Government Interventions in Brazilian Bank Lending: Do Public and

Transcrição

Government Interventions in Brazilian Bank Lending: Do Public and
Private Banks Compete?*
* The opinions expressed herein are those of the authors and not necessarily those of the Banco
Central do Brasil (Central Bank of Brazil).
2
ABSTRACT
This paper investigates the existence of competition between retail government-owned and
private banks in the Brazilian bank lending market. We exploit an exogenous shock to the
lending market, which made governmental banks raise the credit supply combined with a
decrease in the interest rates charged on loans. Our Diff-in-Diff model predicts that public banks
show higher loan growth, non-performing loans, lending returns, operational returns and cost of
funding compared to private peers after the event. In addition, we find evidence of differences in
the asset allocation decisions of banks, as private banks preferred an asset portfolio with a
higher proportion of liquid assets holdings and less loans compared to public banks after the
event. These findings suggest that government-owned retail banks do not compete with private
peers when their objective function is not only to maximize profits given risk.
Keywords: Brazilian bank lending market, federal government interventions, public banks,
private banks, loan annual growth.
3
1. Introduction
Since the international financial crisis in 2007/2008, the academic debate relative to the
role of state-owned banks and their implications for the financial intermediation has been
intensified.
In particular, several empirical studies of recent literature emphasize the stabilizing role
played by public banks in the credit supply provided to the productive sector in periods of
financial crisis and economic recession. Public banks are able to maintain higher rates of loan
growth in times of crisis in comparison with their private peers, which reduce their loan supply
and increase liquidity holdings (Allen et al. 2013; Bertay et al., 2015; Brei and Schclarek 2015;
Cull and Martinez–Peria, 2013; De Haas et al., 2012; Micco and Panizza 2006).
Similar results are reached by Oliveira et al. (2014) and Coleman and Feler (2014) who
find evidence that government-owned banks in Brazil rose credit growth rates, offsetting
declines in lending by private banks during the 2008 financial crisis.
In contrast, the political view, which is another strand of the banking literature, reveals a
less benign perception of the state presence in the banking sector and in the real economy.
For a long period of financial liberalization, started in 1970, a view favorable to the
privatization of the banking sector prevailed. Several empirical papers argued that the presence
of the state in the banking sector generates distortion on the allocation of resources because the
credit decision is politically motivated. State banks tend to increase their lending to companies
that are political supporters of the politicians, particularly during the electoral cycle. As a
consequence of this misallocation, greater government participation in bank ownership tends to
be associated with lower levels of financial development and slower economic growth (Barth et
al. 2001 and 2004; Dinç, 2015; La Porta et al., 2002; Micco et al., 2007; Sapienza, 2004).
Other negative factor posited by this particular strand of the banking literature relates to
the financial instability. Caprio and Martinez-Peria (2002), demonstrate that greater state
presence in the banking system in the beginning of 1980 tends to be associated with higher
probability of banking crisis during 1980-1997.
Detailed individual country studies, with purely Brazilian data, provide consistence
evidence that corroborates this less benign role of public banks in the financial system and in the
real economy (Carvalho, 2014; Claessens et al. 2008).
Other facet of the literature investigates the effect of public banks’ presence on the
banking competition. Coelho et al. (2013) examine the competitive effect by determining how
the conduct of private banks is affected by the entry of a public bank. The results suggest that the
4
presence of a rival private bank toughens competition, but the presence of a public bank does
not affect the conduct of private banks.
We investigate whether retail private banks compete with state banks in the loan
market. We exploit an event occurred in August of 2011, when the federal Brazilian government
adopted several interventions in the bank lending market through public banks to boost credit
supply growth and decrease the loan rates.
Our empirical strategy relies on the double difference model, in which we analyze the
differential effect of the government interventions on annual bank loan growth, non-performing
loans, lending returns, operational results, cost of funding, as well as, differences in the asset
allocation decisions of public banks (treatment group) in comparison with private banks
(control group).
The results show that public banks have higher loan growth, loan quality, lending
returns, operational returns and cost of funding compared to private peers after the treatment.
In addition, we find evidence of differences in the asset allocation decisions of banks, as private
banks preferred an asset portfolio with a higher proportion of liquid assets holdings and less
loans compared to public banks after the treatment. These findings suggest that governmentowned retail banks do not compete with private peers when their objective function is not only
to maximize profits given risk.
Our primary contribution to the banking empirical literature is to investigate the
existence of competition between public and private banks in the event of government
interventions, which impact directly solely one group, the public banks.
Additionally, our analysis differentiates from the previous studies because we observe
the differential effect of government interventions not only on bank performance, but also on
changes in the asset allocation decisions.
This article is organized as follows: Section 2 provides the description of the Brazilian
bank lending; Section 3 contains the empirical strategy; Section 4 describes the data and
presents some summary statistics; Section 5 discusses the results; and Section 6 reveals the
conclusions.
5
2. The Brazilian Bank Lending Structure
The Brazilian bank lending has been growing in a fast pace in recent years. Total bank
lending to GDP has risen significantly from 28.9 % of GDP in 2007 to 47.1 % GDP in 2014.
According to Central Bank of Brazil, the bank credit market in Brazil presents a moderate
concentration level, in which the six leading commercial banks account for, approximately,
80.2% of the total Brazilian banking system in 2014. Meanwhile, the two largest federal public
banks (Banco do Brazil and Caixa Econômica Federal) stand for about 45.1% of the overall
Brazilian banking system in 2014.
A structural transformation has helped raise the Brazilian bank lending, which
contemplates the capital inflows providing liquidity to banks; the development of the domestic
capital market, and the economic stability of the country benefited from the significant increase
in commodity prices and improvements on domestic macroeconomic conditions.
It is important to highlight that this prominent scenario presented some changes with
the rise of the international financial crisis in 2008-2009. Particularly, with the Lehman
Brothers’ bankruptcy in mid-September 2008, the domestic economic activity showed
deterioration, requiring further adjustments on government policies. Therefore, in response to
the decline in economic activity, the federal government implemented an expansionist credit
policy among with a looser macroeconomic policy in 2009.
In this sense, the two largest federal government-owned banks (Banco do Brazil and
Caixa Econômica Federal) played an active countercyclical role compensating for the
retrenchment by private lenders during the global financial crisis.
In 2010, economic activity recovered faster and more strongly than expected, with Gross
Domestic Product (GDP) growing at a fast pace of 7.5%. For this reason, starting in mid-2010
until mid-2011, a range of macroprudential tools target on consumer credit were implemented
by Central Bank of Brazil to address financial stability concerns related to the rapid pace of
credit expansion and, consequently, the increment in the non-performing loans rates.
Since mid-2010 up to mid-2011, in response to the rapid increase of consumer loans, the
government has tightened macroprudential measures in some specific lending products. In
December of 2010, the Central Bank of Brazil announced an increase of the minimum payment
for credit card bills to 15% (previously 10%); anticipated another hike to 20% by December of
2011; and announced a rise of capital requirements for long term consumer loans (the risk
weight for such exposures rose from 100% to 150%). In April of 2011, the Tax on Financial
Operations (IOF) on consumer credit operations was expanded to 3% (previously 1.5%).
6
After strong growth in 2010, the Brazilian economy has persisted slowed down in 2011.
In response to the economic downturn, starting in the second half of 2011, the Central Bank of
Brazil relaxed the macroprudential measures among with a new monetary easing cycle by
cutting the basic reference interest rate (SELIC) to its historical minimal level of 7.25%, in
October of 2012. Both actions aimed to induce economic activity through higher credit
expansion, especially, through the hike of public banks’ lending supply.
These macroprudential policies include: (a) reducing the risk weights on vehicle and
personal loans; (b) increasing the fraction of reserve requirements on time deposits that can be
met by extending loans or purchasing loan portfolios from other banks; (c) allowing banks to
use part of their reserve requirements to increase working capital loans; (d) maintaining the
minimum payment for credit card bills at 15% (previously programmed to be increased to 20%
in December of 2011); (e) reducing IOF tax on credit for consumption (from 3% to 2.5%).
Additionally, in order to transmit the SELIC reduction to the loan interest rates charged
by the banks to the borrowers, in April of 2012 the federal government launched a campaign
aiming to reduce the banks’ spread levels, which were led by the state-owned banks. In
particular, headed by the largest commercial bank (Banco do Brasil) and the largest savings and
loan institution (Caixa Econômica Federal). The campaign focused mainly on the retail credit
segment.
In favor of enabling the federal government strategy of boosting credit supply growth
through public banks combined with diminished loan rates, the Brazilian National Treasury
made several capital injections, which includes BRL 8.1 billion to Banco do Brasil and BRL 13
billion to Caixa Econômica Federal, both in September of 2012.
In a nutshell, the federal government interventions in the Brazilian bank lending, started
in the second half of 2011, are the following:

