metadata: authors: 0: author: Claessens, Stijn
1: author: Sakho,Yaye Seynabou
count: Brazil
docna: 0: docna: Assessing firms' financing constraints
            in Brazil
docty: Policy Research Working Paper
subtopic: Investment and Investment Climate,Business Environment,Legal Institutions of the Market Economy,Financial Regulation & Supervision,Access to Finance
teratopic: Macroeconomics and Economic Growth,Finance and Financial Sector Development
abstracts: cdata!: Firm surveys often indicate that firms
            complain a lot about lack of access to financial services,
            but financing constraints are difficult to identify, given
            demand and supply considerations and with only surveys based
            on firms' perceptions. Specifically, it is difficult to
            separate demand for access to finance of viable firms with
            good growth opportunities from that of firms that are not
            creditworthy and should not deserve financing. In Brazil,
            one of the main constraints to finance is related to the
            high level of interest rates, which affects both bank
            funding costs as well as bank intermediation spreads and, as
            such, the cost of finance and hence the demand and supply of
            bank financing. This paper analyzes a unique loan level data
            set that covers almost a decade of monthly firm bank
            information from credit registry information that is not
            publicly available as well as two cross-sections of
            Brazil's Investment Climate Assessment surveys in 2004
            and 2008 that provide detailed information on firms'
            micro characteristics as well as perceptions of credit. The
            data allow identification of how firms'
            characteristics, banks' characteristics, and macro
            variables affect firms' demand for credit, banks'
            supply of credit, and access to credit. The paper finds
            first that access to finance for firms has improved over the
            decade for small firms, reflecting the deepening of the
            credit markets. However, access to credit depends strongly
            on information availability captured in the positive
            influence of collateral and credit history. Banks perceive
            that it is less risky to lend to firms that the banks know
            or that other banks know. Second, firms' loan demand is
            inelastic to the interest rate at the individual loan
            category level, possibly reflecting some screening and
            pricing; however, when the loans are aggregated, the effect
            of interest rates becomes significant and negative as
            expected. Third, firms loan demand and loan supply are
            affected by the availability of collateral and, in the case
            of loan demand, longer maturity. Policy implications point
            to the importance of reducing asymmetric information between
            lenders and borrowers and on collateral to alleviate
            financing constraints for small firms.
geo_reg_key: 119228,517191
repnme: repnme: Assessing firms' financing constraints
            in Brazil
display_title: Assessing firms' financing
            constraints in Brazil
listing_relative_url: /research/2013/09/18321821/assessing-firms-financing-constraints-brazil
topicv3: Investment Climate m1326293 405,Access to Finance m1327464 1249,Financial Institutions m1505015 3797,Trade Guarantees m1505111 3906,Business Environment m1586411 2530
publishtoextweb_dt: 2013-09-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Finance and Financial Sector Development
no_of_pages: 31
author_keys: 0: author_key: 168298
model_extractions: WPS6624 Policy Research Working Paper 6624 Stijn Claessens Yaye Seynabou Sakho The World Bank Latin America and the Caribbean Region Poverty Reduction and Economic Management Unit September 2013
Policy Research Working Paper 6624

# **Abstract**

Firm surveys often indicate that firms complain a lot about lack of access to financial services, but financing constraints are difficult to identify, given demand and supply considerations and with only surveys based on firms’ perceptions. Specifically, it is difficult to separate demand for access to finance of viable firms with good growth opportunities from that of firms that are not creditworthy and should not deserve financing. In Brazil, one of the main constraints to finance is related to the high level of interest rates, which affects both bank funding costs as well as bank intermediation spreads and, as such, the cost of finance and hence the demand and supply of bank financing. This paper analyzes a unique loan level data set that covers almost a decade of monthly firm bank information from credit registry information that is not publicly available as well as two cross-sections of Brazil’s Investment Climate Assessment surveys in 2004 and 2008 that provide detailed information on firms’ micro characteristics as well as perceptions of credit. The data allow identification of how firms’ characteristics, banks’ characteristics, and macro variables affect firms’ demand for credit, banks’ supply of credit, and access to credit. The paper finds first that access to finance for firms has improved over the decade for small firms, reflecting the deepening of the credit markets. However, access to credit depends strongly on information availability captured in the positive influence of collateral and credit history. Banks perceive that it is less risky to lend to firms that the banks know or that other banks know. Second, firms’ loan demand is inelastic to the interest rate at the individual loan category level, possibly reflecting some screening and pricing; however, when the loans are aggregated, the effect of interest rates becomes significant and negative as expected. Third, firms’ loan demand and loan supply are affected by the availability of collateral and, in the case of loan demand, longer maturity. Policy implications point to the importance of reducing asymmetric information between lenders and borrowers and on collateral to alleviate financing constraints for small firms.

This paper is a product of the Poverty Reduction and Economic Management Unit, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The authors may be contacted at ysakho@worldbank.org and sclaessens@imf.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Assessing firms’ financing constraints in Brazil**

# **Stijn Claessens and Yaye Seynabou Sakho***

**Keywords** : Access to credit, Bank financing , credit shortages, small firms, credit registry data, Brazil **JEL Classification** : G21, G23, G38. Sector Board: EPOL, FSE.
# **1. Introduction and Motivation**

Answering the question whether firms are constrained in their growth by a lack of external financing is a complex question. It requires one to differentiate access to and usage of external financing and requires one to consider both demand and supply side factors. Some firms may not want to borrow even when they have access, relying instead on internal financing (voluntarily excluded). Some firms may want to borrow, and would in principle be able to do so, but may lack access, for example, when there is no banking outlet nearby (involuntarily excluded). Financial institutions may not want to lend to some firms because of creditworthiness concerns (credit rationing). In principle, however, the identification of firms with financing constraints can be done using rich demand and supply side data. So far, this has been mainly done for developed countries and for some developing countries where rich data were available. The issue of financing constraints is of special relevance for Brazil given its still high interest rates for some classes of borrowers, which are argued to be a constraint for growth<sup>1</sup> . However, it has been hard to separate here whether the high rates (high spreads) are due to the compensation for borrowers’ risks and other weaknesses on the demand side, or due to supply constraints, at the level of individual financial institutions (e.g., lack of competition among banks), inefficiencies in financial intermediation (e.g., lack of credit information, financial taxation, etc.), a weak contracting environment due to judicial inefficiencies, or other factors.

Over the past decade Brazil has come a long way in terms of market deepening. Spreads have also come down significantly for households and considerably for firms. This paper presents a simple way to do financing constraint analysis of the impact of interest rates on the demand and supply of bank credit to micro, small and medium firms using a rich dataset including not publicly available firm level transaction data from the Credit Registry System of the Central bank, firm characteristics data from the ICA (Investment Climate Assessment) firm survey and banks characteristics data from the Central Bank. This survey provides detailed information on firms’ characteristics as well as their perception of the business environment and is an important tool in World Bank business environment and financial intermediation analyses. The paper is able to reconcile at the firm level the ICA information with privately available credit registry data from the Central Bank for almost a decade allowing us to have a view at both the bank relationships of the firms at the system level over time as well as the very minute details of who they are at two specific points in time in 2004 and four years later in 2008. This is the first dataset to our knowledge that can have that detailed level of information on these different aspects. We are able to complete the database on the banks using publicly available bank information from the Central Bank over time. The ICA and the bank registry data have not been used for that purpose before to our knowledge, but we argue that by using the uniqueness of bank-firm lending relationships, they provide the ability to separate demand from supply factors.

