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Working Paper Series

The E ffect o f C hanges in Reserve
R e qu ire m en ts on In ve stm e n t and GNP
Prakash Loungani and Mark Rush

Working Papers Series
Issues in Macroeconomics
Research Department
Federal Reserve Bank of Chicago
December 1991 (WP-91-21)

ill® ? i
:;P

FEDERAL RESERVE BANK
OF CHICAGO

THE EFFECT OF CHANGES IN RESERVE REQUIREMENTS
ON INVESTMENT AND GNP

by
Prakash Loungani*
and
Mark Rush**

University of Florida
October 1991

* University of Florida and Federal Reserve Bank of Chicago
** University of Florida

The views expressed in this paper are not necessarily those of the Federal
Reserve Bank of Chicago or the Federal Reserve System.

Introduction
There is a large literature that posits a link between the extent of financial intermediation

performed by banks and aggregate real activity. While the specifics differ from model to model,
the basic idea is that certain types of borrowers, mostly small firms, are unable to borrow directly
by issuing securities on the open market. These borrowers are highly dependent on bank credit
and their borrowing is sensitive to the terms on which it available. Shocks to the supply o f bank
credit can have adverse consequences for investment by depriving such borrowers o f funds.1 In
a recent paper, Gertler and Hubbard (1988) state that "theoretical models which motivate these
types of real-financial mechanisms are now in abundance. The main challenge remaining is to
quantify their importance."
The main source o f evidence for the adverse consequences of declines in intermediary
credit comes from the Great Depression. Bemanke (1983) and Hamilton (1987) have argued that
the collapse of intermediation was very important during the onset o f the Great Depression. To
determine if intermediation matters outside of such exceptional episodes, Bemanke (1986) and
Friedman (1983) have used measures of credit to capture effects from intermediation, while
Gordon and Veitch (1984), Rush (1985,1986) and Manchester (1989) use the money multiplier
between M2 and the monetary base. The credit and money multiplier variables have met with
some success. However, a problem with both these variables is that they contain a large
endogenous component: a fall in GNP reduces the demand for loans and hence causes a decrease

1
See Gertler (1988) for a thorough review o f this literature. Blinder and Stiglitz (1983)
discuss the importance of bank loans in credit creation.

in credit and in the money multiplier.2 Thus, this evidence does not provide unambiguous
support for the importance of financial intermediation.
We propose to investigate the impact o f credit shocks by using a more exogenous "shifter"
o f intermediary activity. In particular, we focus on changes in reserve requirements. Over the
post-WWII period, the majority o f changes in reserve requirements have been carried out for
bank regulatory reasons, rather than as part o f counter-cyclical monetary policy.3 Hence they can
be regarded as exogenous changes in the excise tax on deposit services provided by banks. An
increase in reserve requirements raises the effective tax rate on deposit services and, hence,
lowers the amount o f financial intermediation carried out by banks. If bank loans are special, as
asserted in the financial intermediation literature cited earlier, the increase in reserve requirements
should have adverse real effects.
There is suggestive evidence from the banking microstructure literature that these changes
are important enough to have significant impacts on bank profitability.4 Slovin, Sushka and

2
See King and Plosser (1984) for a model o f this process and Plosser (1991) for empirical
evidence. Manchester controls for at least part o f the endogeneity by including some components
o f the multiplier, such as the currency/ deposit ratios and the excess reserve to demand deposit
ratio, in a VAR system. She finds that there is still a significant correlation between the
multiplier and real GNP.

3
As pointed out by Haslag and Hein (1989) and others, the Federal Reserve generally offsets
changes in reserve requirements by movements in the source base. This suggests that reserve
requirement changes are generally n o t undertaken with the objective o f fine-tuning the economy-the offsetting change in the source base would be counter-productive if that were the objective.

4
This literature builds on the work o f Fama (1985). He points out that bank loans are more
costly than other sources o f external funds, such as commercial paper, since banks face a deposit
tax: they must keep part o f their deposits as non-interest bearing reserves. Why are firms willing
to borrow from what may be a relatively more expensive source o f funds? The answer, suggested
by Fama and others, is that bank loans are a form o f inside debt that signal to outsiders that the

