the real effects of bank capital requirements

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The Real Effects of Bank Capital Requirements

Matthieu BRUN 1, Henri FRAISSE2, David THESMAR3

July 4, 2013

Abstract: We measure the impact of bank capital requirements on corporateborrowing and expansion. We use loan-level data and take advantage of the transitionfrom Basel 1 to Basel 2. While under Basel 1 the capital charge was the same for allfirms, under Basel 2, it depends in a predictable way on both the bank's model and thefirm's risk. We exploit this two-way variation to empirically estimate the semi-elasticityof bank lending to capital requirement. This rich identification allows us to control forfirm-level credit demand shocks and bank-level credit supply shocks. We find verylarge effects of capital requirements on bank lending: 1 percentage point increase incapital requirement leads to a reduction in lending by approximately 10%. At the firmlevel, borrowing is reduced, as well as total assets (mostly working capital); we providesome evidence of impact on employment and investment. Overall, however, becauseBasel 2 reduced the capital requirement for the average firm, our results suggest thatthe transition to Basel 2 supported firm activity during the crisis period.

The opinions expressed in this document do not necessarily reflect the views of the Autorit de

Contrle Prudentiel.

1 ISODEV. Email: [email protected] . This paper was written when Mathieu Brun wasworking at the Banque de France.2 Autorit de Contrle Prudentiel. Email: [email protected] HEC Paris and CEPR. Email: [email protected]


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sample, conditional on their risk category. Looking at these data we observe that banks

assign different default probabilities to firms in the same risk category, which confirms

that internal models vary widely across banks.

Identification rests on two strengths of our research design. First, capital requirements

for lending to a given firm are typically different for two different banks. This is

because Basel 2 allows banks to use their own internal model to evaluate the

probability of default of a firm. Hence, for given firm risk, we can compare the lending

of banks according to the penalties imposed by their internal models, for the same

firm observables. Second, our sample has a sizable number of firms that borrow from

several banks. For these firms, we can control for unobservable firm-level shifts in

credit demand, as the banking literature has done in other contexts (for example,

Kwhaja-Mian, 2005; Iyer,Peydro, Schoar, 2012; Jimenez, Ongena, Peydro, Saurina,

2012). Consistently with this literature, we find that making this adjustment does not

affect our results very much, suggesting that heterogeneity in bank loan portfolios is

not driving the results.

What we find.

Our analysis has caveats that we do our best to address. First, the goal capital

requirements reduce systemic risk. Systemic risk is beyond the scope of this paper,

which focuses only on lending effects. But the welfare benefits of having a safer

banking system may outweigh the costs of depressed lending that we measure.

Second, we measure essentially short-term effects. Because of data availability

constraints, our sample stops in the last quarter of 2011, or three full years after the

transition to Basel II is implemented. Our year-by-year results do not seem to suggest

that the negative impact on lending fades out. But it may be disappear after a longer

period. Last, the transition to Basel 2 that we study occurred in the beginning of 2008,

exactly when the financial crisis started to hit the global financial system. We do our

best to control for this: Our methodology rests on the comparison between firms that

received different treatment from the new regulation. In some specifications, we even

compare lending to the same firm by banks on which the regulation had a different


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impact. Our focus on France also helps a little, as French banks were not extremely

hurt by the crisis. But it could still be argued that our estimates are polluted by the

financial turmoil.

The literature: on regulation: Calomiris paper; macro papers. on Funding shocks:

Kwhaja-Mian, 2005 Iyer,Peydro, Schoar, 2012; Jimenez, Ongena, Peydro, Saurina,

2012). Like these paper, we use firm-time fixed effects to control for the fact that

banks may have different loan portfolios.

2. The Empirical Strategy

a. Calculating Capital Requirements

Since the Basel I accords in 1988, bank regulators around the world have asked banks

to use risk weights to assess the equity needs. The logic is the following. When a bank

makes an investment of 100 in a project (for instance a mortgage, or a corporate

bond), the regulator requires that the bank holds r x100 euros of equity capital, where r

is the equity requirement. Under Basel 1, the equity requirement for corporate lending

was 8%. This meant that if a bank had, say, 10bn of equity, it could not make more

than 125bn of corporate loans, unless, of course, it was ready to issue new stocks.

