Has the Financial Crisis Induced a Credit Crunch for Small and Medium-Sized Enterprises in Germany?

Hat die Finanzkrise zu einer Kreditklemme für KMU in Deutschland geführt?

Master's Thesis, 2009

94 Pages, Grade: 1,3


Table of Contents











Table of Figures

Figure 1: Key Hypothesises of the Masterthesis and Chapters

Figure 2: U.S. Real-GDP and U.S. Consumer Confidence Index

Figure 3: IS-LM Model

Figure 4: Credit Rationing – Profit Functions of Borrower and Lender

Figure 5: Credit Rationing – Credit Price Changes in Relation to Credit Risk Changes

Figure 6: Credit Rationing – Allocation of Banks Capital and Deposits to Credits or Risk Free Bonds

Figure 7: Credit Rationing – Impact of Banks’ Capital Restrictions and Monetary Policy Measures on the Credit Supply

Figure 8: Equity to Asset Ratios of German Companies 1994-2007

Figure 9: Economic Indicators in Germany 2000 - 2009

Figure 10: Basel I Risk Weights

Figure 11: Price for an Initial Rating

Figure 12: Basel II Risk Weights

Figure 13: Credit Price Comparison Basel I to Basel

Figure 14: Default Frequency as a Market Value Dependent Variable

Figure 15: Annual Corporate Defaults by Number (left) and by Value (right)

Figure 16: U.S: Rating Migration Matrices for Years with Macroeconomic Expansion and Contraction

Figure 17: S&P Corporate Ratings Distributions in 2003 versus 2008

Figure 18: 1W, 3M, 12M Euribor compared with Main Refinancing and Marginal Lending Facility

Figure 19: Short-Term Credit Volume and 3M Euribor Spread

Figure 20: Basel II Standard Approach Risk Weights for Corporate Loans

Figure 21: Result of the Loan Pricing Example with Default Risk according to Bangia et al

Figure 22: Analysis of the 10 Biggest German Banks: RWA, Equity Capital, Tier Ratio

Figure 23: Calculation of the Capital Detortion by Rating Migrations

Figure 24: European Issuance of Securitized Products

Figure 25: Development of Total Lending Volume to Non-MFI and GDP Growth in Germany

Figure 26: Credit Volume / GDP Correlation

Figure 27: Investments, New Credits and Credit Stock (Non-MFI in Germany)Year to Year Change

Figure 28: Questionaire in 2004 to German Companies about Credit Lending Restricitions

Figure 29: Income per Total Assets of German Banks in the Credit Business

Figure 30: U.S. Growth of Lending over Six Recessions by Year of Cyclical Peak

Figure 31: Development of Credit Demand Volume and Supply Restrictions in Germany During the Last 3 Month

Figure 32: Equity to Asset Ratios of German Companies by Industry (Source: Own

calculation based on data from Deutsche Bundesbank (2009)

Figure 33: Top-Five Banks per Banking Type in Germany 2007 (Total Assets in €bn), Source: Karsch, W., (2008), P.

Figure 34: Market Shares of German Banks per Banking Type, Source: Bundesverband deutscher Banken, http://www.bankenverband.de/tableprint.asp?id=4068

Figure 36: New Orders in the German Manufacturing Industry, Source: Statistische Bundesamt (2009)

Figure 37: Global Corporate Average Transition Rates, 1981 – 2008 in %, Source: S&P Global Default Study 2008 (2009), P. 41

Figure 38: Calculation of Risk Premiums for the Example Calculation of Standard Risk Costs, Source: Own calculations

Figure 39: Loan Pricing Example Calculation with Default Risk from S&P 2008, Source: Own calculation

Figure 40: IAS 39 – Treatment of Financial Assets, Source: Füllbier, R., (2008), P.

