ESSAYS ON LOCAL BANKING MARKETS
AND THE CAPITAL CRUNCH
DURING THE GREAT RECESSION
BY
DAVID P. GLANCY
B.A., CLARK UNIVERSITY, 2010
M.A., BROWN UNIVERSITY, 2011
A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
IN THE DEPARTMENT OF ECONOMICS AT BROWN UNIVERSITY
PROVIDENCE, RHODE ISLAND
MAY 2016
c Copyright 2016 by David P. Glancy
This dissertation by David P. Glancy is accepted in its present form
by the Department of Economics as satisfying the
dissertation requirements for the degree of Doctor of Philosophy.
Date
Doctor David Weil, Adviser
Recommended to the Graduate Council
Date
Doctor Gauti Eggertsson, Reader
Date
Doctor Neil Mehrotra, Reader
Approved by the Graduate Council
Date
Pete M. Weber, Dean of the Graduate School
iii
VITA
David Glancy was born July 26th, 1988 in San Francisco, California and attended Towson High
School. He studied at Clark University, graduating summa cum laude with highest honors in Eco-
nomics in 2010. While at Brown University, he was a graduate fellow in the research department of
the Federal Reserve Bank of Boston and with Spatial Structures in the Social Sciences (S4). He was
the recipient of the Abramson Award for the best third year paper, the S4 Graduate Paper Prize,
and a Graduate Merit Dissertation Fellowship.
iv
ACKNOWLEDGEMENTS
I first would like to thank David Weil for providing constant feedback and support regardless of how
far my research drifted from his. Without his patience, I would not have been able to pursue the
topics that excited me in graduate school. Gauti Eggertsson, Neil Mehrotra and Nate Baum-Snow
additionally provided invaluable feedback. Every conversation with them greatly improved the qual-
ity of my work. I’m also grateful to Ross Levine for sparking my initial interest in empirical banking
research.
I’d also like to thank the research department at the Boston Federal Reserve Bank for hosting me
for the last two years. The weekly lunch conversations with Victoria Ivashina, Ali Ozdagli, Joe Peek
and Christina Wang have been enlightening and entertaining. These encounters have left me with
a greater understanding and appreciation of the intricacies regarding the workings of the financial
system than I had before.
My friends in the department, Mari´a Jos´e Boccardi Chalela, Greg Casey, Morgan Hardy, Michelle
Marcus, Ang´elica Meinhofer Santiago, Sanjay Singh and Tim Squires, deftly alternated between sup-
porting me in and distracting me from my academic pursuits, depending on whatever was necessary
at the time. Nick Coleman guided me through the ins-and-outs of both banking research and the job
search process. Finally, Aaron Crootof, Anders Ohman, Ximena Ojopi and Brittany Mayweather
took on the Sisyphean task of keeping me sane throughout graduate school, for which I am eternally
grateful.
v
PREFACE
There is a vast literature studying the interactions between bank lending and the local economy.
Whether it was bank failures during the savings-and-loan crisis reducing local income (Ashcraft,
2005), the lending contraction from Japanese banks during the early 90s reducing US construction
activity (Peek and Rosengren, 2000), or bank branch deregulation between 1976 and 1994 increasing
growth (Jayaratne and Strahan, 1996), there is substantial evidence that the performance of local
banks and the local economy are tightly entwined.
However, since those periods, there has been a drastic shift in American credit markets which has
the potential to decouple the health of local banks from the strength of the local economy. The
removal of interstate branching restrictions in 1994 has created a banking system increasingly dom-
inated by large geographically dispersed banks who are less subject to shocks from any given area
(Morgan et al., 2004). Additionally, improvements in information and communication technology
have reduced the importance of proximity in the provision of small business credit, enabling distant
firms to increasingly compete with local ones (Petersen and Rajan, 2002). Finally, the continued
rise of securitization has separated the act loan origination from loan funding, allowing credit risk
to increasingly be held by dispersed investors instead of local banks (Loutskina and Strahan, 2009).
In this dissertation, I demonstrate that despite these changes in the banking system, the linkages
between the health of local banks and the local economy remain strong. Moreover, these linkages
played a fundamental role in propagating local real estate shocks to the general economy during the
Great Recession. Chapter 1 demonstrates that banks locating in weak real estate markets experi-
enced increases in non-performing loans and declines in equity, ultimately resulting in a contraction
in the supply of credit to local businesses. Chapter 2 extends this work to show that this credit
contraction had large employment effects during the Great Recession. Counties whose banks were
exposed to real estate declines elsewhere along their branch network experienced contractions in
small business lending and employment, especially in young firms, firms in financially dependent
industries, and firms close to adversely affected banks. Chapter 3 justifies the emphasis on local
banking markets, showing that bank mark-ups respond to the availability of competitors within 5
miles of their branches instead of the more conventionally used MSA level market definition.
The first two chapters are motivated by the observation that markets in the United States which
experienced large declines in house prices, were plagued by bank failures, contractions in lending
and falling employment during the Great Recession. These early chapters ask whether these declines
in lending and employment were caused by the bank distress, or whether the ultimate cause of this
regional turmoil laid outside the banking section.
Chapter 1 starts by studying how falling house prices impacted the supply of credit. I use a difference-
in-differences approach to test whether commercial lending fell due to low demand, or whether banks
were contracting supply. I exploit variation in banks’ lending portfolios or funding structure which
determine how sensitive a bank’s lending capacity is to house price shocks. First, an increase in loans
losses and a decline in equity should only impact a bank close to their capital constraint. Second,
banks which held more construction loans would take larger losses when house prices fell, and thus
should reduce commercial lending more than other banks facing the same declines. I show that only
highly leveraged banks or construction oriented banks contract commercial lending, indicating that
falling house prices predominantly impacted the supply of bank credit.
vi
vii
Chapter 2 also addresses the question of how falling house prices impacted bank lending, however it
uses geographic variation in bank locations to separate supply from demand. I use bank-by-county
small business lending data from the Community Reinvestment Act to estimate lending growth as a
function of a bank’s multimarket exposure to real estate shocks and county fixed effects. I demon-
strate that bank’s don’t actually reallocate credit away from counties with falling house prices.
Instead, exposed banks cut lending uniformly throughout their network. The county fixed effects
have no impact on the relationship between bank exposure and lending growth, indicating that
lending fell because exposed banks contracted credit everywhere, not because there was low demand
in areas with falling house prices.
More importantly, this chapter shows that this contraction in bank lending impacted local employ-
ment. The exposure of local banks to falling house prices in other counties, MSAs, and geographically
distant counties caused employment to fall within the county from 2007 to 2010. This even holds
controlling for the local decline in house prices. Consistent with a contraction in the supply of bank
lending driving the employment declines, employment fell the most in young firms, firms in finan-
cially dependent industries and in firms close to exposed banks. Overall, I find that bank lending
contractions due to falling house prices explain over half of the relationship between housing wealth
shocks and employment declines and 39% of the decline in employment during the great recession.
Chapter 3 deviates from the topic of the financial crisis and instead justifies the emphasis on local
banking markets in the previous chapters. Motivated by earlier research showing that individuals
and small businesses primarily rely on banks with nearby branches, I construct measures of deposit
concentration in the area directly surrounding a bank’s branches. Although US bank merger policy,
as well as the bulk of academic work on bank market power, emphasize concentration at the MSA
level, I show that this traditional measure of concentration ceases to predict bank mark-ups or
profitability once the more local concentration is accounted for. I show that bank lending rates
depend most strongly on the availability of competitors within 5 miles of their branches, indicating
a limited ability to substitute towards distant banks. This lack of competition also matters for
labor market outcomes. Firms in locally concentrated markets expand less in response to exogenous
demand shocks, indicating the existence of greater financial constraints. This is particularly true of
young firms, who would be the most dependent on banks for external finance.
Contents
1 House Price Declines and the Supply of Bank Credit 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 Previous Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 Empirical Strategy and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.1 Empirical Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.2 Conceptual Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2.3 Relation to Empirical Specification . . . . . . . . . . . . . . . . . . . . . . . . 8
1.2.4 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.3 Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.3.2 Main Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.4 Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.4.1 Commercial Loan Utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.4.2 Are Results Driven by Loan Losses? . . . . . . . . . . . . . . . . . . . . . . . 17
1.4.3 Effects on Other Lending . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
1.4.4 How Much of the Decline is Due to Supply Shocks . . . . . . . . . . . . . . . 20
1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
1.6 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
1.6.1 Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
1.6.2 Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2 Housing Bust, Bank Lending & Employment: Evidence From Multimarket Banks 38
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.1.1 Relation to Previous Literature . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.2 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.2.1 Real Estate Shock Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.2.2 Lending Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.2.3 Labor Market Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
2.3 Empirical Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
2.3.1 Impact of Real Estate Exposure on Small Business Lending . . . . . . . . . . 47
2.3.2 Impact of Real Estate Exposure on Employment . . . . . . . . . . . . . . . . 48
2.4 Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
2.4.1 Exposed Banks Cut Small Business Lending . . . . . . . . . . . . . . . . . . . 52
2.4.2 Bank Exposure Causes Employment Declines . . . . . . . . . . . . . . . . . . 53
2.5 Robustness and Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
2.5.1 Wage Adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
2.5.2 Instrumenting with Earlier Branch Locations . . . . . . . . . . . . . . . . . . 58
2.5.3 Alternative Bank Health Measures . . . . . . . . . . . . . . . . . . . . . . . . 59
2.5.4 Alternative Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
2.7 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
2.7.1 Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
i
CONTENTS vi
2.7.2 Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
2.7.3 Theory Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
3 A Spatial Measure of Banking Competition 83
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
3.2 Background and Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
3.2.1 Bank Concentration and Competition . . . . . . . . . . . . . . . . . . . . . . 87
3.2.2 Importance of Proximity in Banking . . . . . . . . . . . . . . . . . . . . . . . 88
3.2.3 Implications for Bank Market Definition . . . . . . . . . . . . . . . . . . . . . 89
3.3 Local Concentration Measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
3.3.1 Index Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
3.3.2 How Do Local and MSA Concentration Differ? . . . . . . . . . . . . . . . . . 91
3.4 Local Market Structure and Bank Performance . . . . . . . . . . . . . . . . . . . . . 92
3.4.1 Data & Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
3.4.2 Effects of 6km Local Concentration . . . . . . . . . . . . . . . . . . . . . . . . 94
3.4.3 Effects by Distance Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . 96
3.5 Local Concentration and Young Firm Finance . . . . . . . . . . . . . . . . . . . . . . 98
3.5.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
3.5.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
3.5.3 Data and Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
3.5.4 Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
3.7 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
3.7.1 Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
3.7.2 Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
3.7.3 Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
3.7.4 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
3.7.5 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
List of Tables
1.1 Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
1.2 Housing Declines and Loan Non-performance . . . . . . . . . . . . . . . . . . . . . . 26
1.3 Housing Declines and Loan Growth: By Loan Type . . . . . . . . . . . . . . . . . . . 27
1.4 Housing Declines and Commercial Lending Growth: By Leverage . . . . . . . . . . . 28
1.5 Housing Declines and Commercial Lending Growth: By Construction Exposure . . . 29
1.6 Housing Declines and Commercial Loan Utilization: By Leverage . . . . . . . . . . . 30
1.7 Housing Declines and Commercial Loan Utilization: By Construction Exposure . . . 31
1.8 Are Results Driven By Real Estate Losses: Leverage Specifications . . . . . . . . . . 32
1.9 Are Results Driven By Real Estate Losses: Construction Specifications . . . . . . . . 33
1.10 Housing Declines and Total Lending: By Leverage . . . . . . . . . . . . . . . . . . . 34
1.11 Housing Declines and Real Estate Lending: By Leverage . . . . . . . . . . . . . . . . 35
1.12 Housing Declines and Construction Lending: By Leverage . . . . . . . . . . . . . . . 36
1.13 Housing Declines and CRE Lending: By Leverage . . . . . . . . . . . . . . . . . . . 37
2.1 Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
2.2 Growth in Number of Small Loans . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
2.3 Employment Growth: By Tradability . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
2.4 Employment Growth: IV with Out of Market Shocks . . . . . . . . . . . . . . . . . . 69
2.5 Employment Growth: By Financial Dependence . . . . . . . . . . . . . . . . . . . . 70
2.6 Employment Growth: By Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
2.7 Tract Employment Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
2.8 Evidence of Wage Adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
2.9 County Growth: Health of 2002 Branches . . . . . . . . . . . . . . . . . . . . . . . . 74
2.10 Tract Growth: Health of 2002 Branches . . . . . . . . . . . . . . . . . . . . . . . . . 75
3.1 Impact of Concentration on Loan Rates . . . . . . . . . . . . . . . . . . . . . . . . . 111
3.2 Impact of Concentration on Deposit Rates . . . . . . . . . . . . . . . . . . . . . . . . 112
3.3 Impact of Concentration on Bank Profits . . . . . . . . . . . . . . . . . . . . . . . . 113
3.4 Concentration and Response to Demand Shocks . . . . . . . . . . . . . . . . . . . . . 114
3.5 Concentration and Response to Demand Shocks: By Age . . . . . . . . . . . . . . . 115
vii
List of Figures
1.1 Lending Elasticities: By Bank Leverage . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.1 Distribution of Lender Distances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
2.2 Effect of Bank Health: Distance Based Instruments . . . . . . . . . . . . . . . . . . . 63
2.3 Effects of Bank Health on Tract Growth: By Distance . . . . . . . . . . . . . . . . . 64
2.4 Joint Confidence Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.1 Declining Probability of Lending Over Distance . . . . . . . . . . . . . . . . . . . . . 104
3.2 Hypothetical Bank Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
3.3 How Similar Are MSA and Local Concentration? . . . . . . . . . . . . . . . . . . . . 106
3.4 Different Trends in Concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
3.5 Impact of Concentration on Loan Rates . . . . . . . . . . . . . . . . . . . . . . . . . 108
3.6 Impact of Concentration on Commercial Loan Rates . . . . . . . . . . . . . . . . . . 109
3.7 Financial Constraints and Response to Demand Shocks . . . . . . . . . . . . . . . . 110
viii
Chapter 1
House Price Declines and the
Supply of Bank Credit
“Making credit accessible to sound small “It’s absurd to argue we need more
businesses is crucial to our economic re- bank lending when demand is collapsing
covery and so should be front and center throughout the economy”
among our current policy challenges”
-Ben Bernanke -Atif Mian & Amir Sufi
1.1 Introduction
How did falling house prices impact bank lending during the Great Recession? Banks in weak
real estate markets experienced a significant deterioration in loan performance and originated fewer
commercial loans, however the degree to which this decline in lending reflected a contraction in the
supply of credit remains controversial. Given the severity of the employment losses in areas with
falling house prices, identifying the mechanism by which falling house prices impacted bank lending
is a crucial component of understanding the recession.
This chapter presents evidence that falling house prices resulted in a contraction in the supply
of bank credit. The hypothesis is that banks which located in areas where house prices fell took
losses on their loan portfolio, thus harming their capital positions. Since asymmetric information
problems can make issuing new equity prohibitively costly (Myers and Majluf, 1984), banks were
forced to reduce lending in order to deleverage. Consequently, this capital crunch channel can explain
the contraction in lending in areas where house prices fell. Furthermore, as credit supply shocks
have been shown to impact labor markets (Ashcraft, 2005; Benmelech et al., 2011; Chodorow-Reich,
2014), this channel may also have contributed to the employment losses in these areas.
1
However, the relationship between falling house prices and lending growth could also be plau-
sibly attributed to a reduction in the demand for loans. For example, falling house prices could
reduce aggregate demand (Mian and Sufi, 2014) or the value of firm collateral (Fort et al., 2013;
Mehrotra and Sergeyev, 2014). If these effects reduced either the incentive of firms to borrow or the
creditworthiness of local firms, then lending would have fallen regardless of what happened to bank
capital. Under this demand channel, the contraction in lending is due to a dearth of viable lending
opportunities instead of adverse shocks to the health of local banks.
To disentangle supply from demand, I exploit cross sectional variation in the composition of bank
balance sheets to identify banks whose ability to support new lending were more vulnerable to real
estate shocks. General economic turmoil reducing the demand for loans would broadly affect the
commercial lending of local banks. However, the capital crunch hypothesis predicts that some banks
in particular would contract lending much more than others when local house prices fall. First,
only banks close to their capital constraint should cut lending in response to a contraction in house
prices. Banks with an adequate capital buffer could have their equity decline, but would have no
need to shrink their assets in order to remain above regulatory capital ratios. Second, banks which
devoted more of their portfolio to construction loans would experience larger losses.1 Since these
banks would experience a greater shock to equity, they should contract lending the most.
Specifically, I use a difference-in-differences approach to test how house price declines impact the
growth in commercial and industrial (C&I) lending in (1) banks which are highly leveraged versus
banks which are further their capital constraint, and (2) banks which are highly exposed to the
construction sector versus banks which aren’t. Thus I can use banks which face similar house price
declines, or operate in the same market, as a control for local demand. This allows me to assess how
real estate shocks impact the supply of credit.
I consistently find that it was the treated banks (banks with a high construction exposure or
high leverage) who cut lending where house prices fell. Consider the effects of the national 15%
decline in house prices between the beginning of 2007 and 2010. Untreated banks are predicted to
have only minimally responded to this shock, with low leverage banks cutting commercial lending
by 1.2% and low construction banks cutting lending by 0.1% between the start of 2008 and end of
2010. Treated banks are another story; this decline is predicted to have caused a 5.0% decline in
commercial lending in leveraged banks and a 6.1% decline in construction oriented banks over those
three years. Essentially the only banks which reduced lending in response to falling house prices
1 Cole and White (2012); Friend et al. (2013) note that it was predominantly banks whose portfolios were concen-
trated in construction lending who failed during the Great Recession. I also show that falling house prices resulted in
an increase in non-performing construction loans that dwarfed the effects on loan performance in other categories.
2
were the banks whose ability to lend would be sensitive to real estate declines. This suggests the
contraction in lending was essentially due to a contraction in the supply of credit.
This finding is robust to numerous alternative specifications. Whether the treatment variables
are binary or continuous, whether I control for demand using local house price growth or CBSA-
quarter fixed effects, whether or not I weight by the size of the bank’s commercial lending portfolio,
and whether or not I include additional controls, makes little difference for the primary findings. I
consistently find that treated banks exhibit a significantly greater sensitivity to house price declines
than untreated banks.
Next, I take two approaches to provide auxiliary evidence in favor of my proposed channel. First,
one may be concerned that my treatment variables are related to changes in demand. For example,
the decision to operate with little capital may reflect high loan demand during the boom, which
reverted during the bust. To assuage this concern, I study the impact that the treatment variables
have on commercial loan utilization. If treated banks face lower loan demand in areas with falling
house prices, then their customers would be less likely to draw down on their lines of credit, resulting
in a decline in utilization. However, I find little difference in loan utilization in treated and untreated
banks. If anything treated banks have utilization increase when house prices fall, indicating that
they don’t experience differentially low demand.
Second, I show that the findings are explained by poorer loan performance in distressed real estate
markets. Banks with more non-performing loans cut lending, especially if they are highly leveraged.
Once the non-performing loan rate and its interaction with bank leverage are included, the primary
explanatory variables cease to predict lending growth. If leveraged banks or construction oriented
banks cut lending even if their loans continued to pay off, this could indicate that they made loans
to firms whose demand was more sensitive to house price declines. That loan losses take precedence
over falling house prices in predicting lending increases confidence that it is indeed a capital crunch
driven by non-performing loans which precipitated the decline in lending.
Lastly, I extend the analysis to other types of loans. Since the C&I lending category excludes loans
backed by real estate, it may be unsurprising that the contraction reflected a decline in the ability
of banks to lend. However, lending backed by real estate is more likely to fall due to declines in the
value of collateral backing lending or the lack of profitable construction related investments. I find
that national 15% decline in house prices resulted in a 2.4% decline in total lending for unleveraged
banks versus a 5.8% decline for leveraged banks over the course of 2008 to 2010. These effects are
2.5% and 6.3% for lending backed by real estate, 10% and 13.8% for construction lending, and 1.2%
3
versus 3.8% for loans backed by non-farm non-residential properties.2 Although construction lending
seems to have fallen mostly due to low demand, in other categories the decline in house prices had a
dramatically larger effect on lending in leveraged banks. Across the board, leveraged banks reduce
lending in response to this 15% shock by about 3 or 4 percentage points more than unleveraged
banks.
The ramifications of this capital crunch are thus potentially wide reaching. Previous research
has demonstrated that the availability of commercial credit impacts employment and investment
(Almeida et al., 2012; Campello et al., 2010; Chodorow-Reich, 2014), construction and CRE lending
impacts unemployment and construction activity (Peek and Rosengren, 2000; Benmelech et al.,
2011) and mortgage credit impacts employment and output growth (Mondragon, 2014; Loutskina
and Strahan, 2015). The broad decline in the availability of credit across these various lending
categories has the potential to explain much of the observed economic turmoil in distressed real
estate markets during this period. How these bank real estate losses impacted local employment
will be the subject of the second chapter of this dissertation.
1.1.1 Previous Literature
This paper fits cleanly into two literatures. First, there is an old literature demonstrating that
declines in bank capital impact the lending of capital constrained banks. This was widely studied
in the context of the recession in the early 1990s, with a particular emphasis on the New England
banks who were reeling from the bursting of a house price and construction bubble in the late
1980s (Bernanke et al., 1991; Hancock and Wilcox, 1994; Peek and Rosengren, 1994, 1995). Further
evidence in support of the importance of these capital shocks were also presented in the context of
the Japanese banking crisis following the collapse of Japanese land prices in the early 1990s (Peek
and Rosengren, 1997, 2000; Gan, 2007).
Peek and Rosengren (1995) and Gan (2007) are the closest to this paper methodologically. Peek
and Rosengren (1995) shows that deposits fell in poorly capitalized banks with respect to better
capitalized banks during a period of high bank distress in New England. Gan (2007) shows that
firms borrowing from banks whose assets were highly oriented in real estate loans received less
credit following the sharp contraction in Japanese land values. Similar to these papers, I exploit
cross sectional variation in bank leverage or construction exposure to determine how these real estate
shocks impacted the supply of bank lending. I differ by additionally using regional variation in the
2 For construction and non-farm non-residential lending I report estimates from the weighted specifications because
there are many banks who engage in a minimal amount of these types of lending. The differences are starker in the
unweighted specifications.
4
degree of real estate distress, whereas the other papers focused on one area experiencing a negative
shock.
The second strand of literature studies the determinants of bank lending during the Great Re-
cession. Previous work has demonstrated that lending fell for numerous different reasons, including
stresses on bank liquidity (Ivashina and Scharfstein, 2010; Cornett et al., 2011), mortgage backed
security exposure (Santos, 2011), and losses in banks exposed to GSE preferred stocks (Rice and
Rose, 2014).
The most relevant papers to me are Huang and Stephens (2011); Berrospide et al. (2013); Bord
et al. (2014), who show that the multimarket exposure of banks to real estate shocks creates within
county or MSA variation in lending. Namely banks with a greater exposure to real estate declines in
other areas reduce lending relative to less exposed banks, indicating that falling house prices result
in a contraction in the supply of lending. My difference-in-differences approach compliments these
papers as it allows me to study the effects of falling house prices on predominantly single market
banks. As these single market banks account for vast the majority of the banks in the US (6991 out
of 7571), this is important in itself. Additionally, there is little geographically disaggregated lending
data available. Thus by using a methodology only requiring aggregate balance sheet data, I can
increase the scope of the work in terms of the outcome variables I analyze, in addition to expanding
the sample.
The remainder of the paper proceeds as follows: The next section describes the data and empirical
strategy. Section 3 presents the main findings. Section 4 provides additional evidence for the channel,
and extends the methodology to other lending categories. Section 5 concludes.
1.2 Empirical Strategy and Data
1.2.1 Empirical Strategy
The primary analysis is based on the following specifications:
yb,m,q = β1 ∆ln(House P ricem,q ) × T reatb,m,q−1 + β2 ∆ln(House P ricem,q )
(1.1)
+ γXb,m,q−1 + δq + b,m,q
yb,m,q = β1 ∆ln(House P ricem,q ) × T reatb,m,q−1 + γXb,m,q−1 + δm,q + b,m,q (1.2)
5
In the main part of the paper, yb,m,q is the quarterly growth in commercial and industrial lending for
a bank b locating in market m. The market is taken to be the CBSA or rural county, henceforth just
referred to as a CBSA for brevity.3 ∆ln(House P ricem,q ) is the house price growth over the last
year in m, T reatb,m,q−1 is a bank balance sheet variable which influences how sensitive the bank’s
lending capacity is to real estate shocks, δq and δm,q are quarter and CBSA-quarter fixed effects
respectively, and Xb,m,q−1 a set of bank controls including T reatb,m,q−1 .4 The sample period covers
2007 through 2010, and includes banks which held over 50% of their deposits in a single CBSA in
2006. Standard errors are clustered by CBSA.
T reatb,m,q−1 is a measure of either the bank’s exposure to the construction sector or leverage.
I use one of four variables: the ratio of a bank’s tangible assets to tangible equity (Leverage), an
indicator for whether this variable is above the median for the CBSA-quarter (High Leverage),
.
construction lending to bank equity (Construction Equity), and indicator for whether this ratio is
.
above the median for the CBSA-quarter (High Construction Equity).
Many lenders are almost entirely specialized in real estate lending, creating a risk of overly em-
phasizing large percentage swings in C&I lending which actually have an inconsequential influence
on aggregate lending. Thus in addition to OLS specifications, I also weight by the value of out-
standing commercial loans at the end of the previous quarter. In the weighted specifications, I drop
observations more than five standard deviations above the mean in commercial lending.5
1.2.2 Conceptual Framework
In order to interpret the coefficients in this specification, consider the following simple model of banks
engaging in monopolistic competition. A bank in market m lends to a set of borrowers indexed by
i ∈ Im . Total borrowing for every i is a CES composite of borrowing from local banks, generating
a standard iso-elastic demand for each bank b from borrower i.6
rib −
Lbi = ξib Li ( )
ri
3A CBSA contains an urban center of at least 10,000 people and adjacent areas with strong commuting ties.
4 Other control variables include: ln(assets), non-core deposit percent, mortgage back security holdings to assets,
and share of lending in the commercial and industrial loan category.
5 There are some large lenders that only locate in one market because they don’t rely on local branches for lending.
Including them in the weighted specification would skew the findings substantially, as the estimated coefficients would
largely reflect the lending growth in a few banks which aren’t actually predominantly engaging in local lending.
!
−1
−1
6 The amount borrowed from each b, Lb , is done so as to minimize b) 1
R b b
(Lb ) db
R
i r L
i i db s.t. (ξi i = Li .
Ωm Ωm
1
ξib (rib )1− db) 1− , gives the above demand curve.
R
Solving this minimization problem, and denoting ri = (
Ωm
6
Where rib is the interest rate charged by bank b to borrower i, ri is the overall cost of bank borrowing
for i, is the elasticity of demand, and ξib is a demand shifter integrating to 1.
Given a certain level of aggregate lending Lb , the bank will allocate credit across borrowers in
order to maximize profit subject to demand curves and the adding up constraint.
X
maximize Lbi rib
{rib }
i∈Im
X rib −
subject to Lbi = Lb , {Lbi = ξib Li ( ) } ∀i
ri
i∈Im
This results in a constant optimal lending rate for bank b:
Db 1
rb = ( ) ∀i (1.3)
Lb
where Db = ξib Li ri measures the level of demand for loans from b.
P
i∈Im
Next, assume that the marginal cost of lending for a bank is an increasing function c() of its
Lb
leverage Eb
, where E b is the bank’s equity. Using the previous expression and equating the marginal
revenue from lending with the marginal cost, we get the following inverse supply curve:
Lb
rb = c( b ) (1.4)
−1 E
Combining the demand equation in (3) with the supply equation in (4) and log-linearizing produces
the following equations governing the growth in lending and interest rates:
ˆ b = ηb E
L ˆ b + (1 − η b )D
ˆb (1.5)
ηb ˆ b ˆb)
rˆb = (D − E (1.6)
Thus lending growth is a weighted average of demand growth and equity growth, with a weight
b
b ( L b ) −1 −1
on equity growth: η b = (1 + (c0 ( E
L
b)
E
b ) ) . This means that equity shocks matter more if
c( L b )
E
borrowers are more able to substitute between banks ( is high) or if there is a greater elasticity of
b
b ( Lb )
marginal cost with respect to leverage: ( c0 ( E
L
b)
E
b is high).
c( L b )
E
Since this paper focuses on C&I lending instead of total lending, consider the subset of commercial
borrowers Im,CI ⊂ Im . Then the total C&I lending for b is LbCI = (rb )− ξib Li ri . Thus the
P
i∈Im,CI
7
growth in C&I lending is
ˆ b = −ˆ
L ˆb
rb + D
CI CI
(1.7)
ˆb − E
= −η b (D ˆb) + D
ˆb
CI
b
ξib Li ri denotes the level of demand for commercial bank loans for b. Equa-
P
Where DCI ≡
i∈Im,CI
tion (7) shows that the growth in C&I lending can be broken into two parts: a component reflecting
ˆ b ) and a component reflecting the cost of borrowing
changes in demand for commercial lending (D CI
rb ). The object of interest in this paper is the elasticity of lending growth with
from a bank (−ˆ
respect to house price growth:
ˆb
dL ∂rb ∂Dˆb
CI CI
= − +
dHˆm ∂Hˆm ˆm
∂H
| {z } | {z }
Credit Crunch Demand
Channel Channel
Namely I want to test whether falling house prices reduced the supply of commercial lending
b ˆb
∂D
( ∂∂r
Hˆ < 0) or whether they reduced the demand for commercial loans ( ∂ HˆCI > 0). As I will show
m m
below, how this elasticity varies by leverage or construction exposure will be informative in assessing
the magnitude of the demand and credit crunch channels.
1.2.3 Relation to Empirical Specification
Role of Bank Leverage Bank leverage matters because leveraged banks will face a steeper
marginal cost curve. Banks with excess capital can cheaply increase deposits if they want to expand,
whereas banks on their capital constraint would need to raise new equity, which is costly due to
asymmetric information problems (Myers and Majluf, 1984). Additionally higher leverage increases
bank risk and makes uninsured wholesale funding more expensive. As a consequence, leverage
increases η b , making lending growth more sensitive to equity growth.
To relate the theory to empirical strategy, suppose that the key variables in (7) take the following
functional forms:
η b = η0 + ηT Tb
ˆ b = e0 + eH Hˆm
E
(1.8)
ˆ b = d0 + dH H
ˆm
D
ˆ b = c0 + cH Hˆm
D
CI
Where Tb is the treatment variable, indicating whether the bank is highly leveraged. These
8
equations say that falling house prices impact bank equity, overall loan demand and commercial
loan demand the same in leveraged and unleveraged banks. However, shocks to bank equity matter
more for leveraged banks due to the fact that they are closer to their capital constraint (ηT > 0).
Combining (7) and (8) gives commercial lending growth as a function of bank leverage, house
price growth and their interaction as in the estimating equation in (1):
L ˆ m + [η0 (eH − dH ) + cH ] H
ˆ b = [η0 (e0 − d0 ) + c0 ] + [ηT (eH − dH )] Tb H ˆ m + [ηT (e0 − d0 )]Tb
| {z } | {z }
β1 β2
Thus the coefficient on the interaction term between the leverage indicator and house price
growth should provide us with the term ηT (eH − dH ). This is easier to interpret when put in terms
ˆb
∂L b ˆb
∂L b
CI ∂r CI ∂r
of partial derivatives: β1 = ˆ m |T =1
∂ rˆb ∂ H
− ˆ m |T =0 .
∂ rˆb ∂ H
Intuitively, β1 shows the difference in
the supply effect between unlevered and levered banks. A positive coefficient means that leveraged
banks cut lending more when house prices fall. Under the assumption that these types of banks
experience the same demand shocks, this differential can be attributed to increases in the cost of
lending for leveraged banks.