Previous papers using bank registry data have focused only on the information provided to the bank. For instance, some have just focused on one type of credit instrument. Jimenez, Lopez, Saurina (2009) investigate the determinants of the usage of bank credit lines using a database of Spanish corporate credit lines and find that bank monitoring affects firm’s usage of the line as firms with prior default access their credit lines less. Their paper investigates how the relationship between the use of collateral and the credit risk of the borrower plays out in different segments of the credit market (short term and long term loans, new and old borrowers). Khwaja and Mian (2005) use loan level data for the universe of corporate lending in Pakistan and find that politically connected firms have preferential access to public banks loans in Pakistan. Khwaja and Mian (2008) use data that allows them to follow how bank liquidity shocks are passed on to firms and how large firms can compensate for this loss through credit markets whereas small firms are unable to do so and face large drops in overall borrowing. Ioannidou, Ongena, Peydro (2009) use data from the Bolivian Credit Registry database (CIRC) to investigate the impact of monetary policy – as measured by an exogenous increase of the Fed funds rate in a fully dollarized banking system whose economy is not synchronized to the US economy **–** on bank risk

> 1 See Haussman, Rodrik, and Velasco (2005) who argue that the Brazilian economy does not lack profitable investment opportunities but the high opportunity cost of capital means few new investments are profitable enough in order to overcome this hurdle.

2
taking and pricing. Degryse and Ongena (2002) use contract information of 15,000 bank loans from one bank to small firms in Belgium, including the distance between the borrower and the lending bank as well as the distance from the borrower to the competing banks, to study the importance of geographical distance in explaining the pricing and availability of loans to small firms. Petersen and Rajan (2002) use the 1993 National Survey of Small Business Finance, which provides financial information about the firm (balance sheet and income statement) as well as a documentation of the firm’s relationship with financial institutions, to show that lenders are becoming more productive in using alternative sources of information which has eroded the importance of distance for monitoring purpose and increased access to finance for more distant firms.

This paper provides an application of the unique use of credit registry data and ICA data from Brazil to identify the determinants of firms’ demand for external bank credit and to identify the classes of firms unduly rationed. The structure of paper is as follows. The first part of the paper briefly provides a conceptual framework and review relevant literature on the concepts of measuring financing constraints and access to financial services. The paper next presents the specific data we have for Brazil and provide some basic statistics. The paper then presents a methodology to address endogeneity issues that might arise in the analysis as well as the results . Finally, the paper concludes and offers some suggestions about where further data collection and research would be useful.

# **2. Conceptual Framework**

  Availability of external financing and the lack thereof financing constraints depend on demand and supply considerations. The start of the analysis is to differentiate between access and usage of financial services. Usage is the actual consumption of financial services, whereas access is the availability of the supply of financial services at a “reasonable cost”. Hence, there can be a difference between access and usage (Figure 1). While all users have access, it is not the case that all non-users do not have access. When a firm does not use financial services, it may have access but choose not to use it; or it may not have access and thus cannot use it. Furthermore, the reasons for not using financial services are multiple. As with other goods and services, demand for financial services may not exist even when access exists. Many firms in developed countries finance themselves internally and choose not to use bank financing. Or a firm may not have enough growth opportunities to justify external financing. So, while they likely have access, they may (appear) not be burdened by lack of use. There will also be firms that are voluntarily excluded, e.g., they think they will be rejected, but would actually have been accepted, and involuntarily excluded, e.g., firms that are discriminated against.

Equally important, and even in the best financial systems, financial service providers may not wish to supply financial services to all firms since it is not profitable or sustainable to do so. Financial institutions typically say it a too high-risk, high-cost proposition to cater to small firms. High transactions costs for small volumes, combined with difficulties in contract design and enforcement make for not conducive supply. Banks will not be able to profitably run branches in remote areas to cater to firms. This does not reflect any market failures, but rather indicates that finance, like other services, has its own supply forces.

3
Figure 1

## **DIFFERENCE BETWEEN ACCESS TO AND USAGE OF SERVICES**

<!-- Start of picture text --> = Access to financial services<br>= No access to financial services<br>Users<br>of financial services<br>No need/awareness<br>Voluntary exclusion<br>Population • Inability to use due to<br>price/income<br>• Assume to be rejected<br>People not<br>using financial services • Rejected due to high<br>risk/bad credit<br>Involuntary exclusion • Rejected due to discrimination<br>•Rejected due to price/product<br>or income/client features<br>Source: Claessens (2005).<br><!-- End of picture text --> Especially for developing countries, often market structure, incentives and features of the institutional environment affect the willingness and ability of financial institutions to provide financial services to small firms. Supply factors then can constrain access to financial services, even when sustainable demand for external financing is there (in the sense that the firm would have gotten financing if it were placed in a better institutional environment). The importance of the effects of financial, legal and other institutional environment and reforms can best be analyzed in a cross-country context, where financing (constraints) can be compared, controlling for firm characteristics (see Beck, Demirguc-Kunt and Maksimovic (2008 ) for work along these lines). At the single country level, a study of financing constraints can help identify the type of firms most constrained and the forms of financing for which constraints are more binding. To resolve the identification problem and to investigate the importance of supply versus demand factors requires though some specific approaches and some specific data. We review the approaches used to date, and then present our approach.

# **Demand side, econometric techniques**

Traditionally, financing constraints have been investigated from the demand side. The demand for sustainable external financing and the sensitivity of investment to the presence of internal funds, i.e., the degree of financing constraints, have been investigated using one of two approaches, each with their own strengths and shortcomings: investment-internal cash-flow regressions or Euler equations estimations. The first approach is based on the Q theory of investment suggested by Tobin (1969) and has been widely used in the finance literature after the influential paper of Fazzari et al. (1988). In the reduced form q-model, a measure of available internal funds (e.g., cash flows) is directly included as one of the independent variables. In order to investigate the presence of financing constraints, the sample is divided using a priori classifications of firms’ financing constraints, and the investment-cash-flow sensitivities of the different sub-samples are then compared. Higher sensitivity for the samples of a priori more constrained classified firms is interpreted as evidence of tighter financing constraints.

The empirical investigation of the sensitivity between investments and internal funds based on the q-model has, however, several problems. The a priori classification of firms in different groups is often set arbitrarily and 4
often not made time-dependent (Kaplan and Zingales, 1997). Also, the average Tobin's Q may be an imprecise proxy for the unobservable marginal Tobin's Q (Hayashi, 1982). Internal funds could be a proxy for the profitability of investment and the positive sensitivity of investment to cash flow cannot solely be interpreted as capital and credit market imperfections but rather as firms with better liquidity also attaining superior investment possibilities (Hoshi et al., 1991; Schiantarelli, 1996).

An alternative approach to examine financing constraints is through estimating the Euler equation for the capital stock. The Euler equation uses a structural model to capture the influence of current expectations of future profitability on current investment decisions. Unlike the q-model, the Euler-equation approach measures how internal funds indirectly affect investment via a Lagrange multiplier and does not use the market value of q. The advantage of this is that future profitability, i.e. marginal q, does not need to be specified or observed. The major shortcoming of the Euler-equation approach is that it incorporates dynamics, which can complicate the   estimation and adapt needs, and like the q model of investment it needs to make some assumptions, like a geometric depreciation rate and convex adjustment costs.

