Bank Lending and Performance of Micro, Small and Medium Sized Enterprises (MSMEs ... ·...

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Bank Lending and Performance of Micro, Small and Medium Sized Enterprises (MSMEs): Evidence from Bulgaria, Georgia, Russia and Ukraine Karin Jõeveer* Francesca Pissarides** Jan Svejnar*** September 2006 * Keele University ** EBRD *** University of Michigan and CERGE-EI The paper was written with a financial and institutional support of the Japan Europe Development Fund and EBRD. We would like to thank members of the Office of the Chief Economist at EBRD for useful comments. The usual disclaimer applies.

Transcript of Bank Lending and Performance of Micro, Small and Medium Sized Enterprises (MSMEs ... ·...

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Bank Lending and Performance of Micro, Small and Medium Sized Enterprises (MSMEs):

Evidence from Bulgaria, Georgia, Russia and Ukraine

Karin Jõeveer*

Francesca Pissarides**

Jan Svejnar***

September 2006

* Keele University

** EBRD

*** University of Michigan and CERGE-EI

The paper was written with a financial and institutional support of the Japan Europe Development Fund and EBRD. We would like to thank members of the Office of the Chief Economist at EBRD for useful comments. The usual disclaimer applies.

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Bank Lending and Performance of Micro, Small and Medium Sized Enterprises (MSMEs):

Evidence from Bulgaria, Georgia, Russia and Ukraine 1. Introduction

In view of the important contribution which entrepreneurs and micro, small

and medium-sized enterprises (MSMEs) can make to economic growth, innovation

and employment creation, both researchers and policy makers emphasise the need to

obtain a better understanding of the factors that influence the rise and performance of

these firms. Academic research has identified particular constraints on the availability

of finance for MSMEs, such as informational asymmetries between borrowers and

lenders, lack of credit history on the part of many MSMEs, poor legal and institutional

infrastructure, scarcity of appropriate credit skills in banks, and economies of scale in

lending. To overcome these impediments, many governments, international financial

institutions and non-government organizations (NGOs) have established programmes

that target the delivery of medium- to long-term credit to MSMEs through financial

intermediaries. The European Bank for Reconstruction and Development (EBRD),

being the largest development finance lender in the transition economies and one of

the largest in the world, has for instance been implementing micro and SME lending

programmes that aim to build credit skills for MSME lending in existing participating

banks (PBs) and newly established specialised banks known as microfinance

institutions (MFIs). EBRD’s lending also aims to develop PBs’ credit procedures that

reduce lending costs and to help borrowers build a credit history and lower banks’

perceptions of risk associated with this type of lending. Interestingly, while the

objectives of MSME lending programmes are widely accepted as being important,

little evidence is available on the impact and longer-term financial sustainability of

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these programmes (see e.g., Hulme and Moseley, 1996, Morduch, 1999, and Brown,

Earle and Lup, 2002).

The purpose of this paper is to assess the impact on MSME performance of the

provision of credit by banks to these firms. During the period covered by our study,

standard bank credit to MSMEs in the transition economies under consideration was

very limited. There was hence an important gap in the financial market and anecdotal

evidence suggests that the few firms that benefited from bank credit had to provide

extraordinarily large amounts of collateral, and sometimes had to use a series of short

term loans to finance longer term capital investments. In view of this challenge,

EBRD started credit programmes targeting MSMEs utilising lending methodologies

that allowed banks to reach a larger number of firms than traditional methodologies

made possible and using a more flexible definition of collateral than normally used by

other banks in the same countries. In order to carry out our analysis, in 2005 we

administered a survey to a sample of firms that had received a loan from the EBRD

MSME lending programmes in 2002 and to a sample of similar firms that had never

received an EBRD program loan. The latter sample represents our control group. In

both groups, some firms had received loans from non-EBRD sources prior to 2002

and some had not. In the survey, we obtain this information as well as data on EBRD

and non-EBRD loans that the firms obtained between 2002 and 2004, as well as

performance indicators for all firms between 2002 and 2004. A more detailed

discussion of the sample is provided below.

There are two key questions that we address in this research. First, did

MSMEs that had received less flexible and less well tailored (non-EBRD) versus

more flexible and better tailored (EBRD) loans prior to 2002 subsequently attain

greater recourse to bank finance than firms that had not received bank credit prior to

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2002? Second, what has been the effect of EBRD versus non-EBRD credit on MSME

performance? In order to provide a relatively comprehensive understanding, we use

several indicators of firm performance: survival, capital formation (fixed assets),

revenues, labour cost, profit, employment, and market share.

The paper is organised as follows. Section 2 describes the EBRD financing projects

for MSMEs, while Section 3 outlines the hypothesized effects of these programs.

Section 4 discusses the main features of the survey and basic statistics, while Section

5 presents the analytical framework that we use. The empirical results are discussed in

Section 6 and the conclusions are drawn in Section 7. Appendix 1 describes the

individual EBRD programmes whose clients were surveyed up to 2002. Appendix 2

describes effects on firm survival rates and net job creation estimated using a different

control group then the one used to analyzed the impact of EBRD credit on firm

performance.

2. EBRD projects targeting provision of finance to MSMEs

One of EBRD’s operational priorities is to support MSMEs in its region of

operation with loans better tailored to the MSMEs’ needs because it regards the lack

of appropriately tailored loans (e.g., of loans with maturity better tailored to the

intended use of loan, or of loans supported by collateral other than real estate) as

resulting in a suboptimal scale of MSME activity. There are two important market

failures that EBRD addresses in this respect. One concerns the inadequate incentive

structure to allow for capacity building within the MSME lending institutions. The

second failure results in an underdeveloped culture of credit in the MSME segment of

the banking market on both sides of the market.

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The EBRD pursues its objective through the provision of credit via financial

intermediaries and through the channelling of technical assistance funds to these

financial intermediaries or directly to the MSMEs. In particular, based on the belief

that the provision of lending to this group of enterprises is still inadequate, the EBRD

provides the aforementioned (indirect) MSME lending as an additional instrument to

accelerate the development of this specific type of finance -- beyond its support to

foreign banks’ entry and bank privatisation. This intervention by the EBRD is

designed to bring benefits to both the MSMEs and the financial institutions that

engage in MSME financing. The support is designed with a view to make these

activities financially sustainable in the long run. Microfinance institutions of the type

supported by EBRD operate on a commercial basis by providing loan finance to

clients that profit-maximising financial institutions would not yet serve (or not on an

adequate scale) based on transactions costs and risk/return considerations. In

particular EBRD loan programmes directed to MSMEs result in banks extending

loans to enterprises based on their ability to repay the loan (mainly based on cash-

flow considerations) rather than based on an assessment of the profitability of the

intended use of the loan or on the presence of significant real estate value offered as

collateral. This allows for the extension of very large number of loans and for shorter

loan processing times, The latter permits the enterprises to access the credit when they

need it.

The paper evaluates the overall impact of three types of EBRD financing

operations aimed at supporting MSMEs: Those with (1) de novo dedicated micro-

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finance banks, (2) existing banks participating in broad micro-lending programmes

and (3) existing banks participating in the EU/EBRD-SME Facility.1

Microfinance banks are set up by both private and public shareholders to

provide finance on a purely commercial basis to micro and small businesses. Most –

but not all – shareholders of the micro-finance banks are multilateral institutions (such

as the EBRD), bilateral donors, not-for profit private charities and NGOs. These

banks benefit from technical assistance to finance initial set up costs and, later, branch

expansion. Among all EBRD programmes targeting the provision of finance to

MSMEs, microfinance banks are associated with the greatest scale of activity.2 The

rationale for establishing the initially costly microfinance banks, as opposed to

working with existing local partner banks, is to create a reliable, permanent delivery

mechanism for MSME finance. The microfinance banks can also play an important

part in financial sector development by demonstrating the commercial viability of

MSME lending to other market participants. The EBRD microfinance banks were set

up under two different sets of circumstances: a) post-war reconstruction situations

and/or dysfunctional crisis-ridden financial sectors; b) lack (or under-performance) of

viable local banks.

When and where it is possible, carefully selected local banks are provided by

EBRD with medium- to long-term finance which is then on-lent to small and

medium-sized enterprises. The provision of credit lines is accompanied by technical

1 Due to a limited sample size, in our estimations we treat these three programs as a single initiative (we check, however, whether one particular bank -- Hebros in Bulgaria -- yields different results). EBRD also uses SME credit lines within other programmes, stand-alone SME credit lines, credit lines to leasing companies within the EU/EBRD SME Facility, dedicated SME equity funds and, in special cases, direct equity investments in SMEs. 2 EBRD (2004), “Transition Impact and Subsidies in the ERBD’s Micro, Small and Medium-Sized Enterprise Financing Operations”.

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assistance which finances capacity building.3 This is the case for both existing banks

participating in broad micro-lending programmes and existing banks participating in

the EU/EBRD-SME Facility. 4 In addition to the technical assistance component,

banks participating in the EU/EBRD-SME Facility receive a performance fee

associated with the provided credit line. This fee is a conditional subsidy – a discount

on the interest rate charged by the EBRD to participating bank at the end of each

interest payment period, on outstanding amounts drawn down from the Facility’s

credit lines.5

The subsidies provided to the financial intermediaries participating in EBRD

MSME programmes are not transferred to the MSMEs. The effective rates charged by

EBRD sponsored microfinance banks and banks participating in EBRD programmes

are on average in line with the rates charged by their competitors.

In terms of collateral requirements, however, there are significant differences

between what microfinance banks and banks participating in EBRD micro-lending

programmes accept from their clients as collateral as compared to what other banks

(including banks participating in the EU/EBRD-SME Facility) require. Both

microfinance banks and banks participating in micro-lending programmes managed

by EBRD use very flexible definitions of collateral that allows MSMEs, which would

not have otherwise been able to access bank credit, to benefit from bank lending.

In terms of maturity, banks participating in the EU/EBRD SME Facility tend

to offer the longest terms for their loans. These banks offered loans with an average 3 The exact elements of capacity building typically vary from case to case ranging from investments in skills, branch expansion and information technology 4 EBRD also uses SME credit lines within other programmes, stand-alone credit lines and credit lines to leasing companies within the EU/EBRD SME Facility. 5 The discount on the interest rate is supposed to be granted only on the condition that the amounts of the credit line drawn down by the financial intermediaries satisfy the following conditions: (1) the funds need to be used to provide finance to MSMEs with ceilings both on the size of the loan and the size of the enterprise eligible for loans; (2) the final beneficiaries must be new clients of the participating financial institutions; and (3) the quality of the loans/leases made by the participating financial institutions must be of at least a certain standard (measured by arrears).