ˆb
∂L b ˆb
∂D
ˆ m (β2 ) gives us the term η0 (eH − dH ) + cH , which is CI ∂r
The coefficient on H ˆ m |T =0 + ˆm .
CI
∂ rˆb ∂ H ∂H
Thus the coefficient on house price growth will give the sum of the demand effect of falling house
prices, and the supply effect for untreated banks. Given that η0 > 0, and the fact that β1 > 0 =⇒
ˆ m provides an upper
eH > dH , we know that η0 (eH − dH ) > 0. As a result, the coefficient on H
ˆb
∂D
bound for the demand effect ( ∂ HˆCI ).
m
The intuition here is that shocks to equity matter more for leveraged banks. If real estate shocks
impact lending by increasing loan non-performance and decreasing bank equity, this matters more
for banks closer to a regulatory capital ratio. A bank up against a capital constraint would need
to either reduce lending or raise equity to maintain their current leverage after a period of poor
loan performance. A bank further from their constraint could take losses without cutting lending or
raising new equity, since they are permitted to operate at a higher level of leverage.
Conversely, demand shocks matter less for leveraged banks. Low leverage gives the flexibility to
expand or contract lending as warranted by changes in demand. Banks on their capital constraint
would continue to lend similar amounts after a demand shock because their previous lending was
below what was necessitated by demand due to the capital constraint. Figure 1 demonstrates
graphically that demand shocks impact lending in banks which are not capital constrained, while
loan losses impact lending in banks which are constrained.
In short, the credit crunch hypothesis predicts that β1 > 0 when Tb,m,q−1 measures bank leverage,
9
because shocks to equity matter more for banks closer to their capital constraint. The demand
hypothesis predicts falling house prices predominantly impacts loan demand (dH > EH ) meaning
that β1 < 0, because unconstrained banks will cut lending most when demand falls.
Role of Construction Lending Construction exposure matters because it determines how severely
house price declines impact bank equity. When house prices fall in a bank’s market, loan non-
performance and charge-offs increase most drastically for constructions loans. Banks with a high
exposure to construction loans were also disproportionately likely to fail during this period (Cole
and White, 2012; Friend et al., 2013).
To reflect this, now assume the following functional forms for the variables in equation (7):
ηb = η
ˆ = e0 + eH H
ˆ m + eT H Tb H
ˆm
E
(1.9)
ˆ b = d0 + dH H
ˆ m + dT H Tb H
ˆm
D
ˆ b = c0 + cH Hˆm
D
CI
Thus equity growth is more sensitive to house price shocks in construction oriented banks, as con-
struction and land development loans performed so poorly when house prices fall. Overall demand
for loans is assumed to also to be more sensitive to house price declines, because the profitability
of new construction projects decreases when house prices fall. However, demand for commercial
lending is assumed to be the same across banks facing similar local house price movements, since
commercial lending isn’t real estate backed. Under these assumptions, the equation defining lending
growth is:
ˆ b = [η(e0 − d0 ) + c0 ] + [η(eT H − dT H )] Tb H
L ˆ m + [η(eH − dH ) + cH ] H
ˆ m + [η(e0 − d0 )]Tb
| {z } | {z }
β1 β2
The coefficient on the interaction term thus reflects the degree to which a construction heavy
loan portfolio impacts bank equity versus overall loan demand. If falling house prices impact lending
through the credit crunch channel, it should be banks which had previously originated construction
loans who cut lending the most, as losses on these construction loans cause equity to fall. Alter-
natively, if falling house prices predominantly impact loan demand, then this should impact banks
regardless of their previous construction lending. Again, β1 > 0 would be evidence in favor of the
capital crunch channel.
10
1.2.4 Data
Data on lending, bank equity and non-performing loans come from the Call Reports, a quarterly
filing of bank balance sheet variables. Since bank holding companies are expected to be a source of
strength for distressed subsidiaries (Houston et al., 1997; Ashcraft, 2004), I aggregate the balance
sheet variables of multibank holding companies to the level of the highest holding company. I use
observations from the first quarter of 2007 through the end of 2010.
Dependent Variables The main dependent variable of interest is the growth in commercial and
industrial lending: ∆ln(C&I Loans)b,m,q . This measures the quarterly growth in the value of loans
outstanding to businesses. This excludes business loans backed by real estate (which are reported
separately). I additionally test how falling house prices impact the growth in bank assets, securities
holdings, equity, as well as various other lending variables. Since these variables sometimes contain
extreme outliers, I drop the highest and lowest 1% of observations of the dependent variable in the
regressions.
I also demonstrate how locating in a market with falling house prices impacts loan performance.
A loan is considered non-performing if it is 90 days past due or not accruing interest. The non-
performing loan rate for a particular type of loan, e.g. construction loans, is the ratio of the value
of non-performing loans to total lending for the category.
The final dependent variable of interest is the change in the commercial and industrial loan
utilization rate: ∆C&I U tilizationb,m,q . The preferred measure of the utilization rate would be:
.
C&I U tilizationb,m,q = C&I Loansb,m,q (C&I Loansb,m,q + U nused C&I Loansb,m,q )
However, unused commitments on commercial and industrial loans is unavailable until 2010, so I fol-
low Kashyap et al. (2002) and use ”other commitments” which predominantly reflects commitments
to make C&I loans instead. This variable reflects changes in the tendency of firms to draw on their
lines of credit. If a certain type of bank experiences a decrease in the demand for loans, this could
show up as a decrease in the utilization of available credit.
Independent Variables I calculate leverage as the ratio of tangible assets to tangible equity:
.
Leverage = (Assets − Intangible Assets) (Equity − Intangible Assets). Where intangible assets
include variables such as good will or mortgage serving rights.7 My measure of exposure to the
7 Results are essentially unchanged using tier 1 leverage or the ratio of assets to capital.
11
.
construction sector is Construction Equity, where Construction is the value of outstanding con-
struction and land development loans. High Leverage is an indicator variable taking the value of
.
1 if Leverage is above the median for the CBSA-quarter. High Construction Equity is defined
analogously. When I use the variables in their continuous form, I winsorize at the 5% level.8
Finally, house price data comes from the FHFA, who construct a repeat sales index on single
family properties securitized by Fannie Mae or Freddie Mac. For banks locating in an MSA, I use
the MSA level index. For banks outside an MSA, I use the index for rural areas within the state.9
I match banks to CBSAs or counties using deposit data from the 2006 Summary of Deposits. The
dependent variable of interest is the growth in house prices over the previous year in the CBSA
where the bank held the majority of their 2006 deposits. I exclude banks (or holding companies)
which didn’t have any individual market containing at least 50% of their deposits.
Summary statistics are found in Table 1. Focusing on the variables weighted by commercial
lending,10 we see that banks faced an average annual decline in house prices of 2%, with a standard
deviation of 6%. The ratio of construction loans to equity averaged about 1 with a considerable
spread; the interquartile range runs from .34 to 1.48. The leverage ratio averages 11.1 with a
standard deviation of 2.1. The mean quarterly growth rate of commercial lending is virtually 0, but
with a standard deviation of 9%.
1.3 Findings
1.3.1 Background
Before turning to the difference-in-differences approach to distinguish between the capital crunch
and demand channel, I first demonstrate that falling house prices did indeed result in increases in
non-performing loans, and a contraction in the size of bank balance sheets.
Housing Declines and Loan Performance Table 2 estimate the non-performing loan rate for
different types of loans as a function of the house price growth over the last year in the bank’s market
and quarter fixed effects. Banks in regions with falling house prices unsurprising see increases in
non-performing loans. Focusing on panel 1 with the unweighted regressions, a 5% decline in house
8 This matters little for the construction variable, however it is important for the findings with the leverage variable.
Banks at either extreme of leverage will be either already past their capital constraint, or not needing to worry about
their capital constraint, so it would make sense that incremental changes in leverage at the extremes wouldn’t impact
the sensitivity of loan supply to equity shocks.
9 Results are mostly unchanged when just using MSAs, although it reduces the sample substantially.
10 These exclude banks over five standard deviations above the mean as in the regressions.
12
prices is found to increase the overall non-performing loan rate from by 0.8% relative to a mean of
2.2%. Columns 2 and 3 show that this coefficient largely reflects the effect that falling house prices
has on construction loans. The 5% decline in house prices increases the non-performing loan rate for
non-construction loans by only 0.4%, while it raises the non-performing loan rate for construction
loans by 2.5%. Thus while construction lending accounted for less than 10% of bank lending, it
was a primary culprit for the rise in non-performing loans in banks located in distressed real estate
markets.
The effects of falling house prices on loan performance in non-construction categories is typi-
cally an order of magnitude smaller than for construction loans. The non-performing loan rate for
commercial loans and loans to individuals rise by 0.2% and 0.1% respectively in response to a 5%
decline in house prices. Even the performance of other real estate loans are much less sensitive to
real estate shocks, with a 5% decline raising the non-performing loan rate by 0.3% for commercial
real estate (CRE) loans and 0.6% for loans backed by multifamily property.11 Every coefficient is
significant at the 1% level, except for loans to individuals which is significant at the 5% level.
Panel 2 presents regressions of the same specification, but weighting by the value of loans out-
standing in the category to account for the fact that some banks may participate in very little of
some types of lending and may thus have noisier non-performing loan rates. Overall, the insights
from the OLS regressions remain in tact. A 5% decline in house prices raises the non-performing
loan rate on construction loans by 2.2%, compared to 0.4% for non-construction loans. The coeffi-
cient for multifamily lending falls, and the coefficient for loans to individuals becomes insignificant
despite increasing somewhat in value. However, the key take away doesn’t change: banks in areas
with falling house prices saw a deterioration in loan performance which was largely attributable to
construction loans.
Housing Declines and Lending Growth How did falling house prices impact the size and
composition of local bank balance sheets? Table 3 shows that banks in areas with falling house
prices shrink across the board. The 15% decline in house prices experienced in the US between the
start of 2007 and start of 2010 is predicted to have lowered assets by 2.9%, equity by 7.4%, lending
by 4.1% and commercial lending by 3.4%.12 The lending declines are stronger for real estate loans,
mostly due to a large decline in construction lending.
Findings are mostly comparable in the unweighted (Panel 1) and weighted (Panel 2) specifica-
11 CRE loans here refer to loans secured by non-farm non-residential property, and don’t include construction and
land development loans as in some definitions.
12 An average of a 5% per year decline over the course of 12 quarters (2008-2010) would reduce lending by .05×12×β.
Where β is the coefficient on house price growth in Table 3.
13
tions. I find evidence of securities holdings falling only in the unweighted specification, while I find
evidence of CRE lending falling only in the weighted specification. The effects on equity are smaller
when I weight (4.6% decline after 3 years of 5% annual declines) but the effects on commercial
lending are larger (4.2% decline over 3 years).
Recall the mechanism in the capital crunch channel: (1) falling house prices cause loans to
perform poorly, (2) loan losses result in declines in equity pushing banks towards their capital
constraint, (3) banks reduce lending in order to decrease their leverage. All of these elements
are present in the data; banks in distressed real estate markets did have poorly performing loans
and declining lending and equity. However, this isn’t sufficient to prove that the capital crunch
channel was operative. If demand fell faster than the capacity for banks to lend, then the decline
in lending wasn’t due to the decline in equity. It is still possible that banks were able to raise new
equity affordably, but declined to do so due the lack of viable lending opportunities. In order to
demonstrate that the real estate losses actually caused the decline in lending, I next turn to the
analysis using variation across banks facing the same house price declines.
1.3.2 Main Findings
Difference-in-Differences By Leverage Table 4 shows that it was predominantly highly lever-
aged banks who cut commercial lending in response to falling house prices. In Column 1 of the first
panel, I estimate quarterly C&I lending growth as a function of the house price growth over the
previous year, an indicator for whether the bank is above the median in leverage for the area, the
interaction of leverage and house price growth and quarter fixed effects. I find that the elasticity of
quarterly lending growth with respect to house price growth is 0.02 for low leverage banks, but 0.08
for highly leveraged banks, with the difference being significant at the 1% level. The 15% decline in
house prices would thus be predicted to have reduced lending by 5.0% in high leverage banks and
only 1.2% in low leverage banks over the course of 2008 to 2010.
In the second column, I include CBSA-quarter fixed in order to compare lending of banks which
are operating in the same market, instead of just markets with similar house price appreciation.
The difference in the elasticity of lending growth with respect to house price growth between high
leverage and low leverage banks increases modestly (from 0.0632 to 0.0652) but is almost the same
as before. The extra bank controls in the third column change nothing.
One might be concerned that many predominantly single market banks have a very small com-
mercial loan portfolio, making the average growth in commercial lending potentially not reflective
14
of actual changes in credit availability. Columns 4-6 account for differences in the importance of
banks in commercial lending by weighting by the value of the bank’s commercial loan portfolio at
the end of the previous quarter. Here the findings are qualitatively similar and still significant, albeit
somewhat less stark. The lending elasticity is 0.04 for low leverage banks, and statistically different
from zero unlike in the previous specification. For leveraged banks, the elasticity is again 0.08 and
statistically different from the elasticity for unleveraged banks.
In the bottom panel, I use leverage in its continuous form instead of an indicator and get similar
results. A bank at the 25th percentile in the leverage distribution (8.65) would have an elasticity
of 0.0, namely they don’t reduce commercial lending when house prices fall. A bank at the 75th
percentile (12.05) however has an elasticity of 0.09, comparable to the high leverage group in the
previous specification. The inclusion of the CBSA-quarter fixed effects diminishes the effect some-
what, with the movement along the interquartile range of leverage now increasing the elasticity by
0.07 instead of 0.09. The difference is still significant at the 1% level, and is unchanged by the
inclusion of the bank controls.
The findings with the continuous form of leverage are weaker in the weighted regressions, but more
consistent in magnitude. Regardless of controls or fixed effects, movement along the interquartile
range of leverage now increases the elasticity of lending with respect to housing price growth by 0.04,
although it is insignificant in the specification with CBSA-quarter fixed effects and bank controls.
Difference-in-Differences By Construction Table 5 repeats the same analysis, except using
exposure to the construction sector instead of leverage. Here we see that the decline in commercial
lending in areas experiencing negative real estate shocks was entirely due to contractions in banks
holding a high value of construction loans. In Column 1 of panel 1, I find that the elasticity of lending
growth with respect to house price growth is 0.00 for banks below the median in construction lending
to equity, but 0.10 for banks highly exposed to the construction sector, with the difference significant
at the 1% level. The 15% national decline in US house prices would thus be predicted to reduce
lending by 6.1% in construction oriented banks but only 0.1% in other banks over the course of 2008
to 2010.
The inclusion of CBSA-quarter fixed effects and bank controls in Columns 2 & 3 only strengthens
the findings, raising the difference in elasticities from 0.10 to 0.11. Weighting by the size of the
commercial loan portfolio also strengthens the findings slightly, producing differences in elasticities
between 0.10 and 0.12.
Turning to the specifications using the ratio of construction lending to equity in its continuous
15
form in the bottom panel, we reach a similar conclusion. A bank at the 25th percentile in the
distribution (0.12) is found to increase quarterly commercial lending by 0.046% in response to a 1%
decline in house prices.13 A bank at the 75th percentile of the distribution (1.02) has an elasticity
which is 0.07 higher than at the 25th percentile. The inclusion of the CBSA-quarter fixed strengthens
the finding marginally, with the interaction between house price growth and construction exposure
remaining consistent throughout.
Weighting by commercial lending in Columns 4-6 results in slightly smaller differences in elastic-
ities across different values of construction exposure. Moving from the 25th to the 75th percentile
in construction exposure raises the lending elasticity from 0.012 to 0.053. However, the qualitative
result is consistent throughout: bank’s with minimal exposure to construction loans mostly didn’t
cut lending in response to falling house prices. The bulk of the contraction in commercial lending
in weak real estate markets occurred in banks who had initially been originating construction loans,
and hence were taking large losses on their loan portfolio.
1.4 Extensions
1.4.1 Commercial Loan Utilization
Showing that leveraged and construction oriented banks respond more to housing declines can only
be taken to be indicative of a supply shock if these banks aren’t subject to different demand shocks.14
As one cannot directly observe loan demand, I test for a differential effect on a proxy for loan demand:
loan utilization. If treated banks have demand fall more in distressed real estate markets, then their
customers would be less willing to draw down any unused loan commitments, resulting in a decline
in utilization.
Tables 6 and 7 repeat the primary difference-in-differences specifications as in the previous two
tables, but using the change in the percentage of commercial loan commitments which are utilized
as the dependent variable. A positive interaction between the treatment variable and house price
growth would indicate that clients of treated banks are less prone to draw down their lines of credit
when house prices decline, indicating potentially larger demand shocks for treated banks.
Overall, the utilization rate of commercial loan commitments rises when house prices fall. If
falling house prices were to result in a reduction in the availability of credit from local banks, this is
13 An important caveat in this finding is that construction exposure is correlated with house price movements, so
some of the lending effects of falling house prices may be appearing in the coefficient on construction exposure, which
is much more strongly negative than in the other specifications.
14 In ∂Dˆb ∂Dˆb
the context of the model, ˆ m |T =1
∂H
= ˆ m |T =0
∂H
16
precisely what would be expected. When less credit is available from other sources, this could make
a firm more likely to lean on existing commitments.
However, there is little evidence of utilization responding to real estate shocks differently in high
leverage banks. The interaction between the leverage variable and house price growth is significant
at the 5% level in only 2 out of 12 specifications, and in none of the specifications with CBSA-quarter
fixed effects. When it is significant, I find that utilization rises more in leveraged banks when house
prices fall, contrary to what would be likely to happen were demand to be falling disproportionately
in these banks.
There is more evidence of a difference in utilization when construction exposure is the treatment
variable. I find that utilization rises in construction oriented banks when house prices fall. This
.
consistently holds true when Construction Equity, is the treatment variable, but is less robust
.
for High Construction Equity. Again, this indicates that the declines in commercial lending in
construction oriented banks wasn’t due to firms desiring less credit.
1.4.2 Are Results Driven by Loan Losses?
Another way to provide evidence that falling house prices impact lending by impairing loan per-
formance is to include non-performing loans in the specification. If falling house prices resulted
in a contraction in lending because banks took losses on their loan portfolio and forced capital
constrained banks to deleverage, then falling house prices should cease to be important when the
interaction of leverage and non-performing loan rates are controlled for. If banks within distressed
real estate markets cut lending even when their loans continue to pay out, it could indicate that the
contraction reflects something different than a reduced capacity to make loans.
Tables 8 and 9 support the claim that the declines in commercial lending are indeed due to loan
losses. When I include the non-performing loan rate, bank leverage and their interaction in the
primary specifications presented in Tables 4 and 5, I find that: (1) banks with high non-performing
loans cut lending, (2) this contraction is amplified in leveraged banks and (3) house price growth is
mostly insignificant once loan losses are controlled for.
For the first finding, banks with more non-performing loans are consistently shown to contract
lending. The first panel of Table 8 adds the non-performing loan rate and its interaction with
High Leverage to the primary difference-in-differences specifications with High Leverage as the
treatment variable. Across the six specifications, banks below the CBSA median in leverage are found
to reduce quarterly lending growth by between 0.24% and 0.29% in response to a 1 percentage point
17
increase in the non-performing loan rate. The first panel of Table 9 repeats this for the specifications
.
with High Construction Equity as the treatment variable. Here, the elasticity lending growth with
respect to non-performing loans is found to run between -0.28 and -0.34 for unleveraged banks. The
coefficient on non-performing loans is significant at the 1% level in all 12 specifications.
For the second finding, the declines in lending are even greater in leveraged banks. The interaction
between non-performing loans, and the measure of leverage is significant at at least the 5% level in
22 of 24 specifications, and significant at the 10% level in the other two.15 The coefficient on the
interaction of High Leverage and N on-perf orming Loans runs between -0.11 and -0.17. Taking
the mid point of the range of values, a 1% higher non-performing loan rate over the course of a
year would thus reduce C&I lending by roughly 1.16% in unleveraged banks and 1.72% in leveraged
banks.
Finally, and most relevant to the questions at hand, controlling for non-performing loans causes
house price growth to lose its explanatory power. House price growth is always insignificant. The
interaction of house price growth with the treatment variable is only significant at the 5% level in the
.
two specifications with High Construction Equity as the treatment variable and CBSA-quarter
fixed effects. This isn’t merely caused by inflated standard errors stemming from the inclusion of
correlated regressors, the magnitude of the coefficients on the majority of the housing variables drop
drastically once non-performing loans are controlled for. The interaction effect between house price
growth and the treatment variable drops by over half in 22 of 24 specifications, usually substantially
more.
1.4.3 Effects on Other Lending
How did falling house prices impact the supply of credit in other loan categories? Since C&I lending
only accounts for about 15% of the lending of the banks, ignoring other loan categories gives an
incomplete picture of how falling house prices impacted the supply of credit. Furthermore, since
C&I lending isn’t backed by real estate, there is less of a reason to expect falling house prices to
impact lending for reasons other than the health of local banks. Real estate backed lending may
fall because people or firms experience a decline in the value of their real estate assets and can thus
have less collateral to support borrowing. Additionally, construction lending is likely to fall because
there aren’t good investments to be made. Thus it is more likely that even banks far from their
capital constrain would cut lending, indicating a greater role for demand than I found for commercial
15 The 24 specifications come from combinations of the following: (leverage,construction)×(indicator,continuous)×(quarter
fixed effect, CBSA-quarter fixed effect, CBSA-quarter fixed effect and bank controls)×(unweighted,weighted).
18
lending.
Tables 10-13 test whether leveraged banks disproportionately cut different types of lending in
response to falling house prices. I examine how bank leverage impacts the elasticity of lending
growth with respect to house price growth for total lending (Table 10), lending backed by real estate
(Table 11), construction lending (Table 12) and commercial real estate lending (Table 13). Overall,
there is more evidence of demand driven lending declines when the loan category includes real estate
loans, although the capital crunch channel significantly impacts every type of lending.
In Table 10, we see that total lending responds more to house price declines in highly leveraged
banks. However, unlike with commercial lending, unleveraged banks cut lending too. The elasticity
of quarterly total lending growth with respect to house price growth is 0.040 for low leverage banks
and 0.097 for leveraged banks. This difference in elasticities isn’t substantially changed by the
inclusion of CBSA-quarter fixed effects, bank controls, or weighting by initial loan volume. Similar
insights are drawn from the use of leverage in its continuous form in the bottom panel. A 15%
decline in house prices over the course of three years would reduce lending by 2.4% for banks below
the median in leverage, and 5.8% for banks above the median.
In Table 11, we see that the findings for lending backed by real estate is very similar to the
findings for overall lending. The elasticity of quarterly real estate lending growth with respect to
house price growth is 0.042 for low leverage banks and 0.106 for leveraged banks in the unweighted
specification. These elasticities are 0.055 and 0.096 in the weighted specification. Again, using more
conservative fixed effects, additional bank controls, or the continuous measure of leverage don’t
materially change the finding.
Table 12 shows that the decline in construction lending seems to be more driven by declines in
demand in areas where house prices fall. The elasticity of construction loan growth with respect to
house price growth is 0.303 for low leverage banks and 0.389 for leveraged banks in the unweighted
specification. These elasticities are 0.169 and 0.231 in the weighted specification. Although falling
house prices decrease construction lending significantly more for highly leveraged banks, low leverage
banks contract lending to a strong degree, indicating that the declines predominantly reflected a lack
of desirable construction investments.
Commercial real estate lending on the other hand follows patters more similar to the non-real
estate backed C&I loans. Table 13 shows that the contractions in lending were predominantly in
leveraged banks. In the unweighted specification, unleveraged banks are found to increase non-
farm non-residential lending by .023% in response to a 1% decline in house prices, while leveraged
banks decrease lending growth by 0.031% in response to a 1% decline in house prices. As many
19
banks originate very few CRE loans, the weighted specification might be more reflective of changes
in credit. Here we find that the elasticity of CRE lending with respect to house prices is 0.021
for unleveraged banks and 0.063 for leveraged banks, with the difference still being statistically
significant.
1.4.4 How Much of the Decline is Due to Supply Shocks
In this section I attempt to quantify the degree to which banks cut lending due to demand versus
supply factors. Recall that the elasticity of quarterly lending growth in some category G with respect
to last year’s house price growth was decomposed into a credit crunch channel and a demand channel:
ˆb
dL ∂Lˆ b ∂rb ∂D ˆb
G G G
= +
ˆm
dH ˆm
∂ rˆb ∂ H ∂Hˆm
| {z } | {z }
Credit Crunch Demand
Channel Channel
Between 2007 and 2010, house prices declined on average by 5% per year. Over the course
of 12 quarters, this means that the predicted lending decline due to the credit crunch effect is:
ˆb
∂L b ˆb
∂D
CI ∂r
.05 × 12 × E( ˆm )
∂ rˆb ∂ H
and the expected decline due to demand is: .05 × 12 × E( ∂ HˆCI ).
m
In oder to determine these expectations, recall that the coefficients from (1) can be interpreted
as:
ˆ b ∂rb
∂L ˆ b ∂rb
∂L
G G
β1,G = | − |
ˆ m T =1
∂ rˆb ∂ H ˆ m T =0
∂ rˆb ∂ H
ˆ b ∂rb
∂L ∂D ˆb
G G
β2,G = | +
ˆ m T =0
∂ rˆb ∂ H ˆm
∂H
Since we have two equations and three unknowns, I need to make an additional assumption in
order to identify how the regression coefficients map into these elasticities. Note that the parame-
terization in equation 8 implies that:
ˆ b ∂rb
∂L ˆ b ∂rb
ηo + ηT ∂ L
G CI
|T =1 = × |
ˆm
∂ rˆb ∂ H η0 ˆ m T =0
∂ rˆb ∂ H
| {z }
1+χ
ηT
Denoting the ratio η0 ≡ χ, one can solve this system of 3 equations for all of the elasticities of
interest as a function of the regression coefficients and χ.
20
ˆb
∂D β1,G
ˆ
∂H
G
= β2,G − χ
m
ˆb
∂L b
G ∂r 1 (1.10)
ˆ m |T =0 =
∂ rˆb ∂ H χ β1,G
ˆ b ∂r b
∂ LG 1+χ
ˆ |T =1 = χ β1,G
∂ rˆb ∂ H
m
ˆb
∂D β1,G
Thus the decline in lending due to falling demand is: .6 × E( ∂ Hˆ G ) = .6 × (β2,G − χ ). Since half
m
of the banks are high leverage and half low leverage, the decline due to the credit crunch channel is:
ˆ b ∂r b
∂L 1+.5χ
.6 × E( G
ˆm )
∂ rˆb ∂ H
= .6 × χ β1,G .
To bound these effects, one must make an assumption regarding the value of χ, which parameter-
izes how much more important equity shocks are to leveraged banks than unleveraged banks. A high
χ means that unleveraged banks minimally respond to equity shocks, meaning that the coefficient
ˆ m mostly reflects the influence on falling house prices on demand. Low values of χ mean that
on H
even low leverage banks are sensitive to equity shocks, and thus give larger predicted supply effects.
Consider two polar sets of assumptions to bound potential supply and demand effects. First,
lets make the assumption that maximizes the predicted demand effect. Specifically, assume that
ˆ b ∂r b
∂L
low leverage banks can take losses without it impacting the cost of lending ( ˆ m |T =0 = 0).
G
∂ rˆb ∂ H
This corresponds with χ = ∞, making the credit crunch channel equal to: .3β1,G and the demand
channel equal to .6β2,G .
Second, assume that the entire contraction in C&I lending was due to supply. Based on equation
β1,CI 6.32
10 and the findings in Table 4, this implies that χ = β2,CI = 2.02 . I can then use this χ to assess
how supply and demand shocks impacted lending in other categories.
Using the estimated coefficients from Table 4, and Tables 10-13 gives the following estimates of
how the 15% decline in house prices in the US impacted bank lending through the credit crunch
and demand channels under the two assumptions regarding χ. Note that for construction loans and
CRE loans I use the weighted specification, as these types of lending are more concentrated in fewer
banks, whereas real estate lending in more generally an important part of a bank’s portfolio.
21
Demand Friendly Assumption Supply Friendly Assumption
6.32
χ=∞ χ= 2.02
Loan Type Demand Channel Supply Channel Demand Channel Supply Channel
Commercial 1.2% 1.9% 0.0% 3.1%
Overall 2.4% 1.7% 1.3% 2.8%
Real Estate 2.5% 1.9% 1.3% 3.1
Construction 10.1% 1.9% 9.0% 3.0%
CRE 1.3% 1.3% 0.5% 2.1%
Under the demand friendly assumption, which attributes any lending declines in unleveraged
banks as the result of declining loan demand, we typically have falling house prices resulting in a
contraction in the supply of credit accounting for slightly less than a 2% decline across the different
lending categories. Under the supply friendly assumptions, which assume that the decline in com-
mercial lending was entirely a supply side phenomenon, we have falling house prices explaining a
roughly 3% decline in lending. Although this may sound small, total lending in commercial banks
was around $7 trillion before the crises, thus this implies a supply driven contraction of between
$140-210 billion.
1.5 Conclusion
One of the most salient features of the Great Recession was the strong relationship between falling
house prices and the local employment losses. While these housing declines were undoubtedly central
to the economic turmoil, the exact mechanism is unclear. Were the employment losses a consequence
of the loss in housing wealth and a resultant decline in consumption? Were they a consequence of
the extreme losses in construction and related industries? Were the rash of bank failures a cause or
a consequence the economic turmoil?
This paper takes an important step in evaluating these questions, namely I ask whether falling
house prices impacted the supply of credit. I show that falling house prices didn’t uniformly impact
the commercial lending of local banks, instead lending fell in the banks for whom falling house
prices would have resulted in the greatest contraction in the capacity to lend. Specifically, only
construction oriented banks and leveraged banks reduced lending in areas with falling house prices.
However, one must be cautious in interpreting this result as it ignores the sort of adjustments
which should occur in equilibrium. I have shown that lending predominantly fell due to supply
shocks instead of demand shocks at the bank level. However, a contraction in one bank may increase
22
the demand for loans from another. With perfect banking markets, firms would move out of capital
constrained banks and into the banks in a position to lend more cheaply. Overall lending would fall
due to demand, and the differentials between banks would be uninformative regarding the aggregate
availability of credit.
Despite this cause for caution, there are good reasons to believe that substituting away from
a bank is costly. Chodorow-Reich (2014) shows that even for the relatively large firms borrowing
from very large banks, banking relationships are sticky. A shock to a firm’s bank was shown to
have a large impact on employment decision of firms who didn’t have access to the bond market.
Given that the customers of the small single market banks in my sample disproportionately make
information sensitive loans to small opaque firms (Berger et al., 2005), it is unlikely that they’d be
more able to substitute away from a banking relationship than the firms active in syndicated loan
markets appearing in the sample in Chodorow-Reich (2014).
In short, this large disruption in the supply of lending at the bank level has the potential to
disrupt the economy, but it is difficult to quantify the degree with this approach. This will be the
goal of the next chapter.
23
1.6 Appendix
1.6.1 Figures
M C Lending M C Lending
M C 2 M C1 M C 2 M C1
rf rf
MR MR
Lending Lending
L?1 = L?2 L?2 L?1
(a) Supply Shock: Low Leverage (b) Supply Shock: High Leverage
M C Lending M C Lending
MC MC
rf rf
M R2 M R1 M R2 M R1
Lending Lending
L?2 L?1 L?1 = L?2
(c) Demand Shock: Low Leverage (d) Demand Shock: High Leverage
Figure 1.1: Lending Elasticities: By Bank Leverage
Notes: This figure demonstrates the differential effect of bank leverage on the responsiveness of bank lending to a supply versus
a demand shock. Deposits are supplied inelastically at the market rate rf , but banks have a leverage constraint so that lending
cannot be greater than a certain multiple of their equity, causing the supply curve to go vertical. The top panels show that a
decline in equity (a leftward shift of the supply curve) only impacts lending if a bank is on its capital constraint. The bottom
panels show that a decline in demand only impacts lending if a bank is not on its capital constraint. This means that the empirical
finding that falling house prices result in a contraction in lending for leveraged banks implies that these declines predominantly
impact the supply of lending.