Overall, the aforementioned approaches rely on strong theoretical assumptions, which in the event they are not met, render the models misspecified. Econometric advances have provided some solutions. For instance, Erickson and Whited (2000, 2002) proposed a class of GMM estimators that alleviate the measurement problems associated with Tobin’s q, by utilizing the information in the higher order moments of the regression variables. Also, in order to tackle the a priori classification of firms, Hansen (1999) introduces a threshold investment model based on a panel estimation method using a fixed-effects transformation where all parameters are determined simultaneously with the determination of the threshold value of the uncertainty measure. The main advantage of Hansen’s model is that the estimates of the thresholds are conditional on the model specification as a whole.

Nevertheless, these advances do not overcome all limitations. Remaining limitations, as highlighted by recent findings by Alti (2003) and Gomes (2001), include that investment-cash-flow sensitivities can be positive even in the absence of financial frictions. These findings illustrate the need for alternative empirical methodologies in identifying financing constraints. One alternative method is used by Demirgüç-Kunt and Maksimovic (1998) who estimate the degree of financing constraints by using a financial planning model. They obtain the maximum growth rate that firms could attain without access to long-term financing and then compare these predicted rates to the actual growth rates. Another empirical method is to depart from the standard model of reversible investment and combine the literature on financing constraints and with the literature on investment uncertainty. In this way, one can take into account investment irreversibility and the possibility to postpone the investment decision.

# **Demand side, survey techniques**

Even with these adjustments, such tests can typically only be done, however, for listed and large firms (since stock price data will be needed) and with much firm specific information (especially for the Euler equation models where a longer time series is needed). As a consequence, one is not able to do these studies for many developing countries (few have active stock markets) and definitely not for smaller firms. The approach for small (and large) firms has been to use surveys. This is also the typical approach used in developing countries. Enterprise surveys measure business perceptions of the investment climate, and can be used to analyze the link to job creation and productivity growth. Surveys provide quantitative indicators, also of financing constraints. A good example here is the World Business Environment Survey (WBES), administered to enterprises in 80 countries in late 1999 and early 2000, that utilized standard a core WBES survey in measuring constraints to access to finance, and has been extensive used to document the (lack of) financing constraints and the barriers to financing. In contrast, the Investment Climate Assessment (ICA), which has been conducted in 91 countries and is based on surveys of more than 58,000 firms, has not been much used for assessing financing constraints. This approach leads to statements such as “X percent of firms complain about lack of access to finance,” or 5
“financing is the second most important complaint firms state when asked about the constraints to higher growth opportunities.”

# **Endogeneity Issues**

At the same time, when surveying firms on their ease of access to finance, one cannot avoid problems of endogeneity, ambiguity in the definition of access, and often the lack of good conceptual framework for the data being collected. First, dependent and independent variables in empirical analyses using data from survey responses often share a common parameter that is omitted in the survey and is usually the result of self-selection in survey participation. But these can suffer from biases. Specifically, firms that are better may not complain about their access to finance while worse firms, e.g., firms with weak growth opportunities and poor collateral, more likely will complain the most about lack of financing. As a consequence, complaints about access to finance cannot be used directly as independent variables to predict in turn firms’ performance as a dependent variable. Financial and other data may also be biased or endogenous to external financing constraints. The interest rate the firm pays and the amount of financing it gets, for example, are not useful control measures, as they depend on firms’ creditworthiness and degree of access. Also, the cross-sectional nature of most surveys does not allow the tackling of any simultaneity bias between survey responses that are used as dependent and independent variables, respectively. Furthermore, at times the perception of firms has been used as indicators of the business environment itself, which introduces another endogeneity (e.g., with many firms complaining, the business environment may be considered poor, but then it may also be that firms are weak themselves, and thus complain more).

Second, defining access to finance is not an easy task. Generally, access to finance refers to the availability of supply of quality financial services at reasonable costs, with a focus on whether investment is constrained. However, depending on what one considers ‘quality’ services and ‘reasonable’ costs, the measurement of access to finance may need to be altered accordingly. Measurement of access to finance is also influenced by the definition and priority of its various dimensions. For instance, one can distinguish the dimensions of reliability, convenience, continuity and flexibility, with each requiring a different measure. A related issue is that firms obtain various forms of external financing, e.g., short-term trade finance, working capital financing and investment financing. Each of these forms of financing may have different degree of financing constraints, e.g., a firm may not be constrained in trade finance but may not be able to get investment financing.

Third, a major problem with measuring and evaluating firms’ access to finance is the absence of a unified conceptual framework for data collection. Somewhat surprisingly, theoretical models and empirical evidence on the topic of access to finance has not yet resulted in a commonly accepted framework for data collection. Thus, currently collected data are often of an ad-hoc nature, with varying definitions over time. As a result, the data are often not comparable across countries and do not necessarily yield appropriate variables for model testing. The data collected are rarely of the experimental form, where one controls for the firm characteristics by randomizing treatment. For instance, Duflo, Glennester and Kremer (2007) provide a practical guide to build in randomization as part of research design in the field. Randomization would allow solving selection bias to control for firms characteristics

# **Supply Side**

Supply data can help with the identification of financing constraints, but have been little used to date. We know from theory (Rajan 1992) and confirmed empirically (Petersen and Rajan, 1994) that access, especially for smaller, more opaque firms can depend on their relationships with financial institutions. Banks may invest in information acquisition in the expectation of future returns, and then be able and willing to lend more to some classes of firms. Therefore, bank characteristics can relate to the degree of lending to some classes of firms (one can consider these clientele effects). Small banks, for example, may specialize in lending to smaller firms. Or banks with certain lending, technology and human resources management policies that allow for the use of 6
soft information in lending may do better with SME lending. Petersen and Rajan (1994), for example, show how ties between a firm and its creditors affect the availability and cost of funds to the firm.

This importance of bank characteristics and banking markets also applies over time. It means that changes in banks’ and banking systems’ characteristics can drive the degree of access over time. As such, a panel of firm data combined with banking system characteristics becomes a very powerful tool to analyze financing constraints. Petersen and Rajan (1995), for example, study the effects of changes in banking system structures in the U.S. on small firms’ access to financing and find that the increase in competitiveness reduced the access to financing for small firms.

# **The Disequilibrium Approach**

We use a disequilibrium approach to investigate the determinants of credit demand, credit supply, and access to finance for small and medium firms in Brazil from 2003 to 2009. Cunha, de Jesus and Sakho (2011) estimation of the determinants of credit in Brazil over the 2000 to 2010 decade at the aggregate level shows that indeed for firms throughout the decade, credit is driven purely by supply factors for long periods of time, indicating that such disequilibrium model might be the right instrument to use to look at credit in Brazil. In addition, the paper points throughout the decade to long episodes of credit shortages for firms, where credit shortages arise when under prevailing interest rate, the quantity of credit demanded by agents is larger the quantity supplied by financial institutions.

