Financing Micro and Small Firms
in the Great Recession
Megha Patnaik
Job Market Candidate
November 3, 2016
Department of Economics
Stanford University
Motivation
I
Small businesses high share of firms, employment, payroll
I
Face asymmetric information in credit markets
I
More financially constrained
Constraints high in recessions:
I
I
Great Recession - Kauffman firm survey (2008):
I
I
I
I
A third of firms were denied or had trouble accessing credit
18% did not apply for funding fearing rejection
Transmit shocks and amplify fluctuations (Bernanke, 1983)
Linked to aggregate dynamics in the Great Recession
I
entry, exit and employment growth
.15
Mean credit growth
0
.05
.1
-.05
2007
2008
2009
2010
Year
Small businesses
2011
2012
2013
Compustat
Annual QoQ growth in average total liabilities for small businesses in the sample and Compustat firms. Both firm-level datasets
are filtered to keep only firms with at least 4 quarters of data and a moving average of three quarters taken over growth. Sample
data is restricted to positive values of long-term liabilities for firm-quarters and winsorized at the 1% level.
This Paper
Question:
Do credit-supply shocks affect credit for small businesses? How do these effects vary
across firm size?
Two shocks in the Great Recession:
I Failure of insolvent banks with FDIC intervention
I
lending relationships (Petersen & Rajan 1994, Drexler & Schoar 2014)
I Movements in house prices
I
owner’s personal collateral (Evans & Leighton 1990, Hurst & Lusardi 2004)
Data from the leading online accounting software:
I Very small firm sizes including micro firms (BLS: <10 employees)
I Data on financials - credit
I Measures of both shocks at firm level - links to banks and owner’s home address
Challenge
Demand side shocks:
I County-quarter fixed effects as controls
Bank failures:
I FDIC insured banks are dispersed with many branches
I Performance of firms doesn’t predict bank failure
House price movements:
I House prices determined at owner’s home ZIP
I Tradable industries (Mian and Sufi, 2009)
Result
Differences in sensitivity to shocks across firm size I
micro firms: 2-10 employees
I
small firms: 10-250 employees
Credit:
Micro
Banking
Housing
!
Small
!
Sensitivity of small business credit to banking shocks driven by small
firms and to housing collateral shocks driven by micro firms
Context
Different sources for micro and small firmsSurvey of Small Business Finances (2003):
Share collateralized 1221122112211221
Micro firms
Small firms
Housing collateral
27%
13%
Business value
9%
29%
Cost to banksI
Lending relationships costly
I
Loan volume proportional to firm size
Contribution
I
Two types of shocks to credit supply in the Great Recession
I
Banking shocks
I
I
Greenstone, Mas, Nguyen (2014);Chodorow-Reich (2014); Nguyen (2015);
Darmouni (2016); Duygan-Bump, Levkov, Montoriol-Garriga (2015), Amiti,
Weinstein (2013)
Housing shocks
I
Chaney, Sraer, Thesmar(2012); Adelino, Schoar, Severino (2012);
Ersahin,Irani (2015); Schmalz, Sraer, Thesmar(2013); Fort, Haltiwanger,
Jarmin, Miranda (2013)
I
Both banking shocks and housing shocks at the firm-level for small
businesses
I
Sensitivity to banking driven by small firms, to housing driven by
