Local bank competition and small business lending after the onset of the financial crisis

Transcription

1 Local bank competition and small business lending after the onset of the financial crisis Jaakko Sääskilahti 1 The Jyväskylä University School of Business and Economics Abstract August 2014 This paper examines whether the impact of the financial crisis on the volumes and prices of small business loans was dependent on a local competition environment. To address this question, I employ a unique data set of Finnish cooperative banks. I find that the monthly volumes of new business loans decreased and the average loan margins increased during the crisis period. More important, the decrease in the volumes and the increase in the margins were greater in the more competitive local banking markets. The results are more robust for the loan margins than for the volumes. Auxiliary analyses suggest that the most likely explanation for the greater impacts in the more competitive markets is that competition reduced after the crisis disproportionately in the more competitive markets. Keywords: financial crisis, small business lending, local bank competition JEL classification: G01, G21, L11 1 I thank OP-Pohjola group and Toni Honkaniemi, Arto Kuhmonen, Kirsi Saari and Ari Saarinen for providing the bank data. I thank Manuel Bagues, Ari Hyytinen, Juha Junttila, Panu Kalmi, Kari Kemppainen and Jaakko Pehkonen, participants at the Allecon seminar (Jyväskylä) and the Finnish Economic Association XXXVI Annual Meeting (Kuopio) for valuable comments. Financial support from the OP-Pohjola Group s Research Foundation is gratefully acknowledged. address: jaakko.s.saaskilahti@student.jyu.fi Tel.:

2 1 1 INTRODUCTION The availability and cost of bank loans is crucial for many small businesses because they do not often have other possibilities for external funding (Carbó-Valverde et al. 2009). Small business lending has traditionally had a local nature because small firms are often informationally opaque (Agarwal & Hauswald 2010). Thus small businesses availability and cost of loans can be dependent on the behavior of competing local banks or branches. When uncertainty is high especially during crises, the loan availability for opaque small businesses is often reduced the most (e.g. ECB 2014). In this paper, I examine whether, and if so how, the effects of the recent financial crisis on small business lending depended on the local competition environment. In the previous literature, changes in loan supply during crises have been explained by bank balance sheet factors (e.g. Bernanke & Lown 1991; Jimenez et al. 2012b). Studies concerning the availability and costs of bank loans during the current crisis focus especially on the role of bank funding structure (Ivashina & Scharfstein 2010; Cornett et al. 2011) and additionally examine the effects of loan losses (Puri et al. 2011; Santos 2011) and more indirect factors, like changes in risk tolerance (Albertazzi & Marchetti 2010). I contribute to this literature by examining whether the market structure and pre-crisis competition of local banks are also relevant factors for explaining heterogeneous changes in the volumes and prices of small business loans after the onset of the financial crisis. The research question is built on the literature concerning the relationship between market structure or bank competition and loan availability and prices (e.g. Hannan 1997; Carbo- Valverde et al. 2009; Rice & Strahan 2010; Chong et al. 2013). I consider the implications of

3 2 this relationship for changes in lending behavior during the crisis. Different competition environments can explain heterogeneous changes in loan volumes and prices during the crisis, because changes in competition during the crisis (see Ruckes 2004) 2 and characteristics relevant for crisis time lending like risk-taking, relationship banking and funding structure can be dependent on competition environment (see e.g. Keeley 1990; Boyd & Nicolo 2005; Petersen & Rajan 1995; Graig and Dinger 2013). On this reasoning, I also consider the various explanations for the relationship between competition environment and changes in lending during the crisis. I do so by examining how competition changed in various competition environment and how specific characteristics of banks are dependent on local market structure and competition. I employ a unique data of the local cooperative banks of Finnish OP-Pohjola Group containing monthly information about the volumes and average margins of new small business loans during the period 2004 through I combine local economic characteristics and number of branches of banks for each local banking market. My empirical identification is based on the fact that the cooperative banks operate in various local competition environments but are similar in many respects due to the same business model and common culture. The group structure also means that bank specific data are consistent. The data consist of detailed lending information and control variables affecting loan demand and supply. As the measurement of bank competition is not unambiguous, I use two measures. My first measure is Herfindahl-Hirschmann index (HHI). It mirrors the market structure based on the number of branches of various banks in the operating area of each cooperative bank. My 2 Besides this theoretical consideration, for example, The ECB Bank Lending surveys point that the reduced pressure of competition was one reason for tightened lending standards during the current crisis.

