Creditor Rights and Credit Creation by Banks in Transition Economies: Evidence from Banking Environment and Performance Survey

Takeo Hoshi *Graduate School of International Relations and Pacific Studies, University of California,

San Diego, NBER, and TCER

November, 2006

* I thank Steven Fries, Anita Taci, and the European Bank of Reconstruction and Development for allowing me to use the results of the Bank Environment and Performance Survey. I benefited from discussion with Krislert Samphantharak. Jamie Thomas provided research assistance.

1. Introduction This paper studies the creditor rights and other legal environment in transition economies and their impacts on credit creation, using the results of the Banking Environment and Performance Survey (BEPS) conducted by EBRD.

There is now a large literature that establishes the link between financial development and economic growth. For example, Levine (1997) and Wachtel (2001) provide nice surveys of the literature. Developing financial system has been especially important for the former socialist economies, where the financial system that intermediates household saving and corporate investment did not really exist.

In the classical socialism, flow of money just followed flow of goods that is determined

by central planning. As a consequence, the banking system, which handles the monetary transactions, was fully controlled by the central planning. For example, Kornai (1992, p.133) describes the “inventory monitoring” role of banks as a major task of the banking system under classical socialism.

If a firm is judged according to a variety of empirical norms to be overstocked, the bank limits the availability of working-capital credits. More important, it sends inspectors around and makes warning reports to the firm’s superior bodies. This function might as well be performed by an “inventory-monitoring office.

Thus, building a banking system that plays functions similar to those in the capitalist economies, was a major challenge in the transition economies, as Anderson and Kegels (1998) argued.

Another large literature has shown that the financial development is influenced by the development of legal system. The representative research includes Carlin and Mayer (2003), La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1998), and Levine (1998). This literature suggests that the legal development will be an important determinant of the development of financial system in transition economies. This paper focuses on the development of banking system in the transition economies and examines the impacts of the development of creditor rights and other legal environment. We use the data collected by the BEPS for 216 banks in 20 transition economies. The survey allows us to construct measures of the (perceived) development of creditor rights and other legal environment each bank faces. The paper studies how these measures are related to the banks’ use of collateral and credit creation. Haselmann, Pistor and Vig (2005) explores a similar set of questions by examining how the volume of bank loans responded to legal changes in 12 transition economies. Using the bank level data, as we do in this paper, they find that lending volumes increase following legal changes to improve the creditor rights. They also find that the response to legal changes is stronger for new entrants than for incumbent banks.

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The paper is organized as follows. The next section discusses several questions in the BEPS about the legal environment of banks, and how numerical measures of the (perceived) quality of legal system are constructed from the answers. The section also provides country means for these measures. Section 3 starts by introducing the variables that show how often banks accept particular types of assets as collateral, again constructed by the responses to the BEPS. Then, the section examines how the measures of the quality of legal system are related to the collateral variables and the average loan growth of banks at the country level. Since we construct all the measures at bank level, we can study the relation between the (perceived) legal environment, use of collateral, and credit expansion at bank level, which Section 4 does. Section 5 concludes. 2. Measures of Creditor Rights and Other Legal Environment for Banking Using the responses to several questions in the BEPS, we construct two measures of how strong the banks believe creditor rights to be. First, PLEDGE is based on the responses to Question 32, which asks respondents if they (1) strongly disagree, (2) disagree in most cases, (3) tend to disagree, (4) tend to agree, (5) agree in many cases, (6) strongly agree, or (7) do not know with the following four statements about the country’s law concerning pledges (loans secured by movable assets).

a. The laws provide adequate scope of security (e.g., types of assets received as collateral, types of debt that can be secured)

b. The laws enable efficient creation and perfection of security rights (simple, cheap, fast)

c. The laws enable efficient enforcement of security rights (simple, cheap, fast) d. The laws adequately protect secured creditor rights

The following scores are assigned for the response to each of the four questions. 1 if the bank responds “strongly disagree” or “disagree in most cases” 2 if the bank responds “tend to disagree” 3 if the bank responds “do not know” 4 if the bank responds “tend to agree” 5 if the bank responds “agree in many cases” or “strongly agree” Then, PLEDGE measure for each bank is constructed by adding the scores for all four questions. Thus, PLEDGE takes the number between 4 and 20, and gives the bank’s perception about the country’s laws on pledges. A large number would suggest that the bank views that the current laws define, create, protect, and enforce creditor rights secured by movables very well.

The variable MORTGAGE is created in the same way using the responses to a similar question (Question 34) on laws related to mortgages (loans secured by immobile assets). The value of MORTGAGE lies between 4 and 20 for each bank, and shows the bank’s perception about the country’s laws on mortgages. A large number would suggest that the bank considers

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the current laws do well in defining, creating, protecting, and enforcing creditor rights secured by immovable assets, such as land.

The survey also includes questions about the changes of the laws regarding pledges and mortgages. Question 33 asks the bank if the laws related to pledges have (1) become significantly worse, (2) become somewhat worse, (3) not changed, (4) improved somewhat, or (5) improved significantly since 2001, or (6) do not know. Question 35 asks the same question for laws related to mortgages. Assigning the following scores for the response to each question, we come up with two measures of the bank’s perception on “change” of the creditor rights. 1 if the bank responds “have become significantly worse” 2 if the bank responds “have become somewhat worse” 3 if the bank responds “have not changed” or “do not know” 4 if the bank responds “have improved somewhat” 5 if the bank responds “have improved significantly”

The two measures are called PLEDIMP (Question 33) and MORTIMP (Question 35) below.

