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Economic
Review
Federal Reserve B811lt
of San Francisco
Winter 1989

Number 1

Michael C. Keeley

The Stock Price Effects of
Bank Holding Company Securities Issuance

Frederick T. Furlong

Commodity Prices as a Guide for
Monetary Policy

Bharat Trehan

Forecasting Growth in Current Quarter Real GNP

Carolyn Sherwood-Call

Undocumented Workers and Regional
Differences in Apparel Labor Markets

Table of Contents

The Stock Price Effects of Bank Holding Company
Securities Issuance . . . . . . . . „«, . . . . . . . . . . . . . . . „ . . . . . . . . . . . . . . . . . . „ » . . . . . • . . 3
Michael C. Keeley

Commodity Prices as a Guide for M onetary Policy . . . . . » . . . . . . . . . . . • „ » . . . . . . . 21
Frederick T. Furlong

Forecasting Growth in Current Quarter Real GNP . „ „ . » . . . . . » » . . . . . . ° . . . . . .«, 39
Bharat Trehan

Undocumented Workers and Regional Differences in
Apparel Labor Markets . . . . . . . . . . . « , . „ . . . . . . 0. . . . ©. . . . . . „ „ » . . . . . . . . . . . » . 53
Carolyn Sherwood-Call

Federal Reserve B ank o f San F rancisco

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E conom ic R eview / W inter 1989

The Stock Price Effects of Bank Holding Company
Securities Issuance

Michael C. Keeley
Research Officer, Federal Reserve Bank of San Francisco. Christopher James supplied some of the data used in
this study. Barbara Bennett, Jonathan Neuberger, and
Randall Pozdena provided helpful comments, and Alice
Jacobson and Stacey Dogan provided expert research
assistance. Editorial Committee members were Brian
Motley and Randall Pozdena.

This paper examines the announcement effects of bank
holding company (BHC) securities issuance on their common stock prices. A key finding is that since December
1981 when objective minimum capital regulations wereput
into place, announcements ofcommon stock issuance have
been associated with statistically significant negative abnormal common stock returns for BHCs under regulatory
pressure to boost capital. No such effects were found for
highly-capitalized BHCs that were not under regulatory
pressure to boost capital. These results suggest that
poorly-capitalized BHCs will be reluctant to issue common stock to meet capital requirements. They also suggest
that the deadweight costs associated with common stock
issuance by well-capitalized banking organizations are
small or nonexistent.

Federal Reserve Bank of San Francisco

Bank and bank holding company (BHC) capital regulation is becoming an increasingly important tool to limit
banking risk. More capital relative to assets provides a
greater cushion to absorb losses. Moreover, as Furlong and
Keeley (l987a, 1987b) show, more capital relative to assets
reduces banks' incentives to increase asset risk. Thus, an
increase in BHCs' capital-to-asset ratios should reduce the
risk exposure of the deposit insurance system.'
Capital regulation was strengthened- in December 1981
when specific bank and bank holding company minimum
capital standards were introduced for the first time, a
departure from the previous subjective peer-group typ~ of
capital regulation. In addition, these minimum capIt~1
requirements were modified in 1983 to include the multinational bank holding companies and again in 1985 to
standardize the minimum requirements for all banks and
bank holding companies. 3
In early August 1988, the Board of Governors adopted
an even more stringent set of "risk-based" capital requirements for BHCs based on an international agreement
among the twelve leading industrial countries. These new
standards represent an important departure from the current ones in that they require different amounts of capital
based on an assessment of an asset's risk class. They also
require capital to be held against off-balance-sheet items.
Finally, they require more capital for assets in the highest
risk class than do current standards and also define capital
differently than the current U.S. rules do.
To meet these new capital-to-asset ratio requirements,
many banks and bank holding companies either will have
to sell assets or increase capital by retaining a higher
proportion of earnings and/or raising external ~apital.
BHCs raise external capital by selling a range of different
types of securities, including common stock, preferred
stock, mandatory convertible debt, convertible debt, and
straight subordinated debt.
Ideally, capital regulations should be designed to attain
a given degree of risk exposure of the deposit insurance
system while minimizing the deadweight costs i~posed ~n
the banking organizations subject to the regulations. ThIS
paper examines the stock market's reaction to BHCs'
securities issuance to learn more about the effects of
capital regulation on the banking firm. Specifically, the
effects on BHCs' stock prices following the announcement

of the issuance of different kinds of securities may reveal
whether increasing capital imposes costs on banking organizations and whether increasing capital reduces the risk
exposure of the deposit insurance fund.
Two novel aspects of this study are its focus on the
differences between the stock price effects for BHCs under
regulatory pressure to augment capital and those that raise
external capital voluntarily, and its analysis of the changes
in these effects after the new specific, objective minimum
capital regulations were instituted in December 1981. I find
statistically significant negative stock price effects associated with common stock issuance for banking organizations under regulatory pressure to augment capital and

positive, but not statistically significant effects for other
BHCs. Thus, unlike some studies that argue that announcement effects should be absolutely smaller for BHCs
that are known to be under close regulatory scrutiny, I find
just the opposite.
This study is organized as follows. Section I reviews the
theory and evidence regarding the effects of securities
issuance by nonbank firms and discusses the implications
for BHCs' securities issuance. Section II reviews the prior
studies of BHC securities issuance. Section III discusses
the methodology and data employed in this study and
Section IV presents the results. Section V presents a
summary and conclusions.

I. Theory and Evidence from Nonbanking Firms: Implications for Bank Holding Companies
There is now an extensive literature regarding the valuation effects of securities issuance by industrial and utility
firms. Modigliani and Miller (1958) have shown that in
competitive markets without distortions, such as taxes,
bankruptcy costs, agency costs, and asymmetric information, a firm's capital structure is irrelevant. If so, securities
issuance should not affect a firm's stock price."
However, as Smith (1986) points out, empirical studies
have found statistically significant negative stock price
effects of common stock issuance by industrial firms of
approximately - 3.14 percent, as well as negative significant effects associated with the issuance of preferred stock
and bonds that are convertible into common stock. No
statistically significant effects are found for other types of
securities, although usually the point estimates are negative. Utility firms also have negative, but much smaller,
announcement effects associated with common stock issuance, averaging about -.75 percent.
In an attempt to explain these empirical findings, theory
has developed along two main lines. One argues that the
existence of such distortions as taxes, bankruptcy costs,
and agency costs means that capital structure does matter
and that securities issuance will affect stock prices. The
other line of reasoning relies on information asymmetries
and signalling. Below, these two types of theories are
discussed.

Capital Structure Theory
Although firms may indeed have optimal capital structures, it is unclear whether the existence of optimal capital
structures could explain the negative stock price effects
associated with common stock issuance by industrial and
utility firms. The reason is that voluntary securities issuance should always represent a movement toward (and cer-

tainly not away from) a firm's optimum capital structure.
As a result, the effects of a voluntary securities issuance
(which affects capital structure) should be positive or zero.
Thus, while capital structure theory might be able to
explain the negative effects of involuntary securities issuance, it seems unlikely that it could explain the negative
effects associated with voluntary securities issuance. As a
result, most of the literature has focused on signalling
theories to try to explain the stock price announcement
effects of securities issuance.

Signalling Theories
A variety of signalling theories have been built on the
premise that management has information about the value
of a firm that is not available to outside investors. Thus, the
announcement of a security issuance is taken by investors
as a signal that reveals at least some of management's
inside information.
For example, Miller and Rock (1985) argue that net new
external financing is a signal of lower earnings because
internal financing would be used if earnings were sufficient. However, this argument implies that all types of
external financing should have negative announcement
effects and thus fails to explain the different effects of
different types of securities issuance.
Myers and Majluf (1984) argue that management has an
incentive to issue new stock when they believe the firm's
stock is overvalued. However, investors realize that the
firm's managers have such an incentive and take the
information of a new stock issuance as a signal that the
firm's stock is overvalued, which in turn causes the stock's
price to fall.
This theory can explain why managers would be reluctant to issue new stock even to fund positive net present

Economic Review / Winter 1989

value opportunities. If the manager knows that the firm's
stock is undervalued, it would not be optimal to issue
securities to fund a new project that had a modest positive
net present value. It also would explain a preference for
internal financing as well as for the use of low-risk securities, the values of which do not strongly depend on the
firm's value.
However, as Dybvig and Zender (1988) point out, investors would anticipate the tendency to pass up profitable
new projects and would pay a lower initial offering price
than if managers could somehow be induced to follow an
optimal investment policy. (That is, if initial investors
could be certain managers always would undertake positive net present value projects, they would be willing to pay
more for the stock at the initial offering.) Dybvig and
Zender go on to show that an optimal contract for managers
can be devised to overcome the underinvestment problem.
Nonetheless, Dybvig and Zender show that even with
optimal managerial contracts, the existence of information
asymmetries between a firm's managers and its investors
will cause investors to treat securities issuance as a signal.
When the manager has good news about both the new and
old projects, internal financing can and will be used to
undertake new projects so that lack of need for external
financing will be viewed as a positive signal. Similarly,
when a manager has good news about a new project and
bad news about an old project, debt is issued, which has a
minimal effect on stock prices. However, when the manager has bad news about both the new and old projects,
equity will be issued, providing a negative signal, which
causes the stock's price to fall.
In sum, even though the Myers and Majluf story may be
incomplete, securities issuance probably conveys information about the performance of the existing assets of the firm
as well as the prospects for new projects. Thus, it seems
most likely that stock issuance is some sort of signal. 5

Implications for Bank Holding Companies
It seems likely that signalling theory would apply to
securities issued by BHCs, as well as by nonbanking
firms. However, effects for BHCs might differ from those
of industrial firms because BHCs are so highly regulated.
The most common argument regarding the effects of
regulation is that the market's knowledge of regulatory
policy reduces the information that otherwise would be
revealed by a security issuance. For example, the stock

Federal Reserve Bank of San Francisco

price effects associated with announcements of utility
firms' common stock offerings are on average absolutely
smaller than those for industrial firms. Utility firms' tendency to make repeated stock offerings (due to regulation)
and the fact that utilities' stock offerings often require prior
regulatory commission approval appear to diminish the
information content of actual announcements and thus may
explain why utilities have smaller absolute stock price
announcement effects than industrial firms.
Likewise, the information content and stock price announcement effects of BHCs' securities issuance might be
smaller (in absolute value) than those for nonregulated
firms, even though a BHC need not obtain prior regulatory
approval to issue new securities. The market's knowledge
of the BHC regulatory process might well dilute the
information content associated with a BHC's security
offering, particularly for organizations known to be under
regulatory pressure to boost capital. Moreover, since BHC
capital regulation shifted to objective, minimum standards
beginning in 1981, one would expect smaller absolute
stock price effects during the post-1981 period.
On the other hand, there are several reasons why the
stock price announcement effects associated with BHCs'
securities issuance might be more negative than those of
industrial firms. First, if the value of the deposit insurance
guarantee is capitalized in a BHC's common stock value, a
security issuance that is forced on a BHC by its regulator in
an effort to diminish the risk exposure of the deposit
insurance fund could lead to a larger negative effect
because such an issuance would diminish the (option)
value of the deposit insurance guarantee. In particular, one
would expect BHCs with low capital positions to experience larger negative announcement effects than would
highly-capitalized BHCs. 6 Similarly, a regulatory-induced
increase in capital could result in larger negative announcement effects because distortions such as taxes or
agency costs could make a forced change in capital structure away from the BHC's private optimum costly.
One final reason that the announcement effects for
BHCs may be more negative than those for industrial and
utility firms is that regulators may have inside information
obtained during bank and bank holding company examinations. Thus, a securities issuance by a BHC known by the
market to be under regulatory pressure to augment capital
might convey information about the firm's earning prospects.

II. Previous Empirical Research on BHC Securities Issuance
Since there are theoretical arguments both for larger and
for smaller announcement effects for BHCs' securities
issuance than for industrials' , the question regarding which
forces dominate is basically an empirical one. Thus, in this
section the available empirical studies are reviewed. There
are several unpublished papers dealing with the effects of
bank: holding companies' securities issuance. These are
papers by Isberg and Brown (1987), Wansley and Dhillon
(1987), Wall and Peterson (1988), and Polonchek, Slovin,
and Sushka (1987).

datory convertible debt, and subordinated debt issuance by
BHCs from 1982 through 1986. One innovation of their
study is that they obtain the announcement day from the
Dow Jones News Service instead of the WallStreetJournal
Index as the other studies do. They argue that this allows
them to pinpoint the actual first trading day that would be
affected by the announcement. (Thus, they use only a oneday event period.) They find a statistically significant
- 1.5 percent abnormal return for common stock issuance,
butuo significant effects for other types of securities
issuance.

Isberg and Brown
Isberg and Brown (1987) argue that for the 1981 to 1985
period," new common stock issues were the only type of
security issuance associated with statistically significant
negative common stock returns for BHCs both above and
below the contemporaneous capital standards. Although
two-day cumulative average prediction errors and Z statistics are not reported, it appears that they found a -l.l
percent effect for BHCs meeting the capital standards and a
- 2.0 percent effect for BHCs below the standards. However, since many BHCs issued capital prior to the implementation of new capital standards to be in compliance, it
appears that many of the events characterized by this study
as common stock issues by BHCs above the current standards were really issues intended to bring the holding
company into compliance with expected future standards.