Figure 41: Bad Bank Model of the German Government, Source: BMF, (2009b), 2009-06-23, http://www.bundesfinanzministerium.de/DE/Buergerinnen__und__Buer ger/Gesellschaft__und__Zukunft/finanzkrise/130509__BadBank.html

Table of Symbols

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Table of Abbreviations

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The following MBA-Thesis analyses how the financial crisis has affected different pre­conditions for the credit lending behaviour of banks. Additionally the demand for loans of potential borrowers will be considered. A “credit crunch” is defined as a restriction of the credit supply which leads to a mismatch of supply and demand.

It will be shown that the financial crisis caused a still ongoing macroeconomic contraction. The macroeconomic contraction has a direct effect on the calculation of rating grades which downgrade. Parameters of credit risk assessments will be analysed and their pro-cyclicality will be tested. Corporate rating grades already downgraded significantly in 2008.

The downgrading of firms has an effect on the credit pricing which influences also the credit demand. This effect will be shown by explaining a basic cost-plus loan pricing model. The sensitivity of the loan pricing towards changes of attributed capital, refinancing costs and cost of default will be exemplarily shown. It will be shown that the rating downgrades have a significant effect on the credit price. This price increase which might affect the demand is however per definition not a “credit crunch”.

The Basel II Accord regulation links the requirements for banks’ capital to rating grades. Prior to the financial crisis the capital of banks has already been close to the regulatory minimum level. This made the banks vulnerable for impairments on assets. As new sources for capitalisation and possibilities to off-balance assets are currently limited banks have to reduce risk-weighted assets to fulfil the minimum capital requirements.

A reduction of risk-weighted assets could be inter alia done by a restrictive corporate lending policy. The restrictions on banks’ capital will not decrease quickly as especially impairments on the credit book might increase due to upcoming defaults.

Nevertheless a supply restriction gets only visible if the demand for credits is robust. The demand correlates among other points especially to the investments which have decreased since the end of 2008.

“Students of logic will recall the discussion of the damp squib thrown by A to land at B’s feet, by B to C, C to D, and so on, only explode after Y threw it in Z’s face. Who is to blame? A, causa remota? Or Y, causa proxima? Causa remota of the crisis is speculation and extended credit; causa proxima is some incident that snaps the confidence of the system, makes people think of the dangers of failure and leads them to move [...] into cash.. In itself, causa proxima e.g. a refusal of credit to some borrower maybe trivial. Prices fall. Expectations are reversed. The movement picks up speed. [...]As prices fall further, bank loans turn sour. [...] The credit system itself appears shaky and the race for liquidity is on.”

Charles P. Kindleberger1

1 Introduction

1.1 Description of the problem and course of the thesis

“The Financial Crisis has hit the German „Mittelstand“. One out of five entrepreneurs is facing a credit crunch”2 or as Kindleberger calls it “the damp squid might have hit Z”. Anecdotal evidence exists that even good firms are finding it nowadays difficult to obtain credit to finance production and investment. However empirical data do not unequivocally support the assertion that a credit crunch is occurring.3 Has the current financial crisis induced a credit crunch for German Small and Medium-Sized Enterprises (SME) till now or will it do it in future? This question will be therefore analysed and possible future developments will be discussed in this thesis.

With 25.5% equity SME are historically dependent on the availability of debt financing. Further on they play with a share of 99.7% of all firms in Germany, with 40.8 % of total German taxable turnover and 70.5% of the total employees in Germany an important role in the economy.4

The phenomenon of credit crunch means a mismatch of credit demand and supply or better an over exceeding demand compared to the level of supply. For this thesis the most commonly shared definition of Bernanke et al is chosen which defines a credit crunch “as a significant leftward shift in the supply curve for bank loans5, holding constant both the safe real interest rate and the quality of potential borrowers.”6

Therefore first in chapter 2 impacts on the credit demand will be analysed. Factors which could have for example influenced the quality of potential borrowers will be determined. It will be shown based on macroeconomic data how credit ratings migrate along the business cycle. Credit ratings are defined by the framework of the Basel II Accord which will also be introduced. The Basel II Accord further on connects the rating grade to the required capital attribution for a loan.