The first two columns of Table 1 shows the country means for PLEDGE and MORTGAGE. The third column shows the measure of the extent of legal provisions on security interest constructed by Pistor, Raiser, and Gelfer (2000) (PRG henceforth). They checked whether land can be used as collateral, whether security interests can be created in movable assets without transferring the assets, and whether the law provides information registry about the existence of security interests in debtor’s assets. If the country’s law has all the three characteristics, the measure takes the value 3, which is the maximum. The PRGcollat1998 in the table shows the measure for each country coded using the laws in 1998.

Note that there are two major differences between our measure of creditor rights and this

measure in PRG. First, PRG measure is based on the laws found on the book, while our measure is derived from the bankers’ perception revealed by the survey. Second, PRG index measures the status of laws in 1998, while our survey was conducted in 2005.

We can make three observations from the first three columns of Table 1. First, there is

substantial variation of the average view on the legal protection of creditor rights across countries. PLEDGE ranges from a little over 8 (for Albania, Croatia, and Serbia-Montenegro), which means that the banks in these countries on average “tend to disagree” with all the four statements about legal effectiveness of protecting creditor rights, to above 16 (for Latvia and Slovakia), which means the banks on average “tend to agree” with all the four statements about legal protection of creditor rights. Similarly, MORTGAGE ranges from around 11 (Albania, Belarus, Croatia, and Serbia-Montenegro) to around 17 (Estonia, Kazakhstan, Latvia, and Slovakia).

Latvia and Slovakia show high values for both measures, while Albania, Croatia, and

Serbia-Montenegro have low values for both measures. Indeed PLEDGE and MORTGAGE are highly correlated, which is the second observation. The correlation coefficient is 0.880. Thus, a

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country that is considered (by their banks) to have effective legal protection of pledges also tends to have effective legal protection of mortgages.

Finally, both PLEDGE and MORTGAGE are correlated with the similar measure by

PRG (2000). PLEDGE and MORTGAGE tend to be high in those countries with PRGcollat of 3 (highest) in 1998. Thus, the countries which had the laws with stronger protection of creditor rights in 1998 still attracted high opinion of bankers (in their own country) in 2005. The average values for PLEDGE and MORTGAGE for the countries with PRGcollat1998 of 3 are 14.15 and 15.16 respectively, while the averages for the rest of the countries are 12.56 and 12.93. However, there are some important exceptions. Two countries with high values of PLEDGE of MORTGAGE (Latvia and Slovakia) actually had score of only 1 for PRGcollat1998.

The last two columns of Table 1 show the measures of bank perception on changes of the

creditor rights since 2001, PLEDIMP and MORTIMP. In contrast to PLEDGE and MORTGAGE, we do not observe much variation in cross-country means of these “change” variables. For many countries, the means of PLEDIMP and MORTIMP are between 3.0 and 4.0, suggesting that most banks in many countries views the laws on pledges and mortgages have not changed or have improved somewhat since 2001. Either way, they do not perceive drastic changes in these laws regarding creditor rights. This is consistent with the result that our measures of creditor rights constructed from the 2005 survey are highly correlated with the measure in PRG that is based on the information in 1998.

Three countries (Macedonia, Slovakia, and Ukraine) have both PLEDIMP and

MORTIMP above 4.0, suggesting the banks in these countries see the creditor rights improved somewhat or significantly since 2001. This set of countries include both countries with relatively weak creditor rights in 1998 (Slovakia and Ukraine) and that with relatively strong creditor rights in 1998 (Macedonia). The set of countries also include both those with relatively weak (perceived) creditor rights in 2005 (Macedonia and Ukraine) and that with relatively strong creditor rights in 2005 (Slovakia). In other words, it is hard to see the relation between the improvement of creditor rights since 2001 and the strength of creditor rights in 1998 or in 2005. Indeed the correlation between the “change” variables (PLEDIMP and MORTIMP) and other creditor rights measures is weak.1 The BEPS contains several other questions about banks’ perception about the legal and regulatory environment they face. We make three additional variables that we use in the analysis below. First, Question 44 asks the banks how often they associate certain descriptions with the court system in resolving business disputes. They are asked to choose among (1) never, (2) seldom, (3) sometimes, (4) frequently, (5) almost always, (6) always, and (7) do not know. The descriptions of the court system that are given in the questionnaire are:

1 The correlation coefficients between PLEDGE and PLEDIMP, MORTGAGE and MORTIMP, PRGcollat1998 and PLEDIMP, and PRGcollat1998 and MORTIMP are 0.137, -0.018, -0.260, and 0.067 respectively.