Such shortages can arise both in equilibrium and in disequilibrium and both temporarily and in the long term. Disequilibrium could arise temporarily; when an economy is hit by exogenous shocks and price stickiness delay adjustments, so that rationing occurs during the transition to a new equilibrium. However, it can also be a long term feature, when there are permanent restrictions to price or quantity adjustments. For instance, shocks in Brazil that could further cause shortages in disequilibrium are related to Price Stickiness – Mild (average price duration in services - including financial services- around 5 months; . Aith (1998) discusses as examples of the “non-standard behavior” by courts that in Rio Grande do Sul, local judges rule on “limiting interest rates for loan contracts by banks to 12% (as defined in the Constitution), even though the Supreme Federal Court has established such cap depends on regulating.” Asymmetric information between borrowers and lenders can cause shortages in equilibrium, for instance in Stiglitz and Weiss (1981), the interest rate a bank charges may itself affect the riskiness of the pool of loans and the profit maximizing interest set by banks might differ from market clearing levels. In Brazil, asymmetric information is an issue. Until recently, public and private data banks used to contain only negative information (debtors in default) and there were no comprehensive credit record systems.(Beck 2000). In addition, Contract enforcement is another possible cause for shortages- Brazil’s judicial system scores considerably below countries in the upper middle income group as evidenced by cross-country indicators of contract enforceability. World Bank (1999) shows that there are 3,129 cases pending per judge in Sao Paulo, compared to only 58 in Singapore and 244 in Hungary. Pinheiro and Cabral (1999), a judicial execution to recover a creditor claim can take between 1 and 10 years. More recent evidence from Arida, Bacha, and Lara-Resende (2004) further argue that jurisdictional uncertainty is the main culprit linking the inexistence of local long-term domestic credit to the persistence of high short-term interest rates in Brazil. In particular, uncertainties associated to the settlement of contracts in the Brazilian jurisdiction are to blame. Gonçalves, Holland and Spacov (2007) using panel data find positive correlation-albeit not significant- between jurisdictional uncertainty and the level of short term real interest rate. They use a concept of jurisdictional uncertainty defined as an anti-saver and anti-creditor bias, which manifests itself as ''the risk of acts of changing the value of contracts before or at the moment of their execution, and as the risk of an unfavorable interpretation of the contracts in case of a court ruling.” 7
<!-- Start of picture text --> D. = XB, T Fay<br><!-- End of picture text --> D. = XB, T Fay S; = X,,B, T £o5 Cc, = min(D,,5,) <!-- Start of picture text --> S; = X,,B, T £o5<br><!-- End of picture text --> <!-- Start of picture text --> Cc, = min(D,,5,)<br><!-- End of picture text -->
Where denotes the quantity of credit demanded, denotes the quantity of credit supplied and is the actual credit observed in the market. and are the explanatory variables affecting credit demand and supply respectively, and are parameters.

# **Bayesian estimation method**

We estimate the model using a Bayesian estimation approach. Simple MLE would thus lead to biases similar to the well-known linear model. It is also found that the correlation coefficients would be underestimated. This approach is computationally intensive, but allows to circumvent some of the shortcomings related to the numerical optimization problems faced by the Maximum Likelihood approach. The use of the Bayesian estimation method allows us to robustly solve the issues of endogeneity and simultaneity.<sup>2</sup> The Bayesian estimator is the one that minimizes the quadratic loss function which is the mean of the posterior distribution of the parameters (conditional distribution of the parameters given the sample). In order to obtain the posterior’s mean we use the Metropolis-Hastings algorithm to obtain draws from the posterior distribution. With these draws at hand we average them getting our estimator. Bayesian estimation was originally proposed by Bauweans and Lubrano (2006). This approach uses the data augmentation principle to estimate the latent variables – credit demand and credit supply of our disequilibrium model – via Markov Chain Monte Carlo (MCMC) method. One advantage of this technique is that it provides the whole distribution of the parameters and latent variables (the posterior distribution).

The most important identifying characteristic is the interest rate at which the bank lends to a specific firm. This rate is related to this clientele effect, with banks that cater to SME lending, for example, lending at higher exante rates, as transactions costs and risks of non-recovery are higher. Firms’ characteristics allow us to control for those. The rate is also part of a broader terms set including, amount of the loan, collateral required, and maturity of the loan. In principle, other bank characteristics can play a role as well in financing. For example, we may expect banks that are more profitable, that intermediate more in general, and that are sounder to be more likely to grant loans to firms. We use these bank characteristics therefore as control variables in our regressions. Those characteristics would have further allowed us also to identify the supply equation compared to the demand equation, however the Bayesian estimation method takes care of the identification issue altogether.

# **4. Data**

2 The use of ML method to estimate disequilibrium models was first proposed by Maddala and Nelson (1974) in their seminal paper and has since been exploited in a number of studies testing the empirical significance of credit shortages (see, for example, Pazarbasioglu, 1996; Nehls and Schmidt, 2002; Baek, (200) Hurlin and Kierzenkowski, 2006, Laurence Allain and Nada Oulidi, 2009). A variety of estimators for models of this type have been proposed – see, e.g., Fair and Jaffee (1972), Fair and Kelejian (1974), Amemiya (1973), Maddala and Nelson (1974) ML estimation has the advantage of being relatively simple and well known in the literature. However, the method depends on the underlining assumption of stationary residuals. If the residuals are not stationary, then Maximum Likelihood estimation may provide biased parameters estimates. The existence of measurement error could be due to: (i) unobservable variables that are omitted from the estimation equations and are instead captured by the error term and (ii) a non-random selection process for credit observations, causes covariates to be contaminated by additive errors. Firstly, desired credit demanded by firms and desired supply by banks are latent (not observable); we only observe a value equal to the minimum between the two, a Leontief function. Secondly, selection issues arise as credit demand and supply are truncated from below at zero. Simple MLE would thus lead to biases similar to the well-known linear model. It is also found that the correlation coefficients would be underestimated. As this condition cannot be assured in some specifications of the model, we estimate the model using a Bayesian estimation approach .

9
The 2004 Brazil ICA survey data provide firm level information on 1,642 firms interviewed in 2003. The ICA sample is composed such that 20 percent of firms in the sample are micros (1 to 19 employees), 52 percent are small (20 to 99), 23 percent are medium (100 to 499), and 5 percent are large (above 500 employees)<sup>3</sup> . In other words, 95 percent of firms are micro or small and medium enterprises. This ratio is similar regardless of the definition by employees (95 percent) or sales (96 percent).<sup>4</sup> The ICA questionnaire asks each firm whether it has a loan and whether it has applied for a loan. It also asks which bank is the firm’s principal finance provider as well as the number of banks they does business with. Of all firms, 567 or 34.5 percent have a bank loan, and the remaining 1075 firms do not have bank loan. Many firms that decide not to apply for a bank loan report that they do not need a loan (431 firms), probably substituting other sources of finance for bank loan. Few firms report that their loan demand has been rejected by a bank (113 firms). The Brazil ICA survey also asks firms to rank potential obstacles to growth. High interest rates are the principal reason cited for not applying for a loan as reported by firms of all size. Application procedures and collateral requirement are next in importance, affecting especially micro and small firms. The cost of finance is reported to be the main obstacle to growth for 57 percent of all surveyed firms. Access to finance (and specifically collateral) is ranked seventh (34.5 percent of surveyed firms) in the obstacles to growth list, after cost of finance, tax rates corruption, economic and regulatory policy uncertainty, and macroeconomic instability. However, the ranking varies by firm size: access to finance and cost of credit are less binding obstacles for large than for smaller firms. Large firms are more likely to cite tax rates and corruption as their main constraint to growth than micro and small firms. Micro and small firms are charged higher interest rates on overdraft (around 5 percent per month) compared to medium and large firms (3 and 4 percent respectively per month). This analysis, which is typical in ICAs, shows some links between high lending interest rates and poor firm financing, and limited growth, but does not present causal evidence. In particular, the survey does not control for the endogeneity of firm responses.

