Diversification and banks’ willingness to lend to small businesses: Evidence from Islamic and conventional banks in Indonesia

Journal of Economic Behavior & Organization 103 (2014) S39–S55

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Journal of Economic Behavior & Organization journal homepage: www.elsevier.com/locate/jebo

Diversification and banks’ willingness to lend to small businesses: Evidence from Islamic and conventional banks in Indonesia Mohamed Shaban a,∗ , Meryem Duygun a,1 , Mokhamad Anwar b,2 , Bahrullah Akbar c,3 a b c

University of Leicester, School of Management, University Road, Leicester LE1 7RH, United Kingdom Faculty of Economics and Business, University Padjadjaran (UNPAD), Indonesia Institute of Public Administration (IPDN), Jl. Raya Jatinagor KM 20, Sumedang, West Java, Indonesia

a r t i c l e

i n f o

Article history: Received 2 January 2013 Received in revised form 4 March 2014 Accepted 19 March 2014 Available online 13 April 2014 JEL classification: G21 G28 D21 C14 Keywords: Small businesses Diversification Risk Cost and profit efficiency Managerial behaviour

a b s t r a c t This is the first study to provide a comprehensive analysis of banks’ willingness to lend to small businesses (SBs) by differentiating between conventional and Islamic banks’ behaviour in Indonesia. In our initial analysis we examine the determinants of banks’ willingness to lend to SBs and in the second part we investigate the Granger-causes of diversification towards SB lending and banks’ efficiency and ex-post risk. Our results reveal that large banks are less interested in SB lending compared to small banks. Profitability is an important determinant for Indonesian banks to lend to SBs. Islamic banks, however, benefit more from lending to SBs given the substantial improvement in their net interest margin and lower capital compared to conventional banks. Our findings signal overpricing behaviour by Islamic banks, represented by a relatively high unadjusted rate of return given the risk exposure of their products. It is also evident that Islamic banks’ managers seem to hold less capital, counting on the benefits of portfolio diversification towards SBs lending. As expected the moral hazard hypothesis is only evident for Islamic banks in terms of loan and income portfolio diversification. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Small businesses (SBs) constitute a significant proportion of the businesses in many developed and developing nations in terms of their share of the number of enterprises and employment (Hallberg, 2001; Ayyagari et al., 2007). However, the literature on assessing their role in economic development and industrialisation processes has been controversial (Snodgrass and Biggs, 1996; Beck et al., 2005). According to Beck and Demirgüc¸-Kunt (2006), one of the factors which hinders the contribution of SBs to economic growth is limited access to finance. In most countries, SBs were found to have less access to

∗ Corresponding author. Tel.: +44 116 2231820; fax: +44 116 2525515. E-mail addresses: [email protected] (M. Shaban), [email protected] (M. Duygun), [email protected] (M. Anwar), [email protected] (B. Akbar). 1 Tel.: +44 116 2525328; fax: +44 116 2525515. 2 Tel.: +62 22 250 9055; fax: +62 22 250 9055. 3 Tel.: +62 22 779 8252; fax: +62 22 779 8253. http://dx.doi.org/10.1016/j.jebo.2014.03.021 0167-2681/© 2014 Elsevier B.V. All rights reserved.

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external finance, in particular debt financing.1 Indeed, information asymmetry, lack of collateral and inexperienced management, among other factors, are likely to be the cause of SBs inability of to access external finance (Owualah, 1990; Petersen and Rajan, 1994). The literature emphasises that country case studies are crucial to investigating this further and identifying relevant policies and financing tools that can alleviate the financing constraints of SBs (Beck and Demirgüc¸-Kunt, 2006). In our study, we focus on an emerging economy; Indonesia, to investigate the behaviour of banks towards SBs lending from the point of view of loan portfolio diversification. Indonesia has a sizeable micro and small businesses sector: around 99.91% of all businesses can be classified as micro and small businesses.2 This particular category’s contribution to GDP is 42.24% with a total employment of 91.3 million.3 In the last decade there have been intensified efforts by policy makers in Indonesia to stimulate the lending activities of commercial banks towards SBs. Similar to other banking sectors around the world, banks in Indonesia may perceive SBs as information opaque economic entities due to the owners of SBs not having the incentives to sustain reliable records of business transactions (i.e. financial records) as well as them not reliably disseminating information to the public. The early 2000s witnessed a significant growth in SB lending as a proportion of commercial banks’ total loans in Indonesia. Both conventional and Islamic banks were more involved in SB lending in recent years as a response to the Indonesian government’s economic stimulation policies. However, the proportion of SBs to total loan portfolio in conventional banks were declining over time during the 2002–2010 period. Islamic banks, in contrast, showed an increase in the proportion of SB loans to total loans portfolio during the same period (see Table A.1 in Appendix). Islamic banks use murabaha contracts4 when lending to SBs. The Murabaha contract (biaa) or sale constitutes a bank buying an asset on behalf of the client and selling it to the client at a mark-up price. The main criterion that may assist Islamic banks to expand or diversify their loan portfolio towards SB lending is that the bank maintains the ownership of the asset (collateral-by-contract)5 until the terms of the contract come to an end. This eases the collateral obstacle faced by SBs when they seek lending from an Islamic bank. This is because, in the case of a Murabaha contract, the small business client does not need to provide any collateral in advance, in contrast with conventional banks where the collateral is usually an essential pre-condition for borrowing. We argue that the structure of a Murabaha contract may persuade Islamic bank managers to be less concerned with the asymmetry of information associated with SB lending. Therefore, it may exacerbate the moral hazard risk associated with the loan underwriting. In this case, we believe that the structure of a Murabaha contract may provide a competitive advantage to Islamic banks when it comes to SB lending because the collateral-by-contract feature may represent a partial safeguard to an Islamic bank against default risk when compared to conventional contracts that lack such feature. In this study, our contribution to the literature is threefold: first, we comprehensively review banks’ willingness to lend to SBs in Indonesia. The existence of both Islamic and conventional banks in Indonesia provides an interesting country case study that we focus on in our study. We are not aware of previous studies which investigate diversification and bank lending to SBs and thus, differentiate between Islamic and conventional banks’ behaviour. Second, specific bank data on SBs lending is not always available, and such constraints are likely to affect the number of contributions in the literature (Ardic et al., 2011). We use a unique dataset obtained from Bank Indonesia on banks’ SBs lending and thus, contribute to the slim literature on country case studies. Finally, our findings have policy implications in that they highlight some issues that policy makers could use to improve the flow of capital to SBs. We investigate two important areas of concern with respect to banks’ SB lending: first, we examine the determinants of Islamic and conventional banks’ willingness to lend to SBs. Given the differences in the contracts used, it is crucial to discover the existence of any discrepancies in banks’ willingness to lend to SBs. We follow the framework of Berger and Udell (2002) to identify the main drivers of both Islamic and conventional banks to engage in lending activities to SBs. Second, we investigate the impact of diversification towards SBs lending on banks’ cost efficiency, profit efficiency, and ex-post risk (represented in loan loss provision). We consider the expansion of banks’ lending activities to SBs as a core banking operation that directly relates to loan portfolio diversification. This assumption allows investigation of the expansion in lending to SBs within the framework of the well-developed theory of portfolio diversification. We adopt the managerial behaviour model (Berger and DeYoung, 1997) and the modified managerial behaviour model (Rossi et al., 2009), to investigate the behaviour of Indonesian banks. We extend our analysis by differentiating the behaviour of an Islamic bank manager from the conventional bank manager. This allows us to explore whether the risk characteristics of the Islamic loan (Murabaha) encourage Islamic bank managers to behave differently to conventional bank managers. Our results reveal that large banks are less interested in SBs lending than small banks, and profitability is an important determinant for Indonesian banks to lend to SBs. Islamic banks benefit more from lending to SBs given the substantial improvement in their net interest margin and lower capital compared to conventional banks. However, our findings signal

1

See Galindo and Schiantarelli (2003), Cressy (2002) and IADB (2004) for empirical evidence from Latin America, the UK and US. In Indonesia, small businesses (SBs) are defined as business units with total initial assets of up to IDR 200 million, or an annual sales value of up to IDR 1 billion (US$100,000) (State Ministry of Cooperative and Small and medium enterprises, Menegkop and UKM). 3 Annual report, Department of Cooperatives, Republic of Indonesia, 2008. 4 Islamic Banking Statistics, Bank Indonesia (2010, p. 18). 5 We use this jargon to highlight the important feature of a Murabaha contract that differentiates it from a conventional loan. 2