The Basel 2 accords made capital requirements much more heterogeneous across

banks and firms. These new agreements were published in 2004. The idea was to

reoptimize regulation so as to take into account the rising complexity of banking

activities (for instance, securitization), and also to avoid regulatory arbitrage by

adapting capital requirements to the riskiness of investment. Indeed, a flaw the Basel 1

was that, since capital requirements were the same for risky or safe corporations,

banks would have strong incentives to only lend to risky banks, so as to maximize

expected profits under regulatory constraints.


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Our study exploits one key feature of Basel 2: The capital requirement for a loan

depends in a predictable way on both the firm's risk and bank's model. When moving

to Basel 2, banks may be authorized to follow the "internal risk based" (IRB) approach,

whereby they can use their own estimate of the probability of default of the firm to

calculate the capital requirement. Remaining banks are required to take the

"standardized approach", where the capital requirement is a known function of the

firm's official rating.

Let us start with IRB banks. For a loan to firm f by bank b , the capital requirement is

then given by the following formula:

r bf = FIRB (PDbf , LGDbf , M bf ) (1)

where PD bf is firm f 's regulatory probability of default, as estimated by bank b , or by

the regulator (more on this below). LGD bf is the loss given default M bf is the maturity of

the loan. As banks do in most circumstances, we take LGD bf =.45 and M bf =2.5.F IRB (.) is

a known function.

The regulatory probability of default PD bf is not to be confused with the one directlyobtained from an internal model based on a scoring approach. The scoring model

allows putting each firm into a risk class and each firm of this class bears the same PD bf

which is then used for the computation of the capital requirement. In order to limit the

procyclicality induced by the Basel II framework, the French regulatory framework

imposes that the PD were computed through the cycle meaning the PD of a risk class

does not vary over time. However, the rating of a firm might vary over the cycle.

Therefore, firms might be associated to different class of risk overt time and mightdisplay a time varying PD.

We do observe the regulatory default probability PD bf for a large sample of bank-firm

linkages in 2008. We have the rating issued by the Bank of France, available for all

firms and all years in our sample. Using these observations, we calculate the average

effective regulatory PD per rating level. Therefore, we are able to map the Bank de


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France rating system into each internal rating system and to associate to each firm a

regulatory default probability each year. More formally, we have the relationship:

PDbf = 'b. f (2)

where ft is a set of dummies for each value of the firm's official credit rating (which is

discrete). b captures the fact that the model to evaluate the PD may differ from bank

to bank.

Banks that cannot use their own model of PD have to follow the standardized

approach. Under this approach, the capital requirement is direct and known function

of the official bank of France credit rating of the firm. For bank b and firm f , this leads

to:

r bf = Fstandardized ( f ) (3)

where F standardized (.) is a known function. Under the standard approach, all firms that

have the same official rating have the same capital requirement.

Overall, the capital requirement for each bank-firm linkage can be computed anddepends both on the firm's rating and the bank's model. This allows identifying the

impact of the capital charge separately from the firm's credit rating. Note that if all

banks followed the standardized approach, this would not be possible. Fortunately for

us, a large fraction of our sample includes banks that were authorized to adopt the IRB

approach. For these banks, the capital charge is not perfectly correlated with the firm

rating: It also depends on the bank's internal model, summarized by the vector b,

which we can identify using a specific dataset on PDs effectively used by IRB banks.

The identification also exploits the variation of capital requirement across banks

portfolio under the Basel II design. The capital requirement is also a function of the

bank's exposure (overall lending) to the firm and the turnover of the firm. Exposure

matters in that it allows loans to firms below 50m of turnover and below 1m of

exposure to be reclassified as "retail", and therefore benefit from lower capital

requirements. In sum, the Basel II leads to four regimes of capital requirement: the


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retail treatment versus the corporate treatment and the advanced approach

versus the standard approach. An internal model is validated by the regulator at the

level of the portfolio (retail versus corporate) of the judicial entity. Therefore within a

bank holding company, a firm exposure can be treated under the standard approach

by one entity and under the IRB approach by another.

We exploit these contrasting regimes in order to assess the impact of higher capital

requirement.

b. The Model

Our objective is to test whether equity requirements affect bank lending policy. We

focus on the change in lending between before and after the implementation of the

Basel 2 regulation, and ask whether changes in equity requirements are correlated

with changes in lending. We discuss here our exact estimation strategy, and discuss

the sources of identification in the next Section.

