1. finance
2. bank

Unconventional monetary policy spillovers, internal liquidity, and local credit
provision1
Nicholas Coleman
Ricardo Correa
Federal Reserve Board
Federal Reserve Board
Leo Feler
Jason Goldrosen
Johns Hopkins University
Federal Reserve Board
April 2015
JEL codes: E51, F36, G21
Keywords: Brazil, bank lending, lending channel.
1
Correspondence: [email protected], [email protected], [email protected],
[email protected]. The views in this paper are solely the responsibility of the authors and should not be
interpreted as reflecting the views of the Board of Governors of the Federal Reserve System or of any other person
associated with the Federal Reserve System. 1 1. Introduction and Background
The wave of financial globalization that started in the 1980s transformed financial markets
and institutions around the world. As a result of this trend of financial integration, global
banks increased their footprint within their domestic markets and across both emerging and
advanced economies. In this process, banks developed different business models to manage
the funds raised from external sources (CGFS, 2010). One of those business models relies
intensively on the management of liquidity within the banking organization.
This paper studies the patterns of internal liquidity management for large banks in Brazil
and how these business practices affect bank lending to non-related borrowers. In particular,
we try to answer two question: How do banks manage liquidity within their organizations
after suffering a liquidity shock? And what is the impact of liquidity management within the
banking organization on bank lending and the real economy?
To answer these questions, we use a novel dataset with information on the Brazilian
banking sector. The main advantage of these data is that they capture the balance sheets of
branches that belong to the same banking organization aggregated by municipality. This
information is recorded at a monthly frequency, which helps us investigate the effect of
liquidity shocks on the aggregate balance sheet of the banking organization and of its local
branches. More important, these data include the net lending of branches to other parts of the
organization. This allows us to map, at the micro level, the degree of liquidity management
that takes place within the organization as external factors change.
We need a second piece of information to answer our questions. More precisely, we have
to find an external shock that affects Brazilian banks’ liquidity conditions, without this shock
being correlated with the solvency of those banks or the economic activity of the
2 municipalities in which these banks operate. In our particular sample period, the closest
shock with these characteristics is the so called “taper tantrum” (Fischer, 2014). In the spring
of 2013, the Chairman of the U.S. Federal Reserve announced that the pace of asset purchases
that the central bank was conducting at the time would decelerate in the near future. Financial
markets reacted strongly and flows moved quickly out of some emerging markets. Brazilian
banks were not immune to this shock and they lost roughly $20 billion in external funding in
two quarters. This shock allows us to identify the reaction of banks within Brazil to the
change in liquidity conditions and in particular, their adjustment in net lending within their
banking organization as a result of the reduction in external financing.
In our first set of tests, we investigate whether banks follow specific liquidity management
patterns across their network of branches. Specifically, we test whether banks use branches in
their headquarters location to fund branches located in other municipalities. We do not find
any evidence that banks use branches in their headquarters location to raise additional funds
and funnel them to other areas. We next investigate whether banks move resources between
rich and poor areas. We find evidence that there are net transfers to rich areas from poor
areas. This may be because there are more, or better, projects in relatively wealthier areas that
need financing so banks find it profitable to put the money to work in those areas.
In our second set of tests, we study whether the liquidity shock that took place after the
U.S. monetary taper decision changed the pattern of liquidity management within banks in
Brazil. We find that following the taper, there is no differential impact on net lending
between branches in headquarter locations and those in non-headquarters municipalities.
However, we do observe that the net lending within government banks, compared to private
banks, became significantly larger following the taper. We also find that the net internal
3 borrowing of branches located in wealthy cities became larger, indicating that bank funding
stress induced by the decision to taper did change the way that banks funds their branches
internally.
Lastly, we test whether the U.S. monetary taper’s impact on internal liquidity management
by Brazilian banks had any impact on their lending non-related customers. We find that
banks with more intrabank liabilities tend to have more lending, consistent with the view that
this intrabank funding is actually useful in providing additional credit to the real economy and
risk sharing within the banking organization. This effect becomes significantly larger
following the start of the taper.
The study of liquidity management within banking organizations and its impact on the
lending activities that these banks conduct has been an active field of research over the past
15 years. Starting with the work of Campello (2002), several papers in the literature have
found that risk sharing within banking organizations help mitigate external shocks, such as
changes in monetary policy. This is particularly true for banks that have a large global
footprint that allows them to move funds between countries that face different sets of
uncorrelated shocks (Cetorelli and Goldberg, 2012).
Another strand of the literature focuses on the real effects of having banking sectors with
more geographically diversified banks (Morgan, Strahan, and Rime, 2004). This literature
finds that as bank linkages across regions increase, the fluctuations in the business cycles of
those states decrease, but at the same time, the fluctuations of these regions tend to converge.
Similar evidence exists in the economic development literature of risk-sharing across
households, where a households’ consumption varies less with its own income than with the
average income of other households in its village, caste, or ethnic group. Townsend (1994),
4 for example, finds that while income is highly variable across households within Indian
villages, consumption is not, with households reallocating income to equalize the marginal
utility of consumption. Udry (1994) examines channels by which such reallocations occur.
In his study of households in Northern Nigeria, he finds that households extensively borrow
and lend to one another. When a household experiences a negative shock, it will demand
payments on loans that it has made while delaying payments on its debts, and thereby smooth
income and consumption.