4 3 second measure is the Lerner index. It refers to gap between price and marginal costs and is a more direct competition measure widely used in the banking literature (e.g. Carbo et al. 2009). My results indicate that the competitiveness of the local banking markets has an impact on how lending changes because of the crisis. First, I find that after the onset of the financial crisis, the volumes of new small business loans decreased more in the more competitive local markets. In line with the previous literature, the volumes decreased also more in banks that were the most dependent on market based funding. However, some of the other factors emphasized in previous studies, such as liquidity and capitalization, appear not to have had similar impact on the transmission of the crisis (see Cornett el. 2011; Gozzi & Goetz 2010; Ivashina & Scharfstein 2010; Iyer et al. 2014; Albertazzi & Marchetti 2010). Second, I find that the margins of new business loans increased more in the more competitive local markets. The loan margins were lower in the more competitive banking markets before the crisis lower in line with previous studies (e.g. Hannan 1997). However, this difference diminished significantly during the crisis. This result indicates that differences in competition environments explained the heterogeneous impacts of the crisis on loan prices in a situation where the differences in strength of balance sheets did not affect (see Gambacorta & Mistrulli 2014). My auxiliary analysis suggests that unequal changes in competition explain why the effects of the crisis differ across local markets. I find that the values of competition measures and prices of various types of deposits and loans converged between the more and less competitive banking markets. My findings do not support the view that changes in the credit risks of small businesses or the risk-taking of banks would explain the greater increase in the loan margins in the more competitive local markets. However, relatively smaller deterioration in

5 4 average credit rating of the banks in the more competitive markets tentatively supports the flight to quality explanation for greater decrease in volumes. I find also that the banks facing greatest competition are more dependent on market-based funding. This may have had an indirect effect on the competition environment. Overall, my results suggest that the procyclical lending may be related to the degree of bank competition. This is relevant for bank regulation and supervision because one of the key issues on the agenda is how to control excessive procyclicality of lending. Moreover, the results suggest that the heterogeneous effects of crises on local economies through local banks (see Gozzi & Goetz 2010) can be dependent also on bank competition environment. 3 The remaining of this paper proceeds as follows: In the next section, I review previous theoretical and empirical literature to lay the foundations for my paper. Section 3 describes the data and empirical approach. The main results are presented in section 4. I discuss the results in section 5 where I also report a series of auxiliary analyses. Section 6 concludes. 2 COMPETITION ENVIRONMENT AND LENDING IN NOR- MAL AND CRISIS TIMES In this chapter I consider channels through which competition environment can affect loan availability and prices during normal and crisis times. Competition environment can affect, firstly, the behavior of banks and how its changes in crisis times and secondly, certain bankspecific characteristics that affect lending differently in normal and crisis times. 3 Adams and Amel (2011) study how local market structure affect the pass-through of the federal funds rate and point the importance to understand heterogeneous effects in various areas.

6 5 Loan pricing and lending standards The market power hypothesis suggests that competition lowers loan prices and improves availability (e.g. Carbo-Valverde et al. 2009). Many empirical papers support this hypothesis. Hannan (1997) examines US small business loans at the MSA level and finds that banks charge lower loan rates in more competitive markets. Corvoisier and Gropp (2002) use country level European data and find a positive relationship between concentration and loan prices. Degryse and Ongena (2005) find that firm s distance to competing banks is positively related the loan rates that it pays. Petersen and Rajan (1995) find, a little contrary to the hypothesis, that higher concentration i.e., lower competition decreases loan rates for young and small firms in the U.S. but the effect diminishes as firms get older. Ruckes (2004) describes theoretically how price competition varies between recessions and booms but, to my knowledge, there is no empirical analyses how price changes during crises are related to changes in competition. Lending standards i.e., the strictness of various loan terms, is one way to explore changes in loan availability. The ECB Bank Lending survey asks lending standards from banks and divides the relevant factors affecting them into the three groups: competition, balance sheet position and risk perception (Berg et al. 2005). The lending standards of this survey include prices, collateral requirements, maturity, size, covenants and loan-to-value ratio of loans. Empirical studies find that changes in lending standards based on surveys 4 explain changes in credit growth and loan availability (Lown and Morgan 2006; Demiroglu et al. 2012) 4 For example, the Fed makes similar surveys

7 6 The relationship between competition and lending standards other than price has not been considered very much. The theories of Ruckes (2004) and Dell Ariccia and Marquez (2006) explain how strategic competitive behavior influences variations in lending standards over business cycles but these papers do not explicitly consider the effect of level of competition on lending standards at various times. Empirical work supports the view that lending standards are countercyclical and their role in procyclical lending behavior but does not examine the role of competition in this context (Asea & Blomberg 1998; Maddaloni & Peydro 2011; Dell Ariccia et al. 2012). Jimenez and Saurina (2006) describe increasing competition as one of the possible reasons for looser lending standards in normal times but do not isolate its effects from the other determinants. Risk-taking The traditional view is that bank competition increases risk-taking. According to the competition-fragility view, more competition leads to lower profit margins and franchise value which encourages to more risk-taking (Keeley 1990). In contrast, the competition-stability view suggests that higher prices in less competitive markets may cause greater repayment problems and worsen moral hazard and adverse selection problems (Boyd and Nicolo 2005). The model of Martinez-Miera and Repullo (2010) combines these opposite views and proposes a U-shaped relationship between competition and risk-taking. Empirical studies find U-shaped, inverse U-shaped, positive and negative relationships (e.g. Jimenez et al. 2013; Tabak et al. 2013; Kick and Prieto 2013). Berger et al. (2009) illustrate how various measures for competition and risks can explain the different results. Fiordelisi

