• No results found

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

N/A
N/A
Protected

Academic year: 2021

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

Copied!
41
0
0

Loading.... (view fulltext now)

Full text

(1)

Local bank competition and small business lending

af-ter the onset of the financial crisis

Jaakko Sääskilahti1

The Jyväskylä University School of Business and Economics

July 2014

Abstract

This paper examines the effects of the financial crisis on small business lending. In particular I examine whether the magnitude of the changes in volumes and prices of small business loans is dependent on pre-crisis local bank competition and market structure. I use a unique data set of Finnish cooperative banks and the locations of all branches of banks in Finland between 2004 and 2010. The data enable to examine the effect of competition environment based on the homogeneous set of the banks regarding culture and business model, possibil-ity to control uniformly other affecting factors, and significant differences in competition environments. I find that the monthly volumes of new business loans decreased and the average loan margins increased during the crisis period. More important, I find evidence of the larger decrease in volumes and increase in margins in the banks operating in the more competitive local markets. The results are more robust for the prices than for the volumes. 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, partici-pants at the Allecon seminar (Jyväskylä) and the Finnish Economic Association XXXVI Annual Meeting (Kuopio) for val-uable comments. Financial support from the OP-Pohjola Group’s Research Foundation is gratefully acknowledged. E-Mail address: jaakko.s.saaskilahti@student.jyu.fi

(2)

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 a local nature due to informational issues (Petersen & Rajan 2002) and thus local small businesses are often exposed to 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.

Changes in bank driven effects on lending i.e, in loan supply during crises are mostly ex-plained 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 significant role of bank funding structure (Ivashina & Scharfstein 2010; Cornett et al. 2011) and additionally examine the effects of loan losses (Puri et al. 2011; San-tos 2011) and more indirect factors like changes in risk tolerance (Albertazzi & Marchetti 2010).

Market structure and bank competition can affect loan availability and prices (e.g. Hannan 1997; Carbo-Valverde et al. 2009; Rice & Strahan 2010; Chong et al. 2013). As 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), this imply that different competition environments can also

2Besides this theoretical consideration, for example, The ECB Bank Lending surveys point that the reduced

(3)

explain heterogeneous changes in lending behavior during crises which has not considered in previous literature.

In this paper, I examine first how market structure and pre-crisis competition of local banks are associated with heterogeneous changes in the volumes and prices of small business loans during the crisis. Second, I examine whether the relationship between competition environment and changes in lending during the crisis is due to different changes in compe-tition and/or various characteristics of banks depending on local market structure and com-petition.

I employ unique data of the Finnish local cooperative banks containing monthly infor-mation about the volumes and average margins of new small business loans during the period 2004 through 2010. I combine local economic characteristics and number of branches of cooperative and other banks for each individual cooperative bank. My empirical identi-fication is based on the homogeneous cooperative banks regarding common culture and consistent information but heterogeneous local competition environments. The data pro-vide fully comparable and detailed lending information and various control variables af-fecting loan demand and supply. As the measurement of bank competition is not unambig-uous, I use two kind of measures. Herfindahl-Hirschmann index (HHI) is a market structure measure based on the number of branches of various banks in the operating area of each cooperative bank. The Lerner index is a more direct competition measure widely used in the banking literature (e.g. Carbo et al. 2009).

My results indicate a relevant role of competition environment in changes in lending during the crisis. First, I find a decrease in the volumes of new withdrawn small business loans relative to the total assets and an increase in the margins of new business loans after the

(4)

onset of the financial crisis. More important, I find larger changes in the most competitive local bank markets3 and the results are more robust for prices.Second, I find that the aver-ages of competition measures and various types of deposit and loan prices converged be-tween the most competitive and other banks. Additionally, funding structures are quite dif-ferent in the most competitive and other banks which tentatively suggest also this indirect effect of competition environment. Instead, risks and changes in risk –taking do not seem to explain the results.

Overall, the results suggest that competition environment explains the heterogeneous ef-fects of the crisis on small business lending at least when basic problems observed in the previous literature, particularly bad funding conditions and large loan losses, are not very large.More generally, the findings provide tentative support for the effect of competition on pro-cyclical lending which is relevant for bank regulation and supervision. Moreover, as the heterogeneous effects of crisis on various local banks or bank markets can cause hetero-geneous effects of crisis on local economies (see Gozzi & Goetz 2010), local competition en-vironment may have a role in this. 4

In the next section, I review previous theoretical and empirical literature to lay the founda-tions for my paper. Section 3 describes the data and empirical approach. The results of the main empirical analysis are presented in section 4. I make auxiliary analysis and discuss the results in section 5 and conclude in section 6.

3 In my data operating area of each bank forms a single local market and thus competitiveness of local bank and local market are used as synonyms.

4 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.

(5)

2

COMPETITION ENVIRONMENT AND LENDING IN

NOR-MAL AND CRISIS TIMES

In this chapter I consider based on previous theoretical and empirical research various pos-sible channels through which competition environment can affect variations in loan availa-bility and prices between good and crisis times. Competition environment can affect, firstly, the behavior of banks and its changes in crisis times and secondly, bank-specific character-istics that affect lending differently in good and crisis times.

Prices and other lending standards

The market power hypothesis suggests that competition lowers loan prices and thus im-proves 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 under more competition, banks lower loan rates. Corvoisier and Gropp (2002) use coun-try 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 to its loan rates. 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

(6)

prices, collateral requirements, maturity, size, covenants and loan-to-value ratio of loans. Empirical studies find that changes in lending standards based on surveys5 explain changes in credit growth and loan availability (Lown and Morgan 2006; Demiroglu et al. 2012) The relationship between competition and other lending standards 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 they do not explicitly consider the effect of level of competition on lend-ing standards at various times. Empirical works support the view of countercyclical lendlend-ing standards and its role in procyclical lending behavior but do not actually examine the role of competition (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 good times but do not examine its role separated from other determinants.

Risk-taking

The traditional view is that bank competition increases risk-taking. According to the com-petition-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 higher repayment problems and tendency to moral hazard and adverse selection (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.

