Foreign bank entry and credit allocation in emerging markets∗

Hans Degryse
CentER - Tilburg University, TILEC, European Banking CentER, and CESifo
Department of Finance
PO Box 90153, NL 5000 LE Tilburg, The Netherlands
Telephone: +31 13 4663188, Fax: +31 13 4662875
E-mail: h.degryse@uvt.nl
Olena Havrylchyk
CEPII
9 rue Georges Pitard, 75015, Paris, France
Telephone: +33 15368550
E-mail: olena.havrylchyk@cepii.fr
Emilia Jurzyk
International Monetary Fund
700 19th Street NW, Washington DC, 20431, USA
Telephone: +1 202 623 4223
E-mail: ejurzyk@imf.org
Sylwester Kozak
National Bank of Poland
E:mail: Sylwester.Kozak@mail.nbp.pl* This paper’s findings, interpretations, and conclusions are entirely those of the authors and do notnecessarily represent the views of the International Monetary Fund, its Executive Directors, or the countries
they represent. Part of this paper circulated previously as “The effect of foreign bank entry on the cost of
credit in transition economies: which borrowers benefit most?”. The authors would like to thank Stijn
Claessens, Paul De Grauwe, Adam Glogowski, Marta Golajewska, Christa Hainz, Vasso Ioannidou,
Matthias Koehler, Alina Luca, Daniel E. Nolle, Steven Ongena, Damiaan Persyn, Viorel Roscovan,
Rajdeep Sengupta, Dobromil Serwa, Patrick Van Cayseele, Rudi Vander Vennet, as well as participants at
the XVI Tor Vergata Conference on Banking and Finance, 2008 FMA European and International
Conferences, 6th INFINITI Conference on Banking and Finance, and seminars at the IMF, the National
Bank of Poland, CEPII, VU Amsterdam, and Tilburg University for many helpful comments andsuggestions. The paper was completed while Olena Havrylchyk and Emilia Jurzyk were visiting the
National Bank of Poland, whose hospitality is gratefully appreciated. Emilia Jurzyk gratefully
acknowledges financial support from the Research Council of the KU Leuven, in the framework of Central
and Eastern European Initiatives. The remaining errors are the sole responsibility of the authors. Hans
Degryse holds the TILEC-AFM Chair on financial market regulation.
1

Foreign bank entry and credit allocation in emerging markets

Abstract
We employ a unique data set containing bank-specific information to explore how
foreign bank entry determines credit allocation in emerging markets. We investigate theimpact of the mode of foreign entry – greenfield and takeover – on banks’ portfolio
allocation to borrowers with different degrees of informational transparency, as well as
by maturities and currencies. The impact of foreign entry on credit allocation may stemfrom the superior performance of foreign entrants (“performance hypothesis”), or reflectborrower informational capture (“portfolio composition hypothesis”). Our results arebroadly in line with the theoretical models underpinning the portfolio composition
hypothesis, showing that borrower informational capture determines bank credit
allocation. In particular, we find that foreign banks that enter via greenfield investment
devote a higher share of their portfolios to transparent borrowers, lend more at shorter
maturity and in foreign currency. We find few differences between the portfolio
composition of takeover and domestic private banks. We also document that there is asignificant convergence over time between foreign and domestic banks in terms of groupsof borrowers they lend to, while there is no convergence in terms of maturity andcurrency. Finally, we do not find any impact of bank ownership and mode of entry on
lending rates.

JEL classification: G21, G28, G34, L11
Keywords: banks, ownership, credit allocation

2

1. Introduction
Credit allocation is an important determinant of economic growth (see e.g.
Levine, 2005). Recent theories predict considerable impacts of different modes of foreignbank entry on credit allocation. Foreign banks may overcome cross-border informational
disadvantages when they are efficiently managed and have a superior performance (the“performance hypothesis”; see Berger et al., 2000). Foreign banks then extend loans to all
borrower types, and charge lower lending rates than domestic banks (Martinez Peria andMody, 2004; Claeys and Hainz, 2007). Foreign banks however are often accused of“cherry picking” the best borrowers, and in general, of lending more to large transparent
firms at the expense of small and medium sized enterprises (SMEs), implying a different
portfolio composition (the “portfolio composition” hypothesis; see e.g. Dell’Ariccia andMarquez, 2004; Sengupta, 2007; Claeys and Hainz, 2007; Gormley, 2007a; Detragiache
et al., 2008). Also, foreign banks could have only short-term objectives in their hostcountries, making them less likely to extend long-term loans (Rodrik and Velasco, 1999).
Finally, foreign banks may increase the supply of loans in foreign currency, which can be
detrimental to the financial stability of the host country if the borrowers do not hedge thiscurrency risk (Sorsa et al., 2007). While there is a growing number of empirical studies
analyzing lending to borrowers by foreign banks (see e.g. Berger et al., 2001; Clarke et.
al., 2005; Clarke et al., 2006; Gormley, 2007b; Giannetti and Ongena, 2008a,b), up tonow, no comprehensive investigation of all dimensions of banks’ credit allocation was
possible due to lack of detailed data. Our unique data set containing bank-specificinformation on several dimensions of credit allocation allows for a detailed investigationof the theoretical predictions of foreign bank entry on credit allocation.
We study the impact of different modes of foreign bank entry (greenfield andtakeover) on the supply of loans to transparent and opaque borrowers, as well as the3

impact on loan maturity, currency, and loan rates for these groups of borrowers. We also
investigate the impacts of foreign bank entry on the portfolio composition and risks ofdomestic banks. We differentiate between greenfield banks (foreign banks that enter via
greenfield investment) and takeover banks (foreign banks that acquire an existing
domestic institution), because theory suggests that the impacts of foreign banks’ behavior
depends on their mode of entry (see e.g. Claeys and Hainz (2007)). Takeover banks forexample may encounter difficulties when trying to improve credit standards or risk
management procedures, as the acquired institutions are burdened by non-performing
loans and non-transparent organizational structure. Greenfield banks, while free of suchconcerns, are disadvantaged in their access to borrower information, whereas takeoverbanks possess relationships with incumbent firms they inherit from the acquired bank.
An appropriate test of the impacts of different modes of foreign bank entry on
credit allocation requires the presence of both an important fraction of foreign entrants
and of domestic banks. Our unique detailed data set on the Polish banking industry
fulfills this requirement, as currently the share of foreign investors in Polish banks
amounts to 74 percent, and banks of all types of ownership and mode of entry are
represented. The data set provides us with quarterly information on the portfolio
composition of all Polish banks regarding different groups of borrowers, loan maturity,
currency, and loan rates for the period 1996-2006. We believe that our paper is the first to
study all dimensions of banks’ credit allocation in a comprehensive way.
Our findings can be summarized as follows. First, we show that greenfield banks
devote a higher share of their portfolios to transparent borrowers, lend more at shorter
maturity and in foreign currency. Second, there is a significant convergence between
foreign and domestic banks in terms of groups of borrowers they lend to, while there isno convergence in terms of maturity and currency, possibly stemming from foreign4banks’ refinancing possibilities. Third, we show that both modes of foreign bank entry
had a detrimental impact on the loan portfolio quality of domestic banks. Finally, ourfindings support the “portfolio composition” hypothesis, and thus contradict studies thatargue that greenfield banks charge lower lending rates than domestic private banks due totheir superior performance only. Previous results in the literature could stem from the factthat greenfield banks have a higher share of the most transparent borrowers in theirportfolio, whose cost of credit is lower than that of opaque borrowers. Moreover, earlier
studies argue that there is a convergence of interest rates between banks of different typesof ownership, whereas we show that this effect can be attributed to portfolio composition,as, with time, greenfield banks start lending less to transparent borrowers.
The effect of bank ownership on the cost of credit for different types of borrowers
is an important question, since lending rates might be prohibitive for some borrowers,
damaging economic growth. For example, Calvo and Coricelli (1993) argue that thecredit contraction in Central and Eastern Europe explains partly the heavy decline in
output in this region during the period 1989-1990. Therefore, lower interest rates offoreign banks might speak in favor of removing entry barriers for foreign banks.
However, it is important to examine whether foreign banks charge lower lending rates toall borrowers, or only to large transparent firms at the expense of opaque SMEs which
may continue to suffer from the informational capture of incumbent banks. In mostcountries SMEs account for the majority of firms in the economy and a significant shareof employment (Hallberg, 2001, Ayyagari et al., 2007), hence their access to financing
has important implications for the level of economic development and growth. The
existing empirical evidence shows that lending rates of foreign banks in developing
countries are lower than those of domestic banks. This is particularly true for greenfieldbanks which are not burdened with non-performing loans and inefficient organizational5structures (Martinez Peria and Mody, 2004; Claeys and Hainz, 2007). We show that thisempirical finding may stem from a portfolio composition effect in that foreign banks
serve more transparent borrowers. Our findings are in line with Dell’Ariccia and Marquez(2004) and Sengupta (2007)’s theoretical models. Dell’Ariccia and Marquez (2004)
argue that foreign banks have advantages in targeting more transparent new clients(transactions-based lending), whereas domestic banks are better placed to lend to firms
based on soft information (relationship lending). Sengupta (2007) shows that foreign
entrants may exploit their cost-advantage by offering collateralized loans to largetransparent firms whereas incumbent banks retain more risky borrowers. Such market
segmentation could lead to reduced access to finance of firms relying on domestic lenders(Gormley, 2007b). Giannetti and Ongena (2008b) show however that all firms benefit
from foreign entry as indirect effects outweigh.1
The portfolio composition effect can additionally manifest itself in otherdimensions of credit allocation such as collateral requirements (Sengupta, 2007), and thematurity and currency denomination of loans. In recent years, loans in foreign currencies
gained popularity in some emerging economies due to their perceived lower costs (Brown
et al., 2008). Indeed, loan rates in domestic currencies have been higher than the ones inforeign currencies in most transition economies, and borrowers – willing to take on
foreign exchange and interest rate risks – preferred to take loans denominated in foreign
currencies. In some countries appreciation of the domestic currency made loans in foreign
currency appear even cheaper. Foreign banks also supplied more easily foreign currency
loans, since they have better access to international capital markets and to financing fromtheir parent institutions. Further, foreign bank lending may involve more short-term loans
to solve asymmetric information problems as wells as “hot” money that is readily
1

There is also some empirical evidence showing that foreign-owned banks are less likely to lend to
informationally opaque small businesses than domestically-owned banks (Berger et al., 2001; Clarke et al.,6

retracted during crises.2 We therefore also study how the mode of foreign bank entry
impacts the maturity and currency denomination of loans, as well as the impact ofmaturity and currency denomination on the lending rate.
The rest of this paper is organized as follows. Section 2 reviews the theoreticalpredictions and empirical evidence. In Section 3 we present our data and summary
statistics. Sections 4 and 5 describe our empirical findings on portfolio allocation andloan rates, respectively. Section 6 deals with the impacts of foreign entry on domestic
banks. Section 7 concludes.