This paper is related to these three strands of the literature, as we examine whether
lending by bank branches within bank networks varies more depending on their own deposits
or on the deposit base of their parent banks. We further examine the channels by which any
smoothing in lending occurs. Namely, we can observe the interbranch transfers within a bank
to determine whether branches are obtaining resources from their branch network or lending
resources to their branch network.
2. Brazilian Banking Sector.
Brazil’s modern banking history dates back to the early-1800s, with the establishment of
foreign banks and domestic banking houses that helped finance the initial debts of the
country. During several early banking crises, both national and state governments acted as
“insurers against failure” (Musacchio and Lazzarini, 2014 p.81) and assumed control of
troubled, private banks. The role of Brazil’s government banks expanded during the twentieth
century to include the promotion of state-level development projects, the generation of
employment, and the distribution of patronage (Ribeiro and Guimaraes 1967; Triner 2000;
Beck et al. 2005).
5 Government banks were so politically valuable that by the 1970s, the federal government
owned five of them and every state at the time owned at least one of them. The five national
banks are Banco do Brasil, which was founded in 1808, served as Brazil's monetary authority
until the creation of the Central Bank of Brazil in 1964, and was officially part of the National
Treasury until 1987; Caixa Economica Federal, which was established in 1861 as a savings
institution; Banco da Amazonia, founded in 1942 to finance rubber cultivation and later reorganized to provide general banking services to the Amazon region; Banco do Nordeste do
Brasil, established in 1952 to provide banking services and promote development in the
Northeast region; and Banco Nacional de Desenvolviment Economico e Social (BNDES), a
wholesale development bank founded in 1952 to provide long-term financing to infrastructure
and strategic sectors. Banks owned by individual state governments were present in all but
two of Brazil’s 27 states (including the Federal District), and these two states were formed
only more recently. State-owned banks were historically mismanaged. Brazil’s monetary
authority intervened 71 times in the state-owned banks of 18 states between 1955 and 1996.
Given the history of mismanagement, the Brazilian federal government incentivized the
recapitalization and privatization of state-owned banks, beginning in 1996, and now only
seven states have these institutions.
The location decisions of state and government bank branches also do not appear to react
to changes in localities' economic or social characteristics over time. While the initial entry of
government bank branches into a locality likely corresponds to the locality's contemporaneous
economic and social circumstances, government bank branches almost never exit a locality.
This suggests that while a locality's economic and social characteristics evolve, it is not
necessarily the case that its bank branch composition evolves with it.
6 Given their development objectives, a plausible hypothesis is that government banks
might lend differently and allocate intrabank funds in a different manner than private-sector
banks, especially during times of crisis. With consolidated data for all of a bank’s branches in
each municipality in Brazil, we are able to examine how intrabank funds flow across different
types of localities. Particularly, we can assess whether government banks differentially
capture resources from or lend resources to poorer localities or localities with greater external
financial dependence, as measured by their employment composition.
3. Data and Empirical Framework
This section discusses the sample selection, the data, and provides summary statistics
3.1 Sample
For our analysis, we focus on the period between 2011Q1 and 2014Q4 and divide the
sample into a pre- and post-“taper” period. Our “taper” variable takes a value of 1 starting in
2013Q2 when the Federal Reserve’s Federal Open Market Committee’s (FOMC) began
publicly discussing plans to scale down its quantitative easing program.2
As of 2010, Brazil has 5,565 municipalities as of 2010, which subdivide the states into
smaller administrative entities. Because municipalities split and recombine over time, we
collapse municipalities into spatially constant units, which we term “localities.” More
specifically, we use municipal borders from 1970 to further collapse municipalities that are
part of the same urban agglomeration (metropolitan area) into 3,659 individual units. Our
2
The actual exit from the third LSAP was announced after a Federal Open Market Committee (FOMC) meeting
ending on December 18, 2013.
7 final sample includes the 2,214 localities that have at least one bank branch, roughly
corresponding to individual labor and credit markets.3
Currently, approximately one-third of Brazil's nearly 20,000 bank branches belong to
federal government banks, approximately half to private sector banks, and the remainder to
state-government banks. Collectively, state and federal government banks account for
approximately 45% of total bank assets in Brazil (Barth et al. 2013). Our sample of 28 banks
consists of government banks and privately-owned domestic and foreign banks. Wanting to
exclude some smaller banks that could drive the results, we first trim the sample to include
only those banks that make up 99% of total assets in the banking sector. We then trim the
sample by the banks’ overall net due to position. Without any reporting errors, we would
expect internal borrowing and lending between branches to equal one another when
aggregating across all branches for a given bank. Thus, we exclude banks that are believed to
be inaccurate reporters when the difference in these net positions are nontrivial (> 1% of
consolidated bank assets).
3.2 Data and Summary Statistics
3.2.1
Data
Due to data limitations, previous research has been unable to provide a robust analysis of
intrabank funding and how it is used in times of funding stress. For example, the U.S.
