Bad Loans and Efficiency in Italian Banks Angelo Zago∗ Paola Dongili Dipartimento di Scienze Economiche - Università di Verona Revised version - November 2006 Abstract We estimate banks’ technical efficiency taking into account bad loans by using directional distance functions, a generalization of the radial distance functions. Using these functional representations of technology it is possible to credit banks for their efforts to increase outputs and decrease bad loans and resource use. We find that once bad loans are considered, the efficiency of banks increase significantly. In addition, omitting bad loans may underestimate the performance of better credit quality banks. These results suggest that a significant aspect of banking production - credit quality - needs to be considered when evaluating banks’ performances for regulatory purposes. JEL: C14, D24, E50, G21, G28. Keywords: Banking; credit quality; technical efficiency; risk and stability; regulation.
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Corresponding author: Angelo Zago, Viale dell’Università, 4 - Verona 37129 - Italy. Phone +390458028414, fax +39045828529, email:
[email protected].
1. Introduction Notwithstanding the development of financial markets and different financial intermediaries, banks maintain their central role in the economic systems of many developed and developing countries. Indeed, there is a century-long debate on the relative merits of bank versus market-based financial systems, and even recent results confirm that although overall financial development is robustly linked to economic growth, there is no unanimous support for either the bank or the marketbased view (Claessens and Laeven, 2005; Dell’Ariccia et al., 2005; Demirguc-Kunt and Levine, 1999; Guiso et al., 2004; Leahy et al., 2001; Levine, 2002; Rajan and Zingales, 1998). The problems related to banks’ activities and performances are thus the focus of a large body of literature. Credit quality in particular is a critical issue, since it is linked with macroeconomic, competition and banking supervision aspects, and thus it is especially important and relevant for bank-based economic systems, such as those in many European countries (Belaish et al., 2001; De Bandt and Davis, 1999; European Central Bank, 2003; Gambacorta, 1998; Goddard et al., 2001). The aim of this paper is to measure the efficiency of a banking system taking into account the role that credit quality may play at a microeconomic level. For this purpose, we estimate technical efficiency using primal measures based on directional distance functions, a generalization of the radial distance functions which, since Shephard’s (1970) contributions, have been used to give a singlevalued representation of production relations in case of multiple inputs and multiple outputs (Chambers et al., 1996 and 1998). Using directional (technology) distance functions it is possible to credit banks for their efforts to increase outputs while simultaneously decreasing problem loans and resource use. With the efficiency measures introduced1 in this study, we can look at whether high levels of problem loans, usually seen as a signal of the financial distress of a bank, necessarily imply bank’s inefficiency. Indeed, we can explicitly investigate the effects of credit quality on bank’s efficiency. Although there are already some contributions that try to answer these and related questions, as we review in the next section, most of them treat problem loans indirectly, thus not properly 1 While directional measures are quite well known and applied, for instance, in measuring the performances of manufacturing firms crediting their success in reducing pollution (for a recent survey see, e.g., Ball et al., 2004), to the best of our knowlegde no such applications are to be found in the banking sector.
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crediting banks for their efforts to reduce them. It is worth to emphasize that quality of credit, together with its availability and cost, is important for both resource allocation and growth. The models of delegated monitoring and liquidity creation emphasize the role of banks in the evaluation of borrowers’ credit worthiness and hence in the resource allocation process. Poor credit quality, often seen as a signal of excessive credit risk, may cause greater volatility in total credit with possible backward linkages to the banking system itself (Berger and Udell, 2004; Bernauer and Koubi, 2004; Cavallo and Majnoni, 2001; Dell’Ariccia et al., 2005; Jiménez and Saurina, 2005). The globalization of financial markets, the increasing competition and the new activities carried out by banks do not diminish the importance of credit risk. To the contrary, credit quality is an important intermediate target for regulators in order to dampen possible financial crisis, and it is the focus of the Basel reform in the banking supervision system (European Central Bank 2003 and 2004). In addition, credit quality is a major instrument in banking competition to the extent that it may lead to an efficient cost structure. A bad credit screening moreover makes the bank’s lending subject to the winner’s curse (Freixas and Rochet, 1997). Quality of credit is then a specific signal of the soundness of the banking sector as excessive credit risk could impair the efficient allocation of capital. But bad credit may also impair the performance of banking institutions. Indeed, “.. virtually all research [..] find that failing institutions have larger proportions of non-performing loans prior to failure, and that asset quality is a statistically predictor of insolvency ..” (Berger and DeYoung, 1997: 850). In addition, some authors argue that a significant relationship is to be found between the efficiency of the banking system and economic growth, and at the same time the efficiency of the banking system could influence the performance of the real sector of the economy (Koetter and Wedow, 2006; Lozano-Vivas and Pastor, 2006; Lucchetti et al., 2001). The idea of this study therefore is to explicitly credit banks for their effort and success in reducing the “bad output” associated with banking production, that is problem loans. In this endeavor, the non-parametric efficiency measures based on directional distance functions are the natural choice, and we apply them to estimate the efficiency of the Italian banking system.2 Using these performance 2
Although it is quite difficult to find comparable data for different countries over time, recent analysis suggest that Italy in the 90’s was among the developed countries with a higher incidence of bad loans. In 1995, the ratio of non-performing to total loans was 9% for Italy and 6.6% for the UE, while in 1998 it was 8.9% and 3.9% respectively (Belaish et al., 2001).
