Trusting the Bankers:
A New Look at the Credit Channel of Monetary Policy ∗
Matteo Ciccarelli Angela Maddaloni European Central Bank
José-Luis Peydró
15 February 2011
Abstract
Credit supply and demand are mostly unobserved, thus identifying completely the credit channel is unfeasible. Bank lending surveys by central banks, however, contain reliable quarterly information on credit supply and demand’s quantity and quality.
Using the U.S. and the unique Euro area surveys, we find that the credit channel amplifies a monetary policy shock on GDP and inflation, through the balance-sheets of households, firms and banks. For corporate loans, amplification is highest through credit supply; for households, demand is the strongest channel. Finally, a credit crunch
for firms and tighter standards for mortgages significantly reduced GDP during the
financial crisis.
JEL classification: E32, E44, E5, G01, G21.
Keywords: Credit channel, Firm and household balance-sheet channels, Bank lending channel, Credit crunch, Credit supply, Monetary policy.
∗We are grateful to Egon Zakrajsek for providing us with some of the data from the Senior Loan Officer Survey and to Francesca Fabbri for her excellent work as research assistant. We also thank Fabio Canova, Francesco Caselli, Gabriel Fagan, Hanna Hempell, Jordi Galí, Michele Lenza, Jesper Lindé, Simone Man- ganelli, Benoit Mojon, Eva Ortega, Enrico Perotti, Huw Pill, Frank Smets, Ilya Strebulaev, Silvana Teneyro, Mirko Wiederholt, an anonymus referee for the ECB Working Paper Series, and all the participants to the CREI, BIS, DNB, Bank of Italy, ECB seminars, the CREDIT conference in Venice, the workshop “Finan- cial Markets and the Macroeconomy: Challenges for Central Banks” in Stockholm, the “6th International Research Forum on Monetary Policy” at the Board of Governors in Washington, the Workshop of the Basel Committee’s Research Task Force on the Transmission Channels (RTF-TC) in Paris, the Econometric Society World Congress 2010 in Shanghai, 30th CIRET conference in New York, and the American Economic Asso- ciation Meetings 2011 in Denver for useful suggestions and comments. Any remaining errors are our respon- sibility. The views expressed are our own and do not necessarily reflect those of the European Central Bank or the Eurosystem. Email contacts: matteo.ciccarelli@ecb.europa.eu, angela.maddaloni@ecb.europa.eu, and jose-luis.peydro-alcalde@ecb.europa.eu.
“[...] extremely important outstanding questions for research. One is the [. . . ] role of the credit channel in our understanding of economic fluctuations and monetary policy. The literature in this area remains thin, and this thinness reflects difficulty in specifying the relevant mechanisms andfinding the supporting empirical evidence.”
Boivin, Kiley and Mishkin, 2010, Handbook of Monetary Economics
1 Introduction
The events of the last few years suggest that the financial sector — the banking sector in particular — is a crucial determinant of business cyclefluctuations. The worstfinancial crisis in Europe and in the U.S. since the Great Depression was followed by a severe recession.
A key channel through which banks affect the economy is the provision of credit to fund private investment and consumption. During periods of crisis, a credit reduction may be the result of weaker demand, lower net worth offirms and households and, possibly, tighter credit supply due to banks’ solvency and liquidity problems. In these circumstances, central banks support aggregate demand and credit supply through monetary policy. Identifying and quantifying the linkages between monetary policy, credit provision and business cycles is therefore of utmost importance.
The main objective of this paper is to test the credit channel of monetary policy.1 We address the following questions: (i) Does monetary policy affect GDP and inflation through credit availability? (ii) How important are the different transmission channels — credit de- mand, non-financial borrower balance-sheet, and bank lending channels? (iii) Does the relative importance of these channels depend on whether the borrowers are households or firms? (iv) How did credit and monetary policy affect the real activity during the recent financial crisis?
The credit channel theory implies that monetary policy has real effects through credit supply and demand. A tightening of monetary policy reduces loan supply by increasing the external financing cost for banks (bank lending channel). At the same time, loan demand declines due to the higher direct cost of loans (classical interest rate channel) and to the higher external finance premia faced by non-financial borrowers (firm and household balance-sheet channel). Since changes of credit supply and demand are mostly unobserved, the complete identification of both the credit channel and its subchannels is challenging. The academic literature using both a macro and a micro approach has not yet addressed this fundamental identification challenge in a satisfactory manner.
1For the definitions of the credit channel and of the different subchannels, see Bernanke and Gertler (1987 and 1995), and Bernanke (2007).
From a macro perspective, credit aggregates do not convey enough information to identify supply (Bernanke and Gertler, 1995): If the average borrower’s quality did not change, average credit quantities and prices would suffice to isolate demand and supply; however, after a monetary tightening, aflight to quality of banks to borrowers of better quality occurs (Bernanke, Gertler and Gilchrist, 1996).2
Given these limitations, the literature has tried to improve identification with micro data by testing the cross-sectional predictions from theory (see Bernanke and Gertler, 1995).
However, the micro approach cannot fully identify the credit channel. As pointed out by Kashyap and Stein (2000), the micro identification cannot analyze thetotal effect of a mon- etary policy shock on real activity, but only a difference-in-difference effect by comparing banks (see e.g. Kashyap and Stein, 2000) or non-financial borrowers (see e.g. Gertler and Gilchrist, 1994) with different sensitivity to monetary policy. Moreover, financially con- strained borrowers may obtain credit from constrained banks, thus making balance-sheet and bank lending channels difficult to disentangle (Gertler and Gilchrist, 1994). Further- more, analyses based on micro data use actual credit granted and thus are forced to make restrictive assumptions on credit demand.3 Kashyap and Stein (2000), for instance, assume that banks with different liquidity levels face similar changes in loan demand as a response to a monetary policy shock.
In this paper, we tackle the problem of unobserved supply and demand of credit by using the detailed answers of the confidential and unique Bank Lending Survey (BLS) for the Euro area and of the Senior Loan Officer Survey (SLOS) for the U.S. Euro area national central banks and regional Feds request from banks quarterly information on the lending standards that banks apply and on the loan demand that banks receive fromfirms and households. The detailed information reported in the surveys is very reliable, not least because the surveys are carried out by central banks, which are in most cases the bank supervisors and can cross-check the information received with exhaustive hard bank information.4
The information refers to the actual lending standards that banks apply to the whole pool of borrowers (not only to accepted loans). Moreover, the surveys — especially the BLS
— contain information on the factors affecting banks’ lending standard decisions. These
2For the effects of business cycles on credit composition, see Matsuyama (2007). Note also that after a monetary tightening bank loan demand may increase to finance working capital and inventories, due to a limited access to marketfinance (Bernanke and Gertler, 1995; Friedman and Kuttner, 1993).