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27 months maturity versus an average of 18 months offered by microfinance banks

and 12 months offered by banks participating in micro-lending programmes. Banks

which were not participating in EBRD MSME programmes usually extend loans of

shorter maturity than banks participating in EBRD programmes to the same size

category of clients.

The beneficiaries of the first two types of EBRD programmes are private

entrepreneurs and enterprises, ranging from self employed one person businesses to

companies with up to 100 employees. In order to create substantial access to finance

for MSEs, all sectors of the economy in as many regions as possible are targeted,

independent of the size of the loan required. Loans start as low as USD 20 (e.g. for an

open bakery on a Central Asian market to buy flour) up to about USD 200,000 (e.g.

for the purchase of upholstery equipment for a furniture producer in Ukraine). The

typical micro-enterprise client has from 2 to 7 employees, has been in operation from

3 months to 10 years (for more advanced countries) and has total assets ranging from

USD 3,000 to 50,000. A small enterprise typically has from 10 to 60 employees, has

been in existence from 1 to 15 years (for more advanced countries) and has total

assets from USD 50,000 to 500,000. The banks working within the EU/EBRD-SME

Facility target micro, small and medium sized enterprises whose size ranges from 1 to

249 employees. On average, banks operating under this programme effectively

finance relatively larger clients. At the end of 2003, the average client had 16

employees.

3. EBRD MSME Programmes and Hypothesized Effects

In general, evaluations of providers of finance to the MSME sector have been

limited in their scope to assessments of outreach (measured as the number of

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enterprises served), the quality of the loans (typically measured by loan portfolio

arrears ratios), the efficiency of the use of public funds invested (measured as the ratio

of subsidies to the number and volume of loans) and as loan officer efficiency

(typically measured as the number of outstanding loans divided by the number of

trained and retrained loan officers in the programme), the sustainability of the

financial intermediaries (full cost return on equity, and of various social objectives

(number of women borrowers, number of clients below poverty line, average loan

balance per borrower in relation to GNI per capita, and regional dispersion of loans).6

EBRD’s internal evaluations of its MSME programmes have been limited to an

assessment of the impact of these programmes on the ability of the banking sector to

provide finance to MSMEs on a sustainable basis. There has been no evaluation of the

impact of these programmes on the enterprises that benefited from the associated bank

finance.

As evidenced by a number of enterprise surveys, access to external sources of

finance remains an important business constraint for small firms in transition

economies. In particular the 2005 Business Environment and Enterprise Performance

Survey (BEEPS) showed that, although access to external finance is becoming with

time a less severe business constraint, MSMEs in transition economies suffer from

poor access to external finance to a larger degree than MSMEs in mature economies.7

By managing to relieve MSMEs of one of the most frequently quoted (and

most highly rated) constraints to doing business and expansion, we expect that firms

receiving bank finance may have an overall better performance and a higher survival

rate than firms that do not manage to access bank loans. We also expect that firms that

benefit from bank loans would rate finance as a lesser obstacle to doing business than

6 http://www.mixmarket.org/en/demand/demand.profile.comparison.asp 7 See also Pissarides, Singer and Svejnar (2003) for earlier systematic evidence.

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firms which do not manage to obtain a bank loan. Moreover, due to the different

lending methodology adopted by the micro-lending programmes and microfinance

banks, which typically results in faster disbursements and relatively easier loan

application procedures due to the more flexible use of collateral, we would expect

clients of these types of programmes to perceive finance as a lesser business obstacle

overall than clients of other banks. Longer maturity bank finance offered by banks

under the EBRD programmes is expected to result in higher investment ratios for their

clients than for other firms.

To test these hypotheses we constructed a questionnaire covering the following

areas of enterprise behaviour:

1. Financial performance (profit, output, sales, exports, investment, and leverage

ratio)

2. Employment dynamics (changes in both full-time and part-time staff)

3. Market expansion (changes in market share, changes in sectors of activity)

4. Relations between firms and financial providers (ease of obtaining external

finance prior to 2002 and after 2002, access to other bank loans)

5. Perception of obstacles to doing business

4. The Survey, Sample and Basic Statistics

During the first half of 2005 we administered the questionnaire to a sample of

1,272 MSMEs (defined as firms with fewer than 250 employees) in Bulgaria,

Georgia, Ukraine, and Russia.8 In each country, these MSMEs represent a stratified

8 The selection of countries in which the survey was run was based on a number of factors. The first is the number of loans extended by each financial intermediary in the EBRD programme(s) being statistically significant (for statistical purposes this had to be at least 250). Second, to allow for a comparison of the impact on MSMEs of the different quality of finance provided by different types of financial intermediaries, the presence of both dedicated microfinance institution and existing local

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random sample of manufacturing and trade sector enterprises that in 2002 received

finance from EBRD’s MSME financial intermediaries (roughly two thirds of the

overall sample per country) and enterprises that by the time of the survey had not

received finance from EBRD intermediaries but were in existence in 2002 (one third

of the overall sample per country). The former MSMEs represent our treatment group

and the latter ones constitute our control group. The treatment group firms are a

random sample stratified by employment size and sector.9 The treatment group is

composed of two sub-samples (roughly equal in size), one of which includes

enterprises that received finance from a microfinance bank in 2002 and the other

including enterprises which received finance in 2002 from a local bank participating

either in a micro-lending programme or in the EU/EBRD SME Facility.

We restricted the scope of the current research to the trade-retail and

manufacturing sectors, but no quotas were applied to the sectors. In practice most

interviewed enterprises were in the trade sector, as the majority of companies which

borrowed from these banks are in the trade sector. Micro-enterprises constitute the

bulk of the loan portfolio clients of the financial intermediaries used by the EBRD. In

the case of microfinance institutions and micro-lending programmes through

participating banks, micro-enterprises account on average for two-thirds of the

volume and 90 per cent of the number of loans.10 As the role of the micro enterprises

banks administering targeted credit lines being desirable. Finally, in the case of a large country, the selected regions needing to overlap with regions in which the 2002 BEEPS was run. 9 Except for Ukraine it was not possible to find sufficient enterprises in the last employment category as most of the banks working for EBRD did not extend a sufficient number of loans to this category of enterprises. Also in the case of TUB in Georgia it was impossible to interview the specified quota of 100 enterprises per each bank due to the small number of loans extended by this bank in 2002 combined with business failures and inability to reach the enterprises which benefited from TUB loans. This failure was compensated by adding more enterprises from the Procredit Bank in Georgia. In Bulgaria, the Hebros Bank and Procredit Bank had several inaccurate contact entries and the sample was hence drawn with replacement. 10 In the case of the programmes run under the EU/EBRD SME Facility these data is unknown as monitoring of the use of the proceeds of the Facility is based on its sub-loans’ size rather than on its sub-borrowers’ size.

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in the financial intermediaries portfolios is so large, this is reflected in the size of

sample strata by size class11. Because we wanted to analyse the impact of EBRD

finance on enterprises of all sizes, we aimed at having all size classes represented in

the sample. Yet, due to total sample size limitations, in some cases the sample

stratification does not necessarily match exactly the financial intermediaries portfolio

composition, although it gives a heavier weight to micro-enterprises (54 per cent of

total number of surveyed enterprises) than small (36 per cent) or medium sized

enterprises (10 per cent).

Table 1 shows the sample composition by size class and sector for both

control and treatment groups. The control group firms were selected in 2005 as a

stratified random sample from marketing lists, internet databases, yellow pages, and

interviewers’ walk-ins in order to match the treatment group in each country by

categories12 of location, employment size and sector.

The summary statistics related to the key variables used in our analysis are

provided in Table 1A. As may be seen from the table, the variables have reasonable

values and display considerable variation in within and across countries. Given that

the matching of the control group to the treatment group was structured around

employment size and sector of the firms, other variables than employment show a

larger variation. For example, both in Bulgaria and Russia, the presence of some

companies with large revenues in the control groups is evidenced by much larger

mean values for revenues in the control group than in the treatment group.

11 Quotas were specified for the size composition of the sample of enterprises to be interviewed (50 per cent of the sample had to employ up to 9 workers, 20 per cent between 10 and 24 workers, 15 per cent between 25 and 49 and 15 per cent between 5 and 249). 12 The matching was not on a one-to-one basis, but by categories of location, size and sector. 14 Any time-invariant effects of these variables (i.e., effects on the level of performance) are captured in αι.

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5. The Analytical Framework

Our main goal is to analyze the effects that short- and longer-term loans have

on the performance of MSMEs. In carrying out our analysis, we need to take into

account the fact that our sampled firms differ in terms of whether they received an

EBRD loan in 2002 (treatment versus control group) and also whether and when they

received other loans. In particular, firms in the treatment group may have received

other EBRD or non-EBRD loans before and after 2002, while firms in the control

group may have received non-EBRD loans at any time. From an analytical standpoint

there may hence be significant selection problems, with better performing firms for

instance being more able to obtain EBRD and/or non-EBRD loans. If one did not

control for this non-random assignment of firms to loans, one could mistakenly

attribute all of the superior post-2002 performance to loans rather than recognizing

that part may be due to inherently superior performance of the firms that receive

loans. In view of the design of our sample, we strive to control as much as possible

for the treatment and performance of different firms up to 2002, and then focus on

analyzing the impact of subsequent EBRD and non-EBRD loans on performance.

Formally, in the spirit of Ashenfelter and Card (1985), Heckman and Hotz

(1989), and Hanousek, Kocenda and Svejnar (2005), we specify a panel-data

treatment evaluation procedure that fits our context and we supplement it with a set of

instrumental variable estimates. Let Xijt be a given performance indicator, with

subscript i denoting an individual firm with loan of type j (EBRD v. non-EBRD), in

year t. Moreover, let Lijt be a dummy variable which assumes value 1 if a loan of type

j is awarded to firm i in year t and 0 in all other cases. (Given the relatively short time

span of our panel, we specify the effect of a given loan to be time invariant and Lijt is

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hence a dummy variable that has value 0 before year t and value 1 in year t and

thereafter.) A model of performance may be specified in a logarithmic form as

ijtjijtjijtjijjijiijt DttLLtXtLtX υϕθδγβαα +++++++= )()()()(ln 11 (1)

where t is a time trend and D is a vector of dummy variables measuring individual

country, sector and year effects. Our interest is in estimating the average, time

invariant (one shot) performance effect δj and the linearly time-varying effect θj of

EBRD and non-EBRD loans Lijt obtained in the 2002-04 period. In practice, we find

that the time-invariant component is statistically insignificant. Equation (1) is

relatively flexible in that in estimating δj and θj, the equation allows the 2002-04

performance of firms to reflect all time-invariant differences αι that exist across

individual firms, a possible time-varying effect βj of pre-2002 EBRD and non-EBRD

loans Lij1, a possible time-varying effect γj of the firm’s 2001 (base) year performance

Xij1, and time varying effects ϕ that are specific to dummy variables reflecting

individual countries, industries, and years D.14 We take firms that received no loans as

the base and their logarithm of performance is permitted to vary over time at the rate

α. For ease of interpretation, the effect of pre-2002 loans βj is measured relative to α.