Expanded bank credit through the hike of public banks’ lending supply;

New monetary easing cycle by cutting the basic reference interest rate;

Launched a campaign aiming to reduce the banks’ spread levels led by stateowned banks;

Brazilian National Treasury made several capital injections in public banks;

Central Bank of Brazil relaxed the macroprudential measures.
Taking into consideration these federal Brazilian government interventions, a structural
break in the lending growth rate by ownership type occurred in August of 2011, resulting in the
public banks regaining the market share leadership in terms of loan growth; meanwhile, the
private banks responded with a heterogeneous lending behavior by diminishing the loan growth
rate pace.
7
In chart 1 it is possible to visualize the heterogeneous lending behavior according to
ownership type, as well as, the structural break occurred in August of 2011.
[INSERT CHART 1 HERE]
8
3. Empirical Strategy
Our empirical methodology relies on the use of double difference model to evaluate the
differential effect, public banks vs. private banks, of the government interventions on the
following variables: a) annual loan growth pace in nominal and real terms; non-performing
loans; c) lending return; d) operational return; e) securities’ revenue participation; f) cost of
funding. The state-owned banks are defined as the treatment group because they were directly
affected by the government interventions, and the private banks are classified as control group.
In order to better reflect the Brazilian bank lending market we did not treat all
observations equally in the regressions, as some banks are more informative than others. For
this reason, we weighted the observations differently by using the natural logarithm of total
loans of each bank.
The estimates were conducted by using robust standard errors in order to avoid
heteroscedasticity.
Also we adjusted the standard errors to account for within group correlation by
reporting clustered standard errors at bank-level, totaling 17 clusters. If we had assumed that an
observation of a given bank were independent of all other observations for the same bank in the
data set, the standard errors produced would be underestimated and the statistical significance
would be overstated.
Some empirical studies highlight that the decision to use clustered-robust standard
errors should be based on the number of clusters. If the number of clusters is small, there is no
guarantee that the cluster-robust estimator will improve inference in relation to OLS standard
errors estimators.
In particular, Rogers (1993) indicates that clustered- robust standard errors with 20 or
less balanced clusters would suffer from an upward bias. In respect of the number of clusters,
the study of Nichols et al. (2010) highlights the following statement:
“If the number of clusters is small, this will substantially increase the
critical values relative to those computed from the standard Normal (t
with large degrees of freedom)”.
Regarding that our data contain 17 balanced clusters, our clustered- robust standard
errors estimates may suffer from an upward bias leading to extremely conservative results.
9
Effect on Annual Loan Growth Pace
To investigate if the increase in public banks’ credit supply diminishes the annual loan
growth pace of the private banks, we estimate the following equation:
Li,t     1 Publici,t   2 After _ August 2011t
  3 Publici,t  After _ August 2011t 
 Sizei, June2011  GDPt   i,t
(1)
where Δ Li,t denotes the annual loan growth of bank i in time t; Public i,t is a dummy
variable obtaining a value 1 if public bank, and 0 if private; After_August 2011t is a dummy
variable obtaining a value 1 if after August 2011 and o for the remainder periods; (Publici,t
×After_August 2011t) is the interaction term that assumes a value 1 if public bank and after
August 2011. Size and GDP are controlled variables that capture the specific characteristic of the
bank and the macroeconomic trends, respectively. Finally εi,t are the clustered - robust standard
errors.
The β1 coefficient measures the difference between the average annual loan growth rate
of public banks and private banks for the whole sample period; the β2 coefficient measures the
difference between the average annual loan growth rate of all banks for the period before and
after the treatment (August 2011). Finally, the β3 coefficient consists of the Diff-in-Diff estimator,
our main parameter of interest, which captures the effect of the government interventions on
the annual loan growth rate of public banks in comparison with private banks.
Effect on Non-performing Loans
A large literature argues that banks reduce their lending standards and become riskier
during periods of excessive lending growth. Therefore, it indicates that higher rates of bank loan
growth are associated with greater risk-taking, leading to elevated non-performing loans.
Since public banks exhibited significantly higher rates of loan growth than their private
competitors, they may have lowered their lending standards and collateral requirements in
order to boost credit demand.
To analyze if the rise in public banks’ credit supply deteriorated the non-performing
loans of the private banks, we estimate the following equation:
10
NPLi,t     1 Publici,t   2 After _ August 2011t
(2)
where Δ NPLi,t denotes the non-performing loans of bank i in time t; Publici,t is a dummy
errors.
The β1 coefficient measures the difference between the average non-performing loans of
public banks and private banks for the whole sample period; the β2 coefficient measures the
difference between the average non-performing loans of all banks for the period before and after
the treatment (August 2011). Finally, the β3 coefficient consists of the Diff-in-Diff estimator, our
main parameter of interest, which captures the effect of the government interventions on the
non-performing loans of public banks compared to private banks.
Effects on Lending Return
Credit growth, non-performing loans and loan rate are important determinants of
lending return. In this sense, existing studies suggest that non-performing loans negatively
influence the bank’s lending return, while loan rate shows a positive association.
Taking into consideration that public banks reported significantly higher rates of lending
growth, and also decreased interest rates charged on loans in comparison to their private
competitors, those actions may have negatively impacted their credit portfolio return.
To investigate if the increase in public banks’ credit supply diminished the lending return
of the private banks, we estimate the following equation:
Lreturni,t     1 Publici,t   2 After _ August 2011t
(3)
11
where ΔLreturni,t denotes the lending return of bank i in time t; Publici,t is a dummy
errors.
The β1 coefficient measures the difference between the average lending return of public
banks and private banks for the whole sample period; the β2 coefficient measures the difference
between the average lending return of all banks for the period before and after the treatment
(August 2011). Finally, the β3 coefficient consists of the Diff-in-Diff estimator, our main
parameter of interest, which captures the effect of the government interventions on the
lending return of public banks in comparison with private banks.
Effect on Operational Return
Economic theory does not uniquely predict the sign of the relationship between
operational return and loan growth rate because the association can stem from several reasons.
Higher loan growth can be negatively associated with bank operational return, if banks relax
their underwriting standards in order to achieve higher credit growth, contributing to elevated
non-performing loans.
In contrast, higher loan growth may have a positive impact on operational return if
banks expand into lower margin lending businesses without taking on higher-risk loans.
Another reason for the differential impact on operational return of public banks in
comparison with private peers relies on the private banks heterogeneous behavior as they
shifted from credit supply to invest in safer assets (i.e. securities) that may result in smaller
operational return due to the positive risk-return association.
To investigate if the increase in public banks’ credit supply decreased the operational
return of the private banks, we estimate the following equation:
ROAi,t     1 Publici,t   2 After _ August 2011t
 Sizei, June2011  SELIC t   i,t
(4)
12
where ΔROAi,t denotes the operational return of bank i in time t; Publici,t is a dummy
errors.
The β1 coefficient measures the difference between the average operational return of
public banks and private banks for the whole sample period; the β2 coefficient measures the
difference between the average operational return of all banks for the period before and after
the treatment (August 2011). Finally, the β3 coefficient consists of the Diff-in-Diff estimator, our
main parameter of interest, which captures the effect of the government interventions on the
operational return of public banks compared to private banks.
Effect on Securities’ Revenues Participation
A large literature emphasizes that public banks are able to maintain higher rates of loan
growth in times of financial crisis and economic adversities in comparison with their private
peers, which reduce their loan supply and increase liquidity holdings in response to a rise in risk
aversion.
To investigate if the increase in public banks’ credit supply leaded to an investment
reallocation for the private banks from loans to securities, we estimate the following equation:
Participationi,t     1 Publici,t   2 After _ August 2011t

