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«Christa Hainz ∗ ifo Institute, CESifo and WDI Tatjana Nabokin ♦ University of Munich Abstract: Access to finance is a prerequisite for economic ...»

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Access to versus Use of Loans: What are the True

Determinants of Access?

Christa Hainz ∗

ifo Institute, CESifo and WDI

Tatjana Nabokin ♦

University of Munich

Abstract: Access to finance is a prerequisite for economic development.

Existing studies measure access by the use of finance. We develop a direct

measurement for access to finance, using data from the Business

Environment and Enterprise Performance Survey 2005 data. Thereby we

determine whether a firm without a loan is indeed credit-constrained or merely does not have demand for external finance and control for potential selection bias. We show considerable differences between the determinants of access and the determinants of use. This implies the conclusion that analyzing information on the use of finance is not sufficient to identify financially constrained firms.

Keywords: Access to Finance, Use of Finance JEL classification: G21, O16 This draft: October 4, 2010 **Please do not quote and circulate without the permission of the authors.** ∗ CORRESPONDING AUTOR: ifo Institute for Economic Research, Poschingerstr. 5, 81679 Munich, Germany; hainz@ifo.de ♦ University of Munich, Germany.

1. Introduction It is a well-established fact that access to finance is a major determinant of economic growth (Rajan and Zingales, 1998; Beck, Levine, and Loayza, 2000). 1 In the current financial crisis, financially constrained firms do not invest (Campello, Graham, Harvey, 2009). Therefore, access to finance, which has been one of the core topics in development for quite some time (see, for instance, Claessens, 2006; World Bank, 2008),

has emerged on the agenda of nearly all governments. The important policy question is:

what measures must be taken to foster access to finance?

A prerequisite to answering this question is to know which factors determine access to finance. However, in reality it is difficult to measure access to finance. In many studies, the use of finance is taken as a proxy for access to finance. This approach, however, neglects the fact that firms that do not use loans can be firms that either do not have access (and were denied loans) or firms that do not have the need for a loan, rather than suffer from a lack of access. For the purpose of analyzing access to finance only the first group is relevant, i.e. those that do not get a loan despite having demand. This implies that studying the factors affecting access to finance by analyzing the use of loans may be misleading.

In contrast to the existing literature we directly measure access and do not proxy for it. Our analysis uses cross-section data on firm-level from the Business Environment and Enterprise Performance Survey (BEEPS), which was conducted in 2005 among 9655 firms in 27 countries in Europe and Central Asia. This data set provides a unique source of information because firms were not only asked whether they had a bank loan or not, but also for reasons why the firm did not have any loans. The reasons were either that it did not have access or that it did not have the demand for one (which is the case for about one third of the total population of firms in our sample). Our measure of access takes into account whether a firm that needs a loan is successful in obtaining one. Thus, firms that do not need a loan are not lumped together with firms that are denied access. Thereby, we For developing countries, there are several studies that use either policy changes or controlled experiments to estimate the effect of credit constraints on firm performance (for a survey, see Beck and Demirgüc-Kunt, 2008). Karlan and Murdoch (2009) provide a comprehensive overview on the topic in the context of development economics.

use more information than all other measures do, which allows us to study the true determinants of access.

To highlight the differences between the access and the use approach we question whether both methods identify the same determinants as having a significant impact on the probability of being financially constrained. We start by estimating the determinants of use in a probit regression. For access we take two different approaches. Firstly, we restrict our sample to those firms that have demand for loans and perform a probit regression to identify which explanatory variables determine access. Secondly, we use a Heckman selection model to control for the selection into the “demand” group, which might not be random and would therefore bias the estimations. Finally, we compare the significant determinants in the different specifications.

Our empirical analysis shows that there exist major differences in the determinants for use and access both on the firm and on the country-level. First, we show that there exist age groups that are financially restricted, but that are not identified in the use approach. The reason is that these groups have a higher loan demand and therefore the use approach cannot identify financially constrained firms. Second, we show that approximating access by the use of finance can also be misleading for particular firms, e.g. foreign-owned companies. They seem to be restricted in the use approach, although having better access to finance. The Heckman selection model shows that this result is due to the lower demand for loans by foreign-owned firms. Third, the most substantial differences appear for the firms’ sector. Despite controlling for a wide range of determinants, firms from many sectors have a smaller probability of using a loan. The access analysis shows, however, the reduced probability is often due to differences in demand. Finally, we demonstrate that among the country-specific factors, the protection of creditor rights is only significant for explaining access and not use. Moreover, foreign bank presence does not improve access although it increases the use of finance.

Our results show that taking the use of loans as a proxy for access to loans can be misleading in many respects. This finding has important policy implications. In particular, with respect to different sectors we see that some industries have a much lower use of loans simply because they do not need loans. Using the access approach, these sectors significantly do not face more constraints than other sectors. Given the distortions in financial intermediation caused by the current crisis many firms and sectors demand financial support from the government. If this support was provided in the form of easier access to loans, sector-specific programs will be very inefficient, unless they are based on data that adequately measure access to finance. Therefore, we emphasize how important it is to possess data about access to finance before reforms or other policy measures are undertaken.

In the existing literature, firms’ financial situations are evaluated by using balancesheet data or by surveying the firms. Investigating the balance-sheet data provides information on which sources of finance are actually used and the extent of that use.

