapter Thirteen filings, however a year after the BAPCPA was passed, the difference between the two fell by 85%.

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IV. Methodology

The model has been designed to produce reliable and accurate results which can be analysed to understand the effect a State’s Proportion of Filings has on the respective State’s Consumption. In order to isolate the effect that the Proportion of Filings has, it is necessary to include a number of control variables which have a statistically significant impact on the dependent variable, Consumption. The various independent variables considered for the model have been selected from research surrounding current literature on the consumption function and are as follows:

Income

It is commonly accepted that the biggest factor affecting aggregate consumption is aggregate Income. This has been the case in Macroeconomic theory since Keynes made it the keystone of his theoretical structure in The General Theory (Freidman, 1957, pg.3). Although Income is the biggest driver of Consumption, Keynes (1937) went on to State that Income does not increase Consumption by an equal absolute amount, as in general, a greater proportion of Income is saved as real Income increases.

Even though aggregate Income is the most important contributing factor towards aggregate Consumption, taxation can greatly alter an individual’s final income. Along with nationwide taxation regulations, state specific and local regulations are also in place in the US. To mitigate the problem of differing tax rates for the different communities, Disposable Income per Capita has been used in the model as a measure of Income. As this excludes taxation it will give a true measure of the Income a household is gaining each month.

Income Expectations

Alongside Income, Income Expectations play a vital role in the change in Consumption levels of an economy. Flavin (1981) tells us that as permanent Income is uncertain and likely to change over time, an individual’s consumption provisions will be revised on a monthly period as new information about future Income is available. Furthermore, as permanent Income is heavily related to Consumption in each period, and permanent Income is determined by estimates using current information, this means that Income Expectations will have a large effect on current Consumption. Similarly to Aron, Duca, Muellbauer, Murata and Murphy (2012), information on Income Expectations is sourced from Thomson Reuters/University of Michigan consumer sentiment index.

Unemployment Rate

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Unemployment is another important variable that should be controlled for when investigating the effect a variable has on Consumption. Davis (1984) highlights the fact that as Unemployment levels increase in an economy, individual’s uncertainty also increases meaning an increased level of precautionary saving. Malley and Moutos (1996) carried out more recent research in the US (1952-1992) and used the number of motor vehicles purchased as a proxy for Consumption. They found that the Unemployment level had an inversely significant effect on Consumption, even after Income and Interest Rates have been controlled for. For these reasons Unemployment will be tested in the model.

Interest Rate

It was only 10 years prior to Blinder and Deaton’s (1985) investigation into the consumption function that Interest Rates varied enough for analysis to be carried out on interest elasticity of consumption. The authors go on to theorise that an increased Interest Rate means that individuals holding funds in saving accounts feel an increase in wealth and are more likely to increase Consumption.

In addition to this, a decision on whether to use the real Interest Rate or nominal Interest Rate needs to be made. Firstly, Aron et al. (2012) found that whilst formulating a consumption function for the UK from 1967-2005 that the real Interest Rate produced insignificant results, yet the nominal Interest Rate produced significant results. In addition to this, Mishkin (1976) and Hamburger (1967) concluded that nominal Interest Rate showed a strong inverse relationship on consumer expenditures on durable goods (Gylfason, 1981). On these grounds nominal Interest Rate will be used in the model.

Mortgage Rate

Through the research conducted by Hurst and Stafford (2004), they were able to find that there is a correlation between Mortgage Rates and household Consumption. As a result of the rate dropping, consumers are able to benefit from lower monthly repayments, leading to increased disposable Income. Another response that households take is that in periods of relatively low Mortgage Rates, the household can refinance and gauge their new mortgage to the lower rate. They can benefit from a lower stream of mortgage payments and subsequently receive an increase in lifetime wealth, referred to as the “financial motivation”.

Inflation

Paradiso, Casadio and Rao (2012) were able to state that Inflation also has an effect on Consumption, due to the uncertainty it creates for consumers. Increased Inflation also leads to pessimism about the future, encouraging consumers to save more for a worst case scenario. Households also have the incentive of holding real assets rather than assets fixed in nominal values, including consumer durable purchases.

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Average House Price

Campbell and Cocco (2007) showed through their research that there was a relationship between the Composition of the Household Portfolio (Average House Price) and Consumption. The research was able to estimate the largest house price elasticity of consumption and homeowners, and was even able to show that dependent on age, their elasticity differed. In recent years both the UK and the US have experienced rising property prices and increased levels of Private Consumption.

Further support on the topic was also provided by Flavin and Yamashita (2002), who described in their paper that by virtue of housings markets’ magnitude, it has a specific and important role on the consumption bundle.