24
1.6.2 Tables
Table 1.1: Summary Statistics
Mean Standard Percentile Obs
Deviation 25th 75th
Primary Variables
Weighted by C&I Lending
House P rice .
Growth (annual) -0.020 0.062 -0.042 0.014 68895
Construction Equity 0.995 0.787 0.344 1.475 68895
Leverage 11.145 2.084 9.853 12.577 68895
∆ln(C&I Loans) -0.001 0.087 -0.044 0.044 67739
Unweighted
House P rice .
Growth (annual) -0.010 0.050 -0.026 0.017 70639
Construction Equity 0.697 0.755 0.116 1.022 70639
Leverage 10.344 2.369 8.654 12.052 70639
∆ln(C&I Loans) 0.002 0.114 -0.053 0.056 67945
Other Outcome Variables (unweighted)
Non-performing Loans (%)
Overall 0.022 0.033 0.004 0.028 70430
Real Estate 0.024 0.036 0.003 0.031 70082
Construction 0.058 0.122 0.000 0.059 65198
M ultif amily Residential 0.020 0.091 0.000 0.000 52782
HELOC 0.009 0.047 0.000 0.003 51241
Commercial and Industrial 0.020 0.043 0.000 0.022 69119
Individuals 0.009 0.025 0.000 0.009 69899
Quarterly Growth Rates
Assets 0.008 0.035 -0.012 0.030 69912
Equity 0.006 0.049 -0.008 0.028 69511
T otal Lending 0.008 0.041 -0.015 0.029 69628
Real Estate Lending 0.010 0.045 -0.015 0.031 69155
Construction Lending -0.011 0.235 -0.102 0.077 63374
CRE Lending 0.015 0.084 -0.024 0.047 68219
M ultif amily Residential Lending 0.020 0.207 -0.024 0.026 51058
Home Equity Lines of Credit 0.020 0.143 -0.031 0.063 49915
Lending to Individuals -0.011 0.098 -0.052 0.027 68827
Credit Utilization (%)
∆CI Loan U tilization 0.001 0.062 -0.027 0.030 68440
Notes: This table reports summary statistics of the main bank-quarter level variables in this paper. The most
important explanatory variables (leverage, construction exposure and annual local house price appreciation) and
the key outcome variable (quarterly commercial lending growth) are summarized both as weighted by the level of
commercial lending at the end of the previous quarter and unweighted. As in the regressions, I drop any observation
more than five standard deviations above the mean in commercial lending when weighting. Other outcome variables
pertaining to loan performance, balance sheet growth, or credit utilization are unweighted.
25
Table 1.2: Housing Declines and Loan Non-performance
(1) (2) (3) (4) (5) (6) (7)
Total Non-Construction Construction Commercial Individual CRE Multifamily
Panel 1: Unweighted
House P rice Growth -0.155∗∗ -0.0798∗∗ -0.496∗∗ -0.0484∗∗ -0.0172∗ -0.0529∗∗ -0.127∗∗
(0.0197) (0.0129) (0.0613) (0.0110) (0.00708) (0.0126) (0.0347)
Constant 0.0209∗∗ 0.0166∗∗ 0.0527∗∗ 0.0195∗∗ 0.00864∗∗ 0.0214∗∗ 0.0188∗∗
(0.00107) (0.000680) (0.00348) (0.000636) (0.000284) (0.000703) (0.00142)
Observations 70430 70430 65198 69119 69899 69479 52782
R2 0.106 0.074 0.093 0.019 0.004 0.031 0.011
Panel 2: Weighted by Level of Lending
House P rice Growth -0.136∗∗ -0.0755∗∗ -0.431∗∗ -0.0412∗∗ -0.0207 -0.0525∗∗ -0.0779+
26
(0.0292) (0.0204) (0.0852) (0.0121) (0.0144) (0.0158) (0.0464)
Constant 0.0246∗∗ 0.0170∗∗ 0.0753∗∗ 0.0175∗∗ 0.00828∗∗ 0.0200∗∗ 0.0248∗∗
(0.00230) (0.00123) (0.00715) (0.000724) (0.00107) (0.00130) (0.00365)
Observations 70186 70174 64452 68861 69764 69047 51856
R2 0.128 0.117 0.236 0.055 0.015 0.102 0.026
Quarter Fixed Effect Yes Yes Yes Yes Yes Yes Yes
Standard errors in parentheses
+ p < 0.10, ∗ p < 0.05, ∗∗ p < .01
Notes: This table reports coefficients from regressions of quarterly bank level non-performing loan rates for different types of loans on local house
price growth and quarter fixed effects. The dependent variable is the non-performing loan rate overall in (1), on loans besides construction and land
development loans in (2), construction and land development loans in (3), commercial loans in (4), loans to individuals in (5), loans backed by non-farm
non-residential properties in (6), and loans backed by multifamily properties in (7). The non-performing loan rate is the percentage of the value of
loans which are 90 days past due or not accruing interest. The first panel is estimated with OLS. The second panel weights by the level of lending in
the given category at the end of the previous quarter, dropping observations more than 5 standard deviations above the mean in the lending category.
The sample runs from 2007 to 2010 and includes banks which hold over 50% of their deposits in one CBSA or county. Standard errors are clustered by
CBSA/county.
Table 1.3: Housing Declines and Loan Growth: By Loan Type
(1) (2) (3) (4) (5) (6) (7) (8)
Assets Equity Securities Lending C&I RE Construction CRE
Panel 1: Unweighted
House P rice Growth 0.0481∗∗ 0.124∗∗ 0.0511∗∗ 0.0684∗∗ 0.0569∗∗ 0.0767∗∗ 0.350∗∗ 0.00788
(0.00715) (0.0129) (0.0178) (0.0116) (0.0144) (0.0131) (0.0297) (0.0117)
Observations 69912 69511 68223 69628 67945 69155 63374 68219
R2 0.024 0.109 0.008 0.075 0.017 0.057 0.022 0.009
Panel 2: Weighted by Initial Level
House P rice Growth 0.0436∗∗ 0.0771∗∗ 0.0686∗∗ 0.0697∗∗ 0.0820∗∗ 0.212∗∗ 0.0446∗∗
27
-0.00712
(0.0166) (0.0272) (0.0401) (0.0212) (0.0146) (0.0150) (0.0417) (0.0121)
Observations 69790 69347 68159 69402 67739 68928 63118 67855
R2 0.033 0.072 0.017 0.128 0.054 0.121 0.120 0.031
Quarter Fixed Effect Yes Yes Yes Yes Yes Yes Yes Yes
Standard errors in parentheses
+ p < 0.10, ∗ p < 0.05, ∗∗ p < .01
Notes: This table reports coefficients from regressions of quarterly bank level growth rates for different balance sheet variables on local house price
growth and quarter fixed effects. The dependent variable is the growth rate of assets in (1), equity in (2), securities in (3), total lending in (4),
commercial lending in (5), lending backed by real estate in (6), construction and lend development lending in (7), and lending backed by non-farm
non-residential properties in (8). The first panel is estimated with OLS. The second panel weights by the initial level of the balance sheet variable
at the end of the previous quarter, dropping observations more than 5 standard deviations above the mean. The sample runs from 2007 to 2010 and
includes banks which hold over 50% of their 2006 deposits in one CBSA or county. Standard errors are clustered by CBSA/county.
Table 1.4: Housing Declines and Commercial Lending Growth: By Leverage
(1) (2) (3) (4) (5) (6)
Panel 1: Indicator for High Leverage
High Leverage -0.00227∗ -0.00233∗ -0.00235+ 0.000626 -0.000845 -0.000652
(0.00105) (0.00118) (0.00122) (0.00208) (0.00224) (0.00241)
House P rice Growth 0.0202 0.0383∗
(0.0137) (0.0182)
High Leverage × 0.0632∗∗ 0.0652∗∗ 0.0653∗∗ 0.0413∗ 0.0547∗∗ 0.0528∗∗
House P rice Growth (0.0204) (0.0230) (0.0229) (0.0185) (0.0171) (0.0181)
Observations 56214 56214 56214 56013 56013 56013
R2 0.020 0.245 0.246 0.065 0.324 0.325
Panel 2: Leverage
Leverage -0.000866∗∗ -0.000815∗ -0.000874∗ -0.000448 -0.000568 -0.000575
(0.000250) (0.000331) (0.000355) (0.000627) (0.000677) (0.000739)
House P rice Growth -0.235∗∗ -0.0660
(0.0418) (0.0510)
Leverage × 0.0268∗∗ 0.0203∗∗ 0.0205∗∗ 0.0119∗ 0.0118+ 0.0116
House P rice Growth (0.00414) (0.00591) (0.00589) (0.00471) (0.00699) (0.00718)
Observations 66672 66672 66672 66468 66468 66468
R2 0.020 0.290 0.291 0.062 0.360 0.360
Quarter Fixed Effect Yes No No Yes No No
CBSA-Quarter Fixed Effect No Yes Yes No Yes Yes
Weighted No No No Yes Yes Yes
Standard errors in parentheses
+ p < 0.10, ∗ p < 0.05, ∗∗ p < .01
Notes: This table reports coefficients from regressions of quarterly bank level commercial and industrial lending
growth on bank leverage, CBSA house price growth and their interaction. Leverage is the ratio of tangible assets to
tangible equity. In first panel, I use an indicator for whether leverage is above the median for the CBSA-quarter. The
second panel uses leverage in its continuous form, winsorized at the 5% level. Columns 1 & 4 include quarter fixed
effects, while Columns 2,3,5 & 6 include CBSA-quarter fixed effects. Columns 1-3 are unweighted, while columns 4-6
weight by the size of the bank’s C&I loan portfolio in the previous quarter. The sample runs from 2007 to 2010 and
includes bank holding companies which hold over 50% of their deposits in one CBSA or county, dropping observations
in the highest or lowest percentile of lending growth. The weighted regressions additionally drop banks 5 standard
deviations above the mean in commercial lending. Standard errors are clustered by CBSA/county.
28
Table 1.5: Housing Declines and Commercial Lending Growth: By Construction Exposure
(1) (2) (3) (4) (5) (6)
Panel 1: High Construction
. Exposure
High Construction Equity -0.000571 -0.000597 -0.000577 0.00331 0.00283 0.00323
(0.000988) (0.00112) (0.00126) (0.00262) (0.00239) (0.00259)
House P rice Growth 0.00117 0.00928
(0.0152) (0.0275)
.
High Construction Equity × 0.101∗∗ 0.108∗∗ 0.111∗∗ 0.104∗∗ 0.120∗∗ 0.118∗∗
House P rice Growth (0.0196) (0.0222) (0.0221) (0.0321) (0.0332) (0.0321)
Observations 55766 55766 55766 55575 55575 55575
R2 0.020 0.246 0.247 0.066 0.328 0.329
Panel 2: Construction
. to Equity Ratio
Construction Equity -0.00321∗∗ -0.00175 -0.00244∗ -0.00251+ -0.00160 -0.00219
(0.000650) (0.00109) (0.00122) (0.00128) (0.00204) (0.00251)
House P rice Growth -0.0551∗∗ 0.00617
(0.0196) (0.0235)
.
Construction Equity × 0.0773∗∗ 0.0814∗∗ 0.0821∗∗ 0.0459∗∗ 0.0739∗∗ 0.0739∗∗
House P rice Growth (0.0148) (0.0177) (0.0176) (0.0155) (0.0197) (0.0196)
Observations 66672 66672 66672 66468 66468 66468
R2 0.020 0.290 0.291 0.063 0.361 0.361
Quarter Fixed Effect Yes No No Yes No No
CBSA-Quarter Fixed Effect No Yes Yes No Yes Yes
Weighted No No No Yes Yes Yes
Standard errors in parentheses
+ p < 0.10, ∗ p < 0.05, ∗∗ p < .01
Notes: This table reports coefficients from regressions of quarterly bank level commercial and industrial lending growth
on bank construction exposure, CBSA house price growth and their interaction. Construction exposure is the ratio
of bank construction lending to equity. In first panel, I use an indicator for whether construction exposure is above
the median for the CBSA-quarter. The second panel uses construction exposure in its continuous form, winsorized
at the 5% level. Columns 1 & 4 include quarter fixed effects, while Columns 2,3,5 & 6 include CBSA-quarter fixed
effects. Columns 1-3 are unweighted, while columns 4-6 weight by the size of the bank’s C&I loan portfolio in the
previous quarter. The sample runs from 2007 to 2010 and includes bank holding companies which hold over 50% of
their deposits in one CBSA or county, dropping observations in the highest or lowest percentile of lending growth. The
weighted regressions additionally drop banks 5 standard deviations above the mean in commercial lending. Standard
errors are clustered by CBSA/county.
29
Table 1.6: Housing Declines and Commercial Loan Utilization: By Leverage
(1) (2) (3) (4) (5) (6)
Panel 1: Indicator for High Leverage
High Leverage -0.000108 -0.0000898 -0.000259 -0.000307 0.0000111 -0.000226
(0.000475) (0.000537) (0.000551) (0.000759) (0.000855) (0.000931)
House P rice Growth -0.0260∗∗ -0.0124
(0.00686) (0.0109)
High Leverage × 0.00318 0.00399 0.00447 -0.00395 0.00894 0.0117
House P rice Growth (0.00702) (0.00793) (0.00799) (0.00880) (0.0118) (0.00976)
Observations 57818 57818 57818 57685 57685 57685
R2 0.006 0.240 0.241 0.014 0.293 0.297
Panel 2: Leverage
Leverage 0.0000257 0.0000760 0.0000726 -0.000253+ -0.000161 -0.000211
(0.0000968) (0.000148) (0.000154) (0.000147) (0.000163) (0.000200)
House P rice Growth 0.00803 0.0485
(0.0170) (0.0347)
Leverage × -0.00323∗ -0.00322 -0.00299 -0.00590∗ -0.000399 0.00000137
House P rice Growth (0.00148) (0.00218) (0.00219) (0.00268) (0.00261) (0.00238)
Observations 68440 68440 68440 68304 68304 68304
R2 0.006 0.292 0.293 0.014 0.337 0.340
Quarter Fixed Effect Yes No No Yes No No
CBSA-Quarter Fixed Effect No Yes Yes No Yes Yes
Weighted No No No Yes Yes Yes
Standard errors in parentheses
+ p < 0.10, ∗ p < 0.05, ∗∗ p < .01
Notes: This table reports coefficients from regressions of changes in quarterly bank level commercial and industrial
loan utilization on bank leverage, CBSA house price growth and their interaction. Leverage is the ratio of tangible
assets to tangible equity. In first panel, I use an indicator for whether leverage is above the median for the CBSA-
quarter. The second panel uses leverage in its continuous form, winsorized at the 5% level. Columns 1 & 4 include
quarter fixed effects, while Columns 2,3,5 & 6 include CBSA-quarter fixed effects. Columns 1-3 are unweighted, while
columns 4-6 weight by the size of the bank’s C&I loan portfolio in the previous quarter. The sample runs from 2007
to 2010 and includes bank holding companies which hold over 50% of their deposits in one CBSA or county, dropping
observations in the highest or lowest percentile of ∆C&I U tilizationb,m,q . The weighted regressions additionally drop
banks 5 standard deviations above the mean in commercial lending. Standard errors are clustered by CBSA/county.
30
Table 1.7: Housing Declines and Commercial Loan Utilization: By Construction Exposure
(1) (2) (3) (4) (5) (6)
Panel 1: Indicator for
. High Construction Exposure
High Construction Equity 0.00170∗∗ 0.00178∗∗ 0.00170∗∗ 0.00192∗ 0.00320∗∗ 0.00222∗∗
(0.000351) (0.000401) (0.000416) (0.000812) (0.00124) (0.000759)
House P rice Growth -0.0163∗ -0.0101
(0.00634) (0.00921)
.
High Construction Equity × -0.0157∗ -0.0152+ -0.0137 -0.0196 -0.000247 0.00246
House P rice Growth (0.00796) (0.00902) (0.00897) (0.0122) (0.0213) (0.0207)
Observations 57320 57320 57320 57190 57190 57190
R2 0.006 0.240 0.240 0.015 0.298 0.301
Panel 2: Construction
. Lending to Equity Ratio
Construction Equity 0.00176∗∗ 0.00163∗∗ 0.00144∗∗ 0.00119∗ 0.00218∗∗ 0.000996
(0.000237) (0.000375) (0.000384) (0.000498) (0.000769) (0.000806)
House P rice Growth 0.0000881 0.0209
(0.00771) (0.0149)
.
Construction Equity × -0.0169∗∗ -0.0179∗∗ -0.0174∗∗ -0.0309∗∗ -0.0187∗ -0.0191∗
House P rice Growth (0.00452) (0.00612) (0.00626) (0.00761) (0.00863) (0.00871)
Observations 68440 68440 68440 68304 68304 68304
R2 0.007 0.292 0.293 0.016 0.338 0.341
Quarter Fixed Effect Yes No No Yes No No
CBSA-Quarter Fixed Effect No Yes Yes No Yes Yes
Weighted No No No Yes Yes Yes
Standard errors in parentheses
+ p < 0.10, ∗ p < 0.05, ∗∗ p < .01
Notes: This table reports coefficients from regressions of changes in quarterly bank level commercial and industrial
loan utilization on bank construction exposure, CBSA house price growth and their interaction. Construction exposure
is the ratio of bank construction lending to equity. In first panel, I use an indicator for whether construction exposure
is above the median for the CBSA-quarter. The second panel uses construction exposure in its continuous form,
winsorized at the 5% level. Columns 1 & 4 include quarter fixed effects, while Columns 2,3,5 & 6 include CBSA-
quarter fixed effects. Columns 1-3 are unweighted, while columns 4-6 weight by the size of the bank’s C&I loan
portfolio in the previous quarter. The sample runs from 2007 to 2010 and includes bank holding companies which
hold over 50% of their deposits in one CBSA or county, dropping observations in the highest or lowest percentile of
∆C&I U tilizationb,m,q . The weighted regressions additionally drop banks 5 standard deviations above the mean in
commercial lending. Standard errors are clustered by CBSA/county.
31
Table 1.8: Are Results Driven By Real Estate Losses: Leverage Specifications
(1) (2) (3) (4) (5) (6)
Panel 1: Indicator for High Leverage
High Leverage 0.00135 0.00105 0.000792 0.00553∗ 0.00381 0.00393
(0.00130) (0.00148) (0.00155) (0.00282) (0.00298) (0.00327)
House P rice Growth -0.00181 0.0163
(0.0186) (0.0211)
High Leverage × -0.00558 0.00166 0.00150 0.0102 0.0189 0.0180
House P rice Growth (0.0217) (0.0241) (0.0239) (0.0214) (0.0220) (0.0217)
N on-perf orming Loans -0.293∗∗ -0.265∗∗ -0.278∗∗ -0.286∗∗ -0.242∗∗ -0.252∗∗
(0.0443) (0.0577) (0.0591) (0.0706) (0.0765) (0.0788)
High Leverage × -0.166∗∗ -0.157∗∗ -0.157∗∗ -0.136∗ -0.130∗ -0.137∗
N on-perf orming Loans (0.0396) (0.0461) (0.0471) (0.0613) (0.0639) (0.0632)
Observations 57317 57317 57317 57114 57114 57114
R2 0.028 0.241 0.243 0.073 0.323 0.324
Panel 2: Leverage
Leverage -0.000150 -0.000169 -0.000269 0.00109 0.000637 0.000636
(0.000316) (0.000427) (0.000442) (0.000940) (0.000877) (0.00103)
House P rice Growth -0.0641 -0.00608
(0.0533) (0.0586)
Leverage × 0.00549 0.00126 0.00151 0.00255 0.00491 0.00477
House P rice Growth (0.00490) (0.00633) (0.00623) (0.00532) (0.00523) (0.00539)
N on-perf orming Loans -0.0608 -0.00461 -0.0166 -0.0353 -0.0208 -0.0231
(0.0849) (0.107) (0.107) (0.160) (0.145) (0.144)
Leverage × -0.0287∗∗ -0.0307∗∗ -0.0308∗∗ -0.0301∗ -0.0268∗ -0.0280∗
N on-perf orming Loans (0.00783) (0.00953) (0.00949) (0.0135) (0.0122) (0.0123)
Observations 67945 67945 67945 67739 67739 67739
R2 0.028 0.286 0.287 0.071 0.361 0.362
Quarter Fixed Effect Yes No No Yes No No
CBSA-Quarter Fixed Effect No Yes Yes No Yes Yes
Weighted No No No Yes Yes Yes
Standard errors in parentheses
+ p < 0.10, ∗ p < 0.05, ∗∗ p < .01
Notes: This table reports coefficients from regressions of quarterly bank holding company level commercial and
industrial lending growth on MSA level house price growth, the bank level non-performing loan rate, and the interaction
of these variables with bank leverage. Leverage is the ratio of tangible assets to tangible equity. In first panel, I use
an indicator for whether leverage is above the median for the CBSA-quarter. The second panel uses leverage in
its continuous form, winsorized at the 5% level. Columns 1 & 4 include quarter fixed effects, while Columns 2,3,5
& 6 include CBSA-quarter fixed effects. Columns 1-3 are unweighted, while columns 4-6 weight by the size of the
bank’s C&I loan portfolio in the previous quarter. The sample runs from 2007 to 2010 and includes bank holding
companies which hold over 50% of their deposits in one CBSA or county, dropping observations in the highest or
lowest percentile of lending growth. The weighted regressions additionally drop banks 5 standard deviations above
the mean in commercial lending. Standard errors are clustered by CBSA/county.
32
Table 1.9: Are Results Driven By Real Estate Losses: Construction Specifications
(1) (2) (3) (4) (5) (6)
Panel 1: Indicator for
.High Construction Exposure
High Construction Equity 0.0023∗ 0.0019 0.0016 0.0065∗ 0.0065∗∗ 0.0060∗
(0.0011) (0.0013) (0.0014) (0.0030) (0.0024) (0.0025)
House P rice Growth -0.0192 -0.0081
(0.0184) (0.0290)
.
High Construction Equity × 0.0320 0.0410+ 0.0441+ 0.0491 0.0849∗ 0.0821∗
House P rice Growth (0.0219) (0.0247) (0.0244) (0.0358) (0.0382) (0.0374)
N on-perf orming Loans -0.3026∗∗ -0.2718∗∗ -0.2828∗∗ -0.3126∗∗ -0.2582∗∗ -0.2614∗∗
(0.0455) (0.0612) (0.0624) (0.0673) (0.0747) (0.0784)
High Leverage 0.0002 0.0001 0.0000 0.0035 0.0019 0.0024
(0.0014) (0.0016) (0.0016) (0.0028) (0.0030) (0.0033)
High Leverage × -0.1516∗∗ -0.1433∗∗ -0.1437∗∗ -0.1137∗ -0.1147+ -0.1236+
N on-perf orming Loans (0.0383) (0.0453) (0.0462) (0.0556) (0.0674) (0.0678)
Observations 53738 53738 53738 53548 53548 53548
R2 0.029 0.252 0.253 0.076 0.329 0.330
Panel 2: Construction
. Lending to Equity Ratio
Construction Equity 0.0021∗∗ 0.0030∗ 0.0020 0.0023∗ 0.0026 0.0017
(0.0007) (0.0013) (0.0014) (0.0011) (0.0019) (0.0023)
House P rice Growth -0.0232 0.0258
(0.0206) (0.0272)
.
Construction Equity × 0.0183 0.0182 0.0186 -0.0022 0.0334 0.0320
House P rice Growth (0.0141) (0.0217) (0.0217) (0.0174) (0.0222) (0.0221)
N on-perf orming Loans -0.0467 -0.0075 -0.0161 -0.0197 -0.0036 -0.0048
(0.0791) (0.1012) (0.1018) (0.1470) (0.1322) (0.1269)
Leverage -0.0003 -0.0004 -0.0004 0.0009 0.0004 0.0005
(0.0003) (0.0004) (0.0004) (0.0010) (0.0009) (0.0011)
Leverage × -0.0309∗∗ -0.0315∗∗ -0.0313∗∗ -0.0332∗∗ -0.0286∗ -0.0293∗∗
N on-perf orming Loans (0.0074) (0.0092) (0.0092) (0.0123) (0.0113) (0.0110)
Observations 67945 67945 67945 67739 67739 67739
R2 0.028 0.286 0.288 0.072 0.361 0.362
Quarter Fixed Effect Yes No No Yes No No
CBSA-Quarter Fixed Effect No Yes Yes No Yes Yes
Weighted No No No Yes Yes Yes
Standard errors in parentheses
+ p < 0.10, ∗ p < 0.05, ∗∗ p < .01
Notes: This table reports coefficients from regressions of quarterly bank holding company level commercial and
industrial lending growth on the interaction between bank exposure to construction loans and MSA house price
growth and the interaction between the bank non-performing loan rate and leverage. Construction exposure is the
ratio of bank construction lending to equity and leverage is the ratio of tangible assets to tangible equity. In first
panel, I use an indicator for whether construction exposure or leverage is above the median for the CBSA-quarter.
The second panel uses construction exposure and leverage in their continuous forms, winsorized at the 5% level.
Columns 1 & 4 include quarter fixed effects, while Columns 2,3,5 & 6 include CBSA-quarter fixed effects. Columns
1-3 are unweighted, while columns 4-6 weight by the size of the bank’s C&I loan portfolio in the previous quarter.
The sample runs from 2007 to 2010 and includes bank holding companies which hold over 50% of their deposits in one
CBSA or county, dropping observations in the highest or lowest percentile of lending growth. The weighted regressions
additionally drop banks 5 standard deviations above the mean in commercial lending. Standard errors are clustered
by CBSA/county. 33
Table 1.10: Housing Declines and Total Lending: By Leverage
(1) (2) (3) (4) (5) (6)
Panel 1: Indicator for High Leverage
High Leverage -0.00304∗∗ -0.00307∗∗ -0.00295∗∗ -0.00183+ -0.00241∗ -0.00217∗
(0.000558) (0.000630) (0.000679) (0.000936) (0.000953) (0.000966)
House P rice Growth 0.0404∗∗ 0.0328
(0.0130) (0.0298)
High Leverage × 0.0565∗∗ 0.0559∗∗ 0.0543∗∗ 0.0592∗∗ 0.0505∗∗ 0.0522∗∗
House P rice Growth (0.0116) (0.0130) (0.0128) (0.0201) (0.0193) (0.0201)
Observations 58872 58872 58872 58651 58651 58651
R2 0.079 0.321 0.324 0.136 0.393 0.397
Panel 2: Leverage
Leverage -0.00100∗∗ -0.00105∗∗ -0.00113∗∗ -0.000884∗∗ -0.000875∗∗ -0.000842∗∗
(0.000147) (0.000181) (0.000200) (0.000238) (0.000284) (0.000296)
House P rice Growth -0.139∗∗ -0.0972∗∗
(0.0295) (0.0350)
Leverage × 0.0198∗∗ 0.0149∗∗ 0.0144∗∗ 0.0152∗∗ 0.00956∗ 0.0103∗∗
House P rice Growth (0.00283) (0.00356) (0.00344) (0.00310) (0.00379) (0.00352)
Observations 69628 69628 69628 69402 69402 69402
R2 0.083 0.362 0.365 0.139 0.419 0.423
Quarter Fixed Effect Yes No No Yes No No
CBSA-Quarter Fixed Effect No Yes Yes No Yes Yes
Weighted No No No Yes Yes Yes
Standard errors in parentheses
+ p < 0.10, ∗ p < 0.05, ∗∗ p < .01
Notes: This table reports coefficients from regressions of quarterly bank level lending growth on bank leverage, CBSA
house price growth and their interaction. Leverage is the ratio of tangible assets to tangible equity. In first panel,
I use an indicator for whether leverage is above the median for the CBSA-quarter. The second panel uses leverage
in its continuous form, winsorized at the 5% level. Columns 1 & 4 include quarter fixed effects, while Columns 2,3,5
& 6 include CBSA-quarter fixed effects. Columns 1-3 are unweighted, while columns 4-6 weight by the size of the
bank’s C&I loan portfolio in the previous quarter. The sample runs from 2007 to 2010 and includes bank holding
companies which hold over 50% of their deposits in one CBSA or county, dropping observations in the highest or
lowest percentile of lending growth. The weighted regressions additionally drop banks 5 standard deviations above
the mean in commercial lending. Standard errors are clustered by CBSA/county.
34
Table 1.11: Housing Declines and Real Estate Lending: By Leverage
(1) (2) (3) (4) (5) (6)
Panel 1: Indicator for High Leverage
High Leverage -0.00220∗∗ -0.00226∗∗ -0.00198∗∗ -0.00262∗∗ -0.00291∗∗ -0.00260∗∗
(0.000614) (0.000695) (0.000735) (0.000923) (0.000929) (0.000974)
House P rice Growth 0.0424∗ 0.0551∗∗
(0.0165) (0.0187)
High Leverage × 0.0633∗∗ 0.0637∗∗ 0.0586∗∗ 0.0404∗∗ 0.0517∗∗ 0.0464∗∗
House P rice Growth (0.0132) (0.0148) (0.0142) (0.0140) (0.0176) (0.0173)
Observations 58422 58422 58422 58202 58202 58202
R2 0.061 0.277 0.288 0.128 0.370 0.383
Panel 2: Leverage
Leverage -0.000742∗∗ -0.000817∗∗ -0.000884∗∗ -0.00101∗∗ -0.00101∗∗ -0.00100∗∗
(0.000158) (0.000198) (0.000213) (0.000250) (0.000305) (0.000306)
House P rice Growth -0.155∗∗ -0.0678
(0.0317) (0.0532)
Leverage × 0.0220∗∗ 0.0183∗∗ 0.0168∗∗ 0.0131∗ 0.0110+ 0.00984+
House P rice Growth (0.00276) (0.00328) (0.00317) (0.00534) (0.00662) (0.00566)
Observations 69155 69155 69155 68928 68928 68928
R2 0.063 0.318 0.328 0.129 0.399 0.412
Quarter Fixed Effect Yes No No Yes No No
CBSA-Quarter Fixed Effect No Yes Yes No Yes Yes
Weighted No No No Yes Yes Yes
Standard errors in parentheses
+ p < 0.10, ∗ p < 0.05, ∗∗ p < .01
Notes: This table reports coefficients from regressions of the growth quarterly bank level real estate backed lending
on bank leverage, CBSA house price growth and their interaction. Leverage is the ratio of tangible assets to tangible
equity. In first panel, I use an indicator for whether leverage is above the median for the CBSA-quarter. The second
panel uses leverage in its continuous form, winsorized at the 5% level. Columns 1 & 4 include quarter fixed effects,
while Columns 2,3,5 & 6 include CBSA-quarter fixed effects. Columns 1-3 are unweighted, while columns 4-6 weight
by the size of the bank’s C&I loan portfolio in the previous quarter. The sample runs from 2007 to 2010 and includes
bank holding companies which hold over 50% of their deposits in one CBSA or county, dropping observations in the
highest or lowest percentile of lending growth. The weighted regressions additionally drop banks 5 standard deviations
above the mean in commercial lending. Standard errors are clustered by CBSA/county.