micro firms
Overview
I
Data
I
Banking shock
I
Housing shock
I
Revenue and credit
I
Conclusion and future research
Overview
I
Data
I
Banking shock
I
Housing shock
I
Revenue and credit
I
Conclusion and future research
Firm-data
Transactions-level data for &140,000 companies from the leading online
accounting software:
I
Financial variables - short and long-term credit, revenue, expenses
I
Background characteristics - Employment, Address of owner and
firm, industry and age
I
Companies linked to banks (&77,000 firms)
Variable definitions
I
Credit: Sum of all positive long-term liabilities in a quarter
I
loans and credit lines, SBA loans, loans from family, transfers from
personal bank account
I
Employment: Hiring and release dates of payroll employees
I
Location: Business address and the owner’s home address
I
Industry: home address NAICS industry from Dun & Bradstreet
I
Age: minimum of age from software and from Dun & Bradstreet
Summary statistics
aaaaaaaaaaaaaaaaa
Meanui
Std.Devui
uaMinu
aaaaaMax
Median
All firms:
Size (employees)
Revenue ($)
Credit ($)
Credit ($) >0
11.92
37.13
0
5103
3
1,557,643
430,244,608
0
9,570,071
317,640
62,521
6,943,580
0
842,454
0
335,579
16,083,934
40
4,053,002
39,544
2.72
2.54
0
9
2
872,260
55,399,336
0
7,040,920
228,486
Micro firms:
Size (employees)
Revenue ($)
Credit ($)
Credit ($) >0
55,375
8,097,182
0
652,750
0
347,510
20,281,938
43
3,919,374
35,328
Small firms:
Size (employees)
Revenue ($)
Credit ($)
Credit ($) >0
36.59
65.00
10
255
20
3,396,628
820,702,464
0
14,115,132
715,018
81,695
1,287,384
0
1,299,130
0
315,857
2,516,734
35
4,321,989
46,262
Sample is 844,882 firm-year observations for the 141,678 firms in the sample.
Size distribution
Employmenta1221k
0-4
5-9
10-14
15-19
20-24
25-49
50-99
100+
1Share (Census)1
61.89
17.34
6.82
3.54
2.17
5.78
1.31
1.14
1a Share (Sample)
49.14
19.24
9.39
5.55
3.65
7.42
3.59
1.94
Mid-March employment shares in the population and the sample for 2010. Population statistics
are sourced from the Statistics of U.S. Businesses published by the Census Bureau (total number
of firms is 5,734,538). The number of employees is sourced from hiring and release dates of
employees for 2010 (total number of firms is 76,918).
Age distribution
Age (years)
0
1
2
3
4
5
6-10
11-15
16-20
21-25
26+
1Share (Census)1
8.93
6.67
5.50
5.13
5.29
4.96
20.17
14.04
10.05
7.91
11.36
1a Share (Sample)
1.51
7.97
10.87
8.18
7.99
7.99
25.25
12.09
6.02
3.69
8.43
Comparison of population for 2012 from Business Dynamics Statistics and the sample for 2012
March on age, with 4,577,659 firms in the population and 91,571 in the population.
Sector distribution
Sectoraaddad2222diii
Service
Retail
Construction
Manufacturing
Mining
Agriculture
aa Share (Census) aa
70.91
11.97
11.44
4.87
0.43
0.38
aa Share (Sample)
77.00
7.85
9.01
4.68
0.24
1.19
Distribution of firms across 1 digit NAICS Sectors for March 2010. Population statistics from
the Statistics of U.S. Businesses, US Census Bureau. The total number of firms is 5,734,538.
Sample data uses the industry from matching to Dun and Bradstreet for 76,918 firms in 2010.
Firms under “Unclassified” and “Public Administration” have been removed.