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a. fair and impartial b. honest and uncorrupted c. quick and efficient d. affordable e. able to enforce its decisions Using the responses to these questions, we construct a variable COURT, which shows the bank’s perception about the court system. First, the following scores are assigned for the response to each of the five questions. 1 if the bank responds “never” or “seldom” 2 if the bank responds “sometimes” 3 if the bank responds “do not know” 4 if the bank responds “frequently” 5 if the bank responds “almost always” or “always” Then, COURT for each bank is constructed by adding the scores for all five questions. Thus, COURT takes the number between 5 and 25, and gives the bank’s perception about the court system in resolving business disputes. A large number would suggest that the bank views that the court system provides fair, honest, efficient, affordable, and efficient mechanism for business dispute resolution. Next, Question 45 asks the respondents if the information on the banking laws and regulations was easy to obtain and if the interpretations of those were consistent and predictable. Combining the result for 2004 with the responses for Question 48, which asks the bank’s view on the banking regulator in a format very similar to Question 44, we construct REGULAT, which measures the bank’s view on banking regulation. In Question 45, the banks are asked to what degree they agree with the following statements.

a. In 2004, information on the banking laws and regulations affecting my bank was easy to obtain.

c. In 2004, interpretations of the banking laws and regulations affecting my bank were consistent and predictable.

There were questions about 2001 as well, but we use only the questions about 2004 here. The respondents are asked to choose among (1) strongly disagree, (2) disagree in most cases, (3) tend to disagree, (4) tend to agree, (5) agree in many cases, (6) strongly agree, and (7) do not know. The following scores are assigned to each of the two responses in Question 45. 1 if the bank responds “strongly disagree” or “disagree in most cases” 2 if the bank responds “tend to disagree” 3 if the bank responds “do not know” 4 if the bank responds “tend to agree” 5 if the bank responds “agree in many cases” or “strongly agree”

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In Question 48, the banks are asked how often they associate certain descriptions with the banking regulator. They are asked to choose among (1) never, (2) seldom, (3) sometimes, (4) frequently, (5) almost always, (6) always, and (7) do not know. The descriptions of the bank regulator that are given in the questionnaire are: a. fair and impartial b. honest and uncorrupted c. quick and efficient d. able to enforce its decisions The following scores are assigned for the response to each of the four questions. 1 if the bank responds “never” or “seldom” 2 if the bank responds “sometimes” 3 if the bank responds “do not know” 4 if the bank responds “frequently” 5 if the bank responds “almost always” or “always” The variable REGULAT is created by summing up all the scores for two responses for Question 45 and four responses for Question 48. Thus, REGULAT takes the number between 6 and 30, and gives the bank’s perception about the bank regulations and regulators. A large number would suggest that the bank believes the current bank regulation to be clear and the bank regulator to be fair, uncorrupted, efficient and effective. Finally, the BEPS contains a couple of questions about the bank’s view on the prevalence of corruptions in banking regulation and court system. Question 50 asks banks to evaluate the following two statements.

a. It is common for banks to have to pay some irregular “payments/gifts” to central bank officials/banking regulators.

b. It is common for banks to have to pay some irregular “payments/gifts” to court officials.

For each statement, the respondents are asked to evaluate how often the statement would be true. They choose among (1) never, (2) seldom, (3) sometimes, (4) frequently, (5) almost always, (6) always, and (7) do not know. The following scores are assigned for the response to each of the two questions. 1 if the bank responds “never” or “seldom” 2 if the bank responds “sometimes” 3 if the bank responds “do not know” 4 if the bank responds “frequently” 5 if the bank responds “almost always” or “always”

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The variable CORRUPT is constructed by adding the scores for the two responses. CORRUPT takes a value between 2 and 10, and a high value means that the bank believes bank regulators and/or court system are corrupt. Table 2 shows the country means for COURT, REGULAT, and CORRUPT. Again we find substantial cross-country variations for these measures. COURT ranges from below 10 (for Ukraine), which means the banks only “sometimes” associate the five positive images to their court system, to above 20 (for Hungary), which means the banks “frequently” associate positive views with the court system. REGULAT ranges from around 20 (for Russia and Ukraine) to more than 28 (Estonia). For CORRUPT, the value takes 2.0, which is the minimum possible value for 6 countries (Albania, Czech Republic, Hungary, Lithuania, Moldova, and Slovenia). Thus, the banks in these countries respond that they “never” or “seldom” observe the corruption in court or bank regulation. CORRUPT is larger than 4 for two countries (Russia and Ukraine), which means that the banks in those countries observe corruption by court officials or bank regulators at least “sometimes.” COURT and REGULAT are positively correlated, suggesting a country where the banks view their court system favorably also tend to view their bank regulation favorably. The correlation coefficient is 0.594. CORRUPT is negatively correlated with these two measures with the correlation coefficients -0.681 and -0.480 respectively. This is not surprising because a high value of CORRUPT means that the banks find court officials and bank regulators are corrupt. 3. Creditor Rights and Credit Creation: Country Level Evidence How do the different perceptions about the creditor rights and other legal environment influence the bank behavior? This section examines the question by looking at country average data. As the indicators of the bank behavior, we consider two types of measures. First one is constructed from Question 36 of the BEPS, which asks the frequency of the bank accepting particular assets as collateral in 2004. For ten categories of assets, each bank was asked if the frequency of the assets used as collateral was (1) never, (2) seldom, (3) sometimes, (4) frequently, (5) almost always, (6) always, or (7) do not know. The ten asset classes are: a. Land b. Buildings c. Vehicles d. Other tangible movable property (e.g., business equipment) e. Inventory (stocks of goods to be sold) f. Accounts receivable g. Financial collateral (such as cash and securities) h. Personal guarantees i. Guarantees by other private corporations j. Guarantee by government entities

