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

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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 13
4 Johansen Cointegration Test: Max-Eigenvalues 14
5 Granger Causality Test 15
6 Forecast Error Variance Decomposition 16
List of Figures
Figure Number Figure Description Page No.
1 Initial Ordinary Least Squares Regression Model:
8
2 Revised Ordinary Least Squares Regression Model:
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
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.

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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
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

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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.
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

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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
𝐿𝑁𝑌𝑖𝑡 = 𝛼 + 𝛽1 𝑋𝑖𝑡 + 𝛽2 𝐷𝐼𝑃𝐶𝑖𝑡 + 𝛽3 𝐼𝐸𝑖𝑡 + 𝛽4 𝑈𝑅 𝑖𝑡 + 𝛽5 𝐼𝑅 𝑖𝑡 + 𝛽6 𝐼𝑖𝑡 + 𝛽7 𝐴𝐻𝑃𝑖𝑡 + 𝛽8 𝑀𝑅𝑖𝑡 + 𝜀𝑖𝑡
i = {1997, 1998…..2012}
The dependent variable, 𝐿𝑁𝑌𝑖𝑡, is the consumption per capita where i is the consumption
rate, at time period t. The independent variable, 𝛽1 𝑋𝑖𝑡, 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 𝐷𝐼𝑃𝐶𝑖𝑡,
income expectations, 𝛽3 𝐼𝐸𝑖𝑡, the unemployment rate, 𝛽4 𝑈𝑅𝑖𝑡, the interest rate, 𝛽5 𝐼𝑅𝑖𝑡, the
inflation rate, 𝛽6 𝐼𝑖𝑡, the average house price, 𝛽7 𝐴𝐻𝑃𝑖𝑡 and finally the mortgage rate,
𝛽8 𝑀𝑅𝑖𝑡. The model also included an error term, 𝜀𝑖𝑡. 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.

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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
𝐿𝑁𝑌𝑖𝑡 = 𝛼 + 𝛽1 𝑋𝑖𝑡 + 𝛽2 𝐷𝐼𝑃𝐶𝑖𝑡 + 𝛽3 𝐼𝐸𝑖𝑡 + 𝛽4 𝑈𝑅 𝑖𝑡 + 𝛽5 𝐼𝑅𝑖𝑡 + 𝛽6 𝑀𝑅𝑖𝑡 + 𝜀𝑖𝑡
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.
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
𝐿𝑁𝑌𝑖𝑡 = 𝛼 + 𝛽1 𝑆𝐸𝑖𝑡 + 𝛽2 𝐷𝐼𝑃𝐶𝑖𝑡 + 𝛽3 𝐼𝐸𝑖𝑡 + 𝛽4 𝑈𝑅 𝑖𝑡 + 𝛽5 𝐼𝑅𝑖𝑡 + 𝛽6 𝑀𝑅𝑖𝑡 + 𝜀𝑖𝑡
i = {1997, 1998…..2012}

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Where the proportion of filings, 𝛽1 𝑋𝑖𝑡, was removed and the state exemption levels,
𝛽1 𝑆𝐸𝑖𝑡, 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 variables
Ha = There are more than r cointegrating variables
Where r = Number of cointegrating variables
The Hypotheses for the Eigenvalues are:
Ho = There are r cointegrating variables
Ha = 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 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).

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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 Consumption
Ha = The Proportion of Filings Granger causes Consumption
Ho = Consumption does not Granger cause the Proportion of Filings
Ha = 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 Filings
L=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***
MortgageRate -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 1
Significant 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
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.

14 | P a g e
Ordinary Least Squares Regression
Ln(consumption): dependent variable
Variable Coefficient
State Exemption Level 0.0000000796***
Disposable Income per Capita 0.0000253***
Income Expectations -0.002423***
Interest Rate 0.003444
Mortgage 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.

15 | P a g e
Johansen Cointegration Test
Trace Test
Hypothesized No. Of CE(s) None At most 1
Trace Test L=0 61.65717** 8.634005**
Critical Value at 5% 15.49471 3.841466
Trace Test L=1 43.05188** 10.16350**
Trace Test L=2 37.44316** 10.84350**
Trace Test L=3 60.53358** 20.67134**
Trace Test L=4 53.48191** 9.225262**
Trace Test L=5 34.71973** 6.327052**
Trace Test L=6 21.33830** 3.281636
Table 3
Max-Eigenvalues
Hypothesized No. Of CE(s) None At most 1
Eigen values L=0 53.02316** 8.634005**
Eigen values L=1 32.88838** 10.16350**
Eigen values L=2 26.59967** 10.84350**
Eigen values L=3 39.86223** 20.67135**
Eigen values L=4 44.25665** 9.22562**
Eigen values L=5 28.39268** 6.327052**
Eigen values L=6 18.05666** 3.281636
Table 4