  3 Publici,t  After _ August 2011t
 Sizei, June2011  SELIC t   i,t

(5)
where ΔParticipationi,t denotes the securities’ revenues participation of bank i in time t;
Publici,t is a dummy variable obtaining a value 1 if public bank, and 0 if private; After_August
2011t is a dummy variable obtaining a value 1 if after August 2011 and o for the remainder
periods; (Publici,t ×After_August 2011t) is the interaction term that assumes a value 1 if public
bank and after August 2011. Size and GDP are controlled variables that capture the specific
characteristic of the bank and the macroeconomic trends, respectively. Finally ε i,t are the
clustered - robust standard errors.
13
The β1 coefficient measures the difference between the average securities’ revenues
participation of public banks and private banks for the whole sample period; the β2 coefficient
measures the difference between the average securities’ revenues participation of all banks for
the period before and after the treatment (August 2011). Finally, the β3 coefficient consists of the
Diff-in-Diff estimator, our main parameter of interest, which captures the effect of the
government interventions on the securities’ revenues participation of public banks in
comparison with private banks.
Effect on Cost of Funding
The excessive public banks loan growth rate may have generated a demand for
additional capital in order to support their lending expansion, rising their funding costs.
Consequently, this action may have increased the funding competition between public and
private banks, elevating the private banks’ cost of liabilities.
To analyze if the increase in public banks’ credit supply increased the private banks’ cost
of funding, we estimate the following equation:
Cost i,t     1 Publici,t   2 After _ August 2011t
 Controls i, June2011   i,t
(6)
where ΔCosti,t denotes the cost of funding of bank i in time t; Publici,t is a dummy variable
obtaining a value 1 if public bank, and 0 if private; After_August 2011t is a dummy variable
obtaining a value 1 if after August 2011 and o for the remainder periods; (Publici,t ×After_August
2011t) is the interaction term that assumes a value 1 if public bank and after August 2011.
Controls i,June 2011 are control variables to capture the specific characteristic of the bank. Finally εi,t
are the clustered - robust standard errors.
The β1 coefficient measures the difference between the average cost of funding of public
banks and private banks for the whole sample period; the β2 coefficient measures the difference
between the average cost of funding of all banks for the period before and after the treatment
(August 2011). Finally, the β3 coefficient consists of the Diff-in-Diff estimator, our main
parameter of interest, which captures the effect of the government interventions on the cost of
funding of public banks in comparison with private banks.
14
4. Data Description
The empirical analysis is based on a sample of 17 commercial banks focusing on the
retail credit segment, in which 9 are public banks and 8 are private banks, for the period 2009–
2014 in a quarterly frequency, totaling 408 observations. The time period chosen contemplates
the federal government interventions in the Brazilian bank lending market by directly
interfering in the public banks.
In terms of data set for the commercial banks, we selected the top 50 banks in Brazil,
classified by loans and leases in the data-base June 2011. In addition, we excluded development
banks, cooperatives, manufacturers’ banks and banking institutions that operate in a niche
credit market, such as “middle market loan” and “corporate loan”.
It is worth highlighting that commercial private banks show a heterogeneous lending
behavior because they operate in distinct loan segments, such as retail, middle market and
corporate.
The ideal data-set should have contained, besides the commercial public banks, all the
commercial private banks. In this empirical framework, we should have included in the model a
control variable for type of loans to address the heterogeneity behavior concern.
However, this approach was not feasible because the loan-level data is private
information registered at the Credit Information System of the Central Bank of Brazil (SCR), and
we could not obtain from this source.
Given this loan-level data restriction, as a solution we selected commercial private banks
with a more homogeneous behavior in the credit market, focused on the retail segment as this
loan segment had been more directly affected by the government interventions.
The resulting sample of 17 banks responds for 90.55% of the total loan provided by
commercial banks, as of June 2011.
In order to classify according to type of ownership, we used the Central Bank of Brazil
definition, which considers as state-owned banks if more than 50% of controlled stake belongs
to the state or federal government, the remainder of the institutions were defined as private
banks. There is no time variation in ownership for the financial institutions in our sample
period.
Our source of bank-level data is the Central Bank of Brazil, specifically collected from
IF.Data (Selected Information on Supervised Institutions). These bank-level data have a
quarterly frequency that coincides with the released data for financial reports which are June,
September and December of each year.
15
The macroeconomic control variables were taken from the Time Series Management
System (SGS) of Central Bank of Brazil (BCB) and from the Geography and Statistics Brazilian
Institute (IBGE).
Table 1 contains definitions of all the variables included in our empirical analysis and the
respectively sources. It is worth highlighting that we adopted a similar methodological approach
to the work of Delis et al. (2014).
We conducted estimations controlling for size, which is calculated as the natural
logarithm of total assets as of June 2011 for the whole data-set period, in order to obtain an
unbiased diffin-diff estimators.
In the cost of funding estimations, particularly for the bank characteristics’ control
variables added to model, we adopted a similar methodological approach to the work of Barros
et al. (2015). The bank specific controls for the whole data-set period were calculated at preevent levels, as of June 2011.
[INSERT TABLE 1 HERE]
Table 2 reports the descriptive statistics for the variables employed by ownership type,
as well as, before and after the structural break.
Interestingly, it is possible to observe that public banks and private peers have a
different lending behavior by analyzing the variables’ mean previously and after the structural
break. This pattern is consistent with our finding, which shows that public banks expanded the
loan supply; meanwhile, the private peers diminished the loan growth rate pace in response to
the federal government interventions.
The variable size shows significantly distinguished mean values according to the
ownership type, mainly this is due to the presence of estate-owned banks in the sample, which
are significantly smaller in comparison with the two major players (Banco do Brasil and Caixa
Econômica Federal) and to the private banks contemplated in the analysis.
16
5. Results
Effect on Annual Loan Growth Pace
Table 3 reports the results of Equation 1, which measures the effect of government
interventions on the annual loan growth pace, in nominal and real terms.
Even though the β1 coefficients (reported in Columns I and V) are not statistically
significant at usual level, the commercial public banks show in average annual loan growth, in
nominal and real terms, smaller than the reported by the private banks. This finding is,
essentially, influenced by two factors: the public banks’ counter-cyclical active role played in
2009 and the macro-prudential tools implemented in the credit market by the Central Bank of
Brazil in 2010.
After August 2011 (results reported in Columns I and V), all banks show in average a
decrease in the annual loan growth pace, in nominal and real terms, and the β2 coefficients are
negative and statistically significant at 10% level. This finding was determined, essentially, by