35
Table 1.12: Housing Declines and Construction Lending: By Leverage
(1) (2) (3) (4) (5) (6)
Panel 1: Indicator for High Leverage
High Leverage -0.0110∗∗ -0.0109∗∗ -0.00842∗∗ -0.00596∗ -0.00809∗∗ -0.0115∗∗
(0.00192) (0.00223) (0.00237) (0.00238) (0.00290) (0.00242)
House P rice Growth 0.303∗∗ 0.169∗∗
(0.0366) (0.0370)
High Leverage × 0.0857∗ 0.0871∗ 0.0818+ 0.0616+ 0.0782∗ 0.0714∗
House P rice Growth (0.0364) (0.0430) (0.0438) (0.0354) (0.0319) (0.0311)
Observations 53435 53435 53435 53182 53182 53182
R2 0.023 0.300 0.301 0.127 0.377 0.380
Panel 2: Leverage
Leverage -0.00360∗∗ -0.00286∗∗ -0.00220∗∗ -0.00215∗∗ -0.00240∗ -0.00354∗∗
(0.000477) (0.000605) (0.000670) (0.000749) (0.00119) (0.00108)
House P rice Growth -0.0268 0.122
(0.101) (0.104)
Leverage × 0.0354∗∗ 0.0309∗∗ 0.0295∗∗ 0.00792 0.0154+ 0.0136+
House P rice Growth (0.00914) (0.0106) (0.0108) (0.0101) (0.00888) (0.00784)
Observations 63374 63374 63374 63118 63118 63118
R2 0.024 0.347 0.348 0.121 0.405 0.408
Quarter Fixed Effect Yes No No Yes No No
CBSA-Quarter Fixed Effect No Yes Yes No Yes Yes
Weighted No No No Yes Yes Yes
Standard errors in parentheses
+ p < 0.10, ∗ p < 0.05, ∗∗ p < .01
Notes: This table reports coefficients from regressions of the growth quarterly bank level construction and land
development lending on bank leverage, CBSA house price growth and their interaction. Leverage is the ratio of
tangible assets to tangible equity. In first panel, I use an indicator for whether leverage is above the median for
the CBSA-quarter. The second panel uses leverage in its continuous form, winsorized at the 5% level. Columns
1 & 4 include quarter fixed effects, while Columns 2,3,5 & 6 include CBSA-quarter fixed effects. Columns 1-3 are
unweighted, while columns 4-6 weight by the size of the bank’s C&I loan portfolio in the previous quarter. The sample
runs from 2007 to 2010 and includes bank holding companies which hold over 50% of their deposits in one CBSA
or county, dropping observations in the highest or lowest percentile of lending growth. The weighted regressions
additionally drop banks 5 standard deviations above the mean in commercial lending. Standard errors are clustered
by CBSA/county.
36
Table 1.13: Housing Declines and CRE Lending: By Leverage
(1) (2) (3) (4) (5) (6)
Panel 1: Indicator for High Leverage
High Leverage 0.000342 0.000318 -0.000761 0.000187 0.0000452 -0.000235
(0.000913) (0.00104) (0.00105) (0.00115) (0.00128) (0.00121)
House P rice Growth -0.0226 0.0210
(0.0189) (0.0152)
High Leverage × 0.0539∗∗ 0.0566∗∗ 0.0514∗ 0.0418∗ 0.0492∗ 0.0412∗
House P rice Growth (0.0189) (0.0214) (0.0208) (0.0169) (0.0197) (0.0184)
Observations 57671 57671 57671 57321 57321 57321
R2 0.010 0.233 0.236 0.033 0.288 0.291
Panel 2: Leverage
Leverage -0.000114 -0.000185 -0.000647∗ -0.000644∗ -0.000373 -0.000555
(0.000206) (0.000293) (0.000293) (0.000324) (0.000394) (0.000374)
House P rice Growth -0.159∗∗ -0.0519
(0.0425) (0.0562)
Leverage × 0.0158∗∗ 0.0153∗∗ 0.0136∗∗ 0.00875+ 0.0130∗∗ 0.0108∗
House P rice Growth (0.00355) (0.00512) (0.00498) (0.00456) (0.00462) (0.00449)
Observations 68219 68219 68219 67855 67855 67855
R2 0.010 0.275 0.278 0.032 0.329 0.332
Quarter Fixed Effect Yes No No Yes No No
CBSA-Quarter Fixed Effect No Yes Yes No Yes Yes
Weighted No No No Yes Yes Yes
Standard errors in parentheses
+ p < 0.10, ∗ p < 0.05, ∗∗ p < .01
Notes: This table reports coefficients from regressions of the growth quarterly bank level non-farm non-residential
real estate backed lending on bank leverage, CBSA house price growth and their interaction. Leverage is the ratio
of tangible assets to tangible equity. In first panel, I use an indicator for whether leverage is above the median for
the CBSA-quarter. The second panel uses leverage in its continuous form, winsorized at the 5% level. Columns
1 & 4 include quarter fixed effects, while Columns 2,3,5 & 6 include CBSA-quarter fixed effects. Columns 1-3 are
unweighted, while columns 4-6 weight by the size of the bank’s C&I loan portfolio in the previous quarter. The sample
runs from 2007 to 2010 and includes bank holding companies which hold over 50% of their deposits in one CBSA
or county, dropping observations in the highest or lowest percentile of lending growth. The weighted regressions
additionally drop banks 5 standard deviations above the mean in commercial lending. Standard errors are clustered
by CBSA/county.
37
Chapter 2
Housing Bust, Bank Lending &
Employment: Evidence From
Multimarket Banks
2.1 Introduction
Prior to the financial crisis of 2008, house prices in the US dropped precipitously. During the ensuing
recession, employment dropped by over 8.5 million as banks drastically tightened lending standards.1
By the time real GDP per capita rebounded to its previous peak at the end of 2013, over 500 banks
had failed. This economic turmoil was predominantly concentrated in the regions that experienced
the greatest decline in house prices, raising the question of how real estate declines impacted the
supply of credit and the general economy.
The previous chapter established that banks which were exposed to falling house prices contracted
the supply of commercial credit. Given prior work demonstrating that contractions in the availability
of credit can impact labor markets (Ashcraft, 2005; Chodorow-Reich, 2014), this suggests that a
contraction in bank lending may have contributed to the strong relationship between real estate
declines and employment growth during the Great Recession. This chapter quantifies the effect that
declining bank health due to falling house prices had on employment during this period.
1 The Survey of Terms of Business Lending reports that the quarterly spread on commercial and industrial loans
over the intended federal funds rate varied between 3.05 and 3.49 in the two years following the failure of Lehman
Brothers. It had never previously exceeded 2.67 since the survey began in 1986. Banks responding to the Senior Loan
Officer Opinion Survey also reported an unprecedented tightening of lending standards during the recession (Bassett
et al., 2014).
38
The primary difficultly in isolating this effect is that falling house prices can impact the economy
through numerous other channels. One leading explanation is that declining house prices reduce
household wealth and the ability to borrow against home equity, thus reducing aggregate demand
and employment (Midrigan and Philippon, 2011; Mian et al., 2013; Mian and Sufi, 2014; Eggertsson
and Krugman, 2012; Guerrieri and Lorenzoni, 2015). Other explanations include real estate declines
impairing business collateral or prompting a sectoral shift out of construction.2
I disentangle the quantitative significance of the bank lending channel from these other channels
using variation in the exposure of multimarket banks to real estate declines in other counties. While
each theory predicts that declining house prices within a county will result in falling lending and
employment, they differ in predictions regarding the impact of declines elsewhere along the branch
network of locally operating banks. If adverse shocks matter through bank losses, then borrowers
located in strong real estate markets will still face a contraction in credit if their bank is exposed
to real estate declines elsewhere. On the other hand, if real estate shocks predominantly impact
demand, then outside declines should induce a substitution of credit towards local borrowers and
not be associated with adverse outcomes.
In short, I ask whether it was falling house prices locally which impacted the economy, or whether
it was instead the overall exposure of local banks to housing declines. This approach is enabled by
three characteristics of the banking sector. First, most bank lending is done by large multimarket
banks. Thus, the health of a typical borrower’s bank depends largely on conditions in other counties
where the bank has branches. Second, most small business lending is highly local. Thus, a reduction
in the supply of credit from a bank will predominantly affect counties in which the bank operates,
especially within a small window around its branches. Finally, asymmetric information problems
make switching banks costly. Consequently, the incidence of a shock to a bank will largely fall on
the bank’s existing customers instead of on the aggregate supply of credit. Taken together, these
characteristics imply that the availability of credit in a particular area will be impacted by conditions
in different and often distant markets where the nearby banks have other branches.
First, I demonstrate that banks which were exposed to falling house prices contracted credit.
Using county level small business lending data from the Community Reinvestment Act (CRA), I
estimate lending growth as a function of a bank’s exposure to falling house prices, and county controls
or fixed effects. A one standard deviation decline in bank health, i.e. a greater average decline in
housing wealth where the bank has branches, results in a 13.4% reduction in county originations of
2 See Chaney et al. (2012); Fairlie and Krashinsky (2012); Adelino et al. (2013); Fort et al. (2013); Mehrotra and
Sergeyev (2014) for evidence of the collateral channel and Hadi (2011); Rognlie et al. (2014); Hoffmann and Lemieux
(2014) for evidence of the construction channel.
39
small business loans. The magnitude of this estimate increases slightly when real estate controls
or county fixed effects are included. Banks which are exposed to real estate declines uniformly cut
lending throughout their network, not in the actual counties experiencing falling house prices. This
is consistent with real estate declines impacting the supply of credit instead of demand.
Second, and most importantly, I show that this contraction in lending caused a decline in em-
ployment. A one standard deviation increase in the exposure of local banks to negative housing
wealth shocks, controlling for the shock in the county, decreases total employment by 1.2% between
2007 and 2010. Although changes in housing wealth do directly relate to employment growth, bank
exposure is found to a more important driver of employment declines.
Given that bank health impacts the availability of mortgage credit (Berrospide et al., 2013) and
mortgage credit impacts house price growth (Loutskina and Strahan, 2015), falling house prices
locally might be a consequence of bank distress. This would make controlling for the local growth in
house prices overly conservative. To overcome this, I next use an alternative method to separate the
bank lending channel from the direct effects of house price declines which doesn’t require the use of
the endogenous control. Specifically, I instrument for the health of local banks using only exposure
to real estate declines in other markets. When I instrument for bank health using the exposure of
locally operating banks to real estate shocks in different CBSAs, I find that a one standard deviation
decline in bank health reduces employment by 2%.3 Similar results are found when instrumenting
for bank health using only the exposure to shocks in geographically distant counties.
The IV estimate of the effect of bank health on employment growth is virtually identical to the
corresponding OLS estimate: a 2.0% employment response to the one standard deviation shock
for IV vs. 2.1% for OLS. Thus the relationship between bank health and employment growth is
not driven by local house price movements. Instead, it is the total multimarket exposure of the
local banks which relates to employment growth, with falling house prices elsewhere along the
branch network having a virtually symmetric effect on employment growth as declines in the actual
county. Furthermore, the relationship between bank health and employment growth is not driven
by spillovers from neighboring counties, as declines in geographically distant counties or in other
CBSAs also impact local employment growth. This is consistent with an adverse shock to a bank
in one area propagating through the bank’s internal capital market and uniformly impacting the
availability of credit anywhere the bank operates.
Finally, I use two sources of within county variation to provide further support for the proposed
mechanism. First, employment losses are concentrated in firms most reliant on bank finance. Some
3A CBSA contains an urban center of at least 10,000 people and adjacent areas with strong commuting ties.
40
industries are more prone to rely on external finance for technological reasons reflecting, for example,
the degree of economies of scale or length of product gestation periods (Rajan and Zingales, 1998). I
show that employment growth in industries with a high dependence on external finance is over 50%
more sensitive to local bank health. Similarly, as young firms have had little opportunity to retain
earnings and have minimal access to direct finance, they tend to rely more on bank lending (Black
and Strahan, 2002; Cetorelli and Strahan, 2006; Robb and Robinson, 2012). I show that firms under
six years old are over twice as sensitive to bank health as mature firms.
Second, employment losses disproportionately occur in tracts close to unhealthy banks. Since
small business lending is highly local and switching lenders is costly, differential shocks to the health
of local banks creates fine spatial variation in the availability of credit. A one standard deviation
decline in the health of the banks within 3km of a tract decreases employment by 1.7% relative to
other tracts in the county. As the county fixed effect should account for local demand or any other
county level confounder, this provides strong evidence that bank losses from real estate declines
result in a contraction in bank lending and harm to the general economy.
Overall, my findings suggest that these effects are economically large. Even conservative esti-
mates have declining bank health from exposure to real estate shocks explaining roughly half of the
relationship between housing net worth shocks and employment declines, and 39% of the decline in
employment from 2007 to 2010.
2.1.1 Relation to Previous Literature
This paper contributes to several strands of the macroeconomics and finance literature. I add to
a vast literature identifying the degree to which shocks to the supply of bank credit impact the
economy.4 Additionally, I contribute to work studying the transmission of shocks through the
internal capital markets of geographically dispersed banks.5 This paper also complements work on
the roles of relationships and distance in the provision of bank credit, as the highly localized effects
I find require either high switching costs or difficulty obtaining a loan from distant banks.6
First and foremost, this paper fits into the literature studying the effect of credit market frictions
on employment in the Great Recession. Existing work provides mixed evidence regarding the channel
4 Gertler and Gilchrist (1994); Peek et al. (2003); Ashcraft (2005); Bassett et al. (2014) find that bank credit supply
shocks impact employment or investment, Driscoll (2004); Ashcraft (2006); Jim´ enez et al. (2014) on the other hand
find no real effects.
5 Peek and Rosengren (2000); Chava and Purnanandam (2011); Cetorelli and Goldberg (2012) for example study
spillovers through multinational banks, while Morgan et al. (2004); Huang and Stephens (2011); Berrospide et al.
(2013); Bord et al. (2014) focus on the United States.
6 Slovin et al. (1993); Khwaja and Mian (2008); Chodorow-Reich (2014) demonstrate adverse outcomes for clients
of banks who experienced a negative shock. Kwast et al. (1997); Petersen and Rajan (2002); Brevoort and Hannan
(2006); Brevoort and Wolken (2009) find that small businesses rely on nearby banks.
41
emphasized in this paper. Chodorow-Reich (2014) finds that a contraction in bank lending can
explain roughly 40% of the decline in employment in small and medium sized firms in the year
following Lehman’s bankruptcy. However, Greenstone et al. (2014) find that the contraction in small
business lending explains less than 3% of the decline in small business employment. Regarding the
role of falling house prices, Huang and Stephens (2011); Bord et al. (2014) find that banks exposed
to real estate declines reduced small business lending. On the other hand, Mian and Sufi (2014)
find that the employment losses in counties with falling house prices were attributable to falling
aggregate demand instead of credit supply. As my paper relates closely to each of these studies, I
will briefly describe each one in turn.7
My approaches to identifying supply shocks closely follows Huang and Stephens (2011) and
Bord et al. (2014). They similarly create bank level measures of exposure to real estate declines, and
demonstrate that highly exposed banks cut small business lending relative to other banks operating in
the same area. My primary contribution is to assess how the supply contraction impacts employment.
As these papers largely rely on area fixed effects to account for local loan demand, they have more
to say about the composition of which banks lend in the county than on aggregate impacts.
The findings of this paper are most similar to Chodorow-Reich (2014). He finds that the large
contraction in syndicated lending documented by Ivashina and Scharfstein (2010) translates into
employment declines in firms with a prior relationship to banks which contracted credit. He takes
advantage of impressive data on firm-lender relationships to document that banking relationships are
sticky and that shocks to banks impact their customers. However, he acknowledges two difficulties
in the identification of aggregate effects. First, there is a concern about external validity. The credit
contraction only significantly influenced employment in firms with fewer than 1000 employees, a
category severely underrepresented in the syndicated loan data.8 If the rare small firm borrowing in
the syndicated loan market is more financially dependent than the norm, the aggregate employment
effects could be overstated. Second, employment losses at the firm level are difficult to relate to
losses in aggregate without precise knowledge of the extent of general equilibrium effects. Wage or
price adjustments may influence the quantity of labor demanded by unconstrained firms and offset
much of the decline in employment due to a shock to a particular firm.
7 This review is far from exhaustive. Additionally, Almeida et al. (2012) and Duchin et al. (2010) find evidence of
financial constraints among Compustat firms, while Kahle and Stulz (2013) does not. Campello et al. (2010) uses a
survey approach to identify real effects of financial constraints. Duygan-Bump et al. (2015) find that workers in small
firms and financially dependent industries were most likely to become unemployed. Goetz and Gozzi (2010) show
MSAs with banks relying on wholesale funding experienced larger employment declines.
8 His data includes 798 large firms (1000+ employees) and 1242 small/medium firms (<1000 employees). Based
on the 2008 Business Dynamics Statistics, there were 10,839 large firms in the US versus 5.2 million small/medium
firms.
42
Using county level data in my study lessens these concerns. The employment data includes
almost every firm in the county, and the bank data includes every bank or savings and loan with a
branch in the county, so external validity is no longer a problem. Additionally, using the county as
the unit of observation means that general equilibrium effects felt within the county are incorporated
into the aggregate effects. If a person is laid off by a constrained firm, but hired by another firm in
the same county, this would not appear as a decline in employment in my data.9
The estimating equations in this paper closely follow Mian and Sufi (2014), who demonstrate
that non-tradable employment falls in counties experiencing adverse real estate shocks. They argue
that the lack of an employment response in tradable industries indicates that the employment losses
are due to low aggregate demand stemming from the loss in housing wealth. My empirical approach
separates the employment losses attributable to the decline in bank health from the direct effects
of local house price declines. I find that much of the decline in employment is due to differences in
bank exposure instead of directly due to local house price movements.
I do find that bank exposure disproportionately impacts non-tradable employment, however this
can be reconciled with a credit supply shock in a couple ways. First, unhealthy banks may cut
consumer credit and thus reduce local aggregate demand. This is supported by Gropp et al. (2014),
who find that renters reduced their levels of debt in places with falling house prices, suggesting
that the decline in credit operated through channels besides household wealth. Second, the general
equilibrium effects of a shock to business credit would cause a shift of employment into tradable
industries. A credit contraction reduces the demand for labor (Benmelech et al., 2011), then falling
wages and employment decrease demand for local non-traded goods. In the appendix, I demonstrate
that a contraction in credit has an ambiguous effect on tradable employment due to wage adjust-
ments. In support of this explanation, I show in the robustness section that counties with exposed
banks experience declines in the cost of unskilled labor.
Finally, Greenstone et al. (2014) investigate the role of small business lending in explaining the
contraction in employment. They identify shocks to the supply of small business lending using the
CRA data, and argue that the reduction in small business lending can only account for a small
fraction of the overall decline in employment.10 Like this paper, they test how the average health
9 This does not completely address the concern about general equilibrium effects. Falling incomes locally will reduce
the demand for traded goods elsewhere, and rising prices of constrained firms would increase demand for competing
firms. However, these effects are likely less concerning than within county wage adjustments offsetting a reduction in
a firm’s employment.
10 Specifically, they use the CRA data to estimate bank×county level loan growth using county and bank fixed
effects, taking the bank fixed effect to be a bank level supply shock. They instrument for small business lending with
the average supply shock of the banks operating in the county, and find that the elasticity of employment growth with
respect to lending is too small to have played a large role in the overall losses during the Great Recession.
43
of the banks in a county impact employment. However, they rely on a sample of large banks11 and
a narrow type of bank lending: loans to firms with less than $1 million in revenue which aren’t
backed by real estate. However, small banks disproportionately lend to small firms and are more
prone to rely on soft information in lending decisions (Berger and Udell, 2002; Berger et al., 2005).
As a result, this sample excludes many lending relationships, and particularly ones for which the
adverse selection problem in switching banks would be most severe. Furthermore, bank losses could
impact mortgage lending, other lending to individuals, commercial lending backed by real estate and
lending to larger firms, none of which are included in their data. My county level measure of bank
health incorporates the condition of every bank or savings and loan which locates within a county,
and more generally reflects the availability of credit for any type of lending which occurs through
bank branches. This broader scope may explain why I find much larger effects of bank distress than
Greenstone et al. (2014).
The remainder of the paper proceeds as follows: Section 2 describes the data. Section 3 discusses
the empirical strategies. Section 4 presents the findings. Section 5 discusses robustness with regard
to alternative outcome variables, bank health measures and controls. Section 6 concludes.
2.2 Data Sources
2.2.1 Real Estate Shock Variables
I follow Mian and Sufi (2014) and measure real estate distress using the change in household net
worth due to house price movements. Their primary explanatory variable is:
Hc,2006
∆N etW orthc = ∆ln(HouseP rice)c,06−09
N Wc,2006
Where ∆ln(HouseP rice)c,06−09 is house price growth from Core Logic, Hc,2006 is housing wealth
and N Wc,2006 is the net worth of residents in the county.12
This variable estimates the loss in home equity for residents of a county as a fraction of their
net worth. More leverage means a given decline in house prices has a larger wealth effect. However,
since the majority of household wealth is held in housing (Iacoviello, 2011), high leverage is also
11 In their sample, 654 banks operate at some point from 1997-2010, with the number being smaller during the crisis
due to a change in reporting thresholds in 2005.
12 Housing wealth is imputed from 2000 census data on the housing stock and subsequent house price growth, while
the net worth of residents is the sum of housing wealth and financial wealth less debt. Stock and bond holdings are
imputed based on IRS Statistics of Income data on stock and bond income combined with market returns. Debt data
come from Equifax. See Mian et al. (2013) for more details. This variable was downloaded from the replication files
available on the Econometrica website.
44
indicative of high loan to value ratios on the mortgages in the county. Consequently, banks in areas
where housing net worth declines steeply would experience greater loan losses.
My measure of bank distress is based on the degree to which a bank locates in counties with
declining housing net worth. I use deposit and branch location data from the FDIC’s Summary
of Deposits (SOD) to determine a bank’s exposure to a particular county.13 A bank is deemed
unhealthy if it holds deposits in weak real estate markets.14 A county is deemed unhealthy if local
deposits are in unhealthy banks. Specifically:
X Db
c
Bank Healthb = ∆N etW orthc
Db
c∈C
X Db
c
Bank Health Cntyc = Bank Healthb
Dc
b∈B
Where Dcb is the deposits held by bank b in county c and Db and Dc are the aggregate deposits
in the bank and the county respectively. Deposits are as of June 30th, 2006 for the main analysis,
although I additionally use bank health based on 2002 deposits as a robustness check. I additionally
try weighting by the value of 2006 mortgage originations and get similar results.
For the tract level employment regressions, I additionally create a variable calculating the health
of the banks within a certain distance of a tract:
X
Bank Health T ractt (d) = ωc,b (d)Bank Healthb
b∈B
where ωc,b (d) is the share of deposits within dkms of tract t’s centroid held in branches of b.15
2.2.2 Lending Growth
I use Community Reinvestment Act data for county level small business lending. This includes
business loan originations under $1 million in value originated by banks with over $1 billion in
assets in 2005 dollars. The variable of interest is growth in originations during the crisis (2008-2010)
relative to the pre-crisis period (2004-2006). I drop county×bank observations if the bank acquired
another bank in the same CBSA during the period of observation, if it is in the highest or lowest
13 The SOD is an annual survey of branch offices for all FDIC-insured institutions, including commercial banks,
thrifts, and US branches of foreign banks.
14 Banks which are part of a multibank holding company are aggregated to the level of the regulatory high holder.
Houston et al. (1997) shows that subsidiary loan growth depends more on the cash flows and capital of the holding
company than of the subsidiary, making the holding company the proper unit of analysis.
15 I use the ARCGIS10 North American Address Locater to geocode the location for each bank branch in order to
calculate the distances to census tracts.
45
percentile of lending growth or if the bank didn’t have a branch within the county in 2006.
2.2.3 Labor Market Outcomes
County×industry employment data comes from the County Business Patterns (CBP). CBP provides
mid-march employment at the county level for NAICS industries constructed from the US Census
Bureau’s Business Register. I use the Mian and Sufi (2014) classification of 4-digit NAICS industries
as tradable or non-tradable.16 I follow Rajan and Zingales (1998) and measure financial dependence
based on the ability of firms in the industry to fund capital expenditures with cash flows. An
industry is classified as financially dependent if the median Compustat firm in the 3-digit industry
had capital expenditures exceeding cash flows from operations.17 Total employment for a given
category (e.g. non-tradable or financially dependent) is calculated by aggregating the employment
across the industries within a category.18 The dependent variable of interest is employment growth
from 2007 to 2010, either overall or for a particular set of industries.
I use Quarterly Workforce Indicators (QWI) for data disaggregated by firm age or worker edu-
cation. QWI combines data from Unemployment Insurance matched with employer characteristics
from the Business Dynamic Statistics. The quarterly employment growth in a given age category is
the difference in the log of stable employment from the previous quarter.19 Growth from 2007 to
2010 is the sum of the quarterly growth rates from Q2 2007 to Q1 2010.20 Young firms are defined
as firms which are five years old or younger and mature firms are older than five.
Additionally, I use QWI data on earnings and employment by education category to measure
wage growth. The wage for a particular type of worker (e.g. college educated workers) is the total
earnings of stable employees divided by the number of stable employees. I then calculate wage
growth from 2007 to 2010.21
Tract employment comes from the Longitudinal Employer-Household Dynamics Origin-Destination
Employment Statistics (LODES) Workplace Area Characteristics. LODES provides employment at
the 2-digit NAICS level for 2010 census blocks, omitting Massachusetts and DC. Data are con-
16 Restaurant and retail industries are classified as non-tradable, industries prominent in the world trade data are
classified as tradable.
17 Cash flows and capital expenditures come from Compustat data on U.S. firms in the 1990s. This was used in
Coleman and Feler (2014). Thanks to Nick Coleman for sharing this with me.
18 Some county×industry observations have data suppressed and instead provide a range containing the employment
value. Following Mian and Sufi (2014), I take the employment to be the midpoint of the range.
19 Stable employment in a given quarter is the number of jobs where the employee was employed at the beginning
and end of the quarter.
20 One cannot simply look at changes in employment by age group between 2007 and 2010 as firms will change age
categories in that period.
21 People with low education disproportionately lost their jobs in areas with unhealthy banks. The increased
share of employment in highly educated people masks changes in average earnings in the county. Consequently, the
disaggregation by education type is needed to demonstrate how the cost of labor changed.
46
structed from state unemployment insurance records which are combined with Quarterly Census
of Employment and Wages data. I aggregate this to the 2010 census tract level. Growth is again
measured between 2007 and 2010.
Table 1 presents summary statistics of the main variables of interest. The average county ex-
periences a decline in housing net worth of 9.5% from 2006 to 2009 with a standard deviation of
10%. The average decline in Bank Health Cnty is similar at 8.4%, but less variable with a standard
deviation of 4.2%. Banks in places with severely falling house prices tend to have other branches in
stronger markets, mitigating some of the damage from local real estate losses. However, there is still
far from perfect integration, creating cross county variation in the health of banks. Employment
dropped by 7.7% on average from March 2007 to March 2010.
2.3 Empirical Strategy
The goal of this paper is to demonstrate two things: First, banks which were exposed to real estate
declines contracted credit. Second, this credit contraction resulted in employment declines in areas
reliant on exposed banks.
The primary difficulty is that bank exposure is likely correlated with other variables impacting
the demand for credit or demand for labor. In this section, I describe the empirical approaches to
control for these demand side effects. In the appendix, I present a simple model of bank credit and
employment to assist in the interpretation of the regression coefficients from the different approaches.
2.3.1 Impact of Real Estate Exposure on Small Business Lending
While the last chapter focused on smaller, predominantly single market banks, using cross sectional
differences across the banks locating in the same area, here I focus on larger multimarket banks. I
again separate supply from demand by comparing banks lending in the same area. However, now
I exploit geographic variation in the degree of exposure to house price declines elsewhere along the
bank’s branch network as in Huang and Stephens (2011) and Bord et al. (2014). I estimate the
equation:
∆ln(SB Loans)b,c = β1 Bank Healthb + βx0 Xb,c + δc + b,c
The dependent variable is the growth in the number of small business loans the bank originated in
the county during the crisis (2008-20110) relative to before the crisis (2004-2006). As the county
fixed effect δc should account for demand, the coefficient on Bank Health should reflect the effect
47
that bank exposure to real estate declines has on the supply of credit. The hypothesis of this paper
is that banks which were exposed to real estate declines reduced lending, meaning that β1 > 0.
The difference in the estimated effect of bank health in the fixed effect and simple regression
omitting δc will be informative as to the degree to which the relationship between bank health and
lending growth is driven by loan demand. If county lending growth has a supply and a demand
component: ∆ln(SB Loans)b,c = dc + sb + b,c , then the difference between the estimate in the fixed
cov(Bank Health,dc )
effect and simple regression should be var(Bank Health) . If Bank Health reflects the supply of
credit, then the fixed effect and simple regression should be similar. If Bank Health mostly reflects
demand, then including the fixed effect should reduce the point estimate.
Similarly I assess how the real estate shock variables impact the demand for loans by estimating:
∆ln(SB Loans)b,c = β1 Bank Health Cntyc + β2 ∆N etW orthc + βx0 Xb,c + δb + b,c
The bank fixed effect δb accounts for bank level supply shocks, thus the coefficients reflect how
local real estate movements and changes in the health of local banks impact the demand for credit
from a particular bank. The hypothesis of this paper is that the health of the banks in the county
impacts the supply of credit. If this is true, we would expect β1 < 0. Namely, a contraction in
lending among other banks in the county would increase the demand for credit for a particular
bank, causing it to increase lending relative to other areas in which it operates. Conversely, if high
values of Bank Health Cnty reflected some omitted variable positively related to demand, then
individual banks should increase lending when competitors are measured as healthy, making β1 > 0.
2.3.2 Impact of Real Estate Exposure on Employment
Employment Approach 1: OLS, County Controls
The simplest approach to identifying the employment effect of bank losses from real estate exposure
is to estimate the equation:
∆ln(Emp)i,c = βB Bank Health Cntyc + βH ∆N etW orthc + βx0 Xc + i,c (2.1)
Where ln(Emp)i,c is the employment for a given category i (total, non-tradable, or tradable), and
Xc is a set of variables giving the share of 2006 employment in each 2 digit NAICS industry.
The assumption underlying this approach is that the control for ∆N etW orth adequately accounts
for direct demand side effects of declining house prices so that βB reflects the employment effects
48
of a credit contraction, instead of a direct effect of falling house prices. The bank lending channel
would predict that βB > 0, meaning that counties with healthier banks perform better than counties
with similar house price shocks, but local banks more exposed to other distressed counties.
There are two major flaws in this strategy. First, real estate shocks are not exogenous. Falling
house prices could be a result of decreased mortgage lending from unhealthy banks, in which case
I would be over-controlling.22 Moreover, omitted variables impacting employment could correlate
with house price movements and bias the regression coefficients.23 For these reasons, the inclusion
of the control could bias βB downward. Second, the effects of low bank health could reflect spillovers
from declining wealth in other areas instead of declining lending. For example, people in adjoining
counties might reduce spending in local restaurants if they experience a negative wealth shock. This
could create an upward bias in βB . The next approach overcomes these concerns.
Employment Approach 2: Instrument with Exposure from Distant Markets
A similar strategy is to instrument for bank health using the exposure of local banks to house price
declines in other markets. This has two major benefits. First, this allows me to remove the effects of
own county house price declines without introducing an endogenous control. Thus the IV approach
without the control may fix a downward bias in the estimated coefficient on bank health in the OLS
specification including the control.
Second, this approach reduces concerns about bank health reflecting spillovers from neighboring
counties. Falling house prices in other MSAs or hundreds of miles away are unlikely to directly affect
local demand as nearby declines would, thus any relationship between outside bank exposure and
local non-tradable employment can more comfortably to attributed to the bank lending channel.
Thus, I run IV regressions:
Healthc + βx0 Xc + i,c
Second Stage: ∆ln(Emp)i,c = βB Bankd
(2.2)
Healthi,c = ρB Outside Shockc + ρ0x Xc + ui,c
First Stage: Bankd
The outside shock is the component of bank health coming from the exposure of local banks to real
22 Loutskina and Strahan (2015) find that an expansion in mortgage lending due to exogenous changes in the ease
of GSE securitization results in greater house price growth and employment growth.
