Market Concentration and the Cost of Bank Borrowing ∗
Fabián Duarte
SBIF and Central Bank of Chile
Andrea Repetto
Universidad de Chile
Rodrigo O. Valdés
Central Bank of Chile
November 30, 2004
Preliminary and Incomplete — Comments are Welcome - Do Not Quote
Abstract
Over the last decade the Chilean banking industry underwent an unprecedented
increase in concentration, mostly through mergers and acquisitions (M&As). For
instance, in 1990 the market share of the largest four banks was about 49%. By 2002,
this index had reached almost 60%. The goal of this paper is to study the effects
on bank clients of these changes in market concentration and M&As, by analyzing
their effects on the loan rates paid by a sample of Chilean manufacturing firms over
the 1990-98 period. Using a unique data set on credit transactions between banks
and their clients, we study whether borrowers have benefited from the increase in
industry concentration. To identify these effects we first study the role of changes
in the market share of the banks a firm effectively borrows from. We then focus on
M&As, comparing the change in the borrowing conditions of firms that experienced
a bank merger first hand (i.e., at the time of the merger they held loans from banks
that merged) with the change for firms that were not directly affected by the merger.
Our results are consistent with the hypothesis that bank lending is characterized by
borrower capture — perhaps due to informational monopolies and other sources of
switching costs —, as the firms that suffer most from increased market concentration
are those that have no alternative lending sources. The efficiency gains of increased
concentration are shared only with firms that hold loans from multiple banks.
JEL Codes: E51, G21, G32.
Key Words: Bank Concentration; Bank M&As.
∗
We thank Álvaro García and Francisco Nahmías for their help in building the data set. We also
thank the SBIF for sharing their data and their infrastructure with us, and comments from Gabriel
Aparici, Soledad Arellano, Raphael Bergoeing, Solange Berstein, Alexander Galetovic, Patricio Meller,
and seminar participants at the University of Chile and the Central Bank of Chile. Financial support
from FONDECYT (#1040798) and an institutional grant to CEA from the Hewlett Foundation are
gratefully acknowledged. This paper represents the views of the authors and does not necessarily reflect
the opinion of either the Superintendency of Banks and Financial Institutions or the Central Bank of
Chile. All remaining errors are our responsibility.
1
1
Introduction
The Chilean banking industry has changed dramatically over the last three decades.
The pre-1973 period was characterized by heavy government intervention and financial
repression, whereas the 1974-81 period was characterized by market liberalization. After
the deep banking crisis of the early 80s, and since the mid 80s, policy moved towards a
prudential regulation framework . Technical and financial developments have also shaped
the transformation of the industry, as advances in telecommunication and information
processing technologies have dramatically reduced transactions costs. Furthermore, the
gradual deregulation of flows to and from international markets have also been a central
driving force behind the industry changes.
One outcome of these developments has been the increasing concentration of the
Chilean banking industry as measured by standard indexes. For instance, in 1990 the
market share of the largest four banks (C4) was about 49%. By 2002, this index had
reached almost 60%. The number of commercial banks in operation also declined steadily
over the period from 40 to 26. This decline was partly a result of mergers and acquisitions
(M&As). The goal of this paper is to study the effects on bank clients of these changes
in market concentration and M&As, by analyzing their effects on the loan rates paid by
a sample of Chilean manufacturing firms over the 1990-98 period.
In most markets, the effects of concentration and mergers can be analyzed by focusing
on market power and efficiency. That is, if a bank gains market share as a result
of increased efficiency — through economies of scale and scope, through synergies, or
through the selection of the best producers — then costumers may gain through price
reductions and larger traded volumes (Farrell and Shapiro, 1990). However, if suppliers
gain market power as they consolidate, then clients’ welfare may be reduced through
lower good provision and higher prices.
This analysis misses, however, that informational frictions characterize lending markets. Banks invest in building relationships with their costumers in order to better
evaluate the risk of any given loan (Diamond, 1984). If the information that is gathered
through the lending process is not easily conveyed to outsiders, then banks can build up
informational monopolies that allow them to extract rents from clients. This hold up
problem can make it costly for a firm to switch lenders as it may signal that the most
informed bank is not willing to lend to the firm. Firms, thus, have incentives to establish
relationships with multiple lenders (Sharpe, 1990; Rajan, 1992). These informational
switching costs become particularly relevant during episodes of rising market share. For
instance, a firm that has established relationships with two banks that ex-post merge,
losses its ability to limit lenders’ power through switching its funding source. Rising
concentration and mergers thus produce borrower capture. Moreover, relationships are
built through repeated contact between the client and particular bank officers. If these
2
matches are broken over a merger, then valuable information on client’s risk may be lost.