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overpricing behaviour by Islamic banks, represented in a relatively high unadjusted rate of return given the risk exposure of their products. Our second set of results focus on the impact of both loan portfolio diversification and income diversification on Indonesian banks’ efficiency, and ex-post risk. In terms of loan portfolio diversification, both Islamic banks and conventional banks provide evidence in support of the classical diversification hypothesis, bad management hypothesis and monitoring hypothesis. In terms of income diversification, we find evidence of lack of expertise hypothesis and quite life hypothesis in Islamic banks as opposed to conventional banks. Our findings suggest that Islamic bank managers seem to hold less capital, counting on the benefits of portfolio diversification towards SBs. As expected the moral hazard hypothesis is only evident for Islamic banks in terms of loan and income portfolio diversification. The paper is organised as follows: Section 2.1 presents the theoretical discussion on SBs lending in two parts. The first part identifies the determinants of banks’ willingness to lend to SBs following Berger and Udell (2002) framework. Section 2.2 concentrates on loan portfolio diversification and provides a detailed explanation of managerial behaviour models. Section 3 details the methodology and the data used. Section 4 discusses the empirical findings in two sections; Section 4.1 presents results on bank’s willingness to SBs lending and Section 4.2 presents the impact of portfolio diversification on efficiency and risk. Section 5 concludes. 2. Theoretical framework 2.1. Lending to small businesses This section aims to highlight the determinants of banks’ willingness to lend to SBs. Banks’ lending to SBs, in general, can be influenced by internal and external factors. Internal factors such as banks’ profitability, risk and lending technology may lead to an increase or decrease in lending activities to a particular economic segment, for example, small businesses. Similarly, external factors which are out of the bank manager’s control, i.e. contracting monetary policy or global financial crisis, may hamper or alleviate banks’ willingness to lend to SBs. In the spirit of the framework proposed by Berger and Udell (2002) banks’ size and organisational structure are likely to have significant effects on SB lending due to the contracting problems associated with relationship lending.6 Small banks, for example, may have a competitive advantage in lending to SBs because of fewer managerial layers. Small banks are also likely to avoid any organisational diseconomies that stem from co-ordination and miss-communication problems often associated with large banks. Such advantages seems to solve many of the contracting issues that might arise between the loan officer and the borrowers, i.e. the information about the relationship loans is communicated to fewer parties in a small bank compared to a large one. This partially explains the reliance of large banks on hard information in their lending decisions, in other words, adopting transaction-based lending (for a more detailed discussion, see Berger and Udell, 2002). Other factors that might affect lending activities are internal rules and regulations, and the lending technology adopted by the bank, e.g. some banks insist on the availability of collateral when underwriting small business loans; therefore the lack of adequate collateral often results in loans being denied (Owualah, 1990; Petersen and Rajan, 1994). This predicament has encouraged banks and regulators to seek alternative technologies or solutions that may ease the lending process to small businesses. For conventional banks the relationship lending system is one possible solution to this dilemma (Berger and Udell, 2002). However, in the case of Islamic banks, one can argue that the structure of the Murabaha7 contract may moderate the contribution of information asymmetry risk towards the default risk associated with SB lending in the absence of collateral. Such structures may encourage Islamic banks to bypass the need for applying a relationship lending system as this is likely to take a longer time and greater effort by the loan officers. As a result, Islamic banks might have an edge compared to their conventional counterparts because of the structure of the Murabaha financing contract. By capitalising on the structure of the Murabaha contract, the Islamic bank can expedite the penetration process to that segment of loan market without incurring excessive cost to shift their lending technology. The lending process to SBs has implications on the banks’ costs, profits and risks. This is due to the high risk associated with the information asymmetry; which may result in high transaction costs. However, it is assumed that bank managers appropriately assess the risk associated with the loan underwriting, and hence require a risk adjusted return from the customer. The probability of moral hazard behaviour by bank managers whilst processing a small business loan transaction usually emerges from taking on board high risk, and hence charging excessive interest. Thus, this may be reflected by high levels of profitability and risk associated with the expansion in SB lending. The high dependence on bank credit makes SBs more vulnerable to exogenous shocks to the banking system. Hence, severe shocks may have a significant impact on the supply of credit to SBs. Berger and Udell (2002) also postulate that economic shocks can significantly affect the contracting hierarchy and the propensity of banks to make relationship loans. In turn, these

6 Under relationship lending, a bank manager obtains soft information about the client over time through direct contact with the business, the owner, and the local community in which the business operates. The bank manager then utilises this information to decide on the availability and terms of credit to the client. An influential lending technology that reduces information asymmetry problems in small business lending is “relationship lending”. 7 The Murabaha product became the workhorse for Islamic banks and currently represents more than 75% of Islamic Bank assets (for a more detailed discussion, see El-Gamal, 2005 and El-Gamal, 2006).

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shocks can be transmitted to small businesses in the form of a smaller allocation of loans. Among the suggested shocks, we focus on the following economic shocks: (i) regulatory regime shift, represented by changes of bank capital requirements; (ii) monetary policy changes, represented by changes in interest rate; and (iii) macroeconomic changes, represented by GDP growth. We summarise our hypotheses for the determinants of banks’ willingness to lend to small businesses as follows: H1: Islamic banks will be more willing to lend to small businesses given the competitive advantage embedded in the Murabaha contract. H2: Large banks will be less likely to engage in small business lending due to their sophisticated organisational structure. H3: Tight monetary policy, represented by an increase in the lending rate will lower the level of lending to small businesses. H4: Increases in capital requirement will decrease the level of the loans to small businesses. H5: Banks will tend to pursue a pro-cyclical and expansionary lending policy. 2.2. Loan portfolio diversification The second objective of this article is to investigate the impact of loan portfolio diversification towards SB lending on banks’ cost and profit efficiency and the ex-post realised risk (ERR) of the banks.8 Within this framework, we adopt both the managerial behaviour model (Berger and DeYoung, 1997) and the modified managerial behaviour model (Rossi et al., 2009) to unveil the differences between the managerial behaviour of Islamic and conventional banks towards SB lending. We assume that the managers of both Islamic and conventional banks are concerned about their bank’s efficiency, profitability, and risk. Therefore, we start the formulation of our framework by assuming that bank managers are either risk-averse or risk-seekers (displaying either positive or negative moral hazard behaviour), and that their behaviour will have an influence on loan portfolio management and diversification decisions. A bank manager can decide to diversify to reap the benefits suggested by the traditional finance theory, with an aim to reduce the ex-post realised risk (classical diversification hypothesis). However, there seems to be an unobserved threshold to the level of diversification, along with other factors, that might lead to adverse results of diversification (see the recent empirical findings of Rossi et al., 2009; Baele et al., 2007 and Acharya et al., 2006). The adverse results may arise if: (i) the diversification decision is not based on sufficient managerial expertise (i.e. knowhow to handle the new market sectors or segments9 ) and/or (ii) the infrastructure at the bank in terms of labour and capital is insufficient. These factors may lead to adverse outcomes of diversification, which can then proliferate banks’ ex-post realised risk. In the case of loan diversification to SBs, an adverse outcome might result from banks’ low capacity to monitor the borrowers (lack of expertise hypothesis). A diversification decision towards SB lending does not necessarily lead to a reduction in provisions. In some cases the quality of the portfolio composition is the main determinant of the amount of provisions. For example, a bank might achieve a low ERR with a less diversified portfolio, that is, one focusing on low risk lending activities, i.e. lending to well-established corporates or public sector clients. In this case, the need for higher provisioning diminishes compared to a bank with a more diversified portfolio that encompasses high risk assets. Consequently, it is worth investigating the quality of a bank’s loan portfolio while inspecting the impact of diversification on ERR. We do so by including economic capital to correct for risk-weighted assets. Banks’ operating efficiency plays a significant role in increasing (decreasing) banks’ ERR. Therefore we investigate the link between banks’ operating efficiencies (in terms of both cost and profit efficiency) and the ERR. We distinguish between cost and profit efficiency and inspect their impact on banks’ ERR. This allows us to test four hypotheses: the monitoring hypothesis, the idiosyncratic hypothesis, the classical diversification hypothesis and the quiet life hypothesis. According to the monitoring hypothesis, diversification will have two contrasting effects on banks’ cost efficiency. A high level of diversification will diminish banks’ cost efficiency because excessive monitoring processes that will require the recruitment of more qualified personnel due to banks’ internal regulatory codes. Moreover, in order to construct a high quality loan portfolio, a risk-averse manager will put in place a sophisticated monitoring system to monitor the new segment of clients (in our case, SBs). This will lead to contracting or allocating more resources (labour, software, third-party datasets, etc.), particularly expensive10 ones, to this sub-operation. This can magnify the banks’ cost and hence, decrease banks’ cost efficiency. In some cases the manager’s strict behaviour towards risk may be augmented by linking his incentive to the loan’s quality (performance) rather than successful loan underwriting. Contrary to the former argument, diversification may lead to higher cost efficiency. Diversification is expected to lead to a reduction in the risk associated with lending to a narrow group of sectors or clientele segments; therefore mitigating the idiosyncratic risk by means of diversification might lead to monitoring complacency by a bank manager. Counting on