3
Because Brazil lacks a system of credit registries, lending, especially to smaller firms and individuals, tends to be
highly localized and dependent on long-standing relationships with local branch managers. While we have detailed
information only on the lending operations of bank branches, and not on the locations of the borrowers obtaining
these loans, from a 2004 cross-sectional dataset provided by the Central Bank of Brazil, we were able to ascertain
that the sum of all lending within municipalities was approximately equivalent to the sum of all borrowing by firms
and residents within those municipalities, excluding major money centers. We assume the same relationship holds
for deposits, namely, that the deposits of local bank branches come from individuals and businesses within the local
market. This is important for our paper given that we are examining the extent to which local lending depends on
local deposits and the mechanisms that banks use to smooth local demand and supply and of credit. 8 Summary of Deposits data include information on branch locations and deposits but does not
provide broader balance sheet information at the branch or locality level. We overcome this
shortcoming in the literature by using a rich database for Brazilian banks, which includes
comprehensive financial statements at various levels of aggregation. We utilize both
consolidated bank balance sheets and bank balance sheets disaggregated by municipality,
which are published by the Central Bank of Brazil at a monthly frequency. For our analysis, we
have collapse the data to quarterly averages. In the context of internal liquidity management,
the granularity of the data allow us observe how different branches within a banking network
shift deposits between each other in response to an external funding shock or local economic
conditions.
After identifying the link between an external funding shock, intrabank lending, and
external credit provision, we can examine how these changes is bank operations translate to
effects on real outcomes. To measure the local economic impact of these changes in lending,
we will utilize the Brazilian yearly employment census, Relaçao Anual de Informaçoes Sociais
(RAIS). The RAIS identifies all employees on the payroll of formal sector firms as well as the
self-employed who pay into the social security system. The data cover approximately 2.5
million establishments and 36 million workers. Finally, we will use information on localitylevel GDP and control variables including measures of urbanization, education, income,
population, and exports, which all come from Brazil’s Institute of Applied Economic Research
(IPEA).
9 3.2.2
Summary Statistics
Figure 1 reveals some interesting trends that illustrate, in broad terms, the interplay between
the Federal Reserve’s unconventional monetary policies and the Brazil-U.S. exchange rate as a
proxy for Brazilian banks’ external funding costs. More specifically, the figure shows Brazil’s
benchmark interest rate (SELIC) alongside the dollar-real exchange rate between 2010 and
2014. During this time period, the U.S. Federal Reserve announced a series of unconventional
monetary policies. In 2010 and early 2011, the real strengthens against the dollar as the Central
Bank of Brazil gradually increases its policy rate. Consistent with intuition, the real then
depreciated as the Central Bank of Brazil began to cut its target rate in late 2011. Around the
time the market perceived the Federal Reserve’s exit from QEIII, the Central Bank of Brazil
acted quickly, raising its target rate 2.5 percentage points, cumulatively, during the summer of
2013. Nevertheless, over the same time period, the real depreciated roughly 20 percent. By the
end of 2013, when the Federal Reserve officially announced that it would scale down QEIII
asset purchases, the real weakened again despite further increases in the SELIC rate.
In Table 1A, we present the summary statistics for total assets, loans (net of loss
provisions), deposits, and intrabank funding flows for a given bank i, in municipality j, at time
t. Altogether, the branch networks of the 28 banks in our sample span 2,214 Brazilian
municipalities. Between 2011 and 2014, the median bank branch held roughly 29.2 million
Brazilian real in total assets. The median branch also respectively held 20.8 million in loans
and 21.4 million in deposits. Net due to measures the internal transfer of funds between
branches within a single bank’s branch network. Thus, net due to, which is calculated as
liabilities less assets, provides a net measure of the branch’s interbranch, intrabank borrowing
(or lending) position. When we focus on the subsample of “net borrowers” – branches that
10 borrow more from their bank’s network than they lend out – the median branch’s internal
borrowing (net of internal lending) is roughly 38 percent of total assets. Similarly, for median
branch in the subsample of “net lenders”, the amount of funds lent out to other branches (net of
internal borrowing) is roughly 32 percent of total assets.
In Table 1B, we report sample means for a similar set of variables when dividing the
sample into separate subgroups. For example, when differentiating between branches in
municipalities that are above and below our sample’s median municipality GDP per capita in
2010 (4,922 Brazilian real), we find that branches in “high income” municipalities are both
larger and rely more heavily on internal borrowing, on average. We find similar trends for
branches in population centers relative to counterparts in less populated municipalities. The
results in Panel C show that branches in non-headquarter locations lend more internally (net of
borrowed funds) to other branches, on average, as a share of total assets than headquarter
locations. Lastly, Panel E presents an interesting trend that average branch of a governmentowned bank is a net borrower whose net internal borrowing accounts for 9 percent of total
assets. In contrast, the average branch of a privately-owned bank is a net lender with net
internal lending accounts for 30 percent of total assets. The results from Table 1B suggest that
there is some heterogeneity in internal funding flows and other balance sheet characteristics
across banks and municipalities, motivating the need to examine these relationships
econometrically.
3.3 Empirical Framework
This paper aims to understand the impact that bank funding stress has on the intrabank
market and how this, in turn, impacts local lending and real economic outcomes. To attribute
11 a causal impact, we use the so-called “Taper tantrum” event when the market began to
anticipate the Federal Reserve’s shift away from accommodative monetary policies as an
exogenous shock to bank funding conditions in Brazil. In this section we describe our
econometric methodology and wait until section 3 to discuss the results.
We first want to document the net internal funding positions of bank branches in Brazil.
Because the headquarter office of a bank is likely to have greater access to external funding,
we can differentiate bank branches as being either in the headquarters city or not. Figure 2a
shows the geographic distribution of the headquarters of banks in Brazil. In particular, our
sample’s headquarter locations are largely concentrated in Sao Paulo and Brasilia.