23 Contrary to this, Mian and Sufi (2014) find larger estimated effects when they instrument for changes in housing
net worth with the Saiz (2010) measure of the housing supply elasticity. However, the elasticity measure is highly
spatially correlated and only available at the MSA level. As a result, the instrument actually better correlates with
Bank Health Cnty than ∆N etW orth. This may contribute to why Mian and Sufi (2014) find larger point estimates
in their IV specifications.
49
estate shocks in other markets:
X X Dcb Dcb0
Outside Shockc = ∆N etW orthc0
Dc Db
b∈B c0 ∈{C\m(c)}
where m(c) denotes the set of counties in the same market as c. I define the market as the county,
the 2010 Core Based Statistical Area/rural county (CBSA) or the set of counties within a certain
distance of c.
If the coefficient on bank health merely reflects spillovers from neighboring counties, then exclud-
ing variation from nearby counties should remove the effect of bank health. If real estate declines
matter though changes in bank equity, then it doesn’t matter whether the bank is exposed to de-
clines locally or in distant branches as the effect on the aggregate effect on the bank balance sheet
would be symmetric. In the extreme case with no direct demand effects, the OLS and IV estimates
should be the same.
Employment Approach 3: Difference in Differences by Bank Dependence
Once it is established that declining bank health results in employment losses, a natural follow-up
question is to what extent are the employment losses due to a contraction in commercial credit
versus consumer credit. One way to assess the role of declining business credit, is to test whether
bank dependent firms reduce employment more in areas with unhealthy banks.
I use two definitions of bank dependence. First, I use the industry based classification of Rajan
and Zingales (1998). They measure financial dependence based on the cash flows and capital ex-
penditures of the firms in the industries. Being in an industry in which the majority of the firms
were unable to finance their capital expenditure with cash flows is taken to be an indicator of a
technological reliance on external finance.
Second, I use an age based classification. Young firms have had little time to accumulate and
retain earnings. They also generally lack the transparency or size to make direct finance feasible.
As a result they are more reliant on banks than mature firms.
I estimate equations as in the previous two sections and compare the coefficient for employment
growth in the high dependence and low dependence category. If the relationship between growth and
bank health solely reflects omitted demand related variables or a decline in consumer credit, there
would be little reason for them to impact financially dependent firms or young firms more. However,
as these firms are more reliant on banks, a greater sensitivity of employment to bank health would
support the claim that declining commercial credit contributed to the employment losses.
50
Employment Approach 4: Tract Growth, County Fixed Effects
Lastly, I exploit within county variation in proximity to exposed banks. A wide range of studies have
demonstrated that small business lending disproportionately occurs within a couple miles of a bank’s
branches (Kwast et al., 1997; Petersen and Rajan, 2002; Brevoort and Hannan, 2006; Brevoort and
Wolken, 2009). These findings are replicated in Figure 1, which plots the distribution of distances
between small businesses and their lender using data from the Survey of Small Business Finance
and Community Reinvestment Act.
If either switching lenders is costly as in Sharpe (1990) or Chodorow-Reich (2014), or the reliance
on local banks reflects a spatial friction in lending as in Degryse and Ongena (2005) or Agarwal and
Hauswald (2010), then this would mean that an adverse shock to local banks would impact nearby
firms. In the first case, the localized nature of banking is a pure equilibrium outcome, but proximity
proxies for relationships. A shock to a bank would impact it’s existing customers due to the adverse
selection problem in switching lenders, and would thus disproportionately impact the firms near
their branches as they are most likely to have a relationship with that bank. In the second case,
distant firms are a poor substitute for near firms due to an actual advantage of proximity. For
example, if there is location specific soft information, only local lenders may be willing to make a
particular loan. If these lenders are contracting credit, it would be difficult to obtain a loan even
for new firms without an existing relationship. Either way, this spatial variation in the ability of
local banks to lend generates within county variation in the availability of credit. This allows me to
estimate:
∆ln(Emp)t,c = βB Bank Health T ractt (d) + βx0 Xt + δc + t
where t indexes census tracts, Bank Health T ract(d) is the average health of the branches within
dkms of t, δc is a county fixed effect and Xt is a set of variables giving employment shares by 2-digit
NAICS industries. By comparing growth in tracts within the same county, I can remove any county
level demand effects and be more confident that the findings are due to a contraction in bank credit.
Additionally I include an interaction with an indicator for whether the tract disproportionately
has employment in small establishments (establishments with fewer than 100 workers).24 Although
establishment size is an imperfect measure of financial dependence, having small establishments is a
necessary condition for having small firms. As such, these tracts should have an increased sensitivity
24 Zip Code Business Patterns provides counts of establishments by size bin and industry. I can thus estimate the
amount of employment in small establishments and large establishments at the zip code × 2-digit industry level using
the midpoint of the size range. I then allocate this to the tract level using the 2006 two digit block level employment
data from LODES as a cross-walk between zip codes and census tracts.
51
to the health of nearby banks.
2.4 Findings
2.4.1 Exposed Banks Cut Small Business Lending
Table 2 estimates the impact of Bank Health on county level small business lending growth. A
10% shock to a bank causes it to reduce small business loan originations by 19%. When controls for
county shocks to net worth or county fixed effects are included, the predicted effect increases to 21%
or 22%. If real estate shocks impacted the demand for loans, then the reduction in lending would be
concentrated in the actual counties experiencing the shock. The fact that exposed banks uniformly
cut lending throughout their branch network indicates that real estate shocks predominantly impact
the supply of credit.
That controlling for demand has such a minimal effect on the relationship between Bank Health
and lending may seem anomalous, but specifications (5) and (6) provide an explanation of what is
happening. The inclusion of bank fixed effects account for credit supply shocks, allowing one to
observe how county level variables influence loan demand. Counties with declining net worth see
demand increase, albeit insignificantly. However, once the average health of the banks in the county
are accounted for, the coefficient on ∆N etW orth switches signs. Banks cut lending where house
prices fall, but they allocate significantly more credit to places where the other banks are unhealthy.
A 10% decline in the health of the banks in the county, controlling for ∆N etW orth, results in an
11% increase in loan originations relative to other markets the bank lends in. This suggests that
demand doesn’t fall in places with declining net worth because of a contraction in the supply of
credit of competitors.
This last point merits elaboration as it will support the findings in the employment section.
Conditional on ∆N etW orth, banks increase lending in counties with unhealthy banks, indicating an
elevated return to lending. This increase in demand could be due to a contraction in the supply of
loans from competing banks, or an increase in the overall demand for credit among small businesses.
Given that firms in these counties are laying off workers, and exposed banks are cutting lending, it
is unlikely that overall demand is rising. Consequently, this finding substantiates the claim that the
exposure of local banks to real estate declines adversely impacts the supply of bank credit.
52
2.4.2 Bank Exposure Causes Employment Declines
Having now informally established the “first stage” whereby the health of locally operating banks
impacts the supply of credit (conditional on county real estate conditions), the remainder of this
paper will be devoted to determining the labor market effects of this decline in bank health.
Employment Approach 1: OLS, County Controls
Table 3 directly estimates the relationship between employment growth and the real estate shock
variables as in equation 1. Adverse shocks to the net worth of households and to the health of
local banks are associated with declines in total employment and employment in the non-tradable
sector. When the variables of interest are included separately, a one standard deviation decline
in Bank Health Cnty (.042) reduces total employment by 2.1% and non-tradable employment by
2.7%. A one standard deviation decline in ∆N etW orth (.1) reduces overall employment by 2% and
non-tradable employment by 2.1%.
The more informative regressions are those in columns (3) and (6), which include both variables.
A one standard deviation decrease in Bank Health Cnty, controlling for ∆N etW orth, reduces
employment by 1.2% and non-tradable employment by 2.7%. Bank Health Cnty remains significant
in both specifications.
Combined with the finding of the previous section, we have that counties with exposed banks
(conditional on ∆N etW orth) see increased lending from large healthy banks but experience a con-
traction in employment. This indicates a high return to lending despite poor economic conditions,
consistent with a negative shock to the supply of bank credit.
Tradable employment is not significantly related to either variable. As Mian and Sufi (2014)
note, the fact that I only find effects for non-tradable industries, i.e. industries reliant on local
demand, could be suggestive that employment declines are due to low aggregate demand. However,
as falling wages or employment from a credit disruption also would influence local demand, this
doesn’t rule out a shock to the supply of bank credity. In the robustness section, I present evidence
that the cost of labor fell in counties with unhealthy banks, a factor which could offset the effects
of a contraction in credit for tradable firms.
Employment Approach 2: Instrument with Exposure from Distant Markets
In Table 4, I instrument for Bank Health Cnty using only bank exposure to real estate declines in
different counties or CBSAs. Panel 1 presents the findings omitting the control for ∆N etW orth.
53
Interestingly, the estimated effects of a shock to bank health on overall or non-tradable employment
are virtually identical whether I instrument or not. The elasticity of employment growth with
respect to bank health only falls from .5 in the OLS specification to .47 in the IV specifications. For
non-tradable employment growth, the elasticity is .63 in the OLS specification and .62 or .66 in the
IV specifications. These results show that the relationship between bank health and employment
growth is not driven by falling house prices within the actual county, nor is it driven by falling house
prices in economically integrated areas. As individuals living in other MSAs account for a small
portion of the demand for local non-traded goods, this should increase confidence that the results
are actually driven by bank lending instead of aggregate demand.
Accounting for direct effects of house price declines by excluding declines within the same market
may result in a better estimate than the previous approach. A contraction in the supply of bank
credit due to real estate losses would be likely to particularly impact mortgage availability, as banks
would be unwilling to take on additional real estate risk. This could make real estate declines in
part a consequence of poor bank health so that controlling for it would result in an understatement
the true effects. However, due to spatial correlation in house price movements and bank locations,
the outside exposure of local banks is likely to still be correlated ∆N etW orth for reasons beyond
the health of local banks impacting house prices.
Panel 2 additionally controls for ∆N etW orth to address this concern. Again, point estimates
for the effects on overall and non-tradable employment are nearly identical in the OLS and the
two IV specifications. Instrumenting for Bank Health Cnty, causes the elasticity of employment
growth with respect to bank health decline from .28 in the OLS specification to .27 in both the
IV specification. For non-tradable employment growth, the estimates are .60 and .72 in the IV
specifications compared to .64 in the OLS specification. Bank Health Cnty is significant at the
5% or 10% level for overall employment growth and significant at the 1% level for non-tradable
employment growth, while ∆N etW orth remains insignificant. Both real estate shock variables
remain insignificant in the tradable employment specifications.
Figure 2 presents a similar analysis using distance based market definitions. For each distance
up to 300kms, I calculate the component of bank health coming from counties more than that
distance from the county in question, and plot the coefficient on bank health in the employment
regression. In the total employment regressions, the estimate falls from .5 in the OLS specification
to .37 when instrumenting for bank health with exposure from counties over 300 km away. The
estimate when ∆N etW orth is controlled for drops from .28 to .18, although estimates quickly
become imprecise. Point estimates for non-tradable employment are remarkably stable. Without
54
the control, the estimated effect of bank health rises from .63 to .65, with the control it rises from
.64 to .68.
Overall, this approach demonstrates that adverse shocks to banks have a roughly symmetric
effect on employment whether they occur locally or whether they occur in distant counties with
common banks. This is consistent with real estate declines causing a uniform contraction in lending
through out a bank’s branch network, but contrary to the demand channels where only local house
price declines should impact employment.
Employment Approach 3: Difference in Differences by Bank Dependence
Table 5 shows that these effects are magnified in firms in financially dependent industries. The
difference in the elasticity of growth with respect to bank health between highly and lowly dependent
industries is .23 in the IV regression using out of county exposure, .27 in the OLS specification, and
.33 with the outside CBSA instrument. Differences are significant at the 5% or 10% level. In the
OLS specification, a one standard deviation decline in bank health reduces employment by 1.6% in
low dependence industries but 2.8% in high dependence industries.
In the second panel, I control for ∆N etW orth. Bank health is always significant for growth
in highly dependent industries. The difference in elasticities is still positive in every specification,
although it is no longer statistically significant. The estimated effect of local net worth shocks is
always small and statistically insignificant.
A concern with the Rajan and Zingales (1998) definition of financial dependence is that it is
based on the financing needs of large Compustat firms. As a result, it may not be representative
of the financial dependence of the types of businesses which would rely on local banks. Another
approach is to test how the sensitivity varies by firm age.
Table 6 shows that employment growth in young firms responds more to differences in bank
health. When omitting ∆N etW orth control, the difference in the elasticity between young firms
and mature firms varies between .43 and .67 and is always significant at at least the 5% level. In the
OLS specification, a one standard deviation decline in bank health is found to reduce employment
by 1.6% in mature firms and 3.6% in young firms. Additionally controlling for the own county shock
causes the difference in elasticities to shrink to .25 and .2 in the OLS and outside county specification,
and become insignificant. When instrumenting with outside CBSA exposure, the difference increases
to .85 and remains significant at the 5% level despite the larger standard errors.
In short, the decline in employment occurred predominantly in the firms most likely to be im-
pacted by a contraction in commercial credit. As there is little reason to think that these firms
55
would be more sensitive to demand shocks, this provides evidence that the findings are largely not
driven by a decline in consumer credit or some other demand related variable.
Employment Approach 4: Tract Growth, County Fixed Effects
A decline in small business lending from a particular bank would also differentially affect firms based
on their location within the county. Since small business lending is very local, the firms within a
couple miles of the exposed banks should face the greatest employment declines. Table 7 shows that
having the banks within 3km of a tract be highly exposed to falling house prices results in lower
employment growth relative to other tracts in the same county. I find that a 10% shock to nearby
banks reduces employment growth by 3.9%.
Exposure to unhealthy banks matters predominantly for tracts with more employment in small
establishments. A 10% shock reduces growth by 2.4% in tracts above the county median in the share
of employment in large establishments and 4.6% in tracts more reliant on small establishments. I
still find a significant impact on employment and a significantly larger effect on small establishments
when I do not weight by 2006 tract employment, although the point estimates are smaller.
In the last four columns, I measure Bank Health T ract with an indicator for whether the banks
within 3kms are healthier than the county median. Tracts close to unhealthy banks experience
employment growth 1.4% lower than tracts near healthy banks. When I include the interaction for
whether the tract is reliant on small establishments, I find that tracts reliant on large establish-
ments, regardless of bank health, and tracts reliant on small establishments near healthy banks all
perform almost identically. However, tracts with small establishments and unhealthy banks expe-
rience growth which is 2.1% lower than the rest of the county. Omitting the employment weights
again results in qualitatively similar findings but lower magnitudes.
Figure 3 shows how the finding depends on the distance used to define a tract’s banking market.
The left panel plots coefficients from regressions of tract employment growth on bank health and
county fixed effects. For each value d on the x-axis, bank health is measured as the deposit weighted
health of the branches within dkm of the tract. For distances from 2km to 16km, there is little
noticeable trend, with the coefficient on bank health being mostly around .3 or .4 and always
significantly different from 0.
The right panel presents similar results, except using the discrete bank health measure, and
including the interaction for whether the tract is composed of small establishments. Each line shows
the predicted growth rate for a tract of a given health and size category relative to a tract with
large establishments near healthy banks. Here the distance parameter is more relevant. For low
56
values (2-7km) we see that tracts with healthy banks or large establishments mostly have similar
growth rates, with employment growth in tracts with small establishments and unhealthy banks
being about 2% lower. Larger distance parameters attenuate the predicted effect of bank health on
small establishment growth.
Much like with the heterogeneous effects by bank dependence, this finding provides evidence
that a contraction in commercial credit from unhealthy banks resulted in employment losses. Two
firms in the same county are likely to face similar market wages, and similar levels of demand from
consumers. However, by virtue of banking markets being more local than labor or housing markets,
there would still be within county spatial variation in the availability of credit. The concentration of
employment losses in the firms most likely to borrow from unhealthy banks provides strong evidence
in favor of the bank lending channel.
2.5 Robustness and Extensions
2.5.1 Wage Adjustment
Most of the evidence in this paper supports the theory that real estate declines resulted in a contrac-
tion in bank commercial credit and, as a result, a decline in employment. The observed declines in
lending to small businesses, employment in bank dependent firms, and employment in firms near un-
healthy banks would all be explained by this theory. However, the greater sensitivity of employment
in non-tradable industries may be taken as evidence of a demand channel.25
The minimal relationship between tradable employment and bank health can however be rec-
onciled with a credit supply shock if falling wages offset the effects of tighter credit conditions for
tradable firms. If a contraction in credit lowers labor demand, this would result in a reductions in
wages and employment. This decline in local income would in turn reduce the demand for firms
in local non-tradable industries. The impact on tradable employment would thus depend on the
strength of wage adjustments. With an inelastic labor supply and flexible wages, tradable employ-
ment would rise as workers move into the industries with relatively higher demand. With perfectly
rigid wages, tradable employment falls due to the contraction in credit. With imperfect adjustment,
the falling wages and credit availability could offset, leaving employment unchanged. Under this
chain of events, non-tradable employment does fall due to low local demand, but that low demand
is a consequence of falling labor demand instead of the cause.
25 Note that this demand channel needn’t be the housing wealth channel. Bank deleveraging could cause a reduction
in consumer credit resulting in a contraction in demand. This would explain why bank health matters beyond local
real estate declines.
57
For this to be a credible explanation however, I need to demonstrate that wages fell in places
with unhealthy banks. Mian and Sufi (2014) address this issue, demonstrating that the average wage
growth largely did not respond to changes in the net worth of households.26 Their wage measure, the
average payroll of the workers in the county, might not accurately reflect the cost of labor though.
If temporary or low skilled workers have lower search or training costs, they could be more readily
laid-off. Wage declines could then be masked by a change in the composition of the workforce to
include a higher percentage of high wage individuals.
To mitigate this issue, I use the QWI earnings data disaggregated by education category. Table
8 provides evidence that places with unhealthy banks did indeed have declining labor costs. The
first panel estimates the impact of bank health on changes in average earnings from 2007 to 2010.
Specification (1) shows that the average wage overall doesn’t significantly respond to bank health,
with a 10% shock to bank health only reducing wages by 1.6%. However, specifications (2)-(4) show
that a 10% shock reduces the cost of young workers (under 25 years) by 3.8%, workers without a
high school diploma by 2.9%, and workers with no college by 2.3%. All are significant at the 1 or
5% level. Workers with a college education see a modest and insignificant decline in wages, similar
in magnitude to the average decline in wages in (1).27
The second panel shows that declining bank health disproportionately results in employment
losses for workers under 25 and workers who did not complete high school. As these individuals
have the lowest wages, their job losses obscure the actual changes in the cost of labor as observed
using overly aggregated data. Given that education is an imperfect proxy for skill, it is likely that this
disaggregation by education bin doesn’t entirely fix the composition bias. Any worker with higher
match specific human capital is likely to generate surplus for both the employer and employee,
making the job higher paying than for similarly educated people, and making the employee less
likely to lose their job.
2.5.2 Instrumenting with Earlier Branch Locations
While most of this paper focuses on ruling out demand side explanations, another threat is that bank
locations are not exogenous. A bank may have a high exposure to real estate declines not because
of historical accident, but because it specifically chose to enter markets with certain characteristics.
Thus a relationship between unhealthy banks and unemployment declines could reflect an omitted
26 Other work on the wage rigidities during the Great Recession using national data have found mixed results. Daly
et al. (2012) presents evidence of downward nominal rigidity in wages. Elsby et al. (2013) argues that this rigidity
may be less binding than typically hypothesized.
27 These findings should be taken as weaker than the employment findings, as including the control for ∆N etW orth
makes both variables insignificant.
58
variable which induced particular banks (subprime specialists for example) to enter the market.
To overcome this concern, I test the robustness of my main findings to using 2002 branch locations
to compute the bank health measures. As the spike in subprime mortgage originations and private
label securitization occurred in 2004, there should be less concern about locations in 2002 reflecting
unobserved variables relating to the housing boom. In Tables 9 and 10, I replicate the primary
county level and tract level findings instrumenting for the bank health measures using analogous
measures constructed from 2002 branch locations and deposit levels, instead of the 2006 ones as in
the main paper. The results are virtually identical to the equivalent specifications in Tables 3 and
7, alleviating concerns about endogenous location decisions in response to the housing boom driving
my results.
2.5.3 Alternative Bank Health Measures
There are numerous potential measures of county real estate distress or ways of measuring a bank’s
exposure to a given county. The first six tables of the supplementary materials show that the
primary findings are not sensitive to my measure of bank health. In addition to ∆N etW orthc , I
also measure county real estate health as (1 − Delinquencyc ), where Delinquencyc is the December
2009 delinquency rate on mortgages in county c from the New York Fed Consumer Credit Panel
(a 5% sample of US consumers with a credit report). I additionally measure a bank’s exposure
to a county based on the value of mortgage originations for home purchases in 2006, using Home
Mortgage Disclosure Act county lending data. This has the benefit of picking up exposure from
mortgages originated by non-depository affiliates of bank holding companies or other mortgage
lending in counties where the bank doesn’t have branches.28 Combinations of these two exposure
measures with the two county shocks provide four potential bank health measures.
Which measure of bank health appears to matter relatively little on average. Using one of
the three new measures tends to strengthen the out of market IV findings and external financial
dependence findings, but weakens the findings by age and at the tract level. However, using a
different measure of bank health never changes the qualitative predictions.
2.5.4 Alternative Controls
Tables 7-9 in the supplemental materials also present robustness for four different control sets. I
either include nothing besides the variables of interest, 2-digit industry shares as in the paper,
28 Since small banks and banks outside of MSAs are exempt from filing, I limit the sample to MSAs in the employment
regressions.
59
industry shares along with the demographic controls used in Mian and Sufi (2014)29 , or the industry
controls with state fixed effects. Overall, the estimates provided in the paper tend to be on the
lower end, although qualitatively similar to the findings with the alternative control sets. The
largest difference occurs when state fixed effects are used. The state fixed effects strengthen the
findings based on external financial dependence. However, their inclusion makes ∆N etW orth and
Bank Health Cnty insignificant for young and mature firms alike.
2.6 Conclusion
Determining the reason that falling house values translated into employment declines is difficult
due to the similarity in the aggregate implications of different channels. Both declining aggregate
demand and loan supply would result in falling consumption, lending and employment.
Yet despite the near observational equivalence of the channels, the policy prescriptions can vary
substantially. Proponents of the demand view might favor empowering bankruptcy judges to reduce
the amount of principle owed on a mortgage, a provision of the Helping Families Save Their Homes
Act of 2009 which was dropped in the senate. Those believing the banking channel would advocate
for the liquidity support and capital injections as were undertaken by government during the height
of the crisis. Forced principle reductions here could be counterproductive if they caused further
losses to banks in areas with depressed real estate values. These starkly different policy implications
of similarly plausible channels has resulted in a contentious debate regarding the policy response to
the crisis.
In this paper, I demonstrate that losses in the banking system contributed to the decline in
lending and employment between 2007 to 2010. Counties with exposed banks saw increases in
lending from large healthy banks, but falling employment. This set of outcomes is consistent with a
contraction in credit in these counties, but is hard to reconcile with demand based narratives. The
disproportionate employment declines in young firms, financially dependent firms and firms close to
unhealthy banks lends further credence to the banking view.
A back of the envelope calculation indicates that the employment declines due to this contraction
in bank lending were substantial. The less conservative approach in estimating the aggregate effect
of these bank losses is to assume that county housing net worth declines are endogenous and thus
controlling for them causes a downward bias on the coefficient for bank health. Instrumenting for
29 This includes: percentage white, median household income, percentage owner-occupied, percentage with less than
a high school diploma, percentage with only a high school diploma, unemployment rate, poverty rate, and percentage
urban.
60
bank health with real estate movements in counties over 300km away produces an estimated elasticity
of employment growth with respect to bank health of .37. If one is more concerned about spatial
correlation in house prices than their endogeneity, the specification controlling for ∆N etW orth
would be appropriate, generating a coefficient of .28. The average population weighted decline in
county level bank health was .085, thus these estimates imply that declining bank health from real
estate exposure reduced employment by between 2.4% and 3%. US employment at the beginning
of the sample in March 2007 was 137.72 million, thus this amounts to a loss of between 3.3 and 4.1
millions jobs accounting for between 39% and 48% of the aggregate decline in employment.
In addition to the bank lending channel being significant in aggregate, it also goes a long way
in explaining why employment fell in places with falling house prices. Controlling for bank health
causes the coefficient on ∆N etW orth to fall from .2 to .11, thus bank health also explains almost
half of the relationship between shocks to the net worth of households and employment.30 I am
by no means dismissing the existence of a direct effect of housing declines on employment, or the
efficacy of policies to assist homeowners during the crisis. However, my analysis demonstrates that
those direct effects are not the entire story. The ability of banks to lend was a significant driver of
labor market strength in the Great Recession, and played a large part in explaining why employment
declines were so pronounced in places with falling house prices. This all suggests that the dramatic
actions by the Fed and the Treasury to rehabilitate the financial system after the failure of Lehman
Brothers was vitally important in preventing the crisis from being worse than it already was.
30 Even though Bank Health Cnty is consistently statistically significant and ∆N etW orth is never more than
marginally significant when controlling for Bank Health Cnty, one must still be cautious in interpreting their relative
importance. Figure 4 plots the bootstrap distribution of coefficients on Bank Health Cnty and ∆N etW orth. The
joint 95% confidence interval for aggregate employment growth includes values where ∆N etW orth matters as much
or more than Bank Health Cnty.
61
2.7 Appendix
2.7.1 Figures
.3
1
.8
.2
Probability of lending
Density
.6
.1
.4
.2
0
0 10 20 30 40 50 0 2 4 6 8 10
Distance to Lending Institution (miles) Distance to Nearest Branch (miles)
Figure 2.1: Distribution of Lender Distances
Notes: The left figure shows the distance to the depository institution offering a small business a line of credit from
the 2003 Survey of Small Business Finance, excluding loans in banks more than 50 miles away. The right figure shows
how the probability of a CRA reporting institution making a small business loan in a particular census tract varies
with the distance between the tract centroid and the nearest branch of an institution. Probabilities are determined
by local linear regression on the 0-1 loan indicator using an Epanechnikov kernel with a bandwidth of .2 miles. This
is done for the year 2006.
62
Employment Growth Non−tradable Growth
No County Control No County Control
.2 .4 .6 .8
1
.2 .4 .6 .8
0
0 100 200 300 0 100 200 300
Distance (kms) Distance (kms)
Employment Growth Non−tradable Growth
Net Worth Control Net Worth Control
1.5
−.2 0 .2 .4 .6
1
.5
0
0 100 200 300 0 100 200 300
Distance (kms) Distance (kms)
Coefficient on Bank Health 95% CI
Figure 2.2: Effect of Bank Health: Distance Based Instruments
Notes: This figure presents the results of IV regressions as done in Table 4. However, instead of using county or
CBSA market definitions, I use distance based definitions. Specifically, Bank Health Cnty is instrumented using
Db,c Db,c0
∆N etW orthc0 where md (c) is the set of counties within dkms
P P
Outside Shock c = b∈B c0 ∈{C\md (c)} Dc Db
of c. The graph plots the coefficient and 95% confidence for Bank Health Cnty for different values of the distance
parameter d. The left panels have total employment growth from 2007 to 2010 as the independent variable, while
the right have non-tradable employment growth. The top panels omit the control for ∆N etW orth, while the bottom
panels include it.
63
Bank Health and Growth Bank Health and Growth: By Size
.02
.8
.6
.01
.4
0
.2
−.01
0
−.02
−.2
0 5 10 15 0 5 10 15
Distance (kms) Distance (kms)
Regression Coefficient Small, Unhealthy Small, Healthy
95% CI Large, Unhealthy
Figure 2.3: Effects of Bank Health on Tract Growth: By Distance
Notes: Both panels demonstrate how the health of banks within a certain distance of a census tract impact the
employment growth from 2007 to 2010. The left panel plots the coefficient and 95% confidence interval from the
regression ∆ln(Emp)t,c = βB Bank Health T ractt (d) + βx0 Xt + δc + t weighting by 2006 employment, following
specification (1) of Table 7. Bank Health T ractt (d) is the deposit weighted health (bank exposure to real estate
appreciation) of the branches within d kms of the tract centroid. The right panel plots the expected growth of tracts
within different categories. “Large” tracts have below the county median share of employment in small establishments,
”Unhealthy” tracts have a value for Bank Health T ract(d) below the county median. The expected growth for each
size-health pair relative to the Large-Healthy category are plotted. These coefficients are derived from regressions
following specification (6) in Table 7.
64
Figure 2.4: Joint Confidence Regions
Notes: These figures plot the bootstrap coefficients and 95% confidence interval for the coefficients on ∆N etW orth
and Bank Health Cnty. These figures correspond to specifications (3) and (6) in Table 3 and (2) and (4) in Panel
2 of Table 4. The IV specification uses the outside CBSA instrument for bank health. I use 500 replications. The
elongated confidence regions are a consequence of a high correlation between the key explanatory variables. This
shows that although one can be confident that the coefficient on Bank Health Cnty is greater that 0, one can’t
precisely assess the relative magnitude of the banking channel and the demand channel.
65
2.7.2 Tables
Table 2.1: Summary Statistics
Mean Standard Percentile Obs
Deviation 10th 90th
Bank Level
Bank Health -0.059 0.070 -0.144 0.001 2092
Bank × County Growth
∆ln(SB Loans) -0.038 1.160 -1.269 1.485 20995
County Level (Weighted by Households)
Bank Health Cnty -0.084 0.042 -0.153 -0.038 944
∆N etW orth -0.095 0.100 -0.218 -0.006 944
Outside Shock (County) -0.065 0.031 -0.108 -0.030 944
Outside Shock (CBSA) -0.057 0.030 -0.101 -0.025 944
Employment Growth:2007-2010
T otal -0.077 0.053 -0.146 -0.012 944
N ontradable -0.056 0.065 -0.112 0.018 944
T radable -0.188 0.156 -0.371 -0.034 944
HighDependence -0.105 0.088 -0.208 -0.009 944
LowDependence -0.062 0.060 -0.124 0.004 944
Y oung 0.267 0.113 0.152 0.392 900
M ature -0.040 0.051 -0.101 0.019 900
Tract Level (Weighted by Employment)
∆ln(Emp tract) -0.049 0.272 -0.308 0.196 55843
Bank Health T ract (3km) -0.084 0.043 -0.144 -0.037 46828
66
Table 2.2: Growth in Number of Small Loans
(1) (2) (3) (4) (5) (6)
Bank Health 1.987∗∗ 2.105∗∗ 2.202∗∗ 2.249∗∗
(0.711) (0.751) (0.763) (0.793)
∆N etW orth -0.265+ 0.300+ -0.0762 0.305∗
(0.151) (0.155) (0.0907) (0.137)
Bank Health Cnty -1.618∗∗ -1.113∗∗
(0.558) (0.397)
Observations 17519 17519 17519 17519 17519 17519
R2 0.147 0.147 0.149 0.185 0.423 0.424
Fixed Effect CNTY BANK BANK
Standard errors in parentheses
+ p < 0.10, ∗ p < 0.05, ∗∗ p < .01
Notes: This table reports coefficients from regressions of bank×county growth in small business originations on bank
health measures. The dependent variable is the growth in the number of small business loans originated by the bank
in the county from 2008-2010 relative to 2004-2006. Bank Health is the average change in household net worth in
b’s counties, weighted by b’s deposits in the county. ∆N etW orth is the percentage change in household net worth
from falling house prices for the actual county, while Bank Health Cnty is the deposit weighted average value of
Bank Health of the banks in the county. County fixed effects are used in (4), while bank fixed effects are used in
(5) & (6). I additionally control for 2-digit industry shares and the natural logarithm of the number of loans made
from 2004-2006. Standard errors are clustered by bank. I drop any observation in which the bank holding company
acquired another bank within the CBSA, observations in the highest or lowest percentile of growth, and observations
where the bank didn’t have a 2006 branch within the county.