Our specification in equation (1) thus controls for the effects on performance

of fixed differences among all firms. It also controls for any linearly time-varying

differences among firms that received or did not receive EBRD or other loans before

2002, inter-firm differences in the initial (2001) performance, country-specific fixed

effects, industry-specific fixed effects (proxying for factors such as the degree of

competition or differences in technology), and annual shifts (such as macro shocks).

A particular concern is that we should ensure that our estimates capture the effect of

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loans rather than other factors such as competition. As may be seen from equation (1),

we do so by controlling for these other factors by the firm-specific fixed effects, the

effect of initial performance interacted with the time trend, and the industry-specific

and annual time dummy variables interacted with time.

In our empirical work, we also allow for two specifications of the effect of

credit: one where the effect does not vary with the amount of credit and one where the

effect of credit varies with loan size.

For estimating purposes, it is useful to let yijt be the percentage change of Xijt

from t - 1 to t and express equation (1) in the annual rate of change (first-difference)

form as

ijtjijtjijtjijjijijt DLLXLy εϕθδγβα +++∆+++= 11 (2)

where εijt = υijt - υijt-1 is the error term. This specification is more parsimonious and

allows us to estimate all the parameters of interest. It also turns out that in estimating

equation (2) we find that the coefficients on pre-2002 loans Lij1, 2001 performance

Xij1, and the time invariant (one shot) performance effect of a loan ijtL∆ are

statistically insignificant and their exclusion does not affect the other parameter

estimates. In the empirical part of the paper we hence report the estimates from

specifications that exclude these three variables.

There are three key econometric issues that we need to account for in our

analysis: omitted variables bias, measurement error, and endogeneity of receiving

loans. We address omitted variables bias by including a number of important control

variables that we describe above. In dealing with measurement error in loans,

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performance and other variables, we note that the error can induce standard

attenuation as well as more complicated biases in estimated coefficients. As discussed

earlier, in collecting the data set we have placed particular emphasis on identifying

precisely individual loans, as well as carefully collecting several indicators of

performance for the current and preceding periods. We have also checked that there

are no outliers that would seriously affect our estimates.

As to endogeneity of receiving loans, there is a danger that the inherently

superior performance of the firms selected for receiving EBRD or non-EBRD loans

could be attributed to loans rather than the non-random assignment of firms to loans.

In the present study, we address this problem as follows. In addition to matching the

control group firms with the treatment groups on a number of characteristics

discussed above, we use the panel data specification in equations (1) and (2) with the

aforementioned covariates as a panel data treatment evaluation procedure. This

provides a control for the possibility that firms are not assigned to loans at random

and that lending institutions may give loans to firms that are inherently superior or

inferior performers. Second, to deal with what we consider a relatively remote

possibility that firms that received pre-2002 loans from a given source may differ

among themselves with respect to some unobserved characteristics correlated with the

rate of change of performance and not captured adequately by Lij1 and Xij1, we also

estimate equation (2) by an instrumental variable procedure.

Instrumental Variables

We use the Wu-Hausman and Durbin-Wu-Hausman specification tests, the

Heckman selection test and the Hansen J overidentification test for assessing

endogeneity / selection of the loan status of firms. In the IV procedure, we treat all

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2002 and subsequent loans as potentially endogenous and we use the following set of

firm-specific instrumental variables that we expect influence the probability that a

firm receives EBRD or non-EBRD loan and that can be excluded from the second

stage, rate of change of performance equation: one year lagged dummy variables

detecting the change in EBRD and non-EBRD loan status, 16 dummy variable

reflecting whether the firm adopted international accounting standards at least one

year before a loan is granted and the firm’s initial leverage in 2001. For the

regressions predicting the size (relative to revenues) of the EBRD and non-EBRD

loans, we also include as IVs two dummy variables indicating whether the firm

received an EBRD or non-EBRD loan two years ago. These latter two variables have

a strong explanatory power in the loan size regressions, indicating that firms that

received a loan in the past are more likely to receive a larger loan later.18 There may

be concern that these two variables might not be uncorrelated with the error term in

the performance equation if this error term had a fixed component. While this is not

very likely in a first difference equation such as (2), it cannot be ruled out. As it turns

out, all the instrumental variables pass the Hansen over-identification test.

6. The Empirical Results

We present our empirical estimates in two parts. First, we discuss the results

related to the overall effects on MSME performance of the presence of EBRD and

non-EBRD loans, irrespective of the size of these loans. We carry out this estimation

for all firms together and separately by firm size, using three different size categories

16 The variable takes on value 1 if firm had received EBRD (non-EBRD) related credit in given year but had no EBRD (non-EBRD) related credit in previous year, has value 0 if firm had received EBRD (non-EBRD) related loan two years in row or if firm had not received EBRD (non-EBRD) related loan two years in row, has value –1 if firm had received EBRD (non-EBRD) related loan last year but not this year. 18 Using a dummy variable for the initial (2000 or 2001) loan status instead of the two year lagged loan status yields similar results.

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of firms. Second, we examine the extent to which the effects of loans vary with the

size of the loan.

The Effects of Loans on Performance

The results that we present in the tables 4-13 come from the OLS and IV

estimations of equation (2). As mentioned earlier, in estimating equation (2) we find

that the coefficients on pre-2002 loans and 2001 performance, as well as the

coefficient capturing the time invariant effect of the 2002-04 loans, are statistically

insignificant and their exclusion does not affect the other parameter estimates. In what

follows we hence report the estimates from specifications that exclude these variables,

but include all the other variables discussed above. In each table, we present estimates

for all firms taken together and for three categories of firm size. Since we have firms

ranging from micro enterprises to medium sized firms, we estimate the effects for

firms with 1-5 employees (including the self-employed and working family

members), firms with 6-15 employees and firms with 16 or more employees.

Although they do not match the specifications of size categories for micro, small and

medium sized enterprises which we used to stratify the sample, these size categories

provide us with a similar number of observations in each group of firms and are the

best groupings to isolate the effects related to different firm size categories.

In Table 4 we report the linearly time varying effects of EBRD and

non-EBRD loans on fixed assets. With the exception of the estimates for firms with

16 and more workers, the results of the Wu-Hausman, Durbin-Wu-Hausman and

Heckman tests suggest that OLS is an adequate estimation method relative to IVs. The

OLS estimates of the average effects of a loan, based on data for all firms, suggest

that an EBRD loan results in a 10.5% increase and non-EBRD loan in a 14% increase

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in the rate of growth of fixed assets of the firm. (The IV estimates are higher,

implying EBRD and non-EBRD loan effects on the rate of growth of fixed assets of

19% and 29% respectively.) Since the non-EBRD loans are on average three times as

large as the EBRD loans, the percentage effect per dollar of loan is higher for the

EBRD than non-EBRD loans. However, since firms receiving non-EBRD loans are

on average about 75% larger than firms receiving EBRD loans, the effects of the two

types of loans in terms of dollars of fixed assets generated by a dollar of loan are

similar. The results based on firm size suggest that both EBRD and non-EBRD loans

have a positive effect on capital formation in all three size categories of firms.

Moreover, for EBRD loans the percentage effect rises and becomes more statistically

significant with firm size, while the effect of non-EBRD loans is more uniform and if

anything it is greatest at 23% for the very small firms with 1-5 employees and only

marginally significant (at 10%) for the medium size firms (16 or more workers).

Overall, the results in Table 4 are interesting because they provide support for the

anecdotal evidence that firms often use short-term loans for investment purposes,

including investment in fixed assets. They also suggest that the phenomenon is

present in all size categories of firms, and especially so in the micro- and small ones.

The average effects of EBRD and non-EBRD loans on firm revenues are

reported in Table 5. Except for the micro-enterprises, the OLS estimates appear to be

adequate relative to the IV ones since the Wu-Hausman, Durbin-Wu-Hausman and

Heckman tests indicate that endogeneity/selection is not likely to be an issue. (In the

case of the micro-firms, the OLS and IV estimates yield qualitatively similar results.)

The overall OLS estimates indicate that the average effects of EBRD and non-EBRD

loans on the rate of growth of MSME revenues are positive and statistically

significant. They suggest that receiving an EBRD loan on average results in a 4%

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higher rate of growth of revenue than would be the case if a firm did not receive such

a loan. The average effect of a non-EBRD loan is estimated at 6%. The estimates by

size categories of firms indicate that EBRD loans do not have a statistically

significant effect on the revenues of micro-enterprises (1-5 workers), but do have a

positive effect on small (6-15 worker) and medium sized (16 or more worker) firms.

The effect of non-EBRD loans in turn is significant for micro and small firms, but

insignificant for medium sized firms. The overall effects of loans on revenues are

consistent with the positive effect of loans on fixed assets and they suggest that with

some notable exceptions, firms use loans to expand their scale of operations. The

exception are micro-enterprises that register a positive effect of EBRD loans on fixed

assets but not revenues, and the medium size firms that generate a similar finding for

non-EBRD loans. This suggests that in terms of expansion of revenues micro-

enterprises benefit more from non-EBRD loans, while small and medium size firms

tend to benefit more from EBRD loans. The finding is consistent with the hypothesis

that micro-enterprises (especially self-employed individuals) do not tend to use loans

to expand production and instead tend to use them for unproductive investments and

possibly consumption. The latter hypothesis is consistent with answers given by many

micro entrepreneurs to our questionnaire in the area of use of loans – many stated that

they use loans to maintain production but very few claimed to use them to expand

production or introduce new products and technologies.

In Table 6 we report the estimated effects of loans on the rate of change of

labour cost. Except for the medium sized firms, the OLS estimates appear to be

adequate relative to the IV ones by the Wu-Hausman, Durbin-Wu-Hausman and

Heckman tests. As may be seen from the table, the overall OLS estimates suggest that

the average effects of both EBRD and non-EBRD loans are positive at 5% and 10%,

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respectively. The estimates by firm size parallel those found for revenues, implying

that the effect of EBRD loans is insignificant for micro-enterprises but significant for

small and medium sized firms, while the effect of non-EBRD loans is significant for

micro and small firms, but insignificant (in the IV estimation) for medium size firms.

