Credit Supply Shocks, Network Effects,
and the Real Economy
Laura Alfaro
Manuel Garcı́a-Santana
Harvard Business School and NBER
UPF, Barcelona GSE, and CEPR
Enrique Moral-Benito
Banco de España
December 9, 2016
Abstract
We consider the real effects of the bank lending channel and how bank-lending shocks permeate through the economy through input-output linkages. We combine a matched bank-firm
loan dataset with information on the universe of corporate loans in Spain over the period 20022013. Using the empirical approach from the matched employer-employee literature, we identify
bank-specific credit supply shocks for each year in our sample. We then construct firm-specific
credit supply shocks and estimate their effects on firm credit growth, employment growth, and
investment. We find sizable effects at the firm level. A one standard deviation increase in credit
supply shocks generates an increase of 3.4 pp. in firm credit growth. Turning to employment
growth and investment, the estimated effects are 0.3 and 0.7 pp., respectively. The effects of
credit supply of employment growth have different effects during expansions and contraction
periods. In terms how these credit shocks permeate the economy, we find that this propagation
effect is of the same magnitude as the direct effect typically estimated in the literature. Around
1.6 pp. of annual output growth during 2004-2007 were due to positive credit supply; 0.9 pp.
from direct effects and 0.7 pp. from propagation effects. In the period 2008-2010, around -1.7
pp. of annual output growth were due to negative credit supply; -1.0 pp. from direct effects
and -0.7 pp. from propagation effects.
JEL Codes: E44, G21, L25.
Keywords: credit supply, bank lending channel, matched employer-employee, employment, investment, input-output linkages.
1
1
Introduction
In this paper we study the real effects of the bank lending channel and how bank-lending shocks
permeate the economy through input and output linkages.
Figure 1 displays the growth of output in Spain from 2002 to 2014 in the left axis and credit
growth in the right access. An analysis of these series suggests not only important correlation but
also causation.
Investigating the link between credit shocks and real variables, however, poses several challenges.
As noted by Khwaja and Mian (2008), in terms of identification, one needs to estimate a bank
lending-channel or the bank-specific shock, and a firm borrowing-channel, a firms’ ability (or lack of)
to borrow from alternative sources of funds.1 Even after identifying the shocks, the real consequences
to an economy are complex. The effects may differ across different type of firms but also involve
direct and indirect effects via buyer-supplier (input-output) relations. Effects, moreover, may differ
during expansions, recessions, and in particular, financial crises.2 The exercise is very demanding
requiring-firm level data linking credit information to outcome variables (employment, investment,
etc.). To overcome these challenges, we proceed in several ways.
To identify the bank-lending-specific credit supply shocks from the firm-borrowing channel, we
exploit firm-loan-bank relations together with matched employer-employee techniques. In particular,
we construct a new data set that merges loan-level data on credit in the domestic banking sector
from the Central Credit Register (CIR) of Banco de España, and administrative data on firm-level
characteristics taken from the Spanish Commercial Registry for the period 2002-2013. The novel
data set covers the quasi-census of Spanish, close to two million firms, documenting their credit
relations over 2002-2014 involving a period which includes expansion and the financial crisis.
We use the empirical approach from the matched employer-employee literature to identify bankspecific credit supply shocks for each year in our sample. We identify bank specific effects through
1
Firms may be able to undo a particular bank negative supply shock by resorting to another bank or other sources
of funds. Kashyap, Stein, and Wilcox (1993), Kroszner, Laeven, and Klingebiel (2007), and Adrian, Colla, and Shin
(2012) find that firms are able to substitute to other forms of credit supply in the presence of loan supply shocks,
while Klein, Peek, and Rosengren (2002) and Hubbard, Kuttner, and Palia (2002) stress the difficulties that firms have
substituting loans from one bank with loans from another. Viriliu and Xu, AER (2013) stress the role of self-financing.
2
The literature finds different results. Khwaja and Mian (2008) find liquidity shocks to matter for small but not
large firms in Pakistan. On the other hand, looking at a sample of publicly listed firms in Japan, Amiti and Weisntein
(2016) find evidence that bank supply shocks do not matter for firms that borrow little but affect investment rates of
those firms that borrow heavily from banks. Analyzing the impact of the Great Recession, Chodorow-Reich (2014)
finds employment effects for medium and smaller firms, but no significant effect on larger firms using syndicated loan
data covering medium and larger firms in the U.S. Focusing also in this period, Bottero et al (2015) find effects for
small firms in Italy, while finding a strong bank transmission mechanism in Spain, Jimenez et al. (2014) find its
aggregate (net) credit and real impact at the firm-level to be substantially reduced due to the crowding out effects for
large segments of the borrowers. We further discuss related work in Section 3.
2
differences in credit growth between banks lending to the same firm (bank lending channel at the
firm-loan level). The inclusion of firm fixed effects, possible as firm borrow from different banks,
allows accounting for demand effects.
We then look at the bank lending channel at the firm level and analyze the change in the credit
for a particular firm considering the supply shocks estimated for all banks in relationship with a
particular firm. We find sizable effects at the firm level. A one standard deviation increase in credit
supply shocks generates an increase of 3.4 pp. in firm credit growth.
Having matched the credit registry information to firm-level administrative data, we look at
the effects on employment, investment and output. We regress annual employment, output, and
investment growth on the estimated bank supply shocks at the firm level controlling for other firmspecific characteristics.
We find that the estimated effects on real variables are also sizable: For instance, a one standard
deviation increase in credit supply generates an increase of 0.3 and 0.7 pp. in employment growth
and investment respectively. Moreover, credit supply shocks have different effects on employment
during expansions and contraction periods, being much stronger during the 2008-2009 credit collapse.
As mentioned, the real effects of lending shocks can have complex direct and indirect effects. With
the identified shocks, we then formulate general equilibrium economy with buyer-supplier relations
as in Acemoglu et al (2012). We then study how these exogenous supply shocks are amplified though
the economy using the Spanish Input-Output relations. We plug the credit supply shocks that we
estimated using credit registry data into the model and analyze their aggregate implications in terms
of direct and indirect effects. We find that this propagation effect is of a similarly magnitude as
the direct effect typically estimated in the literature: around 1.6 percentage points of annual output
growth during 2004-2007 were due to positive credit supply. 0.9 pp. from direct effects and 0.7 pp.
from propagation effects; during the crisis period, close to -1.7 percentage points of annual output
growth during 2008-2010 were due to negative credit supply, with -1.0 pp. from direct effects and
-0.7 pp. from propagation effects.
The paper relates to two main literatures. First, the banking lending channel literature has taking
an empirical approach to disentangle effects, e.g. Khwaja and Mian (2008); Amiti and Weinstein
(2016); Chodorow-Reich (2014); Jimenez, Mian, Peydro and Saurina (2014); Cingano, Manaresi and
Sette (2015); Bentolila, Hansen and Jimenez (2016). Second, there is also a renewed interest in
networks, which has taken mostly a theoretical approach, e.g. Acemoglu, Carvalho, Ozdaglar and
Tahbaz-Salehi (2012); Acemoglu, Akcigit, Kerr (2016); Carvalho (2014); Bigio and La?O (2016).
Our paper contributes to these literatures by identifying the lending shocks and analyzing how they
permeate through the economy via input output linkages.3
3
Note than differently from Acemoglu et al (2016) we do not need to rely on IV estimation to identify the shocks.
3
The rest of the paper is organized as follows. The remainder of the paper is organized as follows.
