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295295
ISSN 1518-3548
The External Finance Premium in Brazil:empirical analyses using state space models
Fernando Nascimento de Oliveira
October, 2012
Working Paper Series
ISSN 1518-3548 CNPJ 00.038.166/0001-05
Working Paper Series Brasília n. 295 Oct. 2012 p. 1-52
Working Paper Series Edited by Research Department (Depep) – E-mail: [email protected] Editor: Benjamin Miranda Tabak – E-mail: [email protected] Editorial Assistant: Jane Sofia Moita – E-mail: [email protected] Head of Research Department: Adriana Soares Sales – E-mail: [email protected] The Banco Central do Brasil Working Papers are all evaluated in double blind referee process. Reproduction is permitted only if source is stated as follows: Working Paper n. 295. Authorized by Carlos Hamilton Vasconcelos Araújo, Deputy Governor for Economic Policy. General Control of Publications Banco Central do Brasil
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The External Finance Premium in Brazil: empirical analyses using state space models
Fernando Nascimento de Oliveira12
Abstract
The Working Papers should not be reported as representing the views of the Banco Central do Brasil. The views expressed in the papers are those of the author(s) and do not necessarily
reflect those of the Banco Central do Brasil.
Our objective in this paper is to estimate the external finance premium (EFP), which is a non observable variable, of firms in Brazil using state space models. For this purpose, we built an original database with confidential and public data containing balance sheet information of 5,026 public and private firms from the third quarter of 1994 to the fourth quarter of 2010. Our results show that the average and volatility of the EFP of small firms is higher than those of large firms and that the EFP is more sensitive to monetary policy for small firms than for large firms. We also find that the sensitivity in relation to EFP of inventories, short term debt and net operational revenues, all in proportion of total assets, is higher for small firms than for large firms. These empirical evidences point to the importance of the balance sheet channel of the transmission mechanism of monetary policy in Brazil.
Keywords: External Finance Premium (EFP), State Space Models, Kalman Filter, Monetary Transmission Mechanism, Balance Sheet Channel. JEL Classification: G30, G32
1 Central Bank of Brazil, Research Department. E-mail: [email protected] 2 We thank Eduardo Klumb (IBMEC/RJ) and Alberto Ronchi (Previ S/A) for research assistance. I am especially thankful for Eduardo Klumb for obtaining the SERASA database and making it available for this paper. We also thank INSPER/SP for making the Gazeta Mercantil database available as well for this paper. Both SERASA and Gazeta Mercantil databases are confidential.
3
1 – Introduction
The external finance premium (EFP), defined as the difference between the cost of raising
funds externally and the opportunity cost of using internal funds, is a fundamental variable
in economics. While internal finance is available relatively cheaply, obtaining external
funds through loans, bonds or equity possibly implies substantial costs.
The EFP is a crucial variable to understand several microeconomic decisions of firms,
such as capital structure, dividend and compensation policies, demand for investment
among others. It is also important in a macroeconomic context because it is the key
variable related to the credit channels of monetary transmission mechanisms, like the bank
lending and balance sheet channels.34
However, as Bernanke and Gertler (1995) discuss, a major problem for empirical studies
in this area is that the EFP is a non observable variable. There are currently two
approaches that tackle this fact. 5
The first approach relies on finding readily available financial market indicators that are
arguably good indicators for the EFP, such as corporate bond spreads for instance. The
3 Credit channel theories can be broken down into two distinct theories: the bank lending and the balance sheet theories. In the former, monetary contractions increase the adverse selection problems between firms and banks, which may decrease the volumes of loans from banks to firms and households. The reason for this is that banks experience a decrease in the volume of demand deposits that can lead to a decrease in the volumes of loans if they are not able to replace demand deposits with other financial instruments. The balance sheet channel of monetary policy arises because policy shifts affect not only market interest rates, but also the borrowers’ financial positions, both directly and indirectly. A tight monetary policy directly weakens borrowers’ balance sheets in at least two ways. First, a rise in interest rates directly increases interest expenses, reducing net cash flows and weakening the borrowers’ financial position. Second, a rise in interest rates is also typically associated with declining asset prices, which, among other things, shrink the value of the borrowers’ collateral. In the aggregate, these effects could lead to a substantial impact on aggregate demand. 4 The credit channel considers the existence of a financial premium, that is a difference between the cost of funds raised externally (issued by equity or debt) and the opportunity costs of funds raised internally (by retaining earnings). The size of the external finance premium reflects imperfections in credit markets. The explanation of the dynamics of this premium can improve the timing and strength of monetary policy provided by traditional mechanism. The credit view as a whole is interesting and important for several reasons. First, if the credit view is correct, it means that monetary policy can affect the real economy without much variation in the open-market interest rates. Second, the view can explain how monetary contraction influences investment and inventory behavior. Finally, the credit view also implies that the impact of monetary policy on economic activity is not always the same. It is also sensitive to the state of firms’ balance sheet and health of the banking sector. 5 Bernanke and Gertler (1995) use the inverse of coverage ratio.
4
fact that these indicators have substantial predictive content for business cycle fluctuations
is often interpreted as evidence for the existence of financial frictions, as Gertler and
Lown (1999) and Mody and Taylor (2003) argue. Another approach is adopted by Levin
et al. (2004) that use microeconomic financial frictions along with balance sheet and bond
market data, to estimate the external finance premium for a group of listed firms in the
USA. 6
In this paper, we contribute to the literature by taking a different approach from the
mentioned above to estimate the EFP. We estimate it for Brazilian private and public firms
using a state space framework. The EFP for each one of the firms in our database is a
smooth Kalman filter of the state variable. In our estimation of the state space model, we
use several signal and control variables related to financial indicators of the firms highly
correlated with credit market imperfections.
To achieve our objectives, we use an original and confidential database composed of
unbalanced balance sheet information of 291 public firms and 4,735 private firms. Of the
private firms, 102 disclose quarterly information while all the others disclose only end of
the year information.7 The information of the public firms comes from Comissão de
Valores Imobiliários (CVM) and Economatica and the information of the private firms
come from Valor Econômico and from confidential data of SERASA and Gazeta
Mercantil. 89
We choose size defined as total assets as our criteria to classify firms in small or large. We
verify that size is highly correlated to other financial characteristics of firms that indicate
the degree in which firms access the financial markets.
6 There is a vast empirical literature that uses one or these approaches. See Grave (2008) for a good discussion about this literature and about the EFP in the USA. 7 All public corporations disclose quarterly balance sheet information. We use their consolidated balance sheet information. 8 SERASA is a privately held company that has one of the largest databases of financial and accounting information of firms and individuals in the world. The data is related to debt of firms and individuals in Brazil. The information of SERASA is provided to banks, to trade shops, small, medium and large companies, with the goal of giving support to credit decisions and thus make business more cheap, fast and reliable. The data from SERASA goes from 1998 to 2007, and is both quarterly and annual. 9 The data of Gazeta Mercantil is annual and goes from 1998 to 2007 and is based on the balance sheet information of private firms published in this newspaper. The information of Valor Econômico is annual and goes from 2009 to 2010 and is based on the balance sheet information available on the 1000 Maiores Empresas publication.
5
Our results show that small firms in Brazil have a much higher average as well as
volatility of EFP than large firms. The EFP of small firms is much more elastic to the
interest rate than the EFP of large firms. We also find that the elasticity of inventories,
short term debt and net operational revenues all as a proportion of total assets relative to
EFP is higher for small firms than large firms. Finally, we have empirical evidence that
these elasticities decrease in the case the firm had an outstanding loan with Brazil’s
development bank, Banco Nacional de Desenvolvimento Social (BNDES), in our sample
period. Our results seem robust to different specifications of the state space models.
Therefore, these results seem to indicate the relevance of the balance sheet explanation of
the monetary transmission mechanism in Brazil.
Brazil is a very special case of an emerging market where asymmetries of information
could play a very important role in the transmission mechanism of monetary policy. Brazil
has a very interesting financial system. In some of its aspects, like its means of payments,
for instance, the Brazilian financial system rivals that of developed countries. However, as
far as volume of credit to households and firms and depth of the capital markets is
considered, Brazil still lags behind OECD countries.10
The cost of capital in Brazil is very high when compared to international standards. The
spread banks charge on their loans, even for very well rated companies, is well above what
is charged worldwide. This high cost of capital creates enormous agency costs between
private agents and financial institutions.11
Another very important characteristic of corporations in Brazil is that, due to the high
costs of capital, many of them look for a public development bank (BNDES – Brazilian
Social and Economic Development Bank)- for long-term financing. Not only are interest
rates much lower, but also maturities are much longer. Monetary policy affects only
indirectly the long-term interest rates set by the BNDES in its loans.