8

The ex-post realised risk of the banks is the provision of problem with bad loans. New economic sectors (i.e. infrastructure projects, leisure industries, mortgage and real estate development) or economic segments (i.e. small and medium enterprises). 10 Notice that loan officers who have credible expertise in small business lending are scarce in the industry. Hence a marginal change in their demand will result in change in prices (i.e. the salaries they will require from the bank). 9

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Table 1 Descriptive statistics for model (1) variables. Bank type

Stat/variable

SBFPCT

CAR

LDR

PROV

NIM

SIZE

LENDINGRATE

GDPG

Conventional banks

Obs Mean Std. dev. Min Max

980 0.16 0.20 0.00 0.60

980 0.24 0.16 0.02 0.26

980 0.77 0.38 0.00 3.35

980 0.03 0.03 0.00 0.15

980 0.07 0.03 0.01 0.27

980 14.90 1.84 9.88 19.83

980 15.45 1.55 13.21 18.76

980 5.39 0.64 4.50 6.30

Islamic banks

Obs Mean Std. dev. Min Max

30 0.25 0.21 0.00 0.86

30 0.16 0.10 0.08 0.55

30 0.93 0.15 0.69 1.35

30 0.08 0.06 0.01 0.15

30 0.07 0.02 0.04 0.16

30 15.48 0.99 13.04 17.30

30 14.96 1.52 13.21 18.76

30 5.49 0.67 4.50 6.30

SBFPCT is the proportion of small businesses loans to total loans; a proxy for banks’ willingness to lend to small businesses. CAR is capital adequacy ratio; a proxy for banks’ risk. LDR is loan deposit ratio; a proxy of intermediation and liquidity risk. PROV is the loan loss provision to total loans; a proxy of banks’ ex-post risk. NIM is net interest margin; a proxy of profitability. SIZE is the natural logarithm of total assets; a proxy for bank size. LENDINGRATE is lending rate by Bank Indonesia; a proxy for monetary policy. GDPG is Gross Domestic Product at real prices growth rate. We include a dummy variable ISLAMIC for Islamic banks in the model to unveil whether Islamic banks are more willing to lend to small businesses compared to conventional counterparts.

such benefits, a bank manager may relax his monitoring efforts and consequently decrease the monitoring cost, and thus improve cost efficiency (idiosyncratic risk hypothesis). With respect to profit efficiency, classical finance theory suggests a positive relationship between diversification and profit efficiency. One would expect a higher risk adjusted return for a well-diversified portfolio compared to an undiversified one (the classical diversification hypothesis). The monitoring hypothesis also applies to profit efficiency in the presence of a riskaverse manager (i.e. lower profit efficiency). In this case, a higher cost allocated to monitoring activities by a risk-averse manager might not only lead to higher cost efficiency but also to lower profit efficiency (quiet life hypothesis). Bank capitalisation in the form of the equity-to-assets ratio reflects the risk and benefits associated with assets or income diversification. Highly diversified banks may expand their loan portfolio with the expectation of higher capitalisation. Hence they will be reluctant to hinder their lending capacity by retaining a high level of capital to cover unexpected losses of loan default. Therefore we predict that highly diversified banks will be less capitalised compared to less diversified banks (economic capital hypothesis). In the context of Islamic banks’ loan portfolio diversification towards SBs lending, we proceed with our argument that Islamic banks seem to have a competitive advantage concerning SB lending. This may lead them to diversify and expand their lending activities to this particular stratum of economic entity via capitalising on the concept of collateral-by-contract embedded in the Murabaha contract. Nonetheless, the same advantageous feature of the contract may exacerbate the moral hazard risk associated with the contract. This occurs if the loan officer overlooks other related risk to the same borrower by being satisfied with the collateral and charging a high interest rate. 3. Data and model specifications 3.1. Data In our empirical analysis, we use a unique data set from the Central Bank of Indonesia (Bank Indonesia) on 114 commercial banks during the 2002–2010 period. The unbalanced panel dataset is composed of that from 107 conventional banks and 7 Islamic banks. It is worth noting that the Islamic banks comprise the 100% of the total Islamic banks in Indonesia in 2010. The conventional banks consist of 4 state-owned banks, 56 private banks, 26 local government banks, 14 joint-venture banks and 7 foreign banks. Table 1 presents summary statistics for the variables used in the first part of our empirical analysis. 3.2. Models and hypotheses To investigate the determinants of banks’ willingness to lend to SBs, we employ a dynamic panel GMM-System model (Blundell and Bond, 1998 and Windmeijer, 2005): SBFPCTkt = f1 (NIMkt , CARkt , LDRkt , PROVkt , SIZEkt , LENDINGRATEkt , GDPGkt ) + εkt

(1)

The dependent variable SBFPCT is the proportion of SBs loans to total loans, that is, a proxy for banks’ willingness to lend to small businesses. CAR is capital adequacy ratio, a proxy for banks’ risk. LDR is loan deposit ratio, a proxy of intermediation and liquidity risk. PROV is the loan loss provision to total loans, a proxy of ex-post banks’ risk. NIM is net interest margin, a proxy of profitability. SIZE is the natural logarithm of total assets, a proxy for bank size. LENDINGRATE is lending rate by Bank Indonesia, a proxy for monetary policy. GDPG is Gross Domestic Product at real prices growth rate. We include a dummy variable ISLAMIC [1,0] for Islamic banks to unveil whether Islamic banks are more willing to lend to small businesses

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compared to the conventional banks. To further investigate the performance of Islamic banks, we interact the ISLAMIC dummy variables with independent variables and re-estimate the model in (1).

3.3. The modified managerial behaviour model To estimate the managerial behaviour model we firstly estimate banks’ cost and profit efficiency for our sample. We employ the parametric approach Stochastic Frontier Analysis (SFA) to estimate the two frontier models (cost frontier and alternative profit frontier). The main advantage of the parametric approach is that unlike the deterministic Data Envelopment Analysis approach (DEA), it allows us to specify a stochastic (error) term; hence the estimates are less vulnerable to the influence of random events and measurement errors (Kumbakhar and Lovell, 2000). The general form of the cost frontier is ln Ckt = C(qkt , wkt , t; ˇ) + vkt + ukt [2]

k = 1, ..., K t = 1, ..., T

where Ckt is the total cost of bank k at time t, qkt is a vector of outputs, wkt is a set of input prices, t is a time trend that measures technical progress and ˇ is a vector of unknown parameters to be estimated. The error term has two components: ukt and vkt . The random error vkt is assumed to be independent, identically distributed N(0, vi 2 ) and to be independent of ukt . The variables of the cost frontier are similar to those in the literature on bank performance. The Ckt is the total operating cost (the sum of interest expenses, salaries, employee benefits and other operating costs). Outputs are other loans (y1 ), small business loans (y2 ) and investment in securities (y3 ). The loans variable is calculated as the difference between the gross loans and the reserves allocated for non-performing loans (Bikker and Bos, 2008). The input prices are: (i) the cost of loanable funds (w1 ), calculated as the ratio of interest expenses to total deposits; (ii) the cost of labour (w2 ), calculated as personnel expenses relative to total assets; and (iii) the cost of physical capital (w3 ), calculated as the depreciation expenses relative to the book value of fixed assets. Linear homogeneity is ensured by normalising the dependent variable and all the input prices by w3 . Finally, following Berger and Mester (1997), we control for the level of equity as a quasi-fixed input to control for differences in risk preferences.11 Using a multi-output translog functional form, the cost frontier function in (1) becomes:

ln

 TC  w3

kt

= ˇ0 +

3 

ˇi ln Qkit +

i=1

ˇi ln

w 

i=1

 2

2 

2

+ 0.5

i=1 j=1

ˇij ln

w 

w3

+ 0.5

ln ( kit

ˇij ln Qkit ln Qkjt

i=1 j=1

kit

 wi ) + ˇij ln Qkit ln w3 kjt 3

i

w3

3 3  

i

2

i=1 j=1

w  i

w3

kjt

+ ˇ21 ln (EQUITY )kt + ˇ22 YEAR + ˇ23 YEAR2 + (vkt + ukt ) k = 1, . . ., K, t = 1, . . ., T