8 7 and Mare (2014) study the relationship between competition and risk using data on European cooperative banks and find that competition increases stability. Risk-taking behavior during normal times can affect realized risks and thus willingness and ability to lend during crises. Jimenez and Saurina (2006) describe how competition can reduce the net interest margin and profitability of banks, thus encouraging to raise the volumes at the expense of quality. This does not necessarily lead to problem loans in the short run or during normal times, allowing strong loan growth to continue. However, this can cause greater losses if the economy is hit by a crisis. Appetite for risk can also change during crises. Bernanke et al. (1996) develop model for so called flight to quality behavior which means that during bad times the relative decline in lending is greater to borrowers with high agency costs. Their empirical analysis supports the theory. Albertazzi and Marchetti (2010) examine this with Italian data during the current financial crisis. They find that large and low capitalized banks started to grant relatively less loans to riskier firms. Funding structure and costs When banks compete also for deposits, competition environment can affect the funding structure and costs. The market power hypothesis proposes lower deposit rates in more concentrated markets. The empirical evidence supports this view (e.g. Berger & Hannan 1989; Hannan 1997; Hannan & Prager 2004). There is only a little empirical evidence on how competition affects banks funding structures. Graig and Dinger (2013) describe how tight deposit competition raises deposit rates and thus incentives to use wholesale funding. However, they do not examine how competition affects the relative amount of deposit and

9 8 wholesale funding. Instead, they explore relationship between deposit market competition and risk-taking and find that fiercer competition increases the risks of banks. According to many previous studies, bank funding structure had a significant role in lending behavior during the recent financial crisis. Banks with relatively large share of wholesale funding and thus small share of core deposits were the most vulnerable to freeze in global money markets and reduced lending more (e.g. Ivashina & Scharfstein 2010; Iyer et al. 2014). Relationship banking An essential part of banking and lending decisions is asymmetric information between lender and borrower. Banks develop close relationships with customers to ease such informational asymmetries (Boot 2000). According to the information hypothesis, high bank competition can weaken the incentives of banks to invest in soft information and thus relationship building. This mechanism means weaker loan availability in more competitive markets (Petersen & Rajan 1995). In contrast, the model of Boot and Thakor (2000) suggests that competition encourages to relationship lending because that enables to maintain satisfactory prices in competitive markets. Empirical studies support both hypothesis (see Degryse et al p ). Theoretical and empirical literature indicates a specific role of relationship banking during crises. The model of Bolton et al. (2013) suggests that relationship banking diminishes the effects of crisis on lending. Relationship banks charge higher interest margins during normal times but do not change their loan terms as much as transaction banks during crises. Empirical analyses find that relationship banking diminished the overall increase in loan

10 9 prices and decrease in availability during the current crisis (Puri et al. 2011; Gambacorta & Mistrulli 2011; Bolton et al. 2013; Cotugno et al. 2013) Summary The previous literature shows that the relationship between competition and lending behavior is multifaceted. The previous theoretical and empirical studies imply that tighter competition decreases interest rates on loans and deposits and loosens lending standards. Instead, the effect of competition on risk-taking and relationship lending is controversial. In this paper, I examine the effect of competition environment on changes in lending during the crisis. As the previous literature suggests that competition decreases during crises, I examine whether the effect of this is different in various competition environments. The previous studies indicate that changes in risk-taking, realized risks, relationship banking and funding structure are relevant factors for lending behavior during crises. The effect of competition environment can come also through these factors. In auxiliary analysis, I examine contributions of the various factors to the effect of competition environment. 3 DATA AND EMPIRICAL APPROACH In this section, I first describe the data used in this paper. Then, I present my empirical specifications.

11 The cooperative banks of OP-Pohjola group OP-Pohjola group consists of an amalgamation of independent cooperative banks that own OP-Pohjola Central Cooperative. This central institution controls, supervises and steers the cooperative banks. OP-Pohjola Central Cooperative has also many subsidiaries of which Pohjola Bank plc (Pohjola) is the most important. Pohjola is a commercial bank that acts as OP-Pohjola Group s central bank and manages Group s liquidity and international operations. It also manages corporate lending for large and mid-size firms. The group structure and the same business model make these banks homogeneous but at the same time, the banks are heterogeneous, especially regarding size and operational environment. 6 The following characteristics are relevant to my analysis. First, the individual cooperative banks are local and have non overlapping operating areas that are defined at the zip code level and cover the whole Finland. The general principle is that they do not compete against each other. However, many banks are very small and they can have shared business loans. Additionally, there are regional corporate banks that manage larger business loans in wider regional operating areas. This can cause larger shares of business loans in larger banks but should not be a significant problem when considering the development of volumes over time. Second, the cooperative banks get guidelines and constraints from the Central Cooperative but make independent decisions. Third, liquidity management and market funding of the cooperative banks are dealt with Pohjola. The cooperative banks have a checking account with overdraft facility and they can take a short term debt funding from 5 Situation at the end of Hyytinen and Toivanen (2004) use the data of the same cooperative banks to study whether the banks use branch network to invest monitoring and/or market power and how this affect loan interest rates and credit losses