Overview
I
Data
I
Banking shock
I
Housing shock
I
Revenue and credit
I
Conclusion and future research
Bank failures
I FDIC assisted bank failures from 2007 to 2013
I 530 banks were dissolved and merged into healthier institutions
I 130 are matched to the banks firms in the data hold accounts with
Estimation
OLS at firm-quarter level
I
Separately for micro and small firms
Log (Creditit ) = βFailit + θtc + fi + eit
I Outcome: Log (Creditit )
I Failit : dummy for quarter of bank failure and upto 6 following quarters
I θtc : county-quarter fixed effect
I fi : 6 digit NAICS fixed effect / firm fixed effect
Bank failure and firm credit
Log Credit
All
All
Micro
Micro
Small
Small
-0.606***
-0.296***
-0.192
0.005
-0.716***
-0.361***
(0.170)
(0.099)
(0.127)
(0.091)
(0.220)
(0.129)
235,790
235,790
135,253
135,253
100,537
100,537
Qtr-County
Yes
Yes
Yes
Yes
Yes
Yes
NAICS2
Yes
Failure
Obs
Firm
Yes
Yes
Yes
Yes
Yes
The independent variable takes value 1 for the quarter the firm faces bank failure and the following 6 quarters. The sample
is all firms with linked banks. Columns (1) and (2) is the sample of firms of all sizes, columns (3) and (4) is restricted to
micro firms (with less than 10 employees), columns (5) and (6) is small firms (which have more than 10 employees). Credit
is measured as the sum of all transactions categorized as long-term liabilities to a firm. The dependent variable is Log(Credit)
measured as new long-term liabilities to the firm. Dependent variables are winsorized at the top and bottom 1%. Regressions
are weighted by employment. All standard errors are clustered at the firm level.
Cutoff: micro and small = 5
Log Credit
Bank Failure
Observations
All
All
-0.606***
-0.296***
(0.170)
(0.099)
aMicro a
aMicro a
Small
Small
-0.149
0.052
-0.641***
-0.316***
(0.128)
(0.116)
(0.186)
(0.108)
235,790
235,790
74,705
74,705
161,085
161,085
Qtr-County
Yes
Yes
Yes
Yes
Yes
Yes
NAICS2
Yes
Firm
Yes
Yes
Yes
Yes
Yes
The independent variable takes value 1 for the quarter the firm faces bank failure and the following 6 quarters. The sample is
all firms with linked banks. Credit is measured as the sum of all transactions categorized as long-term liabilities to a firm. The
dependent variable is Log(Credit) measured as new long-term liabilities to the firm. Dependent variables are winsorized at the
top and bottom 1%. Regressions are weighted by employment. All standard errors are clustered at the firm level.
Cutoff: micro and small = 15
Log Credit
Bank Failure
Observations
All
All
Micro
Micro
Small
Small
-0.606***
-0.296***
(0.170)
(0.099)
-0.241**
-0.135
-0.746***
-0.382***
(0.116)
(0.083)
(0.253)
(0.148)
235,790
235,790
166,019
166,019
69,771
69,771
Qtr-County
Yes
Yes
Yes
Yes
Yes
Yes
NAICS2
Yes
Firm
Yes
Yes
Yes
Yes
Yes
The independent variable takes value 1 for the quarter the firm faces bank failure and the following 6 quarters. The sample is
all firms with linked banks. Credit is measured as the sum of all transactions categorized as long-term liabilities to a firm. The
dependent variable is Log(Credit) measured as new long-term liabilities to the firm. Dependent variables are winsorized at the
top and bottom 1%. Regressions are weighted by employment. All standard errors are clustered at the firm level.
Monotonic in firm size
Coefficient of Log Cred on closure
-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4
Coefficient across employment size bins
2-5
6-10
11-50
51-101
101-200
200+
Employee size bin
95% confidence interval
beta
Correlation of firm credit to bank failures across firm size. The x-axis is based on standard size categories followed by the US
Census Bureau and the y-axis is the coefficient of the regression with dependent variable log credit and independent variable a
dummy that equals 1 if the firm is impacted by bank closure in the current or the previous year. Fixed effects are at the NAICS
2 and Year-State levels. Standard errors are clustered at the firm level.