(3)

To obtain banks’ profit efficiency, we estimate an alternative profit frontier (Berger and Mester, 1997). The specification of the alternative profit frontier is similar to the cost frontier where our profit proxy is the operating profits (PBTit ), which replace the total cost in (1). The profit frontier does not need to be homogenous, therefore this does not require the profit proxy to be normalised. A few banks in the sample had negative operating profits. We transform the operating profit as follows: ln(PBT + |(PBT)min |+1 where |(PBT)min |) is the minimum absolute value of operating profits for all the banks in the sample. For comparability with profit efficiency scores, we index the estimated cost as follows: CEFit = 1/CEit .12 To estimate the managerial behaviour model we secondly follow the same modelling procedures proposed by Berger and DeYoung (1997) and Rossi et al. (2009) to investigate the inter-temporal relationship between banks’ efficiency, portfolio diversification and ex-post realised risk. The managerial behaviour model suggests that there is causality in the relationship between management behaviour and bank efficiency. Berger and DeYoung (1997) focus on the temporal relationship between banks’ bad loans, capitalisation and efficiency. The modified managerial behaviour model of Rossi et al. (2009) builds on that of Berger and DeYoung (1997) and focuses on the impact of inter-temporal relationships between banks’ profit and cost efficiency, portfolio diversification and ex-post realised risk. We investigate the hypotheses proposed by both models and formulate the hypotheses summarised in Table 2.

11

Using the level of equity rather than the equity to assets ratio is standard in the literature (Duygun Fethi et al., 2012). The cost (in)efficiency can take a value between one and infinity while the profit (in)efficiency may take a value between zero and one. In both cases, a value closer to one indicates higher efficiency. 12

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Table 2 Summary of the managerial behaviour model and the modified managerial behaviour model. Berger and DeYoung (1997) Managerial behaviour model

Rossi et al. (2009) Modified managerial behaviour model

(i) The bad management hypothesis: Inadequate operating cost management will lead to poor loan portfolio management. Hence, low efficiency will exacerbate banks’ ex-post realised risk (ii) The skimping hypothesis: Achieving cost efficiency by lowering the monitoring cost will affect the quality of banks’ loan portfolios and will lead to an increase in ex-post realised risk (iii) The bad luck hypothesis: External factors will decrease banks’ cost efficiency due to the possibility of increase in problem loans and the monitoring cost associated with these (iv) The moral hazard hypothesis: The managers of undercapitalised banks will be less risk averse. Hence undercapitalisation may cause an increase in problem loans

(i) Classical diversification hypothesis: A bank with a well-diversified portfolio will exhibit high risk adjusted return and hence, high profit efficiency and low ex-post risk (ii) Lack of expertise hypothesis: Banks with a lack of expertise in managing and monitoring small businesses will have a high ex-post realised risk (iii) Idiosyncratic risk hypothesis: Banks with a high diversification level will relax their loan portfolio monitoring effort and consequently the cost associated with it; this will lead to cost efficiency (iv) Monitoring hypothesis: A bank manager will allocate excessive resources to monitoring lending to new business segments and hence banks will incur high costs and this in turn will deteriorate the bank’s cost efficiency (v) Quiet life hypothesis: The high cost allocated to monitoring activities by a risk-averse manager will lead to low profit efficiency as well as high cost efficiency (vi) Economic capital hypothesis: Highly diversified banks will expand their loan portfolio to gain from higher capitalisation

In order to test our hypotheses, we follow Rossi et al. (2009) by modelling the inter-temporal causal relations between our dependent and independent variables. We estimate the three models as follows: PROVkt = f1 (PROVklag , EFFklag , CAPklag , HHIINklag , HHILklag , GDPGlag ) + ε1kt

(4)

EFFkt = f1 (PROVklag , EFFklag , CAPklag , HHIINklag , HHILklag , GDPGlag ) + ε1kt

(5)

CAPkt = f1 (PROVklag , EFFklag , CAPklag , HHIINklag , HHILklag , GDPGlag ) + ε1kt

(6)

Table 3 provides a summary of the statistics used in models (4)–(6). Table 4 provides a summary of our hypotheses and the expected signs according to the system of equations (4)–(6). We specify our model using lags for both the dependent and explanatory variables. We employ an Arellano–Bond dynamic panel data model to estimate the equations in (4)–(6). Our model’s specification is slightly different from that of Rossi et al. (2009) because we include the lag difference of the GDP growth to control for: (i) any unobserved cyclicality in our data, hence we opt not to limit our estimation period to specific years as Rossi et al. (2009); and (ii) the behaviour of bank risk in businesses cycles. Banks are exposed to more risk during periods of financial turmoil (Hellmann et al., 2000). In the system of equations presented in (4)–(6) PROV is a measure of banks’ loan portfolio quality and is calculated as loan loss provision relative to banks’ loans; EFF is either banks’ cost or profit efficiency scores; HHIL is a normalised Herfindahl–Hirschman index that measures the diversification of a bank’s loan portfolio (other loans, small businesses loans), a value of 1 means that the bank portfolio is fully concentrated; HHIIN is a normalised Herfindahl–Hirschman index that measures the degree of banks’ income (interest income, investment and securities income, fees and commission income). A value of 1 means the bank income is from one source. CAP is equity to total assets, a proxy for economic capital that measure Table 3 Descriptive statistics for models (4)–(6) variables. Variable

PROV

PROFIT EFFICIENCY

COST EFFICIENCY

HHIL

HHIIN

CAP

Conventional banks

Obs Mean Std. dev. Min Max

980 0.03 0.03 0.00 0.15

971 0.89 0.10 0.10 0.96

980 0.85 0.12 0.35 0.98

980 0.81 0.17 0.50 1.00

980 0.84 0.14 0.35 1.00

980 0.16 0.11 0.08 0.36

Islamic banks

Obs Mean Std. dev. Min Max

30 0.08 0.06 0.01 0.15

30 0.90 0.02 0.85 0.94

30 0.85 0.10 0.63 0.94

30 0.71 0.15 0.50 1.00

30 0.82 0.07 0.70 0.99

30 0.10 0.06 0.06 0.31

PROV is a measure of banks’ loan portfolio quality and is calculated as loan loss provision relative to banks’ loans; PROFIT EFFICIENCY is banks’ profit efficiency scores; COST EFFICIENCY is banks’ cost efficiency scores; HHIL is a normalised Herfindahl–Hirschman index that measures the diversification of a bank’s loan portfolio (other loans, small businesses loans) towards small businesses loans. A value of 1 means that the bank portfolio is fully concentrated; HHIIN is a normalised Herfindahl–Hirschman index that measures the degree of banks’ income (interest income, investment and securities income, and fees and commission income). A value of 1 means that the bank income is from one source; CAP is the equity to total assets ratio; a proxy for economic capital that measures bank capitalisation.