Wansley and Dhillon
Wansley and Dhillon (1987) examine the valuation effects of six types of securities issuance by BHCs between
1978 and 1985: common stock, preferred stock, convertible preferred stock, straight debt-non-shelf, straight
debt-shelf, and debt-for-equity swaps. They find statistically significant abnormal returns for common stock of
-1.5 percent, significant positive returns for preferred
stock of 0.8 percent and no significant abnormal returns for
other types of securities issuance. Since their estimate of
the size of the announcement effect associated with common stock issuance is much smaller than that found for
industrial firms, they argue that banking regulation, like
utility regulation, reduces the uncertainty and information
content of new securities issuance and therefore reduces
the absolute size of the stock price announcement effect. 8

Wall and Peterson
Wall and Peterson (1988) examine the valuation effects
of common stock, preferred stock, convertible debt, man-

Polonchek, et al.
Finally, Polonchek, Slovin, and Sushka (1987) follow a
methodology that is closest to that of this paper. They
examine the valuation effects of various types of securities
issuance for the 1975 to 1985 period and distinguish the
pre-1981 period from the post-1981 period. They also
distinguish the effects for multinational BHCs from those
for other BHCs. 9
They find statistically significant negative abnormal
returns for common stock issuance prior to December 1981
( -1. 7 percent) but not for any other types of securities. 10
After December 1981 abnormal returns also are negative
( -l.l percent) but are not statistically significant. Even
though the absolute decline in abnormal returns appears
not to be statistically significant, they argue that the
explanation for the decline is that during the post-1981
period, capital decisions were determined more by regulatory factors and thus contained a smaller (negative) information component.
They also find larger negative point estimates for multinational BHCs' issuance of common stock during the
1982-1984 period than for those of other BHCs (-1.9
percent for multinationals versus - 0.8 percent for others),
but it appears that the difference is not statistically significant. They argue that this apparent pattern arises because
the multinationals were not subject to capital requirements
until 1983. However, the main reason that the multinationals were not subject to capital requirements until 1983 is
that none of these banking organizations would have met
the 1981 requirements in December 1981 (see Keeley
[1988]). That is, they were given time to raise capital and
bring themselves into compliance. This suggests that the
multinationals were, in fact, under regulatory pressure to
boost their capital by a large amount. Since the multinationals actually were under severe regulatory pressure to
raise capital to meet the 1983 and 1985 standards before

Economic Review I Winter 1989

those standards took effect, the evidence that their abnormal returns were larger (in absolute value) than those of
other BHCs actually contradicts the hypothesis that regulation would cause abnormal returns to decline in absolute
value.
Summary
On the whole, these studies support the hypothesis that
there are negative announcement effects associated with
common stock issuance by BHCs. The absolute values of
the effects for BHCs appear to be smaller than those found
for industrial firms but larger than those found for utilities.
Although these results are broadly consistent with the
hypothesis that BHC regulation dilutes the information
content of securities offerings (since the absolute sizes of
the BHC effects are smaller than those for industrial firms),

they are not inconsistent with a number of other hypotheses .•Moreover, these results tell us little about which
aspects. of the regulatory process may account for the
smaller stock price effects.
With the exception of Polonchek, Slovin, and Sushka,
none of the papers tries to distinguish the announcement
effects before and after the December 1981 change in
capital regulation. Similarly, none of the papers tries to
distinguish the announcement effects for BHCs that had to
issue capital to meet the guidelines from those that did not,
although Isberg and Brown do compare the results based
(apparently) on contemporaneous compliance with capital
guidelines. Moreover, none of the papers distinguishes the
effects before and after December 1981 for BHCs that
would have met the guidelines from those that would not
have." In the analysis below, I address these issues.

III. Methodology and Data
This paper employs the market model to estimate the
abnormal stock price returns associated with BHCs' securities issuance. The model is estimated with data on each
BHC's daily stock returns for a 60-day period beginning 80
trading days before and ending 20 trading days before the
announcement of each security issuance in order to provide
a forecast of what the stock's returns would have been
absent the announcement of a security issuance. (A stock's
rate of return is defined as the change in the stock's price
plus dividend payments, if any, divided by the original
stock price.) Then estimates of abnormal stock price
returns around the announcement date of securities issuance are computed as the difference between the actual and
predicted value.
The market model is:

Rjt= aj + bj Rmt + ejt

(1)

The prediction error for firm j on event day t is defined
as:

PEjt = Rjt - (aj + hj Rmt),
(2)
where the symbol "A " denotes an estimated value.
The daily prediction errors can be averaged over events
of a particular type (for example, common stock issuance)
to produce daily average prediction errors:
APEt = (lIN) I j PEjt,
(3)
where N is the number of events in the sample category.
Tests of statistical significance are based on standardized
prediction errors (see Mikkelson and Partch [1986]). Each
standardized prediction error (SPEjt) is defined as
SPEjt = PEjt /Sjt

(4)

where

where:

Sjt = {Vj[l + 11M + (Rmt-RmF II;(R mi-RmFlY/2 (5)

Rjt = rate of return on BHC j's common stock
over period t,

"Jis the residual variance offirmj's market-model regression, M is the number of days in the period used to estimate
the market model (60 days), the summation over index i
indicates summation over the period used to estimate the
is the mean market return over the
market model, and
estimation period. The average standardized prediction
error is:

Rmt = rate of return on the CRSP value-weighted market
index over period t,

aj , bj are coefficients for BHC i,
ejt = the error term for BHC j at time t, and
t is a time index in event time, that is, t = 81

(6)

is the announcement date.

Federal Reserve Bank of San Francisco

Assuming the individual daily prediction errors are
normally distributed, each SPEir is distributed Student t. If
the individual prediction errors are cross-sectionally independent, the following Z statistic is asymptotically distributed unit normal under the hypothesis that the average
standardized prediction error equals zero:

Z =

Vii (ASPEr)·

(7)

The empirical analysis focuses on abnormal returns
associated with the announcement of a security issuance.
Abnormal returns are defined as the sum of the prediction
errors for the day preceding and the day the announcement
is reported. This procedure allows for the possibility that
the announcement may have been made during trading
hours the previous day and then reported the next day.
To test the hypothesis that the two-day prediction error averaged over N events (in a given category) is zero,
I compute the average two-day standardized prediction
error:

(8)

and thus the Z statistic is:

Z = v'N(AISPEr_,,t)
(9)
Data on the returns of each BHC's security and the
overall market's returns are from the Center for Research
on Securities Prices (CRSP) daily returns tapes. Data on
securities issuance are from Irving Trust's Capital Securities Issued: CommercialBanking for the 1977 through
1986 period. Data from Compustat also are used to identify the quarters when major securities issues took place.
The announcement date is defined.as thedate of the first
report of a security issuance in the Wall Street Journal or
the SEC registration date, whichever was first. Announcementdates were obtained by searching the Wall Street
Journal Index for the year of and the year before the actual
issuance. The assumption is that the market generally only
becomes aware of a security issuance after it is formally
announced or that the probability of a security issuance
increases upon a formal announcement. Security issues not
reported in the WallStreetJournal were not included in the
sample.

IV. Results
Dollar Volume of Securities Issuance
Charts 1, 2, and 3 plot the dollar value of debt, common
stock, and preferred stock issued by all BHCs included in
Irving Trust's publication. Since this publication includes
many very small issues, including those of small holding
companies, it appears to be a fairly complete account of
publicly-traded BHCs' securities issuance. Charts 1,2, and

$ Billions

3 generally show increased security issuance in response
to capital regulation.
Chart 1 shows that the dollar volume of debt issued
increased greatly following the change in capital regulation in December 1981. Since subordinated debt counts as
total capital and mandatory convertible debt counts as
primary capital, the large rise in debt issuance is not
surprising.

Chart 1
Dollar Value of Public Debt Issued

Post 1981

Capital
Re9ulation

14
12
10

8
6

O-F:::::::;:::~..---.--,-..,.......,.--,-..,--,r--r-""'-=h--...-..,.........--r--.--.
1967

1970

1973

1976

1979

1982

1985

1988

Economic Review / Winter 1989

Chart 2 shows a rise in the dollar volume of common
stock issued in 1981 and also an even larger rise in 1986.
Some of the increased issuance in 1981 could be in anticipation of the new capital guidelines. However, it is unclear
whether the even larger increase in 1986 can be explained
by capital regulation unless it was in anticipation of the

$ Billions

risk-based guidelines which were very much in public
view at the time.
Finally, Chart 3 shows a large rise in preferred stock
issuance in 1982, apparently in response to the new capital
guidelines.

Chart 2
Dollar Value of Common Stock Issued

3.0

2.5
Post 1981
Capital
Re9ulatlon

2.0
1.5
1.0

Post 1985
Capital
Regulation

0.5

o.0 -f:::;=;:::;::~-=;::=~--r--r--~-1-.,---.---r--I-T""""""T---'
1967

$ Billions

1970

1973

1976

1979

1982

1985

1988

Chart 3
Dollar Value of Preferred Stock Issued

3.0

2.5
2.0
1.5
1.0
Post 1981
Capital
Regulation

0.5
O.0

-+-,--,......,--,.

1967

Federal Reserve Bank of San Francisco

1970

~~¥-..--,....::;::.....,.-+-....-r--r-..,..-..--,.....,

1973

1976

1979

1982

1985

1988

Sample Characteristics

Table 1 displays the distribution of the sample of securities announcement events analyzed in this study by
type and by year. All of these securities met the regulatory
definition of either primary or total capital used between
1981 and 1986. Consistent with the evidence in Charts I
through 3, there are more security offerings per year
during the 1982-1986 period than during the 1975-1981
period. Also, debt issues were the most common, followed
by preferred stock, with common stock the least frequent
type of offering.
Table 2 shows the distribution of the sample of securities
offerings by BHC and type of issue. It shows that 34 bank
holding companies were responsible for the 155 security
offerings studied here. It also shows that most of the
holding companies issued several different types of securities over the 1975 to 1986 period.

Prediction Errors 1975-1986

Abnormal returns-that is, two-day (cumulative) prediction errors-averaged over the entire 1975-1986 period
separately for each of seven classes of securities and
associated Z statistics are presented in Table 3. In addition, average abnormal returns and Z statistics are presented for simultaneous issues of debt and common stock
and debt and preferred stock.

This disaggregation of security type is based on the
regulatory definition of primary and total capital that was
used throughout the 1982-1986 period. All of the debt
issues analyzed meet the maturity requirement for inclusion in the definition of total capital. All holding company
debt legally is subordinated to deposits. Nonetheless, I
also examined separately debt that was explicitly called
subordinated from that not explicitly called subordinated.
No significant differences were found, however. 12
The results in Table 3 indicate that, on average, there are
negative abnormal returns associated with the issuance of
common stock and mandatory convertible debt (which
eventually will be converted into common stock). The
estimated magnitude of the announcement effect for common stock is 1.5 percent, a similar magnitude to that
found in the Wansley and Dhillon (1987) and Polonchek
et al. (1987) studies, both of which cover similar time
periods. Simultaneous issues of common stock and debt
also have significant negative announcement effects, as
might be expected due to the negative effect of the common
stock issuance. 13
Significant positive abnormal returns of 1.1 percent are
found for perpetual preferred stock, a result similar to that
of Wansley and Dhillon (1987), who find an abnormal
return of 0.8 percent, and Polonchek et al., who find an
abnormal return of 1.57 percent for non-multinational
BHCs during the 1982-1984 period.
These results are somewhatsurprising, since, in terms of

Economic Review / Winter 1989

risk characteristics, perpetual preferred is most like common stock. However, there are two important differences.
First, the market may have viewed preferred stock as
implicitly insured in light of the FDIC's resolution of the
Continental Illinois failure in 1984. (The FDIC implicitly
insured preferred stock holders as well as debt holders
since the BHC was never declared insolvent.) Thus, preferred stock would have risk characteristics more similar to
bank deposits than to common stock. Second, an issuance
of preferred stock may contain information about the

Federal Reserve Bank of San Francisco

ability of the organization to meet preferred stock dividends, which the market would view favorably.
I also find significant (at the 10 percent level) negative
prediction errors ( - 0.74 percent) for mandatory convertible debt. None of the other studies find such an effect, but,
except for Wall and Peterson (1988), neither do they
distinguish between mandatory convertible debt and convertible debt. Since convertible debt is usually convertible
at the issuer's option, it is much more like straight debt,
whereas mandatory convertible debt has risk characteris-

tics similar to those of common stock and should have
similar announcement effects. However, aside from their
selection of a different sample of events, it is unclear why
Wall and Peterson's results differ.
As in all of the studies reviewed, I do not find significant
abnormal returns associated with straight debt. This finding is similar to that for industrial and utility firms and thus
may reflect the low-risk nature of this security. Moreover,
the market may have regarded straight debt issued by
BHCs during this period as having a high probability of
being FDIC-insured following the Continental episode.
I also examined cumulative prediction errors for the 18day period between the estimation period and the (2-day)
announcement period and for the 18-day period after the
announcement period averaged over each type of security,
but none of the average cumulative prediction errors were
statistically significantly different from zero. This suggests

that the stock price announcement effects are permanent.
Moreover, the market model was estimated over two other
sample periods, one beginning 20 days after the announcement period (days 100-158) and another including both the
pre- and post-announcement period samples (days 1-60
plus days 100-158) to test the robustness of the results.
The results were remarkably similar for all three estimation
periods.
In sum, these results strongly suggest negative announcement effects for issues of common stock and securities with risk characteristics similar to common stock,
such as mandatory convertible debt. 14 In the next sections,
I test for possible differences in effects over time and
between groups to determine whether deposit insurance
effects are important and through what avenues capital
regulation may affect the size of the announcement effects.

Economic Review / Winter 1989

Differences over Time
Table 4 presents two-day average prediction errors for
the period prior to the new capital regulations, January 1,
1975 through November 30, 1981, and for the period after the institution of the regulations, December 1, 1981
through December 31, 1986. There is a striking decline in
the absolute size of the announcement effect associated
with common stock issuance from - 2.6 percent to - 0.79
percent, .which is statistically significant at the one percent
level. No other significant differences are found. Thus, it
appears that the institution of capital regulation did have a
major effect on the stock price effect associated with
common stock issuance.
Polonchek et al. also find an absolute decline in the
(negative) effect of common stock issuance, although it is
half as large and not statistically significant. These differences in results may be due to the more powerful statistical techniques and/or the longer sample period used in this
paper. My results for the 1982-1986 period, however,
differ in magnitude from those of Wall and Peterson, who
find negative statistically-significant effects for common

Federal Reserve Bank of San Francisco

stock issuance of -1.5 percent for this period, possibly
because of the somewhat different sample they employ.
The standard explanation for the apparent decline is that
increased capital regulation made equity offerings more
predictable andthus diminished their information content
especially compared to the information conveyed by offerings made during the pre-December 1981 period, when
theywere more likely to be voluntary. To test this explanation, I examine differences in announcement effects between the: group of BHCs voluntarily issuing capital and
those under regulatory pressure to do so. If the information
content argument were correct, BHCs under regulatory
pressure to boost capital would experience less negative
announcement effects associated with stock issuance after
the new regulations took effect than would the other group
ofBHCs.