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measures, in the paper we find that the (average) technical efficiency of Italian banks increase once we recognize their efforts to reduce problem loans. This stastistically significant effect is consistent for different output specifications and for all the years considered, implying that omitting credit quality when measuring banks’ performances may give biased results. In addition, when further analyzing the differences among banks, we find that the impact of considering credit quality in the efficiency estimation is more important for those banks that are most successful in obtaining good credit quality. In other words, not considering bad loans in performance measurement may underestimate (overestimate) the performance of the good (bad) credit quality banks. We argue that these results are particularly important given the potential use of performance evaluation for regulatory purposes and the need for consistent efficiency estimates (Bauer et al., 1998). Although we do not investigate the temporal relationships between efficiency and bad loans along the lines of Berger and De Young (1997), we believe the evidence we present is more consistent with the bad management hypothesis. The next section reviews the literature that take into account problem loans in the production process and efficiency evaluation of banks. Then we introduce the notation, the model and the empirical algorithms we use in the study. After introducing the data in section 4, we present and discuss results in section five. Section six concludes the paper with the limitations of the methodology presented and suggestions for further research work.
2. Review of the literature The study of Berger and De Young (1997, henceforth BDY) is a central contribution for the discussion of the channels through which problem loans may influence banks’ efficiency. They find a negative relationship between cost efficiency and credit quality measured by non-performing loans (henceforth NPL) in failed banks. Observing that failing institutions have larger proportions of NPL and that an average institution incurs higher costs and lower profits than best practice banks, they test different hypothesis to describe the intertemporal relationship among problem loans, cost efficiency and financial capital. They call it ‘Bad luck’ when loan quality problems may arise due to events beyond the control of the bank’s management, while their ‘Bad management’ hypothesis relates assets quality to the performances of the management who may have poor skills in credit scoring or difficulties in monitoring and control4
ling borrowers. Their ‘Skimping hypothesis’ explains the relationships as a bank’s trade-off between short term operating costs and future loan performance problems, while their ‘Moral hazard hypothesis’ explains the incentives to take more risks by a bank with low capital levels. Using Granger causality techniques, their analysis of US commercial banks in 1984-1995 suggests that cost efficiency may be an important predictor of future problem loans and problem banks. In other words, when banks are less cost efficient they end up having also more problem loans (bad management). However, they also find that when NPL increase this is followed by a deterioration of cost efficiency, consistently with the bad luck hypothesis. More recently, Williams (2004) applies the same methodology to European savings banks during the nineties. Contrary to the US experience, his econometric results suggest that the most pressing problem for European banks is bad management, i.e., that poorly managed banks tend to have more poor quality loans. Even after recognizing some shortcomings,3 the European results are thus inconsistent with the findings on US banks, with the only common finding between studies being the statistical evidence of bad management at US commercial banks and European saving banks as well. The result thus “.. has policy implications for banks regulators and supervisors, and bank owners and managers. We concur with Berger and De Young (1997) and emphasize the importance of efficiency (and the need to test the robustness of different efficiency measures.) [..] The policy implication is that bank regulators and supervisors (and bank management) should adopt more sophisticated measures of bank performance for regulatory purposes ..” (Williams, 2004: 2452). Before the BDY’s study few papers had explicitly taken into account the relationships between credit risk and efficiency. Hughes and Mester (1993), beginning a line of research to investigate the role of financial capital in affecting scale and overall efficiency, are among the first to specify loans by different product lines such as commercial, consumer, real estate and other loans - as an initial step to take into account risk in the estimation of multioutput cost functions. In addition, they add NPL in the output vector and hence in the production possibilities set as a quality measure for total outputs. Moreover, in the empirical specification of the cost function - either translog or Fourier - they model credit quality as an 3
Due to limitations in European bank data, the proxy used for credit quality is loan loss provisions which “.. may be subject to managerial discretion and may be over or under stated in any given year ..” and “.. could contain an element of endogeneity, rather than the amount of problem loans ..” (Williams, 2004: 2452 and note 5).