Bendeck (1990) find that announcements of increases in reserve requirements depress bank stock
returns, while raising stock returns in nonbank financial firms. Santoni (1985) studies the effects
o f the Monetary Control Act of 1980 which imposed uniform reserve requirements across all
financial firms by lowering the requirements for member banks while raising them for non
members. He finds that this change raised the after-tax earning streams and stock prices o f
member banks, while lowering earnings and stock prices of non-member banks.
Where our work complements these studies is in showing that the impact o f reserve
regulation is felt not just on bank profitability, but on the amount o f financial intermediation and
on real activity in general, particularly aggregate investment. We find that changes in reserve
requirements over the post-World War II period appear to have had a significant impact on real
activity, particularly on aggregate investment.5 We also provide evidence that the impact o f
reserve requirements on real activity occurs at least partly through it impact on credit activity,
which we measure as commercial and industrial loans provided by banks.

firm’s expected prospects are good. This hypothesis has received empirical support in an
important paper by James (1987). He finds that announcements o f bank credit agreements boost
the borrowers stock returns while announcements of other kinds o f debt have no such impact.

5
For the pie-WWII period, Friedman and Schwartz (1963) attribute the sharpness o f the 1937
recession to the Fed’s doubling o f reserve requirements in 1936-37. However, as discussed in
Friedman and Schwartz, there was disagreement among commentators on whether the Fed’s
action represented a shock to the financial intermediation process (a "credit" shock) or a
"nominal" disturbance, a shock to the stock of money.

Theoretical Framework
Several theoretical frameworks generate a correlation between changes in reserve

requirements and real activity. One o f these is outlined in Barro (1990). As stated in the
introduction, a basic assumption is that banks are more efficient than households or nonfinancial
firms at evaluating and collecting loans made to firms. For simplicity, assume that banks make
only one kind o f loan— which the interest rate charged is R—
on
and accept one kind o f deposit—
on which the interest paid is Rd. In order for the bank to engage in intermediary activity, the
spread R-Rd must cover the costs o f intermediation, which include the costs o f holding some
noninterest-bearing reserves. As Barro points out, an increase in the required reserve ratio
operates like a tax on this intermediary activity, as banks are required to hold more reserves and
thus make fewer loans. To the extent that bank-dependent borrowers are unable to find alternate
sources o f funding, the reduction in loans translates into declines in aggregate investment and
output.
Similar results can be derived from the model discussed by Bemanke and Blinder
(1988).6 To the two assets contained in the IS-LM model, money and bonds, Bemanke and
Blinder add a third asset, loans. They assume that loans are imperfect substitutes for bonds, for
the reasons outlined earlier. Denoting the interest rate on bonds by i, the interest rate on loans
by R, the quantity o f bank deposits by D, and the required reserve ratio by T, the loan supply
is
(1)

= f(R, i)D (l-T )

6
A related model that generates a negative correlation between investment and changes in
the required reserve ratio is presented in Jefferson (1989).

Loan demand is given by:
(2)

Ld = g(R, i, y)

The loan market clears by equating supply and demand:
()
3

g(R, i y = f R i)D(l-T)
, ) (,
As (3) makes clear, unanticipated and once-and-for-all-changes in the required reserve

ratio, T, lower loan supply and hence the quantity of intermediation.7 The decline in the quantity
of intermediation causes output and investment to fall, under the maintained assumption that
bonds are not perfect substitutes for loans. Whether or not these changes have a significant
impact on macroeconomic variables is an empirical issue, which we address in a later section.
We begin our empirical analysis by discussing ways of measuring T, the required reserve ratio.

Measures of the Reserve Requirements Tax
A history of changes in reserve regulations over our sample period is provided in

Appendix A. One thing that is apparent is that these changes are frequently quite complex. For
instance, the 1951 increase in the required reserve ratio actually breaks down into an increase
from 22% to 24% on demand deposits held at central reserve banks; a 18% to 20% increase on
demand deposits at reserve city banks; an increase from 12% to 14% on demand deposits at
country banks; and a 5% to 6% increase in time deposits at all classes of banks. Additionally,
changes in reserve requirements are often accompanied by other complicated policy decisions,

7
In contrast to our work, the focus of the Bemanke and Blinder paper is on "exogenous"
shocks to the f(...) function. The two examples that they provide of such shocks are the collapse
of credit during the Great Depression and the credit controls of March-July 1980. As with
changes in T, shocks to the f(...) function lower the quantity of intermediation and output.