2. Literature review: Theoretical predictions and empirical evidence
2.1. Theory
Theory predicts different impacts of foreign bank entry on credit allocation. We
start with the impacts on banks’ portfolio allocation and loan rates to transparent andopaque borrowers. Berger et al. (2000) formulates the global advantage hypothesis,
according to which efficiently managed foreign institutions are able to overcome crossborderdisadvantages(distance,

monitoring

costs,

differencesininstitutional

environment, language and culture) and operate more efficiently than their domestic
competitors. Foreign banks may have higher efficiency when operating in other nations
as they are able to spread their superior managerial skills or best-practice policies andprocedures over more resources, thus lowering costs.3 We label this the “performance
hypothesis”. The “performance” effect is expected to be identical for all borrowers when
2006, Gormley, 2007b, Mian, 2006).
For example, Dooley and Shin (2000) argue that foreign creditors’ run from Korean banks triggered thecrisis in Korea in 1997.
3
Foreign entrants may also raise revenues through superior investment or risk management skills, byproviding better service quality/variety that some customers prefer, or by obtaining diversification of risks
that allows them to undertake higher risk-higher expected return investments. Further, foreign banks intransition and developing economies additionally benefit from their better access to international capital
markets and funding from their parent companies. This diminishes their cost of funds, which in turn should27foreign entrants pass on the efficiency gains in a similar fashion to both transparent andopaque borrowers.4 The performance hypothesis should be strongest for foreign banks
entering transition economies that have nascent banking markets characterized by low
competition and efficiency, and for greenfield banks relative to takeover banks as the first
type did not inherit bad loans and inefficient organizational structures. Takeover banks incontrast need transitional time in order to modernize their lending practices and to clean
up the loan portfolio which is left from previous owners.
An alternative hypothesis explaining banks’ choices of clients can be labeled the“portfolio composition hypothesis”. Several theoretical papers argue that foreign banks
have a comparative advantage in lending based on hard information, i.e. in lending tolarge transparent firms that have a long credit history and detailed financial statement
information (Dell’Ariccia and Marquez, 2004; Gormley, 2007a; Gormley, 2007b;
Sengupta, 2007; Detragiache et al. 2008). Moreover, foreign banks are better suited tolend to multinational corporations from their home countries. In contrast, domestic banks
use “relationship lending” to gain knowledge about opaque firms that can produce lesshard information about the quality of the firm, but can provide soft information (Berger etal., 2001).5
Besides having a disadvantage in using soft information, foreign banks, and inparticular greenfield institutions, might be less willing to do so. This idea is modeled by
Stein (2002) and introduced into the banking literature by Berger et al. (2005). Steinbe translated into lower lending rates, benefiting borrowers. Moreover, foreign banks might enjoy lower
cost of deposits due to their superior reputation.
4
The following empirical papers show that foreign banks exhibit indeed higher efficiency (Bonin, et al.
2005; Weill, 2003), experience faster and more stable loan growth (De Haas and Lelyveld, 2006), andenjoy higher profitability than domestic banks (Havrylchyk and Jurzyk, 2007).
5
Hard and soft information differ in their degree of transferability. Hard information refers to credible andpublicly verifiable data, such as firms’ balance sheets, credit history, collateral and guarantees. Soft
information cannot be verified by a third person and is gained a result of the relationship between a bankand a borrower. For example, through repeated interviews with an owner of a young firm, a bank manager
might be convinced that the firm’s owner is a smart, honest and hard working entrepreneur with a high8(2002) argues that organizations with more hierarchical structures are more likely to relyon hard information as opposed to organizations with flatter structures. The reason is thatflatter organizations have better control and information on their managers, and thus canafford to give them more discretion, which allows them to use soft information. The
modeling in Stein (2002) and Berger et al. (2005) can be easily applied to foreign
entrants, which are usually part of large multinational banking groups, and where
communication of soft information is obstructed not only by the hierarchy, but also by
cultural and linguistic barriers. We can also assume that funds are more easily moved
to/from greenfield institutions than to/from takeover banks that have large deposit
networks and are thus more independent in their financing. This would additionallydiminish the incentives of managers of greenfield institutions to invest time in gathering
soft information and engaging in relationship lending.6
Dell’Ariccia and Marquez (2004) also demonstrate the impacts on loan rates when
foreign entrants compete with better informed domestic banks. They show that loan rates
charged to transparent borrowers are lower than those charged to opaque clients, and thisis due to differences in the borrowers’ elasticity of demand for credit. Transparent
borrowers have a more elastic demand because they can signal their information tooutside lenders, which leads to higher competition and, thus, lower cost of funds for largetransparent firms. Opaque firms, on the other hand, cannot signal their worth and are
captured by their creditors leading to higher borrowing costs for them.
probability of success. However, this soft information cannot be transferred to other potential lenders(Petersen, 2004).
6
New studies question the argument that large and foreign banks are not capable to lend to SMEs (Berger
and Udell, 2006; de la Torre et al., 2008). The reasoning is that latest advances in credit scoring
methodologies coupled with enhanced computer power and increased data availability make transaction
lending technologies to be well suited for funding small firms (Mester, 1997; Petersen and Rajan, 2002).
This is especially true when credit scores are based on the owner’s personal consumer data obtained fromconsumer credit bureaus, which is combined with data on the SME collected by the financial institutions.
These studies still agree that small domestic banks have an advantage to gather and process soft
information, but they argue that large and foreign banks are also able to lend to SMEs, but using “hard”

9The above two results – the fact that foreign banks would prefer to lend to moretransparent borrowers and that lending rates for this type of client are lower – could
explain the negative impact of foreign bank ownership on lending rates. Claeys andHainz (2007) model the impact of different modes of foreign bank entry when competingwith domestic banks. Greenfield and takeover banks both enjoy superior screening
technology whereas only takeover banks enjoy access to soft information. Domestic
banks then have to shade their bids more when competing with takeover banks than withgreenfield banks. Their model then predicts that greenfield banks charge lower loan rates
than takeover banks for opaque borrowers. Further, greenfield banks will aim to shy awayfrom opaque borrowers for whom soft information is important.
Foreign entry could also impact loan maturity and currency composition.
Foreign banks might bring in hot money, which can be easily withdrawn in case of crisis(Rodrik and Velasco, 1999). Foreign banks may also use shorter loan maturities tomitigate borrower risk and asymmetric information (Ortiz-Molina and Penas, 2008). The
theoretical impact of loan maturity on cost of credit is ambiguous as it reflects two
opposite effects. A borrower that issues short-term debt can face costly liquidations at
expiration which motivates it to opt for longer-term debt. At the same time, lenders preferto give short term loans because of agency problems, such as asset substitution andunderinvestment. As a result, borrowers are willing to incur and lenders demand higher
lending rates for loans with longer maturity. Alternatively, lenders might ration credit torisky borrowers and force them to take short-term loans, which would decrease averagelending rates on long-term loans. Empirical evidence supports both hypotheses forcorporate loans and bonds (Gottesman and Roberts, 2004; Helwege and Turner, 1999).
For an individual firm, the spread typically increases with maturity, reflecting greater
information-based technologies. In this case, we should not observe differences in portfolio allocations ofloans between different types of banks.

10uncertainty. At the same time, safer firms tend to issue longer-dated bonds or have accessto long-term bank credit, which causes the average spread to decline with maturity.7
The currency composition of loan portfolios can also be influenced by the entry of
foreign banks (Sorsa et al., 2007), because ability and willingness of banks to supply
loans in foreign currency depends primarily on their access to foreign funds and/or on
hedging opportunities.8 In this respect, foreign banks have an advantage overdomestically-owned banks, since they enjoy better access to international capital markets,
including their own parent banks.9 Currency composition may also be an important
determinant of lending rates. In fact the popularity of loans denominated in foreign
currencies stems from lower lending rates that are charged on this type of loans. Brown etal. (2008) for example show that opaque firms have an additional incentive to declare that
their revenues are in foreign currency in order to profit from cheaper cost of credit. This
interest rate advantage can be considered as a compensation for the inherent foreignexchange risk. For instance, Beer et al. (2008) find that more risk-loving households are
more likely to take a loan in a foreign currency. However, a survey undertaken in 11
CEECs shows that most borrowers, particularly households and SMEs, are not aware of
involved currency risk (ECB, 2006). This is due to historically low exchange rate
volatility in some countries, which created a belief in low foreign exchange rate risk. The

7

The situation in our data set (i.e. Poland) is additionally complicated because yield curves were
downward-sloping till 2003, reflecting market expectations of diminishing inflation and interest rated
convergence to the EU level. Short-term interest rates declined dramatically from 20.6% at the end of 1996
till 5.7% in 2003, with a yield curve taking an upward shape after 2003. In such economic environment,
firms would agree to take long-term loans only if they had lower interest rate than short-term ones. It
should be noted that this situation was not unique and is still observed in some CEECs.
8
Very often loans that are contracted in foreign currency are actually extended to borrowers in domestic
currency, even though they have all the characteristics of foreign currency loans, namely interest rate andexchange rate risks. In this case banks are not obliged to have access to foreign currency funding, but rather
they should be able to hedge their exposure to foreign currency risk, which is easiest for banks with goodstanding on international financial markets.
9
To illustrate this point in our sample, the share of interbank liabilities of greenfield banks from nonresident banks has increased from 7 to 20 percent of total banks’ assets, reflecting an increasing
attractiveness of Poland for foreign investors. At the same time, the share of non-resident interbank
liabilities of domestic banks, both private and state-owned, was virtually zero.
11

willingness to borrow in foreign currency is additionally enhanced by appreciation of thelocal currency, which is also true for Poland.
To sum up, we expect that loan maturity and currency could be substantiallydifferent across banks with different ownership and could play an important role inexplaining lending costs. Since foreign banks are more likely to lend short term and inforeign currency, we will also have to control for these portfolio characteristics in order
to distinguish between the performance and portfolio composition hypotheses.

