Maria A Garcia-Valiñas
email: mariangv@uniovi.es

Manuel A Muñiz

María A García-Valiñas

IS DEA USEFUL IN THE REGULATION OF WATER UTILITIES? A DYNAMIC EFFICIENCY EVALUATION

Keywords: water supply, efficiency, Data Envelopment Analysis, price regulation JEL Classification: D24, Q25, Q28

d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Throughout the ages, water has been considered one of the most important natural resources that has made the development of civilization possible [START_REF] Marshall | Memorials of Alfred Marshall, Kelley and Millman[END_REF]; [START_REF] Gibbons | The Economic Value of Water, Resources for the Future[END_REF]]. Water resource availability has conditioned the location and growth of towns and contributed towards public health improvements. Its significance combined with its relative scarcity have made water the object of several conflicts. Its allocation among alternative uses, in order to attain high social welfare levels, has become one of more frequently discussed issues in the field of the economic theory of natural resources.

In this research, our aim is to make a contribution towards improving water allocation by studying the productive process. In particular, we have focused on analysing potential cost savings corresponding to the final stage of water distribution, with a view to be applied to price regulation in this sector, and introduce incentives to get productivity improvements. In this sense, we base our approach on that followed by Thanassoulis (2000, a, b), using Data Envelopment Analysis methodology (DEA) and including some changes in the basic model in order to adapt it to the Spanish case. Scarcity problems which water suppliers in Spain have to face have motivated the inclusion of initial endowments of water resource as a non-controllable input for suppliers, as it is a factor that has conditioned the service in several Spanish regions.

The study is structured as follows. First of all, a price regulation rule has been proposed, thereby justifying the usefulness of the DEA technique. Next, the 3 methodology applied to obtain information about the relative productive performance of entities is described. In the two following sections, we describe the data and variables used and the productive units considered in the DEA analysis. In the next section, we present the main results of this research. Finally, the paper ends with some concluding remarks.

2.-A PROPOSAL FOR PUBLIC REGULATION OF WATER RESOURCES IN SPANISH MUNICIPALITIES

Regulation takes place when a public entity defines a behaviour framework for different economic agents (households, firms), establishing several obligatory constraints. Some services, such as water, electricity or gas supply, are habitual focuses of regulatory activity. In particular, quality and price restrictions are frequent in water distribution services. In this study we want to emphasize price regulation, because of the importance that pricing policies have in this context (OECD, 2001). Spanish legislation defines urban water distribution as a public utility that has to be provided by municipalities. Prices of this service are subject to a twostep approval process, in the context of an authorized tariff regime. First of all, prices are provisionally approved by the municipality, with the Autonomous Regions, through its Price Commissions 1 , providing the definitive approval of the prices proposed by local governments. In this process, price structure is not conditioned, and there are no explicit rules that limit the evolution of tariffs. This latter fact generates important differences in Spanish price levels [START_REF] Mma | Libro Blanco del Agua en España[END_REF]; INE (1999)], which are never justified strictly by cost or other supply conditions. In this sense, it was shown that the expectations of a cheap resource availability and the subventions linked to water prices are the source of current problems, leading to insufficient incentives for promoting water savings [START_REF] Mma | Libro Blanco del Agua en España[END_REF]. In our country, water bill amount to a very small proportion of the total households' expenditure, so that fact reduces the effectiveness of prices as a mechanism to manage water demands in urban contexts (Martinez-Espiñeira and Nauges, 2004). To address this situation, the European Directive 2000/60/CE establishes the framework to guide water policies for EEC members, with a focus on cost recovery. Its basic objective consists of avoiding highly subsidized, below-cost pricing caused by political reasons.

In a parallel way of water tariff design (OECD, 1987;[START_REF] Muñiz | Separating managerial inefficiency and external conditions in Data Envelopment Analysis[END_REF], it would be interesting to study some rule that set global limits on price evolution. These kinds of restrictions, called price-cap regulation, have been defended because of its superiority with respect to other regulatory methods, such as rate-of-return or cost-plus regulation [START_REF] Schmalensee | Good regulatory regimes[END_REF]; [START_REF] Liston | Price-cap versus rate-of-return regulation[END_REF]]. The best-known example in this context is RPI -X regulation, where RPI is a retail price index and X is a factor connected with supplier productivity or efficiency. The flexibility of this rule has been highlighted in that price ceilings are defined for a bundle of the firm's products and /or services.

In this research, framed as it is in a public management environment, we have chosen a specification of this methodology which depends on the specific aid F o r P e e r R e v i e w 5 that supplier is searching for 2 . The features of units evaluated in this study lead us to consider the hypothesis that those units are social welfare-maximizing, or, even, vote-maximizing. Under this assumption, price ceilings would have to be fixed in minimum terms, to avoid reducing prices too sharply [START_REF] Bös | Pricing and Price Regulation[END_REF]. In this case, price variation, P, could not be lower than the following limit:

X RPI P (1)
According to the preceding expression, more efficient entities face a lower limit, and would have to raise prices less than inefficient ones. Incentives to save costs are clear. Suppliers would try to increase efficiency, and consequently the X factor, thereby achieving a lower ceiling in the next period.

The X-index can be exogenously given as far as the regulated entity is concerned, in which case we speak of a political regulation, or it can be fixed in an endogenous manner, which can be referred to as productivity-related regulation. Under both variants, the regulator provides incentives for improving supplier performance, and it would be necessary to evaluate the temporal evolution of each productive unit efficiency. This fact introduces a role for Data Envelopment Analysis (DEA) as an instrument with which productivity performance can be analysed, and which is described in the next section. As it was noted in the introduction, the nature of this research implies, as an intermediate step, the evaluation of potential cost savings of entities that supply water in an urban context in order to define a valid regulatory approach. We have used DEA methodology, in accordance with several studies applied to water services [START_REF] Aida | Evaluating water supply services in Japan with RAM: a Range-Adjusted Measure of inefficiency[END_REF]Thanassoulis (2000, a;b)].

This is a non-parametric methodology, in that unlike most econometric methods, no parameters specifying a particular functional form need to be calculated. First developed by [START_REF] Charnes | Measuring the efficiency of Decision Making Units[END_REF], it is a general linear programming-based technique to evaluate productive efficiency. Assuming homogeneous productive units, a frontier formed by efficient units is designed. In the primal optimization programme, and considering a sector with K suppliers, which produce s outputs denoted by y, using m inputs denoted by x, the efficiency of productive unit k 0 would require solving the following constrained programme: where x 1 is the only input considered in his study, y 1 , y 2 and y 3 are three output categories produced by the supplier, EFF is the potential cost objective that must be reached by each supplier and UC* y1,2,3 are the optimal weights generated endogenously by adapting DEA programming and which are equivalent to the unitary cost of each output. In this sense, for those companies that verify EFF x 1k0

= 0 k h Máx. = s j jk j y u 1 (2) s.a. 1 1 0 = = m i ik i x v i=1,...,m 0 1 1 = = s j m i ij i jk j x v y u j=1,…,s , j u i v , K k k , , , , 1 0 K K = Page 7
< x 1k0 , their potential cost savings would be (x 1k0 -EFF x 1k0 ) .

In our opinion, and considering that our final aim is the measurement of potential cost savings of the regulated entities, the adaptation of the basic DEA Following this reasoning, in the current study we use the original version of the DEA programme orientated to input minimization in order to detect cost savings for each municipality and year considered. These savings will be used to implement the proposal for price regulation to be finally applied in each municipality.