The hypothesis will therefore be tested that due to a macroeconomic contraction the credit ratings migrated downwards and therefore the requirements for attributed capital increase. However as the attributed capital or the cost of capital is only one part of a credit price based on a cost-plus credit pricing model it will be shown which other parameters could affect a credit pricing and increase potentially in a macroeconomic contraction the credit prices. The influence of the monetary policy will be also analysed. The introduced loan pricing model will be applied in an idealized example calculation to display how the credit price for an SME could have evolved since mid of 2007. If the credit pricing drives the credit demand down and therewith the total credit lending volume, the existence of a credit crunch can be qua definition excluded. Further on in chapter 2 it will be analysed how potentially increased requirements for capital attribution due to downward migrations of ratings could have a restrictive effect on the supply of credits, especially if the capital to asset ratio has been negatively affected by impairments on banks trading and credit assets a restriction of the credit supply could have occurred after the onset of the financial crisis. Therefore the recent development of the available capital of banks in Germany will be analysed on the basis of empiric data in chapter 2.2.1. By the introduction of the loan pricing model it will be shown that an important part of the credit pricing is influenced by the availability and costs of banks’ deposits. Therefore the refinancing possibilities and costs will be also briefly explained. After in chapter 2 the single impacts on the lending demand and supply have been analysed in chapter 3 the development of the lending volume will be shown.

The thesis starts with defining the framework of the thesis consisting of the financial crisis, basic economic models, SME and the banking system in Germany. The tested dependencies and hypothesises are summarized below:

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Figure 1: Key Hypothesises of the Masterthesis and Chapters7

1.2 Framework

1.2.1 Financial Crisis

A healthy and vibrant economy requires a financial system that moves funds to economic agents who have the most productive investment opportunities, e.g. those that have a positive net present value. Financial crises interfere with this process because they can drive the economy away from equilibrium with high output in which financial markets perform well to one in which output declines sharply because the financial system is unable to channel funds to those with the best investment opportunities.8

Numerous theories have explained the nature and impact of financial crises. Monetarists like Friedman/Schwartz9 describe financial crises as a result of banking panics where bank deposits are withdrawn due to upcoming uncertainty and a shortage of money supply as the consequence.10 According to Kindleberger11 a financial crisis involves sharp declines in asset prices, failures of both large financial and non-financial firms, disinflation, disruptions in foreign exchange markets or some combination of all of these.12

Recent studies include the impacts of asymmetric information namely adverse selection and moral hazard as a root cause for financial crises.

Similarly to Kindleberger’s observation the current financial crises had different root causes which are briefly and not comprehensively described.

Root causes should be distinct from catalyst. One catalyst for the development of a bubble of asset prices and credits was the supply of “cheap money” by the central banks and especially the US Federal Reserve Bank.13 After the burst of the dot-com bubble the Federal Fund Target Rate was reduced from 650 bps in mid of 2000 to 100 bps in mid of 2004.14 This “cheap money” was one reason for an increased spending in different asset classes. Especially the US residential housing market prospered. Accompanied by a politically driven relaxation of the mortgage lending regulation by the U.S. government the average size of new mortgage loans increased to 4.6 times of the American households’ median income.15 The home ownership rate in the U.S. rose from 64 % in 1990 to 69 % in 2005. Additionally investors shifted their portfolios heavily towards real estate16 and as a consequence the real estate prices rose heavily disproportional to the Consumer Price Index (CPI).17 This development was only made possible by declining lending standards – creditworthiness was minor accounted than the superficially good prospects of the collateral value. In 2005 for example 57 % of the new loans in the U.S. were granted to customers without a reported stock of equity.18 The subprime segment as the worst out of four mortgage categories rose over proportionally to 14 % of the total market size accounting for a value of approximately $ 1000tr in 2007.19

In order to pool and tranch the risk, banks securitized the loans by on-selling the claims or forming structured products like Asset-Backed Securities (ABS), Mortgage Backed Securities (MBS) and Collateralized Debt Obligations (CDO).