Section 2 describes the data. Section 3 disentangles the banking lending channel. Section 4 presents
the direct real effects of the lending shock. Section 5 describes a model to understand the overall
effects of the credit shock, including the direct and the network propagation effects and quantifies
the effects. Section 6 concludes.
2
Data
We use two data sets: loan-level data on credit in the domestic banking sector from the Central
Credit Register (CIR) of Banco de España, and administrative data on firm-level characteristics
taken from the Spanish Commercial Registry and constructed by Almunia, Lopez-Rodriguez, and
Moral-Benito (2016).
The Central Credit Register (CIR) is maintained by the Bank of Spain in its role as primary
banking supervisory agency, and contains detailed monthly information on all outstanding loans
over 6,000 euros to non-financial firms granted by all banks operating in Spain since 1984. Given
the low reporting threshold, virtually all firms with outstanding bank debt will appear in the CIR.
Jiménez, Ongena, Peydró, and Saurina (2012) provide more details on this dataset.
The CIR also contains loan application data. Banks receive monthly information from the CIR
on their borrowers (e.g. total indebtedness or defaults). Moreover, banks can also obtain this
information on any firm that seriously approaches the bank to obtain credit. Therefore, any request of
information from a bank about a given firm can be interpreted as a loan application. By matching the
monthly records on loan applications with the stock of credit we infer whether the loan materialized.
If not, either the bank denied it or the firm obtained funding elsewhere.
For each loan the CIR provides the identity of the parties involved so that we can match the
loan-level data from CIR with administrative data on firm-level characteristics. While CIR data are
available at the monthly frequency, firm-level characteristics are only available on a yearly basis.
Therefore, we collapse the monthly loan-level data to the annual frequency in order to merge both
datasets. At the monthly level, each bank-firm relationship is understood as a loan by aggregating all
outstanding loans from each bank-firm-month pair. Annual bank-firm credit exposure is computed
as the average value of monthly loans between bank i and firm j. We end up with a bank-firm-year
database covering 9 years from 2002 to 2010, 235 banks, 1,555,806 firms, and 18,346,144 bank-firmyear pairs (our so-called loans).
Turning to the firm-level characteristics, we use the so-called SABI-CBI database taken from the
Spanish Commercial Registry, which contains the balance sheets of the universe of Spanish companies
given the firms’ legal obligation to deposit their balance sheets on the Commercial Registry. The
4
SABI-CBI database covers around 90% of the firms in the census for all size categories in terms
of both turnover and number of employees. Moreover, the correlation between micro-aggregated
employment (and output) growth and the National Accounts counterparts is around 0.95 over the
2002-2013 period.
Our final sample covers balance sheet information for a total of 1,801,955 firms with an average
of 993,876 firms per year.4
3
The bank lending channel
3.1
Relation to the literature
Khwaja and Mian (2008) consider a regression of bank-firm loan growth on a bank liquidity shock
and firm-specific effects to account for the demand side (given the presence of multibank firms). The
coefficient on the bank liquidity shock is the so-called bank lending channel. To be more concrete,
they consider the collapse in bank liquidity of bank with deposits in US dollars due to nuclear tests
in Pakistan causing sanctions and huge withdrawals of US dollars. They also consider the same
regression at the firm level rather than at the loan level. However, they cannot control for demand
shocks as they only have one observation per firm. They argue that their estimated effects at the
firm-level represent a lower bound given the negative correlation between bank liquidity shocks and
the firm demand shocks. Their results indicate that banks pass their liquidity shocks on to firms
but large firms are able to undo these shocks by additional borrowing through the credit market. In
contrast, small firms are unable to do so and experience large drops in their overall borrowing.
Along these lines, Chodorow-Reich (2014) exploits a sample of 2,000 medium-sized and large firms
in the U.S. with loan-level information from the Dealscan syndicated loan database. He considers
U.S. bank exposure to the Lehman default as an exogenous shock to the availability of credit to
borrowers. He finds that U.S. bank exposure to the Lehman default had a sizable influence on
employment for medium and small firms but no significant effect for large firms, which resembles the
size-dependent results in Khwaja and Mian (2008).
Focusing also on the effects of the Great Recession, Cingano, Manaresi, and Sette (Forthcoming)
exploit data from the Italian credit register and consider the 2007 liquidity drought in interbank
markets as a source of variation in banks credit supply. They find that this credit supply shock
induced a significant downsizing of firms’ activity as measured by investment, employment, value
added, labor costs, and intermediate input expenditure.
4
Almunia, Lopez-Rodriguez, and Moral-Benito (2016) document the construction and provide an in-depth analysis
of the coverage and representativeness of the SABI-CBI database.
5
Using data from the Spanish Central Credit Register (CIR), Bentolila, Jansen, Jimenez, and
Ruano (2015) and Jimenez, Mian, Peydro, and Saurina (2014) estimate the real effects of two different
bank supply shocks.5 While Jimenez, Mian, Peydro, and Saurina (2014) consider exposure to real
estate as a measure of access to securitization and thus bank credit supply, Bentolila, Jansen, Jimenez,
and Ruano (2015) exploit the difference between banks’ health as identified by those banks that were
bailed out by the Spanish government and curtailed lending relative to the other banks. Jimenez,
Mian, Peydro, and Saurina (2014) do not find any effect on real outcomes -employment and sales
growth- in Spain arguing that firms can undo the bank supply shocks by resorting to other banks
over the period 2004-2007.6 In contrast, Bentolila, Jansen, Jimenez, and Ruano (2015) find sizable
effects of credit supply on employment growth from 2006 to 2010.
Finally, Amiti and Weinstein (2013) estimate the effect of credit supply on firms’ investment
and find a significant effect only for firms with high loan-to-assets ratios. They exploit a sample of
around 150 banks and 1,600 listed firms in Japan over a 20-year period (1990-2010) from the Nikkei
NEEDS FinancialQUEST matched bank-firm loan database. Instead of observed supply shocks (e.g.
liquidity in Khwaja and Mian (2008) or securitization in Jimenez, Mian, Peydro, and Saurina (2014))
they decompose the bank-firm credit growth into bank- and firm-specific fixed effects. They then
use the estimated bank-specific effects as proxies of bank credit supply shocks.
Our empirical approach is very close to Amiti and Weinstein (2013). We also estimate bank- and
firm-specific effects in order to decompose credit growth between supply and demand. However, by
using methods from the matched employer-employee literature we are able to estimate year-by-year
supply shocks for more than 200 banks and demand shocks for more than 700,000 firms. Year-by-year
regressions at the firm level indicate that the effect of credit supply on firms’ employment growth
is larger during the 2008-2009 credit collapse than in expansion (2003-2007). The effect of one SD
deviation of the bank supply shock on firm employment growth is 0.20 percentage points during the
expansion while this effect is 0.60 pp. during the 2008-2009 credit collapse.
3.2
Estimating bank-specific credit supply shocks
Let us consider the following decomposition of credit growth between bank i and firm j in year t:
∆ ln cijt = δit + λjt + ijt
5
(1)
In order to account for demand in the firm-level regressions, Jimenez, Mian, Peydro, and Saurina (2014) correct
their estimates using the covariance between supply and demand inferred from the loan-level regressions with and
without firm effects and Bentolila, Jansen, Jimenez, and Ruano (2015) argue that they can control for demand shocks
by including a rich set of firm controls because their bank lending estimates based on within-firm variation a la Khwaja
and Mian (2008) are very close to those including firm observed covariates instead of firm dummies.
