Bank lending and personal income
in the Great Recession
Simon Wehrmüller∗
October 27, 2014
Abstract
This paper suggests a new way to identify how credit supply affects income. I
use fluctuations in a bank holding’s balance sheet positions along with the number
of branches within a United States core-based statistical area to create a measure of
an exogenous shocks to this area’s supply of credit in the latest financial crisis. The
identification assumes the exposure of a region to be proportional to the number of
bank branches. I find causal evidence that an adverse shock to a bank affects its
borrowers negatively. A one standard deviation drop in the loan shock leads to a 0.2
percentage points drop in income growth. The effect is statistically significant and
robust to a number of extensions like the inclusion of additional banks in the analysis
or a geographic restriction to big cities.
1
Introduction
How did the 2007-2008 financial crisis affect United States households? A fall in asset
prices in 2007 and 2008 caused a run on the shadow banking system that left some conventional depository banks in dire financial straits and forced them to cut back lending. I
calculate a local loan supply shock based on the number of branches a bank holding company (BHC) has within a core-based statistical area (CBSA) to find a channel through
which bank distress affected local economies.
There is an apparent problem when trying to identify a bank lending channel. A
reduction in the observed amount of lending in an economy can stem from shifts in demand
or supply (or a combination thereof). A negative productivity shock may reduce the
amount of positive net present value projects firms have and therefore their demand for
loans. Furthermore, as Bernanke (1983) pointed out for the case of the Great Depression,
a reduction in the borrower’s net worth, and therefore the capital it can pledge as collateral
against a loan, makes those loans more expensive, hence reducing demand further. On the
other hand, a negative shock to the balance sheet of the bank might force the bank to cut
back on its lending due to liquidity constraints. This means that there may be positive
∗
Department of Economics, Stockholm School of Economics. Email: [email protected].
1
net present value projects that cannot be executed. Both, a drop in demand and in supply
will result in the same observation – a drop in credit and output. Kaminsky and Reinhart
(1999) show that, indeed, during bank distress output and credit decrease, and Bernanke
and Blinder (1992) find a correlation between bank liquidity, loan volumes and economic
activity. Yet, these studies say little about causality, much less about its direction.
The Great Recession created interesting new data to tackle this identification problem.
The effects of the 2007-2008 financial crisis were noted globally, but even within the United
States big regional disparities were observed regarding output and unemployment. At the
same time, some banks took a larger hit than others. The goal of this paper is to link the
regional differences in output and the differences in the performance of banks.
The bursting of the real-estate bubble in the United States and the related drop in
value of securities held by financial intermediaries led to liquidity problems for a number
of conventional banks. Doubts about the solvency of banks led short-term bank creditors
to withdraw their funds and concerns about the availability of future credit led borrowers
to draw down their credit lines (Ivashina and Scharfstein, 2010). Seasonally adjusted
commercial and industrial (C&I) loans rose in the beginning of the crisis and only peaked
one month after Lehman Brothers filed for bankruptcy protection. From then on, C&I
loans by domestically chartered commercial banks fell by 24 percent until October 2010.
A long-standing bank-firm relationship can help to overcome the information asymmetries that are inherent in the market for credit, lowering the effective borrowing costs
for firms. Firms can therefore find it difficult to switch banks in order to roll over their
credit or apply for new ones and it is hard, in particular for small firms, to issue debt
on the market themselves. A forced contraction in the total lending of a bank will lead
to a reduction in the supply of loans to qualified lenders. Borrowers of banks that have
been more affected by the financial crisis are expected to be more affected by the crisis
themselves.
The key identifying assumption is that the distribution of branches of bank holding
companies throughout the United States does not directly affect the economic performance
of the region. Local businesses are exposed to the risk that local banks cut their credit
lines. In the wake of the recent financial crisis, some bank branches were forced to cut
credit because the financial high holder runs out of money. Such a drop in credit is
therefore an exogenous supply shock to the CBSA. Along with Chodorow-Reich (2014) I
rely on the fact that the financial crisis did not have its roots in C&I lending, but that
C&I lending much rather reflects bank distress whose cause is orthogonal to lending.
The identification of such a C&I lending shock has to rely on big banks that operate
offices throughout the country. Furthermore, I will exclude the largest cities from the
analysis and thereby reduce the risk of reverse causality.
I use fluctuations in bank holding companies’ balance sheet positions, as collected by
the Federal Reserve, as exogenous shocks to the supply of credit for local businesses and I
use data on the location of bank branches by the Federal Deposit Insurance Corporation
(FDIC) to estimate the impact of these fluctuations on CBSA.
2
The analysis excludes loans by non-banks from the analysis, but this is of no concern
to the identification of a loan channel as we expect the main effect to work through small,
local businesses. The aim of such an analysis can not be to create a measure of the total
amount of loans available, rather, I try to identify one particular channel through which
the supply of loans to a local economy works.
The analysis includes dummy variables for the states and a number of regional characteristics, like the employment level by industry. Nevertheless one might fear that the error
term be correlated with the explanatory variables in an OLS regression due to historical
selection of certain banks into certain regions. I use historical realisations of the identified
loan shock as an instrument. If the drop of credit in a region was due to the fact that
this region was subject to a particularly “aggressive” bank strategy prior to the crisis, the
historical realisations of the loan shock are correlated with the actual loan shock, but not
with the error term of the OLS regression.
Small and medium-sized enterprises (SME) are particularly important for this analysis.
There are at least two ways in which the drop in C&I loans may have hit SME more
severely than large firms in the financial crisis. Firstly, lending to smaller firms seems to
have dropped relatively more than lending to large firms. Cole (2010 and 2012) finds that
the percentage drop in total bank loans to small businesses was twice as big as for all
firms together. Also, distressed banks disbursed fewer funds to commercial and industrial
borrowers under precommitted credit lines than banks that were not in distress. Huang
(2010) concludes that credit lines only provide contingent, partial insurance and finds that
not just riskier borrowers and borrowers with a shorter bank relationship, but also smaller
borrowers run a bigger risk when relying on credit lines instead of holding cash. Secondly,
as Driscoll (2004) points out, many small firms are bank dependent in a way that they
find it particularly hard to replace bank loans by other forms of financing, i.e., they lack
a direct access to financial markets.
Small firms indeed found it hard to access financial resources in the Great Recession.
In fall 2008, loans to the smallest one-third of firms dropped drastically where the largest
two-third of the firms recovered faster.
I find a statistically and economically significant effect of the identified loan channel
on local income. Over the observation period of three years following the outbreak of the
financial crisis in 2007, a one standard deviation in the loan shock lead to a 0.6 percentage
point drop in income. The effect is robust to a number of extensions like the inclusion of
more banks in the analysis, or the limitation of the data set to metropolitan areas.
Previous studies that have looked at bank lending during the 2007-2008 financial crisis,
use data from DealScan or other sources that mainly cover large syndicated loans. These
sources have the advantage that they cover a wider base of lenders, in particular, they also
cover loans by non-banks. But as those studies miss out on most non-syndicated loans,
small lenders are left out from these analyses. On the other hand, as small lenders find it
hard to issue commercial papers or bonds, they are particularly dependent on banks and
therefore we can assume that the main mechanism exploited in here works through SME.
3
Another distinguishing feature of this analysis is the outcome measure. Studies investigating the effect of credit restrictions during the Great Recession mainly looked at firm
performance, mostly of listed firms. This study considers income differences on a local
level.
The links between access to credit and macroeconomic outcomes, such as income and
unemployment, have been widely studied in the literature. In their analysis of the Great
Depression, Friedman and Schwartz (1963) identified a sharp decline in money supply,
caused by the public withdrawing bank deposits, as the main mechanism through which
banking panics affected the economy. Bernanke (1983), considering the same financial
turmoil, identified two channels through which a worsening of the Great Depression occurred. For one thing, a reduced activity by banks destroys “informational capital” that
the customer-bank relation created and for another thing a drop in output reduced the
amount of potential collateral firms could pledge to secure their loans. Both channels, one
working through the banking system the other through the creditworthiness of borrowers
lead to a worsening of the crisis.
Bernanke and Gertler (1989) show how small shocks can have long lasting effects on an
economy when one considers the effect such a shock may have on the difference between
the costs a firm faces between internal and external financing, a figure that came to be
known as the external finance premium. Broadly speaking, there is an inverse relationship
between the external finance premium and the general financial conditions. A drop in
productivity now leads to lower cash flow, which worsens the financial condition of the
firm and therefore increases its external finance premium which will force the firm to cut
its leverage and thereby its production. A number of papers have investigated this effect.
Among them are Bernanke, Gertler and Gilchrist (1996) who first used the term “financial
accelerator” to refer to the amplification of shocks to the economy by its effect on financial
markets, and Kiyotaki and Moore (1997).
The amplification of shocks through financial markets is equally useful to understand
the transmission of monetary shocks. Besides the cost-of-capital channel, there is a credit
channel, which can be split in two components (Bernanke and Gertler, 1995). Firstly, the
balance-sheet channel reflects the idea that changes in interest rates affect the cash flow
of firms and is therefore similar in nature to the financial accelerator above. Secondly, the
bank-lending channel captures the destruction of information capital.
There is also a literature on how labour and credit markets interact. Greenwald and
Stiglitz (1993) use a new-Keynesian approach to show that a firm’s labor demand fluctuates with its balance sheet positions. Acemoglu (2001) sees new jobs mainly created
by innovative firms, limiting access to credit prevents the emergence of new firms and
hence jobs. He argues that the persistently higher levels of unemployment in Europe as
compared to the United States are due to higher credit market frictions. In his model coordination failure can lead to a high equilibrium unemployment even though steady state
unemployment rates are low (entrepreneurs who need credit will get it eventually).
An interesting study is Ashcraft (2005) who finds local area effects of bank distress.