10 The total credit to the private sector is around 50% of GNP, while in the USA, for example, it is over 100% of GNP. 11 This is how the literature defines credit market imperfections in general terms.
6
There is a vast literature both empirical and theoretical about EFP. Most papers focus on
the macroeconomic aspects of EFP. Just to cite some empirical papers, we could point to
Gertler and Gilchrist (1994) that use the inverse of coverage ratio as a proxy for EFP of
firms in United States. Gertler and Gilchrist conclude that balance sheet effects can be
more relevant for smaller firms (defined by the relative size of its total assets in relation to
large firms).
Oliveira (2009) undertakes a similar work to Gertler e Gilchrist (1994) for Brazil adopting
also the firm’s size as a measure of credit market access. The empirical analyses were
conducted over a database of public and private firms firms between the third quarter of
1994 and the fourth quarter of 2007. Following Gertler and Gilchrist (1994), Oliveira
concludes that smaller firms are more sensitive to EFP than large firms.
Gilchrist and Himmelberg (1995, 1998) investigate the influence of fundamental
(expected return and present value) and financial (availability of internal and external
funds) factors on firms investment decisions considering capital market imperfections.
Among others characteristics, the authors adopted the existence of debt rating as a
criterion to measure the credit market imperfections. According to the authors, considering
that most companies that issues public debt obtains a bond rating, this strategy permits to
split the sample into firms that have, or not, issued public debt in the past. If the company
didn’t issue debt, it must have faced more constraints in credit market access. Their
empirical analyses indicated that non rating firms are more sensitive to EFP than large
firms.
The rest of this paper is organized as follows. Section 2 describes the data. Section 3
presents our model. Section 4 presents the empirical analyses. Section 5 concludes.
2 – Data
We built an original and confidential database of an unbalanced panel of balance sheet
information of 291 public firms and 4,735 private firms from the third quarter of 1994 to
the fourth quarter of 2010. Of the private firms, 102 disclose quarterly information while
7
all the others disclose only end of the year information.12 The information of the public
firms comes from Comissão de Valores Imobiliários (CVM) and Economatica and, and
the information of the private firms come from Valor Econômico and confidential data of
SERASA and Gazeta Mercantil.
We take size, measured by total assets, as our classification criteria for credit access
following Gertler and Gilchrist (1994). We observe that size is highly correlated with
other financial variables that indicate the capacity firms have to access the financial
markets. We classify firms into small and large. We will show that our small firms have
relatively less access to the financial markets than do large corporations.
Our interest in separating firms into large and small ones is that, as Gertler and Gilchrist
(1994) point out, is that by doing this we can infer the level of access of the firms to the
financial markets. In theory, small firms will depend much more on bank loans than will
large firms. The latter will also issue much more short-term and long-term debts and will
have more inventories.
In the case of firms with quarterly information, we consider a possible candidate for small
firm one whose logarithm of total assets is less than or equal to the 30th percentile of the
distribution of total assets in at least one quarter of our sampling periods. In a similar
fashion, we consider a possible candidate for large firm, one whose logarithm of total
assets is greater than or equal to the 30th percentile in at least one year of our sampling
periods. Thus, we obtain 112 small firms and 68 large firms. Of the 68 large firms, five are
private ones. Of the 112 small firms, 36 are private ones.
In the case of firms with yearly information, we consider a firm to be small if its logarithm
of total assets is less than or equal to the 30th percentile in at least one year. A firm is
large if its logarithm of total assets is greater than or equal to the 70th percentile in at least
one year. Thus, we obtain 108 large firms and 181 small firms.
12 All public corporations disclose quarterly balance sheet information. We use their consolidated balance sheet information.
8
We look at the skewness of the distribution of small and large firms in every quarter or
year. We could have problems in our sample selection if the distribution of small firms
were skewed to the right or if the distribution of large firms were skewed to the left. This
could indicate that our cut-off for small and large is not a good one. The averages of
quarterly skewness (considering all periods) were 0.89 and 1.59 for small and large firms,
respectively. In the case of end-of-the-year information, the skewness (considering all
periods) was 0.79 for small firms and 1.21 for large firms. These results indicate that our
classification scheme is not a bad one as far as the cut-off is concerned.
Panel A of Table 1 shows all firms, private and public, classified by sectors of the
economy. As one can see, public firms come mostly from the food and beverages sector
(13.74%), while private firms come mostly from the services sector (23.44%).
Panel B of Table 1 shows the small and large public and private firms with quarterly
information organized by the sector of the economy they belong to. As one would
imagine, large firms (23%) include concessionaries of public services, followed by the
food and beverage sector (16%) while small firms include mostly the service sector (11%)
followed by the textile sector (11%).
Panel C of Table 1 presents the mean values of some financial characteristics of small and
large firms for the whole sample relative to their assets. As we can easily verify, large
firms have, on average, greater long-term and short-term debt than do small firms. Large
firms also have more fixed assets and net operational revenues as a percentage of total
assets. Finally, 53% of large firms (36 firms) have much more outstanding loans with the
BNDES compared to only 18.0% of small firms (22 firms).
Panel D of Table 1 shows some mean tests for these characteristics considering the
financial statements of the last quarters of the years 1999, 2002 and 2010. As one can see,
all p-values of the differences in the mean values for the characteristics between large and
small firms are close to 0. Therefore, it seems that small firms in our sample of quarterly
data differ from large firms as far as access to the financial market is concerned. They
have less access to the financial markets.
9
Panel E of Table 1 shows the small and large private firms with end-of-the-year
information organized by the sector of the economy they belong to. We have 4,735 non-
financial firms in our database with balance sheet information for all years from 1998 to
2007. There are 108 large firms and 181 small firms. Of the large firms, 18% pertain to the
food and beverage sector. In the case of small private firms, 26% belong to the service
sector.
Panels F and G of Table 1 present the financial characteristics of small and large private
firms with end-of-the-year balance sheet information as well as their mean tests. Large
private firms have, on average, greater long-term and short-term debt than do small private
firms and more net operational revenues. Therefore, it seems that small private firms in
our sample differ as well from large firms as far as access to the financial market is
concerned. They seem to have also less access to the financial markets.
Finally, Panel H of Table 1 presents information about outstanding loans of firms in our
sample of firms with BNDES during our sample period. As one can see, there are 106
firms (21.09%) with outstanding loans. Most come from the food and beverages sector
(16.98%).13
In the next section, we will describe our state space model.
3 – The State Space Model
The EFP estimated for each firm in our data sample is a smooth Kalman filter of the state
variable of the following state space model defined in equation (1), the state equation, and
equations (2), the signals equations. 14
(1)tttt
wBuAxx ++= ++ 11
(2)tttt
vDhCxz ++=
13 To obtain the information on BNDES we looked at off balance sheet information of public firms as well as information disclosed on the homepage of BNDES at the Internet. 14 See Harvey (1994) for an excellent introduction of state space models and Kalman Filters.
10
where the disturbances (wt and vt) are white noise and independent over time and across
firms, ut and ht are control variables and the matrix coefficients (A, B, C and D) are
constant over time.
Following Oliveira (2009) and Gertler e Gilchrist (1994) we will model 3 signals:
inventories divided by total assets, short term debt divided by total assets and net
operational revenues divided by total assets. Equations (3) to (5) below are the signal
equations (i indexes the firm from 1 to 5026, t indexes the quarter from 1994Q3 to
2010Q4 and the disturbance are white noise and independent across firms and time).