2.2. Empirical literature
The empirical evidence on foreign-owned banks and SME credit availability indeveloping countries is rather inconclusive, and findings depend a lot on the analyzed
country and region. The empirical studies focusing on foreign bank entry and lending to
SMEs in CEECs do not provide direct evidence of whether foreign banks’ lending isbiased towards large transparent borrowers. De Haas and Naaborg (2006) conduct
focused interviews with managers of foreign parent banks and their affiliates in Central
and Eastern Europe and document that foreign banks expanded into SME and retailmarkets. Giannetti and Ongena (2008a) use firm level data and find that foreign bankpresence in Central and Eastern Europe stimulates growth of financial loans, and eventhough large firms benefit more from foreign lending, smaller companies profit as well.
But it is not clear whether loans to SMEs are supplied by foreign banks or whether
domestic institutions decided to expand to this sector because of increased competition in
the market for transparent borrowers.
Many empirical studies on foreign bank entry focus on Latin American countries.
Berger et al. (2001) find that foreign-owned institutions have difficulties extending loans
to opaque small firms in Argentina, particularly when foreign banks come from far-away

12

countries. Interestingly, banks that are headquartered in other Latin American countries
do not differ in their lending practices from domestic banks. Clarke et al. (2006) find that
foreign banks in Chile and Peru lend less to SMEs than domestic private banks, whereas
the difference is not significant in Argentina and Colombia. They also find that large
foreign banks increase their lending to SMEs faster than domestic banks.
Studies that focus on Asian markets come up with even more pessimistic
conclusions. Mian (2006) finds that greater cultural and geographical distance between
foreign banks’ headquarters and local branches in Pakistan leads them to avoid lending to“informationally difficult” but sound firms that require relationship lending. Gormley(2007b) finds that only profitable firms benefit from foreign bank entry in India, whereas
there is a significant drop in loans to informationally opaque firms.
Unlike above country studies, Clarke et al. (2006) rely on a firm survey conducted
in 35 developing and transition economies. They conclude that SMEs benefited fromforeign bank entry in the form of lower financing obstacles. However, they cannot
determine whether this improvement came thanks to more lending by foreign institutions,or whether domestic banks increased their lending to SMEs because of the increased
competition on the market for large transparent borrowers. Moreover, their study suffersfrom the lack of time series dimension, which does not allow them to analyze theobserved impact over time. Giannetti and Ongena (2008b) find for a set of emerging
countries that relationships with foreign banks are less likely to be terminated. Further
they also show that firms benefit indirectly from the presence of foreign investors as itincreases the probability that firms establish bank relationships, improving access tocredit for all firms. And Detragiache et al. (2008) find for a set of 89 middle and low
income countries that countries with a larger foreign bank presence have shallower credit
markets and exhibit slower credit growth with a larger initial foreign bank presence.

13

Most of empirical studies for developing and transition countries show thatgreenfield banks charge lower lending rates and spreads than domestic banks andtakeover banks. Martinez Peria and Mody (2004) study banking markets in five Latin
American countries during the late 1990s and find that foreign banks have lower spreads
than domestic banks and takeover banks have higher spreads than greenfield banks.
Claeys and Hainz (2007) document that greenfield banks charge the lowest lending rates
in ten CEECs. However, absence of sufficiently detailed data does not allow them todiscriminate between the two main complementary hypotheses we discussed above, i.e.
the performance hypothesis and the portfolio composition hypothesis.

3. Data: Polish banking industry and summary statistics
We test our hypotheses using a unique data set that was kindly provided by the
National Bank of Poland. It contains quarterly information on 110 Polish banks10 between
December 1996 and December 2006. In addition to standard information from balance
sheets and income statements (like bank assets, capitalization, costs and profits), it
contains data on interest income, amount of granted loans, and non-performing loans fortwo borrower types: private firms and individual entrepreneurs.11 Our data gives us aunique opportunity to construct banks’ portfolio shares, interest rates, market shares,
concentration measures (Herfindahl index), and non performing loans for each borrower
type separately.
The distinction between the two groups of borrowers mentioned above isgrounded in Polish law. A borrower is classified as a “private firm” if the firm is owned
by private investors (either entirely, or where the private share exceeds 50%), and is10

We define a bank as Polish if it is registered in Poland and the National Bank of Poland collects
information on it.

14either subject to commercial law or is subject to civil law and employs more than 9
workers. Additionally, such firms have to comply with accounting regulations that
require full bookkeeping. “Individual entrepreneurs (for short, entrepreneurs)”, incontrast, are small firms employing up to 9 workers, subject to civil law, and using
simplified accounting procedures.
In theory, transparent firms have reliable financial statements, long credit history,
and good collateral, all of which help the bank to evaluate borrower’s creditworthiness.
We are confident that private firms in our sample correspond to this definition; hence welabel them also as transparent borrowers.12 We classify entrepreneurs as opaque because
they are small and often young entities, and that makes it more challenging for a bank tojudge their capacity and willingness to repay. This is particularly acute in emerging
markets, where many small firms are informal. It implies that a firm might have larger
turnover and assets than it declares officially, but it also implies that the firm has
unrecorded, contingent senior liabilities to its employees (de la Torre et al., 2008).
The Polish banking sector provides a good testing ground for our hypotheses
because, similar to other CEECs, it experienced massive foreign direct investments into
the banking sector. At the end of 2006, the share of foreign investors in Polish banking
constituted 74 percent. This is less, however, than in other major CEECs – for example in
Hungary more than 80 percent of banking assets is in foreign hands, and in the Czech
Republic and Slovakia foreign banks control more than 95 percent of assets. The
relatively smaller presence of foreign banks in Poland gives us an additional strong
argument to use Poland as a case study, since there are still local private and state-ownedbanks left which serve as a benchmark.
11

We have information on three other groups of borrowers, namely state-owned enterprises, individuals,
and farmers. Due to the difficulties in classifying these borrowers in terms of transparency, we decided touse only the two groups mentioned in the text.

15

During 1996-2006, there were a number of domestic mergers and acquisitions in
the Polish banking sector, hence we treat merged institutions as two before the merger
and as one afterwards13. For our estimations we have deleted the first four quarters ofoperations for both greenfield and takeover banks in order to exclude the initial setting-up
and transformation period.

<Insert Tables 1 and 2 about here>

Tables 1 and 2 present variable definitions and summary statistics for thevariables that we employ in our analysis, respectively. Let us look first at the compositionof banks’ portfolios. As predicted by the portfolio composition theory, foreign banks
extend their loans predominantly to transparent borrowers: on average, these loans
constitute 54 percent of the total portfolio. At the same time, domestic banks focus muchless on transparent firms – they constitute only 31 percent of their portfolio, and thedifference between the two is statistically significant. The mode of entry plays a verysignificant role too, as greenfield banks extended almost 67% of their loans to private
firms, which is 25 percentage points more than takeover banks, and twice as much as
domestic banks, both private and public. Domestic private banks, on the other hand,appear to possess a comparative advantage in lending to opaque borrowers i.e.
entrepreneurs. Our data shows that they devote 15% of their portfolio to these borrowers,
which is one and a half times more than other banks.

12

Most private firms have considerably more than 9 workers as firms have a tendency to stay either below
or go far above this cutoff. The average statistics therefore are clearly driven by large firms.
13
We also investigated the effect of domestic mergers on banks’ lending rates by including a dummy thattakes a value of one if the bank had undergone a domestic merger, and zero otherwise. Our estimations
revealed that this merger dummy was never statistically significant and, therefore, we decided to exclude it
from our final results.
16

The differences in banks’ portfolios are not constant over 1996-2006. First, it isinteresting to note that greenfield banks over time start lending more to entrepreneurs,
and this stands in contrast to the behavior of all other banks. In the same time, they
decrease the share of loans to private firms in their portfolio. It seems, therefore, thatgreenfield banks over time acquire skills needed for lending based on soft information.
Alternatively, we could hypothesize that they implement methodologies used forassessment of hard information provided by their large clients for the purposes of lending
to SMEs, in spirit of Berger et al. (2001).
The summary statistics reveal that loan maturity differs according to the mode offoreign entry. Greenfield banks issue more and takeover banks less short-term loans thandomestic private banks (53, 36 and 41 percent, respectively). At the same time, publicbanks extend more long-term loans than any other type of bank, followed by takeoverbanks. This might be explained by less stringent portfolio management techniques andtheir lower volatility of deposits due to implicit government guarantees. It is interesting to
see that, contrary to the findings for borrower type, the differences between banks withrespect to loan maturity are persistent over our sample period. While all banks eventually
start granting more long-term loans, the share is still the highest for public banks, whilegreenfield banks held the highest stare of short-term loans.
We now turn to the currency composition of loan portfolios for different types ofbanks. One of the characteristics of many CEECs is the high proportion of loans inforeign currency. Their share ranges from around 10-20 percent in Czech Republic andSlovakia, to 60-70 percent in the Baltic states. As we see in the data, in 2006 Polish banks
extended 23 percent of their loans in foreign currency, which is not very high incomparison to other countries. Still, this poses significant risks for the banking sector, as
many borrowers, especially he SMEs, are not hedged against currency and interest rate17

risk.14 As expected, foreign banks, particularly greenfield institutions, give more loans inforeign currency than domestic banks, which is probably due to their better access tointernational capital markets and parent companies. These differences are persistent over
our sample period.
A comparison of loan rates across all bank types reveals that greenfield banks
charge the lowest loan rates: 14.7 percent on average for all borrowers, whereas private
domestic banks charge the highest: 21.7 percent. Takeover banks offer lower lending
rates than both types of domestic banks, but charge more than greenfield institutions.
These results are in line with the existing literature. We also test the differences in rates
of greenfield, takeover, and state-owned banks with respect to rates charged by domestic
private banks, and find that all the differences are statistically significant.
Next, we compare rates on loans to different types of borrowers. Lending rates
offered to private firms – the most transparent borrowers – are significantly lower thanlending rates charged to entrepreneurs. The difference between them is 2.3 percentage
points on average and is statistically significant. This is in line with the theoreticalconsiderations that lending rates in the competitive markets with the smallest
informational asymmetries should be the lowest whereas lending rates for opaqueborrowers are the highest due to high switching costs stemming from informational
asymmetries.15