(7)

Berger et al. (2009) illustrate how the empirical results are sensitive to various measures of competition and risks and empirical studies actually find U-shaped, inverse U-shaped, pos-itive and negative relationships (e.g. Jimenez et al. 2013; Tabak et al. 2013;Kick and Prieto 2013). Fiordelisi and Mare (2014) study the relationship between competition and risk in European cooperative banks and find that competition increases stability.

Risk-taking behavior during good times can affect realized risks and thus willingness and ability to lend during crises. Jimenez and Saurina (2006) describe how competition can re-duce the net interest margin and thus profitability of banks which can encourage to raise the volumes at the expense of quality. When this does not lead to problem loans in the short run, strong loan growth can continue. However, this causes greater losses during 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 larger 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 and find that large and low capitalized banks moved to less riskier loans.

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 and the empirical evidence supports that (e.g. Berger & Hannan 1989; Hannan 1997; Hannan & Prager 2004). There is only a little empirical evidence on how com-petition affects banks’ funding structures. Graig and Dinger (2013) describe how tight de-posit competition raises dede-posit rates and thus incentives to use wholesale funding. How-ever, they do not actually examine how competition affects the relative amount of deposit

(8)

and wholesale funding, but how deposit rates affect risks. They find that higher deposit rates, that implicates higher deposit competition, increase the risks of banks.

According to many previous studies, bank funding structure had a significant role in lend-ing behavior durlend-ing the current crisis. Banks with relatively large share of wholesale fund-ing and thus small share of core deposits were the most vulnerable tofreeze in global money markets and reduced lending more (e.g. Ivashina & Scharfstein 2010; Iyer et al. 2014).

Relationship banking

The essential part of banking and lending decisions is asymmetric information between lender and borrower. Banks develop close relationships with customers to ease asymmetric information (Boot 2000). According to the information hypothesis, high bank competition can weaken the incentives to invest in soft information and thus relationship building which 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 rela-tionship lending in order to maintain satisfactory prices in competitive markets. Empirical studies support both kind of hypothesis (see Degryse et al. 2009 p. 119-120).

Asymmetric information generally increases during crises and theoretical and empirical lit-erature indicates the increased role of relationship banking. The model of Bolton et al. (2013) suggests that relationship banking diminishes the effect 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 common increase in loan prices and decrease in availability during the current crisis (Puri et al. 2011; Gambacorta & Mistrulli 2011; Bolton et al. 2013; Cotugno et al. 2013)

(9)

Summary and purposes of this paper

The previous literature shows that the relationship between competition and lending be-havior 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 first examine the effect of competition environment on changes in lending during the crisis and thus the general effect of various channels discussed above. Second, I consider the contributions of various factors. The previous literature implies that competi-tion decreases during the crisis. I study whether the change in competicompeti-tion is different in various competition environments. The previous studies concerning especially the current crisis 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 even if previous literature is controversial about the relationship between these and competition. I examine, as far as possible, the role of these indirect factors of competition environment in changes in lending during the crisis.

3

DATA AND EMPIRICAL APPROACH

In this section, I first describe the data used in this paper and provide more detailed descrip-tion about small business loan data and competidescrip-tion measures. Then, I present my empirical specifications.

(10)

3.1

The cooperative banks of OP-Pohjola group

OP-Pohjola group consists of an amalgamation of 1976 independent cooperative banks that own OP-Pohjola Central Cooperative. This central institution controls and supervises coop-erative banks and produces e.g., information services. OP-Pohjola Central Coopcoop-erative has also many subsidiaries of which Pohjola Bank is the most important. Pohjola is a commercial bank that acts as OP-Pohjola Group’s central bank and manages Group’s liquidity and in-ternational operations. It also manages corporate lending for large and mid-size firms. The group structure and the same business model make, in one sense, these banks homoge-neous but at the same time, the banks are quite heterogehomoge-neous especially regarding size and operational environment.7 The following characteristics are relevant to my analysis. First, the individual cooperative banks are purely local and have non –overlapping operating ar-eas 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. Second, the cooperative banks get guidelines and constraints from the Central Cooperative but make independent decisions. Third, liquidity issues and market funding of the cooperative banks are managed with Pohjola. The cooperative banks have a checking account with overdraft facility and they can take a short term debt funding from 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

6 Situation at the end of 2012

7 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

(11)

individual cooperative banks but costs vary over time according to the price of wholesale funding of Pohjola which certainly can affect willingness to use this kind of funding in var-ious times. Fourth, it is possible that cooperative banks reacted differently to the crisis than competing banks due to the different business models.8

3.2

Data sources

The unique bank data collected from inside the OP-Pohjola Central Cooperative include detailed monthly lending information for all cooperative banks between January 2004 and October 2010. The price and volume information is available for both the outstanding and new withdrawn business loans, mortgages and consumer loans. The data contain also de-tailed balance sheet, income statement and deposit information about each cooperative bank. Additionally, the information about small business lending is complemented by the loan-specific data that is available since September 2008.

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 enter-prise (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 statisti-cal databases StatFin and Altika of Statistics Finland.

The number of the branches of all banks is joined 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.

(12)

3.3

Small business lending data

The dependent variables are the new withdrawn business loans a month divided to 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 companies9.

The new withdrawn loans consist mainly of new contracts but include also drawdowns of existing loan commitments. This 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. As our interest is in the purely new lending activity, the effect of drawdowns of existing loan com-mitments is diminished by controlling the amount of off-balance sheet items that include these limits.

The average margin of new business loans is the average interest margin on floating rate loans with either one of the Euribor or banks’ own prime rate as a reference rate. As the margin is the pricing element of loan contracts, it is appropriate measure for changes in banks’ loan pricing10.

9 Based on loan specific data between 10:2008 and 06:2013, 89 percent of new corporate loan contracts were under 250 000 euros and 96 percent under one million. The shares of total volume were 43 and 69 percent, respectively.

10 In fact, the average total interest rates on small business loans decreased during the crisis due to signifi-cantly loosened monetary policy and decreased market rates.

(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 be-ginning of the crisis period which is determined based on the collapse of Lehman Brothers on 15 September 2008.

A

B

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 cooper-ative banks of OP-Pohjola group for the January 2004/2005 to September 2010. Panel A shows the 12 months moving average of banks’ average monthly loan volumes that is measured as the percentage of new with-drawn business loans divided by total assets in the previous month. Panel B shows index of monthly aver-age margin of cooperative banks’ new withdrawn business loans.

