Explaining The Variation In The Severity Of The 2008 Financial Crisis Across U.S. States: A Cross-Sectional

Approach

Matthew Bonshor April 10th, 2015

Professor Mike Kennedy Honours Seminar in Macroeconomics: Economics 491

Intro

The Financial Crisis of 2007 and its subsequent effects on markets, liquidity, credit and

housing markets were felt in both the United States and around the globe. The majority of

empirical literature that analyzes the effects of the crisis in the United States focuses on

aggregate data. This focus on aggregate data alone paints the story of the crisis with too

broad a brush and fails to account for large variation in experiences that was felt across

different U.S. states. This paper shows that increases in employment in construction in

the years before 2008 was an early and powerful predictor of the drop in personal

disposable income per capita following the U.S. 2008 financial crisis. Using state level

cross-sectional data this paper demonstrates that those states that experienced the greatest

increase in gross employment in construction from first quarter of 2001 to the first

quarter of 2006 experienced the largest drop in personal disposable income per capita

from the first quarter of 2007 to the first quarter of 2009. Over the same time frame, this

paper demonstrates that those states that had largest run up in their housing price index

experienced the sharpest post 2008 financial crisis decline in house prices. Controlling

for oil production is shown to be highly significant and my estimates demonstrate that oil

production acted as an important cushion to oil producing states which softened the blow

of the post 2008 recession. This is an important finding, especially since in future

recessions a commodity boom may not exist to cushion the effects of that recession.

Section I of this paper outlines the aggregate data on both consumption and

housing prices in the United States during the financial crisis and the limitations of that

aggregate in explaining cross state variation. Section II discusses previous approaches

that have used a similar cross-sectional methodology that this paper ultimately employs

and outlines some important findings. Section III presents the methodology that is

utilized in my analysis and the results. Section IV offers areas for improvement, future

research and conclusions.

Section I - Overview of Consumption and House Prices During The Crash

Viewing aggregate data on U.S. Consumption as well as Housing Price Indexes shows

that the U.S. experienced a significant recession1. Durable goods consumption was hit

the hardest, with a peak to trough drop of 15.56% from 2007 Q4 to 2009 Q2 which did

not show recovery to its pre-crisis level until 2012 Q3. Non-durable goods consumption

was hit hard as well, falling 9.54% from 2008 Q3 to 2009 Q1 but recovered faster than

durable goods consumption, returning to its levels by the fourth quarter of 2010. The

impact on consumption of services showed little change during the recession in

aggregate, experiencing only a 0.797% drop from its 2008 Q3 peak to trough of 2009 Q2

and making a strong recovery by 2010 Q1. Aggregate housing price indexes meanwhile

show a significant decline, falling 18.43% from a peak in the first quarter of 2007 to a

trough in the second quarter of 20122. Unlike aggregate consumption, at the time in

which this paper was written, aggregate house prices in the U.S. still remain well below

their pre-crisis levels, demonstrating a more prolonged decline and only a moderate

comeback.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!1!Figures!1,!2!and!3!plot!aggregate!U.S.!consumption!for!durable!goods,!non;durable!goods,!and!services!respectively!!2!Figure!4!plots!the!all!transactions!house!price!index!for!the!United!States!!2!Figure!4!plots!the!all!transactions!house!price!index!for!the!United!States!

Many explanations of the causes of the financial crisis concentrate their effort on

aggregate explanations. For instance, Paul Langley (2008) outlines the instability created

by the securitization of mortgages, leveraged investment and the culture of risk that they

created. Ralph De Haas and Neeltje Van Horen (2012) analyze the bankruptcy of Lehman

Brothers and the subsequent credit crisis that blossomed, affecting banks domestically

and internationally. Country level analysis and the aggregate data outlined above is

undoubtedly an important part of the picture when understanding the financial crisis. For

example, the collapse of Lehman Brothers brought derivatives trading, capital

requirements and financial stability to the fore of regulators minds (De Haas and Van

Horen, 2012). However, aggregate explanations fail to capture the full range of

experiences during the financial crisis. It is important to recognize that there was a

significant range of deviation in experiences across states. For example from 2006 Q3 to

2011 Q1, the all transactions HPI fell 40%, whereas North Dakota experienced an

increase of 12% over the same time frame3. The fall in the housing price index was by no

means consistent across states with a standard deviation of 55. The table below

summarizes top five most affected and least affected states on two different

measurements, the fall in the all transactions house price index and the fall in personal

income per capita4.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!3!Source:!FRED!economic!data,!all!transactions!house!price!index!4!These!changes!are!measured!from!a!2007!Q1!peak!to!a!2009!Q1!trough!following!the!methodology!used!throughout!this!paper!

