Euro Area Policies: Selected Issues

International Monetary Fund

doi:10.5089/9781451879896.002.A001

Jump to Content Jump to Main Navigation

Euro Area Policies

Selected Issues

Author: International Monetary Fund

Publication Date:: 04 Aug 2008

eISBN:: 9781451879896

Language:: English

Keywords:: ISCR; CR; bank; money market; inflation; monetary policy; rate; money demand demand shock; demand and supply effect; financing condition; bank loan supply; bank loan variable; Corporate bonds; Loans

Download PDF (2.3 MB)

This Selected Issues paper analyzes euro area policies and discusses the implications of the 2007–08 financial sector turbulence for real economic activity. It examines the linkages between the financial and real sectors in the euro area. The paper discusses the European Central Bank’s (ECB) monetary analysis and the role of monetary aggregates in central banking, surveying the ongoing theoretical and empirical debate. The paper also describes the introduction of a “European Mandate” for financial sector authorities in the European Union (EU), a proposal that is under consideration by EU member states.

Abstract

I. From Subprime Loans To Subprime Growth? Evidence for the Euro Area¹

A. Introduction and Main Findings

1.The impact of the global financial turbulence on the euro–area real sector is animportant unresolved issue. Since mid–2007, the sub–prime mortgage crisis in the United States has sparked a reassessment of risk across global markets. Risk premia in money and credit markets have spiked, raising the cost of interbank and corporate financing, including in the euro area. The tighter financial conditions associated with the turbulence can affect euro–area activity through a number of channels, including:

An increase in bank funding costs (due to higher money market premia and rates), which may be passed on to firms and consumers via higher lending rates. Indeed, some reaction of retail lending rates can already be observed (Figure 1).
In response to their own deteriorated balance sheets and financial conditions, banks may limit the amount of credit available to borrowers for any given price. This could be in the form of stricter lending standards. The latest data indicate that quantitative bank lending conditions have tightened appreciably since mid–2007 (Figure 2).
The costs of corporate bond and equity financing may also be higher, limiting the scope for substitution from bank financing. The corporate bond and credit default spreads of all maturities and ratings have jumped up, and the stock market has fallen since the start of the turbulence (Figure 3).
Tighter financing conditions could create “financial accelerator effects” by depressing asset prices and reducing the value of collateral. Available data indeed confirm that asset prices are declining (Figure 3); this has an impact on collateral values, but the evidence on the accelerator effects has been only anecdotal so far.

2. This chapter examines empirically the linkages between the financial and realsectors in several alternative but complementary ways. It may be too early to observe in full how the deterioration in financing conditions will affect the euro–area economy, but it is still useful to examine the linkages between the financial and real sectors in the euro area, using a combination of past and recent data. The recent data show that bank credit to the private sector continues to grow at a brisk pace (due to strong loan growth to the non–financial corporate sector), while equity and bond issuance by (non–financial) firms has been holding up (Figure 4). This chapter focuses on linkages between:

Bank characteristics and lending behavior (using data on individual euro–area banks). This analysis helps to understand how financing conditions for banks, which are a crucial part of the financial intermediation in Europe, translate into banks’ lending behavior, i.e. into financial conditions of banks’ clients. The key finding of this chapter is that a deterioration in the financial health of banks could translate into significantly lower bank loan supply.
Bank loan supply and aggregate output (using country–level data). This analysis allows to examine the relationship between bank credit supply and economic activity. The key finding in this part is that a cutback in bank loan supply is likely to have a negative impact on economic activity in the euro area; again, this effect is statistically significant, but relatively small. These findings are not dissimilar from those in the literature on the bank lending channel in the United States, which generally finds strong evidence that banks decrease their loan supply in response to tighter financing conditions, but little evidence that the cutback in bank loan supply leads to lower real activity.
Corporate sector financing conditions and economic activity (using data on corporate bond spreads and output). This part of the analysis allows to gauge how a change in corporate sector financing conditions affects industrial output. This part of the calculations suggests that higher costs of corporate bond financing (which could also reflect broader financial conditions in the economy) tend to lead to a significant negative response of industrial production growth.

B. Linkages Between Bank Characteristics and Lending Behavior

3. To assess the extent to which bank supply in the euro area is affected by deteriorating financing conditions, the “bank lending channel” was analyzed. Two key part s of the bank lending channel are: (i) an adverse effect of higher financing costs on bank loan supply (e.g., because banks are not able to fully shield their loan portfolios from changes in financing costs); and (ii) a negative effect of the declining loan supply on economic activity (e.g., if a substantial group of borrowers is not be able to insulate their spending from the reduction in bank credit).