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For each asset class, we create a dummy variable that takes 0 if the bank answered “never”, “seldom”, or “do not know” as the frequency of the assets accepted as collateral, and 1 otherwise. Thus, the dummy variable shows if the bank accepted the particular type of assets as collateral at least “sometimes.” Second bank behavior variable comes from the bank balance sheets obtained from the BankScope database. We calculate the growth rate of loans from 2001 to 2004 for each bank and call it LoanG. In the analysis below, we exclude the banks that experienced the loan growth of more than 100% in any year between 2001 and 2004. Table 3 shows the country means of the collateral variables constructed in this way. For each country and each asset category, the number shows the proportion of banks that accept the assets in the category as collateral at least “sometimes.” We again find substantial variation across countries. For example, all banks accepted land as collateral (at least “sometimes”) in Albania, Estonia, Hungary, and Romania, but no banks in Macedonia did so (in 2004). Only one of 9 banks surveyed in Belarus accepted land as collateral. The correlations between series are not very high in general. Except for the correlation between “Vehicles” and “Other movables” (0.867), the correlations between country means range from -0.368 (between “Accounts receivable” and “Other movables”) to 0.549 (between “Personal guarantee” and “Guarantee by private entities”). Thus, the types of collateral accepted seem to differ across countries. Table 4 shows the country means of the growth rate of loans from 2001 to 2004. The growth rates are calculated from the amount of loans in local currencies in the BankScope database. The CPI inflation rate from 2001 to 2004 is subtracted from the number to get the real growth rate. The simple average across the banks for each country is reported in the table. Any bank that has experienced more than 100% of real loan growth in any one year is excluded from the calculation. Overall the table shows many banks experienced substantial loan growth between 2001 and 2004. The simple average of country means is about 66% for three years, or 22% a year. Looking at the correlations between these bank behavior measures and the measures of bank perceptions of creditor rights and other legal environment, we can find country level evidence on the relation between (bank perception on) legal infrastructure and bank performance. Table 5 shows the correlation coefficients between the seven measures of legal environment that were constructed in the last section and the two kinds of bank behavior measures: use of collateral and loan growth. We note that some measures of collateral use are highly correlated with PLEDGE and MORTGAGE variables. For example, the proportion of banks that accept land as collateral is positively correlated with MORTGAGE with the correlation coefficient of 0.394, suggesting (not surprisingly) bank are more willing to accept land as collateral in a country with strong protection of mortgage rights. Similarly, the use of inventory as collateral is correlated with both PLEDGE (with correlation coefficient 0.429) and MORTGAGE (with correlation coefficient 0.433). The other assets that are more likely to be used as collateral in countries with high values of PLEDGE and MORTGAGE are accounts receivable and personal guarantees. Interestingly, the use of buildings as collateral is negatively correlated with PLEDGE and MORTGAGE.

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Interestingly, many measures of collateral use (including land and accounts receivable in particular) are negatively correlated with PLEDIMP and MORTIMP, the measures of recent improvement in the creditor rights. This may suggest that it takes time for the legal rules on creditor rights to influence banks’ willingness to accept particular collaterals so that recent improvement of creditor rights has not increased the uses of collaterals. The use of land as collateral is also correlated positively with the bank’s image of the court system and the bank regulator, as the fourth and fifth columns of the table show. It is negatively correlated with the bank’s perception of corruption by court officials and/or bank regulators, as the last column shows. The use of accounts receivable as collateral is also positively correlated with positive view on the court system and negatively correlated with perceived corruption, but the correlation with REGULAT is small. The use of inventory as collateral is not much correlated with any of the three measures. The use of government guarantee seems to be positively correlated with COURT and REGULAT but negatively with CORRUPT. The last row of Table 5 shows the correlation between loan growth and bank perception of legal and regulatory environment. The correlation coefficients with both PLEDGE and MORTGAGE are positive, suggesting that the loan growth tends to be high in countries with better protection of pledge and mortgage rights. The loan growth is also positively correlated with PLEDIMP and MORTIMP. Thus, the countries that improved the creditor rights recently also tend to have higher loan growth. Surprisingly, the correlation coefficient with CORRUPT is also positive and even higher, suggesting the loan growth was actually higher in countries where court officials and bank regulators are considered more corrupt. At the country level, the loan growth is negatively correlated with the perceived quality of the court system and the bank regulation. 4. Creditor Rights and Credit Creation: Bank Level Evidence Because countries can differ in many dimensions, including but not limited to legal environments and credit creation, some of the correlations identified above can be spurious. Since our measures of (perceived) legal environment are constructed at bank level, we can study their influence on credit creation using bank level observations. This is what we do in this section. We restrict the sample to those banks that have valid observations all the legal and collateral variables that we use and have real loan growth rate not exceeding 100% in any year between 2002 and 2004. This leaves us 117 banks. Table 6 shows the number of banks in the sample for each country. Unfortunately, we end up having only one bank each for Hungary, Macedonia, and Slovakia. Table 7 is the bank level version of Table 5, and shows the correlation coefficients between collateral variables and legal environment variables at the bank level. The last row of the table reports the correlation between the real loan growth and legal variables. The table suggests that the use of land, inventories, and accounts receivable are positively correlated with PLEDGE and MORTGAGE. These are consistent with the results we find in the country level