In this paper we analyze whether bank clients’ terms of lending improve or worsen
around shifts in market concentration. We first explore the effects of changes that
occur in a continuous manner to then focus on bank M&As. The use of micro data
allows us to identify exogenous sources of variation in lending concentration. That is,
from the individual firm’s perspective, changes in market concentration (either small
and continuous or large and discrete) are exogenous events that do not depend on any
particular borrower’s decisions. The analysis of bank concentration and mergers using
aggregate data cannot identify an exogenous “treatment” effect, as the extent of banking
concentration depends upon the relative efficiency of the system, and hence the interest
rates charged and the volume of loans granted. In other words, on aggregate, market
concentration and bank mergers are endogenous, but are exogenous to each individual
borrower in the market.
Our methodology allows for a heterogeneous response of firms, depending upon the
identity and the number of banks the firms borrowed from prior to changes in market
concentration. Our benchmark results show that if the average market share of the banks
a firm borrows from rises, then the typical firm will experience a drop in the interest
rates paid. Only firms that borrow from a single bank suffer from rising borrowing
costs. Specifically, our point estimates indicate that for every percentage point rise in
the single lender’s market share, the interest rate charged increases by 0.36 percentage
points. However, the rate paid by firms that borrow from three or more banks falls by
about 0.5 percentage points.
We then extend our analysis to the M&As that occured over the 1990-98 period. Using a difference-in-difference approach we analyze whether bank clients’ terms of lending
improve or worsen around merger events. To identify these effects we compare the change
in borrowing behavior of firms that directly faced a bank merger (i.e., at the time of
the merger held loans from the merging banks) to the change for firms that were not
directly affected by the merger. We find that firms that exclusively borrowed from the
banks involved in the merger suffer the most.
Our results point out to the fact that having alternative lending sources isolates
firms from the adverse effects that rising concentration and mergers may convey. Thus
our findings are consistent with the existence of informational monopolies and switching
costs. Moreover, the efficiency gains that result from larger market shares are passed on
only to clients that face lower switching costs.
The remainder of the paper is organized as follows. In section 2 we provide a brief
characterization of the Chilean banking industry. In section 3, we review the construction
and properties of our data set to then report the estimation results in section 4. In the
fifth section we analyze the M&A episodes. We first describe our methodology and build
3
treatment and comparison groups. We then present our estimation results. We conclude
in section 6.
2
Concentration in the Chilean banking industry
Over the years 1990-1998 Chile experienced a period of high and sustained growth. GDP
per capita grew at an annual average rate of 6.5%; i.e, in only 8 years, income per capita
increased by 65%. Banking loans amounted to 60% of GDP in 1990-98 on average. This
share grew at a fast pace over the period: by 1998 it was equal to 71.5%. Although large
compared to other Latin American countries, the ratio of banking loans to GDP is still
low at international standards.
The banking system consolidated over the decade. After a number of regulatory
changes — that responded to the massive failure of banks during the crisis of 198283 — the industry experienced strong growth and a sharp increase in its international
integration. The concentration of the system also increased. Figure 1 plots the evolution
of two loan based concentration measures: the Herfindahl and the share of the largest
four banks (C4). Both indicators show a large rise in concentration The loan based
Herfindahl index reached its maximum level (1426) by the end of 2002. At the time, the
C4 concentration index was equal to 67.5%. Despite this tendency, the concentration of
the Chilean system is still relatively low, compared to international standards (Levine,
2000).
The figure also shows that market concentration experienced a number of discrete
changes (1996, 1997, 2001, and 2002). All these episodes correspond to M&A activity.
The arrows in the figure mark all the M&A events that occured over the period. The
first three episodes involved a mid sized and a small bank, and did not change the
concentration of the industry in a significant manner. The next four involved larger
banks, and thus had an effect on market concentration. The M&A activity also led to
a significant decline in the number of commercial banks operating in Chile. Figure 2
shows the evolution of the number of banks. By December of 1990 there were 40 banks
in business in Chile. By December of 2002 only 26 banks were in operation.