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output and not as a control variable.4 Among other studies along these lines, Mester (1996) uses a stochastic cost frontier accounting for the quality and riskiness of bank output. Again, output quality is measured by the average volume of non performing loans and this measure is included in the output vector of the input requirement set, thus explicitly taken into account in the technological relationship. Hughes and Mester (1998) amend the standard cost model to account for the role of financial capital in banking, modelling the demand for financial capital so that it can be used as a cushion against insolvency. Once again, the technology is represented by a transformation function defined on a vector of variables characterizing output quality, among which bad loans.5 Other related papers in the stochastic frontier approach use a slightly different specification. Berger and Mester (1997), for instance, compare cost and profit efficiency and consider credit quality as an environmental variable. In an attempt to avoid the endogeneity problem, i.e., the issue of whether bad loans are due to exogenous factors or management problems, they use the ratio NPL/total loans both at the bank and State level.6 Mester (1997) explicitly takes into account banks’ heterogeneity estimating both a single cost frontier and a model allowing for different frontiers and error terms across Federal Reserve Districts. She estimates translog cost functions, taking financial capital and loan quality into account.7 Hughes et al. (1996) look a the impact of geographical diversification using a structural model of a profit function with endogenous risk and modelling risk preferences. So the consideration of managers’ trade offs between risk and return, allowing for the possibility of non-risk neutrality, leads to the inclusion of NPL as an indicator of underlying credit quality of the bank, in addition to financial capital, which is otherwise usually done in a cost function context. Besides these contributions using parametric and stochastic approaches, few others have been suggested by DEA scholars. Probably the first to consider credit 4
Output quality is interacted with output, factor prices, capital, etc. and not specified as a simple fixed netput or environmental variable, i.e., not interacted. In the first case we have scaling, in the latter we would have translating effects (Gorman, 1976; Lewbel, 1985). 5 Altunbas et al. (2000), claiming they are following Mester (1996), study the impact of risk and quality factors on Japanese banks’ costs by using a stochastich cost frontier and including loan losses provisions as a proxy for loan quality. However, the loan quality proxy is not interacted with any other output or price variables. 6 They do not interact credit quality neither with outputs, financial capital nor factor prices, i.e, they treat it as a fixed netput. 7 However, this latter is not interacted with factor prices.
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quality, following expert advice from a banking specialist, Charnes et al. (1990) consider loan loss provisions as an additional input together with actual loan losses. Thus in the DEA approach the first attempt to consider credit quality considers a measure of bad loans as an input, i.e., something to be reduced. Berg et al. (1992), in a Malmquist study of productivity growth during the deregulation of Norwegian banking in the period 1980-1989, compare the results when they also include the (negative) of loan losses in the year considered.8 Even though they recognize that such indicator of credit quality may be imperfect - not least because present losses arise mostly from loans granted in earlier years - they find limited effects and little differences with respect to the specification without such loan losses, concluding that the additional constraint for loan losses is thus mostly not binding. Resti (1997) in a joint application of parametric and non-parametric techniques to measure the efficiency of Italian banks during the 1988-1992 period, finds a negative and significant correlation between efficiency indexes and the bad to total loans (BTL henceforth) ratio.9 He then uses the BTL ratio as a non-discretionary variable as suggested by Banker and Morey (1986), that is a variable describing the amount or quality of the inputs and outputs that cannot be freely modified by the producer. In other words, he considers the BTL ratio as a control variable to rule out ‘unfair’ comparisons among banks with different quality. With this DEA specification he finds that “.. the units labelled as most inefficient when credit quality is not considered [..] do not increase their scores more than other banks [..]. Therefore, it seems highly unlikely that their extra costs arise from a more effective monitoring of the loan portfolio. [..] seemingly counterintuitive findings (higher costs with lower quality) ..” (Resti, 1997: 243, italics in the original article). Drake and Hall (2003) uses DEA to analyze the technical and scale efficiency in Japanese banking in 1997. Following the intermediation approach, they use provisions for loan losses (PLL henceforth) as an indicator to the extent of problem loans - in the sense they are acting as a proxy for the resources and expenses involved in dealing with problem loans - and incorporate them as a categorical variable in DEA analysis thus following Banker and Morey (1986). They find that 8
Within the value added approach, the authors take into account three outputs: short term loans, long term loans, and non bank deposits, while the negative loan losses are interpreted as an indicator of the quality of the loans. 9 In a previous study, in the output vector Resti (1994) considers directly only performing loans, i.e., total loans net of NPL.