3An exception is Jiménez et al. (2010a) who use loan applications and loan (bank-firm) level data;
however, their objective is only to identify the bank lending (supply) channel.
4See e.g. Del Giovane, Ginette and Nobili (2009) for an example of cross-checking with Italian data. It should also be noted that the lending standards from the surveys are correlated with actual lending rates and volumes, and are good predictors of credit and output growth (see Lown and Morgan, 2006, for the U.S.
and De Bondt et al, 2010, for the Euro area).
factors can be grouped into (i) credit supply — related to bank balance-sheet capacity — and (ii) quality of loan applicants — related to the net worth and risk of firms and households.5 This detailed information is therefore crucial to identify loan supply shocks, and to quantify the transmission of a monetary policy shock on real activity through the different channels — the (supply) bank lending, the non-financial borrower balance-sheet, and the credit demand channel.
Since lending standards and loan demand may react to — but also influence — business cyclefluctuations, we embed the rich information from the lending surveys into an otherwise standard vector autoregressive (VAR) model to account for the linkages between the credit and the business cycle. The VAR is estimated over the sample 1992:Q3—2009:Q4 for the U.S., and over the sample 2002:Q4—2009:Q4 for a panel of 12 Euro area countries. For the identification strategy of monetary policy shocks, we follow Christiano, Eichenbaum and Evans (1999) and Angeloni, Kashyap and Mojon (2003), and use the overnight rate as the monetary policy instrument. The overnight rate in the Euro area is a sensible measure of monetary policy, also during the crisis period when credit enhancement actions were introduced (see ECB, 2009, and Lenza et al. 2010). For the sake of symmetry we consider the federal funds rate as the measure of monetary policy for the U.S. during the whole period, although the Federal Reserve implemented a wider set of actions during the crisis.
The main results of the paper are nevertheless robust to a shorter sample, ending before the introduction of unconventional monetary policy measures.
For the identification of the credit shocks, using the information from the surveys, we interpret an innovation to the answers related to the demand for loans as a shock to credit demand, and an innovation to changes in total lending standards as a shock to credit avail- ability (broad credit channel). The latter can be further decomposed in bank lending and non-financial borrower channel using the answers related to the factors affecting the changes in standards. We interpret an innovation to changes of standards due to changes in bank balance-sheet strength and competition as a shock to credit supply (bank lending channel), and an innovation to changes of standards due to changes in firm/household balance-sheet strength as a shock toborrower’s quality (firm/household balance-sheet channel). A visual inspection of the credit availability shocks estimated from the model suggest that they are consistent with episodes of restrictions and expansions in the credit markets both in the Euro
5The information contained in the Euro area BLS isunique in different dimensions. Compared to the SLOS, it contains more comprehensive information on the factors affecting banks’ decisions to change their lending standards, which is crucial to identify loan supply. Lending standards and loan demand are also sig- nificantly less correlated in the Euro area than in U.S. Finally, in the Euro area banks are the main providers of funds to the private sector, differently from the U.S. where markets and other financial intermediaries have a more important role (Allen et al., 2004).
area and in the U.S. Shocks to credit supply in the Euro area are also in line with periods of changes in bank capital, liquidity and competition.
Three sets of results emerge from the analysis. First, the broad credit channel is oper- ational. A monetary policy shock affects credit availability, and a credit availability shock affects GDP growth and inflation. Results are significant for business, mortgage, and con- sumer loans, with differences in the size and timing of the impact across borrowers and economic regions. When we disentangle credit availability shocks into credit supply and borrower’s quality shocks, wefind that the bank lending channel and thefirm and household balance-sheet channels are operational.
Second, we quantify the importance of the channels by analyzing the different impacts through appropriately designed counterfactuals. Overall, the credit channel is more impor- tant in the Euro area than in the U.S. In the Euro area, for business loans, the amplification of monetary policy shocks is higher via the bank lending than via the demand and balance- sheet channels. Credit demand is the most important channel for household loans. In the U.S., results suggest that the bank lending channel is not significant and a monetary policy shock seems to be transmitted to real activity mainly through the firm balance-sheet and demand channels. However, these results should be interpreted with caution because the information provided by the SLOS on credit supply is somewhat less complete and accurate than the one provided by the BLS.
We also provide some additional analysis based on answers for lending standards applied by small and large banks, and for loans to small and large enterprises. Results suggest that heterogeneity of firms and banks matters for the credit channel of monetary policy, with differences depending on thefinancial structure and on the borrower’s category.
Finally, we implement a shock decomposition of GDP growth during the financial crisis.
In the Euro area results suggest that a reduction of credit supply to firms (due to weaker bank capital and liquidity positions) significantly contributed to the decline of GDP growth.
In other words, there was a credit crunch forfirms with real implications (see Bernanke and Lown, 1991). Monetary policy seems to have supported GDP growth, by lowering interest rates and relaxing balance-sheet constraints of banks. In the U.S. restrictions to credit availability for mortgage loans contributed significantly to GDP decline. Unfortunately, using information from the SLOS, we cannot disentangle the effects on credit availability of low net worth of households and weak bank capital and liquidity positions.
Our paper makes a key contribution to the literature on monetary policy transmission.
We disentangle the effects of monetary policy on loan supply and demand in a novel and direct way by using unique data on (i) the lending standards applied by banks, (ii) the reasons behind banks’ decisions to change them (changes in the net worth of borrowers
or banks), and (iii) the loan demand from firms and households.6 This strategy allows to identify the impact of a monetary policy shock on aggregate output and prices through the different channels of transmission — credit demand, supply, and the non-financial borrower balance-sheet channels (Bernanke and Blinder, 1992; Bernanke and Gertler, 1995; Diamond and Rajan, 2006; Gertler and Kiyotaki, 2010, Boivin, Kiley and Mishkin, 2010; Adrian and Shin, 2010). Following up on Den Haan et al. (2007), we also show the differential effects of monetary policy shocks on supply and demand for business, mortgage and consumer loans.
Finally, we contribute to the emerging literature on the recent crisis and study the effect of credit and monetary policy shocks on GDP (Diamond and Rajan, 2009; Gertler and Karadi, 2009). Our results shed light on theories linking monetary policy, business cycles and the financial sector, and have important policy implications for central banks and governments.
The rest of the paper is structured as follows. Section 2 describes the data and reviews the empirical identification. Section 3 presents and discusses the results. Section 4 summarizes the paper and discusses the implications.
2 Data and empirical identification
The main testable prediction of the credit channel is that a contractive monetary policy shock reduces aggregate output and prices through a reduction of loan supply (bank lending channel) and a tightening of lending standards due to lower quality of the pool of non- financial borrowers (firm and household balance-sheet channel).7 The main challenges are to disentangle credit demand from supply, and to identify the different balance-sheet channels.