the private banks’ lending behavior as they slowed their loan growth after the government
interventions in the credit market through the public banks.
In Column I the Diff-in-Diff estimator of our baseline specification is economically large,
corresponding to a predicted 11.7 percentage points more nominal growth in loans of public
banks compared to private banks after the event. The inference based on real terms (shown in
Column V) is also economically large, indicating an increase of approximately 11.0 percentage
points in loan growth of public banks in comparison with private banks after August 2011. For
both regressions our coefficient of interest (Public × After August 2011) is statistically significant
at 10% level.
Recall that our clustered- robust standard errors may suffer from an upward bias. Even
when we apply this extremely conservative procedure, our coefficients of interest for both
regressions remain statistically significant.
Additionally, we re-conducted estimates for our baseline specification using nonweighted observations. The results reported in Columns II and VI slightly changes the magnitude
of our coefficients of interest (Public × After August 2011), but do not significantly alter our
inferences.
In Columns III and VII, when we control for GDP, the Diff-in-Diff coefficient for both
regressions remains unchanged. Also the inclusion of GDP and Size as additional controls
(Columns IV and VIII) do not alter the Diff-in-Diff coefficients obtained in our baseline
specification.
17
Using quarterly time dummies, instead of GDP as macroeconomic control variable, we reestimated Equation 1. The Diff-in-Diff estimators remain essentially the same not only in terms
of econometric significance, but also in terms of economic magnitude (results not reported here
due to space considerations but available on request).
Further estimations for Equation 1 are carried out with non-clustered robust standard
errors; the Diff-in-Diff coefficients obtained in all regressions are significantly higher in terms of
econometric significance (results reported in the Appendix).
In a nutshell, after August 2011, the banking institutions according to ownership type
showed a different lending behavior, in which the public banks led the credit supply expansion,
meanwhile the private peers lent at a slower growth rate.
It is possible to observe a change in private banks’ lending policies as they became
gradually more selective in the loan supply after August 2011. This result might be explained by
changes in the banks’ perception of risk due to deterioration in the Brazilian economic scenario
with reflections in the non-performing loans rate of the private banks.
In addition, the public banks reduced more sharply the loan interest rates charged to
borrowers compared to the private peers, which allowed the increase in credit demand and the
rise of the portability mechanism.
Note that the Central Bank of Brazil allows borrowers to transfer outstanding loans
between banks, the so-called portability mechanism; usually the decision is based on loan
interest rates.
The control variable, size, is not determinant to the annual growth loan rate.
Regarding the controlled variable related to macroeconomic trends (GDP), we found a
positive statistical significance at 10% level under the real terms procedure.
Effect on Non-performing Loans
interventions on the non-performing loans.
In Column I the β1 (Public) and β2 (After August 2011) coefficients of our baseline
specification are not statistically significant at conventional level.
Our Diff-in-Diff estimator (reported in Column I), although weak, suggests that the nonperforming loans was higher for government-owned banks compared to private banks after the
18
government interventions. Note that our clustered- robust standard errors may suffer from an
upward bias, leading to extremely conservative results.
In addition, we re-conducted estimates for our baseline specification using non-weighted
observations. The result reported in Columns II does not change the magnitude of our coefficient
of interest (Public × After August 2011).
In Column III, when we control for GDP, the Diff-in-Diff coefficient remains unchanged.
Also the inclusion of GDP and Size as additional controls (Columns IV) does not alter the Diff-inDiff coefficient obtained in our baseline specification.
Using quarterly time dummies, instead of GDP as macroeconomic control variable, we reestimated Equation 2. The coefficient of interest (Public × After August 2011) remains
essentially the same not only in terms of econometric significance, but also in terms of economic
magnitude (results not reported here due to space considerations but available on request).
These findings suggest that the significant public banks’ lending expansion after the
government interventions did not lead to signs of deterioration in their loan portfolio compared
to private banks.
A combination of factors helps explain why the expansionist lending behavior of public
banks reflected positively in their non-performing loans.
Firstly, loans’ interest rates reduction encouraged by public banks was a crucial
determinant for the increase in the portability mechanism. As the public banks reduce more
sharply the loan interest rates charged to the borrowers, borrowers’ outstanding loans from
private banks were significantly transferred to them. Specially, outstanding loans categorized as
low credit risk, such as, real-estate loans and payroll-linked loans.
Secondly, after August 2011, the composition of public banks’ loan portfolio reported a
significant expansion in loans categories with less credit risk. For instance, rural loans, realestate loans and Payroll-deducted lending.
On the other hand, the private banks lend out at a slower pace than public banks
increased their loans’ maturity. This factor justifies, in parts, the higher loan loss provisions of
private banks in comparison to public banks.
19
The control variable, size, is an important determinant to the non-performing loans, as
reports a negative coefficient with statistical significance at 5% level.
positive statistical significance at 1% level.
Effect on Lending Return
interventions on lending return.
In Column I β1 coefficient of our baseline specification is negative and statistically
significant at 5% level, given that public banks report in average smaller lending returns
compared to private banks. This finding is, essentially, influenced by the public banks’ countercyclical active role played in 2009 and their reduction in the lending growth pace in 2010.
After August 2011 (result reported in Column I), the whole banks’ sample show in
average a decrease in lending returns as β2 coefficient presents negative statistical significance
at 1% level. This result was determined, essentially, by the private banks’ lending behavior as
they slowed loan growth after the government interventions in the credit market through the
public banks.
In Column I the Diff-in-Diff estimator of our baseline specification, although weak (pvalue of 0.112), indicates that the public banks’ lending returns were higher compared to
private banks after the event (predicting approximately 1.5 percentage point additional increase
in lending returns for public banks after August 2011).
Note that our clustered- robust standard errors may suffer from an upward bias, leading
to extremely conservative results.
In Column III, when we control for GDP, the Diff-in-Diff coefficient remains unchanged.
Also the inclusion of GDP and Size as additional controls (Columns IV) does not alter the Diff-inDiff coefficient obtained in our baseline specification.
Using quarterly time dummies, instead of GDP as macroeconomic control variable, we reestimated Equation 3. The coefficient of interest (Public × After August 2011) remains
20
Even though the public banks reported differential at loan pricing in comparison with
private peers, their lending returns were supported by their higher loan growth pace after the
treatment.