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Table 4 Summary and explanations of the expected signs of the managerial behaviour model. Dependent variable

Modified managerial behaviour model (Rossi et al., 2009)

Managerial behaviour model (Berger and DeYoung, 1997)

Classical diversification hypothesis

Ex-post risk

Lack of expertise hypothesis

Ex-post risk

(4) provides evidence of the effect of diversification on ex-post realised risk. A positive (negative) sign on the HHIL and HHIIN implies the classical diversification hypothesis (the lack of the expertise hypothesis)

A negative (positive) sign on the efficiency variables implies the bad management hypothesis (the skimping hypothesis) A negative sign on the capitalisation variable (CAP) implies the moral hazard hypothesis

Idiosyncratic risk hypothesis

Cost efficiency

Monitoring hypothesis Classical diversification hypothesis Quiet life hypothesis

Cost efficiency Profit efficiency Profit efficiency

Economic capital hypothesis

Capitalisation

(5) provides evidence of the effect of diversification on banks’ efficiency. For cost efficiency a negative (positive) sign on the HHIL and HHIIN implies the idiosyncratic risk hypothesis (the monitoring hypothesis)

A negative sign on the provisions variable implies the bad luck hypothesis

For profit efficiency as the dependent variable, a negative (positive) sign implies the classical diversification hypothesis (the quite life hypothesis) (6) closes the model and provides evidence for the economic capital hypothesis. Here, we would expect a positive sign on the two HHIL and HHIIN which implies that banks hold less capital, capitalising on their highly diversified loan and income portfolios

bank capitalisation; GDPG is Gross Domestic Product growth at real prices; a proxy for economic cycle. ε is an error term. We provide a summary of our hypotheses and expected signs in Table 4. 4. Empirical results 4.1. Determinants for banks’ willingness to lend to SBs We estimate the model in (1) using Ordinary Least Squares (OLS), and Fixed Effects (FE) and System-GMM to investigate the determinants of banks’ willingness to lend to SBs. In Table 5, we report the results from the three models for comparison purposes. For our analysis, we will focus on the results obtained from the system-GMM model, since it is more efficient and consistent compared to the OLS and FE results. The System-GMM results (Table 5, column 3) are stable and show that the coefficient of the lagged dependent variable lies between the ones estimated by OLS and the FE model (Roodman, 2009) and that the Hansen statistic infers no problem of over identification in our model. We consider the endogeneity of size suggested by Berger and Udell (2002) and include size as endogenous. In this section, we first present our findings on the whole sample followed by our further investigation on Islamic banks. In general, our results on the determinants of banks’ willingness to lend to SBs reveal that profitability is an important and significant driver for banks to engage in lending to SBs (Table 5, column 3). The coefficient of NIM (0.299) is positive and significant, which implies that lending to SBs improves banks’ net interest margin. Hence, banks tend to charge SBs higher interest rates to compensate for the risk associated with these information opaque businesses. Further, larger banks in Indonesia are less willing to engage in lending to SBs. The coefficient of size (−0.012) is negative and significant. This reflects the finding of Berger and Udell (2002) that sophisticated hierarchies in large banks are likely to act as an obstacle for lending to SBs. It seems that large banks in Indonesia focus on large corporates for which hard information is available (using transaction-based lending) compared to SBs that are more suited to a relationship lending approach. When risk is considered, it seems that lending to SBs is associated with high loan-loss provisions. As expected, this reflects the relatively high risk associated with lending to information opaque SBs. The coefficient of PROV (0.172) is positive and significant, suggesting that the higher risk represented by ex-post risk is associated with a high proportion of SBs loans within the Indonesian banks’ portfolios. Such evidence is consistent with the profitability proxy, which has a high coefficient value. The results provide evidence of lower liquidity risk associated with a high proportion of loans to SBs. The coefficient of LDR is negative, which implies less liquidity risk to the bank. This could be due to the lower number of loans relative to deposits, given that SB loans are smaller in size and shorter in maturity. Expanding the diversity of loan portfolios towards SB lending seems to be reflected by a lower capital requirement. The coefficient of CAR (−0.069) is negative and significant, which implies that the higher the CAR, the lower the proportion of SB lending in banks’ loan portfolios (see Figs. 1 and 2 in Appendix). Our results also reveal that banks in Indonesia seem to adopt a countercyclical policy in lending to SBs. This could be a profitability smoothing approach, e.g. banks can improve their

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Table 5 Determinants of banks’ willingness to lend to small businesses. VARIABLES

OLS SBFPCT 1

FE SBFPCT 2

GMM-SYS SBFPCT 3

LSBFPCT

0.822*** (0.0155) −0.00280 (0.00374) −0.00694 (0.00716) 0.186* (0.0904) −0.0322† (0.0190) −0.0125 (0.00811) 0.115 (0.0959) −0.00335† (0.00173) 0.0660*** (0.0186) 0.150† (0.0811) Y

0.515*** (0.0277) −0.00225 (0.00451) −0.00305 (0.00669) 0.0793 (0.167) −0.0567* (0.0261) −0.0520*** (0.0128) −0.0481 (0.120) −0.0251** (0.00922)

893 0.813

893 0.423 114

0.717*** (0.00420) −0.00608*** (0.000757) −0.00714*** (0.000660) 0.299*** (0.0241) −0.0609*** (0.00428) −0.0216*** (0.00207) 0.174*** (0.0159) −0.0120*** (0.00119) 0.0900*** (0.00809) 0.349*** (0.0284) Y 0.004 0.985 0.256 89 893 – 114

LENDINGRATE GDPG NIM CAR LDR PROV SIZE ISLAMIC Constant Year dummies AR1 AR2 Hansen No. of instruments Observations R-squared Number of banks

0.548** (0.203) −

Standard errors in parentheses * p < 0.05. ** p < 0.01. *** p < 0.001. † p < 0.1. The dependent variable SBFPCT is the proportion of small businesses loans to total loans; a proxy for banks’ willingness to lend to small businesses. LSBFPCT is the lagged dependent variable. CAR is capital adequacy ratio; a proxy for banks’ risk. LDR is loan deposit ratio; a proxy of intermediation and liquidity risk. PROV is the loan loss provision to total loans ratio; a proxy of banks’ ex-post risk. NIM is net interest margin; a proxy of profitability. SIZE is the natural logarithm of total assets; a proxy for bank size. LENDINGRATE is lending rate by Bank Indonesia; a proxy for monetary policy. GDPG is Gross Domestic Product at real prices growth rate. We include a dummy variable ISLAMIC for Islamic banks in the model to unveil whether Islamic banks are more willing to lend to their small businesses compared to conventional counterparts.

Fig. 1. Capital asset ratio (CAR) versus the proportion of small business lending to total loans (SBFPCT) for the whole sample.

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Fig. 2. Capital asset ratio (CAR) versus the proportion of small business lending to total loans (SBFPCT) for Islamic and conventional banks.

NIM in times of lower demand for loans by large corporates through the expansion of SB lending. However, contractionary monetary policy represented by an increase in the lending rate seems to have a negative shock on SB lending. Regarding Islamic banks; our results support our hypothesis that Islamic banks are more inclined towards SBs lending when compared to their conventional counterparts. Islamic banks in our data set are represented with a dummy variable ISLAMIC, and the coefficient of ISLAMIC (0.09) is positive and significant, which implies that Islamic banks behave differently when it comes to SB lending (Table 5, column 3). To further investigate the behaviour of Islamic banks among other different types of ownership, we run an additional estimation in which we include dummy variables for different types of ownership within the conventional banks sub-sample. As expected, we find that state-owned banks are equally keen to lend to SBs. There was no evidence that private or foreign owned banks are willing to lend to SBs. To further investigate our hypothesis on Islamic banks’ behaviour towards SB lending, we interact some bank specific variables with the dummy ISLAMIC variable. We repeat this for the two sets to avoid any multicollinearity problems. The models we generated are stable and there is almost no variation in the results (see Table 6, columns 2 and 3). Within the Islamic banks sub-sample, the largest banks behave similarly to large conventional banks in that they are less willing to lend to SBs. However the coefficient of SIZEISLAMIC (−0.009) is negative and significant, which reflects that the magnitude of unwillingness is diminished among large Islamic banks when compared to their conventional counterparts, It seems that profitability is the important driver for Islamic banks when engaging in lending to SBs when compared to their conventional counterparts. The positive and significant coefficient of NIMISLAMIC (3.49) is considerably higher compared to the coefficient of NIM (0.159) for the whole sample (see Table 6). This demonstrates that Islamic banks have benefited more from biasing their lending towards SBs when compared to conventional banks, and hence it had more impact on their NIM. Furthermore, if we consider that the majority of the lending by Islamic banks is done through Murabaha contracts, then Islamic banks seem to generate risk-unadjusted return from lending to SBs. This has slightly improved their NIM (see Fig. 3 in Appendix) compared to that of their conventional counterparts, given the less risky structure of Murabaha contract. It is worth noting that other findings in the literature confirm a higher NIM for Islamic banks compared to conventional banks. However, these studies tend to relate such results to the efficiency of the Islamic banks, which is not consistent with the classic portfolio theory13 (see, for example, Ahmad et al., 1999; Iqbal, 2001 and Kader and Asarpota, 2007). The profitability evidence is further confirmed by the negative and significant value of the ex-post risk proxy PROVISLAMIC (−0.534) that opposes the positive value for the whole sample coefficient. This result implies that Islamic banks have a significant comparative advantage when it comes to SB lending due to the collateral-by-contract concept associated with the Murabaha contract. Hence, the results reveal that higher lending to SBs is associated with an impressive NIM for Islamic banks, and a reduction in loan loss provision when compared to their conventional counterparts. Islamic banks benefited more from lending to SBs by both higher returns and lower risks. Despite the fact that the structure of their lending products is based on Murabaha contracts (a rather suitable financing product for small businesses) they also tend to exploit SBs by charging higher interest rates (unadjusted rate of return given the risk exposure). Fig. 3 in Appendix shows that even when the proportion of lending to small businesses swings the NIM of Islamic banks, this tends to be stable. This matter seems to encumber the majority of lending activities to SBs, even in the case of conventional banks; however, it seems rather exacerbated in Islamic banks. These findings may appeal to policy makers who are concerned with improving the capital