(3)
itεSELICSmalla)
it(fixassetsα
SELICBNDESSmallα)(SELICα)it
(EFPα
)it
(EFPα)it
(EFPα)it
(EFPα)it
(Sαait
S
t+−+
−
+−+−+−
+−
+−
+−
+−
+=
))1(*(18
)1(**7
))1(645
34231211
9
0
(4)
itεSELICSmalla)
it(fixassetsα)
it(DSmallα
SELICBNDESSmallαSmallα(SELICα)it
(EFPα
)it
(EFPα)it
(EFPα)it
(EFPα)it
(Dα)it
(Dαait
D
t+−+
−+
−+
−++−+−
+−
+−
+−
+−
+−
+=
))1(*(1111
*10
)1(**98
))1(746
3524132211
12
0
(5)
itεSELICBNDESSELICSmall
)it
(fixassets)it
(RSmallα
SELICBNDESSmallαSmallα)it
(EFPα
)it
(EFPα)it
(EFPα)it
(EFPα)it
(Rα)it
(Rαait
R
t+−+−+
−+
−+
−++−
+−
+−
+−
+−
+−
+=
)1()*)1(*(1111
*10
)1(**9846
3524132211
1312
0
αα
α
In equations (3) to (5), R represents net operational revenues divided by total assets, S is
inventories divided by total assets and in equation (4) D is short term debts divided by
total assets. To control for the existence of agency costs, we use the ratio of fixed assets to
total assets in all equations. This ratio gives an idea of the level of collateral firms can
11
potentially have available to offer to banks. The greater this ratio, the lower the agency
costs. We include also the following variables: the small dummy variable that indicates a
small firm; an interaction term between the small variable and Selic rate that indicates
monetary policy; and an interaction term between Small and BNDES and Selic
(Small*BNDES*Selic(-1)) indicating that a small firm had outstanding debt with the
BNDES during our sampling period. 15
To build our state equation and find the EFP as a state variable for each firm we need to
understand what financial variables can explain EFP in Brazil. To do this, we will describe
below a theoretical model that can explain what financial variables are relevant in the
Brazilian case.
Our model will be a partial equilibrium and static one related to the optimal decision of
capital level of a Brazilian firm so as to maximize expected profit. We have a continuum
of firms. The main idea is taken from Bernanke et al (1999) (BGG). The BGG model
incorporates the costly state verification mechanism debt (CSV).
In the BGG model, there is only one lender, that we call a market lender. In our model, we
include a second lender, a Social Development Bank (BNDES) that lends with lower
interest rate than the market lender.
The reason for this is a particular feature of Brazil’s credit market, as we stressed before.
In Brazil, BNDES is a key player in the implementation of government’s industrial policy
and the main long term financing provider for private investment. The funds offered by
BNDES have better costs and maturity conditions compared with other financing agents
from Brazil’s credit market. Furthermore, the long term interest rate charged for funds
obtained in the development bank16 are just marginally affected by the short term interest
rate that Central Bank controls. In such a context, firms that have more access to BNDES
funds must present more resilience to external finance premium variation.17
15 We use robust standard errors and perform IM, Pesaran and Shin unit root test for panel data, which confirms that all series are stationary. 16 TJLP – “Taxa de Juros de Longo Prazo” (Long Term Interest Rate). 17 In the homepage of BNDES, one can verify that the volume of loans as well as the number of firms that have received loans has increased over time, particularly in recent years.
12
We will assume that the probability a firm has to obtain a loan at BNDES is BP . To apply
for financing with BNDES resources, the client must meet the following minimum
requirements: be up to date with tax obligations; have ability to pay; have sufficient
guarantees against the risk of the operation; not be under a credit recovery regime; and
comply with legislation on the imports, in the case of financing for imports of machinery
and equipment; finally comply with environmental laws.
The firms are assumed to be risk neutral and have finite horizons. They acquire capital K
at a price Q at the end of period t for use in production in period t+1. At the end of period
t, the firm j has available net worth jtN 1+ and finances capital with internal funds
supplemented by external borrowing from a financial intermediary:
jtjttjt NKQB 1111 ++++ −= .
Ex-ante the expected revenue from investment project depends if the lender obtained a
loan at BNDES or at the market bank. It is given by ttKtB KQRw 1+ in the case of the market
bank and ttKtM KQRw 1+ in the case of BNDES as a lender, where Mw and Bw are
productivity disturbance for a firm that obtains a loan at the market lender and at BNDES
respectively. These disturbances are iid across firms and time.
Adopting the CSV approach, an agency problem arises because intermediaries (market
Bank and BNDES) cannot observe BwMw , and need to pay an auditing cost if they
wish to observe the outcome. The financial contract is a standard debt contract including
the following bankruptcy clause: if _
MM ww > or BB ww_
> the firm pays off the loan in
full from revenues and keeps the residual. The lender receives ttKtB KQRw 1
_
+ in the case of
BNDES and ttKtM KQRw 1
_
+ in the case of a market bank.
If the firm defaults on its loan, the lender pays an auditing cost Bμ in the case of BNDES
and Mμ in the case of the market lender. BNDES receives what is found, namely (1-
13
)Bμ ttKtB KQRw 1
_
+ and the market bank receives (1- )Mμ ttKtM KQRw 1
_
+ . A defaulting firm
receives nothing.
It is reasonable to assume that the lender will issue the loan only if the expected gross
return to the firm equal´s the lenders relevant opportunity costs of lending. Because the
loan risk is perfectly diversifiable, the relevant opportunity cost of the lender is the riskless
rate Rt+1 (Selic rate) in the case of the market lender and ρ Rt+1 ρ <1 in the case of the
BNDES.
Let FB(w) and FM(w) be the probability of default in the case of a firm that obtained a loan
at BNDES and market bank respectively. Let ⎥⎦
⎤⎢⎣
⎡=
+
+
1
1
t
Kt
R
REs be the discounted return on
capital or the EFP.
The following propositions relate the EFP to the probability of default of firms and to the
probability a firm has to obtain a loan at BNDES.
Proposition 1
Considering the structure of the model defined above, the external finance premium, EFP,
is an increasing function of the probabilities of default of firms at BNDES and the market
lender.
Demonstration: See Appendix A
Proposition 2
Considering the structure of the model, defined above, the external finance premium, EFP,
is a decreasing function of the probability of the firm to obtain a loan at BNDES if the
expected profit of BNDES is less than the expected profit of the market lender.
Demonstration See Appendix A
14
Taking in consideration Propositions 1 and 2 above, EFP is related to the probability of
default of firms and to the probability of obtaining a loan at BNDES. The probability of
default is a function of agency costs that depend on leverage, the return on capital, the
price of capital and default costs.
We follow Smith and Stulz (1985) and take total debt divided by total assets as an
empirical approximation of the leverage ratio. We also use the ratio between current assets
and current liabilities. This variable shows the degree of the firm’s current liquidity.
Extremely liquid businesses will have less probability of bankruptcy.
Myers (1977) demonstrates that indebted businesses have distorted incentives in terms of
their policies for investment. To summarize, the distortion occurs due to the priority that
the creditors have over the shareholders for receiving cash flow generated by corporations.
Given this priority, the shareholders do not have incentives to contribute resources for
investments whose returns—because of the highly indebted situation—will likely be used
in the payment of debt. Excessive debt, however, can impede lucrative projects from being
implemented. Thus, creditors anticipate the conflict of interest and incorporate their costs
in the interest rate. We will use the Selic rate , lagged one period so as to avoid problems
of endogeneity, as an approximation for this interest rate.
As Jensen and Meckling (1976) argue the higher the ratio between the fixed assets and
total assets, the greater the firm’s capacity to offer real collateral to creditors, that can
reduce the creditors’ loss due to financial stress and, consequently, reduce the incentives
to distort the investment policy. Therefore, a greater ratio between fixed assets and total
assets reduces the probability of default.
Rajan and Zingales (1995) show that a high ratio between a corporation’s market value
and the book value suggests that future gains (embedded in the market value of the firm’s
shares) still do not correspond to the value of the existing assets. Such a corporation
should have greater difficulty offering real collateral to creditors compatible with the
profitability of the existing investment opportunities. So we will use this ratio as a control
variable as well.
15
Another characteristic of a firm related to its cost of agency with creditors is its size.
Larger firms, in general, have greater reputation, a fact that can reduce costs of agency.
Therefore, we can expect that the size, defined by the total assets, reduce the probability of
the firm using hedge or speculation as explained by Rajan and Zingales (1995).
We also consider an explanatory variable that is related to both the costs of bankruptcy
and to the cost of agency with creditors: the firm’s profitability, as Rajan and Zingales
(1995) discuss. The firm’s profitability is defined as the ratio of the company’s net
revenue to its net worth. This variable gives an idea about the capacity of the corporation
to internally finance itself, avoiding the capital market or bank loans. The less a company
needs to finance externally, the less are the costs of bankruptcy.
We also follow Bernanke and Gertler (1995) and use the inverse of the coverage ratio as
control variable for EFP.
To summarize, EFP in our model in time t will be a function of the following variables
also in time t with the exception of Selic rate, that is lagged one period: fixed assets
divided by total assets; size, measured by a dummy variable equal to 1 if the firm is small;
profitability, measured by net revenues divided by net worth; inverse of coverage ratio;
market value divided by book value; current assets divided by current liabilities; total debt
divided by total assets and a binary variable that indicates that the firm obtained an
outstanding loan at BNDES in our sample period interacted with the dummy small and the
Selic rate.