To show net positions relative to headquarters, we run the following specification:
(1)
where
is the net due to position, calculated as
, for
bank , in locality , in month . A positive net due to position implies that a bank branch is a
net borrower from other bank branches within the banking organization, and a negative
position implies that the bank branch is a net lender from other bank branches. This
regression includes bank fixed effects, and we additionally include time fixed effects in
alternative specifications. Note that that dummy variable for being a government banks is
omitted as it is collinear with the bank fixed effect. We additionally run regressions where
instead of including a dummy for the headquarters locality of a bank, we include an indicator
of economic development. In this regressions, we are interested in understanding whether
12 intrabank transfers flow from rich to poor localities or vice-versa. In all of our estimations in
this and the following sections, we cluster at the bank level.
Next, we are interested in understanding the impact of changes in liquidity provision
through the interbranch network following the start of the Federal Reserve’s taper. In our
analysis, we treat the taper as an exogenous shock to the ability of banks to access funding in
international markets, and thus may require banks to rely more heavily on their branch
networks. To test this, we run the following specification:
(2)
where again
is the net due to position as calculated above. In these regressions we include
either bank and locality fixed effects or bankXlocality fixed effects in alternative
specifications.
We ultimately aim to test what impact this shock to bank funding has on lending at the
locality level. To test this question, we run the following specification:
where
(3)
is the natural logarithm of total credit operations for bank in locality in time .
We include both deposits at the bank-by-locality level and deposits at the banking group level
to determine whether it is specifically the deposit-base at the branch-level itself or if it is total
13 deposits that could be potentially transferred through other branches within a banking group
that matters. We additionally include the size of the intrabank positions for each branch over
time. Our hypothesis is that bank branches will lend more if their intrabank liabilities are
higher
0 because it is precisely these liabilities that will allow them to continue their
credit expansion if they run out of deposits to lend.
The last test that we run expands equation (3) to include the “post” dummy to see if
these relationships held in the environment with higher bank funding stress. We will be
expanding this analysis to see if the differential changes in lending caused by the taper had
any impact on local employment, wages, and firm growth.
4. Results
We first present results that show how the net due to positions of bank branches vary by
being located in the headquarters of the bank or not and how the net due to position varies by
the wealth of localities. We then proceed to examine if this changed following the shock to
bank funding (the taper). Finally, we conduct an analysis on lending at the locality level by
banks to see how this funding shock impacted local lending.
4.1
Net Due To Position
Table 2 shows the results from estimating equation (1) for quarterly net due to position at
the bank, locality level on a dummy variable for whether that locality is the headquarters of
that bank. In column (1), the coefficient estimates suggest that the headquarters of a bank
have an average net due to position of 20% (insignificant) less than non-headquarters
localities. A higher net due to position means that the bank has more intrabank liabilities
which implies that headquarters localities are associated with a higher share of intrabank
14 assets. This is consistent with the hypothesis that banks raise money in their headquarters
locality and then transfer it internally to their branches. In column (2), we include the
interaction term of the headquarters locality with a dummy for whether the bank is owned by
the government. Note that because we are including bank fixed effects, the dummy variable
for government ownership will not enter the regression. We find that the effect of
headquarters on the net due to position for government banks is 16.8% larger than for private
banks. In columns (3) and (4) we additionally include locality fixed effects and in columns
(5) and (6) we include time fixed effects which produce stable, but insignificant, coefficients.
All of our regressions are clustered at the bank level.
Table 2 reports the average net due to position in the headquarters vs. non-headquarters
which yielded potentially economically meaningful differences between headquarters and
non-headquarters localities, but they were statistically indistinguishable. Instead of the
distinction between headquarters and non-headquarters, Table 3 distinguishes between areas
with comparing high or low income levels, calculated at gross municipal product per capita
(GMPPC). In column (1) we report that high income localities, defined as those above the
median GMPPC, have a 7.2% (statistically significant) higher net due to position than low
income localities. This implies that banks located in poorer areas are lending money to their
areas with higher incomes. We do not, however, find any difference in this relationship
between government and private banks (reported in column (2)). These results include bank
fixed effects, but they also hold and are stable if we additionally include time fixed effects, as
seen in columns (3) and (4). The remaining columns provide the continuous measure of
GMPPC instead of a dummy for being above the median.
15 To understand the impact of a shock to bank funding, we need to compare the net due to
positions before and after the Federal Open Market Committee’s decision to taper the
quantitative easing program. This decision caused emerging market economies, including
Brazil, to experience large capital outflows, depreciating local currencies (see Figure 1), and
increased bank funding stress. Importantly, the decision by the Federal Open Market
Committee to taper was exogenous to Brazil which provides us with a natural experiment
where we can examine how banks funding change under stress.
In Table 4 we reports the results of this test. As seen in column (1), there is no statistical
difference in the net due to position before the taper and after for either the headquarters
locality or the non-headquarters locality. Column (2), however, finds that the average net due
to position for private banks decreased by almost 9%, but actually increased by about 8% for
government banks following the taper. There is no differential impact on localities that are
headquarters depending on whether the bank is government or privately owned, however.
These results are robust to the inclusion of localityXtime fixed effects (columns (4)-(6))
instead of bank and locality fixed effects (as we included in columns (1)-(3)).