The indicators presented in Figure 3 show that the system has remained profitable
and healthy. Profits as a percentage of assets averaged 5.86%, whereas past-due loans
as a fraction of total loans averaged 1.17%. Moreover, efficiency, measured as expenses
over gross interest margin, decreased slightly from 61% to 58%.
4
3
Data
The data in this study come from two sources. The first data set gathers information on
all credit transactions between commercial banks and firms. The information is collected
by the Superintendencia de Bancos e Instituciones Financieras (SBIF), the commercial
bank regulatory and supervision government agency. The data set contains information
on the amount borrowed by each firm from each commercial bank, and the fraction of
outstanding and overdue loans (including also data on credits paid late). In Chile, all
firms and individuals are assigned a unique identification or taxpayer code when they
are born or legally incorporated, known as Rol Único Tributario or RUT. This code is
recorded in the data set, and allows us to follow firms over time.1
This data set has been matched with the second source we use, the Encuesta Nacional Industrial Anual or ENIA, a survey of manufacturing firms conducted annually by
the statistics government agency (Instituto Nacional de Estadísticas, INE). The ENIA
covers all manufacturing plants with at least ten employees. Thus, it includes all newly
created and continuing plants with ten or more employees, and it excludes plants that
ceased activities or reduced their hiring below the survey’s threshold. The ENIA covers about 50% of total manufacturing employment.2 It collects detailed information on
plant characteristics, such as manufacturing subsector (at the 4 digit ISIC level), ownership status, sales, employment, location, investment, and interest payments including
inflation adjustments and bank commissions paid.3 Although not reported in the publicly available data set, the survey records the firms’ RUT, so the two data sets can be
matched.
To construct a proxy of interest rates, we use total interest payments from ENIA
and the average outstanding debt from SBIF. Unfortunately, matching firms across
surveys may induce a series of measurement problems. First, since interest includes
payments accrued to both banking and non-banking debt we may overestimate the true
interest rate paid. However, debt is possibly overestimated, since the SBIF data gathers
information on all the firm’s activities, whereas the ENIA only records manufacturing
related activities. Thus, if a firm produces manufacturing and non-manufacturing goods
and services under the same RUT, the SBIF data will represent a broader set of activities
than the ENIA. This means that we may overestimate the debt, relative to other firm
characteristics, and thus may reduce any possible overestimation of the interest rate.
Finally, the ENIA records information at the plant level, and not at the firm level. Still,
we were able to add up information on plants belonging to the same firm as long as they
1
To protect the firms’ identity, RUTs were deleted from our sample by SBIF and Central Bank
statisticians. Firms were randomly assigned a new identification code that allows us to follow them over
time.
2
Industrial employment represents roughly 16% of total Chilean employment.
3
Inflation adjustments on financial contract interest rates are due to the widespread use of indexation
clauses in Chile.
5
produce under the same RUT.
These and other possible measurement problems lead to the existence of a number
of important outliers in the distribution of interest rates paid. The top panel of Table
1 presents some basic sample statistics. For instance, whereas the mean interest rate
paid is extremely high, the median behaves in a manner similar to aggregate rates in
the economy (Figure 4).4 To partly correct for these extreme outliers, we excluded firms
that report rates over a hundred percent.5 We also excluded firms with either no debt or
no interest payments, and those that had no bank loans outstanding. The lower panel
of the Table presents the sample statistics after exclusions were made. Finally, since the
median, unlike the mean, is less affected by extreme observations, the regression analysis
below is based on Least Absolute Deviations (LAD) methods — with bootstrap standard
errors —and not on OLS.6
Thus our data set contains thus almost 8000 observations on more than 1000 firms
over the 1990-1998 period. Nominal figures were deflated using the value added and
gross production deflators constructed by ECLAC/UN at the three digit ISIC level (see
Yagui, 1993). These adjustments take into account that stock variables are recorded at
year end prices, whereas the prices of flow variables represent within year averages.
4
The effect of market concentration on the cost of bank
borrowing
In this section we present our empirical estimates of the effects of banking concentration
on the cost of bank borrowing using our sample of Chilean firms. We first estimate
the model without market concentration controls. We then add concentration measures
allowing for a heterogeneous response of interest rates paid depending upon the number
of banks firms borrow from.
4
It is worth noting that these rates are not completely comparable. First, our sample rates include
commissions paid. Second, these rates include commercial loans only. Finally, the aggregate rates refer
to loans given for a 90 days to one year period.
5
It is prohibited by law to charge interest rates over a certain maximum (tasa máxima convencional ).