12 11 Pohjola. The price of a certain short term debt at a given time is the same for all banks and reflects the price of Pohjola s own wholesale funding. This indicates that the availability of short term funding is not a problem for the individual cooperative banks but costs vary over time according to the price of wholesale funding of Pohjola. This can affect willingness to use this kind of funding in various times. 3.2 Data sources The bank data collected from inside the OP-Pohjola Central Cooperative include detailed monthly lending information for all cooperative banks between January 2004 and October The price and volume information is available for both the outstanding and new business loans, mortgages and consumer loans. The data contain also detailed balance sheet, income statement and deposit information for each cooperative bank. Additionally, the information about small business lending is complemented by the loan-specific data that is available since September The data about locations of the branches of all Finnish banks is from the establishment data of business register of Statistics Finland. Yearly data between 2004 and 2010 include enterprise (bank) name and business ID, establishment (branch) name and code, municipality and zip-code. Local economic data are at the municipal level and collected from the statistical databases StatFin and Altika of Statistics Finland. The number of the branches of all banks is assigned to each cooperative bank based on zip codes. The local economic data are combined at the municipality level so that local variables are calculated based on all municipalities where a cooperative bank operates.

13 Small business lending data The dependent variables are the new withdrawn business loans per month divided by total assets in the previous month and the average margin on monthly new business loans. The corporate loans of the cooperative banks are mainly small business loans because Pohjola Bank manages the loans for larger companies 7. The new withdrawn loans consist mainly of new contracts but include also drawdowns of existing loan commitments 8. As our interest is in the purely new lending activity, the effect of drawdowns of existing loan commitments is diminished by using lagged amount of offbalance sheet items, that include the undrawn commitments, as control variable. My measure describes better the changes in loan supply and demand than the change in outstanding loans used in many previous papers (e.g. Cornett et al. 2011) because the latter measure is affected also by the repayments of loans. The loan margin is the pricing element of loan contracts. The average margin of new business loans is the average interest margin on floating rate loans with either one of the Euribor rates or banks own prime rate as a reference rate 9. 7 Based on loan specific data between 10:2008 and 06:2013, 89 percent of new business loan contracts were under euros and 96 percent under one million. The shares of total new business loan volumes were 43 and 69 percent, respectively. 8 I use the term new loans throughout the text although the exact measure is new withdrawn loans 9 The average total interest rates on small business loans is dependent on market rates and decreased during the crisis due to decreased market rates even though loan margins increased.

14 13 The general developments of the volumes and margins of the new business loans in the cooperative banks are presented in figure 1. Vertical lines in October 2008 indicate the beginning of the crisis period which is determined based on the collapse of Lehman Brothers on 15 September A Ratio of new business loans to total assets(12 months MA of cooperative banks) % m1 2006m1 2007m1 2008m1 2009m1 2010m1 Time B Margin of new business loans (average of cooperative banks) m1 2006m1 2008m1 2010m1 Time Figure 1: The development of new business loans in Finnish cooperative banks The figure shows the average development of the volumes and margins of new business loans in the cooperative banks of OP-Pohjola group for the January 2004/2005 to September Panel A shows the 12 months moving average of banks average monthly loan volumes that is measured as the percentage of new business loans divided by total assets in the previous month. Panel B shows index of monthly average margin of cooperative banks new business loans. The index is used for reasons of confidentiality of the data.

15 Competition data Bank competition measures are typically classified based on the Traditional and New Empirical Industrial Organization (IO) literature. The traditional IO literature uses market structure measures 10 whereas new measures try to gauge market power more directly 11. Carbo et al. (2009) compare various competition measures and find that these are only weakly related. The recent literature often argues that national concentration is an inappropriate measure of bank competition (Claessen & Laeven 2003; Schaeck et al. 2009; Bikker et al. 2012). Schaeck et al. (2009) note that if bank markets are local, national concentration measures can in principle be a wrong way to gauge competition. I use both kinds of measures and take into account the different characteristics of them in analysis. The market structure measure of this paper is based on locations of the bank branches of the cooperative banks and all other banks in Finland. The Herfindahl Hirschman Index (HHI) is computed assuming that the share of branches represents the market shares, like e.g. in Degryse and Ongena (2007) and Chong et al. (2013). I modify this such that different banks branches have different effects on market share because sizes of branches can vary. The branch weights are based on the banks total business loan market shares in Finland related to their total number of branches. For example, a weight of certain bank s branch is over one if the bank s share of business loans is larger than the share of branches in the whole Finland. (See Appendix 1 for details.) 10 Traditional measures include e.g. concentration ratios, number of banks or Herfindahl indices (Beck et al. 2010) 11 The new IO measures include e.g. the Lerner index and Panzar and Rosse H-statictics. Other used competition measures are e.g. net interest margin and return on assets (see e.g. Carbo et. al 2009)