Overall, the estimates in Tables 4-6 indicate that by and large firms use EBRD and

non-EBRD loans to invest in fixed assets and augment both revenues and labour

costs.

In Table 7 we complement our findings on labour cost by examining the effect

of loans on the rate of change in total employment (results based on full time

employment are similar), thus assessing if the labour cost effects are brought about

primarily by effects on wages or employment. The OLS estimates are preferred to the

IV estimates on the basis of the Wu-Hausman, Durbin-Wu-Hausman and Heckman

tests. The results based on the sample of all firms suggest that both EBRD and non-

EBRD loans have a positive effect on the rate of growth of employment (5.8% and

9.5%, respectively) and they imply that the overall labour cost effect of loans is

primarily accounted for by the positive effect of loans on employment. The results

with respect to the three size categories of firms are similar to the previous findings –

EBRD loans have a positive effect on small and medium size firms but not the micro-

enterprises, while non-EBRD loans have a positive effect on micro- and small firms

but not the medium size ones. The fact that the distinction between the effect of

EBRD and non-EBRD loans exists not only for revenues and to a lesser extent capital

formation, but also for labour cost and employment, suggests that micro-enterprises

tend to use EBRD loans for non-productive purposes rather than for expanding

employment and revenues – a phenomenon that is consistent with the aforementioned

answers to our questionnaire.

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Having examined the effect of loans on revenues and costs – the two principal

components of profit -- we next examine directly the effect of loans on profit. As may

be seen from Table 8, the Wu-Hausman, Durbin-Wu-Hausman and Heckman tests

suggest that the OLS estimates are adequate relative to the IV estimates. The pooled

estimates suggest that while EBRD loans on average increase the rate of growth of

profit by 8%, the effect of non-EBRD loans is 6% but statistically insignificant.19 The

estimates by firm size yield a significant positive coefficient for EBRD loans for

micro-enterprises, a marginally (10%) significant effect for small firms and

insignificant effect for medium sized firms, while the non-EBRD loans generate a

statistically significant positive effect only for small firms. The relative insignificance

of the effect of loans on profit is not surprising. MSMEs operate in a competitive

setting and loans are hence likely to result in an increased scale of operations but not

necessarily higher profit. Moreover, as we have seen in the preceding tables, loans are

usually found to have a positive effect on both revenues and labour costs, with the net

effect on profit thus being a residual effect. The limited effect of loans on profitability

is also seen when we use profit/revenues as the dependent variable (not reported

here). With that dependent variable the effect is insignificant in all specifications. It

must be noted, however, that since the effects of loans on revenues are on the whole

positive, estimates based on dependent variables expressed in a per revenue form will

be statistically insignificant if the positive effect of loans on the numerator does not

exceed the effect on revenues that are in the denominator.

Effects by Loan Size

19 Fewer than 5% of the observations have negative values of profit. These observations have been excluded from estimation. When we estimate the profit equation using the percent change formula, the results are statistically insignificant.

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In the estimates presented so far, we have been capturing the effect of the

presence or absence of a loan. While this approach is fruitful in getting a sense of the

overall effect, in this section we complement these results by presenting estimates that

allow the loan effects to vary with the size of the loan, defined as loan size relative to

firm’s revenues.

The estimated effects of loans on fixed assets are reported in Table 9, with the

results of the Wu-Hausman, Durbin-Wu-Hausman, and Heckman tests suggesting that

with the possible exception of firms with 6-15 workers, the OLS estimates are an

adequate estimation method relative to IVs. The estimated coefficients indicate that

the effects of EBRD and non-EBRD loans on fixed assets basically do not vary with

the size of the loan. The only exception is the effect of EBRD and non-EBRD loans

on micro-enterprises, which vary negatively with loan size at the 10% significance

test level. As the calculated critical values in the table indicate, at this significance test

level, the effect of EBRD and non-EBRD loans on fixed assets turns from positive to

negative at 123% and 244% above the mean value of the EBRD and non-EBRD loans

to these firms, respectively.

The estimates with respect to the effects of loans on revenues, reported in

Table 10, indicate that OLS is preferred to IV in regressions for firms with 6-15 and

16 or more workers, while the IV estimates are preferred for firms with 1-5 workers.

The IV estimates are also preferred in the overall regression for all firms if one uses a

10% rather than 5% significance test level for the Wu-Hausman and Durbin-Wu-

Hausman tests. The results suggest that for all three categories of firms, the effect of

non-EBRD loans is invariant with respect to the loan size. The estimated effect of

EBRD loans is invariant with respect to the loan size for micro- and small enterprises,

and varies negatively with loan size in the case of the medium-sized firms. The

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critical value at which the effect of EBRD loan on revenue turns from positive to

negative for these firms is estimated to be 155% above the mean loan value.

The estimated effects of loans on labour costs (Table 11) also mostly do not

vary with loan size. The model selection tests suggest that OLS is an adequate

estimation method relative to IVs in all regressions except possibly those for firms

with 16 or more workers. The two effects that vary with loan size are found for non-

EBRD loans to micro-enterprises (with a critical value estimated to be at 45% above

the mean value of the loan), and in the OLS (but not IV) estimation for EBRD loans

to firms with 16 or more workers (with a critical value at 389% above the mean loan).

The estimates with respect to employment, reported in Table 12, indicate that

OLS is an adequate estimation method relative to IVs at 5% test level in all the

regressions. However, it is not preferred at the 10% test level for micro- and small

firms by the Wu-Hausman and Durbin-Wu-Hausman tests, while the Heckman

selection procedure signals that selection may be a problem for small and medium

sized firms. The estimates are similar to those for labour costs in that the variation of

the loan effect with loan size is minimal – the effect of EBRD loans varies negatively

with loan size for micro-enterprises in OLS but not IV estimation, and the effect of

non-EBRD loans varies negatively with loan size in the case of medium size firms in

OLS but not IV estimates. In all other cases the effect is invariant with respect to loan

size.

Finally, in the profit regressions (Table 13), OLS is preferred to IV in the

aggregate regression for all firms and in the regression for small firms. In the runs on

micro- and medium sized firms, OLS is adequate relative to IV at the 5% but not 10%

significance test level. The estimates indicate that the OLS effect of EBRD loans

varies negatively with loan size in all cases except small firms, while the IV estimates

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point to a time invariant effect of EBRD loans also for micro- and medium sized

firms. The effect of non-EBRD loans is estimated to be invariant with respect to loan

size in all categories of firms.

7. Conclusions

Our estimates suggest that non-EBRD and EBRD bank loans have a

significant positive effect on most performance indicators of micro, small and

medium sized enterprises (MSMEs), defined as firms with 1-5 workers, 6-15 workers

and 16 or more workers, respectively. In a panel data treatment evaluation framework,

both EBRD and non-EBRD loans have a positive effect on fixed assets, suggesting

that all three types of firms use bank loans for investment in fixed capital. In terms of

dollars of fixed assets generated by a dollar of loan, the effects of the two types of

loans are similar.

The positive effect of both EBRD and non-EBRD loans is by and large also

found with respect to revenues, labour cost and employment. In particular, the overall

pooled estimates for all firms indicate that the loans serve the purpose of enabling the

MSMEs to expand production beyond the scale that they could achieve without this

source of credit. Moreover, the positive labour cost effect is primarily a positive effect

of loans on employment. Interestingly, the two sets of loans differ in their effect on

profitability. The EBRD loans have a positive effect on profit while the non-EBRD

ones have no significant effect. The relatively limited effect on profitability is

intuitively acceptable, given that firms use the loans to expand both revenues and

input use. Moreover, in a competitive environment, one would expect the effect to

translate primarily into expanding scale.

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Estimates carried out separately on three different size categories of firms

indicate that with the exception of fixed assets, where the effect of all loans is

uniformly positive, the positive effect of EBRD loans on the various performance

variables is stronger for firms with 6-15 and 16 and more employees, while the effect

of non-EBRD loans is stronger for firms with 1-5 and 6-15 employees. In this sense,

the micro-enterprises benefit more from non-EBRD loans, while small and medium

size firms benefit more from EBRD loans. This differential impact raises the

hypotheses that the few micro-enterprises benefiting from non-EBRD loans are

inherently better performers than their peers given that non-EBRD banks would only

lend to micro-enterprises that are considered to be associated with very low risk,

whereas the same is not true for micro-enterprises benefiting from EBRD

programmes, since these are only selected by the banks on their perceived ability to

repay the loan. Thus the results for micro-enterprises can be seen as positively biased

in the case of non-EBRD loans. However, since the above differential effect of loans

exists also for labour cost and employment, it is plausible that micro-enterprises

(often self-employed individuals) do not use EBRD loans to expand production and

instead employ them for unproductive investments and possibly consumption – a

phenomenon that is consistent with the responses of micro-entrepreneurs to our

questionnaire, with many stating that they use loans to maintain production but very

few claiming to use them to expand production or introduce new products and

technologies.

We also find that none of the effects of non-EBRD loans varies with loan size,

while the effect of EBRD loans increases with loan size (relative to revenues) for

fixed assets and labour costs, and decreases with loan size for employment. The

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effects of both types of loans on revenues and profit are invariant with respect to loan

size.

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References:

Brown, J.D., J. Earle and D. Lup (2002), “Determinants of Small Firm Growth: Finance, Human Capital, Technical Assistance and the Business Environment,” Heriot-Watt University and Central European University mimeo. Morduch, J. (1999), “The Microfinance Promise,” Journal of Economic Literature, 37, 1569 – 1614. Pissarides, F., M Singer and J. Svejnar (2003), “Objectives and constraints of entrepreneurs: evidence from small and medium size enterprises in Russia and Bulgaria,” Journal of Comparative Economics, 31, 503-531.