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controlling for problem loans raises the technical and scale efficiency level of all banks, suggesting that potential economies of scale may be overestimated when risk factors are excluded, in line with the findings of other studies. A common feature of some of these studies therefore is the attempt to estimate efficiency controlling for the credit risk as an environmental variable which influences the performance of the banks. However, as emphasized in the Berger and Humphrey’s (1997) survey article on financial institutions’ efficiency, including problem loans as an explanatory variable is most appropriate when they are caused by bad luck events exogenous to the bank. When, on the other hand, bad management causes dominate then “.. problem loans are essentially endogenous to financial efficiency and should not be controlled for in the analysis of efficiency ..” (Berger and Humphrey, 1997: 194). Following these remarks, more recently Pastor, in a series of papers dealing with this topic, tries to explicitly decompose the credit risk components due to bad luck from those due to bad management. Using PLL as an indicator of credit risk, in Pastor (1999) he adopts DEA with a sequential procedure including credit risk and some environmental variables - business cycle and structural economic indicators - to account for exogenous factors beyond management control.10 In Pastor (2002), the same methodology is applied to the banks of four European countries - France, Germany, Italy and Spain - for the period 1988 to 1994. The interesting finding that emerges is that on average “.. about 80% of bad loans is due to factors internal to firms, while the rest is attributable to circumstances exogenous to firms ..” (Pastor, 2002: 909). For the case of Italy, where supposedly the banking system is less efficient in risk management, the fraction is higher and thus the proportion of bad loans due to internal factors is 87%, compared to 81%, 74% and 73% respectively for France, Spain and Germany. Pastor and Serrano (2005), recognizing that the few studies obtaining efficiency measurement adjusted for risk do so not properly,11 use a profit function frontier 10
Using data from Spanish banks in 1985-1995, in a first step he calculates risk management efficiency and hence the component due to bad management. Then he uses this risk efficiency measure recovering the volume of PLL that are due to bad management and considers it as an input to reduce, as in Charnes et al. (1990), in a subsequent efficiency evaluation. Finally, in a subsequent step he further controls for environmental variables. 11 Pastor and Serrano suggest that Hughes and Mester (1993), for instance, adjust for risk by “.. directly including bad loans as an additional input in the estimation ..” (2005: 632) of a cost frontier, thus suffering from bias to the extent that part of bad loans may be due to factors exogenous to the banks, i.e., unfavourable economic conditions, and being incomplete since they are derived from a cost efficiency estimation, thus omitting the revenue effects.