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analysis. Banks that believe creditor rights are well protected to tend to use land, inventories, and accounts receivable more often as collateral. The creditor rights variables are also positively correlated with the use of financial assets, which we did not find in the country level analysis. On the other hand, we lose the correlation between creditor rights and the use of personal guarantee, which suggests there may be a third factor at the country level that is correlated with the use of personal guarantee and the strength of creditor rights. The measures of improvement in creditor rights (PLEDIMP and MORTIMP) are not correlated with the uses of collaterals very much. The only exception is that the (perceived) improvement in the laws on pledges is positively (mildly) correlated with the use of buildings as collateral. The use of land collateral continues to be positively correlated with COURT and negatively correlated with CORRUPT. The correlation with REGULAT disappears at the bank level. The use of accounts receivable does not seem correlated with these measures at the bank level. The positive correlation between the use of government guarantee with COURT and REGULAT is still present at the bank level, and the negative correlation between the use of government guarantees with CORRUPT is also present. The real loan growth from 2001 to 2004 is also correlated positively with the creditor rights variables PLEDGE and MORTGAGE at bank level. Banks that believe creditor rights are well protected to tend to increase their lending more. The loan growth is also correlated with the improvement of creditor rights, although the correlation with MORTIMP is small. The puzzling positive correlation between corruption and real loan growth is still found at the bank level. Also, the loan growth is negatively correlated with the perceived quality of bank regulation and regulators again at bank level. These correlations suggest that a bank that has favorable perception about the legal status of pledges and mortgages in the country is more likely to accept particular types of assets, especially land, and increase their loans more rapidly. The relation between the loan growth and creditor rights may be partially indirect through the use of collaterals. Banks that are willing to accept particular types of assets may also increase the loans.

Simple correlations also suggest the (perceived) corruption reduces the use of some types of assets as collateral (especially land and government guarantee), but overall increases the real loan growth. The quality of the court system and the bank regulation also promotes the use of some types of collaterals (especially land and government guarantees) but reduces the real loan growth.

To check these relations more in details, Tables 8 through 10 report results of simple

regression analyses. The sample is 117 banks, for which all the relevant variables in the analyses are available. Table 8 shows regressions of real loan growth (from 2001 to 2004) on the variables on legal environments. Panel A of Table 8 shows the regressions of real loan growth on PLEDGE and MORTGAGE. Each column shows the coefficient estimates (and standard errors in the parentheses) for a different specification. The coefficient estimates of the constant term and country dummies (when included) are not reported. The results in Columns (1) and (2)

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show that PLEDGE and MORTGAGE influence the loan growth positively, when they are put into regression one by one, corroborating the results in the earlier correlation analysis. But, the coefficient estimates are not much statistically significant. The next two specifications (3 and 4) in Panel A include 20 country dummies. When these country dummies are included in the analysis, the effects of PLEDGE or MORTGAGE are not statistically significant, suggesting the direct correlation between the creditor rights measures and real loan growth comes mostly from co-variations across different countries. The last two specifications (5 and 6) put real deposit growth as an additional control variable. The estimated coefficient on the deposit growth is positive and statistically significant. The coefficient estimates on PLEDGE and MORTGAGE are positive but statistically insignificant. The same set of regressions has been estimated with PLEDIMP and MORTIMP replacing PLEDGE and MORTGAGE. The result (not reported in the table) is very much similar: the coefficients on these creditor rights variables are positive but not statistically significant. Panel B of Table 8 reports regressions of loan growth on COURT, REGULAT, and CORRUPT. As the earlier correlation analysis suggested, COURT and REGULAT variables are negatively correlated with the loan growth, and CORRUPT variable is positively correlated with the loan growth, but none of the coefficient estimates are statistically significant. The last two columns show that the point estimate of the coefficient on CORRUPT turns negative when the country dummies are included (models (10) and (11)) and when the deposit growth is added as a control (model (11)). Summarizing the results in Table 8, we can find weak evidence that the loan growth is positively correlated with the (perceived) quality of creditor rights. The relation, however, disappears when the country dummies are added to the regression, suggesting most of the correlation is due to variations at country level. The other three variables, COURT, REGULAT, and CORRUPT, do not seem to have strong impacts on the credit creation. Table 9 reports the regressions of loan growth on some collateral variables. The specifications in Panel A do not include the country dummies, but those in Panel B do. The results in Panel A show that the use of some assets as collateral is significantly related to the real loan growth rate. For example, model (1) suggests that the banks that more often accept land as collateral tend to have high loan growth rate, after controlling for the real deposit growth rate. Similarly, the use of building assets and personal guarantees are also correlated positively with the credit growth. Finally, model (6) shows that the banks that more often accept government guarantees as collateral tend to have low growth of loans.