Structuring the Panel Dataset and Corresponding Model

We have chosen the sample period 1997-2012, which covers an era of varying circumstances in the USA, including the financial recession and depression, as well as different natural disasters occurring over this time period. The dataset includes all 52 states in the USA and uses a panel data structure, which came from various sources including the US Bureau of Economic Analysis, the US Courts Bank Statistics, the US Bureau of Labor Statistics, University of Michigan, the Federal Reserve, US legal information from a third party, the Lincoln Institute of Land Property and the Federal Housing Finance Agency.

A logarithmic function was applied to the dependent variable, Consumption, whilst the other variables remained in their original state. This was decided because it would likely produce a better representation of the movement of the values and fluctuations among different years and different States. This helps simplify the interpretation of the fluctuations and results of the tests on the dependent variable. The decision to take the logarithmic value of the dependent variable follows the example set out by Filer and Fisher (2002). These values were then put into the model. Initially we started with nine variables in order to construct our econometric model. As shown below:

Figure 1

LNY ¿=α+β1 X¿+β2 DIPC ¿+β3 IE¿+β4 UR¿+β5 IR¿+β6 I ¿+ β7 AHP¿+ β8 MR¿+ε¿

i = {1997, 1998…..2012}

The dependent variable, LNY ¿ , is the consumption per capita where i is the consumption rate, at time period t. The independent variable, β1 X¿, is the Proportion of Chapter Seven to Chapter Thirteen Bankruptcy Filings in each year, at time period t. The eight remaining variables are control variables in order to outline the influence the independent variable had on the dependent variable. These included the disposable income per capita,β2 DIPC¿ , income expectations, β3 IE¿, the unemployment rate, β4 UR¿, the interest rate, β5 IR¿, the inflation rate, β6 I ¿, the average house price, β7 AHP¿ and finally the mortgage

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rate, β8 MR¿. The model also included an error term, ε ¿. A variable of tax was going to be included into the model, but as we are using disposable income per capita tax has already been deducted from income.

Ordinary Least Squares Model

This study uses an OLS (ordinary least squares) regression model with the application of Eviews, a piece of econometric computer software, to explore whether all the variables included in the analysis were significant and therefore influential on the level of Consumption. The chosen variables are theoretically influential on Consumption, however numerical backing of this claim needs to be provided in order to strengthen our theory. It is common in previous literature when analysing Consumption that the variables are evaluated through an OLS model, making it highly appropriate for this piece of research.

In order to decide which variables are insignificant a probability value is calculated. If this value is higher than 0.1 then we consider these variables to be insignificant, however if the probability value produced is lower than 0.1, we accept the corresponding variable at the 10% level of significance.

After applying an OLS regression to the model we found that two variables were insignificant at the 10% level of significance. These were the Average House Price and the Inflation Rate. As stated, any insignificant variables would be removed from the model.

These two variables were removed from our initial model, which resulted in all variables being significant including the interest rate which was originally considered insignificant.

Based from these findings the model was reduced to:

Figure 2

LNY ¿=α+β1 X¿+β2 DIPC ¿+β3 IE¿+β4 UR¿+β5 IR¿+β6 MR¿+ε¿

i = {1997, 1998…..2012}

Alongside the selected model, an additional OLS model is employed in order to produce coefficient values, which gives a basic understanding to how influential the independent variable and control variables are on the level of consumption. This test is run for six time lags to account for households being unable to file under Chapter Seven for six years after their initial filing.

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In addition to the models mentioned above, a similar model has been formulated to determine the effect State Exemption Level has on consumption, using the same control variables. As show below:

Figure 3

LNY ¿=α+β1 SE¿+ β2 DIPC¿+β3 IE¿+ β4UR¿+ β5 IR¿+β6 MR¿+ε ¿

i = {1997, 1998…..2012}Where the proportion of filings, β1 X¿, was removed and the state exemption levels, β1 SE¿, replaced this variable.

Test of Cointegration

As with (Shittu, 2012, p174) a Johansen Cointegration test will be applied to the independent and dependent variables in order to add further demonstration of the link between the level of Chapter Seven and Chapter Thirteen filings and the level of Consumption per Capita within the USA. Cointegrating variables are said to be independent of one another but still move in the same direction (Enders, 2004). In our case this would mean that if the Proportion of Filings increases the level of Consumption would also increase. The test runs two procedures, one being a trace test and the second producing Eigen values in order to test for cointegration. A null and alternative hypothesis will be constructed for each procedure and will have critical values computed at the 5% level of significance. The Trace test and Eigen values have the same null hypothesis but varying alternative hypotheses with the Eigen values pin pointing exactly how many cointegrating variables there are and the Trace test showing the least amount of cointegrating variables there are. These results will determine whether the null hypothesis is rejected or accepted.