6
Note also that they consider a sample of around 30,000 large firms due to computational reasons.
6
where cijt refer to the yearly average of outstanding credit of firm j with bank i in year t. δit and
λjt can be interpreted as supply and demand respectively. δit captures bank-specific effects that are
identified through differences in credit growth between banks lending to the same firm. Imagine one
firm and two banks in year t − 1; if the credit of the firm grows more between t − 1 and t with the
first bank, we assume that this is because the credit supply of the first bank is larger than that of the
second bank. This is so because demand factors are kept constant given the inclusion of firm-specific
effects (λjt ). This identification strategy resembles that of the bank lending channel by Khwaja
and Mian (2008) but instead of considering observed bank supply shocks (e.g. liquidity shocks) we
consider unobserved shocks estimated by means of bank-specific effects. Finally, ijt captures other
shocks to the bank-firm relationship assumed to be orthogonal to the bank and firm effects.
A common approach for estimating the model in (1) is to include the bank effects as dummy
variables and to sweep out the firm effects by the within transformation. This approach is typically
labeled as “FEiLSDVj” because it combines the fixed-effects (FE) and the least-squares dummy
variable (LSDV) methods. However, the dimension of our dataset precludes us from considering this
option as our sample contains 24,490,973 bank-firm-year observations and 2,820 bank-years.7 We
thus resort to matched employer-employee techniques (see Abowd, Kramarz, and Margolis (1999))
in order to estimate the model.8 Given the sparsity of typical matrices involved in the estimation
of high-dimensional fixed effects, the methods used in this literature consider an efficient storage
of these matrices in compressed form so that the “FEiLSDVj” approach is feasible with standard
computers (see for instance Cornelissen (2008)).
Turning to identification, the bank- and firm-effects are identified only in relative terms within
each group.9 A group here can be understood a set of bank and firms that are connected, which
means that the group contains all the firms that have a credit relationship with any of the banks in
the group, and all the banks that provide credit to at least one firm from the group. In contrast, a
group of banks and firms is not connected to a second group when no bank in the first group provides
credit to any firm in the second group, nor any firm in the first group has a credit relationship with
a bank in the second group. In practice, we identify 11 groups in our data using the algorithm in
Abowd, Creecy, and Kramarz (2002). Each group corresponds to a calendar year in our data because
all firms and banks are connected within a year but there are neither banks nor firms connected across
7
Under the assumption that one cell of the data matrix consumes 8 bytes, storing the matrix of bank dummies
consumes 552 GB, which makes the problem computationally intractable. This is the case when working in highprecision mode in STATA.
8
In analogy with the matched employer-employee methods, banks and firms in our data correspond to firms and
workers in typical matched employer-employee panels. Also, for each firm in our data we have the number of banks
as the time dimension in standard matched employer employee datasets.
9
To be more concrete, we fix the omitted category to be BBVA, so that individual bank dummies can be interpreted
relative to BBVA.
7
years. Therefore, time evolution of the dummies does not have any meaningful interpretation. Note
also that this identification scheme implies that one must rely on multibank firms, which represent
around 75% of the bank-firm-year relationships in our sample.
Since the credit registry data has a monthly frequency, we could also estimate equation (1) with
quarterly or even monthly data. Using annual data we have more firms per bank so that the bank
effects are better estimated. However, using quarterly/monthly data, we would better control for
demand shocks because the firm effects are allowed to vary within a year. Having this trade-off in
mind, we have finally decided to use annual data in order to merge the estimated effects with the
dataset on firm-level characteristics that is available at a yearly frequency.
Validating bank-specific credit supply shocks In order to assess the plausibility of the δ̂it
estimates, we divide our sample into healthy and weak banks as in Bentolila, Jansen, Jimenez, and
Ruano (2015). Figure 1 shows the time evolution of the average difference in credit supply shocks
between healthy and weak banks as identified by the bank dummies (δ̂it ). Weak banks had higher
supply shocks until 2006 and lower afterward, which coincides with the narrative in Bentolila, Jansen,
Jimenez, and Ruano (2015). We interpret this evolution as clear evidence in favour of the plausibility
of our estimated bank supply shocks.
−.14
−.1
−.06
−.02
.02
.06
Figure 1: Average difference in bank supply shocks (weak - healthy)
2004
2005
2006
2007
Average difference
2008
2009
2010
90% CI
Notes. This plot is based on year-by-year regressions of the bank-level dummies on a constant and a dummy for weak
banks as identified in Bentolila, Jansen, Jimenez, and Ruano (2015). For each year we plot the coefficient on the weak
bank dummy, which estimates the average difference in supply shocks by type of bank (weak or healthy).
8
If our identified bank-specific credit shocks really capture supply factors, a bank with a larger
dummy (δ̂it ) should grant more loans for a given firm. Loan application data allows testing this
hypothesis. In particular, we can regress a loan granting dummy on the bank dummies and a set of
firm fixed effects to account for demand factors. Identification relies again on multibank firms, but
now, the firms in our sample cannot have any credit exposure with the banks in the regression as
otherwise they would not be observed in the loan application data. Therefore, the bank-firm pairs
exploited in this exercise are not used in the identification of the bank dummies in (1). In particular,
we run the following regression for each year from 2004 to 2010:
Loan grantedij = γ δ̂i + λj + ij
(2)
where Loang rantedij is a dummy variable taking the value 1 if firm j has at least one loan granted
with bank i and zero if all loan applications from firm j to bank i were not materialized. δ̂i refers
to our estimated bank supply shock for bank i, and λj captures firm-specific effects to account for
demand. The γ parameter captures the effect of our credit supply shocks on the probability of loan
acceptance. A positive and significant estimate can be interpreted as evidence in favour of our bank
dummies capturing credit supply. Intuitively, the same firm applying to two different banks -with
no previous credit relationship with the firm- has a higher probability of getting the loan accepted
in the bank with the larger bank dummy if γ is positive.
Figure 2 plots the estimated γ coefficient for each year. The effect of the bank-specific shocks is
positive and significant in all years, which we interpret as additional evidence in favour of the validity
of our identified bank supply shocks.
Finally, we follow Amiti and Weinstein (2013) and we also explore how well our predicted banks’
credit growth explains the banks’ actual credit growth. For that purpose, we compute the R-squared
of year-by-year regressions of the bank’s actual credit growth (∆ ln cit ) on the bank’s credit growth
predicted from our model (∆ ˆln cit ).10 The average R2 over the 2004-2010 period is 60%, which
indicates that the estimated bank- and firm-specific effects explains most of the variation in bank
lending.
3.3
Loan-level effects
Following Khwaja and Mian (2008) and Jimenez, Mian, Peydro, and Saurina (2014) we first estimate
the magnitude of the so-called bank lending channel at the bank-firm (loan) level. Based on the
simple model outlined by Khwaja and Mian (2008), quantifying the bank lending channel amounts
10
Note that ∆ ˆln cit =
P
j
Pcijt−1 ∆ ˆ
ln cijt
j cijt−1
where ∆ ˆln cijt = δ̂it + λ̂jt .