Equation (6) below shows the state equation (i indexes the firm from 1 to 5026, t indexes
the quarter from 1994Q3 to 2010Q4, L is the lag function and the disturbance is white
noise and independent across firms and time).18
18 The number of lags for each firm is obtained by the Akaike information criteria. L represents the lag function and ∏ a matrix of constant coefficients over time.
16
(6)
itεSELICSELICSmallacontrol
SELICBNDESSmallαSmallα)it
L(EFPait
EFP
it
tititi
+−+−+Π
+−++−
Γ+=
)1(*)))1(*(6
)1(**2110
5α
4 – Empirical Analysis
One problem to estimate our state space model, equations (3) to (6), is that we have much
not available information of firms, particularly private firms with annual balance sheet
information only. So we backcast all control and signal series used in equations (3) trough
(6).
There are many techniques to do this.19 We follow Issler et al (2009). In this case, missing
values will be the state variables described by the smooth Kalman filter state variable of
the model below for each variable of interest and for each firm (i indexes the firm from 1
to 5026 and t indexes the quarter from 1994Q3 to 2010Q4). 20
ititSI Δ=Δ for t= 1994Q3 to 2010Q4
tIΔ missing
ititII Δ=Δ otherwise (7)
ititititXSS εα +Β+Δ=Δ −1
where,
IΔ - series of interest used as control or signal in our state space from (3) to (6)
measured in growth rate;
X – control variables: growth rate of real GDP, growth rate of real industrial production
and growth rate of services GDP;
SΔ - state variable at t;
19 See Chon e Lin (1971), Harvey e Pierse (1984) and Silva and Cardoso (2001). 20 We use R2 in first differences as a measure of fit, as defined in Issler et al (2009).
17
– White noise, α a constant parameter and B a matrix of constant parameters.
After backcasting our controls and signal series, we estimate our state space model,
equations (3) to (6) for each firm. The EFP is the smooth kalman filter of the state
equation(6).
Table 2 presents the average of all EFP estimated for several classifications of firms. One
can observe that small firms in our sample have higher average and volatility of EFP than
large firms independently if they have quarterly or annual information or had outstanding
loans at BNDES during our sample period.
In Table 3 Panels A and B, we present the results of the estimation of the state equation
(6). We are interested in the sign of the following regressors: Selic, the dummy variable of
the small firms alone and interacted with the Selic rate and the interaction between the
Selic rate, the small variable and the BNDES dummy variable. If the balance sheet
explanation of the monetary policy is relevant, the coefficients of Selic, small and the
interaction between the two are positive and significant. Due to the credit market
characteristics of the credit market in Brazil, we would expect also the coefficient variable
of the interaction between BNDES, small and Selic to be negative and significant.
Table 3 Panel A presents the results of the estimation of the state variable based on
aggregate data of all the series involved in our estimation for the whole sample, only for
firms that have quarterly information and for those firms with only annual information.
We aggregate the signal and control series using equal weights. As we can see all
coefficients have the right sign and are significant in all estimations for all types of firms.
Table 3 Panel B presents the average of the coefficients of the estimation of the state
variables for each firm for the whole sample, only for firms that have quarterly
information and for those firms with only annual information.. As in Panel A, the
coefficients have the right sign and are statistically significant once more.
Panels A, B and C of Table 4 present the estimated coefficients of the signal equations,
that is the dynamics of inventories/assets, short-term debt/, and net operational
18
revenues/assets for aggregate data of all types of firms (equations (3) to (6)). We
aggregate the series of signals, controls and EFP using equal weights once more.
For the dynamics of inventories /assets, short-term debt/assets, we are interested in the
sign of the following coefficients: the sum of the EFP coefficients; Selic; the dummy
variable of the small firms alone and interacted with the Selic rate; and the interaction
between the Selic rate, the small variable and the BNDES dummy variable. If the balance
sheet explanation of the monetary policy is consistent, the sum of EPP coefficients, and
the coefficients of Selic, small and the interaction between the two are positive and
significant. Due to the credit market characteristics of the credit market in Brazil, we
would expect the coefficient variable between BNDES, small and Selic to be negative and
significant. As one can observe, all coefficients have the right sign and are significant.
For the dynamics of net operational revenues/assets, we are interested in the sign of the
following coefficients: the sum of EFP coefficients; the coefficients of Selic;, the dummy
variable of the small firms alone and interacted with the Selic rate and the interaction
between the Selic rate, the small variable and the BNDES dummy variable. If the balance
sheet explanation of the monetary policy is relevant prevalent, the sum of EFP
coefficients, the coefficients of Selic, small and the interaction between the two are
negative and significant. Once more, due to the credit market characteristics of the credit
market in Brazil, we would expect the coefficient variable between BNDES, small and
Selic to be positive and significant. As we can observe, all coefficients have the right sign
and are significant.
Panels A, B and C of Table 5 present the averages of the estimated coefficients of the
signal equations, that is the dynamics of inventories/assets, short-term debt/, and net
operational revenues/assets for the estimations with individual data of all types of firms.21
The coefficients reported are averages of the coefficients of all firms (standard deviation
of the averages in parenthesis). As we can see, all coefficients have the correct sign (as we
discussed above) and are statistically significant.
21 We use robust standard errors in our regressions to correct for autocorrelation and heteroskedasticity.
19
The results of the dynamics of the three variables – inventories/assets, net operational
revenues/assets, short-term debt/assets - using individual data seem to confirm the results
obtained with aggregated data. They indicate that small and large public firms react very
differently to monetary policy. Small firms seem to be more sensitive to monetary policy
than large firms.
Maybe our previous results have some relation to the fact that we have more small firms
than large firms. To verify whether this is driving our results, we decreased the number of
small firms in all estimations presented above such that it would match the number of
large firms. Due to space restrictions, we do not report our results, but they confirm, in
general, the previous ones.
We also did several other robustness exercises. We used several different specifications
for the state and signal equations. We used the growth rate of BOVESPA and IBRX as
control variables in the backcasting estimations. We aggregated the data in Table 3 and 4
using weights proportional to the total assets of firms. We use medians instead of averages
for the coefficients estimated in Table 3 Panel B and Table 5 Panels A, B and C. We
included a control variable that indicates a financial crisis in Brazil in our sampling period
in the state and signal equations. In general, our results do not change. Due once again to
space restrictions, we do not report the results.
All our empirical results above seem to show a relevant asymmetry in the reaction of
small and large firms to monetary contractions. This asymmetry reflects different access
of Brazilian corporations to the financial markets. Large public and private firms have
more financing alternatives than do their small counterparts and therefore are able to
suffer less discontinuity in terms of investment, revenues and short-term financing after
monetary contractions.
Since the borrowers’ financial position affects their external premium and thus the overall
terms of credit they face, fluctuations in the quality of borrowers’ balance sheets should
likewise affect their investment and spending decisions.
20
This approach has been supported by a wide range of empirical work linking balance sheet
and cash flow variables to firms’ decisions concerning fixed investments, inventories and
other factor demands, as well as to household purchases of durables and housing.
The balance sheet channel of monetary policy arises because shifts in the central bank
policy not only affect the market interest rate per se, but also the borrowers’ financial
positions both directly and indirectly. Our paper is in line with this overall empirical
evidence of the literature for OECD countries.
5 – Conclusion
This paper investigates the balance sheet explanation of the monetary transmission
mechanism in Brazil. One problem to investigate this is that a fundamental variable for
this purpose, the external finance premium (EFP), is non observable. This paper
contributes to the literature by estimating the EFP of firms in Brazil using state space
models.
Our estimations are based on an original database with confidential and public data
containing information of 5,026 public and private firms from the third quarter of 1994 to
the fourth quarter of 2010.
Our results show that the average and volatility of the EFP of small firms is higher than
those of large firms and that the EFP is more sensitive to monetary policy for small firms
than for large firms. We also found that the elasticity of EFP in relation to inventories,
short term debt and net operational revenues, all as a proportion of total assets is higher for
small firms than for large firms. These empirical evidences point to the importance of the
balance sheet channel as an explanation for the transmission mechanism of monetary
policy in Brazil.
Our results indicate that small firms are much more sensitive to monetary policy than large
firms and that BNDES plays a relevant role in decreasing this sensitiveness. The results
are robust to several different specifications of the space model.