14

Many foreign currency loans are extended in Swiss Francs, on which lending rates are lower than on
Euro loans. This characteristic is shared by other countries in CEECs, such as Hungary and Slovenia. This
trend comes from Austria where most of loans in foreign currency are denominated in Swiss Franc.
Originally this was constrained to regions bordering Switzerland where firms and individuals had a natural
hedge against currency risk since their income was often in Swiss Franc. However, lending in Swiss Francs
is now extended to other parts of Austria and to CEECs where Austrian banks are active.
15
While there was a general trend for all lending rates to decrease over 1996-2006, the spread between
lending rates to private firms and entrepreneurs has not changed in a significant way. Two countervailingforces may lead to this finding: opaque borrowers may have become more transparent and foreign banks
may have increased competition more in the transparent segment of the market.
18

4. Empirical analysis - portfolio allocation
4.1. Empirical model
We first study the impact of foreign entry and bank ownership on the allocation ofloans with respect to borrower type, maturity and currency. We model banks’ portfolio
shares as a function of ownership and mode of entry, as well as controls for bankcharacteristics, macroeconomic environment, and market structure.
To be more formal, we estimate the following model for different types ofborrowers, maturities and currency:Pln( it ) = α 0 + α 1Ownershipit −1 + α 2 Bank characteristicsit −1 + α 3 Macrot −1 +1 − Pit(1)+ α 4 Market Structureit −1 + Seasont + ε it ,where Pit is the share of loans: by borrower, by maturity, or by currency in loan portfolioof bank i at time t; Ownershipit - dummy variables that capture the effect of bankownership (State-owned) and mode of foreign bank entry (Takeover and Greenfield) for
bank i at time t; Bank

characteristicsit - variables that control for return on assets(ROA), Capitalization, Costs, and the share of non-performing loans (NPL) in theportfolio of bank i at t; Macrot - variables that control for Inflation, real short-term
interest rate (Real interest rate) and real GDP growth (GDP) at t; Market Structureit variables that control for bank size (Bank size) at time t. We lag all explanatory variables
by one period and also include seasonal dummies.
Following Berger et al. (1998) and Clarke et al. (2006) we estimate all shareequations in log-odds logit form which allows us to get around the problem that shares bydefinition are bounded between 0 and 1. Since our dataset is in panel version, initially we19have to choose between panel and pooled estimation methods. The former, however,presents significant problems: while the Hausman tests indicates that we should allow for
unobserved fixed effects in the error term, fixed effects estimation does not allow us toestimate the time-invariant coefficients (i.e. effect of greenfield ownership). Random
effects estimation however will produce inconsistent parameter estimates. Consequently,
we opt for a pooled model with clustered error terms. We also check the robustness of ourresults by estimating our regressions using Beck and Katz (1995) panel-corrected
standard errors methodology, allowing for heterogeneity and autoregressive process oforder 1 in the standard errors. Our results reported below are robust to this procedure andare available upon request.16
Our main variables of interest are three dummy variables that correspond to typesof ownership and modes of entry (state-owned, greenfield, takeover), while private
domestic banks serve the role of benchmark for our estimation. We also split thesedummy variables for two periods – before and after 2001 – to capture changes in lending
over time. As the choice of this threshold might appear arbitrary, we check the robustnessof our results with other thresholds, but our findings remain unchanged. We believe thattime period in which foreign banks operates is more important than banks’ age. For
example, a foreign bank that enters Poland in the later period is likely to have alreadybeen present in other Central and Eastern European countries, and therefore has at thattime already a lot of experience in operating in a transition economy. In fact, we noticed
that banks that were the first ones to enter Polish banking markets in the early 90s arealso those that lend most to large firms, which reflects the fact that they came to Poland
following their clients and were not interested in the local market.

16

We also test for non-stationarity of the banks’ interest rate data using panel unit root tests of Levin, Lin
and Chia-Shang (2002), Im, Pesaran and Shin (2003), Maddala and Wu (1999) and Hadri (2000). The null
hypotheses for the first three tests are the existence of the unit root, whereas Hadri (2000) tests the null
hypotheses of stationarity of time series. We find that all time series in the sample are stationary.
20

As bank-level characteristics, we include ROA, Capitalization, Costs, Bank size
and NPL (non-performing loans) to capture the financial health and performance ofbanks, which might be correlated with bank strategies. We admit that these variables
might be endogenous, even though we have lagged them. To further overcome thispotential endogeneity problem we test the robustness of our results by including initialsample values of bank-characteristics, which does not change our main results in asignificant manner. However, we prefer to present results with lagged bankcharacteristics, because they are time-varying and, thus, serve as better controls. It is
important to note, though, that we do not attempt to rely on these variables to explain thecausal relationship between bank characteristics and loan growth.
The expected signs of the bank characteristics are unclear because of conflicting
hypotheses. Healthier banks with higher profits, better loan quality and higher
capitalization might be able to grow faster over time and to expand to areas where it takes
time and effort to acquire know-how of the business, like lending to SMEs. Similarly,
healthier banks should be able to extend more foreign currency loans and loans at longer
maturity, because they have better access to foreign and long-term funding. On the other
hand, banks that suffer from financial distress can be required by government
supervisors, depositors, and capital market investors to reduce their risk profile. This
would lead to a decrease in relationship lending to informationally opaque small
businesses because the risk of these loans cannot be easily verified (Berger et al., 2001).
Hainz (2005), in contrast, argues that firms can more easily switch from distressed banks
as asymmetric information problems are lowered. Similarly, regulators might ask
distressed banks to reduce their lending in foreign currency to reduce their currency risk.
Therefore, we might expect a positive relationship between bank health and the share ofloans to opaque clients, at longer maturity and in foreign currency.

21

Contrary to the above hypothesis, failing banks might be more willing to “gamble
for resurrection” and lend to riskier borrowers, such as SMEs (Clarke et al., 2006).
Moreover, large firms might be less willing to continue their relationship with suchbanks, whereas opaque clients might have more difficulties switching to other lenders,and distressed banks might benefit from their “captured” clients. They might also be
more willing to lend in foreign currency disregarding currency risk considerations.
It should be mentioned that some of our bank-level variables, such as ROA, Costs,
Capitalization, and Bank size, are calculated at the bank level, whereas non-performing
loans (NPL) are calculated both at a bank and at a bank-portfolio level, i.e. separately for
entrepreneurs and large firms. In the first case, we control for the general health of thebank, whereas in the second case we talk about the health of the particular part of a loan
portfolio.
To control for macroeconomic environment, we include real short-term interest
rate (Real interest rate), inflation rate (Inflation) and real GDP growth (GDP). In general,
we expect that periods of high economic growth with low interest rates and low inflation
should be more beneficial for riskier and opaque clients, such as small entrepreneurs.
Benign economic conditions should also increase lending in domestic currency and at
longer maturity.

4.2. Results for borrower type – portfolio allocation
We first estimate the impact of foreign bank ownership on borrower compositionof bank portfolios (share regressions). The results are reported in Table 3: columns 1-2
present findings with the share of private firms in the bank portfolios as dependent
variable, and columns 3-4 with the share of entrepreneurs. The key explanatory variablesin these regressions are the greenfield and takeover dummies, which we additionally split

22for the period before and after 2001 to analyze changes over time. Our findings show
that, controlling for other factors, foreign banks that entered via greenfield investment
devote almost 25 percent less of their portfolio to entrepreneurs (column 3) while theylend over 90 percent more to private firms (column 1) than domestic private banks. This
result supports the portfolio composition theory that points to comparative disadvantagesof foreign banks in lending to opaque clients. It is interesting to note, however, that the
takeover dummy is not statistically significant. That can be interpreted as a sign that thesedisadvantages can be overcome by foreign banks if they take over existing institutions
and thus acquire access to local knowledge via local personnel and existing relationships
with firms.

<Insert Table 3 about here>

We also find that there is a convergence between banks in terms of portfolio
composition as differences between greenfield and domestic private banks diminish after
2001 (columns 2 and 4). Even though greenfield banks still lend a larger share of theirportfolios to private firms than domestic private banks, this share is smaller than in thefirst half of the period in a statistically significant manner. At the same time, thedifference in terms of loans to entrepreneurs between greenfield and domestic privatebanks disappears after 2001. These findings reflect growing interest of greenfield banksin lending to SME, which may be brought by two main developments: 1) fierce
competition in the credit market for large corporations and 2) improved ability of foreignbanks’ subsidiaries to finance relatively opaque SMEs (De Haas and Naaborg, 2006).
Even though greenfield banks were still less able and less willing to engage in
relationship lending, they developed other technologies that helped them to overcome

23opaqueness of entrepreneurs, such as small business credit scoring, asset-based lending,or fixed assets lending. They were able to do this in the later period, as legal andaccounting systems have become more sophisticated, making some SMEs moretransparent.
Our summary statistics suggested that takeover and state-owned banks also devotea smaller share of their portfolios to entrepreneurs than domestic private banks; however
this result disappears in regressions when taking into account our control variables. This
stems from the fact that these banks are usually large institutions, and their inability tolend to opaque clients is captured by the size variable. Our estimation results clearly showthat larger banks prefer to lend more to private firms, whereas small banks are better at
lending to entrepreneurs. As to other bank-specific variables, we find that a bankdecreases the share of loans to a specific borrower type if the non-performing loans(NPL) in its loan portfolio for that borrower type increase.

4.3. Results for loans at different maturities – portfolio allocation
The results of share regressions for loan portfolios at different maturities arereported in Table 4. We find that, controlling for other factors, greenfield banks lend
more at short-term (up to 1 year), and less at long-term (over 5 years) than domestic
private banks. The numbers are economically significant too, as the greenfield banks
have over 73 percent more of short-term, and over 26 percent less of long-term loans intheir portfolios than domestic private banks. These results are stable over time, and we do
not observe substantial convergence in terms of portfolio maturity between banks. Even
though the coefficients in the second half of the period appear to be smaller for short-termloans and larger for long-term loans, these differences are not statistically significant. Ourfindings suggest that only foreign banks that enter via greenfield investment prefer to24

lend short term, as there are no differences between takeover and domestic private banks(the base case).

<Insert Table 4 about here>

Our results also show that low inflation and low real interest rates contribute tohigher share of long-term loans, which is explained by higher certainty of businessconditions and better possibilities of long-term planning, and higher precision of
forecasting. We also find that healthier banks (measured by higher profits, smaller nonperforming loans, and higher capitalization) lend less short-term and more long-term.

4.4. Results for loans in foreign currency – portfolio allocation
Table 5 reports the results of our portfolio share regressions for loans in foreign
currency. We find that greenfield banks offer a higher share of loans in foreign currency.
This result holds for the entire analyzed period – the difference in coefficients forgreenfield banks before and after 2001 is not statistically significant (model 2).