9
−.02
0
.02
.04
Figure 2: Effect of the bank shocks on loan granting
2004
2005
2006
2007
2008
Effect on loan granting (γt)
2009
2010
90% CI
Notes. This plot is based on year-by-year regressions of the loan granted dummy on the bank-level dummies and a
set of firm fixed effects. In particular, the γ parameter plotted here estimates the effect of the bank dummies on the
probability of acceptance of a loan request. Standard errors are clustered at the bank level.
to estimate the β coefficient in the following model:
∆ ln cij = α + β δ̂i + ηj + vij
(3)
where ∆ ln cij refers to the credit growth between bank i and firm j in a given year. δi represents
a bank-specific supply shock and ηj accounts for firm-specific demand shocks. The lending channel
corresponds to the parameter β. Crucially, the availability of firms borrowing from different banks
allows including firm fixed-effects (ηj ) in the regression to account for the demand side (see Khwaja
and Mian (2008)). The bank supply shocks δi are proxied by exogenous changes in deposits in Khwaja
and Mian (2008), or access to securitization in Jimenez, Mian, Peydro, and Saurina (2014). In our
case, we exploit the bank supply shocks estimated above (see section 3.2) standardized to have zero
mean and unit variance. In contrast to previous literature, we can also estimate the evolution over
time of the bank lending channel because we have a set of bank supply shocks for each year. Given
the lack of comparability over time of our bank supply shocks, we consider year-by-year regressions
that allow us to identify time varying parameters of the bank lending channel βt .
Figure 3 plots the resulting time-varying estimates of the bank lending channel. Despite using only
multibank firms, our sample comprises, on average, 1,667,718 loans and 887,992 firms in each year.
10
Therefore, the coefficients are very precisely estimated (note that standard errors are multi-clustered
at the bank and firm level - see Cameron, Gelbach, and Miller (2011)). The magnitude of the bank
lending channel is sizable: on average, an increase of one standard deviation in bank supply generates
an average increase of 4.8 percentage points in the growth of each bank-firm credit (∆ ln cij ). The
largest average bank-firm credit growth is 1.8% in 2007, which illustrates the magnitude of the bank
lending channel. Moreover, Figure 3 also points to an increase in the relevance of the bank lending
channel during the crisis.
3
percentage points
5
7
Figure 3: Time-varying estimates of the bank lending channel at the loan level
2004
2005
2006
2007
Bank lending channel (βt)
2008
2009
2010
90% CI
Notes. This figure plots the β estimates from year-by-year regressions given by equation (3). The estimation sample
comprises, on average, 1,667,718 loans and 887,992 firms in each year. Standard errors used to construct the confidence
bands are multi-clustered at the bank and firm level.
Finally, it is worth mentioning two additional results. First, when estimating equation (3) without
firm-specific effects on the same sample of multibank firms, we observe that the bank lending channel
is less important, as the average effect drops from 5.2 pp. to 4.3 pp. This reduction indicates that
banks’ supply and firms’ loan demand shocks are negatively correlated in the cross-section as also
found by Khwaja and Mian (2008). Second, we also repeated the estimation of equation (3) but
including our firm-specific effects (λ̂jt ) estimated in section 3.2 instead of the unobserved firm-specific
effects. As expected, the estimates of the bank lending channel remain unaltered, which indicates
that the λ̂jt estimates are a good proxy for loan demand shocks at the firm level (see below).
11
3.4
Firm-level effects
The bank lending channel appears to be very important given the estimates at the loan level in
section 3.3. However, it may well be that firms are able to undo a negative bank supply shock by
resorting to other banks. If this is the case, a large drop in the credit of a client firm with a bank
affected by a negative supply shock would not capture the actual effect of credit supply on credit
growth. In order to obtain such an estimate we consider the following year-by-year regressions at
the firm level:
∆ ln cj = αF + β F δ j + γ F λ̂j + uj
(4)
where δ j represents a firm-specific credit supply shock constructed as a weighted average of the
supply shocks estimated for all the banks in relationship with firm j with weights given by the share
of credit of each bank with this firm in the previous period:
δj =
X
i
c
P ij(−1) δ̂i
i cij(−1)
(5)
Given this specification, the bank lending channel at the firm level can be estimated from β F as
in Khwaja and Mian (2008) and Jimenez, Mian, Peydro, and Saurina (2014). However, as in the loan
level case, we can obtain time-varying estimates of the bank lending channel from our year-by-year
regressions (i.e. βtF ).
We also account for demand shocks at the firm level. In the case of loan level data, the inclusion
of firm unobserved heterogeneity is possible due to the presence of firms borrowing from different
banks. This approach is no longer possible when using firm-level data. Under these circumstances,
Khwaja and Mian (2008) and Jimenez, Mian, Peydro, and Saurina (2014) resort to the correlation
between supply and demand effects implied by the differences between OLS and FE estimates at
the loan level, to correct the biased OLS estimate of β F . In particular, they exploit the fact that
the difference between the OLS and the FE estimates at the loan level from equation (3) provide
a quantification of the covariance between δi and ηj given the formula of the omitted variable bias.
In our case, we include in the firm level regression the firm-level demand shocks (λ̂j ) estimated
in section 3.2 by means of matched employer-employee techniques. Cingano, Manaresi, and Sette
(Forthcoming) show that both approaches are equivalent but including the estimated demand shocks
we can easily compute appropriate standard errors.
Figure 4 plots time-varying estimates of the bank lending channel at the firm level. In this case,
our sample comprises, on average, 887,992 firms per year. The magnitude of the bank lending channel
is still sizable at the firm level: on average, an increase of one standard deviation in bank supply
12
generates an average increase of 3.8 percentage points in credit growth at the firm level (∆ ln cj ).
The highest credit growth at the firm level in our data is 6.7% in 2006, which illustrates that the
bank lending channel still operates at the firm level.
−1
percentage points
3
7
Figure 4: Time-varying estimates of the bank lending channel at the firm level
2004
2005
2006
2007
2008
F
Bank lending channel (β t)
2009
2010
90% CI
Notes. This figure plots the β F estimates from year-by-year regressions given by equation (4), which identify the bank
lending channel at the firm level. The estimation sample comprises, on average, 841,911 firms in each year. Standard
errors used to construct the confidence bands are clustered at the main bank level, i.e., the largest lender for a firm.
In terms of comparisons with the literature, Jimenez, Mian, Peydro, and Saurina (2014)find that
credit supply shocks have no significant effects on credit growth at the firm level between 2004 and
2007. However, both results are not strictly comparable given the differences in the nature of the
bank supply shocks and the data sample. On the one hand, they analyze supply shock identified
through larger access to securitization of real state assets; on the other hand, the sample in Jimenez,
Mian, Peydro, and Saurina (2014) covers loans above e60,000 mainly corresponding to large firms
(the average multibank firm employs 37 workers in their sample) that may better undo bank supply
shocks by borrowing from other banks.
Finally, it is worth mentioning that including firm-level demand shocks in the model has a crucial
effect on the estimates. In particular, re-estimating the model in (4) by OLS without including firmlevel effects (λ̂j ), the 2004-2010 average estimate of β F drops from 3.4 pp. to 0.7 pp., which indicates
that banks’ supply and firms’ loan demand shocks are negatively correlated in the cross-section as
found in the loan-level case.
13
4
The real (and direct) effects of financial shocks
In order to estimate the effects of the bank lending channel on real outcomes, we match the credit
registry information with administrative data at the firm level providing annual information on
different firm characteristics. To be more concrete, we consider the effects of credit supply on firms’
employment and output growth.