4. The empirical evidence on the bank lending channel in Europe has been less than decisive. Most of the literature on the banking lending channel deals with the U.S. economy (for a survey, see e.g., Bernanke and Blinder, 1995; Bernanke and Gertler, 1995), and generally finds strong evidence that banks decrease their loan supply in response to tighter financing conditions (in particular for small balance sheet–constrained banks), although there is little evidence that the cutback in bank loan supply leads to lower real activity (e.g., Driscoll, 2003). For Europe, the available studies (e.g., Altunbaş, Fazylov, and Molyneux, 2002; Angeloni and Ehrmann, 2003) are rather inconclusive, but suggest that the bank lending channel may be effective in countries with banking systems characterized by many small banks, weak capitalization and liquidity, and limited non–bank sources of funds.

5. Estimating the factors behind credit developments is complicated by the interplay of cyclical and long–term factors that influence both credit demand and credit supply. On the credit demand side, these include a combination of cyclical developments and structural shifts. On the credit supply side, the impact of the economic downturn on financial markets and the financial situation of the banks seems to have influenced their lending.

6. A supply–demand disequilibrium model was used to analyze the bank lending channel in the euro area. Equilibrium approaches, such as VEC/VAR models or single–equation estimates can provide only a limited answer to the causes of credit slowdown, because they do not address the question whether the demand or supply function determines the credit. Following the examples of Pazarbasioglu (1997), and Barajas and Steiner (2002), a credit demand– and a credit supply–function are estimated under the restriction that the minimum of the two determines the credit. This strategy avoids the identification problem of equilibrium models, and allows to make a statement on the existence of a credit crunch.²

7. The disequilibrium model was estimated bank–by–bank panel data for a sample of the 50 largest euro–area banks from 1997Q1 to 2007Q4.³ The specification of the demand side follows Bundesbank (2002). The specification of the supply side is close to Pazarbasioglu ^(1997), but with the distance to default among the supply–side variables. The distance to default was used to approximate banking sector vulnerability as a possible source of credit supply strain.⁴ The advantage of using individual bank data is that it allows for testing whether weaker banks are more likely to restrain their credit.

8. The estimated model provides a plausible explanation of the factors contributing to credit developments in the major euro area banks (Table 1). All the key coefficients have the expected signs and are significant. The model explains year–on–year real growth rates of customer loans as a function of a bank’s distance to default (with an expected positive sign, as higher distance to default is associated with greater soundness, making it easier for banks to expand lending), the real GDP growth rate as a proxy for overall economic activity (positive sign), the lending rate and net interest margin (expected negative signs, reflecting more expensive lending for borrowers), and bank size approximated by total value of loans (expected negative sign). The key variable of interest is distance to default, which captures the effect of bank financial conditions on credit supply.

Table 1.

Demand and Supply in the Disequilibrium Model, 1997–2007 1/

(Dependent variable: year–on–year real growth rate of a bank’s total credit)

Source: Authors’ calculations based on data from BankScope and Datastream.

^{^1/}Maximum likelihood estimation. Log likelihood = 125.31.

Table 1.

Demand and Supply in the Disequilibrium Model, 1997–2007 1/

(Dependent variable: year–on–year real growth rate of a bank’s total credit)

Explanatory variables	Demand		Supply
	Parameter	Sth. Error	Parameter	Sth. Error
Constant	−10.24	0.48	−8.32	2.31
Real GDP growth	1.18	0.09
Lending rate	−0.03	0.01
Net interest margin	−0.09	0.05
Distance to default			0.03	0.01
Log (total loans)			−0.02	0.01

Source: Authors’ calculations based on data from BankScope and Datastream.

^{^1/}Maximum likelihood estimation. Log likelihood = 125.31.

Table 1.

Demand and Supply in the Disequilibrium Model, 1997–2007 1/

(Dependent variable: year–on–year real growth rate of a bank’s total credit)

Explanatory variables	Demand		Supply
	Parameter	Sth. Error	Parameter	Sth. Error
Constant	−10.24	0.48	−8.32	2.31
Real GDP growth	1.18	0.09
Lending rate	−0.03	0.01
Net interest margin	−0.09	0.05
Distance to default			0.03	0.01
Log (total loans)			−0.02	0.01

Source: Authors’ calculations based on data from BankScope and Datastream.

^{^1/}Maximum likelihood estimation. Log likelihood = 125.31.

9. Based on the estimated coefficients, the effect of bank soundness on loan supply is significant, but relatively small. The estimate implies that, for instance, a one–standard–deviation drop in the distance to default is associated with a year–on–year real growth of credit that is 1.5 percentage points lower than otherwise. As a side result, Figure 5 illustrates the development of the excess demand for credit in the model. It is an aggregate number, calculated by aggregating the demand and supply estimates for all the individual banks. The figure suggests that in 2000 there was a period of excess supply of credit, while 2003 and 2004 were characterized by excess demand for credit. Since then, demand and supply have been relatively balanced.

Box 1.

Interest Rates, Credit Volume, and Euro Area Output

To examine in more detail the lags between financial conditions, lending volumes, and output, a series of vector autoregression (VAR) and vector error correction (VEC) models has been estimated. Presented here is an impulse‐response graph from a VAR model estimating linkages between interest rates, lending volumes, and output on aggregate quarterly data for the euro area. The VAR calculations confirm that higher interest rates transmit to loan volumes and output with lags. The maximum impact of higher rates on loans comes with a 6 quarter lag. The first 3 quarters are characterized by very little impact on corporate credit (shown here). For household credit, there is even a small “hump” initially.