4
Cross-guarantee provisions permit the FDIC to charge the expected loss of one bank
subsidiary to the capital of another one of the same bank holding company. Ashcraft’s
analysis is concerned with two cases in 1988 and 1992, where such claims led to the failure
of “healthy” banks in Texas. He finds significant effects on economic activity.
The Great Recession generated interesting data to further investigate the interdependence of credit and macroeconomic outcomes. Disruptions in the banking system were
enormous and the differences in the way the crisis affected people, e.g., through available
income, were big. Recent studies on the effect of financial crises on borrowers include
a survey by Campello, Graham, and Harvey (2010) investigating the effects of financial
constraints on corporate spending plans. The paper, surveying CFOs in late 2008 suggests
that that financially constrained firms plan cuts in tech spending, employment and capital spending. Furthermore, constrained firms drew more heavily on credit lines for fear of
future credit restrictions. Chava and Purnanandam (2011) and Ivashina and Scharfstein
(2010) show that the more crisis-affected a bank was, the more it decreased lending in the
Russian debt crisis of fall 1998 and the recent financial crisis, respectively. Both papers
use DealScan data, and thus mainly capture (large) syndicated loans.
Greenstone and Mas (2012) attempt to separate supply and demand effects, regressing
small business lending on a county and a bank fixed effect. They use Community Reinvestment Act disclosure data. Gozzi and Goetz (2010) use a sharp decline in liquidity in
the market for short-term wholesale funding. They assume that exposure of local banks to
this class of instruments, that banks use to fund themselves other than through demand
deposits, is orthogonal to the demand for loans in a metro area. Gozzi and Goetz find
that the top quartile of the metro areas in terms of exposure to wholesale funding saw a
decrease in employment of 0.9 percent, while the bottom quartile saw a reduction of 0.6
percent.
In an analysis of firm level data, Chodorow-Reich (2014) finds that the withdrawal of
credit accounts accounts for one-half to one-third of the employment decline in small and
medium firms in the year following the Lehman bankruptcy. He uses DealScan data on
syndicated loans, matched with confidential employment data from the Bureau of Labor
Statistics.
The remainder is organized as follows: The next section looks at small firms’ credit
constraints in the financial crisis, shows how bank holding companies in the United States
are organized and how the banking system has gradually shifted from a unitary system to
a to a branching system. Section 3 lists the various sources of the data, section 4 describes
the empirical method, section 5 discusses the results and the final section 6 concludes.
5
2
Credit supply in the financial crisis
The drying up of liquidity in the second semester of 2007, commonly measured by soaring
TED spreads1 led banks cut the origination of new credits. Cornett, McNutt, Strahan,
and Tehranian (2011) find that the reduction in lending was correlated to the amount
of illiquid assets held by banks. As off-balance sheet liquidity risk materialized, bank
exchanged new credit with liquidity.2 These off-balance sheet risks were partly credit lines
guaranteed to private businesses prior to the crisis. As the demand from businesses for
liquidity also increased, business loans continued to increase after the outbreak of the crisis
400
25000
600
30000
Loans
800
35000
Income
1000
40000
1200
45000
and only cumulated in the second half of 2008, see figure 1.
1995q1
1999q3
2004q1
Loans
2008q3
2013q1
Income
Figure 1: Commercial and Industrial Loans of Domestically Chartered Commercial Banks
in USDbn (not seasonally adjusted), and income.
The testable hypothesis of this paper is that adverse shocks to a bank affect those
regions negatively in which it is over-represented. It is therefore important to know how the
shocks to the banks pass through to the regions. Cornett et al. (2011) found considerable
differences in the reaction of banks to the liquidity constraints in the inter-bank market.
And there is some evidence that small firms found it harder to access credit in the financial
crisis.
Banks are most likely to give out loans to businesses in their vicinity. But as banks
were not all hit in the same by by the financial crisis, some businesses were potentially
hit harder than others. This is why the geographic location of a firm business matters. In
1
The TED spread is the difference between the three-month London Interbank Offered Rate and the
three-month Treasury rate. The TED spread rose from below 100 basis points to temporarily above 200
in the second half of 2007 and above 450 basis points in 2008, at a very high volatility.
2
See Strahan (2012) for an illustration of how banks adjusted their balance sheet to this “liquidity
pressure”.
6
this section, I briefly discuss small firms’ credit constraints in the financial crisis and the
structure of the US banking market that allowed for liquidity shocks to a particular bank
holding company to be transmitted throughout the country.
2.1
Small firms’ credit constraints in the financial crisis
Small businesses are an important factor in the United States economy. The Business Dynamics Statistics by the U.S. Census for March 2007 show that 29 percent of all employees
worked for firms with less than 50 employees, and 50 percent of all employees worked for
a firm with less than 500 employees.
Small firms create more jobs, destroy more jobs, and, normally, they tend to have
a higher net job growth rate. An analysis by Neumark, Wall, and Zhang (2013) shows
an inverse relationship between firm size and net job growth. Haltiwanger, Jarmin, and
Miranda (2013) find the same correlation, but attribute job growth to firm age. Either
way, small firms create more jobs than large ones.
But in the recent crisis, small and firms suffered disproportionately greater net job
losses. According to the Congressional Budget Office (2012), firms with fewer than 50
workers experienced a net job loss of 7.1 percent in the period from December 2007 to
December 2010, and firms with 50 to 499 workers saw a decline of 8.1 percent. Within
firms with 500 or more workers the number of workers only dropped by 5.4 percent. More
specifically, as Charnes and Krueger (2011) point out, small firms have suffered a steeper
decline in early stages of the crisis and showed a slower recovery after mid-2009.
Gertler and Gilchrist (1994) show that small firms’ production reacts disproportionally
to monetary tightenings and they conjecture that this is due to liquidity constraints.
Petersen and Rajan (2002), in an influential analysis of a National Survey of Small
Business Finance (SSBF), show that the physical distance between small firms and the
banks has increased in the period from 1972 to 1993 and they believe that this trend
has accelerated since the end of the observation period of their data. Petersen and Rajan
attribute the declining importance of distance to the greater use of information technology.
But, using the 1993, 1998, and 2003 SSBF, the the more recent analysis by Brevoort,
Holmes, and Wolken (2010) suggests that the provision of bank credit remains largely
local. In particular, the authors find “no evidence that information opacity has decreased
in its importance as a predictor of distance. In fact, for younger and smaller firms, which
are generally considered to be the most opaque, the growth of distance lagged those of
older and larger firms. There is, therefore, little reason to believe, based upon these
data that credit access to these firms from more distant suppliers has increased over the
decade.” (p. 27) The authors find that in 2003, 14.5 percent of small businesses borrowed
from a bank that was more than 30 miles away.
It is important for a small firm to uphold an existing relationship with a bank. Petersen
and Rajan (1994), among others show that closer ties with a bank mean higher availability
of credits for the firm.
7
The U.S. Census Bureau provides a Quarterly Financial Report for Manufacturing
Corporations (QFR). This data is stratified by asset size where the highest category consists of firms with one billion USD in assets or more. For this exposition, we define all
firms not in this category as small.3 Figure 2 shows the total liabilities for small firms (up
to USD 1bn in assets) and large firms (more than USD 1bn in assets). The numbers are
.6
.8
1
1.2
normalised to equal one in the second quarter of 2008.
2001q1
2004q1
2007q1
quarter
Assets up to USD 1bn
2010q1
2013q1
Assets more than USD 1bn
Figure 2: Liabilities of small and large firms. Source: QFR.
The graph shows that the liabilities for small firms were more affected by the financial
crisis, falling (with the exception of one quarter) from the third quarter of 2008 until the
second quarter of 2010. The liabilities for large firms only fell for two quarters.
The drop in liabilities and the above mentioned studies by Cole (2010 and 2012) are
suggestive of a restricted access for small firms in the recent crisis. But, again, it cannot be
the goal of this study to motivate such a channel through small firms, rather it motivates
the idea, that the mechanism works through small firms. Switching banks is costly for
the firm and the bank and costs may not be linear to the size of the firm. In this case
it’s possible, that small firms do not just find it harder to substitute bank credit by other
finance, but also have a harder time to switch banks. Moreover, small firms are more
likely only to be represented in one region and therefore being close to fewer banks.
2.2
Bank holding companies
The United States has a strong tradition of a unit banking system in which a single
bank operates without any branches. Oftentimes such banks managed to establish a local
3
An important limitation of this dataset for this expostion is that category sizes are variable over time.
Small firms accounted for between 23 and 31 percent of total assets, with a falling trend.
8
monopoly. Decision-making is quick and no transfer from more to less profitable regions
takes place. But branch banking has become more and more important, in particular by
the end of the 20th century. Avraham, Selvaggi, and Vickery (2012) report that the share
of assets controlled by the ten largest Bank Holding Companies (BHC) has more than
doubled from less than 30 to over 60 percent between the years 1991 and 2011.
BHC as one form of branch banking emerged in the early 20th century. In 1933,
the Glass-Steagall Act of 1933 forbid banks the participation in securities dealing and
underwriting business but did not prevent the concentration of commercial credit. Only
the Bank Holding Company Act (BHCA) of 1956 limited the branch banking system.
Omarova and Tahyar (2011) write that “... the BHCA was designed principally as an
anti-monopoly law that sought to close the key ‘routes to a national banking empire.’
The primary policy goal of the new statute was to restrict geographic expansion of large
banking groups and, more broadly, to prevent excessive concentration in the commercial
banking industry.” (p. 120) Soon after the enactment, the focus of regulation on BHC
began to shift towards the separation of banking and commerce. BHC’s activities were
limited to a set of activities that were “closely related to banking.”
“Throughout the 1980s and 1990s, federal banking regulators gradually extended the
scope of permissible banking and “closely related to banking” activities, in order to ensure
the continuing economic viability of the United States banking industry in the increasingly
competitive global environment.” (Omarova and Tahyar, 2011, p. 125) Through technological innovation and and changes in the statute-implementing regulations restrictions in
the the geographic reach of banking institutions had been eroding, but it was only in 1994,
when the Riegle-Neal Interstate Banking and Branching Efficiency Act formally repealed
interstate branching restrictions in the BHCA (Kane 1996).
