Bank lending and performance of micro, small and medium sized

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***

March 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, 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 a being important, little evidence is

available on the impact and longer-term financial sustainability of these programmes

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(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 short-, medium- and long-term credit by banks to these firms. During the

period covered by our study, standard bank credit in the transition economies under

consideration was of a short-term nature, focusing on providing the firms with

working capital. There was hence an important gap in the financial market and

anecdotal evidence suggests that firms sometimes tried to use a series of short term

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

started providing medium- to long-term credit to enable the firms to finance capital

investment and other forms of longer term restructuring. 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 short-term 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 short-term (non-EBRD) versus medium- to long-term

(EBRD) loans prior to 2002 subsequently attain greater recourse to bank finance than

firms that had not received bank credit prior to 2002? Second, what has been the

effect of short- versus longer-term credit on MSME performance? In order to provide

a relatively comprehensive understanding, we use several indicators of firm

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performance: survival, investment, revenues, profit, labour cost, 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.

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 medium- to long-term loans because it regards the lack of longer

maturity loans (i.e., those for fixed as opposed to only working capital investment) in

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.

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

beyond its support to foreign banks’ entry and bank privatisation to accelerate the

development of this specific type of finance. This intervention by the EBRD is

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

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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.

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

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

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

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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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

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

participating in broad micro-lending programmes and for 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 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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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

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 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

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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

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

6 http://www.mixmarket.org/en/demand/demand.profile.comparison.asp

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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 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 benefit

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

7 See also Pissarides, Singer and Svejnar (2003) for earlier systematic evidence.

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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

random sample of 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 split into 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 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 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.

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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

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 mirror 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 as a stratified

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

interviewers’ walk-ins.

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

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. 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).

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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.

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 BEEP survey and in 2002

expressed willingness 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 of 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 2 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

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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 Georgia and Ukraine the mortality 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

job creation. Many donors, governments and politicians believe, rightly or wrongly,

that supporting SMEs will result in 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 3 contains the net job creation rates

for firms in both treatment and control groups for each country12. 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 12 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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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 are 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).

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

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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, in year t. Moreover, let Lijt

denote loan of type j of firm i in year t (this is a dummy variable having value 1 if

firm received the credit of type j in year t and zero otherwise). A model of

performance may be specified in a logarithmic form as

ijtjijjijjijiijt DtLtXtLtX υϕδγβαα τ ++++++= )()()(ln 11 . (1)

Equation (1) is relatively flexible in that in estimating the performance effect δj of

loans Lijt obtained in the 2002-04 period, 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 individual countries, industries,

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and years D.13 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 economy-wide shifts (such as

macro shocks or degree of openness to trade). A particular concern is that we should

ensure that our estimates capture the effect of 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 specifying equation (1) we allow both current and previous year’s loans to

have an equal effect on current year’s performance. This is an acceptable

approximation since in virtually all cases estimations that allowed the two effects to

be different did not result in significantly different coefficients. The reason for this

lack of difference may be that the accounting information on performance refers to

year-end values, while loans are disbursed throughout the year. In empirical work, we

also allow for two specification 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.

13 Any time-invariant effects of these variables (i.e., effects on the level of performance) are captured in αι.

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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 as14

ijtjijjijjijijt DLXLy εϕδγβα τ ++∆+++= 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.

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,

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, we have already mentioned that 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.

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

14 Equation (2) may also be viewed as coming from a framework such as that invoked in the endogenous growth literature (e.g., Temple, 1999; Barro and Sala-i-Martin, 1995), where the rate of change of the dependent variable may depend on its initial level (e.g., rate of change of performance being related to an initial level of investment) and some other variables. One may also want to ensure that our estimates capture the effect of loans rather than other factors such as competition. As may be seen from equation (1), we do so by controlling for the extent of competition 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.

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aforementioned covariates as a panel data treatment evaluation procedure to 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) with firm specific random and fixed effects.15 Finally, since the above approaches

may not fully address all types of endogeneity, especially those where the effect is

time-varying, we also employ an instrumental variable procedure.

Instrumental Variables

We use the Hausman (1978) specification test for assessing endogeneity of the

initial pre-2002 loan status. We employ the first-difference IV method in which we

treat the initial loan status as potentially endogenous and instrument it by IVs that we

describe presently. The test is carried out by differencing the two sets of parameter

estimates and standardizing the vector of differences by the difference in the

covariance matrices of the two sets of estimates. The resulting quadratic form is

asymptotically chi-squared with degrees of freedom equal to the number of

parameters being tested.16

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 can be

excluded from the second stage, rate of change of performance equation: A second

15 In the firm specific fixed effects case, we exclude initial performance and country and industry dummy variables since their effect is captured by the firms specific fixed effects. 16 In practice, some diagonal elements of the covariance matrix are negative. As usual, we carry out the test only for parameters corresponding to the positive diagonal elements, with a corresponding correction to the degrees of freedom, using the generalized inverse matrix (procedure YINVO in TSP 4.5).

19

order polynomial of the distance of the firm from the nearest regional capital (Q7.1),

this distance interacted with time, industry-specific average perception of the

respondents about the availability of financing from banks in a given year (Q10.1 –

industry-specific average perception of the respondents about the corruption of bank

officials in a given year, industry-specific average perception of the respondents about

the number of days it takes to obtain a loan in a given year, dummy variable reflecting

whether the firm adopted international accounting standards at least one year before a

loan is granted, the ratios of full time to part time employees and full time to

temporary employees,17 gender of the CEO and CEO gender interacted with lagged

firm size (total employment). 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 effects of EBRD (longer term) and non-EBRD (short-term) loans on

MSME performance. In the second section we examine the determinants of whether a

firm receives a loan and what the loan size is.

The Effects of Loans on Performance

The results that we present in the tables in the text come from OLS estimations

of equation (2). The corresponding random effects estimates are virtually identical.

The fixed effects estimates have greater noise to signal ratio and while generally

yielding similar point estimates of the relevant coefficients, they tend to generate

larger standard errors of the estimates. The random effects, fixed effects and IV

estimates are presented in the tables at the end of the paper.

17 These input ratios can be treated as exogenous under the assumption that the firm is price taker in the input market, minimizes cost and has a Cobb Douglas production technology.