10.The bank lending channel operates with appreciable lags. In addition to the disequilibrium model presented in Table 1, a series of pairwise Granger causality tests were run to assess the relationships between real credit growth, real output growth, and banking sector vulnerability (approximated again by distance to default). The results of the exercise suggest that banking sector vulnerability, measured by distance to default, is influenced by real GDP and real credit in the horizon of 2–4 quarters. The distance to default influences real credit, but not GDP, with a lag of 6 quarters.⁵ Similarly, VEC/VAR models with interest rates, credit growth, and output find that interest rates have significant effects on output, but this “interest rate channel” operates with lags of about 6 quarters (Box 1).

C. Linkages Between Bank Loan Supply and Aggregate Output

11. In the next step, the relationship between the supply of bank credit and economic activity was examined. Output tends to move together with bank credit to the private sector (Figure 6), but this does not necessarily mean that the supply of bank loans has a significant effect on output. An alternative and equally plausible possibility is that as economic activity slows, the demand for bank loans declines, leading to a positive relationship between the two series. Disentangling the demand and supply effects (i.e., solving the identification problem) is hard, since these effects tend to occur jointly but only the equilibrium outcome is observed.

12. The identification problem was addressed by using an instrumental variables technique to isolate the loan supply effect on real output. Shocks to country–specific money demand are used as an instrument for shocks to loan supply, as proposed by Driscoll (2004) in addressing the same question for the United States. The logic behind this approach is based on the premise that country–specific shocks to money demand should lead to country–specific changes in the supply of loans, and therefore changes in output. This would allow to isolate the effect of loan supply on real activity.⁶ The identification scheme involves the following three steps (see Appendix I for details):

The overall effect of bank credit on output is investigated by regressing output growth on the growth rate of bank loans (and its lagged value), as well as its own lagged values. The resulting coefficient will reflect both the supply and demand effects of bank credit on real activity.
The shocks to money demand are recovered after estimating money demand functions for each euro–area country in the sample. Then the growth rate of bank loans is regressed on its lagged values and the estimated money demand shocks, in order to establish whether the latter are a good instrument for shocks to loan supply.
The effect of bank credit on output is re–estimated using the country–specific shocks to money demand as instruments. The resulting coefficient of bank loans is indicative of the supply effect, as the demand effect has been stripped out. Assuming that shocks to loan demand and supply are positively correlated, one could expect the instrumented coefficient of bank loans to be smaller than the non–instrumented one.

13. The estimations are done using country–level data from 2003Q1 to 2007Q3. The sample includes 11 euro–area countries (Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Portugal, Spain).⁷ The key variables used in the analysis are: real GDP, M3, deposit rates, and bank loans to non–financial corporations. For each country, the money supply (M3) and bank loan variables are deflated by the corresponding GDP deflator. Except for deposit rates, all other variables are in logarithmic form.

14. The estimation results from the first step confirm the positive relationship between bank credit and economic activity. Real bank credit has a significant and positive effect on output (Table 2). The size of the coefficient suggests that an increase in bank credit (in real terms) by 10 percentage points is associated with an increase in real GDP by about 1.5 percentage points.

Table 2.

OLS Regression of Output on Loans Dependent Variable: $Δ {\tilde{y}}_{i t}$

Source: IMF staff calculations.Notes: 1. All variables are demeaned by their cross–sectional averages.2. Critical values for 1, 5, and 10 percent are denoted by (^***), (^**) and (^*), respectively.

Table 2.

OLS Regression of Output on Loans Dependent Variable: $Δ {\tilde{y}}_{i t}$

Regressors	Coefficient	Standard Error
$Δ {\tilde{y}}_{i t - 1}$	–0.2135	(0.1805)
$Δ {\tilde{y}}_{i t - 2}$	–0.0899	(0.1756)
$Δ {\tilde{l}}_{i t}$	0.1486	(0.0324)^***
$Δ {\tilde{l}}_{i t - 1}$	0.0115	(0.0342)
Number of observations	232
R–squared	0.09

Table 2.

OLS Regression of Output on Loans Dependent Variable: $Δ {\tilde{y}}_{i t}$

Regressors	Coefficient	Standard Error
$Δ {\tilde{y}}_{i t - 1}$	–0.2135	(0.1805)
$Δ {\tilde{y}}_{i t - 2}$	–0.0899	(0.1756)
$Δ {\tilde{l}}_{i t}$	0.1486	(0.0324)^***
$Δ {\tilde{l}}_{i t - 1}$	0.0115	(0.0342)
Number of observations	232
R–squared	0.09

15. The second step estimates suggest that positive money demand shocks are associated with higher growth in bank loans. The shocks to money demand are constructed using estimates of country–specific money demand functions (Appendix I). Their impact on bank loans is illustrated by the positive and significant coefficient of the (country–specific) residuals from the estimated money demand functions on the growth of bank loans, even after controlling for lagged values of output (see Table 3). Therefore, the money demand shocks can be used as an instrument for loan demand in the next step.