Cutoff: Lag Employment (2 years)
Log Credit
Bank Failure
Observations
All
All
aMicro a
aMicro a
Small
Small
-0.606***
-0.296***
-0.183
0.049
-0.711***
-0.383***
(0.170)
(0.099)
(0.130)
(0.092)
(0.219)
(0.129)
235,790
235,790
118,862
118,862
116,928
116,928
Qtr-County
Yes
Yes
Yes
Yes
Yes
Yes
NAICS2
Yes
Firm
Yes
Yes
Yes
Yes
Yes
The independent variable takes value 1 for the quarter the firm faces bank failure and the
following 6 quarters. The sample is all firms with linked banks. Credit is measured as the
sum of all transactions categorized as long-term liabilities to a firm. The dependent variable is
Log(Credit) measured as new long-term liabilities to the firm. Dependent variables are winsorized
at the top and bottom 1%. Regressions are weighted by employment. All standard errors are
clustered at the firm level.
Size and age
5
Mean Age
10
15
Average age across size bins
9.0
10.8
12.2
12.3
12.6
6-10
11-50
51-100
101-200
200+
0
6.7
2-5
Average age for firms in different size bins. Standard size bins as used by the US Census Bureau.
Robust to controlling for age
Log Credit
All
All
Micro
Micro
Small
Small
Bank Failure
-0.626***
-0.305***
-0.185
-0.013
-0.739***
-0.372***
(0.173)
(0.101)
(0.131)
(0.094)
(0.222)
(0.131)
Log Age
-0.087***
0.046
-0.110***
0.101**
-0.142***
0.025
(0.024)
(0.063)
(0.016)
(0.045)
(0.032)
(0.089)
224,827
224,827
125,958
125,958
98,869
98,869
Qtr-County
Yes
Yes
Yes
Yes
Yes
Yes
NAICS2
Yes
Observations
Firm
Yes
Yes
Yes
Yes
Yes
The independent variable takes value 1 for the quarter the firm faces bank failure and the following 6 quarters. The sample
is all firms with linked banks. Credit is measured as the sum of all transactions categorized as long-term liabilities to a firm.
The dependent variable is Log(Credit) measured as new long-term liabilities to the firm. firm age measured as the difference in
years between the current year and the minimum of the first year of business recorded in Dun and Bradstreet of the firm and
the registration date of the firm in the software. Dependent variables are winsorized at the top and bottom 1%. Regressions
are weighted by employment. All standard errors are clustered at the firm level.
Selection problem: Balance in 2006
Variable
Failure
No failure
Credit
Log(Credit)
Log(Credit/Sales)
Employment
Log Employment
61,401
10.42
-2.57
12.26
1.86
51,166
10.36
-2.89
12.54
1.95
Diff. p-value
(Raw)
0.284
0.727
0.072
0.819
0.145
Diff. p-value
(with FE’s)
0.174
0.965
0.116
0.695
0.322
Balancing tests for firms that faced bank failures and firms which did not in 2006.
Differences in p-values calculated directly as well as with county and 2 digit industry
fixed effects
Placebo: No effect 6 quarters prior to failure
Log Credit
All
All
Micro
Micro
Small
Small
-0.110
0.069
-0.147
-0.040
-0.102
0.098
(0.188)
(0.116)
(0.153)
(0.098)
(0.228)
(0.144)
137,133
137,133
65,717
65,717
71,416
71,416
Qtr-County
Yes
Yes
Yes
Yes
Yes
Yes
NAICS2
Yes
aaaaaaaavaaaaaaaaaa
Bank Failure - 6
Observations
Firm
Yes
Yes
Yes
Yes
Yes
Placebo test for the response of firm credit to bank closure measured six quarters before bank failure. The sample is all firms
with linked banks. Columns (1) and (2) is the sample of firms of all sizes, columns (3) and (4) is restricted to micro firms
(with less than 10 employees), columns (5) and (6) is small firms (which have more than 10 employees). Credit is measured
as the sum of all transactions categorized as long-term liabilities to a firm. The dependent variable is Log(Credit) measured as
new long-term liabilities to the firm. Dependent variables are winsorized at the top and bottom 1%. Regressions are weighted
by employment. All standard errors are clustered at the firm level.
Credit around the event of bank failure
Pattern of credit - quarters around bank failure:
1.
2.
3.
4.