.2 6 .2 8 .3 .3 2 .3 4 .3 6 1 2 mo n th s MA ( % ) 2005m1 2006m1 2007m1 2008m1 2009m1 2010m1 Time

The average of new business loans/total assets

60 70 80 90 1 0 0 1 1 0 Ja n u a ry 2 0 0 4 = 1 0 0 2004m1 2006m1 2008m1 2010m1 Time

(14)

3.4

Competition data

Bank competition measures are typically classified based on the Traditional and New Em-pirical Industrial Organization (IO) literature. The traditional IO literature uses market structure measures11 whereas new measures try to gauge market power more directly12. Carbo et al. (2009) compare various competition measures and find that these are only weakly related which highlights the complexity of the issue. The recent literature often ar-gues that national concentration is an inappropriate measure of competition (Claessen & Laeven 2003; Schaeck et al. 2009; Bikker et al. 2012). However, Schaeck et al. (2009) note that if bank markets are local, national concentration measures can in principle be a wrong way to gauge competition and refer to findings that show the relationship between local concen-tration and competition. I use both kinds of measures and take into account the different characteristics of them in analysis. 13

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 by using the weighted effects of different banks’ branches based on banks’ total business loan market shares in Finland related to their total number of branches. For example, a weight of certain bank’s

11 E.g. concentration ratios, number of banks or Herfindahl indices (Beck et al. 2010)

12 The new IO measures include the Lerner index, 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)

(15)

branch is over one if the bank’s share of business loans is higher than the share of branches in the whole Finland.

The Lerner index is a more direct competition or market power measure based on the gap between the marginal 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 mostly computed by using total revenues or interest revenues and the marginal cost of total funding and operating costs.14 I construct the Lerner index by using total interest revenues.

3.5

Control variables and descriptive statistics

I control the key transmission channel of crisis on loan supply observed in previous litera-ture, 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; Gam-bacorta & Mistrulli 2014). Size, capitalization, liquidity and non-performing loans are the basic 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 be-cause my volume measure, new withdrawn loans, includes also drawdowns of existing loan commitments. In addition, I use local economic variables and province-time trend to control local loan demand.

Bank variable definitions are based on previous studies (e.g., above mentioned) and charac-teristics of the data. The funding structure variable is the share of short term market based funding that consist in the cooperative banks mainly ofshort term loans from Pohjola and

14 In some papers the Lerner index is calculated more precisely to different products (see e.g. Jimenez et al. 2013)

(16)

negative checking account in Pohjola. The capitalization variable is the regulatory capital adequacy ratio. Liquid assets consist of cash, demand deposits in banks (especially in Poh-jola) and debt securities eligible for refinancing with central banks. The bank size is meas-ured as logarithm of total assets and non-performing loans are the loans having been in default for 90 days. The relative size of off-balance sheet is measured by the amount of ir-revocable 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 pe-riods before and during the crisis. The competition and local economic variables are meas-ured yearly and all other variables monthly. The average of new business loans divided by total assetsdecreased 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 con-siderably lower during the crisis which should mean increased competition. However, this reflects rather the problem of the Lerner index in measuring competition over time. For ex-ample, in this case, the sharp reduction in market rates during the crisis combined with large share of floating rate loans is likely to have a significant effect on the values of the Lerner indices. For this reason, my analysis is based on banks’ differences in the Lerner indices in certain time and relative changes in the Lerner indices after the onset of the crisis.

(17)

Table 1. Descriptive statistics before and during the crisis

mean standard deviation number of observations before crisis crisis before crisis crisis before crisis crisis Dependent variables

new business loans/total assets (%) 0,34 0,28 0,34 0,30 11032 4728 margin of new business loans (%-points) 1,29 1,58 0,49 0,52 10140 4322 Competition variables

Lerner index 0,26 0,11 0,10 0,13 959 392 Branch HHI 0,65 0,68 0,27 0,28 980 392 Bank characteristics

short term market-based funding/assets (%) 5,02 5,82 5,02 8,09 10938 4707 capital adequacy ratio (%) 21,27 23,04 5,88 6,02 11229 4728 liquid assets/total assets (%) 3,01 2,30 2,90 2,47 11229 4728 the log of the total assets 18,18 18,41 1,10 1,10 11229 4728 nonperforming loans/total assets (%) 0,66 0,59 0,55 0,53 11229 4728 OBS commitments/total assets (%) 4,80 4,87 1,86 1,90 11229 4728 Local economic conditions

unemployment rate (%) 10,06 10,68 3,79 3,05 985 384 personal income (thousand euros) 22,74 23,84 3,04 2,79 985 394 the change in number of corporations (%) 2,79 1,13 2,42 2,46 980 392 the change in sales of establishments (%) 6,39 -3,43 9,40 13,17 985 394

3.6

Empirical specifications

I use difference-in-difference (DID) methods to analyze whether the changes in margins and volumes of new small business loans were different in different local bank competition en-vironments after the onset of the financial crisis.The empirical identification is based on the data on the local cooperative banks of the same group with independent decision making and heterogeneous local competition environments. Some of the cooperative banks compete with many other banks including large global banks but some of the cooperative banks op-erate in local markets with less or not at all competing banks. Besides the market structure, there can be also other factors affecting the tightness of competition or differences in market power in different local markets. For that reason, I use both market structure, HHI, and more direct market power measure, Lerner index, as the competition variable.

(18)

(1)Yit = α + β1Crisis𝑡+ β2Comp𝑖+ β3Comp𝑖xCrisis𝑡+ β2Short debt𝑖+

β3Short debt𝑖xCrisis𝑡+ ∑𝐽𝑗=1δ𝑗Xj,i,t−1+ ∑𝐾𝑘=1θ𝑘Rk,i,t+ εit,

where Yit is in one specification the monthly volume of new withdrawn business loans di-vided 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 start by comparing the changes in average price and volume measures in the most com-petitive and in the other banks during the crisis. The pre-crisis 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 most competitive banks 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 the most competitive are banks at the first quintile15. The 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 this crisis period. In order to better identify the effect of competition environment on lending, I take next into account this by setting dummy variable Short debt to one for the banks that were the most dependent on short term market-based 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.