In this section we explain our empirical strategy, focusing mainly on the data that are cru- cial to address the identification challenges. In particular, Section 2.1 summarizes the Euro area and the U.S. surveys. Section 2.2 describes the credit and macroeconomic variables.
6Other papers have used the data from the SLOS. In particular, Lown and Morgan (2006) analyze the predictive power of lending standards for GDP in a similar VAR model. To this aim they use all the available time series and focus only on the C&I loans. Answers on loan demand and on the reasons affecting changes in credit standards were included in the survey only in the 90s, therefore they use only the main question on lending standards. Our work is different in aim and scope, as we test the credit channel of monetary policy and fully exploit the broad information arising from the survey.
7See Bernanke and Gertler (1995). Holmstrom and Tirole (1997), Stein (1998 and 2010), and Diamond and Rajan (2006) show that bank loan supply is shaped by the frictions stemming from the agency costs of borrowing: between banks and their borrowers (firms and households), and between banks and their providers of funds (retail and wholesale depositors, and equity-holders). Monetary policy influences the severity of these frictions by changing the net worth (and externalfinance premia) of non-financial andfinancial borrowers, thus affecting credit demand and supply and, in turn, aggregate output and prices (Bernanke and Blinder, 1988; Bernanke and Gertler, 1989; Kiyotaki and Moore, 1997, 2008; Bernanke, Gertler, Gilchrist, 1999;
Adrian and Shin, 2009; Stiglitz and Greenwald, 2003; and Stiglitz and Weiss, 1981). In this paper bank lending channel, bank balance-sheet channel and (bank) credit supply channel are synonimous (see Bernanke, 2007).
Finally, Section 2.3 presents our empirical identification.
2.1 The surveys
2.1.1 The Euro area Bank Lending Survey
The national central banks of the Eurosystem request a representative sample of banks in each country to provide quarterly information on the lending standards that banks apply to customers and on the loan demand that banks receive. The survey contains 18 specific questions on past and expected (bank) credit market developments. Banks are asked about lending standards applied and loan demand received over the previous three months, and on the expectations of the same figures over the following quarter. The survey focuses on two borrower sectors,firms and households. Loans to households are further disentangled in loans for house purchase and for consumer credit.8
The questions imply only qualitative answers and no figures are required. The question- naire is sent to senior loan officers, such as the chairperson of the bank’s credit committee.
The analysis reported in this paper is based on the aggregate answers at the country level received from a sample of around 90 banks, which comprises banks of different size.9 The response rate has been 100%. The regular questions have been kept fixed throughout the sample while a number of ad-hoc questions were added at times to shed light on specific issues. We do not use the answers to these questions since they are available only for few quarters.
The Euro area results of the survey (which are a weighted average of the results obtained for each Euro area country) are published every quarter on the website of the ECB.10 In very few countries the aggregate answers of the domestic samples are published by the respective national central banks. However, the overall sample including all the answers at the country and bank level is confidential.
The questionnaire covers both demand and supply of bank loans. Two questions relate to demand: one concerns the change in demand received from each type of borrower (firm and household), and the other the factors affecting loan demand (investment needs, limited access to other sources of finance, etc.).
Concerning supply, addressed in ten different questions, attention is given to changes in lending standards, to the factors responsible for these changes, and to credit conditions and
8Berg et al. (2005) describe in detail the setup of the survey. The survey was first used for research purposes in Maddaloni and Peydró (2010).
9At the start, there were 87 banks answering the survey. In 2008, the sample was enlarged for Italy and Germany, and with the inclusion of Cyprus, Malta and Solvenia, the number of banks became 112.
10See http://www.ecb.europa.eu/stats/money/surveys/lend/html/index.en.html.
terms applied to customers — i.e. whether, why andhow banks change lending standards for each type of borrower, firms and households.11 First, banks are asked whether they have changed lending standards over the previous quarter and what they expect for the following quarter.12 Next, the survey askswhy banks have modified the standards. In particular, ques- tions concern the effects of changes in bank balance-sheet capacity, competitive pressures, and borrowers’ creditworthiness and net worth. Finally, the third set of questions relates to how banks change terms and conditions for the loans, e.g. via changes in loan spread, size, collateral, maturity and covenants.
For the purpose of this paper we concentrate only on few questions from the BLS that we describe in detail in the Appendix. In particular, to identify shocks to credit supply we use the answers related to whether and why lending standards have changed, while the answers related to the change in loan demand received by banks are used to identify shocks to credit demand. Moreover, since the US survey does not include expected changes in lending standards, for the sake of comparability, we only concentrate on variations in lending standards over the previous three months.
To use a balanced panel, we restrict the analysis to the 12 countries which comprised the Euro area at the inception of the survey (2002:Q4). The answers cover the period from 2002:Q4 to 2009:Q4. Over this period we consistently have quarterly data for 12 Euro area countries (Austria, Belgium, France, Finland, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, and Spain).
2.1.2 The U.S. Senior Loan Officer Survey
The Federal Reserve publishes every quarter the results of a survey on bank lending stan- dards, the Senior Loan Officer Opinion Survey on Bank Lending Practices (SLOS). The Survey covers both business and household loans, and focuses on availability and demand for bank loans. The focus is on past developments, without regular questions on expectations.
The current sample is composed of around 60 banks, usually the largest in each of the 12 Federal Reserve Districts. The Survey is conducted by the regional Fed involved. The response rate is virtually 100%. More information on the setup of the survey can be found in Lown and Morgan (2006).13 Similarly to the Euro area BLS, for business (C&I) loans the SLOS asks about the changes in lending standards and the factors that have determined these decisions. These factors are broadly related to bank balance-sheet positions, bank
11Lending standards are the internal guidelines or criteria for a bank’s loan policy (see Loan and Morgan, 2006, and Freixas and Rochet, 2008).
12In cases when foreign banks are part of the sample, the credit standards refer to the loans’ policy in the domestic market which may differ from guidelines established for the headquarter bank.
13The results of the survey are available at http://www.federalreserve.gov/boarddocs/SnLoanSurvey.
competition factors, and borrower risk/outlook. Unfortunately, and differently from the BLS, the SLOS information on factors is not available for mortgage and consumer loans.14
The survey was introduced for the first time in 1967. Since then, however, the questions and frequency of the survey have changed several times. Therefore, the time series that can be used for a consistent econometric analysis exploiting the full information of the survey is considerably shorter. Since the first years of the 1990s the survey is carried out four times per year, it includes questions on credit standards and demand for bank credit, as well as different answers for small and large firms. Based on these observations and on the data available to us, we start the U.S. analysis in 1991:Q3.15 We use in our benchmark analysis the answers related to large enterprises.16 For the purpose of this paper we concentrate only on few questions from the SLOS that we describe in detail in the Appendix.