Table 3.

First Stage IV Regression: Loans on Money Demand Shocks Dependent Variable: $Δ {\tilde{l}}_{i t}$

Source: IMF staff calculations.Notes: 1. All variables are demeaned by their cross–sectional averages2. Critical values for 1, 5, and 10 percent are denoted by (^***), (^**) and (^*), respectively.3. Money demand shocks are denoted by.

ϵ_{i t}

Table 3.

First Stage IV Regression: Loans on Money Demand Shocks Dependent Variable: $Δ {\tilde{l}}_{i t}$

Regressors	Coefficient	Standard Error
$Δ {\tilde{y}}_{i t - 1}$	–0.2478	(0.3474)
$Δ {\tilde{y}}_{i t - 2}$	–0.0119	(0.3287)
$Δ {\tilde{ϵ}}_{i t}$	0.0679	(0.0466)
$Δ {\tilde{ϵ}}_{i t = 1}$	0.2205	(0.0492)^***
Number of observations	232
R–squared	0.08

ϵ_{i t}

Table 3.

First Stage IV Regression: Loans on Money Demand Shocks Dependent Variable: $Δ {\tilde{l}}_{i t}$

Regressors	Coefficient	Standard Error
$Δ {\tilde{y}}_{i t - 1}$	–0.2478	(0.3474)
$Δ {\tilde{y}}_{i t - 2}$	–0.0119	(0.3287)
$Δ {\tilde{ϵ}}_{i t}$	0.0679	(0.0466)
$Δ {\tilde{ϵ}}_{i t = 1}$	0.2205	(0.0492)^***
Number of observations	232
R–squared	0.08

ϵ_{i t}

16. Once demand effects are taken into account, the loan supply effect on output is positive and statistically significant, but relatively small. The coefficient of the bank loan variable is still positive but smaller than in the first step (0.10 instead of 0.15) when the instrumental variables estimation is implemented (Table 4). Overall, the estimation results suggest that an increase (decrease) in the supply of bank loans by 10 percentage points is likely to lead to an increase (decrease) in real GDP by about 1 percentage point. Therefore, the analysis implies that a cutback in bank loan supply is likely to have a negative impact on economic activity.

Table 4.

Second Stage IV Regression of Output on Loans Dependent Variable: $Δ {\tilde{y}}_{i t}$

Table 4.

Second Stage IV Regression of Output on Loans Dependent Variable: $Δ {\tilde{y}}_{i t}$

Regressors	Coefficient	St. Error
$Δ {\tilde{y}}_{i t - 1}$	–0.1514	(0.0582)^**
$Δ {\tilde{y}}_{i t - 2}$	–0.0178	(0.0447)
$Δ {\tilde{l}}_{i t}$	0.0955	(0.0496)^**
$Δ {\tilde{l}}_{i t - 1}$	0.0178	(0.0447)
Number of observations	232

Table 4.

Second Stage IV Regression of Output on Loans Dependent Variable: $Δ {\tilde{y}}_{i t}$

Regressors	Coefficient	St. Error
$Δ {\tilde{y}}_{i t - 1}$	–0.1514	(0.0582)^**
$Δ {\tilde{y}}_{i t - 2}$	–0.0178	(0.0447)
$Δ {\tilde{l}}_{i t}$	0.0955	(0.0496)^**
$Δ {\tilde{l}}_{i t - 1}$	0.0178	(0.0447)
Number of observations	232

D. Linkages Between Corporate Financing Conditions and Economic Activity

17. To address the question of how corporate sector financing conditions affect activity, the relationship between the corporate bond spread and euro–area output has been analyzed. The corporate bond spread is defined as the difference between the yield on a corporate bond (risky asset) of a given maturity and quality and the yield on a government bond (riskless asset) of the same maturity. The corporate bond risk premium has been shown to be a good predictor of real activity in the United States (Chan–Lau and Ivaschenko, 2002; Mody and Taylor, 2004) and in the euro area (De Bondt, 2002; Ivaschenko and Koeva Brooks, 2008), which is consistent with the presence of a financial accelerator in the economy.⁸ As corporate bond spreads tend to move together with the tightness of bank lending standards in the United States (Duca, 1999; Gertler and Lown, 2000), they also can be treated as a proxy for corporate sector financing conditions.

18. The analysis was conducted using vector autoregressions run for 1999M1– 2008M1. The key variables were the corporate bond spread, the annual growth in output, and the annual change in the real effective exchange rate. The number of lags in the vector autoregression was set to 3.⁹ As regards the corporate bond spread, aggregate euro–area data on corporate bond yields were utilized for securities of different maturities and quality. The spreads for AAA, AA, A, and BBB 7–year corporate bonds in the euro area (in relation to a 7–year government bond) are shown in Figure 3. The regression results presented here are based on the BBB yield minus the government bond yield, but other spreads have also been used as robustness tests, and yielded similar results. Given the high frequency nature of the data, monthly industrial production (instead of real GDP) is used as an indicator for economic activity.