Differences Between Groups Over Time
Although objective minimum capital regulations were
phased in over the 1981 to 1985 period, I would argue that
the 1985 standards were the ultimate goal even as early as

1981. The main reason the 1985 standards were not immediately imposed was to give institutions time to raise the
necessary capital to bring them into compliance. In keeping with this interpretation, this paper distinguishes those
banking organizations that would have met the 1985 primary capital requirements in 1981 from those that would
not have. (See Keeley (1988].) Throughout the paper 1refer
to the former as "capital sufficient" and the latter as
"capital deficient" banking organizations. As shown in
Keeley (1988), capital deficient banking organizations did
in fact increase capital both absolutely and relative to
capital sufficient organizations. 15
Table 5 presents separate estimates of the effects of
common stock issuance for capital deficient and sufficient
organizations both before and after the 1981 change in
capital regulation. I also examined the announcement
effects for each of the other types of securities issuance
analyzed in Table 4, but no significant differences between
the time periods or between capital sufficient and deficient
groups were found.
The results in Table 5 suggest that the stock price effects
for capital sufficient BHCs changed from -1.2 percent in
the pre-1981 period to positive 1.5 percent in the post-1981
period. This change is statistically significant. Moreover,
although capital deficient BHCs' estimated effect declined
in absolute value, the change was not statistically significant.
If increased capital regulation reduced the signal content of common stock issuance, one would expect the

announcement effects to be less negative during the post1981 period. While Table 5 does show such a pattern for
each group separately, the change is not statistically significant for the capital deficient organizations. Moreover,
simple signalling theory also would predict that capital
deficient BHCs' returns should be less negative than
capital sufficient BHCs' returns, which would be less
predictable and thus should contain more information.
These results thus cast doubt on this simple signalling
hypothesis since the pattern of results is opposite to that
which it would predict.
An alternative interpretation of these results is that
securities issuance diminishes the value of the deposit
insurance guarantee. The larger negative stock price effects for capital deficient banking organizations, especially
during the post-198l period, are consistent with the view
that the value of (underpriced) deposit insurance is capitalized in the share prices of capital deficient banking
organizations and that increases in their capital diminished
the value of that asset.
A second, but not mutually exclusive hypothesis is that
regulators have inside information which is revealed to
investors by the nature of a security issuance. 16 Below, I
explore these two hypotheses further.

Capital Structure Effects
If the results in Table 5 primarily reflect a diminution
of the value of deposit insurance, in theory, issues that
have greater proportional effects on the capital-to-asset

Economic Review / Winter 1989

ratio should have more negative abnormal returns. Consequently, I regress abnormal returns on the size of the issue,
measured by the percentage change in the capital-to-asset
ratio caused by the common stock issue. 17
The results of such regressions, estimated using generalized least squares with individual error variances calculatedusing Equation 5, are reported in Table 6. Separate
estimates are presented for capital deficient and sufficient
banking organizations for three time periods: the entire
sample period, 1975-1986; the period prior to the new
capital regulations, December 1, 1975 through November
30, 1981; and the period after the new regulations were
introduced, December 1,1981 through December 31, 1988.
Although the results of these regressions should be
viewed with caution because of the very small sample
sizes, they nevertheless do suggest a marked change in the
relationship between capital deficient organizations' abnormal returns and the percentage effect of the common
stock issuance on the market value capital-to-asset ratio. IS
During the early period before explicit capital guidelines
were in place, issues that had larger effects on the capitalto-asset ratio had less negative abnormal returns. This
suggests that issues during this period were voluntary, even
by banking organizations with low capital-to-asset ratios.
However, during the post-December 1981 period, the point
estimate suggests a negative relationship, although it is not
statistically significant. In theory, if the negative mean
abnormalreturns were due to a diminution of the value of
the deposit insurance guarantee, the relationship between
the size of the issue and abnormal returns should be
negative. Thus, these results are not inconsistent with this
hypothesis. However, given the small sample sizes and the
lack of statistical significance, neither do these results
provide strong support for this hypothesis.
The results for the capital sufficient banking organizations are more striking. They show, during the postDecember 1981 period, a statistically significant positive
relationship between abnormal returns and the size of the
issue. Thus, large issues (relative to capital) by organizations already meeting the capital requirements appear to be
taken by the market as positive signals. Since such issues
are voluntary,'? presumably they would not reflect a diminution in the value of the deposit insurance guarantee or an
implicit regulatory tax.
In sum, the results of these regressions provide some
support for the capital structure theory, which predicts that
issue size relative to capital is important and that stock
price effects should become more negative for capital
deficient organizations as the size of the issue increases.
They also suggest that the deadweight costs of common

Federal Reserve Bank of San Francisco

stock issuance for well-capitalized banking organizations
are small or nonexistent since, on average, stock price
announcement effects are not negative and even become
more positive as the relative size of the issue increases.

Inside Information
These results also are consistent with the second hypothesis that the type of securities issued conveys inside
information about earning prospects obtained by regulators during bank and bank holding company examinations.
Since a banking organization's balance sheet is available
to outside investors, the market can readily determine

whether the BHC is under regulatory pressure to increase
its capital ratio. However, the market does not necessarily
know the future prospects of the BHC or the method the
BHC will use to augment capital.
It seems likely that investors would look for information about a BHe's prospects in the type of securities it
issues. Capital deficient BHCs that issue common stock
may be viewed by investors as needing to do so because
they are under regulatory pressure not to issue securities
that require increased payouts from earnings, such as debt
or preferred stock. Thus, a common stock issuance by a
capital deficient BHC may be a signal of management and
regulator skepticism about the BHC's ability to generate
sufficient future earnings to meet the cash flow requirements of additional debt or preferred stock or to generate
cash flow sufficient to permit the accumulation of retained
earnings to meet the new capital requirements. On the
other hand, if regulators and bank management believe
that the banking organization's future earnings prospects
are very good, retained earnings rather than a security
issuance can be used to meet higher future capital require-

ments. Moreover, a voluntary issue of common stock by a
capital sufficient BHC would not provide a negative signal
and might even signal the availability of a positive net
present value project.
The positive effects of issue size on the abnormal returns
associated with securities issuance by capital sufficient
BHCs also might be explained by this hypothesis. Prior to
the institution of specific minimum capital guidelines,
market participants would have been unsure whether a
banking organization's common stock issuance was due to
regulatory pressure. Since there was some chance that it
was, there was a small mean negative announcement effect
even for capital sufficient organizations. However, afte
specific capital guidelines were introduced, market pat
ticipants could be confident that a common stock issue by a
capital sufficient BHC was not a signal that regulators
viewed the organization's earning prospects unfavorably.
As a result, in the post-1981 regulatory period, the estimated mean abnormal returns associated with capital
sufficient BHCs' common stock issuance were positive
and were positively related to the size of the issue.

V. Conclusions
The results of this paper yield some important conclusions regarding the stock price effects of BHCs' securities
issuance, especially securities issued by weakly-capitalized banks under regulatory pressure to boost capital.
These findings are particularly important in light of the
new risk-based capital requirements, which will require
many banking organizations to increase their capital-toasset ratios.
First, common stock issuance appears to have negative and statistically significant announcement effects for
weakly-capitalized banking organizations under regulatory pressure to raise capital. Moreover, the effects are
fairly large, implying a mean abnormal return of - 2
percent (which represents a dilution effect of about - 30
percent) for capital deficient banking organizations during
the post-1981 period. Thus, contrary to the implication of
some previous studies, one cannot be sanguine that the
more objective capital regulation in place since December
1981 has significantly reduced the announcement effects
associated with common stock issuance for those BHCs
under regulatory pressure to augment capital. However, no
evidence of negative announcement effects is found for
BHCs that are meeting or exceeding regulatory capital
guidelines.
Second, common equity (and debt that will be converted into common equity) might appear to be the most
costly form of capital from the banking organization's

standpoint since it has the largest negative announcement
effects. Straight subordinated debt and limited life preferred have no significant stock price effects, and perpetual preferred actually appears to have positive effects.
However, it is difficult to draw any strong policy conclusions from these results. One reason is that market participants may have viewed subordinated debt and preferred
stock as being at least partially implicitly insured throughout this period.P Another reason is that the estimated
announcement effects presumably result from optimizing
decisions at the banking organization level. Thus, if alternatives to common stock issuance were used instead, it is
unclear whether they would have lower costs. Finally,these
results may reflect a decrease in the risk exposure of the
deposit insurance fund (and a corresponding reduction of
the.capitalized value of the deposit insurance guarantee),
which was the objective of the capital regulations in the
first place.
Third, the data do not permit us to determine whether the
negative announcement effects associated with common
stock issuance simply reflect a negative signal about
institutions' values or whether they are the result of a
diminution of the capitalized value of the deposit insurance
guarantee. While the estimated announcement effects of
common stock issuance for capital deficient banking organizations appear to be negatively related to the relative
size of the issue, as the deposit insurance hypothesis

Economic Review / Winter1989

predicts, the relationship is not statistically significant.
Nonetheless, regulators may wish to pursue a policy of
requiring more capital since neither explanation for the
negative abnormal returns implies that there are social
costs associated with more stringent capital regulation and
more stringent capital regulation does reduce the risk
exposure of the deposit insurance system.
Finally, these results suggest that banking organizations
with weak capital positions will attempt to resist regulatory
pressure to issue common stock in order to meet capital

requirements because of the negative effects on the value of
their stock. However, since there is no evidence of negative
effects for strongly-capitalized BHCs, they may not be
reluctant to issue stock to finance new, positive net presentvalueprojects. 2 1 Moreover, the absence of negative
stock price announcement effects for strongly-capitalized
banking organizations suggests that the deadweight costs
associated with common stock issuance are small or nonexistent for such firms.

ENDNOTES
1. Deposit insurance can be viewed as a put option on the
bank's assets at a striking price equal to the promised
maturity value of the insured deposits (see Merton [1977]).
The value of the put increases as capital relative to assets
decreases or as asset risk increases since both factors
increase default risk.
2. Keeley (1988) finds that even though bank holding
companies partially circumvented the more stringent capital regulations promulgated in the early 1980s, they did
nevertheless boost capital-to-asset ratios in response to
the regulations. Thus, it appears that BHes do respond to
more stringent capital regulations.
3. .tn December 1981, minimum primary capital was set at
six percent of assets for banks and bank holding companies with assets less than $1 billion and five percent for
organizations with assets of $1 billion or more except for
"multinational" bank holding companies which were exempted. In June 1983, the five percent requirement was
extended to the multinationals. Finally, in June 1985, a
uniform 5.5 percent minimum primary capital-to-asset
ratio was set for all banking organizations regardless of
size.
4. Many financial executives argue that issuing shares at
a price below book value depresses the stock's price
because it represents a "dilution" of share value. While
such a stock issuance does decrease book value per
share, it should not depress the market value of the stock
as long as the proceeds from the stock issuance can be
invested in assets that are at least as profitable as the
firm's current assets.
5. In contrast, Scholes (1972), following a different line
of reasoning, argues that the demand for a stock is
downward sloping due to heterogeneous expectations.
Although it is difficult to reconcile heterogeneous expectations with market equilibrium (since people who think
the stock is undervalued should buy, thereby driving up
the stock's price, and vice versa) differential taxation
might explain heterogeneous demand tor a stock. Downward sloping demand, in turn, would cause price pressure when there is a new issue. This would explain why
only risky securities have negative stock price effects
associated with their issuance (since there would not be
heterogeneous expectations for riskless securities).

Federal Reserve Bank of San Francisco

6. As Furlong and Keeley (1987a, 1987b) show, the diminution in the value of the deposit insurance option associated with a given capital infusion is greatest for banking
organizations with the lowest capital ratios.
7. It is unclear why they included the year 1981 since the
first capital requirements did not go into effect until
December 1981.
8. There are two important limitations of the Wansley and
Dhillon study, First, they do not allow for potential changes
in abnormal returns due to the changed capital regulatory
regime beginning in 1981. Second, their announcement
period is the day of and the day after the announcement,
unlike the standard practice of using the day before
and the day of the announcement. Thus, they may have
underestimated the announcement effects, (I find that the
largest negative residual is on the day before the announcement. )
9. Abnormal returns are calculated using the mean returns method, a procedure Brown and Warner (1985)
show is not very powerful if the events are clustered in
calendar time,
10, They also find significant negative abnormal returns
associated with the announcement of dividend reductions
both before and after December 1981, which is consistent
with the negative abnormal returns associated with common stock issuance.
11. Moreover, several of these papers use statistical
techniques with low power, Dhillon and Wansley use an
unconventional event period, Isberg and Brown use a
one-day instead of two-day event period, and Polonchek,
Siovin, and Sushka use the mean adjusted return model
instead of the preferable market model and use an unconventional three-day event period. Several of the studies do
not adequately disaggregate different types of debt and
preferred stock securities (that is, convertible versus mandatory convertible debt, limited life versus perpetual preferred stock),
12. I also tested for the possibility that shelf-registered
debt would have different abnormal returns and found no
significant differences,
13. Although the point estimate of the effect of the announcement of a simultaneous common/debt issue is

larger than that for common alone, the difference is not
statistically significant.
14. I also found a positive announcement effect associated with perpetual preferred stock.
15. Moreover, the probability of security issuance increased for capital deficient organizations relative to capital sufficient organizations with statistically significant
increases for preferred stock and debt issuance. See
Keeley (1988b).
16. Another possible explanation for this pattern of returns is that the size of the offerings relative to the initial
value of the firms in the two groups differs systematically. I
tested for this by examining the relative dilution effects of
the two groups' common stock offerings. Dilution is defined as the ratio of the change in the aggregate equity
value of the outstanding shares (percent change in share
price, times share price, times number.of shares, divided
by 100) to the total dollar proceeds of the issue. A dilution
ratio of zero percent means that the announcement of a
new offering does not affect the share price of existing
shares, and a dilution ratio of -100 percent means that the
decline in existing share value equals the value of the new
capital raised by the issue. Dilution is interesting to examine because it could be that firms with the smallest abnormal returns also had very small dollar value issues and
thus large dilutions.
For all BHCs I found a mean dilution effect of -27 percent,
about the same as the -31 percent dilution effect found by
Asquith and Mullins (1986) for industrial firms. Thus, even
though the percentage stock price effect for bank holding
companies is much smaller than that found for industrial
firms, the dilution effect is about the same, presumably
because banking organizations' stock issues typically
raise far less funds in proportion to their pre-issue value
than do industrial firms.
More importantly, the pattern of dilution effects is basically
the same as the stock-price announcement effects. Capital deficient BHCs have more negative dilution effects

than capital sufficient BHCs and both groups show less
negative effects during the post-1981 period. Thus, systematic differences in issue size do not appear to explain
the pattern of abnormal returns across capital deficient
and sufficient organizations.
17..: This variable is equal to. the value of the issue divided
by the pre-issue market value of the firm's equity minus the
value of the issue divided by the pre-issue market value of
the firm's assets.
18. Two periods were pooled and a model was estimated
Which al.lowedthe intercept and the coefficient to differ in
the two periods. For capital deficient organizations, the
change in both the intercept and the coefficient was
statistically significant at the 5 percent level. For capital
sufficient banking organizations, neither parameter was
significantly different.
19. Keeley (1988c) argues that insured banks voluntarily
would issue capital in order to protect their valuable
charters, which would be forfeited in the event of bankruptcy.
20. As long as subordinated debt and preferred stock are
not implicitly insured, there is no apparent theoretical
reason to restrict their use as a type of banking capital.
They provide the same protection to the deposit insurance
fund as common equity and they may have lower costs.
See Furlong and Keeley (1987c).
21. These results are consistent with several empirical
studies (Marcus and Shaked [1984], Ronn and Verma
[1986], and Pennacchi [1987]) which find that for many
large banking organizations, the fair value of deposit
insurance appears to be less than its price. However,
since the value of deposit insurance need not be capitalized into the value of the banking organization and
instead may benefit bank depositors and/or borrowers,
one cannot conclude from these results that more capital
would not significantly reduce the risk exposure of the
deposit insurance system.

