When Credit Dries Up: Job Losses in the Great Recession
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When Credit Dries Up:
Job Losses in the Great Recession
Samuel Bentolila Marcel Jansen
CEMFI U. Autónoma de Madrid
Gabriel Jiménez Sonia Ruano
Banco de España Banco de España
Presentation at IMéRA-AMSE Conference on “Growth in Europe?”,
Marseille 24 September 2015
This paper is the sole responsibility of its authors. The views presented here do not necessarily re‡ect those
of either the Banco de España or the Eurosystem
1 / 29Motivation
I Do shocks to the banking system have real e¤ects and, if so,
do they give rise to employment losses?
I Policymakers in Europe and the US are concerned because the
decline the lending during the Great Recession is seen as a
primary factor in the slow recovery (IMF, 2013)
I These concerns led to massive injections of capital in banks
with solvency problems (over e 600 Bn in Europe)
I However, recent evidence on the real e¤ects of credit supply
shocks and the economic bene…ts of bailouts is scarce. Why?
I Lack of good data on bank credit to …rms (in the US)
I Challenging identi…cation issues
2 / 29The basic challenge
I Disentangle credit supply and credit demand shocks:
I A …nancial crisis may force banks to reduce credit supply, but
it may also induce …rms to reduce credit demand
I The troubles of …rms may cause the hardship of banks,
inducing reverse causality
I Firms may have alternative sources of funding
I Selection, with an over-representation of vulnerable …rms in
weak banks
In recent years most studies exploit quasi-experimental techniques
to overcome these identi…cation problems
3 / 29The Spanish experience in the Great Recession (GR)
The Spanish economy o¤ers an ideal setting to explore how shocks
to credit supply spillover to the real economy:
I A large employment fall: 9% over 2008-2010
I Spanish …rms rely heavily on bank credit and the high leverage
ratios of many …rms, mostly SMEs, made them vulnerable to
the reduction in credit supply during the GR
I This credit supply shock, reducing credit by 40% in real terms
from 2007 to 2010, was originated by a boom-bust cycle in
housing prices with catastrophic e¤ects on bank solvency
4 / 29Our approach
We exploit large cross-sectional di¤erences in lender health at the
onset of the crisis
I The weakest banks, all of them cajas de ahorros (savings
banks), were bailed out by the State –mostly after 2010. The
rest survived without …nancial assistance
I Bailed-out or weak banks overinvested in loans to real estate
during the boom
I Weak banks reduced credit more during the recession
I We compare the change in employment from 2006 to 2010 at
two sets of Spanish …rms: those with a pre-crisis exposure to
weak banks in 2006 and those with no exposure
5 / 29Our strength
Data set
I We have access to a unique dataset with con…dential
information about all bank loans to non-…nancial …rms and
loan applications to non-current banks
I These data are linked to …rm and bank balance sheet data
I The data allow us to reconstruct the entire banking relations
and credit histories of close to 170,000 non-…nancial …rms
To the best of our knowledge, it is the most extensive database
ever assembled for the analysis of credit supply shocks
In our empirical analysis we include an exhaustive set of …rm
controls to account for selection and have instruments generating
exogenous variation in weak-bank exposure
6 / 29Summary of results
I Controling for selection, weak-bank exposure caused an extra
employment fall of around 2.2 pp from 2006 to 2010
I This corresponds to about one-quarter of aggregate job losses
in …rms exposed to weak banks in our sample
I The results are very robust and reveal sizable di¤erences
depending on …rm …nancial vulnerability and …rm size
I Di¤erences in employment destruction and …rm exit are
concentrated at multi-bank …rms
7 / 29Plan of the talk
I The …nancial crisis in Spain
I Data and treatment variable
I Empirical strategy
I Empirical results:
I Baseline (DD)
I Transmission mechanism and exogenous exposure
I Treatment heterogeneity
I Probability of exit
I Job loss estimates
I Conclusions
8 / 29The …nancial crisis in Spain
I The Spanish cycle
I Expansion, 2002-2007: GDP 3.7%; employment 4.2% (p.a.)
I Recession, 2008-2010: GDP -3.0%; employment -9.0%
I Bank credit boom-bust: Real annual ‡ow of new credit to
non-…nancial …rms by deposit institutions
I 2003-2007: 23%, 2007-2010: -38%
I Euro membership + Expansionary monetary policy (ECB):
Sharp drop in real interest rates and easy loans to real estate
developers and construction companies (REI): 14.8% of GDP
2002 to 43% in 2007
! Housing bubble: 59% rise in real housing prices over
2002-2007 (-15% over 2008-2010)
9 / 29Savings banks and the credit collapse
I Savings banks: same regulation and supervision, di¤erent
ownership and governance
I Market shares and exposure to REI (%):
Credit to Non- Loans to REI/
Fin. Firms Loans to NFF
Weak banks 32 68
Healthy banks 67 37
I Di¤erential credit growth:
I Expansion (2002-2007): Weak 40% v. healthy 12%
I Recession (2007-2010): Weak -46% v. healthy -35%
I Both at intensive and extensive margins
10 / 29Savings banks and the credit collapse
New credit to non-…nancial …rms by bank type (12-month backward
moving average, 2007:10=100)
11 / 29Savings banks and the credit collapse
Acceptance rates of loan applications by non-current clients, by bank
type (%)
12 / 29The bank restructuring process
Steps:
1. Nationalization and reprivatization (2 WBs, 3/2009-7/2010):
0.44% GDP
2. Mergers (26 WBs) and takeovers (5 WBs), from 3/2010:
1.1% GDP by 12/2010
3. Further consolidations and nationalizations (since 2011). Loan
from European Financial Stability Facility to …nance
weak-bank recapitalization (6/2012, 4% GDP)
So:
I Weak bank de…nition: nationalized, merged with State
support, or taken over by another bank
I 2009-2010: Run by own managers (exc. 2 weak banks in 1.)