4.1
Empirical strategy
We exploit two alternative empirical strategies. First, we regress annual employment (and output)
growth on the bank supply shocks at the firm level controlling for other firm-specific characteristics:
∆ ln wj = θw δ j + π w Xj + νjw
(6)
where wj = {Ej , Yj } refers to either employment (number of employees) or output (Euros) of firm
j.11 δ j is the bank supply shock defined in equation (5), and Xj represents a vector of firm-specific
characteristics. The vector Xj includes the firm-specific credit demand shocks (λ̂j ) as well as other
firm covariates, namely, size, sector of activity, lagged investment, lagged loan-to-assets ratio, and
lagged productivity. The model in (6) is estimated year-by-year so that we obtain time-varying
estimates of the reduced-form effects of the bank lending channel on real outcomes.
Second, we also estimate the strength of the credit channel by regressing real firm growth on
credit growth instrumented with our firm-specific credit supply shocks:
w
∆ ln wj = φw ∆ ln cj + πIV
Xj + u w
j
(7)
w
∆ ln cj = ψ w δ j + Φw
IV Xj + vj
where the identification assumption is that bank credit supply (δ j ) affects firm growth only through
its effect on credit. Note that the first stage is basically equal to the bank lending channel at the
firm level estimated in (4) but including some additional controls. Moreover, the reduced form effect
in (6) is equal to this bank lending channel multiplied by the pass-through of credit to firm growth:
θw = ψ w × φw .
11
Results considering ∆(1 + ln Ej ) and (Ej − Ej,−1 )/(0.5 × (Ej − Ej,−1 )) as dependent variables remain unaltered.
These alternative definitions are considered by Bentolila, Jansen, Jimenez, and Ruano (2015) and Chodorow-Reich
(2014), respectively.
14
4.2
Results
Figure 5 reports the estimated reduced-form effects of the bank lending channel on firm growth
in terms of employment (left panel) and output (right panel). We find a positive and statistically
significant effect of credit supply shocks on employment growth at the firm level for all years in
our sample. Admittedly, the statistical significance is only marginal over the years 2004-2007. On
average, an increase of one standard deviation in bank supply generates an average increase of 0.3
percentage points in annual employment growth at the firm level. Annual employment growth in
our sample is, on average, 2.9%. Interestingly, the left panel of Figure 5 points to larger real effects
of the credit channel during the 2008-2009 credit collapse. The estimated effect for these two years
is 0.5 pp., which more than doubles the average effect of 0.2 pp. on the remaining years. In order to
gauge the economic magnitude of this effect, we can compare the 0.5 pp. increase with the average
employment growth of 2.6% over these two years.
.6
0
0
percentage points
.4
percentage points
.2
.4
.8
Figure 5: Reduced-form effects of the bank lending channel on firm growth
2004
2005
2006
2007
2008
E
Effect on employment growth (θ )
2009
2010
2004
2005
2006
2007
2008
Y
90% CI
Effect on output growth (θ )
2009
2010
90% CI
Notes. This figure plots the θE and θY estimates from year-by-year regressions given by equation (6), which identify
the reduced-form effect of the bank lending channel on employment and output growth at the firm level. In particular,
the figure plots the effect of a one SD increase in the credit supply shock on annual employment and output growth
in percentage points. The estimation samples comprise, on average, 354,029 and 344,908 firms in each year. Standard
errors used to construct the confidence bands are multi-clustered at the main bank and industry level.
Turning to output growth, the right panel of Figure 5 reports the corresponding reduced-form
effects. We also find a positive and statistically significant effect of credit supply shocks on output
growth at the firm level for most years in our sample. A one standard deviation (SD) increase in the
credit supply shock generates an average increase of 0.2 pp. on firm output growth, which accounts
for 20% of the average output growth of 1.0% observed over the 2004-2010 period.12 . Overall, the
12
Note that the average output growth in the pre-2008 period was around 2.4% while that of the post-2008 period
15
output effects of the bank lending channel appear to be similar to those of employment.
Results reported in Figure 5 are based on samples of 354,029 and 344,908 firms per year. The
sample sizes are reduced with respect to that of section 3.4 for two reasons: first, our administrative
data does not cover the universe of firms in the credit registry; second, many firms are lost due to
missing values in some of the covariates included in (6). Indeed, if we do not include the vector
of covariates Xj , the samples contain 545,327 and 531,277 firms on average. Moreover, the results
remain similar when the firm-specific controls are not included, which points to the exogeneity of the
bank supply shocks with respect to firm characteristics.
Estimates of the credit channel in (7) are reported in Figure 6. Overall, they appear to be
more stable than the corresponding reduced form effects but they are also less precisely estimated.
According to the left panel of Figure 6, a 1 pp. increase in credit growth generates an average
increase of 0.09 pp. in annual employment growth. This figure is similar to that of Bentolila, Jansen,
Jimenez, and Ruano (2015) who find an effect of 0.52 pp. over the four-year period 2006-2010.
The estimated effects is statistically significant during the 2008-2009 credit collapse while the 90%
confidence interval contains zero for all the remaining years. With respect to the output effects in the
right panel of 6, the average effect of a 1 pp. increase in credit growth on output growth is around
0.05 pp, being larger during the 2008-2009 years (0.07 pp.).
In both cases, the reduced-form effect reported in Figure 5 coincides with the the elasticity of
firm growth to credit growth in Figure 6 multiplied by the bank lending channel estimates at the firm
level reported in Figure 4. Finally, note also that the reduced form coefficient ψ w is highly significant
and conventional F statistics confirm the absence of a weak instruments problem.
4.3
Aggregate effects
In this section, we provide a quantification of the aggregate effects of bank credit supply shocks
on employment and output growth. For that purpose, we estimate year-by-year counterfactual
employment growth (and output) at the firm level in the absence of credit supply shocks using the
estimates from (7). To be more concrete, we first estimate the firm-level credit growth due to the
bank supply shocks:
^
∆
ln cj = β̂ F δ j
(8)
^
Armed with the credit growth induced by supply factors (∆
ln cj ), we can estimate the counterfactual employment and output growth that we would have observed in the absence of credit supply
was -0.8%.
16
−.06
−.06
percentage points
.04
.14
percentage points
0
.06
.24
.12
Figure 6: Credit-driven effects of the bank lending channel on firm growth
2004
2005
2006
2007
2008
E
Credit effect on employment growth (φ )
2009
2010
2004
2005
2006
2007
2008
Y
90% CI
Credit effect on output growth (φ )
2009
2010
90% CI
Notes.This figure plots the φE and φY estimates from year-by-year IV regressions given by (7), which identify the
effect of credit supply on employment and output growth at the firm level through the access to credit. In particular,
the figure plots the effect of a one percentage point increase in credit growth on annual employment and output growth
in percentage points. The estimation samples comprise, on average, 354,029 and 344,908 firms in each year. Standard
errors used to construct the confidence bands are multi-clustered at the main bank and industry level
shocks as follows:
^
^
∆
ln wj = ∆ ln wj − φ̂w ∆
ln cj
(9)
where wj = {Ej , Yj } refers to either employment or output of firm j. φ̂w refers to the estimate
obtained from (7).
Firm-specific employment and output growth measures (both observed and counterfactual) can
be aggregated as follows:
^
∆
ln w =
X
∆ ln w =
X
^
ϕi ∆
ln wi
(10)
ϕi ∆ ln wi
(11)
i
i
(12)
where ϕi refers to the employment (or output) weight of firm i in the previous year (ϕi =
w
P i(−1) ).
j wj(−1)
Note that one can also aggregate at the industry level to obtain sector-specific credit supply shocks
in terms of output and employment. Indeed, Figure 7 plots yearly shocks to each sector (NACE rev2
^
classification) resulting from the aggregation of the firm-specific shocks given by φ̂w ∆
ln cj . The
estimated shocks in Figure 7 clearly point to positive credit supply shocks in 2004-2007 for all the
64 sectors in our data. In contrast, the shocks appear to be negative in the 2008-2010 period, being
17
the largest those corresponding to the year 2009.