20

In estimating equation (2), we find that the coefficients on pre-2002 loans and

2001 performance are uniformly statistically insignificant. In what follows we hence

report the estimates from specifications that exclude these two variables.

In Table 4 we report the effects of EBRD and non-EBRD loans on investment,

with Panel A providing estimates based on the presence or absence of a loan and

Panel B giving estimates based on the size of the loan relative to revenue. Since in

35% of observations the responding firms report undertaking no investment, we need

to address this issue of zero values in estimating our logarithmic regression. We have

taken several approaches to handling this problem. The results that we report in Table

4 come from specification where we use the annual percentage change in investment

I, calculated as (It - It-1)/[(It + It-1)0.5], with firms reporting zero investment in two

consecutive years being given the value of zero for this percentage change. We have

also estimated a specification of equation (2) in which zero investment is replaced

with country specific median investment level times 0.001. This specification reflects

the assumption that even firms reporting no investment most likely carry out some

minimal investment activities during the year in question. We have tested the

sensitivity of our results to the imputed value of investment and found the results to

be qualitatively similar.18 Finally, we have estimated the change in investment rates

(investment to capital and investment to revenue ratios) and, as a lowerbound

estimate, we have estimated equation (2) only for the firms reporting positive

investment in consecutive years. We do not report all these estimates, but they

indicate that both EBRD and non-EBRD loans have a positive effect on investment

and fixed assets.

18 In particular, we have used replacing the zero investment with country of incorporation mean investment a value time 0.0001 and found the coefficients to be slightly larger but similarly statistically significant.

21

As may be seen from Panel A of Table 4, with the dependent variable being (It

- It-1)/[(It + It-1)0.5], the EBRD loans on average result in a 38 log points (46%)

increase in investment, while non-EBRD loans on average raise investment by 55 log

points (73%). The two estimates are statistically different from each other, implying

that non-EBRD loans have a greater effect on investment that the EBRD ones. The

estimates vary by country, with the greatest investment effects of the presence of

EBRD and non-EBRD loans being reported in Russia (68 log point or 97% and 98 log

points or 166%, respectively), followed by Ukraine (40 log points or 49% and 43 log

points or 54%, respectively) and Bulgaria (25 log points or 28% and 58 log points or

79%, respectively). The estimated EBRD coefficient in Georgia is 23% and

statistically significant, while the coefficient for non-EBRD loans is again positive but

statistically insignificant. The estimates based on the size of the loan, reported in

Panel B of Table 4, indicate that the investment effect of EBRD loans varies with the

size of the loan. The effect is sizable and positive in Bulgaria, Georgia and Russia, but

it is small, negative and only significant at the 10% test level in Ukraine. The effect of

non-EBRD loans on investment does not vary with the size of the loan in Bulgaria

and Russia, but the estimated effect is positive in Georgia and negative in Ukraine.19

19 When we assign firms reporting zero investment the value of 0.001 of the national investment average of the firms that report positive investment, we find a strong positive effect of both EBRD and non-EBRD loans on investment in all four countries (except for non-EBRD loans in Georgia). The estimated effect is in most cases too large to be completely credible, indicating that the 0.001 scaling factor may be too small. The estimates hence provide an upper bound on our estimation. The overall effect across countries 127 log points (256%) for EBRD loans and 169 log points (442%) for non-EBRD loans. The two estimates are statistically different from each other at 7% statistical test level, implying that non-EBRD loans have a greater effect on investment that the EBRD ones. The estimates vary by country, with the greatest effects of the presence of EBRD and non-EBRD loans being reported in Russia (226 log point or 858% and 307 log points or 2054%, respectively), followed by Ukraine (153 log points or 362% and 153 log points or 362%, respectively) and Bulgaria (50 log points or 65% and 127 log points or 256%, respectively). The estimated EBRD coefficient is large in Georgia (98 log points of 166%), but the coefficient for non-EBRD loans, while positive, is statistically insignificant. In the estimates based on the size of the loan, the loan amount is an insignificant determinant of investment growth in the overall regression. In Bulgarian, Georgian and Russian country specific regressions, the EBRD loan size is positively related to performance. In Ukraine the EBRD loan amount is negatively related to investment growth, while in Georgia the larger non-EBRD loan is related to higher investment growth. It is interesting to note that when we run our regressions only on

22

As may be seen from Table 1A, the lack of size effect of non-EBRD loans is not

brought about by a small variance of the loan size within each country.

The estimated effects of loans on fixed assets, based on equation (2), are

reported in Table 5. The estimates of average effects of a loan, reported in Panel A,

are in line with those on investment. In a pooled regression they suggest that an

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

fixed assets of the firm. The estimates vary by country, with the greatest average

effects being reported in Bulgaria (15% and 22%, respectively), followed by Ukraine

(12% and 14%) and Russia (10% and 13%). The estimated coefficients in Georgia are

smaller (4% and 5%) and they are statistically insignificant. The estimates based on

the size of the loan return less significant results. When we take into account the size

of the loan in Panel B of Table 5, we show that the effect of the size of an EBRD loan

is statistically insignificant in all countries except for Bulgaria, where the effect is

positive. Hence, the positive effect of loan size that we found with respect to

investment does not translate as readily into the effect on fixed assets. As with

investment, the effect of non-EBRD loan size on fixed assets is only positive in

Georgia. The effect is actually negative in Russia and negative but only marginally

significant in Ukraine.

Overall, the results in Tables 4 and 5 are interesting because they confirm the

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

including investment in fixed assets. Moreover, the coefficient estimates in Panels A

suggest that non-EBRD loans have a somewhat larger effect on investment than

EBRD loans, although the EBRD and non-EBRD loan effects are not statistically

firms reporting positive investment in consecutive years, we still find an overall effect of 24 log points (27 %) for the presence of an EBRD loan and 45 log points (57 %) for the presence of non-EBRD loans.

23

different with respect to fixed assets. The differential effect on investment is

intuitively acceptable in that non-EBRD loans are on average larger than EBRD loans

(see Table 1A). Moreover, EBRD loans are meant to complement (rather than

substitute) other sources of loans.