I Institutional Protection System: separate legal entities
13 / 29Data: Six di¤erent databases
1. Central Credit Register of the Bank of Spain (CIR):
I All loans above e 6,000: identity of bank and borrower,
collateral, maturity, etc.
I Firms’credit history: non-performing loans and potentially
problematic loans
2. CIR: Loan applications by non-current borrowers
3. Annual balance sheets and income statements of …rms from
Spanish Mercantile Registers via SABI
I Exclude construction, real estate, and related industries:
169,295 …rms
I Coverage: 21% …rms, 32% value added, 48% private employees
4. Firm entry and exit from Central Business Register
5. Bank balance sheets from supervisory Bank of Spain database
6. Bank location database
14 / 29The treatment variable
I WBi : Dummy variable that takes the value 1 if the …rm had
any loans with weak banks in 2006
I Alternative continuous treatment: WB intensityi : loans from
weak banks to asset value
I Sample data:
I Employment fell by 8.1%
I 39% of …rms had credit from weak banks
15 / 29Firm characteristics
There are signi…cant di¤erences in the characteristics of …rms in
the treatment and control groups. Treated …rms are on average:
I Older and larger
I Financially worse: less capitalized, liquid, and pro…table, more
indebted with banks
I More loan applications to non-current banks, more frequent
defaults
These di¤erences lead to di¤erent unconditional trends. We rely on
random selection conditional on controls
16 / 29Empirical strategy
Di¤erences in di¤erences (DD), estimated in di¤erences:
∆4 log 1 + nijkt = α + βWBi + Xi γ + dj δ + dk λ + uijkt
I ∆4 =4-year di¤erence, nijkt =employment at …rm i in
municipality j, industry k, year t=2010 ! Trends in all control
variables
I nijkt set to zero for …rms in the sample in 2006 but not in
2010 because they closed down ! log(1 + nijkt ) ! Surviving
and closing …rms
I β measures Average Treatment e¤ect on the Treated (ATT)
17 / 29Threats to identi…cation
I Demand e¤ects (Mian-Su…, 2014): Lending in REI / certain
areas ! Larger drop in household demand and higher density
of (non-REI) …rms exposed to WB ! Job losses from
consumption rather than credit
! Dummies by: Municipality (3,697) and Industry (80)
I Non-random matching: laxer loan-approval criteria at WB
may cause bias in risk pro…le and reduce access to credit. And
aggregate shocks may di¤erentially a¤ect productivity
(Paravisini et al., 2015) ! Firm controls:
I Productivity: age, age2 , size, ROA, share of temp contracts
I Finance: bank debt, short-term and long-term bank debt,
liquidity, own funds, no. past loan applications to non-current
banks and whether all accepted, past loan defaults, current
loan defaults, credit lines, no. banking relationships and
square, share of uncollateralized loans
18 / 29Baseline: Di¤erence in di¤erences
Dependent variable: ∆4 log 1 + nijkt
No …rm Some …rm Baseline Placebo
controls controls (’02-’06)
WBi -0.067 -0.059 -0.022 0.004
(0.010) (0.010) (0.007) (0.008)
R2 0.050 0.063 0.074 0.156
No. obs. 169,295 169,295 169,295 126,997
Municipality and industry …xed e¤ects, and …rm controls included
(No controls: -7.7% di¤erence.)
19 / 29Alternative speci…cations
I Selection: Exact matching within municipality, industry, and
…rm control cells (0-1 dummies)
I Selection: Panel with …rm-speci…c trends (t=2007,...,2010):
-3.3 pp
∆ log (1 + nijkt )=αi0 +WBi dt β0 +Xi dt γ0 +dj dt δ0 +dk dt λ0 +dt φ + vijkt
I Demand: traded-goods sectors, based on high concentration -
highest quartile Her…ndahl (Mian-Su…, 2014)
I Alternative de…nition of weak banks: 2006 loan exposure to
…rms in REI within upper quartile
I E¤ect is expected to increase with the degree of exposure
I WB Intensityi = Loans from weak banks / Asset Value
I At average intensity of exposure, the e¤ect is 1.9 pp
20 / 29Alternative speci…cations
Dependent variable: ∆4 log 1 + nijkt
Exact Tradable Loans Intensity
matching goods to REI
WBi -0.034 -0.049 -0.021
(0.010) (0.020) (0.008)
WBi Intensity -0.104
(0.028)
R2 0.001 0.109 0.074 0.074
No. obs. 169,295 21,029 169,295 169,295
Municipality and industry …xed e¤ects, and …rm controls included
21 / 29Is credit the channel?