The data also show that 45 percent of firms do business mainly with public banks, while domestic private banks cater to 42 percent of firms in the sample and foreign private banks cater to 12.7 percent of the sample firms. In particular, Banco de Brasil, a public bank is the principal bank for 593 firms or 36 percent of the sample; it is the main bank for small firms. The second most important Bank is Bradesco, a private domestic bank that cater to firms of all size. Micro firms mostly work with Caixa Economica Federal, a public bank. The firms in our sample tend to work with more banks on average as their size increase, with on average 2 banks for micro firms, 3 banks for small firms, 4.8 banks for medium firms, and 8.2 banks for large firms.

For 2003 and SCR, Table 1 shows various summary statistics for firms (1642 for 2003 and 694 for SCR) according to their size. Most of the firms in the sample are small (73%) (and 23% of medium size). Most of the small and medium firms are dedicated to the Garment Industry, and most of the large firms are in the Food Processing market. As expected, the larger the firm, the more it operates in the exterior, the more it sells abroad. Larger firms are older and employ more people (by construction).With respect to capital origin, the table shows that most capital for smaller firms comes from private and domestic sources, whereas larger firms have more access to private and foreign sources. Unsurprisingly, larger firms have larger equity and liability levels. These trends are similar for 2008, except for the fact that most small firms operate in the Furniture Industry, median firms in the Machinery business, and large firms in Auto Parts (Table 2).

Table 1. ICA data on Firms’ Characteristics in 2003 and 2008 and SCR

> 3 The classification of firm by size (when size is defined according to the number of employees) is as defined by the Ministry of Industrial Development and External Trade.

> 4 The ICA dataset for Brazil has been well described by Kumar and Francisco (2005), most of the descriptive that follow draw on their analysis.

10
|'|||% of firms<br>|2003<br>% of sales sol|d domestically|F|oundatio|nyear||# Emplo|yees||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Firms Size|Obs|Industry mode|operating in<br>other countries|Mean|Std. Dev.|Mean|Std. Dev.|Min Max|Mean|Std. Dev|. Min|Max|
|Small (<100 employees)|1191|Garments|7.74|95.99|14.93|1987|13.26|1882 2002|37|23.03|6|99|
|Medium (100 to 500 employees)|376|Garments|21.54|83.22|29.30|1978|20.10|1880 2002|205|97.69|100|498|
|Large (>500 emplyees)|75|Food processing|40|76.83|26.59|1961|26.26|1880 2002|1201|1231.74|500|7500|
|||||2008|||||||||
||||% of firms|% of sales sol|d domestically|F|oundatio|nyear||# Emplo|yees||
|Firms' Size|Obs|Industry mode|operating in<br>other countries|Mean|Std. Dev.|Mean|Std. Dev.|Min Max|Mean|Std. Dev|. Min|Max|
|Small (<100 employees)|1432|Furniture||97.54|11.15|1990|13.22|1912 2008|28|22.00|1|98|
|Medium (100 to 500 employees)|272|Machinery|NA|91.38|20.62|1981|18.10|1894 2007|202|97.82|100|480<br>|
|Large (>500 emplyees)|98|Auto Parts||85.58|24.27|1968|25.14|1880<br>2003|1761|1708.37|500|9716<br>|
|||||SCR|||||||||
||||% of firms|% of sales sol|d domestically|F|oundatio|nyear||# Emplo|yees||
|Firms' Size|Obs|Industry mode|operating in<br>other countries|Mean|Std. Dev.|Mean|Std. Dev.|Min Max|Mean|Std. Dev|. Min|Max|
|Small (<100 employees)|506|Garments|7.71|95.56|15.18|1991|12.15|1882 2002|40|23.28|6|99|
|Medium (100 to 500 employees)|158|Garments|19.62|85.99|27.44|1984|17.32|1901<br>2002|192|88.61|100|467<br>|
|Large (>500 emplyees)|30|Food processing|33.33|77.43|28.00|1968|17.71|1934 2002|1249|853.39|560|3500|

# Table 2. ICA data on Firms’ Capital Structure in 2003 and 2008 and SCR

|Firms' Size|Mean<br>%Private|Std. Dev.<br>& Domestic|Mean<br>%Private<br>Capita|Std. Dev.<br>& Foreign<br>l Origin|Mean<br>2003<br>%Gove|Std. Dev.<br>rnment|Total<br>Mean|equityin<br> Std. Dev.|2002(m<br>Min|illions)<br>Max|Total li<br>Mean|abilities in<br>Std. Dev.|2002<br> Min|(millions)<br>Max|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Small (<100 employees)|97.50|14.66|2.38|14.49|0.08|2.04|1.68|10.10|-23.50|259.00|3.95|27.50|0.00|741.00|
|Medium (100 to 500 employees)|91.47|26.69|8.21|26.27|0.31|5.21|13.90|74.20|-154.00|1,200.00|46.20|267.00|0.04|4,240.00|
|Large (>500 emplyees)|79.66|38.47|19.69|38.59|0.65|3.20|73.40|130.00|-157.00|813.00|192.00|306.00|0.22|1,570.00|
|'|||<br>Capita|<br>l Origin|2008<br>||Total|equityin|2002(m|illions)|Total li|abilities in|2002|(millions)|
|Firms Size|Mean<br>%Private|Std. Dev.<br>& Domestic|Mean<br> %Private|Std. Dev.<br>& Foreign|Mean<br>%Gove|Std. Dev.<br>rnment|Mean|Std. Dev.|Min|Max|Mean|Std. Dev.|Min|Max|
|Small (<100 employees)|97.28|15.66|2.36|14.72|0.04|1.08|||||||||
|Medium (100 to 500 employees)|90.19|27.98|9.16|27.47|0.00|0.00||NA||||NA|||
|Large (>500 emplyees)|76.06|40.56|21.25|39.74|0.25|2.22|||||||||
||||||SCR||||||||||
||||Capita|l Origin|||Total|equityin|2002(m|illions)|Total li|abilities in|2002|(millions)|
|Firms' Size|Mean<br>%Private|Std. Dev.<br>& Domestic|Mean<br> %Private|Std. Dev.<br>& Foreign|Mean<br>%Gove|Std. Dev.<br>rnment|Mean|Std. Dev.|Min|Max|Mean|Std. Dev.|<br>Min|Max|
|Small (<100 employees)|96.21|17.70|3.52|17.39|0.20|3.12|1.30|5.28|-13.60|69.60|2.99|11.50|0.00|125.00|
|Medium (100 to 500 employees)|91.16|26.88|8.74|26.88|0.09|1.19|17.70|100.00|-22.20|1,200.00|33.30|144.00|0.00|1,670.00|
|Large (>500 emplyees)|77.50|39.25|21.40|39.45|1.10|4.19|93.50|116.00|3.81|525.00|205.00|260.00|9.03|1,250.00|

11
Figure 2. ICA data on Firms’ Financing by Capital Origin <!-- Start of picture text --> Firm Financing by Type<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2003 2008 2003 2008 2003 2008 (blank)<br>Large (>500 Medium (100 to Small (<100 (blank)<br>emplyees) 500 employees) employees)<br>Private Domestic Private Foreign Government<br><!-- End of picture text --> Table 3 shows the main financing constraint of firms that declare to have been denied a loan request. The most important source of financial constraint is “Lack of collateral” (38%), followed by “Poor Credit History” (26%). Among the financially constrained firms, the majority are small firms (82%). For small firms, the problem of lack of collateral is even more binding, being the cause of 42% of the financial constraints. However, for medium and large firms this is not the case: the most important financial constraint is “Poor Credit History”. When looking at industry types of the financially constrained firms, the sectors most affected by this restriction are Garments (34%) and Furniture (32%). For both these industries, the main reason for the restriction was “Lack of collateral”.