This rate reached a peak of over 80% in 1991 during our sample period.
6
See Amemiya (1986) for a derivation of the estimator and a proof of its consistency, and Garcia et
al. (2003) for a broader description of the distribution of our outcome variable.
6
4.1
The benchmark model
Our benchmark econometric model controls for a number of variables in order to correct
for characteristics that may make firms more or less likely to borrow, and or more or
less risky. These controls include firm characteristics such as the number of employees,
the natural log of sales, the natural log of the capital stock, regional dummies, sectoral
dummies at the two-digit ISIC level, and a dummy indicating whether the firm has had
overdue loans in the past. Our controls also include an indirect measure of age to control
for firm quality and for a selection bias due to exit. We do not observe directly the date
in which the firm was created. However, RUTs are assigned by the Internal Revenue
Service chronologically; i.e., a younger firm has a larger RUT number than an older firm.
These identification numbers are assigned within ownership categories. For instance,
individuals have RUTs between 1 and 48 million, limited liability corporations have
RUTs between 77 and 80 million, and publicly traded companies have RUTs between
90 and 97 million. Since we are not allowed to directly observe the RUTs, Central Bank
statisticians created a variable we call “rank RUT”. This variable is an ordering from
larger to smaller RUT (so the lowest number is assigned to the youngest firm) within
ownership categories. There are 11 categories in our data set; however, over 90% of the
sample is represented by individuals, limited-liability corporations and publicly traded
companies. Finally, all of our regressions include time dummies.
The first column of Table 2 presents our benchmark regression results without any
controls for market concentration. The results show that even though interest rates are
mismeasured, our model is able to explain the variability observed. First, we find that
age is correlated with interest rates paid, but that the effect depends crucially upon the
ownership status of the firm. Limited liability companies pay lower rates as they age, a
result consistent with selection and learning, as older firms have been able to compete
profitably for longer periods of time.Firms owned by individuals pay higher rates over
time, whereas the rate paid by publicly traded firms is not related to age.
Our results also indicate that larger firms pay lower rates, when firm size is measured
by sales, by the number of employees, and by the stock of capital. The estimation
results indicate that if a firm hires 100 more employees (about one third of one standard
deviation of employment in the sample), interest rates fall by 0.3 percentage points.
Similarly, a 1% increase in sales reduces rates paid by a little over a half percentage
point. The effect of a 1% larger stock of physical capital is to reduce rates by almost
0.7 percentage points. This result is also consistent with the fact that firms with higher
collateral are less risky, and thus are charged lower rates.
Finally, the point estimate of the coefficient on the dummy that indicates whether a
firm had overdue loans in the past (90 days or more) is positive, as expected. However,
this coefficient is not statistically different from zero.
7
4.2
Concentration
The last three columns of Table 2 present alternative specifications that include measures
of market concentration. In column (2) we include the average market share of the banks
that currently lend to the firm; i.e., if S
firm i is borrowing from ni banks at date t, then
ni
sjt
the concentration measure is given by jni , where sjt represents the loan based market
share of bank j in year t. This measure is exogenous to the firm, since changes in market
participation of any given bank does not depend upon the identity of a particular client.
The result in column (2) shows that market concentration reduces the rate paid by firms.
The estimated effect indicates that for every one percent rise in the average market share
of the banks a firm borrows from, rates paid are reduced by 0.2 percentage points. Thus
our result suggests that banks possibly share the efficiency gains they get when they
become larger.
The next regression explores whether the effect of concentration depends upon the
number of banks a firm borrows from. Conditional on size, age and risk, a firm that
borrows from a single bank has less bargaining power than a firm that currently borrows
from two, three or more banks. If the single lender becomes larger, the firm cannot easily
change its credit source. Switching costs are much lower if the firm holds loans from
more banks, and can threat to move its business elsewhere if a lender charges higher
interest rates. Alternatively, a firm that holds loans from multiple banks is more likely
to face rate reductions when its lending source becomes larger and gains efficiency.
In column (3) we thus report the effects of concentration conditional on the number
of banks the firm currently borrows from. Specifically, we interact our concentration
measure with three dummies related to the number of lenders: whether the firm borrows
from a single bank, from two banks, or from three or more banks. We also include these
number-of-banks dummies (without interactions) as controls. The results are consistent
with the existence of a hold-up problem: firms that borrow from a single bank pay higher
interest rates when the bank that they borrow from becomes larger in the market.