2.2 The variables
2.2.1 Credit demand and supply variables
We use the answers from the bank lending surveys to construct credit supply and demand variables. Wetrust the bankers and interpret their assessment as truthfully reflecting condi- tions in the bank credit markets.
That lending surveys are carried out by the central banks — often supervisory authorities with very detailed data to cross-check information — contributes to the reliability of the information received and to the overall credibility of the survey. For example, Del Giovane, Ginette and Nobili (2009) show the consistency at the bank level between the BLS answers and detailed credit data using supervisory information from Italy. Theyfind that the answers from the survey are reliable indicators of actual developments in bank loans, and that changes in BLS variables are translated into changes in actual lending standards in around one quarter.
Credit demand is defined by using the answers related to loan demand that banks receive
from firms and households (questions Q4 and Q13 of the BLS; Q4, Q10 and Q18 of the
SLOS). The answers related to the lending standards that banks apply to customers define credit availability (questions Q1 and Q8 of the BLS; Q1, Q9 and Q15 of the SLOS). We use the factors affecting banks’ decisions on lending standards to separate changes in credit
14In addition, some of the questions about the factors in the SLO were added only in recent years, which further restrict the use of these answers over the entire time period.
15Questions related to the demand for consumer loans were included only in 1995:Q4.
16The series on lending standards for large and small enterprises have a correlation of 96%. The series on demand for loans from large and small enterprises have a correlation of 93%. For the Euro Area the general question onfirms refers toall firms, with specific questions referring to lending standards applied to large and smallfirms, where the correlation is significantly lower than in the USA.
availability into (i) changes in credit supply (factors related to bank balance-sheet capacity and competition pressures — factors A and B in questions Q2, Q9 and Q11 of the BLS; factor A in Q3 of the SLOS) and (ii) changes inborrower’s quality (factors related to the quality of loan applicants such as outlook, quality and risk of borrowers — factor C in questions Q2,Q9 and Q11 of the BLS; factors B and C in Q2 of the SLOS).17 The definition of credit supply and borrower’s quality is possible for all type of loans in the Euro area survey and only for business loans in the U.S. survey.
The questions asked in the BLS and in the SLOS allow for five possible replies. The an- swers range from “eased considerably” to “tightened considerably” for the questions related to changes in lending standards, and from “decreased considerably” to “increased consider- ably” for the questions related to the demand for loans. We follow Lown and Morgan (2006) and quantify the different answers by using net percentages.18
When measuring credit availability, the net percentage is the difference between the percentage of banks reporting a tightening of lending standards and the percentage of banks reporting a softening of standards in each country and for each quarter. The net percentage of banks that have changed standards due to factors linked to bank balance-sheet capacity and competition defines credit supply. The net percentage of banks that have changed standards due to factors linked tofirm (household) balance-sheet capacity defines borrower’s quality. In both cases a positive value implies that there is a net tightening of lending standards and therefore a restriction of credit availability. For changes in credit demand, the net percentage is the difference between the percentage of banks reporting an increase in the demand for loans and the percentage of banks reporting a decrease. In this case, a positive figure indicates a net increase in demand. Figure 1A plots the Euro area aggregate and the U.S. figures for credit demand, credit availability, credit supply, and borrower’s quality.
Credit supply and demand are not highly correlated, as shown in Figure A of the Appen- dix. The three graphs report the correlation between credit demand and (i) credit availability, (ii) borrowers’ quality, and (iii) credit supply. The correlations between credit demand and supply are never higher than 40 percent.19
17See Appendix for the detailed questions.
18The use of these statistics implies that no distinction is made for the degree of tightening (easing) of lending standards and, similarly, for the degree of decrease (increase) of demand. This issue can be addressed using diffusion indexes. In this case, results do not differ qualitatively from those obtained with net percentages.
19Results are similar if correlations are computed with loans to households in the Euro area. The same detailed infomation is not available for the SLO.
2.2.2 Macroeconomic variables
We include in the analysis three macroeconomic variables: aggregate output, prices and the monetary policy rate. The output variable is the yearly growth rate of real GDP for each Euro area country and for the U.S. Prices are proxied by the yearly growth rate of the GDP deflator. Finally, the monetary policy interest rate is the yearly change in the overnight money market rate, EONIA for the Euro area and the effective federal funds rate for the U.S. In the U.S. the fed funds rate has been extensively used as an indicator of the stance of monetary policy (see e.g. Bernanke and Blinder, 1992; and Christiano, Eichenbaum and Evans, 1999; Bernanke and Mihov, 1997). In the Euro area, the Governing Council of the ECB decides the interest rate on the main refinancing operations (MRO) which is directly linked to the EONIA (see also Angeloni, Kashyap and Mojon, 2003).
In response to the financial crisis, in October 2008 the ECB eased the monetary policy stance by reducing the policy rate and introducing a measure of credit enhancement. The latter allows the Eurosystem to lend to banks through fixed-rate full-allotment liquidity auctions. The implementation of this policy brought the EONIA significantly below the MRO (Trichet, 2009; ECB, 2009; Lenza et al. 2010). Based on this observation, we believe that the EONIA rate is still the sensible measure of monetary policy even during the crisis time.20 For the sake of consistency, we consider the federal funds rate as the measure of monetary policy for the U.S. However, the actions taken by the Fed during the crisis were directed towards several markets (Bernanke, 2009, and ECB, 2009), and therefore the overnight rates may not be a comprehensive measure of monetary policy stance during the crisis. Nonetheless, the main results of the paper are robust to a shorter sample, ending in 2008:Q3 — the time of Lehman Brothers’ failure and the introduction of unconventional measures of monetary policy.
2.3 Empirical identification
We process the credit and macro variables with a standard VAR model:
=()−1+ (1)
where= 1 denotes time,is an—dimensional vector of endogenous variables,() is a matrix polynomial of order in the lag operator , and is a vector of white noise
20As a robustness check, in non-reported analyses we have also used the 3-month Euribor rate and the overnight interest swap rate on EONIA (OIS). These measures carry additional information compared with the overnight rates and, therefore, the results obtained may be more difficult to interpret. The 3-month Euribor also reflects a component of bank credit risk. The OIS rate is a proxy of expectations of monetary policy, but it may also be affected by liquidity in the swap market.
residuals.
For the U.S. the available time series cover almost twenty years of quarterly observations (1992:3-2009:4). For the Euro area, as the sample is rather short (2002:4-2009:4), we estimate a panel VAR on a data set of the 12 countries comprising the Euro area in 2002, with fixed- effects and common slope.21 This framework pools diverse information from all countries, while controlling for heterogeneity in the constant term. Also, it takes into account the common monetary policy and some degree of cross-country heterogeneity in the business and credit cycles. The VAR is estimated with Bayesian techniques using one lag and assuming
a flat prior on the parameters and normality of the error terms (see e.g. Kadiyala and
Karlsson, 1997).