In Table 5, we present further results about the impact of the taper on the net due to
positions on banks but instead of a dummy variable for whether a locality is the headquarters
for a particular bank, we include an indicator variable for whether the locality has a high
income, as measured by the gross municipal product per capita, as we did in Table 3. In
Column (3) we show that there is an overall decrease in the net due to position of bank
branches following the taper of 13.4%, but this decrease was smaller (only about 6%) for high
income areas. Government banks saw a relative increase in their net due to positions. We do
16 not see any differential impact of government banks for high compared to low incomes areas,
though. Interestingly, in columns
4.2
Lending
The previous section showed how the net due to position of bank branches changed when
bank funding became stressed. This section presents results on how lending is related to the net
due to position of banks and then how it was impacted by the taper.
Table 6, shows how lending is related to deposits at both the bank branch level and at the
group level. Next we show how the lending results vary with intrabank assets and intrabank
deposits. In column (1) we find evidence that is consistent with the development literature of
risk-sharing across households. We find that not only is lending related to the deposits at the
bank branch level, but it is also related to deposits at the banking group level. Consistent with
this story is that banks are able (or willing) to lend more if they are able to borrow more from
within its banking group, if needed.
In column (4) we include the size of a bank branch’s intrabank assets and liabilities and find
that these significantly predict the lending of a bank branch. We find that banks with higher
intrabank assets lend less, consistent with their offering money to other bank branches instead of
to the locality residents or businesses. We also find that banks with higher intrabank liabilities,
i.e. the bank branches that borrowed internally, have a higher lending. This implies that banks
that required additional funds to lend borrowed them internally and lent the money to the local
economy. We would not expect banks to be borrowing internally unless there is a binding
funding constraint and this is precisely what we find.
Table 7 provides evidence of the relationship between intrabank funding and lending
following the FOMC’s decision to taper. In column (1) we regress total credit operations on
17 bank branch deposits, banking group deposits, the net due to position of the bank branch and a
dummy equal to one after the taper rumors started in 2013Q2. We report a negative coefficient
on the net due to position implying that banks with higher intrabank liabilities, i.e., those that
borrowed internally, had higher lending than those with higher intrabank assets. Interestingly,
the taper had no effect on lending. Column (4) includes interactions between the deposit
measures and the intrabank funding measure to see if the determinants of lending changed
following the increase in bank stress. We find that the interaction between the taper and the net
due to position is positive and statistically significant. This implies that following the Fed taper
rumors, when banks had funding pressures, the intrabank market became a more important part
of banks’ ability to continue and actually increase lending.
4
Discussion and Conclusion
Using a unique dataset that allows us to see the interbranch network, we analyze how banks
utilize their intrabank market to raise funding, e.g. take deposits, from certain locations and
transfer them to other branches within their banking group. As far as we know, this is the first
analysis that has been able to use bank balance sheets at the city level which allows us to clearly
identify the dynamics of internal bank liquidity provision.
We first investigate whether there is a relationship between the net due to position of banks
located in the headquarters city or a city not located in the banks’ headquarters. We do not find
any evidence that banks use their headquarters locality to raise additional funds and funnel them
to other areas. We next investigate whether there is a relationship between rich and poor areas.
We find evidence that there are net transfers to rich areas from poor areas. This may be because
18 there are more or better projects in relatively wealthier areas that need financing so banks find it
profitable to put the money to work in those areas.
We also are interested in how these relationships evolve over time, in particular, we are
interested in whether these intrabank relationships changes when banks are under funding stress.
To investigate this, we use the FOMC’s decision to taper the quantitative easing program which
lend to large capital outflows from emerging market economies, including Brazil and created
financial stress. The tapering decision is convenient because it is orthogonal to any individual
bank or locality within Brazil as the Federal Reserve’s mandate is to maximize employment with
stable inflation in the United States. The mandate does not require any consideration of other
countries.
We find that following the taper, there is no differential impact on the net due to position of
headquarters and non-headquarters localities, but we do see that the net due to position of
government banks, compared to private banks, became significantly larger following the taper.
We also found that the net position of banks located in wealthy cities became larger, indicating
that bank funding stress induced by the decision to taper did change the way that banks are
funded.
Lastly, we examine whether the taper’s impact on the intrabank funding market had any
impact on lending. We find overall that banks with more intrabank liabilities tend to have more
lending, consistent with the view that this intrabank funding is actually useful in providing
additional credit to the real economy. This effect becomes significantly larger following the start
of the taper.
19 Taken together, this paper provides the first branch-level evidence of the way that banks
ration liquidity both in normal and in stressful times and the importance of this for banks to
continue lending.
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22 Figure 1: Brazil’s Policy and Dollar Exchange Rates
23 Figure 2a: Headquarters Locations for Brazilian Banks
Figure 2b: Net Lender vs. Borrower Locations of Bank of Brazil Branches
24 Table 1A:
This table reports summary statistics for total assets, loans (net of loss provisions), and deposits for bank i, in
municipality j, at time t. Also reported are banks’ “net due to” flows or intrabank borrowing and lending. The net
due to variable is calculated as the difference in intrabank borrowing (liabilities) and intrabank lending (assets). The
underlying monthly balance sheet information are collapsed and averaged to a quarterly series for the time period
2011Q1 to 2014Q4. Unless otherwise noted, the data are reported in nominal Brazilian real. Source: Central Bank of
Brazil.