<Insert Table 5 about here>

The higher share of foreign currency loans granted by greenfield banks mightreflect their better access to international capital markets either directly or via their parent
banks. We are able to test this hypothesis by including the share of interbank liabilities
with non-resident banks to capture foreign funding (model 3).17 This additional
explanatory variable turns out to be significant and has a positive sign, indicating that25

banks that have higher share of their funding from non-resident banks are more likely togive loans in foreign currency as well. However, the inclusion of this variable does notchange by a lot the magnitude of coefficient of greenfield dummy. Therefore, higher
propensity of greenfield banks to extend loans in foreign currency stems not only fromtheir better access to international capital markets, but also other factors, such as currency
risk management, or the servicing of different segments of customers.
Among other variables, bank size appears to be significant in the shareregressions, which might indicate that larger banks have on average better access toforeign currency liabilities, which they lend on to borrowers. The decrease in the inflationand interest rates made lending in foreign currency less attractive for borrowers,
diminishing the growth in these loans on average.
To summarize the results obtained up to this point, we found that foreign banks
that enter via greenfield investment lend more to transparent firms (private firms) and lessto opaque firms (entrepreneurs), they extend more loans at short-term, and in foreign
currency. We also found that there is some convergence between banks in terms of
portfolio composition as over time greenfield banks lend more to opaque and less totransparent borrowers. It is interesting to note, however, that there is not muchconvergence with respect to currency and maturity of loans between foreign and domestic
banks. We find that greenfield banks lend more short-term and more in foreign currency
than domestic private banks, and these differences are not disappearing over time.

5. Empirical analysis – loan rates
5.1. Empirical model17

We can test this hypothesis only for a limited time period, as the data on interbank liabilities is reported
only after 2001. We do not present these results in the paper, but they are available upon request.
26

Next, we look at the determinants of bank lending rates. In particular, weinvestigate whether there are differences between interest rates charged by banks withdifferent ownership structure after we control for the portfolio composition. Section 4 has
clearly shown that the portfolio composition is different across bank types. To identify
the importance of portfolio composition, we first estimate a baseline lending rate modelwith specification similar to other studies. We do that to be able to compare our resultswith the existing literature (Martinez Peria and Mody, 2004; Claeys and Hainz, 2007). In
this specification, we use lending rate for all borrowers as a dependent variable andexamine the effect of bank ownership and mode of entry on costs of loans for an averageborrower, controlling for bank characteristics, macroeconomic environment and market
structure.
To be more formal, we estimate the following model:
Lit = α 0 + α 1Ownershipit −1 + α 2 Bank characteristicsit −1 + α 3 Macrot −1 ++ α 4 Market Structureit −1 + Seasont + ε it ,(2)

where the variables are as defined before (see Table 1 and 2) except for Lit , the lending
rate of bank i during quarter t, and Market Structureit , which includes variables that
control for market concentration and market power (Herfindahl Index and Market Share).

5.2. Results for the average bank lending rate
The results of estimating model (2) where we do not distinguish between the
different borrower types are presented in the first column of Table 6. We find thatgreenfield banks charge their borrowers 0.9 percent less per quarter (or 3.6 percent on anannual basis), whereas takeover banks do not charge less than domestic banks, which areomitted in our estimations and, thus, serve as a benchmark. Among bank specificvariables, the deviation from the median non-performing loans, costs, and market share27

are significant and have the expected signs. Banks that have higher costs and face higher
credit risk are more likely to charge higher lending rates. Large banks appear to reap
economies of scale which they transfer to their customers in the form of lower lending
rates.

<Insert Table 6 about here>

In line with the literature that analyzes whether benefits of foreign ownership areconstant over time, we split our takeover and greenfield variables separately into
dummies that take the value of 1 if a bank was established (for greenfield) or acquired(for takeover) before 2001 and after that date (in line with our share regressions). Ourfindings (column 2) show that the impact of greenfield mode of foreign entry disappears
with time, which is usually interpreted in the literature as convergence between banks ofdifferent types of ownership due to competition or changes in portfolio composition. Our
results corroborate previous finding in the literature and therefore, our data reflects thesituation in developing and transition countries and does not just deal with a particular
Polish case (Martinez Peria and Mody, 2004; Claeys and Hainz, 2007). As a robustness
check, for all interest rate regressions we also split the greenfield and takeover variables
into dummies that take the value of 1 if a bank was established (for greenfield) or
acquired (for takeover) less than three years ago and banks that were over three years old.
We repeated all estimations. The results are robust to this, and are available upon request.
As mentioned earlier, the disadvantage of the above model is the lack of
information on borrower type, which does not allow us to separate the reasons for lower
lending rates of greenfield banks, i.e. their superior performance or their portfolio
composition targeted to more transparent borrowers. Our first step to remedy this is to28

include the share of transparent and opaque borrowers in banks’ portfolio into ourbaseline regression. The results, presented in column 3 of Table 6, clearly show that the
impact of bank ownership and foreign banks’ mode of entry disappears: average lending
rates between banks are no longer statistically different. It seems, therefore, that theprevious findings suffered from the omitted variable bias, which rendered some of theownership variables significant. Our results also hold if we account for the dynamic
effects (column 4): we no longer see any evidence of convergence in foreign bank’s
interest rates. Consequently, our findings present us with an initial proof of the portfolio
composition hypothesis.

5.3. Results for borrower types – lending rate
To further test the portfolio composition hypothesis, we estimate model (2)separately for private firms and entrepreneurs. Since we estimate our models withhomogeneous borrowers in each sample, the composition effect is removed from ourestimations and we succeed to observe the pure effect of bank ownership and mode ofentry on lending rates. As a result, any remaining differences between banks with respect
to ownership would serve as evidence for our performance hypothesis. If we do not findsuch differences, this will be a proof for the portfolio composition hypothesis.
We present our results for transparent borrowers – private firms – in Table 7a, andfor opaque ones – entrepreneurs – in Table 7b. Our results (column 1 in both tables) show
that, once we control for the portfolio composition effect, the mode of entry of foreignbanks is not an important determinant of lending rates. This means that the mode offoreign bank ownership has no impact on lending rates, which contradicts the existing
literature on the impact of the mode of foreign bank entry on bank lending rates(Martinez Peria and Mody, 2004; Claeys and Hainz, 2007; de Haas and Lelyveld, 2006).

29

Our different results stem from the fact that previous studies were not able to control for
portfolio composition of banks’ loan portfolios.

<Insert Table 7a and b about here>

As mentioned above, currency and maturity may also have important impact oninterest rates charged by banks. To control for currency denomination of loans, we
augment our model with a variable that controls for the share of loans in foreign currency
in banks’ portfolios (column 2 in Tables 7a and 7b). As expected, higher share of foreign
currency loans has a negative impact on average lending rates. However, this fact has
only a slight impact on our final results. We still find that foreign bank ownership andmode of entry do not influence lending rates. However, our results indicate that stateowned banks offer lower lending rates once we control for loan currency. Annualized, thedifference amounts to 2.8 percent, and is both statistically and economically significant.
As shown in the descriptive statistics, state-owned banks extend more loans in domestic
currency than foreign banks. Since these loans are on average more expensive thanforeign currency loans, the failure to control for this factor makes loans extended bystate-owned banks appear to be more expensive as well.
To control for loan maturity we augment our model with variables that capture theshare of short-term and long-term loans in banks’ portfolios (Tables 7a and 7b, column
3). Our results show that maturity is not a significant determinant of lending rates, which
is probably due to a complexity of various factors that play in different directions. More
importantly however, when we look at the impact of foreign bank ownership and themode of entry on lending rates after controlling for loan maturity, our results are robust. It

30

is also the case if we control for both currency and maturity simultaneously (Tables 7aand 7b, column 4).

5.4. Does time influence lending rates?
Even though we do not find an impact of foreign bank ownership and mode ofentry on banks’ lending rates, we would like to analyze whether it is temporary andmaybe banks with longer presence in the market are more able to capitalize on theiradvantages. To do this, we again split our takeover and greenfield variables separately
into dummies that take the value of 1 if bank was newly established (for greenfield) or
acquired (for takeover) before 2001, and banks that entered (or were acquired) after that
date. The results are presented in Tables 8a and 8b for transparent and opaque borrowers,
respectively.

<Insert Table 8a and b about here>

Our findings suggest that time does not play an important role and even after
many years of operations lending rates of foreign banks are not different from those ofdomestic private banks. Our results are very robust to different specifications of variables
accounting for time dynamics. To check the stability of our results we use different year
in which we split the sample, we use bank age (as mentioned above) or we use interaction
variable between age and time and foreign bank dummies assuming a linear relationship.
Our results remain the same and we do not find any age or time dynamic effects. These
results are not reported in the paper for brevity, but are available upon request.
Our results are contrary to the existing literature, which shows that lending rates
of greenfield banks are lower on average but converge with lending rates of other banks

31

in the longer term. We argue that previous findings are due to changing portfolio
composition of foreign banks, and not due to convergence in performance. If we look at
the descriptive statistics in Section 3, we observe that greenfield banks decreased theshare of their loans to large private firms, and more recently, they started to extend lessloans in foreign currency. Both of these factors should contribute to an increase of
average lending rates of greenfield banks over the analyzed period, but this is purely aportfolio composition effect.

6. Findings for domestic banks
From the perspective of public policy, it is not important if foreign banks prefer totarget more transparent clients as long as domestic banks continue to lend to SMEs
(Detragiache et al., 2008). However, Dell’Ariccia and Marquez (2004) predict thatdomestic banks increase the share of opaque clients in the wake of foreign bank entry, but
this brings about the deterioration in their overall portfolio quality. To test thesehypotheses, we analyze the impact of foreign bank entry on the supply of loans andportfolio quality of domestic banks. To this end, we estimate two models on the sample
of domestic banks. First, we analyze the portfolio shares:Pln( it ) = δ 0 + δ1Stateit −1 + δ 2 Share greenfieldt −1 + δ 3 Share takeovert −1
1 − Pit+ δ 4 Bank characteristicsit −1 + δ 5 Macrot −1 + Seasont + ε it ,(3)

Then we look at the non-performing loans:
NPLit = γ 0 + γ 1Stateit −1 + γ 2 Share greenfieldt −1 + γ 3 Share takeovert −1+ γ 4 Macrot −1 + Seasont + ε it ,(4)

where in all regressions Share greenfieldt is the share of greenfield and Sharetakeovert is the share of takeover banks in the total banking loans at quarter t, and NPLt
is the measure for non-performing loans, and the other variables are as defined above.