However, it is necessary to specify some features that have to be taken into account in the Spanish case, so we have proposed some modifications on the basis of the preceding model. For example, in several Spanish regions suppliers have to face relative scarcity of water resources. We can say that there are high pressures on water in Spain, to the extent that it appears at the top of a ranking corresponding to a group of European countries (OECD, 1997 With a view to including in our analysis the effect of this technological characteristic, we have incorporated the rainfall of each geographical area and period, which would be defined as a non-controllable input by suppliers. In DEA literature, the importance of giving a differentiated analytical processing to these non-controllable inputs evaluated is accepted. Otherwise, the final results would be biased, thereby invalidating the information obtained in the analysis 3 . In light of this, two alternative models (M1 and M2) have been considered, where in the second one (M2) a non-controllable input has been included in accordance with the methodological guidelines existing in the literature for this kind of variable [START_REF] Banker | Efficiency analysis for exogenously fixed inputs and outputs[END_REF].

The definitive method used to calculate potential costs savings of a specific period and supplier has consisted of carrying out a separate DEA analysis in each municipality, oriented to input maximization. As productive units to be compared, we have used the results of the same municipality during the period analysed.

Moreover, in order to check for its effects, an alternative version of the basic model which includes rainfall as a non-controllable input has been applied. The choice of variables is a controversial issue in DEA analysis 4 . We have decided to follow the guidelines proposed by Thanassoulis (2000, a). Once he had settled on a group of variables, he finally connected a single input (operational costs) with a subset of outputs, such as water delivered, the level of properties and the length of mains. In our case, the variables which are included in the efficiency analysis are shown in Table 1:

[INSERT TABLE 1]
In line with the situation described in the previous section, it is desirable to introduce an additional factor into the basic model in order to capture the effect of variables which cannot be controlled by the supplier and which can have an influence in the results. Thus, the density of rainfall taken up in the Hydrographical Basin where each municipality is located is incorporated in order to approximate the scarcity problems of water suppliers.

As can be seen, three outputs have been included. 13 Sewerage in the central area of Asturias (CADASA). This association was created to deal with scarcity problems in the region, which had a high population density together with a notable level of industrialization. Currently, it supplies around the 50% of the municipality consumption.

Finally, in Table 2 we show statistics of the variables defined above for each municipality, and it is possible to see clear divergences among them.

[ INSERT TABLE 2] 6.-RESULTS

The quantitative results from applying the models described in section 3 (M1; M2) appear in the Appendix. This information is shown in Graphs 1 to 3, with comments immediately after in order to facilitate the visual interpretation of the evolution of performance corresponding to each municipality.

With regard to global results, in the three cases analysed it was verified that the model M2 (which includes the effect of the non-controllable input) leads to higher or equal efficiency indexes than those obtained under the model M1 (which does not include that effect). This is a result inherent in the methodology used and its significance in this case is not a very important issue since, as will be shown below, the important thing is to analyse the temporal evolution of efficiency indexes in each municipality and any divergence that may arise between the results of models M1 and M2. Studying the three graphs, it is possible to see differences across municipalities as to the evolution of efficiency in water distribution service over time. This is an especially interesting result because it is a direct consequence of the feature (mentioned when the Spanish sector was described) that there are large variations between the conditions and results of Spanish regions with regard to water supply, caused by differences in performance and in which economic and politic aspects intermingle. For this reason, it is more appropriate to comment on the results separately for each municipality, since, in the Spanish case, there are not common features with regard to supply conditions.

[INSERT GRAPH 1]

Beginning with Seville, we can observe two different trends. At the beginning of the time series, performance is steady and, in relative terms, positive.

This pattern is broken from the year 1996, when productive efficiency becomes notably worse. Low efficiency levels remain until the last year of our sample.

However, the use of model M2 shows that, in the second lower-efficiency stage (1996)(1997)(1998)(1999)(2000), the year 1998 is an exception, showing that bad results are attributable to the effect of climatic conditions and not to the poor management of the supplier, as can be deduced from the recuperation of the M2 index for that year.

[

INSERT GRAPH 2] F o r P e e r R e v i e w 15
With regard to the municipality of Elche, the most peculiar result is that climatic conditions have no effect on productive efficiency. As a consequence, the results of the models M1 and M2 are the same. This is probably explained because the fact that this municipality has got to adapt supply conditions to climatic fluctuations. Confirming this idea, the local authority alluded that it was not necessary to interrupt water supply, despite precarious climatic conditions, because of the existence of adequate infrastructures to face up to this kind of situations. Moreover, and in relation to evolution over time, an improvement of productive efficiency (with ups and downs) can be observed until the year 1995, after which the results become stabilized at lower levels than in that year but much higher than those in initial years.

Finally, with respect to Gijón, we find again a different evolution from the previous municipalities. In this case, and after some initial years in which the results are very good, we can see a sharp reduction of productive efficiency which starts in 1988 and touches bottom in 1991. From this year, a progressive recovery is initiated, levelling off over the later years. In this municipality, as in the case of Seville, climatology has an huge effect. In particular, if we take into account its influence, the pronounced decreasing of efficiency in the intervening years is held up, so an adverse climatology would explain the bad results obtained in 1898 and 1990. Likewise, the inclusion of climatic influence allows us to observe that the recovery of efficiency in the latest years continues until the year 2000, with similar levels to those obtained in initial years.

[INSERT GRAPH 3] After obtaining the results of efficiency in the three municipalities (which are linked to potential costs savings that could be achieved in each case), we would be able to connect those results with price regulation in the sector. In order to guide the evolution of future tariffs, the regulator could have an efficiency objective (X-factor), based on average efficiency over the period analysed (or another descriptive indicator) for each municipality, calculating as a expected deviation with respect to that average. The supplier would try to achieve efficiency improvements in order to reduce the ceiling on pricing. In this way, the public regulator gets to provide incentives for saving costs.

7.-CONCLUSIONS

In developing countries, the attention paid to the regulation of provision (and production) conditions of the water supply service has increased in recent years. This study has been focused on the regulation of water prices in order to motivate suppliers to cut costs. For this purpose, a rule has been proposed to guide price evolution, based on price-cap regulation. The main objective of that instrument will be to provide public authorities an instrument that allows them to set prices according to economic rather than political criteria.

A prior step to setting water supply tariffs has been the analysis of the productive efficiency of suppliers in previous years, or to be more accurate, the measurement of the potential cost savings that they would have been able to achieve if their performance had been the best possible. part in the price authorization process, such as members of the Regional administration, the water sector or consumer organizations.

2 Price-cap regulation could be proposed as a minimum or maximum ceiling, depending on the objective set by the supplier. For more details, see [START_REF] Bös | Pricing and Price Regulation[END_REF]. 3 See, for example, [START_REF] Charnes | Efficiency Analysis with Non-Discretionary Resources[END_REF], [START_REF] Mccarty | Technical efficiency in New Jersey school districts[END_REF], [START_REF] Ruggiero | Non-discretionary inputs in data envelopment analysis[END_REF] or [START_REF] Muñiz | Separating managerial inefficiency and external conditions in Data Envelopment Analysis[END_REF].

  the remaining notation, u r and v j denote, respectively, the weights to be assigned to outputs and inputs, efficiency index of the k 0 supplier. As can be seen, the preceding programme is orientated towards output maximization, under constant returns to scale (CRS). This basic model has been adapted by Thanassoulis (2000, a), who estimated potential cost savings in the specific context of the British water distribution sector. He used DEA methodology as an instrument, transforming the previous model (output maximization orientated) into an alternative version which made the interpretation of the model more transparent and whose final result is the calculation of the cost savings of the companies. Specifically, Thanassoulis obtained the following expression: EFF x 1k0 = UC* y1 y 1k0 + UC* y2 y 2k0 + UC* y3 y 3k0(3)

  maximize output proposed by the author does not contribute to improving the transparency of the technique used. The reason is that under the constant returns to scale defended by the author the efficiency indexes assigned to each supplier (which are those which will ultimately generate a measure of potential savings) are the same, independently of the orientation of the DEA model. Instead of the proposed version of adapting the basic model to maximize output, and since the final result is the same, a DEA model orientated to input minimization seems to be more appropriate as it has a direct interpretation in terms of potential cost savings.