21
Large firms in Brazil, which are likely to obtain loans from the BNDES, respond to an
unanticipated decline in cash flows in a different manner from small firms. They can at
least temporally be able to maintain their levels of production and employment in the face
of higher interest costs and declining revenues through other sources of short-term and
long-term financing. However, this is not the case for small firms. These firms, which
have more limited access to the financial markets, tend to lose inventories and revenues
and to cut work hours and production.
These differences in access have many possible reasons. Some have to do with a
bankruptcy legislation that makes it difficult for creditors to size the assets of firms; others
relate to high spreads that are still prevalent in Brazil; another reason as well may be
related to a segmented credit market, where long-term financing basically comes from the
BNDES and is much easier for large firms, which meet the necessary requisites for the
loans, than for small firms.
22
References
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24
Table 1. Descriptive Analysis of the Database
Our sample is composed of 291 non-financial public corporations and 4,735 private firms. Our sample period goes from the third quarter of 1994 to the fourth quarter of 2010. Of the private firms, 102 disclose quarterly information as well as yearly information while all the others disclose yearly information only. The information on the public corporations comes from the Brazilian Securities and Exchange Commission (CVM), and Economatica and the information on the private firms comes from Valor Econômico and confidential information from SERASA and Gazeta Mercantil. We classify a firm as being large when its logarithm of its total assets is above the 70th percentile in at least one quarter or one year of our sampling period. We classify a firm as small when the logarithm of its total assets is below the 30th percentile at least in one quarter or year of our sampling period. Panel A shows the number of private and public firms separated by sectors of the economy. Panel B shows small and large private and public firms with quarterly information organized by sectors of the economy. Panel C shows some financial characteristics of small and large firms with quarterly financial statements. Panel D shows the results of the mean tests for the financial characteristics of small and large firms with quarterly financial statements. Panel E shows small and large private firms with end-of-the-year information organized by sectors of the economy. Panel F shows some financial characteristics of small and large private firms with end-of-the-year financial statements. Panel G shows the results of the mean tests for the financial characteristics of small and large private firms with end-of-the-year financial statements. Panel H shows the number of private and public firms that had loans with BNDES during our sample period. Panel A Total Number of Firms Classified by type (private or public) and sectors
Public Private
Chemical/Petroleum 36 273 Foods and Beverage 40 90 Mining/Metalurgy 8 31 Eletrical/Eletronic 14 92 Transportation 18 268 Public Services 30 91 Textile 35 75 Services 39 1110 Others 71 3815 Total 291 4,735
25
Panel B Small and Large Firms with Quarterly information by Sectors of the Economy
Sectors Large Small T
ota
N Log(Assets) Net Operational Revenues/Assets
N Log(Assets) Net Operational Revenues/Assets
Chemical/Petroleum 4 19.21 0.69 1 18.36 0.45 15
Food and Beverages 11 18.24 0.51 10 17.56 0.34 24
Mining/Metallurgy 4 18.31 0.44 8 17.90 0.71 26
Electro/Electronic Equipment
3 18.61 0.58 8
17.81
0.45 32
Transportation 5 18.49 0.41 6 17.86 0.51 20
Public Services
16 18.39 0.71 6 17.51 0.73
46
Textiles 4 18.67 0.55 13 16.21 0.54 29
Services 3 11.80 0.31 14 9.72 0.49 35
Others 18 10.32 0.72 54 10.45 0.31 166
Private Firms 5 11.41 0.38 39 8.32 0.56 102
Total 68 120 393
26
Panel C Financial Characteristics of Firms with Quarterly Information
Financial Characteristics
Large Firms (A)
Small Firms (B)
N Mean Median Standard Deviation
N Mean Median Standard Deviation
Log(Assets) 68 18.31 18.05 4.19 120 17.17 17.05 3.51
Operational revenues/Assets
68 0.68 0.60 0.85 120 0.36 0.18 0.58
Financial Expenses/Assets
68 0.19 0.18 0.35 120 0.19 0.19 0.42
Fixed Assets/ Assets
68 0.47 0.53 0.46 120 0.36 0.36 0.83
Short-term Debt/Assets)
68 0.68 0.65 0.91 120 0.49 0.17 0.15
Long-term Debt/Assets
68 0.23 0.19 0.17 120 0.09 0.12 0.13
BNDES Loans
36
21
Panel D Mean Tests for Financial Characteristics of Large and Small Firms with Quarterly Information Mean Tests 4Q1994 4Q2002 4Q2010
Ln(Assets) 4.33 (0.03)
4.96 (0.03)
5.70 (0.03)
Ln (inventories) 2.55 (0.06)
3.66 (0.01)
2.95 (0.02)
Ln(net operational revenues)
3.44 (0.01)
3.06 (0.01)
4.52 (0.02)
Ln(short-term debt) 3.470 (0.00)
3.09 (0.02)
4.87 (0.01)
Ln(long-term debt) 1.82
(0.01) 1.99
(0.01) 1.58
(0.02)
27
Panel E Small and Large Private Firms with End-of-the-Year Information and Sectors of the Economy
Sectors Large Small T
ota
N Log(Assets) Net Operational Revenues/Assets
N Log(Assets) Net Operational Revenues/Assets
Chemical/Petroleum 10 12.18 0.61 8 9.26 0.59 115
Food and Beverages 20 9.26 0.43 10 10.46 0.36 139
Mining/Metallurgy 10 11.25 0.23 16 10.24 0.27 129
Electro/Electronic Equipment
7 10.17 0.53 12 11.14
0.18
34
Transportation 9 9.20 0.56 21 8.75 0.24 101
Public Services
14 8.30 0.49 5 7.29 0.40
42
Textiles 13 8.21 0.16 14 9.27 0.78 145
Services 6 19.54 0.24 49 11.30 0.64 104
Others 13.23 0.38 7.09 0.45
3,988
Total 108 181
4,797
28
Panel F Financial Characteristics of Private Firms with End-of-the-Year Information
Financial Characteristics
Large Firms (A)
Small Firms (B)
N Mean Median Standard Deviation
N Mean Median Standard Deviation
Log(Assets) 108 11.87 11.0 3.51 181 8.32 8.70 4.76
Net Operational revenues/Assets
108 0.61 0.42 2.65 181 0.31 0.47 0.49
Financial Expenses/Assets
108 0.15 0.05 1.28 181 0.19 0.16 0.29
Fixed Assets/ Assets
108 0.63 0.35 0.43 181 0.47 0.31 0.61
Short-term Debt/Assets)
108 0.41 0.41 0.61 181 0.39 0.14 0.51
Long-term Debt/Assets
108 0.32 0.05 0.31 181 0.28 0.23 0.29
Panel G Mean Tests for Financial Characteristics of Large and Small Private Firms with End-of-the-Year Financial Statements Mean Tests 1998 2002 2004
Ln(Assets) 3.161 (0.01)
6.23 (0.02)
2.34 (0.02)
Ln(Inventories) 1.42 (0.02)
1.76 (0.02)
2.378 (0.01)
Ln(Net operational revenues)
2.43 (0.01)
3.62 (0.02)
4.45 (0.03)
Ln(Short-term debt) 3.03 (0.02)
4.43 (0.01)
4.32 (0.10)
Ln(Long-term debt) 1.32
(0.01) 1.14
(0.04) 1.25
(0.09)
29
Panel H BNDES Outstanding Loans
BNDES No BNDESSector
Retail
Non-metallic minerals
27 Construction 6
4
4
7 10
Foods and beverages 18 22
Industrial machinery 3
Electro-electronics 3 13
0
3
Mining 4 2
Oil and gas 6
Textile 9 31
29
Pulp and paper 5 4
Metallurgy and steelmaking 11
Vehicles and Spare Parts 3
11
Chemical 11 17
Transportation 6
Total 106 4920
Agriculture and fisheries 0
19
5
44 17 Others
30