such as changes in which cities are deemed "country" or the massive rewriting of reserve
requirement regulations in the 1980s, that affect banks’ ability to create credit. These
considerations preclude a strategy of simply "reading off" tax rate changes from the reserve
requirements schedule and using these changes as an independent regressor.
Instead, we suggest two measures which represent attempts to summarize these complex
changes in regulations in one number. The first variable is the ratio of "required reserves held
by member banks" to "total member bank deposits subject to reserve requirements." We refer to
this variable as the required reserve ratio (T). In theory, the behavior of T could be driven largely
by shifts from one type of bank to another, or from one type of deposit to another; these shifts
may be caused by factors other than reserve requirement changes. However a look at the time
series behavior of log changes in T (DT) shown in Figures 1(a) and 1(b), should allay these fears.
By matching this figure to the history given in Appendix A, one can verify that almost all of the
"blips" in this series correspond fairly closely to dates of actual changes in reserve requirements.
The second variable makes use of data from the St. Louis Federal Reserve Bank, which
makes an adjustment to the monetary base to reflect changes in reserve requirements. In
particular, the St. Louis Fed "adjusted monetary base" (AMB) is calculated as
(4)

AMB = B + RAM

where B is the source base and RAM is the reserve adjustment magnitude. As an illustration of
how RAM is computed, suppose that there is only class of deposits that are subject to reserve
requirements. Then, if the required reserve ratio is changed from some initial base period value
T0 to Tj, RAM is computed as
(5)

RAM = (T0 - Tt)D

where D is the current level of deposits that are subject to reserve requirements. An increase in
reserve requirements (Tx > T0) absorbs reserves whereas a reduction "frees up" reserves.
In practice, of course, the computation of RAM is quite complicated because of differences in
requirements across types of deposits and types of banks.
We could use changes in RAM as an alternative summary measure of changes in reserve
requirements. However, it is likely that the impact on real activity of, say, a $5 billion RAM
would be greater if the source base was $6 billion than if it were $400 billion. To capture this
effect, the variable we use, denoted F, is calculated as the ratio of the adjusted monetary base
to the source base:8
(6)

F = AMB/B

Note that log differences of this ratio (DF) are approximately,
(7)

DF *

(RAM/B)

The time series behavior of DF is shown in Figure 1(a) and 1(b) as well. As with the DT
variable, major changes in this variable are associated with reserve requirements changes. Indeed,
the simple correlation between DT and DF is -0.772.

8
The potential explanatory power of this variable for real activity was suggested to us by
Milton Friedman (in correspondence with Mark Rush).

Empirical Results

Empirical Specification
Obviously, factors other than reserve requirements determine the evolution of real activity.

Hence, we estimate reduced form OLS equations for investment and output as functions of
changes in reserve requirements and other macroeconomic variables, which we specify below.
The two measures of reserve requirements that we use are (log) changes in T and F,
denoted by DT and DF respectively. To capture one obvious source of macroeconomic
fluctuations, we include the log of real federal purchases (LF) in the regressions for output. The
impact of this variable on investment was never significant, so we excluded it from those
regressions; no results hinge on this exclusion. We use four alternate measures of monetary
policy: the growth rate of the monetary base (DB), the growth rate of M l (DM), the change in
the 3-month Treasury bill rate, and the spread between the short-term T-bill rate and the short
term commercial paper rate.9 Broadly speaking, all four measures of monetary policy were
significantly correlated with real activity, and there was little reason to choose one measure over
the other on empirical grounds.1 More important, conclusions about the impact of changes in
0
reserve requirements on real activity—
which is our primary focus— not depend crucially on the
do

9 We did not pursue a decomposition of money growth into anticipated and unanticipated
components in view of the conclusions of Bano and Rush (1980) and Frydman and Rappoport
(1987) that, with quarterly data, both components of the money supply matter for output.
1 We also tried specifications in which two of the policy measures were entered
0
simultaneously: the base and M l, the base and interest rates, M l and the interest rate. Again, no
clear "winner" emerged. This may seem somewhat surprising since several studies find that
interest rates dominate monetary aggregates in explaining real activity. Two factors may explain
our results. First, many of the studies do not use the monetary base. Second, and perhaps more
important, our sample period starts in 1947 whereas these studies typically focus on the post-1959
period.