The Gramm-Leach-Bliley Act of 1999 partly repealed the Glass-Steagall Act, its goal
was to “enhance competition in the financial services industry by providing a prudential
framework for the affiliation of banks, securities firms, insurance companies, and other
financial service providers...” Similar to the interstate branching restrictions, and in line
with previous experience from the legislative process in the financial-service industry (Kane
1996), the enforcement system anticipated statutory changes.4
It is not possible to date the changes exactly that lead to the pre-crisis rules in the
financial-service industry in the United States, but it is important to know that the banking
system has gradually shifted form a unitary system to a branching system, where few big
BHC control the large majority of assets. Calomiris and Mason (2003) used the fact that
banking was dominated by the unitary system in the Great Depression to investigate the
effect of bank distress on the real sector. They use state-level deposit growth as a source
of exogenous variance, exploiting the fact that bank deposits would mainly remain within
one bank office. In my analysis, I will use the facts that few banks hold most assets
4
E.g., the so-called Section 20 subsidiaries, referring to the Federal Reserve’s interpretation of the
respective section of the Glass-Steagall Act, allowed some commercial banks to hold investment bank
subsidiaries.
9
and that a negative shock to their ability to lend money will be reflected throughout the
nation, and that banks’ business was not limited to accepting deposits and making loans.
A final question is, to what extend BHC guarantee for their subsidiaries. The Board
of Governors stated that it expects a BHC to act as a source of managerial and financial
strength to its subsidiaries. In 1978 the United States Supreme Court approved a “weak
form of the source-of-strength doctrine” that envisages to decline applications for new
lines of business and mergers by a parent company that fails to do so. In the 1980’s
several statements by the Board of Governors made it clear that it would force a parent
bank holding company to provide financial support to a distressed banking subsidiary. In
1989, Congress passed the Financial Institutions Reform, Recovery, and Enforcement Act
which granted to the FDIC the authority to charge off any expected losses from a failing
banking subsidiary to the capital of non-failing affiliate banks, the so called cross-guarantee
provision (cf. Ashcraft (2004) and Keeton (1990)).
Foreign holding companies, holding United States banks are dealt with as follows:
United States subsidiaries of a non-U.S. banks are listed under their foreign parent’s
name. The loans and branches of all United States subsidiaries are added.
3
Data
I combine data from various sources. The FDIC collects annual data on the location of
bank branches. For each branch, the FDIC identifies the financial high holder and the
county the branch is located in. I link the counties to the CBSA. For these regions I have
data on income, business patterns, and population from various sources. Through the
financial high holder, I can merge the data with quarterly (for small banks semi-annual)
statistics regarding balance sheet items and income, that commercial banks report to the
Federal Reserve and savings institutions used to report to the Office of Thrift Supervision.
FDIC Summary of Deposits
All FDIC-insured institutions with a main office and
one or more branches file an annual report to the FDIC reporting the deposits in each
branch/office and its precise location. A branch/office “is any location, or facility, of a
financial institution, including its main office, where deposit accounts are opened, deposits
are accepted, checks paid, and loans are granted.” ATM are not included. The FDIC
augments its data with the information unit banks file in their Call Reports. All FDIC
Summary of Deposit data are as of June 30.
In 2007 there were 8594 commercial banks and savings institutions in the United States,
operating 97263 offices. The last two columns of table 1 shows the 20 banks with the most
branches nationwide. The top 10 banks at the time had 27356 and the top 20 36724 offices.
Bank of America, the BHC with the most offices at the time, had 5733 offices in 31 states.
Figures 7 to 9 in the Appendix show how the branches of the 10 largest financial high
holders in 2007, adjusted for mergers until 2010, are spread throughout the contiguous
10
Table 1: Banks by number of branches, 2007, adjusted for mergers until 2010.
adjusted for mergers until 2010
Bank
Rank Branches CBSA States
Wells Fargo & Company
1
6782
631
39
Bank of America Corporation
2
6142
455
32
JPMorgan Chase & Co.
3
5334
262
26
U.S. Bancorp
4
2796
430
26
PNC Financial Services Group
5
2687
166
13
Regions Financial Corporation
6
2094
290
16
BB&T Corporation
7
1830
251
12
Suntrust Banks, Inc.
8
1776
122
9
U.K. Financial Investments Limited
9
1665
94
12
Fifth Third Bancorp
10
1224
113
10
Toronto-Dominion Bank, The
11
1140
62
12
Citigroup Inc.
12
1026
78
14
Keycorp
13
1002
138
14
Huntington Bancshares
14
762
72
6
Banco Santander Central Hispano, S.A.
15
745
33
6
BNP Paribas
16
728
173
20
Bank of Montreal
17
714
73
12
Capital One Financial Corporat.
18
714
53
6
Allied Irish Banks, P.L.C.
19
674
54
3
Banco Bilbao Vizcaya Argentaria, S.A.
20
623
78
7
Non-surviving top 10 banks by number of branches in 2007
Wachovia Corporation (acquired by Wells Fargo & Company)
Washington Mutual Bank (acquired by JPMorgan Chase, & Co.)
Royal Bank of Scotland Group (acquired by U.K. Financial Investments Ltd)
National City Corporation (acquired by PNC Financial Services Group)
2007
Branches States
3315
23
5733
31
3127
24
2591
26
1135
10
2087
16
1509
12
1747
12
1218
627
1044
965
424
807
733
237
714
674
245
10
8
18
14
6
9
22
5
6
8
4
3414
2180
1653
1451
22
15
13
8
Source: FDIC Summary of Deposits, 2007 to 2010, Chicago Fed.
United States5 State dummies in the main identification will take care of the apparent
over-representation of the largest banks on the east coast, as well as the historical legal
geographic restrictions as described above.
A brief look at the development of the number of offices and the number of financial
high holders from 1994 to 2013 shows that the number of financial high holders has steadily
declined, where the number of offices has increased from 1995 to 2009. This corroborates
the view expressed above, that banking has seen a shift from the unitary system to a
system where fewer and fewer banks dominate the market. In 1994, 81297 offices were run
by 10416 separate financial high holders, where in 2009 99550 offices belonged to 7219 high
holders. The number of offices has dropped since 2009, inverting the long time trend.6
Figure 3 shows how the number of offices and financial high holders evolved since 1994.
Changes in the ownership structure of banks in the time period under consideration
will be important for the analysis. I adjust the 2007 data for changes until 2010. Below,
in subsection 4.2, I explain in detail how the adjustment works. The first four columns
of table 1 show the top 20 banks by number of branches, as of 2007 when adjusted for
changes in the financial high holder until 2010. Wells Fargo, the third largest bank by
5
Counties with at least one branch are highlighted. The more branches the darker the county is in the
map. The intervals are as follows: 1 to 4 branches; 5 to 9 branches; 10 or more branches.
6
Electronic data is only available from 1994 onwards, but as Berger, Kashyap, and Scalise (1995) write,
the number of banking organizations have been falling since 1979.
11
7000
8000
9000
10000
Number of inancial high holders
100000
6000
Number of offices
85000
90000
95000
80000
1995
2000
2005
Number of offices
2010
2015
Number of inancial high holders
Figure 3: Offices and financial high holders. Source: FDIC Summary of Deposits 19942013.
number of branches in 2007, took over Wachovia, the second largest, in the end of 2008.
Additionally, Wells Fargo also took over a handful of small banks until June 2010. For this
analysis, I consider a total of 6782 branches in 39 states as belonging to this BHC. C&I
loans for all top tier bank of these 6782 branches are added at three points in time: for
the first quarter of 2004, the third quarter of 2008, and the second quarter of 2010. I do
not expect to overestimate the total amount of C&I loans due to a lack of consolidation,
because interbank lending is not included in C&I loans.
Holding Company Data
The BHCA defines BHC as “a company that owns or controls
one or more U.S. banks. Although the definition of control for purposes of determining
whether an entity is a BHC is complicated and fact-dependent, the statute generally
presumes the existence of control where an entity owns more than twenty five percent of
any class of voting shares of a bank.” (Avraham, Selvaggi, and Vickery, 2012, p. 118)
A commercial bank can have one or more branches and can itself be part of a BHC.
A BHC can be part of other BHC. Depending on their size and the category they fall in,
those different institutions file different reports, that are being used in this analysis. Every
institution is assigned a number that links it to the regulatory top holder.
The highest tier BHC, or the commercial bank, if not part of a BHC, is the top regulatory holding company (also called financial high holder). The Federal Reserve Board
defines: “Regulatory top holder is any company that (i) directly or indirectly owns, controls or has power to vote 25 percent or more of a bank’s or direct holding company’s
shares or (ii) controls in any manner the election of a majority of the directors or trustees
12
of a bank or direct holding company or (iii) exercises a controlling influence over the
management or policies of a bank or direct holding company.”
The Chicago Fed provides quarterly data on holding companies, collected by the Federal Reserve Board.7 This dataset has information on BHC on a consolidated basis. From
the various balance sheet items listed, I use C&I loans to United States addressees by
big banks as a loan shock in my analysis. The Chicago Fed also has data for mergers of
BHC. It is important to keep track of the ownership structure of BHC. Our analysis is
concerned with a big shock to the banking system, where a number of mergers among big
banks happened.