Using bank-firm loan-level data, we estimate the following equation:

Lbf = f + b + .rbf + Xf + bf (3)

where Lbf is the growth of lending by bank b to firm f . rbf is the change in regulatory

capital requirement between before and after the reform. Since it is constant, equal to

8% under Basel 1, we take without loss of generality the risk weight in the "post"

(Basel 2) period as our measure of change. f is a firm fixed effect, designed to capture

the fact that some firm (for instance, risky firms) may simultaneously experience an

increase in risk weight, and a reduction of lending, without any causal link between the

two. As we discuss below, the model with fixed effects is identified only for firms

borrowing from several banks. b is a bank-specific fixed effect, which controls for

bank-wide lending decisions; It is identified because the same bank lends to several

firms. X f are sets of firm specific controls designed to capture observable differences in


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lending policy that are unrelated to regulation (firm profitability, size, credit rating). bf

are error terms, which we cluster at the bank b level.

The null hypothesis is that =0. In this case, bank lending is not constrained byregulation, for two possible reasons. First, the bank is well capitalized. In this case, the

requirement does not matter, as the bank has enough equity to make the loan, as long

as it is NPV>0. Since the NPV of the loan is firm specific (it does not depend on the

bank), it should be absorbed by the firm fixed effect. Second, the bank has little equity,

but can issue more without friction. Under these conditions, the MM theorem is valid:

controlling for loan NPV (the firm fixed effect), the capital requirement does not

matter, since the cost of capital is unaffected by the fraction of equity used to finance

the bank.

One last issue with equation (3) is that we need to estimate r bf at the bank-firm level

under the Basel 2 regime. To do this, we use equation (1), which connects r bf with the

probability of default PD bf , and equation (2), which connects PD bf with the Bank of

France credit ratings that we observe in our data. At this stage we need the

coefficients b, which establish the correspondence between the ratings and bank-specific default probabilities. To calibrate b, we use a dataset that reports the PDs

effectively used by banks. Since this dataset is not exhaustive but covers a small subset

of firms, we calculate the average PD per category of credit rating, and use these

numbers to impute the PD for all bank-firm match.

c. Identification

Our estimation technique allows for a sharp identification of the effects of regulation

on bank lending.

First, equation (3) is a first difference equation. Bank and firm fixed propensities to

borrow/lend are already absorbed in the implicit level equation. In its simplest form,

(3) is identified of off the correlation between loan growth and capital requirement


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increase. At this stage, however, the problem remains that risky firms (those with high

capital requirement in the post Basel 2 period) may be borrowing less after Basel 2 was

implemented. The problem is especially acute since the post Basel 2 period in our

sample coincides with the beginning of the financial crisis, which may have had a

stronger effect on the demand and supply of credit to risky firms.

Second, the coefficient on the capital charge, , is identified, even when one controls

for credit rating of the firm. For now, assume no firm fixed effect f in equation (3).

This is because, for a firm with a given credit rating f , different banks face different

capital requirements, depending on the model they use. There are two useful sources

of variation here. First, the difference between the standard and the IRB approaches:

As long as b differs between the two approaches, a bank under IRB faces a different

capital requirement than a bank under the standard approach, even if the firm's credit

rating is the same. Second, within the IRB approach, bank b and b' also face different

capital charges for given rating as long as the models they are using are different

enough: b has to be different from b' . This source of identification is the core of our

identification strategy.

Formally, our identification technique can be understood by combining equations (1)-

(3). To clarify exposition, let us assume that the Basel formula F(.) is a linear function of

the default probability only (in our empirical approach we take the other inputs as

given and constant, like most banks do in practice): we posit F(x)=ax. Let us then take

two firms: f , which borrows from bank b , and f' , which borrows from bank b' . Assume

both firms have the same credit rating . Then, equation (3) for both firms writes:

Lbf = a.b + . + bf (4)

Lb'f' = a.b' + . + b'f' (5)

In these two equations, we abstract from bank fixed effects (because they do not

affect the reasoning here) and from firm fixed effects (to which we return in the next

paragraph). Substracting the (5) from (4), we obtain that:

Lbf - Lb'f' = a.(bb' ) + (bf - b'f' )


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which shows that the coefficient of interest is identified as soon as banks b and b'

have different models: b b' . The key to our identification of separately from the

direct effect of ratings is that banks have different models. We establish this by (1)

controlling for ratings and (2) showing that b b' holds in the data. This discussion

remains the same if b follows the standardized approach: Then, it is key for

identification that b' follows IRB.