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29 Table 1. Sample size and stratification by size class

Number of Employees Sector Bulgaria 0-9 10-24 25-49 50-249 Trade Industry Service Total

Quota 50 20 15 15 50 40 10 100 Control Group 50 37 20 13 34 41 45 120 Procredit Bank 73 41 17 15 69 46 31 146 Hebros 20 20 7 7 28 22 4 54 Total 143 98 44 35 131 109 80 320

Number of Employees Sector Georgia 0-9 10-24 25-49 50-249 Trade Industry Service Total

Quota 50 20 15 15 50 40 10 100 Control Group 77 21 8 7 54 34 25 113 ProCredit Bank 88 15 6 2 86 16 9 111 TUB 71 17 4 1 53 18 22 93 Total 236 53 18 10 193 68 56 317

Number of Employees Sector Russia 0-9 10-24 25-49 50-249 Trade Industry Service Total

Quota 50 20 15 15 70 20 10 100 Control Group 64 31 11 9 75 25 15 115 KMB 42 39 11 14 71 16 19 106 NBD 64 28 13 9 71 12 31 114 Total 170 98 35 32 217 53 65 335

Number of Employees Sector Ukraine 0-9 10-24 25-49 50-249 Trade Industry Service Total

Quota 50 20 15 15 50 40 10 100 Control Group 47 22 15 16 49 41 10 100 ProCredit Bank 47 23 14 16 52 40 8 100 PrivatBank 50 19 16 15 51 40 9 100 Total 144 64 45 47 152 121 27 300 Total surveyed enterprises 693 313 142 124 693 351 228 1272

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30

Table 2. Summary Statistics in 2001 (financial data is expressed in thousands of USD)

Control group Treatment group Mean Median St. Dev. Mean Median St. Dev.

Bulgaria Revenue 390 113 1037 180 90 312 Investment 10 0 24 10 2 23 Fixed assets 96 19 240 62 11 189 Net profit 39 9 107 23 10 58 Labor cost 32 9 92 13 5 30 Total employment 21 10 33 14 6 35 EBRD loan size 0 0 0 16 (59) 9 25 Non-EBRD loan size 97 (13) 45 130 14 (22) 9 13

Georgia Revenue 137 24 436 119 42 385 Investment 18 0 85 3 0 7 Fixed assets 104 5 361 24 5 73 Net profit 17 3 67 41 8 207 Labor cost 11 3 35 6 2 12 Total employment 17 4 37 6 3 10 EBRD loan size 0 0 0 7 (61) 3 13 Non-EBRD loan size 61 (9) 49 49 11 (4) 4 15

Russia Revenue 869 78 5074 284 133 610 Investment 20 0 85 21 7 70 Fixed assets 118 10 651 94 17 317 Net profit 71 11 188 72 23 307 Labor cost 26 8 46 18 9 27 Total employment 19 7 47 13 6 22 EBRD loan size 0 0 0 11 (67) 5 15 Non-EBRD loan size 76 (7) 17 164 6 (5) 5 3

Ukraine Revenue 297 54 1409 174 19 659 Investment 12 0 47 10 1 56 Fixed assets 153 6 1329 34 4 137 Net profit 46 6 184 76 5 423 Labor cost 14 4 26 23 3 117 Total employment 14 8 19 15 5 29 EBRD loan size 0 0 0 21 (65) 4 46 Non-EBRD loan size 23 (19) 9 42 0 (3) 0 0

Note: Figures are adjusted to producer prices. For all countries the country specific producer price index is used except for Russia for which the Nizhny Novgorod regional producer price index is used. The number of loans is given in parentheses.

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31

Table 3. Summary Statistics in 2004 (financial data is expressed in thousands of USD)

Control group Treatment group Mean Median St. Dev. Mean Median St. Dev.

Bulgaria Revenue 948 179 2838 437 174 913 Investment 53 12 204 45 9 121 Fixed assets 171 60 313 164 59 452 Net profit 89 20 235 54 22 135 Labor cost 59 21 134 32 13 56 Total employment 23 12 31 20 10 35 EBRD loan size 0 0 0 37 (136) 15 72 non-EBRD loan size 69 (29) 36 94 68 (16) 27 93

Georgia Revenue 263 28 1327 140 51 333 Investment 12 0 52 3 0 7 Fixed assets 126 5 396 29 7 76 Net profit 36 4 127 40 10 135 Labor cost 20 3 91 8 3 16 Total employment 14 4 27 8 3 15 EBRD loan size 0 0 0 14 (117) 4 28 non-EBRD loan size 29 (18) 20 34 36 (9) 19 37

Russia Revenue 588 69 3407 290 132 574 Investment 26 0 116 27 7 77 Fixed assets 87 7 433 87 23 241 Net profit 51 9 135 65 23 251 Labor cost 23 8 44 22 12 35 Total employment 20 7 42 20 10 29 EBRD loan size 0 0 0 18 (117) 8 26 non-EBRD loan size 43 (22) 10 137 20 (14) 10 22

Ukraine Revenue 347 56 1445 480 23 2568 Investment 9 0 26 22 1 104 Fixed assets 110 6 890 68 4 328 Net profit 80 6 373 304 5 2327 Labor cost 17 5 29 33 4 161 Total employment 21 10 27 27 10 47 EBRD loan size 0 0 0 16 (111) 5 32 non-EBRD loan size 16 (26) 7 26 6 (11) 5 5

Note: Figures are adjusted to producer prices. For all countries the country specific producer price index is used except for Russia for which the Nizhny Novgorod regional producer price index is used. The number of loans is given in parentheses.

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Table 4 – Loan effects on fixed assets

All All Size1-5 Size1-5 Size6-15 Size6-15 Size16+ Size16+ OLS IV OLS IV OLS IV OLS IV EBRD loan 0.105 0.191 0.049 -0.123 0.094 -0.091 0.19 0.675 [0.018]*** [0.092]** [0.025]** [0.153] [0.026]*** [0.160] [0.045]*** [0.185]*** NonEBRD loan 0.14 0.292 0.233 0.6 0.095 0.295 0.145 0.468 [0.032]*** [0.169]* [0.093]** [0.391] [0.045]** [0.421] [0.047]*** [0.279]* Constant 0.179 0.102 0.186 0.301 0.194 0.302 0.139 -0.166 [0.031]*** [0.071] [0.069]*** [0.128]** [0.044]*** [0.118]** [0.057]** [0.147] Observations 3632 3615 1313 1307 1286 1276 1033 1032 R-squared 0.04 0.04 0.09 0.03 Hansen J 6.03 0.97 1.98 1.04 P-value 0.05 0.62 0.37 0.59 Wu-Hausman 0.59 1.40 1.64 3.00 P-value 0.56 0.25 0.19 0.05 Durbin-Wu-Hausman 1.18 2.84 3.32 6.08 P-value 0.55 0.24 0.19 0.05 Heckman's lambda -0.03 0.05 0.16 -0.35 [0.09] [0.11] [0.14] [0.21]

Notes: Dependent variable is defined ln(Ft)-ln(Ft-1). EBRD (non-EBRD) loan dummy equals 1 if firm received credit from EBRD (non-EBRD) related bank in given year. All regressions included industry, country and year dummies. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%.

Table 5 – Loan effects on revenues

All All Size1-5 Size1-5 Size6-15 Size6-

15 Size16+ Size16+ OLS IV OLS IV OLS IV OLS IV EBRD loan 0.043 0.105 0.029 -0.186 0.044 0.212 0.057 0.245 [0.012]*** [0.072] [0.018] [0.121] [0.020]** [0.140] [0.026]** [0.143]* NonEBRD loan 0.063 0.24 0.165 0.534 0.054 0.143 0.024 0.257 [0.022]*** [0.134]* [0.058]*** [0.276]* [0.028]* [0.256] [0.035] [0.219] Constant 0.132 0.067 0.184 0.34 0.105 -0.006 0.165 0.012 [0.025]*** [0.061] [0.073]** [0.116]*** [0.036]*** [0.104] [0.042]*** [0.127] Observations 3705 3686 1351 1345 1307 1295 1047 1046 R-squared 0.04 0.06 0.07 0.05 Hansen J 4.36 4.16 1.92 1.23 P-value 0.11 0.12 0.38 0.54 Wu-Hausman 1.33 3.31 1.29 1.29 P-value 0.27 0.04 0.28 0.28 Durbin-Wu-Hausman 2.67 6.68 2.61 2.62 P-value 0.26 0.04 0.27 0.27 Heckman's lambda -0.08 -0.05 -0.07 -0.14 [0.05] [0.07] [0.09] [0.10]

Notes: Dependent variable is defined ln(Rt)-ln(Rt-1). EBRD (non-EBRD) loan dummy equals 1 if firm received credit from EBRD (non-EBRD) related bank in given year. All regressions included industry, country and year dummies. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%.

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Table 6 – Loan effects on labour costs

All All Size1-5 Size1-5 Size6-15 Size6-

15 Size16+ Size16+ OLS IV OLS IV OLS IV OLS IV EBRD loan 0.051 0.081 0.002 -0.092 0.052 -0.051 0.103 0.401 [0.014]*** [0.079] [0.024] [0.148] [0.019]*** [0.130] [0.028]*** [0.158]** NonEBRD loan 0.1 0.022 0.159 0.269 0.092 -0.056 0.097 0.178 [0.026]*** [0.155] [0.085]* [0.313] [0.036]** [0.277] [0.038]*** [0.279] Constant 0.135 0.124 0.192 0.228 0.116 0.186 0.129 -0.026 [0.024]*** [0.062]** [0.086]** [0.131]* [0.033]*** [0.095]* [0.035]*** [0.134] Observations 3460 3442 1113 1108 1299 1287 1048 1047 R-squared 0.03 0.03 0.06 0.04 Hansen J 1.04 2.52 1.13 0.01 P-value 0.6 0.28 0.57 1 Wu-Hausman 0.45 0.39 0.41 3.51 P-value 0.64 0.67 0.66 0.03 Durbin-Wu-Hausman 0.90 0.80 0.83 7.11 P-value 0.64 0.67 0.66 0.03 Heckman's lambda 0.00 -0.03 0.03 -0.10 [0.06] [0.09] [0.10] [0.12]

Notes: Dependent variable is defined ln(LCt)-ln(LCt-1). EBRD (non-EBRD) loan dummy equals 1 if firm received credit from EBRD (non-EBRD) related bank in given year. All regressions included industry, country and year dummies. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%.