67
Table 2.3: Employment Growth: By Tradability
All Industries Nontradable Tradable
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Bank Health Cnty 0.497∗∗ 0.279∗ 0.630∗∗ 0.642∗∗ 0.221 -0.131
(0.0977) (0.111) (0.102) (0.108) (0.216) (0.292)
∆N etW orth 0.201∗∗ 0.105+ 0.214∗∗ -0.00568 0.125 0.170
68
(0.0477) (0.0577) (0.0493) (0.0655) (0.0953) (0.124)
Observations 944 944 944 944 944 944 944 944 944
R2 0.414 0.413 0.423 0.241 0.208 0.241 0.075 0.077 0.077
Standard errors in parentheses
+ p < 0.10, ∗ p < 0.05, ∗∗ p < .01
Notes: This table reports coefficients from regressions of county level employment growth from 2007 to 2010 on measures of bank health and shocks to housing net worth. The
dependent variable is the growth in total employment in 1-3, the growth in non-tradable employment (restaurants and retail establishments) in 4-6, and growth in tradable
employment in 7-9. ∆N etW orth is the percentage change in net worth of the households of a county and Bank Health is average multimarket exposure to ∆N etW orth of local
banks weighted by local deposit share. Controls for the share of employment in each 2 digit NAICS industry are included in each regression. I weight by the total households
in the county and cluster the standard error by state.
Table 2.4: Employment Growth: IV with Out of Market Shocks
All Industries Nontradable Tradable
Market CNTY CBSA CNTY CBSA CNTY CBSA
(1) (2) (3) (4) (5) (6)
Panel 1: No County Control
Bank Health Cnty 0.475∗∗ 0.466∗∗ 0.616∗∗ 0.660∗∗ 0.244 0.0604
(0.0709) (0.0718) (0.0903) (0.112) (0.216) (0.217)
Observations 944 944 944 944 944 944
R2 0.414 0.414 0.241 0.240 0.075 0.073
F 419.853 216.516 419.853 216.516 419.853 216.516
Panel 2: Own County Control
Bank Health Cnty 0.272∗ 0.270+ 0.599∗∗ 0.715∗∗ 0.0202 -0.440
(0.118) (0.153) (0.179) (0.230) (0.376) (0.449)
∆N etW orth 0.107 0.108 0.00894 -0.0308 0.118 0.276
(0.0725) (0.0781) (0.0910) (0.103) (0.156) (0.173)
Observations 944 944 944 944 944 944
R2 0.423 0.423 0.241 0.240 0.077 0.076
F 166.787 127.954 166.787 127.954 166.787 127.954
Standard errors in parentheses
+ p < 0.10, ∗ p < 0.05, ∗∗ p < .01
Notes: This table reports coefficients from IV regressions of county level employment growth from 2007 to 2010 on
measures of bank health and shocks to housing net worth. The dependent variable is the growth in total employment
in 1-3, the growth in non-tradable employment (restaurants and retail establishments) in 4-6, and growth in tradable
employment in 7-9. The endogenous variables is Bank Health, which is average multimarket exposure to ∆N etW orth
of local banks weighted by local deposit share. This is instrumented for using bank exposure to real estate declines
from other counties in odd columns, and other CBSAs in even columns. Controls for the share of employment in
each 2 digit NAICS industry are included in each regression. In panel 2, I additionally control for ∆N etW orth, the
percentage change in net worth of the households of a county. I weight by the total households in the county and
cluster the standard error by state.
69
Table 2.5: Employment Growth: By Financial Dependence
OLS IV:Outside Cnty IV:Outside CBSA
Financial Dependence: High Low High Low High Low
(1) (2) (3) (4) (5) (6)
Panel 1: No County Control
Bank Health Cnty 0.661∗∗ 0.391∗∗ 0.627∗∗ 0.394∗∗ 0.689∗∗ 0.363∗∗
(0.109) (0.103) (0.100) (0.0835) (0.115) (0.0870)
Difference: .27+ .233+ .325∗
(.15) (.131) (.144)
Observations 944 944 944 944 944 944
R2 0.339 0.264 0.338 0.264 0.338 0.264
Panel 2: Own County Control
Bank Health Cnty 0.451∗ 0.291∗∗ 0.406∗ 0.323∗ 0.593∗∗ 0.251
(0.199) (0.0955) (0.201) (0.139) (0.216) (0.195)
∆N etW orth 0.101 0.0485 0.117 0.0375 0.0531 0.0622
(0.0698) (0.0591) (0.0792) (0.0804) (0.0790) (0.0937)
Difference: .161 .083 .342
(.221) (.245) (.291)
Observations 944 944 944 944 944 944
R2 0.341 0.265 0.341 0.265 0.340 0.265
Standard errors in parentheses
+ p < 0.10, ∗ p < 0.05, ∗∗ p < .01
Notes: This table reports coefficients from regressions of county level employment growth from 2007 to 2010 on
measures of bank health and shocks to housing net worth. The dependent variable is the growth in employment in
industries with a high dependence on external finance in odd columns, and growth in industries with low financial
depdendence in even columns. The first two columns are estimated with OLS, while the last four instrument for
Bank Health Cnty using only the component coming from other counties or CBSAs. The difference in the coefficients
for highly and lowly financially dependent industries is reported below the regression coefficient. Controls for the share
of employment in each 2 digit NAICS industry are included in each regression. In panel 2, I additionally control for
∆N etW orth, the percentage change in net worth of the households of a county. I weight by the total households in
the county and cluster the standard error by state.
70
Table 2.6: Employment Growth: By Age
OLS IV:Outside Cnty IV:Outside CBSA
Young Old Young Old Young Old
(1) (2) (3) (4) (5) (6)
Panel 1: No County Control
Bank Health Cnty 0.853∗∗ 0.384∗∗ 0.794∗∗ 0.361∗∗ 0.926∗∗ 0.257∗∗
(0.171) (0.114) (0.163) (0.0917) (0.120) (0.0640)
Difference: .469∗ .432∗ .669∗∗
(.206) (.187) (.136)
Observations 900 900 900 900 900 900
2
R 0.299 0.286 0.298 0.286 0.298 0.279
Panel 2: Own County Control
Bank Health Cnty 0.599+ 0.349∗∗ 0.491 0.294∗ 0.877∗ 0.0229
(0.316) (0.117) (0.310) (0.127) (0.357) (0.204)
∆N etW orth 0.121 0.0165 0.157 0.0353 0.0268 0.127
(0.128) (0.0648) (0.135) (0.0775) (0.168) (0.108)
Difference: .25 .198 .854∗
(.337) (.335) (.411)
Observations 900 900 900 900 900 900
R2 0.301 0.286 0.301 0.286 0.299 0.272
Standard errors in parentheses
+ p < 0.10, ∗ p < 0.05, ∗∗ p < .01
Notes: This table reports coefficients from regressions of county level employment growth from 2007 to 2010 on
measures of bank health and shocks to housing net worth. The dependent variable is the growth in employment for
young firms (five years and younger) in odd columns, and growth in mature firms in even columns. The first two
columns are estimated with OLS, while the last four instrument for Bank Health Cnty using only the component
coming from other counties or CBSAs. The difference in the coefficients for young and mature firms is reported below
the regression coefficient. Controls for the share of employment in each 2 digit NAICS industry are included in each
regression. In panel 2, I additionally control for ∆N etW orth, the percentage change in net worth of the households
of a county. I weight by the total households in the county and cluster the standard error by state.
71
Table 2.7: Tract Employment Growth
Continuous Health Measure High Bank Health Indicator
(1) (2) (3) (4) (5) (6) (7) (8)
Bank Health T ract 0.391∗∗ 0.240+ 0.139∗ 0.00846 0.0135∗∗ -0.000112 0.00848∗∗ 0.00263
(0.0909) (0.121) (0.0598) (0.0798) (0.00384) (0.00698) (0.00268) (0.00410)
Bank Health T ract × SM 0.222+ 0.262∗∗ 0.0209+ 0.0121∗
(0.112) (0.0832) (0.0109) (0.00471)
SM 0.00901 -0.00139 -0.0199∗ -0.0301∗∗
72
(0.00970) (0.00790) (0.00900) (0.00618)
Observations 45923 45901 45923 45901 45923 45901 45923 45901
R2 0.103 0.104 0.055 0.057 0.103 0.104 0.055 0.057
Fixed Effect CNTY CNTY CNTY CNTY CNTY CNTY CNTY CNTY
Weighted? Y Y N N Y Y N N
Standard errors in parentheses
+ p < 0.10, ∗ p < 0.05, ∗∗ p < .01
Notes: This table reports coefficients from regressions of tract level employment growth from 2007 to 2010 on tract level measures of bank health and county
fixed effects. In the first four columns, Bank Health T ract is the average health of the banks within 3km of the tract centroid, weighted by the deposits in
branches within 3km of the tract. In the last four columns, Bank Health T ract is an indicator for whether the aforementioned measure is above the county
median. I additionally control for 2-digit NAICS employment shares of the tract in 2006. Even columns additionally include an interaction for whether the
tract is above the county median in terms of the share of employment in small establishments. Regressions are weighted by 2006 tract employment in 1,2,5,
&6 and unweighted in the rest. Standard errors are clustered by state. Observations in the highest and lowest percentile of employment growth are dropped.
Table 2.8: Evidence of Wage Adjustment
ALL Under Less Than High Some College
25 yrs High School School College
(1) (2) (3) (4) (5) (6)
Panel 1: Wage Growth
Bank Health Cnty 0.157 0.383∗∗ 0.286∗∗ 0.231∗ 0.172 0.143
(0.113) (0.0852) (0.0879) (0.101) (0.109) (0.126)
Observations 929 931 927 929 928 919
2
R 0.161 0.378 0.340 0.238 0.179 0.127
Panel 2: Employment Growth
Bank Health Cnty 0.344∗∗ 0.480∗∗ 0.509∗∗ 0.241 0.330∗∗ 0.319∗
(0.124) (0.144) (0.133) (0.149) (0.116) (0.122)
Observations 932 932 928 932 932 931
R2 0.249 0.268 0.308 0.281 0.237 0.191
Standard errors in parentheses
+ p < 0.10, ∗ p < 0.05, ∗∗ p < .01
Notes: This table reports coefficients from regressions of 2007 to 2010 employment or wage growth within an education
category on bank health. The first panel uses wage growth as the dependent variable, while the second uses stable
employment growth. The wage in a given year is the income of stable workers divided by the number of stable workers
of a given education type. Column 1 includes all workers, while the proceeding columns include only (2) workers under
25, (3) less than high school, (4) high school, (5) some college, and (6) college. Bank Health is average multimarket
exposure to ∆N etW orth of local banks weighted by local deposit share. Controls for the share of employment in each
2 digit NAICS industry are included in each regression. I weight by the total households in the county and cluster
the standard error by state.
73
Table 2.9: County Growth: Health of 2002 Branches
All Industries Nontradable Tradable
(1) (2) (3) (4) (5) (6)
Bank Health Cnty 0.507∗∗ 0.273∗ 0.637∗∗ 0.672∗∗ 0.218 -0.217
(0.0949) (0.134) (0.0997) (0.160) (0.203) (0.342)
∆N etW orth 0.107 -0.0159 0.200
(0.0703) (0.0837) (0.156)
Observations 944 944 944 944 944 944
R2 0.414 0.423 0.241 0.241 0.075 0.077
Standard errors in parentheses
+ p < 0.10, ∗ p < 0.05, ∗∗ p < .01
Notes: This table reports coefficients from IV regressions of county level employment growth from 2007 to 2010 on measures of bank health
and shocks to housing net worth. The dependent variable is the growth in total employment in 1-3, the growth in non-tradable employment
(restaurants and retail establishments) in 4-6, and growth in tradable employment in 7-9. ∆N etW orth is the percentage change in net worth of
the households of a county and Bank Health Cnty is average multimarket exposure to ∆N etW orth of local banks weighted by local deposit
share. I instrument for Bank Health Cnty using the equivalent definition as in the text except with 2002 branch deposits. Controls for the
share of employment in each 2 digit NAICS industry are included in each regression. I weight by the total households in the county and cluster
the standard error by state.
74
Table 2.10: Tract Growth: Health of 2002 Branches
Continuous Health Measure High Bank Health Indicator
(1) (2) (3) (4)
Bank Health T ract 0.391∗∗ 0.181∗ 0.00991 0.0191∗∗
(0.127) (0.0898) (0.00779) (0.00629)
Observations 44898 44898 44898 44898
R2 0.105 0.056 0.105 0.056
Fixed Effect CNTY CNTY CNTY CNTY
Weighted? Y N Y N
Standard errors in parentheses
+ p < 0.10, ∗ p < 0.05, ∗∗ p < .01
Notes: This table coefficients from IV regressions of tract level employment growth from 2007 to 2010 on tract level
measures bank health and county fixed effects. In the first two columns, Bank Health T ract is the average health
of the banks within 3km of the tract centroid, weighted by the deposits in branches within 3km of the tract. In the
third and fourth column, Bank Health T ract is an indicator for whether the aforementioned measure is above the
county median. I instrument for Bank Health T ract using the equivalent definition as from the text, except with
2002 branch locations and deposits. I additionally control for 2-digit NAICS employment shares of the tract in 2006.
Regressions are weighted by 2006 tract employment in 1 & 3 unweighted in 2 & 4. Standard errors are clustered by
state. Observations in the highest and lowest percentile of employment growth are dropped.
75
2.7.3 Theory Appendix
To motivate the empirical work, I present a simple model of bank credit and employment. First, I
derive how how banks choose their aggregate lending and allocate credit across counties. Then, I
calculate how county and industry employment are affected by credit and demand shocks. In the
end, I discuss the implications of the model for the different empirical approaches.
Model
Bank’s Problem Suppose that banks engage in monopolistic competition in the provision of
business loans. A borrower i in county c uses a CES composite of borrowing from a continuum of
banks to engage in production. They choose the amount to borrow from banks b, Lbc,i , so as to
! −1
R b b R b −1 b
minimize rc,i Lc,i db s.t. (Lc,i ) db = Lc,i . Where rc,i is the interest rate charged by
Ωc Ωc
bank b to borrower i in county c, Ωc is the set of banks operating in county c and Lc,i is aggregate
borrowing. This implies a demand curve.
b
rc,i
Lbc,i = Lc,i ( )−
rc,i
R b 1− 1
where rc,i = ( (rc,i ) db) 1− is the cost of bank borrowing.
Ωc
Given a certain level of aggregate lending Lb , the bank will allocate credit across borrowers in
order to maximize profit subject to demand curves and the adding up constraint. Denoting the set
of counties b operates in Cb and the set of borrowers in the county Ic , b’s maximization problem is:
X X
maximize Lbc,i rc,i
b
b }
{rc,i
c∈Cb i∈Ic
b
X X rc,i
subject to Lbc,i = Lb , {Lbc,i = Lc,i ( )− } ∀c, i
rc,i
c∈Cb i∈Ic
This results in a constant optimal lending rate for bank b:
Db 1
rb = ( ) ∀i, c (2.3)
Lb
where Db =
P P
Lc,i rc,i measures the level of demand for loans from b. This is increasing in the
c∈Cb i∈Ic
aggregate demand for bank finance among clients and in the interest rate charged by competitors.
Next, to determine the optimal size of the bank’s loan portfolio, suppose that the marginal cost
Lb
of funds for a bank is an increasing function c() of its leverage Eb
, where E b is the bank’s equity.
76
Then the first order condition implicitly defining the bank’s optimal lending is:
− 1 Db 1 Lb
( b ) = c( b )
L E
This equation equates marginal revenue to marginal cost, and thus defines the bank’s supply curve.
Log-linearizing this equation, and combining it with the demand curve in (3), we find that the
growth in lending and interest rates as a function of the growth in demand and equity:
ˆ b = η BE E
L ˆ b + (1 − η BE )D
ˆb (2.4)
b b
ηbBE ˆ b ˆb)
rˆb = (D − E (2.5)
b
b ( L b ) −1 −1
where ηbBE = (1 + (c0 ( E
L
b)
E
b ) ) parameterizes the degree of bank financial constraints. Note
c( L b )
E
that growth in lending is more related to equity growth if the elasticity of marginal cost with respect
to leverage is high. In the extreme, if banks have a constant marginal cost of funds up until they
hit a binding leverage constraint, ηbBE would be 1 for any bank at the regulatory capital ratio, and
0 for any bank below it.
Bank County Lending Summing over the industry demand curves within a county, and using
that the optimal bank interest rate is constant at the bank level, we get that the county level lending
for b is Lbc = (rb )− Lc,i rc . Thus the growth in lending at the bank-county level is:
P
i∈Ic
ˆ b = −ˆ
L ˆc
rb + D
c
(2.6)
ˆb − E
= −ηbBE (D ˆb) + D
ˆc
Lc,i rc denotes the level of demand for bank loans in county c and rc =
P
Where Dc ≡
i∈Ic
1
( (rb )1− db) 1− is the interest rate charged for bank lending in county c.
R
Ωc
Firm’s Problem
To endogenize the loan demand, and understand how changes in credit spreads impact employment,
consider the following optimization problem. A representative firm in industry i in county c produces
77
output Yc,i with a constant returns to scale production function using labor Nc,i such that:
Yc,i = Ac,i Nc,i
The wage bill wc Nc,i is financed by a combination of bank finance Lc,i and direct finance dc,i
c,i δ 1−δc,i
according to the relationship wc Nc,i = Lc,i dc,i . Recall the cost of bank finance is rc and denote
the cost of direct finance ρ. The firm’s cost minimization problem is:
c,i δ 1−δc,i
min rc Lc,i + ρdc,i s.t. wc Nc,i = Lc,i dc,i
The marginal cost of output and demand for bank funds are then:
wc
mcc,i = co rcδc,i ρ1−δi,c
Ac,i
δc,i −1
rc
Lc,i = bo wc Nc,i
ρ
Where co and bo are constants.31
Assuming perfect competition and a unit elastic demand curve, expenditure on i’s good is fixed
Xc,i
at Xc,i and output is priced at marginal cost. Consequently Yc,i = mci,c . Normalizing ρ to one for
notational simplicity, labor demand is
Yc,i −1
Nc,i = = Xc,i co rcδc,i wc
Ac,i
Combining the previous two expressions gives us the demand for bank finance:
Xc,i
Lc,i = δc,i
rc
Aggregate Employment Effects Log linearizing the firm’s labor demand:
ˆc,i = X
N ˆ c,i − δc,i rˆc − w
ˆc
To determine Xc,i , let expenditure growth be a weighted average of national consumption growth
ˆ
¯ and local consumption growth, with weight θc,i on local consumption growth. Furthermore, let
C,
−δ
1−δc,i
δc,i
31 c
o = δc,i c,i (1 − δc,i )δc,i −1 , bo = 1−δc,i
78
ˆc ≡ w
local consumption growth be determined by the growth in income W ˆc and housing wealth
ˆc + N
ˆ c with elasticities η XW and η XH respectively. Growth in expenditure for an industry’s output is
H
then:
ˆ c,i = (1 − θc,i )Cˆ¯ + θc,i (η XW W
X ˆ c + η XH H
ˆ c)
To pin down wages, let the inverse labor supply curve be wc = Ncψ , then total payments to labor
ˆ c = (ψ + 1)N
are wc Nc = Ncψ+1 . This implies income growth W ˆc . Combining this with the previous
two equations thus yields the county×industry employment growth:
ˆc,i = α(θc,i ) + θc,i η XH H
N ˆ c − δc,i rˆc + β GE (θc,i )N
ˆc (2.7)
Where α(θc,i ) = (1 − θc,i )Cˆ
¯ denotes the growth in demand from national sources, and β GE =
θη XW (ψ + 1) − ψ summarizes the general equilibrium effect of changes in aggregate labor demand
on industry labor demand.
This equation says that a decline in house values reduces local demand (assuming η XH > 0) and
impacts employment in industries which are dependant on local demand (i.e. θc,i is high). This is
the channel emphasized in Mian and Sufi (2014). Also, an increase in the cost of bank credit impacts
employment in firms which are bank dependent (δc,i is high). This is the channel emphasized by
this paper and Chodorow-Reich (2014). Third there are general equilibrium effects which depend on
the tradability of the industry. A contraction in employment lowers the cost of labor for all firms,
however there is an offsetting effect on firms which are dependent on local demand, as the decline
in income reduces expenditure.
To calculate aggregate employment growth, sum over the growth rates of the industries within
a county (weighting by initial employment) to get:
ˆc = µc (α(θc ) + θc η XH H
N ˆ c − δc rˆc ) (2.8)
1
where µc ≡ 1−β GE (θc )
is a multiplier reflecting the extent of general equilibrium wage and income
effects and θc and δc denote the (weighted) average value of the corresponding industry level param-
eters across the industries within the county.
Implication for Empirical Approaches
Small Business Lending, County Fixed Effects Equation (6) provides the formula for the
growth in lending at the county level. It has a supply component corresponding to changes in
79
the interest rate the bank charges, and a demand component reflecting the availability of credit
from competitors and the demand for the firm’s product. Regressing county lending growth on
bank health (denoted BH b ) without county fixed effects would produce a point estimate: β L,BH =
b b ˆ c ,BH b )
cov(ˆ
r ,BH )
var(BH b )
+ cov(D
var(BH b )
. This includes both the effect on interest rates, and on demand. To
isolate the effect of bank health on the cost of credit, I add county fixed effects to absorb the
ˆ c . This should produce the point estimate reflecting the impact of bank health on lending rates:
D
b b
β L,BH |dc = cov(ˆ
r ,BH )
var(BH b )
Similarly, using a bank fixed effect allows me to isolate supply shocks and test how changes in
housing net worth impacts demand. Regressing county lending growth on the shock to housing
cov(Dˆ b ,H
ˆc)
ˆ and bank fixed effects should provide an estimate: β L,H |δ =
net worth, H, ˆc) . The fact
c
b var(H
that this is found to be indistinguishable from 0, and slightly negative, indicates that the decline in
lending wasn’t due to falling demand in places with declining housing net worth.
Employment Approaches 1& 2 Equation (8) calculates employment growth as a function of
ˆ c . Assume that Bank Health Cnty
the credit supply shock rˆc and the housing net worth shock H
(denoted BHc for brevity) isn’t related to demand except through it’s influence on lending rates so
that rˆc = η rB BHc + uc , where the residual is orthogonal to demand, then equation 8 becomes:
ˆc = µc α(θc ) + θc η DH H
N ˆ c − δc η rB BHc − δc uc (2.9)
ˆ c and the controls
Approach 1 directly estimates this equation using ∆N etW orthc to control for H
for industry shares to control for differences in θc . If the model is correctly specified and the variables
∂N ˆ ∂ rˆ
are exogenous, the coefficient on BHc should be µδη rB (or more plainly: ∂ rˆ ∂BH ).
ˆ c to be exogenous, a condition which is unlikely to hold
However, direct estimation requires H
as unobservables increasing demand for labor will impact house prices as a consequence. Thus
the second approach is to omit the control, and instrument for Bank Health Cnty using only the
exposure of banks to falling house prices in markets which don’t directly impact local employment.
Employment Approach 3: Difference in Differences by Bank Dependence Note from
equation (7) that a regression of employment growth in an industry i on Bank Health Cnty will
produce a coefficient:
ˆ BHc )
cov(H, cov(ˆ
r, BHc ) ˆ , BHc )
cov(N
plim βiN,BH = θi η XH − δi + β GE (θi )
var(BHc ) var(BHc ) var(BHc )
80
Suppose that industries differ only by their bank dependence, with the difference in importance of
bank credit between the high and the low group being denoted ∆δ ≡ δhigh dep − δlow dep . Then the
difference in point estimates for high dependence and low dependence firms would be:
N,BH N,BH cov(ˆ
r, BHc )
plim(βhigh dep − βlow dep ) = ∆δ = ∆δη rB
var(BHc )
Thus a positive differential would demonstrate that Bank Health Cnty impacts the supply of credit,
since the high and the low group experience the same shocks to demand and wages.
Reconciliation with Mian and Sufi My findings suggest that declining house prices translate
to employment losses due to a contraction in bank credit. This can be reconciled with the finding in
Mian and Sufi (2014) that deficient aggregate demand was an important driver in the relationship
between house price movements and employment.
Note from (7) that a completely tradable firm (θi =0) would experience employment growth:
ˆc,perfectly tradable = Cˆ¯ − δc,i rˆc − ψ N
N ˆc
= Cˆ¯ − δc,i rˆc − ψµc α(θc ) + θc η XH H
ˆ c − δc rˆc
This shows that a contraction in credit has an ambiguous effect on tradable employment. On one
hand, it costs more to borrow (δc,i rˆc ↑), on the other labor becomes cheaper due to constraints at
other firms (ψµc δc rˆc ↑).
More generally, a regression of employment growth on house price growth would produce the
estimate:
cov(ˆ ˆ c)
r, H ˆ, H
cov(N ˆ c)
plim βiN,H = θi η XH − δi + β GE (θi )
var(Hˆ c) var(Hˆ c)
Now, consider the difference in growth rates in two groups of firms which differ only in tradability.
Non-tradable industries are more reliant on local demand (∆θ ≡ θc,nontrade − θc,trade > 0). Then
the difference in the effect of a house price decline in tradable and non-tradable industries will be
81
estimated as
ˆc , H
cov(N ˆ c)
H H
plim( βnontrade − βtrade ) = ∆θη XH + ∆β GE
V ar(Hˆ c)
ˆc , H
cov(N ˆ c)
= ∆θη XH + ∆θ(η XW (ψ + 1)
ˆ
V ar(Hc )
!
ˆ
XH XW XH cov(ˆ
r c , H c
= ∆θ η + (η (ψ + 1) µ θ c η − δc
| {z }
| {z } V ar(H ˆ c)
Direct Sensitivity of Demand | {z }
Effect to Labor Demand Labor Demand
Change
ˆ
This equation shows that if falling house prices adversely impacts the supply of credit ( cov(ˆrc ,Hc
V ar(Hˆ ) <
c
0), then this will impact non-tradable industries the most. Thus a greater sensitivity of employment
growth with respect to house price growth doesn’t prove the existence of a housing wealth effect.
Even if η XH = 0, you would still find that non-tradable industries respond more.
Intuitively a decline in credit in a county has three effects: (1) a direct effect on labor demand by
increasing the cost of capital for bank dependent firms, (2) an offsetting general equilibrium effect
on the cost of labor through declining wages, and (3) an amplifying effect through the reduction
in local demand due to lower wages or employment. The first two effects hit both tradable, and
non-tradable industries, while the third effect in only borne by non-tradable industries. In short,
although the decline in labor demand in non-tradable industries suggests a decline in local demand,
this does not necessarily rule out credit supply. Weak labor market conditions stemming from credit
disruptions can also cause spending to decline, thus harming industries which are reliant on local
demand.
82
Chapter 3
A Spatial Measure of Banking
Competition
3.1 Introduction
Banks play a fundamental role in the allocation of capital within an economy. When these banks
can exert market power, it may have a significant adverse effect on the ability of the economy to
function efficiently. An understanding of the competitive pressures within the banking system is
thus of vital importance.
The first step of any empirical analysis of banking competition is proper measurement. Although
there is a vast literature attempting to measure the influence of bank competition, there is still no
consensus on how competition itself should actually be measured. When regulators are trying to
determine the probable competitive effects of a merger, their starting point is to use a Herfindahl-
Hirschman Index (HHI) of deposits within a market, where the market is defined roughly as an
MSA or a rural county.1 This methodology is also common in the academic literature when trying
to either control for bank competition or determine its impacts.
However, this market definition is at odds with the literature on distance and banking. Studies
utilizing various sources of data and time periods have found that individuals and small businesses
rely predominantly on very nearby banks, and that banks price discriminate based on location. If
banks are able to geographically segment the market and exert market power in those segments, a
B
1 The s2b where sb is the share of the total deposits
P
conventional Herfindahl measure is defined as: HHImkt =
b=1
in the market held by bank b.
83
market may be uncompetitive without being concentrated. The conventional MSA level Herfindahl
is a flawed measure of competition because it fails to take into account the location of banks within
the MSA. Thus, while it may address the ability of banks to collude on pricing, it fails to pickup
how the location of banks within a market can impact market power arising from spatial frictions
in lending.
The primary contribution of this paper is the introduction of a new measure of banking con-
centration which takes into account the local nature of banking. Specifically, I create a tract level
measure of concentration, which amounts to the deposit Herfindahl for all branches within a certain
distance of the tract centroid. My market level measure, referred to from here on out as the Local
Herfindahl, is then the average concentration of the tracts within the market. This measure of
concentration reflects both the availability of competitors within a market as well as the degree to
which they geographically segment the market.
With this new measure of concentration, I weigh in on three existing questions in the banking
literature. (1) Does bank market structure impact pricing and profitability? (2) What is the proper
geographic extent of banking markets? (3) Is bank market power detrimental to the ability of young
firms to obtain finance?
The first question this paper addresses is how bank market structure impacts the ability of
banks to behave uncompetitively. If concentration results in less competition, this should impact a
bank’s pricing decisions and ultimate profitability (the Structure-Conduct-Performance hypothesis).
Consistent with this, concentration has been found to be associated with low deposit rates (Berger
and Hannan, 1989; Heitfield and Prager, 2004), deposit rates which are less prone to rise with market
rates (Hannan and Berger, 1991; Neumark and Sharpe, 1992), high loan rates (Hannan, 1991; Hannan
and Liang, 1995), and high profitability (Pilloff and Rhoades, 2002; Rhoades, 1995). However, other
papers have questioned the hypothesis that market structure impacts bank performance and argue
that concentration may reflect high market shares of more efficient firms, and thus higher profitability
(Smirlock, 1985; Rhoades, 1985; Evanoff and Fortier, 1988; Berger, 1995). Additionally, Weiss (1989)
finds that the positive relationship between concentration and loan rates is inconstant across different
studies.
Measuring concentration using the Local Herfindahl results in stronger findings in support of
the Structure-Conduct-Performance hypothesis. Local concentration is consistently more predictive
of bank market power than conventional concentration. A one standard deviation increase in the
conventional MSA Herfindahl raises average loan rates by 3 basis points. A one standard deviation
increase in the new Local Herfindahl, even controlling for the conventional Herfindahl, raises lending
84
rates by 10 basis points and makes the conventional Herfindahl insignificant. Similar results holds for
other indicators of bank market power; banks in locally concentrated markets charge higher interest
rates on commercial and industrial loans, have higher service charges, offer lower deposit rates, and
as a result experience higher net profit margins and returns on equity.
The greater magnitude and significance for local concentration suggests that previous studies
using the conventional Herfindahl suffered from attenuation bias, possibly explaining the lack of
consistent findings. Furthermore, since variation in the Local Herfindahl comes from different loca-
tion patterns within the market instead of only differences in aggregate market shares, the results
are less likely to be explained by heterogeneity in bank efficiency.
The second question is how one should define a banking market. Regulators define markets at
roughly the level of the MSA or the rural county. This market definition has been supported by
numerous studies of bank pricing and profitability (Heitfield, 1999; Heitfield and Prager, 2004; Pilloff
and Rhoades, 2002). However, several authors have argued that financial innovations and deregula-
tion has resulted in markets which may be statewide or national (Jackson III and Eisenbeis, 1997;
Radecki, 1998; Petersen and Rajan, 2002). In the other direction, the observation of a disproportion-
ate reliance on local banks (Kwast et al., 1997; Brevoort and Hannan, 2006; Brevoort and Wolken,
2009) and on spatial price discrimination (Degryse and Ongena, 2005; Agarwal and Hauswald, 2010;
Bellucci et al., 2013) raises the possibility of bank markets being substantially smaller due to either
transport costs or information problems associated with distant lending. An understanding of the
appropriate market definition is important for both the regulators responsible for approving mergers,
and academics studying the sources and implications of bank market power.