However, if the firm borrows from three or more banks, then rates charged lower as
bank shares rise. We find a negative but not statistically significant effect on firms
that currently borrow from exactly two banks. The point estimate, however, is lower in
absolute value than the estimate for firms with three or more lending banks.
The estimated coefficient for single—lender firms indicate that for every point of
market share the bank gains, interest rates rise by 0.364 percentage points. Alternatively,
we can illustrate the relevance of this effect by looking at effective market evolution.
Specifically, the largest bank by the end of 1998 had a 16.5% share of the Chilean
market. At the beginning of our sample period, its share was about 9%. Thus a firm
that only borrowed from this particular bank experienced a rise in interest rates of almost
8
3.1 percentage points solely due to the increase in market share of the bank.7 The gain
in reduced rates is much larger for firms with multiple lenders: every one point rise in
average shares, reduces rates by more than half a percentage point.
These estimates assume that changes in market shares happen in a continuous manner. Nevertheless, the M&A activity over this sample changed shares discretely, a fact
we explore in the next section. In this section, however, we correct for the fact that
the number of banks a firm borrows from is reduced from n to n − 1 at the same time
market shares are rising. In column (4) we add M&A controls to isolate the effect of the
rise in market participation. Specifically, we interact our concentration measures with
dummies indicating whether the firm was currently borrowing from a bank that merged.
The estimation results presented in column (3) are robust to these events: a one point
rise in average market shares increases the rates that firms that borrow from a single
bank pay by about 0.39 percentage points. Firms borrowing from more banks do not
face higher rates. Firms with three or more lending relationships face rate reductions of
the order of 0.56 percentage points.
5
Bank M&As
Our analysis so far looks at changes in market shares that occur in a continuous manner.
In this section, using a difference-in-difference approach, we exploit the fact that market
shares vary exogenously in a discrete manner around bank M&As. These events allow us
to analyze in more detail the role of switching costs, since we can compare firms that are
tied to banks that merge with firms that also have few lenders, but did not experience
a merger first hand.
5.1
The experiment
We use a difference-in-difference (DD) approach to estimate the effects of bank mergers
on interest rates paid by firms and on the volume of loans granted to firms. The DD
approach identifies the effect of a treatment (the merger) on an outcome variable (interest
rates charged on loans). The method compares the difference in the outcome variable
before and after the treatment for groups affected directly by it, to this difference for
groups unaffected by the treatment. This DD approach provides a powerful identification
method as long as the treatment is truly exogenous to the individuals under study, an
assumption that certainly holds in our data base. In other words, bank mergers are
exogenous to individual firm decisions: the decision of whether to merge depends on
7
The evolution of market rates plotted in Figure 4 show that average rates in the system amounted
to 26.8% over the sample period.
9
variables such as bank regulations, efficiency, market power, and the like, and not on any
individual firm’s behavior. The analysis of mergers using aggregate data cannot identify
an exogenous “treatment” effect, as the extent of M&As in the banking sector depends
upon the relative efficiency of the system, and hence on the interest rates charged and
the volume of loans granted. In other words, at the aggregate level, bank mergers are
endogenous, but are exogenous to each individual borrower in the market.
Formally, let yist represent the outcome variable; i.e., either the interest rate paid by
firm i in group s in year t (i = 1,..., I, s = 0, 1, and t = 1,..., T ). The s index identifies
firms in the treatment group (s = 1) from untreated firms (s = 0). Let Xist represent
a set of variables that control for firm characteristics, such as size, age, industry, credit
history, and location. Finally, let dt be a dummy that indicates the period when the
merger occurred, ds a dummy of whether the firm belongs to the treated group, and dst
a dummy that indicates treated firms in the treatment period. That is, dst indicates
whether the merger directly affected the borrower at time t. To measure this differencein-difference effect, we estimate the coefficient δ in
yist = αds + γdt + δdst + Xist β + η ist
where η ist represents mean zero error, and Xist represents the same vector of control
variables we used in the previous section..
Our data base allows us to perform multiple treatment-control experiments. First, we
observe five different M&A episodes over our sample period (1990-1998). Three events
involve relatively small banks that did not change market concentration significantly
ex-post. The remaining mergers involve banks of similar size, with average market share
of about 8%. Table 3 lists the M&A episodes we analyze in this paper, including the
average market share of the relevant banks a year before the merger occurred.