Thus, the use of land, buildings, and personal guarantees, seems to contribute to more rapid credit growth, while the dependence on government guarantees tend to slow the credit expansion. Panel B indicates, however, that all the coefficient estimates on the collateral variables become statistically insignificant once country dummies are included in the regression,

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although their signs remain the same. Thus, the important effects again seem to work at country level. Although we find only weak evidence that creditor rights measures influence the credit growth in Table 8, it is possible that the development of creditor rights motivates the banks to use land and other assets as collateral, and indirectly leads to credit expansion. To check this possibility, Probit models of the use of land as collateral were estimated. The results are reported in Table 10. Each rows the coefficient estimate from a model that tries to explain the frequency of the use of land collateral (note this is a 0-1 variable) by the legal environment variable specified in the first column. The point estimates of the coefficients suggest that the use of land as collateral tends to increase with PLEDGE, MORTGAGE, PLEDIMP and COURT, and fall with CORRUPT. The results are reasonable, but only the one for MORTGAGE is statistically significant. The banks that view mortgage rights are well protected tend to accept land more often as collateral. Combining the findings from Tables 8 through 10, we can paint a consistent picture about the relation between legal environment and credit creation in the transition economies, although some of the links are weak. Strong creditor rights measured as higher PLEDGE and MORTGAGE encourage banks to accept land and other assets more often as collateral. The use of these assets as collateral allows the banks to expand credit rapidly. Thus, the development of creditor rights seems to contribute to credit expansion indirectly. 5. Conclusion The paper used the BEPS to construct some new measures of the banks’ perception of the legal environment in the transition economies and their use of collateral in extending loans. The paper examined how these variables are correlated at both country level and bank level. We have found substantial variations in the perceived quality of creditor rights and other legal environments among banks and especially across different countries. There is some evidence that the perceived legal environment is correlated with the choice of collateral for bank loans and the growth rate of loans. For example, stronger mortgage rights encourage banks to accept land as collateral more often, and wider acceptance of land tends to increase loan growth. The BEPS provides some additional information which may be relevant to credit creation by banks in the transition economies but is not analyzed in this paper.2 For example, many banks in these countries are owned by foreign banks and foreign non-financial firms. Does the loan growth at these foreign-owned banks more or less sensitive to the perceived quality of creditor rights? Are the foreign-owned banks more likely to accept certain types of assets as collateral? The BEPS also contains information for the percentages of customer loans to different types of customers, such as households, large corporations, state firms, and so on. Are

2 The BEPS also include several questions that attempt to gauge the extent of risk each bank takes. For a paper that analyzes the risk management at banks in the transition economies using the BEPS, see Haselmann and Wachtel (2006).

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the banks that lend primarily to private corporations influenced more by creditor rights than some banks that lend primarily to state-owned firms? Do the banks that lend primarily to small corporations ask for personal guarantees more frequently? These questions are left for future research. References Anderson, Ronald W., and Chantal Kegels (1998). Transition Banking: Financial Development

of Central and Eastern Europe. Oxford, UK: Oxford University Press. Carlin, Wendy, and Colin Mayer (2003). “Finance, Investment, and Growth,” Journal of

Financial Economics, 69, 191-226. Haselmann, Rainer, Katharina Pistor, and Vikrant Vig (2005). “How Law Affects Lending,”

manuscript, Columbia Law School. Haselmann, Rainer, and Paul Wachtel (2006). “Bank Risk and Bank Management in Transition:

A Progress Report on the EBRD Banking Environment and Performance Survey,” manuscript, Stern School of Business, New York Univeristy.

Kornai, János (1992). The Socialist System: The Political Economy of Communism. Princeton,

NJ: Princeton University Press. La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer, and Robert W. Vishny (1998).

“Law and Finance,” Journal of Political Economy, 106, 1113-1155. Levine, Ross (1997). “Financial Development and Economic Growth: Views and Agenda,”

Journal of Economic Literature, 35, 688-726. Levine, Ross (1998). “The Legal Environment, Banks, and Long-Run Economic Growth,”

Journal of Money, Credit and Banking, 30, 596-613. Pistor, Katharine, Martin Raiser, and Stanislaw Gelfer (2000). “Law and Finance in Transition

Economies,” Economics of Transition, 8, 325-368. Wachtel, Paul (2001). “Growth and Finance: What Do We Know and How Do We Know It?”

International Finance, 4, 335-362. White, Halbert, 1980, “A Heteroscedasticity-Consistent Covariance Matrix Estimator and a

Direct Test for Heteroscedasticity,” Econometrica, 48, 817-838.

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Table 1. Creditor Rights Measures (Country Means)

Country PLEDGE MORTGAGE PRGcollat-1998

PLED-IMP

MORT- IMP

Albania 8.4 11.2 1 4.0 3.2Belarus 12.1 11.0 1 3.4 3.7Bosnia/Herzegovina 10.9 11.7 0 4.1 3.6Bulgaria 14.7 15.4 3 3.2 3.1Croatia 8.2 10.9 1 3.0 3.5Czech Republic 13.0 12.3 1 3.7 3.1Estonia 15.0 16.6 3 3.0 3.4Hungary 13.7 15.3 3 3.0 3.0Kazakhstan 15.8 17.4 3 3.8 3.6Latvia 16.5 17.1 1 3.8 3.3Lithuania 14.6 15.8 3 3.6 3.6Moldova 12.9 13.3 3 4.1 3.9Poland 14.1 14.3 3 3.6 3.9R. Macedonia 12.5 13.2 3 4.0 4.2Romania 13.0 13.9 1 3.5 3.6Russia 11.8 11.6 2 3.5 4.1Serbia/Montenegro 8.2 11.3 NA 3.6 3.5Slovakia 17.9 16.9 1 4.4 4.4Slovenia 13.6 12.6 1 3.7 3.1Ukraine 12.8 13.1 2 4.1 4.4

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Table 2. Other Legal and Regulatory Environment Measures (Country Means)