Economic Review / Winter 1989

REFERENCES
Asquith, Paul and David W. Mullins, Jr. "Equity Issues and
Offering Dilution," Journal of Financial Economics,
Vol. 15, No. 1/2, Jan.lFeb. 1986.
Brown, Stephen J. and Jerold B. Warner. "Using Daily
Stock Returns: The Case of Event Studies," Journal of
Financial Economics, Vol. 15, Jan.lFeb. 1986.
Dybvig, Philip H. and Jaime F. Zender. "Capital Structure
and Dividend Irrelevance with Asymmetric Information," Cowles Foundation Discussion Paper No. 878,
Vale University, July 1988.
Isberg, Steven C. and Donald M. Brown. "The Effect of
New Capital Issues on the Prices of Holding Company
Shares," Proceedings of a Conference on Bank Structure and Competition: Merging Commercial and Investment Banking, Federal Reserve Bank of Chicago,
1987.
Furlong, Frederick T. and Michael C. Keeley. "Bank Capital Regulation and Asset Risk," Economic Review,
Federal Reserve Bank of San Francisco, Spring,
1987a.
_ _ _ _ . "Does Bank Capital Regulation Affect Risk
Taking?," Working Paper 87-06, Federal Reserve Bank
of San Francisco, September, 1987b.
_ _ _ _ . "Subordinated Debt as Bank Capital," FRBSF
Weekly Letter, October 23, 1987c.
Irving Trust. Capital Securities Issued: Commercial Banking. Irving Trust, Corporate Financial Counseling Department, One Wall Street, New York, New York,
1976-1986.
Keeley, Michael C. "Bank Capital Regulation in the Early
1980s: Effective or Ineffective?," Economic Review,
Federal Reserve Bank of San Francisco, Winter
1988a.
"Bank Capital Regulation: Effective or Ineffective?," Proceedings of a Conference on Bank
Structure and Regulation, Federal Reserve Bank of
Chicago, 1988b.
_ _ _ _ . "Deposit Insurance, Risk and Market Power
in Banking," Working Paper 88-07, Federal Reserve
Bank of San Francisco, 1988c.
Marcus, Alan J. and Israel Shaked. "The Valuation of FDIC
Deposit Insurance Using Option-Pricing Estimates,"
The Journal of Money, Credit and Banking, November 1984, Part 1.

Federal Reserve Bank of San Francisco

Merton, Robert C. "An Analytic Derivation of the Cost
of Deposit Insurance Loan Guarantees," Journal of
Banking and Finance, June 1977.
Mikkelson, Wayne H. and M. Megan Partch. "Valuation
Effects of Security Offerings and the Issuance Process," Journal of Financial Economics, Vol. 15, No. 1/2,
Jan.lFeb. 1986.
Miller, Merton and Kevin Rock. "Dividend Policy Under
Asymmetric Information," Journal of Finance, Vol. 40,
September 1985.
Modigliani, Franco and Merton Miller. "The Cost of Capital, Corporation Finance and the Theory of Investment," American Economic Review, Vol. 53, 1958.
Myers, Stewart C. and Nicholas S. Majluf. "Corporate
Financing and Investment Decisions when Firms
Have Information that Investors Do Not Have," Journal
of Financial Economics, Vol. 13, 1984.
Pennacchi, George. "A Reexamination of the Over- (or
Under-) Pricing of Deposit Insurance," Journal of
Money, Credit and Banking, Vol. 19, No.3, August
1987.
Polonchek, John, Myron B. Siovin, and Marie E. Sushka.
"The Valuation Effects of Commercial Bank Securities Offerings: A Test of the Information Hypothesis,"
mimeo, 1987.
Ronn, Ehud and Avinansh K. Verma. "Pricing Risk-Adjusted Deposit Insurance: An Options-Based Model,"
The Journal of Finance, September 1986.
Scholes, Myron. "The Market for Securities: Substitution
Versus Price Pressure and the Effects of Information
on Share Prices," Journal of Business, Vol. 45, 1972.
Smith, Clifford W. "Investment Banking and the Capital
Acquisition Process," Journal of Financial Economics, Vol. 15, No. 1/2, Jan.lFeb. 1986.
Wall, Larry D. and Pamela P. Peterson. "Valuation Effects
of New Capital Issues by Large Bank Holding Companies," mimeo, Federal Reserve Bank of Atlanta,
1988.
Wansley, James W. and UpinderS. Dhillon. "Determinants
of Valuation Effects for Commercial Bank Security
Offerings," mimeo, July 1987.

Economic Review / Winter 1989

Commodity Prices as a Guide for Monetary Policy

Frederick T. Furlong
Research Officer, Federal Reserve Bank of San Francisco. Editorial committee members were Brian Motley
and Adrian Throop.

This paper evaluates the usefulness of a commodity
price index as an indicator variable for monetary policy
purposes. Commodity prices are found to be statistically
significant in explaining changes in monetary policy goal
variables and to improve somewhat out-of-sampleforecast
errors ofpolicy variables. However, commodity prices do
not by themselves fill the void left by the loss of Ml as an
intermediate target, nor do the findings support using a
commodity price index in place of M2 as a guide to
monetary policy.

Federal Reserve Bank of San Francisco

The U.S. shed the last vestige of the gold standard in
1971 with the official decision to suspend gold convertibility. This decision actually was just the final step in a
long transition, starting at the time of World War I, from a
commodity-based monetary system to a pure fiat monetary
system. Aside from the brief official consideration of returning to the gold standard in the early 1980s, little serious
thought has been given to abandoning the current fiat
system in favor of a commodity standard.'
Even though the current fiat system is firmly in place,
there are important proposals to link the monetary system
to commodity prices. These new proposals generally call
for using a commodity index to guide monetary policy,
either as an intermediate target or as a more general
indicator variable. For the most part, these proposals rely
on a basket of commodities, rather than on a single
commodity price such as that of gold. 2
To be used as a guide for monetary policy, a commodity
price index should have a reliable relationship with the
ultimate variables of concern to monetary policy, such as
inflation, economic growth, and unemployment. Movements in the commodity index also ought to precede those
of the ultimate policy variables to provide policymakers
with an early indication of the impact of their policies.
Accordingly, the purpose of this paper is to examine the
relationships between selected commodity price indexes
and policy variables to evaluate the usefulness of such
indexes in the conduct of monetary policy. The paper
begins with a discussion of the prerequisites for a commodity price index to serve as a guide to policy and
considers the criticisms raised concerning the use of such
an index. The empirical analysis then focuses on two
composite indexes, those of the Commodity Research
Bureau (CRB) and the Journal of Commerce (JOC). The
commodities included in those indexes are listed in the
Box in the Appendix. The objective of the analysis is to
determine whether changes in the commodity price indexes contain information useful to policy beyond that
contained in the intermediate targets like MI and M2.

I. Commodity Prices in a Fiat System
In a fiat monetary system, a commodity price index can
be used to guide monetary policy in one of two ways: as an
intermediate target or as an indicator variable. The use of a
commodity price index as an intermediate target is referred
to as a "price rule." Adherence to such a price rule means
that monetary policy generally would ease when the price
index for the selected basket of commodities was below the
predetermined target and tighten when the index was
above its target.
Any variable taking on such a vital role in the conduct of
policy, first and foremost, would have to lead reliably the
ultimate policy variables. That is, policymakers would
haveto be confident that movements in the price index were
signalling future changes in inflation and the performance
of the economy. But that alone is not enough. The index
also would have to be responsive to changes in monetary
policy. An intermediate target that was a reliable indicator
of the policy variables but did not respond relatively
quickly and predictably to the actions of policymakers
would be of little use.
The one intermediate target that satisfactorily exhibited
these characteristics for many years was Ml. Until 1985,
Ml was the primary intermediate target for the Federal
Reserve. Since then, evidence of distortions in the relationship between Ml and economic activity and inflation
prompted the Federal Reserve to drop Ml as an intermediate target. Targets still are set for the broader monetary
aggregates, M2 and M3, but these aggregates generally
are not as reliable as Ml once was.
For this reason, a number of economists have looked at
commodity indexes as a possible substitute for MI. However, these studies generally have rejected the use of a
commodity price index as an intermediate target. Hafer
(1983), Gamer (1985), and Defina (1988) reject a "price
rule" in part because commodity prices cannot be expected
to react to open market operations with a sufficiently
short and reliable time lag. Moreover, empirical analysis presented in the Appendix suggests that commodity

price indexes are affected predominantly by non-monetary
shocks. A number of studies also question whether available statistical evidence concerning the relationships between commodity prices and monetary policy objectives is
adequate to justify using a commodity price index as the
central guide to policy. 3
However, still open is the question whether a commodity
price index would be useful in a more limited role as an
indicator variable to guide monetary policy. In the absence
of a reliable intermediate target, indicator variables can be
useful in formulating monetary policy. Such variables can
be used to help forecast movements in the ultimate policy
variables.
Currently, this is done systematically through the
macro-econometric models that are used at the Board of
Governors of the Federal Reserve System and at some of
the Reserve Banks (see Judd [1988]). These models are
used to project the path for monetary policy that leads to
the desired values of the ultimate variables. Indicator
variables can be used in this context to improve forecasts of
the ultimate policy variables, and, thus, to help determine
the appropriate course for monetary policy.
Movements in the commodity price index would not in
themselves prescribe a particular path for monetary policy,
but would influence policy only to the extent that they
affect policymakers' views on the outlook for the ultimate
policy objectives. With this more limited role, the criteria
for whether a commodity index could be of use in formulating monetary policy can be less stringent than those
applied to an intermediate target. In general, the usefulness of an indicator variable can be measured by its
marginal contribution to reducing the error in predicting
policy variables. 4
In the remainder of the paper, then, the discussion
focuses on the extent to which different commodity price
indexes convey information about the future values of two
key policy variables, inflation and unemployment.

II. The Usefulness of Commodity Prices
The Supporting Case
One advantage of commodity prices as indicator variables is that they are reported on a more frequent and
timely basis than are the data on the policy variables
themselves. Data on the prices of many commodities are
reported daily with no more than a one day lag. Data on the
CPI and employment, on the other hand, are available

monthly with about a one month lag. Other measures of
economic activity and inflation, such as GNP and the GNP
deflator, are available only quarterly.
More fundamentally, some argue that commodity prices
ought to lead movements in the ultimate policy variables
because they are more flexible and adjust more quickly

Economic Review / Winter 1989

current price of storable commodities, particularly metals,
will rise or fall to reflect higher or lower inflation expectations. However, since nominal interest rates also should
reflect inflation expectations and nominal interest rates are
a principal carrying cost for commodities, movements in
commodities prices may not be a perfect yardstick. 7

than do overall prices and labor markets. Their greater
flexibility and speedier adjustment stem from the depth
and. sophistication of international spot markets. Moreover; because commodities generally are basic inputs that
enter at the beginning of the production process, it is
reasonable to expect shifts in aggregate demand to be
reflected in the prices of commodities before those of
finishedgoods.5
Supply shocks are a third factor that makes commodity
price indexes potentially attractive as indicator variables.
Examples ofrecent supply shocks include the reduction in
agricultural production due to the drought in the U.S. and
the increased supply of oil associated with a breakdown in
the cohesion of OPEC. Such shocks affect aggregate
supply and tend to be inversely related to output and
employment. The effect on output, holding money constant, in tum affects the overall level of prices. Movements
in a commodity price index containing the commodities
subject to a shock would be expected to precede changes in
the general price level. 6
A fourth reason sometimes given for using commodity
prices as an indicator variable, at least for overall inflation,
is that the movement in the prices of certain commodities
may provide information concerning market participants'
inflation expectations. According to this argument, the

Criticisms and Qualifications
The first argument in favor of considering a commodity
price index as an indicator variable-that is, the ready
availability of data-is not very compelling since data on
other potentially useful economic variables are also readily
available. For example, data on interest rates and exchange
rates also could provide information to policymakers, and
are available on as frequent and as timely a basis as are
commodity prices. 8
Second, the features of commodity price indexes that
make them potentially beneficial for monetary policy
purpose actually may limit their usefulness. For example,
the flexibility and rapid adjustment of commodity prices
may increase the "noise-to-information" content of a
commodity price index. Indeed, commodity prices tend to
be quite volatile compared with prices generally. This can
be seen in Chart I, which traces the annualized monthly

Chart 1
CPI and Commodity Indexes
(Growth rates, monthly data, annualized)
Percent

80
60

Joe

40
20
O..fllll'lJl~

-20

-40
-60

+-"-,.-,.....,.....,.-,....,..,.....,-,-......-r-.-.--r---r-1r-r-.......-r-,.-,.....,.....,.-,....,..,.....,....,
60

Percent

200
160
120

80
40

o ~~~,., w~'w!
-40
-80
- 12 0

+-r-,.......,.-,-,....,..,.....,-,-......-r-.-.--r---r-1r-r-.......-r-,,.....,.....,.-.--r-..,....,....,
60