19. The estimation results show that a positive shock to the corporate bond spread leads to a significant negative response of output. The impulse responses of the baseline regressions (Figure 8) illustrate that a one–standard–deviation shock to the corporate bond yield (about 60 basis points) has an adverse effect on the growth rate of industrial output, which peaks at about 0.25 percent in 8–20 months. This effect is statistically significant, as shown by the 95 percent confidence bands. A limitation of these estimates is that simultaneity might be an issue in the basic VAR estimation. Nonetheless, these results are fairly robust across alternative specifications.

E. Quantitative Implications

20. Based on the “bank lending channel” estimates, the impact of the estimated banking losses on euro–area output could be 0.2–0.3 percentage points. This section presents two different estimates of the impact. The calculations illustrate that there are linkages between the financial sector soundness and real economic developments. They also illustrate the challenges of quantifying the exact relationship, and the uncertainties surrounding the estimates.

21. One approach to estimating the impact is to start from the current estimates oflosses in the banking sector; these would imply a negative 0.2 percentage point impact on euro area GDP. The following explains the estimate:

Estimates of the total subprime–related losses in euro–area global banks were around US$45 billion as of March 2008 (IMF, 2008). The estimated losses for the whole of Europe were much larger (about US$121 billion), but substantial chunks of these losses were in global banks based in the United Kingdom and Switzerland. The US$45 billion is based on the recent IMF staff calculations, but it is also consistent with estimates by other analysts and academics, such as Greenlaw and others (2008).
The US$45 billion estimated losses correspond to about 2.0 percent of the euro–area banks’ capital and reserves. If nothing else happened, the ratio of equity to (unweighted) assets for euro area banks would decline from 6.7 percent to 6.5 percent, and the banks’ leverage would increase correspondingly.
A plausible assumption is that the banks target a certain leverage ratio. One option is to find investors to inject more capital. Another option is to shrink assets. To keep the leverage ratio unchanged, assets would have to fall by 2.0 percent. It is assumed that banks cut down their loan supply by the same amount.
From the estimate in the previous section, a decline in the supply of bank loans by 10 percentage points is likely to lead to a decline in real GDP by about 1 percentage point. A loan decline by 2.0 percent therefore corresponds to 0.2 percentage point drop in real GDP.

22. An alternative approach, based on0 stock price developments, suggests a0.3 percentage point fall in output. As a market– based indicator that incorporates market participants’ view on banks’ situation and outlook, distance to default can provide an Sources: Staff calculations based on data from Datastream. Note: Distance to default is the difference between the expected value of assets at maturity and the default threshold, which is a function of the value of the liabilities. A higher distance to default is associated with a lower probability of default. alternative assessment of the likely impact of the shocks that hit the banks. The average distance to default in January 2008 was 1.9 standard deviations lower than the average distance to default in July 2007. Using the estimates in the previous section, this translates into a decline in real credit by 2.9 percentage points. That in turn (using again the estimates from the previous section) translates into a real GDP decline by some 0.3 percentage points. In other words, this method yields a broadly similar, but somewhat lower, estimate of the likely GDP impact than the method based on projected capital losses.

23. The difference between the two approaches reflects a variety of factors. This includes the extent to which the banks will (or will not) be recapitalized. The extent of recapitalization is not trivial to estimate, making the market’s guess a useful alternative input.

24. The above estimates should be taken with a grain of salt. In particular, they focus only on losses to the euro–area economy stemming from losses in euro–area banks, and do not cover the impact on euro–area residents of losses in, say, Swiss banks. Also, the underlying estimates are based on commercial and investment banks, leaving out other financial institutions that could have exposures (such as thrifts, insurance companies, or hedge funds). The impact of bank losses on lending, and thereby on output, can be lower if banks increase their capital–to–asset ratios (decrease leverage) through capital injections rather than (or in addition to) asset manipulation. The impact can also be bigger if banks aim to de–leverage, i.e., decrease their leverage target, which is quite likely given the overall increase in risk aversion (see, e.g., IMF, 2008), and if they get hit by additional shocks, such as stock price declines.

Appendix

Identifying the Linkages Between Bank Loan Supply and Aggregate Output

The theoretical framework used to derive the empirical specification of the model is an IS/LM model, which adds a credit channel of monetary transmission to the traditional interest rate channel (Bernanke and Blinder, 1988). A possible solution to the problem of identifying loan supply effects within this framework is offered by Driscoll (2004) in investigating the analogous question for the U.S. economy. As noted by Driscoll, “the approach could also be applied to regions in other countries, or other collections of small open economies under fixed exchange rates, such as the European Union.”