Core Based Statistical Areas
The identification of a local credit supply shock relies
on an integrated market. Many SME rely on local banks where I assume that lending
practices are determined by a notion of socio-economic integration rather than political
boarders. Such local markets for credit do not necessarily follow county boarders, as
some county and even state boarders run through cities. The Office of Management and
Budget defines Core Based Statistical Areas (CBSA) as a group of one or more counties
(or equivalents) with at least one core i.e., an “urbanized area or urban cluster” of at
least 10000 people, plus adjacent counties with a high social and economic integration
(as measured by commuting to work). A CBSA is either a metropolitan statistical area
(metro area) or a micropolitan statistical area (micro area), depending on the size of its
core. A CBSA with a core of 50000 or more is a metro area. There are 381 metro areas and
536 micro areas in the United States (excluding unincorporated territories). Roughly 2000
counties remain that do not have a core and whose population’s socio-economic integration
with a such an area is low. In 2007, 94 percent of the United States population were living
in a CBSA. This analysis in therefore based on a sample that leaves roughly every 20th
person out, but I will censor the at the top in order to avoid endogeneity concerns regarding
big cities economic performance affection a bank.
County Business Patterns In the survey on county business patterns (CBP), the US
Census Bureau reports county level employment by industry during the week of March 12.
The county business patterns’s classification of industries is according to the 2007 North
American Industry Classification System (NAICS). I use the 2-digit NAICS industries.8
7
This dataset combines data from the filers of FR Y-9C (all domestic holding companies on a consolidated basis), FR Y-9LP (all large domestic holding companies on an unconsolidated parent only basis) and
FR Y-9SP (all small domestic holding companies on an unconsolidated parent basis) on a quarterly basis
since 1986. FR Y-9C and FR Y-9LP data are collected quarterly. FR Y-9SP is collected semiannually.
Notice that these data differ from the so called Call Reports (e.g., the Consolidated Reports of Condition
and Income for a Bank with Domestic and Foreign Offices, FFIEC 031). Note: Effective March 31, 2006,
the FRY-9C and the FRY-9LP filing threshold was increased from $150 million to $500 million or more
and the reporting exception that required each lower-tier bank holding company with total consolidated
assets of $1 billion or more to file the FRY-9C was eliminated.
8
These are: Agriculture, forestry, fishing and hunting; Mining, quarrying, and oil and gas extraction;
Utilities; Construction; Manufacturing; Wholesale trade; Retail trade; Transportation and warehousing; Information; Finance and insurance; Real estate and rental and leasing; Professional, scientific, and technical
services; Management of companies and enterprises; Administrative and support and waste management
13
The Census Bureau adds noise to the employment data to avoid disclosure. Where data
do not meet publication standards or to avoid disclosing data of individual companies, the
Census Bureau only gives the range in which the number of employees lie. In this case I
chose the midpoint. I divide the number of employees by industry by the total number of
employees in the county.
Income
The Bureau of Economic Analysis (BEA) has yearly data on income and pop-
ulation for counties, metro and micro areas. I use the per capita personal income, both
on the left and the right and side of my regression. As a outcome variable I consider
the change in per capita income over a certain period of time and as controls I use per
capita income at the outbreak of the crisis and past income growth. The BEA defines
personal income as the income received by all persons from all sources. It includes wage
and salary disbursements, employer contributions for pension and insurance funds, as well
as property income and net transfers. It does not include taxes, nor capital gains from
the sale of assets.
In figure 1, we see that yearly growth in personal income for the US was only negative
in one year (2009). Figure 4 shows the kernel density estimation for yearly changes in
the logs of the income for all CBSA.9 We can see that the mass that represents negative
growth is elevated for the years 2008 through 2010. This is suggestive of the financial
turmoil being reflected in income data for 2008, 2009, and 2010. In my main specification,
I will use the difference in the logarithms of personal income per capita between 2007 and
2010. As a robustness check I also consider the difference between 2007 and 2009.
4
Empirical method
4.1
Identification of a loan supply effect
The goal of this study is to identify the effect of a credit supply shock. C&I loans dropped
sharply by the end of 2008 and my identification strategy is based on this drop. The
decline in loans in the wake of the financial crisis was created outside the corporate loan
sector. I can use this fact to separate an exogenous shock to local credit supply within the
United States. Figure 1 above shows how C&I loans of domestically chartered commercial
banks, in USDbn, and income evolved since 1995.
Furthermore, I use the fact that branches of bank holding companies are not spread
evenly throughout the country and that bank-borrower relationships tend to be sticky.
This allows me to identify a source of exogenous variation in the supply of credit to local
businesses. Based on the market share of a top regulatory bank holding company in every
and remediation services; Educational services; Health care and social assistance; Arts, entertainment, and
recreation; Accommodation and food services; Other services (except public administration); Industries
not classified.
√
√
9
I use an Epanechnikov kernel function of the form 3/4(1 − z 2 /5)/ 5 if |z| < 5 and 0 else, with a
bandwidth of 0.005.
14
.2
−.2
−.1
0
2009
.1
.2
25
−.2
−.1
0
2007
.1
.2
−.2
−.1
0
2010
.1
.2
−.1
0
2008
.1
.2
−.2
−.1
0
2011
.1
.2
20
0
5
10
15
20
0
5
10
15
20
15
10
5
0
−.2
25
.1
25
0
2006
0
5
10
15
20
25
0
5
10
15
20
25
20
15
10
5
0
−.1
25
−.2
Figure 4: Kernel density estimation of yearly log changes in income.
region, I create a measure of a loan shock and estimate its effect on the income in this
region during the Great Recession.
The market share of a high holder in a region is the number of bank branches, belonging
to this high holder divided by the total number of bank offices within this region. The
loan supply shock Lr to region r is defined as follows:
Lr = f (sr )∆l
(1)
f (sr ) is a vector-valued function of the region-specific vector of market shares of the
regulatory high holders sr . For the baseline regression I set f (sr ) = sr . This functional
form has the most straight-forward economic interpretation, but further calculations show
that the result is robust when f is a quadratic, square root or indicator function. ∆l
contains the log differences of the total amount of commercial and industrial loans to
United States addressees by the respective bank at the beginning and the end of the
observation period. Figure 1 shows that C&I loans peaked in the third quarter of 2008
and fell until the second quarter of 2010. I use this as the observation period.
The dependent variable is the difference in the logarithms of personal income per capita
between 2007 and 2010. I call this difference for region r, ∆yr . The main regression model
is
∆yr = α + βLr + xr γ + r .
(2)
α is a constant, β measures the effect of the loan supply shock. The vector γ measures
the effect of a set of control variables. xr contains (in the main specification) income
per capita in 2007 and change in income per capita from 2004 to 2007, as well as the
15
following numbers for 2007: population, dividends, interest and rents, transfers, the share
of employment in 20 different categories (NAICS), and dummies for the state in which
most people of the respective CBSA live.10
Recall the basic assumptions about the 2007-2008 financial crisis underlying this analysis, which are that the crisis originated outside C&I lending and that bank-borrower
relationships are sticky. I attempt to measure the effect of a cutback in loans by a bank
on local income, thus identifying the effect of a loan shock. The loan shock, as defined in
equation (1), assumes the weight of a bank in region r to be a function of the vector of
market shares sr in this region. The baseline regression assumes a liner relation between
the market share and the weight of a bank. This means, I assume f (sr ) = sr . A linear
relationship follows from a very simple assumption about how cuts in lending affect local
borrowers. A bank lowering lending by 20 percent over the time period from the third
quarter of 2008 to the second quarter of 2010 terminates 20 percent of the lending relationships (in value) in all regions and the borrowers left without a bank have to give up
their projects. Moreover, the assumption that the shock is proportional to the market
share has the advantage that regression coefficients have a straight-forward interpretation.
But, there might be spillover effects that cause non-linearities in the way loans shocks
are transferred to regions. The failure of an SME due to a shortage of money can affect the
demand its subcontractor faces. A leftward shift of the subcontractor’s demand curve may
turn some of its positive net present value projects non-profitable and therefore reduces the
subcontractor’s demand for credit. The higher the market share of the general contractor’s
bank, the more likely the subcontractor is to be borrowing from the same bank. We would
therefore expect the marginal effect to decrease. To check for the robustness of my results
to decreasing marginal effects, I use two different specifications for the function f . First,
I use a square root function and second I use an indicator function, defined to be 1 when
the market share is strictly positive and 0 else.
I cannot exclude the opposite effect either. If bank-lender relationships are not perfectly sticky, some of the positive net present value projects might find another bank. An
increase in the market share from 10 to 20 percent, say, now means that the numbers of
lenders affected doubles, but at the same time the share of banks to provide for alternative resources decreases by 11 percent. I use a quadratic function to capture the idea of
increasing marginal effects.
A concern with the regression model (2) might be reverse causation. A negative demand shock leads to a drop in income and therefore also in the demand for loans. Notice
that by assumption, the crisis started outside C&I lending. Nevertheless, the regressions
exclude CBSA with more than 5 million persons to reduce the threat to the identification
strategy to suffer from demand shocks from single regions.11
Another concern is that certain banks have selected into certain regions prior to the
outbreak of the crisis. I introduce state dummies in all regressions to control for selection
10
11
CBSA include counties from up to four different states.
The inclusion of the 9 CBSA with more than 5 million inhabitants has very little effect on the results.
16
into states. Selection into states is likely, as banks are partly regulated on a state level and
also the boundaries of Federal Reserve Districts largely follow state borders. I control for
selection into CBSA within states by past changes in income, population, dividend and
interests, transfers, as well as the share of workers in various professional categories (this
is the local industry composition).
A region that saw a big influx of loans prior to the crisis might be more likely to have
suffered from financial turmoil as its economy was more leveraged. In order to account
for bank behavior up to the crisis, I use a measure for the loan supply shock from the
trough to the peak in C&I loans, i.e., from the first quarter of 2004 to the third quarter
of 2008, see figure 1, as an instrument for the loan supply shock Lr . This instrument
is uncorrelated with the error term in the regression model (2) and it is correlated with
the loan shock Lr . An example will explain how the IV strategy works. If a region was
exposed to a bank that was willing to finance projects from which other banks would shied
away and therefore saw a bigger exposure to the financial crisis, this region might have
suffered more in terms of income than another region with more conservative banks. The
instrument captures the loose policy by the bank without directly affecting the drop in
income in the wake of the financial crisis.