16 15 The Lerner index is a more direct competition or market power measure. It is the gap between the price and the marginal cost of an individual bank (e.g. de Guevara et al. 2005; Carbo et al. 2009; Beck et al. 2013). The Lerner index can be computed in various ways depending on the purposes and data availability. It is typically computed by using total revenues or interest revenues and the marginal cost of total funding and operating costs. 12 I construct the Lerner index by using total interest revenues. (See Appendix 1 for details.) 3.5 Control variables and descriptive statistics I control the key transmission channel of crisis on loan supply observed in previous literature, i.e., the dependence on short term market-based funding (e.g. Iyer et al. 2014) and other bank characteristics that can affect loan supply and pricing (e.g. Jimenez et al. 2012a; Gambacorta & Mistrulli 2014). Size, capitalization, liquidity and non-performing loans are the basic bank specific control variables and funding structure is a relevant one particularly in the context of the current crisis. Off-balance sheet commitments is an important control in my setting because my volume measure, new loans, includes also drawdowns of existing loan commitments. In addition, I use local economic variables and province-time trends to control for the dynamics of loan demand. I measure the control variables as follows: The funding structure variable refers to the share of short term market based funding to total assets. The short term market based funding consists in the cooperative banks mainly of short term loans from Pohjola and negative checking account in Pohjola. The capitalization variable is the regulatory capital adequacy ratio defined as the Tier 1 capital divided by total risk-weighted assets. Liquidity variable is 12 In some papers the Lerner index is calculated more precisely to different products (see e.g. Jimenez et al. 2013)

17 16 liquid assets divided by total assets. Liquid assets consist of cash, demand deposits in banks (especially in Pohjola) and debt securities eligible for refinancing with central banks. The bank size is measured by the logarithm of total assets. Non-performing loans are the loans that have been in default for 90 days and they, too, are scaled by total assets. The relative size of off-balance sheet is measured by the amount of irrevocable commitments to customers divided by total assets. The local economic variables include the unemployment rate, the growth of sales in establishments, the personal income and the growth in the number of corporations (see e.g. Adams & Amel 2011; Keeton 2009). Table 2 provides descriptive statistics of the variables used in estimations separately for periods before and during the crisis. The competition and local economic variables are measured yearly and all other variables monthly. The average of new business loans divided by total assets decreased from 0,34 % before the crisis to 0,28 % during the crisis and average loan margin increased from 1,29 percentage points to 1,58. The average Lerner index is considerably lower during the crisis which should mean increased competition. However, this reflects rather the possible problem of the Lerner index as a competition measure in a low interest rate environment that here coincide with the crisis period. This is particularly relevant if deposit funding has a significant role and loans have floating interest rates which is the case in my data. In a low interest rate environment, the deposit rates can face the lower bound and funding costs become rigid for market rates while floating loan rates continues to change along with market rates. For this reason, my analysis is based on banks differences in the Lerner indices before the crisis and relative changes in the Lerner indices after the onset of the crisis.

18 17 Table 1. Descriptive statistics before and during the crisis Dependent variables mean standard deviation number of observations before crisis crisis before crisis crisis before crisis crisis new business loans/total assets (%) 0,34 0,28 0,34 0, margin of new business loans (%-points) 1,29 1,58 0,49 0, Competition variables Lerner index 0,26 0,11 0,10 0, Branch HHI 0,65 0,68 0,27 0, Bank characteristics short term market-based funding/assets (%) 5,02 5,82 5,02 8, capital adequacy ratio (%) 21,27 23,04 5,88 6, liquid assets/total assets (%) 3,01 2,30 2,90 2, the log of the total assets 18,18 18,41 1,10 1, nonperforming loans/total assets (%) 0,66 0,59 0,55 0, OBS commitments/total assets (%) 4,80 4,87 1,86 1, Local economic conditions unemployment rate (%) 10,06 10,68 3,79 3, personal income (thousand euros) 22,74 23,84 3,04 2, the change in number of corporations (%) 2,79 1,13 2,42 2, the change in sales of establishments (%) 6,39-3,43 9,40 13, Empirical specifications I employ difference-in-difference (DID) approach to analyze whether the changes in margins and volumes of new small business loans were different in different local bank competition environments after the onset of the financial crisis. Thus, the empirical identification is based on two sources of variation: the time before and after the financial crisis as well as the cross section of cooperative banks in the more and less competitive local environments. I estimate the following econometric model: (1)Y it = α + β 1 Crisis t + β 2 Comp i + β 3 Comp i xcrisis t + β 2 Short debt i + J β 3 Short debt i xcrisis t + j=1 δ j X j,i,t 1 + k=1 θ k R k,i,t + γ l Province l t before L l=1 κ l Province l t crisis + ε it, K L l=1 +

19 18 where Y it is in one specification the monthly volume of new business loans divided by total assets of the bank in the previous month and in other specification the average margin of new business loans in bank i at time t. I first estimate the model without controls i.e., using only Crisis and Comp dummies and their interaction term. Hence, I compare the changes in average margins and volumes of new business loans in the most competitive and in the other local banking markets. The precrisis time period is between 2004:01 and 2008:09 and the crisis period between 2008:10 and 2010:09 when Crisis dummy takes value 1. Comp dummy takes value one for the banks in the most competitive environments and zero for the other banks. The classification is based on either the average HHI or the average Lerner index before the crisis such that a bank is in the most competitive environment if the competition measure is at the first quintile 13. Next, I add various control variables to better identify the effect of competition environment. Short debt is one for the banks that were the most dependent on short term marketbased funding before the crisis. The classification is based on one year averages of the share of short term market based funding of total assets before the beginning of the crisis and the most dependent banks are those at the top quintile. This take into account the effect of funding structure on lending during the crisis as serious problems in money markets and thus the difficulties in short term market based funding of banks especially after the collapse of Lehman Brothers was a key characteristic of the crisis period. Bank-specific balance sheet factors X controls differences between banks and includes variables for capitalization, size, non-performing loans, liquidity and OBS exposure. Demand 13 Lower values of the Lerner index and the HHI mean fiercer competition.