Federal Reserve Bank of San Francisco

growth rates for the CPI relative to those for the two widely
cited composite indexes from the CRB and the JOC. Over
the period covered by the chart, growth rates for the CRB
and the JOC indexes are four to seven times more volatile
than the growth rate for the CPl.
Moreover, Table 1 shows that high volatility is not
unique to the two indexes examined in this paper. Over the
past eight years, the annualized growth rates of other
commodity price indexes were 3 1/ 2 to 20 times more volatile
than was the growth in the CPI. The commodity price
indexes were also much more volatile than were either Ml
orM2.
This volatility makes it difficult to discern from shortrun movements in commodity prices the implications for
overall inflation. Indeed, as shown in the last column of
Table 1, simple correlations for monthly data on CPI
inflation and changes in commodity prices are quite low.
Thus, month-to-month changes in commodity prices provide little information about overall inflation. On the other
hand, short-run movements in the monetary aggregates do
not provide much information, either. The correlations for
CPI inflation and the month-to-month changes in Ml and
M2 are only slightly higher than those for the commodity
price indexes, and those for the monetary aggregates have
the wrong sign.
The more critical question is whether movements in
commodity prices over longer periods precede movements
in overall prices in a reliable and predictable manner. The

Economic Review / Winter 1989

variable. Not only are the general swings in growth rates
for commodity prices indexes more pronounced, they are
more frequently interrupted by relatively sharp reversals.
Had these indexes been used to guide policy at the time,
such reversals would have given false signals regarding
overall inflation. The signals are false in the sense that they
would have been assumed to represent true turning points
to contemporaneous observers.
The last observation is that the rise and fall in CPI
inflation relative to the rise and fall in the growth rates for
the composite commodity price indexes was much larger
in the 1980s than in the 1970s. This by itself does not
necessarily mean that the relationship between the CPI and
the commodity price indexes is unstable; the difference in
the magnitudes of the swings in CPI inflation may have
been due to the influence of other variables. However, it
does suggest that policy makers should not rely on a simple
relationship between a commodity price index and inflation.?

panels in Chart 2, which plot 12-month moving average
growth rates, provide some perspective on this question.
From the chart, a number of observations can be made. In
support of the usefulness of commodity indexes, movements in the commodity indexes did tend to precede
movements in overall inflation. Peaks and troughs in the
moving average of CPI inflation were preceded by turning
points in both of the commodity indexes.
However, the usefulness of the commodity indexes is
limited for several reasons. To start with, the number of
months by which changes in the commodity price indexes
preceded turning points in CPI inflation varied unpredictably over the 28 years covered in the chart. Table 2 shows
that the range for each of the two indexes is wide. The
range for the JOC index, at three to 22 months, is only
slightly wider than is the range for the CRB index.
Another weakness of the commodity price indexes, as
Chart 2 shows, is that even when the series are smoothed,
growth rates for the commodity price indexes still are more

Chart 2
CPI and Commodity Indexes
(Growth rates. 12 month moving averages)
Percent

Percent

CPI ...

-10

- 2 0 +--r-,-,...,-...,--,-,-,...,-,-,...,-...,--,-,-,--,-,-,....,....,--,-,-.,.-,-,-,....,.-+
60

Percent

CPI ...

-10

o
60

Federal Reserve Bank of San Francisco

III. Empirical Evidence on Commodity Prices
In this section, the potential contributions of the CRB
and the JOC indexes as indicator variables for monetary
policy are examined empirically. The analysis employs
Vector Autoregressions (VARs). The basic model comprises four equations. The dependent variables are a monetary aggregate (either MI or M2), a commodity price
index, and two policy variables. One policy variable is
represented by the CPI. The other policy variable, NUR,
measures the strength of economic activity relative to
potential. NUR is the difference between the actual civilian unemployment rate and the Congressional Budget
Office's measure of full employment unemployment.

With the exception of NUR, the variables in the VARs
are log-first differences. In the VARs, the explanatory
variables in the equations are lagged values of all the
dependent variables in the system and a constant. Separate
lag lengths were selected for each variable in each of the
equations to minimize the one-period-ahead prediction
errors, allowing for a maximum lag of eight quarters for
each variable.
The analysis is directed first at whether the CRB index or
the JOC index might have been useful for monetary policy
purposes had either index been used in conjunction with
Ml. This analysis uses quarterly data for the period cover-

Economic Review / Winter 1989

ing the early 1960s to the early 1980s. This is a period for
which M1 was a reasonably good indicator of monetary
policy. As Judd and Trehan (1987) show, the usefulness of
M1 as a guide to policy deteriorated in the early 1980s as
that aggregate became highly interest sensitive and subject
to greater portfolio substitution. 10
The discussion then turns to the performance of the CRB
index and the JOC index when they are used with M2. For
the most part, the sample period used for the systems that
include M2 covers the early 1960s to the end of 1987.
In the VARs, dummy variables are included in the CPI
(inflation) equations to control for the effects of the imposition and subsequent lifting of wage and price controls in

Federal Reserve Bank of San Francisco

the early 1970s. Dummy variables also are included in the
monetary aggregate equations to control for the effects of
Credit Controls in 1980.

Ml and Commodity Price Indexes
Tables 3-5 present evidence on the statistical and
economic importance of the commodity price indexes in
predicting. inflation and NUR from the mid-1960s to the
early 1980s. Table 3 reports the results from three different
YARrnodels used to explain changes in the CPI: one
\Vith(}ut acommodity price index, one that includes JOC,
and one that includes CRB. Table 3 shows that for the
period considered, the JOC index did better than the CRB

index in predicting inflation. The F-statistics on the lagged
values of the JOC index are statistically significant, but not
on those of the CRB index.
The variance decompositions in the bottom portion of
Table 3 also favor the JOC index. Variance decomposition
is used to assess the relative importance of the variables in
"explaining" movements in the dependent variable. This
technique permits us to decompose the variation in the
forecast errors of the equations into proportions associated
with "shocks" to the various explanatory variables in the
system. (Shocks are defined as movements in the explanatory variables that are not predicted by the system of
equations.) The higher is the proportion of the error
variance that is attributed to a particular variable, the
greater is that variable's influence on inflation. 11 The
variance decomposition in Table 3 shows that shocks to the
JOC index account for about twice as much of the variance
in the forecast error as does the CRB index. The variances
are for the errors in forecasting the level of CPI.
The variance decompositions in Table 3 also indicate
that the percent of the variance of the forecast error
attributable to lagged values of the policy variables drops
noticeably when the commodity price indexes are included
in the model. This suggests that the commodity price
indexes are affected by non-Ml shocks that also affect the
policy variables. But it does not necessarily mean that the
shocks affect the commodity price indexes sooner than the
policy variables: when the commodity price indexes are
ordered last, the percent of the variance of the policy
variable forecast errors attributed to the commodity price
indexes becomes quite small. However, since the information on the commodity prices becomes available before
that on the policy variables, there may have been at least a
small advantage to considering commodity prices along
with MI in the past. Such an advantage does not argue in
favor of placing much weight on the commodity price
indexes, though.
Perhaps more important than the variance decomposition for evaluating the role of the commodity price indexes
as indicator variables is the extent to which they reduce the
errors in predicting the CPI. The upper portion of Table 5
presents the standard errors of the forecasts for CPI. In
contrast to the picture presented by the variance decompositions, the standard errors for the equation with the JOC
index included are not appreciably lower than are those for
the equation with the CRB index. In addition, neither
commodity index appreciably reduces the standard errors
compared with the system with only MI and the policy
variables.
The results relating to NUR are slightly more favorable

to the inclusion of a commodity price index, if only
because MI itself performs rather poorly. In the top panel
of Table 4, the lagged values of the JOC index are
significant, but not those for MI and the CRB index. From
the variance decompositions, the JOC index is somewhat
more important in predicting NUR than is MI over the
short to intermediate term. By far the most important
variable is NUR itself. Taken together, the lagged values of
the policy variables are much more important than those of
either MI or the commodity indexes in explaining NUR.
Still, the inclusion of the JOC index does reduce somewhat
the standard deviation of the forecast errors for NUR over
the intermediate term, as shown in Table 5. Once again,
these results point to a possible role for commodity prices,
but not a particularly prominent one.

Economic Review / Winter 1989

M2 and Commodity Price Indexes
Distortions to Ml in the 1980s induced the Federal
Reserve to cease targeting that aggregate. As noted earlier,
the Federal Reserve continues to set targets for M2. Thus,
it would be helpful to know whether a commodity price
index might augment the information contained in M2.
Indeed, when considered in conjunction with M2, a
commodity price index may be more important on the
margin since M2 is not as good an intermediate target
variable as Ml once was. As shown in Table 5, for the
sa.mple period ending 1982:3, the standard errors of the
forecasts from a VAR that includes M2 and the policy

Federal Reserve Bank of San Francisco

variables are larger than those from the model with MI.
Over the longer period ending 1987:4, the model with M2
performs somewhat better relative to MI. However, that is
mainly because of the deterioration in the relationship
betweenMland the policy variables.
The results from the systems with M2 in Tables 6-8 are
a-bit more favorable to the CRB index than those from the
systems with Ml. From Table 6, the lagged values of the
CRBindex in the CPI equation are statistically significant.
The F-statistic for the lagged values of the JOC index, on
the other hand, is relatively low. Nevertheless, from the
variance decompositions in Table 6, it is clear that the JOC

and CRB indexes are about equally important in predicting
the level of CPl. Both indexes are more important than M2
up to eight quarters ahead, but less important over a longer
horizon.
The statistics on the CPI prediction errors in Table 8 for
the systems that include the JOC and the CRB index are
not much different, and do not provide a basis for choosing
one index over the other. Moreover, a comparison of the
prediction errors for the CPI from the system that does not
include commodity prices with the errors from systems
that do include the JOC or the CRB reveals that the
inclusion of either commodity price index would mean
only modest improvement in the near-term forecast. In the

longer term, the standard errors are somewhat larger for the
systems that include commodity prices. The forecast errors
are uniformly larger for the systems that include a commodity price index but not M2.
On balance, the inclusion of the commodity price indexes does not improve inflation predictions very much.
Once again, because the commodity price indexes apparently are affected by non-M2 (as well as non-Ml) shocks
that also affect policy variables, the principal advantage of
incorporating a commodity price index appears to be that
the data for commodity prices are available sooner than
those for the policy variables.
The results from the NUR equations including M2 and

Economic Review / Winter 1989

the commodity indexes parallel those derived earlier with
MI. The one exception is that the lagged values of M2 are
significant, as shown in Table 7. Otherwise, the commodity price indexes are significant but tend to account for
only a small proportion of the variance in the forecast error

Federal Reserve Bank of San Francisco

for NUR. The policy variables are far more important than
M2 or the commodity indexes. And, from Table 8, we see
that standard errors tend to be slightly lower with the JOC
index and slightly higher with the CRB index, compared
with the model that has only M2 and the policy variables. 12

IV. Problems with Stability
A question concerning the stability of the relationships
between the commodity price indexes and the policy
variables was raised in connection with the discussion of
Chart 2. This issue can be examined using the VARsfrom
the previous section. From the VARs, it is possible to
derive the reactions of the policy variables to shocks to the
commodity price indexes. The relationship between the
policy variables and the indexes would be considered
stable if thereactions, or impulse responses, of the policy
variables are similar when the models are estimated over
different time periods.
To test for stability, then, the VAR models with M2, a
commodity index (JOC or CRB), and the two policy

variables were estimated for the periods 1965:1 to 1975:4
and 1976:1 to 1987:4. Chart 3 plots the responses of the
CPI, expressed as a percent of the initial' level of CPI, to
one standard deviation shocks to the growth rates of the
JOC and the CRB indexes. Both panels point to instability.
The response of the CPI is more pronounced when the
model is estimated over the second period. The differences
in the responses tend to widen as the number of quarters
after the shock increases.
The responses of NUR to shocks to the commodity price
indexes also show instability. In Chart 4, the response of
NUR to a shock to JOC 16 quarters 1ateris about the same
in both time periods. However, for shocks to both the JOC

Chart 3
Impulse Response Functions for CPI
(response of CPI as a percent of the Initial level of CPU

Percentage
Point

3.5
Shock to JOC Index

3.0
2.5
2.0

1976-1987

1.5

Quarters after the shock

Percentage
Point

3.0
Shock to CRB Index

2.5
2.0
1.5

1976-1987

1.0
0.5
1965-1975

0.0
2

Quarters after the shock

Economic Review / Winter 1989

and CRB indexes, the paths of the interim responses differ
for the two time periods.
Given that the relationships between changes in the
commodity price indexes and the policy variables are
subject to structural shifts, the earlier VAR results could
understate the statistical significance of commodity prices
and misrepresent their economic importance. It is possible
that estimating the models over separate time periods
would control for structural shifts and would result in
stronger relationships between the commodity price indexes and the policy variables in each period. However,
better in-sample fits for the two subperiods would not

necessarily tell us whether those relationships provide
useful guidance for monetary policy in the future because
they do not eliminate problems caused by subsequent
structural shifts.
In an effort to gauge how instability might affect the
usefulness of the commodity price indexes for policy
purposes, I generated a series of out-of-sample forecasts
using the VARs identified in Table 8. The forecasts were
for.four and eight quarters ahead. The sample period for
the first set of forecasts ends on 1980:4. The first fourqUarter ahead forecast, then, is for 1981:4 and the first
eight-quarter ahead forecast is for 1982:4. The estimation

Chart 4
Impulse Response Functions for NUR
Percentage
Point

2.1

2.0

Shock to JOC Index

1.9
1.8
1.7
1976-1987

1.6

1.5
1.4
1.3
1. 2 +--,----r--,-,.--.,.--,..----r--,-,.--..,.--,..----r----,;--,..--..,.--,
2

Quarters after the shock

Percentage
Point

2.1
Shock to CRB Index

2.0
1.9

1968-1975

1.8
1.7
1.6

1976-1987

1.5
2

Quarters after the shock

Federal Reserve Bank of San Francisco

period is extended one quarter each time for successive
forecasts, Thelast forecast is for 1988:2, The mean forecast
errors and the absolute valuesofthemean forecast errors
from this exercise are reported in Table 9,
11l.ebenchmark •for gauging tljecontributiOll of the
commodity price. indexes is thethree variable VAR with
M2, CPI,andNUR, In setting this benchmark,itis
recognizedthatthe relationship betweenM2and the policy
variableslikely also shiftedto someextehtin the 1980s,13
Thus, the three variable VARwithM2 does not necessarily
give the best (lowest predictiol.lerror}forecast ofthe pol~
icyvariables-c-Cl'I: inflation and NUR, Nonetheless,' this
VAR is the appropriate benchmark since the Federal
Reserve still sets targets for M2 and the. aggregate still
affects policy considerations,
The first observation is that, among the three variable
systems, the models with the commodity price indexes

tend to perform the worst in predictingCPLThe out-ofsample forecasts support the earlier conclusion that the
commodity priceindexes should not be used to replace M2
asa target variable,
Second,ctespite the problems with structural shifts
raised earlier, including the composite commodity price
indexes along with M2does tend to improve the prediction
errors for CPI eight quarters ahead, However,judging from
the. mean •absolute errors, sizable. mistakes-remain in
predicting·.C.PI,
Regarding NUR, itisevidenLin Table 9 thatincluding
the commodity price indexes does not improve the out-ofsample forecasts for NUR, and in most cases, the price
indexes make the forecasts worse, The three variable
model with M2 and the policy objective variables generally does the best.