Third, we use firms borrowing from multiple banks to control for unobservable firm-

level credit demand shocks - and not just observed rating. This is represented in

equation (3) by the fixed effect f . For each firm who borrows from different banks,

identification relies on the comparison between bank lending depending on the bank's

capital charge. We can do this because the capital charge depends on both the firm

and the bank (see discussion above). This approach is well known in the banking

literature (see for instance: Khwaja and Mian, 2008, Iyer et al, 2011, Jimenez et al,

2012).

3. Data

a. Bank-Level Data

Our sample is made of the six largest banking groups operating in France. They

represent around 80% of the total asset of the French banking system. The sample

related to the estimation of equation (3) has 256 banks owned by one of these six

groups.

b. Loan-level data


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We start from a large dataset of bank-firm linkages available at the Bank of France

(Centrale des Risques ). A linkage between bank b and firm f exists and is reported in

the data as soon as bank b has an exposure of more than 25,000 euro to firm f . The

data is thus exhaustive above a certain exposure threshold. The dataset is quarterly,

and provides us with the total exposure, as well as the identifiers of the bank and of

the firm. Exposure is lumpy on the way up, but continuous on the way down. Exposure

increases abruptly when the bank extends a new loan to the firm, and then goes down

progressively as the firm repays the loan. We define "lending" as the quarterly change

in exposure. We then set lending to zero if exposure decreases - in the data such

occurrences typically corresponds to principal repayment.

We then create a quarterly panel firm-bank pairs. We include all firm-bank pairs that

appear at least once in the large linkage dataset between 2006Q1 and 2012Q4. We

then assume that the pair exists throughout the period that we study (2006Q1 -

2012Q4). If, at a given date, the pair is absent from the linkage data, we posit that

exposure is equal to zero.

We then merge with firm-level accounting and rating information, also available fromthe Bank of France ( Centrale des Bilans ). Such information is updated annually.

Accounting information follows the tax forms that firms have to fill in and provides us

with extremely detailed data on the balance sheet and the income statement. Credit

ratings are awarded by a special unit at the Bank of France, which is in charge of

maintaining the public credit registry. The credit registry covers a vast number of firms.

Because we want to focus on the effect of the Basel 2 implementation, we collapse the

dataset into two sub-periods: 2006Q1-2007Q4 (before the reform), and 2008Q1-

2012Q4 (after the reform). For each bank-firm pair, we take the sum of lending in each

sub-period. We then take the log of the sup-period lending plus one. Thus, if banks

does not lend during one period, the log is zero.

We restrict the sample of firms to the firms that provide balance sheet and income

statement over the entire period. We take averages of all firm observables. We end up


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with 421,901 bank-firm pairs. We provide summary statistics for the resulting dataset

in Table 1A.

We finally merge the data with bank-level information on the transition to Basel 2. Thisdata provides us with the transition date (2008Q2 for all banks), and the approach

approved by the French supervisor: IRB vs standardized. We focus on banks that are

continuously present throughout the sample. On the sample on which regression (3) is

estimated, 256 banks are present. All of them do not have exposures within each of

the four Basel II regimes. As for the retail portfolio, 142 out 239 transit to the IRB

approach. As for the corporate portfolio, 165 out 237 adopt the standardized

approach. In terms of bank-firm relationship, out of 429,901 pairs that we follow

between before and after the Basel 2 implementation, 291,162 transit to IRB, while

the remaining 130,739 follow to the standardized approach.

Firms that borrow from several banks make up a large fraction of our sample. 91% of

the bank-firm pairs in our sample (387,961 out of 429,901) correspond to the firms

borrowing from at least two different banks. Over the period, 73% of the firms have

had an exposure to several judicial entities. These observations are especially useful asthey allow us to include a firm specific fixed effect in equation (3), thereby controlling

for differences, across banks, in the unobservable riskiness of firms. In Table 1B, panel

A, we report summary statistics for two subpopulations of our loan-level sample: loans

in which the firm borrows from several banks, and loans in which the firm borrows

from one bank only. We see that the statistics are similar in the two subsamples, even

though the differences in means are statistically significant. We will control for this

observables in our regressions. But more convincingly, we will show that regressions

results, before controlling for firm fixed effects, are identical in the two subsamples.