20 19 conditions are controlled by local market factors R (the unemployment rate, the growth of sales in establishments, the personal income and the growth in the number of corporations) and the province-time trends. These are separately for periods before and during the crisis such that t before =1,,57 between 2004:01 and 2008:09 and zero otherwise and t crisis =1,,24 between 2008:10 and 2010:09 and zero otherwise. Finally, I estimate the model with bank and time fixed effects. Bank fixed effects control unobservable fixed differences between banks and their local environments. Time fixed effects control the common changes in lending environment, like significantly loosened monetary policy, increased uncertainty and new regulatory initiatives after the onset of the financial crisis. Comp and Short debt dummies are not used when bank fixed effects α i are included. 4 EMPIRICAL ANALYSIS This section provides results of the main empirical analysis for the effect of competition environment on the changes in volumes and prices of small business loans during the current financial crisis. In addition, the section includes several robustness checks. 4.1 Main results Table 2 presents bivariate results of the mean DID estimates of volumes and margins of new business loans for the banks in the competitive environments and for the other banks. 14 The 14 The same results as figures when the classification is based on the Lerner index are presented in the appendix 2.

21 20 classification is based on the Lerner index in panels A and C and on the HHI in panels B and D. Table 2. Volumes and margins of new business loans before and after the onset of the financial crisis (bivariate tests) This table presents mean difference-in-difference (DID) estimates when the dependent variable is in panels A and B the percentage of monthly new business loans divided by total assets in the previous month and in panels C and D the average margin of monthly new business loans. Dependent variables are winsorized at the 99th percentile. The cooperative banks are classified into two groups: competitive, when cooperative bank s pre-crisis average of competition measure is at the first quantile; other, otherwise. The classification is based on the Lerner index in panels A and C and on the HHI in panels B and D. The DID estimates are printed in bold. Standard errors (not reported) are clustered at the bank level. ***, **, and * denote that the coefficients are statistically significantly different from zero at the 1%, 5%, and 10% level, respectively. Competitive Other Difference Panel A: New loans/ Total assets in the previous month, Competition measure: Lerner index Before October ,410 0,298-0,113*** After October ,333 0,255-0,078*** Difference -0,078*** -0,043*** -0,035** Panel B: New loans / Total assets in the previous month, Competition measure: HHI Before October ,404 0,299-0,105*** After October ,339 0,253-0,086*** Difference -0,065*** -0,046*** -0,019 Panel C: Margin of new business loans, Competition measure: Lerner index Before October ,085 1,323 0,238*** After October ,518 1,561 0,044** Difference 0,433*** 0,238*** 0,195*** Panel D: Margin of new business loans, Competition measure: HHI Before October ,028 1,340 0,312*** After October ,459 1,578 0,119*** Difference 0,431*** 0,238*** 0,193*** The volume of new business loans with relation to total assets was on average significantly lower during the crisis than before the crisis in the banks in the most competitive environments as well as in the other banks (Panels A and B of Table 2). The more important for the purpose of my study, the interaction terms of competition environment and crisis i.e., the DID estimates are negative and also statistically significant when the competition classification is based on the Lerner index in panel A. Whereas the average of new business loans

22 21 divided by total assets in previous month decreased 19 % (0,410 ->0,333) among the banks in the competitive environments, the decrease was 14 % (0,298 ->0,255) among the other banks. The margins of new business loans increased significantly after the onset of the financial crisis (Panels C and D of Table 2). The average loan margin is lower in the competitive local markets but the difference almost vanished during the crisis. The results are similar with both competition measures and indicate that the average loan margin of the banks in the competitive markets increased about 43 basis points and 24 basis points in the other banks and the differences i.e., the DID estimates are also statistically significant. Table 3 presents results for volumes (Panel A) and prices (Panel B) when the effect of dependence on short term market-based funding is taken into account (column 2) and the bank-specific and local environment controls are included (columns 3-4). The competition environment classification is based on pre-crisis Lerner index in these estimations and in column 5 that includes bank and time fixed effects. Column 6 present results when HHI is used to the competition environment classification and all controls and bank and time fixed effects are included. Panel A of table 3 shows that the coefficient of the interaction term of the competition and crisis dummies is negative in all specifications which means that decrease in volumes during the crisis was on average greater in the banks in the competitive environment than in the other banks. Compared to the bivariate result, the coefficient remains negative but reduces and becomes statistically insignificant when dependence on short term market based