16 | P a g e
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
Proportion of
Filings does
not Granger
Cause LN
Consumption
2.E-11** 2.E-21** 0.0010** 1.E-06** 3.E-10** 0.0086** 0.0101**
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% = ***

17 | P a g e
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
Period Disposable
Income
period
Capita
Income
Expectations
Interest
Rate
Mortgage
Rate
Proportion
of Filings
Unemployment
Rate
Ln
Consumption
1 14.65 7.05 4.14 0.718 0.04 0.33 73.06
2 27.97 10.39 2.19 0.95 0.18 0.76 57.56
3 28.03 14.12 4.42 2.91 0.14 0.58 49.79
4 32.21 11.96 10.49 2.77 0.27 0.52 41.78
5 35.57 9.63 13.51 2.14 0.22 0.83 38.09
6 38.97 8.15 13.1 2.25 0.24 0.81 36.46
7 40.63 7.48 11.78 2.87 0.63 1.36 35.81
8 40.79 7.98 10.63 3.2 1.26 2.39 34.78
9 39.71 10.57 9.51 2.89 1.77 3.23 33.14
10 38.24 14.58 8.47 2.67 1.94 3.47 30.87
11 37.19 17.93 8.32 2.79 1.91 3.31 28.38
12 37.25 19.26 9.3 2.91 1.83 3.07 26.14
13 38.4 18.98 10.52 2.75 1.86 2.92 24.42
14 40.13 18.14 11.01 2.51 2.11 2.92 23.18
15 41.67 17.36 10.7 2.41 2.67 2.99 22.2
16 42.56 16.91 10.06 2.35 3.45 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.

18 | P a g e
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.
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).

20 | P a g e
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 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.

21 | P a g e
VIII. Appendix
Tables 1 and 2
Dependent Variable: CONSUMPTION
Method:Panel Least Squares
Date: 03/05/15 Time: 15:08
Sample: 1997 2012
Periods included: 16
Cross-sections included: 52
Totalpanel (unbalanced) observations: 816
Variable Coefficient Std. Error t-Statistic Prob.
C 4846.163 1274.141 3.803475 0.0002
PROPORTION_OF_FILINGS 18.05896 4.530482 3.986101 0.0001
DIPOSABLE_INCOME_PER_CA
P 0.832694 0.013587 61.28735 0.0000
INCOME_EXPECTATIONS -28.36582 8.151064 -3.480014 0.0005
INTEREST_RATE 223.1740 69.60812 3.206148 0.0014
MORTGAGE_RATE -346.6108 146.4283 -2.367102 0.0182
UNEMPLOYMENT_RATE 41.30357 40.47482 1.020476 0.3078
R-squared 0.919785 Mean dependent var 28240.78
Adjusted R-squared 0.919190 S.D. dependent var 6585.962
S.E. of regression 1872.199 Akaike info criterion 17.91616
Sum squared resid 2.84E+09 Schwarz criterion 17.95651
Log likelihood -7302.792 Hannan-Quinn criter. 17.93164
F-statistic 1546.063 Durbin-Watson stat 0.282678
Prob(F-statistic) 0.000000
ABOVE TIMELAG= 0 (no average house prices & inflation rate)

22 | P a g e
Date: 03/05/15 Time: 15:08
Sample: 1998 2012
C 4040.414 1315.567 3.071233 0.0022
PROP1 20.81317 4.704342 4.424248 0.0000
P 0.830553 0.013841 60.00521 0.0000
INTEREST_RATE 211.5163 69.45743 3.045265 0.0024
MORTGAGE_RATE -274.6066 148.3381 -1.851220 0.0645

23 | P a g e
Date: 03/05/15 Time: 15:08
Sample: 1999 2012
C 3587.721 1361.150 2.635802 0.0086
PROP2 23.50441 4.988134 4.712064 0.0000
P 0.821972 0.014209 57.84950 0.0000
INTEREST_RATE 253.1126 72.93631 3.470324 0.0006
MORTGAGE_RATE -339.6450 153.8326 -2.207887 0.0276

24 | P a g e
Date: 03/05/15 Time: 15:09
Sample: 2000 2012
C 3348.721 1417.831 2.361862 0.0185
PROP3 21.32449 5.288028 4.032597 0.0001
P 0.819304 0.014663 55.87684 0.0000
INTEREST_RATE 310.6324 75.24324 4.128375 0.0000
MORTGAGE_RATE -460.5854 156.0773 -2.951009 0.0033