Top 5 least affected Change in HPI Top 5 least

affected Change in personal income per capita

North Dakota 18.55 Denver 7416

South Dakota 12.13 North Dakota 7167

Texas 9.16 South Dakota 5261

Oklahoma 8.48 Alaska 5049

Wyoming 8.35 Idaho 4182

Top 5 most affected Change in HPI Top 5 most affected

Change in personal income per capita

Rhode Island -118.47 Hawaii -60

Arizona -146.38 Illinois -347

Florida -172.56 Arizona -819

Nevada -177.09 Georgia -1343

California -230.23 Nevada -3001

Mian and Sufi (2009) aptly surmise this shortfall when they recognize that

“aggregate patterns [only] hint at the importance [of our variables of interest].” What,

then, is the best approach to explore this variability? One possible approach is to look at

cross-sectional data, rather than aggregate data. This is the focus of the following section.

Section II – Cross-Sectional Framework

Two economists from the University of Chicago, Amir Sufi and Atif Mian (2009) used a

cross-sectional approach to understand the effects of the financial crisis on consumption.

Mian and Sufi were interested to determine whether or not the rapid increases household

debt that preceded the financial crisis significantly predicted the severity of the crash5.

They collected data on 123 U.S. counties from a wide geographic distribution. A unique

aspect of their analysis is that they posit that many of the macroeconomic factors that

caused the financial crisis were in play before the year 2006 and conclude, “household

leverage is an early and powerful predictor of the 2007 to 2009 recession” (Mian and

Sufi; 2009). Their analysis shows that countries in the U.S. that “experienced a large

increase in household leverage from 2002 to 2006 showed a sharp relative decline in

durable consumption starting in the third quarter of 2006 (full year before any significant

change in unemployment)6”.

Akins et al (2014) also employ a cross-sectional framework, though at the state

level rather than the county level. They were interested in exploring the relationship

between inter-bank competition and the subsequent risk level taken by banks before the

financial crisis. By collecting state level data on two measures of competition, total

assets, loan to assets ratios, loan to real estate ratios and several other controls, they

determine that “states with greater competition have stricter lending standards in the form

of a greater fraction of rejected mortgage applications” (Akins et al, 2014).

Both papers presents some fascinating findings on the importance of household

finance, debt leverage and risk preferences. But most importantly they offer a valid

framework and lens through which to analyze the financial crisis that explores the

variation felt by individual regions, whether that is states or counties.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!5!Figure!5!plots!household!debt!service!payments!as!a!percentage!of!disposable!income!for!the!United!States!6!Their!results!are!presented!in!figure!6!

Section III – Framework, Analysis and Results

Extending the methodology of these previous studies I look at evidence from the cross-

sectional state level data. I am interested in exploring why some states were hit so much

harder than others following the crash of 2008. Following the methodology of Mian and

Sufi (2009) I divide my variables into ‘run up’ variables that measure a change from

2002 to 2006 and ‘crash’ variables that measure a change from 2007 to 2009. To start, I

need to determine some ‘crash’ variables; those being dependent variables which

measure the severity of the recession in each state. Following this, I need to determine

‘run up’ variables of interest, or those factors that were in play prior to the crash that will

help explain the severity of the recession.

Data

The majority of the data I use is real, quarterly, seasonally adjusted data taken from the

Economic Data bank at the St. Louis Federal Reserve Bank7. I collect cross sectional

evidence from 44 states. Following the methodology established by Mian and Sufi

(2009) my variables take the form of either ‘run up’ effects or ‘crash effects’. ‘Run up’

effects measure a change from 2002 Q1 to 2006 Q1 with the hypothesis that these early

variables would have a significant effect on the severity of the ‘crash effects’ that

measure changes from 2007 Q1 to 2009 Q1. In my regression table, independent variable

‘run up’ effects are denoted with a ‘(+)’ beside them, while the dependent variable ‘crash

effects’ are denoted with a ‘(-)’8.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!7!For!a!complete!summary!of!the!data!and!its!source!refer!to!figure!7!8!!Refer!to!figure!8!for!the!regression!tables!

Crash Variables

A logical first measure of the crash, as outlined by Mian ad Sufi, is personal consumption

expenditures. Sharp drops in consumption are felt strongly by households and explaining

the variability in consumption across states would be useful to a variety of stakeholders.