Second, the M&As affect different firms in different ways, depending on the identity
and the number of banks the firm was borrowing from at the time of the merger, allowing
us to construct multiple treatment and control groups. For instance, firms may choose
to borrow from two banks to reduce the extent to which banks can extract rents from
them, as transactional and informational costs may make it difficult to switch to a third
bank. Thus a firm that borrows exclusively from two banks that merged faces a discrete
change in concentration, whereas a firm that borrows from two banks that were not
involved in the merger can still easily move its business across banks without incurring
in extra costs. Therefore, we identify one treatment group as those firms that were
borrowing from two banks that merged, and define its control group as firms that were
borrowing from any two banks not directly affected by the merger. The treatment firm
faces an exogenous reduction in the number of lending banks, whereas control firms do
not face any direct change.
10
We build other treatment/control pairs in a similar fashion. Figure 5 and Table
4 illustrate our identification choices. Assume there are many banks in the economy
(A,B,C,...), and many firms (1,2,3,...). Assume further that banks A and B merge.
Client firms belong to a treatment or a control group depending upon the identity of the
bank it borrows from (banks A or B versus other banks not involved in the merger), and
upon the number of banks it borrowed from prior to the M&A (1, 2, or more banks).
For instance, firm 1 in the Figure borrows exclusively from one of the merging banks.
The effect of the merger on this firm can be estimated comparing the change in the
interest rate it pays and the amount it borrows, to the change experienced by firm 6 —
i.e., a firm that held loans exclusively from a bank that did not participate in the merger.
Alternatively, we can compare the outcomes of firm 2, one that borrowed from A and B
only, to the outcome of firm 7, that borrowed from any other two banks. We can also
compare firm 2 to firm 5, that also borrowed from A and B, but had already established
a relationship with a third bank, allowing the firm to keep some of its bargaining power.
Table 4 presents our classification of firms in treatment and control groups, and the
comparisons we perform in our statistical experiments.
It is worth emphasizing that the DD literature assumes that control groups are totally
unaffected by the treatment. However, if mergers change market equilibria, then control
firms may be charged different interest rates and may receive different levels of credit
in the post M&A equilibrium. Thus our results must be interpreted as the effect that
the merger has over and above the effect it may have on control firms. Nevertheless,
our sample period comprises three merger episodes that had little effect over market
concentration, and thus have a better chance of isolating control firms. Moreover, our
multiple treatment/control approach allows us to partially disentangle the heterogeneous
effects that M&As may have on clients with different pre-merger bargaining power.
5.2
Results
Table 5 presents our diff-in-diff estimation results. The first column assumes that the
effects across mergers are equal. All other columns represent a merger episode in chronological order. The two lines at the bottom of the table show two measures of the market
“relevance” of each merger. The first is the sum of the average merging banks share a
year prior to the consolidation episode. The second is the average market share of the
acquired bank. Thus the figures show that the first three mergers are marginal relative
to the fourth and fifth mergers.
The estimated effects on loan rates are mostly positive, but not statistically significant. Unfortunately in some of the small mergers there are very few firms that were
directly affected by the merger. In fact, in the first three mergers there are no firms
that borrowed exclusively from the banks involved (type 2 firms). These small cell sizes
11
reduce the precision of our estimates (see Table 4). All coefficients that turned out
significant are positive, however, indicating that firms experience rises in lending rates
after a merger occurs. Moreover, all statistically significant results refer to type-1 firms;
i.e., those that only borrow from a single M&A bank.
Some of the estimated effects are quite large. These firms are worse off even if we
compare them to firms that also borrowed from an M&A bank but had at least one extra
lender to switch to in case the newly consolidated bank refused a loan or threatened to
increase its rates. In this case, the estimated effects are much larger than those predicted
by our earlier model, where we analyzed all changes in concentration, even those not
associated to mergers. For instance, a type 1 firm borrowing from the acquired bank
around the second M&A experienced a rise in its average share from 0.4% to 6.37% (see
the shares in the lower panel of Table 5). Our previous model would have predicted
a rise of about 2.4 percentage points in interest rates paid. Our merger estimates are
much larger, a result that is consistent with the notion that these firms are those with
the least bargaining power, as their ability to quickly switch banks is most likely limited
by switching costs and by an ex-post informational monopoly. It is also consistent with
the fact that infrequent but discrete episodes may have larger effects on borrowers than
equivalent changes that happens over a continuum of small events.