13

We refer to the lower risk associated with Islamic bank’s financing contracts (i.e. Murabaha) when compared to loans by conventional banks.

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Table 6 Determinants of banks’ willingness to lend to small businesses with focus on Islamic banks and banks’ ownership. VARIABLES

GMM-SYS SBFPCT

GMM-SYS SBFPCT

GMM-SYS SBFPCT

L.SBFPCT

0.736*** (0.00459) −0.00605*** (0.000655) −0.00799*** (0.000688) 0.159*** (0.0240) −0.0571*** (0.00450) −0.0156*** (0.00189) 0.0867*** (0.0148) −0.0158*** (0.00133) 0.00218 (0.00390) 0.0996*** (0.00991) 0.0127*** (0.00325) −0.00277 (0.00353) 0.0919*** (0.00833) – – – – – – – – – – 0.407*** (0.0315) Y 0.002 0.983 0.199 93 893 114

0.715*** (0.00610) −0.00668*** (0.000656) −0.00631*** (0.000780) 0.0999** (0.0313) −0.0637*** (0.00551) −0.0135*** (0.00239) 0.0722*** (0.0148) −0.0168*** (0.00138) 0.00178 (0.00435) 0.109*** (0.0117) 0.0169*** (0.00371) −0.00149 (0.00341) – – 3.496*** (0.0510) −0.00980*** (0.000464) – – – – – – 0.426*** (0.0314) Y 0.002 0.948 0.355 95 893 114

0.745*** (0.00703) −0.00547*** (0.000627) −0.00702*** (0.000705) 0.135*** (0.0246) −0.0523*** (0.00409) −0.0151*** (0.00268) 0.133** (0.0434) −0.0148*** (0.00140) 0.00121 (0.00425) 0.0963*** (0.00920) 0.0128*** (0.00343) 0.000555 (0.00336) – – – – – – −1.056*** (0.0709) −0.534*** (0.0428) 0.295*** (0.00439) 0.374*** (0.0317) Y 0.003 0.963 0.258 96 893 114

LENDINGRATE GDPG NIM CAR LDR PROV SIZE FB SOB LGOB PB ISLAMIC NIMISLAMIC SIZEISLAMIC CARISLAMIC PROVISLAMIC LDRISLAMIC Constant Year dummies AR1 AR2 Hansen No. of instruments Observations Number of NO

Standard errors in parentheses ** p < 0.01. *** p < 0.001. The dependent variable SBFPCT is the proportion of small businesses loans to total loans; a proxy for banks’ willingness to lend to small businesses. LSBFPCT is the lagged dependent variable. CAR is capital adequacy ratio; a proxy for banks’ risk. LDR is loan deposit ratio; a proxy of intermediation and liquidity risk. PROV is the loan loss provision to total loans ratio; a proxy of banks’ ex-post risk. NIM is net interest margin; a proxy of profitability. SIZE is the natural logarithm of total assets; a proxy for bank size. LENDINGRATE is lending rate by Bank Indonesia; a proxy for monetary policy. GDPG is Gross Domestic Product at real prices growth rate. We include a dummy variable ISLAMIC for Islamic banks in the model to unveil whether Islamic banks are more willing to lend to small businesses compared to the conventional counterparts. FB is a dummy variable for foreign banks. LGOB is a dummy variable for Local Government bank. SOB is a dummy variable for State-owned bank. PB is a dummy variable for private-owned bank. The dummy variable for Joint-venture-banks (JVB) is not included in the model to avoid multicolinearity. NIMISLAMIC is the product of NIM and the dummy variable for Islamic banks. SIZEISLAMIC is the product of SIZE and the dummy variable for Islamic banks. CARISLAMIC is the product of CAR and the dummy variable for Islamic banks. PROVISLAMIC is the product of PROV and the dummy variable for Islamic banks. LDRISLAMIC is the product of LDR and the dummy variable for Islamic banks.

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Fig. 3. Net interest margin (NIM) versus the proportion of small business lending to total loans (SBFPCT) for Islamic and conventional banks.

inflow to small businesses. The lending activities by Islamic banks should be scrutinised by regulators and policy makers to prevent exploitation of SBs by overpriced products.

4.2. Impact of loan diversification towards SBs lending on banks’ efficiency and risk Our second objective in this paper is to investigate the Granger-causes between banks’ diversification, efficiency and ex-post risk. We estimate the system of equations in (4)–(6) to provide empirical evidence from the Indonesian banks by testing the hypotheses in the managerial behaviour model (Berger and DeYoung, 1997) and the modified managerial model (Rossi et al., 2009). We follow Rossi et al. (2009) and employ the Arellano–Bond dynamic panel data model to obtain our results. Following a similar structure to the previous section, we first present our findings on the whole sample followed by a further investigation of Islamic banks. For the whole sample, we estimate two groups of results: PANEL A and PANEL B. In the former, we include cost efficiency as banks’ efficiency measure, and in the latter we replace it with profit efficiency (see Table 7). We discuss our results in the same sequence as Eqs. (4)–(6). First we elaborate on our results of the impact of loan portfolio diversification towards SBs loans. The sum of the coefficients of the HHIL (the proxy for diversification towards SB loans) is positive and significant. This implies that higher levels of concentration (low diversification) are associated with higher risk provisions for Indonesian banks. This supports the classical diversification hypothesis. At sample average, banks managers seem to allocate more resources to monitor the performance of SBs loans, which in turns diminishes cost efficiency (Table 7, column 2A). This provides evidence to support the monitoring hypothesis. Regarding profit efficiency, the coefficient of HHIL is negative and significant (Table 7, column 2B). This result implies that banks with portfolios less diversified towards SBs loans seem to be more efficient. This finding supports the classical diversification hypothesis. The negative value of the sum of the HHIL coefficients (Table 7, column 3A) refutes the economic capital hypothesis. HHIIN is the proxy for the diversification of banks’ income from various sources. Its positive value supports the classical diversification hypothesis. That is, at the sample average, the higher diversification (higher concentration) of income is associated with lower (higher) risk (Table 7, column 1A). Unlike loan diversification, higher income concentration (high diversification) is associated with lower (higher) cost efficiency, i.e. banks with high income diversification levels tend to relax their monitoring efforts, which in turn creates a cost reduction and a higher cost efficiency. This result supports the idiosyncratic risk hypothesis. Regarding income diversification and profit efficiency, the value of HHIIN is negative and significant (Table 7, column 2B) which supports the classical diversification hypothesis. In terms of the effect of income diversification on capital (CAP), the coefficient of HHIN is positive and significant (Table 7, columns 3A and 3B) which is consistent with the economic capital hypothesis. This reflects the fact that banks which capitalise on their highly diversified income portfolio hold less capital. The negative sum of the coefficients of the cost efficiency variable (EFF) (Table 7, column 1A) supports the bad management behaviour of Berger and DeYoung (1997). At the sample average, high (low) cost efficiency Granger-causes low (high) risk, i.e. inadequate operating cost management leads to poor loan portfolio management, and therefore low efficiency may exacerbate banks’ ex-post risk. The sum of the coefficients of CAP is positive and significant (Table 7, columns 1A and 1B), providing evidence to refute the moral hazard hypothesis at the sample average. In Indonesian banks, the higher the banks’ capitalisation, the higher the level of provisions for problem loans. In other words, banks in Indonesia seem to take on higher