The results in Panel A of Table 6 indicate that the overall average effects of

EBRD and non-EBRD loans on MSME revenue, measured with data pooled across

the four countries, are positive and statistically significant. Receiving an EBRD loan

on average results in 4.3% higher 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 6.3%. The

two effects are significantly different from zero but not from each other. Estimates by

country indicate that these average effects of EBRD loans are statistically significant

in Georgia (6%), Russia (3%) and Ukraine (12%), while the effect of non-EBRD

loans is significant only in Georgia (13%). The remaining country-specific estimates

are by and large positive but statistically insignificant. The estimates based on the size

of a loan, reported in Panel B, indicate that larger EBRD loans result in lower

revenues than larger loans, especially in Russia and Ukraine. The effect of non-EBRD

loan amount on revenue is only significant in Ukraine and it is negative. Hence, while

loans tend to have a positive effect on firm revenue, the larger loans seem to be “too

large” to have a beneficial impact.

In Table 7 we report estimated effects of loans on labour cost. As may be seen

from Panel A of the table, when measured with data pooled across the four countries

the average effects of both EBRD and non-EBRD loans are positive at 5.1% and 10%,

respectively. Again, the two effects are significantly different from zero but only

significantly different from each other at 6% test level. Estimating these effects by

country indicates that the average effect of EBRD loans on labour cost is statistically

24

significant in Bulgaria (6.4%), Russia (4.8%) and Ukraine (9.5%), while the effect of

non-EBRD loans is significant only in Bulgaria (12.8%) and Ukraine (12.5%). The

remaining country-specific estimates are all positive but statistically insignificant. The

estimates based on the size of a loan, reported in Panel B, indicate that in Ukraine

small EBRD and non-EBRD loans have a positive effect on labour cost but that both

effects decline with the size of the loan. In contrast, in Georgia the effect of non-

EBRD loans varies positively with loan size. The important finding is that most

coefficient on loan size are insignificant, suggesting that the effect of loans on labour

cost does not vary systematically with loan size.

Overall, the estimates in Tables 4-7 indicate that firms use EBRD and non-

EBRD loans to invest and augment both revenues and labour costs. The exceptions

are firms in Bulgaria and Georgia, with the former ones increasing labour cost but not

revenues and the latter ones increasing revenues but not labour costs.

In Table 8 we complement our findings on labour cost by examining the effect

of loans on total employment (results based on full time employment are similar). As

may be seen from Panel A of Table 8, the cross country pooled estimates show that

both EBRD and non-EBRD loans have a positive effect on employment (5.8% and

9.5%, respectively). Again, the two effects are significantly different from zero but

not from each other. Country-specific estimates are positive for both sets of loans in

Bulgaria (13.3% and 11.4%) and Ukraine (8.1% and 24.6%), but they are

insignificant in Georgia and Russia. The labour cost effect of loans in Bulgaria and

Ukraine is hence primarily accounted for by the positive effect of loans on

employment, while in Russia it appears to be primarily driven by the effect of

increased wages (labour cost per worker). The estimates based on the size of a loan,

reported in Panel B, indicate that the employment effect of a larger loan, when

25

significant, is negative. This is the case with EBRD and non-EBRD loans in Georgia,

and also EBRD loans in Bulgaria. It must be noted, however, that the EBRD effects

are only significant at the 10% test level.

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

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

be seen from Panel A of Table 9, the cross country pooled estimates indicate that

while EBRD loans on average increase profit by 8%, the effect of non-EBRD loans is

statistically insignificant at 6%.20 Again, in view of the size of the associated standard

errors, the two effects are not significantly different from each other. Country-specific

estimates indicate that the overall effect of EBRD loans on profit is brought about by

a strong effect in Ukraine (13.7%) and to a lesser extent in Georgia (9.2%). The effect

of EBRD loans is statistically insignificant in Bulgaria and Russia, while the effect of

non-EBRD loans is statistically insignificant in all four economies. The estimates

based on the size of a loan, reported in Panel B, indicate that there is a statistically

weak negative effect of EBRD loan size on profit in Ukraine, but that in all other

cases the data suggest that the effect does not vary with the size of the loan. The

relatively frequent insignificance of the effect of loans on profit is not surprising.

MSMEs operate in a competitive setting and it is hence quite likely to see loans result

in an increased scale of operations but not necessarily higher profit. Moreover, as we

have seen in the preceding tables, loans are found to have a positive effect on both

revenues and 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. In this specification the effect is insignificant in all specifications

except for the effect of EBRD loans in Russia, which is -1.9%. 20 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 same percent change formula for profit as we did for investment, the results are similar but statistically insignificant.

26

Finally, it must be noted that since the effects of loans on revenues are on the

whole positive, many estimates based on dependent variables expressed in a per

revenue form are statistically insignificant.

Determinants of Loans

The data we collected permit us to address the question of what factors

determine whether a MSME receives an EBRD or non-EBRD loan and what the loan

size is. In Table 10 we present estimates explaining the probability that a firm

receives an EBRD or non-EBRD loan in a given year. The results in Tables 10A and

10B relate to whether the firm receives a loan or not and they are based on a probit

estimation method. The results in Tables 10C and 10D are Tobit estimates because the

variable has a lowerbound at zero. The dependent variable in Tables 10C and 10D is

the ratio of loan size over revenues. The results on Tables 10A and 10C are based on

firms, which had received EBRD loan on 2002 and hence the estimation is done only

for the following years 2003 and 2004.

Results in Table 10A indicate that the firms, which had received an EBRD

loan before 2002 have a higher probability to receive an EBRD loan in 2003 and

2004. In Georgia and Russia we observe a negative effect of having receiving a non-

EBRD credit before 2002 on receiving EBRD credit later on. One possible

explanation is linked to the lack of available information to lenders of borrowers’

credit history. The World Bank’s Credit Information Index21 is 0 (lowest) in Georgia,

Russia and Ukraine and 3 (medium) in Bulgaria. However even in the case of

Bulgaria the public registry cover was extremely poor (2004) at 5/1000 capita and

was almost irrelevant for micro-finance providers as at the time it did not record 21 http://www.doingbusiness.org/ . This index ranges from 0 to 6 and measures the rules affecting the scope, accessibility and quality of credit information available to lenders through either public or private bureaus.