23 22 funding is controlled. The result does not change much when bank characteristics, local economic variables and time-province trend are added as control variable. The result holds also with time and bank fixed effects when the coefficient is also statistically significant. The values of these coefficients are about that are roughly half of the average decrease of all banks during the crisis (see Table 1). The coefficient is lower and statistically insignificant when competition environment classification is based on the HHI instead of the Lerner index. Panel B of table 3 shows that the coefficient of the interaction term of the competition environment and crisis dummies does not change much when the controls are added and is also statistically significant in all specifications. The result holds also when the HHI is used for the classification of the competition environment. This confirms the result of the bivariate analysis that the increase in the margins of new small business loans during the crisis was greater in the banks in the most competitive environments than in the other banks. The increase of the average margin is about 19 basis points greater in the banks in the most competitive environments and the economic relevance becomes clear when this is compared to the average increase of all banks that is 29 basis points (Table 1).

24 23 Table 3. Volumes and margins of new business loans before and after the onset of the financial crisis (multivariate tests). This table presents regression of loan volumes and margins on crisis, competition, dependence on short term market-based funding, and their interactions plus bank and local environment controls. The dependent variable is in panel A the percentage of monthly new business loans divided by total assets in the previous month and in panel B the average margin of monthly new business loans. Dependent variables are winsorized at the 99th percentile.crisis takes value one after september 2008 and zero otherwise. Competitive takes value one when cooperative bank s pre-crisis average of competition measure is at the top quantile and zero otherwise. Standard errors,clustered at the bank level,are reported in parentheses. ***, **, and * denote that the coefficients are statistically significantly different from zero at the 1%, 5%, and 10% level, respectively. Panel A. Dependent variable: New loans/ Total assets in the previous month (1) (2) (3) (4) (5) (6) Crisis 0.043*** 0.038*** 0.026*** (0.008) (0.008) (0.009) (0.016) (0.038) (0.037) Competitive (Lerner) 0.113*** 0.090*** (0.021) (0.024) (0.027) (0.027) Crisis x Competitive (Lerner) ** * (0.014) (0.016) (0.017) (0.017) (0.017) Crisis x Competitive (HHI) (0.016) Short market-based debt dummy 0.079*** 0.041* 0.046* (0.029) (0.025) (0.025) Crisis x Short market-based debt ** * * * (0.017) (0.017) (0.017) (0.018) (0.017) Bank specific controls no no yes yes yes yes Local economic variables no no yes yes yes yes Time-province trends no no no yes yes yes Time-fixed effects no no no no yes yes Bank fixed effects no no no no yes yes Number of observations R-squared F-statistic Panel B. Dependent variable Margin of new business loans (1) (2) (3) (4) (5) (6) Crisis 0.238*** ** 0.265*** *** *** *** (0.017) (0.019) (0.022) (0.034) (0.070) (0.071) Competitive (Lerner) *** *** (0.036) (0.036) (0.044) (0.036) Crisis x Competitive (Lerner) 0.195*** 0.181*** 0.196*** 0.197*** 0.189*** (0.033) (0.031) (0.031) (0.029) (0.029) Crisis x Competitive (HHI) 0.162*** (0.033) Short market-based debt dummy * (0.034) (0.029) (0.029) Crisis x Short market-based debt (0.030) (0.031) (0.031) (0.031) (0.032) Bank specific controls no no yes yes yes yes Local economic variables no no yes yes yes yes Time-province trends no no no yes yes yes Time-fixed effects no no no no yes yes Bank fixed effects no no no no yes yes Number of observations R-squared F-statistic

25 Robustness checks I conduct various robustness checks to consider possible problems in the main analysis. The difference-in-difference analysis based on the two groups, the banks in the more and less competitive environments, assumes the discontinuous effect of the competition measures because the variables take only the value one or zero. Now I use continuous values instead of dummies for the pre-crisis competition measures and keep the estimations otherwise intact. The results are reported in Table 4. They are comparable to Table 3 and shows that my main results hold with continuous competition variables. 15. In the estimation for volumes, the coefficients of the interaction terms of the crisis and the pre-crisis average of the competition measures are positive and mainly statistically significant with various control variables when the Lerner index is used as a competition measure (Panel A of table 4). As greater value of the Lerner index means lower competition, these positive coefficients for interaction terms indicate that the decrease in new small business loans during the crisis was lower in less competitive environments. Evaluated at the tenth and ninetieth percentile of the average pre-crisis value of the Lerner index, the coefficient in the fifth column indicates that the decrease in volume measure was 0.03 percentage points lower in the less competitive banks while the average decrease of all banks was 0.06 percentage points (see table 1). 15 I have dropped observations if the HHI equals one which explains the lower amount of observations in columns 6 of Table 4. The dropped observations consist mainly of very small banks.