Unfortunately it was not possible to find personal consumption at the state level, so I had

to turn to proxies for these variables. For consumption I decided to use personal income

per capita. Intuitively, and as microeconomic theory would suggest, when personal per

capita income falls consumption will also fall in turn9. This is especially true for durable

goods consumption, which experienced the most severe drop following the crash of 2008

as seen earlier in this paper. Coupled with what economic theory would suggest, the two

variables have a correlation coefficient of 0.992 at the aggregate country level which

makes a strong case that personal per capita income makes for a good proxy for

consumption. Thus, those states that experienced the sharpest decline in personal income

per capita felt the crash harder than most states.

My second measure of the crash is the change in the all transactions house price

index. The rapid depreciation of house prices was again felt strongly by households.

Further, declining house prices in some states led to increased mortgage default rates as

homeowners found themselves underwater. Thus it is logical to think that the states that

experienced the sharpest decline in house prices felt the recession more strongly than

those that did not. For example, Bhutta et al. 2010 show that “borrowers from Arizona,

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!9!Refer!to!the!OECD!report!on!the!framework!for!statistics!on!the!distribution!of!household!income,!consumption!and!wealth,!pg.!102!

California, Florida, and Nevada who purchased homes in 2006 using non-prime

mortgages with 100 percent financing” suffered a staggering 80% default rate10.

Run Up Variables

The first run up variable that I employ, extending the Mian and Sufi paper, would be

household debt leverage. Unfortunately this data was not available at the state level. As a

proxy for the run up in household debt leverage I use the increase in the all transactions

house price index. In the years leading up to the crash easy access to credit fueled a

housing bubble. Rising house prices encouraged households to further stretch their line of

credit by taking on more debt (Mian and Sufi, 2009b). Thus it is reasonable to expect that

the states that experienced the sharpest rise in housing prices would likely also have been

those that in turn took out the most debt. Additionally over the period of 2002 Q1 to 2006

Q1 the two variables have a correlation coefficient of 0.73. Interestingly there is the

highest correlation between the two variables over the range in which we are concerned

(2002 to 2006), but much less so in the period from 2000 to 201411 which may lend some

credibility to this channel of transmission12 (Mian and Sufi, 2009b).

Neither proxies (consumption or debt leverage) that I have chosen are perfect and

any results drawn from my analysis can only be loosely applied to the results from the

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!10!In!the!following!order,!California,!Nevada,!Florida!and!Arizona!were!the!4!states!that!experienced!the!greatest!fall!in!HPI!from!2007!to!2009!11!!Over!this!range!aggregate!all!transactions!HPI!and!household!debt!service!payments!as!a!percentage!of!disposable!income!have!a!correlation!coefficient!of!only!0.11!12!The!channel!I!refer!to!is!that!rising!house!prices!fueled!more!borrowing,!which!fueled!higher!house!prices!which!in!turn!fueled!more!borrowing!etc.!!!

Mian and Sufi paper. However, each of these proxies are important variables in their own

right and any significant results can be interpreted on their own. For instance,

understanding determinants of personal per capita income is important to both

households and regulators alike. Similarly the determinants of house prices are important

to the same group of stakeholders.

A second measure of leverage that I include in is the amount of outstanding loans

issued by commercial banks. If the results presented by Akins et al. (2014) hold true, we

might expect that those states whose commercial banks increased their total loan portfolio

in the years leading up to the crash would experience the crash harder than those who did

not13.

A third important run up factor pertains to the housing market. The years leading

up to the crash were characterized by an unprecedented rise in house prices, rapidly

accelerating increases in new housing starts and rising levels of employment in

construction, all of which were “disconnected from the businesses cycle [in an]

unprecedented way” (Girouard et. al, 2006). To this end I control for these variables in

my regression and anticipate that one or some combination of them will significantly

predict the variation in how hard states felt the crash.

Lastly, I added a dummy variable that denoted whether or not a state was a top 10

oil producer in 2009. The idea behind this was to capture the presumed ‘cushion’ effect

that U.S. oil production would have on the top 10 states acting to soften the blow of the

recession on both personal disposable income per capita and house prices.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

Framework

Again, my dependent variables consist of measures of the ‘crash’: the change in personal

per capita income and the change in the all transactions HPI. My independent variables

consist of the aforementioned proxies and run up variables. I use ordinary least squares

regressions to estimate my model.