(19)

The differences between the banks and local environments can cause differences in loan supply and demand and I control these by using one month lagged bank-specific balance sheet factors X and local market factors R described in the previous section.

Further, in order to control better the differences in regional economic (loan demand) con-ditions, the changes in common lending conditions and unobservable differences between banks, I estimate the following model:

(2)Yit = αi+ λ𝑡+ β1Crisis𝑡+ β2Comp𝑖+ β3Comp𝑖xCrisis𝑡+ β4Short debt𝑖+ β5Short debt𝑖xCrisis𝑡+ ∑j=1J δjXj,i,t−1+ ∑k=1K θkRk,i,t+ ∑Ll=1γlProvince𝑙∗ tbefore+

∑L κlProvince𝑙∗ tcrisis

l=1 + +εit,

I add first to the model 1 the province-time trends separately for periods before and during the crisis such that tbefore=1,…,57 between 2004:01 and 2008:09 and zero otherwise and

tcrisis=1,…,24 between 2008:10 and 2010:09 and zero otherwise. Finally, I include bank fixed

effects to control unobservable fixed differences between banks and their local environ-ments and time fixed effects to control the common changes in lending environment like significantly loosened monetary policy, increased uncertainty and new regulatory initia-tives after the onset of the financial crisis. Comp and Short debt dummies are not used when bank fixed effects αi are included in this final model.

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 cur-rent financial crisis. In addition, the section includes several robustness checks.

(20)

4.1

Main results

Tables 2 and 3 present bivariate results of the mean DID estimates of volumes and margins of new business loans for the competitive and other banks.16 The classification is based on the Lerner index in panels A and on the HHI in panels B.

Table 2 indicates that the volume of new business loans with relation to total assets is on average significantly lower during the crisis than before the crisis in both the competitive and other banks. The more important for the purpose of my study, the interaction terms of competition 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 divided by total assets in previous month decreased 19 % (0,410 ->0,333) among the competitive banks, the decrease was 14 % (0,298 ->0,255) among the other banks.

Table 3 shows that the margins of new business loans increased significantly after the onset of the financial crisis. The average loan margin is lower in the competitive banks but the difference almost vanished during the crisis. The results are very similar with both compe-tition measures and indicate that the average loan margin of the competitive banks in-creased about 43 basis points and 24 basis points in the other banks and the differences i.e., the DID estimates are also statistically significant.

16 The same results as figures when the classification is based on the Lerner index are presented in the appen-dix 2.

(21)

Table 2. Business loans volumes 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 the percentage of monthly new business loans divided by total assets in the previous month that is 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 panel A and on the HHI in panel B. 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(t-1), Competition measure: Lerner index

Before October 2008 0,410 0,298 -0,113*** After October 2008 0,333 0,255 -0,078*** Difference -0,078*** -0,043*** -0,035**

Panel B: New loans / Total assets(t-1), Competition measure: HHI

Before October 2008 0,404 0,299 -0,105*** After October 2008 0,339 0,253 -0,086*** Difference -0,065*** -0,046*** -0,019

Table 3. The 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 the average margin of monthly new business loans that is 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 top quantile; other, otherwise. The classification is based on the Lerner index in panel A and on the HHI in panel B. 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: Margin of new business loans, Competition measure: Lerner index

Before October 2008 1,085 1,323 0,238*** After October 2008 1,518 1,561 0,044** Difference 0,433*** 0,238*** 0,195***

Panel B: Margin of new business loans, Competition measure: Branch HHI

Before October 2008 1,028 1,340 0,312*** After October 2008 1,459 1,578 0,119*** Difference 0,431*** 0,238*** 0,193***

Tables 4 and 5 present results for volumes and prices when the effect of dependence on short term market-based funding is taken into account and the bank-specific and local en-vironment controls are included. I use mainly the competition dummy based on the Lerner index and make comparison using the HHI in the model with all controls.

Table 4 indicates that the coefficients of the interaction term of the competition and crisis dummies remain negative and rather the same size in all specifications which means that

(22)

decrease in volumes during the crisis is higher in the most competitive banks than in the other banks. The values of these coefficients are about 0.02-0.03 that are roughly half of the average decrease of all banks during the crisis (see Table 1). The coefficients are not statisti-cally significant in all estimations which at least partly reflect the high volatility of the vol-ume measure. The coefficients of the interaction term of dependence on short term market-based funding and crisis are negative. One more important result is that the coefficients of relative amount of off-balance sheet items are positive and statistically significant. This is an important control variable as the purpose is to examine the volumes of new loans and the dependent variable is new withdrawn business loans that includes also drawdowns of existing loan commitments.

The results in table 5 show that the coefficient of the crisis-competition interaction term is positive and statistically significant in all models and also when the HHI is used as the com-petition classification measure. This confirms the result of the bivariate analysis that the increase in the average margins of new small business loans during the crisis was higher in the most competitive banks than in the other banks. The values of coefficients are quite sim-ilar in different specifications and indicate that the increase of the average margin is about 19 basis points higher in the most competitive banks. The economic relevance becomes clear when this is compared to the average increase of all banks that is 29 basis points (Table 1).

(23)

Table 4. Business loan volumes before and after the onset of the financial crisis (multivariate tests).

This table presents regression of loan volumes on crisis, competition, dependence on short term market-based funding, and their interactions plus bank and local environment controls. The dependent variable is defined as the monthly new business loans divided by total assets in the previous month and it is 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.