  model which we use to study efficiency, we now turn to a description of the information used in the empirical application. In this section the definition and justification of variables included in the DEA analysis is presented.

  are very heterogeneous organizations. Several agents take

  The first one, number of m 3 delivered, is habitual in this context. The remaining output variables (KMNET, POPULATION) are related to the spread of service coverage from a geographical

	point of view, and both have an important influence on costs (Thanassoulis, 2000, emergency periods. The leakages percentage registered in the network is high,
	a). reaching, in average terms for the period considered in this research, around 23%.
	5.-MUNICIPALITIES ANALYSED Elche is a municipality located in eastern Spain, whose population density
	is about 600 inhabitants per km 2 . It is made up of 22 towns, with different
	With the aim of identifying the evolution of drought periods, and in order characteristics, including village settlements and tourist areas. However, 90% of
	to evaluate efficiency trends over time, we use a time series from 1985 to 2000, the population is concentrated in the city of Elche. It is in an arid area with a
	both years inclusive, for three Spanish municipalities of different characteristics concentration of rainfall in autumn. The water supply had been directly delivered
	F o r F o r by the municipal administration without the mediation of any company or F o organism. Water input is bought entirely from external suppliers. The Association r
	P e P e of Taibilla Channels is the main source (supplying 80%-90%, depending on the period considered), followed by the Villena Channel (10%-20%). Average P e
	e e leakages during the period were about 18%. e
	r r r
	R e v i e w R e v i e w The third and last municipality analysed in our study is Gijón, located in R e the north of Spain, with a population density close to 1,500 inhabitants per km 2 . It v i is made up of 105 towns/villages, with a wide geographical dispersion. Again, 95% of the inhabitants are located in the city of Gijón, and the remaining e w
	progressive incorporation of those towns to the distribution service has made the population is spread out over the other villages. The climate is characterized by
	supply system more complex. According to company sources, these towns had mild temperatures, precipitations and seasonal differences. A public firm supplies
	"five different network systems and singular pumping stations, together with a water in the municipality (EMA). The EMA was constituted in 1965 with the
	greater depth than that which was usually set in its basic distribution network principal objective of resolving the deficiencies in the system at that time.
	(…)"(EMASESA, 1997, p. 39). The company manages several reservoirs directly, Nowadays, these shortcomings have been eradicated, and the leakages percentage
	so it does not need to buy water input from external suppliers, except in
	11 12

(Seville, Elche and Gijón), whose main features are described below. There are differences in terms of size, climate and socioeconomics aspects, which allow us to check the adaptation of the proposed model to several productive environments.

Seville constitutes the biggest municipality included in the study. Its population density is around 5,000 inhabitants per km 2 . It is located in the south of Spain and is characterized by a continental climate with Atlantic influences, with high temperatures and poor rainfall. Currently, supply is delivered by a local public firm. The Municipal Water Company of Seville (EMASESA) is the organism that supplies the resource in the capital and in some adjacent towns. The has been under 15% in recent years. Part of the supply needs are covered by water from reservoirs which are managed by the Consortium of Water Supply and

Table 1 . Input-output variables in water distribution

 1

	The DEA methodology is

Table 2 . Descriptive statistics of variables

 2

	Page 23 of 25
		SEVILLE	ELCHE	GIJÓN
		mean	stan.dev.	mean	stan.dev.	mean	stan.dev.
	M3SUM	90,368,750.0 9,513,619.6 12,864,826.2 1,679,528.8 22,622,792.3 1,670,654.3
	KMNET	2,891.6	779.8	519.9	35.1	864.4	109.6
	POPULATION	1,120,888.0	115,793.0	186,855.0	6,756.0	264,014.0	4,110.0
	COSTOP	47,889,353.5 8,919,579.0	6,150,437.0	473770.6	7,503,024.9 1,412,952.1
	RAINFALL	575.5	197.9	455.8	118.9	1,321.9	141.5

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Submitted Manuscript

For specific details, see[START_REF] Lewin | Evaluating the administrative efficiency of courts[END_REF],[START_REF] Charnes | Sensitivity and stability analysis in DEA[END_REF] o[START_REF] Smith | Measuring efficiency in the public sector[END_REF].

D24 -Production|Capital and Total Factor Productivity|Capacity < D2 -Production and Organizations < D -Microeconomics, Q25 -Water < Q2 -Renewable Resources and Conservation|Environmental Management < Q -Agricultural and Natural Resource Economics, Q28 -Government Policy < Q2 -Renewable Resources and Conservation|Environmental Management < Q -Agricultural and Natural Resource Economics

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript


Lukas Menkhoff
email: menkhoff@gif.uni-hannover.de

G21 -Banks|Other Depository Institutions|Mortgages < , O16 -Financial Markets|Saving and Capital Investment

Keywords: financial system, Thailand, emerging economies, financial crisis, bank lending JEL-Classification: G 21, O 16 Financial system, bank lending, relationship lending, financial crises, emerging economies, Thailand

Evidence from credit files is provided to examine bank lending determinants of Thai commercial banks. Their lending practice follows reasonable patterns as a standard set of variables, including indirect risk variables, explains much of the variance in interest rate spread. Reflecting institutional differences with mature markets, we find higher importance of relationship banking and risk control via credit availability. Information about later default reveals prudent relationship lending. However, banks could have made better use of available information about borrowers' riskiness. These findings do not support a general verdict of bad banking but indicate room to improve lending decisions.

Introduction

Banks are more important in developing economies than in industrialized countries [START_REF] Levine | Financial development and economic growth: views and agenda[END_REF]. Moreover, bank-based financial systems seem to be more successful in developing economies than in industrialized countries. These results reflect the general opinion that developing countries ought to be judged different in their institutions. Thus, we cannot expect that our insights in respect to bank lending in mature markets will remain when examining emerging markets. Despite this fact, there is hardly any micro-based empirical analysis of bank lending in developing countries. Indeed our case study does show that lending decisions of Thai commercial banks are determined differently to those in industrialized economies. We find that relationship lending is more enunciated than in mature markets, credit availability is used as an instrument of risk control, and that information on default risk is not extracted very well from balance sheet data.

The Thai case seems to be of particular interest beyond covering an emerging market, because the Asian crisis of 1997/98 started in Thailand. Consequently, contagion as an explanatory variable can be excluded here (see [START_REF] Park | Institutional investors, trade linkage, macroeconomic similarities, and contagion of the Thai crisis[END_REF].

Moreover, the domestic financial sector should be analyzed as its problems preceded the overall economic crisis in Thailand (see e.g. [START_REF] Warr | What happened to Thailand?[END_REF][START_REF] Alba | Financial liberalization and the capital account: Thailand 1988-1997[END_REF][START_REF] Rajan | Ir)Relevance of currency-crisis theory to the devaluation and collapse of the Thai Baht[END_REF]. However, we entertain doubts to some general verdict of bad banking practices: before the Asian crisis, Thailand's banks were regarded as a positive element in the economy and Thailand was among the countries classified as resourceefficient (World Bank, 1993). The resulting comparatively high total factor productivity would be difficult to explain in light of a large and inefficient banking sector [START_REF] Menkhoff | Bad banking in Thailand? An empirical analysis of macro indicators[END_REF]. Thus, first-hand micro-level evidence helps to improve our understanding of the rationale of bank lending before the crisis: were lending determinants any different from those in mature markets, reflecting different institutions and therefore indicating any degree of functionality? Was risk-taking -the main indicator of bad banking practices -inappropriate? Were relations between the bank and its borrowers -a typical motivation for bad banking -too close to allow for prudent banking? We conducted an in-depth case study of the lending decisions of Thai commercial banks before the Asian crisis, based on their internal credit files. Thus, our study fits into a large new literature on the functioning of financial systems in emerging markets. The unexpected outbreak of the Asian crisis, and the fact that there was no historical precedent for the nature of its progress, initiated this wealth of new research.