6
Conclusions
In this paper we have explored the effects on interest rates paid by a sample of Chilean
manufacturing firms of changes in banking concentration. Our results show that bank
concentration has heterogeneous effects on firms. In particular, a key variable is whether
firms hold loans from single or multiple lenders. Moreover, our analysis of the effects
of bank M&As on the terms of lending show that firms that borrow exclusively from
the banks involved in a merger are those that suffer most. These findings are consistent
with the hypothesis of informational monopolies and switching costs: the ability to obtain
better loan rates from a third party reduces the extent to which any given firm loses
bargaining power against a bank that consolidates.
12
References
[1] Amemiya, T. (1986), Advanced Econometrics, Harvard University Press.
[2] Diamond, D. (1984), “Financial Intermediation and Delegated Monitoring”, Review
of Economic Studies, 51, 393-414.
[3] García, A., A.Repetto, S.Rodríguez, and R.Valdés (2003), “Concentration, HoldUp and Information Revelation in Bank Lending: Evidence From Chilean Firms”,
Economía Chilena, December 2003.
[4] Farrell, J., and C.Shapiro (1990), “Horizontal Mergers: An Equilibrium Analysis”,
American Economic Review, 80(1), 107-126.
[5] Levine, R. (2000), “Bank Concentration: Chile and International Comparisons”,
Documento de Trabajo Banco Central de Chile No. 62, January.
[6] Rajan, R. (1992), “Insiders and Outsiders: The Choice Between Informed and Arm’s
Length Debt”, The Journal of Finance, 47, 1367-1400.
[7] Sharpe, S. A. (1990), “Asymmetric Information, Bank Lending, and Implicit Contracts: A Stylized Model of Customer Relationship”, Journal of Finance, 45, 10691087.
[8] Yagui, E. (1993), “Un Deflactor para la Encuesta Nacional Industrial Anual (Base
1989=100)”, Estadística y Economía, 6, 129-163.
13
Table 1. Borrowing Level and Cost
No exclusions
All firms
Mean
8817
Interest Payments/Debt
Median
22
St.Dev
475370
By number of employees (size quintiles)a
I (smallest)
1530
II
1325
III
407
IV
4501
V (largest)
34365
11
22
27
26
22
59522
34221
8552
197338
1013527
With exclusion of outliers
All firms
30
26
22
By number of employees (size quintiles)a
I (smallest)
30
26
II
32
29
III
30
27
IV
30
26
V (largest)
27
22
a. Quintiles are defined within each subsample.
23
23
21
21
20
Table 2: The Determinants of The Cost of Borrowing
(Dependent Variable: Interest Rate Paid, %)
(1)
Concentration
(2)
-.2011
(0.0988)**
Concentration - One lender
Concentration - Two lenders
Concentration - Three or more lenders
(3)
(4)
.3640
(0.1514)**
.3935
(0.1604)**
-.1112
(0.2201)
-.0886
(0.2242)
-.5174
(0.2409)**
-.5627
(0.2449)**
Rank Rut - Individuals
.0104
(0.0021)***
.0108
(0.0019)***
.0106
(0.0020)***
.0105
(0.0019)***
Rank Rut - Limited Liability
-.0007
(0.0003)***
-.0007
(0.0003)***
-.0008
(0.0003)***
-.0009
(0.0003)***
Rank Rut - Publicly Traded
-.0010
(0.0011)
-.0009
(0.0010)
-.0011
(0.0010)
-.0011
(0.0014)
Sales (natural log)
-.5335
(0.2193)**
-.5681
(0.2438)**
-.4750
(0.1590)***
Employment
-.0034
(0.0012)***
-.0038
(0.0011)***
-.0045
(0.0012)***
-.0044
(0.0015)***
Physical capital stock (natural log)
-.6867
(0.2224)***
-.7422
(0.1849)***
-1.111
(0.2720)***
-1.0677
(0.2385)***
1.0987
(0.9943)
1.4380
(0.9548)
1.1458
(1.1875)
1.3629
(1.2819)
M&A controls
No
No
No
Yes
Number of lenders dummies
No
No
Yes
Yes
Loan overdue 90 days +
Number of obs.
7967
7967
7967
All regressions include a full set of year, regional and 2-digit-ISIC sector dummies. The age control variables
are shown for the largest three categories only. Results for other groups are available upon request.
Bootstrap standard errors in parentheses.