I restrict the analysis to the top 10 (top 20 as a robustness check) banks by number
of branches. The more banks I include (with branches in fewer and fewer states), the
more the identification suffers from small (local) banks inflating the standard error of the
estimation. Consider a state that was hit particularly hard by the crisis. Let us assume
that this state has historically had a lax banking regulation and that local banks, for
liquidity reasons, were forced to cut back on loans a lot. If I include small banks in the
analysis, the respective state dummy will be correlated with the loan shock, our variable
of interest, and will therefore be less precisely measured.
4.2
Closure of banks and change of top regulatory bank holding company
The identification strategy employed requires me to keep the structure of the banking
sector within a region constant. The shutdown of a bank branch in a CBSA might be
endogenous to this county’s economic performance during the financial crisis. I therefore
hold the number of branches per bank in each county constant. The FDIC survey of bank
branches is as of the end of June, I fix the number of bank branches at their 2007 levels.
A crucial point in this analysis are changes in the ownership structure of a bank. This
is in particular an issue as the econometric analysis is concerned with a big shock to the
banking system.
It is, again, important to keep in mind what effect we are after in this analysis. Whereas
the closure of a bank branch and the failure of a bank or a small bank holding company
can be endogenous to the local economy, the well-being of large bank holding companies
is by assumption not. In the baseline case I consider banks that were merged by June
17
2010 as one unit from the beginning. This means that I add the C&I loans from all nonsurviving banks to the surviving from the beginning of the sample period. Notice that this
procedure yields non consolidated values. In the case of C&I loans this is of no concern,
as C&I loans exclude the interbank market.
When Royal Bank of Scotland got in distress, a new institution was created, U.K.
Financial Investment Limited, that took over more than 90 percent of the branches of
Royal Bank of Scotland in 2009. It is important to notice, that this take-over does not
show up in the merger data the Chicago Fed. Whenever a top regulatory bank holding
company loses 50 or more percent of its branches to another top regulatory bank holding
company within one year, I consider this a merger as well. I use the annual FDIC data to
identify take-overs.
It can happen that a bank is taken over more than once by another bank if defined as
thus. This was the case for two banks among the top 100 by number of branches. Mellon
Financial Corporation lost 92 percent of its branches to Royal Bank of Scotland in 2001
and 78 percent of the remaining to the Bank of New York Mellon in 2007. Bank of New
York lost 93 percent of its branches to JP Morgan Chase in 2006 and all the remaining
branches to the Bank of New York Mellon in 2007. In cases like this, I count the first
take-over only.
Among the top 20 top regulatory bank holding companies, none just vanished. Either
it was merged with, or the majority of its branches were forfeited to another company.
An example will clarify how I account for closures of bank branches and changes of
top regulatory bank holding companies. As of 2007, CBSA M has 2 branches of bank A,
one branch each of bank B and C, and one branch each of small bank S1 and S2 . In 2008,
bank A is acquired by bank D and bank B acquires bank E. In 2009, bank D acquires
bank F, bank G takes over 95 percent of bank B’s branches and bank C closes its branch
in M.
Reducing sr to its non-zero component and adjusting l accordingly, we find the county
shock LM = sM ∆l, where
sM =
1 1 1
, ,
3 6 6
and

loans 2010 by D


loans 2007 by A,D,F





∆l = ln loans 2010 by G − ln  loans 2007 by B,E,F  .
loans 2010 by C
loans 2007 by C
Figure 10 in the Appendix illustrates this example.
18
5
Results
5.1
Baseline
In the main specification of the regression, I consider the largest 10 banks by number of
branches in order to calculate the loan shock Lr . This means that all components of sr in
equation (1), referring to banks outside the top 10, are set to zero.
The median loan shock, considering the top 10 banks, is -0.052. Figure 5 compares
the (unweighted) average personal income in current USD for CBSA that experienced a
loan shock below and above the median shock. CBSA with a loan shock above median are
defined as those regions who were hit more through the credit channel identified in this
paper. CBSA that experienced an above median shock have a higher income throughout
the period from 1969 to 2012. Splitting the sample in metro and micro areas does not
change this result, but we then find that the difference between metro and micro areas
is larger than within those groups of CBSA. The dotted line in figure 5 shows the ratio
between CBSA that experienced a shock above and below median. This line has a local
maximum in 2006 from where it fell through 2012, reflecting the narrowing of the gap
between the solid and the dashed line for this time period. It is this drop in the ratio
between the two lines that I analyse. The fact that the ratio between the two lines was
roughly between 1.07 and 1.08 between 1981 and 2005 is suggestive of the fact that the
two groups of regions were not affected differently by shocks in the economy during that
time, but things changed in the beginning of the 21st century, where the ratio first rose
and then dropped to the lowest level in 40 years.
Table 2 shows the OLS regression results of model (2). The number of banks is limited
to the top 10 by number of branches. In table 2, I run the regressions using all Metropolitan
and Micropolitan Statistical areas (CBSA) with a population of less than 5 million. In
the appendix, table 6, I show an alternative specification that differs in the sample sizes.
In this regression, I restrict the sample to the CBSA that are home to at least one of the
top 10 banks.
The results in table 2 show that the effect of the loan shock L is only lightly affected
by the inclusion of additional regional characteristics. The loan shock is only significant
on a 10 percent level. A one unit increase in the loan shock implies a drop in income by
roughly 8 percentage points. The minimal specification (i) includes dummy variables for
the states and the per capita income in 2007. As we concluded from figure 5, income per
capita in 2007 is an important factor. A CBSA whose income p.c. was USD 1000 higher
than the (unweighted) average, expected its income to increase by 0.4 percentage point
less than the average.
In specification (ii), I include the change in income from the trough to the of C&I lending in 2004 to the outbreak of the financial crisis in 2007. There is a positive relationship
between the the change in income from 2004 to 2007 and 2007 to 2010. This is due to the
large effect income p.c. in 2007 has on the results. In fact, a regression of L, change in
income p.c. between 2004 to 2007 and state dummies on the change in income between
19
1.04
1.06
1.08
1.1
Ratio (above/below median shock)
40000
20000
USD
10000
1.02
5000
1970
1980
1990
year
Above median shock
Ratio (right scale)
2000
2010
Below median shock
Figure 5: Personal income (in current USD, unweighted) in CBSA which experienced
an above and below median loan shock, excluding metro areas with more than 5 million
persons and the ratio between the two lines.
p.c. (not reported) cannot reject the null hypothesis of no effect of the change in income
p.c. between 2004 to 2007.
Specification (iii) suggests that large cities have experienced a bigger drop in income.
Specification (iv) includes dividends and interest and transfers p.c. in 2007. Both variables
are significantly different from zero and together seem to better predict changes in income
than income p.c. in 2007. But, throughout the loan shock L is little affected by the
inclusion of further covariates.
Specification (v) includes the full set of covariates. In addition to the abovementioned
variables, I include here employment during the week of March 12 2007 for 20 different
industries, thus controlling for local industries. The inclusion of industries, again, does not
change the estimated effect of a loan shock. I only report finance and insurance and real
estate, rental and leasing both have a slightly negative effect, their statistical significance
is low. Among the non-reported coefficients agriculture, forestry, fishing and hunting has
the statistically most significant effect. The estimated coefficient is 0.329 with a standard
deviation of 0.140.
The analysis is based on a few big banks that operate nation-wide. A reason why one
might be hesitant to give the partial correlations reported in 2 a causal interpretation, is
that banks may have differed in their policy of granting loans. Past observations of the
loan shock is therefore a suitable instrument. It is likely to be correlated with observations
during the financial crisis and it is uncorrelated with the error term, as we assume the
financial crisis not to have originated in C&I lending.
20
Table 2: Loan shock and income. Dependent variable: Change in income from
2007 to 2010.
Loan shock L
Income p.c.
(USDmio) 2007
Change in income
2004 to 2007
Population
(100mio) 2007
Dividends and interest
p.c. (USDmio) 2007
Transfers p.c.
(USDmio) 2007
Finance and insurance
(CBP) 2007
Real estate, rental and
leasing (CBP) 2007
R2
N
CBSA w/o big banks
CBP
State dummies
(i)
0.079
(0.045)*
-4.057
(0.463)***
(ii)
0.076
(0.044)*
-4.449
(0.477)***
0.205
(0.069)***
(iii)
0.068
(0.044)
-4.383
(0.533)***
0.201
(0.070)***
-0.232
(0.336)
(iv)
0.074
(0.042)*
-0.703
(0.678)
0.179
(0.063)***
-1.138
(0.320)***
-7.284
(1.013)***
3.719
(1.278)***
0.55
908
Yes
No
Yes
0.57
908
Yes
No
Yes
0.57
908
Yes
No
Yes
0.61
908
Yes
No
Yes
(v)
0.082
(0.042)*
-0.090
(0.794)
0.113
(0.064)*
-0.753
(0.269)***
-7.394
(1.198)***
1.654
(1.447)
-0.075
(0.081)
-0.379
(0.212)*
0.65
908
Yes
Yes
Yes
All regressions include state dummies for the state in which most people of the CBSA live.
County Business Patterns (CBP) for 18 of 20 NAICS not reported. Heteroscedasticity robust standard errors in parentheses. *,**,*** mean statistical significance at the 10, 5, and
1-percent level, respectively.
Table 3 shows the first stage regressions. Table 7 in the appendix shows the respective
regressions for the alternative specification, excluding CBSA without a big bank. There
is a strong negative relation between L and its counterpart for the period 2004 to 2007.
A region that saw a more pronounced increase in loans form big banks in the time where
C&I loans rose countrywide, saw also a bigger outflow in the period when they dropped
countrywide. Furthermore, we observe a similar pattern to the OLS estimates above: the
loan shock for the period 2007 to 2010 is negatively correlated with income p.c. in 2007,
but the respective coefficient is no longer statistically significant once we include dividends
and interest and transfers.