The Hypotheses for the Trace test are:Ho = There are r cointegrating variablesHa = There are more than r cointegrating variables

Where r = Number of cointegrating variables

The Hypotheses for the Eigenvalues are:Ho = There are r cointegrating variablesHa = There are r+1 cointegrating variables

Where r = Number of cointegrating variables

The test will produce two values, one being a Trace test value/Eigen value and the second being a computed 5% critical value. If the value of the Trace test/Eigen value is larger than the critical value then the null hypothesis for no cointegration is rejected in favour of

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the alternative hypothesis indicating at the 95% confidence level there are cointegrating variables (Enders, 2004).

The main difference between the Trace test and the Eigen values is that the Trace test is a joint test where the null hypothesis is that the number of cointegrating vectors is less than or equal to r (level of cointegrating variables), against a general alternative that there is a greater number of cointegrating vectors than r (Shittu, 2012).

Granger Causality Test

The Granger Causality test will outline a similar approach to the test of cointegration, in that it outlines the influence the dependent and independent variable have on one another (Asterious & Hall, 2011). In this case the influence the Proportion of Filings has on Consumption levels and how Consumption influences the Proportion of Filings. In order to undertake this approach two hypotheses will be computed, each with one null hypothesis and one alternative hypothesis. This test was similarly run by (Hassan, Sanchez & Suk, 2011) when conducting their macroeconomic research.

These are as follows:

Ho = The Proportion of Filings does not Granger cause ConsumptionHa = The Proportion of Filings Granger causes Consumption

Ho = Consumption does not Granger cause the Proportion of FilingsHa = Consumption Granger causes the Proportion of Filings

A probability value is calculated, which in turn determines whether we reject or accept the null hypothesis. If the probability value is lower than 0.1 or 0.05 than the null hypothesis is rejected at the 10% and 5% levels of significance respectively. This indicates that one variable influences the other.

Forecast Variance Decomposition Model

The last test is the simplest test to be undertaken. Hassan, Sanchez & Suk (2011) ran a Forecast Error Variance Decomposition test and is going to be used in the analysis to help provide an insight to how influential variables are on the level of Consumption and also outline the overall importance of each variable on the level of Consumption. The results are simple to interpret; the entire values given total a total value of one hundred. The level of influence on the dependent variable is given as a percentage value and is simply interpreted as the higher this consequent value, the higher the influence.

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V. Data Analysis and Results

Ordinary Least Squares Regression

Ln(consumption): dependent variable

Variable Coefficient at Time Lag (L) on Proportion of FilingsL=0 L=1 L=2 L=3 L=4 L=5 L=6

Proportion of Filings

0.000565*** 0.000789*** 0.000947*** 0.000870*** 0.001052*** 0.001060*** 0.001118***

Disposable Income per Capita

0.0000261***

0.0000256***

0.0000247***

0.0000242***

0.0000240***

0.0000234***

0.0000231***

Income Expectations

-0.002303*** -0.002302*** -0.001924*** -0.001320*** -0.000949*** -0.000954*** -0.000733**

Interest Rate 0.005413* 0.005925** 0.009945*** 0.013245*** 0.015795*** 0.016958*** 0.016110***

Mortgage Rate -0.018448*** -0.14804** -0.021613*** -0.027774*** -0.005611*** -0.028541*** -0.025195***

Unemployment Rate

-0.005356*** -0.002840* 0.000107 0.002340 0.003484* 0.003085* 0.003469*

Table 1Significant to: 10% = *5% = **1% = ***

Table 1 details the separate coefficients for the various independent variables which have an effect on the dependent variable, Ln Consumption. The results are interpreted as a one unit change in the independent variable having a change to the magnitude of the respective coefficient on the dependent variable. For example, at lag length zero, we find that a one unit change in Disposable Income Per Capita has an increasing effect of 0.0000261 on Ln Consumption.

Our results imply that the Proportion of Filings has a positive impact on Ln Consumption in every time lag, significant at the 1% level of confidence. Our results show that at a lag

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level of zero, a State which experiences a 1% increase in the Proportion of Filings with respect to another State; should also experience an increase of 0.000565 in their Ln Consumption. The results are similar for every time lag as they all show the Proportion of Filings have a positive impact on Ln Consumption. The greatest effect on Ln Consumption, as a result of an increase in the Proportion of Filings, appears to be in the sixth time lag. This is consistent with the theory that individuals do not return to their initial pattern of Consumption until they have the safety net of being able to file for bankruptcy under Chapter Seven in place.

Ordinary Least Squares Regression

Ln(consumption): dependent variable

Variable CoefficientState Exemption Level 0.0000000796***Disposable Income per Capita 0.0000253***Income Expectations -0.002423***Interest Rate 0.003444Mortgage Rate -0.020525***Unemployment Rate -0.008569***

Table 2

Significant to: 10% = *5% = **1% = ***

Table 2 gives a description of the results when the Proportion of Filings is substituted for the State Exemption Level. There are no time lags tested for the State Exemption Level, given that the State Exemption Levels stay constant across all time periods tested. However our results did not include seven States which had unlimited Exemption Levels, due to the skewed results they would have produced in E-views. However, the results again show that an increase in the State Exemption Level also has a positive effect on Consumption. This can be interpreted as States with a higher Exemption Level also experiencing a higher Consumption per Capita. However the coefficient shows the State Exemption Level appears to have a minimal effect on Consumption. This is surprising as it was expected the Proportion of Filings, and State Exemption Levels would be extremely correlated and would therefore show similar impacts on Consumption.