The basis model consists of four equations for each country iin the euro area. There are three markets: a loan market, a money market, and a goods market.

In the loan market, banks face the following loan demand $l_{it}^{d}$ from households and firms:

\begin{matrix} l_{i t}^{d} = τ r - {χ ρ}_{i t} + {\bar{ω} y}_{i t} + ν_{i t} & (B .1) \end{matrix}

where у_it denotes output, ρ_it is the interest rate on loans, r_tis the interest rate on bonds (i.e., the price of financing expenditures from an alternative source), and ν_it is a demand shock.

The loan rate is allowed to vary across euro area countries, while the bond rate is assumed to be the same for all countries. This is consistent with the evidence on a well–integrated bond market and segmented loan markets.

The loan supply function is specified by the following equation:

\begin{matrix} l_{i t}^{s} = - λ r + μ_{i t} + β (m_{i t} - p_{i t}) + w_{it} & (B .2) \end{matrix}

where $(m_{i t} - P_{i t})$ denotes money supply, and W_it is the shock to loan supply. The supply of loans depends on deposits as a way to generate loans and the interest rates on loans (ρ_it) and bonds (r_t ). The underlying assumption is that loans and bonds are imperfect substitutes.

The money market equilibrium for each country is given by:

m_{i t} \begin{matrix} - P_{i t} = {γy}_{i t} - δ (r_{t} - r_{i t}^{d}) + ϵ_{i t}, & (B .3) \end{matrix}

where $r_{i t}^{d}$ is the country–specific rate on deposits, ε _it and is a country–specific money demand shock. The money supply m _it is determined by the European Central Bank.

Finally, aggregate output is specified as function of the interest rate on bond (r_t), the interest rate on loans (ρ_it), and a country–specific shock (z_it):

\begin{matrix} y_{i t} = - θ r_{t} + α ρ_{it} + z_{it} & (B .4) \end{matrix}

Then the model is solved for output and loans, producing the following relationships:

\begin{matrix} y_{i t} = \frac{θ}{χ + \bar{ω} α} r_{t} + \frac{α}{χ + \bar{ω} α} l_{i t} - \frac{α}{χ + \bar{ω} α} ν_{i t} + \frac{χ}{χ + \bar{ω} α} z_{i t} (B .5) \end{matrix}

\begin{matrix} \begin{matrix} l_{i t} = \frac{θ}{χ + \bar{ω} α} r_{t} + \frac{α β}{χ + μ} ϵ_{i t} + \frac{χ β γ + \bar{ω} μ}{χ + μ} y_{i t} - \frac{μ}{χ + μ} ν_{i t} + \frac{χ}{χ + μ} w_{i t} + \frac{χ δ β}{χ + μ} r_{i t} (B .6) \end{matrix} \end{matrix}

These two equations illustrate the problem of identifying demand and supply effects in bank lending (i.e., separating the bank lending and interest rate channels), as bank loans and output are endogenous (jointly determined) as describe above.

Following Driscoll (2004), the identification problem is addressed by demeaning each variable with its This effectively “shuts down” the interest rate channel. Specifically, after transforming each variable x_it into a deviation from its

cross–sectional mean. ${\tilde{x}}_{i t} = \frac{1}{N} Σ_{i = 1}^{N} x_{i t},$ the model can be re-written as follows:

\begin{matrix} \begin{matrix} {\tilde{l}}_{i t}^{d} = - χ {\tilde{ρ}}_{i t} + (\bar{ω} \tilde{y_{it}} + v_{i t} (B . 1^{,}) \end{matrix} \end{matrix}

\begin{matrix} {\tilde{l}}_{i t}^{s} = - μ {\tilde{ρ}}_{i t} + β ({\tilde{m}}_{i t} - {\tilde{p}}_{i t}) + w_{i t} & (B . 2^{,}) \end{matrix}

\begin{matrix} {\tilde{m}}_{i t} - {\tilde{P}}_{i t} = γ {\tilde{y}}_{i t} + δ {\tilde{r}}_{i t}^{d} + ϵ_{i t} & (B . 3^{,}) \end{matrix}

\begin{matrix} {\tilde{y}}_{i t} = - α {\tilde{ρ}}_{i t} + Z_{i t} & (B . 4^{,}) \end{matrix}

The corresponding expressions for the (demeaned) country–specific output and loans are:

\begin{matrix} {\tilde{y}}_{i t} = \frac{α}{χ + \bar{ω} α} {\tilde{l}}_{i t} - \frac{α}{χ + \bar{ω} α} ν_{i t} + \frac{χ}{χ + \bar{ω} α} z_{i t} (B . 5^{,}) \end{matrix}

\begin{matrix} \begin{matrix} {\tilde{l}}_{i t} = \frac{χ β}{χ + μ} ϵ_{i t} + \frac{χ β γ + ω μ}{χ + μ} {\tilde{y}}_{i t} - \frac{μ}{χ + μ} v_{i t} + \frac{χ}{χ + μ} w_{i t} + \frac{χ δ β}{χ + μ} {\tilde{r}}_{i t} (B . 6^{,}) \end{matrix} \end{matrix}

The last two relationships indicate that the money demand shock ϵ_it is correlated with ${\tilde{l}}_{it}$ but not with ${\tilde{l}}_{it}$ but does not affect ${\tilde{y}}_{it}$ independently of its effect on ${\tilde{l}}_{it}$ , i.e. it is uncorrelated with the disturbance terms in equation (B.5’). This makes money demand shocks a good candidate for an instrumental variable.