32

We have seen so far that foreign banks that enter via greenfield investment lend
more to large firms and less to entrepreneurs, they extend more loans at short-term, and inforeign currency. However, the entry of foreign banks might influence the supply of loans
not only directly via lending by these banks, but also indirectly by influencing the
behavior of domestic institutions. For example, higher competition in the market fortransparent borrowers might induce domestic banks to lend more to opaque clients. At thesame time, if greenfield banks cherry pick the best borrowers, the quality of loan
portfolios of domestic banks could deteriorate. To test these hypotheses we include theshare of loans granted by greenfield and takeover banks into the share and nonperforming loans regressions, and run them on the sample of domestic banks.

<Insert Table 9 about here>

The results presented in Table 9 show that domestic banks increased the share ofentrepreneurs in their portfolios in the wake of foreign bank entry. Even though this
impact is only significant in the period after 2001 and for the share of loans granted bygreenfield banks, it is consistent with theoretical predictions of Dell’Ariccia and Marquez(2004). As a result of greenfield entry, domestic banks appear to refocus their lending
more towards opaque clients for whom they have the largest comparative advantages.
In Table 10, we show the impact of foreign bank entry on the level of nonperforming loans of domestic banks. Our results show that foreign banks have an
opposite effect on the quality of loan portfolios of domestic banks depending on theirmode of entry. The entry of greenfield banks decreased the quality of loan portfolios ofdomestic banks to both transparent and opaque clients. This is consistent with the
hypothesis that greenfield banks attract the best clients forcing domestic banks to lend to33

higher risk borrowers, which were likely to be turned down by foreign lenders who
possess superior screening techniques (Claeys and Hainz, 2007). Among entrepreneurs,
greenfield banks might have attracted the relatively more transparent borrowers to whomthey could lend relying on transaction-based lending techniques. This deteriorating
portfolio quality of domestic banks in the wake of foreign bank entry via greenfield
investment is consistent with predictions of Dell’Ariccia and Marquez (2004).

<Insert Table 10 about here>
At the same time, the entry of takeover banks appears to decrease the amount of
non-performing loans of domestic banks. This could be attributed to positive spillovers
with respect to lending techniques, loan monitoring or risk management. The fact that theobserved improvement concerns only loans to private firms suggests that domestic banks
lending to entrepreneurs might rely mostly on relationship lending.

7. Conclusions
Using a unique novel data set providing us with detailed information on bankportfolios, we explore how foreign bank entry determines credit allocation in emerging
markets. In particular, we investigate the impact of the mode of foreign entry – greenfield
and takeover – on banks’ portfolio allocation to borrowers with different degrees of
informational transparency, as well as by maturities and currencies. The impact of foreign
entry on credit allocation may stem from the superior performance of foreign entrants(“performance hypothesis”), or reflect borrower informational capture (“portfolio
composition hypothesis”). Our results are broadly in line with the theoretical models
underpinning the portfolio composition hypothesis, showing that informational capture

34

determines bank credit allocation (see e.g. Dell’Ariccia and Marquez, 2004, or Sengupta,
2007).
Our main result can be further summarized as follows. First, we show that themode of entry is a very important determinant of foreign banks’ portfolio composition.
Greenfield banks devote a higher share of their portfolio to transparent firms and asmaller one to opaque firms than domestic banks, whereas there are no large differences
between takeover banks and domestic private banks. We further find that over time
greenfield banks shifted away from transparent borrowers towards more opaque ones.
This is consistent with theories arguing that greenfield banks have comparativeadvantages at lending to transparent borrowers using hard information initially, but overtime also become privately informed and start servicing opaque borrowers based on soft
information.
Second, we argue that the result established in the existing literature indicatingthat the average lending rate of greenfield banks is lower than that of domestic privatebanks has to be attributed to a different portfolio composition: greenfield banks offer
more loans to transparent borrowers that exhibit a lower cost of credit. When the interest
rates offered to different borrower types are analyzed, the effect of foreign ownership
disappears.
Third, our results reveal that greenfield banks extend more loans in foreign
currency. This reflects their better access to foreign currency funding in international
capital markets either directly or via their parent companies, and the fact that they lend
more to multinational corporations and exporting firms. Moreover, greenfield banks
extend more loans at shorter maturities and less loans at longer maturities, which may
reflect their short-term commitment to host economies. Furthermore, we find very little
evidence of convergence between greenfield and private banks in terms of loan maturity

35and currency. The composition of portfolios has remained the same during the analyzed
period and greenfield banks consistently lend more at shorter maturities and in foreign
currency.
Finally, higher participation of greenfield banks had negative impacts on the loan
portfolio quality of domestic banks. Interestingly, the entry of takeover banks had no
impact on the loan portfolio of domestic banks for opaque firms, but improved the loan
portfolio quality of transparent firms. This suggests spillover effects as domestic
institutions learn to use transaction-based lending techniques in lending to transparent
borrowers.

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40

Table1. Definition of variables used
VariableState-owned
TakeoverGreenfield
Lending rate
NPL
Market share
Capitalization
Cost
Herfindahl Index

Definition
A dummy variable which takes the value of one if more than 50 percent of the bank isowned by the state, zero otherwise
A dummy variable which takes the value of one if more than 50 percent of the bank isowned by the foreign investor, which entered the market via acquisition of an
existing bank, zero otherwise
A dummy variable which takes the value of one if more than 50 percent of the bank isowned by the foreign investor, which entered the market via establishing a new bank,zero otherwise
The ratio of interest income to total loans. This variable is calculated for all
borrowers, and for private firms and entrepreneurs separately. Calculated quarterly,
unless explicitly stated otherwise
The ratio of non-performing loans to total loans. This variable is calculated for all
borrowers and for private firms and entrepreneurs separately.
Share of loans of a bank in the total loans of banking sector in host country. Thisvariable is calculated for all borrowers, and for private firms and entrepreneurs
separately.
The level of risk adjusted capital
The ratio of personnel and administrative costs to total assets
Herfindahl index, calculated as the sum of squared shares of loans. This variable iscalculated for all borrowers and for private firms and entrepreneurs separately.
The ratio of loans to private firms (entrepreneurs) in bank’s portfolio.

Share private(entrepreneur)
Share greenfield (takeover) The ratio of loans granted by greenfield (takeover) banks to total loans granted by allbanks in quarter t
FX loans
The ratio of loans in foreign currency in bank’s loan portfolio. This variable iscalculated for all borrowers, and for private firms and entrepreneurs separately.
Short-term loans
The ratio of loans with maturity less than 1 year in a bank’s loan portfolio. Thisvariable is calculated for all borrowers, and for private firms and entrepreneurs
separately.
Long-term loans
The ratio of loans with maturity over 5 years in a bank’s loan portfolio. This variable
is calculated for all borrowers, and for private corporations and entrepreneurs
separately.
GDP
Real quarterly growth rate of GDP
Inflation
Quarterly inflation rate
Real interest rate
Real short-term interest rate, calculated using 3 month WIBOR (Warsaw interbank
offered rate) interest rate and inflation rate by Fisher equation

41

Table 2. Summary statistics
Variable

Share of loans to private firms in portfolios ofgreenfieldtakeoverprivatestate-owned
Share of loans to entrepreneurs in portfolios ofgreenfieldtakeoverprivatestate-owned
Share of loans up to 1 year in portfolios ofgreenfieldtakeoverprivatestate-owned
Share of loans from 1 to 5 years in portfolios ofgreenfieldtakeoverprivatestate-owned
Share of loans over 5 years in portfolios ofgreenfieldtakeoverprivatestate-owned
Share of loans in foreign currency in portfolios ofgreenfieldtakeoverprivatestate-owned
Lending rate
Lending rates on loans issued bygreenfieldtakeoverstate-owned
privateLending rate on loans to
Private firms
by greenfieldby takeoverby state-owned
by private
Entrepreneurs
by greenfieldby takeoverby state-owned
by private
Non-performing loans
Non-performing loans to private firms bygreenfieldtakeoverprivatestate-owned

Obs

Mean Std. Dev. A B

750
738
563
330

0.667
0.404
0.329
0.292

0.348 *** ***0.263 ***
0.225
0.166 ***

750
738
563
330

0.082
0.098
0.151
0.103

0.141 *** ***0.082 ***
0.120
0.099 ***

750
738
563
330

0.537
0.358
0.411
0.296

0.327 *** ***0.178 ***
0.214
0.169 ***

750
738
563
330

0.306
0.381
0.374
0.350

0.283 *** ***
0.177
0.177
*
0.204

750
738
563
330

0.157
0.261
0.215
0.353

0.179 *** ***0.194 ***
0.203
0.267 ***

750
738
563
330
2073

0.302
0.216
0.107
0.108
0.174

0.235 *** ***0.208 ***
0.161
*
0.127
0.126

632
605
307
529

0.147
0.165
0.174
0.217

0.089 ***
0.161 ***
0.086 ***
0.126

2151
618
600
307
524
1836
329
598
307
523
2151

0.167
0.152
0.155
0.161
0.212
0.190
0.145
0.173
0.197
0.235
0.368

0.141
0.138 ***
0.153 ***
0.085 ***
0.155
0.170
***
0.199 ***
0.152 ***
0.197 ***
0.180
1.233

739
738
563
330

0.078
0.444
0.808
0.565

1.241 *** ***1.466 ***
1.746
1.167 **

42

Table 2. Summary statistics (continued)
Variable
Non-performing loans to entrepreneurs bygreenfieldtakeoverprivatestate-owned
Non-resident interbank liabilities bygreenfieldtakeoverprivatestate-owned
ROA
Cost
Capitalization
Herfindahl Index
private firms
entrepreneurs
Market share
GDP growth
Inflation
Real interest rate

Obs

Mean Std. Dev. A B

451
736
563
330

0.135
0.363
0.843
0.103

2.189 ***
1.281 ***
1.718
0.099

**

375
401
117
107
2196
2284
2284
2270
2270
2270
2269
2160
2160
2160

0.140
0.054
0.002
0.016
0.001
0.013
0.205
0.072
0.068
0.070
0.017
0.016
0.015
0.018

0.193 *** ***0.114 ***
0.008
0.032 ***
0.074
0.022
0.327
0.009
0.009
***
0.012
0.031
0.086
0.015
0.013

*** **, , and * correspond to 1%, 5% and 10% significance levels that the difference: in column A between
greenfield, takeover, state-owned and private banks, and in column B between private firms andentrepreneurs is different from zero.