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Johansen Cointegration Test

Trace Test

Hypothesized No. Of CE(s) None At most 1Trace Test L=0 61.65717** 8.634005**Critical Value at 5% 15.49471 3.841466Trace Test L=1 43.05188** 10.16350**Critical Value at 5% 15.49471 3.84166Trace Test L=2 37.44316** 10.84350**Critical Value at 5% 15.49471 3.84166Trace Test L=3 60.53358** 20.67134**Critical Value at 5% 15.49471 3.84146Trace Test L=4 53.48191** 9.225262**Critical Value at 5% 15.49471 3.841466Trace Test L=5 34.71973** 6.327052**Critical Value at 5% 15.49471 3.841466Trace Test L=6 21.33830** 3.281636Critical Value at 5% 15.49471 3.841466

Table 3

Max-Eigenvalues

Hypothesized No. Of CE(s) None At most 1Eigen values L=0 53.02316** 8.634005**Critical Value at 5% 14.26460 3.841466Eigen values L=1 32.88838** 10.16350**Critical Value at 5% 14.26460 3.841466Eigen values L=2 26.59967** 10.84350**

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Critical Value at 5% 14.26460 3.841466Eigen values L=3 39.86223** 20.67135**Critical Value at 5% 14.26460 3.841466Eigen values L=4 44.25665** 9.22562**Critical Value at 5% 14.26460 3.841466Eigen values L=5 28.39268** 6.327052**Critical Value at 5% 14.26460 3.841466Eigen values L=6 18.05666** 3.281636Critical Value at 5% 14.26460 3.841466

Table 4

Significant to:10% = *5% = **1% = ***

The tables above prove to be consistent with other finding and theory, regarding the additional time lags. This is due to the fact that each null hypothesis is rejected at the 5% level of significance as both the Trace test and Eigen values produce a value greater than the critical value at 5%. The combination of both tests results indicate that both ln-Consumption and the Proportion of Filings co-integrate. This goes on to show that with each lag level they are considered cointegrating variables, apart from in lag 6 where the value for both tests for at most one cointegrating variable are insignificant and only have at most one cointegrating value. Overall the ln Consumption influences the level of Proportion of Filings and also that the Proportion of Filings effect ln Consumption. The Trace test values and Eigen values fluctuate throughout this robustness test, but the results for ‘None’ are higher than the results for ‘At most 1’. Therefore it shows that ln-Consumption and Proportion of Filings can be considered cointegrating variables.

Granger Causality Test

Null

Hypothesis

L

=0

L

=1

L

=2

L

=3

L

=4

L

=5

L

=6

Propo

rtion of

Filings does

not Granger

Cause LN

2.

E-11**

2.

E-21**

0.

0010**

1.

E-06**

3.

E-10**

0.

0086**

0.

0101**

16 | P a g e

Consumption

LN

Consumption

does not

Granger Cause

Proportion of

Filings

4.

E-07**

3.

E-05**

0.

9540

1.

E-15**

0.

0090**

2.

E-06**

4.

E-05**

Table 5

Significant to:10% = *5% = **1% = ***

Following the Granger Causality tests done between ln Consumption and Proportion of Filings, it is proven that Consumption has an influence on the Proportion of Filings, and that the Proportion of Filings, similarly has an influence on Consumption. One anomalous result was discovered when analysing lag 2, with a figure showing that consumption is insignificant with its influence over proportion of filings. Similar to the Johansen Cointegration test that was run, although confirming our initial thoughts, these co-coefficients are inconsistent and follow no sort of trend.

Forecast Error Variance Decomposition

P

eriod

Di

sposable

Income

period

Capita

Inco

me

Expectations

I

nterest

Rate

M

ortgage

Rate

Pr

oportion of

Filings

Unempl

oyment Rate

Ln

Consumption

1 14. 7.05 4 0. 0.0 0.33 73.06

17 | P a g e

65 .14 718 4

2 27.

97

10.39 2

.19

0.

95

0.1

8

0.76 57.56

3 28.

03

14.12 4

.42

2.

91

0.1

4

0.58 49.79

4 32.

21

11.96 1

0.49

2.

77

0.2

7

0.52 41.78

5 35.

57

9.63 1

3.51

2.

14

0.2

2

0.83 38.09

6 38.

97

8.15 1

3.1

2.

25

0.2

4

0.81 36.46

7 40.