Regarding the contribution of the various financial sources to new investments and working capital, for the former, most of the funding comes from internal sources; while for the latter most of the funding is from external sources. This could be in line with banks’ reluctance to fund riskier projects, accounted for under “New Investments”, rather than safer bets like “Working Capital” (cash, inventories, etc.). For both working capital and new investments, most of the external financing comes from Commercial Banks. However, for new investments, the picture is more even and Trade Credits and Investment Funds are also relevant actors in the funding scheme.

12
# <u>Table 3.</u> Firms’ Financing Constraints by Firm Size **Most important financing constraints (2003) Freq. Percent Cum.**

|**Most important financing constraints(2003)**<br> <br>**All Firms**|**Freq. **<br>|**Percent **<br>|**Cum.**<br>|
|---|---|---|---|
|Lack of collateral|43|38.7|38.7|
|Poor credit history|29|26.1|64.9|
|Incompleteness of application|18|16.2|81.1|
|Perceived lack of feasibility of project|11|9.9|91.0|
|Other|10|9.0|100.0|
|Total|111|1000||
|<br>**Small Firms**||.||
|Lack of collateral|38|41.8|41.8|
|Poor credit history|21|23.1|64.8|
|Incompleteness of application|16|17.6|82.4|
|Other|9|9.9|92.3|
|Perceived lack of feasibility of projec|7|7.7|100.0|
|Total|91|100.0||
|<br>**Medium Firms**||||
|Poor credit history|7|38.9|38.9|
|Lack of collateral|4|22.2|61.1|
|Perceived lack of feasibility of projec|4|22.2|83.3|
|Incompleteness of application|2|11.1|94.4|
|Other|1|5.6|100.0|
|Total|18|1000||
|**Large Firms**||.||
|Poor credit history|1|50.0|50.0|
|Lack of collateral|1|50.0|100.0|
|Total|2|100.0||

The data on the “banks side” cover all the banks that are lending to the firms in the data, and include bank specific characteristics from 2003 to 2009. They capture leverage (debt to equity ratio), profitability (net profit/net capital), size (assets are in the top 90 percentile of the banking system), risk (as measured by the Basel index), liquidity (immobilization index), intermediation (operation of credit/revenues from intermediation operations).

The data are complemented by information on the main partner bank from the Central Bank of Brazil’s credit registry, the Central de Risco, on the bank’s five most common types of lending products, the flows of lending by product, and the interest rates charged. The five most common corporate loan categories are: discount receivables, working capital, overdraft, goods acquisition finance, and vendor. Working Capital is a collateralized credit line that is geared towards meeting the cash-flows needs of a firm during its operational cycle. Overdraft is a line of credit that can be used without collateral by a firm until a certain credit limit is reached. Hence, overdraft commands the highest interest rates in our sample. Discount receivable is a type of loan based on an invoice that the firm will receive in the future. Vendor is a short term credit line (180 days or less in general) through which a company can finance their clients. It is highly collateralized, and has the lower interest rates in our sample. Finally acquisition of goods and services is a credit line for the purchase of goods. Collateral is required, usually the good itself but sometimes it could be a promissory note.<sup>5</sup> The Central de Risco data is monthly from2003 to 2009.

5

> • working capital [working capital]: credit lines characterized for a period exceeding 30 days, contract signing and presentation of specific guarantees, intended to finance the operational activities of enterprises;

> • guaranteed account [overdrafts]: credit linked to the corporate bank account in which a limited resource is made available for use in accordance with customer convenience;

> • discount bills and promissory notes [discountable]: advance of funds from the duplicates in collection or promissory notes, which

13
Different loan categories differ significantly in terms of maturity, interest rates, collateral, firms’ outstanding debts, and loan value. For example, goods acquisition finance loans are of significantly longer maturity (averaging approx. 25 months) compared to the average of other categories (ranging between 2 and 5 months). Similarly, real interest rates charged on overdrafts are significantly higher than that charged on other loan types. Collaterals are most often used for these two before-mentioned loan categories. Firms which issue goods acquisition finance loans tend to be those with highest outstanding debts. Loans of largest value are issued through working capital and overdrafts. Examining these trends more closely helps us identify characteristics of each of the loan categories.

Lending rates vary widely among banks, even when considering specific lending products. Looking at real interest rates for each of the categories of loans, the minimum rates for all categories is approximately -14%. The standard deviations and maximum rates are indicators of lending rate variations. Overdrafts register the highest variability according to both indicators, with interest rates ranging between 34 percent and 167 percent. In contrast, rates are more stable for other types of loans. In decreasing order of interest rate volatility, as measured by the standard deviation, are working capital, vendor, discountable, and goods acquisition finance loans. Among banks, smaller banks tend to lend relatively more to small and medium firms, and to “Food Processing”, “Garments” and “Furniture” industries. Larger banks lend more to smaller firms, and to the same industries. This is consistent with a better monitoring capacity of larger banks, which allows lending to smaller (and potentially riskier) firms. More profitable, stable, and liquid banks (lucro liquido/patrimonio liquido; basil index<15.15; and imobilizationindex<2.259) lend mostly to medium sized firms; and to “Food Processing”, “Textiles” and “Garments” industries.

Considering the loan type distribution, most of the loans were in the form of “Discountables” (82%), followed by “Working Capital” (8%). The mean loan value for these two loan types is markedly different, of R$32,000and R$190,000, respectively. This shows that banks grant many small loans in the form of “Discountables”, but most of the amounts granted are concentrated in a few “Working Capital” operations. Most loan types have relatively short-term maturities, of around 3 months, except for “G&A”, that has a mean maturity of 25 months. The higher real interest rates are charged on “Overdraft” loans, in contrast to those charged for “Vendor” loans (43% vs 2%). This pattern also applies to the mean number of collaterals associated with the loan: “Vendor” loans have the lowest number of collateral; meanwhile “Overdraft” (and “G&A” to a similar extent) has a higher number of collaterals associated. This could indicate that “Vendor” is a relatively safe loan for the bank.

constitute the very guarantees of operation;

> • vendor, sales finance operation based on the principle of credit assignment, which allows a company to sell their product to run and receive the cash payment. The seller transfers its credit to the bank and this, in exchange for an intermediation fee, paid in cash and the seller finances the buyer;

> • procurement of goods [goods acquisition]: traditional operations funding to individuals and companies in which the credit is tied to the acquisition of the asset that is almost always a guarantee of operation;