Table 7 – Loan effects on employment

All All Size1-5 Size1-5 Size6-15 Size6-15 Size16+ Size16+ OLS IV OLS IV OLS IV OLS IV EBRD loan 0.058 0.079 0.045 0.028 0.063 0.003 0.061 0.157 [0.013]*** [0.079] [0.028] [0.183] [0.019]*** [0.116] [0.022]*** [0.107] NonEBRD loan 0.095 -0.027 0.169 0.311 0.166 -0.031 0.037 -0.031 [0.025]*** [0.142] [0.083]** [0.394] [0.048]*** [0.244] [0.028] [0.171] Constant 0.054 0.063 0.079 0.095 0.039 0.083 0.114 0.1 [0.023]** [0.064] [0.084] [0.180] [0.034] [0.083] [0.031]*** [0.089] Observations 3335 3304 1006 996 1288 1270 1041 1038 R-squared 0.06 0.05 0.09 0.12 Hansen J 0.1 0.37 1.51 0.62 P-value 0.95 0.83 0.47 0.73 Wu-Hausman 0.96 0.15 0.28 1.41 P-value 0.38 0.86 0.75 0.24 Durbin-Wu-Hausman 1.92 0.30 0.57 2.87 P-value 0.38 0.86 0.75 0.24 Heckman's lambda -0.02 -0.11 0.06 -0.07 [0.06] [0.11] [0.10] [0.08]

Notes: Dependent variable is defined ln(Et)-ln(Et-1). EBRD (non-EBRD) loan dummy equals 1 if firm received credit from EBRD (non-EBRD) related bank in given year. All regressions included industry, country and year dummies. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%.

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Table 8 – Loan effects on net profits

All All Size1-5 Size1-5 Size6-

15 Size6-

15 Size16+ Size16+ OLS IV OLS IV OLS IV OLS IV EBRD loan 0.08 0.073 0.096 -0.24 0.081 0.032 0.055 0.404 [0.025]*** [0.154] [0.036]*** [0.226] [0.042]* [0.258] [0.056] [0.329] NonEBRD loan 0.062 0.197 0.144 0.629 0.155 0.078 -0.052 0.357 [0.048] [0.296] [0.096] [0.403] [0.077]** [0.650] [0.080] [0.590] Constant 0.09 0.068 0.064 0.306 0.097 0.127 0.131 -0.146 [0.045]** [0.122] [0.099] [0.198] [0.072] [0.185] [0.078]* [0.302] Observations 3520 3502 1277 1271 1252 1240 991 991 R-squared 0.02 0.02 0.02 0.03 Hansen J 2.91 1.92 2.48 0.36 P-value 0.23 0.38 0.29 0.84 Wu-Hausman 0.22 1.76 0.03 0.90 P-value 0.80 0.17 0.97 0.41 Durbin-Wu-Hausman 0.45 3.56 0.06 1.83 P-value 0.80 0.17 0.97 0.40 Heckman's lambda -0.06 -0.03 0.05 -0.31 [0.10] [0.15] [0.18] [0.20]

Notes: Dependent variable is defined ln(Pt)-ln(Pt-1). EBRD (non-EBRD) loan dummy equals 1 if firm received credit from EBRD (non-EBRD) related bank in given year. All regressions included industry, country and year dummies. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%.

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Table 9 – Loan effects on fixed assets, controlling for loan size

All All Size1-5 Size1-5 Size6-15 Size6-15 Size16+ Size16

OLS IV OLS IV OLS IV OLS IV EBRD loan 0.107 -0.221 0.057 0.04 0.097 -0.351 0.191 0.654 [0.019]*** [0.484] [0.025]** [0.162] [0.028]*** [0.284] [0.046]*** [0.265]NonEBRD loan 0.137 -0.503 0.263 0.498 0.078 -0.092 0.14 0.341 [0.034]*** [0.642] [0.101]*** [0.388] [0.046]* [0.300] [0.050]*** [0.404Loan size to revenue * EBRD loan -0.01 1.277 -0.026 0.101 -0.012 1.457 -0.002 -0.871 [0.011] [1.930] [0.014]* [0.852] [0.035] [1.399] [0.014] [0.744Critical EBRD loan to revenue size (%) 1071.514 17.275 222.503 821.507 24.119 11546.400 75.144 [1136.97] [13.071] [142.774] [2357.52] [7.891]*** [97592.96] [42.734Loan size to revenue * NonEBRD loan 0.01 2.982 -0.077 -0.872 0.065 -0.903 0.018 -0.341 [0.035] [3.081] [0.040]* [1.386] [0.072] [2.711] [0.052] [1.404Critical non-EBRD loan to revenue size (%) 16.880 343.545 57.063 100.1 [6.087]*** [151.091]** [66.715] [298.2Constant 0.178 0.252 0.186 0.135 0.194 0.405 0.14 -0.0 [0.031]*** [0.151]* [0.069]*** [0.163] [0.044]*** [0.129]*** [0.058]** [0.11Observations 3631 3614 1313 1307 1286 1276 1032 103R-squared 0.04 0.05 0.09 0.03 Hansen J 0.31 1.38 0.14 0.98P-value 0.86 0.5 0.93 0.61Wu-Hausman 0.95 0.35 2.05 1.55P-value 0.43 0.85 0.08 0.18Durbin-Wu-Hausman 3.84 1.40 8.32 6.32P-value 0.43 0.84 0.08 0.18Heckman's lambda 0.05 0.07 0.15 -0.0 [0.04] [0.05] [0.06]** [0.10

Notes: Dependent variable is defined ln(Ft)-ln(Ft-1). EBRD (non-EBRD) loan dummy equals 1 if firm received credit from EBRD (non-EBRD) related bank in given year. All regressions included industry, country and year dummies. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%.

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Table 10 – Loan effects on revenue, controlling for loan size

All All Size1-5 Size1-5 Size6-15 Size6-

15 Size16+ Size16+ OLS IV OLS IV OLS IV OLS IV EBRD loan 0.051 0.197 0.04 0.056 0.055 -0.046 0.063 0.221 [0.012]*** [0.224] [0.018]** [0.133] [0.020]*** [0.156] [0.026]** [0.128]* NonEBRD loan 0.07 0.209 0.212 0.782 0.058 0.076 0.02 0.176 [0.024]*** [0.325] [0.063]*** [0.326]** [0.028]** [0.202] [0.037] [0.202] Loan size to revenue * EBRD loan -0.028 -0.611 -0.036 -0.246 -0.053 0.249 -0.025 -0.27 [0.008]*** [0.900] [0.014]*** [0.721] [0.032]* [0.658] [0.010]** [0.301] Critical EBRD loan to revenue size (%) 178.774 32.301 109.928 22.921 105.204 18.602 254.727 81.860 [61.399]*** [13.027]** [60.633]* [23.364] [64.185] [26.620] [136.085]* [56.359] Loan size to revenue * NonEBRD loan -0.024 -0.27 -0.121 -1.959 -0.014 -1.302 0.015 -0.083 [0.032] [1.500] [0.025]*** [1.749] [0.025] [1.819] [0.035] [0.678] Critical non-EBRD loan to revenue size (%) 289.833 77.408 175.166 39.914 417.001 5.865 211.490 [352.086] [312.460] [36.148]*** [25.342] [740.396] [9.696] [1502.181] Constant 0.13 0.066 0.184 0.199 0.101 0.149 0.161 0.055 [0.025]*** [0.068] [0.073]** [0.154] [0.036]*** [0.090]* [0.042]*** [0.077] Observations 3705 3686 1351 1345 1307 1295 1047 1046 R-squared 0.05 0.07 0.07 0.05 Hansen J 2.72 0.25 2.41 1.26 P-value 0.26 0.88 0.3 0.53 Wu-Hausman 2.27 6.84 0.48 1.19 P-value 0.06 0.00 0.75 0.31 Durbin-Wu-Hausman 9.11 27.27 1.97 4.85 P-value 0.06 0.00 0.74 0.30 Heckman's lambda -0.02 0.01 0.02 -0.08 [0.02] [0.03] [0.04] [0.05]*

Notes: Dependent variable is defined ln(Rt)-ln(Rt-1). EBRD (non-EBRD) loan dummy equals 1 if firm received credit from EBRD (non-EBRD) related bank in given year. All regressions included industry, country and year dummies. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%.

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Table 11 – Loan effects on labour costs, controlling for loan size

All All Size1-5 Size1-5 Size6-15 Size6-

15 Size16+ Size16+ OLS IV OLS IV OLS IV OLS IV EBRD loan 0.055 0.168 0.004 -0.018 0.047 -0.087 0.11 0.381 [0.014]*** [0.247] [0.025] [0.080] [0.021]** [0.161] [0.028]*** [0.164]** NonEBRD loan 0.109 0.093 0.223 0.451 0.088 0.051 0.093 0.158 [0.034]*** [0.293] [0.091]** [0.321] [0.039]** [0.216] [0.048]* [0.234] Loan size to revenue * EBRD loan -0.012 -0.369 -0.004 0.031 0.023 0.582 -0.022 -0.415 [0.007]* [0.987] [0.016] [0.439] [0.033] [0.720] [0.008]*** [0.402] Critical EBRD loan to revenue size (%) 443.540 45.489 107.250 58.460 14.912 489.074 91.968

[263.6]* [56.110

] [696.606] [618.142

] [12.710] [198.776]*

* [58.543] Loan size to revenue * NonEBRD loan -0.032 0.017 -0.153 -0.84 0.016 -0.718 0.015 -0.056 [0.107] [1.334] [0.032]*** [1.264] [0.027] [1.742] [0.184] [0.807] Critical non-EBRD loan to revenue size (%) 338.197 145.465 53.734 7.169 281.075

[1049.108

] [45.899]**

* [58.052] [19.648] [3643.38

] Constant 0.136 0.084 0.187 0.139 0.118 0.175 0.13 -0.001

[0.024]*** [0.079] [0.086]** [0.133] [0.034]**

* [0.082]*

* [0.035]*** [0.076] Observations 3459 3441 1113 1108 1299 1287 1047 1046 R-squared 0.03 0.04 0.06 0.04 Hansen J 0.8 2.7 0.77 0.12 P-value 0.67 0.26 0.68 0.94 Wu-Hausman 0.44 0.96 0.37 2.31 P-value 0.78 0.43 0.83 0.06 Durbin-Wu-Hausman 1.76 3.92 1.50 9.35 P-value 0.78 0.42 0.83 0.05 Heckman's lambda -0.03 -0.01 -0.01 -0.08 [0.03] [0.05] [0.04] [0.06]

Notes: Dependent variable is defined ln(LCt)-ln(LCt-1). EBRD (non-EBRD) loan dummy equals 1 if firm received credit from EBRD (non-EBRD) related bank in given year. All regressions included industry, country and year dummies. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%.