27

information on loans below Leva 10,000 (US$ 6,000). This means that lenders only

have information on their former clients rather than on new clients who may have

borrowed from different lenders. Thus receiving a bank loan does not affect the

probability of receiving a bank loan from a different provider through having gained a

credit history. Lagged change in EBRD credit over years has a positive effect on

receiving credit. Also firms which adopted international accounting standards at least

a year before 2002 are more likely to receive an EBRD loan than firms which did not

adopt such standards. Similar results are observed with respect to receiving non-

EBRD credit (see panel B). Firms which received non-EBRD credit before 2002 are

more likely to receive non-EBRD credit in years 2002-2004. Also older firms, firms

with international accounting standards and firms with male CEO are more likely to

receive non-EBRD credit than young firms, firms which have not adopted

international accounting standards and firms with female CEO.

Results in Tables 10C and 10D indicate that the size of the EBRD loan is

related to the firm’s past credit record and to it having adopted international

accounting standards. The size of non-EBRD loans is dependent on the age of the

firm and the gender of the CEO matters.

7. Conclusions

Our study indicates that bank loans have a significant positive effect on most

performance indicators of micro, small and medium sized enterprises (MSMEs) in the

transition economies. In particular, exit rates related to cessation of business are

higher for companies that did not benefit from EBRD loans (even though they may

have benefited from non-EBRD loans) than for companies that benefited from EBRD

28

loans. Net job creation rates are positive and almost everywhere larger for companies

that benefited from EBRD loans than for companies that did not. Both EBRD and

non-EBRD loans have a positive effect on investment and fixed assets, and they

suggest that in the imperfect capital markets characteristic of the transition economies

firms use even short-term (non-EBRD) loans for investment in fixed capital. The

positive effect of EBRD and non-EBRD loans extends to revenues, labour cost and

employment. Hence, the loans serve the purpose of enabling the MSMEs to expand

production beyond the scale that they could achieve without this source of credit.

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 loans 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.

As might be expected, the effect of loans on the performance indicators varies

somewhat across countries and further country-specific analyses will be useful.

Moreover, we find that many of the effects do not vary with the size of the loan and

that some loans may be too big in the sense that they bring about a diminishing return

or even decline in performance. This finding deserves further study as it indicates that

the absorptive capacity of MSMEs may need to be more carefully taken into account.

In terms of determinants of loans, we confirm in the transition economy

context that prior credit (loan) history is an important determinant of the ability of

firms to obtain subsequent loans from the same provider but not from different

providers. The (older) age of the firm, the adoption of international accounting

standards and having a male CEO increase the probability of receiving credit from a

29

non-EBRD provider. In terms of determinants of the size of EBRD loans what matters

is the past credit history and having adopted international accounting standards.

30

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.

31 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

32

Table 1A. Average Summary Statistics over 2001-2004

Control group Treatment group

Mean Median St. Dev. Mean Median St. Dev.

Bulgaria

Revenues 1,072 250 3,067 494 221 922 Investment 49 5 178 42 8 120 Fixed assets 224 53 482 178 51 514 Net profits 104 24 283 63 30 148 Labour costs 76 24 190 37 15 73 Total employment 24 12 34 18 9 38 EBRD loan size 0 0 0 39,941 (526) 18,819 72,783 non-EBRD loan size 154,014 (83) 68,669 219,702 50,266 (213) 34,334 62,822 Georgia

Revenues 360 47 1,674 241 89 680 Investment 28 0 149 6 0 16 Fixed assets 210 8 702 50 10 140 Net profits 48 7 172 78 16 351 Labour costs 28 5 113 13 5 27 Total employment 14 4 31 7 3 12 EBRD loan size 0 0 0 19,069 (509) 7,485 36,894 non-EBRD loan size 131,387 (46) 46,456 28,8170 16,368 (251) 8,420 26,407 Russia

Revenues 20,456 2,145 123,056 8,378 3,956 17,081 Investment 706 0 3,148 737 200 2,192 Fixed assets 2,896 237 15,487 2,649 593 8,283 Net profits 1,745 288 4,679 1,994 668 8,092 Labour costs 695 223 1,309 587 300 892 Total employment 19 7 42 17 9 25 EBRD loan size 0 0 0.00 423,388 (559) 197,770 815,225 non-EBRD loan size 1,637,040 (54) 316,431 448,2485 384,107 (311) 200,000 493,348 Ukraine

Revenues 1,723 300 7,558 1,653 120 8,323 Investment 51 0 170 117 9 643 Fixed assets 731 35 6,170 247 24 1,086 Net profits 320 27 1,401 931 28 7,196 Labour costs 82 26 147 145 18 726 Total employment 17 8 23 21 7 39 EBRD loan size 0 0 0 92,777 (450) 22,533 200,665 non-EBRD loan size 108,390 (86) 50,000 184,515 47,384 (242) 15,569 91,805

Note: Figures (except total employment) in Table 1 are in thousands of local currency units adjusted to producer prices.22

22 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.

33 Table 2. 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


37 19


23 56

Georgia


27 75


0 11


22 32

Russia


16 43


35 23


16 137

Ukraine


19 160

34 Table 3. 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


-0.1 61

Bulgaria


-0.13 82


-0.07 19


-0.15 56

Georgia


-0.12 75


0.61 11


0.04 32

Russia


0.34 43


-0.42 23


0 137

Ukraine


0.1 160

35 Table 4A. OLS regression of the effect of loans on investment with loan dummies ALL Bulgaria Georgia Russia Ukraine EBRD loan dummy 0.375 0.247 0.229 0.682 0.397

[0.034]**

* [0.067]**

* [0.081]**

* [0.062]**

* [0.061]**

* Non-EBRD loan dummy 0.547 0.582 0.154 0.976 0.43

[0.063]**

* [0.122]**

* [0.172] [0.132]**

* [0.092]**

* Georgia -0.252

[0.041]**

* Russia -0.154

[0.036]**

* Ukraine -0.123

[0.036]**

* Year 2003 -0.169 -0.006 0.2 -0.496 -0.279

[0.049]**

* [0.101] [0.132] [0.067]**

* [0.086]**

* Year 2004 -0.091 -0.18 0.453 -0.214 -0.339

[0.045]** [0.091]** [0.122]**

* [0.070]**

* [0.064]**

* Constant 0.126 0.176 -0.342 -0.06 0.101 [0.053]** [0.105]* [0.144]** [0.100] [0.060]* Observations 3724 952 894 982 896 R-squared 0.05 0.03 0.02 0.21 0.09 Investment growth is defined as (It-It-1)/0.5(It+It-1). If (It+It-1)=0 then investment growth has assigned 0. All regressions included industry dummies. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1% Table 4B. OLS regression of the effect of loans on investment with loan size