Country COURT REGULAT CORRUPT Albania 16.5 26.0 2.0 Belarus 17.0 23.3 2.7 Bosnia/Herzegovina 13.5 24.0 3.1 Bulgaria 11.4 25.2 3.6 Croatia 13.7 23.1 2.1 Czech Republic 15.3 25.0 2.0 Estonia 17.2 28.2 2.3 Hungary 21.0 26.6 2.0 Kazakhstan 11.0 24.1 3.6 Latvia 16.2 24.8 3.0 Lithuania 17.2 27.4 2.0 Moldova 13.8 24.6 2.0 Poland 14.1 24.7 2.1 R. Macedonia 11.0 25.8 3.2 Romania 13.6 24.7 2.2 Russia 14.1 20.6 4.7 Serbia/Montenegro 14.4 25.4 3.3 Slovakia 18.3 26.2 2.4 Slovenia 17.4 25.0 2.0 Ukraine 9.3 21.4 4.6

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Table 3. Country Means of Collateral Variables

Country Land Buildings Vehicles Other Movables Inventory

Accounts Receivable

Financial Assets

Personal Guarantee

Guarantee (Private)

Government Guarantee

Albania 1.00 1.00 0.75 1.00 0.50 0.25 0.75 0.25 0.00 0.25Belarus 0.11 1.00 1.00 1.00 1.00 0.56 0.22 0.67 0.89 0.33Bosnia/Herzegovina 0.69 1.00 0.92 0.92 0.31 0.31 0.85 0.92 0.85 0.08Bulgaria 0.90 0.90 0.70 0.90 0.90 0.90 0.90 0.90 0.78 0.44Croatia 0.82 1.00 0.64 0.55 0.27 0.91 1.00 0.82 0.91 0.91Czech Republic 0.29 0.86 0.29 0.43 0.43 0.86 0.57 0.57 0.71 0.57Estonia 1.00 1.00 1.00 1.00 0.80 0.80 0.80 1.00 0.80 0.60Hungary 1.00 1.00 0.50 0.50 1.00 1.00 0.50 0.50 0.50 1.00Kazakhstan 0.78 0.89 0.89 0.89 1.00 0.44 1.00 0.71 0.75 0.29Latvia 0.82 0.94 0.88 0.82 0.82 0.82 0.50 0.88 0.65 0.29Lithuania 0.80 1.00 0.40 0.40 0.60 0.40 0.40 1.00 0.60 0.00Moldova 0.89 1.00 0.67 0.89 0.89 0.56 0.78 0.44 0.56 0.33Poland 0.67 0.73 0.60 0.60 0.60 0.73 0.60 0.53 0.67 0.33R. Macedonia 0.00 1.00 0.83 0.83 0.33 0.17 0.83 0.83 0.50 0.50Romania 1.00 1.00 0.75 0.83 0.75 0.92 0.75 0.75 0.42 0.25Russia 0.50 0.88 0.87 0.92 0.87 0.35 0.71 0.74 0.78 0.22Serbia/Montenegro 0.47 1.00 0.47 0.74 0.79 0.32 0.95 0.79 0.95 0.47Slovakia 0.60 0.80 0.50 0.75 0.75 0.75 0.80 1.00 0.60 0.60Slovenia 0.71 1.00 0.29 0.43 0.57 0.71 0.71 0.86 0.86 0.43Ukraine 0.50 0.88 0.88 0.88 0.75 0.38 0.63 0.75 0.75 0.13

Table 4. Country Means of Real Loan Growth Rates: 2001-2004

Country LoanG (%) Albania 66.2Belarus 52.2Bosnia/Herzegovina 99.8Bulgaria 95.5Croatia 57.8Czech Republic 34.0Estonia 63.6Hungary 36.0Kazakhstan 111.4Latvia 86.5Lithuania 96.0Moldova 87.5Poland 25.2R. Macedonia 63.8Romania 52.4Russia 69.7Serbia/Montenegro 19.7Slovakia 45.2Slovenia 47.5Ukraine 101.9

Table 5. Correlation between Creditor Rights Measures and Collateral Use Measures: Country Level Data

PLEDGE MORT-GAGE

PLED-IMP

MORT-IMP

COURT REGULAT CORRUPT

Land 0.119 0.394 -0.296 -0.435 0.257 0.301 -0.262Buildings -0.446 -0.286 -0.246 -0.382 0.200 0.192 -0.123Vehicles -0.003 0.045 -0.011 0.281 -0.378 -0.268 0.433Other Movables -0.062 -0.027 0.195 0.274 -0.359 -0.167 0.420Inventory 0.429 0.433 -0.192 -0.042 0.158 0.048 0.177Accounts receivable 0.373 0.357 -0.552 -0.430 0.380 0.110 -0.327Financial assets -0.235 -0.013 0.065 0.063 -0.476 -0.031 0.235Personal guarantee 0.390 0.370 -0.064 0.193 -0.061 0.161 0.289Guarantee (private) 0.010 -0.106 -0.280 0.047 -0.175 -0.328 0.295Gov’t guarantee -0.055 0.040 -0.495 -0.290 0.382 0.216 -0.278 LoanG 0.225 0.293 0.240 0.142 -0.482 -0.232 0.483

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Table 6. Number of Banks in the Sample for Each Country