The vector is composed of three sets of variables, as in Christiano, Eichenbaum and Evans (1999): the macroeconomic variables (GDP growth and inflation), the credit variables and the monetary policy rate. We use two specifications related to the transmission channels that we want to analyze. In particular, we consider the following models:
• Model 1 (Credit channel) includes credit demand and credit availability for non-
financialfirms, mortgages and consumption.
• Model 2 (Bank, firm and household balance-sheet channels) includes credit demand, borrower’s quality and credit supply for non-financial firms, mortgages and consump- tion.
We also analyze an alternative specification of Model 1 and 2 where we only consider loans to firms, for the sake of comparability with the previous literature (see Bernanke and Gertler, 1995), and also since the SLOS contains information on the factors affecting lending standards only for business loans.22 It should be noted that business loans are only a fraction of bank loans (51% in EA and around 30% in the US) and, as already pointed out by Bernanke et al. (1996), the credit channel may be more relevant for loans to households than to firms. In addition, Den Haan et al. (2007) point out the importance of the whole portfolio of bank loans when analyzing a monetary tightening. The volume of loans granted to different borrowers (business, consumer and real estate) may react differently to an interest rate shock due to the strategic decision of banks in reallocating their loan portfolio.
For the identification of the monetary policy shock, we assume that the monetary au- thority observe current output, prices and the responses of loan officers when deciding the
21Even though there is no official CEPR dating of the Euro area business cycle after the 1990s, the Euro area shows the features of a complete business cycle from 2002 to 2009.
22In the paper we report only a selection of results. The complete set of results is provided in a separate Appendix available from the authors.
policy rate. Therefore, all these variables do not change at time in response to a time policy shock and the policy rate is ordered after the macro and the credit variables.23 Never- theless, we conduct several robustness checks using different ordering of variables in and the results obtained are robust to the different specifications.
For the identification of the credit shocks, we interpret an innovation to the answers re- lated to the demand for loans as a shock to credit demand, and an innovation to changes in lending standards as a shock tocredit availability (to analyze the broad credit channel). Simi- larly, we interpret an innovation to changes of standards due to changes in bank balance-sheet strength and competition as a shock to credit supply (to analyze the bank lending channel), and an innovation to changes of standards due to changes in firm/household balance-sheet strength as a shock toborrower’s quality (to analyze thefirm/household balance-sheet chan- nel).
A visual inspection of thecredit availabilityshocks estimated from the model (Figure 1B) suggest that they are consistent with episodes of restrictions and expansions in the credit markets both in the Euro area and in the U.S. Figure 1B also plots the shocks to credit supply as estimated from the model and the actual credit supply variable from the surveys.
It is important to note that shocks to credit supply mimic well over time the developments in related lending standards in the Euro area. In particular, during the period 2004-2006 lending standards were relaxed mainly as a results of competition pressures in financial markets; in the period starting in August 2007 lending standards were tightened because of worsened bank liquidity and capital positions.24 Conversely, in the U.S., shocks to credit supply do not mimic well lending standards related to credit supply, thus suggesting that the available data from the SLOS may not be sufficient to disentangle the sub-channels of the broad credit channel (bank lending and non-financial borrower balance-sheet channel).
3 Results
We present the results in four main subsections. First, we analyze the full dynamics of the credit channel. Second, we focus on its sub-channels, both the bank lending and the firm and household balance-sheet channels. Third, we interpret the results discussing also the differences between the Euro area and U.S. Finally, we perform a shock decomposition of
23Note that this ordering differs from what typically assumed in the previous literature, as for instance in Christiano, Eichenbaum and Evans (1999). This choice is certainly justifiable in the Euro area, where the monetary policy strategy is based on a two-pillar approach and explicitly takes into account information from credit aggregates.
24The shadow banking system (competition from non-banks) was a key element in exerting competitive pressures in the period from 2004 to 2006.
GDP growth during the recent financial crisis and assess the relative contribution of the various shocks.
The results are presented by means of impulse response functions and historical decom- positions. All responses have been normalized and divided by their innovation variances, thus they can be compared on a single scale. We show the median responses along with 68 and 90 percent Bayesian credible intervals.
3.1 The credit channel (Model 1)
In this subsection we analyze the existence and the importance of the credit channel of monetary policy. First, we look at the impact of a monetary policy shock on credit availability and demand. Next, we assess the effect of a shock to credit availability and demand on aggregate output and prices. Finally, by means of appropriately designed counterfactual experiments, we quantify the credit channel by reporting the amplification of a monetary policy shock due to credit availability.25 We measure the amplification effect on the responses of GDP growth and inflation.
3.1.1 Impulse response functions
As explained in Section 2.3, the VAR of Model 1 includes credit demand and credit availabil- ity variables for business, mortgage and consumer loans.26 Figure 2A shows the responses of credit demand and credit availability to a one-standard deviation monetary policy shock for the Euro area and the U.S. Note that a one-standard deviation shock corresponds to an increase of the overnight interest rate of around 60 basis points in the U.S. and of 30 basis points in the Euro area. Consistently with theory, when monetary policy is tightened credit availability is restricted, whereas credit demand declines in both economic areas.27
In the Euro area, the level and persistence of the median responses of credit availability are generally similar across different types of loans: the median responses peak between four and five quarters. For credit demand, instead, the median responses peak between three and six quarters, with the response of mortgage loans being larger and less persistent than the responses of other loans. When comparing credit demand and credit availability, the responses are different across types of loans. In particular, the response of credit availability is
25For similar counterfactual analysis, see e.g. Sims and Zha (2006).
26In the SLO, the information on consumer loans is available only since 1996. Given the short time series and the limited importance of consumer loans in banks’ loans portfolio (around 15% over the last 20 years), for the U.S. we show the impulse responses obtained when including only business and mortgage loans since 1992.
27It is important to recall here that apositive shock to credit availability is atightening of lending stan- dards, while apositive shock to credit demand indicates an increase in demand.
more sizeable and less persistent for business loans, whereas demand responds more strongly and less persistently for mortgage loans.
In the U.S., the median responses of credit availability for both types of loans are more sizeable than the responses of credit demand. Results are, however, less precise than in the Euro area. The median responses are higher and more persistent than in the Euro area, except the response of credit demand for mortgage loans, whose significance is statistically negligible.
Next, we analyze the impact of shocks to credit variables on GDP growth and inflation.
Figure 2B plots the responses of these variables to a one standard deviation shock to credit availability and credit demand in the Euro area and in the U.S.