ALL Bulgaria Georgia Russia Ukraine EBRD loan dummy 0.375 0.094 0.153 0.614 0.416 [0.035]*** [0.079] [0.092]* [0.065]*** [0.063]*** Non-EBRD loan dummy 0.545 0.433 0.115 0.952 0.472 [0.068]*** [0.142]*** [0.184] [0.152]*** [0.103]*** EBRD*loan amount 0.001 0.821 0.444 0.547 -0.034 [0.034] [0.311]*** [0.198]** [0.204]*** [0.018]* Critical level of EBRD loan amount 1219.3 [630.1]* Non-EBRD*loan amount 0.006 0.558 0.112 0.177 -0.103 [0.064] [0.394] [0.040]*** [0.433] [0.041]** Critical level of non-EBRD loan amount 460.25 [137.56]*** Georgia -0.252 [0.041]*** Russia -0.154 [0.036]*** Ukraine -0.123 [0.036]*** Year 2003 -0.169 -0.014 0.188 -0.501 -0.281 [0.050]*** [0.102] [0.131] [0.067]*** [0.086]*** Year 2004 -0.092 -0.199 0.444 -0.223 -0.338 [0.045]** [0.091]** [0.121]*** [0.070]*** [0.064]*** Constant 0.128 0.206 -0.339 -0.054 0.101

36 [0.053]** [0.106]* [0.145]** [0.101] [0.061]* Observations 3718 947 894 982 895 R-squared 0.05 0.05 0.03 0.21 0.09

Investment growth is defined as (It-It-1)/0.5(It+It-1). If (It+It-1)=0 then investment growth has assigned 0. All regressions included industry dummies. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%

37 Table 5A. OLS regression of the effect of loans on fixed assets with loan dummies

ALL Bulgaria Georgia Russia Ukraine EBRD loan dummy 0.105 0.154 0.042 0.097 0.123 [0.018]*** [0.038]*** [0.026] [0.030]*** [0.046]*** Non-EBRD loan dummy 0.14 0.219 0.052 0.131 0.14 [0.032]*** [0.067]*** [0.040] [0.059]** [0.069]** Georgia -0.092 [0.022]*** Russia -0.21 [0.023]*** Ukraine -0.145 [0.025]*** Year 2003 -0.018 0.059 -0.038 -0.036 -0.057 [0.023] [0.042] [0.028] [0.033] [0.070] Year 2004 -0.08 0.028 -0.009 -0.109 -0.235 [0.018]*** [0.035] [0.028] [0.035]*** [0.041]*** Constant 0.179 0.075 0.165 -0.052 0.09 [0.028]*** [0.050] [0.069]** [0.036] [0.037]** Observations 3632 893 876 967 896 R-squared 0.04 0.04 0.02 0.04 0.03 All regressions included industry dummies. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1% Table 5B. OLS regression of the effect of loans on fixed assets with loan size

ALL Bulgaria Georgia Russia Ukraine EBRD loan dummy 0.107 0.093 0.054 0.068 0.134 [0.018]*** [0.045]** [0.027]** [0.032]** [0.049]*** Non-EBRD loan dummy 0.137 0.166 0.025 0.158 0.169 [0.034]*** [0.072]** [0.039] [0.068]** [0.076]** EBRD*loan amount -0.01 0.327 -0.072 0.236 -0.02 [0.009] [0.166]* [0.062] [0.213] [0.011]* Critical level of EBRD loan amount 1071.5 75.3 667.9 [975.2] [64.7] [308.7]** Non-EBRD*loan amount 0.01 0.197 0.074 -0.214 -0.07 [0.034] [0.188] [0.010]*** [0.105]** [0.036]* Critical level of non-EBRD loan amount 73.9 240.4 [25.6]*** [104.2]** Georgia -0.092 [0.022]*** Russia -0.21 [0.023]*** Ukraine -0.144 [0.026]*** Year 2003 -0.018 0.056 -0.037 -0.037 -0.058 [0.023] [0.042] [0.028] [0.033] [0.070] Year 2004 -0.08 0.021 -0.007 -0.112 -0.234 [0.018]*** [0.035] [0.028] [0.035]*** [0.041]*** Constant 0.178 0.087 0.154 -0.05 0.09 [0.028]*** [0.050]* [0.070]** [0.035] [0.038]** Observations 3631 893 876 967 895 R-squared 0.04 0.05 0.02 0.05 0.03

All regressions included industry dummies. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%

38 Table 6A. OLS regression of the effect of loans on revenue with loan dummies

ALL Bulgaria Georgia Russia Ukraine EBRD loan dummy 0.043 0.018 0.06 0.032 0.069 [0.012]*** [0.030] [0.024]** [0.014]** [0.026]*** Non-EBRD loan dummy 0.063 0.026 0.128 -0.004 0.09 [0.025]** [0.043] [0.051]** [0.024] [0.057] Georgia 0.004 [0.019] Russia -0.123 [0.016]*** Ukraine -0.037 [0.020]* Year 2003 -0.043 -0.015 -0.031 -0.04 -0.085 [0.014]*** [0.034] [0.027] [0.015]*** [0.034]** Year 2004 -0.076 -0.032 0.016 -0.087 -0.203 [0.012]*** [0.026] [0.025] [0.015]*** [0.028]*** Constant 0.132 0.167 0.185 0.01 0.101 [0.025]*** [0.044]*** [0.075]** [0.031] [0.036]*** Observations 3705 935 894 982 894 R-squared 0.04 0.01 0.05 0.06 0.06

All regressions included industry dummies. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1% Table 6B. OLS regression of the effect of loans on revenue with loan size