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Table 7 Determinants of banks’ willingness to lend to small businesses with focus on Islamic banks and banks’ ownership. VARIABLES

L.PROV L2.PROV L.EFF L2.EFF L.CAP L2.CAP L.HHIL L2.HHIL L.HHIIN L2.HHIIN L.GDPG L2.GDPG Constant Observations Number of NO AR1 Sargan

PANEL A: cost efficiency

PANEL B: profit efficiency

1A PROV

2A COSTEFF

3A CAP

1B PROV

2B PROFEFF

3B CAP

0.0231*** (0.00625) 0.0517*** (0.00737) −43.88*** (2.219) 42.09*** (2.152) 0.0166*** (0.00274) 0.00195 (0.00230) 0.0265*** (0.00389) −0.0168*** (0.00129) 0.00420** (0.00143) 0.00508** (0.00162) −0.000256 (0.000241) −0.00100** (0.000381) −0.00355*** (0.000510)

−8.10e−07 (1.75e−06) 1.11e−06 (2.10e−06) 1.870*** (0.00143) −0.876*** (0.00137) −6.40e−07 (6.88e−07) −4.65e−07* (2.01e−07) 1.37e−06* (5.61e−07) 4.89e−07 (5.27e−07) −9.55e−07** (3.13e−07) −1.62e−06*** (2.14e−07) −2.33e−08 (1.54e−08) −1.94e−08 (1.79e−08) 2.36e−06*** (1.97e−07)

0.420*** (0.0106) −0.182*** (0.0104) −26.90*** (3.304) 26.19*** (3.221) −0.0551*** (0.00749) −0.0212*** (0.00432) 0.0159** (0.00615) −0.0560*** (0.00381) 0.0285*** (0.00293) −0.00349 (0.00506) −0.00275*** (0.000447) 0.00382*** (0.000595) 0.00216** (0.000804)

0.0288*** (0.00616) 0.0328*** (0.00601) −0.380 (0.296) 0.447 (0.275) 0.0239*** (0.00251) −0.00111 (0.00270) 0.0262*** (0.00523) −0.0163*** (0.00136) 0.00434*** (0.00124) −0.00495*** (0.00122) −0.000820*** (0.000188) −0.000838* (0.000352) −0.00132** (0.000479)

−0.000131*** (1.00e−05) −3.92e−05*** (3.35e−06) 2.057*** (0.000459) −1.061*** (0.000443) −7.13e−06*** (2.07e−06) −1.18e−05*** (1.97e−06) −2.42e−06 (2.77e−06) −1.71e−06*** (4.29e−07) −5.67e−06*** (1.01e−06) −1.17e−06* (5.76e−07) 4.04e−07*** (7.64e−08) 4.69e−07*** (1.14e−07) −2.66e−05*** (5.23e−07)

0.316*** (0.0143) −0.0753*** (0.0126) −1.400 (0.912) 1.245 (0.836) −0.0285*** (0.00817) 0.0126* (0.00491) 0.0408*** (0.00507) −0.0388*** (0.00747) 0.0478*** (0.00244) 0.0215*** (0.00336) −0.00187*** (0.000468) 0.00129* (0.000573) 0.00490*** (0.00117)

667 112 0.0003 0.137

667 112 0.008 0.323

667 112 0.001 0.466

661 111 0.0004 0.376

661 111 0.005 0.536

661 111 0.001 0.502

Standard errors in parentheses * p < 0.05. ** p < 0.01. *** p < 0.001. † p < 0.1. PROV is a measure of banks’ loan portfolio quality and is calculated as loan loss provision relative to banks’ loans; EFF is either banks’ cost or profit efficiency scores; HHIL is a normalised Herfindahl–Hirschman index that measures the diversification of a bank’s loan portfolio (other loans, small businesses loans) towards small businesses loans, a value of 1 means that the bank portfolio is fully concentrated; HHIIN is a normalised Herfindahl–Hirschman index that measures the degree of banks’ income (interest income, investment and securities income, and fees and commission income) a value of 1 means the bank income is coming from one source; CAP is equity to total assets; a proxy for economic capital that measures bank capitalisation; GDPG is Gross Domestic Product growth at real prices; a proxy for economic cycle. L refers to the lag of one year of the variables. L2 is the lag of two years of the variables.

risk compensating this with higher levels of capital. The negative and significant coefficients of CAP (Table 7, column 2A) support the bad luck hypothesis. We expand on the models of Berger and DeYoung (1997) and Rossi et al. (2009) by including GDPG into our model. The coefficients of GDPG (Table 7, columns 1A and 1B) are negative and significant, which confirms a cyclical approach of bank managers in dealing with risk. In other words, bank managers in Indonesia seem to increase (decrease) loan loss provision during periods of economic downturn (economic growth). In order to disentangle the Islamic bank manager’s behaviour in our models (4)–(6), we interact the dummy variable for Islamic banks with some of the key variables in our hypotheses and re-run the models. For the sake of brevity, in Table 8 we present only the additional interaction variables,14 and we separate the analysis of the hypothesis on diversification (Table 8, columns 1–3C and 1–3D) from others (Table 8, columns 4C and 4D) to avoid multicollinearity. The results reveal that in the case of Islamic banks in Indonesia, the higher levels of concentration in loan portfolio (i.e. low diversification towards SB lending) Granger-causes higher provisions for risk. The sum of the coefficients of ISLAMHHIL is positive and significant for the two specifications (Table 8, columns 1C and 1D) which supports the classical diversification hypothesis. Regarding the impact of loan portfolio diversification on cost efficiency; our results support the monitoring hypothesis. It seems that Islamic bank managers allocate more resources to monitor the performance of SB loans, which in turns diminishes

14

Full results are available upon request from the corresponding author.

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Table 8 Determinants of banks’ willingness to lend to small businesses with focus on Islamic banks and banks’ ownership. Variables

L2.ISLAMIHHIL L.ISLAMIHHIIN L2.ISLAMIHHIIN

PANEL D: profit efficiency

1C PROV

2C EFF

3C CAP

4C PROV

1D PROV

2D EFF

3D CAP

4D PROV

Y 1.030*** (0.0357) −0.136*** (0.0108) 0.0331* (0.0167) −0.0985*** (0.0236)

Y 5.72e−06 (1.35e−05) 1.52e−05† (8.45e−06) 3.30e−06 (2.44e−06) 5.01e−06* (2.01e−06)

Y 0.473*** (0.132) 0.00671 (0.0302) −0.0749* (0.0326) −0.332*** (0.0631)

Y

Y 1.017*** (0.0267) −0.102*** (0.00833) 0.0993*** (0.0110) −0.0815*** (0.0155)

Y 3.55e−06 (3.35e−05) 4.35e−06 (5.28e−06) 3.55e−05*** (8.26e−06) 7.51e−05*** (1.24e−05)

Y 0.417** (0.161) −0.0989* (0.0440) −0.334*** (0.0481) −0.342*** (0.0646)

Y

Constant

−0.00428*** (0.000562)

1.91e−06*** (2.78e−07)

0.00237* (0.000981)

−0.419*** (0.0432) −1.020*** (0.0535) 105.8*** (24.64) −107.8*** (23.66) −0.00407*** (0.000584)

Observations Number of NO AR1 Sargan

667 112 0.0001 0.235

667 112 0.0002 0.3778

667 112 0.001 0.486

667 112 0.0003 0.182

L.ISLAMICAP L2.ISLAMICAP L.ISLAMIEFF L2.ISLAMIEFF

−0.00424*** (0.000481)

−2.58e−05*** (8.85e−07)

0.00873*** (0.00107)

−0.484*** (0.0645) −1.070*** (0.0566) −39.25* (18.63) 31.83† (17.86) −0.00243*** (0.000521)

661 111 0.0009 0.2216

661 111 0.0001 0.356

661 111 0.001 0.566

661 111 0.0005 0.3717

Standard errors in parentheses * p < 0.05. ** p < 0.01. *** p < 0.001. † p < 0.1. ISLAMIEFF is the interaction between the Islamic banks dummy variable and with either banks’ cost or profit efficiency scores; ISLAMIHHIL is the interaction between the Islamic banks dummy variable and the normalised Herfindahl–Hirschman index that measures the diversification of a bank’s loan portfolio (other loans, small businesses loans) towards small businesses loans, a value of 1 means that the bank portfolio is fully concentrated; ISLAMIHHIIN is the interaction between the Islamic banks dummy variable and the normalised Herfindahl–Hirschman index that measures the degree of banks’ income (interest income, investment and securities income, and fees and commission income) a value of 1 means the bank income is coming from one source; ISLAMICAP is the interaction between the Islamic banks dummy variable and the equity to total assets a proxy for economic capital that measure bank capitalisation.