63

7.48 1

1.78

2.

87

0.6

3

1.36 35.81

8 40.

79

7.98 1

0.63

3.

2

1.2

6

2.39 34.78

9 39.

71

10.57 9

.51

2.

89

1.7

7

3.23 33.14

1

0

38.

24

14.58 8

.47

2.

67

1.9

4

3.47 30.87

1

1

37.

19

17.93 8

.32

2.

79

1.9

1

3.31 28.38

1

2

37.

25

19.26 9

.3

2.

91

1.8

3

3.07 26.14

1

3

38.

4

18.98 1

0.52

2.

75

1.8

6

2.92 24.42

1 40. 18.14 1 2. 2.1 2.92 23.18

18 | P a g e

4 13 1.01 51 1

1

5

41.

67

17.36 1

0.7

2.

41

2.6

7

2.99 22.2

1

6

42.

56

16.91 1

0.06

2.

35

3.4

5

3.4 21.26

Table 6

The variance decomposition indicates the amount of information each variable contributes to the other variables in the auto regression. It determines how much of the forecast error variance of each of the variables can be explained by exogenous shocks to the other variables. It is standard in VAR analysis that a variable explains a huge proportion of its forecast error variable. In our case, this is ln Consumption, which decreases as time decreases. Other than ln Consumption, the most influential variable is Disposable Income per Capita. This variable stays consistently high throughout the forecast period.

The main aim is to show how Proportion of Filings influences ln Consumption. According to the results above, a shock in Proportion of Filings accounts for the lowest level of impact in fluctuation of ln Consumption. This implies that although the Proportion of Filings has an effect on Consumption, its effect is not as great as other variables such as Disposable Income per Capita, Income Expectations and the Interest Rate.

19 | P a g e

VI. Limitations

Although the majority of literature analysing Consumption changes does not control for age, Deaton (2005) explained that consumers make intelligent choices about how much they wish to consume at each age, due to making provisions for retirement. This means working individuals build up and run down assets in order to tailor their consumption patterns at different stages in their life. Further research could include Average Age per State as it will make the effect that the Proportion of Filings has on Consumption more distinguishable and mitigate this problem.

Another limitation to our research is the fact that there is evidence to suggest that it is empirically beneficial to separate Income changes into anticipated and unanticipated effects. This is due to individuals making rational consumption decisions based on expectations (Blinder & Deaton, 1985). This was not included as it is beyond the scope of an undergraduate paper with limited time and resources. Further research could analyse anticipated and unanticipated changes to determine the exact coefficient of Income.

A further drawback in the model is that it does not account for fundamental disasters that greatly altered consumption, e.g. Hurricane Katrina hitting southern America in 2005 and causing over $100 billion in damage (Knabb, Rhome & Brown, 2005). Furthermore, other events that drastically transform consumption levels could also be controlled for by using dummy variables for events such as the 2007-2012 financial crisis.

20 | P a g e

The Exemption Level analysis also experiences a number of problems that could alter the outcomes of the study. Firstly, the Household Exemption Levels used were for able bodied working age single people. This means the model excludes the fact that some states have different regulations for retired and disabled people. Also, many states have rules in place where if working age adults are married and living in the same property as their spouse, then the Household Exemption Level doubles. This has not been accounted for in the research as information on the number of married couples filing for bankruptcy was not available. Furthermore, 7 states did not have a definitive Household Exemption Level and were not included in the research, meaning the whole of the US has not been accounted for.

The research could also be improved by expanding the effect of the Proportion of Filings on Consumption to overall GDP. A higher proportion of filings in some States has been found to show there is less confidence from lenders and creditors and also less credit availability to consumers (Filer and Fisher, 2002). This is because those who are borrowing are more likely to have their debts written off. This in turn may lead to a negative impact on Investment in these States. Further research is required to assess if the effect on Consumption translates to the same effect on overall GDP given that Consumption, is the biggest driver of GDP (Anbao & Danhua, 2011).

VII. Conclusion

There have been a number of debates regarding the impacts of bankruptcy laws within the United States of America, namely the decision to introduce the BAPCPA creating a system which offers greater protection for creditors. In this study, we examine whether despite the introduction of BAPCPA, individuals should be encouraged, from an economic point of view, to file under Chapter Seven as it means they can return quickly to a normal pattern of Consumption.

The model uses a panel data set for all 52 states over a sixteen-year period from 1997-2012 in order to examine to what extent a State’s Proportion of Chapter Seven to Chapter Thirteen Bankruptcy Filings has an effect on the respective State’s Consumption per Capita value, both in the same year and the subsequent six years. This was accomplished through the use of tests on the panel data in the form of an OLS regression, a Johansen cointegration test and a VAR analysis which included a Granger-causality and forecast error variance decomposition tests.