ALL Bulgaria Georgia Russia Ukraine EBRD loan dummy 0.051 0.04 0.082 0.042 0.083 [0.012]*** [0.035] [0.030]*** [0.016]*** [0.027]*** Non-EBRD loan dummy 0.07 0.034 0.124 -0.007 0.118 [0.027]** [0.048] [0.053]** [0.026] [0.064]* EBRD*loan amount -0.028 -0.122 -0.129 -0.083 -0.026 [0.010]*** [0.108] [0.131] [0.044]* [0.007]*** Critical level of EBRD loan amount 178.8 32.9 63.6 50.9 319.6 [68.2]*** [28.9] [53.1] [25.7]** [114.0]*** Non-EBRD*loan amount -0.024 -0.028 0.012 0.026 -0.069 [0.031] [0.071] [0.014] [0.056] [0.022]*** Critical level of non-EBRD loan amount 289.8 119.9 172.1 [340.4] [278.3] [56.4]*** Georgia 0.004 [0.019] Russia -0.124 [0.016]*** Ukraine -0.032 [0.020] Year 2003 -0.042 -0.015 -0.028 -0.04 -0.086 [0.014]*** [0.034] [0.027] [0.015]*** [0.034]** Year 2004 -0.075 -0.029 0.019 -0.086 -0.201 [0.012]*** [0.026] [0.025] [0.015]*** [0.028]*** Constant 0.13 0.164 0.179 0.009 0.099 [0.025]*** [0.043]*** [0.076]** [0.031] [0.036]*** Observations 3705 935 894 982 894 R-squared 0.05 0.02 0.05 0.06 0.07


39 Table 7A. OLS regression of the effect of loans on labour costs with loan dummies

ALL Bulgaria Georgia Russia Ukraine EBRD loan dummy 0.051 0.064 0.009 0.048 0.095 [0.014]*** [0.027]** [0.028] [0.022]** [0.036]*** Non-EBRD loan dummy 0.1 0.128 0.058 0.062 0.125 [0.025]*** [0.047]*** [0.042] [0.053] [0.050]** Georgia -0.038 [0.020]* Russia -0.077 [0.018]*** Ukraine -0.044 [0.021]** Year 2003 0.007 0.035 -0.034 0.055 -0.044 [0.017] [0.031] [0.031] [0.022]** [0.048] Year 2004 -0.089 -0.042 -0.058 -0.033 -0.24 [0.014]*** [0.029] [0.030]* [0.023] [0.031]*** Constant 0.135 0.142 0.157 0.025 0.11 [0.025]*** [0.038]*** [0.061]** [0.037] [0.037]*** Observations 3460 909 763 965 823 R-squared 0.03 0.03 0.02 0.03 0.07

All regressions included industry dummies. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1% Table 7B. OLS regression of the effect of loans on labour costs with loan size

ALL Bulgaria Georgia Russia Ukraine EBRD loan dummy 0.055 0.075 0.032 0.051 0.103 [0.015]*** [0.032]** [0.032] [0.024]** [0.039]*** Non-EBRD loan dummy 0.109 0.123 0.038 0.061 0.169 [0.030]*** [0.052]** [0.051] [0.056] [0.056]*** EBRD*loan amount -0.012 -0.062 -0.128 -0.026 -0.012 [0.006]* [0.080] [0.093] [0.055] [0.006]* Critical level of EBRD loan amount 443.5 122.2 24.6 197.8 866.6 [234.1]* [141.2] [21.8] [392.1] [425.8]** Non-EBRD*loan amount -0.032 0.021 0.051 0.01 -0.107 [0.047] [0.073] [0.015]*** [0.149] [0.038]*** Critical level of non-EBRD loan amount 338.2 158.6 [448.8] [54.3]*** Georgia -0.037 [0.020]* Russia -0.077 [0.018]*** Ukraine -0.04 [0.022]* Year 2003 0.007 0.034 -0.032 0.056 -0.047 [0.017] [0.031] [0.031] [0.022]** [0.046] Year 2004 -0.089 -0.041 -0.055 -0.032 -0.242 [0.014]*** [0.029] [0.030]* [0.024] [0.030]*** Constant 0.136 0.141 0.147 0.024 0.113 [0.025]*** [0.038]*** [0.063]** [0.037] [0.038]*** Observations 3459 909 763 965 822 R-squared 0.03 0.03 0.02 0.03 0.08


40 Table 8A. OLS regression of the effect of loans on employment with loan dummies

ALL Bulgaria Georgia Russia Ukraine EBRD loan dummy 0.058 0.133 -0.007 0.03 0.081 [0.013]*** [0.026]*** [0.023] [0.020] [0.031]** Non-EBRD loan dummy 0.095 0.114 -0.015 0.024 0.246 [0.026]*** [0.038]*** [0.047] [0.037] [0.060]*** Georgia -0.032 [0.017]* Russia -0.014 [0.017] Ukraine 0.061 [0.021]*** Year 2003 0.085 0.383 -0.019 -0.013 -0.045 [0.013]*** [0.025]*** [0.021] [0.023] [0.027]* Year 2004 -0.083 -0.288 -0.024 -0.061 0.049 [0.016]*** [0.034]*** [0.021] [0.025]** [0.038] Constant 0.054 0.032 0.126 0.101 0.049 [0.024]** [0.039] [0.056]** [0.037]*** [0.031] Observations 3335 886 780 908 761 R-squared 0.06 0.42 0.04 0.02 0.06

All regressions included industry dummies. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1% Table 8B. OLS regression of the effect of loans on employment with loan size

ALL Bulgaria Georgia Russia Ukraine EBRD loan dummy 0.06 0.169 0.018 0.033 0.082 [0.013]*** [0.037]*** [0.027] [0.022] [0.032]** Non-EBRD loan dummy 0.11 0.095 0.033 0.006 0.246 [0.028]*** [0.048]** [0.042] [0.038] [0.064]*** EBRD*loan amount -0.011 -0.212 -0.144 -0.028 -0.005 [0.012] [0.128]* [0.078]* [0.062] [0.008] Critical level of EBRD loan amount 533.7 79.8 12.7 120.0 1751.2 [568.7] [38.3]** [15.9] [244.1] [2772.3] Non-EBRD*loan amount -0.052 0.072 -0.126 0.127 0 [0.053] [0.112] [0.019]*** [0.096] [0.053] Critical level of non-EBRD loan amount 209.2 26.2 [198.3] [32.4] Georgia -0.032 [0.017]* Russia -0.016 [0.017] Ukraine 0.062 [0.021]*** Year 2003 0.085 0.381 -0.014 -0.014 -0.044 [0.013]*** [0.026]*** [0.021] [0.023] [0.027] Year 2004 -0.084 -0.287 -0.021 -0.061 0.05 [0.016]*** [0.035]*** [0.021] [0.025]** [0.038] Constant 0.054 0.03 0.135 0.102 0.046 [0.024]** [0.039] [0.053]** [0.037]*** [0.031] Observations 3330 882 780 908 760 R-squared 0.06 0.42 0.06 0.02 0.06