Balance-sheet data can be used to measure whether a firm is financially constrained, for instance, by studying the sensitivity of investment to cash flows (Fazzari, Hubbard, Petersen, 1988). However, there is a considerable debate about this approach (Kaplan and Zingales, 1997, 2000) just as there is for other approaches, such as Tobin’s q. Sometimes information from the annual report indicates that financial constraints exist because firms cannot fulfill covenants. Since reliable balance-sheets are needed, this approach is most appropriate when studying big corporations. For small- and medium enterprises (SMEs) data availability is often an issue. Alternatively, data from surveys can be used. For instance, the World Business Environment Survey (WBES) conducted by the World Bank in many emerging and developing economies considered how problematic access to finance and cost of finance are for the operation and the growth of a firm.

Our paper is related to the literature on access to finance and how it is determined by firm- and country-specific characteristics. In this literature a variety of data sources is used. On the firm-level, most studies include ownership and size as explanatory variables. Interestingly, the results of these determinants are ambiguous. Some studies find that small firms use loans more intensively (Giannetti and Ongena, 2009 using loans/ total assets from balance sheets data as dependent variable) while others obtain the opposite result (Beck, Demirgüç-Kunt, and Maksimovic, 2008 using bank loan total finance from survey data and Brown, Jappelli, and Pagano, 2009 using total debt/ assets from survey data). The same is true for ownership. In this area, only Brown, Jappelli, and Pagano (2009) find that state-owned firms use loans more intensively. Beck, DemirgüçKunt, and Maksimovic (2005) use survey data where firms assess how much access to finance is an obstacle for their growth. Interestingly, access to finance is perceived as more difficult by small and state-owned firms (Beck, Demirgüç-Kunt, and Maksimovic., 2005). One explanation for these contradictory results might be that the studies investigate different regions and time periods. Probably even more important is the fact that they use proxies for access to finance that do not fully reveal financial constraints.

On the country-level, the results are less ambiguous. It is generally agreed that better protection of creditor rights increases the use of finance (Giannetti and Ongena, 2009, Beck, Demirgüç-Kunt, and Maksimovic, 2008, Brown, Jappelli, and Pagano, 2009).

When investigating the legal provisions made to protect creditor rights, the quality of the legal system and the enforcement of these rights are complements (Safavian and Sharma, 2007). The analysis on the impact of information show that the results may depend on the type of measure for access to finance. When access to finance is measured by a perception index, the existence of information sharing arrangements increases access to finance for all firms. However, if the dependent variable is total debt/ assets, information sharing is beneficial only for small firms and those firms in countries with weak creditor rights (Brown, Jappelli, and Pagano, 2009).

We contribute to the literature on access to loans by creating a direct measure of access and identifying problems in the former approaches. Since we can contrast the factors determining access with those determining use, we can evaluate whether the policy measures discussed so far, which are based on measuring the use of loans, are appropriate to foster access to loans.

The paper is organized as follows. In section 2, we describe our data set and the methodology. The results from the empirical analysis are presented in section 3. In the empirical analysis we discriminate between firm and country-specific determinants of access to finance and control for selection bias. We conclude in section 4.

2. Data and Methodology We use the Business Environment and Enterprise Performance Survey (BEEPS) collected by the European Bank for Reconstruction and Development (EBRD). The BEEPS intends to assess the environment for private enterprise and business development. We use the survey that was conducted in March and April 2005. It covers 27 countries in Europe and Central Asia. Per country between about 200 and 900 firms were interviewed, depending on the size of the country. We leave out data from Uzbekistan and Tajikistan as information about institutional characteristics of these countries is missing and answers might be distorted for political reasons. We also leave out Turkey because we focus on transition countries. So finally we analyze 6659 firms in 24 countries.

The questionnaire contains information about the general characteristics of the firm and a whole section about its financing. In this section firms are asked about their most recent loan. Table I shows how the answers are distributed. In our sample 56% of all firms currently have a loan, 44% do not have one. If the firm does not currently have a loan, it can provide different reasons for it. About 4 % of the firms were rejected, 94% even have not applied for a loan and for 2% the applications are still pending. In addition, firms which did not apply for a loan were asked for the reasons. Firms could give multiple answers, which can be summarized into two different categories. First, the firm did not apply because it was discouraged due to the following reasons: collateral requirements are too strict, interest rates are too high or informal payments need to be made to obtain a loan. Second, a firm actually did not apply because it does not have demand for loans. This differentiation is essential for the following analysis.

2.1. Dependent Variables We have two different dependent binary variables, use and access. The difference between these two dependent variables is depicted in Figure 1.

Use is equal to 1 if a firm has a loan and 0 otherwise. This measure is equivalent to former studies analyzing the use of finance. This method does not differentiate between firms that are financially constrained and firms that actually have no demand for a loan.

To study access to finance and to identify credit constrained firms, only firms with demand for a loan should be examined. Accordingly the binary variable demand equals 1 for the following three cases: 2 Ideally, we would like to discriminate firms with demand further into those that are creditworthy and those that are not creditworthy. However, such a differentiation is very difficult to be done in practice.

There is one study in which a bank newly entering the market evaluated the creditworthiness of households (Johnson and Murdoch, 2008) − the firm has a loan [1] − the firm does not have a loan because its application was turned down [2] − the firm does not have a loan because it was discouraged from applying[3].

Finally, demand is 0 if the firm does not have a loan because it has no demand for a loan and therefore has not applied [4]. The number of firms whose application is still pending is very small and being unclear whether they will get access or not, we do not include them.

To analyze access to finance we consider only of those firms which have demand for loans (demand=1). Therefore the variable access is 1 if the firm has a loan (and by definition has demand). Access is 0 if it does not have a loan although it has demand for it; this means that the group consists of discouraged firms and of firms that applied for a loan but have been rejected.

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