*** indicates 1% significance.
** indicates 5% significance.
** indicates 10% significance.
-.5187
(0.2078)***
7967
Table 3. Merger Episodes
Bank 1
Average share in
year prior to the
merger (%)
5,20
Bank 2
Average share in
year prior to the
merger (%)
Name
Centro Hispano
1,21
Date
June 1993
Name
O´Higgins
December 1993
O´Higgins
5,97
Hong Kong
0,40
January 1996
BHIF
4,63
Banesto
0,94
July 1996
Osorno
7,42
Santander
6,63
January 1997
Source: SBIF.
O´Higgins
8,10
Santander
9,94
Table 4. Firm Classification Strategy
Treatment groups
F1: Firms borrowing from one M&A bank and no other banks.
% of obs.a
2,0
F2: Firms borrowing from both M&A banks and no other banks.
0,2
F3: Firms borrowing from one merged bank and one non M&A bank.
2,8
F4: Firms borrowing from one merged bank and at least two non M&A bank.
13,9
F5: Firms borrowing from both merged bank and at least one non M&A bank.
4,1
Control Groups
F6: Firms borrowing from only one (non M&A) bank.
15,1
F7: Firms borrowing from exactly two (non M&A) banks.
20,3
F8: Firms borrowing from 3 or more (non M&A) banks.
41,7
Experiments
Firm 1 versus Firm 3
Firm 1 versus Firm 6
Firm 2 versus Firm 5
Firm 2 versus Firm 7
Firm 3 versus Firm 7
Firm 4 versus Firm 8
Firm 5 versus Firm 8
Note: The classification is based on firm status in the year prior to the M&A occurrence.
a. Average % across mergers.
Table 5. Bank Mergers and Loan Rates
Firm 1 vs. Firm 3
All M&As
3.014
(3.385)
Dependent Variable: Interest rate, %
Merger
1
2
3
8.258
23.38
-3.265
(9.381)
(12.025)**
(14.766)
15.884
(7.474)**
26.06
(8.786)***
5
-1.022
(5.399)
0 .716
(4.793)
2.827
(4.773)
Firm 1 vs. Firm 6
3.545
(2.777)
Firm 2 vs. Firm 5
-9.942
(8.344)
-14.90
(10.343)
-7.775
(12.313)
Firm 2 vs. Firm 7
-9.197
(8.208)
-11.52
(10.200)
-9.339
(12.103)
Firm 3 vs. Firm 7
-0.233
(2.311)
6.027
(6.355)
0.904
(8.674)
15.10007
(10.511)
-0.799
(3.980)
0.396
(3.800)
Firm 4 vs. Firm 8
0.657
(1.179)
2.627
(2.697)
-1.107
(3.070)
-3.214334
(3.435)
0.703
(2.246)
1.213
(0.792)
Firm 5 vs. Firm 8
-1.061
(0.870)
0.185
(5.956)
3.314
(8.948)
4.906844
(7.439)
-0.438
(2.988)
0.110
(1.187)
5.57
14.05
18.04
0.94
6.63
8.1
Merger size (pre-merger
6.41
6.37
sum of shares, %)
Share of acquired bank (%)
1.21
0.40
Note: First entry is the estimated coefficient; the second entry is its standard error.
All regressions include controls, as described in the text and in Table 2.
13.00
(10.712)
4
5.985
(5.426)
Figure 1. Banking System Concentration
Chile, 1990-2002
1600
70
65
1400
60
55
50
1000
45
800
40
600
35
30
1990
400
1991
1992
1993
1994
1995
1996
Herfindahl
1997
1998
Share of Largest 4
1999
2000
2001
2002
Herfindahl
% largest 4 (loans)
1200
Figure 2. Number of Banking Institutions
Chile, 1990-2002
45
40
35
30
25
20
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
Figure 3. Health Indicators
2,4
11,0
10,0
2,2
9,0
2,0
8,0
1,8
7,0
1,6
6,0
1,4
5,0
1,2
4,0
1,0
3,0
2,0
0,8
1990
1991
1992
1993
1994
1995
Profits/Capital stock
1996
1997
1998
Past due loans/Total loans
1999
2000
2001
2002
Figure 4. Lending Interest Rates
(%)
35
30
25
20
15
10
1990
1992
1994
1996
Nominal rate 90-365 days
1998
2000
Sample median
2002
2004
Figure 5. Firm Classification Strategy
Bank
A
Firm
1
Firm
3
Bank
B
Firm
2
Firm
5
Bank
C,D,...
Firm
6
Firm
7
Firm
4
Firm
8