Variable:
Obs
Mean
SD
10%
25%
Median
75%
90%
Total Assets (Mill)
85,311
968.38
21,768.98
5.09
13.53
29.23
70.77
204.61
Net Loans (Mill)
85,311
311.42
4,756.66
1.45
6.67
20.84
61.72
165.30
Deposits (Mill)
85,311
401.19
8,321.62
3.02
9.78
21.38
49.18
143.69
Net Due To (Mill)
85,311
0.09
1,729.12
-27.36
-8.60
-0.04
4.25
29.36
Net Due To/Assets
85,311
-8.87%
45.65%
-73.84%
-45.44%
-0.05%
21.18%
55.77%
Ln(Total Assets)
85,311
17.31
1.68
15.47
16.44
17.21
18.10
19.16
Ln(Total Lending)
85,225
16.75
2.03
14.20
15.72
16.85
17.94
18.92
Ln(Total Deposits)
85,311
16.85
2.05
14.92
16.10
16.88
17.71
18.78
Ln(Intrabank Assets)
85,311
12.50
5.34
0.00
10.04
14.02
16.15
17.34
Ln(Intrabank Liabilities)
85,311
7.14
7.99
0.00
0.00
0.00
15.84
17.49
25 Table 1B:
This table reports the subsample means for our key variables on interest. The net due to variable is calculated as the
difference in intrabank borrowing (liabilities) and intrabank lending (assets) and is scaled by assets. The natural logs
of total assets, lending, deposits, and intrabank borrowing and lending are also reported. The underlying monthly
balance sheet information are collapsed and averaged to a quarterly series for the time period 2011Q1 to 2014Q4.
For each subsample comparison, we calculate a two-sample t-test for equal means. ***, **, and * respectively
indicate significance at the 1%, 5%, and 10% level. In Panel A, the are municipalities are split by those above and
below the sample median gross municipal product per capita in 2010 (4,922 Brazilian real). Panel B compares
separately examines bank branches that are located inside and outside major population centers. Panel C
distinguishes between headquarter and non-headquarter locations. Branches in the “major city” subsample are
located in municipalities that rank in the top 15 by population – municipalities greater than 1.5 million residents. To
see how branch balance sheets evolved over time, Panel D compares branch balance sheets before and after the
Federal Reserve’s monetary policy announcement in December 2014 to scale back asset purchases. Panel E
compares publically- and privately-owned banks where latter group includes both domestic and foreign banks.
Source: Central Bank of Brazil.
Table 1B: T-Statistics
Panel A
Variable:
High Income
Observations
Mean
Low Income
Observations
Mean
Difference
Significance
Net Due To/Assets
Ln(Total Assets)
Ln(Total Lending)
Ln(Total Deposits)
Ln(Intrabank Assets)
50,634
50,634
50,568
50,634
50,634
-5.154%
17.736
17.267
17.150
12.406
34,677
34,677
34,657
34,677
34,677
-14.287%
16.692
16.005
16.411
12.639
9.133%
1.044
1.262
0.739
-0.233
***
***
***
***
***
Ln(Intrabank Liabilities)
50,634
8.011
34,677
5.876
2.135
***
Difference
Significance
Panel B
Variable:
Major City
Observations
Mean
Non-Major City
Observations
Mean
Net Due To/Assets
Ln(Total Assets)
Ln(Total Lending)
Ln(Total Deposits)
Ln(Intrabank Assets)
2,344
2,344
2,321
2,344
2,344
2.880%
21.456
20.779
18.724
13.425
82,967
82,967
82,904
82,967
82,967
-9.198%
17.195
16.641
16.796
12.475
12.078%
4.261
4.138
1.928
0.950
***
***
***
***
***
Ln(Intrabank Liabilities)
2,344
15.727
82,967
6.900
8.827
***
Difference
Significance
Panel C
Headquarters
Observations
Mean
Non-Headquarters
Observations
Mean
Net Due To/Assets
Ln(Total Assets)
Ln(Total Lending)
Ln(Total Deposits)
Ln(Intrabank Assets)
448
448
448
448
448
-0.796%
23.812
22.880
22.578
13.593
84,863
84,863
84,777
84,863
84,863
-8.909%
17.277
16.722
16.819
12.495
8.113%
6.535
6.158
5.759
1.098
***
***
***
***
***
Ln(Intrabank Liabilities)
448
11.568
84,863
7.120
4.449
***
26 Panel D
Variable:
Pre-Taper
Observations
Mean
Post-Taper
Observations
Mean
Difference
Significance
Net Due To/Assets
Ln(Total Assets)
Ln(Total Lending)
Ln(Total Deposits)
Ln(Intrabank Assets)
57,508
57,508
57,459
57,508
57,508
-8.974%
17.237
16.667
16.784
12.395
27,803
27,803
27,766
27,803
27,803
-8.645%
17.467
16.934
16.986
12.720
-0.329%
-0.230
-0.267
-0.202
-0.326
***
***
***
***
Ln(Intrabank Liabilities)
57,508
7.098
27,803
7.235
-0.137
**
Difference
Significance
Panel E
Variable:
Private Bank
Observations
Mean
Government Bank
Observations
Mean
Net Due To/Assets
Ln(Total Assets)
Ln(Total Lending)
Ln(Total Deposits)
Ln(Intrabank Assets)
38,569
38,569
38,487
38,569
38,569
-30.233%
16.862
15.758
16.313
12.527
46,742
46,742
46,738
46,742
46,742
8.764%
17.683
17.574
17.292
12.479
-38.996%
-0.822
-1.815
-0.979
0.047
***
***
***
***
Ln(Intrabank Liabilities)
38,569
5.414
46,742
8.570
-3.156
***
27 Table 2:
This table reports the effect of municipality and bank characteristics on a branch’s net due to position. The net due to
variable is calculated as the difference in intrabank borrowing (liabilities) and intrabank lending (assets) and is
scaled by assets. The underlying monthly balance sheet information are collapsed and averaged to a quarterly series
for the time period 2011Q1 to 2014Q4. “Headquarters Locality” is an indicator variable for whether or not the
reporting branch is the headquarter location of their corresponding banking network. “GovBank” is an indicator
bank representing whether or not the bank is government-owned. T-statistics are reported in parentheses. Standard
errors are clustered by bank. ***, **, and * respectively indicate significance at the 1%, 5%, and 10% level.