A shock to credit availability dampens GDP growth in both economies. In the Euro area, responses to business and mortgage loans restrictions are more significant than those to consumers’ credit, possibly due to the relatively low importance of the latter (around 10%
of the total loans). GDP growth reacts more to a shock to business loans than to a shock to mortgage loans. The converse is true in the U.S. Moreover, comparing across economies, a shock to business loans restrictions has a higher impact in the Euro area than in the U.S., whereas a shock to mortgage loans has a larger and more persistent effect in the U.S.
The responses of inflation to a credit availability shock follow similar patterns, with the median U.S. responses being higher and more persistent — albeit more uncertain — than in the Euro area. There is a significant “price puzzle” for Euro area business loans, with inflation increasing right after the tightening of credit availability — possibly due to the higher costs of external finance premia for firms — and then decreasing after three quarters.
Shocks to credit demand increase GDP growth in the Euro area and U.S. for all types of loans. The median effects for mortgage loans are higher than for business loans. The impact on inflation is more subdued and not significant in the U.S. In the Euro area a shock to demand for business loans has an immediate positive effect on inflation, whereas for mortgage loans the impact is slightly more sizeable and delayed.
All in all, albeit with some differences across economic areas, type of loans, intensity and timing of the impacts, the results suggest that a credit channel of monetary policy is active in both economies and for all types of loans. Tight monetary policy restricts credit availability for firms and households, which in turn reduces GDP growth and inflation.
3.1.2 Counterfactual analysis
We now quantify the economic relevance of the credit channel. In particular, we focus on the following questions: Does credit availability amplify the impact of a monetary policy shock on GDP growth and inflation? How does the impact differ across loans to firms and
households? What is the relative importance of the credit channel and the (credit) demand channel?
We answer these questions with counterfactual experiments. Results are reported in Figure 2C, where we compare the dynamics of the responses of GDP growth and inflation to a 100 basis points monetary policy shock (grey interval) with the counterfactual responses of the same variables obtained when closing down either the credit availability or the credit demand for each type of loan (solid black line).28
For the Euro area, the charts show that the impact of a monetary policy shock on GDP growth and — less evidently — on inflation would be significantly different if we closed the credit availability channel for business and mortgage loans. Closing this channel would imply, in particular for business loans, not only a significantly reduced effect at the peak but also a different timing, with the response of GDP growth peaking one or two quarters earlier.
For business loans, the credit channel is quantitatively more important than the demand channel. Results indicate that, when the credit channel is shut down, the median response of GDP growth to a monetary policy shock would be reduced by about 50 percent. However, for mortgage loans, closing down the demand channel would have a stronger effect on GDP growth and inflation than closing down the credit channel.29
For the U.S. results are more uncertain than for the Euro area. However, when looking at median responses, closing down the credit channel for business loans would imply a 30%
reduction in the peak response of GDP growth and a 75% reduction in the response of inflation. For mortgage loans, the credit channel is negligible.
Based on these results, we conclude that the impact of the credit channel of monetary policy is quantitatively more important in the Euro area.
3.2 Firm, household and bank balance-sheet channels (Model 2)
In this subsection we analyze the main sub-channels of the credit channel of monetary policy.
We assess the relative importance of the transmission mechanisms of monetary policy shocks through the balance-sheets of banks, firms and households — the bank lending channel and
thefirm and household balance-sheet channels.
For this purpose, we use a VAR with the credit supply and the borrower’s quality variables
28The 68% Bayesian credible intervals in grey represent the responses in a system where all the channels are active. The black lines are the median responses computed from a system where the credit availability or the demand channel has been closed down. We perform the exercise by choosing a sequence of credit availability or demand shocks that exactly neutralizes the impact of the monetary policy shock on these variables.
29Note that the response of inflation to a monetary policy shock is also somewhat amplified by credit demand for consumption.
(see Section 2.2 for the definition of the variables and Section 2.3 for the description of Model 2). As discussed in Section 2.1, in the SLOS these two variables are available only for business loans. As a consequence, we present results for all the balance-sheet channels in the Euro area, while for the U.S. we discuss the results only for business loans.
3.2.1 Impulse response functions
Figure 3A shows that the responses of credit supply, borrower’s quality and credit demand to a monetary tightening are significant for all types of loans and have the expected signs. In particular, a monetary tightening increases lending standards related to both credit supply and borrower’s quality, and reduces credit demand. The responses are broadly similar across types of loans. However, for credit supply, the median responses for business loans are higher and peak earlier than for mortgage and consumer loans. For borrower’s quality, business loans react faster although the size is very similar across loan categories. For credit demand, the reaction of mortgage loans is faster and stronger than the reaction of business or consumer loans.
Figure 3B shows the responses of GDP growth and inflation to shocks to credit supply, borrower’s quality and credit demand. Concerning the impact on GDP, all responses are in line with expectations. In particular, restrictive shocks to credit supply and tightened standards due to borrower’s quality have a negative impact on real activity, whereas a positive shock to credit demand has a positive impact. The strongest effects stem from shocks to credit supply for business loans and from shocks to credit demand for mortgage loans. Shocks to borrower’s quality have a significant effect on both business and mortgage loans. On the other hand, shocks to consumer loans have a negligible impact.
Shocks to credit supply and to borrower’s quality have a mixed impact on inflation. They generally give rise to a negative response, with the impact being more significant for business loans through shocks to credit supply. There are some notable “price puzzles”, in particular for shocks to borrower’s quality for business loans and shocks to credit supply for mortgage loans. A shock to credit demand gives rise to responses of inflation that are significant and have the expected sign for all types of loans. For loans to households a demand shock is always more important than a credit supply or borrower’s channel shock.
Results for U.S. business loans are reported in Figure 4 (Panel A and B). Overall, they are more uncertain than in the Euro area, and point to somewhat different conclusions. A monetary tightening clearly implies, as in the Euro area, a tightening of standards related to credit supply and to borrower’s quality, as well as a reduction of credit demand (Panel 1). However, unlike in the Euro area, only a shock to borrower’s quality has a notable and significant effect on both GDP growth and inflation, while the bank credit supply seems
irrelevant.30
3.2.2 Counterfactual analysis
As in Model 1, to assess the economic relevance of the different channels of transmission we run a counterfactual experiment, reported in Figure 3C. Results for the Euro area indicate that, for business loans, a monetary policy shock has a stronger impact on GDP growth and inflation through (bank) credit supply — the bank lending channel. The counterfactual experiments say that if we shut down this channel, the median effect on GDP growth of a monetary policy shock would be reduced at the peak by about 50% for both GDP (from -1%
to -0.5%) and inflation (from -0.35% to -0.18%).
Monetary policy shocks are transmitted to GDP through all channels. Credit supply is the most important channel for corporate loans and demand for mortgage loans. Closing down the demand channel would, however, imply a smaller overall effect than closing down the bank-lending channel. Finally it is interesting to note that the credit demand channel for consumption has a significant impact only on inflation.