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Table 12 – Loan effects on employment, controlling for loan size

All All Size1-5 Size1-5 Size6-15 Size6-15 Size16+ Size16+ OLS IV OLS IV OLS IV OLS IV EBRD loan 0.06 0.122 0.064 0.102 0.062 0.149 0.06 0.165 [0.013]*** [0.089] [0.031]** [0.311] [0.023]*** [0.145] [0.022]*** [0.119] NonEBRD loan 0.11 0.067 0.151 0.356 0.175 0.144 0.058 0.081 [0.026]*** [0.207] [0.083]* [0.565] [0.052]*** [0.190] [0.028]** [0.169] Loan size to revenue * EBRD loan -0.011 -0.267 -0.087 -0.813 0.002 -0.581 -0.005 -0.071 [0.012] [0.368] [0.051]* [2.123] [0.066] [0.651] [0.011] [0.268] Critical EBRD loan to revenue size (%) 533.693 45.838 73.933 12.603 25.613 1285.434 231.708 [572.949] [33.453] [41.863]* [8.208] [10.452]** [3016.583] [724.219] Loan size to revenue * NonEBRD loan -0.052 0.182 0.051 -0.288 -0.036 -0.022 -0.08 0.069 [0.043] [0.863] [0.175] [2.626] [0.056] [1.373] [0.035]** [0.548] Critical non-EBRD loan to revenue size (%) 209.160 123.495 490.095 654.782 72.588 [162.883] [954.657] [732.738] [40184.36] [41.525]* Constant 0.054 0.021 0.07 0.087 0.04 0.015 0.117 0.055 [0.023]** [0.044] [0.085] [0.152] [0.034] [0.070] [0.031]*** [0.056] Observations 3330 3303 1005 996 1286 1270 1039 1037 R-squared 0.06 0.06 0.09 0.13 Hansen J 0.8 0.12 0.42 1.52 P-value 0.67 0.94 0.81 0.47 Wu-Hausman 0.35 2.06 0.68 1.23 P-value 0.84 0.08 0.61 0.30 Durbin-Wu-Hausman 1.42 8.37 2.75 4.99 P-value 0.84 0.08 0.60 0.29 Heckman's lambda 0.02 0.03 0.12 -0.07 [0.02] [0.05] [0.04]*** [0.04]**

Notes: Dependent variable is defined ln(Et)-ln(Et-1). EBRD (non-EBRD) loan dummy equals 1 if firm received credit from EBRD (non-EBRD) related bank in given year. All regressions included industry, country and year dummies. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%.

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Table 13 – Loan effects on net profits, controlling for loan size

All All Size1-5 Size1-5 Size6-15 Size6-15 Size16+ Size16+ OLS IV OLS IV OLS IV OLS IV EBRD loan 0.087 0.387 0.111 0.191 0.081 -0.353 0.061 0.316 [0.025]*** [0.432] [0.037]*** [0.220] [0.045]* [0.427] [0.056] [0.289] NonEBRD loan 0.063 0.52 0.142 0.69 0.16 0.043 -0.036 0.152 [0.052] [0.396] [0.106] [0.470] [0.081]** [0.555] [0.089] [0.571] Loan size to revenue * EBRD loan -0.031 -1.38 -0.055 -0.549 0.003 1.35 -0.024 -0.725 [0.013]** [1.751] [0.023]** [1.219] [0.097] [1.963] [0.011]** [0.660] Critical EBRD loan to revenue size (%) 278.885 28.024 201.293 34.800 26.142 249.621 43.526 [134.461]** [8.094]*** [95.346]** [42.690] [12.850]** [249.371] [24.725]* Loan size to revenue * NonEBRD loan -0.003 -1.752 0.004 -2.087 -0.019 -2.699 -0.074 0.074 [0.041] [1.776] [0.056] [2.680] [0.061] [4.272] [0.107] [1.792] Critical non-EBRD loan size (%) 1951.579 29.694 33.093 852.761 1.604 [23830.38] [11.424]*** [28.344] [2654.83] [18.631] Constant 0.087 -0.031 0.069 0.112 0.097 0.301 0.129 -0.052 [0.045]* [0.150] [0.099] [0.280] [0.073] [0.213] [0.078]* [0.181] Observations 3520 3502 1277 1271 1252 1240 991 991 R-squared 0.02 0.02 0.02 0.03 Hansen J 0.63 0.29 0.49 0.53 P-value 0.73 0.87 0.78 0.77 Wu-Hausman 1.90 2.18 0.84 1.92 P-value 0.11 0.07 0.50 0.10 Durbin-Wu-Hausman 7.65 8.84 3.42 7.82 P-value 0.11 0.07 0.49 0.10 Heckman's lambda -0.02 -0.01 0.02 -0.10 [0.05] [0.07] [0.08] [0.09]

Notes: Dependent variable is defined ln(Pt)-ln(Pt-1). EBRD (non-EBRD) loan dummy equals 1 if firm received credit from EBRD (non-EBRD) related bank in given year. All regressions included industry, country and year dummies. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%.

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Table 14 – probability of receiving a loan

EBRD loan

NonEBRD loan

Loan size to revenue * EBRD loan

Loan size to revenue * NonEBRD loan

Change in EBRD loan (t-1) 0.161 -0.019 0.091 -0.005 [0.017]*** [0.008]** [0.028]*** [0.013] Change in non-EBRD loan (t-1) -0.051 0.149 0.01 0.093 [0.030]* [0.029]*** [0.026] [0.024]*** Independent auditor (t-2) -0.14 0.065 0.001 -0.019 [0.023]*** [0.016]*** [0.032] [0.011]* Leverage at 2001 0.177 0.181 -0.042 0.02 [0.051]*** [0.037]*** [0.032] [0.045] EBRD loan (t-2) 0.174 -0.021 [0.040]*** [0.010]** NonEBRD loan (t-2) -0.033 0.138 [0.021] [0.029]*** Constant 0.548 0.12 0.053 0.027 [0.028]*** [0.019]*** [0.029]* [0.015]* Observations 3780 3780 3779 3779 R-squared 0.1 0.08 0.03 0.03 F-statistics of IV’s 40.3 18.6 9 8.3

Notes: Dependent variable in the 1st column is EBRD loan dummy, which equals 1 if firm received credit from EBRD related bank in given year. Dependent variable in the 2nd column is NonEBRD loan dummy, which equals 1 if firm received credit from NonEBRD related bank in given year. Dependent variable in the 3rd column is EBRD loan dummy interacted with the loan size. Dependent variable in the 4th column is NonEBRD loan dummy interacted with the loan size. Change in EBRD (non-EBRD) loan has value 1 if firm had received EBRD (non-EBRD) related credit in given year but had no EBRD (non-EBRD) related credit in previous year, has value 0 if firm had received EBRD (non-EBRD) related loan two years in row or if firm had not received EBRD (non-EBRD) related loan two years in row, has value –1 if firm had received EBRD (non-EBRD) related loan last year but not this year. Independent auditor is a dummy variable, which equals 1 if firm had the independent auditor at least a year before receiving loan. Leverage is a ratio of debt over the sum of debt and equity. All regressions included industry, country and year dummies. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%.

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41Table 15. Exit rates over 2002-2005

Country Source of credit or control group

Exit rate (in percent)

Total number of screened companies

Partner bank clients 0 156 Microfinance bank clients 1 273 BEEPS Control Group: loan recipients

0 21

BEEPS Control Group: not loan recipients

15 61

Bulgaria

BEEPS Control Group: total

11 82

Partner bank clients 12 212 Microfinance bank clients 13 302 BEEPS Control Group: loan recipients

37 19

BEEPS Control Group: not loan recipients

23 56

Georgia

BEEPS Control Group: total

27 75

Partner bank clients 7 228 Microfinance bank clients 5 306 BEEPS Control Group: loan recipients

0 11

BEEPS Control Group: not loan recipients

22 32

Russia

BEEPS Control Group: total

16 43

Partner bank clients 13 303 Microfinance bank clients 7 208 BEEPS Control Group: loan recipients

35 23

BEEPS Control Group: not loan recipients

16 137

Ukraine

BEEPS Control Group: total

19 160

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42Table 16. Net job creation, 2002-2005 Country Source of credit or

control group Net job creation Total number of

screened companies

Partner bank clients 0.42 156 Microfinance bank clients 0.33 273 Treatment Group total 0.35 BEEPS Control Group: loan recipients

-0.21 21

BEEPS Control Group: not loan recipients

-0.1 61

Bulgaria

BEEPS Control Group: total

-0.13 82

Partner bank clients 0.1 212 Microfinance bank clients 0.06 302 Treatment Group total 0.08 BEEPS Control Group: loan recipients

-0.07 19

BEEPS Control Group: not loan recipients

-0.15 56

Georgia

BEEPS Control Group: total

-0.12 75

Partner bank clients 0.17 228 Microfinance bank clients 0.07 306 Treatment Group total 0.11 BEEPS Control Group: loan recipients

0.61 11

BEEPS Control Group: not loan recipients

0.04 32

Russia

BEEPS Control Group: total

0.34 43

Partner bank clients 0.28 303 Microfinance bank clients 0.11 208 Treatment Group total 0.17 BEEPS Control Group: loan recipients

-0.42 23

BEEPS Control Group: not loan recipients

0 137

Ukraine

BEEPS Control Group: total

0.1 160

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44 Appendix 1 The financial intermediaries

Microfinance banks:

ProCredit Bank, Bulgaria (formerly known as Microfinance Bank of Bulgaria), was established in 2001 to provides financial services to MSMEs. The founding shareholders are: the EBRD (20 per cent), Commerzbank AG (20 per cent), International Micro Investitionen AG (IMI) (20 per cent), Deutche Investitions- und Entwicklungsgesellschaft mbH (DEG) (20 per cent) and International Finance Corporation (IFC) 20 per cent). ProCredit Bank began operating in Sofia and in 2002 had a network of 10 branches and employed 38 trained loan officers. As of end of 2002 the bank provided 5,919 loans to Bulgarian MSEs in a total volume of € 39.6 million (average loan size € 7,000). The quality of its loan portfolio was excellent with as few as 0.05 per cent of loans in arrears over 30 days. Its borrowers had on average 28 employees and the loans had on average 16 months maturity

Procredit Bank Georgia (formerly MBG) is a specialised micro-enterprise bank established by the German-Georgian Foundation for the Promotion of Private Sector Development, which owns 31 per cent of the shares, jointly with the International Finance Corporation (IFC) with 20 per cent, FMO [Nederlandse Financierings-Maatschappij voor Ontwikkelingslanded N.V.] (10 per cent), IMI [International Micro Investitionen AG] (10 per cent) and four Georgian banks with an aggregate holding of 19 per cent. MBG received its commercial banking licence in May 1999 and started operations shortly afterwards. The shareholder structure of MBG changed slightly after the capital increase, with Commerzbank (Germany) acquiring 15 per cent and the EBRD's stake increasing from 6 per cent to 10 per cent. This allowed the EBRD to be represented on MBG's Supervisory Board. At end 2002 this bank had issued 9,973 loans to MSMEs for a value of € 47 million (average loan size € 5,000). Quality of the portfolio was good with 3.1 per cent of all loans in arrears over 30 days. The bank had a network of 16 branches and outlets and 12 trained loan officers. Its average client had 4 employees. It took an average of 6 days to process a loan application, and issued loans with 18 months maturity on average.