Can examine for the affected firm, need to control for trend
Unaffected firm can act as control
Do not have date of failure for unaffected bank
Use matching
Matching
Firms with similar propensity score based on pre-failure variables
I Propensity score based on naics4, state, log(age) and log(credit) in the previous
year with caliper 0.01 for the distance in propensity score within a year
I Difference in Log Credit for years leading up to closure and after between firms
whose banks failed and similar firms whose banks did not fail
I Difference significant for 2008 and 2009 during the Recession, when the highest
number of bank closures occured.
I Standard errors bootstrap the difference in log credit between the two types of
firms
Credit around bank failure
-2-1.5-1 -.5 0 .5 1 1.5 2 2.5 3
Coefficient of Log Cred on closure
Difference in log credit around closure (matched)
-5
0
5
10
Quarters from closure
1 Standard Deviation
beta
Difference between log credit around bank closure of small firms whose banks failed and matched firms whose banks did not
fail. Firms matched using propensity score based on 2 digit NAICS, state, log employment and log age a year before closure,
with one match per affected firm and caliper for propensity score 0.01. Standard errors are bootstrapped with 500 draws from
the sample.
No effect 6 quarters after failure
Log Credit
aaaaaaaavaaaaaaaaaa
Bank Failure - 6
Observations
All
All
Micro
Micro
Small
Small
-0.353*
0.053
0.013
-0.084
-0.410*
0.075
(0.182)
(0.140)
(0.154)
(0.120)
(0.242)
(0.190)
118,806
118,806
67,330
67,330
51,476
51,476
Qtr-County
Yes
Yes
Yes
Yes
Yes
Yes
NAICS2
Yes
Firm
Yes
Yes
Yes
Yes
Yes
Response of firm credit to bank closure measured six quarters after bank failure. The sample is all firms with linked banks.
Columns (1) and (2) is the sample of firms of all sizes, columns (3) and (4) is restricted to micro firms (with less than 10
employees), columns (5) and (6) is small firms (which have more than 10 employees). Credit is measured as the sum of all
transactions categorized as long-term liabilities to a firm. The dependent variable is Log(Credit) measured as new long-term
liabilities to the firm. Dependent variables are winsorized at the top and bottom 1%. Regressions are weighted by employment.
All standard errors are clustered at the firm level.
Heterogeneity: banking relationships
Number of relationships at time of bank failure
I 75% have 1 linked bank, 20% have 2, 5% have more
Log (Creditit ) = βFailit + Log (Empit ) + θtc + fi + eit
I Outcome: Log (Creditit )
I Failit : dummy for quarter of bank failure + 6 quarters after
I θtc : county-quarter fixed effect
I fi : 2 digit NAICS fixed effect
Higher effect for fewer linked banks
Log Credit
All
Bank Failure
Obs
1 bank
2 banks
-0.646***
(0.110)
Small
3-5 banks
1 bank
2 banks
3-5 banks
-0.485***
-0.203
-0.758***
-0.562***
-0.202
(0.129)
(0.148)
(0.142)
(0.166)
(0.188)
234,274
231,923
231,065
99,806
99,011
98,666
Qtr-County
Yes
Yes
Yes
Yes
Yes
Yes
NAICS2
Yes
Yes
Yes
Yes
Yes
Yes
Placebo test for the response of firm credit to bank closure measured six quarters before bank failure. The sample is all firms
with linked banks. Columns (1) and (2) is the sample of firms of all sizes, columns (3) and (4) is restricted to micro firms
(with less than 10 employees), columns (5) and (6) is small firms (which have more than 10 employees). Credit is measured
as the sum of all transactions categorized as long-term liabilities to a firm. The dependent variable is Log(Credit) measured as
new long-term liabilities to the firm. Dependent variables are winsorized at the top and bottom 1%. Regressions are weighted
by employment. All standard errors are clustered at the firm level.