(1) (2) (3) (4) (5) (6) Crisis -0.0430*** -0.0375*** -0.0261*** 0.0184 0.0106 0.0081

(0.0080) (0.0082) (0.0092) (0.0155) (0.0375) (0.0372) Competitive (Lerner) 0.1125*** 0.0896*** -0.0120 -0.0060

(0.0213) (0.0238) (0.0273) (0.0268)

Crisis x Competitive (Lerner) -0.0346** -0.0236 -0.0258 -0.0248 -0.0285* (0.0144) (0.0155) (0.0166) (0.0166) (0.0173)

Crisis x Competitive (HHI) -0.0160 (0.0162) Short market-based debt dummy 0.0790*** 0.0410* 0.0458*

(0.0292) (0.0245) (0.0249)

Crisis x Short market-based debt -0.0379** -0.0306* -0.0318* -0.0265 -0.0307* (0.0170) (0.0174) (0.0171) (0.0177) (0.0170)

Capital adequacy ratio t−1 -0.0049*** -0.0042*** -0.0002 -0.0002

(0.0013) (0.0012) (0.0019) (0.0019)

Liquid assets/total assets t−1 -0.0040*** -0.0045*** -0.0018* -0.0020*

(0.0015) (0.0015) (0.0011) (0.0011)

The log of the total assets t−1 0.0320*** 0.0290*** 0.0297 0.0307

(0.0085) (0.0087) (0.0666) (0.0673)

Nonperforming loans/total assets t−1 0.0009 -0.0003 -0.0138** -0.0135*

(0.0091) (0.0087) (0.0069) (0.0069)

OBS commitments/total assets t−1 0.0208*** 0.0220*** 0.0296*** 0.0294***

(0.0046) (0.0045) (0.0034) (0.0034) 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 15602 15602 15419 15419 15419 15419 R-squared 0.0288 0.0382 0.0953 0.1095 0.0470 (w) 0.0468 (w) F-statistic 30.5648 21.1165 14.2178 - -

(24)

Table 5. Business loan prices before and after the onset of the financial crisis (multivariate tests).

This table presents the regression of business loan prices on crisis, competition, dependence on short term market-based funding, and their interactions plus bank and local environment controls. The dependent variable is defined as the monthly average margin of new business loans and it is 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.

(1) (2) (3) (4) (5) (6) Crisis 0.2382*** 0.2313*** 0.2651*** -0.3422*** -0.5101*** -0.5102***

(0.0171) (0.0189) (0.0215) (0.0338) (0.0700) (0.0708)

Competitive (Lerner) -0.2383*** -0.2305*** -0.0390 -0.0414

(0.0358) (0.0361) (0.0440) (0.0357)

Crisis x Competitive (Lerner) 0.1947*** 0.1814*** 0.1961*** 0.1968*** 0.1888*** (0.0334) (0.0308) (0.0310) (0.0289) (0.0294)

Crisis x Competitive (HHI) 0.1616*** (0.0325)

Short market-based debt dummy -0.0264 0.0544* 0.0231

(0.0340) (0.0289) (0.0289)

Crisis x Short market-based debt 0.0451 0.0039 0.0162 0.0235 0.0386 (0.0299) (0.0305) (0.0308) (0.0305) (0.0317)

Capital adequacy ratio t−1 -0.0028 -0.0007 -0.0076** -0.0084**

(0.0025) (0.0024) (0.0038) (0.0039)

Liquid assets/total assets t−1 0.0018 -0.0015 0.0004 0.0008

(0.0038) (0.0036) (0.0027) (0.0027)

The log of the total assets t−1 -0.1076*** -0.0947*** -0.0327 -0.0299

(0.0157) (0.0137) (0.1209) (0.1245)

Nonperforming loans/total assets t−1 0.0991*** 0.0500** -0.0015 -0.0053

(0.0235) (0.0198) (0.0111) (0.0110)

OBS commitments/total assets t−1 -0.0212*** -0.0234*** -0.0282*** -0.0268***

(0.0080) (0.0071) (0.0048) (0.0049)

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 14317 14317 13980 13980 13980 13980 R-squared 0.1037 0.1041 0.2010 0.3192 0.3194 (w) 0.3182 (w)

F-statistic 149.4866 91.4491 52.8999 .

4.2

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 competitive and the other banks, assumes the nonlinear effect of the competition measures because the competition variables take only the value one or zero. Now I use continuous values instead of dummies for the pre-crisis competition measures and make otherwise the similar estimations. Results in Tables 6 and 7 are comparable to Tables 4 and 5.

(25)

Table 6 shows that the coefficients of the interaction terms of the crisis and the pre-crisis average of the competition measures are positive and mainly statistically significant. As higher value of the competition measures means lower competition, these results indicate that the decrease in new small business loans during the crisis and tightness of pre-crisis competition are positively related. 17. To assess the economic significance, I compare the predictions of the model 3 for the effects of the crisis in the tenth and ninetieth percentile of the average pre-crisis value of the Lerner index. The decrease of the volume of lending is 17 % evaluated in the tenth percentile and 7 % in the ninetieth percentile. In sum, the results are in line with my main analysis.

Table 7 shows that the coefficients of the interactions terms of the crisis and the pre-crisis average of competition measures are negative and highly statistically significant in all spec-ifications which means higher increase in loan margins in the more competitive banks. Eval-uated 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.

17 I have dropped observations if the HHI equals one which explains the lower amount of observations in columns 6 in Tables 7 and 8. The dropped observations consist mainly of very small banks.

(26)

Table 6. Business loans volumes before and after the onset of the financial crisis (cf. table 4).

This table presents the regression of loan volumes on crisis, competition, dependence on short term market-based funding, and their interactions plus bank and local environment controls. The dependent variable is defined as the monthly new business loans divided by total assets in the previous month and it is 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.

(1) (2) (3) (4) (5) (6) Crisis -0.1005*** -0.0671*** -0.0660*** -0.0200 -0.0275 -0.0281

(0.0180) (0.0211) (0.0218) (0.0242) (0.0416) (0.0516) Average Lerner before crisis -0.5921*** -0.4546*** 0.0339 0.0091

(0.0814) (0.0943) (0.1220) (0.1204)

Crisis x Average Lerner before crisis 0.1918*** 0.1132 0.1498** 0.1413** 0.1499** (0.0681) (0.0726) (0.0710) (0.0679) (0.0706)

Crisis x Average HHI before crisis 0.0511 (0.0450) Short market-based debt/assets 0.4265*** 0.2708** 0.2586*

(year average before crisis) (0.1596) (0.1211) (0.1337)

Crisis x Short market-based debt -0.2541*** -0.1997** -0.2025** -0.1607* -0.2409** (0.0885) (0.0853) (0.0914) (0.0895) (0.1151)