This report finds a significant positive correlation between a State’s Proportion of Filings and its respective Consumption per Capita value, with State Exemption Levels also proving to have a significant positive effect on Consumption. The robustness tests showed results which supported the aforementioned theory, with the Johansen

21 | P a g e

cointegration and Granger causality reiterating a strong relationship between the two investigated factors; Proportion of Filings and Consumption. This study acts as the foundation for research into US bankruptcy filing investigations and provides new insights into the current bankruptcy and Consumption debates. However, the report realises that it is not without limitations which should be taken into account when the research is expanded. Other Independent Variables should be included in order to more accurately analyse the effect the Proportion of Filings has on Consumption, whilst further controls should take place for natural disasters. The research can be expanded to assess if the Proportion of Filing’s effect on Consumption translates to the same effect on overall GDP.

VIII. Appendix

Tables 1 and 2

Dependent Variable: CONSUMPTIONMethod: Panel Least SquaresDate: 03/05/15 Time: 15:08Sample: 1997 2012Periods included: 16Cross-sections included: 52Total panel (unbalanced) observations: 816

Variable Coefficient Std. Error t-Statistic Prob.

C 4846.163 1274.141 3.803475 0.0002PROPORTION_OF_FILINGS 18.05896 4.530482 3.986101 0.0001

DIPOSABLE_INCOME_PER_CAP 0.832694 0.013587 61.28735 0.0000

INCOME_EXPECTATIONS -28.36582 8.151064 -3.480014 0.0005INTEREST_RATE 223.1740 69.60812 3.206148 0.0014

22 | P a g e

MORTGAGE_RATE -346.6108 146.4283 -2.367102 0.0182UNEMPLOYMENT_RATE 41.30357 40.47482 1.020476 0.3078

R-squared 0.919785 Mean dependent var 28240.78Adjusted R-squared 0.919190 S.D. dependent var 6585.962S.E. of regression 1872.199 Akaike info criterion 17.91616Sum squared resid 2.84E+09 Schwarz criterion 17.95651Log likelihood -7302.792 Hannan-Quinn criter. 17.93164F-statistic 1546.063 Durbin-Watson stat 0.282678Prob(F-statistic) 0.000000

ABOVE TIMELAG = 0 (no average house prices & inflation rate)



C 4040.414 1315.567 3.071233 0.0022PROP1 20.81317 4.704342 4.424248 0.0000




23 | P a g e

R-squared 0.914152 Mean dependent var 28803.48Adjusted R-squared 0.913471 S.D. dependent var 6391.836S.E. of regression 1880.208 Akaike info criterion 17.92527Sum squared resid 2.68E+09 Schwarz criterion 17.96777Log likelihood -6840.454 Hannan-Quinn criter. 17.94163F-statistic 1343.480 Durbin-Watson stat 0.165723Prob(F-statistic) 0.000000




C 3587.721 1361.150 2.635802 0.0086PROP2 23.50441 4.988134 4.712064 0.0000




R-squared 0.908224 Mean dependent var 29380.38

24 | P a g e

Adjusted R-squared 0.907443 S.D. dependent var 6193.466S.E. of regression 1884.248 Akaike info criterion 17.93023Sum squared resid 2.50E+09 Schwarz criterion 17.97514Log likelihood -6376.161 Hannan-Quinn criter. 17.94758F-statistic 1162.794 Durbin-Watson stat 0.133941Prob(F-statistic) 0.000000




C 3348.721 1417.831 2.361862 0.0185PROP3 21.32449 5.288028 4.032597 0.0001




R-squared 0.899936 Mean dependent var 29954.03

25 | P a g e

Adjusted R-squared 0.899017 S.D. dependent var 6013.261S.E. of regression 1910.886 Akaike info criterion 17.95907Sum squared resid 2.38E+09 Schwarz criterion 18.00672Log likelihood -5919.493 Hannan-Quinn criter. 17.97754F-statistic 978.8059 Durbin-Watson stat 0.151221Prob(F-statistic) 0.000000




C 1960.211 1572.053 1.246912 0.2129PROP4 24.65468 5.324345 4.630556 0.0000


INCOME_EXPECTATIONS 3.586654 10.28694 0.348661 0.7275INTEREST_RATE 363.7340 77.71978 4.680070 0.0000


R-squared 0.894216 Mean dependent var 30505.97Adjusted R-squared 0.893160 S.D. dependent var 5871.625

26 | P a g e

S.E. of regression 1919.219 Akaike info criterion 17.96867Sum squared resid 2.21E+09 Schwarz criterion 18.01945Log likelihood -5455.476 Hannan-Quinn criter. 17.98842F-statistic 846.7352 Durbin-Watson stat 0.127858Prob(F-statistic) 0.000000