−2
Output shock due to credit supply
−1
0
1
2
Figure 7: Sector-specific credit supply shocks
−1.5
−1
2004
−.5
0
.5
Employment shock due to credit supply
2005
2006
2007
2008
1
2009
2010
Notes. This figure plots the industry-specific shocks in terms of employment (x axis) and output (y axis) due to credit
^
supply. These shocks are constructed from the aggregation of the firm-level shocks φ̂w ∆
ln cj .
Turning to the total economy effects, Figure 8 plots year-by-year credit growth,13 employment
growth and output growth together with the estimated counterfactuals in the absence of credit supply
^
^
^
shocks (∆
ln E, ∆
ln Y , and ∆
ln C). The upper left panel plots the figures for credit growth (both
^
actual ∆ ln C and counterfactual ∆
ln C). Annual credit growth was around 23% over the 2004-2007
period while, according to our estimates, this figure would have been 16% in the absence of credit
supply shocks. In contrast, actual credit growth was 0.7% in 2008-2010 while the counterfactual is 8%.
These figures indicate that an aggregate credit supply shock took place in 2004-2007 (counterfactual
lower than actual) and a negative supply shock took place in 2007-2010 (counterfactual larger than
actual credit growth).
The upper right panel of Figure 8 plots the aggregate effects of credit supply on employment.
Pre-crisis annual employment growth was 3.9% (2004-2007 average) while this figure was -4.1%
during the crisis years (average 2008-2010). According to our estimates, these figures would have
been 3.2% and -3.5% in the absence of credit supply shocks. These differences point to positive
and sizable credit supply shocks during the pre-crisis period, and a negative and large bank supply
shock over the crisis. For instance, over the 2004-2007 period, 1.872 million jobs were created in
13
Credit growth is aggregated as employment or output but using weights for aggregation based on the share of
total credit of each firm rather than employment.
18
terms of full-time equivalent workers. This figure would have been 1.548 in the absence of credit
supply shocks, which implies a positive contribution of 323,626 full-time equivalent workers from the
supply of credit by Spanish banks (17% of total employment creation). Turning to the 2008-2010
period, around 235,000 out of the 1.6 million jobs destructed were due to the negative credit supply
shock, which accounts for 15% of total employment destruction over the crisis. Needless to say,
these figures must be interpreted with caution given large uncertainty surrounding these estimates,
especially those corresponding to the 2004-2007 that were estimated less precisely.
Turning to the bottom left panel of Figure 8, the aggregate effects of the bank lending channel
on output are a bit larger than those of employment. The aggregate output growth rate was around
7% during the pre-crisis years 2004-2007 and -0.1% over the 2008-2010 period. Our counterfactual
estimates of aggregate output growth in the absence of credit supply shocks are 6% and 0.9%,
respectively. Therefore, the credit supply contribution to aggregate output growth was 1 pp. during
the pre-crisis period while the contribution was -1 pp. during the crisis.
Figure 8: Aggregate fluctuations in the absence of credit supply shocks
Employment
−4
−10
6
−5
16
0
26
5
Credit
2004
2005
2006
2007
Actual growth
2008
2009
2010
Counterfactual
2004
2005
2006
2007
Actual growth
2008
2009
2010
Counterfactual
−4
0
4
8
Output
2004
2005
2006
2007
Actual growth
2008
2009
2010
Counterfactual
Notes. This figure plots aggregate credit, employment and output growth rates. Actual refers to National Accounts
figures for employment and output, and Banco de España for credit. Counterfactual refers to the estimated growth
^
^
^
rates in the absence of credit supply shocks (∆
ln E, ∆
ln Y , and ∆
ln C). See the main text for more details.
19
4.4
A time-varying credit supply indicator
The aggregate estimates reported in the previous section clearly point to a positive credit supply
shock during the boom (2004-2007) and a negative credit supply shock over the bust (2008-2010). In
this section, we present an alternative approach to estimate a time-varying indicator of credit supply
that confirms this pattern. Intuitively, we use the loan-level data to estimate bank-specific time
trends of credit supply after accounting for demand shocks (i.e. firm fixed effects). The resulting
bank-specific trends can then be aggregated to construct an aggregate indicator of credit supply over
time.
We consider the following model:
∆ ln cijt = µjt + ζi + Ki0 × T + ξijq
(13)
where ∆ ln cijt refers to credit growth between bank i and firm j in quarter t. Ki0 × T captures a
bank-specific time trend that aims to identify the time evolution of bank-specific credit supply. In
particular, for our baseline quartic trend we define Ki = (κ1,i , κ2,i , κ3,i , κ4,i )0 and T = (t, t2 , t3 , t4 ).
Therefore, bank-specific time trends in credit supply can be estimated as K̂i0 × T .
Identification of bank-specific credit supply time trends is based on the inclusion of firm-quarter
effects (µjt ) that account for time-varying demand shocks as well as time invariant bank-specific
effects (ζi ) that account for constant heterogeneity in supply factors at the bank level. Note that we
focus now on quarterly data to get a better identification of the time trends that are now the focus
of our analysis.14
Figure 9 plots the resulting indicators of credit supply when considering cubic and quartic time
trends. Interestingly enough, all the three cases considered point to a credit supply increase during
2004-2007 while it was dramatically reduced starting in 2008. This pattern fully coincides with our
counterfactual estimates in section 4.3. We consider these three exercises to illustrate that the use
of 30 or 50 largest banks does not have a significant impact on the estimated cubic trend, and that
the type of trend (cubic or quarter) does not alter the patters of credit supply. Note that a quartic
trend with the 50 largest banks is not feasible due to computational constraints.
14
The matched employer-employee techniques employed above allow us to accommodate the firm-quarter (µjt )
and bank dummies (ζi ). However, the bank-specific time trends also represent a challenge from a computational
perspective, especially given the use of quarterly data which multiplies by a factor of four the number of annual
observations. This is so because each bank-specific time trend must be stored as an additional set of variables to be
included in the regression. For instance, in the case of a quartic trend, quarterly loan-level data up to 2010 comprises
58,017,961 bank-firm-quarter observations and the inclusion of a quartic trend for each bank in the sample implies
that 168 × 4 = 672 variables must be included in the regression in addition to the firm-quarter and bank dummies
handled by the FEiLSDVj approach. This estimation requires around 311 GB of memory which makes the problem
computationally intractable. We therefore restrict the analysis to the 30 and/or 50 largest banks in our sample that
account for 88% and 96% of the total credit, respectively.
20
−.2
−.15
−.1
−.05
0
.05
Figure 9: Aggregate credit supply over time
2004q1
2005q3
2007q1
2008q3
Quartic trend (30 largest banks)
Cubic trend (30 largest banks)
2010q1
Cubic trend (50 largest banks)
Quartic trend (50 largest banks)
Notes. This figure plots the aggregate credit supply indicator resulting from averaging the bank-specific trends given
by K̂i0 × T . In particular, quartic and cubic trend are plotted here. The value in the first quarter is normalized to 0.