Overview
I
Data
I
Banking shock
I
Housing shock
I
Revenue and credit
I
Conclusion and future research
House price data
Zillow House Price Index:
I Monthly time series of house price index at the zipcode level for the US from
2007 to 2013
I Index uses all houses with estimated price even without sale (single family +
condominium + cooperative)
I Advantage over other indices: at the zipcode level - highly correlated across
indices (Guerrieri et al 2013)
I Average over months to create quarterly series
Estimation
OLS at firm-quarter level
I
Separately for micro and small firms
Log (Creditit ) = βLog (HPIzt ) + θtc + fi + eit
I Outcome: Log (Creditit )
I Log (HPIzt ): log of the house price index at the owner’s zipcode (quarterly)
I θtc : county-quarter fixed effect
I fi : 6 digit NAICS fixed effect/firm fixed effect
House prices and firm credit
Log Credit
Log HPI
Observations
All
All
Micro
Micro
Small
Small
0.090
-0.043
0.225***
0.339***
0.083
0.281*
(0.055)
(0.185)
(0.031)
(0.087)
(0.065)
(0.151)
448,866
448,866
245,522
245,522
203,344
203,344
Qtr-County
Yes
Yes
Yes
Yes
Yes
Yes
NAICS2
Yes
Firm
Yes
Yes
Yes
Yes
Yes
The correlation between firm credit and house prices. The dependent variable is the log of credit determined by aggregating all
transactions which are long-term liabilities to the firm, and winsorized at the top and bottom 1%. Standard errors are clustered
at the firm level.
Monotonic in firm size
Coefficient of Log Cred on log HPI
-.8 -.6-.5-.4-.3-.2-.1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
Coefficient across employment size bins
2-5
6-10
11-50
51-100
100+
Employee size bin
95% confidence interval
beta
Correlation of firm credit with house price index across firm size. The x-axis is based on standard size categories followed by
the US Census Bureau and the y-axis is the coefficient of the regression with dependent variable log credit and independent
variable the ZIP code level house price index measured at the owner’s address. Fixed effects are at the NAICS 2 and Year-State
levels. Standard errors are clustered at the firm level.
Tradability
I
County-Quarter FE’s to control for demand may be insufficient
I
Tradable industries - demand is less localized
Tradable sectors (Mian and Sufi 2009)
I
I
I
I
4 digit NAICS industry as tradable if it has the sum of imports and exports
to be higher than $10,000 per employee or exceeding $500 million.
Retail industries, restaurants and grocery are non-tradable.
Strategy follows Adelino, Schoar, Severino (2014)
Tradability - micro firms
Log Credit
dad
Log HPI
Observations
22Allda
All - Construction
All - Non-Tradables
Manufacturing
0.211***
0.220***
0.216***
0.171
(0.027)
(0.029)
(0.030)
(0.142)
259,742
233,776
214,359
17,929
Qtr-County
Yes
Yes
Yes
Yes
NAICS2
Yes
Yes
Yes
Yes
The correlation between firm credit and house prices. The dependent variable is the log of credit determined by aggregating all
transactions which are long-term liabilities to the firm, and winsorized at the top and bottom 1%. Standard errors are clustered
at the firm level.