In 1992 the EBRD established a private sector banking institution in Russia, the Russia Project Finance Bank (RPFB) to finance investment programs and projects in Russia and CIS. Following the August 1998 financial crisis in Russia, the EBRD decided to restructure RPFB into KMB, a specialised bank focused on micro and small enterprises. This restructuring served a dual purpose, since RPFB could not survive as a boutique investment bank and the Russia Small Business Fund (RSBF) needed a vehicle for both collecting assigned sub-loans and making new loans in the Programme. KMB would therefore safeguard the human and financial capital of the RSBF in the aftermath of the crisis. As of end 2002 KMB had issued a total 28,819 loans to MSMEs for a value of € 220.4 million (average loan size € 8,000). The quality of the loan portfolio was excellent with less than 1 per cent of all loans in arrears over 30 days. The bank had a network of 66 branches and outlets and 61 trained loan officers. It took on average 8 days to process a loan application and the loans issued had a maturity of 18 months. The average borrower had 11 employees. In 2002 KMB shareholders were EBRD (31.4%), Mourant Trust (nominal holder for EBRD) (3.8), Soros Economic Development Fund (35.2%), DEG (21.9%) and Triodos (7.7%).

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45ProCredit Bank Ukraine (formerly MFB) was registered in late 2000 and started operating in February 2001. It was originally set up by EBRD (20.0%), IFC (20.0%), Western NIS Enterprise Fund (20.0%), the German-Ukrainian Fund (20.0%), IMI (12.0%), and the Doen Foundation (8.0%) with a total equity capital of €10.0 million. At end 2002 this bank had issued 7,518 loans to MSMEs (average loan size was € 6,000 for 16 month maturity), for a total value of over € 42 million, through a total of 18 branches and 70 loan officers. It used to take an average of 10 days to process a loan request. Its borrowers had on average 12 employees. Quality of the loan portfolio was excellent with only 0.5% of all outstanding loans in arrears over 30 days. Local banks participating in micro-lending programmes: Tbiluniversal Bank (TUB) was founded in 1995 as a private commercial bank. TUB belongs to a group of 5 smaller banks20 which are opposed to the “big five”21,accounting for 12.75% of total bank assets, and is controlled by two relatives (Tariel and Sulkhal Gvalia) with a cumulative share of 38%. TUB strategy focuses on targeting small and medium enterprises as its core clientele. Lending is the main income source for TUB. In terms of its market position TUB’s long term strategy is focused on private enterprises and “middle wealthy” individuals as the core clientele of the bank. Naturally, financing MSE and SME has been a key objective in its business strategy. TUB, lacking sufficient structure and with limited resources, has focused on service quality to strengthen its competitive position in the market. TUB joined the EBRD micro-lending programme (SELP) in March 2000 and at end 2002 it offered micro-loans through two branches in Tbilisi with the support of 9 trained loan officers. It took 16 working days to process a loan application. Between the beginning of the programme and end 2002 TUB had issued a total of 300 micro-loans for a total amount of € 1.4 million (average loan size was € 4,600 size. No loans were at that point in arrears. The joint Ukraine Micro Lending Programme (UMLP) involves the EBRD, Tacis, US Agency for International Development (USAID) and the German-Ukrainian Fund. The aim is to foster the development of micro and small enterprises by facilitating their access to bank credit. Funds are distributed by the National Bank of Ukraine through five commercial banks - Aval Bank, Agio Bank, KyivPrivat bank, Privatbank and VA Bank. PrivatBank joined the UMLP in 1999. At end of 2002 it had issued 4,411 loans to MSMEs for a total volume of € 24.4 million (average loan size € 5,500). The quality of the portfolio was excellent with only 0.63% of total loans in arrears over 30 days. It used a network of 45 branches and 130 trained loan officers. The average borrower employed 11 staff and loans were on average extended for 11 months maturity. It took 10 days on average to process a loan. NBD is a medium-sized regional bank based in Nizhny Novgorod. It was founded as an open joint stock company in Nizhny Novgorod in 1992 by a group of regional industrials and started its operations with special programmes for defence companies. NBD was one of the RSBF pilot private banks in 1994. NBD offers a standard range of banking products to individuals and enterprises and is focused mainly on MSMEs. The bank seeks to trade on its image as an independent local bank with a foreign shareholder and co-operates closely with international partners to differentiate itself from local competitors. Between the Russian crisis of 1998 and end of 2002 NBD issued 1,484 for a volume of € 10.1 million (average loan size € 6,800). The quality of the portfolio was excellent with only 1.03% of total loans in arrears over 30 days. It used a network of 10 branches and 22 trained loan officers. No information is available on the average borrower size or the number of days to process loan applications. Local bank participating in EU/EBRD SME Facility:

20 These are: Intellectbank, People’s Bank, TbilUniversalbank, BasisBank, Commercial Bank of Greece. 21 The “big five” by the size of assets are TBC Bank, Bank of Georgia, UGB, ProCredit Bank (former MBG) and Cartu Bank.

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46 Hebros Bank joined the EU/EBRD SME Facility in September 2001. At end of 2002 it had issued 284 loans to MSMEs for a total volume of € 7.7million (average loan size € 27,200). The quality of the portfolio was excellent with only 0.03% of total loans in arrears over 30 days. It used a network of 35 branches and 43 trained loan officers. The average borrower employed 25 staff and loans were on average extended for 18 months maturity. It took 10 days on average to process a loan.

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47Appendix 2.

Loans and Firm Survival and Job Creation

While in the case of the MSME clients of EBRD programmes we know which firms

exited and which survived, the above control group does not include enterprises that were in

existence in 2002 and exited thereafter. We therefore cannot compare the survival rates of the

(EBRD) treatment and the control group of firms. As a result, in order to carry out such a

comparison, we selected as another control group for this purpose enterprises that were

respondents in the 2002 BEEPS survey and in 2002 agreed to be re-interviewed in 2005. The

response (re-interviewing) rate was 41% in Bulgaria, 36% in Georgia, 12% in Russia, and 40%

in Ukraine. The reasons for these less than 100% re-interviewing rates most importantly a refusal

to co-operate, followed by firm exit (death). We can distinguish these reasons, and for the

purposes of calculating firm survival, we have a complete count for the BEEPS firms. The

BEEPS firms that agreed to be re-interviewed in 2005 all answered a reduced version of the

questionnaire covering mainly employment dynamics. In our analysis, we divide these firms into

those that were recipients and non-recipients of loans and we estimated net job creation of EBRD

programmes.

In Table 15 we present the exit (death) rate of screened companies subdivided into five

categories for each country: companies that received a loan from EBRD owned microfinance

banks; companies that received a loan from an EBRD partner bank; companies in the BEEPS

control group; companies in the BEEPS control group that received a loan; companies in the

BEEPS control group that did not receive a loan in 2002. The exit rates over the 2002-05 period

(all companies existed in 2002) is calculated on the basis of ascertained cessation of business.

The exit rates of the control groups, taken as a whole, are significantly higher than those of the

enterprises which benefited from EBRD loans. Exit rates of the part of the control groups that

benefited from a bank loan in 2002 are also consistently higher than exit rates of SMEs which

benefited from an EBRD loan. However, within the control group exit rates for bank loan

recipients are not always lower than exit rates of SMEs that did not receive bank loans. In

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48Georgia and Ukraine the exit rate for loan recipients in the control group is in fact higher

than for companies that did not receive a loan. This raises the possibility that the quality of the

loan finance received by enterprises in these two countries might not have been appropriate for

the recipients (e.g., loan maturity may have been too short or with conditions that did not suit the

firm’s needs).

An interesting question that arises in evaluations of programmes that support provision of

finance to the smallest enterprises is whether these programmes result in net job creation. Many

donors, governments and politicians believe, rightly or wrongly, that supporting SMEs will result

in net job creation. Their direct and indirect support to the development of such programmes

often has been targeted, even if only implicitly, to increasing employment. We calculated net job

creation rates for firms in both the treatment group and the control group, and for firms in the

control group stratified according to having received a loan or not. Table 16 contains the net job

creation rates for firms in both treatment and control groups for each country22. Net job creation

rates are positive for firms in the treatment group as a whole, for firms in the treatment group in

each country and for firms which were clients of each financial intermediary used by the EBRD.

The net job creation rates are in excess of the net job creation rates for firms in the control group

as a whole in each country but Russia. In Russia net job creation rates for the treatment group are

positive but lower than job creation rates for the part of the control group that benefited from a

non-EBRD loan, but are higher than the net job creation rates for the part of the control group

which did not benefit from any bank lending. Net job creation rates in the control group are

negative in Bulgaria, Georgia and Ukraine and positive for Russia. Results for Russia might be

biased by the fact that there were not sufficient BEEPS companies that could be screened in the

Nizhny Novgorod area where the treatment group is based. Thus the Russia BEEPS control

group was complemented by BEEPS firms in other regions which might have experienced faster

economic growth rates than Nizhny Novgorod. Within the control group net job creation rates

22 Data for 2002 employment in the companies that received finance from the EBRD partner bank in Russia had to be approximated, as the participating bank did not provide exact employment figures for this group of companies, but rather provided a range.

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49are higher for the companies that received a loan than for those who didn’t in Georgia and

Russia. The reverse is true in Bulgaria and Ukraine, which suggests that within the control

groups in these two countries bank lending was associated with investments leading to

substitution of labour with capital (if associated with the effect on exit rates in the same group,

this is particularly true for Bulgaria).

Our findings on the effect of EBRD bank loans on firm survival rates and on net job

creation rates are potentially inflated by two sources of selection bias. The first one stems from

the fact that firms that apply for a bank loans may differ from firms in the control group in that

they are possibly more growth oriented, and better performers than firms who do not apply for a

loan. The second potential bias stems from the administrative selection: banks’ loan officers

choose to lend to some applicants and not to others. If loan officers do a good job they

necessarily select the best performers among the applicant group. Thus the fact that the EBRD

loan recipients have better survival rates and net job creation rates than firms in the control group

cannot necessarily be attributed to the EBRD lending. In analysing the impact of EBRD lending

on other aspects of firm performance we have employed techniques that minimize these potential

biases and we also controlled for selection bias in part by using the control sample selected by

matching firms by categories of location, size and sector, as described in section 4 of the paper.