choice of the monetary policy measure. In the interests of brevity, therefore, we only report
results based on the monetary base and Ml measures.
We include an additional independent variable that is motivated by the empirical findings
of Barro (1989). Using reduced form equations similar to the ones we estimate, Barro found that
the real stock market return has a strong impact on subsequent aggregate investment. Moreover,
stock returns dominated both a Tobin’s-q variable and cash-flow variables. We follow Barro by
including in our regressions real stock return, called DS, where the stock market aggregate used
is the Standard and Poor’s 500.u
Finally, we also need to take account of the trend growth in output and investment.
Deciding whether these variables are trend-stationary or difference-stationaiy is far from the focus
of our paper; hence we explore both methods of detrending. In one set of regressions we include
a time trend and lags of the dependent variable. In another set, we use first differences of the log
of output and investment.
For all the variables, except the trend, we included lags. Clearly there is no theoretical
basis for the number of lags to be included. Including more lags than justified lowers efficiency
but including fewer biases the results. Hence, we were generous with the lags. In the trend
stationary case, we included eight lags of the independent variables and two lags for LF and the
lagged dependent variable; in the difference stationary regressions we reduced the dependent
variable to one lag. To check the sensitivity of our results, we tried different lag structures. For

1
1
In Loungani, Rush and Tave (1991), we have explored the impact of stock market
dispersion on aggregate investment. Since the results to be presented below are robust to the
inclusion of dispersion, we exclude it from the current specification.

instance, we increased the number of lags for the independent variables up to twelve; and
changed the lags for the dependent variable up to four. We also estimated regressions omitting
the stock return variable. Our conclusions were, in general, robust to these changes, and the
added lags rarely attained standard levels of significance. The measure of output used is real
GNP, while for investment we used gross private domestic investment plus consumer durable
expenditures.
To summarize, our estimated trend-stationary equations are of the form:
(8)

LI = a + bt + C(L)DT [or C(L)DF] + D(L)DB + E(L)DS + F(L)LI + enror

(9)

LY = a’ + b’t + C’(L)DT [or C’(L)DF] + D’(L)DB + E’(L)DS + F ’(L)LY + G’(L)LF + error

where LI and LY are the log of investment and output, respectively, and the X(L)’s are
polynomials in the lag operator. The difference-stationary regressions are similar, except that the
first-differences of LI and LY are used and the trend term is omitted. For our intermediation
variables, we expect the coefficients of C(L) and C’(L) to be negative when DT is used and
positive when DF is used.

Benchmark Results
We estimated the regressions specified above for the period 1950:1 to 1987:4. To

conserve space, the results of the estimation are summarized as follows. First, for each
independent variable, we report the sum of the current and eight lagged coefficients and the
standard error of the sum. For the reserve requirements variables, we also provide some evidence

on the short run impact of these variables on real activity by reporting the sum of the current and
four lagged coefficients.1
2
Looking at the first row in Table 1, we find a significant role for changes in the money
supply: In both the output and investment regressions, the sum of the base money coefficients
is highly significant. Next, in keeping with Barro’s work, the sums of the stock return coefficients
are also highly significant. Declines in the extent of financial intermediation— measured by
as
either DT or DF-have the predicted negative impact on real activity. The short run impact is
significant at 1% in three of the four equations and at 10% in the output regression with DT. The
sum of the current and eight lagged coefficients is again significant at 1% in three equations but
insignificantly different from zero in the output equation with DT.
Also reported in Table 1 are the results of estimating the regressions using the growth
rates of output and investment as dependent variables. Estimating the regressions in first
difference form modifies the interpretation of the estimated coefficients. This specification
implies that when the sum of the DT (DF) coefficients is significantly negative (positive), then
then a one-period change in the level of T or F has a permanent impact on the level of GNP and
investment.1
3

1 The data used in the estimation and the complete set of results are contained in an
2
appendix available from the authors.
1 Since we measure these variables as growth rates, this may be appropriate. For instance,
3
a permanent decrease in the required reserve ratio causes a one-period decrease in DT and a oneperiod increase in DF. But the fact that the change in the required reserve ratio is permanent may
imply a permanent increase in GNP as the tax on credit creation is permanently lower. This sort
of permanent effect on GNP and investment is not allowed in the trend stationary case, because
with a finite number of lags for DT and DF, GNP must eventually return to its trend.