26 25 Table 4 Volumes and margins of new business loans before and after the onset of the financial crisis when competition measures are continuos (cf. table 3). This table presents the regression of loan volumes and margins on crisis, competition, dependence on short term market-based funding, and their interactions plus bank and local environment controls. The dependent variable is in panel A the percentage of monthly new business loans divided by total assets in the previous month and in panel B the average margin of monthly new business loans. Dependent variables are winsorized at the 99th percentile. Crisis takes value one after september 2008 and zero otherwise. The competition measures are calculated as pre-crisis average. Standard errors,clustered at the bank level,are reported in parentheses. ***, **, and * denote that the coefficients are statistically significantly different from zero at the 1%, 5%, and 10% level, respectively. Panel A. Dependent variable: New loans/ Total assets in the previous month (1) (2) (3) (4) (5) (6) Crisis *** *** *** (0.018) (0.021) (0.022) (0.024) (0.0416) (0.052) Average Lerner before crisis *** *** (0.081) (0.094) (0.122) (0.120) Crisis x Average Lerner before crisis 0.192*** ** 0.141** 0.150** (0.068) (0.073) (0.071) (0.068) (0.071) Crisis x Average HHI before crisis (0.045) Short market-based debt/assets 0.427*** 0.271** 0.259* (year average before crisis) (0.160) (0.121) (0.134) Crisis x Short market-based debt *** ** ** * ** (0.089) (0.085) (0.091) (0.090) (0.115) Local economic and bank controls no no yes yes yes yes Time-province trends no no no yes yes yes Time-fixed effects no no no no yes yes Bank fixed effects no no no no yes yes Number of observations R-squared F-statistic Panel B. Dependent variable Margin of new business loans (1) (2) (3) (4) (5) (6) Crisis dummy 0.580*** 0.524*** 0.572*** *** *** (0.043) (0.047) (0.048) (0.049) (0.074) (0.098) Average Lerner before crisis 1.295*** 1.268*** 0.514** 0.460** (0.150) (0.158) (0.236) (0.226) Crisis x Average Lerner before crisis *** *** *** *** *** (0.163) (0.165) (0.167) (0.140) (0.136) Crisis x Average HHI before crisis *** Short market-based debt/assets (year average before crisis) (0.173) (0.181) (0.162) (0.099) Crisis x Short market-based debt 0.410*** *** (0.157) (0.164) (0.161) (0.165) (0.269) Local economic and bank controls no no yes yes yes yes Time-province trends no no no yes yes yes Time-fixed effects no no no no yes yes Bank fixed effects no no no no yes yes Number of observations R-squared F-statistic

27 26 In the estimation for prices, the coefficients of the interactions terms of the crisis and the precrisis average of competition measures are negative and highly statistically significant in all specifications which means greater increase in the loan margins of the banks in the more competitive environments (Panel B of Table 4). Evaluated at the tenth and ninetieth percentile of the average pre-crisis value of the Lerner index, the coefficient in the fifth column indicates that the increase in loan margin is 21 basis points lower in the less competitive banks. The difference is 13 basis points when evaluation is based on the average pre-crisis values of the HHI in column 6. Finally, I use varying competition variables instead of dummies and pre-crisis averages to consider the effect of competition environment in normal and crisis times. I employ yearly data for all variables because the competition variables are measured annually. The Lerner indices are included as one-year lagged to avoid endogeneity problems. The results, presented in appendix 3, are in line with the main results. 5 DISCUSSION AND AUXILIARY ANALYSIS The results indicate that the average margins of new business loans were lower before the crisis in the less concentrated or more competitive local markets which supports the market power hypothesis (e.g. Hannan 1997). The more important for the purpose of my study, the increase of the average loan margins after the onset of the crisis was significantly greater in the more competitive environments. This result suggests that competition environment can explain heterogeneous effects of the crisis on loan prices in addition to factors, like loan losses and liquidity, considered in previous literature (Santos 2011; Gambacorta & Mistrulli 2014).

28 27 The results also show that the volumes of new business loans decreased more in more competitive environments during the crisis but the effect is not as robust as on loan prices. Together with the results for loan prices, this suggest that banks in more competitive environments reduced loan supply i.e., tightened lending standards and/or increased rationing more during the crisis. This can explain the heterogeneous changes in loan supply besides factors, like funding problems and loan losses, considered in previous literature (e.g. Puri et al. 2011; Iyer et al. 2014). However, this interpretation must be considered with caution, because I cannot control loan demand completely. Moreover, the results of changes in loan volumes are possibly affected by the lending behavior of the cooperative banks related to other banks in the same local market. 16 OP-Pohjola group s market share of business loans in the whole Finland increased from 26,8 % to 29,2 % between 2008 and 2010 which can indicate that at least some rival banks increased prices, tightened their lending standards and/or increased rationing more than the cooperative banks. This could mean that increased loan prices and tightened lending standards did not affect volumes so much as without this rival effect. In particular, this effect might be greater in the more competitive local markets with more rival banks. Next, I analyze alternative explanations for the main result that competition environment had a role in lending changes after the onset of the financial crisis. First, I examine the effect of the changes in competition. Second, I examine whether the changes in average riskiness of small business loans were different in the banks in the most competitive environment and in the other banks. Third, I examine tentatively the role of the relationship between 16 See discussion about the effects on cooperative business model on lending behavior during the crisis e.g. in Groeneveld and de Vries (2009) and EACB (2010)