My measure of concentration is well suited for addressing this question because it includes a
distance parameter pertaining to how far from a tract a branch can be and still be included in the
tract’s market. I can thus test how the relationship between concentration and bank pricing varies
depending on the market definition. I find that the Local Herfinadhl with a distance parameter of 4
km is most predictive of lending rates. Additionally, I find that the Tract Herfinadhl reflecting the
concentration in the 8kms surrounding a bank’s branches is most predictive of its lending rates.2
The final question I address is how market structure impacts firm financial constraints and
the allocation of capital. Bank market power may help young risky firms obtain credit, as banks
will be more able to build a relationship with them and earn rents in the future (Information
2 Note that these measures reflect different things. The Tract Herfindahl reflects the availability of competitors
within a certain distance of a bank’s branches. The Local Herfindahl is the average concentration of the tracts within
a conventionally defined market. This accounts for the fact that segmentation elsewhere in an MSA may impact the
outside option of nearby borrowers.
85
Hypothesis). Consistent with this, Petersen and Rajan (1995) and Cetorelli and Gambera (2001)
find concentration benefits young firms. Alternatively, young firms are less able to fund themselves
or obtain non-bank external finance, making financial frictions due to bank market power potentially
more damaging (Market Power Hypothesis). Cetorelli and Strahan (2006) and Chong et al. (2013)
find that young firms particularly struggle in concentrated markets.
One reason for the lack of consistent findings could be the inability to measure market power
properly with the conventional Herfinadahl. The within US studies use MSA/rural county market
definitions, while most of the cross country studies assume national banking markets. Ignoring the
spatial distribution of bank branches would be particularly problematic for studying small business
lending, as the opaque information environment exacerbates asymmetric information problems.3
Furthermore, the small loan sizes make any distance related costs to lending proportionally more of
a deterrent.
In order to analyze how market structure relates to firm financial constraints, I test how bank
market structure impacts the ability of firms to expand following a shock to investment demand.
Imperfect financial markets make firm growth more dependent on the availability of internal funds
than on demand, reducing the synchronization between the firm’s growth and the national growth
rate of the industry. I find that in locally concentrated markets firm employment growth is less
responsive to changes in national employment growth, especially for firms less than 6 years old.
However, the conventional MSA concentration is not significantly related to this elasticity for young
firms, suggesting a weaker correlation with firm financial constraints.
Taken together, this paper provides evidence that banks obtain market power stemming from
spatial frictions in lending. More segmented markets allow higher bank markups and result in higher
financial constraints for young firms. This is significant in understanding the potential effects of the
recent consolidation of the banking system. Since the removal of interstate branching restrictions
following the 1994 Riegle-Neal Interstate Banking and Branching Efficiency Act, there has been a
dramatic consolidation of the banking system resulting in more concentrated MSAs. However, the
remaining banks have been more spread across sub-markets within MSAs, causing concentration at
finer levels to decline. As local concentration is found to be what matters for bank pricing, this
suggests an increase in the the competitiveness of the banking sector since 1994, while previous
findings with regard to the trend and effect of market concentration would suggest the opposite.
3 Claessens and Laeven (2005) and Carb´ o-Valverde et al. (2009) present evidence that concentration fails to measure
bank market power as is relevant to young firms. They find that competition is beneficial to growth, when measured
using New Empirical Industrial Organization based measures of market power. However, competition as measured by
low concentration is found to be either unimportant or to have the opposite effect.
86
The remainder of the paper will proceed as follows: Section 2 documents the evidence that
banking is a local activity, and motivates the new measure of concentration. Section 3 describes
the construction of the Local Herfindahl, and compares it to the conventional measure. Section 4
justifies that the new measure better relates to lending rates, deposit rates, and profitability. Section
5 presents an application to the study of financial frictions for young firms. Section 6 concludes.
3.2 Background and Motivation
3.2.1 Bank Concentration and Competition
A primary factor determining the effectiveness of the banking system is its competitiveness. A
monopolist bank may be able to offer low rates on deposits and high rates on loans, bringing
about an inefficiently low quantity of intermediation. Such a bank may also face minimal costs to
inefficiency and be able to enjoy a quiet life with minimal effort engaged in screening investments,
thus harming allocative efficiency. Finally, a bank with sufficient market power may act so as to
support incumbents to whom they’ve made loans at the expense of new entrants and inhibit the
process of creative destruction. Given that a well developed financial system is critical for economic
growth (King and Levine, 1993; Levine, 2005), supporting competition in the banking sector is an
important policy goal.
One of the determinants of the competitiveness of the banking sector seems to the availability
of competitors in the local market. Numerous academic studies have found evidence that banks in
concentrated markets (MSAs or rural counties) behave uncompetitively. High level of concentration
have been found to be associated with low deposit rates (Berger and Hannan, 1989; Heitfield and
Prager, 2004), deposit rates which are less responsive to interest rates (Hannan and Berger, 1991),
high loan rates (Hannan, 1991), and low efficiency (Berger and Hannan, 1998).
The effects of bank competition have also been shown to extend beyond just the banking system.
Within the US, the increase in banking competition following the removal of branching restrictions
was found to increase growth by improving the quality of lending (Jayaratne and Strahan, 1996),
decreasing loan rates (Jayaratne and Strahan, 1998), and decreasing financial barriers to firm entry
(Cetorelli and Strahan, 2006). Cross country evidence also suggests that banking competition is
important. Restrictions on the entry of foreign banks have been found to increase the likelihood
of a banking crisis (Barth et al., 2004), while nationwide concentration in assets has been found to
increase firm financing obstacles in developing countries (Beck et al., 2004).
87
This evidence that bank competition is important, and that banks in concentrated markets
behave uncompetitively, supports US competition policy regarding bank mergers. When evaluating
a potential merger between two banks, the key approach to assessing the likely effect on competition
is to look at the effect on the MSA level deposit Herfindahl. Typically, if the merger leaves the
Herfindahl for an MSA under .18 or doesn’t raise the Herfindahl by over .02, then the Federal
Reserve board will not look an further (ABA, 2006).4
Despite this emphasis on leaving markets unconcentrated, the removal of restrictions on within
state bank branching between 1976 and 1994 and cross-state branching after 1994 have dramatically
changed the structure of the market. Whereas in 1980 there were 14,434 FDIC-Inusured commercial
banks, by 2012 there were only 6,096.5 However, during this time period, the number of offices
increased from 53,172 to 89,805 as larger banks took advantage of the ability to expand into new
markets. This simultaneous decline in the number of institutions and increase in the geographic
spread of these institutions makes how one defines a banking market of critical importance in de-
termining the probable competitive impacts of this consolidation. The exit of small banks makes
concentration at more aggregate levels rise, while the geographic expansion of the branch network
of the remaining banks may have the opposite effect on concentration measured at a finer level.
3.2.2 Importance of Proximity in Banking
Although the MSA level Herfindahl is effective at predicting bank behavior, it is still an open question
whether or not this market definition is the appropriate one for measuring bank concentration. A
second strand of the banking literature has studied the importance of proximity in banking, and
found banking to be very local. Due to transportation costs and the existence of local knowledge,
nearby banks will have an advantage relative to distant banks. This may result in banks achieving
market power depending on the availability of other banks in close proximity.
Kwast et al. (1997) and Brevoort and Wolken (2009) use survey data to document that individual
financial services and small business lending disproportionately occur in branches of banks within a
few miles of their customers. Brevoort and Hannan (2006) use disclosure data from the Community
Reinvestment Act to study the location of small business lending. They find that the probability of
a bank lending to a tract falls rapidly as the distance between the tract and the nearest branch of
a bank grows.
4 As a point of reference, a market where 4 banks held 20% of the deposits each, and two banks held 10% of the
deposits each, would have a Herfindahl of .18. If the two smaller banks merged, this would increase the Herfindahl
by .02.
5 http://www2.fdic.gov/hsob/HSOBRpt.asp
88
Figure 1 demonstrates the Brevoort and Hannan (2006) findings graphically. We see that the
probability of an institution making a loan to a particular tract is declining rapidly in the distance
from the tract centroid to the nearest branch of the institution. Although the levels shift for different
years, the slope seems to change little, suggesting that distance is not becoming less important.
Overall, the probability of a bank making a loan to a tract declines from over 80% if the bank is
very close, to about 30% if the bank is 5 kms away, and the rate of decline slows down thereafter.6
There is also evidence of spatial price discrimination, suggesting that these lending patterns
reflect actual spatial frictions instead of merely being an equilibrium outcome. Degryse and Ongena
(2005) and Agarwal and Hauswald (2010) show that loan rates for a particular bank vary based
on proximity of the borrower to the bank, as well as the proximity of the borrower to the nearest
competitor. Although the former attributes the findings to transportation costs and the latter
location specific soft information, both papers suggest that distant banks may be a poor substitute
for near banks.
3.2.3 Implications for Bank Market Definition
In short, the research on distance and lending has consistently found that proximity to depository
institutions remains highly relevant to the majority of individuals and small firms. These customers
almost exclusively use banks within a few miles of them and are subject to price discrimination
based on their location. Transportation costs and asymmetric access to soft information over space
make it so that distant banks are often not viable substitutes for local banks. The finding that there
are significant difficulties to lending at a distance raises questions regarding the appropriateness of
the MSA level market definition.
To see how the MSA level concentration measure could be problematic, consider the two stylized
banking markets presented in Figure 2. In the left figure, each institution locates in one neighbor-
hood, in the right, each institution is evenly spread across neighborhoods. These markets will have
the same concentration as measured by the MSA Herfindahl, as this measure only depends on the
total market share of each institution without regard to the location of their branches. However, if
people bank predominantly in their own neighborhood, then the competitiveness of the two markets
differ substantially. In one case, every bank has a local monopoly, in the other, every bank has three
competitors wherever it operates.
6 This probably understates the true effect of distance. For one, there is measurement error in distance due to
occasional geocoding errors and due to the fact that I measure distance to centroids instead of actual borrower
locations. These errors will tend to attenuate the observed relationship between lending and distance. Secondly, only
large banks need to report. These large banks are more prone to rely on hard information than engage in proximity
dependent relationship lending, making distance more of a deterrent for the small local banks which don’t report.
89
Although the allocation of branches in Figure 2a is highly stylized, it still may be relevant. Take
for example banking laws in Illinois in the early 90s: banks could have up to five branches, two
could be out of county so long as they were within ten miles of the home office (Rice and Davis,
2007). Even this was a liberalization as previously the only branching allowed was a drive up facility
within 1500 feet of the loan office. Furthermore, banks had one mile of “home office protection”
prohibiting other banks from operating within one mile of their home bank without their permission.
This restriction to branching only locally and protection from outside entry could easily produce the
type of market structure shown in Figure 2a. Although such restrictions have since been removed,
opening a new branch is costly enough that the effects on market structure could persist. As a
result, firms or individuals in a market deemed competitive by the conventional metric could still
contend with having limited banking options within their area.
3.3 Local Concentration Measure
3.3.1 Index Definition
If distance creates obstacles for lending or imposes costs on bank customers, then market level
concentration measures can be misleading. In this case, the level of concentration relevant to a firm
or individual could be the concentration of banks within a certain radius of their location. This is
what I measure.
In order to construct this more local measure of concentration, I use the ARCGIS10 North
American Address Locater to geocode the location of each bank branch in the US from 1994 to
2012. Branch addresses and deposits come from the FDIC’s Summary of Deposits (SOD), an annual
survey of branch offices for all FDIC-insured institutions. It includes commercial banks, thrifts, and
US branches of foreign banks.
I then calculate the deposit Herfindahl for each census tract, using the set of branches within
a certain distance of the tract centroid. Denote sb,d,c the portion of the total deposits in branches
within d kilometers of the centroid of census tract c which are held by institution b ∈ B.7 Then the
tract level herfindahl for the distance parameter would be:
X
HHI tractdc = s2bdc (3.1)
b∈B
7 For example, suppose there are three branches within 2km of tract c: 2 for bank of America and 1 for Wells Fargo.
If the branches have equal deposits, then sBA,2,c = 32 and sW F,2,c = 13 , and thus the Tract Herfindahl would be 95 .
90
My measure of concentration for market m is then the average tract Herfindahl of census tracts in
the market. Denoting Cm the set of census tracts in market m, my measure of concentration is:
1 X X 2
HHI locdm = sbdc (3.2)
|Cm |
c∈Cm b∈B
Consider again the market structure hypothesized in Figure 2 and assume each bank holds equal
deposits. While the conventional Herfindahl would treat the two markets as equally concentrated
at 14 , the Local Herfinadahl would take into account the segmentation within the markets and thus
differ. Where every bank had an equal share in each market in panel (b), the local Herfindahl would
remain 14 , however, where every bank had a local monopoly, the local Herfindahl would be 1.
3.3.2 How Do Local and MSA Concentration Differ?
If local concentration is indeed a better indicator for competition, how well does the conventional
measure capture this? Figure 3 provides scatter plots for the MSA Herfindahl and the Local Herfind-
ahl for various distance parameters. The left panel is for the year 1994 and the right 2012. Of the
most interest are the leftmost columns which have the MSA Herfindahl on the x axis and the Local
Herfindahl at a particular distance on the y axis. Although the MSA and the local concentration
measures are strongly positively correlated, the relationship is far from perfect, especially for finer
distance measures. The bottom panel shows how the correlation of the Local Herfindahl with the
MSA Herfindahl varies by the distance parameter. For the year 2012, the correlation is around .2 at
a distance of 2 kms, and rises to around .8 at 16 kms. The key take away is that if banks compete
over only a small area, the MSA Herfindahl may be a poor proxy for the competitiveness of the local
banking sector.
Another question is how concentration changed following the allowance for interstate branching.
The ability of large banks to expand and take over small banks could result in increases in concen-
tration. However, the removal of entry restrictions could also allow these banks enter areas which
previously had limited banking options. Figure 4 plots average values (weighted by the 2010 popu-
lation) for the MSA Herfindahl and the Local Herfindahl at various parameters for the years 1994
to 2012. Although conventionally measured MSA concentration rises drastically from .105 to .146,
concentration when measured at a more local level is actually declining over time. This suggests
that although larger banks may be expanding their market share and taking over small local banks,
they are increasingly competing over a wider area within each market and thus possibly making
each locality more competitive.
91
To understand what it means for more aggregate concentration to be rising, and local concen-
tration falling, note that the Local Herfindahl can be decomposed into a term relating to the MSA
concentration and a term relating to the variance of banks’ market shares across the sub-markets:
X X
HHI locdm = var(sb ) + s¯2b (3.3)
b∈B b∈B
1 1
(sbc − s¯b )2 , and s¯b =
P P
Where var(sb ) = |Cm | |Cm | sbc . The second term is nearly identical
c∈Cm c∈Cm
to the MSA Herfindahl. Thus to have MSA concentration rising, but local concentration falling,
means that the variance of market shares is falling faster than aggregate concentration has been
rising. Namely, banks are competing more widely within markets, causing deposits in a particular
bank to be spread more evenly throughout MSAs instead of concentrated in one area.
3.4 Local Market Structure and Bank Performance
3.4.1 Data & Methodology
Local and MSA concentration are thus different, but which better reflects the competitiveness of
the banking market? In this section, I demonstrate that the Local Herfindahl and Tract Herfindahl
better relate to variables pertaining to bank pricing or profitability. Namely, the location pattern of
the banks within the market impacts the competitiveness, not just aggregate market shares.
I use balance sheet data for US banks from the Consolidated Report of Condition and Income
(Call Report) to assess how concentration in a bank’s markets impacts their decisions and perfor-
mance. The variables computed from the Call Reports frequently have a few extreme outliers, most
likely due to reporting errors or small denominators, so I drop the lowest and highest 1% of the ob-
servations for each variable every year. I also drop any institutions which merged with or acquired
another institution in a given year.
I don’t have branch level data, so I can’t assess, for example, how Bank of America behaves
in concentrated versus unconcentrated markets. Instead, I use the geocoded branch locations to
calculate the deposit weighted average concentration in the markets (MSA or Rural County) or
tracts that the bank locates in.8 If a bank i has a set of branches j ∈ Ji , with Dj,i,c,m,t denoting
the deposits in branch j, tract c, market m and year t, then the relevant measures of concentration
will be:
8 Over half of the institutions hold deposits in only one market, and about 95% have a single market accounting
for over 50% of their deposits. Results are robust to using concentration in the largest market as the institution level
measure of exposure to concentration
92
1 X
HHI mkti,t = Dj,i,c,m,t HHI mktm,t
Di,t
j∈Ji
1 X
HHI locdi,t = Dj,i,c,m,t HHI locdm,t
Di,t
j∈Ji
1 X
HHI tractdi,t = Dj,i,c,m,t HHI tractdc,t
Di,t
j∈Ji
Where Di,t is aggregate deposits for bank i in year t. Thus I have three bank specific measures
of concentration: market concentration reflecting the availability of competitors within the MSA or
county, local concentration reflecting the availability of competitors in the MSA or county as well
as the degree to which those banks geographically segment the market, and tract concentration,
reflecting the availability of competitors in the areas directly surrounding a bank’s branches. I then
run regressions to assess which concentration measure impacts bank decision variables:
yi,t = α + β 0 HHIi,t−1 + γ 0 Xi,t + δt + ηi + i,t (3.4)
Where yi,t is the institution level outcome variable from the Call Reports data relating to either
bank pricing or profitability. δt and ηi are year and institution fixed effects respectively. HHIi,t−1
is a vector of variables relating to the institution’s exposure to concentration, and Xi,t is a set of
controls.9 Standard errors are clustered by bank.
If the relevant market is the more aggregate one, as assumed by most the previous literature, then
one would expect the conventional market Herfindahl to be significant, and the others insignificant
after controlling for the conventional measure. In this situation, tract concentration would be subject
to measurement error due to the fact that it excludes far off banks which are in fact relevant. Local
concentration would be subject to measurement error as the component of the measure relating to
the spread of the institutions within the market would just be adding noise to the proper measure.
However, if the relevant market is highly local, we get different predictions. In this case, it is more
important to have competing banks in the near area than in the MSA. The conventional Herfindahl
would be flawed as it identifies an area as competitive if there is low concentration at a highly
aggregate level, even if individual banks are able to dominate smaller segments of the market. The
Local Herfindahl would account for this segmentation and should thus better predict bank markups.
The Tract Herfindahl directly measures the concentration in the area around the bank’s branches,
9X
i,t includes log total deposits, log number of branches, share of market deposits in banks headquartered in
another state, and the bank’s market share in its largest market. None of the estimates are very sensitive to the
control set, so it is emphasized little throughout the rest of the paper.
93
and should thus also predict markups better than the conventional Herfindahl. Generally, the data
support the claim that local concentration is what matters.
3.4.2 Effects of 6km Local Concentration
Lending Rates Table 1 shows the relationship between concentration and lending rates. In
loan income 10
Columns 1-3, the dependent variable is the average rate charged for lending: 100 × loans .
If higher concentration results in greater market power, then this should allow banks to charge
borrowers higher interest rates.
Column 1, suggests that this is indeed the case. The coefficient implies that a one standard
deviation increase in market concentration raises the lending rate by about 3 basis points, which
is economically small but statistically significant at the 1% level. When local concentration is
included in a horse race regression in Column 2, the coefficient on the conventional Herfindahl falls
by almost 75% and becomes insignificant while local concentration is significant at the 1% level. Even
controlling for the conventional Herfindahl, a one standard deviation increase in the local Herfindahl
raises the lending rate by about 9 basis points. Column 3 additionally includes the concentration in
the 6km neighborhood surrounding the bank branches, which is significant at the 1% level with a
one standard deviation increase raising the average lending rate by 6 basis points, after controlling
for the market level concentration measures. The local concentration measure remains significant at
the 5% level and larger in magnitude than the conventional Herfindahl.
This measure of lending rates can be problematic as not all lending is likely to be sensitive to
local market structure. For example, mortgage lending is less sensitive to the common information
problems from lending at a distance due to the fact that the loans are collateralized and easily sold
on the secondary market.
Next I look at the rate charged on commercial and industrial loans. If a bank is close to a firm, it
may reduce the costs of monitoring and thus help the bank overcome some of the agency problems
associated with debt finance. Proximity may be particularly important for small businesses for
whom hard information such as a history of financial statements is unavailable. If soft information
is proximity dependent, then it may be that only banks close to a business are willing to lend.
This would make the distinction between market concentration and local concentration particularly
important for commercial lending.
In Columns 4-6, the dependent variable is the average interest rate charged on commercial and
10 Income and expenditure measures like loan income and deposit interest are totals for the calendar year, while
stock variables like loans and deposits are the average of the first and fourth quarter reported value.
94
industrial loans (business loans not secured by real estate). In Column 4, we see that the magnitude
of the effect of conventionally measured concentration is higher by about a third compared to
when average loan rate was the dependent variable, although it is insignificant.11 Adding local
concentration in Column 5 causes the coefficient to become virtually zero. A one standard deviation
increase in local concentration, controlling for market concentration, is found to raise commercial
and industrial lending rates by 20 basis points, although it is only significant at the 10% level. The
most dramatic effect is seen for the concentration in the 6kms surrounding a bank’s branches. A
one standard deviation increase in the tract concentration, holding market and local concentration
constant, raises the lending rates by 17 basis points. The coefficient on local concentration falls by
almost 60% and becomes insignificant while, conventionally measured concentration remains near 0.
Deposit Rates How are depositors are impacted? Table 2 shows that the increased returns on
loans in concentrated markets are not passed on to depositors. In Columns 1-3, the independent
interest on deposits
variable is the interest rate on deposits: 100 × deposits , in Columns 4-6 it is the service
service charges on deposits
charge rate on deposits: 100 × deposits .
In Column 1, we see that higher conventional concentration is associated with lower rates offered
on deposits. A one standard deviation increase lowers the average deposit rate by 2.5 basis points,
and is significant at the 1% level. Controlling for local concentration causes the coefficient to fall
by almost half, while local concentration is significant at the 1% level. A one standard deviation
increase in local concentration lowers the deposit rate by 4.5 basis points. Tract concentration in
Column 6 is insignificant.
The distinction between local and MSA concentration is less significant for deposit rates than
for other variables. This could be attributed to deposit insurance ensuring that deposit accounts are
roughly homogeneous, thus removing any room for local information to be relevant. Alternatively,
since ATMs are not counted as bank branches, the geographic spread of branches may not reflect
access to bank services as well as for loans, where the interactions are with loan officers located in
a bank branch.
Columns 4-6 show that local concentration not only results in lower interest payments to deposi-
tors, but also results in depositors needing to pay more in service charges. Conventionally measured
concentration in Column 4 is unrelated to the service charge rate, however local concentration is
found to increase the service charge rate and is significant at the 1% level. When the 6km concentra-
11 Until 2001, only larger banks had to report how much income was derived from commercial and industrial loans.
The lack of significance is likely attributable to the greater weight place on crisis years, and on larger banks who are
less sensitive to local market structure
95
tion is added, it is also significant at the 1% level in the same direction, while the local concentration
measure remains significant at the 1% level.
Profits Finally, Table 3 demonstrates that the increased interest margins translates into higher
N et Income
profits. In the first three columns, profits are measured by the return on equity: 100 × Equity ,
N et Income
in the second three it is measured as the Net Profit Margin: 100 × Income . Qualitatively the
results are similar for either measure of profitability. Banks in more concentrated markets earn
higher profits, with market concentration being significant at the 5 or 10% level. Including local
concentration makes market concentration insignificant, while the predicted impact of a 1 standard
deviation increase in concentration is about 4 times as large as with the conventional concentration
measure. Tract concentration is insignificant.
Overall, these results provide support for the idea that the local market structure impacts bank
performance. In concentrated markets, banks charge higher interest rates, offer lower deposit rates,
have higher service charges, and earn higher profits. These effects are uniformly larger in magni-
tude and significance when local concentration is used instead of market concentration. Market
concentration only remains significant in the horse race regression for deposit rates, even then the
predicted impact is greatly diminished. This is all consistent with local concentration being the
superior measure of market power, and raises the possibility that previous failures to support the
structure-conduct performance paradigm were due to attenuation bias stemming from the mismea-
surement of market power.
3.4.3 Effects by Distance Parameter
For which distance parameter does concentration best relates to bank markups? The results thus
far with the 6km distance parameter suggest that market structure at a finer level than the MSA
determines the competitive pressures faced by banks, but they do not answer the question of how
fine a measure is needed. Here I assess the proper geographic extent of bank markets by testing
which distance best relates to bank markups.
This exercise is particularly important in light of the findings in Figure 4, which shows that while
concentration at the MSA level has been rising over time, concentration at a finer level has been
falling. On average, there are more banks available within 4km than in the past, but fewer banks
available within 10km. This makes the presumed competitive effects of the recent consolidation
depend critically on the proper market definition. If concentration for higher parameter values is
found to matter, then the recent consolidation is likely to have harmed competition. If instead lower
96
parameters are found to be more predictive, then the decline in highly local concentration would
suggest that the diminished tendency of banks to segment the market would more than make up for
the increase in concentration at more aggregated levels.
Figure 5 shows how the findings for the relationship between concentration and lending rates
vary by distance parameter. Each point represents the result of a horse race regression following
the specification in equation 3. The left panel includes the conventional Herfindahl and the Local
Herfindahl for a particular distance parameter. The distance parameter is given on the X-axis, with
a value of 0 being the regression with just the conventional Herfindahl. The solid black line gives the
coefficient for the Local Herfindal, and the dashed line the 95% confidence interval. The light gray
line is the coefficient on the conventional Herfindahl. To facilitate comparison, all the concentration
measures are standardized, so the value on the y-axis can be interpreted as the percentage point
increase in lending rates from increasing concentration by one standard deviation.
The coefficient on local concentration is maximized when the distance parameter is 4 kms. Here
a one standard deviation increase in local concentration is found to raise the lending rate by about
10 basis points, compared to about 1 basis point for the conventional Herfindahl. The coefficient for
local concentration declines monotonically as the distance parameter increases after 4.
The right panel presents the same analysis as the left, except it uses the concentration around
the institution’s actual branches instead of average local concentration in the markets where the
institution has branches. For every distance, tract concentration is significant at at least the 5%
level and larger than the coefficient on the conventional Herfindahl. The maximum occurs at a
distance of 8km, with a coefficient just under .075.
Figure 6 does the same for the commercial and industrial loan rate. The pattern with the local
Herfindahl in the left panel is comparable to before. The coefficient is maximized at 4kms, mostly
declines thereafter, and the effect of local concentration is found to be higher than for conventional
concentration so long as the distance parameter is less than 12km. The magnitude of the effect is
much higher than before, with the coefficient taking a maximum value around .25 as opposed to .1,
but it is much less precisely estimated, with the effect only being statistically significant from 0 at
the 5% level for the distance parameter of 4kms.
The right panel has a clearer message. The coefficient for tract concentration is maximized
at 8km, and declines linearly for smaller or larger coefficients. A one standard deviation increase
in concentration in the 8km around a bank’s branches raises the rate charged on commercial and
industrial loans by over 25 basis points, with the coefficient on conventionally measured concentration
essentially being 0. Increasing or decreasing the distance parameter by 4km, cuts the predicted
97
impact by almost half. Namely it is the banks within 5 miles that account for the competitiveness
of a bank’s commercial lending rates, and when this concentration is controlled for, conventional
concentration has no explanatory power.
3.5 Local Concentration and Young Firm Finance
3.5.1 Background
One direction where an improved measure of banking concentration could yield substantial gains
is in the study of how bank market structure impacts the allocation of credit. Whether bank
market power is beneficial or harmful to the ability of young firms to obtain finance is theoretically
ambiguous while empirical work has been inconclusive.
The findings of the last section support the claim that local concentration provides banks with
market power, however the aggregate implications of this market power regarding the allocation of
credit are not straight forward. One school of thought is that concentration is beneficial for young
firms. Petersen and Rajan (1995) argue that banks may lend to young firms at subsidized rates
because they can extract rents in the future. In competitive markets without the same potential for
future capture, banks may be unwilling to lend to young, informationally difficult, firms profitably.
Using data from the Survey of Small Business Finance, they demonstrate that in concentrated
markets (measured as the deposit Herfindahl for the MSA or rural county), younger firms are
charged lower interest rates and receive more credit. Similarly, Cetorelli and Gambera (2001) find
that bank concentration promotes the growth of financially dependent sectors by improving credit
access to young firms, despite exerting a negative effect on growth overall.12 Seeing as young firms
are a significant driver of job creation (Haltiwanger et al., 2013), this raises the question of whether
the greater market power is actually a problem.
However, there are good reasons to think that young firms would be especially harmed by con-
centration. Young firms may struggle to obtain non-bank external finance due to their opaqueness or
their size making the fixed costs of securities issuances problematic. Furthermore, young firms may
struggle to self finance due to not having had sufficient time to accumulate earnings. This is further
amplified if young firms face start up costs resulting in low initial profitability. Consistent with this,
Cetorelli and Strahan (2006) shows that MSA concentration has a negative effect on business entry
while Beck et al. (2004) and Chong et al. (2013) find that concentration is associated with greater
12 See Fischer (2000) and Zarutskie (2003) for more evidence in support of the information hypothesis
98
financial constraints for small businesses.
3.5.2 Methodology
On top of the difficulties measuring banking concentration, determining the extent to which firms
are financially constrained is problematic. First, one can not simply run growth regressions with
concentration as an explanatory variable as high growth for young firms could either be attributed to
a lack of financial constraints fueling capital accumulation, or to the existence of financing constraints
forcing firms to enter at an inefficiently small scale then grow fast with the use of retained earnings.13
Second, comparing the age or size distribution of firms in concentrated and unconcentrated markets
is problematic as the entry of new firms could allow more banks to profitably operate, creating
concerns over the direction of causality. Finally, firm level measures of constraints tend to only be
available for publicly listed firms who are less financially constrained and less bank dependent.
In this section I test how well firms in an MSA are able to expand following a demand shock.
With perfect capital markets, firms should face an elastic capital supply curve and thus be able to
increase their scale substantially when it is profitable to do so. However, if firms are financially
constrained, investing more than can be financed with their own cash flows may be prohibitively
costly. If labor and capital are complements, this diminished sensitivity of capital to demand due
to financial frictions will result in a similarly diminished sensitivity of employment to demand.
If firms use finance to hire labor, the effects can be even more dramatic, with financial frictions
directly inhibiting employment expansion instead of operating indirectly through capital. Either
way, financially constrained firms will increase labor less in response to a positive.
Specifically, I instead investigate how the elasticity of local employment growth with respect to
the national growth in the local industries varies by bank market structure.14 An increase in national
employment in a given industry is likely to reflect some sort of national shock to productivity, demand
or expectations, increasing the return to investment in areas with employment in the industry, but
unrelated local financial markets. In the appendix, I show that this elasticity is a decreasing function
of the elasticity of the marginal cost of capital with respect to capital of the firms in the city.
Figure 7 provides the graphical intuition for the relationship between financial frictions and the
response to demand shocks. Consider, a firm with a downward sloping capital demand curve for
capital. For a given shift in demand, the output response will depend on the shape of the loan offer
13 Kerr and Nanda (2010) show that following an increase in competition due to branch deregulation, firms entered
closer to their maximum size.
14 This exercise is similar in spirit to Wurgler (2000), which demonstrates that countries with a larger financial
sector allocate more investment to growing industries and less to declining industries.
99
function. If there is premium for external finance as in Gerali et al. (2010) or C´
urdia and Woodford
(2010), then firms for whom the marginal product of capital is within the interest rate wedge would
not adjust their capital stock. Since high local concentration is found to be associated with higher
rates on commercial loans, this means that there is a wider range of productivities such that firms
would be unwilling to borrow in response to a demand shock. These firms will have capital growth
determined by cash flows and thus be less synchronized with national employment growth. If firms
have leverage constraints as in Buera and Shin (2013) or Moll (2014), then productive firms will
be unable to borrow to fund expansion up to the point where the marginal product of capital is
equal to the interest rate. Again constrained firms will have growth depend on asset growth instead
of productivity growth. Finally, if higher leverage increases expected bankruptcy costs as in costly
state verification model (Townsend, 1979), this would result in an upward sloping marginal cost of
capital curve.