Panel B, Table 1B investigates the difference between banks that adopt the standard

and banks that adopt the IRB approach. The difference between these two categories

might contribute to identification: two firms in the same risk class, but borrowing from

two banks following different approaches, will face different equity requirements. Our

empirical strategy will test if this difference in equity requirements is systematically


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related to differential lending growth. In Panel B, we report the difference in loan-level

data for these two subsamples. We find that the summary statistics are similar, even

though, once again, averages are significantly different. It may thus be argued that the

two subsamples are different. We will deal with this concern by showing that

regression results are similar whether we include, or not, banks transiting to the

standard approach. Thus, the standard/IRB difference do not importantly contribute to

our identification.

c. Common Counterparties

The first dataset ("common counterparties") allows us to measure how bank models

for default probabilities differ across banks, for given firm characteristics. This dataset

is a survey run in 2007 by the French prudential supervisor, on a sample of firms that

borrow from at least two different French banks. For each of these common

counterparties, banks report the default probability that they are using to calculate the

capital charge of the loan. Thus, this dataset allows to directly compare the PD models

that banks are using for the same firms. The dataset contains a lot of additional

information, such as the LGD used by the bank (almost always equal to 45%), and firm-

level characteristics, such as the credit rating given by the Bank of France.

We use these data to calculate a bank-specific correspondence between credit rating

and default probabilities. We first collapse the 11 bank of France rating categories into

6 categories: This is because, to attribute equity requirements under the standardizedapproach, the regulator only uses these 6 categories. We then calculate, for each bank

and each of the 6 risk categories, the average regulatory PD of the firms in the sample

(not the effective probability of default, but the one that banks assign according to the

CC survey). This is how we obtain the b parameter in equation (2). We then use this

bank-specific parameter to impute, in our entire sample (described in Section above),

the default probabilities for each bank-firm pair. We then transform these default

probabilities into capital charges using the Basel formula (1). We provide summary


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statistics on the resulting capital charges in Table 1A. For the average firm, the

transition to Basel 2 led to a decrease in capital charge by approximately 1.9

percentage points (from 8% to 6.1 on average). This is consistent with the general idea

that Basel 2 aimed at fostering SME financing. It should be noted however that the

cross sectional s.d. of the change in equity requirements is about 2%, so that a

significant fraction of the firms in our sample experienced increases capital charges.

We exploit this variation in our empirical Section.

Key to our identification strategy is that banks have different models for default

probabilities, i.e. that the b are different across banks. We check this in Table 2,

where, for each class of credit rating separately, we regress the imputed PD on a full

set of bank dummies. We then test whether we can reject the null hypothesis that all

banks use the same model, i.e. that the b are all equal, conditional on the rating. The

first three columns of Table 2 show that, conditional on firm ratings, there is

substantial heterogeneity in probabilities of default. Columns 4-8 show that the null

hypothesis that all bank fixed effects are equal is strongly rejected by the data for all

ratings groups. Moreover, bank fixed effects explain a considerable amount of the

variance of PDs. The unexplained variation comes from the fact that we average

observations across quarters of a sub period. Since banks may change credit rating, we

take the integer part of the average rating, as an approximation for the average ration

across periods. This creates composition effects that add noise to our construct.

d. Firm-level data

Last, we collapse the dataset constructed in Section 3.a. into firm-level data. The

objective here is to evaluate the impact of the Basel 2 reform on firm-level decisions

and outcome such as capital structure and investment. In particular, an important

question is whether firms that can borrow more from one bank because of the

transition to Basel 2 end up borrowing more in total, or whether they reduce they

borrowing from other banks. We start from the loan-level data described in Section


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3.a. For all observations corresponding to the same firm, we then take the average of

all variables. Firm-level accounting variables are not affected by this procedure, as they

are by definition the same across all firm-bank linkages corresponding to the same

firm.