For the U.S., our results suggest that the bank lending channel is not significant (Figure 4C). As explained in Section 2.2, estimated shocks to (bank) credit supply in the U.S. do not mimic well actual shocks to bank liquidity, capital and competition, contrary to what we observe for the Euro area. This suggests a cautious interpretation of the results as evidence against the importance of the bank lending channel for U.S. A monetary policy shock is transmitted to real activity mainly through the firm balance-sheet and demand channels.
For both channels, the median effect in the counterfactuals would be reduced at the peak by about 50% for both GDP (from -0.3% to -0.15%) and inflation (from -0.10% to -0.05%).
3.3 Discussion of the results
The previous findings show that the credit channel is operational and amplifies the impact of a monetary policy shock on GDP and inflation through the balance-sheets of households, firms and banks. Based on the results of our analysis, the credit channel is overall more important in the Euro area than in the U.S. Moreover, in the Euro area, for business loans, the
30The available empirical evidence on the U.S bank lending channel is not conclusive and comprises analyses supporting the existence of the bank lending channel (e.g., Kashyap and Stein, 2000) and of a more conventional transmission mechanism (Romer and Romer 1990; Ramey 1993). Peek and Rosengren (1997 and 2009) find significant real effects of credit supply shocks stemming from Japanese banks (real estate bubble burst in Japan). Monetary policy may also affect bank risk-taking (compositional changes of credit supply). For evidence, see Jiménez et al. (2010b), Ioannidou et al. (2010), and Maddaloni and Peydró (2010). For evidence on the household balance-sheet channel, see Mishkin (1977) and (1978). For evidence on thefirm balance-sheet channel, see Gertler and Gilchrist (1994) and Lang and Nakamura (1995).
amplification of monetary policy shocks is higher via the bank lending than via the demand and balance-sheet channels. For household loans, credit demand is the most important channel. In the U.S. the bank lending channel seems irrelevant and a monetary policy shock is transmitted to real activity mainly through the firm balance-sheet and demand channels.
The differences in the results obtained for the Euro area and for the U.S. can be largely interpreted in light of thefinancial structures in the two areas. A relevant feature of the Euro area is that corporations fund themselves externally mainly through bank loans: around 70%
of external financing in the balance sheets of Euro area firms is constituted by bank loans, while in the U.S. this percentage is around 20% (Trichet 2009). Moreover, a large fraction of corporate equity in the Euro area is non-quoted, i.e. not issued in financial markets, as opposed to the U.S. (see ECB, 2007 and Allen et al., 2004).
The limited liability feature offirms’ shares implies the existence of relevant agency prob- lems affecting corporate financing (see Freixas and Rochet, 2008). These agency problems impose binding financial constraints onfirms that are more subject to shocks affecting bank credit supply. A monetary policy shock transmitted through the bank lending channel may therefore have a higher impact on real activity in the Euro area than in the U.S. wherefirms can, at least partly, diversify theirfinancing needs between banks, other intermediaries (not included in the sample of the bank lending surveys) and markets.
Shocks to credit supply may be less important for household loans, especially in the Euro area. Households are also financially constrained because of human capital inalienability (Hart and Moore 1994). However, in most Euro area countries, mortgage loans are with recourse (as opposed to the U.S.) — the borrower is responsible for any remaining debt after foreclosure. This implies that mortgage loans are highly collateralized (not only with the value of the house but also other assets of the borrower) and agency problems may be less severe. In this case, financial constraints are less binding and monetary policy affects the economy significantly more through the demand and the (collateral) balance sheet channel than through the credit supply channel.
Finally, for household loans, the results for the U.S. may be more subdued than for the Euro area because a significant part of U.S. mortgage loans are granted by non-bankfinancial intermediaries not included in the bank lending surveys. Therefore, our analysis for the U.S.
mortgage market may be less comprehensive than for the Euro area and our results are likely to underestimate the importance of credit channels for U.S. mortgage loans.
In the next subsection, we further qualify these results by providing some limited analysis on the role of heterogeneity of borrowers and lenders on the importance of the credit channel.
3.4 Heterogeneity and the credit channel
The impact of the credit channel of monetary policy may differ according to the heterogeneity of borrowers and lenders, notably differences infirm size (Gertler and Gilchrist, 1994) and in bank size (Kashyap and Stein, 2000). In particular, monetary policy effects should be higher on credit granted to smallerfirms or by smaller banks, typically morefinancially constrained.
The lending surveys help shed light on this aspect. In the Euro area, the BLS contains answers for lending standards applied by small and large banks, and for loans to small and large enterprises. Correlations among the answers of banks of different size is on average not greater than 50%, while the answers for large and small firms are relatively more correlated (around 80%). In the SLOS there is information only on lending standards to small and large firms, with avery high correlation of around 96% (see section 2 above).
Figure 5A plots the counterfactuals as in figure 2C showing the impact offirm size. The impulse responses show that the impact of monetary policy on GDP through the credit channel is higher via large firms in the Euro area, and via small firms in the U.S. (the latter result consistent with e.g. Gertler and Gilchrist, 1994). The difference across the two areas can be rationalized with differences in thefirms’ financing. In the Euro area, the effect on GDP through large firms is higher because these firms borrow mainly from banks (Trichet, 2009) and their overall impact on the economy is larger, notwithstanding the fact that changes in monetary policy affect more credit to small firms. Differently, in the U.S.
the credit channel is stronger through small firms since bank loans to large firms do not represent their main source offinancing.
Figure 5B plots the counterfactuals according to bank size. This information is available only for the Euro area. The charts clearly show that the bank lending channel of monetary policy for business loans is stronger through small banks. For household loans, as discussed earlier, the borrower’s quality and the demand channels are most relevant, and the bank lending channel is not significant regardless of bank size. These results are somewhat con- sistent with Kashyap and Stein (2000) — the bank lending channel is stronger through small banks, but only for business loans.
All in all, results suggest that heterogeneity offirms and banks may matter for the credit channel of monetary policy. Differences depend on thefinancial structure (in particular the importance of the banking system as provider of funds to nonfinancial sectors) and on the borrower’s category.
3.5 Shock decomposition of GDP during the financial crisis
Finally in this subsection, we analyze the relative importance of different shocks during the financial crisis that started in 2007:Q3 and we shed some light on the relationship between credit provision, monetary policy and business cycles. Figure 6 reports shock decomposi- tions using the specification of Model 1 (Figure 6A) and Model 2 (Figure 6B). The bars in the charts represent the effects at time of innovations to other variables which explain movements in GDP growth.31
Figure 6A reports the contribution to GDP growth of shocks to credit availability and demand in the Euro area. It is interesting to note the impact as well as the timing of these shocks.