Clearly the sums of the DS coefficients are still strongly significant. However, the impact
from the money supply as measured by the sums of the coefficients is sharply diminished. Most
likely this suggests that money is neutral in the long-run (neutrality requires that the sum of the
coefficients equal zero) and indeed, most of the longer lagged coefficients are negative. Our main
interest, however, centers around the financial intermediation variables. It is clear that our
intermediation variables, especially the shorter lagged variables, remain highly significant. In
particular, the sums of the current and first four lagged coefficients fall a bit in absolute value
but their overall level of significance is unaffected. Looking at the longer lags, it is interesting
to note that the estimated sums all fall in size and significance relative to the trend-stationary
specification. In the output regressions, the sum of the DT variables remains insignificant and the
sum of the DF variables barely attains significance. The sums in the two investment remain quite
significant, but are substantially smaller than in the trend stationary specifications. These
qualitative results suggest that in the long-run, a change in the required reserve has no effect on
the level of GNP and the effect on investment shrinks.1
4

1
4
As pointed out by one of the referees, the fact that the last several lags of the DF variable
tended to have negative coefficients suggested that including longer lags might further reduce the
total sum of the coefficients. Thus, we also estimated regressions with 12 lags of our
intermediation variables to examine this possibility. None of the lags attained anything close to
conventional levels of statistical significance. Somewhat surprisingly to us, adding the extra lags
did not have too much impact on the overall sums; however, the significance of the sums was
reduced.

Overall, these results strongly support our main qualitative prediction that increases in
reserve requirements have an adverse impact on real activity, after controlling for the impact of
standard macroeconomic variables, including measures of monetary policy.1
5

Tests of Robustness
We undertook tests along a couple of dimensions to further examine the robustness of our

results. First we wanted to ascertain how our results were affected by any endogeneity. Given
the sources in our regressions of possible endogeneity (e.g., endogenous Federal Reserve policy)
it is difficult to think of variables that could legitimately be used as exogenous instruments in a
conventional 2SLS regression. Hence, we employ the following strategy.
To start, recall that both intermediation variables can be affected by the public’s
(endogenous) actions in shifting between deposit types. However, as we noted earlier, most of
the pronounced blips in the DT and DF series correspond to policy actions by the Fed. This
suggests that focusing on "large" changes in these series is one way to alleviate the potential bias
caused by shifting between deposit types. Hence, we estimate regressions that use only large
values for DT and DT, where we define large as being greater than 0.7 standard deviations away
from the mean.1 The sums and standard errors of the coefficients from regressions using these
6
"large" values are reported in Table 2. Briefly, comparing these with the previous results, we see

1 Two recent papers, Plosser (1991) and Haslag and Hein (1991), use bivariate and
5
multivariate VAR’s, respectively, and they also find that the reserve adjustment component of
the base is significantly correlated with output.
1 We used 0.7 standard deviations because this range captures about 50% of observations
6
from a normal distribution. We did not experiment with other ranges--0.7 is the only range we
used.

little effect in the investment regressions. In the output regressions the main impact is to lower
the significance of the sums with all the lagged coefficients, though it slightly raises the
significance of the sums with only four lagged coefficients.1
7
The second dimension along which we test our results is to consider the impact of using
an alternate measure of monetary policy. It has been suggested that the impact of reserve
requirements on real activity that we report may be partially "proxying" for a correlation between
M l and real activity. It would appear that re-estimating our regressions with the monetary base
replaced by M l would be a test of this suggestion. While we do follow this route below, it is
useful to keep in mind that this procedure suffers from a potential pitfall: In this paper, we are
interested in studying the effect on aggregate real variables from exogenous changes in bank
intermediation, that is, exogenous fluctuations in bank loans. Now, consider the following
hypothetical scenario. Suppose that all investment is financed by bank loans and bank loans
finance only investment. Then, due to the endogenous correlation between bank loans and
investment, the inclusion of bank loans in a reduced form regression will eliminate the impact
of other variables that influence investment through impacts on bank loans. Further, assume that
funds for all banks loans are obtained through banks’ intermediation from demand deposits and
that M l fluctuates only because of the demand deposit component. Then, including M l in the

1
7
To conserve space, we do not report the details of two other tests that investigate the
robustness of our results to potential endogeneity problems. First, we estimate regressions that
omit contemporaneous values of the independent variables. The results here are quite similar to
what we obtained using only "large" values for intermediation: Little effect in the investment
regressions and a general reduction in the significance of the longer lagged sums in the output
regressions. Second, we estimate a VAR system with output, investment, DT (or DF), DB and
DS. Here again, the impact of DT and DF on real activity emerges as statistically significant.

regression will have the same impact as including loans: No other variables would emerge as
significant determinants of investment. Obviously, in reality the correlations between investment
and bank loans, between bank loans and demand deposits, and between demand deposits and Ml
are not perfect. Nonetheless, these endogenous correlations exist and they reduce the likelihood
of isolating a separate impact from changes in reserve requirements, once a broader monetary
aggregate is included in the regression. Hence we would argue that finding any relationship
between our intermediation measures and aggregate variables from such a regression is very
strong evidence in favor of theories that stress a role for credit creation.
Once again, we estimate both trend and difference stationary regressions over the same
time period as our benchmark specification. The results from this are summarized in Table 4. In
light of the issue discussed above, the intermediation variables perform quite well. In all cases,
the sum of at least the current and first four lagged coefficients is significantly different from
zero at the 10% level or better. And the qualitative results we obtained were identical to those
obtained using the monetary base: Intermediation has a stronger effect on investment than on
total GNP. Thus, we find these results to be quite supportive of a role for credit creation in
influencing aggregate variables.