Table 2 EFP Means and Means Tests Our sample is composed of 291 non-financial public corporations and 4,735 private firms. Our sample period goes from the third quarter of 1994 to the fourth quarter of 2010. Of the private firms, 102 disclose quarterly information as well as yearly information while all the others disclose yearly information only. The information on the public corporations comes from the Brazilian Securities and Exchange Commission (CVM), and Economatica and the information on the private firms comes from Valor Econômico and confidential information from SERASA and Gazeta Mercantil. We classify a firm as being large when its logarithm of its total assets is above the 70th percentile in at least one quarter or one year of our sampling period. We classify a firm as small when the logarithm of its total assets is below the 30th percentile at least in one quarter or year of our sampling period. period goes from the third quarter of 1994 to the fourth quarter of 2010. Of the private firms, 102 disclose quarterly information as well as yearly information while all the others disclose yearly information only. The Table presents the means, means difference and means differences tests for EFP between large and small firms. In the first 2 columns of Panel A, under parentheses we show the standard deviations. In the third column, under parentheses we have the p-value of the t tests. EFP Large EFP Small Means Differences
(p-value )
All sample 0.26 (0.04)
0.41 (0.05)
-0.15 (0.00)
Quarterly 0.35 (0.07)
0.42 (0.08)
-0.07 (0.03)
Annual 0.44 (0.03)
0.71 (0.09)
-0.27 (0.05)
BNDES quarterly 0.28 (0.11)
0.35 (0.01)
-0.07 (0.07)
BNDES annual 0.33 (0.02)
0.42 (0.08)
-0.09 (0.00)
31
Table 3 EFP and Monetary Policy Our sample is composed of 291 non-financial public corporations and 4,735 private firms. Our sample period goes from the third quarter of 1994 to the fourth quarter of 2010. Of the private firms, 102 disclose quarterly information as well as yearly information while all the others disclose yearly information only. The information on the public corporations comes from the Brazilian Securities and Exchange Commission (CVM), and Economatica and the information on the private firms comes from Valor Econômico and confidential information from SERASA and Gazeta Mercantil. We classify a firm as being large when its logarithm of its total assets is above the 70th percentile in at least one quarter or one year of our sampling period. We classify a firm as small when the logarithm of its total assets is below the 30th percentile at least in one quarter or year of our sampling period. period goes from the third quarter of 1994 to the fourth quarter of 2010. Of the private firms, 102 disclose quarterly information as well as yearly information while all the others disclose yearly information only. Panel A presents the estimation of the state space equation, equation (6), for aggregate data obtained with equal weights. Panel B presents the averages of the coefficients estimated of the state space equation, equation (6), with individual data. P-values are shown in parentheses. Panel A Aggregate Data EFP All Sample Quarterly Data Annual Data
Constant 0.21 (0.02)
0.21 (0.03)
-0.18 (0.32)
EFP(-1) 0.49 (0.04)
0.31 (0.18)
0.16 (0.13)
Selic (-1) 0.043 (0.02)
0.004 (0.06)
0.09 (0.09)
Small 0.021 (0.06)
0.012 (0.08)
0.032 (0.04)
Small*Selic(-1) 0.01 (0.00)
0.13 (0.02)
0.08 (0.05)
BNDES*Small*Selic(-1) -0.03 (0.02)
-0.04 (0.02)
-0.07 (0.06)
Control Variables
Sample 1994Q3 2010Q4
32
Panel B Individual Data: Averages of Estimated Coefficients EFP All Sample Quartely Data Annual Data
Constant 0.11 (0.02)
0.45 (0.03)
-0.18 (0.32)
EFP(-1) 0.21 (0.02)
0.31 (0.19)
0.11 (0.03)
Selic (-1) 0.021 (0.023)
0.023 (0.06)
0.195 (0.02)
Small 0.01 (0.01)
0.062 (0.02)
0.052 (0.03)
Small*Selic 0.01 (0.00)
0.13 (0.02)
0.08 (0.05)
BNDES*Small*Selic -0.01 (0.08)
-0.04 (0.06)
-0.023 (0.04)
Control Variables
Sample 1994Q3 2010Q4
Table 4 EFP and the Business Cycle: Aggregate Data on Public and Private Firms Our sample is composed of 291 non-financial public corporations and 4,735 private firms. Our sample period goes from the third quarter of 1994 to the fourth quarter of 2010. Of the private firms, 102 disclose quarterly information as well as yearly information while all the others disclose yearly information only. The information on the public corporations comes from the Brazilian Securities and Exchange Commission (CVM), and Economatica and the information on the private firms comes from Valor Econômico and confidential information from SERASA and Gazeta Mercantil. We classify a firm as being large when its logarithm of its total assets is above the 70th percentile in at least one quarter or one year of our sampling period. We classify a firm as small when the logarithm of its total assets is below the 30th percentile at least in one quarter or year of our sampling period. period goes from the third quarter of 1994 to the fourth quarter of 2010. Of the private firms, 102 disclose quarterly information as well as yearly information while all the others disclose yearly information only. Panel A presents the estimation results for the dynamics related to the aggregate value of inventories/assets. Our main specification follows equation (3) in the text. Panel B presents the estimation results for the dynamics related to the aggregate value of short-term debt/assets. Our main specification follows equation (4) in the text. Panel C presents the results for the dynamics related to the aggregate value of net operational revenues/assets. P-values are shown in parentheses.
33
Panel A Inventories/Assets
All Sample Firms
Quartely Information
Firms Annual
Information
Constant 0.42 (0.31)
043 (0.15)
0.41 (0.58)
Selic(-1) 0.15 (0.03)
0.06 (0.08)
0.31 (0.43)
BNDES.Selic(-1).Small -0.23 (008)
-0.03 (0.09)
-0.02 (0.34)
Small 0.43 (0.00)
0.31 (0.02)
0.51 (0.01)
Small*Selic(-1) 0.21 (0.03)
0.91 (0.00)
0.43 (0.03)
Sum EFP
0.26 (0.02)
0.51
(0.01)
0.15
(0.03)
Control Variables
Sample 1994Q3 to 2010Q4
Panel B Short-Term Debt/Assets
All Sample Firms
Quartely Information
Firms Annual
Information
Constant -0.18 (0.33)
-0.69 (0.28)
-0.40 (0.23)
Selic(-1) 0.15 (0.03)
0.06 (0.08)
0.31 (0.43)
Small 0.82 (0.03)
0.42 (0.01)
0.52 (0.04)
Small.Selic(-1) 0.72 (0.00)
0.53 (0.03)
0.41 (0.01)
BNDES.Selic(-1).Small -0.51 (0.08)
-0.42 (0.02)
-0.43 (0.04)
34
Sum EFP 0.16 (0.00)
0.311 (0.03)
0.18 (0.09)
Control Variables
Sample 1994Q3 to 2010Q4
Panel C Net Operational Revenues/ Assets
All sample Firms
Quartely Information
Firms Annual
Information
Constant -0.71 (0.21)
-0.81 (0.29)
-0.21 (0.69)
Selic(-1) -0.15 (0.03)
-0.06 (0.08)
0.31 (0.43)
Small -0.43 (0.03)
-0.61 (0.05)
-0.93 (0.08)
Selic(-1).Small -0.52 (0.03)
-0.85 (0.01)
-0.76 (0.00)
BNDES.Selic*Small(-1) 0.27 (0.08)
0.51 (0.01)
0.62 (0.04)
Sum EFP -0.22 (0.04)
-0.50 (0.06)
-0.19 (0.09)
Control Variables
Sample 1994Q3 to 2010Q4
35