41 Table 9A. OLS regression of the effect of loans on net profit with loan dummies

ALL Bulgaria Georgia Russia Ukraine EBRD loan dummy 0.08 0.06 0.092 0.036 0.137 [0.021]*** [0.049] [0.048]* [0.031] [0.044]*** Non-EBRD loan dummy 0.062 0.015 0.162 -0.024 0.126 [0.043] [0.086] [0.105] [0.077] [0.077] Georgia 0.044 [0.032] Russia -0.12 [0.027]*** Ukraine -0.014 [0.030] Year 2003 -0.05 0.02 -0.058 -0.045 -0.137 [0.031] [0.065] [0.064] [0.045] [0.073]* Year 2004 -0.035 -0.015 0.119 -0.138 -0.107 [0.028] [0.058] [0.065]* [0.042]*** [0.059]* Constant 0.09 0.136 0.185 -0.008 0.054 [0.040]** [0.078]* [0.094]* [0.056] [0.055] Observations 3520 899 824 978 819 R-squared 0.02 0.01 0.03 0.02 0.02

All regressions included industry dummies. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1% Table 9B. OLS regression of the effect of loans on net profit with loan size

ALL Bulgaria Georgia Russia Ukraine EBRD loan dummy 0.087 0.122 0.087 0.052 0.152 [0.021]*** [0.058]** [0.052]* [0.032] [0.045]*** Non-EBRD loan dummy 0.063 0.055 0.164 -0.063 0.125 [0.046] [0.093] [0.119] [0.081] [0.086] EBRD*loan amount -0.031 -0.333 0.032 -0.132 -0.03 [0.017]* [0.215] [0.133] [0.082] [0.015]* Critical level of EBRD loan amount 278.8 36.5 39.3 515.4 [156.8]* [20.1]* [29.7] [275.2]* nonEBRD*loan amount -0.003 -0.144 -0.015 0.296 0.003 [0.038] [0.120] [0.468] [0.350] [0.043] Critical level of non-EBRD loan amount 1951.6 38.2 1068.3 21.2 [22308.0] [60.1] [32114.8] [29.3] Georgia 0.044 [0.032] Russia -0.12 [0.027]*** Ukraine -0.009 [0.030] Year 2003 -0.049 0.022 -0.059 -0.045 -0.137 [0.031] [0.065] [0.064] [0.045] [0.073]* Year 2004 -0.034 -0.009 0.119 -0.137 -0.106 [0.028] [0.057] [0.065]* [0.042]*** [0.059]* Constant 0.087 0.126 0.186 -0.008 0.052 [0.040]** [0.077] [0.095]* [0.055] [0.056] Observations 3520 899 824 978 819 R-squared 0.02 0.01 0.03 0.03 0.02


42 Table 10A. Probit regression of the effect of firm characteristics on receiving EBRD credit (marginal effects)

ALL Bulgaria Georgia Russia Ukraine Year 2003 -0.03 -0.094 -0.18 0.038 0.072 [0.032] [0.057]* [0.085]** [0.077] [0.051] Georgia -0.158 [0.045]*** Russia -0.132 [0.044]*** Ukraine -0.146 [0.047]*** EBRD credit before 2002 0.156 0.102 0.235 0.235 0.076 [0.029]*** [0.062] [0.064]*** [0.055]*** [0.067] non-EBRD credit before 2002 -0.083 0.073 -0.432 -0.425 0.201 [0.068] [0.080] [0.161]*** [0.107]*** [0.165] Lagged change in EBRD credit 0.1 0.133 0.169 0.069 0.061 [0.023]*** [0.045]*** [0.060]*** [0.056] [0.039] Lagged change in non-EBRD credit -0.118 0.114 -0.641 -0.232 -0.088 [0.069]* [0.104] [0.142]*** [0.158] [0.138] Age 0.004 -0.019 0.011 -0.001 0.008 [0.006] [0.037] [0.010] [0.037] [0.039] Age squared 0 0.001 0 0 0 [0.000] [0.002] [0.000]* [0.002] [0.002] Distance from regional capital 0.002 -0.002 0.004 [0.002] [0.005] [0.002] Distance squared 0 0 0 [0.000] [0.000] [0.000] Established at 2002 0.06 0.12 0.004 0.097 [0.068] [0.132] [0.093] [0.214] International accounting standards 0.311 0.339 0.267 [0.037]*** [0.062]*** [0.124]** Woman CEO 0.021 0.086 0.094 -0.034 -0.046 [0.030] [0.058] [0.065] [0.065] [0.070] Woman CEO interacted with total employment -0.001 -0.002 -0.009 -0.002 0.003 [0.001] [0.002] [0.008] [0.003] [0.002] Observations 1648 397 406 438 377