Table 2: Baseline Regressions
Net Due To Position
Headquarters Locality
(1)
(2)
(3)
(4)
(5)
(6)
-0.201
-0.247
-0.144
-0.222
-0.144
-0.222
(-1.44)
(-1.32)
(-0.93)
(-0.96)
(-0.94)
(-0.96)
HeadquartersXGovbank
0.168
0.243
0.242
(0.79)
(0.84)
(0.84)
Fixed Effects:
Bank
X
X
Locality
X
X
X
X
X
X
X
X
X
X
Time
Observations
Banks
Localities
85311
85311
85311
85311
85311
85311
28
28
28
28
28
28
2214
2214
2214
2214
2214
2214
t statistics in parentheses
28 Table 3:
This table reports the effect of municipality and bank characteristics on a branch’s net due to position. The net due to
variable is calculated as the difference in intrabank borrowing (liabilities) and intrabank lending (assets) and is
scaled by assets. The underlying monthly balance sheet information are collapsed and averaged to a quarterly series
for the time period 2011Q1 to 2014Q4. “High Income” is an indicator variable for whether or not the reporting
branch is located in a municipality that is above or below the sample median for gross municipal product per capita
in 2010 (4,922 Brazilian real). Standardized values of gross municipal product per capita are also included.
“GovBank” is an indicator bank representing whether or not the bank is government-owned. T-statistics are reported
in parentheses. Standard errors are clustered by bank. ***, **, and * respectively indicate significance at the 1%,
5%, and 10% level.
Net Due To Position
High Income
Table 3: Baseline Regressions
(2)
(3)
(4)
(1)
0.0720***
(2.97)
High IncomeXGov Bank
0.0671**
(2.09)
0.0723***
(2.91)
0.00802
(0.17)
(5)
(6)
(7)
(8)
0.0242**
(2.29)
0.0209
(1.13)
0.0244**
(2.24)
0.0212
(1.12)
0.0678**
(2.06)
0.00736
(0.16)
LnGMPPC (standardized)
LnGMPPCXGov Bank
0.00563
(0.25)
0.00530
(0.23)
Fixed Effects:
Bank
Time
X
X
X
X
X
X
X
X
X
X
X
X
Observations
Banks
Localities
85311
28
2214
85311
28
2214
85311
28
2214
85311
28
2214
85311
28
2214
85311
28
2214
85311
28
2214
85311
28
2214
t statistics in parentheses
29 Table 4:
This table reports the effect of municipality and bank characteristics on a branch’s net due to position. The net due to
variable is calculated as the difference in intrabank borrowing (liabilities) and intrabank lending (assets) and is
scaled by assets. The underlying monthly balance sheet information are collapsed and averaged to a quarterly series
for the time period 2011Q1 to 2014Q4. “Headquarters Locality” is an indicator variable for whether or not the
reporting branch is the headquarter location of their corresponding banking network. “GovBank” is an indicator
bank representing whether or not the bank is government-owned. “Taper” is an indicator bank data were reported
after the Federal Reserve announced its plans to scale down its third round of asset purchases (a value of 1
beginning 2013Q2). T-statistics are reported in parentheses. Standard errors are clustered by bank. ***, **, and *
respectively indicate significance at the 1%, 5%, and 10% level.
Net Due To Position
Taper
Table 4: Including Taper Dummy
(1)
(2)
(3)
0.00753
(0.14)
-0.0874***
(-5.41)
0.395**
(2.50)
-0.0684
(-0.80)
GovbankXTaper
0.00524
(0.07)
0.172***
(4.35)
(6)
0.334**
(2.13)
-0.0631
(-0.28)
0.172***
(4.35)
0.335**
(2.15)
0.0982
(0.37)
0.182***
(2.97)
0.182***
(2.96)
HeadquartersXGovbank
0.0489
(0.37)
-0.422*
(-1.77)
HeadquartersXTaperXGovbank
-0.116
(-1.16)
-0.239
(-0.90)
Fixed Effects:
Bank
Locality
LocalityXTime
Observations
Banks
Localities
X
X
85311
28
2214
X
X
85311
28
2214
t statistics in parentheses
30 (5)
-0.0875***
(-5.37)
Govbank
HeadquartersXTaper
(4)
X
X
85311
28
2214
X
X
X
85311
28
2214
85311
28
2214
85311
28
2214
Table 5:
This table reports the effect of municipality and bank characteristics on a branch’s net due to position. The net due to
variable is calculated as the difference in intrabank borrowing (liabilities) and intrabank lending (assets) and is
scaled by assets. The underlying monthly balance sheet information are collapsed and averaged to a quarterly series
for the time period 2011Q1 to 2014Q4. “High Income” is an indicator variable for whether or not the reporting
branch is located in a municipality that is above or below the sample median for gross municipal product per capita
in 2010 (4,922 Brazilian real). Standardized values of gross municipal product per capita are also included.