1 It complements ongoing debates on liberalization and the growth effects of financial development. 2 Our contribution is to explain the lending process from the banks' perspective, as this piece of evidence has been missing until now. We analyze Thai commercial banks under the theoretical perspective of relationship lending [START_REF] Boot | Relationship banking: what do we know?[END_REF], as it is precisely these close relations between banks and their customers that characterize lending in emerging economies. For this purpose, a new data set was compiled in 2000/01, consisting of 560 credit files sourced from the majority of Thai commercial banks. These banks form the core of Thailand's financial system, accounting for around 60% of total assets. 3 The data cover the period 1992 to 1996.

The only other similar kind of paper focusing on an emerging economy of which we are aware (La [START_REF] Porta | Related lending[END_REF] examines related lending in Mexico. The result is striking, as the information gathered, clearly indicates that related lending in Mexico is accompanied by more favorable credit terms for the borrowers and worse repayment for the lending bank than non-related lending. So, risk is not priced appropriately and relations are misused. The Thai case is very different from the Mexican experience.

We discover stable determinants of lending decisions which add new insights to the existing literature from industrialized countries. For the developing economy, as expected from theoretical considerations, relationship indicators have relatively greater importance in explaining the interest rate spread than direct risk variables.

Furthermore credit availability is reduced for more risky borrowers, whereas direct risk proxies do not seem to influence spread. When using the later default of a loan

1 See e.g. La Porta et al., 1998, Corsetti, Pesenti and Roubini, 1999, Sarno and Taylor, 1999, Dooley, 2000, Claessens, Djankov and Lang, 2000, Goh and Groenewold, 2000, Faccio, Lang and Young, 2001, Martinez Peria and Schmukler, 2001, Demirgüç-Kunt and Huizinga, 2004, Agbola and Kunanopparat, 2005. 2 See surveys by [START_REF] Levine | Financial development and economic growth: views and agenda[END_REF], World Bank, 2001; on the role of banks e.g. [START_REF] Beck | Finance and the sources of growth[END_REF], on the impact of liberalization e.g. Arestis et al., 2002. 3 Another 20% of assets are represented by so-called finance companies, and the remaining 20% are held by specialized state banks and foreign banks. The rest of the paper is structured as follows: Section 2 gives a detailed description and analysis of the data used. The systematic examination on the ex ante nature of lending in Thailand is presented in Section 3. The information from the ex post available default variable is analyzed in Section 4. Section 5 concludes with a view on possible objections to the results of this research.

2

The data

Data compilation

The greatest hurdle in conducting this study was to gain the cooperation of Thai banks in allowing researchers to study their credit files. It seems to be obvious that these banks had no direct interest in such work and that -due to the history of the banking system -they feared outcomes that could attach blame to them. In getting the support of banks, three factors were helpful: first, the passing of time helped to heal wounds as many responsible persons changed positions, and procedures were upgraded so that if the outcome might blame anything, it would just be "history".

Second, all participants were promised strict confidentiality so that nobody could identify unprofessional practices and no bank or person can be singled out. Third, the study is a pure research project which is not intended to gain any private information advantage but aims to improve knowledge. It thus produces a public benefit which was honored by a supporting letter from the Thai Ministry of Finance, which is involved in banking supervision.

Starting in August 2000 we approached all 15 Thai commercial banks that existed before the crisis or their successors in case of mergers. Nine banks finally agreed to cooperate and are thus included in our sample. As their number is so limited and we promised confidentiality, we cannot say much about their participation except that it is quite representative for the size and structure of the banking sector.

Regarding the selection of credit files, there is the problem of an uncontrollable selection bias which could distort our sample and produce misleading results. In par-

F o r P e e r R e v i e w 6
ticular it is to be expected that banks want to present themselves in a favorable light, although the personal incentive was probably low due to changing responsibilities and confidentiality should reduce the incentive for strategic file selection. So we tried to get cooperation from as many banks as possible to minimize the impact from distorted selection in a certain bank. Within the banks, we asked for a randomized sample. As the depository of files often follows some criteria, in these cases we decided on a diversified selection. In some cases the banks presented lists of customers revealing size and industry, so we could choose the files (preferring the critical industries construction and real estate).

Between September 2000 and March 2001, the nine participating banks were each visited for about two to three weeks. Between 35 and 85 credit files of the predetermined five-year period from 1992 to 1996 were analyzed in each bank. In all 560 cases, we focus only on a single loan grant. For each loan, we personally extracted the information from the credit file or supervised the bank employee doing so to ensure that the information was compiled in a comparable way.

Data representativeness

Do the data in our study represent the loans made by Thai commercial banks in a reasonable manner? There are basically three ways to find out whether the credit files compiled largely represent the total population. First, the average firm size may be analyzed, second the industry structure of loans can be compared with the market, and third, the share of non-performing loans (NPLs) can be compared with that for all commercial banks (see [START_REF] Menkhoff | The rationale of bank lending in pre-crisis Thailand[END_REF], for more details).

The minimum firm asset size realized in the sample of about one million Baht equals roughly 40,000 USD at the historical exchange rate. The maximum size of about 90 billion Baht equates to around 3.6 billion USD. This indicates what the median value of 10 million USD confirms: the sample does not represent the total economy, but rather the medium and larger sized segments of Thailand's economy. The reason is that the credit files stem from the headquarter offices, which handle all larger loan cases, whereas the really small loans may be decided at the branches. So whatever this study reveals, there is still the possibility that results may differ for really small-scale loans.

The industry structure of loans in the sample also shows some deviation from the total population as it is classified by the central bank (see Overall, the data received represent only firms with bank loans and they are not strictly representative of commercial banks. Loans refer to medium and large size firms, industries are over-or under-represented to a certain degree and the NPL share may be slightly too low. However, bearing this in mind, the data are not misleading and thus appear to be useful for our research.

Description of variables

Due to the purpose of the study, there are two dependent variables, the interest rate spread (IRS) and credit availability. The IRS is measured as the difference between the interest rate charged and the minimum overdraft rate. The latter is the reference rate charged to first class customers for overdraft credit and lines of credit.

Credit availability is more difficult to grasp. Petersen and Rajan (1994, p.18) advise against taking the actual debt ratio as this may be an ambivalent figure, either reflecting credit demand -which is not of interest here (good firms may not need credit)or credit supply as seen from the bank's point of view (the bank possibly rations a firm). We therefore rely on the "bank credit ratio" (BCR) which is the line of credit credit used. The advantage of using the BCR against the debt ratio, however, is the -admittedly imprecise -indication about available credit in the future.

Regarding the independent variables, Table 2 presents the full list and an exact description of variables used. The next group after the dependent variables in the list is three variables that aim to directly capture the riskiness of loans. Higher risk is expected to be indicated by a higher liability-to-asset ratio (or leverage), by a lower current ratio and a lower interest coverage ratio. The following group of variables in Table 2 covers relationship variables. A close relation is expected to be revealed by a positive house bank status, a long relation duration and a small number of competing banks.

Accordingly, control variables are introduced. Some frequently used variables are classified as "indirect risk variables", as large assets, old age and high collateral can be regarded as risk reducing. Moreover, there are three kinds of dummies, representing idiosyncratic influences from individual bank policies, years and industry influences. Finally, in Section 4 of the study, two more variables are used: L/C volume, informing about the volume of all lines of credit by the respective bank to a firm, and default, informing about a non-performing status of the loan in the period between credit granting and data compilation according to the latest regulations.