Capital adequacy ratio t−1 -0.0045*** -0.0038*** -0.0006 0.0020

(0.0015) (0.0014) (0.0019) (0.0021)

Liquid assets/total assets t−1 -0.0035** -0.0042*** -0.0016 -0.0023

(0.0014) (0.0014) (0.0011) (0.0018)

The log of the total assets t−1 0.0343*** 0.0322*** 0.0279 0.0592

(0.0080) (0.0083) (0.0666) (0.0872)

Nonperforming loans/total assets t−1 0.0007 -0.0002 -0.0130* -0.0168

(0.0090) (0.0087) (0.0069) (0.0111)

OBS commitments/total assets t−1 0.0205*** 0.0213*** 0.0288*** 0.0282***

(0.0044) (0.0043) (0.0034) (0.0034) 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 15524 15444 15282 15282 15282 10689 R-squared 0.0386 0.0467 0.0966 0.1101 0.0470 (w) 0.0576 (w) F-statistic 38.6507 25.3024 13.5606 - .

(27)

Table 7. Business loan prices before and after the onset of the financial crisis (cf. table 5).

This table presents the regression of business loan prices on crisis, competition, dependence on short term market-based funding, and their interactions plus bank and local environment controls. The dependent variable is defined as the monthly average margin of new business loans and it is winsorized at the 99th percentile. Crisis takes value one after september 2008 and zero otherwise. The competition measures are calculated as pre-crisis averages.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.

(1) (2) (3) (4) (5) (6) Crisis dummy 0.5796*** 0.5244*** 0.5718*** -0.0166 -0.2128*** -0.3274***

(0.0430) (0.0470) (0.0480) (0.0485) (0.0743) (0.0978)

Average Lerner before crisis 1.2950*** 1.2678*** 0.5136** 0.4599**

(0.1495) (0.1584) (0.2355) (0.2259)

Crisis x Average Lerner before crisis -1.1509*** -1.0215*** -1.0523*** -1.1229*** -1.0740***

(0.1625) (0.1645) (0.1666) (0.1398) (0.1362)

Crisis x Average HHI before crisis -0.3068*** (0.0991) Short market-based debt/assets -0.0839 0.2217 0.0946

(year average before crisis) (0.1731) (0.1807) (0.1619)

Crisis x Short market-based debt 0.4095*** 0.1653 0.1730 0.2555 0.8699*** (0.1570) (0.1635) (0.1614) (0.1647) (0.2691)

Capital adequacy ratio t−1 -0.0044 -0.0016 -0.0050 -0.0061

(0.0029) (0.0028) (0.0037) (0.0037)

Liquid assets/total assets t−1 0.0022 -0.0015 -0.0009 0.0021

(0.0038) (0.0036) (0.0026) (0.0026)

The log of the total assets t−1 -0.0990*** -0.0883*** -0.0451 -0.1428

(0.0163) (0.0150) (0.1191) (0.1462)

Nonperforming loans/total assets t−1 0.0987*** 0.0498** -0.0049 -0.0162

(0.0235) (0.0199) (0.0110) (0.0142)

OBS commitments/total assets t−1 -0.0184** -0.0216*** -0.0260*** -0.0284***

(0.0081) (0.0072) (0.0047) (0.0054)

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 14236 14159 13845 13845 13845 10133 R-squared 0.1197 0.1210 0.2065 0.3254 0.3265 (w) 0.3566 (w) F-statistic 184.7488 120.6504 57.4826 - -

Next I take into account that also the effects of the bank characteristics can be different in good and crisis times by using the interactions terms of the crisis and all the explanatory variables (see Cornett et al. 2011). Additionally, I use lagged values of the competition measures instead of pre-crisis averages. I use yearly data because the competition variables are measured annually. All the explanatory variables are one year lagged and also time fixed

(28)

effects are included. Bank fixed effects are not included because the development of the Lerner index is problematic to describe the changes in competition over time.18.

The results are presented for the loan volumes in Table 8 and for the loan margins in Table 9. The results are in line with the previous analysis also with this yearly data and the lagged and continuous competition variables. The coefficients of competition- crisis interaction terms are positive for the loan volumes and negative for the loan margins. As higher values of the competition measures mean lower competition, the results indicate that the decrease in the loan volumes and the increase in the loan margins during the crisis are positively related to tightness of competition.

5

DISCUSSION AND AUXILIARY ANALYSIS

The main results and various robustness checks 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 im-portant for the purpose of my study, the increase of the average loan margins after the onset of the crisis is significantly higher in more competitive banks. This result implicates that competition environment can be a relevant factor to explain heterogeneous effects of the crisis on loan prices in addition to factors, like loan losses and liquidity, considered in pre-vious literature (Santos 2011; Gambacorta & Mistrulli 2014).

(29)

Table 8. Business loans volumes before and after the onset of the financial crisis with yearly data

This table presents the regression of loan volumes on crisis, competition, bank characteristics, and interactions plus local environment controls and time fixed effects. The dependent variable is defined as the yearly new business loans divided by total assets in the previous year and it is winsorized at the 99th percentile.

Crisis takes value one in 2009 and 2010 and zero otherwise.All the explanatory variables are one year lagged. 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.

(1) (2) (3) (4) (5) Crisis dummy -2.6249*** -2.0599*** (0.2064) (0.2840) Lernert−1 -5.7080*** -3.3904*** 0.7736 0.3101 (0.7705) (0.9629) (1.0184) (1.1235) Crisis x Lernert−1 6.4349*** 4.9431*** 3.6774*** 0.0784 (0.7858) (0.9085) (0.9823) (1.0775) HHIt−1 -2.5816*** (0.9698) Crisis x HHIt−1 1.6328** (0.6674)

Short market − based debt/assetst−1 0.0693*** 0.0168 0.0239 0.0328*

(0.0166) (0.0153) (0.0151) (0.0189)

Short market − based debtt−1 x Crisis -0.0320** -0.0055 -0.0093 -0.0006

(0.0126) (0.0148) (0.0145) (0.0197)

Capital adequacy ratio t−1 -0.0742*** -0.0653*** -0.0335

(0.0221) (0.0220) (0.0227)

Liquid assets/total assets t−1 -0.0511 -0.0546 -0.1182**

(0.0408) (0.0392) (0.0564)