The Federal Fund Target Rate started to increase by the end of 2004 and simultaneously the obligations from mortgage loans with variable interest payments. The number of defaults started to increase. The real estate prices started to decline with a dwindling demand and some emergency sell-offs. Parallel the underlying market value of structured products declined, too. “The proximate cause of the crisis was the turn of the housing cycle in the United States and the associated rise in delinquencies on subprime mortgages, which imposed substantial losses on many financial institutions and shook investor confidence in credit markets. The abrupt end of the credit boom has had widespread financial and economic ramifications. Financial institutions have seen their capital depleted by losses and writedowns and their balance sheets clogged by complex credit products and other illiquid assets of uncertain value. Rising credit risks and intense risk aversion have pushed credit spreads to unprecedented levels, and markets for securitized assets, except for mortgage securities with government guarantees, have shut down.”20 The development of the securization market will be anaylised in detail in chapter 2.2.2.

The financial crisis evolves by increasing write-downs and uncertainty about the value of structured products and their underlying collateral into a crisis of confidence.

Banks become reluctant to lend to each other21 and in a viscous circle banks like Lehman Brothers Inc. defaulted or were close to default due to a lack of liquidity among other reasons.22

Due to the interdependence of national and international financial institutions the liquidity and devaluation problems spread quickly.23

The hypothesis that impairments on assets like structured products and credits by banks had a negative effect on the capital to asset ratio will be tested in chapter 2.2.1.

Besides the impact of the financial crisis on the interbanking market, the securization market, and the capitalization of banks, spill-over effects to the real economy are relevant for this thesis. “Empirical evidence suggests a positive, first-order relationship between financial development and economic growth. A growing body of work would push even more sceptics towards the belief that the development of financial markets and institutions is a critical and inextricable part of the growth process and away from the view that the financial system is an inconsequential side show, responding passively to economic growth and industrialization. There is even evidence that the level of financial development is a good predictor of future rates of economic growth.”24

Anecdotic evidence is given that the lack of liquidity and capital of banks reduced their credit lending and therewith affected the real sector. It has to be falsified if this is true.

However there are other spill-over channels. The crisis of confidence in the financial sector spread especially in the U.S. among consumers and firms and influenced their spending behaviour. The U.S. real GDP got already under pressure by the end of 2007 and suffered more significantly in 200825 compared to the German GDP.26 The financial services sector contributes with 21 %27 largely to the U.S. GDP and Bank restructurings set a significant direct pressure on the GDP and employment. Additionally with decreasing real estate prices the consumer spending as a second significant part of the GDP got under pressure. The consumer confidence index already started to decline in August 2007.28

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Figure 2: U.S. Real-GDP and U.S. Consumer Confidence Index29

The German economy was still well off till mid of 2008. The number of new orders in the industrial sector increased year on year on average in November 2007, December 2007 and January 2008 in real terms by 11.2 %. The export demand was despite a relatively strong Euro compared to the US $ and Japanese ¥ still stable and the domestic demand extraordinary strong.30,31 Even though the German real economy was still running, the confidence of German companies disappeared significantly. The crisis of confidence in the financial sector and the suffering U.S. economy induced the IFO index as a key indicator for the confidence of German companies to decline from 104.6 points in March 2008 to 85.9 points in November 2008.32

How much of this confidence decline was induced by the crisis of confidence in the financial sector and how much by the forerunning real economic recession in the U.S. could not clearly be separated.

1.2.2 Credit Rationing and IS-LM as Theoretical Models

a) IS-LM Model

The macroeconomic IS-LM model can be used to explain generally the interrelationship between interest rates, credit demand and credit supply.

The IS (I nvestment and S avings) schedule explains the dependency of interest rate and economic output (Y). It describes besides investment and savings all spending in a closed economy and is a downward sloping curve:33

Y = C (Y-T) + I(r) + G

With C: Consumer Spending, Y: Gross Domestic Product, T: Tax Rate, I: Investments,r: Interest Rate, G: Governmental Spending

The assumption that the investment is fully interest rate elastic as the model pretends is controversially discussed among economists.34 The credit demand is moreover often inelastic and related to the investment requirements35 of firms. Firms have to invest to stay globally competitive and firms have to finance externally if their internal sources are not sufficient. These credit demand factors are not solely elastic to the interest rate. Therefore in this thesis the credit price will be seen as one factor for the credit demand but the elasticity will not be quantified – meaning the effect of macroeconomic contractions on the credit pricing will be shown but the effect on the credit demand not quantified.