The descriptive statistics of these variables reveal that in particular the risk proxies can behave in an extreme manner. Regarding the liability-to-asset ratio, there are firms where liabilities are larger than assets. The current ratio can lie between 0, i.e. for firms with no liquid assets, and 139, i.e. for firms with hardly any short-term liabilities. Finally, the interest coverage ratio takes values between -37, i.e. where earnings or possibly interest expenses are negative, and 97, i.e. where interest expenses are extremely low. Note, moreover, that this last risk proxy is available only for less than 90% of all cases, indicating that this kind of calculation is not always applied, e.g. because it may be hypothetical in the case of a new firm. These sometimes extreme values of risk proxies justify treating them -in line with the literature -as outliers.

Finally, and consistent with other studies, the variable asset size is right-skewed distributed with extremely few large firms and has thus been transformed into logarithmic values. The same transformation was chosen for the variable number of banks and L/C volume as these variables also increase very strongly for the few large firms.

Characteristics of borrowing firms and loans

The last part in this Section 2 gives average values of variables and their deviation by way of the relation of these variables with the asset size of borrowing firms (see also [START_REF] Petersen | The benefits of lending relationships: evidence from small business data[END_REF]. Firm-related variables are shown in Table 3.

The smallest 10% of firms covered has assets below 22.1 million Baht, i.e. slightly less than 1 million USD. The largest firms have assets of more than 2,947 million Baht, i.e. roughly 118 million USD.

Means of the variables develop with percentiles of increasing asset size. Size related figures, such as age, equity and liabilities clearly go upwards. The behavior of the three risk proxies may therefore be more interesting: the liability-to-asset ratio increases slightly, the current ratio shows a hump-shaped pattern, whereas the interest coverage ratio clearly goes down, with the exception of the smallest firms. This means that larger firms tend to be identified as unanimously more risky than medium sized firms and also as somewhat more risky than small firms.

The characteristics of loans depending on the asset size of borrowing firms is presented in Table 4. In summary, Tables 3 and4 provide evidence that the data set includes economically rational information and that this structure is similar to well-known structures from mature markets. Empirical examinations first analyze bank lending behavior using ex ante information (Section 3), and then using ex post information in addition (Section 4).

Bank lending analyzed by use of ex ante information

We find that lending of Thai commercial banks was by and large functional. The benchmark for this assessment is earlier results on lending determinants of banks from industrialized countries. The comparison with these mature markets shows, moreover, that the importance of lending determinants differs in the emerging Thai We analyze lending behavior by applying the standard empirical technique of the relationship banking literature. According to this procedure, the price and the availability of loans are explained by a set of theoretically reasonable variables. The data used for these regressions stems from the information that was available to the bank at the point of decision making. In our case, this is the information contained in the credit files.

The pricing of loans

The pricing of loans extended by Thai commercial banks does not really support the verdict of bad banking. The extensive research on lending decisions in mature markets found that risk factors play a surprisingly small role, that relationship sometimes matters and that the size of a firm is always important. 4 In the case of an emerging market, such as Thailand, we expect that the credit granting technology is less developed and information from balance sheet data is more opaque so that risk factors are unimportant for loan pricing. In the case of a bad banking environment, one may even expect that lending decisions become largely independent of economic determinants. However, our evidence is different.

Moreover, the institutional environment of an emerging economy seems favorable to the widespread use of relationship lending. Relationship building between lender and borrower can be understood as an institution to partially overcome the incomplete and asymmetric information between the parties involved. The more perfect markets are and the better the quality of information is, the more contracts are expected to be reliable and enforceable and the smaller is any possible advantage from relationship lending. We can thus expect that Thailand is a case where relationship lending may play a prominent role, in particular for smaller firms.

What is less clear is whether relationship lending will make loan prices cheaper or more expensive. Arguing that a close bank has intimate private knowledge of the borrowing firm and can thus assess risks more precisely would justify the realization of lower interest rates. One can, however, also argue that the close bank is in a 4 In addition to studies mentioned on the US see e.g. [START_REF] Degryse | Relationship lending within a bank-based system: evidence from European small business data[END_REF] on Belgium, [START_REF] Elsas | Is relationship lending special? Evidence from credit-file data in Germany[END_REF], [START_REF] Lehmann | Do lending relationships matter? Evidence from bank survey data in Germany[END_REF] and [START_REF] Elsas | Empirical determinants of relationship lending[END_REF] on Germany and [START_REF] Ferri | Bank-firm relationships and allocative efficiency in northeastern and central Italy and in the south[END_REF] on Italy. strong bargaining position as it is the main provider of credit and as its potential withdrawal of credit would provide a strong negative signal to other lenders. In this sense, a firm is to some degree "caught" in the particular relation with a close bank.

In the situation of an emerging economy both arguments seem to be of particular relevance: information asymmetry is very important and would lead to a negative correlation between interest rate spread and the incidence of relationship lending. However, competition between banks is also rather lower than in mature markets and would thus allow a close bank to more easily exploit its position. This would lead to a positive correlation between spread and relationship lending. In summary, higher interest rates for "related" loans indicate low competition in the market. Lower spreads, however, can be problematic too: they indicate bad banking if risk-taking is inappropriate.

To empirically examine the Thai case, we estimate -in accordance with the literature -OLS regressions where variables are included which have been previously

identified as important in such examinations. To consider conflicting aspects, several specifications of the following general form are used:

Interest rate spread = 0 + 1 risk proxies + 2 relationship indicators + 3 indirect risk variables + 4 bank dummies + 5 year dummies + 6 industry dummies + Specification (1) in Table 5 proceeds as suggested by [START_REF] Petersen | The benefits of lending relationships: evidence from small business data[END_REF] and serves as the benchmark. In order to avoid possibly distorting influences from extreme values of the risk variables (see Section 2.3), Petersen and Rajan suggest setting a negative value of the interest rate coverage ratio to zero as well as excluding the most extreme 5% of cases from the three risk variables. The disadvantage of this procedure is that the case number goes down to 416.

In this benchmark specification, the signs of almost all variables are as expected from theory. Neglecting the constant term, it is unfortunately the first risk proxy -the liability-to-asset ratio -which presents an unexpected negative sign, indicating that firms with higher leverage would receive cheaper money. 5 The other two risk proxies have the expected negative sign. Turning to the group of relationship variables, these show negative signs too, indicating that related loans are cheaper (see e.g. [START_REF] Berger | Relationship lending and lines of credit in small firm finance[END_REF]. The lower spread resulting from more lending (see detailed in [START_REF] Menkhoff | Collateral-based lending in emerging markets: evidence from Thailand[END_REF]. Finally, the age variable is expected to show a negative sign, but obviously the effect is already included in other correlated variables, such as size, and the remaining effect is close to zero.

The behavior of the dummies -not shown here -is as follows: the large and different coefficients for the nine banks are remarkable, indicating either quite different borrowers or strategies. The year dummies have low coefficients, indicating slightly growing spreads over time. This time trend may be caused by two effects:

first, the economic conditions have become rather worse over time and second, some disintermediation has happened (see [START_REF] Menkhoff | Bad banking in Thailand? An empirical analysis of macro indicators[END_REF] which may not be fully captured by the other variables. Finally, the coefficients of the industry dummies are of small size and often point to the expected direction, such as higher spreads for construction and real estate and lower spreads for (the preferred industry of) agriculture or banks. Overall, the signs of the coefficients are rational.

Turning to the explanatory strength of the variables, three coefficients are statistically significant, apart from the constant term and bank dummies. First of all, the asset size variable is dominant. The risk proxies are not statistically significant in contrast to the relationship indicators. Among the latter, even two of them are significant, i.e. the house bank variable as well as the number of banks lending to the firm.

6

This indicates the importance of relationship lending, a finding that is robust throughout further analyses. The relationship determinants were insignificant for loan pricing in the USA [START_REF] Petersen | The benefits of lending relationships: evidence from small business data[END_REF] or in Germany [START_REF] Elsas | Is relationship lending special? Evidence from credit-file data in Germany[END_REF].