The shocks $ϵ_{i t}$ are obtained by estimating a money demand function for each euro area country. The first stage of the instrumental–variable estimation aims to estimate if money demand shocks have a significant effect on aggregate lending in a pooled panel ordinary least squares (OLS) regression using the demeaned values of all variables. In the second stage, the money demand shocks are used as an instrument in a regression of loans on output, which helps isolate the supply effect of bank lending on real activity.

References

Altunbaş, Yener, Otabek Fazylov, and Philip Molyneux, 2002, “Evidence on the bank lending channel in Europe,” Journal of Banking & Finance, Vol. 26, No. 11, November, pp. 2093–2110.
- Search Google Scholar
- Export Citation
Angeloni, Ignazio, and Michael Ehrmann, 2003, “Monetary transmission in the euro area: early evidence,” Economic Policy Vol. 18, No. 37, pp. 469–501.
- Search Google Scholar
- Export Citation
Bernanke, Ben, and Alan Blinder, 1988, “Money, Credit and Aggregate Demand,“ American Economic Review, Vol. 78, pp. 901–21.
- Search Google Scholar
- Export Citation
Bernanke, Ben, and Mark Gertler, 1995, “Inside the Black Box: The Credit Channel of Monetary Policy Transmission,” The Journal of Economic Perspectives, Vol. 9, pp. 27–48.
- Search Google Scholar
- Export Citation
Black, Fisher, and Myron Scholes, 1973, “The Pricing of Options and Corporate Liabilities,” Journal of Political Economy, Vol. 81, No. 3, pp. 637–54.
- Search Google Scholar
- Export Citation
Chan–Lau, Jorge, and Iryna Ivaschenko, 2002, “The Corporate Spread Curve and Industrial Production in the United States,” IMF Working Paper No. 02/08 (Washington: International Monetary Fund).
- Search Google Scholar
- Export Citation
De Bondt, Gabe, 2002, “Euro Area Corporate Debt Securities Market: First Empirical Evidence,” ECB Working Paper No.164 (Frankfurt: European Central Bank).
- Search Google Scholar
- Export Citation
Bundesbank, 2002, “The Development of Bank Lending to the Private Sector.” Deutsche Bundesbank Monthly Report, Vol. 54, October.
- Search Google Scholar
- Export Citation
Čihák, Martin, and Petya Koeva Brooks, 2008, “From Subprime Loans to Subprime Growth? Evidence for the Euro Area,” IMF Working Paper (Washington: IMF).
- Search Google Scholar
- Export Citation
Duca, John, 1999, “What credit market indicators tell us,” Economic and Financial Policy Review, Federal Reserve Bank of Dallas, issue Q III, pages 2–13.
- Search Google Scholar
- Export Citation
Driscoll, John, 2004, “Does Bank Lending Affect Output? Evidence From the U.S. States,” Journal of Monetary Economics, Vol. 51, No. 3, April, pp. 451–47.
- Search Google Scholar
- Export Citation
Gertler, Mark, and Cara Lown, 2000, “The Information in the High Yield Bond Spread for the Business Cycle: Evidence and Some Implications” NBER Working Paper W7549.
- Search Google Scholar
- Export Citation
Greenlaw, David, Jan Hatzius, Anil Kashyap, and Hyun Song Shin (2008), “Leveraged Losses: Lessons from the Mortgage Market Meltdown,” U.S. Monetary Policy Forum Conference draft, mimeo.
- Search Google Scholar
- Export Citation
International Monetary Fund (2008), Global Financial Stability Report: April 2008 (Washington: International Monetary Fund).
- Search Google Scholar
- Export Citation
Merton, Robert, 1974, “On the Pricing of Corporate Debt: The Risk Structure of Interest Rates,” Journal of Finance, Vol. 29, No. 2, pp. 449–70.
- Search Google Scholar
- Export Citation
Mody, Ashoka, and Mark Taylor, 2004, “Financial predictors of real activity and the financial accelerator,” Economics Letters, Vol. 82(2), pages 167–72.
- Search Google Scholar
- Export Citation
Pazarbasioglu, Ceyla, 1997, “A Credit Crunch? Finland in the Aftermath of the Banking Crisis,” IMF Staff Papers 44, pp. 315–3.
- Search Google Scholar
- Export Citation

Prepared by Martin Čihák and Petya Koeva Brooks. More details, as well as results of contingent claims analysis for the euro area are provided in Čihák and Koeva Brooks (2008).