43 Table 10B. Probit regression of the effect of firm characteristics on receiving non-EBRD credit (marginal effects) ALL Bulgaria Georgia Russia Ukraine Year 2003 0.058 0.035 0.108 0.053 0.058 [0.011]*** [0.021]* [0.035]*** [0.020]*** [0.023]** Year 2004 0.06 0.051 0.066 0.082 0.059 [0.010]*** [0.019]*** [0.025]*** [0.026]*** [0.026]** Georgia -0.022 [0.010]** Russia 0.002 [0.011] Ukraine 0.003 [0.012] EBRD credit before 2002 -0.039 -0.074 -0.009 -0.046 -0.036 [0.007]*** [0.015]*** [0.008] [0.013]*** [0.021]* non-EBRD credit before 2002 0.236 0.135 0.248 0.244 0.497 [0.037]*** [0.046]*** [0.103]** [0.097]** [0.091]*** Lagged change in EBRD credit -0.016 -0.032 -0.013 -0.004 -0.009 [0.007]** [0.017]* [0.006]** [0.013] [0.017] Lagged change in non-EBRD credit 0.068 0.042 0.045 0.055 0.126 [0.012]*** [0.025]* [0.017]*** [0.020]*** [0.030]*** Age 0.002 0.006 0.001 0.001 0.011 [0.001]* [0.004]* [0.001]* [0.006] [0.012] Age squared 0 0 0 0 -0.001 [0.000] [0.000] [0.000] [0.000] [0.001] Distance from regional capital 0 -0.001 0 [0.000] [0.002] [0.001] Distance squared 0 0 0 [0.000] [0.000] [0.000] Established at 2002 -0.005 0.053 -0.008 0 [0.017] [0.115] [0.009] [0.027] International accounting standards 0.199 0.463 0.162 0.289 0.06 [0.050]*** [0.204]** [0.055]*** [0.155]* [0.065] Woman CEO -0.021 -0.033 0.003 -0.022 -0.038 [0.008]*** [0.018]* [0.009] [0.013]* [0.020]* Woman CEO interacted with total employment 0 0 0 0.001 0 [0.000]*** [0.000]*** [0.000] [0.000]*** [0.000] Observations 3815 936 893 954 868 All regressions included industry dummies. Robust standard errors in brackets. significant at 10%; ** significant at 5%; *** significant at 1%

44 Table 10C. Tobit regression of the effect of firm characteristics on EBRD loan size (marginal effects) ALL Bulgaria Georgia Russia Ukraine Year 2003 -0.075 -0.064 -0.048 0 0.078 [0.060] [0.031]** [0.054] [0.036] [0.207] Georgia -0.118 [0.077] Russia -0.145 [0.074]** Ukraine 0.086 [0.077] EBRD credit before 2002 0.239 0.044 0.183 0.084 0.227 [0.056]*** [0.030] [0.043]*** [0.028]*** [0.226] non-EBRD credit before 2002 -0.089 0.056 -0.363 -0.237 0.011 [0.112] [0.040] [0.154]** [0.073]*** [0.589] Lagged change in EBRD credit 0.141 0.058 0.046 0.022 0.234 [0.048]*** [0.026]** [0.041] [0.028] [0.178] Lagged change in non-EBRD credit -0.089 0.028 -0.495 -0.117 0.194 [0.137] [0.053] [0.165]*** [0.091] [0.553] Age 0.012 -0.008 0.003 0.021 0.214 [0.012] [0.018] [0.007] [0.017] [0.134] Age squared 0 0.001 0 -0.001 -0.013 [0.000] [0.001] [0.000] [0.001] [0.008] Distance from regional capital 0.01 0 0.022

[0.003]*** [0.002] [0.008]*

** Distance squared 0 0 0

[0.000]** [0.000] [0.000]*

* Established at 2002 0.089 0.106 0.002 0.017 1.02 [0.122] [0.082] [0.062] [0.085] [0.949] International accounting standards 0.303 -0.047 0.105 0.425 0.74 [0.110]*** [0.143] [0.054]* [0.095]*** [0.431]* Woman CEO 0.088 0.067 0.086 -0.033 0.183 [0.055] [0.029]** [0.042]** [0.030] [0.230] Woman CEO interacted with total employment -0.002 0 0.001 0 -0.002 [0.002] [0.000] [0.004] [0.001] [0.007] Constant -0.275 0.011 -0.13 -0.109 -1.307

[0.120]** [0.080] [0.091] [0.080] [0.517]*

* Observations 1648 400 408 440 400 All regressions included industry dummies. * significant at 10%; ** significant at 5%; *** significant at 1%

45 Table 10D. Tobit regression of the effect of firm characteristics on non-EBRD loan size (marginal effects)

All regressions included industry dummies. * significant at 10%; ** significant at 5%; *** significant at 1%

ALL Bulgaria Georgia Russia Ukraine Year 2003 0.438 0.188 1.38 0.175 0.377 [0.091]*** [0.103]* [0.368]*** [0.062]*** [0.208]* Year 2004 0.446 0.218 0.789 0.222 0.437 [0.088]*** [0.098]** [0.349]** [0.061]*** [0.207]** Georgia -0.169 [0.100]* Russia -0.023 [0.092] Ukraine 0.075 [0.091] EBRD credit before 2002 -0.465 -0.534 -0.286 -0.233 -0.479 [0.100]*** [0.147]*** [0.283] [0.078]*** [0.226]** non-EBRD credit before 2002 0.871 0.371 1.281 0.262 1.598 [0.086]*** [0.090]*** [0.334]*** [0.063]*** [0.220]*** Lagged change in EBRD credit -0.11 -0.21 -0.404 -0.031 0.118 [0.072] [0.091]** [0.235]* [0.046] [0.168] Lagged change in non-EBRD credit 0.546 0.113 1.043 0.19 1.015 [0.095]*** [0.099] [0.305]*** [0.064]*** [0.261]*** Age 0.025 0.021 0.061 0.011 0.184 [0.008]*** [0.016] [0.016]*** [0.025] [0.103]* Age squared 0 0 -0.001 -0.001 -0.013 [0.000]*** [0.000] [0.000]*** [0.002] [0.007]* Distance from regional capital 0.005 -0.003 0.006 [0.003]* [0.009] [0.004] Distance squared 0 0 0 [0.000] [0.000] [0.000] Established at 2002 -0.031 0.25 -0.104 0.007 -5.164 [0.162] [0.240] [0.363] [0.094] [0.000] International accounting standards 0.684 0.649 1.192 0.358 0.191 [0.133]*** [0.286]** [0.281]*** [0.112]*** [0.388] Woman CEO -0.156 -0.109 0.05 -0.074 -0.163 [0.074]** [0.092] [0.231] [0.050] [0.187] Woman CEO interacted with total employment 0.002 0.001 -0.013 0 0.001 [0.001] [0.001] [0.006]** [0.001] [0.007] Constant -1.688 -0.87 -2.8 -0.448 -2.586 [0.147]*** [0.170]*** [0.516]*** [0.105]*** [0.407]*** Observations 3809 955 950 1005 899

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Bank lending and performance of micro, small and medium sized

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