25 | P a g e
Date: 03/05/15 Time: 15:32
Sample: 2001 2012
C 1960.211 1572.053 1.246912 0.2129
PROP4 24.65468 5.324345 4.630556 0.0000
P 0.822841 0.015185 54.18665 0.0000
INCOME_EXPECTATIONS 3.586654 10.28694 0.348661 0.7275
INTEREST_RATE 363.7340 77.71978 4.680070 0.0000
MORTGAGE_RATE -469.3028 160.1771 -2.929899 0.0035

26 | P a g e
Date: 03/05/15 Time: 15:34
Sample: 2002 2012
C 2043.746 1618.814 1.262496 0.2073
PROP5 23.73550 5.563376 4.266384 0.0000
P 0.819037 0.015882 51.57018 0.0000
INCOME_EXPECTATIONS 4.577176 10.50258 0.435814 0.6631
INTEREST_RATE 375.1436 79.41751 4.723689 0.0000
MORTGAGE_RATE -444.1495 168.0122 -2.643556 0.0084

27 | P a g e
Dependent Variable: LN_CONSUMPTION
Date: 03/05/15 Time: 15:54
Sample: 2003 2012
C 9.609181 0.059655 161.0780 0.0000
PROP6 0.001118 0.000197 5.662968 0.0000
P 2.31E-05 5.41E-07 42.57949 0.0000
INTEREST_RATE 0.016110 0.002990 5.387930 0.0000
MORTGAGE_RATE -0.025195 0.006397 -3.938840 0.0001
S.E. of regression 0.064742 Akaike info criterion -2.623082
Sum squared resid 2.091558 Schwarz criterion -2.564612
Log likelihood 670.6398 Hannan-Quinn criter. -2.600150

28 | P a g e
Tables 3 and 4
No Lag:
Date: 03/20/15 Time:14:26
Sample (adjusted):2000 2012
Included observations:660 after adjustments
Trend assumption: Linear deterministic trend
Series:LN_CONSUMPTION PROPORTION_OF_FILINGS
Lags interval (in first differences):1 to 2
Unrestricted Cointegration Rank Test(Trace)
Hypothesized Trace 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.077196 61.65717 15.49471 0.0000
At most 1 * 0.012997 8.634005 3.841466 0.0033
Trace test indicates 2 cointegrating eqn(s) atthe 0.05 level
* denotes rejection ofthe hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test(Maximum Eigenvalue)
Hypothesized Max-Eigen 0.05
None * 0.077196 53.02316 14.26460 0.0000
At most 1 * 0.012997 8.634005 3.841466 0.0033
Max-eigenvalue test indicates 2 cointegrating eqn(s) atthe 0.05 level
Unrestricted Cointegrating Coefficients (normalized byb'*S11*b=I):
LN_CONSUMPT
ION
PROPORTION_
OF_FILINGS
-4.745836 0.044073
-2.583481 -0.056239
Unrestricted AdjustmentCoefficients (alpha):
D(LN_CONSUM
PTION) 0.005954 -0.000410
D(PROPORTIO
N_OF_FILINGS) 0.157680 0.609856
1 Cointegrating Equation(s): Log likelihood -428.5706

29 | P a g e
Normalized cointegrating coefficients (standard error in parentheses)
LN_CONSUMPT
ION
PROPORTION_
OF_FILINGS
1.000000 -0.009287
(0.00201)
Adjustmentcoefficients (standard error in parentheses)
D(LN_CONSUM
PTION) -0.028257
(0.00388)
D(PROPORTIO
N_OF_FILINGS) -0.748322
(0.99788)
Lag 1:
Date: 03/20/15 Time:14:27
Trend assumption:Linear deterministic trend
Series:LN_CONSUMPTION PROP1
None * 0.052656 43.05188 15.49471 0.0000
At most 1 * 0.016577 10.16350 3.841466 0.0014
None * 0.052656 32.88838 14.26460 0.0000
At most 1 * 0.016577 10.16350 3.841466 0.0014
LN_CONSUMPT
ION PROP1
-4.139697 0.057907

30 | P a g e
3.838188 0.042839
D(LN_CONSUM
PTION) 0.004951 -0.000383
D(PROP1) -0.072196 -0.718816
LN_CONSUMPT
ION PROP1
1.000000 -0.013988
(0.00296)
D(LN_CONSUM
PTION) -0.020498
(0.00358)
D(PROP1) 0.298870
(0.94337)
Lag 2:
Date: 03/20/15 Time:14:28
None * 0.046633 37.44316 15.49471 0.0000
At most 1 * 0.019279 10.84350 3.841466 0.0010
None * 0.046633 26.59967 14.26460 0.0004
At most 1 * 0.019279 10.84350 3.841466 0.0010