A rough tool for distinguishing credit supply and demand factors are the bank lending surveys, organized by the Eurosystem since 2003, and summarizing responses of senior loan officers regarding loan demand and changes in their bank’s lending policy in the previous quarter. Practical problems in interpreting the results of the survey include the qualitative, subjective nature of the survey data, and the short time series available. Empirically, the survey results suggest that both the loan demand and the lending standards are procyclical (Čihák and Koeva Brooks, 2008), but the time series of lending surveys are too short to allow for a more elaborate analysis or to test for breaks in the correlations.

Data are from the BankScope database by Bureau van Dijk for 1997–2006. To explain the factors contributing to credit developments, the following variables are used: total bank assets, total loans, shareholders’ equity, short–term liabilities, long–term liabilities, liquid holdings (cash, ECB and other financial institutions’ securities, and government securities), equity price data (“last price,” daily), and equity shares outstanding (daily).

The distance to default (DD) is an increasingly popular measure of bank soundness. It is based on the valuation model of Black and Scholes (1973) and Merton (1974), who drew attention to the concept that corporate securities are contingent claims on the asset value of the issuing firm. The DD is calculated from market prices of bank shares and balance sheet data on individual banks obtained from the BankScope database.

Detailed results are available upon request.

Greenlaw and others (2008) use the Treasury–Eurodollar (TED) spread as another instrument for credit supply in the United States. As the difference between unsecured and government–backed deposit rates, the TED spread provides a useful measure of credit risk, which is likely to be correlated with credit supply. A weakness of the TED spread is that it may be influenced by “flight to quality” flows that move Treasury bill yields, as well as the funding pressures that drive LIBOR rates.

Cyprus, Malta, Luxembourg, and Slovenia are not included due to data limitations.

The basic story of the financial accelerator is that it is a mechanism linking the condition of borrower balance sheets to the terms of credit, and hence to the demand for capital. Corporate–sovereign bond spreads are a key measure of the credit terms.

Simultaneity may be an issue because the paper does not propose a structural VAR.

Other Publishers

Save
Cite
Email this content

Share Link

Copy this link, or click below to email it to a friend
Email this content
or copy the link directly:

https://www.elibrary.imf.org/view/journals/002/2008/263/article-A001-en.xml
The link was not copied. Your current browser may not support copying via this button.

Link copied successfully

Euro Area Policies: Selected Issues

Author: International Monetary Fund

Volume/Issue:: Volume 2008: Issue 263

Publisher:: International Monetary Fund

ISBN:: 9781451879896

ISSN:: 1934-7685

Pages:: 79

DOI:: https://doi.org/10.5089/9781451879896.002

Keywords
I. From Subprime Loans To Subprime Growth? Evidence for the Euro Area1
References

View raw image

Euro Area: Money Market and Retail Lending Rates

View raw image

Euro area: Changes in Credit Standards to Enterprises and Households, 2005‐07

View raw image

Euro Area: Corporate Bond and Equity Market Prices

View raw image

Euro Area: Growth in Bank Loans and Securities Issuance

View raw image

Euro Area: Excess Demand for Loans, 1997–2007

View raw image

Impulse response to corporate lending rate increase by 1 st.dev.

(64 basis points)

View raw image

Euro Area: Output Growth and Growth in Bank Loans, 2000–07

View raw image

Euro Area: Response of Annual Growth in Industrial Production to One Standard Deviation Innovation in Corporate Bond Spread

View raw image

Global Bank Subprime and Alt-A Lossees, March 2008

(billions of U.S. dollars)

View raw image

Distance to default for 53 large banks, 2000-08

Table 4.

Second Stage IV Regression of Output on Loans Dependent Variable: $Δ {\tilde{y}}_{i t}$

Regressors	Coefficient	St. Error
$Δ {\tilde{y}}_{i t - 1}$	–0.1514	(0.0582)^**
$Δ {\tilde{y}}_{i t - 2}$	–0.0178	(0.0447)
$Δ {\tilde{l}}_{i t}$	0.0955	(0.0496)^**
$Δ {\tilde{l}}_{i t - 1}$	0.0178	(0.0447)
Number of observations	232

Topics

Countries

Series

About

Resources

Abstract

I. From Subprime Loans To Subprime Growth? Evidence for the Euro Area1

A. Introduction and Main Findings

B. Linkages Between Bank Characteristics and Lending Behavior

C. Linkages Between Bank Loan Supply and Aggregate Output

D. Linkages Between Corporate Financing Conditions and Economic Activity

E. Quantitative Implications

Appendix

Identifying the Linkages Between Bank Loan Supply and Aggregate Output

References

Same Series

Other IMF Content

Other Publishers

Asian Development Bank

Inter-American Development Bank

International Labour Organization

The World Bank

Share Link

Table of Contents

Headings

Figures

Metrics

I. From Subprime Loans To Subprime Growth? Evidence for the Euro Area¹