V. Conclusion
The evidence on commodity price indexes suggests that
these series would not fill the void left by MI when it was
dropped as an intermediate target. Nor is there a case for
using these commodity price indexes in place of M2, even
though the performance of M2 falls short of that observed
for MI when that aggregate was the primary intermediate
target. And certainly, neither the in-sample nor the out-ofsample evidence supports according any special status to
commodity prices,

The findings in this paper do provide some support for
considering one of the two composite commodity indexes
along with other information variables, One advantage is
the more timely availability of commodity price data
relative to those on overall prices, Moreover, despite some
concerns about stability, the inclusion of the JOC or the
CRB index tended to improve the near-to-intermediateterm out-of-sample forecasts of the CPI, though not for the
forecasts of unemployment.

Economic Review / Winter 1989

APPENDIX
Commodity Prices and Money
There is evidence that the prices of at least some
commodities do respond to monetary developments. FrankelandHardouvelis (1985) find a positive and statistically
significant response of the prices of several commodities to
surprises in weekly money supply announcements. (See
also Kitchum and Denbaly [1982].) The adjustments occur
\Vithin.the •trading day following .the money supply announcement.
Frankel and Hardouvelis also find, however, that money
supply developments account for only a small fraction of
the variation in commodity prices. Il.l.the short run, movements incommodity prices appear to be dominated by
developments affecting their relative prices rather than by
more. general macroeconomic developments, including
monetary policy.
To measure the relative importance of monetary influences on commodity prices, I used a system of VARs that
includes a commodity index, Ml, the CPI, and a measure
of slack in the labor market, NUR, which is defined in the
text. The data are quarterly and cover the mid-1960s to the
early 1980s. With the exception of NUR, all the variables
are log-first differences.
As stated in the text, separate lag lengths were selected
for each variable in each of the four equations of the models
to minimize the one period ahead predictionerror, allowing for a maximum lag of eight quarters for each variable.
Under this procedure, Ml enters the commodity price
equations with a one-quarter lag and an eight -quarter lag
in the CPI equation. This finding is consistent with the
argument that commodity prices adjust more quickly to
monetary developments than do overall prices.
The results from estimating the two models, one with
the. CRB index and one with the JOC index, are presented
in the Table. In the commodity price index equations for
both models, the lagged values of the changes in the
commodity indexes themselves are highly significant,
while CPI inflation is marginallysignificant, In the model
with the JOC index, Ml is statistically significant in
explaining the commodity price index, but M1 is not
significant in the equation for the CRB index. Experimenting with longer lags did not alter these results.
One possible explanation for the insignificant coefficient on Ml in the CRB equation is that the CRB index
reacts so quickly to Ml that the lagged values of the
aggregate in the VARs do not provide additional explanatory power. However, rapid adjustment to changes in Ml
should imply that lagged values of CRB beyond one

Federal Reserve Bank of San Francisco

quarter do not contain much information either. Yet the
coefficients on both the two- and three-quarter lags for the
CRB index are statistically significant and of the same
magnitude as the coefficient on CRB lagged one quarter.
The variance decompositions presented in the bottom
panel of the Table indicate that shocks to Ml accounted for
a relatively small portion of variance in the forecast errors
for levels of the CRB and the JOC indexes. In contrast, the

commodity indexes themselves account for an extremely
high proportion of the variance in the forecast error. This
suggests that they could be treated as more or less exogenous with respect to the other variables in the system.
Thus, it appears that both indexes are subject to nonmonetary shocks primarily and it is not likely that either
commodity price index can be used as a substitute for an
aggregate like MI.

Economic Review / Winter 1989

ENDNOTES
1. The Presidential Gold Commission, established in
June 1981, was charged with researching the virtues of
reinstating the gold standard. The Commission's report in
March 1982 rejected the idea of adopting a formal monetary role for gold. (See Cooper [1982].)
2. Two recent proposals for incorporating a composite
commodity index into the conduct of monetary policy
were made by then Treasury Secretary James Baker III
(Wall StreetJournal [1987]) and Governor Wayne Angell of
the Board of Governors of the Federal Reserve System
(Angell [1987]).
3. Other empirical evidence on the relationships of commodity prices and possible policy variables is reported in
Yeats (1973), Neftci (1979), and Melton and Smith (1987).
4. The extent to which a commodity price index is responsive to monetary policy is not irrelevant, however.
5. Frankel (1986) shows commodity prices can be expected to overshoot in response to monetary shocks. This
result stems from the effects of monetary shocks to real
interest rates, which in turn is related to the sluggish
adjustment of the general level of prices.
Another complication is that real shocks to aggregate
demand also should affect the real rate of interest, and
real interest rates affect the price of durable commodities.
A positive shock that raises the real rate of interest would
tend to reduce the price of durable (storable) commodities, but not necessarily the prices of goods and services
more generally.
6. Other studies have found that oil prices do help explain
inflation. For example, Throop (1988) includes the change
in the real price of oil in an augmented Phillips Curve
equation in the structural forecasting model of the Federal
Reserve Bank of San Francisco.
7. It is true that an expected change in the relative price of
a commodity can affect current decisions to hold inventories and thereby affect current spot prices. It is also the
case that prices of commodities can be expected to rise
as part of an increase in overall prices. That is one reason
many durable commodities such as gold have been used
as inflation hedges (see Bird [1984]), particularly when tax
rates on capital gains and ordinary income differ. Nevertheless, because nominal interest rates also tend to rise,
expectations of a general rise in prices will not necessarily
mean higher current commodity prices.

Federal Reserve Bank of San Francisco

8. For an analysis of exchange rates and inflation, see
Kahn (1987).
9. From a policy perspective, it is useful to know whether
commodity prices respond more to monetary or" nonmonetary shocks. If monetary influences are moreimportant, then a commodity price index would be a useful
guide to policy even though these indexes exhibit great
volatility. On the other hand, if nonmonetary shocks dominate, it would be difficult to determine whether movements
ina commodity price index were signalling that lTloQetary
policy was off course.
This distinction is important since the loss of M1 as an
intermediate target makes the need for reliable indicator
variables more pressing. In this regard, the contribution of
a commodity price index would be stronger if it were able
to capture information formerly captured by M1. As discussed in the Appendix, this does appear to be the case.
10. These changes in the behavior of M1 are attributed
to deposit deregulation, particularly the introduction of
money market deposit accounts and interest-bearing
NOW accounts.
11. The variance decompositions were derived by ordering the variables as indicated in Table 3. The order in
which the variables are placed can affect the results of the
variance decomposition. The ordering chosen here permits us to develop an upper bound estimate of the impact
of M1.
12. In addition to M2 and the commodity indexes, there
are a number of other variables that policymakers might
consider as guides to monetary policy. Two possibilities
are interest rates and exchange rates. However, the inclusion of the one-year Treasury note rate and the trade
weighted exchange value of the dollar does not weaken
materially the case for limited reliance on the commodity
price indexes.
13. Indeed, impulse responses for the policy variables to
shocks to M2 show that these responses for both the
measure for inflation and for unemployment were different
for the VARs estimated over the two subperiods used for
Charts 3 and 4.

REFERENCES
Angell, Wayne D. "ACommodity Price Guide to Monetary
Targeting," Speech before the Lehram Institute, December 10, 1987.
Bird,P.J.WN .•• "Commodities as a Hedge Against Inflation,"Applied Economics.,.1984,·.16.
Cooper, Richard N. "The Gold Standard: Historical Facts
and Prospects," Brooking Papers on Economic Activity,No.1,1982.
Defina, Robert H .."Commodity Prices: Useful Intermediate Targets for Monetary. Policy?" Business. Review,
Federal Reserve Bank of Philadelphia, May/June
1988.
Frankel,. Jeffrey A •"Expectations and Commodity Price
Dynamics: The Overshooting Model, "American Journal of Agricultural £conomics, May 1986.
Frankel, Jeffrey A and GikasAHardouvel.is. "Commodity
Prices, Money Surprises and Fed Credibility," Journal
of Money Credit and Banking, November 1985.
Garner, C. Alan. "Commodity Prices and Monetary Policy
Reform," Economic Review, Federal Reserve Bank of
Kansas City, February 1985.
Hafer, R.W. "Monetary Policy and the Price Rule: The
Newest Odd Couple," Review, Federal Reserve Bank
of S1. Louis, February 1983.
Judd, John P. "Looking Forward," Weekly Letter, Federal
Reserve Bank of San Francisco, July 8, 1988.

Judd, John P. and Bharat Trehan. "Portfolio Substitution
and the Reliability of M1, M2, and M3 as Monetary
Policy Indicators," Economic Review, Federal Reserve<BankofSanFrancisco, Summer 19.87.
Kahn, George A. "Dollar Depreciation and Inflation,"
Economic Review, Federal Reserve Bank of Kansas
City, November 1987.
Kitchen, <John and Mark Denbaly. "Commodity Prices,
Money Surprises, and Fed Credibility: A Comment,"
JournalofMoney Credit and Banking, May 1987.
Melton, William C. and Bruce Smith. "Do Commodity
Prices Presage Inflation?," Economic Perspective,
IDS Financial Services lnc., December 1987.
Neftci, Salih N."Leading-Lag Relations, Exogeneity and
Prediction of Economic Time Series,"Econometrica,
January 1979.
Throop, Adrian. "A Macroeconomic Model ofthe U.S.
Economy," Working Paper, No. 88-06, Federal Reserve Bank of San Francisco, September 1988.
Wall Street Journal. "Baker Suggests Role for Gold in
Setting World Economic Policy," October 1,1987.
Yeats, AJ. "An Evaluation of the Predictive Ability of the
FRB Sensitive Price Index," Journal of the American
Statistical Association, December 1973.

Forecasting Growth in Current Quarter Real GNP

Bharat Trehan
Economist, Federal Reserve Bank of San Francisco.
Early work on this project was done by Rose McElhattan.
The author wishes to thank Steve Morus and especially
Sherry Brennan for extremely helpful research assistance.
Editorial committee members were Brian Motley and
Reuven Glick.

This paper presents a simple model for obtaining estimates of current quarter real GNP growth using data on
series that are available on a monthly basis. The variables
used to "forecast" GNP growth are industrial production,
real retail sales, and nonfarm payroll employment. The
model'sforecasts compare well with the Blue Chip consensus forecast and contain information about final GNP
beyond what is contained in the advance GNP estimates.

Federal Reserve Bank of San Francisco

Policy actions taken today rarely have an immediate
impact on the economy, and several quarters may elapse
before the effects of these actions begin to show up.
Consequently, policymakers must rely on forecasts of
future economic activity to formulate current policy. The
task of forecasting the course of the economy is complicated by the fact that the relevant data on current activity
are available only with a delay.Thus, an important first step
in this process is to obtain reliable estimates of current
activity. Such information should enable policymakers to
take more timely action by responding to emerging trends.
This paper presents a method of obtaining "forecasts"
of current quarter real GNP growth early in the quarter, in
order to improve upon forecasts of output growth obtained
from econometric models that are estimated using quarterly data only. The method presented here is a statistical
one; it involves forecasting current quarter output using a
small number of variables. It is thus to be contrasted to
techniques that require knowledge of the contemporaneous
values of a large number of series constituting the various
components of GNP. The hope is that an inexpensive
technique that does not require keeping track of a large
number of variables will provide a reasonable estimate of
current quarter output.
The objective of obtaining reliable estimates of GNP
early in the quarter effectively determines the nature of the
exercise carried out here. First, the data series used to
predict GNP must be available on a more frequent basis
than the quarterly GNP data themselves. Fortunately, there
are many monthly data series that, ostensibly at least,
should provide some indication of emerging trends in
economic activity. Second, these monthly series should be
available relatively soon after the end of the month they
cover. Obviously, series that are published with a lag of
several months are not useful for our purposes. A number
of monthly series meet this requirement as well. Finally,
these series themselves should be easy to forecast (over
horizons of one to three months), since we would like to
predict current quarter GNP even before data on all three
months of the quarter are received. Series that can be
forecast reasonably accurately will lead to better estimates
of current quarter output early in the quarter.

From these criteria we were able to choose a small
number of series, called indicator variables, with which to
construct a model for forecasting current quarter GNP. The
equation that is presented here uses contemporaneous
values of nonfarm payroll employment, industrial production, and retail sales, as well as lagged values of real GNP,
to predict current quarter real GNP growth. We present an
analysis of its forecasting performance at different points
in the quarter, when varying amounts of information are
available on the indicator variables. The model is not very
useful in the beginning of the quarter, when we have no
information about the indicator variables. The forecasting
accuracy of the model improves noticeably when information on the first month of the quarter becomes available.
While there is some further improvement when data on the
second and third months of the quarter become available,
this improvement is not large. The model's forecasts compare favorably with the Blue Chip consensus forecast. The
model's forecasts also contain information about final
GNP over and above that contained in the Commerce
Department's advance! GNP release.

The paper is organized as follows. Section I discusses
issues of estimation strategy and variable selection. Section II presents the estimated model, called the monthly
indicators model. It provides details on the forecasting
performanceofthe system used to predict the indicator
variables and presents the equation used to predict real
GNP. The next section presents the results on the model's
forecasting performance over the period from 1978.31988.2, and a comparison of the model's forecast with the
consensus Blue Chip forecast. Section IV considers the issue of combining forecasts, in order to determine whether
the model forecast provides information about the final
value of real GNP beyond that contained in the advance
estimate of GNP released by the Commerce Department,
as well as that contained in the Blue Chip forecast. This
section also evaluates how the model performs relative to
the Blue Chip forecast in predicting the advance GNP
estimates. Section V concludes.