Dynamic Response of Output and Investment
Having established the statistical significance of our results, we now assess their

quantitative importance by tracing out the dynamic response of output and investment to changes
in reserve requirements. For this we use the estimated coefficients from the trend-stationary
regressions reported in Table 1. As a baseline forecast, we set all the future values of DB, DS,

DT, DF, and LF equal to their mean over our sample period, while the trend variable is allowed
to grow at its usual rate. Then, to determine the impact of changes in reserve requirements, we
retain DB, DS and LF at their mean values, but assume that reserve requirements are lowered
for two successive quarters and then return to their mean value. We picked the magnitude of the
reduction to equal the actual change in reserve requirements that occurred in 1958:1 and
1958:2.1
8
The response of output and investment over a 20-quarter period is shown in Figure 2,
using estimates from the DF regression. The policy change raises output above the baseline
forecast, with the peak impact occurring after six quarters-the impact at this point is $120
billion, or about a 3% increase. The impact on investment is much stronger-about $60 at the
peak, representing a 15% increase. The impact on output and investment dies out fairly slowly
over the succeeding quarters. In Figure 3, we show the estimated impact using the DT regression.
This reduces the estimated impact of the policy change, with the peak impact dropping to 1%
and 10% for output and investment, respectively.
To check the sensitivity of our quantitative results, we retain DT as the policy measure
but use estimates from Table 3. Recall that in this regression M l growth was used as the measure
of monetary policy rather than base growth. As shown in Figure 4, the quantitative impact of this
change is fairly small. For instance, the size of the peak impact on output falls slightly below 1%,
whereas the impact on investment is now about 7.5%.

1
8
That is, we set the values of DT and DF in the first quarter equal to their value in 1958-1
and in the second quarter equal to their 1958:2 value.

Extensions of the Basic Results1
9
We present some auxiliary evidence to support the hypothesis that changes in reserve

requirements affect aggregate investment through their impact on bank loans. First, we show that
increases in reserve requirements have an adverse impact on the quantity of bank loans, in
particular, commercial and industrial loans. We estimate a difference-stationary equation for
commercial and industrial loans (DL), specified in a fashion similar to the investment equation:
(10)

DL = a + P(L)DT [or P(L)DF] + T(L)DB + y(L)DS + 8(L)DL + error

The sample period is restricted by the availability of the loan data and starts in 1959:1. The
results of the estimation are reported in Table 4. In the interests of brevity only the sums
of the DT and DF coefficients are reported. For both DT and DF measures, the sums of the
current and lagged coefficients are significantly different from zero. Hence, the results strongly
support the hypothesis that increases in reserve requirements have a negative impact on the
quantity of bank loans.2
0
Finally, we conduct a more direct test of the hypothesis that bank credit contains
"information" that is not contained in other types of credit. We do so by constructing a variable
denoted MIX which is the ratio of bank credit to total credit, where the total is lending by
depository institutions plus the Fed.2 It is easy to show that MIX is inversely related to both
1
1 We are grateful to the referees for suggesting these extensions.
9
20 By splicing our loans series with a series on C&I lending by weekly reporting banks, we
were able to estimate a loans regression starting in 1950. The results from this exercise were
equally supportive. The marginal significance level was .015, .067, .008 and .061 for DT(4),
DT(8), DF(4) and DF(8), respectively.
2 Kashyap, Stein and Wilcox (1990) also construct a mix variable which is the ratio of bank
1
lending to total lending, where the total is defined as bank lending plus commercial paper. They
find this mix variable to be negatively correlated with investment, particularly, with inventories.

the required reserve ratio and the currency/deposit ratio. Increases in MIX therefore correspond
to increases in intermediation and should lead to increases in aggregate investment. To test this
we regress investment on current and eight lagged values of the growth rate of the mix, the
growth rate of the monetary base and stock returns. As shown in Table 5, the results support the
hypothesized positive impact of the MIX variable on aggregate investment.22