The control variable, size, is determinant to the lending return by reporting a negative
statistical significance at 5% level.
negative statistical significance at 1% level.
Effect on Operational Return
interventions on operational return.
Even though the β1 coefficient (reported in Column I) is not statistically significant at
usual level, the commercial public banks show in average operational return smaller than the
reported by the private banks. This finding is, essentially, influenced by the public banks’
counter-cyclical active role played in 2009 and their reduction in the lending growth pace in
2010.
After August 2011 (documented in Column I), the whole banks’ sample show in average
a decrease in operational returns as β2 coefficient presents negative statistical significance at 1%
level. This result was determined, essentially, by the private banks’ lending behavior as they
slowed loan growth after the government interventions in the credit market through the public
banks, and their higher loan loss provisions.
In Column I the Diff-in-Diff estimator of our baseline specification is economically large,
corresponding to a predicted 0.6 percentage point additional increase in operational return for
public banks after August 2011. Also this coefficient of interest is statistically significant at 5%
level.
Recall that our clustered- robust standard errors may suffer from an upward bias. Even
when we apply this extremely conservative procedure, our coefficient of interest remains
statistically significant.
21
Additionally, we re-conducted estimates for our baseline specification using nonweighted observations. The result reported in Columns II slightly changes the magnitude of our
coefficient of interest (Public × After August 2011), but does not significantly alter our inference.
In Column III, when we control for SELIC, the Diff-in-Diff coefficient remains unchanged.
Also the inclusion of SELIC and Size as additional controls (Columns IV) do not alter the Diff-inDiff coefficient obtained in our baseline specification.
Using quarterly time dummies, instead of SELIC as macroeconomic control variable, we
re-estimated Equation 4. The Diff-in-Diff estimators remain essentially the same not only in
terms of econometric significance, but also in terms of economic magnitude (results not
reported here due to space considerations but available on request).
As an implication of changes in the asset allocation of private banks, it appears that the
revenues from securities did not offset the decrease in loans’ revenues of private banks. These
findings could be explained by the risk-return trade-off.
The control variable, size, is determinant to the operational return by reporting a
negative statistical significance at the 10% level.
Regarding the controlled variable related to macroeconomic trends (SELIC), we found a
negative statistical significance at the 10% level.
Effect on Securities’ Revenues Participation
interventions on securities’ revenues participation.
Our Diff-in-Diff estimator (reported in Column I), although weak (p-value of 0.156),
indicates that the Brazilian government interventions in the credit market directly through
public banks determined a negative differential effect on the securities’ revenues participation of
public banks in comparison to commercial private peers (predicting approximately 3.6
22
percentage point additional decrease in securities’ revenues participation for public banks after
August 2011).
Note that our clustered- robust standard errors may suffer an upward bias, leading to
extremely conservative results.
observations. The result reported in Columns II slightly changes the magnitude of our coefficient
of interest (Public × After August 2011), but does not significantly alter our inferences.
In Column III when we control for SELIC, the Diff-in-Diff coefficient remains unchanged.
Also the inclusion of SELIC and Size as additional controls (Columns IV) does not alter the Diffin-Diff coefficient obtained in our baseline specification.
Using quarterly time dummies, instead of SELIC as macroeconomic control variable, we
re-estimated Equation 5. The coefficient of interest (Public × After August 2011) remains
Particularly, private banks’ asset allocation policies changed due to a rise in risk
aversion; consequently, they shifted from loans to safer assets. These evidences are in line with
the findings reported in the international banking literature related to the stabilization role of
public banks.
In addition, the results suggest that government-owned retail banks do not compete with
private peers when their objective function is not only to maximize profits given risk. These
evidences are also in line with the empirical studies about Brazilian banking competition, which
indicate that government-owned banks do not compete with private banks.
Effect on Private Banks’ Cost of Funding
interventions on the cost of funding.
23
Our Diff-in-Diff estimator (reported in Column I) suggests that that the government
interventions in the credit market through public banks did not cause a differential effect on
public banks’ cost of funding compared to private peers.
In Column III when we control for Size, Liquidity, ROE, Low quality loans and Capital
adequacy, the Diff-in-Diff coefficient remains unchanged.
errors; the Diff-in-Diff coefficients obtained in all regressions do not present statistical
significance at conventional level (results reported in the Appendix).
A combination of factors helps explain these results. Firstly, over 2012 and 2013, the
commercial private banks reported high liquidity holdings because their funding was growing in
a higher pace than their lending. This excess liquidity of the private banks is in line with the
results founded in the present paper, as the private banks preferred to expand their liquid assets
instead of loans supply in response to the event.
Secondly, the lending capacity expansion of state-owned banks was partially supported
by capital injections from the Brazilian National Treasury, totaling BRL 22.1 billion in 2012. This
factor could have mitigated the public banks’ needs for obtaining funds.
Regarding the bank characteristics control variables, only ROE is statistically significant
at conventional level.
24
6. Conclusion
This paper investigates the existence of competition between retail government-owned
and private banks in the event of federal government interventions in the Brazilian bank lending
over the 2009 - 2014 period.
Using a Diff-in-Diff empirical approach, we found that public banks show higher loan
growth, non-performing loans, lending returns, operational returns and cost of funding
compared to private peers after the treatment.
In addition, we find evidence of differences in the asset allocation decisions of banks, as
private banks preferred an asset portfolio with a higher proportion of liquid assets holdings and
less loans compared to public banks after the treatment.
These findings suggest that government-owned retail banks do not compete with private
peers when their objective function is not only to maximize profits given risk.
Moreover, the results are in line with the findings reported in the international banking
literature related to the stabilization role of public banks, as well as, with the empirical studies
about Brazilian banking competition, which indicate that government-owned banks do not
compete with private banks.
Lastly, our paper’s findings have some important implications. First, we contribute to the
banking empirical literature by investigating the existence of competition between public and
private banks with a different methodological approach.
Additionally, our results shed light on policymakers’ future actions in the Brazilian bank
lending market, since it suggest that the increase in the lending growth rate of public banks by
government interventions aiming to accelerate economic activity, tightened the private banks’
loan supply policy showing a crowding out effect.
25
7. REFERENCES
ADACHI, Vanessa; MANDL, Carolina; RIBEIRO, Alex. Grandes bancos privados têm recursos em
excesso.
Valor
Econômico,