7

In summary, loan pricing determinants of Thai commercial banks are similar in structure but different in importance to those in industrialized countries. The significant determinants include an indirect risk variable and important relationship factors.

There is some evidence of functional competition in the lending market. All these findings on loan pricing do not signal the existence of bad banking. In order to check the robustness of findings we examine several modifications. Specification (2) aims at better capturing the non-linear firm size effect. Taking account of the increasing interest rate spread for the ten per cent largest firms (see Table 4), we add a second size term to the benchmark specification which effectively models this non-linearity. The result is, indeed, an improvement towards theoretical expectations as the overall fit becomes better, the collateral coefficient becomes significant and the "wrong" sign of the liability-to-asset ratio is now of negligible size. In another examination we follow [START_REF] Berger | Relationship lending and lines of credit in small firm finance[END_REF] and split the total sample at the median firm size of about 300 million Baht (i.e. 12 million USD at historical rate) into two subsamples of small and large firms. It is expected that the small firm sample has a higher influence of relationship variables. Specification (3) shows respective findings for the smaller firms, which has the expected higher overall fit than the regression of larger firms (specification 4). Coefficients do not change in comparison with the benchmark -except for the liability-to-asset ratio becoming positive -but significance of coefficients goes down, probably due to the smaller sample size.

Again, these results underline the existence of principally reasonable lending practices. By contrast, in the large firm specification (4) neither asset size nor house bank status explain anything. Only the variable relation duration is statistically significant and the number of banks becomes significant at a 11% level. This suggests in combination with the comparatively low explained variance two lessons: relationship is always important in Thailand but can change its expression and further influences may be important in the loan market for large firms which are not so well covered here.

In a related exercise, specification (5) picks up the influence from house banks on the liability-to-asset ratio by integrating both variables into a new interactive variable. It is found that the unexpected sign of this risk proxy is generated by house banks only, whereas non-house banks set higher loan rates to firms with higher leverage. This result may be interpreted as imprudent lending by house banks or as the outcome of the house banks' function, such as to finance younger firms or to allow firms' operations with higher leverage. The role of house banks will be examined in more detail later.8 Having addressed pricing, we now turn to the question of availability of loans.

The availability of loans

Thai commercial banks use credit availability as a means of controlling the risk of their loan portfolio. This reveals even more clearly than for the pricing regression that risk matters in lending decisions and it confirms the above finding that emerging markets operate differently from mature markets.

The theory of credit rationing links rationing to informational asymmetry in credit markets [START_REF] Stiglitz | Credit rationing in markets with imperfect information[END_REF]. As asymmetric information between lender and borrower is even more pronounced in emerging markets, one may expect reduced possibilities for discriminative pricing by Thai commercial banks. Instead, banks may then rely more on rationing, i.e. influencing credit availability according to the perceived riskiness of borrowers. Studies from mature markets indicate another consequence of relationship banking: a good relation improves credit availability.

The empirical examination of the Thai case uses the same set of explanatory variables as above. They are regressed on the "bank credit ratio"-variable which aims to capture credit availability. The outcome presented as specification (1) in Table 6 shows, indeed, a reassuring finding: as can be expected from theoretical reasoning, the sign of coefficients is mostly opposite to the pricing regressions -meaning that banks react to the same set of variables with higher spreads and/or relatively lower amount of loans. In addition, two of the three risk proxies show the expected sign and the liability-to-asset ratio is now even statistically significant and of high economic importance. This result -in combination with the earlier findings on loan pricing -suggests that Thai commercial banks address risk more by limiting the amount of credit than by increasing the price of loans. Regarding the concern of bad banking, loan markets seem to be functioning in the sense that risk is considered by setting prices and even stronger by controlling loan volumes.

Further results seem noteworthy: a house bank relation improves credit availability, as identified by earlier literature on mature markets (e.g. [START_REF] Petersen | The benefits of lending relationships: evidence from small business data[END_REF]Rajan, 1994, Lehmann and[START_REF] Lehmann | Do lending relationships matter? Evidence from bank survey data in Germany[END_REF]. The asset size variable does not seem to capture the riskiness of a firm but rather reveals a limitation of our credit availability indicator as large firms rely much less on bank credit and thus also need less open credit lines (see also Table 4). Moreover, the differences between the nine banks are much smaller regarding the provision of credit availability than regarding their loan pricing (not reported). This may be interpreted as a further indication that the systematic component of lending behavior of Thai commercial banks can be better understood by looking at the relative volume of loans rather than at prices.

Further regressions indicate the importance of credit availability for the lending decisions of Thai commercial banks. Splitting the sample into loans to small and large firms does not influence the outcome much, and particularly not so drastically as it did for interest rate spreads. The only new significant result for small firms is the detrimental influence from many lending banks, possibly signaling some problems in attracting large amounts when there are "too many" banks necessary (see specifica- Summarizing Section 3, we find that risk is priced. Risk is controlled even more rigorously by adapting credit volume. Relationship variables are very important, as close relations go along with better prices and better credit availability. Moreover, relationship lending and credit rationing seem to be fruitful concepts in understanding bank lending in emerging markets. The next Section 4 will put the rationale of Thai banks' lending under an ambitious test: how did banks perform from an ex post perspective?

Bank lending analyzed by use of ex post information

The tough test of an ex post analysis, which goes beyond most literature in this field, reveals some weaknesses in Thai banks' lending policy but does not confirm the bad banking hypothesis. We distinguish in our analysis between the motivation towards bad banking, signaled by a misuse of relation, and the practice of bad banking, signaled by underutilization of information on riskiness.

As new ex post information we introduce the default variable. Default happens in our data if the loan has turned into a non-performing loan between loan granting in This means for our sample that in most cases the Asian crisis caused default. Accepting that this crisis was difficult to forecast, the incidence of default is largely influenced by a macro shock and may be thus a noisy indicator for imprudent lending behavior [START_REF] Caprio | Bank insolvency: bad luck, bad policy, or bad banking?[END_REF].

Nevertheless, even a simple correlation analysis of default with lending determinants shows that available credit file information indicated vulnerability of firms.

Analysis (1) in Table 7 shows that two out of three risk variables are significantly related to default and that all of them have the expected sign. This is remarkable as it reveals the potential usefulness of risk proxies to forecast later default. Moreover, size predicts -contrary to conventional wisdom -later default as well as a younger age of firms. Finally, the signs of relationship variables indicate that related firms had a lower probability of default, although only a longer relation duration indicates this significantly. So simple correlations help to understand the relation of non-performing with high risk and the presence of relationship lending: it is clearly risk that matters.

We interpret this as evidence against the bad banking hypothesis that too close relations would hinder prudent lending. It is less clear whether risk should have been anticipated better. We analyze these issues in a multivariate setting by regressing lending determinants on default.

Due to the 1-0-nature of the default variable, probit regressions are used here.

The respective benchmark regression is shown as specification (2) in Table 7. Two of the three risk proxies have the expected sign, the current ratio is statistically highly significant and the interest coverage ratio is almost significant (at 11%). The case of relationship proxies is somewhat different as none of them are statistically significant.

In particular, house bank relations, as well as a smaller number of lending banks, tend to be related with lower rather than higher default probability. In this specification, younger firms and -surprisingly again -larger assets are positively related with default. This result confirms the correlation analysis (see analysis 1 in Table 7).

It raises the question as to which influences may be hidden in the unexpectedly signed asset variable. Splitting the sample into small and large firms shows no contradiction between both regressions -presented as specifications (3) and (4) respectively -but different importance in determinants. The asset factor is a particular problem of large firms. As it is only for house banks where firm size becomes significant (not reported), this indicates that there might be influences on credit granting beyond economic rationale for the group of large and close firms in the sense of La Porta, López- [START_REF] Porta | Related lending[END_REF]. Another finding seems noteworthy, i.e. the opposite signs for the liability-to-asset ratio. The theoretically unexpected negative sign in specification ( 2) is obviously driven by small firm financing where it may represent rather a positive signal from already existing external financial sources.