Shocks to credit availability for firms contributed significantly in reducing GDP growth during the entire crisis period (Figure 6A). During the first period of the crisis they were the only shocks contributing negatively. At the beginning of the financial crisis in August 2007 the capacity of bank balance sheets was impaired by increased capital and liquidity constraints. Banks reacted by restricting supply primarily to riskier borrowers, in particular firms with limited liability. The credit demand from households was dampened by the worse outlook for the economy starting in 2008:Q3. The decline in demand in turn contributed to the recession.
Shocks to credit supply and demand are not sufficient per se to explain the recession.
However, they might have had indirect effects through variables not included in our model specification. These effects may have cumulated during thefirst part of the crisis and realized only later on.
At the same time, overnight interest rate shocks (monetary policy reaction) had a positive contribution to GDP growth, which presumably would have been lower had an aggressive monetary policy not been put in place. Shocks to the policy rate broadly comprise policy rate cuts and the full-allotment policy implemented by the Eurosystem.32 In addition, the poli- cies aimed at sustaining bank’s liquidity may have partly relaxed (bank) credit availability constraints in the second part of the financial crisis.
The same analysis carried out for the U.S. yields different results. Overall, negative shocks to credit availability for mortgages are the most important shock that have contributed to reduce GDP growth, consistently with the role played by the mortgage market in triggering
31The decomposition at each timeis technically performed by estimating the VAR over the whole sample and projecting the variables of interest over the crisis sample (2007Q3-2009Q4). The difference between the realised and the projected values is then decomposed in the contributions of the innovations to all variables between 2007Q3 and. See, e.g. Doan (2009) for more details.
32As explained in Section 2.4, the level of EONIA rate already embeds the effect of the full-allotmentfixed rate policy. For a related discussion, see Lenza et al. 2010.
the crisis. Interest rate shocks played an almost neutral role. This does not come as a surprise, as the Federal Reserve has engaged in a multifaceted strategy of non-conventional monetary policy measures. These measures supported not only thefinancial sector (e.g. by increasing the number of financial institutions that could obtain central bank liquidity and by relaxing requirements on the quality of collateral) but also the corporate sector (e.g. by buying commercial papers). This may partly explain why restrictions to business loans play a minor role as compared to the Euro area, and shocks to the fed funds rate do not contribute significantly to GDP changes during the crisis.
In Figure 6B, using Model 2, we investigate the relative importance of all the channels for the Euro area (no comparable data are available for the U.S.). The decomposition qualifies the results of Figure 6A and shows that a credit supply reduction forfirms has had the highest negative contribution on GDP growth during the period 2007:Q3-2008:Q3. In other words, problems in bank capital and liquidity significantly contributed to the economic recession by inducing a credit crunch forfirms.
4 Concluding remarks
The recent crisis in Europe and U.S. suggests that identifying and quantifying the channels linking monetary policy, credit provision and business cycles is of utmost importance. In this paper we test the credit channel of monetary transmission and explore the dynamics of credit during the recent financial crisis.
Credit supply and demand are typically unobserved, therefore the complete identifica- tion of the credit channel of monetary policy and of its subchannels is generally unfeasible.
However, bank lending surveys by central banks contain reliable information on quantity and quality of both credit supply and demand. Therefore, to analyze the credit channel we use the answers from the unique, confidential Euro area Bank Lending Survey and from the U.S. Senior Loan Officer Survey. National central banks of the Eurosystem and regional Feds request banks quarterly information on the loan demand that banks receive and on the lending standards that banks apply to firms and households, including the factors affecting banks’ decisions to change standards — namely whether standards change due to bank or to non-financial borrower balance-sheet constraints.
Overall our results suggest that the credit channel is operational and amplifies a monetary policy shock on GDP and inflation, through the balance-sheets of households, firms and banks. In the Euro area, all channels are important in transmitting monetary policy shocks to GDP and inflation with credit supply and demand being the most important channels for corporate and mortgage loans respectively. Counterfactual experiments suggest that if we
shut down the bank-lending channel, the median effect on GDP growth of a monetary policy shock would be reduced at the peak by about 50% for both GDP (from -1% to -0.5%) and inflation (from -0.35% to -0.18%).
In the U.S., the bank lending channel does not seem very significant, whereas a mone- tary policy shock is transmitted to real activity mainly through the firm balance-sheet and demand channels. Counterfactual experiments suggest that for both channels, the median effect would be reduced at the peak by about 50% for GDP (from -0.3% to -0.15%) and inflation (from -0.10% to -0.05%).
Further analysis based on disaggregate data suggest that heterogeneity of firms and banks may matter for the credit channel of monetary policy, with differences depending on
the financial structure and on the borrower’s category. In the Euro area, monetary policy
has more impact via credit to largefirms. In addition, the bank-lending channel of monetary policy is stronger through small banks, but only for business loans.
Finally, we implement a shock decomposition of GDP growth during the recent financial crisis. In the Euro area, results suggests that a reduction of credit supply to firms (due to restrictions to bank balance sheets capacity) significantly contributed to the decline of GDP growth — a credit crunch for firms with real implications. Monetary policy, at least in the Euro area, seems to have supported GDP growth, by relaxing balance-sheet constraints of banks and lowering interest rates. In the U.S., restrictions in credit availability for mortgage loans are important to explain GDP decline.
Our analysis has important implications for theory and policy. For economic theory,
ourfindings imply that bank loan supply should be included explicitly when modelling the
linkages between monetary policy, credit provision and the real economy. In turn, this is likely to amplify the mechanisms of the financial accelerator of Bernanke, Gertler, and Gilchrist (1999) — see, in this respect, the recent macro models by Gertler and Kiyotaki (2010), Gertler and Karadi (2010), Del Negro et al. (2010), Angeloni and Faia (2010), Christiano et al. (2010); also the micro finance models by Adrian and Shin (2010), Diamond and Rajan (2006 and 2009) and Stein (2010). Moreover, our results also stress the importance of an accurate calibration of the models, taking into account heterogeneity and differences among financial systems (the bank lending channel is more important in the Euro area than in the U.S.), as well as different lending markets (supply restrictions affect business lending more than household lending).
From a policy perspective, our results suggests that central bank policies based on very low interest rates and measures aimed at relaxing bank capital and liquidity constraints have provided a significant support to the real economy during the crisis. Ourfindings provide a broad support to the different policies implemented during the crisis by the Federal Reserve
and the ECB. In the U.S. the Fed has implemented policies directed at supporting both banks andfirms, whereas in the Euro area interventions were (almost) uniquely targeted to banks. Finally, in this paper we show how bank lending surveys are a useful tool to monitor real-time changes in credit demand, borrower’s quality, and credit supply, with important implications for the monetary policy and financial stability assessment.
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