The LM (L iquidity preference and M oney supply) function is the equilibrium point between the liquidity preference function and the money supply function. The liquidity preference describes the preferences to either hold cash or securities. The money supply function is the interest rate dependent willingness of banks and central banks to lend money. The intersection point between the liquidity preference and money supply functions constitute a single point on the LM curve. Recalling that for the LM curve, interest rate is plotted against the real GDP whereas the liquidity preference and money supply functions plot interest rates against quantity of cash balances, that an increase in GDP shifts the liquidity preference function rightward and that the money supply is constant, independent of GDP - the shape of the LM function becomes clear. As GDP increases, the negatively sloped liquidity preference function shifts rightward. Money supply, and therefore cash balances, are constant and thus, the interest rate increases. The LM function is therefore positively sloped and defined as:36

M/P = L(r,Y)

M/P: Real Money Supply (Money / Price Level), L: Demand for Money

illustration not visible in this excerpt

Figure 3: IS-LM Model37

A credit crunch can be explained by a shift of the curves.38 An exogenous decline in bank lending (resulting, for example, from a shortage of equity capital) is a negative IS shock to the economy. The LM curve moves leftwards. The resulting higher interest rate r2 leads to lower net returns of investing and thus the investment demand of bank-dependent borrowers falls and the IS curve shifts leftwards down.39

b) Credit Rationing Model by Stiglitz/Weiss

Among microeconomic models explaining the credit lending rationale of banks, the credit rationing model by Stiglitz/Weiss40 is most commonly used to explain the occurrence of a credit crunch.41 The model is based on the idea of asymmetric information about the risk of potentially to be financed projects between the lender and the borrower. Credit rationing is defined as the unwillingness of banks to grant credit to the degree potential borrowers are intending to lend or to a price which is not accepted by potential borrowers. The market participants therefore speak about credit rationing.42 In a world with symmetric information in case of a the supply over exceeding demand, banks would increase the interest rates to maximize profits and the supply would increase to an equilibrium. Therefore a credit crunch can theoretically not happen. In case of asymmetric information this might be different according to Stiglitz/Weiss.43

The basic assumptions of the model are that the lender is risk neutral, the lender could distinct borrowers only by their project returns, all projects are of equal size and no collaterals could be granted by the borrower. Due to adverse selection and moral hazard the lenders return and the credit price are not correlating. Therefore banks do a credit rationing and provide in case of credit market equilibrium to some indistinctable borrowers credits but not to all. Adverse selection occurs as with increasing credit pricing borrowers with a lower risk step out and borrowers with a higher risk and therefore higher expected return stay. The borrower’s profit function with r (credit price), X (project return), B (investment) is max[0, X- (1+r)B] (see Figure 4 left). The lenders profit function is min[(1+r)B, X]. The expected profit however is due to increasing bankruptcy cost in relation to X concave. Moral hazard effects are occurring as with an increasing credit price (r) riskier projects with a higher X are more favourable for the borrower.44 The borrower only profits from the upside potential of the riskier projects as in case of bankruptcy the bank loses the face value of the credit.

illustration not visible in this excerpt

Figure 4: Credit Rationing – Profit Functions of Borrower and Lender45

If different credit types could be distinct, e. g. type 1, 2, 3 (see Figure 4 right) each type has a specific concave expected profit curve.46 In a competitive banking environment all lenders set the price r equal to their own refinancing costs rr. Type 2 borrowers get rejected by the lenders as their credit price does not match rr – this phenomenon is called “redlining”. On the other hand type 3 borrowers get fully served but due to competition among banks for a lower than possible expected profit. Only for type 1 borrowers the credit rationing occurs as explained above that some borrowers get credit and some not.