Table 5 EFP and the Business Cycle: Individual Data on Public and Private Firms Our sample is composed of 291 non-financial public corporations and 4,735 private firms. Our sample period goes from the third quarter of 1994 to the fourth quarter of 2010. Of the private firms, 102 disclose quarterly information as well as yearly information while all the others disclose yearly information only. The information on the public corporations comes from the Brazilian Securities and Exchange Commission (CVM), and Economatica and the information on the private firms comes from Valor Econômico and confidential information from SERASA and Gazeta Mercantil. We classify a firm as being large when its logarithm of its total assets is above the 70th percentile in at least one quarter or one year of our sampling period. We classify a firm as small when the logarithm of its total assets is below the 30th percentile at least in one quarter or year of our sampling period. period goes from the third quarter of 1994 to the fourth quarter of 2010. Of the private firms, 102 disclose quarterly information as well as yearly information while all the others disclose yearly information only. Panel A presents the averages of the coefficients estimated for the dynamics related to the aggregate value of inventories/assets. Our main specification follows equation (3) in the text. Panel B presents the averages of the estimated coefficients for the dynamics related to short-term debt/assets. Our main specification follows equation (4) in the text. Panel C presents the averages of coefficients for the dynamics related to net operational revenues/assets. Our main specification follows equation (5) in the text. P-values are shown in parentheses. Panel A Inventories/ Assets (Averages of Coefficients)
All Sample Firms
Quartely Information
Firms Annual
Information
Constant -0.11 (0.31)
-0.61 (0.25)
-0.41 (0.68)
Selic(-1) -0.15 (0.03)
-0.06 (0.08)
0.31 (0.43)
Small 0.73 (0.00)
0.42 (0.08)
0.91 (0.02)
Selic(-1).Small 1.76 (0.03)
2.61 (0.10)
2.99 (0.07)
BNDES. Small.Selic(-1) -0.08 (0.09)
-0.43 (0.03)
-0.62 (0.04)
Sum EFP 0.53 (0.09)
0.42 (0.06)
0.82 (0.05)
Control Variables
Sample 1994Q3 to 2010Q4
36
Panel B Short-Term Debt/Assets (Average of Coefficients)
All Sample Firms with Quartely
Information
Firms with Annual
Information
Constant -0.11 (0.31)
-0.61 (0.25)
-0.41 (0.68)
Selic(-1) -0.15 (0.03)
-0.06 (0.08)
0.31 (0.43)
Small 0.72 (0.00)
0.73 (0.04)
0.42 (0.03)
Selic(-1).Small 1.76 (0.03)
2.69 (0.10)
2.53 (0.04)
BNDES.Small.Selic(-1) (0.89) (0.19) (0.04)
Sum EFP 0.20 (0.00)
0.54 (0.03)
0.11 (0.00)
Control Variables
Sample 1994Q3 to 2010Q4
37
Panel C Net Operational Revenues/ Assets (Average of Coefficients)
All Sample Firms with
Quarter Information
Firms with Annual
Information
Constant -0.11 (0.31)
-0.61 (0.25)
-0.41 (0.68)
Selic(-1) -0.15 (0.03)
-0.06 (0.08)
-0421 (0.03)
Small -0.32 (0.04)
-0.53 (0.02)
-0.83 (0.00)
Selic(-1).Small -1.46 (0.02)
-2.51 (0.11)
-2.19 (0.02)
BNDES.Small.Selic(-1) 0.42 (0.08)
0.92 (0.02)
0.31 (0.04)
Sum EFP -0.16 (0.03)
-043 (0.01)
-0.35 (0.15)
Control Variables
Sample 1994Q3 to 2010Q4
38
Appendix A
In this appendix, we demonstrate Propositions 1 and 2 in the text. Our model adapts the part of the BGG (1999) model related to the decision of the entrepreneur to the Brazilian credit market. The idea is to model this decision using the costly state verification mechanism (CSV). It is a static partial equilibrium model related to the decision of the firm to maximize expected profit. We have a continuum of firms and two types of lender, a market lender and another lender, a social development Bank (BNDES). There is no aggregate risk. The following are definitions of variables we use. Definitions
=BP Probability Loan BNDES
=Bw productivity shock of BNDES loan
=Mw productivity shock of market loan
=Bw_
default cutoff BNDES
=Mw_
default cutoff market
=)(wBf density probability default of loan obtained at BNDES
=)(wMf density probability default of loan obtained at market bank
=)(wBμ monitoring costs BNDES
=)(wMμ monitoring costs market
39
=⎟⎟
⎠
⎞
⎜⎜
⎝
⎛Γ
_BwB expected gross share profits going to BNDES
=⎟⎟
⎠
⎞
⎜⎜
⎝
⎛Γ
_MwM expected gross share profits going to market bank
=⎟⎟
⎠
⎞
⎜⎜
⎝
⎛ _BwBGBμ expected monitoring costs BNDES
=⎟⎟
⎠
⎞
⎜⎜
⎝
⎛ _MwMGMμ expected monitoring costs market bank
( ) ( )dw
w
wBfBwdwww
BwfBwB
B
B
∫∫∞
+=⎟⎟
⎠
⎞
⎜⎜
⎝
⎛Γ
_
_
_
0
_
( ) ( )dw
w
wMfMwdwww
MwfMwM
B
B
∫∫∞
+=⎟⎟
⎠
⎞
⎜⎜
⎝
⎛Γ
_
_
_
0
_
Given the definitions above, one can see that:
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−=Γ
_1'
BwBFB
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−=Γ
_''
BwBfB
40
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−=Γ
_1'
BwBFM
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−=Γ
_1'
BwBFM
∫=⎟⎟
⎠
⎞
⎜⎜
⎝
⎛_
0
)(_ Bw
dwwBwfBBwBGB μμ
∫=⎟⎟
⎠
⎞
⎜⎜
⎝
⎛_
0
)(_ Bw
dwwBwfBBwBGB μμ
The Net Share Profit for BNDES and the market bank are:
BNDES: 0__
>⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛Γ BwBGBBwB μ
Market Bank: 0__
>⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛Γ MwMGMMwM μ
We have the following transversality conditions:
0__
lim
0_
=⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛Γ
→
BwBGBBwB
Bw
μ
41
0__
lim
0_
=⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛Γ
→
MwMGMMwM
Bw
μ
BBwBGBBwB
Bw
μμ −=⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛Γ
∞→
1__
lim_
MMwMGMMwM
Bw
μμ −=⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛Γ
∞→
1__
lim_
The hazards rate for the BNDES and Market bank loans are the following:
Hazard rate for BNDES=
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛
_1
_
BwBF
BwBf
Hazard rate for Market Bank=
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛
_1
_
MwMF
MwMf
As one can see, ⎟⎟
⎠
⎞
⎜⎜
⎝
⎛ __BwhBw is increasing in
_Bw
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛ __MwhMw is increasing in
_Mw
42
There are global maximum for the net profits of firms that obtain a loan at BNDES and at the market bank.
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛−=⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛Γ
__1
_1(
_'
_'
BwhBwBBwFBwBGBBwB μμ >=< for
*_
BwBw >
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛−=⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟⎟
⎠
⎞
⎜⎜
⎝
⎛Γ
__1
_1(
_'
_'
MwhMwMMwFMwMGMMwM μμ
>=< for *
_MwMw >
Proposition 1 Given the structure of the model described above an in the text, the external finance premium, EFP, is an increasing function of the probabilities of default of firms. Demonstration
A firm has the following problem to solve and it chooses: K, Bw_
Mw_
( ) QKR KMMBBBB wPwPMax
⎥⎥⎦
⎤
⎢⎢⎣
⎡
⎟⎟⎠
⎞⎜⎜⎝
⎛⎟⎟⎠
⎞⎜⎜⎝
⎛Γ−−+
⎟⎟⎠
⎞⎜⎜⎝
⎛⎟⎟⎠
⎞⎜⎜⎝
⎛Γ−
__111
s.t.
( )NQKRQKKRBwBGBBwB −=⎥⎥⎦
⎤
⎢⎢⎣
⎡
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟⎟
⎠
⎞
⎜⎜
⎝
⎛Γ ρμ
__
( )NQKRQKKRMwMGMMwM −=⎥⎥⎦
⎤
⎢⎢⎣
⎡
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟⎟
⎠
⎞
⎜⎜
⎝
⎛Γ
__μ
43
We define
10,, <<== ρN
QKk
R
KRs
Where s is the external finance premium, EFP. Therefore, we will have:
( ) skMMBBBB wPwPMax⎥⎥⎦
⎤
⎢⎢⎣
⎡⎟⎟⎠
⎞⎜⎜⎝
⎛⎟⎟⎠
⎞⎜⎜⎝
⎛Γ−−+⎟
⎟⎠
⎞⎜⎜⎝
⎛⎟⎟⎠
⎞⎜⎜⎝
⎛Γ−
__111
s.t.
1__
−=⎥⎥⎦
⎤
⎢⎢⎣
⎡
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟⎟
⎠
⎞
⎜⎜
⎝
⎛Γ kk
sBwBGBBwB ρ
μ
1__
−=⎥⎥⎦
⎤
⎢⎢⎣
⎡
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟⎟
⎠
⎞
⎜⎜
⎝
⎛Γ kskMwMGMMwM μ
Or
( ) skMMBBBB wPwPMax⎥⎥⎦
⎤
⎢⎢⎣
⎡⎟⎟⎠
⎞⎜⎜⎝
⎛⎟⎟⎠
⎞⎜⎜⎝
⎛Γ−−+⎟
⎟⎠
⎞⎜⎜⎝
⎛⎟⎟⎠
⎞⎜⎜⎝
⎛Γ−
__111
s.t.