43

Table 3. Borrower type: share of loans to private firms and entrepreneurs
Table 3 presents the results of equation (1). The dependent variable is the log-odds ratio of the share of loans to private firms andentrepreneurs in banks’ portfolios. The table lists coefficients and standard errors (in parentheses) from regression with pooled OLS
with robust standard errors clustered on banks. All dependent variables except for ownership dummies are lagged by one quarter.
Regressions include seasonal dummies. Definitions of variables are provided in Table 1. ***, **, and * correspond to 1%, 5% and 10%
significance levels, respectively.

Greenfield
TakeoverState-owned
Greenfield*Before 2001

Private firms(transparent)
12.286***[0.546]-0.108[0.529]-0.454[0.374]

Greenfield*After 2001
Takeover*Before 2001
Takeover*After 2001
ROA
Cost
Capitalization
Bank size
NPL
GDP
Inflation
Real interest rate
Observations
No. of banks
R-squared-21.192***[6.915]-105.978***[16.64]
1.666***[0.349]0.238**[0.115]-0.237**[0.105]1.707[1.496]29.458***[7.144]23.402***[6.612]
2133
107
0.365

Private firms(transparent)
2-0.492[0.380]2.641***[0.567]1.906***[0.594]0.232[0.489]-0.452[0.608]-20.561***[6.859]-105.166***[16.463]1.644***[0.338]0.236**[0.118]-0.240**[0.102]1.353[1.533]16.877**[6.880]17.266***[6.171]
2133
107
0.372

Entrepreneurs(opaque)

Entrepreneurs(opaque)-0.373[5.578]0.043[17.493]-1.651[1.018]-0.255***[0.095]-0.440***[0.101]
1.903[2.099]5.559[8.509]7.734[7.142]
1802
101
0.199

-0.223[0.331]-2.046***[0.735]-0.756[0.505]-0.036[0.289]-0.589[0.430]-1.001[5.336]0.308[18.484]-1.472[1.013]-0.229**[0.094]-0.442***[0.103]2.058[2.107]7.322[6.540]9.652[6.408]
1802
101
0.226

3
-1.143**[0.508]-0.396[0.336]-0.188[0.335]

4

44

Table 4. Loan maturity: share of loans by maturity in banks’ portfolios
Table 4 presents the results of equation (1). The dependent variable is the log-odds ratio of the share of loans with maturity up to 1
year (models 1 and 2) and over 5 years (models 3 and 4) in banks’ portfolios. The table lists coefficients and standard errors (inparentheses) from regression with pooled OLS with robust standard errors clustered on banks. All dependent variables except forownership dummies are lagged by one quarter. Regressions include seasonal dummies. Definitions of variables are provided in Table
1. ***, **, and * correspond to 1%, 5% and 10% significance levels, respectively.

Greenfield
TakeoverState-owned
Greenfield*Before 2001

Up 1 year
11.030**[0.397]0.022[0.223]-0.729**[0.335]

Greenfield*After 2001
Takeover*Before 2001
Takeover*After 2001
ROA
Cost
Capitalization
Bank size
NPL
GDP
Inflation
Real interest rate
Observations
No. of banks
R-squared-6.286**[2.525]-15.891[11.495]
0.061[0.275]0.120*[0.067]0.182*[0.094]1.73[1.299]29.968***[4.393]27.045***[4.786]
2155
108
0.213

Up 1 year2-0.728**[0.336]
1.168***[0.438]0.924**[0.402]0.003[0.257]0.005[0.228]-6.205**[2.504]-16.154[11.528]
0.052[0.279]0.114*[0.067]0.180*[0.093]1.618[1.316]27.745***[4.221]25.739***[4.362]
2155
108
0.214

Over 5 years
3-1.043**[0.400]-0.721[0.458]0.196[0.449]-7.517[7.223]-30.477[21.138]
1.448**[0.553]0.441***[0.135]-0.05[0.101]-2.596[2.003]-13.767*[8.235]-12.552[8.087]
2024
108
0.229

Over 5 years
4

0.170[0.447]-1.240**[0.482]-0.964**[0.414]-0.403[0.368]-0.921[0.558]-7.274[7.037]-28.807[21.657]
1.464**[0.579]0.457***[0.141]-0.045[0.102]-2.52[2.043]-15.800**[7.097]-12.778*[7.418]
2024
108
0.235

45

Table 5. Foreign currency: share of loan portfolio in foreign currency
Table 5 presents the results of equation (1). The dependent variable is the log-odds ratio of the share of loans in foreign currency inbanks’ portfolios. The table lists coefficients and standard errors (in parentheses) from regression with pooled OLS (robust standard
errors clustered on banks). All dependent variables except for ownership dummies are lagged by one quarter. Regressions include
seasonal dummies. Definitions of variables are provided in Table 1. ***, **, and * correspond to 1%, 5% and 10% significance levels,
respectively.

Greenfield
TakeoverState-owned
Greenfield*Before 2001

1
1.840***[0.497]0.354[0.579]-0.425[0.512]-13.463[10.570]-30.016[30.239]0.999*[0.594]0.378***[0.110]-0.121*[0.068]-0.413[0.510]1.997***[0.482]
1.739***[0.558]0.220[0.650]0.410[0.618]-13.582[10.575]-30.901[29.790]0.987*[0.585]0.369***[0.109]-0.124*[0.068]0.334[1.866]
9.832[9.098]14.099[10.227]
1662
92
0.223

0.144
[1.855]8.933[10.109]
13.097[10.641]
1662
92
0.225

Greenfield*After 2001
Takeover*Before 2001
Takeover*After 2001
ROA
Cost
Capitalization
Bank size
NPL

2Interbank liabilities
GDP
Inflation
Real interest rate
Observations
No. of banks
R-squared

3
1.689***[0.481]0.307[0.573]-0.463[0.503]-12.168[10.244]-25.788[30.089]
1.055*[0.608]0.397***[0.110]-0.114*[0.066]
1.926***[0.669]0.085[1.875]13.752[9.492]18.912*[10.726]
1662
92
0.234

46

Table 6. Average bank lending rate
Table 6 presents the results of equation (2). The dependent variable is the bank-specific average lending rate. The table lists
coefficients and standard errors (in parentheses) from regression with pooled OLS with robust standard errors clustered on banks. All
dependent variables except for ownership dummies are lagged by one quarter. Regressions include seasonal dummies. Definitions ofvariables are provided in Table 1. ***, **, and * correspond to 1%, 5% and 10% significance levels, respectively.

Greenfield
TakeoverState-owned
Takeover*Before 2001

1
-0.009**[0.0040.002[0.005]-0.002[0.005]

Takeover*After 2001
Greenfield*Before 2001
Greenfield*After 2001
Share private

2-0.001[0.005]0.001[0.004]0.004[0.007]-0.010**[0.004]-0.007[0.005]

Share entrepreneur
Capitalization
Cost
NPL
Market share
Herfindahl Index
GDP
Inflation
Real interest rate
Observations
No. of banks
R-squared

0.006[0.012]0.470***[0.093]0.010**[0.005]-0.114**[0.048]0.228**[0.087]-0.016[0.016]
0.663***[0.101]0.641***[0.126]
2271
106
0.323

0.006
[0.012]
0.469***[0.093]0.010**[0.005]-0.116**[0.052]0.189*[0.097]-0.013[0.017]0.718***[0.092]
0.664***[0.121]
2271
106
0.325

3
0.003
[0.005]0.005[0.005]-0.004[0.005]-0.036***[0.012]-0.011[0.009]0.01[0.012]0.087[0.130]0.011**[0.005]-0.106**[0.041]0.225**[0.087]0.008[0.018]0.705***[0.083]0.719***[0.098]
2271
106
0.399

4

-0.004[0.005]0.002[0.004]0.006[0.006]0.003[0.005]0.003[0.005]-0.036***[0.012]-0.01[0.009]0.01[0.012]0.085[0.131]0.011**[0.005]-0.109**[0.044]0.212**[0.092]0.009[0.018]0.728***[0.084]0.725***[0.101]
2271
106
0.399

47

Table 7a. Loan rates to private firms
Table 7a presents the results of equation (2). The dependent variable is the bank-specific interest rate on loans to private firms (i.e.
transparent). The table lists coefficients and standard errors (in parentheses) from regression with pooled OLS with robust standard
errors clustered on banks. Herfindahl Index, Market Share, FX loans, Short-term loans and Long-term loans are calculated for private
firms. All dependent variables except for ownership dummies are lagged by one quarter. Regressions include seasonal dummies.
Definitions of variables are provided in Table 1. ***, **, and * correspond to 1%, 5% and 10% significance levels, respectively.

Greenfield
TakeoverState-owned
FX loans

1-0.001[0.005]0.004[0.005]-0.006[0.004]

Short-term loans

2
0.005[0.004]0.005[0.004]-0.007*[0.004]-0.036***[0.006]Long-term loans
Capitalization
Cost
NPL
Market share
Herfindahl Index
GDP
Inflation
Real interest rate
Observations
No. of banks
R-squared-0.008**[0.004]0.454**[0.183]0.007***[0.001]-0.169***[0.040]0.287***[0.095]-0.042[0.030]0.646***[0.088]0.781***[0.004]
2239
104
0.267

-0.004[0.003]0.260*[0.143]0.007***[0.001]-0.083***[0.023]0.258***[0.087]-0.034[0.029]0.697***[0.072]0.885***[0.005]
2233
104
0.313

3
-0.001[0.005]0.003[0.004]-0.004[0.003]-0.006[0.010]-0.017[0.012]-0.006*[0.003]0.372**[0.158]0.007***[0.001]-0.130***[0.043]0.254***[0.086]-0.043[0.030]0.628***[0.088]0.780***[0.003]
2233
104
0.284

4
0.004
[0.005]0.005[0.004]-0.006**[0.003]-0.035***[0.006]-0.004[0.008]-0.010[0.010]-0.004[0.003]0.240*[0.144]0.007***[0.001]-0.068**[0.030]0.251***[0.086]-0.033[0.029]0.675***[0.083]0.868***[0.005]
2233
104
0.315

48

Table 7b. Loan rates to entrepreneurs
Table 7b presents the results of equation (2). The dependent variable is the bank-specific interest rate on loans to entrepreneurs (i.e.
opaque). The table lists coefficients and standard errors (in parentheses) from regression with pooled OLS with robust standard errors
clustered on banks. Herfindahl Index, Market Share, FX loans, Short-term loans and Long-term loans are calculated for entrepreneurs.
All dependent variables except for ownership dummies are lagged by one quarter. Regressions include seasonal dummies. Definitionsof variables are provided in Table 1. ***, **, and * correspond to 1%, 5% and 10% significance levels, respectively.