The log of the total assets t−1 0.3193*** 0.3658*** 0.1029

(0.0993) (0.1035) (0.1520)

Nonperforming loans/total assets t−1 0.0602 -0.0946 -0.0381

(0.1685) (0.1783) (0.2469)

OBS commitments/total assets t−1 0.2156*** 0.2361*** 0.1940**

(0.0737) (0.0722) (0.0873)

Capital adequacy ratio t−1 x Crisis 0.0068 0.0343 -0.0042

(0.0245) (0.0244) (0.0216)

Liquid assets/total assets t−1 x Crisis 0.0694 0.0370 0.0999*

(0.0450) (0.0437) (0.0551)

The log of the total assets t−1 x Crisis -0.1057*** -0.0469 -0.0635

(0.0387) (0.0425) (0.0483)

Nonperforming loans/total assets t−1 x Crisis -0.1271 -0.0438 0.1317

(0.1985) (0.1996) (0.2454)

OBS commitments/total assets t−1 x Crisis -0.0105 -0.0626 -0.0055

(0.0696) (0.0691) (0.0806) Local economic variables no no no yes yes Time fixed effects no no no yes yes Number of observations 1144 1120 1120 1106 773 R-squared 0.1370 0.1794 0.3115 0.3470 0.3527 F-statistic 59.7627 42.3086 22.1406 18.1606 16.3383

(30)

Table 9. Business loan prices before and after the onset of the financial crisis with yearly data

This table presents regression of business loan prices on crisis, competition, bank characteristics, and interactions plus local environment controls and time fixed effects. The dependent variable is defined as the yearly average margin of new business loans and it is winsorized at the 99th percentile. Crisis takes value one in 2009 and 2010 and zero otherwise.All explanatory variables are one year lagged measures. 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

(1) (2) (3) (4) (5) Crisis dummy 0.6344*** 0.5500*** (0.0452) (0.0521) Lernert−1 0.6397*** 0.4299*** 0.1111 0.5439*** (0.1404) (0.1453) (0.1795) (0.1928) Crisis x Lernert−1 -1.1825*** -0.9569*** -1.1638*** -0.7891*** (0.1898) (0.1995) (0.2232) (0.2422) HHIt−1 0.3678** (0.1455) Crisis x HHIt−1 -0.4574*** (0.1251)

Short market − based debt/assetst−1 -0.0064*** -0.0001 0.0019 0.0028

(0.0018) (0.0020) (0.0021) (0.0026)

Short market − based debt/assetst−1 x Crisis 0.0064*** 0.0035 0.0014 0.0064*

(0.0023) (0.0025) (0.0024) (0.0033)

Capital adequacy ratio t−1 -0.0018 -0.0029 -0.0021

(0.0033) (0.0035) (0.0037)

Liquid assets/total assets t−1 -0.0028 -0.0034 -0.0046

(0.0058) (0.0060) (0.0092)

The log of the total assets t−1 -0.0743*** -0.0585*** -0.0555**

(0.0166) (0.0171) (0.0231)

Nonperforming loans/total assets t−1 0.1269*** 0.0533 0.0587

(0.0302) (0.0330) (0.0389)

OBS commitments/total assets t−1 -0.0343*** -0.0251** -0.0232*

(0.0115) (0.0112) (0.0118)

Capital adequacy ratio t−1 x Crisis 0.0108*** 0.0046 0.0044

(0.0037) (0.0036) (0.0036)

Liquid assets/total assets t−1 x Crisis 0.0029 0.0041 0.0147

(0.0088) (0.0087) (0.0095)

The log of the total assets t−1 x Crisis 0.0188** -0.0020 -0.0109

(0.0073) (0.0074) (0.0078)

Nonperforming loans/total assets t−1 x Crisis -0.0401 0.0171 0.0412

(0.0415) (0.0369) (0.0482)

OBS commitments/total assets t−1 x Crisis 0.0113 0.0182 0.0292**

(0.0142) (0.0127) (0.0137) Local economic variables no no no yes yes Time fixed effects no no no yes yes Number of observations 1140 1116 1116 1102 780 R-squared 0.2548 0.2718 0.3869 0.4982 0.5381 F-statistic 147.9445 105.8709 52.3763 59.6621 51.7057

(31)

The results also show that the volumes of new business loans decreased more in more com-petitive environments during the crisis but the effect is not as robust as on loan prices. To-gether with the results for loan prices, this suggest that banks in more competitive environ-ments reduced loan supply i.e., tightened lending standards and/or increased rationing more during the crisis which 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. One improvement would be, if it were possible to examine the changes in the whole set of lending standards.

Moreover, the results of changes in loan volumes are possibly affected by the lending be-havior of the cooperative banks related to other banks in the same local market.19 OP-Poh-jola 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, lending standards and/or rationing more than the cooperative banks. This could decrease the effect of increased loan prices and possible tightening of other lending stand-ards on volumes of small business loans of the cooperative banks in more competitive local markets.

Next, I analyze alternative reasons behind the main results based on the discussion in sec-tion 2. First, I examine the effect of the changes in competisec-tion. Second, I examine whether the changes in average riskiness of small business loans were different in the most compet-itive and in the other banks. Third, I examine tentatively the role of the relationship between

19 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)

(32)

competition environment and funding structure. I am unable to consider the role of rela-tionship lending in different competition environments due to data restrictions.

I consider the changes in competition by comparing the average Lerner index values of the competitive and the other banks20 before and after the crisis. I use also interest spread i.e., difference between the average interest rate on new loans and deposits as a complementary measure that aims to roughly capture the combined effect of loan and deposit competition. For more detailed examination, I compare the interest rates on various types of new deposits and loans.

Table 10 shows that the difference in both the competition measures and the interest rates on various types of deposits and loans diminished during the crisis which suggests that the level of (price) competition converged during the crisis in various competition environ-ments and resulted in higher price changes in the competitive banks compared to the other banks. These different changes in the competition measures support the Ruckes’ (2004) the-ory of procyclical price competition although I cannot examine soundly how competition changed generally during the crisis due to the sensibility of the Lerner index to monetary conditions.