Table 4 reports the two stage least square (2SLS) estimates. The coefficients roughly
double as compared to the OLS estimates. And the inclusion of additional covariates has
a slightly stronger effect on the estimated coefficient on the loans shock L.
Table 8 in the appendix shows the IV regressions for the alternative specification,
excluding CBSA without a big bank. In both specifications the estimates for the loan
shock are lower when using OLS. A possible explanation for this is the measurement error
in the loan shock that may occur form the assumption that income is a affine function of
the log differences in the loans and that OLS estimates are therefore biased towards zero.
The standard deviations for the consistent estimates only increase slightly. Statistical
significance is therefore higher for 2SLS estimates.
Per capita income is not significant, but per capita income from dividends and rents
21
Table 3: Loan shock and income. First stage regression. Dependent variable:
Loan shock L.
Loan shock
2004 to 2007
Income p.c.
(USDmio) 2007
Change in income
2004 to 2007
Population
(100mio) 2007
Dividends and interest
p.c. (USDmio) 2007
Transfers p.c.
(USDmio) 2007
Finance and insurance
(CBP) 2007
Real estate, rental and
leasing (CBP) 2007
R2
N
CBSA w/o big banks
CBP
State dummies
(i)
-0.386
(0.018)***
-0.461
(0.153)***
(ii)
-0.386
(0.018)***
-0.490
(0.158)***
0.015
(0.024)
(iii)
-0.373
(0.018)***
-0.237
(0.153)
0.000
(0.024)
-0.846
(0.137)***
(iv)
-0.373
(0.018)***
-0.512
(0.271)*
0.002
(0.024)
-0.778
(0.147)***
0.542
(0.487)
-0.306
(0.844)
0.77
908
Yes
No
Yes
0.77
908
Yes
No
Yes
0.78
908
Yes
No
Yes
0.78
908
Yes
No
Yes
(v)
-0.370
(0.019)***
-0.106
(0.331)
0.003
(0.026)
-0.570
(0.154)***
0.085
(0.569)
0.409
(0.895)
-0.015
(0.048)
-0.071
(0.135)
0.79
908
Yes
Yes
Yes
All regressions include state dummies for the state in which most people of the CBSA live.
County Business Patterns (CBP) for 18 of 20 NAICS not reported. Heteroscedasticity robust standard errors in parentheses. *,**,*** mean statistical significance at the 10, 5, and
1-percent level, respectively.
are. Also significant is the percentage of employees working in real estate and rental and
leasing. Both effects are negative, this means that the more exposed a region is to the
financial sector, either through labor or capital, the more affected it was by the financial
crisis.
From 2007 to 2010, the (unweighted) average income in all CBSA rose by 4.49 percent.
Consider now the 2SLS estimates of specification (v). The measure of the loan shock L
has a standard deviation of 0.045. A one standard deviation drop in the loan shock leads
to a drop in income of 0.045 · 0.129 = 0.006 or 0.6 percent. An average CBSA has 72 bank
branches of which there are 24 from top 10 banks. In the bank that saw the biggest drop in
C&I loans from 2007 to 2010, C&I loans fell by 38 percent (Wells Fargo & Company). In
the bank where C&I loans dropped the least, they fell by 4 percent (BB&T Corporation).
Let’s compare two CBSA with 72 branches each. If one of them had 9 branches from
the least affected bank and the other one had instead 9 branches from the most affected
bank, the difference in the loan shock is one standard deviation, this means that we would
expect a 0.6 percentage points difference in income growth over the time period 2007 to
2010.
How does this number compare to previous research on the credit channel in financial
crises? Calomiris and Mason (2003) find evidence that the supply of bank credit in the
Great Depression has a direct effect on the income growth during that period. A one
standard deviation decrease in the growth of loans makes up for a 4 percent drop in
22
Table 4: Loan shock and income. Two stage least square estimates. Dependent
variable: Loan shock.
Loan shock L
Income p.c.
(USDmio) 2007
Change in income
2004 to 2007
Population
(100mio) 2007
Dividends and interest
p.c. (USDmio) 2007
Transfers p.c.
(USDmio) 2007
Finance and insurance
(CBP) 2007
Real estate, rental and
leasing (CBP) 2007
R2
N
CBSA w/o big banks
CBP
State dummies
(i)
0.162
(0.058)***
-3.939
(0.455)***
(ii)
0.163
(0.057)***
-4.324
(0.468)***
0.204
(0.067)***
(iii)
0.160
(0.059)***
-4.304
(0.520)***
0.202
(0.068)***
-0.071
(0.336)
(iv)
0.149
(0.056)***
-0.626
(0.652)
0.181
(0.061)***
-1.013
(0.315)***
-7.316
(0.981)***
3.677
(1.243)***
0.55
908
Yes
No
Yes
0.57
908
Yes
No
Yes
0.57
908
Yes
No
Yes
0.61
908
Yes
No
Yes
(v)
0.129
(0.054)**
-0.083
(0.758)
0.113
(0.061)*
-0.697
(0.262)***
-7.364
(1.148)***
1.617
(1.392)
-0.073
(0.077)
-0.363
(0.204)*
0.65
908
Yes
Yes
Yes
All regressions include state dummies for the state in which most people of the CBSA live.
County Business Patterns (CBP) for 18 of 20 NAICS not reported. Heteroscedasticity robust standard errors in parentheses. *,**,*** mean statistical significance at the 10, 5, and
1-percent level, respectively.
income for a time period where income dropped nationwide by 17 percent.
Figure 6 compares the loan shock and the change in income from 2007 to 2010 for all
the metro areas in a scatter plot. I also named the the six metro areas that experienced the
highest/lowest loan shocks and saw the largest/smallest income growth (the sets intersect).
There is a positive correlation between the loan shock and the change in income. In
the metro area Phoenix-Mesa-Scottsdale, Arizona, the weighted average of the change in
loans experienced by the exposure to certain big banks (“loan shock”) was −0.213 and
the difference between the logs of the per capita income in 2007 and 2010 was −0.080.
According to the regression results for the instrumental variable estimation (2SLS) in table
4, a unit increase in the loans shock leads to an increase in income of 0.129. The measure
of the loan shock L has a mean of −0.060. Therefore, had the metro area Phoenix-MesaScottsdale experienced an average loan shock, we expect income growth to be (0.213 −
0.060) · 0.129 = 0.020 higher. This means the per capita income in the metro area would
have shrunk by 6 instead of 8 percent.
5.2
Robustness
I perform several robustness checks. The estimates are robust to these extension, but in
some specifications, the tests are less powerful and statistical significance is not always
granted. The restriction to the top 10 banks is arbitrary. The identification strategy just
requires me to limit the sample to banks that are large and unlikely to be influenced by
23
.2
Midland, TX
Jacksonville, NC
Watertown−Fort Drum, NY
Manhattan, KS
Glens Falls, NY
Change in income 2007−2010
−.1
0
.1
The Villages, FL
Danville, IL
Sierra Vista−Douglas, AZ
Urban Honolulu, HI
Lakeland−Winter Haven, FL
Palm Bay−Melbourne−Titusville, FL
Chambersburg−Waynesboro, PA
Kahului−Wailuku−Lahaina, HI
Tucson, AZ
Phoenix−Mesa−Scottsdale, AZ
Elkhart−Goshen, IN
Hilton Head Island−Bluffton−Beaufort, SC
Las Vegas−Henderson−Paradise, NV
Naples−Immokalee−Marco Island, FL
Sebastian−Vero Beach, FL
−.2
Reno, NV
−.2
−.15
−.1
Loan shock
−.05
0
Figure 6: Loan shock and change in income for all metro areas. The six metro areas that
experienced the highest/lowest loan shocks and saw the largest/smallest income growth
are named in the plot.
local demand effects. The results are robust to further limiting or extending the sample
of banks considered “large”. As expected the measured effect of a loan shock is diluted
with the inclusion of more banks, but the relationship is not monotonous. Figure 11 in the
appendix shows stimates of β in the baseline equation (2) for the 2 to 100 largest banks
by number of branches and the four specifications as in tables 2, 4, 6, and 8. The dotted
lines represent the point estimate plus/minus one standard deviation.
Table 5 shows OLS and 2SLS estimates of model (2) where the number of banks is
limited to the top 20 by number of branches. The point estimates drop as compared to
the estimated effects for the top 10 banks, and standard deviations for the 2SLS increase.
Despite the larger sample for the estimates that restrict the sample to the CBSA with big
banks (now top 20), statistical significance of the 2SLS decreases.
Tables 9 and 10 in the Appendix, list further robustness checks. Personal income
only dropped in one year for the national aggregate, but the variance of yearly changes
in income between the CBSA was elevated from 2007 through 2010, see figure 4. This
is why I chose the time from 2007 to 2010 for the before and after analysis of personal
income. Reducing the time interval by one year, reduces also the effect of the loan shock,
the coefficients are no longer significantly different from 0. Prolonging the period by one
year, on the other hand increases the estimated effects.
Reducing the sample to only metro areas, increases both the estimated effects of the
loan shocks and their standard deviations, leaving the statistical significance at about the
same level, see table 10. The inclusion of the change in the number of building permits for
24
Table 5: Loan shock by top 20 banks and income. Dependent variable: Change
in income from 2007 to 2010.