31 | P a g e
LN_CONSUMPT
ION PROP2
-3.373205 0.065391
4.716525 0.030036
Unrestricted Adjustment Coefficients (alpha):
D(LN_CONSUM
PTION) 0.002939 -0.002515
D(PROP2) -0.596483 -0.729390
LN_CONSUMPT
ION PROP2
1.000000 -0.019386
(0.00404)
D(LN_CONSUM
PTION) -0.009915
(0.00323)
D(PROP2) 2.012058
(0.84856)
Lag 3:
Date: 03/20/15 Time:14:28
None * 0.075756 60.53357 15.49471 0.0000
At most 1 * 0.040029 20.67134 3.841466 0.0000

32 | P a g e
None * 0.075756 39.86223 14.26460 0.0000
At most 1 * 0.040029 20.67134 3.841466 0.0000
LN_CONSUMPT
ION PROP3
-5.327734 0.044286
-2.798295 -0.055257
D(LN_CONSUM
PTION) 0.005725 0.001897
D(PROP3) -0.675639 1.029082
LN_CONSUMPT
ION PROP3
1.000000 -0.008312
(0.00204)
D(LN_CONSUM
PTION) -0.030502
(0.00527)
D(PROP3) 3.599626
(1.34835)
Lag 4:
Date: 03/20/15 Time:14:29
Trend assumption: Linear deterministic trend

33 | P a g e
None * 0.092687 53.48191 15.49471 0.0000
At most 1 * 0.020071 9.225262 3.841466 0.0024
None * 0.092687 44.25665 14.26460 0.0000
At most 1 * 0.020071 9.225262 3.841466 0.0024
LN_CONSUMPT
ION PROP4
-6.536257 0.009323
0.957078 -0.071815
D(LN_CONSUM
PTION) 0.006339 -0.001604
D(PROP4) 0.869204 0.656569
LN_CONSUMPT
ION PROP4
1.000000 -0.001426
(0.00157)
D(LN_CONSUM
PTION) -0.041433
(0.00704)
D(PROP4) -5.681339
(1.65751)

34 | P a g e
Lag 5:
Date: 03/20/15 Time:14:29
None * 0.067866 34.71973 15.49471 0.0000
At most 1 * 0.015539 6.327052 3.841466 0.0119
None * 0.067866 28.39268 14.26460 0.0002
At most 1 * 0.015539 6.327052 3.841466 0.0119
LN_CONSUMPT
ION PROP5
-6.658695 0.003226
1.623077 -0.073514
D(LN_CONSUM
PTION) 0.003913 -0.002435
D(PROP5) 0.936653 0.630576
LN_CONSUMPT
ION PROP5
1.000000 -0.000484
(0.00197)

35 | P a g e
D(LN_CONSUM
PTION) -0.026055
(0.00812)
D(PROP5) -6.236885
(2.04769)
Lag 6:
Date: 03/20/15 Time:14:29
None * 0.049866 21.33830 15.49471 0.0059
At most 1 0.009253 3.281636 3.841466 0.0701
None * 0.049866 18.05666 14.26460 0.0120
At most 1 0.009253 3.281636 3.841466 0.0701
LN_CONSUMPT
ION PROP6
1.883249 0.061224
6.897612 -0.039800
D(LN_CONSUM
PTION) 0.001894 -0.002209
D(PROP6) -1.142453 -0.127087

36 | P a g e
LN_CONSUMPT
ION PROP6
1.000000 0.032510
(0.00865)
D(LN_CONSUM
PTION) 0.003566
(0.00247)
D(PROP6) -2.151523
(0.52156)

37 | P a g e
Table 5
Lag 0
Pairwise Granger Causality Tests
Date: 03/05/15 Time: 16:36
Sample: 1997 2012
Lags: 2
Null Hypothesis: Obs F-Statistic Prob.
PROPORTION_OF_FILINGSdoes 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
Date: 03/10/15 Time: 17:03
Sample: 1997 2012
Lags: 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
Date: 03/10/15 Time: 17:04
Sample: 1997 2012
Lags: 2
PROP2 does not Granger Cause LN_CONSUMPTION 608 7.01196 0.0010
LN_CONSUMPTION does not Granger Cause PROP2 0.04705 0.9540

38 | P a g e
lag 3
Date: 03/10/15 Time: 17:05
Sample: 1997 2012
Lags: 2
lag 4
Date: 03/10/15 Time: 17:06
Sample: 1997 2012
Lags: 2
LN_CONSUMPTION does not Granger Cause PROP4 4.75032 0.0090
lag 5
Date: 03/10/15 Time: 17:06
Sample: 1997 2012
Lags:

39 | P a g e
lag 6
Date: 03/10/15 Time: 17:07
Sample: 1997 2012
Lags: 2
Table 6

40 | P a g e
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