In another effort to better understand the influence of the asset variable, specification (5) distinguishes the effect of higher credit volume from asset size. Both variables are highly correlated and therefore neglected in other regressions, but their joint inclusion could be informative here. The credit volume variable has a positive and highly significant coefficient, whereas the asset variable loses significance.

Three more changes towards significance can be recognized when compared with specification (2): in the house bank variable, the interest coverage ratio and the liability-to-asset ratio (this latter variable for small firms only, see above). How to interpret these results?

First, specification (5) confirms earlier evidence that relationship banking does not "generate" default. By contrast, house banks are related to non-default which may be caused by better understanding of the firm and/or by better credit availability for the firm. Second, several direct and indirect risk proxies (current ratio, interest coverage ratio and age) seem to be useful in capturing riskiness. As they have not been used ex ante to full extent, banks may want to improve risk consideration in the future. 9 Third, a high credit volume is rather more important than being a large firm for subsequent default. This result may indicate imprudent aggressive lending policies, in particular regarding large firms. However, it is also consistent with the argument that the credit crunch in Thailand's early post-crisis years hit credit-dependent firms hardest (see [START_REF] Agénor | The credit crunch in East Asia: what can bank excess liquid assets tell us?[END_REF]. The financial structure of firms is then the reason for default, in particular when the economic crisis is complemented by a banking crisis.

In summary, the analysis of the default variable helps to discriminate between the motivation and practice of bad banking: close relations to borrowers do not seem to be the core issue in explaining default with the exception of some easy lending to large, close customers. The major problem in bank lending was rather some underutilization of information on the riskiness of borrowers.

9 Additional analyses on this issue are performed in [START_REF] Menkhoff | The rationale of bank lending in pre-crisis Thailand[END_REF]. that the available risk variables are not important in pricing loans, a result that is similar to that found in mature markets. New is, however, the high weight of relationship in decision making -when compared to mature markets -which is theoretically expected. Also, the importance of credit availability as a preferred instrument for controlling risk is sensible in more opaque financial markets. However, Thai commercial banks did not operate without problems. The analysis of ex post default cases shows that there is an element of too generous credit granting to related large firms. Moreover and quite generally, available risk information could have been used better to restrict default. Both elements of negative related lending as well as underutilization of information may not be unexpected for an emerging market. Given the verdict of bad banking, however, the unimportant or -depending on the specification -stabilizing role of relationship in default regressions is more surprising. Therefore, Thai commercial banks' lending rationale provides a rather "good" example of relationship lending, in contrast to the Mexican case. Thai banks' main shortcoming is quite conventional, as they fail to fully recognize risk factors. This failure indicates that banks could improve operations but it does not indicate outright bad banking.

Several general lessons seem to emerge for the understanding of bank lending in developing countries, although more work appears to be warranted: first, emerging countries can be, but do not have to be, characterized by largely functional lending markets. The lesson from Thai commercial banks is that -despite the heavy financial crisis in 1997 -risk was considered and relations were not generally misused in lending decisions before the crisis (see also Levy-Yayati et al., 2004). Second, the tentatively higher degree of asymmetric information in emerging economies explains the way bank lending works: our case study shows that relationship lending and credit rationing play a larger role than in mature markets (see also [START_REF] Demetriades | Financial restraints in the South Korean miracle[END_REF]. Third, the functioning of a developing banking market can be clearly improved (see e.g. [START_REF] Hahm | Interest rate and exchange rate exposures of banking institutions in pre-crisis Korea[END_REF]. Even the notoriously unreliable balance sheets in The table presents Pearson rank correlations in column (1) and probit regressions with Huber/White robust covariances in columns (2) to ( 5). Negative values of the interest coverage ratio are set to zero and the most extreme 5% cases of each risk proxy are excluded. Specification (3) presents small firms and specification (4) large firms. In specification (5) the variable L/C volume ("line of credit volume") is added.

  variable, relation proxies indicate a positive effect for Thai commercial banks, as close customers did not generally receive too risky credit. There is some evidence, however, that large (and tentatively related) customers did. Moreover, risk proxies help to explain default and thus have obviously not been fully exploited beforehand, indicating limited efficiency in Thai commercial banks' lending practice.

(

  L/C) divided by the sum of L/C plus liabilities. Its central advantage is that the L/C includes available future credit because the L/C is usually not fully used. The larger the BCR the greater the probability of there being unused L/C, indicating good credit availability. This is, of course, still a crude measure of credit availability, as there is no information about the extent to which the L/C has already been used. Moreover, the L/C is also influenced by credit demand and supply factors similar to the actual

  Absolute volume of lines of credit (L/C) goes up with asset size, as well as the default share. By contrast, interest rate spread declines, as well as the bank credit ratio (BCR) and collateral with increasing firm size. Regarding the three indicators for relationship lending, banks have fewer house bank relations with larger firms, relation duration goes up with size and larger firms have more lending banks.

  institutional differences. In particular, relationship banking is pronounced.

  in the industry. The sign of the asset size variable is as theoretically expected. Collateral has a positive sign, indicating that collateral could only partially cover the high risk, collateralized loans being still riskier than others

  tion 2). Regarding large firms, another variable becomes significant, i.e. the positive influence of relation duration (see specification 3), a result already known from the pricing regression. This may indicate that a longer lending relationship increases credit availability because the bank learns more about the borrower. The variable age, however, shows a statistically negative sign, which we interpret as indication of other financing alternatives of established large firms. Finally, specification (4) informs about determinants of house bank relations only. Sign, size and significance of coefficients are very similar to earlier findings, indicating that house banks also conduct risk control via credit availability.

  and data compilation in 2000/01.

  analyzes the behavior of local banks in an emerging market from several hundred credit files of Thai commercial banks. The findings show

Table 1

 1

	). The by far

Table 2 Variable Description

 2

	Variable Name	Description
	Dependent variables
	Interest rate spread (IRS) Interest rate spread over minimum overdraft rate (MOR)
	Bank credit ratio (BCR)	Volume of the line of credit (L/C) granted in relation to the sum
		of liabilities plus L/C
	Direct risk variables
	Liability-to-asset ratio	The book value of liabilities divided by assets in the same year
	Current ratio	Current assets divided by current liabilities in the same year
	Interest coverage ratio	Earnings before interest expense, tax, depreciation and amorti-
		zation divided by interest expense
	Relationship variables
	House bank status	The code is 1 if bank considers itself as a house bank of the
		borrower and 0 if otherwise
	Relation duration	The number of years of an active bank-borrower relationship
		prior to the credit decision, normally the period since first credit
		granting
	Number of banks that	The number of banks that the borrower has relationship with (as
	lend to the borrower	stated in the credit files)
	Indirect risk variables
	Assets	The latest book value of assets of the firm prior to the credit
		decision
	Age	Number of years that the borrower has been in operation prior
		to the credit decision
	Collateral	Collateral value given by the firm as percentage of the line of
		credit granted (degree of collateralization, not its incidence)
	Dummy variables
	Bank	Dummy variables for the nine lending banks covered
	Year	Dummy variable for the years 1992 to 1996
	Industry	Set of 12 dummy variables indicating the industry
	L/C volume	Total volume of lines of credit granted by the respective bank,
		generally representing short-term loans
	Default	Loan became non-performing between granting and data com-
		pilation