Loan shock
Income p.c. (USDmio)
Change in income 2004 to 2007
Population (100mio)
Dividends and interest p. c. (USDmio)
Transfers p.c. (USDmio)
Finance and insurance (CBP)
Real estate, rental and leasing (CBP)
R2
N
CBSA w/o big banks
OLS
(i)
(ii)
0.050
0.072
(0.039)
(0.040)*
-0.048
0.046
(0.797)
(0.814)
0.111
0.114
(0.064)*
(0.067)*
-0.794
-0.713
(0.275)*** (0.275)***
-7.464
-7.667
(1.201)*** (1.229)***
1.624
2.321
(1.451)
(1.539)
-0.079
-0.081
(0.081)
(0.083)
-0.398
-0.483
(0.211)*
(0.219)**
0.65
0.67
908
851
Yes
No
2SLS
(i)
(ii)
0.107
0.140
(0.068)
(0.078)*
0.013
0.119
(0.766)
(0.779)
0.110
0.113
(0.061)*
(0.064)*
-0.732
-0.635
(0.269)***
(0.273)**
-7.483
-7.694
(1.152)*** (1.173)***
1.517
2.093
(1.402)
(1.502)
-0.081
-0.080
(0.078)
(0.079)
-0.389
-0.477
(0.204)*
(0.210)**
0.65
0.67
908
851
Yes
No
All regressions include state dummies for the state in which most people of the CBSA live.
County Business Patterns (CBP) for 18 of 20 NAICS not reported. Heteroscedasticity robust
standard errors in parentheses. *,**,*** mean statistical significance at the 10, 5, and 1-percent
level, respectively.
metro areas increases the estimates and the t-statistics. I use the total number of newly
authorised privately owned housing units from the Census Bureau, this measure is only
available for metro areas.
Table 11 in the appendix uses earnings instead of income as an outcome measure. The
point estimates are larger, but so are the standard deviations. Statistical significance is
smaller.
Finally, tables 12, 13, and 14 report the 2SLS estimates for three alternative functional
forms of f in equation 2.
All in all, the estimated positive effect form the loan shock on personal income during
the Great Recession seem to be robust to changes in the identification method.
6
Conclusion
In a sample of 908 regions in the United States, representing roughly 70 percent of the
population, I find a causal effect of bank lending behavior on the local income. In the time
between 2007 and 2010, a one standard deviation drop in my measure of a loan shock,
leads to a 0.2 percentage points drop in income growth per year. The shock is statistically
significant and robust. The main difference between the credit channel identified in this
paper and in previous works is that this paper does not leave out small firms (in fact,
there is reason to believe that the identified channel mainly works through small firms)
and that the outcome measure is not the performance of firms, but local income.
25
The identification relies on the same two facts that underlie the analysis of ChodorowReich (2014). These are, firstly, sticky bank-borrower relationships and, secondly, the
nature of the recent financial crisis which did not have its roots in C&I lending. The first
fact has been shown empirically and is also understood theoretically through the concept of
“informational capital” that emanates from a relation between a firm and its lender. The
second fact is generally accepted as well, as the underlying causes of the Great Recession
are to be found in the market for mortgage-backed securities, rather than in C&I lending.
This paper does not show that there is a transmission of shocks from Wall Street to
Main Street. But accepting the existence of such a mechanism, my paper identifies a
channel through which these shocks affect local income.
26
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29
Appendix
WELLS FARGO & COMPANY
BANK OF AMERICA CORPORATION
JPMORGAN CHASE & CO.
U.S. BANCORP
Figure 7: Branches of financial high holders in the counties of the contiguous United
States. The financial high holders are ordered according to number of branches as of 2007.
30
PNC FNCL SVC GROUP
REGIONS FINANCIAL CORPORATION
BB&T CORPORATION
SUNTRUST BK
Figure 8: Branches of financial high holders in the counties of the contiguous United
States. The financial high holders are ordered according to number of branches as of 2007.
ROYAL BANK OF SCOTLAND GROUP PLC
FIFTH THIRD BANCORP
Figure 9: Branches of financial high holders in the counties of the contiguous United
States. The financial high holders are ordered according to number of branches as of 2007.
31
2007
2008
2009
D acquires F
A is acquired by D
A
2010
- D
-
D
-
G
-
C
S1
-
...
S2
-
...
B acquires E
G takes over 95% of B
B
-
B
C
- C
C closes branch in W
Figure 10: Illustration of how to account for closures of bank branches and changes of top
regulatory bank holding companies. Bank A has two branches and bank B, C, S1 and S2
have one branches each in county W. S1 and S2 are small banks, their history is only of
interest if they are (directly or indirectly) merged with a large top regulatory bank holding
company.
32
Table 6: Loan shock and income. Dependent variable: Change in income from
2007 to 2010.
Loan shock L
Income p.c.
(USDmio) 2007
Change in income
2004 to 2007
Population
(100mio) 2007
Dividends and interest
p.c. (USDmio) 2007
Transfers p.c.
(USDmio) 2007
Finance and insurance
(CBP) 2007
Real estate, rental and
leasing (CBP) 2007
R2
N
CBSA w/o big banks
CBP
State dummies
(i)
0.076
(0.047)
-4.084
(0.484)***
(ii)
0.073
(0.047)
-4.471
(0.500)***
0.200
(0.077)***
(iii)
0.064
(0.047)
-4.405
(0.565)***
0.196
(0.078)**
-0.215
(0.350)
(iv)
0.071
(0.045)
-0.501
(0.703)
0.175
(0.069)**
-1.125
(0.325)***
-7.576
(1.045)***
4.895
(1.321)***
0.56
830
No
No
Yes
0.57
830
No
No
Yes
0.57
830
No
No
Yes
0.62
830
No
No
Yes
(v)
0.098
(0.044)**
0.074
(0.819)
0.113
(0.072)
-0.672
(0.269)**
-7.696
(1.237)***
2.786
(1.538)*
-0.080
(0.084)
-0.473
(0.223)**
0.67
830
No
Yes
Yes
All regressions include state dummies for the state in which most people of the CBSA live.
County Business Patterns (CBP) for 18 of 20 NAICS not reported. Heteroscedasticity robust standard errors in parentheses. *,**,*** mean statistical significance at the 10, 5, and
1-percent level, respectively.
33
Table 7: Loan shock and income. First stage regression. Dependent variable:
Loan shock L.
Loan shock
2004 to 2007
Income p.c.
(USDmio) 2007
Change in income
2004 to 2007
Population
(100mio) 2007
Dividends and interest
p.c. (USDmio) 2007
Transfers p.c.
(USDmio) 2007
Finance and insurance
(CBP) 2007
Real estate, rental and
leasing (CBP) 2007
R2
N
CBSA w/o big banks
CBP
State dummies
(i)
-0.356
(0.018)***
-0.395
(0.154)**
(ii)
-0.356
(0.018)***
-0.425
(0.160)***
0.016
(0.026)
(iii)
-0.342
(0.019)***
-0.147
(0.154)
-0.003
(0.026)
-0.871
(0.135)***
(iv)
-0.342
(0.019)***
-0.407
(0.277)
-0.002
(0.026)
-0.802
(0.144)***
0.524
(0.476)
-0.163
(0.882)
0.75
830
No
No
Yes
0.75
830
No
No
Yes
0.76
830
No
No
Yes
0.76
830
No
No
Yes
(v)
-0.341
(0.019)***
-0.077
(0.336)
0.001
(0.029)
-0.681
(0.159)***
0.018
(0.573)
0.958
(0.932)
-0.032
(0.048)
-0.035
(0.135)
0.77
830
No
Yes
Yes
All regressions include state dummies for the state in which most people of the CBSA live.
County Business Patterns (CBP) for 18 of 20 NAICS not reported. Heteroscedasticity robust standard errors in parentheses. *,**,*** mean statistical significance at the 10, 5, and
1-percent level, respectively.
Table 8: Loan shock and income. Two stage least square estimates. Dependent
variable: Loan shock.
Loan shock L
Income p.c.
(USDmio) 2007
Change in income
2004 to 2007
Population
(100mio) 2007
Dividends and interest 2007
p.c. (USDmio)
Transfers p.c.
(USDmio) 2007
Finance and insurance
(CBP) 2007
Real estate, rental and
leasing (CBP) 2007
R2
N
CBSA w/o big banks
CBP
State dummies
(i)
0.182
(0.064)***
-3.960
(0.475)***
(ii)
0.183
(0.063)***
-4.339
(0.489)***
0.199
(0.074)***
(iii)
0.182
(0.065)***
-4.332
(0.549)***
0.198
(0.075)***
-0.023
(0.350)
(iv)
0.168
(0.062)***
-0.422
(0.673)
0.178
(0.067)***
-0.975
(0.319)***
-7.624
(1.003)***
4.805
(1.286)***
0.56
830
No
No
Yes
0.57
830
No
No
Yes
0.57
830
No
No
Yes
0.62
830
No
No
Yes
(v)
0.168
(0.059)***
0.086
(0.776)
0.114
(0.069)*
-0.586
(0.262)**
-7.657
(1.177)***
2.655
(1.481)*
-0.076
(0.079)
-0.456
(0.214)**
0.67
830
No
Yes
Yes
All regressions include state dummies for the state in which most people of the CBSA live. County
Business Patterns (CBP) for 18 of 20 NAICS not reported. Heteroscedasticity robust standard
errors in parentheses. *,**,*** mean statistical significance at the 10, 5, and 1-percent level, respectively.
34
OLS (ii)
−.05
0
0
.05
.05
.1
.1
.15
.15
.2
OLS (i)
0
20
40
60
80
100
0
20
60
80
100
80
100
2SLS (ii)
0
0
.05
.1
.1
.15
.2
.2
.3
.25
2SLS (i)
40
0
20
40
60
80
100
0
20
40
60
Figure 11: Estimates of β in the baseline equation (2) for the 2 to 100 largest banks and
the four specifications as in tables 2, 4, 6, and 8. The dotted lines are the point estimate
plus/minus one standard deviation.
35
Table 9: Loan shock and income. Dependent variable: Change in income from
2007 to 2009, and change in income from 2007 to 2011.