5
Network Propagation of the Credit Supply Shocks
The recent work by Acemoglu, Ozdaglar and Tahbaz-Salehi (2010, 2014), Acemoglu, Carvalho,
Ozdaglar and Tahbaz-Salehi (2012), Acemoglu, Akcigit, and Kerr (2016), building on early work
by Long and Plosser (1983) emphasize the role of input-output linkages in propagating shocks and
the macroeconomic implications.15
Following this work, we present a simple in which supply shocks to a given industry affect directly
its output and indirectly the output of other industries.16 Hence, this model is useful to study the
propagation of financial shocks over the economy. We will first focus on the simplest version of the
model, in which we assume that (i) firms have access to a constant returns to scale technology, (ii)
the utility function of the representative consumer is represented by a Cobb-Douglas and (iii) labor
is inelastically supplied by the household.
Technology and market structure: There are n industries in the economy. In each of these
industries i = 1, ..., n there is a representative perfectly competitive firm that has access the following
Cobb-Douglas production function:
yi =
αl
li i
n
Y
a
yijij
(14)
j=1
15
see also Barrot and Sauvagnat (forthcoming), Boehm, Flaaen, and Nayar (2016).
The model is close to Acemoglu, et a. (2012) and Acemoglu, Akcigit, Kerr (2015). In important recent work
Bigio and LaO (2016) extend this work to include financial constrains.
16
21
where yi is the amount of units produced in industry i; yij is the amount of goods produced in
industry j used as inputs by industry i; li is the amount of labor used by industry i. We assume
that these productions functions exhibit constant returns to scale, i.e:
αil
+
n
X
aij = 1,
αil > 0 and aij ≥ 0
(15)
j=1
Financial constraints We assume the existence of working capital. This means that, before
production takes place, firms must pay wages and the cost of intermediate goods in advance. We
also assume that firms must borrow for this. Financial markets are subject to some imperfection
so firms can borrow just up to a fraction φ of their revenue. Hence, firm operating in industry i
maximize its profits subject to:
li +
n
X
pj Xij ≤ φi pi yi
(16)
j=1
Note that, under constant returns to scale, the firm would always like to borrow an amount equal to
pi yi . This means that the constraint will bind whenever φ < 1.
Profit maximization Firm operating in industry i solves the following maximization problem:
max
li ,xij ,∀j
pi yi − li −
n
X
pj Xij
j=1
αl
subject to: yi = li i
n
Y
a
yijij
j=1
li +
n
X
pj Xij ≤ φi pi yi
j=1
This problem can be solved in two stages. In the first stage, for a given level of firm’s expenditure
Ei , the firms decides how to allocate this expenditure across the different production factors. The
solution of this problem is given by:
li = αEi
pj Xij = aij Ei
22
(17)
(18)
In the second stage, the firm decides the level of expenditure Ei . We know that in the absence of
financial frictions this level would be equal to revenue. In the presence of financial frictions, however,
the amount of expenditure Ei is given by:
Ei = min{1, φi }Ri
(19)
where Ri ≡ pi yi .
Preferences: We assume that the economy is populated by a representative household whose
preferences are represented by the following utility function:
u(c1 , ..., cn , ) =
n
Y
cβiji
(20)
i=1
where βi ∈ (0, 1) with
Pn
i=1
βi = 1. We assume that labor is exogenously supplied. The household’s
budget constraint is given by:
n
X
pi ci = wl
(21)
i
where pi is the price of good produced in industry i; w is the competitive equilibrium (the household
and firms maximize utility and profits respectively taking as given prices and the market clearing
conditions for labor and goods are satisfied) price for labor that we normalize to one. (w = 1)
Household’s Maximization Problem Household’s maximisation problem is as follows:
max
ci ,∀i
subject to:
n
Y
cβiji
i=1
n
X
pi ci = wl
i
The FOC of this problem yields to the following condition:
pi ci = βi wL
(22)
Input-output structure: the Cobb-Douglas production function in equation (14) together with
the firms’ FOC imply:
23
aij =
pj yij
pi y i
(23)
The input-output structure if this economy is defined by the direct requirements (DR) matrix:
a11 a12 ... a1n


 a21 a22 ... a2n
A=
 ... ... ... ...

an1 an2 ... ann






where the entry ij shows the dollar amount of good j that industry i uses per dollar of output that
it produces. We also define the total requirements matrix (the Leontief inverse) as:
h11 h12 ... h1n


 h21 h22 ... h2n
=
 ... ... ... ...

hn1 hn2 ... hnn






−1
H ≡ (I − A)
Note that the latter is the one that captures the propagation of a shock in a given industry to the rest
of the economy. A negative credit supply shock to industry j reduces its production and increases
its price. This adversely impacts all of the industries that purchase inputs from industry j. But this
direct impact will be further augmented in the competitive equilibrium because these first-roundaffected industries will change their production and prices, creating indirect negative effects on other
customer industries (downstream effects).
Market clearing condition in the goods market: Total production in a given industry i must
be equal to the amount of intermediate goods produced in that industry and used by all industries
plus the amount of production that goes to final consumption:
y i = ci +
n
X
yji
(24)
j=1
where ci is final consumption of good produced in industry i.
Market clearing condition in the labor market: Total exogenous labor supply must be equal
to total labor demand, i.e, the sum of all labor used across all industries:
L=
n
X
i=1
24
li
(25)
Equilibrium: An equilibrium in this economy is defined as a set of prices {p1 , ..., pn } and allocations
{l1, ...; ln }, {y1 , ..., yn }, {c1 , ..., cn } and {xi 1, ..., xi n}, ∀i, such that:
1. Firms solve their maximization problem, i.e, equations (17), (18), (19), are satisfied.
2. Households solve their optimization problem, i.e, equation (22) is satisfied.
3. Markets clear, i.e, equations (24) and (25) are satisfied
The effect of a credit supply shock: As shown by Acemoglu, Akcigit, and Kerr (2016), the
impact of supply shocks on sector i output can be decompose in two parts: the direct effect and the
network effect. Formally:
dlnyi = dlnφi +
n
X
(hij − 1i=j ) × dlnφj
(26)
j=1
Note that this implies that supply shocks do not have upstream effects.
Identification of changes in financial frictions: Our purpose in practise is to plug in the
credit supply shocks that we estimate using credit registry data into the model, so we can study
their aggregate implications. We proceed as follows:
• First, we estimate in the data what has been the direct effect of credit supply shocks on the
value added of a given industry. In practise, we do this by computing a counterfactual evolution
of value added that had taken place in the absence of the estimated shocks.
• Imagine that we find that, in a given industry, value added would have been a 10% higher in
the absence of credit supply shocks
• Then, conceptually, what we do is to calibrate the change in φi (we don’t really care about
the level) for each industry so the model generates the same direct effect as the one we have
previously estimated in the data.
• In practise, we exploit the structure of the model (equation 26) and we simply, for a given φi
in t = 0, solve for a a φi in t = 1 that makes the log difference of φi between the two periods
equal to the estimated direct effect. Coming back to our previous example. We should solve
for φt=1
such that lnφt=1
− lnφt=0
= 0.10.
i
i
i
25
5.1
Input-Output Tables in Spain
In this section we describe the Input-Output tables that we use to calibrate the model. We use the
IO tables provided by the INE (“Instituto Nacional de Estadstica”). For the moment, we use the IO
tables provided for the year 2010. The reason why we use this year is because the tables constructed
before relied on an industry classification different from that we have in or firm level data (see Table
A.1 for a complete list of the industries available in the IO tables). In Figures A.1-A.3, we show that
IO seem to have remained very stable over time.