3.5.3 Data and Specification
My strategy is to test for financing constraints by examining how responsive firm growth is to a
shock to the return to investment. I follow Bartik (1991) and use national industry level growth
as a shifter of demand which is exogenous to local conditions. By using exogenous demand shocks,
this methodology overcomes the reverse causality concerns mentioned before. Secondly, by using
aggregated data, I’m able to include small and young firms, who would be more sensitive to bank
competition but, by virtue of not being publicly listed, have little available data.
The measure of the demand shock is:
I
X
shockm,t = θi,m,t−1 emp growthi,t
i=1
where θi,c,t−1 is the share of private march employment in MSA m in industry i in previous year,
and emp growthi,t is HP-filtered national employment growth in the industry for the year.
The primary specification used is:
growthm,t = α + β1 shockm,t + β2 shockm,t × HHIm,t + β3 HHIm,t + δt + ηm + m,t
where growthm,t is MSA level wage or employment growth. HHIm,t is a measure of concentra-
tion, either the conventionally measured MSA concentration, or the 6km local concentration. The
concentration measure is normalized, so the coefficient on shockm,t is the elasticity of local growth
100
with respect to national growth in an MSA with an average concentration, while the coefficient
on shockm,t × HHIm,t is how much the elasticity falls when concentration rises by one standard
deviation. If concentration is associated with higher financial frictions, this would imply β2 < 0,
so that higher concentration results in a smaller elasticity of MSA growth with respect to national
growth.
Industry level data comes from the Quarterly Census of Employment and Wages (QCEW) for
the years 1990 to 2006 at the three digit 2002 NAICS level. This is used to calculate the national
growth at the industry level, and the employment share at the industry×MSA level to create the
shock variable. It is also used to calculate wage and employment growth at the MSA level. The
QCEW doesn’t include data disaggregated by firm age. I use the Business Dynamic statistics (BDS)
to calculate the annual employment growth rate of young firms and mature firms at the MSA level,
where I define young firms as firms which are 0-5 years old and mature firms as firms which are 6
years or older. A more detailed description of the data is in the appendix.
3.5.4 Findings
Table 4 presents the results without the age breakdown. The first three columns use the 6km
local Herfindahl as the measure of concentration, and the last three use the conventional MSA
level concentration. National growth in the local industry is unsurprisingly strongly with MSA
employment growth. A one percentage point shock results in employment growth of .87 and .96
percentage points for MSAs with an average local and MSA concentration respectively. Of greater
interest is how this elasticity varies across cities. Looking at the coefficient on the interaction term,
we see that cities with a 1 standard deviation higher local Herfinadahl have an elasticity of growth
with respect to the Bartik instrument which .16 percentage points lower. Having a 1 standard
deviation higher conventional concentration lowers the elasticity by .1 percentage points.
The findings when the QCEW employment growth measure is used is comparable to with the
BDS, with the difference between the effect of local concentration vs MSA concentration being
slightly starker. Unlike the BDS, the QCEW includes wage data, allowing me to check if the
findings are attributable to differences in the elasticity of labor supply. Higher local concentration
results in wages which are less responsive to demand shocks, suggesting that heterogeneity in the
response to demand shocks is attributable to differences in the size of the labor demand shift instead
of differences in the elasticity of labor supply. However, the coefficient is insignificant for local
concentration and even slightly positive for MSA concentration.
101
The goal of the section is to show how concentration relates to financial frictions by age group.
Table 5 conducts the analysis for the BDS data broken down by firm age. The first two columns
demonstrate that local concentration is particularly harmful for firms which are five years old or
younger. A one standard deviation increase in local concentration lowers the elasticity of employment
growth with respect to the Bartik instrument by .34 percentage points for young firms but only .12
for mature firms. These effects are .1 and .08 respectively when conventional concentration is used,
with the effect of concentration on young firms becoming insignificant.
In short, local concentration is found to inhibit growth following a demand shock, particularly for
young firms. This is consistent with the hypothesis that young firms are more reliant on bank finance,
and are thus disproportionately harmed by a low degree of competition. The fact that concentration
is found to inhibit growth for young firms only when the local measure is used, suggests that previous
studies relating concentration to young firm finance may have not been measuring concentration as
is relevant to young firms. Finally, having local concentration matter more for young firms raises
confidence that the channel of influence is actually through bank relationship lending. Young firms
are more bank dependent and informationally opaque, making proximity and the access to soft
information it provides more important. Thus it makes perfect sense that the location patterns of
the banks in an MSA are more important for young than mature firms.
3.6 Conclusion
There has been a debate in the banking literature as to whether the proper extent of a bank
market is the state or something more local, namely the county or MSA. I present evidence that
the proper market is actually even more local than previously suggested. My proposed measure of
concentration, which uses the average concentration of small sub-markets within the conventionally
defined banking markets, consistently outperforms the conventional MSA/county level Herfindahl in
horse race regressions. This suggests that it isn’t enough to have many banks competing in the same
market, instead competitiveness requires banks to locate near competitors as opposed to clustering
and dominating areas within the market. The conventional Herfindahl measure doesn’t account for
the location of branches within markets and thus fails to effectively measure concentration.
These findings are relevant to merger policy, which frequently use the market Herfindahl to assess
the likely competitive effects of a horizontal merger. In light of the results of this paper, it isn’t
sufficient to look at the change in market shares within the market, but instead one must look at
the locations of the acquiring and acquired branches. If the two banks tend to locate in different
102
sub-markets, the merger is unlikely to be damaging. However, if the bank branches tend to be close
to one another, the merger is more likely to result in a decline in the competitiveness of the bank
market.
These findings are also relevant to the academic literature, which frequently uses a market
Herfindahl to measure competition. Studies which use it as a control variable may not be suf-
ficiently controlling for bank market power. Studies which have concentration as the variable of
interest may be understating its effects due to this measurement error. For example, while the
market level Herfindahl isn’t significantly related to the responsiveness of young firms to a demand
shock, the local concentration is strongly related. Consequently, this improvement in the measure-
ment of concentration is important for understanding the effects of bank market structure on the
allocation of credit and growth.
Finally, these findings are important in the construction of an accurate narrative of the consoli-
dation of the banking sector over the last 20 years. The nearly 40% rise in the MSA Herfindahl after
the onset of interstate branching could be a cause for concern given the existing findings that this
concentration is associated with higher interest margins and barriers to firm entry. However, upon
closer analysis, we see that local concentration is what is associated with a lack of competitiveness
on the part of banks. That concentration at this level is falling should assuage some of the fears
regarding the competitive effects of the consolidation of the banking industry.
103
3.7 Appendix
3.7.1 Figures
Impact of Distance−By Year
1 .8
Probability of lending
.4 .6
.2
0 5 10 15
Distance to Nearest Branch (kms)
1997 2001
2005
Figure 3.1: Declining Probability of Lending Over Distance
Notes: This shows how the probability of a CRA reporting institution making a small business loan in a particular
census tract varies with distance between the tract centroid and the nearest branch of an institution. Probabilities
are determined by local linear regression on the 0-1 loan indicator using an Epanechnikov kernel with a bandwidth of
.2 kilometers. This is done separately for the years 1997, 2001 and 2005.
104
(a) Locally Concentrated (b) Locally Competitive
Figure 3.2: Hypothetical Bank Markets
Notes: This figure presents two hypothetical bank markets. Each letter corresponds to a branch location of a particular
institution. The left figure presents a locally concentrated market, as the branches of each institution cluster in one
part of the market, giving it a local monopoly. The right figure presents a locally competitive market, with the
branches spread evenly throughout and competing with the other institutions in every locality.
105
1994 Concentration Measures 2012 Concentration Measures
HHI_msa HHI_msa
.6 .6
.4 .4
HHI_12km HHI_12km
.2 .2
0 0
.6 .6
.4 .4
HHI_8km HHI_8km
.2
.2
0
.8 .8
.6 .6
HHI_4km HHI_4km
.4 .4
.2 .2
0 .2 .40 .2 .4 .6 0 .2 .4 .6 0 .50 .2 .4 .6 .2 .4 .6
MSA and Local HHI Correlation, 1994 MSA and Local HHI Correlation, 2012
1
1
.8
.8
.6
.6
.4
.4
.2
.2
0
0
0 5 10 15 0 5 10 15
distance distance
Figure 3.3: How Similar Are MSA and Local Concentration?
Notes: The top panels present scatter plots for the year 1994 (left) and 2012 (right). The left most column of each
scatter plot has the conventional Herfindahl on the x axis, and the 4, 8, or 12 km local concentration on the y. The
other scatters are of the local concentration measure for different distances. A small number of MSAs with an MSA
concentration above .5 are dropped to help with the display of the points in the more common range of values.
The bottom graphs display the correlation of the local concentration measure with the conventional MSA concentration
measure for different distances. Again 1994 is on the left and 2012 on the right.
106
Concentration Trends
−.04−.03−.02−.01 0 .01 .02 .03 .04
Concentration
1995 2000 2005 2010
Year
MSA HHI 16km HHI
12km HHI 8km HHI
4km HHI
Figure 3.4: Different Trends in Concentration
Notes: This figures display the trend in concentration as measured by the 4, 8, 12, and 16km local concentration
and the conventional MSA Herfindahl from 1994 to 2012. Each point gives the 2010 population weighted average
concentration of the all MSAs in the US for a given year. All values are relative to their respective 1994 concentration
level.
107
Dependent Variable: Loan Rate
.1
.15
.08
.1
Regression Coefficient
Regression Coefficient
.06
.05
.04
0
.02
−.05
0 5 10 15 0 5 10 15
Distance (kms) Distance (kms)
Local HHI MSA HHI Tract HHI MSA HHI
Local 95% CI Tract 95% CI
Figure 3.5: Impact of Concentration on Loan Rates
Notes: Each point reports coefficients from regressions of the form :Loan rate = α + βConcentrationi,t + γXi,t +
δt + ηi + i,t . In the left panel Concentrationi,t includes conventional concentration and local concentration, in the
right it includes conventional concentration and tract concentration. The distance parameter is given on the x-axis,
with a value of 0 being the specification where only conventional concentration is included. Concentration measures
are standardized to having a standard deviation of 1. Standard errors are clustered by bank.
108
Dependent Variable: Commercial & Industrial Loan rate
.6
.4
.3
.4
Regression Coefficient
Regression Coefficient
.2
.2
.1
0
0
−.2
−.1
0 5 10 15 0 5 10 15
Distance (kms) Distance (kms)
Local HHI MSA HHI Tract HHI MSA HHI
Local 95% CI Tract 95% CI
Figure 3.6: Impact of Concentration on Commercial Loan Rates
Notes: Each point reports coefficients from regressions of the form :CI Loan rate = α + βConcentrationi,t + γXi,t +
δt + ηi + i,t . In the left panel Concentrationi,t includes conventional concentration and local concentration, in the
right it includes conventional concentration and tract concentration. The distance parameter is given on the x-axis,
with a value of 0 being the specification where only conventional concentration is included. Concentration measures
are standardized to having a standard deviation of 1. Standard errors are clustered by bank.
109
Figure 3.7: Financial Constraints and Response to Demand Shocks
M C Capital M C Capital M C Capital
Supply
Supply
Supply
rext
Demand Demand Demand
rint rint rint
Capital Capital Capital
assets λ × assets assets
(a) External Mark-up (b) Leverage Constraint (c) Costly Verification
Notes: Each figure shows an example of how financial constraints would make a firm’s growth less responsive to a
demand shock. If there is a premium to external finance as in (a), there is a wedge between the cost of internal funds
and external funds creating a range of productivities such that a firm will not increase capital when it becomes more
productive. In (b), there is a leverage constraint, so that firms above a certain level of productivity will grow only
when assets grow, not when demand grows. In (c), there is an upward sloping loan offer curve reflecting a greater
probability of default at higher leverage. As the curve gets steeper, expansion becomes more costly and firms will
respond less to changes in demand.
110
3.7.2 Tables
Table 3.1: Impact of Concentration on Loan Rates
Loan Rates CI-Loan Rates
(1) (2) (3) (4) (5) (6)
HHI mkt 0.249∗∗∗ 0.0660 0.0528 0.337 -0.0395 -0.0989
(2.80) (0.69) (0.55) (0.95) (-0.09) (-0.23)
HHI loc 0.396∗∗∗ 0.250∗∗ 0.861∗ 0.380
(3.44) (2.12) (1.67) (0.70)
HHI tract 0.169∗∗∗ 0.556∗∗∗
(3.40) (2.77)
Observations 136705 136705 136705 76663 76663 76663
Year FE Yes Yes Yes Yes Yes Yes
Bank FE Yes Yes Yes Yes Yes Yes
Controls Yes Yes Yes Yes Yes Yes
t statistics in parentheses
* p < 0.10, ** p < 0.05, *** p < 0.01
Notes: This table reports coefficients from regressions of the form :yi,t = α + βConcentrationi,t + γXi,t + δt + ηi + i,t .
The dependent variable is the average loan rate in Columns 1-3 and the average loan rate for commercial and industrial
loans in Columns 4-6. In Columns 1 &4, concentration is the average market Herfindahl for the markets in which
the institution holds deposits, weighted by deposits. Columns 2 & 4, add the average 6km local concentration for the
bank. Columns 3 & 6 additionally includes the average deposit concentration of the banks operating within 6km of
tracts where the bank’s branches are located, again weighted by deposits. All specifications cover the years 1994 to
2010 and include year and institution fixed effects and bank controls. Standard errors are clustered by institution.
111
Table 3.2: Impact of Concentration on Deposit Rates
Deposit Rates Service Charge Rates
(1) (2) (3) (4) (5) (6)
HHI mkt -0.212∗∗∗ -0.120∗∗ -0.117∗∗ 0.00315 -0.0794∗∗∗ -0.0828∗∗∗
(-4.05) (-2.03) (-1.97) (0.12) (-2.77) (-2.89)
HHI loc -0.200∗∗∗ -0.166∗∗ 0.178∗∗∗ 0.143∗∗∗
(-2.79) (-2.23) (4.64) (3.67)
HHI tract -0.0384 0.0408∗∗∗
(-1.20) (2.73)
Observations 137196 137196 137196 135829 135829 135829
Year FE Yes Yes Yes Yes Yes Yes
Bank FE Yes Yes Yes Yes Yes Yes
Controls Yes Yes Yes Yes Yes Yes
t statistics in parentheses
* p < 0.10, ** p < 0.05, *** p < 0.01
Notes: This table reports coefficients from regressions of the form :yi,t = α + βConcentrationi,t + γXi,t + δt + ηi + i,t .
The dependent variable is the average rate on deposits columns 1-3 and the average service charge rate in columns
4-6. In columns 1 &4, concentration is the average market Herfindahl for the markets in which the institution holds
deposits, weighted by deposits. Columns 2 & 4, add the average 6km local concentration for the bank. Columns 3
& 6 additionally includes the average deposit concentration of the banks operating within 6km of tracts where the
bank’s branches are located, again weighted by deposits. All specifications cover the years 1994 to 2010 and include
year and institution fixed effects and bank controls. Standard errors are clustered by institution.
112
Table 3.3: Impact of Concentration on Bank Profits
Return on Equity Net Profit Margin
(1) (2) (3) (4) (5) (6)
HHI mkt 1.626∗ -0.374 -0.338 3.348∗∗ -0.924 -0.970
(1.68) (-0.34) (-0.31) (2.29) (-0.55) (-0.58)
HHI loc 4.392∗∗∗ 4.722∗∗∗ 9.345∗∗∗ 8.904∗∗∗
(3.25) (3.38) (4.36) (4.10)
HHI tract -0.385 0.509
(-0.71) (0.65)
Observations 137185 137185 137185 137218 137218 137218
Year FE Yes Yes Yes Yes Yes Yes
Bank FE Yes Yes Yes Yes Yes Yes
Controls Yes Yes Yes Yes Yes Yes
t statistics in parentheses
* p < 0.10, ** p < 0.05, *** p < 0.01
Notes: This table reports coefficients from regressions of the form :yi,t = α + βConcentrationi,t + γXi,t + δt + ηi + i,t .
The dependent variable is the return on equity in Columns 1-3 and the profit margin in Columns 4-6. In Columns 1
&4, concentration is the average market Herfindahl for the markets in which the institution holds deposits, weighted
by deposits. Columns 2 & 4, add the average 6km local concentration for the bank. Columns 3 & 6 additionally
includes the average deposit concentration of the banks operating within 6km of tracts where the bank’s branches are
located, again weighted by deposits. All specifications cover the years 1994 to 2010 and include year and institution
fixed effects and bank controls. Standard errors are clustered by institution.
113
Table 3.4: Concentration and Response to Demand Shocks
Employment Growth Wage Growth Employment Growth Wage Growth
BDS QCEW QCEW BDS QCEW QCEW
Market Definition 6km 6km 6km MSA MSA MSA
(1) (2) (3) (4) (5) (6)
shockc,t 0.870∗∗∗ 0.720∗∗∗ 0.270 0.958∗∗∗ 0.791∗∗∗ 0.288∗
(4.68) (5.12) (1.61) (5.29) (5.58) (1.78)
shockc,t × HHIc,t -0.162∗∗∗ -0.149∗∗∗ -0.0630 -0.0977∗∗ -0.0631∗∗ 0.0140
(-3.88) (-4.88) (-1.45) (-2.44) (-2.44) (0.42)
HHIc,t 0.0000597 -0.00193 -0.000437 -0.000387 -0.00188∗∗∗ -0.00129∗
(0.04) (-1.35) (-0.21) (-0.57) (-2.78) (-1.66)
N 4322 4227 4229 4322 4227 4229
t statistics in parentheses
* p < 0.10, ** p < 0.05, *** p < 0.01
Notes: This table reports regressions of the form growthc,t = α + β1 shockc,t + β2 shockc,t × HHIc,t + β3 HHIc,t +
δt + ηc + i,t . growthc,t is either the growth in employment or payroll per worker for the MSA. shockc,t is the
average growth in national industries, weighted by the share of MSA employment in the industry. HHIc,t is the 6km
local Herfindahl in columns 1-3 and the conventional MSA Herfindahl in columns 4-6, normalized to have a mean of
zero and standard deviation of 1. MSA and year fixed effects are used in each specification, and standard errors are
clustered by MSA.
114
Table 3.5: Concentration and Response to Demand Shocks: By Age
Young Mature Young Mature
Market Definition 6km 6km MSA MSA
shockc,t 1.265∗∗ 0.733∗∗∗ 1.422∗∗∗ 0.799∗∗∗
(2.45) (4.15) (2.70) (4.65)
shockc,t × HHIc,t -0.335∗∗ -0.118∗∗∗ -0.0970 -0.0781∗∗
(-2.57) (-2.96) (-0.89) (-2.18)
HHIc,t -0.00306 -0.000380 -0.00144 -0.000309
(-0.59) (-0.21) (-0.72) (-0.44)
N 4287 4322 4287 4322
t statistics in parentheses
* p < 0.10, ** p < 0.05, *** p < 0.01
Notes: This table reports regressions of the form growthc,t = α + β1 shockc,t + β2 shockc,t × HHIc,t +
β3 HHIc,t + δt + ηc + i,t . growthc,t is the growth in employment for age category in the MSA. shockc,t
is the average growth in national industries, weighted by the share of MSA employment in the industry.
HHIc,t is the 6km local Herfindahl in columns 1 & 2 and the conventional MSA Herfindahl in columns 3
&4, normalized to have a mean of zero and standard deviation of 1. MSA and year fixed effects are used
in each specification, and standard errors are clustered by MSA.
115
3.7.3 Decomposition
One can think of a market as being uncompetitive for two reasons. First, it could be dominated by
a few small firms, enabling tacit collusion with loan or deposit pricing. A second source of a lack of
competitiveness could be that banks tend to concentrate their branches geographically and exhibit
market power in small areas. Conveniently, the local Herfindahl can be simply decomposed into a
component relating to the market level concentration and a component relating to within institution
clustering. After some algebra15 , the local Herfindahl can be written as:
B
X B
X
var(sb ) + s¯2b
b=1 b=1
T T
1 1
(sbt − s¯b )2 , and s¯b =
P P
Where var(sb ) = T T sbt . The first part of this expression can be
t=1 t=1
seen as a measure of the degree to which branches of an institution cluster around other branches
of the institution. A high variance of local deposit shares mean that there are some areas where the
banks hold a very large portion of deposits, and some where they hold very little. Even if a market
is unconcentrated, if the institutions within it dominate the areas in which they operate, then each
locality may be uncompetitive. Lower variance means that banks are more evenly spread across the
market.
B
s¯2b , is similar to the conventional Herfindahl16 . The
P
The second part of this expression,
b=1
difference is that instead of sb being measured as the share of deposits in the market held by bank
b, s¯b is the average share held by bank b across the different sub-markets. These measures of market
share differ for two reasons. First, increasing deposits in an area increase measured MSA market
share more than it raises s¯ if there are already a lot of deposits in that area as the denominator
(total deposits) is larger in that region. Second, there may be double counting as deposits in a bank
included in the intersection of multiple localities will be adding deposits to several markets.
15
T B
1 XX 2
HHIlocal = s
T t=1 b=1 bt
T B
1 XX
(sbt − s¯b )2 + 2sbt s¯b − s¯2b
=
T t=1 b=1
B T
" #
X 1 X
(sbt − s¯b )2 + s¯2b
=
b=1
T t=1
16 It has a correlation coefficient over .7
116
3.7.4 Theory
Suppose that production utilizes technology z and inputs capital k and labor l to produce output
according to the function f (z, k, l). Where f has positive first derivatives, positive cross derivatives
and negative second derivatives. Firms hire labor in a competitive market at cost w. However, due
to financial frictions they face a weakly increasing marginal cost of capital curve c(k, a) where ck ≥ 0
and a indexes borrowing costs so that ca ≤ 0.17 The first order conditions of this maximization
problem implicitly determine the factor demands:
fl (z, k, l) = w =⇒ l? (z, k, w)
fk (z, k, l) = c(k, a) =⇒ k ? (z, l, a)
∂l? x ∂k? x
Letting ηl,x ≡ ∂x l? , ηk,x ≡ ∂x k? denote the elasticity of demand for labor with respect to variable
∂x 1
ˆ≡
x, and x ∂t x denote the growth rate of x, we can calculate the growth rate of labor18 :
ˆl = ηl,z zˆ + ηl,k kˆ + ηl,w w
ˆ
kˆ = ηk,z zˆ + ηk,l ˆl + ηk,a a
ˆ
Combining these equations gives an individual firm’s growth rate:
ˆl = 1
[(ηl,z + ηl,k ηk,z )ˆ
z + ηl,k ηk,a a
ˆ + ηl,w w]
ˆ
1 − ηl,k ηk,l
For brevity, write this as:
ˆl = β z zˆ + β a a
ˆ + βw w
ˆ
The growth in employment in industry i in city c will come from integrating over the firms growth
within the industry city:
Z
ˆli,c,t = ˆl(ω)gi,c,t (ω)dω
Ω
where ω is the firm type, and gi,c,t (ω) the density of employment of the firm type. Assume that pro-
17 If a is taken to be assets, this can be seen as a reduced form version of a costly state verification model where
an increase in capital relative to a firm’s assets raises the default probability and thus the cost of external finance.
Alternatively as a firm grows larger, they may be more able to pay the fixed costs of issuing securities lowering the
cost of capital.
18 By the implicit function theorem and the assumptions on the production function: η fl,z z
l,z = − f l?
> 0, ηl,k =
l,l
f k 1 w fk,z z fl,k l ca c
− fl,k ? > 0, ηl,w = fl,l l?
< 0, ηk,z = − f ? > 0, ηk,l = − f ? > 0, ηk,a = fk,k −ck k?
>0
l,l l k,k −ck k k,k −ck k
117
ductivity growth depends on an industry level shock zˆi,t and idiosyncratic noise which is integrated
ˆi,c,t be determined at the industry×city×year level19 Finally,
out in aggregation. Let wage growth w
assume an elasticity of labor supply ψi,c,t , so that ˆlc,t = ψi,c,t w
ˆc,t .
Under these assumptions, industry MSA employment growth will be:
Z
ˆli,c,t = ψi,c,t
[β¯i,c,t
z
zˆi,t + β a (ω)ˆ
a(ω)gi,c,t (ω)dω]
ψi,c,t − β¯i,c,t
w
Ω
x
β x (ω)gi,c,t (ω)dω, x ∈ {w, z} is the average sensitivity of employment growth to
R
Where βi,c,t ≡
Ω
factor x.
MCA employment growth will then be:
X X
ˆlc,t = θi,c,t ˆli,c,t = β¯c,t
z
θi,c,t zˆi,t + Ec,t (β a a
ˆ)
i∈I i∈I
θi,c,t zˆi,t ψi,c,t
where θi,c,t denotes the share of employment in industry i, and β¯c,t
z
β¯i,c,t
z
P
= P
θi,c,t zˆi,t ( ψi,c,t −β¯i,c,t
w )
i∈I i∈I
is the weighted average sensitivity of industry×city employment growth to national growth. Ec,t (β a a
ˆ) ≡
ψ
β a (ω)ˆ
P R
θi,c,t ψi,c,ti,c,t
−β¯w
a(ω)gi,c,t (ω)dω is the average growth due to asset accumulation.
i,c,t
i∈I Ω
1
Notice that the sensitivity of labor growth to idiosyncratic productivity growth β z = 1−ηl,k ηk,l (ηl,z +
z f l f
ηl,k ηk,z ) depends positively on ηk,z = − fk,zk,z k,l
−ck k and negatively on ηk,l = − fk,l −ck k . Using the
first order condition that c(k ? , a) = fk (z, k ? , l? ) These can be written as:
∂fk z ∂fk l
∂z fk ∂l fk
ηk,z = ∂fk k ∂c k
, ηk,l = ∂fk k ∂c k
∂k fk − ∂k c ∂k fk − ∂k c
∂c k
Thus a greater elasticity of marginal cost of capital with respect to capital ( ∂k c ), ie a steeper capital
supply curve, results in a lower sensitivity of labor growth with respect to productivity growth.20
What this model shows is that labor growth can come from two sources. First, productivity
growth increases the optimal scale of production bringing about growth in employment, particularly
among firms with lower financial constraints (a flatter capital supply curve). Second, productivity
growth can come from a reduction in the cost of capital. For example if external finance is pro-
hibitively costly, firms will grow by accumulating profits. This form of growth reflects credit frictions
prohibiting firms from entering at an efficient scale.
The concern is that asset growth and productivity growth are unobservable. Thus we need a
19 Since the focus is on cyclical movements instead of a long term equilibrium, this seems more reasonable than
allowing frictionless movement across industries.
20 There is also a general equilibrium effect of capital constraints on wages when there is an aggregate productivity
shock, as capital constraints make demand less elastic. But this is second order to the others
118
proxy for productivity growth which is uncorrelated with asset growth to identify differences in β¯c,t
by county. Note that aggregate growth by industry is
ˆli,t =
X ψi,c,t
θ˜i,c,t ˆli,c,t = zˆi,t Ei,t ( β¯i,c,t
z
) + Ei,t (β a a
ˆ)
c∈C
ψi,c,t − β¯i,c,t
w
Where the expectation is the expected value over cities weighted by the density of i’s employment
in city c. Simply using national growth as a proxy for productivity shocks is problematic as trend
growth may be correlated with past profits and thus available assets. Also, correlation of asset
growth is problematic. For example, if productivity follows a jump process, unconstrained firms
may quickly reach their maximum scale, while constrained firms lag behind. However, after the
jump there may be high national growth as many firms want to increase their scale. But it will be
the constrained firms which have high growth, making the correlation related to frictions instead
of the lack thereof. Asset growth will depend on profits and thus the level of productivity instead
of its changes, making it a slow moving variable. Detrending the series should roughly remove the
employment growth attributable to trend productivity or asset accumulation. Thus the cyclical
component of employment growth should be:
ˆlcyc ' zˆcyc Ei,t ( ψi,c,t
β¯i,c,t
z
)
i,t i,t
ψi,c,t − β¯i,c,t
w
Returning to the expression for county-industry growth,
Z
ˆli,c,t = ψi,c,t cyc
[β¯i,c,t
z
(ˆ
zi,t trend
+ zˆi,t )+ β a (ω)ˆ
a(ω)gi,c,t (ω)dω]
ψi,c,t − β¯i,c,t
w
Ω
ψi,c,t
ψi,c,t −β¯i,c,t
w β¯i,c,t
z
ˆli,c,t = ˆlcyc + i,c,t = β¯i,c,t
l ˆlcyc + i,c,t
ψi,c,t ¯z i,t i,t
Ei,t ( ψi,c,t −β¯w βi,c,t )
i,c,t
Where is the growth attributable to trend productivity and to asset growth. Thus the sensitivity
of local growth employment to national employment growth reflects how responsive a city×industry
is to a productivity shock relative to other the industry in the other cities. This sensitivity has been
shown to depend on the level of financial frictions in the city. Aggregating to the city level:
X X
ˆlc,t = θi,c,t ˆli,c,t = β¯c,t
l
θi,c,t ˆli,t +
i∈I i∈I
119
θi,c,t ˆ
li,t ¯l
where θi,c,t denotes the share of employment in industry i, and β¯c,t
l
P
= P β )
li,t i,c,t
θi,c,t ˆ
is the
i∈I i∈I
weighted average sensitivity of industry×city employment growth to national growth. Suppose that
β¯i,c,t
l
= α1 − α2 HHIc,t , then we get the estimating equation used in the empirical section. α2 tells
us how much the bartik sensitivity declines when concentration goes up, thus informing us of the
degree to which firms are facing upward sloping supply curves.
3.7.5 Data Sources
Summary of Deposits The Summary of Deposits (SOD) is an annual survey of all FDIC-insured
institutions. Each branch of commercial banks or savings and loans located within the US reports
the value of the deposits at that branch as of June 30. For institutions with more than one branch,
the assignment of deposits over the institution’s branches are instructed to be done in a “manner
consistent with their existing internal record keeping practices”, for example nearest branch to
account, most active branch of the account, place of origination, etc. I use the years 1994-2012. Also
included in the data are the address of the branch, the range of services available at the branch, and
identifiers for the financial institution and, if applicable, the holding company the branch is part of.
Call Reports Report of Condition and Income data (Call Reports) are available on a quarterly
basis for banks regulated by the Fed, FDIC, and the Comptroller of the Currency. I use data from
1994-2010, the first year SOD data is publicly available to the last year the Chicago Fed makes
the Call Report data. It provides information on the balance sheet, incomes and expenditures of
banks. Banks are linked to their locations in the SOD data using their RSSD ID. Stock variables
(e.g. loans ) are measured as the average of the first quarter and last quarter reported amounts,
while income and expenditure variables (e.g. loan income, interest paid on deposits) use the fourth
quarter reports giving the total received or paid during the calendar year.
Variables often exhibit some extreme outliers, likely due to reporting errors or small denomina-
tors, so the highest and lowest 1% of values every year are dropped.
Quarterly Census of Employment and Wages The Quarterly Census of Employment and
Wages (QCEW) provides payroll and employment data at the MSA level for NAICS industries
using administrative records from state unemployment insurance programs. It covers roughly 97%
of the employment in the US. Employment is measured as filled jobs, so that people working multiple
jobs will be counted multiple times in the data. Each establishment is allocated to an NAICS code,
based on their primary activity.
120
I only include people working in private industries, since public sector establishments aren’t
going to be subject to sensitive to credit market frictions, and won’t make hiring decisions with the
objective of profit maximization.
Business Dynamic Statistics The Business Dynamics Statistics (BDS) is developed by the
Center for Economic Studies using the the US Census Bureau’s Longitudinal Business Database, a
panel of US business establishments. Employment is full and part time employment as of march
12th. A firm is assigned an initial age of the age of the oldest establishment at time of birth. Firm
age then increase by 1 every year thereafter. A firm is considered young in year t if it’s age is five
or below and mature otherwise.
121
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