Summary statistics are reported in Table 3. They show that few composition effects

arise, when compared with Table 1A. The average change in capital charge is the same:

a decrease by about 2.2 percentage points after Basel 2 was implemented. Sales grow

by 10% on average. Lending which accordingly to our definition cumulates the new

loans on the two sub periods- increases by 147%. Note that this large increase is due to

our definition of change in lending which implies for a firm borrowing X in the post

period and nothing in the pre period a change of log(1+X) and which considers a bank-

firm relationship as soon as an exposure appears on the total period. On the sample of

firms that borrow in both periods, the lending increases by 67%. In the following

section, we will test the robustness of our results considering alternative definitions of

what a bank-firm pair is. All these numbers do not differ widely from those obtained in

Table 1A: This is because multi-bank borrowing firms are not very different from

single-bank firms, as already observed in Table 1B. We have added a few more firm-

level accounting variables in this Table. Exposure is the total exposure to all banks,

averaged over quarters and differentiated. Consistently with more borrowing (lending

increases), total bank exposure increases by 58%.

4. Results

a. The Effect of Basel 2 on Bank Lending

Table 4 reports the regression results of various specifications of equation (3). We start

with Panel A. In column 1, there are no firm-level controls, and no fixed effects. In this

very raw specification, we find a negative impact on lending of higher capital

requirement but this impact is not significant at conventional levels. Column 2 includes


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firm-level controls: the pre and post-period average bank of France rating discretized

into 6 categories, as well as the pre-Basel 2 level of log sales and ROA. These variables

are designed to control for firm size and financial health before the reform. Credit

ratings are also included because they serve to calculate the equity requirements, and

we want to make sure that the effect of requirements is not identified on firm credit

risk, but on its interaction with the model of the bank. Including these controls actually

almost doubles the size of our estimate which now turns out to be strongly significant.

In column 3, we further include a bank fixed effects, to alleviate the concern that

conservative banks (whose equity requirement is high under their own internal model)

may be linked to firms that do not borrow very much for other reasons. For instance,

mutual banks may be lending to SMEs that do not seek to grow very much, especially

in times of crises, and may also end up with higher capital requirements, either

because of conservative risk-management practices, or because they were only

allowed to adopt the standard approach (which has higher capital requirements). As

can be seen from Column 3, controlling for bank fixed effects does change our

estimate in a significant manner. We find that a two percentage point decrease in

capital charge (say, from 8 to 6%) leads to an increase by 8.74*0.02=17.5 percentage

point increase in bank lending. This corresponds to about 6% of the sample s.d. of bank

lending growth. Our model thus has a priori reasonable explanatory power for this

kind of microeconomic study. In aggregate, the model estimates that the Basel 2

reform has boosted corporate lending by 0.022*8.74=19 percentage points,

where2.2% is the average reduction in equity requirement coming from Basel 2. This is

14% of the increase in lending observed between the two sub-periods.

Columns 4-5 seek to control for credit demand shocks by including a firm-level fixed

effect in equation (3). The firm fixed effect also allows to control for unobserved

heterogeneity in firm-level credit risk that may affect a bank's decision to lend. Note

that, for this heterogeneity to matter, it would have to explain lending beyond what

the bank of France rating can do. Firm-level risk unobserved heterogeneity is identified

of off firms that borrow from several different banks (they constitute almost 73% of


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b. Additional Tests

We then consider alternative measures of lending and definition of the bank-firmrelationship. In Table 2, recall that once an exposure of the bank b to the firm f was

observed over the whole period, the pair bank-firm was considered. In the panel A of

Table 5, we restrict ourselves to bank-firm pairs when the firm has taken out at least

one new loan from the bank within the entire period. As before, we take the same 5

econometric specifications. The impact of the capital requirement is then magnified

leading to a significant impact of higher capital requirement on lending growth in all

specifications. To measure loan growth at the intensive margin, we take lending

growth of firm-bank linkages for which we observe at least one loan in both periods.

We report the result of this regression in Table 5, Panel B. We find that the intensive

margin effect that we estimate is smaller than the overall effect estimated in Table 4.

The coefficient hovers around 3. This means that 2 percentage point decrease in the

equity requirement (-0.019 is the sample mean) would lead to a 6 points decrease in

lending at the intensive margin, much smaller than the 19 points obtained globally (in

Table 4, Panel A).

We then investigate the transition dynamics, and show that the effect of the reform

increases over the years until 2011 (we do not have firm controls for the year 2012).

We have to compare the lending granted in 2011, 2010 and 2009 taken separately to

the one made in the pre reform period. In order to compare the parameters associated

to the change in capital requirement to the same parameter estimated with the whole

post period we consider another definition of lending. Indeed, if we stick to our former

definition, the parameter will be less comparable since the likelihood to get a new loan

is of course greater when considering more years in the post period and that the

amount of new loans accrue over time. Alternatively, we now define lending as the log

of the average exposure over one year of the post period when considering the post


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period year separately. When we collapse all the post periods, we define lending as the

log of the average exposure over the entire post period.