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efficiency estimation considering both credit risk and environmental variables. Their findings reveal that, for seven countries in the Euro area in the period 19931997, cost efficiency and cost efficiency adjusted for risk hardly differ. However, profit efficiency adjusted for risk is higher than profit efficiency not adjusted, revealing that it is from the profit (revenue) viewpoint that behavior toward risk really matters. To conclude, we would like to highlight that the literature surveyed has some limitations that need to be addressed and that may be summarized with the following. The stochastic cost frontier approach does not distinguish from internal and external causes of bad loans. In addition, we believe that although it is common practice to assume that the output vector is exogenous in most studies estimating cost functions, a substantial problem may be the endogeneity of the output vector. We would indeed argue that it is difficult to defend the exogeneity assumption typically made in cost function estimation when dealing with the problems addressed here. As often recognized, the reduction of bad loans requires an increase in resource use for screening and selecting applicants, monitoring loans, etc. This being the case, it is likely that the endogeneity of the output vector would lead to inconsistent estimates.12 The approach taken by BDY, on the other hand, while allowing to distinguish between the different sources of bad loans, gives results that refer only to the whole banking system instead of offering conclusions at the level of banking firms (Pastor, 1999), making the approach less appealing for regulatory purposes. Moreover, the standard DEA approach either uses credit risk as an input to reduce - thus imputing it mostly to management components13 - or a categorical variable, thus imputing it mostly to exogenous variables considered beyond management control. In sum, as other studies recognize, the methods used so far seem ill equipped for properly handling the problem of credit quality in performance evaluation. In other words, we agree with Williams (2004) on the need for more sophisticated measures of performances. To that end, we have to overcome the fact that the usual “.. efficiency indicators offer only a partial view of firms’ performance. Indeed, they only assess the achievement [..] of one very specific economic objective: minimization of costs or maximizations of profits. They do not consider other as12
As the reader may recall, the seminal paper by Nerlove (1963) on duality using the estimation of a cost function dealt with regulated electricity firms whose output was given. Sheriff (2005) is a recent attempt to take into account the endogeneity of the output vector using GMM. 13 In addition, notice that treating bad outputs as inputs would yield an unbounded output set, which is not physically possible if traditional inputs are given (Färe et al., 2001, 2005).
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pects [..] in the case of banks one of the most important aspect to be considered is credit risk [..] some kind of synthetic indicator is necessary to be able to evaluate both efficiency and credit risk simultaneously ..” (Pastor and Serrano, 2005: 632). In this study we employ a methodology that explicitly consider problem loans as a separate (bad) output jointly produced in a non-separable production process that we model using an axiomatic production theory approach. With this latter, in particular, we model the complementarity between the good output (total loans) and the bad output (bad loans) using the concept of output weak disposability.14 In addition, as a function representation of the technology we use the directional distance functions, a generalization of the radial distance function, introduced to production economics by Chambers et al. (1996) who extended and adapted the idea of the translation functions of Kolm (1976) and Blackorby and Donaldson (1980), and of the benefit function introduced in consumer theory by Luenberger (1992, 1994). The directional distance function allows to compare different firms and to measure their distance from the frontier of the technology moving along a preassigned direction.15 In this fashion, it is possible to evaluate the performance of the firms that need to increase the production of the good outputs and decrease that of bad outputs (Färe and Grosskopf, 2000). In particular, with the directional technology distance function proposed in this study, it is possible to credit banks for their ability to produce good outputs and reduce inputs and bad outputs simultaneously. To the best of our knowledge, this is the first time a relatively new non-parametric methodology is applied to investigate the impact of problem loans on banks’ performances. In the next section we introduce the model and the empirical strategy.
3. Model specification and empirical implementation In this paper we use the directional distance function to incorporate credit quality into the technology and thus estimate bank’s efficiency taking into account credit 14 Weak output disposability means that any proportional contraction of good and bad outputs together is feasible, that is for given inputs, the reduction in bad output(s) is possible if good outputs are reduced proportionally (Färe et al., 2005). In other words, any disposal of the bad output, i.e., the reduction in bad loans, is costly rather than free. 15 This allows to compare firms using a common metrics. In the case of the radial measures, instead, the direction is the radial expansion/contraction toward the frontier and can thus be different for every firm.
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risk. Indeed, since the production of “good” outputs, such as loans and non interest income, is typically accompanied by the joint production of undesirable “bad” outputs, such as the bad or non performing loans, we treat these latter as a bad output. This implies that we recognize that the reduction of the bad output(s) can come only at a cost: either a reduction in the good outputs or/and the use of more resources. In the appendix we explain the idea more formally using an axiomatic approach (see appendix 1). To represent the technology, we use the directional technology distance function, which generalizes both input and output Shephard’s distance functions and provides a complete representation of the technology. When bad outputs are present, following Chambers et al. (1996, 1998), and Chambers (2002), it can be defined as: − → D T (x, y, b; gx , gy , gb ) = max{β ∈ < : (x − βgx , y + βgy , b − βgb ) ∈ T }, (3.1) M H N M H gx ∈