C 2043.746 1618.814 1.262496 0.2073PROP5 23.73550 5.563376 4.266384 0.0000


INCOME_EXPECTATIONS 4.577176 10.50258 0.435814 0.6631INTEREST_RATE 375.1436 79.41751 4.723689 0.0000


R-squared 0.886359 Mean dependent var 31051.37Adjusted R-squared 0.885119 S.D. dependent var 5750.282

27 | P a g e

S.E. of regression 1949.008 Akaike info criterion 18.00052Sum squared resid 2.09E+09 Schwarz criterion 18.05484Log likelihood -5006.144 Hannan-Quinn criter. 18.02173F-statistic 714.9641 Durbin-Watson stat 0.147076Prob(F-statistic) 0.000000


Dependent Variable: LN_CONSUMPTIONMethod: Panel Least SquaresDate: 03/05/15 Time: 15:54Sample: 2003 2012Periods included: 10Cross-sections included: 51Total panel (unbalanced) observations: 506


C 9.609181 0.059655 161.0780 0.0000PROP6 0.001118 0.000197 5.662968 0.0000

DIPOSABLE_INCOME_PER_CAP 2.31E-05 5.41E-07 42.57949 0.0000



28 | P a g e

R-squared 0.853945 Mean dependent var 10.34702Adjusted R-squared 0.852189 S.D. dependent var 0.168396S.E. of regression 0.064742 Akaike info criterion -2.623082Sum squared resid 2.091558 Schwarz criterion -2.564612Log likelihood 670.6398 Hannan-Quinn criter. -2.600150F-statistic 486.2533 Durbin-Watson stat 0.144146Prob(F-statistic) 0.000000


Tables 3 and 4

No Lag:

Date: 03/20/15 Time: 14:26Sample (adjusted): 2000 2012Included observations: 660 after adjustmentsTrend assumption: Linear deterministic trendSeries: LN_CONSUMPTION PROPORTION_OF_FILINGS Lags interval (in first differences): 1 to 2

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.077196 61.65717 15.49471 0.0000At most 1 * 0.012997 8.634005 3.841466 0.0033

Trace test indicates 2 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

29 | P a g e

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.077196 53.02316 14.26460 0.0000At most 1 * 0.012997 8.634005 3.841466 0.0033

Max-eigenvalue test indicates 2 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):

LN_CONSUMPTION

PROPORTION_OF_FILINGS

-4.745836 0.044073-2.583481 -0.056239

Unrestricted Adjustment Coefficients (alpha):

D(LN_CONSUMPTION) 0.005954 -0.000410

D(PROPORTION_OF_FILINGS) 0.157680 0.609856

1 Cointegrating Equation(s): Log likelihood -428.5706

Normalized cointegrating coefficients (standard error in parentheses)LN_CONSUMPT

IONPROPORTION_

OF_FILINGS 1.000000 -0.009287

(0.00201)

Adjustment coefficients (standard error in parentheses)D(LN_CONSUM

PTION) -0.028257 (0.00388)

D(PROPORTION_OF_FILINGS) -0.748322

(0.99788)

Lag 1:

Date: 03/20/15 Time: 14:27Sample (adjusted): 2001 2012Included observations: 608 after adjustmentsTrend assumption: Linear deterministic trendSeries: LN_CONSUMPTION PROP1

30 | P a g e

Lags interval (in first differences): 1 to 2



None * 0.052656 43.05188 15.49471 0.0000At most 1 * 0.016577 10.16350 3.841466 0.0014




None * 0.052656 32.88838 14.26460 0.0000At most 1 * 0.016577 10.16350 3.841466 0.0014



LN_CONSUMPTION PROP1

-4.139697 0.057907 3.838188 0.042839


D(LN_CONSUMPTION) 0.004951 -0.000383

D(PROP1) -0.072196 -0.718816



ION PROP1 1.000000 -0.013988

(0.00296)


PTION) -0.020498 (0.00358)

D(PROP1) 0.298870 (0.94337)

31 | P a g e

Lag 2:

Date: 03/20/15 Time: 14:28Sample (adjusted): 2002 2012Included observations: 557 after adjustmentsTrend assumption: Linear deterministic trendSeries: LN_CONSUMPTION PROP2 Lags interval (in first differences): 1 to 2



None * 0.046633 37.44316 15.49471 0.0000At most 1 * 0.019279 10.84350 3.841466 0.0010




None * 0.046633 26.59967 14.26460 0.0004At most 1 * 0.019279 10.84350 3.841466 0.0010




-3.373205 0.065391 4.716525 0.030036


D(LN_CONSUMPTION) 0.002939 -0.002515

D(PROP2) -0.596483 -0.729390



ION PROP2 1.000000 -0.019386

(0.00404)

32 | P a g e


PTION) -0.009915 (0.00323)

D(PROP2) 2.012058 (0.84856)

Lag 3:




None * 0.075756 60.53357 15.49471 0.0000At most 1 * 0.040029 20.67134 3.841466 0.0000