Change in income
Loan shock
Income p.c. (USDmio)
Change in income 2004 to 2007
Population (100mio)
Dividends and interest p. c. (USDmio)
Transfers p.c. (USDmio)
Finance and insurance (CBP)
Real estate, rental and leasing (CBP)
R2
N
CBSA w/o big banks
OLS
2007-2009
2007-2011
0.046
0.127
(0.035)
(0.060)**
-0.216
0.813
(0.623)
(1.036)
0.027
0.192
(0.054)
(0.108)*
-0.650
-0.893
(0.236)*** (0.326)***
-6.221
-8.076
(1.073)*** (1.578)***
3.162
1.111
(1.251)**
(1.968)
-0.041
-0.198
(0.087)
(0.117)*
-0.351
-0.145
(0.199)*
(0.322)
0.64
0.59
908
908
Yes
Yes
2SLS
2007-2009
2007-2011
0.079
0.201
(0.049)
(0.081)**
-0.212
0.824
(0.595)
(0.989)
0.027
0.193
(0.051)
(0.103)*
-0.611
-0.806
(0.228)***
(0.316)**
-6.200
-8.029
(1.033)*** (1.508)***
3.136
1.053
(1.202)***
(1.900)
-0.040
-0.195
(0.083)
(0.112)*
-0.340
-0.120
(0.190)*
(0.310)
0.64
0.59
908
908
Yes
Yes
All regressions include state dummies for the state in which most people of the CBSA live.
County Business Patterns (CBP) for 18 of 20 NAICS not reported. Heteroscedasticity robust
standard errors in parentheses. *,**,*** mean statistical significance at the 10, 5, and 1-percent
level, respectively.
36
Table 10: Loan shock and income. Dependent variable: Change in income from
2007 to 2010. For Metropolitan Statistical areas only.
Building permits
Loan shock
Income p.c. (USDmio)
Change in income 2004 to 2007
Population (100mio)
Dividends and interest p. c. (USDmio)
Transfers p.c. (USDmio)
Finance and insurance (CBP)
Real estate, rental and leasing (CBP)
Change in building permits 2005 to 2007
R2
N
CBSA w/o big banks
OLS
No
Yes
0.121
0.154
(0.060)**
(0.074)**
-0.337
1.051
(0.771)
(1.088)
-0.010
-0.099
(0.101)
(0.131)
-0.052
0.075
(0.265)
(0.325)
-6.066
-7.911
(1.634)*** (2.172)***
1.471
1.021
(2.338)
(2.925)
-0.106
-0.298
(0.200)
(0.262)
-0.793
-0.718
(0.447)*
(0.548)
0.014
(0.006)**
0.74
0.77
372
339
Yes
Yes
2SLS
No
Yes
0.143
0.206
(0.067)**
(0.074)***
-0.298
1.137
(0.682)
(0.956)
-0.011
-0.101
(0.090)
(0.116)
-0.036
0.114
(0.241)
(0.293)
-6.097
-7.960
(1.450)*** (1.900)***
1.535
1.179
(2.089)
(2.586)
-0.112
-0.307
(0.179)
(0.231)
-0.807
-0.750
(0.400)**
(0.488)
0.014
(0.006)***
0.74
0.77
372
339
Yes
Yes
All regressions include state dummies for the state in which most people of the CBSA live. County
Business Patterns (CBP) for 18 of 20 NAICS not reported. Heteroscedasticity robust standard
errors in parentheses. *,**,*** mean statistical significance at the 10, 5, and 1-percent level, respectively.
Table 11: Loan shock and earnings. Dependent variable: Change in earnings from
2007 to 2010.
Loan shock
Income p.c. (USDmio)
Change in income 2004 to 2007
Population (100mio)
Dividends and interest p. c. (USDmio)
Transfers p.c. (USDmio)
Finance and insurance (CBP)
Real estate, rental and leasing (CBP)
R2
N
CBSA w/o big banks
OLS
(i)
(ii)
0.104
0.140
(0.075)
(0.078)*
0.129
0.321
(0.993)
(1.005)
0.412
0.457
(0.099)*** (0.104)***
-0.758
-0.635
(0.410)*
(0.415)
-3.203
-3.636
(1.636)*
(1.711)**
-14.921
-14.678
(2.674)*** (2.831)***
-0.010
-0.014
(0.140)
(0.147)
-0.946
-1.024
(0.339)*** (0.356)***
0.52
0.53
908
851
Yes
No
2SLS
(i)
(ii)
0.171
0.226
(0.097)*
(0.105)**
0.139
0.332
(0.946)
(0.953)
0.413
0.458
(0.094)*** (0.098)***
-0.679
-0.531
(0.401)*
(0.407)
-3.160
-3.586
(1.554)**
(1.614)**
-14.974
-14.845
(2.568)*** (2.710)***
-0.008
-0.008
(0.134)
(0.140)
-0.923
-0.998
(0.326)*** (0.341)***
0.52
0.52
908
851
Yes
No
All regressions include state dummies for the state in which most people of the CBSA live.
County Business Patterns (CBP) for 18 of 20 NAICS not reported. Heteroscedasticity robust
standard errors in parentheses. *,**,*** mean statistical significance at the 10, 5, and 1-percent
level, respectively.
37
Table 12: Loan shock and earnings. Dependent variable: Change in income
from 2007 to 2010.
Loan shock
(quadratic)
Income p.c.
(USDmio) 2007
Change in income
2004 to 2007
Population
(100mio) 2007
Dividends and interest
p.c. (USDmio) 2007
Transfers p.c.
(USDmio) 2007
Finance and insurance
(CBP) 2007
Real estate, rental and
leasing (CBP) 2007
R2
N
CBSA w/o big banks
CBP
State dummies
(i)
0.325
(0.182)*
-4.143
(0.421)***
(ii)
0.424
(0.184)**
-4.545
(0.432)***
0.217
(0.069)***
(iii)
0.406
(0.184)**
-4.458
(0.503)***
0.212
(0.070)***
-0.259
(0.329)
(iv)
0.370
(0.183)**
-0.794
(0.653)
0.189
(0.062)***
-1.179
(0.308)***
-7.255
(0.969)***
3.780
(1.235)***
0.55
908
Yes
No
Yes
0.56
908
Yes
No
Yes
0.57
908
Yes
No
Yes
0.61
908
Yes
No
Yes
(v)
0.384
(0.181)**
-0.184
(0.752)
0.118
(0.062)*
-0.773
(0.260)***
-7.286
(1.135)***
1.631
(1.387)
-0.071
(0.078)
-0.366
(0.203)*
0.65
908
Yes
Yes
Yes
All regressions include state dummies for the state in which most people of the CBSA live.
County Business Patterns (CBP) for 18 of 20 NAICS not reported. Heteroscedasticity robust standard errors in parentheses. *,**,*** mean statistical significance at the 10, 5, and
1-percent level, respectively.
Table 13: Loan shock and earnings. Dependent variable: Change in earnings
from 2007 to 2010.
Loan shock
(square root)
Income p.c.
(USDmio) 2007
Change in income
2004 to 2007
Population
(100mio) 2007
Dividends and interest
p.c. (USDmio) 2007
Transfers p.c.
(USDmio) 2007
Finance and insurance
(CBP) 2007
Real estate, rental and
leasing (CBP) 2007
R2
N
CBSA w/o big banks
CBP
State dummies
(i)
0.078
(0.023)***
-3.790
(0.486)***
(ii)
0.072
(0.023)***
-4.183
(0.502)***
0.193
(0.067)***
(iii)
0.073
(0.024)***
-4.188
(0.543)***
0.194
(0.067)***
0.022
(0.336)
(iv)
0.069
(0.022)***
-0.500
(0.656)
0.173
(0.060)***
-0.932
(0.316)***
-7.362
(1.007)***
3.477
(1.248)***
0.55
908
Yes
No
Yes
0.57
908
Yes
No
Yes
0.57
908
Yes
No
Yes
0.61
908
Yes
No
Yes
(v)
0.057
(0.022)***
-0.027
(0.765)
0.110
(0.061)*
-0.685
(0.260)***
-7.422
(1.168)***
1.557
(1.392)
-0.070
(0.078)
-0.359
(0.204)*
0.65
908
Yes
Yes
Yes
All regressions include state dummies for the state in which most people of the CBSA live.
County Business Patterns (CBP) for 18 of 20 NAICS not reported. Heteroscedasticity robust standard errors in parentheses. *,**,*** mean statistical significance at the 10, 5, and
1-percent level, respectively.
38
Table 14: Loan shock and earnings. Dependent variable: Change in earnings
from 2007 to 2010.
Loan shock
(indicator function)
Income p.c.
(USDmio) 2007
Change in income
2004 to 2007
Population
(100mio) 2007
Dividends and interest
p.c. (USDmio) 2007
Transfers p.c.
(USDmio) 2007
Finance and insurance
(CBP) 2007
Real estate, rental and
leasing (CBP) 2007
R2
N
CBSA w/o big banks
CBP
State dummies
(i)
0.023
(0.006)***
-3.748
(0.495)***
(ii)
0.021
(0.006)***
-4.144
(0.513)***
0.188
(0.067)***
(iii)
0.021
(0.006)***
-4.149
(0.553)***
0.188
(0.067)***
0.019
(0.332)
(iv)
0.020
(0.006)***
-0.471
(0.661)
0.168
(0.060)***
-0.938
(0.313)***
-7.360
(1.031)***
3.307
(1.243)***
0.56
908
Yes
No
Yes
0.57
908
Yes
No
Yes
0.57
908
Yes
No
Yes
0.61
908
Yes
No
Yes
(v)
0.017
(0.006)***
-0.022
(0.771)
0.108
(0.060)*
-0.718
(0.259)***
-7.439
(1.187)***
1.502
(1.383)
-0.063
(0.079)
-0.359
(0.201)*
0.66
908
Yes
Yes
Yes
All regressions include state dummies for the state in which most people of the CBSA live.
County Business Patterns (CBP) for 18 of 20 NAICS not reported. Heteroscedasticity robust standard errors in parentheses. *,**,*** mean statistical significance at the 10, 5, and
1-percent level, respectively.
39