The credit rationing model especially provides indications as shown in the following part how the credit lending correlates to

a) a change in credit risk,
b) capital restrictions of lenders and
c) monetary political measurements.

Therefore the model fits to explain the relationship between credit lending, a potential credit crunch and macro economical cycles.

A major influence factor on the concave curves is the information asymmetry. As the information asymmetry is higher if the costs for information gathering are higher, borrowers who require high efforts like SME companies are rationed the most.47 In an extended model a criterion to reduce the information asymmetry could be e. g. provided collateral which reduces the adverse selection.48 Once the model is extended to multiple periods the relationship and past experiences with borrowers’ payment morale could be used to reduce the information asymmetry.49 The later on explained “Hausbank” principle which SME often use could therefore reduce the information asymmetry.

A comparative-static analysis50 provides indications about how lenders react to exogenous changes of the credit risk. If the default risks increase the curve of expected returns of the lender is shifted downwards. Originally served borrowers will not receive credits anymore and the total lending volume declines. The reaction of the credit price r depends on different scenarios:

Assuming there are only two types of projects, there are type (a) with low risk and type (b) with high risk. In case of failure the borrowers project profit X is zero and in case of success the profit Xi (i= a,b). The profit Xi occurs with the probability Pi, with Xa < X b and Pa > P b. The total profit is P i (X i -(1+r)B). If the lender sets r equally independent from the project type there is no motivation for the borrower to choose the riskier project:

illustration not visible in this excerpt

If in a recession the success probability of low risk and high risk projects gets reduced proportionally, the credit price will not be changed and only the credit lending volume changes (case 1).

If the success probability of the riskier projects reduces disproportionaly the credit price will increase as dr*/d τ > 0 (case 2).51

illustration not visible in this excerpt

Figure 5: Credit Rationing – Credit Price Changes in Relation to Credit Risk Changes52

How the refinancing abilities of banks have an impact on the credit lending is analysed in the following part. A bank which has the initial capital of at could take money from deposits for rs to invest it into riskless bonds for rg or lend credits for r. With Y as the profit from lending, N as the lending volume, M as the volume of investments in risk-free bonds and T the effective tax rate on deposits the end-capital could be described as:

a t+1 = max [Y(N,r) + M(1+rg) – rs(1+ T) ( N+M-a t ),0]

The level of the risk aversion of the lender could be expressed by assuming that a concave expected utility function should be maximized which is dependent on the arithmetic average and the standard deviation σ of the end-capital.

The curve RR shows that the bank could achieve the highest returns until a point utility value (σY*,µY*). This value is achieved at a credit pricing r* which means that credit rationing occurs. If on the other hand all available capital is invested into risk-free bonds the return S could be achieved. The line SP1 shows efficient combinations. Additionally a bank could lever the invested capital by additional deposits. If rs is larger than rg - without considering tax effects on rs – the alternative investment line will be the flatter P2L (see Figure 6 left).53 If rs is smaller than rg it will make sense with funds from deposits to either invest into risk-free bonds or lend additional credits. In this case the curve would be the steeper curve SP1.54


1 Kindleberger, C. P., (1978), P. 120

2 Bruckner, W., (2008), 2009-06-10, http://www.handelsblatt.com/unternehmen/finanzierung/mehr-geld-von-sparkassen;2098046

3 Ding, W., Domac, I., Ferri, G., (1999), P. 4

4 Ernst, D., (2007), P. 7

5 For details see the description of the LM-curve in chapter 1.2.2

6 Bernanke, B. S., Lown, C. S. (1991), P. 205

7 Own exhibit

8 Mishkin, F., (1992), P. 115

9 see Friedman, M., Schwartz, A.-J., (1963)