For instance, in Table 5, Panel A, we use as dependent variable the change betweenthe log of average post reform exposure and the log of average exposure in the pre

period. In Table 5, Panel B, we use as dependent variable the change between the log

of average exposure in 2009 and the log of average exposure in the pre period. Panel C

focuses on 2010 and Panel D on 2011. In each panel, we take the whole sample and

report results for all the 5 specifications used in Table 4. Looking at all specifications,

we conclude that the effect became more pronounced over the years. This is

consistent with the financial crisis, a time where it was notoriously difficult for banks to

raise external equity.

c. The Effect of Basel 2 on Corporate Outcomes

We now test for the impact of the regulation change on firm-level outcomes. To do

this, we run the following regression at the firm level:

Yf = .rf + Xf + f (6)

where Yf is the change in firm policy (debt, investment etc.), and X f is a set of firm

level controls (the same as in Tables 4-6). rf is the average of all changes in equity

requirement, across all banks to which the firm f is linked. The f are assumed to be

heteroskedastic. We report the results in Table 7.

Note that, since we focus on firm-level outcome (not loan level ones), this

methodology does not permit to control for bank nor firm fixed effects. This is a

limitation of our study. What gives us confidence in our estimates, however, is that the

estimates, with or without bank and firm FE, tend to be stable (see Table 4, Panel A).

This suggests that the potential biases that may arise from bank or firm-level


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heterogeneity (bank-level credit supply shocks, firm-level credit demand shocks etc)

are not too large.

We first show that the reform affected overall firm borrowing. We have shown inTables 4-6 that the change in capital requirement affects bank lending, but it may be

the case that firms can substitute shrinking lending by one bank with borrowing from

another bank. It may alternatively be the case that the ability to borrow from one

particular bank (because of decreasing capital charge) would cause the firm to borrow

less from other banks. In both cases, even though the reform is shown to have an

impact at the loan level, it may have no impact at the firm level. We check this in

columns 1-3, Table 7, using our two different measures of firm borrowing.

In column 1, we look at average firm borrowing across all linkages. This measure is the

closest to the regressions already run in Tables 4-5, except that we aggregate at the

firm level. For each firm and each date, we calculate the sum of loans made across all

bank-firm linkages. We then sum this measure across dates of each sub period. Finally,

we take the log and differentiate. This measure is exactly identical to the loan growth

measure used in Table 4-5 for firms who are linked to only one bank (about half of oursample). As shown in column 1, we obtain an estimate of the impact of the reform that

is slighty lower than the baseline estimates (-3.17 in table 7 to be compared with -5.8

in table 4, suggesting that there is little substitution within our sample given the

precision of the estimates).

In column 2, we use as dependent variable average total bank exposure. In the initial

bank-firm linkage data, we aggregate total exposure across all banks at the firm-

quarter level. We then take average total exposure across quarters of each subperiod,

take the log and differentiate. We find in column 2 that the effect estimated in column

1 is slightly lower than the one displayed in table 6, column 2 at the bank-loan level.

The fact that the estimate has similar size for stocks and flows suggests that the

maturity of the loans tends to be short, so that a reduction in lending would quickly

lead to a reduction in the stock of debt.


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In columns 4-7, we show that a decrease in average equity requirement leads to the

expansion of a firm's assets, working capital and employment. In column 4, we find

that a 2 ppt increase in capital requirement leads to an increase in total assets, as well

as current assets, by about 1 ppt. The effect is a bit smaller, although with a similar

order of magnitude, for employment: For the average firm in our sample, the

transition to Basel 2 helped create 0.31 x 0.022 = 0.68 additional ppt of employment.

This is to be compared with a 3.2% average employment growth in our sample.

5. Conclusion

Although present at the heart of the policy debate on the banking regulation, the

impact of higher capital requirement on lending and real outcome has been rarely

examined in the academic literature at the micro level.