12/07/2013.
Available
in
Portuguese
at:
http://www.valor.com.br/financas/3195904/grandes-bancos-privados-tem-recursos-emexcesso.
ALLEN, Franklin; KRZYSZTOF, Jackowicz, KOWALEWSKI, Oskar. The Effects of Foreign and
Government Ownership on Bank Lending Behavior During a Crisis in Central and Eastern
Europe. Wharton Financial Institutions Center, Working Paper, n. 13-25, 2013.
ALVES, Murilo. Reversão de medidas e queda dos juros turbinam crédito no fim de 2011. Valor
Econômico,
27/01/2012.
Available
in
Portuguese
at:
http://www.valor.com.br/financas/2508028/reversao-de-medidas-e-queda-dos-jurosturbinam-credito-no-fim-de-2011.
BACEN, Banco Central do Brasil. Relatório de Estabilidade Financeira. Brasília: Banco Central,
março/2015. Available in Portuguese at: http://www.bcb.gov.br/?RELESTAB201503.
______. Relatório de Estabilidade Financeira. Brasília: Banco Central, setembro/2013. Available in
Portuguese at: http://www.bcb.gov.br/?RELESTAB201309.
______. Relatório de Estabilidade Financeira. Brasília: Banco Central, março/2013. Available in
______. Relatório de Estabilidade Financeira. Brasília: Banco Central, março/2012. Available in
______. Informações para análise econômico-financeira.
http://www4.bcb.gov.br/top50/port/top50.asp .
Available
in
Portuguese
at:
______. Informações macroeconômicas séries temporais. Available in
https://www3.bcb.gov.br/expectativas/publico/consulta/serieestatisticas.
Portuguese
at:
______.
Circular
3.563/2011.
Brasília,
2011.
Available
in
Portuguese
http://www.bcb.gov.br/pre/normativos/circ/2011/pdf/circ_3563_v2_L.pdf.
at:
______.
Circular
3.514/2010.
Brasília,
2010.
Available
in
Portuguese
http://www.bcb.gov.br/pre/normativos/circ/2010/pdf/circ_3514_v1_O.pdf.
at:
______.
Circular
3.515/2010.
Brasília,
2010.
Available
in
Portuguese
http://www.bcb.gov.br/pre/normativos/circ/2010/pdf/circ_3515_v2_L.pdf.
at:
______.
Circular
3.513/2010.
Brasília,
2010.
Available
in
Portuguese
http://www.bcb.gov.br/pre/normativos/circ/2010/pdf/circ_3513_v3_P.pdf.
at:
______.
Resolução
3.931/2010.
Brasília,
2010.
Available
in
http://www.bcb.gov.br/pre/normativos/res/2010/pdf/res_3931_v2_L.pdf.
at:
Portuguese
BARROS, Lucas; OLIVEIRA, Raquel; SCHIOZER, Rafael. Depositors’ Perception of “Too-Big-toFail”. Review of Finance, v. 19, p. 191-227, 2015.
26
BARTH, James; CAPRIO, Gerald; LEVINE, Ross. The Regulation and Supervision of Banks around
the World: A New Database. World Bank, Policy Research Working Paper, n. 2588, 2001.
______. Bank regulation and supervision: what works best? Journal of Financial Intermediation, v.
13, p. 205-248, 2004.
BERTAY, Ata; DEMIRGÜÇ-KUNT, Asli; HUIZINGA, Harry. Bank ownership and credit over the
business cycle: Is lending by state banks less procyclical? Journal of Banking and Finance, v. 50, p.
326-339, 2015.
BREI, Michael; SCHCLAREK, Alfredo. A theoretical model of bank lending: Does ownership
matter in times of crisis? Journal of Banking and Finance, v. 50, p. 298-307, 2015.
CAMPOS, Eduardo; RIBEIRO, Alex. Bancos recompõem os spreads. Valor Econômico,
26/02/2015. Available in Portuguese at: http://www.valor.com.br/financas/3927792/bancosrecompoem-os-spreads.
CAPRIO, Gerard; MARTÍNEZ PERIA, María Soledad. Avoiding Disaster: Policies to Reduce the
Risk of Banking Crisis. In Monetary Policy and Exchange Rate Regimes: Options for the Middle
East. 1ª ed. Editora: Egyptian Center for Economic Studies. 2002.
CARVALHO, Daniel. The Real Effects of Government-Owned Banks: Evidence from an Emerging
Market. The Journal of Finance, v. 69, p. 577–609, 2014.
CLAESSENS, Stijn, FEIJEN, Eric; LAEVEN, Luc. Political Connections and Preferential Access to
Finance: The Role of Campaign Contributions. Journal of Financial Economics, v. 88, p.554–80,
2008.
COELHO, Christiano; DE MELLO, João Manoel; REZENDE, Leonardo. Do Public Banks Compete
with Private Banks? Evidence from Concentrated Local Markets in Brazil. Journal of Money,
Credit and Banking, v. 45, p.1581–1615, 2013.
COLEMAN, Nicholas., FELER, Leo. Bank Ownership, Lending, and Local Economic Performance
During the 2008 Financial Crisis. International Finance Discussion Papers, Working Paper, n.
1099, 2014.
CULL, Robert; MARTÍNEZ PERIA , María Soledad. Bank ownership and lending patterns during
the 2008–2009 financial crisis: evidence from Latin America and Eastern Europe. Journal of
Banking and Finance, v. 37, p. 4861-4878, 2013.
DELIS, Manthos; KOURETAS, Georgios; TSOUMAS; Chris. Anxious periods and bank lending.
Journal of Banking and Finance, v. 38, p. 1-13, 2014.
DE HAAS, Ralph; KORNIYENKO, Yevgeniya; LOUKOIANOVA, Elena; PIVOVARSKY, Alexander.
Foreign Banks and the Vienna Initiative: Turning Sinners into Saints? International Monetary
Fund, Working Paper, n. 12/117, 2012.
DINÇ, Serdar. Politicians and Banks: Political Influences in Government-Owned Banks in
Emerging Countries. Journal of Financial Economics, v. 77, p. 453-470, 2005.
IBGE, Instituto Brasileiro de Geografia e Estatística. Informações macroeconômicas IBGE.
Available
in
Portuguese
at:
http://www.ibge.gov.br/home/estatistica/indicadores/pib/defaultcnt.shtm.
27
IZAGUIRRE, Mônica. Bancos oficiais turbinam dívida federal. Valor Econômico, 05/02/2013.
Available in Portuguese at: http://www.valor.com.br/financas/2995292/bancos-oficiaisturbinam-divida-federal.
LA PORTA, Rafael; LOPEZ-DE-SILANES, Florencio; SHLEIFER, Andrei. Government Ownership of
Banks. Journal of Finance, v. 57, p. 265-301, 2002.
LOPES, Fabiana. Lucro de bancos cresce com spread maior e bons ativos. Valor Econômico,
23/04/2014. Available in Portuguese at: http://www.valor.com.br/financas/3523280/lucro-debancos-cresce-com-spread-maior-e-bons-ativos.
KARIN, Sato; MANDL, Carolina. Inadimplência do BB cai, mas perda com crédito cresce. Valor
Econômico,
14/08/2013.
Available
in
Portuguese
at:
http://www.valor.com.br/financas/3232236/inadimplencia-do-bb-cai-mas-perda-com-creditocresce.
MANDL, Carolina; LOPES, Fabiana. BB cresce menos no crédito do que bancos privados. Valor
Econômico,
06/11/2014.
Available
in
Portuguese
at:
http://www.valor.com.br/financas/3768784/bb-cresce-menos-no-credito-do-que-bancosprivados.
MICCO, Alejandro; PANIZZA, Ugo; YAÑES, Monica. Bank ownership and performance: Does
politics matter? Journal of Banking and Finance, v. 31, p. 219-241, 2007.
MICCO, Alejandro; PANIZZA, Ugo. Bank Ownership and Lending Behavior. Economics Letters, v.
93, p. 248-254, 2006.
NICHOLS, Austin; SCHAFFER, Mark. Clustered errors in Stata. United Kingdom Stata Users' Group
Meetings, 2007.
NICHOLS, Austin; SCHAFFER, Mark; BAUM, Christopher. Evaluating one-way and two-way
cluster–robust covariance matrix estimates. United Kingdom Stata Users' Group Meetings, 2010.
OLIVEIRA, Raquel de Freitas; SCHIOZER, Rafael Felipe; LEÃO, Sérgio. Atuação de bancos
estrangeiros no Brasil: mercados de crédito e derivativos de 2005 a 2011. RAM, Rev. Adm.
Mackenzie, v.15, p.162-198, 2014.
ÖNDER, Zeynep; ÖZYILDIRIM, Süheyla. Role of bank credit on local growth: Do politics and crisis
matter? Journal of Financial Stability, v. 9, p.13–25, 2013.
PLANALTO, Presidência da República. Medida Provisória 581/2012. Brasília: 2012. Available in
Portuguese at: http://www.planalto.gov.br/ccivil_03/_ato2011-2014/2012/Mpv/581.htm.
______.
Decreto
7.458/2011.
Brasília:
2011.
Available
in
Portuguese
http://www.planalto.gov.br/ccivil_03/_Ato2011-2014/2011/Decreto/D7458.htm.
at:
RIBEIRO, Alex; CAMPOS, Eduardo. Governo retira amarras para tentar acelerar o crédito. Valor
Econômico,
21/08/2014.
Available
in
Portuguese
at:
http://www.valor.com.br/financas/3661956/governo-retira-amarras-para-tentar-acelerar-ocredito.
RIBEIRO, Alex; PERES, Leandra. Caixa deve discutir nova capitalização com Tesouro. Valor
Econômico,
10/05/2013.
Available
in
Portuguese
at:
28
http://www.valor.com.br/financas/3117994/caixa-deve-discutir-nova-capitalizacao-comtesouro.
ROGERS, William. Regression standard errors in clustered samples. Stata Technical Bulletin,
v.13, p. 19-23, 1993.
SAPIENZA, Paola. The effects of government ownership on bank lending. Journal of Financial
Economics, v. 72, p. 357-384, 2004.
29
Table 1 - Variable Definitions and Sources