I. Strategy and Variable Selection
The central issue of this project is which variables to use
to predict real GNP. There are several approaches to this
problem. Traditional, structural macroeconomic models,
for instance, focus on the product side of the National
Income and Product Accounts. An alternative approach is
to obtain GNP estimates from information about factor
inputs-utilize Okun's Law, for example. In contrast to
these two approaches, this paper uses purely statistical
criteria to determine whether a given variable should be
used to forecast GNP. Specifically, a variable is included in
the model if it helps to reduce the "ex-ante" errors in
predicting real GNP and is statistically significant in the
GNP equation. (As mentioned above, only those variables
for which the relevant data are available relatively early are
candidates for inclusion.)
However, minimizing GNP forecast errors is not a single
criterion, since we wish to make forecasts of current
quarter output several times during the quarter as new
information on the indicator variables becomes available.
A variable that is useful in predicting GNP when all three
months of information are available may not be included in
the model, since we are also concerned with the variable's
usefulness when we have less than three months of information on it. Thus, our ideal variable is one that minimizes
GNP forecast errors whether we have one, two or three
months of information for the current quarter. What this
means is that the variable we choose to predict real GNP
should itself be easy to forecast.

This means that the process of choosing the appropriate
set of variables for forecasting real GNP can become
extremely cumbersome, since each time a new variable is
considered for inclusion in the GNP equation it is also
necessary to respecify the equations for forecasting all the
indicator variables. Selecting variables according to the
criteria of minimizing forecast errors also complicates
matters, since we are faced with a rather large list of
potential indicator variables.
In all, more than a dozen monthly series satisfied the
criterion of being available early in the quarter and were
considered for inclusion in the monthly indicators model.
These are listed in the Appendix. Variables that did relatively well when no information on the current quarter was
available but did relatively badly otherwise were dropped
from consideration early in the specification search." In
addition, early work also revealed that variables that did
reasonably well in predicting real GNP when all three
months of data were available also tended to do well when
only one or two months of data were available. (The
reasons for this are discussed below.)
As a consequence, the latter part of the specification
search was carried out in two separate stages. In the first
stage, the focus was on the usefulness of the indicator
variables in predicting real GNP when information on all
three months of the quarter was available. This allowed
elimination of more than half the variables in the original
list. The second stage involved specifying equations for

Economic Review / Winter 1989

forecasting the indicator variables themselves, and then
using these forecasts to obtain forecasts of real GNP.
Bayesian Vector Autoregressions (BVARs) were estimated to obtain forecasts. of the monthly values of the
indicator variables. This is an inexpensive forecasting
technique pioneered by Robert Litterman that has been
shown to produce macroeconomic forecasts comparable to
those obtained from large, commercial forecasting services. (See Litterman [1986] and McNees [1986] for a
comparison.) The technique uses the forecaster's prior
beliefs about the behavior of the variables in question to
modify the coefficients that would be obtained from unrestricted estimation of a vector autoregression. 3 The use of
priors reduces the probability of picking up spurious
correlations in the data. Unrestricted vector autoregressions tend to pick up such correlations and consequently
explain in-sample observations relatively well but tend to
forecast rather badly.
The general form of the prior employed here has come to
be known as the" Minnesota prior," which postulates that

most economic time series behave like random walks with
drift." Consequently, the estimated coefficients are pushed
towards this specification. Specifically, for each variable,
the coefficient on its own first lag is pushed towards one,
while the coefficients on all other right-hand-side variables
are pushed towards zero. How much the coefficients are
pushed towards this prior is determined by examining the
forecasting performance of alternative specifications and
choosing the one that does the best. Considerations of
space preclude a complete description of this prior and the
technique here. The interested reader is referred to Todd
(1984) for a clear, nontechnical discussion. Roberds (1988)
provides a more technical and complete description of how
to set up such a model.
Different BVARs were estimated for each combination
of variables included in the equation used to forecast GNP.
The indicator variable forecasts obtained from each of
these BVARs were then used to obtain forecasts of real
GNP at different points in the quarter. The final model was
selected on the basis of these GNP forecast errors.

II. The Monthly Indicators Model
This section presents the model that was obtained
through this process. Choosing variables on the basis of
forecasting criteria leads to an eclectic set of indicator
variables. The model's GNP equation contains a measure
of production, industrial production (denoted IP); a measure of factor inputs, nonfarm payroll employment (denoted
EMP); and .a measure of consumption, real retail sales
(denoted RRS). An important advantage of the set of
variables used in the model is that all data for a particular
month are available by the middle of the following month. 5
The producer price index for finished commodities (PPI)
has been used to deflate retail sales. At first glance, it
might seem more appropriate to use a consumption deflator. However, the deflator for personal consumption expenditures becomes available more than a month after the
PPI. Another alternative is the consumer price index (CPl).
It turns out that the forecasting performance of the GNP
equation is not very sensitive to whether the PPI or the CPI
is used to deflate retail sales. A benefit of using the PPI is
that it is released about two weeks before the CPI.
Intuition also suggests that a measure of labor hours may
be preferable to a measure of aggregate employment,
because average worker hours can be changed (within
limits) to vary production without changing employment.
However, using aggregate hours instead of employment
leads to no appreciable difference in the GNP forecasts
when all three months of data are available. In addition,

Federal Reserve Bank of San Francisco

forecasting labor hours turns out to be somewhat harder
than forecasting employment. As a result, GNP forecasts
based on one or two months of information are somewhat
worse when hours are used to predict GNP than when
employment is used. Experiments with specifications including various measures of average weekly labor hours in
addition to employment were similarly unsuccessful.
Another potential problem has to do with the retail sales
variable. In the last few years, sales incentives offered by
automobile dealers have led to wide swings in quarter-toquarter automobile sales, distorting quarterly retail sales
data. To correct for these distortions one could omit
automobile sales from consideration altogether and use
retail sales net of autos in the GNP equation. This alternative specification led to poorer forecasting performance
than did the specification that included auto sales. Another
approach would be to include automobile sales as a separate variable in the GNP equation. Although this approach
does lead to a statistically significant impact of changes in
the growth rate of auto sales on real GNP growth, the
estimated coefficient is quite small. Furthermore, there is
no appreciable difference between the forecasting accuracy of the version of the model that contains automobile
sales separately and that which lumps them together with
non-auto retail sales. Consequently, automobile sales were
not included separately in the final version of the model.
Obviously, the small set of indicators used here omits

everybody's favorite variable. Two variables that might
seem particularly important are the merchandise trade
balance and inventories. The merchandise trade balance
was not included primarily because of the lack of a
continuous series over a period long enough to allow
reliable estimation. In addition, including this variable in
the model is not likely to add much information to "realtime" forecasts, since data on the merchandise trade balance for a particular month do not become available until
approximately two months later.
Similarly, it seems that incorporating inventory data
should help, since inventory swings are a significant component of quarterly variation in real GNP growth. However, trials with several alternative measures of nominal
inventories failed to turn up a measure that either was

significant in the real GNP equation or did not worsen its
forecasting performance. Series on real inventories were
significant in the real GNP equation, but these were not
included in the final specification because they become
available with more than a one quarter lag. Attempts to
deflate the nominal inventory data with various price level
measures and so create a useful measure of real inventories
were also unsuccessful.

Predicting the Indicator Variables
The BVAR used to predict the monthly values of the
three indicator variables contains five variables: the indicator variables themselves plus average weekly hours of
production workers on private, non-agricultural payrolls,
and the six-month commercial paper rate. The last two

Economic Review / Winter 1989

variables increase the precision of the forecasts of the
indicator variables, but are not useful in predicting GNP.
Each equation contains 12 lags of each of the variables.
Given the nature of the exercise, presenting the estimated
coefficients does not appear to be particularly useful. 6
(The computer program used to estimate the BVAR is
available from the author on request.) Instead, Table I
presents forecast error statistics for the one-month ahead to
the three-month ahead horizons over a lO-year period
extending from July 1978 to June 1988 (a total of 120
forecasts). Each forecast was obtained by estimating the
BVAR up to the period prior to the first month being
forecast. 7 For comparison purposes, the Table also includes error statistics on forecasts obtained from univariate
autoregressions.
Although both the BVAR and the univariate autoregressions predict the log levels of the indicator variables, the
forecasts have been converted to annualized growth rates
in order to facilitate interpretation of the various error
statistics shown in the table. Four different measures of
forecast accuracy are presented there: the Mean Error, the
Mean Absolute Error (MAE), the Root Mean Square Error
(RMSE), and Theil's U-statistic. A MAE close to the Mean
Error implies that the errors are generally of the same sign,
meaning that the forecasts are generally either too low or
too high. A comparison between the RMSE and the MAE
provides information about the relative size of the errors: if
the errors are roughly of the same size, the two measures
will be close -. A mixture of large and small errors will lead
to a RMSE above the MAE. Theil's U'-statistic is unit free
and provides. a comparison of the model's forecast with the
naive forecast of no change in growth rates. Values larger
than one imply that the model's forecast is worse than the
naive forecast.
As shown in Table 1, there is a substantial difference in
the size of the errors made in predicting the three indicator
variables. For instance, the MAEs and the RMSEs of the
real retail sales forecasts are about eight times larger than
the MAEs and the RMSEs of the employment forecasts.
This is largely because the industrial production and real
retail sales series are much more volatile than the employment series. Over the forecast period, the standard error of
the growth rate of real retail sales is nearly seven times

Federal Reserve Bank of San Francisco

larger than the standard error of the growth rate of employment, while that for industrial production is more than
three times as large as that for employment. 8
A comparison of the MAEs and the RMSEs of the
BVAR and the univariate autoregressions shows that the
BVAR forecasts are better for all three variables. A similar
conclusion holds for the If-statistics shown there. The
Mean Errors from the BVAR are smaller than those for the
univariate autoregressions for both employment and industrial production but are larger in the case of real retail
sales -. While it is possible to respecify the BVAR's prior to
get smaller mean errors for retail sales, doing so raises the
RMSEs of all three variables.

Predicting Contemporaneous GNP Growth
The equation used to predict current quarter GNP is
RGNP t = 0.81 + 0.17 IPt
(2.16) (2.81)

0.14 RRS t
(3.77)

1.13 EMP t
(4.95)

- 0.21 RGNP t _j - 0.09 RGNP t _2 - .26 RGNP t _3
(- 3.01)
(1.41)
( 3.95)

Adjusted R2 = 0.74, S.E.E. = 2.17
Estimation Period: 1968.2 to 1988.2.
t statistics are shown in parentheses
All variables are in (annualized) growth rates. The starting
date was determined by the availability of the retail sales
data. The number of lags was determined by using the FPE
criterion." The Lagrange Multiplier test for first order
serial correlation produced a Chi-Square(l) statistic of 0.2,
with a marginal significance level of 0.6. Hence, first order
serial correlation is not a problem here. (The conventional
Durbin-Watson statistic cannot be used because of the
presence of lagged values of real GNP on the right hand
side. See Pagan [1984] for a discussion of the Lagrange
Multiplier test.) Omitting lagged values of real GNP leads
to serially-correlated residuals and worsens the forecasting
performance of the equation. Experimentation with different priors to restrict the coefficients on the lagged values of
GNP did not lead to an improvement in forecasting performance.

III. Forecasting Performance
Table 2 presents the error statistics for the GNP forecasts. For each forecast, the equation was estimated up to
the previous quarter and the resulting coefficients used,
together with the current quarter values of the indicator
variables, to predict real GNP growth in that quarter. I
present results for two sample periods. The first one
extends from 1983.3 to 1988.2, a total of 20 forecasts. The
intent is to focus upon the most recent period. However, it
is likely that a sample of 20 forecasts is not large enough to
provide a reliable test of the model's performance. Accordingly, Table 2 also presents summary statistics on the
model's forecasting performance over the period from
1978.3 to 1988.2, a total of 40 forecasts.
Four different exercises were performed for each sample
period to duplicate the varying amounts of information
available over the course of the quarter. The first one tests

the forecasting capabilities of the model during the first
month of each quarter, when no information is available on
the indicator variables. In this case, the BVARforecasts the
values of the indicator variables for all three months of the
quarter and these values are used in the GNP equation to
forecast GNP growth. The second assumes that we are in
the second month of the quarter, when data for one month
are available on the indicator variables, and the BVAR is
used to forecast the values of the indicator variables for the
remaining two months of the quarter. Similarly, the third
set of GNP forecasts is based on two months of data for the
indicator variables, and the BVAR is used to forecast the
values of the indicator variables in the third month of the
quarter. Finally, the fourth set is based on all three months
of actual data for the indicator variables, so that no BVAR
forecast is required to forecast GNP growth.

Economic Review / Winter 1989

An important issue in evaluating the forecasting performance of the model has to do with the use of real-time
versus final data. Ideally one would like to duplicate the
data sets that were actually in use at each point of the
sample period to compare the model's forecasts with those
available from other sources. Unfortunately, while it is
possible (with considerable effort) to obtain preliminary
data, it appears virtually impossible to find out the dates at
which subsequent revisions were made for each of the
series in the model. Consequently, it is not possible to
duplicate the data sets that were used for the real-time
forecasts made over this period. Therefore, all the statistics
presented below have been computed on the basis of
currently available (August 1988) data.'?
Table 2 reveals that the real GNP forecasts obtained
when the BVAR forecasts the indicator variables for all
three months of the current quarter (that is, forecasts made
in the first month of the quarter) are not very good, with a
RMSE above 3.25 percent (at an annual rate) in both
sample periods. In fact, for the short sample period Theil's
U'-statistic is greater than one, implying that a naive
forecast of no change in growth rates would have been
better than the monthly indicators model's forecast over
this period. This is not a major shortcoming, however,
since the purpose of the model is to forecast real GNP
using contemporaneous information on the indicator variables.
The performance of the model improves noticeably
when information on the first month of the quarter becomes
available (that is, for forecasts made in the second month of
the quarter), with the RMSE falling below two percent.
Over the shorter sample period, forecasts made in the third
month of the quarter (shown in the third row) are no more
accurate than those made in the second month, although
they are slightly more accurate for the full sample period.
Similarly, forecasts made one month after the quarter has
ended (that is, forecasts that use actual data on all three
months of the quarter) are not much better than forecasts
made in the third month of the quarter. In fact, the RMSE
of the forecast made in the month after the end of the
quarter being forecast is only around 0.1 percentage points
smaller than the RMSE of the forecast made two months
earlier.
The relatively small impact of the second and third
months' data on the model's forecast accuracy reflects the
fact that quarterly growth rates are a weighted average of
monthly growth rates. For example, in computing the
growth rate for the second quarter from monthly data, the
growth rates for February and June get a weight of 1/9
each, those for March and May get a weight of 2/9 each

Federal Reserve Bank of San Francisco

and that for April gets a weight of 3/9. Thus, the arrival of
information on the first month of the quarter doubles the
amount of information we have on the quarterly growth
rate (from one-third to two-thirds). By contrast, information on the third month of the quarter gives us only oneninth of the information required for the quarterly growth
rate. That is why the model's forecasts will not change
significantly when data on the second and third months of
the quarter become available.
Notice that the RMSEs of the real GNP forecasts made
on the basis of three months of information are smaller
than the standard error of the estimated GNP equation.
This implies that the variables that are used to forecast real
GNP are doing more than picking up random movements.
Finally, while the error statistics for the shorter sample
period tend to be somewhat larger than those for the full
sample, the difference is not large enough to suggest that
the forecasting ability of the model has changed over time.