Table 3 Characteristics of Borrowing Firms by Firm Asset Size

 3

	Characteristics			Asset percentiles			Mean
		0-10 10-25	25-50	50-75	75-90	90-100
	Book value of	<	22.1-	76.0-	246.1-	1,000-	>	2,165
	assets 1	22.1	76.0	246.1	1,000	2,947	2,947
	Age (in years)	7.78 13.30	12.78	17.92	18.61	17.20	14.65
	Equity 1	2.69 11.39	34.04	124.11	418.42	3,439	463.87
	Liabilities 1	5.80 36.11	106.92	389.28	1,263	13,917	1,702
	Liability-to-asset	0.61 0.74	0.75	0.76	0.76	0.78	0.74
	ratio
	Current ratio	1.57 3.77	4.64	3.12	1.02	0.93	3.18
	Interest coverage	3.70 4.91	3.00	2.93	3.15	2.30	3.17
	ratio

1 Figures are in million Baht.

Table 4 Characteristics of Loans by Firm Asset Size

 4

	Characteristics			Asset percentiles			Mean
			0-10 10-25	25-50	50-75	75-90	90-100
	L/C volume (in	14.33 39.73	83.97	188.81	492.51	840.30	240.58
	million Baht)
	Interest rate		2.53	1.23	0.74	0.34	0.17	0.26	0.76
	spread
	BCR		0.60	0.45	0.38	0.29	0.23	0.10	0.34
	Default in %	1	16.34 10.92	25.50	22.19	64.88	50.01	45.90
	Collateral in %	78.95 63.40	62.62	44.15	32.77	34.06	52.95
	House bank		76	51	56	47	34	28	49
	status in %
	Relation duration	5.51	7.06	7.36	8.86	8.93	9.26	7.96
	(in years)
	Number of lend-ing banks	1.76	2.50	3.51	5.04	5.78	8.53	4.36

Table 5 Risk and Relationship Factors in the Pricing of Loans

 5 Standard errors are in parentheses. *** Significant at the 1 percent level (**: 5 percent level, *: 10 percent) The table presents OLS regressions with White heteroskedasticity-consistent standard errors. Negative values of the interest coverage ratio are set to zero and the most extreme 5% cases of each risk proxy are excluded. In specification (2) the variable Ln (assets) * Ln (assets) is added. Specification (3) refers to small firms only, specification (4) to large firms respectively and in specification (5) house bank status is interactively linked to the liability-to-asset ratio.

			Dependent variable: Interest rate spread
	Independent variables	(1)	(2)	(3)	(4)	(5)
	Liability-to-asset ratio	-0.231 (0.186)	-0.008 (0.176)	0.067 (0.290)	0.272 (0.181)
	Current ratio	-0.065 (0.042)	-0.031 (0.036)	-0.043 (0.056)	0.016 (0.045)	-0.060 (0.040)
	Interest coverage ratio	-0.015 (0.015)	-0.011 (0.014)	-0.008 (0.028)	-0.007 (0.013)	-0.014 (0.015)
	House bank status * (-1)					0.360***
	Liability to asset ratio					(0.118)
	House bank status	-0.220** (0.095)	-0.181** (0.090)	-0.269* (0.142)	-0.029 (0.117)
	Relation duration	-0.009 (0.007)	-0.011 (0.007)	-0.006 (0.013)	-0.012* (0.007)	-0.009 (0.007)
	Ln (Number of banks)	-0.141** (0.060)	-0.144*** (0.056)	-0.108 (0.111)	-0.106 (0.064)	-0.145** (0.061)
	Ln (assets)	-0.236*** (0.034)	-0.856*** (0.087)	-0.483*** (0.064)	-0.037 (0.043)	-0.235*** (0.033)
	Ln (assets) * Ln (assets)		0.053*** (0.007)
	Age	0.001 (0.004)	0.003 (0.004)	0.004 (0.009)	0.001 (0.003)	0.001 (0.004)
	Collateral	0.002 (0.001)	0.002 (0.001)	0.002 (0.002)	0.001 (0.001)	0.002 (0.001)
	(Constant)	3.143*** (0.307)	4.387*** (0.301)	4.159*** (0.370)	0.341 (0.484)	2.970*** (0.268)
	Bank dummies	yes	yes	yes	yes	yes
	Year dummies	yes	yes	yes	yes	yes
	Industry dummies	yes	yes	yes	yes	yes
	Number of cases	416	416	208	208	416
	Adjusted R 2	0.456	0.527	0.505	0.123	0.461
	F-statistic	11.891	15.010	7.825	1.910***	12.471

Table 7 Risk and Relationship Factors in Explaining Default

 7 Standard errors are in parentheses. *** Significant at the 1 percent level (**: 5 percent level, *: 10 percent)

		Correlations		Dependent variable: Default
		with default
	Independent variables	(1)	(2)	(3)	(4)	(5)
	Liability-to-asset ratio	0.025	-0.577	-2.958***	0.755	-0.783*
			(0.432)	(0.804)	(0.642)	(0.432)
	Current ratio	-0.150***	-0.328**	-0.550**	-0.165	-0.336**
			(0.132)	(0.251)	(0.165)	(0.131)
	Interest coverage ratio	-0.109**	-0.082	-0.400***	-0.071	-0.110**
			(0.051)	(0.152)	(0.061)	(0.051)
	House bank status	-0.044	-0.266	-1.052***	-0.204	-0.486**
			(0.219)	(0.363)	(0.323)	(0.230)
	Relation duration	-0.090*	0.009	-0.034	0.021	0.006
			(0.017)	(0.052)	(0.026)	(0.018)
	Ln (Number of banks)	0.066	0.052	0.227	-0.159	0.021
			(0.137)	(0.286)	(0.179)	(0.142)
	Ln (assets)	0.206***	0.176***	0.301*	0.276***	0.016
			(0.055)	(0.181)	(0.106)	(0.072)
	Age	-0.098**	-0.031**	-0.018	-0.037***	-0.028**
			(0.012)	(0.039)	(0.014)	(0.013)
	Collateral	0.014	0.000	0.013**	0.000	0.001
			(0.002)	(0.006)	(0.003)	(0.003)
	Ln (L/C volume)					0.319***
						(0.082)
	(Constant)		-0.522	-0.526	-1.844*	-0.751
			(0.591)	(0.978)	(1.119)	(0.635)
	Bank dummies		yes	yes	yes	yes
	Year dummies		yes	yes	yes	yes
	Industry dummies		yes	yes	yes	yes
	Number of cases		416	208	208	416
	McFadden R 2		0.299	0.533	0.368	0.337
	LR-statistic		109.845	77.077	78.976	123.776

No significant effects of leverage have been found among others by[START_REF] Berger | Relationship lending and lines of credit in small firm finance[END_REF] Udell, 1995, or Petersen and[START_REF] Petersen | The benefits of lending relationships: evidence from small business data[END_REF], the latter also with a negative sign in two specifications.

We confirm the finding of[START_REF] Elsas | Empirical determinants of relationship lending[END_REF] that a house bank variable probably better captures relationship lending than a duration variable.

Another significant variable -not shown here -is for the construction industry, which makes sense for this highly cyclical and leveraged business.

To check whether results depend on the sample size, various specifications -e.g. with less restrictions on the risk variables -have been run without major changes (see[START_REF] Menkhoff | The rationale of bank lending in pre-crisis Thailand[END_REF].

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UKSubmitted Manuscript

Acknowledgements

For their helpful comments we would like to thank seminar participants in Bangkok, Bonn, Dresden, Hamburg and Hannover, participants at the Money, Macro and Finance Conference in Warwick, the Global Finance Conference in Beijing, the European Economic Association Conference in Stockholm, the German Economic Association Conference in Dresden, Piti Disyatat, Michael Frömmel, Horst Gischer, Olaf Hübler, Don Nakornthas and Doris Neuberger. Alexander Mende and Maik Schmeling provided very useful research assistance. We are grateful to the Thai commercial banks that participated in the survey and the Thai Ministry of Finance for its support. Financial support by the Center for Development Research at the University of Bonn (ZEF Bonn) and by the German academic exchange service (DAAD) is gratefully acknowledged.