None * 0.075756 39.86223 14.26460 0.0000At most 1 * 0.040029 20.67134 3.841466 0.0000




-5.327734 0.044286-2.798295 -0.055257


D(LN_CONSUMPTION) 0.005725 0.001897

D(PROP3) -0.675639 1.029082

33 | P a g e



ION PROP3 1.000000 -0.008312

(0.00204)


PTION) -0.030502 (0.00527)

D(PROP3) 3.599626 (1.34835)

Lag 4:




None * 0.092687 53.48191 15.49471 0.0000At most 1 * 0.020071 9.225262 3.841466 0.0024




None * 0.092687 44.25665 14.26460 0.0000At most 1 * 0.020071 9.225262 3.841466 0.0024



34 | P a g e


-6.536257 0.009323 0.957078 -0.071815


D(LN_CONSUMPTION) 0.006339 -0.001604

D(PROP4) 0.869204 0.656569



ION PROP4 1.000000 -0.001426

(0.00157)


PTION) -0.041433 (0.00704)

D(PROP4) -5.681339 (1.65751)

Lag 5:




None * 0.067866 34.71973 15.49471 0.0000At most 1 * 0.015539 6.327052 3.841466 0.0119




35 | P a g e

None * 0.067866 28.39268 14.26460 0.0002At most 1 * 0.015539 6.327052 3.841466 0.0119




-6.658695 0.003226 1.623077 -0.073514


D(LN_CONSUMPTION) 0.003913 -0.002435

D(PROP5) 0.936653 0.630576



ION PROP5 1.000000 -0.000484

(0.00197)


PTION) -0.026055 (0.00812)

D(PROP5) -6.236885 (2.04769)

Lag 6:




None * 0.049866 21.33830 15.49471 0.0059

36 | P a g e

At most 1 0.009253 3.281636 3.841466 0.0701




None * 0.049866 18.05666 14.26460 0.0120At most 1 0.009253 3.281636 3.841466 0.0701




1.883249 0.061224 6.897612 -0.039800


D(LN_CONSUMPTION) 0.001894 -0.002209

D(PROP6) -1.142453 -0.127087



ION PROP6 1.000000 0.032510

(0.00865)


PTION) 0.003566 (0.00247)

D(PROP6) -2.151523 (0.52156)

37 | P a g e

Table 5

Lag 0

Pairwise Granger Causality TestsDate: 03/05/15 Time: 16:36Sample: 1997 2012Lags: 2

Null Hypothesis: Obs F-Statistic Prob.

PROPORTION_OF_FILINGS does not Granger Cause LN_CONSUMPTION 712 25.4533 2.E-11 LN_CONSUMPTION does not Granger Cause PROPORTION_OF_FILINGS 14.9804 4.E-07

lag 1

Pairwise Granger Causality Tests

38 | P a g e

Date: 03/10/15 Time: 17:03Sample: 1997 2012Lags: 2


PROP1 does not Granger Cause LN_CONSUMPTION 660 51.1667 2.E-21 LN_CONSUMPTION does not Granger Cause PROP1 10.6736 3.E-05

lag 2



PROP2 does not Granger Cause LN_CONSUMPTION 608 7.01196 0.0010 LN_CONSUMPTION does not Granger Cause PROP2 0.04705 0.9540

lag 3



PROP3 does not Granger Cause LN_CONSUMPTION 557 13.9562 1.E-06 LN_CONSUMPTION does not Granger Cause PROP3 36.7558 1.E-15

lag 4

Pairwise Granger Causality TestsDate: 03/10/15 Time: 17:06

39 | P a g e

Sample: 1997 2012Lags: 2


PROP4 does not Granger Cause LN_CONSUMPTION 506 22.8748 3.E-10 LN_CONSUMPTION does not Granger Cause PROP4 4.75032 0.0090

lag 5

Pairwise Granger Causality TestsDate: 03/10/15 Time: 17:06Sample: 1997 2012Lags:


PROP5 does not Granger Cause LN_CONSUMPTION 455 4.80535 0.0086 LN_CONSUMPTION does not Granger Cause PROP5 13.3246 2.E-06

lag 6



PROP6 does not Granger Cause LN_CONSUMPTION 404 4.64595 0.0101 LN_CONSUMPTION does not Granger Cause PROP6 10.4534 4.E-05

Table 6

40 | P a g e

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British Journal of Arts and Social Sciences. 4(2). 164-179.

Subject Area Databases. (2015). Retrieved from the US Bureau of Labor Statistics website: http://www.bls.gov/bls/proghome.htm.

Survey of Consumers: University of Michigan. (2015). Retrieved from the ThomsonReuters website: http://www.sca.isr.umich.edu/.

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http://www.nolo.com/legal-encyclopedia/federal-bankruptcy-exemptions-property.html

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