I Bank-…rm sample (281,016 observations, 98,754 …rms)
∆4 log (1 + Creditibt ) =η i + βWBb +γFBib +εibt
I Creditibt = total credit committed by bank b to …rm i,
t=2010, FBit = …rm-bank controls [log(no. of years with the
bank), past defaults with the bank]
I Fixed e¤ects leave only …rms that work with more than 1
bank, but this speci…cation controls perfectly for credit
demand (Khwaja-Mian, 2008)
I Result: b
β=-0.090 (0.040) ! Weak banks cut credit more to
…rms that were their clients in 2006
22 / 29Instrumental variables: Is credit the channel?
I Transmission mechanism (t = 2010):
∆4 log 1 + nijkt = α00 + β00 ∆4 log 1 + Creditijkt
+Xi γ00 +dj δ00 +dk λ00 +εijkt
∆4 log 1 + Creditijkt = ρ + µWBi +Xi η + dj σ + dk ψ + ω ijkt
I Creditit = total credit committed by banks. Exclusion
restriction: Working with a weak bank a¤ects ∆Employment
only through ∆Credit
I Trade credit: Subsample of …rms (8%), with …nancial
institutions (32% of liabilities) and trade credit (35%)
I Exogenous exposure to WB: regulatory change in 12/1988
allowing savings banks to expand beyond region of origin !
density of weak-bank branches in 12/1988 by municipality.
Exclusion restriction: local weak-bank density only a¤ects
employment through exposure to weak banks
23 / 29Instrumental variables: Is credit the channel?
Dependent variable: ∆4 log 1 + nijkt
Instrumented ∆4 log ∆4 log WBi
variable (1+Creditit ) (1+Creditit )a
0.447 0.849 -0.061
(0.127) (0.367) (0.026)
First stage
WBi -0.048 -0.039
(0.010) (0.019)
Local weak-bank 0.496
densityi (0.071)
Overall e¤ect -0.022 -0.033 -0.030
F test / p value 23.1/0.00 4.09/0.05 13.3/0.00
No. obs. 169,295 12,889 169,295
Industry f.e. and …rm controls included. Col. (1) has municipality f.e.;
Col. (3) has coastal province f.e. a Col. (2) uses total credit
24 / 29Treatment heterogeneity: Financial vulnerability
Dependent variable: ∆4 log(1 + nijkt )
Rejected applicationsi -0.065 log(Total Assets)i 0.018
(0.008) (0.005)
WBi Rejected applic.i -0.027 WBi log(Total Assets)i 0.004
(0.013) (0.006)
Defaultsi -0.212 Single banki 0.033
(0.031) (0.007)
WBi Defaultsi -0.059 WBi Single banki 0.037
(0.033) (0.016)
Short-term debti -0.083 WBi -0.035
(0.013) (0.007)
WBi Short-term debti -0.016 R2 0.071
(0.014) No. obs. 169,295
Municipality and industry …xed e¤ects, and …rm controls included
25 / 29Probability of exit
I Lower WB employment e¤ect on survivors (DD): 1.1 pp
I Dependent variable: Probability of exit from 2006 to 2010i
WBi 0.010
(0.004)
WB Intensityi 0.070 0.080
(0.015) (0.016)
WB Intensityi -0.054
Single banki (0.017)
R2 0.080 0.081 0.078
No. obs. 169,295 169,295 169,295
Municipality and industry …xed e¤ects, and …rm controls included
I WB Intensityi : 9th v. 1st decile ! 1.7% higher probability
(17% of baseline rate)
26 / 29Job loss estimates
Caveat:
I These are not macro e¤ects (Chodorow-Reich, 2014)
I These are only di¤erential e¤ects
Share of observed aggregate job losses in exposed …rms:
I Baseline: 23.7% of job losses
I Survivors: 52% of job losses
I Closing …rms: 19% of job losses
27 / 29Conclusions
I Aim: measure the impact of credit constraints on employment
during the Great Recession in Spain
I Identi…cation: We exploit di¤erences in lender health at the
onset of the crisis, as evidenced by savings banks’bailouts
I We …nd that job losses from expansion to recession at …rms
exposed to weak banks are signi…cantly larger than at similar
non-exposed …rms
I This explains around one-fourth of aggregate job losses at
exposed …rms
28 / 29Conclusions
I The estimated e¤ects vary considerably with the …rm’s
creditworthiness and the structure of its banking relationships,
in particular how many banks it works with
I Credit constraints do not just force …rms to purge jobs but
also cause some of them to close down
I Given our controls, constrained …rms would have received
more credit had they not been attached to weak banks and, in
this sense, these job losses are ine¢ cient
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