Regression 1

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Results

Figure 8 presents the results of the regression where the dependent variable is the fall in

the all transactions housing price. Column 1 of figure 8 shows that the increase in loans

and the increase in housing starts are both negatively associated with the fall in the

housing price index, however neither are significant at the 10% level. Column 2 adds a

control for the run up in employment in construction which, as predicted by the literature

is negatively associated with the change in the housing price index and significant at the

1% level. Column 3 controls for the run up in house prices, or as outlined earlier the

proxy for household debt leverage. Interestingly, the change in outstanding loans made

by commercial banks is negatively associated with the crash and significant at the 5%

level. Housing starts lose its significance which is an important distinction. This

regression suggests that it is really the increase in the amount of employment in

construction that was important rather than the number of housing starts14. Interestingly,

the employment share in construction as a fraction of total employment is not significant

which is contrary to my original hypothesis since it would seem logical that the change in

construction relative to the total labour force would be a better measure of deviation and

thus predict the variation in the crash better15. Another important result from column 3 is

that the run up in house prices is negatively associated with the post recession fall, the

magnitude of the coefficient is large and it is significant at the 1% level. Additionally,

controlling for the run up in house prices greatly increases the R2 of the test from 0.6 to

0.88. Using the run up in house prices as a proxy for household debt leverage, this

corroborates the story told by Mian and Sufi that “household debt leverage was an early

and powerful predictor of the severity of the 2009 recession” (Mian and Sufi, 2009).

Controlling for oil in column 4 we see that, as predicted, it is positively associated with

the change in the housing price index and significant at the 10% level. Thus, the housing

markets in the states that were top 10 oil producers in 2009 benefitted from oil production

and its counter-recession effects.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!14!Consider!the!hypothetical!example!where!new!houses!are!entirely!built!by!robots!and!no!human!capital!is!required.!Thus,!a!fall!in!housing!starts!affects!only!the!few!workers!required!to!service!those!machines.!It!is!the!employment!in!construction!that!is!more!significant!than!the!number!of!houses!being!built!15!Future!research!may!look!to!determine!why!gross!changes!in!employment!in!construction!was!more!significant!than!changes!in!employment!construction!as!a!share!of!total!employment!as!this!is!beyond!the!scope!of!this!paper!

Figure 8

Figure 9

Figure 9 presents the results of the regression where the dependent variable is the

fall in personal disposable income per capita. The results are almost entirely the same as

in figure 8 with the run up variables sharing similar signs, magnitudes and significance

levels. The most striking aspect of Figure 9 is the significance, magnitude and added

explanatory power of controlling for oil. Comparing column 3 and column 4 of Figure 9

we observe the R2 of the test almost double from 0.27 to 0.52. Further, when oil is

controlled for, the run up in house prices no longer has a significant effect on the change

in income levels after the crash. This is an interesting result and one that could be studied

in future research. One hypothesis is that rising house prices were a result of increased

borrowing and leveraging, thus when the housing bubble burst house prices fell but

incomes remained the same, hence why the run up in house prices predicts the fall in

house prices significantly but does little to explain the fall in incomes across states.

Understanding the Importance of Oil Production

Approximately the first quarter of 2009 would turn out to be the bottom of a steep and

exponential rise in the U.S. crude oil production industry. The recent rise has been

described in the Oil & Gas Journal, 2013, as a “game changing revolution…unlocked by

innovations in hydraulic fracturization and horizontal drilling”. Hydraulic fracturization,

or “fracking” as it is commonly referred to is both extremely capital and labour intensive.

One hypothesis about the significance of being an oil producer in cushioning per capita

incomes across states is that the oil and gas produces were ramping up drilling capacity

and building the infrastructure in 2009 providing jobs and some economic security to

many of the citizens of these states. Thus, as shown in my results, the boom in oil acted

as a cushion. Given the volatility of oil prices and the fact that it is a limited natural

resource it is worth noting that in a future financial crisis states may not benefit from a

commodity boom and thus the effects of a financial crisis could be felt even harder16.