Credit risk and thus credit rating is a very important part of loan pricing and the changes in the average credit ratings of banks’ small business customers can reflect the changes in risk-taking in addition to the change in general riskiness of small businesses. I exploit loan-spe-cific data with credit ratings since September 2008 and calculate the average credit ratings

(33)

of outstanding business loans for each cooperative bank at the beginning and at the end of crisis period.

Table 10. Comparison of competition measures, prices and dependence on short term market-based funding in the competitive and other banks before and during the crisis.

The classification into the competitive and other banks is based on the pre-crisis Lerner- index. All numbers are averages of the banks in each group. Interest spread is difference between the average interest rate on new loans and deposits. Interest rates are for new monthly deposits and loans.

Before October 2008 After October 2008

Variable Competitive Other Difference Competitive Other Difference Diff-in-Diff Lerner index 0,145 0,297 0,152 0,045 0,164 0,119 -0,033 Interest spread 1,232 1,624 0,392 1,145 1,376 0,231 -0,161 Interest rates All deposits 3,090 2,885 -0,205 1,799 1,790 -0,009 0,196 Term deposits 3,416 3,324 -0,093 1,943 1,971 0,028 0,121 Business loans 4,436 4,812 0,376 3,340 3,664 0,324 -0,052 Mortgages 3,885 3,949 0,064 2,714 2,778 0,064 0,000 Consumer loans 4,822 5,120 0,299 3,623 3,873 0,250 -0,049 short market-based 0,115 0,034 -0,081 0,094 0,049 -0,045 0,036 debt/asset

When I compare the average credit rating in the most competitive and in the other banks, the result (not reported) is that the average credit rating of small businesses is lower (better) in the competitive banks both in September 2008 and in September 2010. This is line with the competition-stability view that risks are lower in more competitive banks (see e.g. Boyd and Nicolo 2005; Fiordelisi and Mare 2014). However, this does not necessarily indicate the differences in risk-taking because the differences in the average credit ratings are likely af-fected by the general riskiness of businesses in various local areas.

More important here is to compare changes during the crisis. The difference in the average credit rating of small businesses between the competitive and other banks increased during the crisis which has two implications. First, the changes in riskiness of business loans do not explain the higher increases in loan margins in the competitive banks but instead, it should have the opposite effect. Second, I cannot control the change in riskiness of loan applicants

(34)

but the higher difference in average credit rating at the end of crisis can mean that the most competitive banks reduced loan supply to the most risky businesses more than the other banks. This suggests that the “flight to quality” during crises can be higher in more compet-itive banking markets which contributes to previous literature that has considered, for ex-ample, the role of bank size and capitalization in changes in risk-taking (Albertazzi & Mar-chetti 2010).

Finally, I consider the connection between competition and dependence on short term mar-ket based funding. Table 10 shows that the data support the marmar-ket power hypothesis also for deposit rates in line with previous literature (e.g. Hannan 1997; Hannan & Prager 2004) and that the share of short term market-based funding in total assets is on average higher in the competitive banks but this difference reduced during the crisis. This simple evidence suggests that competition can increase the share of other short term funding than deposits and supports the limited literature about that (see Graig & Dinger 2009). My results show that the estimates on the effect of competition environment decreases when the variable measuring dependence on short term market-based funding is included into the analysis. An interesting question that requires more detailed analysis is whether and how competi-tion environment affects funding structure and thus vulnerability of banks to various fund-ing shocks.

Overall, the results suggest that competition environment could be a significant factor in heterogeneous effects of the crisis on small business lending and add our understanding about various transmission channels of the crisis on bank lending. More generally, the re-sults highlight the possible significant role of competition in procyclical lending that is one of the main issues on the agenda of bank supervision and regulation at the moment. My

(35)

data enable appropriately to consider the role of local competition environment but better generalization of the results requires analysis also in other empirical contexts.

6

CONCLUSION

In this paper I examine the role of competition environment in the heterogeneous changes in small business lending after the onset of the financial crisis in September 2008. Using the detailed data on Finnish cooperative banks, I find that the volumes of new small business loans relative to total assets decreased and the average loan margins increased during the crisis and, more important, the both changes were larger in the more competitive local en-vironments. The results are more robust for the loan margins and show that the increase in the average margin of new business loans was almost double in the most competitive banks compared to the other banks. This suggest that the effect of competition environment in the transmission of the crisis can be a relevant factor ignored in the previous studies. At more general level, the results suggest that the effect of competition environment on loan availa-bility and especially on loan prices is different in normal and crisis times.

I also examine possible reasons for the heterogeneous effects of the crisis depending on com-petition environment. My results show that the level of comcom-petition converged in different competition environments during the crisis. In addition, the results tentatively suggest that competition environment is relevant in heterogeneous changes in risk-taking and in differ-ent funding structures which influence the changes in loan availability during the crisis. REFERENCES

Adams, R.M. & Amel, D.F. 2011. Market structure and the pass-through of the federal funds rate. Journal of Banking & Finance

Albertazzi, U. & Marchetti, D.J. 2010. Credit Crunch, Flight to Quality and Evergreening: An Analysis of Bank-Firm Relationships After Lehman.

References

Related documents

We in- vestigate convergence of inflation using unit labour cost (ULC) growth and applying PANIC (Bai and Ng, 2004) and cluster procedures (Hobijn and Franses, 2000, Busetti et

[r]

○ If BP elevated, think primary aldosteronism, Cushing’s, renal artery stenosis, ○ If BP normal, think hypomagnesemia, severe hypoK, Bartter’s, NaHCO3,

* Corresponding author. E-mail address: paola.zappa1@unimib.it.. Controversial too are the empirical findings on the effectiveness of social learning, once contextual variables and

Nos interesa especialmente centrarnos en la disartria como conducta verbal problemática presente en la esquizofrenia que padece el paciente 1 , realizando una

We will explain to you the various credit plans available, including minimum information which the bank may need for processing your loan application, the most important

National Conference on Technical Vocational Education, Training and Skills Development: A Roadmap for Empowerment (Dec. 2008): Ministry of Human Resource Development, Department

More worrisome is the possibility of non-random migration, which is not a real concern with our mortality results (given we measure outcomes at ages 5-9, just a few years after