022____
=+−⎥⎥⎦
⎤
⎢⎢⎣
⎡
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛Γ+
⎥⎥⎦
⎤
⎢⎢⎣
⎡
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛Γ kskMwMGMMwMk
sBwBGBBwB μ
ρμ
44
Solution
( ) +
⎥⎥⎦
⎤
⎢⎢⎣
⎡⎟⎟⎠
⎞⎜⎜⎝
⎛⎟⎟⎠
⎞⎜⎜⎝
⎛Γ−−+⎟
⎟⎠
⎞⎜⎜⎝
⎛⎟⎟⎠
⎞⎜⎜⎝
⎛Γ−= skMMBBBB wPwPL
__111
022____
=+−⎥⎥⎦
⎤
⎢⎢⎣
⎡
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛Γ+
⎥⎥⎦
⎤
⎢⎢⎣
⎡
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛Γ λλμλ
ρμλ kskMwMGMMwMk
sBwBGBBwB
FOC
(i) ⇒=∂∂
0k
L
( ) +
⎥⎥⎦
⎤
⎢⎢⎣
⎡⎟⎟⎠
⎞⎜⎜⎝
⎛⎟⎟⎠
⎞⎜⎜⎝
⎛Γ−−+⎟
⎟⎠
⎞⎜⎜⎝
⎛⎟⎟⎠
⎞⎜⎜⎝
⎛Γ− sMMBBBB wPwP
__111
02____
=−⎥⎥
⎦
⎤
⎢⎢
⎣
⎡
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛Γ+
⎥⎥
⎦
⎤
⎢⎢
⎣
⎡
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛Γ λμλμ
ρλ
MwMGMMwMsBwBGBBwBs
(ii) ⇒=
∂
∂0
_Bw
L
0_
'_
'_
' =⎥⎥⎦
⎤
⎢⎢⎣
⎡
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟⎟
⎠
⎞
⎜⎜
⎝
⎛Γ+⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛Γ− BwBGBBwBk
sBwBBskP μ
ρλ
(iii) ⇒=
∂
∂0
_Mw
L
45
0_
'_
'_
')1( =⎥⎥⎦
⎤
⎢⎢⎣
⎡
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟⎟
⎠
⎞
⎜⎜
⎝
⎛Γ+⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛Γ−− MwMGMMwMskMwMBPsk μλ
Combining (ii) and (iii), we have:
( )
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛Γ+⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛Γ
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛Γ−+⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛Γ
=_
'_
'_
'_
'1
_1
_'
MwMGMMwMBwBGBBwB
MwMBPBwBBP
μμρ
λ
0,,,,,__
>
⎟⎟⎟⎟⎟⎟
⎠
⎞
⎜⎜⎜⎜⎜⎜
⎝
⎛
= MBMBB wwP μμρλλ
( )⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢
⎣
⎡
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛Γ
⎟⎟⎟
⎠
⎞
⎜⎜⎜
⎝
⎛
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛Γ−+⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛Γ
−⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛Γ+⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛Γ⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛Γ
⎥⎥
⎦
⎤
⎢⎢
⎣
⎡
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛Γ+⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛Γ
=
∂
∂
_"
_''1_
'1_
'
_'
_'
_'
_'1_
''
2_
'_
'_
'_
'1
1_
BwBGBBwBMwMBPBwBP
MwMGMMwMBwBGBBwBBwBP
x
MwMGMMwMBwBGBBwBBw
μρ
μμρ
μμρ
λ
It is easy to see that 0_
<
∂
∂
Bw
λ
46
By analogy, 0_
<
∂
∂
Mw
λ.
Using (i), we have (iv): (iv)
( )
⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢
⎣
⎡
⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢
⎣
⎡
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛Γ+
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛−⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛Γ
+
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛Γ−−+⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛Γ−
=
__
__
_11
_1
2
MwMGMMwM
BwBGBBwB
MwMBPBwBBP
s
μρ
μ
λ
λ
λ
λ
∂
∂
∂
∂=
∂
∂ s
BwBw
s__
. As it is easy to see,
( )sBwBw
Bw
ss __0
_0 =⇒>
∂
∂⇒<
∂
∂
λ
By analogy, ( )sww
w
ssMM
M
__
_00 =⇒>
∂
∂⇒<
∂
∂
λ
Therefore s, EFP, is a increasing function of the probabilities of default of the firms.
47
Proposition 2 Given the structure of the model described above and in the text, the external finance premium, EFP, is a decreasing function of the probability of the firm to obtain a loan at BNDES if the expected profit of BNDES is less than the expected profit of the market lender. From (iv) above, we have:
0
112
2[]
__
<⎟⎟⎟
⎠
⎞
⎜⎜⎜
⎝
⎛
⎟⎟⎟
⎠
⎞
⎜⎜⎜
⎝
⎛Γ−−
⎟⎟⎟
⎠
⎞
⎜⎜⎜
⎝
⎛Γ−−
=∂∂
MMBB
B
ww
P
s
λ
:
Where [] is the denominator of (iv)
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛Γ<⎟
⎟
⎠
⎞
⎜⎜
⎝
⎛Γ⇔<
∂∂ __
0 MwMBwBBP
s
48
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253 Bank Efficiency and Default in Brazil: causality tests
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254 Macroprudential Regulation and the Monetary Transmission Mechanism Pierre-Richard Agénor and Luiz A. Pereira da Silva
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255 An Empirical Analysis of the External Finance Premium of Public Non-Financial Corporations in Brazil Fernando N. de Oliveira and Alberto Ronchi Neto
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256 The Self-insurance Role of International Reserves and the 2008-2010 Crisis Antonio Francisco A. Silva Jr
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258 Bancos Oficiais e Crédito Direcionado – O que diferencia o mercado de crédito brasileiro? Eduardo Luis Lundberg
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259 The impact of monetary policy on the exchange rate: puzzling evidence from three emerging economies Emanuel Kohlscheen
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260 Credit Default and Business Cycles: an empirical investigation of Brazilian retail loans Arnildo da Silva Correa, Jaqueline Terra Moura Marins, Myrian Beatriz Eiras das Neves and Antonio Carlos Magalhães da Silva
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261 The relationship between banking market competition and risk-taking: do size and capitalization matter? Benjamin M. Tabak, Dimas M. Fazio and Daniel O. Cajueiro
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262 The Accuracy of Perturbation Methods to Solve Small Open Economy Models Angelo M. Fasolo
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263 The Adverse Selection Cost Component of the Spread of Brazilian Stocks Gustavo Silva Araújo, Claudio Henrique da Silveira Barbedo and José Valentim Machado Vicente
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49
264 Uma Breve Análise de Medidas Alternativas à Mediana na Pesquisa de Expectativas de Inflação do Banco Central do Brasil Fabia A. de Carvalho
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265 O Impacto da Comunicação do Banco Central do Brasil sobre o Mercado Financeiro Marcio Janot e Daniel El-Jaick de Souza Mota
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266 Are Core Inflation Directional Forecasts Informative? Tito Nícias Teixeira da Silva Filho
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271 Optimal Policy When the Inflation Target is not Optimal Sergio A. Lago Alves
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272 Determinantes da Estrutura de Capital das Empresas Brasileiras: uma abordagem em regressão quantílica Guilherme Resende Oliveira, Benjamin Miranda Tabak, José Guilherme de Lara Resende e Daniel Oliveira Cajueiro
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273 Order Flow and the Real: Indirect Evidence of the Effectiveness of Sterilized Interventions Emanuel Kohlscheen
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278 Liquidez do Sistema e Administração das Operações de Mercado Aberto
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50
280 Educação Financeira para um Brasil Sustentável Evidências da necessidade de atuação do Banco Central do Brasil em educação financeira para o cumprimento de sua missão Fabio de Almeida Lopes Araújo e Marcos Aguerri Pimenta de Souza
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282 The Signaling Effect of Exchange Rates: pass-through under dispersed information Waldyr Areosa and Marta Areosa
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283 The Impact of Market Power at Bank Level in Risk-taking: the Brazilian case Benjamin Miranda Tabak, Guilherme Maia Rodrigues Gomes and Maurício da Silva Medeiros Júnior
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284 On the Welfare Costs of Business-Cycle Fluctuations and Economic-Growth Variation in the 20th Century Osmani Teixeira de Carvalho Guillén, João Victor Issler and Afonso Arinos de Mello Franco-Neto
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285 Asset Prices and Monetary Policy – A Sticky-Dispersed Information Model Marta Areosa and Waldyr Areosa
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51
294 Pesquisa de Estabilidade Financeira do Banco Central do Brasil Solange Maria Guerra, Benjamin Miranda Tabak e Rodrigo César de Castro Miranda
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52