Conclusions
To study the effects of financial intermediation on real activity, some exogenous "shifter"

of intermediary activity is needed. The variable considered in this paper is changes in reserve
requirements. As discussed in the paper, these changes are often made for bank regulatory
reasons, and hence appear to be far more exogenous with respect to macroeconomic
developments than the credit variables used in earlier tests. If intermediation has real effects, then
an increase in reserve requirements ought to be followed by declines in output and investment.
We find that changes in reserve requirements have statistically significant and quantitatively
important impacts on real activity. Furthermore, even after we control for the correlation between
M l growth and real activity, changes in reserve requirements continue to exert an independent
influence on real activity. This result provides support for theories that emphasize the credit
channel of monetary transmission.

22
If investment is regressed on the two underlying components of the mix, the required
reserve ratio and the currency/deposit ratio, only the former is significant.

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1941
1948:1
1948:2
1948:3
1949:2
1949:3
1951:1
1953:3
1954:2
1954:3
1958:1
1958:2
1960:3
1960:4
1962:4

Appendix A: Changes in Reserve Requirements
Net demand deposits
Time deposits
Central reserve
Reserve city
Country
(all classes
banks
of banks)
banks
city banks
14
20
6
26
22

24
26
24

22
21

16
15

22
21
20

14
13

5
18
17
161/2

19
18
171/2
I61/2

12
11

I61/2 17
171/2
17

12
121/2

4
Other time
0-5m. Over 5m.
4
3

12 i/2

13
5
Sav.

Net demand deposits
0-2

1972:4
1973:3
1974:4
1975:1
1975:4
1976:1
1976:4

Savings

4
3

Net demand deposits
Other banks
Reserve city
0-5m. Over 5m.
0-5m. Over 5m.
1966:3
1967:1
1968:1
1969:2
1970:4

71/2
7
5
6

100

400+

2
10

10
100

10
101/2

12
12 i /2

71/2

9l/2

Other
0-5
<6
<4
mths. yrs.
3
3

time
5+
<6
<4
mths. yrs.
5
5

>4
yrs
5

113/4 123/4 16i/4

400
171/2 3
13
131/2 18
171/2
I61/2
13

>4
yrs.
3

21/2

R e s e r v e R e q u ir e m e n t s e s t a b l i s h e d u n d e r t h e M o n e t a r y

C o n tro l A c t (M C A )

21/2

o f 1980

Net transactions accounts
Nonpersonal time deposits
Eurocurrency liab,
0-25 million
3
All types
Less than 4 yrs.
3
3
over 25 million
12
4 yrs. or more
0
See Santoni (1985) for information on the phase-in periods and other details of the 1980 MCA.

TABLE 1: BENCHMARK REGRESSIONS

TREND STATIONARY
DT REGRESSIONS
OUTPUT
DB(8) 0.78***
(.30)

DF REGRESSIONS
OUTPUT
1.37***
(*42)

INVESTMENT
2 3i***
DB(8)
(•62)

INVESTMENT
3.15***
(.80)

0.349***
(.088)

DS(8)

0.123***
(.038)

DT(4) -0.148*
(.079)

-0.720***
(.192)

DF(4)

7.19***
(1*95)

17.22***
(5.04)

DT(8) -0.121
(.084)

-0.626***
(.204)

DF(8)

6.95***
(2.35)

20.45***
(5.55)

DS(8)

0.134***
(.040)

0.348***
(.090)

DIFFERENCE STATIONARY
DT REGRESSIONS
OUTPUT
DB(8) 0.26*
(.14)

DF REGRESSIONS

INVESTMENT
0.50*
DB(8)
(.28)

OUTPUT
0.30**
(.15)

INVESTMENT
0.73**
(.33)

0.321***
(.091)

DS(8)

0.130***
(.037)

DT(4) -0.125*
(.074)

-0.717***
(.184)

DF(4)

5.14***
(1.66)

13.79***
(4.25)

DT(8) -0.046
(.063)

-0.363**
(.156)

DF(8)

2.46*
(1.53)

11.45***
(3.89)

DS(8)

0.146***
(.038)

0.284***
(.091)

The top numbers are the sums of the current and lagged coefficients in the specified regression. An
(8) indicates it is the sum of the current and eight lagged coefficients; (4) indicates the sum of the
current and four lagged coefficients. The number in parentheses beneath the estimate of the sum is
the standard error of the sum. *** indicates significance at the 1% level, ** at the 5% level and * at
the 10% level.