“GovBank” is an indicator bank representing whether or not the bank is government-owned. “Taper” is an indicator
bank data were reported after the Federal Reserve announced its plans to scale down its third round of asset
purchases (a value of 1 beginning 2013Q2). T-statistics are reported in parentheses. Standard errors are clustered by
bank. ***, **, and * respectively indicate significance at the 1%, 5%, and 10% level.
Net Due To Position
Taper
Table 5: Including Taper Dummy
(1)
(2)
(3)
0.0000256
(0.00)
-0.0874***
(-5.41)
Govbank
High IncomeXTaper
0.0121
0.0709**
(0.32)
(2.26)
GovbankXTaper
(4)
(5)
(6)
-0.134***
(-12.42)
0.395**
0.334**
0.447***
(2.50)
(2.13)
(4.35)
0.172***
0.215***
0.182***
0.227***
(4.35)
(8.80)
(2.97)
(4.54)
High IncomeXGovbank
0.00312
-0.178**
(0.19)
(-2.12)
-0.0638
-0.0821***
(-1.49)
(-3.68)
High IncomeXTaperXGovbank
Fixed Effects:
Bank
Locality
X
X
X
X
X
X
LocalityXTime
Observations
Banks
Localities
X
X
85311
85311
85311
85311
85311
85311
28
28
28
28
28
28
2214
2214
2214
2214
2214
2214
t statistics in parentheses
31 X
Table 6:
This table reports the effect of bank- and branch-level characteristics on a branch’s total loans (net of loss
provisions). Group level deposits are equal to the total deposits across all branches for a given bank. T-statistics are
reported in parentheses. Standard errors are clustered by bank. ***, **, and * respectively indicate significance at
the 1%, 5%, and 10% level.
Ln(Total Credit Operations)
Table 6: Total Credit Operations
(1)
(2)
(3)
(5)
(6)
Ln(Deposits)
0.658***
(17.04)
0.656***
(17.06)
0.797***
(2.87)
0.762***
(20.24)
0.760***
(19.71)
0.909***
(3.38)
Ln(Group-Level Deposits)
0.658***
(2.89)
0.578***
(2.91)
-0.227
(-0.59)
0.562***
(3.39)
0.484**
(2.50)
-0.123
(-0.47)
Ln(Intrabank Assets)
-0.0337***
(-3.91)
-0.0339***
(-4.00)
-0.0843*
(-1.83)
Ln(Intrabank Liabilities)
0.0301***
(6.69)
0.0298***
(6.71)
0.0630**
(2.20)
X
X
X
X
X
Fixed Effects:
Bank
Locality
Time
LocalityXTime
Observations
Banks
Localities
X
X
X
X
X
X
85225
28
2214
85225
28
2214
85225
28
2214
t statistics in parentheses
32 (4)
X
85225
28
2214
85225
28
2214
85225
28
2214
Table 7:
This table reports the effect of bank- and branch-level characteristics on a branch’s total loans (net of loss
provisions). Group level deposits are equal to the total deposits across all branches for a given bank. The net due to
variable is calculated as the difference in intrabank borrowing (liabilities) and intrabank lending (assets). “Taper” is
an indicator bank data were reported after the Federal Reserve announced its plans to scale down its third round of
asset purchases (a value of 1 beginning 2013Q2). T-statistics are reported in parentheses. Standard errors are
clustered by bank. ***, **, and * respectively indicate significance at the 1%, 5%, and 10% level.
Ln(Total Credit Operations)
Table 7: Total Credit Operations
(1)
(2)
(3)
(5)
(6)
Ln(Deposits)
0.860***
(23.76)
0.860***
(23.42)
0.822***
(4.02)
0.863***
(24.61)
0.863***
(24.00)
0.826***
(3.88)
Ln(Group-Level Deposits)
0.389**
(2.29)
0.421*
(1.76)
-0.0282
(-0.15)
0.394**
(2.15)
0.438
(1.62)
-0.0357
(-0.18)
Net Due To
1.482***
(19.44)
1.484***
(19.22)
2.219***
(10.33)
1.427***
(21.24)
1.429***
(20.95)
2.169***
(11.48)
0.0163
(0.64)
18.00
(0.00)
0.105
(0.11)
18.72
(0.00)
-0.0215*
(-1.80)
-0.0217
(-1.70)
-0.0202
(-0.41)
Ln(Group-Level Deposits)XTaper
0.0108
(0.33)
0.00778
(0.22)
0.0248
(0.58)
Net Due ToXTaper
0.154**
(2.19)
0.154**
(2.25)
0.135
(1.26)
X
X
X
X
X
Taper
Ln(Deposits)XTaper
Fixed Effects:
Bank
Locality
Time
LocalityXTime
Observations
Banks
Localities
X
X
X
X
X
X
85225
28
2214
85225
28
2214
t statistics in parentheses
33 (4)
85225
28
2214
X
85225
28
2214
85225
28
2214
85225
28
2214