Greenfield
TakeoverState-owned
FX loans

1-0.005[0.007]-0.001[0.006]-0.004[0.006]

Short-term loans

20.001[0.006]-0.001[0.006]-0.004[0.005]-0.031***[0.007]Long-term loans
Capitalization
Cost
NPL
Market share
Herfindahl Index
GDP
Inflation
Real interest rate
Observations
No. of banks
R-squared

0.013[0.017]0.533[0.331]0.009***[0.002]-0.115***[0.036]0.137[0.095]-0.099**[0.038]0.617***[0.127]0.529***[0.174]
1924
98
0.189

[0.0150.018][0.4500.329]
0.009***[0.002]-0.083***[0.026]0.158*[0.095]-0.092**[0.038]0.586***[0.123]0.540***[0.174]
1924
98
0.219

3
-0.004[0.007]-0.002[0.006]-0.004[0.005]0.001[0.007]0.001[0.017]0.016[0.017]0.561[0.349]0.009***[0.002]-0.107***[0.036]
0.162*[0.095]-0.103***[0.039]0.629***[0.128]0.540***[0.172]
1924
98
0.196

4
0.001
[0.006]0.001[0.005]-0.004[0.005]-0.031***[0.007]0.001[0.007]0.007[0.017]0.015[0.017]0.459[0.332]0.009***[0.002]-0.085***[0.028]0.162*[0.094]-0.092**[0.038]0.605***[0.120]0.558***[0.168]
1924
98
0.219

49

Table 8a. Loan rates to private firms (with time effects)
Table 8a presents the results of equation (2). The dependent variable is the bank-specific interest rate on loans to private firms (i.e.
transparent). The table lists coefficients and standard errors (in parentheses) from regression with pooled OLS with robust standard
errors clustered on banks. Herfindahl Index, Market Share, FX loans, Short-term loans and Long-term loans are calculated for private
firms. All dependent variables except for ownership dummies are lagged by one quarter. Regressions include seasonal dummies.
Definitions of variables are provided in Table 1. ***, **, and * correspond to 1%, 5% and 10% significance levels, respectively.

Greenfield*Before 2001
Greenfield*After 2001
Takeover*Before 2001
Takeover*After 2001
State-owned
FX loans

1-0.001[0.005]-0.001[0.006]0.004[0.004]0.004[0.007]-0.006[0.004]

Short-term loans

2
0.006[0.005]0.004[0.005]0.006[0.004]0.005[0.006]-0.007*[0.004]-0.036***[0.006]Long-term loans
Cost
Capitalization
NPL
Market share
Herfindahl Index
GDP
Inflation
Real interest rate
Observations
No. of banks
R-squared

0.453**[0.186]-0.008**[0.004]0.007***[0.001]-0.169***[0.044]0.279**
0.129-0.042[0.030]0.654***[0.139]0.784***[0.096]
2239
104
0.267

0.261*[0.144]-0.005[0.003]0.007***[0.001]-0.082***[0.025]0.274**
0.112-0.034[0.030]0.679***[0.115]0.878***[0.088]
2233
104
0.314

3
-0.002[0.005]0.001[0.006]0.002[0.004]0.002[0.007]-0.004[0.003]-0.005[0.010]-0.017[0.013]0.373**[0.160]-0.006*[0.003]0.007***[0.001]-0.128***[0.045]0.258**[0.113]-0.043[0.030]0.625***[0.137]0.780***[0.102]
2233
104
0.285

4
0.005
[0.005]0.003[0.006]0.005[0.004]0.004[0.006]-0.007*[0.003]-0.035***[0.006]-0.004[0.008]-0.010[0.010]0.241*[0.145]-0.004[0.003]0.007***[0.001]-0.066**[0.033]0.276**[0.111]-0.033[0.029]0.647***[0.133]0.856***[0.098]
2233
104
0.315

50

Table 8b. Loan rates to entrepreneurs (with time effects)
Table 8a presents the results of equation (2). The dependent variable is the bank-specific interest rate on loans to entrepreneurs (i.e.
opaque). The table lists coefficients and standard errors (in parentheses) from regression with pooled OLS with robust standard errors
clustered on banks. Herfindahl Index, Market Share, FX loans, Short-term loans and Long-term loans are calculated for entrepreneurs.
All dependent variables except for ownership dummies are lagged by one quarter. Regressions include seasonal dummies. Definitionsof variables are provided in Table 1. ***, **, and * correspond to 1%, 5% and 10% significance levels, respectively.

Greenfield*Before 2001
Greenfield*After 2001
Takeover*Before 2001
Takeover*After 2001
State-owned
FX loans

1-0.006[0.005]-0.004[0.009]-0.002[0.005]0.001[0.008]-0.004[0.006]

Short-term loans

20.001[0.006]0.002[0.008]-0.001[0.004]0.001[0.007]-0.004[0.005]-0.031***[0.007]0.532[0.333]0.013[0.017]0.009***[0.002]-0.116***[0.037]0.136[0.094]-0.097**[0.040]0.644***[0.172]0.541***[0.199]
1924
98

0.449
[0.330]0.015[0.018]0.009***[0.002]-0.084***[0.026]0.158*[0.094]-0.091**[0.040]0.603***[0.166]0.547***[0.197]
1903
98

0.001
[0.007]0.001[0.017]0.561[0.352]0.016[0.017]0.009***[0.002]-0.108***[0.037]0.160*[0.094]-0.102**[0.041]0.660***[0.167]0.555***[0.195]
1903
98

0.189

0.219

0.196

Long-term loans
Cost
Capitalization
NPL
Market share
Herfindahl Index
GDP
Inflation
Real interest rate
Observations
No. of banks
R-squared

3-0.006[0.005]-0.002[0.009]-0.002[0.005]-0.001[0.008]-0.004[0.006]4
0.001[0.005]0.001[0.008]0.001[0.004]0.002[0.007]-0.004[0.005]-0.031***[0.007]0.001[0.007]0.007[0.017]0.458[0.333]0.015[0.017]0.009***[0.002]-0.086***[0.029]0.161*[0.093]-0.091**[0.040]0.623***[0.160]0.566***[0.190]
1903
98
0.219

51

Table 9. Domestic banks: share of loans to private firms and entrepreneurs
Table 9 presents the results of equation (3).The dependent variable is the log-odds ratio of the share of loans to private firms (model 1
and 2) and entrepreneurs (model 3 and 4) in domestic banks’ portfolios. The table lists coefficients and standard errors (in parentheses)from regression with pooled OLS with robust standard errors clustered on banks. All dependent variables except for ownershipdummy are lagged by one quarter. Regressions include seasonal dummies. Definitions of variables are provided in Table 1. ***, **, and*correspond to 1%, 5% and 10% significance levels, respectively.

Private(transparent)
State-owned
Share greenfield (in this segment)
Share takeover (in this segment)
Share of greenfield*Before 2001

1
-0.022[0.399]-1.502[4.375]-0.657[0.470]Share of greenfield*After 2001
Share of takeover*Before 2001
Share of takeover*After 2001
ROA
Cost
Capitalization
Bank size
NPL
GDP
Inflation
Real interest rate
Observations
No. of banks
R-squared

1.401[3.816]-115.361***[31.760]
0.040[0.636]-0.144[0.118]-0.260***[0.083]-0.241[1.506]-3.407[6.503]-3.464[7.792]
775
54
0.237

Private(transparent)

2-0.017[0.401]

3.475[5.707]
2.164[5.586]-0.972[0.631]-0.136[1.108]1.584[3.801]-114.849***[31.456]
0.006[0.635]-0.148[0.119]-0.261***[0.082]0.255[1.949]2.817[7.268]-0.204[7.442]
775
54
0.239

Entrepreneurs(opaque)3-0.13[0.349]7.787**[3.124]-0.378[0.498]-5.183*[2.913]-68.393*[34.312]-1.613**[0.774]-0.260***[0.096]-0.372***[0.090]0.973[1.209]9.807*[5.724]
11.553[7.289]
781
54
0.361

Entrepreneurs(opaque)
4-0.127[0.349]5.127[5.633]13.789***[4.818]-0.329[0.509]-1.299[1.344]-5.381*[2.909]-68.657*[34.354]-1.659**[0.758]-0.264***[0.097]-0.374***[0.089]-0.099[1.407]8.467[6.747]
12.179*[7.057]
781
54
0.363

52

Table 10. Domestic banks: non-performing loans for private firms and entrepreneurs
Table 10 presents the results of equation (4). The dependent variable is the ratio of non-performing loans to total loans for private
firms (model 1 and 2) and entrepreneurs (model 3 and 4), calculated as a deviation from the median. The table lists coefficients andstandard errors (in parentheses) from regression with pooled OLS with robust standard errors clustered on banks. All dependent
variables except for ownership dummy are lagged by one quarter. Regressions include seasonal dummies. Definitions of variables areprovided in Table 1. ***, **, and * correspond to 1%, 5% and 10% significance levels, respectively.

PrivateState-owned
Share greenfield (in this segment)
Share takeover (in this segment)
Share of greenfield*Before 2001

(transparent)

1.057[1.133]-14.256*[7.280]-14.394[8.917]
837
56
0.030

4.353
[4.588]17.052***[6.387]-1.007*[0.576]-2.068**[0.874]0.160[0.961]-13.021*[6.786]-11.131[7.401]
847
56
0.036

1
-0.247[0.278]4.875[5.135]-0.988*[0.531]

Share of greenfield*After 2001
Share of takeover*Before 2001
Share of takeover*After 2001
GDP
Inflation
Real interest rate
Observations
No. of banks
R-squared

Private(transparent)

2-0.248[0.275]

Entrepreneurs

Entrepreneurs

3-0.127[0.405]9.518**[4.368]0.057[0.478]4-0.134[0.400](opaque)

0.583[1.015]-9.716[8.918]-11.404[11.238]
837
56
0.025

(opaque)

7.934[4.826]21.349**[10.042]-0.024[0.537]-1.160[1.079]-0.043[1.068]-9.250[7.674]-8.413[8.524]
847
56
0.031

53