10 Kindleberger, C.-P., (1989), P. 7

11 see Kindleberger, C.-P., (1989), P. 6

12 Mishkin, F., (1992), P. 116

13 Frenkel, M., Rudolf, M., Johanning, L., Sellhorn, T., (2008), P. 3

14 Federal Reserve Bank (2008), 2009-06-09, http://www.federalreserve.gov/fomc/fundsrate.htm

15 DGTPE, (2008), 2009-06-09, http://www.dgtpe.minefi.gouv.fr/TRESOR_ECO/anglais/pdf/2008-014-40en.pdf

16 Frenkel, M., Rudolf, M., Johanning, L., Sellhorn, T., (2008), P. 5

17 Federal Housing Finance Enterprise, (2008), 2009-06-09, http://www.fhfa.gov/default.aspx?Page=14

18 Rehm, H., (2008b), P. 308

19 Frenkel, M., Rudolf, M., Johanning, L., Sellhorn, T., (2008), P. 10

20 Bernanke, B.-S., (2009), P.1

21 Frenkel, M., Rudolf, M., Johanning, L., Sellhorn, T., (2008), P. 13

22 See for details about the interbanking market in Germany chapter

23 Frenkel, M., Rudolf, M., Johanning, L., Sellhorn, T., (2008), P. 16

24 Levine, R. (1997), P. 688

25 Bureau of Economic Research – U.S. Department of Commerce (BEA), (2009), P. 6

26 See for the German GDP chapter 2 page 17

27 Insurance Information Institute (2008), 2009-06-09,http://www.iii.org/financial2/today/gdp/

28 The Conference Board, (2009), 2009-06-09, http://www.conference-board.org/economics/crc.cfm

29 CCI data (2009), 2009-06-09, http://www.pollingreport.com/consumer.htm#Conference and Bureau of Economic Research – U.S. Department of Commerce (BEA), (2009), P. 6

30 Statistisches Bundesamt, (2009), 2009-06-09, http://www.StatistischesBundesamt.de

31 See for the chart of the order development of the industrial sector Appendix XVIII

32 See for details chapter 2 page 17

33 Gordon, R. J., (2006), Macroeconomics, P. 99

34 Gischer, H., Herz, B., Menkhoff, L., (2005), P. 240

35 See also chapter 3, Credit lending volume correlates to the BIB

36 Gordon, R. J., (2006), Macroeconomics, P. 101

37 Gordon, R. J., (2006), Macroeconomics, P. 101

38 Bernanke, B., Blinder, A., (1988), P. 435

39 Bernanke, B., Lown, C., (1990), P. 229

40 Vgl. Stiglitz, J./Weiss, A., (1981)

41 Neuberger, D. (1994) conducts a comprehensive evaluation of different microeconomic models and comes to this conclusion

42 Franke, G., (2000), P. 239

43 Hartmann-Wendels, T., Pfingsten, A., Weber, M., (1998), P. 166

44 Neuberger, D. (1994) P. 7

45 Neuberger, D. (1994) P. 7

46 Vgl. Jaffee /Stiglitz J. (1990) P. 859

47 Calomiris C., Hubbard (1989) P. 93

48 Bester, H. (1987), P. 887

49 Stiglitz J./ Weiss A. (1983)

50 Stiglitz J./Weiss A. (1981)

51 Neuberger D. (1994), P. 14

52 Neuberger, D. (1994), P. 14

53 Stiglitz J. (1992)

54 Greenwald B., Stiglitz J., (1991), P. 19

Excerpt out of 94 pages


Has the Financial Crisis Induced a Credit Crunch for Small and Medium-Sized Enterprises in Germany?
Hat die Finanzkrise zu einer Kreditklemme für KMU in Deutschland geführt?
Otto Beisheim School of Management Vallendar
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Kreditklemme, Credit Crunch, Finanzkrise, KMU, SME, Kreditgeschäft, Bankwesen, Banking, Financial Crisis, Restructuring, Credit, Loans, Restrukturierung
Quote paper
Christopher Heine (Author), 2009, Has the Financial Crisis Induced a Credit Crunch for Small and Medium-Sized Enterprises in Germany?, Munich, GRIN Verlag, https://www.grin.com/document/132485


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