We can directly measure from the data the matrix A, which is represented in Figure (10). Notice
that industries as Real State Services, Wholesale, Electricity Services or Basic Metals are used
intensively by many industries.
51
56
61
Figure 10: A matrix for Spain (2010)
36
Real State Services (44)
31
Wharehousing for transp. (34)
Construction (27)
21
26
output
41
46
Accomodation and food serv. (36)
Wholesale (29)
11
16
Basic Metals (15)
1
6
Electricity (24)
1
6
11
16
21
26
31
36
41
46
51
56
61
input
Notes. This figure shows the IO structure of the Spanish economy for the year 2010. In particular, it represents the
elements in matrix A. A contour plot method is used, showing only those shares greater than 2%, 5%, 10% and 20%.
Source: INE.
26
5.2
Estimated propagation effects
Figure 11 illustrates that the identified network effects are of the same order of magnitude than
the direct effects, which reveals the importance of propagation effects when estimating the impact
of financial shocks on real outcomes (see Table A.1 for a list of industries). On the other hand,
there are certain industries in which the network effect is significant despite these industries are not
directly affected by the credit supply shock. For instance, sector 45 (Legal and accounting activities;
activities of head offices; management consultancy activities) is barely affected directly by the shock
in 2007, but the network effect is substantial due to its dependence on supplier industries that were
hit by the financial shock.
Figure 11: Direct and network effects by industry
53
2
29
45
Network effect
0
1
10
52
44
2
5
5641454
55
243
57
63
−2
−1
64
30 51
394720 323418
19726
16
31
2512
46 4028
27
219138 11
37
5935
58
62
14 15
6
36 4 4933
24 22
61
60171 3 38
48
50 23
−2
−1
2004
2005
0
Direct effect
2006
2007
1
2008
2
2009
2010
Notes. This figure plots the industry-specific effects (in percentage points) on output due to credit supply shocks.
Finally, we aggregate the industry-specific results in Figure 11 to assess the overall impact of the
identified credit supply shocks on aggregate output growth (see Figure 12 and Table 1).
6
Concluding Remarks
27
Figure 12: Aggregate output growth in the absence of credit supply shocks
−4
0
4
8
Output
2004
2005
2006
2007
2008
2009
2010
Actual growth
Direct
Direct+Network
Notes. This figure plots aggregate output growth. Actual refers to National Accounts figures. Direct and network
refer to the estimated growth rates in the absence of credit supply shocks.
Table 1: Aggregate output growth in the absence of credit supply shocks
Year
Direct Effect
due to BLC
Network Effect
due to BLC
Total Effect
due to BLC Actual growth
2004
2005
2006
2007
2008
2009
2010
1.15
0.11
0.85
1.56
-0.71
-1.25
-0.89
0.90
0.09
0.66
1.20
-0.53
-0.96
-0.67
2.05
0.20
1.52
2.76
-1.24
-2.20
-1.56
6.29
7.18
7.40
6.99
4.39
-3.96
-0.65
2004-2007
2008-2010
0.92
-0.95
0.71
-0.72
1.63
-1.67
6.97
-0.07
Notes. All figures refer to annual output growth in nominal terms. Actual is taken from
National Accounts. The estimated direct and network effects are computed as the outputweighted average of industry-specific figures. Total refers to the sum of the direct plus the
network effect.
References
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28
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29
A
Additional Tables and Figures
Table A.1: List of industries
Number Industry
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
Crop and animal production, hunting and related service activities
Forestry and logging
Fishing and aquaculture
Mining and quarrying
Manufacture of food products, beverages and tobacco products
Manufacture of textiles, wearing apparel and leather products
Manufacture of wood and of products of wood and cork, except furniture;
Manufacture of paper and paper products
Printing and reproduction of recorded media
Manufacture of coke and refined petroleum products
Manufacture of chemicals and chemical products
Manufacture of basic pharmaceutical products and pharmaceutical preparations
Manufacture of rubber and plastic products
Manufacture of other non-metallic mineral products
Manufacture of basic metals
Manufacture of fabricated metal products, except machinery and equipment
Manufacture of computer, electronic and optical products
Manufacture of electrical equipment
Manufacture of machinery and equipment n.e.c
Manufacture of motor vehicles, trailers and semi-trailers
Manufacture of other transport equipment
Manufacture of furniture; other manufacturing
Repair and installation of machinery and equipment
Electricity, gas, steam and air conditioning supply
Water collection, treatment and supply
Sewerage; waste collection, treatment and disposal activities; materials recovery;
Construction
Wholesale and retail trade and repair of motor vehicles and motorcycles
Wholesale trade, except of motor vehicles and motorcycles
Retail trade, except of motor vehicles and motorcycles
Land transport and transport via pipelines
Water transport
Air transport
Warehousing and support activities for transportation
Postal and courier activities
Accommodation; food and beverage service activities
Publishing activities
Motion picture, video and television programme production, sound recording and music publishing activities;
Telecommunications
Computer programming, consultancy and related activities; information service activities
Financial service activities, except insurance and pension funding
Insurance, reinsurance and pension funding, except compulsory social security
Activities auxiliary to financial services and insurance activities
Real estate activities
Legal and accounting activities; activities of head offices; management consultancy activities
Architectural and engineering activities; technical testing and analysis
Scientific research and development
Advertising and market research
Other professional, scientific and technical activities; veterinary activities
Rental and leasing activities
Employment activities
Travel agency, tour operator reservation service and related activities
Security and investigation activities; services to buildings and landscape activities; business support activities
Public administration and defence; compulsory social security
Education
Human health activities
Social work activities
Creative, arts and entertainment activities; libraries, archives, museums and other cultural activities; gambling activities
Sports activities and amusement and recreation activities
Activities of membership organisations
Repair of computers and personal and household goods
Other personal service activities
Activities of households as employers; undifferentiated goods- and services-producing activities of households for own use
Activities of extraterritorial organisations and bodies
30
31
36
Figure A.1: The Spanish Input-Output structure 2000
Real State Services (29)
Wholesale (20)
Rent. of M&E, others (30)
.1
11
Basic Metals (12)
DR_coefficient
Construction (18)
16
output
21
26
.2
.05
6
Electricity (17)
1
.02
1
6
11
16
21
26
31
36
input
Notes. This figure shows the IO structure of the Spanish economy for the year 2000. Source: World Input-Output
Database.
31
36
Figure A.2: The Spanish Input-Output structure 2005
Wholesale (20)
Rent. of M&E, others (30)
Basic Metals (12)
DR_coefficient
.2
Construction (18)
16
output
21
26
Real State Services (29)
11
.1
.05
6
Electricity (17)
1
.02
1
6
11
16
21
26
31
36
input
Notes. This figure shows the IO structure of the Spanish economy for the year 2005. Source: World Input-Output
Database.
31
31
36
Figure A.3: The Spanish Input-Output structure 2010
16
output
21
.2
Construction (18)
Wholesale (20)
Rent. of M&E, others (30)
Basic Metals (12)
DR_coefficient
26
Real State Services (29)
11
.1
.05
6
Electricity (17)
1
.02
1
6
11
16
21
26
31
36
input
Notes. This figure shows the IO structure of the Spanish economy for the year 2010. Source: World Input-Output
Database.
32