U.S. Crude Oil Production

Source: U.S. Energy Information Association Section IV – Future Research and Conclusion

This paper provides strong evidence that there were some important structural factors at

play in different U.S. states in the years preceding the financial crisis that explains how

severely the recession was felt in each state. Most of the factors or ‘run up’ variables

were significant and carried the sign that was hypothesized earlier in this paper. In its

current form, the variables used to explain the crash are very broad. Future research could

focus on decomposing these broad variables into their subcomponents. For example, the

run up in house prices differed across states for several reasons including: lending

requirements, bank policies or exposure to a prior housing boom that brought about

changes in legislation. A broad variable like the run up in house prices likely catches

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!16!For!example,!if!oil!prices!hold!at!their!current!low!prices!many!of!these!operations!will!no!longer!be!economically!viable!!

many of the effects of these variables, but by breaking it apart future research could make

some important conclusions about the relative contribution of each variable. Specifically

in Texas, resulting from several previous real estate booms and crashes, “cash-out”

mortgages cannot exceed 80% of their homes appraised value which was “ubiquitous

during the mortgage boom [in other states], as skyrocketing house prices made it possible

for homeowners, even those with bad credit, to use their home equity like an ATM”

(Katz, 2010). Thus it would be illustrative to determine the relative importance of a

variable that measured exposure to a previous real estate crisis versus the relative

importance of lending requirements, all of which are likely captured in my housing price

index ‘run up’ variable. A second area of future research could look to add interaction

terms between the variables that might explore inter state effects, whether that is trade,

cross-state employment or simply proximity.

References

Akins, Brian, Lynn Li, Jeffry Ng, Tjomme O. Rusticus, 2014,“Bank Competition and

Financial Stability: Evidence from the Financial Crisis”, Singapore Management

University, School of Accountancy Research Paper Series Vol. 2, No. 2.

Bhutta, Neil, Jane Dokko and Hui Shan, 2010, “The Depth of Negative Equity and

Mortgage Default Decisions”, Federal Reserve Board of Governors, Finance and

Ecnomics Discussion series: 2010-25.

De Haas, Ralph and Neeltje Van Horen, 2012, “International Shock Transmission after

the Lehman Brothers Collaps: Evidence from Syndicated Lending”, American Economic

Review: Papers & Proceedings 2012, 102(3): 231-237.

Girouard, Natalie, Mike Kennedy, Paul van den Noord and Christophe André, 2006,

“Recent House Price Developments: The Role of Fundamentals”, OECD Economics

Department Working Papers No. 475.

Hurd, Michael and Susann Rohwedder, 2010, “Effects of the Financial Crisis and Great

Recession on American Households”, Network for Studies on Pensions, Aging and

Retirement Discussion Papers.

Langley, Paul, 2008, “Sub-prime mortgage lending: a cultural economy”, Economy and

Society Volume 37 Number 4: 469-494.

Mian, Atif and Amir Sufi, 2009, “Household Leverage and the Recession of 2007 to

2009,” IMF Working Papers.

Mian, Atif R. and Sufi, Amir, 2009b. “House Prices, Home Equity-Based Borrowing, and

the U.S. Household Leverage Crisis”, Working Paper, Chicago Booth.

OECD (2013), OECD Framework for Statistics on the Distribution of Household Income,

Consumption and Wealth, OECD Publishing. http://dx.doi.org/10.1787/9789264194830-

en

Appendix

Figure 1 Figure 2

Figure 3

Personal consumption expenditures on services

Figure 4

All transactions house price index for the United States. Note the decline that began almost 2 years before the decline in

consumption and has yet to return to its pre financial crisis levels

Figure 5

Household debt service payments as a percent of disposable personal income. Note the all time high in late 2008 that has since

been followed with an unprecedented drop.

Figure 6

Figure 7

Variable Description Source

Loans (+) Change in outstanding

commercial bank loans from

2002Q1 to 2006Q1

FRED

Series ID: TLLNUI

Housing Starts (+) Change in privately owned

housing starts authorized by

building permits 2002Q1 to

2006Q1

FRED

Series ID: BP1FHSA

Employment (+) Change in total employment

in construction 2002Q1 to

2006Q1

FRED

Series ID: CONS

Employment Share (+) Change in total employment

in construction as a % of

total non farm employment

2002Q1 to 2006Q1

FRED

Series ID: PAYEMS

HPI (+) Change in all-transactions

house price index 2002Q1 to

2006Q1

FRED

Series ID: STHPI

Oil Producer Dummy variable that assigns

1 to the top 5 oil producing

states in 2012, 0 otherwise

Source:

http://www.eia.gov/state/rankings/

Income (-) Change in personal

disposable income per capita

2007Q1 to 2009Q1

FRED

Series ID: A229RX0Q048SBEA

HPI (-) Change in all-transactions

house price index 2007Q1 to

2009Q1

FRED

Series ID: STHPI

Figure 10

Correlation matrix

Figure 11