TABLE 2: REGRESSIONS USING ONLY "LARGE" VALUES FOR DT AND DF

TREND STATIONARY
DT REGRESSIONS
OUTPUT
DB(8) 0.64**
(.30)

DF REGRESSIONS
OUTPUT
1.21***
(.39)

INVESTMENT
DB(8)
2.35***
(.63)

INVESTMENT
3.58***
(.79)
0.312***
(.083)

0.328***
(.084)

DS(8)

0.097***
(.038)

DT(4) -0.156**
(.077)

-0.634***
(.185)

DF(4)

7.09****
(LSI)

13.05***
(4.23)

DT(8) -0.087
(.083)

-0.524***
(.200)

DF(8)

7 27***
(2^5)

20.97***
(5.41)

DS(8)

0.120***
(.039)

DIFFERENCE STATIONARY
DF REGRESSIONS

DT REGRESSIONS
OUTPUT
DB(8) 0.20
(.13)

INVESTMENT
0.51*
DB(8)
(.28)

OUTPUT
0.23
(.14)

INVESTMENT
0.71**
(.31)

0.297***
(.088)

DS(8)

0.H 2***
(.037)

DT(4) -0.130*
(.072)

-0.670***
(.178)

DF(4)

4.69***
(1.52)

10.49***
(3.80)

DT(8) -0.025
(.060)

-0.286*
(.151)

2.37
(1.60)

10.58***
(3.98)

DS(8)

0.127***
(.037)

DF(8)

0.249***
(.087)

TABLE 3: REGRESSIONS USING M l

TREND STATIONARY
DT REGRESSIONS
OUTPUT
DM(8) 0.34
(.29)
DS(8)

0.087**
(.045)

DF REGRESSIONS

INVESTMENT
1.72**
DM 1(8)
(.78)
0.165
(.104)

DT(4) -0.118*
(.067)

-.455***
(.166)

DT(8) -0.034
(.077)

-.329*
(.195)

OUTPUT
0.41
(.31)
0.060
(.044)

DS(8)

DF(4)

INVESTMENT
2.01**
(.85)
0.110
(.104)

2.53***
(1.26)
1.86
(1.67)

DF(8)

5.34*
(3.06)
6.56
(4.23)

DIFFERENCE STATIONARY
DT REGRESSIONS
OUTPUT
DM1(8) 0.17
(.15)
DS(8)

DF REGRESSIONS

INVESTMENT
0.37
DM1 (8)
(.31)

OUTPUT
0.14
(.14)

INVESTMENT
0.49
(9.32)

0.269***
(.091)

DS(8)

0.083**
(.034)

0.216**
(.089)

DT(4) -0.112*
(.060)

-.510***
(.149)

DF(4)

2.13**
(1.06)

6.20**
(2.52)

DT(8) -0.016
(.064)

-.323**
(.165)

DF(8)

1.11
(1.33)

6.60**
(3.37)

0.103***
(.035)

The top numbers are the sums of the contemporaneous and lagged coefficients in the specified
regression. (8) indicates all 8 lags are summed; (4) indicates the sum of the first 4 lags. The number
in parentheses beneath the estimate of the sum is the standard error of the sum. *** indicates
significance at the 1% level, ** at the 5% level and * at the 10% level.

TABLE 4: COMMERCIAL AND INDUSTRIAL LOANS REGRESSIONS

DT REGRESSIONS

DF REGRESSIONS

SUM
DT(4)

-0.322**

DF(4)

7.77***

DT(8)

-0.469**

DF(8)

11.80***

TABLE 5: INVESTMENT REGRESSION WITH "MIX" VARIABLE
SUM
GM1X(4)

3.75**

GMIX(8)

3.36**

*** indicates significance at the 1% level and ** at the 5% level.

Figure 1(a)
Changes in reserve requirements,1948-65

D T ....... DF

Figure 1 (b)
Changes in reserve requirements,1966-87

D T ....... DF

Fig. 2: Output and Investment Response

Using DF and Monetary Base Growth

output

....... investment

Fig. 3: Output and Investment Response

Using DT and Monetary Base Growth

output

investment

Fig. 4: Output and Investment Response

Using DT and M1 Growth

output

....... investment

Full text of Working Papers (Federal Reserve Bank of Chicago) : The Effect of Changes in Reserve Requirements on Investment and GNP, Working Paper 1991-21

FRASER