This table describes the regression variables. The first column provides the names of the variables that are
used in the empirical analysis, the second column provides operational definitions of the variables, and
the third column indicates their sources.
Variables
Dependent
Annual nominal loan growth
Annual real loan growth
Non-performing loans
Lending return
Operational return
Securities' revenue participation
Cost of funding
Independent
Public Bank
After August 2011
Public Bank × After August 2011
Controls
Bank's characteristics
Size
Low quality loans
Liquidity
ROE
Capital adequacy
Macroeconomic
Δ GDP nominal
Δ GDP real
Δ SELIC
Source: Compiled by authors
Operational Definition
Source
change in the natural logarithm of total loans over the same period of
previous year
change in the natural logarithm of total loans deflated for the year
2009 over the same period of previous year
the ratio of total non-performing loans to the total loans
the ratio of total loans' revenues to the total loans
the ratio of operational return to the total assets
the ratio of securities' revenues to the sum of total loans, securities'
revenues and services' revenues
the ratio of cost of borrowing at market rate to the sum of total
interbank deposits, time deposits, acceptances and repo-repurchase
deflated for the SELIC of the period
BCB
dummy variable obtaining a value 1 if public bank
dummy variable obtaining a value 1 if after August 2011
dummy variable obtaining a value 1 if public bank and after August
2011
BCB
the natural logarithm of total assets
the ratio of loans due for more than 90 days to total assets
the ratio of cash and tradable securities to total assets
the ratio of net income to equity
the ratio of equity to total assets
BCB
BCB
BCB
BCB
BCB
change in the GDP nominal rate over the same period of previous
year (quarterly, seasonally adjusted)
change in the GDP real rate over the same period of previous year
(quarterly, seasonally adjusted)
change in the basic reference interest rate over the previous quarter
IBGE
BCB
BCB
BCB
BCB
BCB
BCB
IBGE
BCB
30
Table 2 - Summary Statistics
This table reports the mean, standard deviation, minimum and maximum values of key variables. We
split all of the observations between public banks and private banks for before and after August 2011. All
of the variables are quarterly measured from 2010 to 2014. Total nominal loans is the value of book
loans; Total real loans is the value of book loans deflated for the year 2009; Non-performing loans is the
ratio of total non-performing loans (i.e., loans due more than 90 days) to total loans; Lending return is the
ratio of total loans' revenues to total loans; Operational return is the ratio of operational return to total
assets; Securities' revenue participation is the ratio of securities' revenues to the sum of total loans,
securities' revenues and services' revenues; Cost of funding the ratio of cost of borrowing at market rate
to the sum of total interbank deposits, time deposits, acceptances and repo-repurchase deflated for the
SELIC of the period; Size is the natural logarithm of total assets.
Mean
Public Banks
Total nominal loans (BRL thousand)
3,711,761.50
Total real loans (BRL thousand)
3,381,903.66
0.05561
Lending return
0.07227
Operational return
0.00935
0.26987
Cost of funding
-0.00057
Size (BRL thousand)
9,298,650.00
Private Banks
Total nominal loans (BRL thousand) 42,398,240.00
Total real loans (BRL thousand)
38,440,322.46
0.06632
Lending return
0.11081
Operational return
0.00681
0.20286
Cost of funding
0.00207
Size (BRL thousand)
109,363,620.50
Std. dev.
Min.
Before August 2011
108,564,770.16 665,313.00
97,766,632.20 623,486.04
0.01750
0.02888
0.04606
0.00840
0.00731
-0.00368
0.09481
0.15739
0.00380
-0.00293
241,393,902.96 1,573,061.00
Before August 2011
84,295,937.97 4,414,894.00
75,701,507.62 4,137,338.05
0.02655
0.02147
0.05257
0.04416
0.01036
-0.00600
0.06533
0.05432
0.00458
-0.07762
238,408,351.55 7,008,646.00
Max.
Mean
359,531,142.00 6,886,756.50
311,175,276.51 5,318,570.66
0.09764
0.04656
0.22661
0.06576
0.03032
0.00644
0.62344
0.25498
0.01528
-0.00002
865,018,721.00 13,281,434.50
259,585,323.00 52,813,834.50
224,671,871.86 39,893,005.27
0.13667
0.06913
0.29914
0.08503
0.05312
0.00252
0.29707
0.21457
0.03236
0.00263
768,663,512.00 127,199,377.50
Std. dev.
Min.
Max.
After August 2011
196,939,786.46 1,389,191.00 645,028,781.00
148,575,650.47 1,182,947.47 450,230,729.38
0.01475
0.02306
0.09002
0.03512
0.02585
0.17391
0.00783
-0.00776
0.03566
0.11960
0.05804
0.49655
0.00506
-0.00318
0.02156
405,736,329.27 2,652,349.00 1,326,982,264.00
After August 2011
121,667,366.91 5,834,664.00 390,476,483.00
93,184,138.74 4,990,398.39 272,552,972.71
0.01534
0.03620
0.12470
0.03459
0.04076
0.19016
0.01001
-0.06186
0.03119
0.07324
0.05026
0.34628
0.00963
-0.01821
0.04888
352,763,949.22 10,272,465.00 1,117,848,197.00
31
Table 3 – Effect on Annual Loan Growth Pace
This table reports regression results for the estimation of equation 1, for nominal and real annual loan
growth, without controls; without controls and using non-weighted observations; with GDP control and
with GDP and Size controls, as indicated. The regressors are defined in section 4 of the paper. All tstatistics in parentheses are heteroscedasticity-robust and clustered at the bank level. The symbols ***, **
and * indicate statistical significance at the 1%, 5% and 10% levels, respectively.
Variables
Public
After_August 2011
Public × After_August 2011
I
Diff-in-Diff
ΔL nominal
II
Diff-in-Diff
ΔL nominal
III
Diff-in-Diff
ΔL nominal
-0.013
(-0.27)
-0.179***
(-3.87)
0.117*
(2.03)
-0.019
(-0.39)
-0.184***
(-3.76)
0.121*
(1.96)
-0.013
(-0.27)
-0.108*
(-1.79)
0.117*
(2.03)
1.430
(1.61)
GDP
Size
IV
V
Diff-in-Diff Diff-in-Diff
ΔL nominal
ΔL real
-0.007
(-0.12)
-0.108*
(-1.79)
0.117*
(2.03)
1.431
(1.61)
0.005
(0.36)
-0.012
(-0.26)
-0.175***
(-4.00)
0.110*
(2.02)
VI
Diff-in-Diff
ΔL real
VII
Diff-in-Diff
ΔL real
VIII
Diff-in-Diff
ΔL real
-0.018
(-0.38)
-0.180***
(-3.89)
0.114*
(1.95)
-0.012
(-0.26)
-0.101*
(-1.78)
0.110*
(2.02)
-0.006
(-0.12)
-0.101*
(-1.78)
0.110*
(2.02)
1.489*
(1.78)
0.005
(0.36)
1.490*
(1.78)
GDP real
Observations
2
R Adjusted
340
340
340
340
340
340
340
340
0.1584
0.1500
0.1723
0.1727
0.1673
0.1588
0.1846
0.1849
32
Table 4 – Effect on Non-performing Loans
This table reports regression results for the estimation of equation 2 without controls; without controls
and using non-weighted observations; with GDP control and with GDP and Size controls, as indicated. The
regressors are defined in section 4 of the paper. All t-statistics in parentheses are heteroscedasticityrobust and clustered at the bank level. The symbols ***, ** and * indicate statistical significance at the 1%,
5% and 10% levels, respectively.
Variables
Public
After_August 2011
I
Diff-in-Diff
ΔNPL
II
Diff-in-Diff
ΔNPL
III
Diff-in-Diff
ΔNPL
IV
Diff-in-Diff
ΔNPL
0.010
(1.02)
0.000
(0.05)
0.009
(0.96)
0.010
(1.02)
-0.000
(-0.01)
0.009
(0.98)
0.010
(1.02)
0.002
(0.31)
0.009
(0.96)
0.089***
(2.96)
0.005
(0.60)
0.002
(0.30)
0.009
(0.96)
0.089***
(2.96)
-0.004**
(-2.73)
408
408
408
408
0.1758
0.1708
0.1902
0.3181
GDP
Size
Observations
2
R Adjusted
33
Table 5 – Effect on Lending Return
and using non-weighted observations; with GDP control and with GDP and Size controls, as indicated. The
regressors are defined in section 4 of the paper. All t-statistics in parentheses are heteroscedasticityrobust and clustered at the bank level. The symbols ***, ** and * indicate statistical significance at the 1%,
Variables
Public
After_August 2011
I
Diff-in-Diff
ΔLreturn
II
Diff-in-Diff
ΔLreturn
III
Diff-in-Diff
ΔLreturn
IV
Diff-in-Diff
ΔLreturn
-0.033**
(-2.20)
-0.025***
(-3.10)
0.015
(1.68)
-0.030*
(-1.91)
-0.025***
(-2.93)
0.015
(1.49)
-0.033**
(-2.19)
-0.027***
(-3.28)
0.015
(1.68)
-0.062***
(-3.28)
-0.041***
(-2.94)
-0.027***
(-3.27)
0.015
(1.67)
-0.063***
(-3.30)
-0.006**
(-2.45)
408
408
408
408
0.1170
0.1022
0.1166
0.1877
GDP
Size
Observations
2
R Adjusted
34
Table 6 – Effect on Operational Return
and using non-weighted observations; with SELIC control and with SELIC and Size controls, as indicated.