This paper evaluates such impact. The implementation of the Basel II regulatory

framework in 2008 in France led banks to substantially modify the regulatory capital

associated to each credit line of their corporate portfolio. Exploiting the French

national credit register and the internal bank rating models, we are able to match eachloan at the firm-bank level with a risk weighted asset charge applied by the bank. We

identify the impact of the capital requirement by contrasting the lending of several

banks charging different regulatory capital to the same firm. These charges differ

across banks because banks are either under different regulatory regimes (e.g.

advanced, foundation or standard approach of Basel II) or simply because they differ in

their internal models. Therefore our approach controls for demand effect.

Depending on our definition of lending at the extensive margin, on new loans or

exposure- we find an increasing impact ranging from 3 to 8 percent of an increase of

capital requirement by one percent. This impact translates to real corporate outcomes

such as employment and investment.


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References

Admati, Anat, DeMarzo, Peter, Hellwig, Martin and Pfleiderer, Paul, 2010, "Fallacies,

Irrelevant Facts and Myths in the Discussion of Capital Regulation: Why Bank Equity is

Not Expensive", Stanford GSB WP 2065

Aiyar, Shekhar, Calomiris, Charles and Tomasz Wiedalek, 2012, "Does Macro-Pru Leak?

Evidence from a UK Policy Experiment", NBER WP 17822

Hanson, Sam, Kashyap, Anil and Stein, Jeremy, 2011, "A Macroprudential Approach to

Financial Regulation", Journal of Economic Perspectives , Vol 25(1), pp 328

Iyer, Raj, Lopes, Samuel, Peydro, Jose-Luis and Antoinette Schoar, 2011, "The Interbank

Liquidity Crunch and the Firm Credit Crunch: Evidence from the 2007-2009 Crisis",

mimeo UPF and MIT

Jimnez Gabriel, Steven Ongena, Jos Luis Peydr and Jess Saurina, 2012, "Credit

supply and monetary policy: Identifying the bank balance-sheet channel with loan

applications, American Economic Review, 102 (5), 2301-2326.

Kashyap, Anil and Stein, Jeremy, 2004, "Cyclical Implications of the Basel II capital

standardss", Economic Perspectives , Federal Reserve Bank of Chicago, vol. 28

Kwhaja, Asim, and Mian, Atif, 2008, "Tracing the Impact of Bank Liquidity Shocks",

American Economic Review , vol 98(4)


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Figures and Tables


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Table 1A: the different Basel II regimes

Note: The unit of observation is a bank-firm linkage. Basel II considers four different regimes ofcapital requirement: the retail exposure (less than one million of exposure and 50 millions ofturnover), the corporate exposure (more than one million of exposure or more than 50millions of turnover). The retail portfolio and the corporate portfolios of a given entity canbe treated either under the standard approach or the advanced approach. This table displaysthe number of banks, the number of exposures, the total amount of exposures in each of thefour regulatory regimes. We start from the universe of bank-firm linkages with bank exposureabove 25,000. These data are quarterly. For a pair to be followed throughout 2006:1 - 2012:4,we require it to be in the bank-firm linkage dataset at least once in the period (2006:1 -2012:4). We then match these data with annual firm-level accounting information. In our finaldataset, we have 421,901 bank-firm linkages. The average exposure is computed over the2008:1-2012:4 period. The regulatory regime of a given entity for a given regulatory portfoliohas been provided by the French supervisory authority.


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Table 1B: Summary Statistics of the Loan-Level Dataset

Note: The unit of observation is a bank-firm linkage. Change in log lending is defined as thedifference between the log of the sum of new loans granted to the firm f by the bank b in thepost period and the log of the sum of new loans granted to the firm f by the bank b in the preperiod pre period. For the other variables, "Change in X" the difference between the averageof X in the post reform period (2008:1 - 2011:1) and the average of X in the pre period (2006:1- 2007:4). "Pre reform X" is the average of X in the pre period (2006:1 - 2007:4). To constructthese averages, we start from the universe of bank-firm linkages with bank exposure above25,000. These data are quarterly. For a pair to be followed throughout 2006:1 - 2012:4, werequire it to be in the bank-firm linkage dataset at least once in the period (2006:1 - 2012:4).We then match these data with annual firm-level accounting information. Finally, we use 2007

bank-risk class averages from the "common counterparties" and the Banque de France ratingof the firm to impute regulatory PDs and capital charges for each bank-firm pair and eachquarter. We then take the average of all variables over the two sub-periods (pre and postreform), and then differentiate. In our final dataset, we have 421,901 bank-firm linkages.

the real effects of bank capital requirements

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