14
Table 4. Loan Summary Statistics by Loan Categories

||Vendor|Working<br>Capital|Overdraft|G&A|Discountable|
|---|---|---|---|---|---|
|Obs|2040|3933|1867|1107|41755|
|Maturity (in mo|nths)|||||
|Mean|2.2|5.3|3.3|25.5|1.9|
|Std Dev|1.3|6.2|1.6|15.5|1.2|
|Min|0.1|0.0|0.0|0.8|0.0|
|Max|12.2|48.8|17.6|146.2|36.8|
|Bank real intere|st rate|||||
|Mean|1.8|17.4|43.4|14.0|18.4|
|Std Dev|12.0|18.8|32.6|8.1|9.2|
|Min|-14.7|-14.7|-14.7|-14.1|-14.6|
|Max|94.2|124.9|179.4|51.7|128.2|
|Number of colla<br>|terals associated<br>|with the loan<br>||||
|Mean|0.02|0.39|0.75|0.73|0.17|
|Std Dev|0.16|0.57|0.55|0.52|0.40|
|Min|0.00|0.00|0.00|0.00|0.00|
|Max|2.00|4.00|3.00|4.00|4.00|
|Outstanding de|bt of a firm to one|bank||||
|Mean|5,619,981|4,051,038|981,927|15,100,000|1,351,568|
|Std Dev|16,800,000|10,500,000|4,203,347|21,300,000|2,967,162|
|Min|10,109|10,036|11,615|6,359|10,163|
|Max|108,000,000|128,000,000|71,000,000|104,000,000|61,700,000|
|Loan Value (re|ais)|||||
|Mean|45,697|186,573|166,879|84,844|31,504|
|Std Dev|210,464|1,213,142|1,344,528|264,060|137,288|
|Min|5,001|5,000|5,011|-|5,000|
|Max|3,881,290|68,200,000|23,200,000|5,953,071|5,616,909|

The most important loan type is “working capital”, with an average amount of R$ 677 million. Its importance has a peak in 2008, of R$886 million and then wanes in 2009 (as do all other loan types, except for “G&A”). The second most important loan type is “Discountable”, with a peak in 2006 of R$331 million, which then falls sharply to a minimum in 2009 of around R$93.2 million. The overall pattern shows that all loan types but “G&A” have been declining in 2008 and 2009. Disaggregating these amounts of lending by firm size, it can be seen that most of the variation in the time trends comes from the medium size firm’s borrowing activity. For example, whereas the amounts lent to small and large firms of “Working capital” loans remains fairly stable at relatively low levels for the period analyzed, the trend for the medium firms is more volatile and reveals higher total amounts of borrowing than for the other two sizes of borrowing firms. This is consistent with the theory that the most dynamic sector of the loan market is made of medium sized firms. Smaller ones are not deemed reliable by banks, and large firms may have access to internal sources of funding.

15
Figure 3: Value of Loans by Firm Size <!-- Start of picture text --> Loan Volumes by Firm Size (R$ millions)<br>Large 2003<br>2004<br>2005<br>Medium 2006<br>20 07<br>2008<br>2009<br>Small<br>0 250 500 750<br><!-- End of picture text -->

# Figure 4: Value of Loans by Firm Sector

<!-- Start of picture text --> Loan Volumes (R$ millions)<br>Food processing<br>Chemicals 2003<br>Electronics 2004<br>Machinery 2005<br>Textiles 2006<br>2007<br>Garments<br>2008<br>Furniture<br>2009<br>Auto-parts<br>Shoes & leather products<br>0 250 500 750<br><!-- End of picture text --> There are large differences in client bases between the banks, suggesting that knowing the bank can help resolve the demand versus supply identification problem. Large banks are shown to lend proportionately more to small and medium-sized firms (especially to small firms) than do small banks. In particular, small banks’ bulk of lending is directed at medium firms, while that of large banks is directed at small ones. Interestingly, profitable stable and liquid banks all appear to have a similar profile of lending, targeting medium-sized firms even more than small banks do. Note that these breakdowns reflect the composition bias of our sample towards micro, small, and medium firms which represent over 95 percent of the total sample.

The industry clientele also varies across banks. For example, food processing is on average the largest category, yet accounts for a range from a minimum of 14% of loans by large banks to a maximum of 23.5% of loans by stable and liquid banks. Large banks lend relatively more to shoes and leather products whereas small banks lend much more to textiles.

16
Table 5. Bank Lending Relationship by Bank Type and Firm Size and Industry

||S|mall Ban|ks|L|arge Ban|ks|Pro|fitable Ba|nks|St|able Ban|ks|L|iquid Ban|ks|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Loans granted by firm size|Freq.|Percent|Cum.|Freq.|Percent|Cum.|Freq.|Percent|Cum.|Freq.|Percent|Cum.|Freq.|Percent|Cum.|
|Small|29,915|41.7|41.7|6,786|62.7|62.7|963|38.2|38.2|7,661|37.9|37.9|7,778|38.4|38.4|
|Medium|32,274|45.0|86.7|3,151|29.1|91.8|1,352|53.7|91.9|10,346|51.1|89.0|10,259|50.6|89.0|
|Large|9504|13.3|100.0|884|8.2|100.0|204|8.1|100.0|2236|11.1|100.0|2222|11.0|100.0|
|Total|71,693|100||10,821|100||2,519|100||20,243|100||20,259|100||
||S|mall Ban|ks|L|arge Ban|ks|Pro|fitable Ba|nks|St|able Ban|ks|L|iquid Ban|ks|
|Loans granted by firm<br>industry|Freq.|Percent|Cum.|Freq.|Percent|Cum.|Freq.|Percent|Cum.|Freq.|Percent|Cum.|Freq.|Percent|Cum.|
|Food processing|13,267|18.5|18.5|1,512|14.0|14.0|524|20.8|20.8|4,727|23.4|23.4|4,769|23.5|23.5|
|Textiles|8,349|11.7|30.2|673|6.2|20.2|405|16.1|36.9|2,970|14.7|38.0|3,526|17.4|40.9|
|Garments|8,340|11.6|41.8|1,543|14.3|34.5|319|12.7|49.5|2,493|12.3|50.3|2,938|14.5|55.5|
|Shoes and leather products|4,026|5.6|47.4|1,325|12.2|46.7|157|6.2|55.8|1,471|7.3|57.6|1,587|7.8|63.3|
|Chemicals|6,791|9.5|56.9|664|6.1|52.8|230|9.1|64.9|1,366|6.8|64.4|1,252|6.2|69.5|
|Machinery|7,440|10.4|67.3|1,272|11.8|64.6|182|7.2|72.1|2,401|11.9|76.2|1,924|9.5|79.0|
|Electronics|9,514|13.3|80.5|1,252|11.6|76.2|339|13.5|85.6|1,368|6.8|83.0|1,117|5.5|84.5|
|Auto-parts|3,617|5.1|85.6|863|8.0|84.1|68|2.7|88.3|1,125|5.6|88.5|856|4.2|88.7|
|Furniture|10,349|14.4|100.0|1,717|15.9|100.0|295|11.7|100.0|2,322|11.5|100.0|2,290|11.3|100.0|
|Total|71693|100||10821|100||2519|100||20243|100||20259|100||

# **5. Empirical Results**

While the ICA-survey shows the problems of firms in accessing external financing relative to other constraints, they suffer from the endogeneity problems mentioned, e.g., we do not know the real reasons for the mentioned obstacles, including lack of external financing. It can be that the firm indeed has good growth opportunities, but banks are not interested in lending to firms in general. Or it can be that the banks make the right judgment in not lending as the firm has limited growth opportunities or is not creditworthy. We can now use the data to more formally assess the demand of the borrower and the supply of banks to provide financing as a function of the financing terms as well as various macro and micro variables including firms and bank characteristics. We estimate a disequilibrium model described in equations (1) to (8), where the set of explanatory variables for loan demand include the set of firm and loan characteristics, macro-economic variables such as measures of inflation and growth and represents a set of banks and loan characteristics and macroeconomic variables.