The regressors are defined in section 4 of the paper. All t-statistics in parentheses are heteroscedasticityrobust and clustered at the bank level. The symbols ***, ** and * indicate statistical significance at the 1%,
Variables
Public
After_August 2011
I
Diff-in-Diff
ΔROA
II
Diff-in-Diff
ΔROA
III
Diff-in-Diff
ΔROA
IV
Diff-in-Diff
ΔROA
0.000
(0.13)
-0.008***
(-3.05)
0.006**
(2.22)
0.000
(0.14)
-0.009***
(-3.11)
0.007**
(2.32)
0.000
(0.13)
-0.009***
(-3.07)
0.006**
(2.22)
-0.245*
(-2.02)
-0.001
(-0.38)
-0.009***
(-3.06)
0.006**
(2.21)
-0.244*
(-2.02)
-0.001*
(-1.82)
408
408
408
408
0.1436
0.1503
0.1503
0.1954
SELIC
Size
Observations
2
R Adjusted
35
Table 7 – Effect on Securities’ Revenues Participation
and using non-weighted observations; with SELIC control and with SELIC and Size controls, as indicated.
The regressors are defined in section 4 of the paper. All t-statistics in parentheses are heteroscedasticityrobust and clustered at the bank level. The symbols ***, ** and * indicate statistical significance at the 1%,
Variables
I
II
III
IV
Diff-in-Diff
Diff-in-Diff
Diff-in-Diff
Diff-in-Diff
ΔParticipation ΔParticipation ΔParticipation ΔParticipation
Public
After_August 2011
0.095**
(2.75)
0.009
(0.80)
-0.036
(-1.49)
0.097**
(2.68)
0.008
(0.73)
-0.034
(-1.41)
0.095**
(2.75)
0.015
(1.20)
-0.036
(-1.49)
4.418***
(7.09)
0.120***
(3.89)
0.015
(1.21)
-0.036
(-1.49)
4.411***
(7.05)
0.018**
(2.38)
408
408
408
408
0.1481
0.1485
0.1723
0.2930
SELIC
Size
Observations
2
R Adjusted
36
Table 8 – Effect on Private Banks’ Cost of Funding
and using non-weighted observations; with controls, as indicated. The regressors are defined in section 4
of the paper. All t-statistics in parentheses are heteroscedasticity-robust and clustered at the bank level.
The symbols ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively.
Variables
I
Diff-in-Diff
ΔCost
II
Diff-in-Diff
ΔCost
III
Diff-in-Diff
ΔCost
-0.004*
(-1.88)
0.000
(0.14)
0.001
(0.23)
-0.005*
(-1.74)
0.000
(0.02)
0.001
(0.34)
-0.004*
(-1.97)
0.000
(0.13)
0.001
(0.24)
-0.000
(-1.20)
-0.001
(-0.12)
1.098***
(3.95)
-0.123
(-1.08)
0.001
(0.06)
408
480
480
0.0231
0.0199
0.1101
Public
After_August 2011
Size
Liquidity
ROE
Low quality loans
Capital adequacy
Observations
2
R Adjusted
37
Chart 1 – Annual Loan Growth Rate by Ownership Type
This chart shows the quarterly loan growth rate of public banks (blue line) and private
banks (red line) in the Brazilian Bank Lending Market from 2010 to 2014. The vertical
dashed line marks the structural break.
35,0%
30,0%
25,0%
20,0%
15,0%
10,0%
5,0%
0,0%
Public
Private
38
Appendix A – Effect on Annual Loan Growth Pace
This table reports regression results for the estimation of equation 1, for nominal and real annual loan
growth, without controls; with GDP control and with GDP and Size controls, as indicated. The regressors
are defined in section 4 of the paper. All t-statistics in parentheses are heteroscedasticity-robust. The
symbols ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively.
Variables
I
Diff-in-Diff
ΔL nominal
II
Diff-in-Diff
ΔL nominal
III
Diff-in-Diff
ΔL nominal
IV
Diff-in-Diff
ΔL real
V
Diff-in-Diff
ΔL real
VI
Diff-in-Diff
ΔL real
-0.013
(-0.51)
-0.179***
(-7.70)
0.117***
(3.51)
-0.013
(-0.50)
-0.108***
(-3.80)
0.117***
(3.48)
1.430***
(2.81)
-0.007
(-0.24)
-0.108***
(-3.80)
0.117***
(3.47)
1.431***
(2.83)
0.005
(1.00)
-0.012
(-0.51)
-0.175***
(-7.96)
0.110***
(3.48)
-0.012
(-0.50)
-0.101***
(-3.83)
0.110***
(3.47)
-0.006
(-0.12)
-0.101*
(-1.78)
0.110*
(2.02)
1.489***
(3.11)
0.005
(0.36)
1.490*
(1.78)
Public
After_August 2011
GDP
Size
GDP real
Observations
340
340
340
340
340
340
R2 Adjusted
0.1584
0.1723
0.1727
0.1673
0.1846
0.1849
39
Appendix B – Effect on Non-performing Loans
This table reports regression results for the estimation of equation 2 without controls; with GDP control
and with GDP and Size controls, as indicated. The regressors are defined in section 4 of the paper. All tstatistics in parentheses are heteroscedasticity-robust. The symbols ***, ** and * indicate statistical
significance at the 1%, 5% and 10% levels, respectively.
Variables
Public
After_August 2011
I
Diff-in-Diff
ΔNPL
II
Diff-in-Diff
ΔNPL
III
Diff-in-Diff
ΔNPL
0.010***
(2.95)
0.000
(0.13)
0.009**
(2.26)
0.010***
(2.99)
0.002
(0.68)
0.009**
(2.28)
0.089**
(2.28)
0.005*
(1.74)
0.002
(0.76)
0.009**
(2.50)
0.089**
(2.49)
-0.004***
(-9.11)
408
408
408
0.1758
0.1902
0.3181
GDP
Size
Observations
2
R Adjusted
40
Appendix C – Effect on Lending Return
This table reports regression results for the estimation of equation 3 without controls; with GDP control
and with GDP and Size controls, as indicated. The regressors are defined in section 4 of the paper. All tstatistics in parentheses are heteroscedasticity-robust. The symbols ***, ** and * indicate statistical
Variables
Public
After_August 2011
I
Diff-in-Diff
ΔLreturn
II
Diff-in-Diff
ΔLreturn
III
Diff-in-Diff
ΔLreturn
-0.033***
(-4.48)
-0.025***
(-3.89)
0.015*
(1.80)
-0.033***
(-4.47)
-0.027***
(-3.96)
0.015*
(1.79)
-0.062
(-0.82)
-0.041***
(-5.71)
-0.027***
(-3.89)
0.015*
(1.86)
-0.063
(-0.85)
-0.006***
(-6.08)
408
408
408
0.1170
0.1166
0.1877
GDP
Size
Observations
2
R Adjusted
41
Appendix D – Effect on Operational Return
This table reports regression results for the estimation of equation 4 without controls; with SELIC control
and with SELIC and Size controls, as indicated. The regressors are defined in section 4 of the paper. All tstatistics in parentheses are heteroscedasticity-robust. The symbols ***, ** and * indicate statistical
Variables
Public
After_August 2011
I
Diff-in-Diff
ΔROA
II
Diff-in-Diff
ΔROA
III
Diff-in-Diff
ΔROA
0.000
(0.30)
-0.008***
(-5.97)
0.006***
(3.79)
0.000
(0.31)
-0.009***
(-5.97)
0.006***
(3.81)
-0.245**
(-2.05)
-0.001
(-0.80)
-0.009***
(-6.02)
0.006***
(3.95)
-0.244**
(-2.10)
-0.001***
(-4.75)
408
408
408
0.1436
0.1503
0.1954
SELIC
Size
Observations
2
R Adjusted
42
Appendix E – Effect on Securities’ Revenues Participation
This table reports regression results for the estimation of equation 5 without controls; with SELIC control
and with SELIC and Size controls, as indicated. The regressors are defined in section 4 of the paper. All tstatistics in parentheses are heteroscedasticity-robust. The symbols ***, ** and * indicate statistical
Variables
I
II
III
Diff-in-Diff
Diff-in-Diff
Diff-in-Diff
ΔParticipation ΔParticipation ΔParticipation
Public
After_August 2011
0.095***
(7.99)
0.009
(0.97)
-0.036**
(-2.14)
0.095***
(8.02)
0.015
(1.49)
-0.036**
(-2.17)
4.418***
(3.62)
0.120***
(10.56)
0.015*
(1.96)
-0.036**
(-2.35)
4.411***
(3.97)
0.018***
(9.73)
408
408
408
0.1481
0.1723
0.2930
SELIC
Size
Observations
2
R Adjusted
43
Appendix F – Effect on Private Banks’ Cost of Funding
This table reports regression results for the estimation of equation 6 without controls; with controls, as
indicated. The regressors are defined in section 4 of the paper. All t-statistics in parentheses are
heteroscedasticity-robust. The symbols ***, ** and * indicate statistical significance at the 1%, 5% and
10% levels, respectively.
Variables
Public
After_August 2011
I
Diff-in-Diff
ΔCost
II
Diff-in-Diff
ΔCost
-0.004***
(-3.04)
0.000
(0.22)
0.001
(0.33)
-0.004***
(-3.27)
0.000
(0.20)
0.001
(0.36)
-0.000***
(-2.63)
-0.001
(-0.20)
1.098***
(5.80)
-0.123
(-1.58)
0.001
(0.19)
480
480
0.0231
0.1101
Size
Liquidity
ROE
Low quality loans
Capital adequacy
Observations
2
R Adjusted

Government Interventions in Brazilian Bank Lending: Do Public and

Transcrição

Documentos relacionados

Speech by the Governor of the Bank of Italy Ignazio

Minsky, Financial Governance, Banking, and Financial Instability in

opinion - Epoch Times | Print Archive

THE PARADIGM OF THE NEW MILLENNIUM

Financial crisis and DEMOCRATIC DEFICIT

expedition - Lewis and Clark RDC

modelling the central bank repo rate in a dynamic - dinâmia`cet-iul

Financial innovation and bank behavior

why should central banks communicate with public?

Car Sale - MAUI COUNTY FCU

Child and youth.indd - Associação Portuguesa de Bancos

Spring 2004 (PDF Format)

Financial Crisis - Direito à moradia

EXHffiIT#la