Overview
I
Data
I
Banking shock
I
Housing shock
I
Conclusion and future research
Aggregate effects
House price changes -
1. Zillow price index: highest $196K (Feb-June 2007) to lowest $152K (Nov
2011-Apr 2012) = decline of 22.44%
2. Micro firms have coefficient β from Log(Credit) & Log(HPI) = 0.33:
corresponds to 7.4% change in credit
3. Mean annual credit volume for micro firms is & $350K this gives a change
of & $25K
4. Median annual revenue for micro firms is & $200K which means a loss of
credit of as a share of revenue & 12.5%
Bank failures -
1. Bank shock led to 30% decline in credit level (1-exp(-0.36))*100
2. Mean annual credit for small firms is & $316K: a decline of & $95K
3. Median annual revenue for small firms is & $715K: a loss of credit as a
share of revenue of & 13.25%
Conclusion
I
Sensitivity of credit to bank closure - small firms
I
I
I
I
not driven by selection
robust to age controls
decreases with more banking relationships
Sensitivity of credit to housing collateral - micro firms
I
holds for tradable industries
I
Both effects monotonic and not sensitive to cutoff
I
Aggregate effects of shocks to credit supply:
I
I
House prices: average micro firm credit decline&12.5% of revenue
Bank failures: average small firm credit decline ˜13.25% of revenue
Future research
I
Existing financial constraints I
I
I
Response of firms to shock
I
I
I
I
I
I
Leverage before credit supply shock
Response of net credit (involves repayments for existing loans)
Employment
Wages
Revenue and Profitability
Expenses - different types
Investment
Broader banking shocks
Thank you
Megha Patnaik
[email protected]
Back-Up Slides
Small businesses in the US economy
Employees
<500
<50
<20
Firms
99%
95%
90%
Share
Emp Payroll
Sales
Source: Statistics of US Businesses (2013)
Back
Banking relationships
Banking and housing shocks
Log Credit
Bank Failure
All
All
Micro
Micro
Small
Small
-0.629***
-0.339***
-0.123
-0.043
-0.765***
-0.399***
(0.142)
(0.201)
(0.111)
(0.139)
(0.105)
(0.260)
0.145**
-0.046
0.218***
0.286
0.146*
-0.109
(0.069)
(0.297)
(0.042)
(0.244)
(0.085)
(0.373)
180,679
180,679
105,748
105,748
74,931
74,931
Qtr-County
Yes
Yes
Yes
Yes
Yes
Yes
NAICS2
Yes
Log(HPI)
Observations
Firm
Yes
Yes
Yes
Yes
Yes
Relationship between log credit and housing and banking shocks. Bank failure is a dummy that equals 1 for the quarter the
firm faces bank failure and the following 6 quarters. House price measure is log of the Zillow monthly index at the ZIP code of
the owner’s address, averaged over months in a quarter. The sample is all firms with linked banks. Columns (1) and (2) is all
firms, columns (3) and (4) is micro firms, columns (5) and (6) is small firms. Credit is measured as the sum of all transactions
categorized as long-term liabilities to a firm. All regressions have been weighted by the number of employees. Standard errors
are clustered at the firm level.
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Borrowing frequency
Number of months in a year that firms borrow. The sample is for 141,678 firms restricted to those with at least one year of
borrowing in the dataset.
House prices and firm credit - Banking sample
Log Credit
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Log HPI
Observations
All
All
Micro
Micro
Small
Small
0.090
-0.043
0.225***
0.339***
0.083
0.281*
(0.055)
(0.185)
(0.031)
(0.087)
(0.065)
(0.151)
448,866
448,866
245,522
245,522
203,344
203,344
Qtr-County
Yes
Yes
Yes
Yes
Yes
Yes
NAICS2
Yes
Firm
Yes
Yes
Yes
Yes
Yes
The correlation between firm credit and house prices. The dependent variable is the log of
credit determined by aggregating all transactions which are long-term liabilities to the firm, and
winsorized at the top and bottom 1%. Standard errors are clustered at the firm level.
Tradable firms - all
Log Credit
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22Allda
All - Construction
All - Non-Tradables
Log HPI
0.165***
0.171***
0.176***
0.182
(0.023)
(0.024)
(0.025)
(0.128)
448,877
401,785
367,345
22,930
Observations
Manufacturing
Qtr-County
Yes
Yes
Yes
Yes
NAICS2
Yes
Yes
Yes
Yes
The correlation between firm credit and house prices. The dependent variable is the log of
credit determined by aggregating all transactions which are long-term liabilities to the firm, and
winsorized at the top and bottom 1%. Standard errors are clustered at the firm level.