Country Number of banks Albania 4Belarus 6Bosnia/Herzegovina 6Bulgaria 7Croatia 8Czech Republic 5Estonia 4Hungary 1Kazakhstan 6Latvia 12Lithuania 4Moldova 3Poland 11R. Macedonia 1Romania 9Russia 10Serbia/Montenegro 6Slovakia 1Slovenia 7Ukraine 6Total 117

Table 7. Correlation between Creditor Rights Measures and Collateral Use Measures: Bank Level Data

PLEDGE

MORT-GAGE

PLED-IMP

MORT-IMP

COURT

REGULAT

CORRUPT

Land 0.145 0.190 0.105 -0.067 0.138 0.008 -0.123Buildings -0.068 -0.047 0.158 -0.055 0.021 0.008 -0.067Vehicles 0.150 0.063 0.064 0.116 -0.083 -0.130 0.093Other Movables 0.100 0.170 0.103 0.116 -0.010 -0.061 0.029Inventory 0.143 0.186 0.021 0.059 0.057 0.072 0.100Accounts receivable 0.180 0.258 -0.037 -0.021 -0.063 -0.076 -0.067Financial assets 0.118 0.172 0.036 0.069 0.046 0.017 -0.002Personal guarantee -0.028 -0.020 -0.079 -0.068 -0.080 0.045 0.082Guarantee (private) 0.001 0.080 -0.069 0.011 0.043 0.009 -0.035Gov’t guarantee 0.018 0.122 -0.079 0.070 0.246 0.186 -0.133 LoanG 0.154 0.130 0.130 0.052 -0.094 -0.153 0.107

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Table 8. Legal Environment and Loan Growth

Panel A

( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) PLEDGE 0.017*

(0.009) 0.010

(0.011) 0.005

(0.010)

MORTGAGE 0.015 (0.009)

0.003 (0.011)

0.003 (0.011)

Deposit Growth

0.335*** (0.094)

0.341*** (0.093)

Country

dummies? No No Yes Yes Yes Yes

Adjusted R-squared

.015 .008 .214 .207 .312 .311

Panel B

( 7 ) ( 8 ) ( 9 ) ( 10 ) ( 11 )

COURT -0.009 (0.010)

REGULAT -0.017 (0.011)

CORRUPT 0.037 (0.028)

-0.037 (0.033)

-0.038 (0.032)

Deposit Growth

0.341*** (0.094)

Country

dummies? No No No Yes Yes

Adjusted R-squared

.000 .015 .003 .216 .319

Notes: Dependent variable is the real growth rate of loans (measured in local currency) from 2001 to 2004. Each column shows coefficient estimates from a specification of the regression model. Robust standard errors developed by White (1980) are reported in parentheses. *** (**, *) shows that the coefficient is statistically significant at 1% (5%, 10% respectively) level. Panel A contains the specifications with PLEDGE and MORTGAGE. Panel B shows the specifications with COURT, REGULAT, and CORRUPT.

Table 9. Use of Collateral and Credit Growth

Panel A

( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 )

Collateral variable

Land Building Inventory Accounts receivable

Personal guarantee

Guarantee (Gov’t)

Collateral 0.238** (0.093)

0.405* (0.215)

0.070 (0.094)

0.042 (0.093)

0.238** (0.118)

-0.181** (0.088)

Deposit growth

0.456*** (0.064)

0.468***(0.065)

0.445*** (0.064)

0.456*** (0.066)

0.462*** (0.066)

0.432*** (0.066)

Country

dummies? No No No No No No

Adjusted R-squared

.302 .302 .258 .256 .295 .283

Panel B

( 7 ) ( 8 ) ( 9 ) ( 10 ) ( 11 ) ( 12 ) Collateral variable

Land Building Inventory Accounts receivable

Personal guarantee

Guarantee (Gov’t)

Collateral 0.187 (0.117)

0.268 (0.275)

0.099 (0.108)

0.131 (0.116)

0.186 (0.139)

-0.136 (0.105)

Deposit growth

0.335*** (0.089)

0.336***(0.090)

0.306*** (0.093)

0.303*** (0.093)

0.321*** (0.098)

0.314*** (0.096)

Country

dummies? Yes Yes Yes Yes Yes Yes

Adjusted R-squared

.344 .339 .327 .330 .346 .346

Notes: Dependent variable is the real growth rate of loans (measured in local currency) from 2001 to 2004. Each regression model uses a collateral variable that is specified in the second

row. Robust standard errors developed by White (1980) are reported in parentheses. *** (**, *) shows that the coefficient is statistically significant at 1% (5%, 10% respectively) level.

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Table 10. Legal Environment and Use of Land as Collateral

Legal Environmental Variable Used

Coefficient Estimate (standard error)

PLEDGE 0.041 (0.025)

MORTGAGE 0.051** (0.026)

PLEDIMP 0.181 (0.164)

MORTIMP -0.099 (0.147)

COURT 0.034 (0.021)

REGULAT 0.002 (0.024)

CORRUPT -0.097 (0.077)

Notes: Dependent variable is the use of land as collateral. Each row represents a different regression model. Each model contains only the legal environmental variable specified and the constant as the explanatory variables. Since the dependent variable is 0-1 variable, the regression models are estimated by Probit. Standard errors are reported in the parentheses. ** shows that the coefficient is statistically significant at 5% level.

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