M. Shaban et al. / Journal of Economic Behavior & Organization 103 (2014) S39–S55

Variables in Table 7 included L.ISLAMIHHIL

PANEL C: cost efficiency

Managerial behaviour model hypotheses

Islamic banks

Loan portfolio diversification towards small businesses loans (HHIL) √ The bad management hypothesis x The skimping hypothesis:  The bad luck hypothesis √ The moral hazard hypothesis

Income portfolio diversification to various sources of income (HHIIN) √ The bad management hypothesis x The skimping hypothesis: The bad luck hypothesis  √ The moral hazard hypothesis

√

Conventional banks √ x √ x

√ x √ x

Modified managerial behaviour model hypotheses

Classical diversification hypothesis Lack of expertise hypothesis Idiosyncratic risk hypothesis Monitoring hypothesis Quiet life hypothesis Economic capital hypothesis Classical diversification hypothesis Lack of expertise hypothesis Idiosyncratic risk hypothesis Monitoring hypothesis Quiet life hypothesis Economic capital hypothesis

Islamic banks

Conventional banks

√

√

x x √

x x √

 √

x x

x √ x √ √ x

√ x x √ x 

denotes the presence of supporting evidence for the hypothesis; x denotes the presence of refuting evidence of the hypothesis;  denotes no evidence provided by the results for the hypothesis.

M. Shaban et al. / Journal of Economic Behavior & Organization 103 (2014) S39–S55

Table 9 A summary of our findings.

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M. Shaban et al. / Journal of Economic Behavior & Organization 103 (2014) S39–S55

cost efficiency (Table 8, column 2C). Regarding profit efficiency; the coefficient of ISLAMIHHIL is positive, but insignificant (Table 8, column 2D) providing no evidence to support the quite life hypothesis. Similar to conventional banks, Islamic banks tend to hold less capital with higher levels of diversification in their loan portfolios. This is confirmed by the positive and significant value of the coefficient of ISLAMIHHIL (Table 8, columns 3C and 3D). Our results provide opposing evidence on the diversification of Islamic banks’ income portfolios when compared to those of conventional banks. The sum of the coefficients of ISLAMIHHIIN; proxy diversification of Islamic banks income from various sources, is negative and significant. This supports the lack of expertise hypothesis and implied that Islamic banks lack the expertise to mobilise income from various sources, and this is reflected in high risk. In other words, the other sources of income in Islamic banks seem to be highly volatile, which Granger-causes higher levels of risk. Unlike loan diversification, higher concentration of income sources (high diversification) is associated with high (low) cost efficiency. This supports the monitoring hypothesis and refutes the idiosyncratic risk hypothesis, i.e. Islamic bank managers seem to allocate excessive resources to monitor income generated from different sources (Table 8, column 2C). Regarding income diversification and profit efficiency, the value of the sum of ISLAMIHHIIN coefficients is positive and significant (Table 8, column 2D) which supports the quite life hypothesis, i.e. the higher cost allocated to monitoring activities by a risk-averse manager in Islamic banks results in lower profit efficiency. In terms of income diversification effect on capital (CAP), the coefficient of ISLAMIHHIN is negative and significant (Table 8, column 3C and 3D), refuting the economic capital hypothesis, i.e. the more Islamic banks diversify their income sources, the more capital they hold. In the sense of Berger and DeYoung (1997)’s hypotheses; for Islamic banks, the negative sum of the coefficient of the cost efficiency variable (EFF) suggests the presence of bad management behaviour (Table 8, column 4C). The coefficients of CAP are negative and significant (Table 8, columns 4C and 4D) supporting the moral hazard hypothesis for Islamic banks. This result confirms that Islamic bank managers are risk-seekers rather than being risk-averse. Finally, we did not find any evidence in support of the bad luck hypothesis in the case of Islamic banks. We provide a summary of our findings in Table 9 as a mapping aid to our results.

5. Conclusion Over the last few decades, the literature argued that SBs are less likely to have access to finance when compared to larger firms. For policy purposes, as well as assisting with the identification of the right financing tool, it is imperative to analyse the willingness of different types of banks to lend to SBs. Country case studies are crucial to exploring the policies and tools which contribute towards financial constraints (Beck and Demirgüc¸-Kunt, 2006). However, we are not aware of any country case study that differentiates between Islamic and conventional banks’ willingness to lend to SBs. Therefore, in this study, we have focused on banks in Indonesia to provide a comprehensive analysis of the banks’ willingness to lend to SBs by differentiating between the behaviour of conventional and Islamic banks. In our initial analysis we have examined the determinants of banks’ willingness to lend to SBs, and in the second part of our analysis we have investigated the Granger-causes of diversification towards SB lending on banks’ efficiency and ex-post risk. Our first set of results reveals that, in general, profitability is an important stimulant for Indonesian banks to lend to SBs. Compared to small banks; large banks are less interested in lending to SBs. Islamic banks seem to benefit more from lending to SBs, given the impressive improvement in their net interest margin and lower capital when compared to conventional banks. Islamic banks’ products and their structure are suitable for SB lending in terms of easing the preconditioned collateral requirements of the conventional banks. Nonetheless, our results signal overpricing behaviour by Islamic banks, represented in a relatively high unadjusted rate of return, given the risk exposure of their products. Our second set of results also provides a significant contribution to the literature. We have adopted the managerial behaviour model (Berger and DeYoung, 1997) and the modified managerial behaviour model (Rossi et al., 2009); and have investigated the impact of both loan portfolio diversification towards SB lending and income diversification on Indonesian banks’ efficiency, and ex-post risk. Regarding loan portfolio diversification towards SB lending; both Islamic banks and conventional banks provide evidence to support the classical diversification hypothesis, the lack of expertise hypothesis, the Idiosyncratic risk hypothesis and the monitoring hypothesis. We find evidence that Islamic bank managers seem to hold less capital, because they rely on the income generated by their portfolios being diversified towards SBs. In the case of diversification of banks’ income portfolio, our results seem to differentiate between the behaviour of Islamic banks compared to the conventional ones in Indonesia. Unlike conventional banks, Islamic banks lack expertise in mobilising a diversified income from different sources of banking operations. We also found evidence that Islamic bank managers seem to allocate significant resources to monitoring their different sources of income, which diminishes their profit efficiency. We provide evidence in support of the moral hazard hypothesis in the behaviour of Islamic banks, but not for conventional banks. Overall, our results have some implications for policymakers. Islamic products could be an answer for obstacles that hinder the flow of funds to SBs. But in this vein, regulators should closely monitor the Islamic banks’ operations to avoid exploitation of SBs.

M. Shaban et al. / Journal of Economic Behavior & Organization 103 (2014) S39–S55

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Appendix. Table A.1 The percentage of small business lending as a proportion of bank’s total loans portfolio. Bank type

2002

2003

2004

2005

2006

2007

2008

2009

2010

Total

Conventional Islamic Total

33.2% 37.9% 33.3%

29.8% 33.6% 29.8%

29.1% 34.7% 29.2%

27.1% 25.9% 27.1%

23.3% 54.2% 24.1%

21.8% 51.7% 22.6%

19.5% 47.5% 20.3%

19.8% 36.3% 20.5%

20.0% 30.6% 20.7%

24.8% 38.2% 25.2%

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