The dependent variable is the value of the contracted loan. The coefficients (betas) represent by how much the amount of a loan increases by increasing one of the regressors. The estimates rely on the disequilibrium model as in Madalla (1974). Since we are estimating 2 equations (demand and supply), supply betas are the estimates for the supply coefficients and demand betas are the estimates for the demand coefficients. The estimation is Bayesian using a quadratic loss function and the posterior was calculated using the Metropolis Hasting's procedure. For each issue we first present the OLS estimates and then the disequilibrium results.

# **Determinants of Firms’ Demand for Bank Financing**

Table 6.A. shows the results of the OLS estimation of the demand for each type of loan and for all loans together. With respect to the loan demand, the lending rate has the expected negative sign in most of the estimations and is significant. The exceptions are working capital and vendor loans. However, the lending rates coefficients coming from the working capital estimations are not significant. The results show that the maturity of the loans has a positive and significant effect on loan demand overall. Also, firms that can provide collateral have a higher demand for credit. The table suggests that macroeconomics variables, such as inflation and GDP 17
growth rates, do not have a significant importance in the loan demand. With respect to firms’ characteristics, big firms have a higher loan demand for G&A, overdraft and working capital loans, and a lower demand for the rest. Exporting firms have a significant higher demand for discountable, vendor and working capital loans. Moreover, we find that firms that are public listed have a higher demand for credit of all types. The same happens with firms that are managed by the owner, with the exception of vendor loans. Finally, the availability of internal sources of funding has an important negative effect on the demand for working capital and discountable loans.

Table 6.B. shows the results of the disequilibrium model estimation of the demand for each type of loan. The table shows that the bank real interest rate associated with the loan is not a significant variable in most of the estimations. The maturity of the loan has a positive and significant impact on the demand for discountable, overdraft and working capital loans. Furthermore, the availability of collateral is a significant variable in all the estimations. In particular, firms that can provide collateral exhibit a higher demand for overdraft, vendor and working capital loans; while a lower demand for discountable and G&A loans. Firms’ size does not seem to affect the demand for discountable and overdraft loans. However, we observe that smaller firms’ demand is higher for working capital loans and lower for vendor and G&A loans. Exporting capacity has a relevant and negative effect on the demand for discountable loans. Finally, access to BNDES loans has an important and negative impact in the demand for overdraft and G&A loans.

Table 7.A. shows the results of the OLS estimation of the supply for each type of loan and for all loans together. We find that the outstanding debt of a firm to one bank before the actual contracted was signed has a significant and positive impact on the supply for credit in all the estimations. Also, the supply of working capital, G&A and overdraft loans is higher for large firms while the opposite is true for discountable and vendor loans. There is also a larger supply of credit for exporting firms. With respect to banks’ characteristics, we observe that size has a significant role. Large banks have a lower supply of working capital, overdraft, vendor and G&A loans, while a higher supply of discountable loans.

Table 7.B. shows the results of the disequilibrium model estimation of the supply for each type of loan. The table shows that the bank real interest rate associated with the loan has a positive and significant effect on the supply of overdraft, G&A and vendor loans, while it is not significant for discountable and working capital loans. We find that the supply of discountable and working capital loans increases for firms that can provide collateral. However, the availability of collateral is not a significant factor affecting the supply of other types of loans.

# **Estimating Access to Bank Credit**

We investigate the determinants of firm access to credit. A firm from our sample with constrained access would not able to get a credit even though it had a credit in the past. We use a COX equation estimation to define the probability that a firm receives a loan at a given period conditioned on the fact that the firm did not get any credit on the past T periods. Table 9. presents results on the COX equation estimation, which shows the probability that the firm receives a loan given that the firm has not had one in the past T periods. The coefficients indicate how a change in one of the explanatory variables changes the probability of getting a loan given that the firm has not gotten a loan for T periods, where T is defined as the date in which a new loan is originated minus the last date in which the firm has contracted a loan.

We find that for each type of loan, the lending rate is not a significant factor affecting the probability of a firm getting a loan. But, for the estimation of all loans aggregated, the lending rate becomes significant and has the negative sign that we would expect. This also happens with the variables firm’s size and GDP growth: they are significant in the estimation for all loans but not for each type of loan independently. When aggregating all loans, the results show that big firms have a lower probability of getting a loan. This, again, is consistent with 18
the fact that larger firms use more internal sources of financing. Moreover, higher GDP growth rates increase the probability of getting a loan. An important factor affecting the probability of a firm getting a loan is the total number of loans that a firm had in the past. This variable has a positive sign and is significant in all the estimations. This may be reflecting the importance of having a credit history in order to get a new loan. The impact of the maturity of the loans is also negative and significant in most of the estimations.

The variables that matter the most for new firms to access bank credit are loan characteristics such as real bank interest rates and loan maturity, firm characteristics such as size (whether or not it is big), macroeconomic conditions such as GDP growth, inflation, and BNDES share in credit), and previous access to credit (number of loans per firm). While firm size seemed to matter a lot in the period 2003-06, this does not appear to be as important in the more recent sample (2007-09). We also observe the increased role of BNDES financing, but with a negative coefficient, a system of public directed crowding out private credit in the case of all loan types except overdraft and goods and acquisition loans. The impact of cyclical downturns appears to be mitigated, as signaled by the decreased significance of GDP growth, though the coefficient increased. This is in line with the small impact of the global financial crisis on credit access in 2009, when despite a contraction in GDP, directed credit growth through BNDES helped maintain credit market liquidity.

# **6. Conclusion**

We investigate the determinants of small-firm access to credit over the past decade in Brazil. We find over the decade improved access to finance for small firms which reflects the deepening of the credit markets. While firm size mattered in the 2003-2006 period to get access to credit, firm size is not as important in the 2007-2009 sample. Loan characteristics such as real bank interest rates and loan maturity, macroeconomic conditions (such as GDP growth, inflation, and BNDES share in total credit), and previous access to credit (number of loans per firm) also matters for access to Bank credit. The latter finding points to the importance of information availability (less information asymmetry) to reduce risk and increase access, which is captured in the positive influence of collateral and credit history in the regression. Banks perceive it less risky to lend to firms that they know or that other Banks know. This is consistent with the spreads decomposition that indicates the importance of risk as one of the main component of spreads. This finding has policy implications on the role of credit registries in increasing access to credit for firms.

The loan demand and loan supply analysis points to the importance of collateral. The availability of collateral is a significant variable in all the estimations. In particular, firms that can provide collateral exhibit a higher demand for overdraft, vendor and working capital loans We find that the supply of discountable and working capital loans increases for firms that can provide collateral. However, the availability of collateral is not a significant factor affecting the supply of other types of loans. Maturity matters for loan demand. The maturity of the loan has a positive and significant impact on the demand for discountables, overdraft and working capital loans indicating that though there have been improvements on increasing loan maturity, loans with longer maturity are wanted. Policy implication for increasing access to longer term loan maturity and on collateral would matter for small firm financing constraints. Finally, further research is needed to better understand the results on the elasticity of loan demand and loan supply to interest rates. We find some inelasticity of loan demand at the loan category level, maybe already reflecting some screening and pricing, however when the loans are aggregated , the effect of interest rates becomes significant and negative as expected. On the supply side, bank real interest rate associated is a positive and significant effect only in the supply of overdraft, G&A and vendor loans.

19