Comparison with the Blue Chip Consensus
Table 2 also includes forecast error statistics for the
consensus real GNP forecast from the Blue Chip survey.
This survey is based on a panel of 51 forecasts and is
contained in a newsletter titled, Blue Chip Economic
Indicators, published by Capitol Publications. The consensus forecast is the average of the 51 individual forecasts.
For the Blue Chip forecasts I have chosen a dating convention based on when the forecasts are released. The official
release date of the survey is the 10th of the month, but the
survey itself is conducted over the first week of the month.
I have dated the forecast released on the 10th of the month
as the forecast for that month. For example, the first quarter
Blue Chip forecast released on the 10th of April is the
forecast that is compared to the model forecast available on
the 15th of April. From a policymaker's perspective, this
comparison is the relevant one, since the two forecasts are
lined up according to the dates when they actually become
available.
However, if we want to assess the relative accuracy of
the two forecasts, it would be better to compare the model
forecast errors in one row with the Blue Chip forecast in the
following row, since the Blue Chip average in any row will
be based on less information about the economy than the
model forecast in the same row. For example, the error
statistics on the model's forecasts available in the second
month of the quarter should be compared to the error
statistics on the Blue Chip forecasts available in the third
month of the quarter. Note, however, that this comparison
will overcompensate in those months where employment
data for a given month are released in the first few days of

the following month, because the Blue Chip survey respondents are likely to have incorporated this information
into their forecasts by the time of the survey.II
Table 2 reveals that over both the 83.3-88.2 sample
period and the 78:3-88:2 sample period, the Mean Error,
MAE and the RMSE of the model forecasts available in the
second month of the quarter are all smaller than the
corresponding error statistics for the Blue Chip consensus
forecast available in each of the following two months. The
model forecast does worse than the Blue Chip forecast only
for forecasts made when no information on the current
quarter is available. Thus, once information about the
first month of the current quarter becomes available, the
monthly indicators model performs better than the Blue
Chip forecast.
Needless to say, this comparison exaggerates the relative advantage of the monthly indicators model, since it
was estimated with the benefit of hindsight and it uses
more accurate data than was available to individuals making real time forecasts over this period. Nevertheless, it
does provide some reassuring evidence on the forecasting
capabilities of the model. In addition, early versions of the
model have been used to make real-time forecasts of output
growth since the third quarter of 1987. These forecasts are
presented in Table 3, along with the Blue Chip consensus
forecast. Over this period (87.3-88.2), the mean error of
the model's real time forecasts made using three months of
data on the indicator variables is 0.7 percent, the MAE is
1.1 percent and the RMSE is 1.5 percent. Over the same

period the mean error of the comparable Blue Chip forecast
is 2.0 percent, the MAE also is 2.0 percent and the RMSE
is 2.4 percent. For the model forecasts based on one month
of information, the mean error is 0.9 percent, the MAE is 1
percent and the RMSE is 1.4 percent. While this sample of
four observations is much too small for the results to be
considered proof of the model's real-time forecasting capabilities, these results are at least consistent with the
statistics presented in Table 2.
Finally, Chart 1 compares real GNP growth and the
forecasts from the model for the period from 1983.3 to
1988.2. Two different forecasts are shown: first, forecasts
made on the basis of one month of data on the current
quarter and second, forecasts made on the basis of three
months of data on the current quarter. The two forecasts are
similar, as the RMSEs reported in Table 2 would suggest.
To summarize the results of this section, the forecast
errors reveal that the monthly indicators model is not very
useful when no information is available on the current
quarter. The forecasting ability of the model increases
noticeably once the first month of information becomes
available, although the improvement is likely to be smaller
when the model makes real-time forecasts because only
preliminary data will be available at first. The model's
forecasts should be much more reliable once data on the
second month are available, especially because data for the
first month of the quarter are often revised at this time and
hence are likely to be more accurate.

Economic Review / Winter 1989

Annual GNP
Growth (%)

Chart 1
Real GNP Forecasts from
the Monthly Indicators Model
Forecast Period 83.3 - 88.2

10
8

6
4
2

O-l------------t,.+--------83Q4

84Q4

85Q4

86Q4

87Q4

Numb'Hs refer to months of current-quarter data
used in making forecast.

IV. Combining Forecasts
The results presented above reveal that the model's
forecasts are reasonably accurate. However, we have not
yet examined the issue of optimality. In other words, are the
model's forecasts the best available, or can they be improved by combining them with information from some
other source? Although it is not possible to determine what
is the best forecast overall, this section considers the
possibility ofcombining the model's forecast with the
advance GNP. estimate and the Blue Chip consensus forecast to determine whether the model's forecasts can be
improved.

The Model Forecast and the Advance GNP Estimate
We begin by looking at what happens when the advance
GNP estimate (which is released by the Commerce Departrnentabout three to four weeks after the end of the
quarter) is combined with the model forecast to predict
final GNP. The first part of Table 4 presents regressions of
final GNP on the advance GNP estimate and on the model
forecast obtained by using all three months of current
quarter data. Once again, results are presented for two
different sample periods. The first two columns of the
Table show that over 1983.2-1988.2 both estimates are
unbiased (that is,the hypotheses that the constant term is
zero and that the coefficient on the forecast is.one cannot be
rejected at conventional significance levels in either equati()n).Also, both equationsexplain about the same share of
the total variation in final GNP.

FederaLReserveBank ofSan Francisco

The third column presents a regression including both
variables. When forecasts are .pooled using regression
analysis itis common practice to exclude the constant term
and constrain the coefficients on the two forecasts to sum to
one. This procedure has the advantage that if the two
individual forecasts are unbiased, the combination forecast
will be unbiased as well. However, Granger (1984) points
outthat the forecast error obtained from such a procedure is
not necessarily uncorrelatedwith the individual. forecasts.
Thus, it is possible that the forecast error itself can be
forecast from one of the individual forecasts, implying that
the combination procedure is not optimal. To avoid this,
Granger recommends that the estimated equations include
a constant and not place any restrictions on .thecoefficients. Accordingly, column (3) of Table 4 presents results
from unrestricted regressions.
The unrestricted regressions produce coefficients on the
model forecast and on the advanceGNPestiInatethi:ltare
about the same size. The standard error ofthis.equatioJ1is
about 10 percent smaller than the equation containing the
advance GNP estimate alone, suggesting that the monthly
indicators model does contain information over andab()ve
that contained in the advance (iNP data. Unfortunately, the
coefficients in equation 3 are not estimated very precisely.
Thus, the 70% confidence interval for the coefficient on
the model forecast extends from. 31 to .83, while the 7Q%
confidence interval for the coefficient on advance GNP
extends from .29 to .88.

Columns (4)-(6) present the same regressions over the
entire sample period. A comparison of these results with
those in columns (1)-(3) reveals that there is not much
difference between the coefficients of either variable
across the two sample periods. However, the adjusted R2
for the full sample period is noticeably higher.
The bottom half of the Table presents the error statistics
obtained when the advance GNP estimate and the model's
forecast are combined to predict the final GNP number.
The sample period extends from 83.3 to 88.2. (The entire
sample period cannot be used since the model's forecasts
over the 78.3-83.2 period are used to estimate the prediction equation.) The procedure is the same as in Table 2,

that is, the forecast for each quarter is obtained by estimating the underlying equation up to the previous quarter.
Combining the two forecasts leads to a MAE of 1.26
percent and a RMSE of 1.65 percent over this period. A
comparison with the results in Table 2 reveals that the
MAE obtained from the combination forecast is about 10
percent less than the MAE of the model's forecast. A
similar reduction is obtained forthe RMSE. This combination forecast is also an improvement on the results obtained
when advance GNP is itself treated as a forecast of real
GNP. If the advance GNP estimate is used by itself to
forecast real GNP over the 83.3-88.2 period, the mean
error is 0.54 percent, the MAE is I. 52 percent and the

Economic Review I Winter 1989

RMSE is 1.82 percent. These errors are roughly the same
size as the errors of the monthly indicators model forecast
based on three months of information (see Table 2). Thus,
pooling the model's forecast and the advance GNP estimate leads to forecasts that are an improvement on either
one considered by itself.

The Model Forecast and the Blue Chip Forecast
Table 5 presents the results of combining the model
forecast and the Blue Chip consensus forecast. The regressions shown in the first part of the table are based on

Federal Reserve Bank of San Francisco

forecasts that become available in the first month after the
end of tbe quarter. Column (1) shows that the Blue Chip
forecast is an unbiased estimator of final GNP, a result that
is not too surprising because the forecast itself is an
average. A comparison of this equation with equation (2)
of Table 4 reveals that the model forecast explains a
somewhat greater share of the in-sample variation of real
GNP than does the Blue Chip forecast. Regressing real
GNP. on both the Blue Chip and the model forecast
(column 2 of Table 5) improves the explanatory power of
the~ql.lation, although this equation does not do quite as

well as the one that contains the model forecast and the
advance GNP estimate. The coefficients are not estimated
vel)' precisely here either. A 70% confidence interval for
the coefficient on the Blue Chip forecast extends from. 30
to .87, while that for the coefficient on the model forecast
extends from .48 to .99. Roughly the same sort of results
are obtained for the full sample period. These are shown in
columns (3) and (4) of the table.
The second part of the table presents the error statistics
obtained when the two forecasts are combined to predict
final GNP. The forecasts are generated in the same way as
they were in Table 4. However, four sets of forecasts are
presented here, to allow for the possibility that the relative
weights on the two forecasts may not be the same at
different points in the quarter. Unfortunately, these results
do not suggest that the two forecasts can be combined vel)'
profitably in the early parts of the quarter. The Blue Chip
forecast made in the first month of the quarter is generally
better than the forecast obtained by combining the model
and the Blue Chip forecast. By contrast, the model forecast
made in the second month of the quarter is better than the
combination forecast. And while the combination forecast

made in the third month of the quarter is an improvement
over the model forecast, the difference between the two is
not striking (for instance, the RMSE falls from 1.95 to
1.90). There is a somewhat larger gain for combination
forecasts made in the first month following the end of the
quarter (the RMSE falls from 1.86 to 1.76); however, these
forecasts are worse than those obtained by combining the
model forecast and the advance GNP estimate.
Finally, it is worth asking if the error in predicting final
GNP can be reduced by combining all three measures: the
model forecast, the Blue Chip forecast, and the advance
GNP estimate. Unfortunately, this does not lead to any
improvement in the GNP forecast. The equation that
contains all three variables turns out to be no better than the
one that contains only the model forecast and the advance
GNP estimate over either sample period. Further, an
equation that contains the advance GNP estimate and the
Blue Chip forecast does no better than an equation that
contains only advance GNP. (For the 83.3-88.2 period,
the coefficient on the Blue Chip forecast is 0.01 while that
on advance GNP is 0.87.)

Economic Review / Winter 1989

Predicting the Advance GNP Estimate
The results presented above suggest that the Blue Chip
consensus forecast is closely related to the advance GNP
estimate. Table 6 provides direct evidence on this issue, in
the form of regressions of the advance estimates of real
GNP on both the model forecast and the Blue Chip
forecast. A comparison of columns (1) and (2) reveals that
while both estimates are unbiased predictors of the advance GNP estimate, the Blue Chip forecast is much more
closely related to advance GNP than is the model forecast
overthe83.2-88.2 period. Column (3) shows that if both
variables are used to forecast advance GNP, the coefficient
on the Blue Chip forecast is three times that on the model
forecast. A comparison of the adjusted R2s and the standard errors of equations (2) and (3) shows relatively little
difference between the two. Thus, the model forecast
provides very little information about advance GNP once

the information available in the Blue Chip forecast has
been taken into account. The results over the entire sample
period are similar, though the model forecast does noticeably better by itself.
The-fact that the Blue Chip consensus is so much better
at predicting advance real GNP than the model probably
reflects the way that the underlying forecasts have been
constructed. Private sector forecasters follow methods that
are very similar to those used in constructing the advance
(iNP release. Since markets react to the advance release, it
seems plausible that market participants will focus their
efforts on obtaining forecasts of this number. In contrast,
estimation of the monthly indicators model has used final
GNP data and no attempt has been made to predict the
advance numbers, since policymakers presumably are
concerned about the actual level of economic activity, and
not its first estimate.

V. Conclusions
This paper has presented a simple model to obtain
estimates of current quarter real GNP growth based on a
small number of variables. Information on the set of
variables that is used to forecast GNP becomes available
relatively early. In addition, these variables are relatively
easy to predict, so that by the middle of the second month
of the quarter being forecast we have a forecast of final
GNP growth with a Root Mean Square Error that is less
than 2 percent at an annual rate. Nor is it very difficult to
generate the GNP forecasts. Obtaining these forecasts

requires keeping track of a small number of monthly
series, and the forecast itself can be generated very quickly
on a personal computer.
The results presented here reveal that the model's forecasts based on one month of data for the current quarter are
about as good as those based on all three months of data.
Further, these forecasts compare well to the consensus
Blue Chip forecast. Finally, the monthly indicators' forecast provides useful information on final GNP even after
the advance GNP estimate is released.

APPENDIX
The set of variables over which I searched to find the
best specification for the monthly indicators model contained:
Nominal Manufacturing Shipments
Nominal Manufacturing Inventories
Book Value of Manufacturing and Trade Inventories
Housing Starts
Six Month Commercial Paper Rate
Ten Year Bond Rate
Producer Price Index-Finished Goods
Consumer Price Index
Aggregate Labor Hours Index
Average Nonfarm Hours
Average Manufacturing Hours
Automobile Sales
Retail Sales Net of Autos
Industrial Production
Nonfarm Payroll Employment
Retail Sales

Federal Reserve Bank of San Francisco

ENDNOTES
1. This release was known as the preliminary GNP estimate prior to 1988.3.
2 .. In.general, these are variables that .are relativeiyeasy
to forecast but are not ascloselycorrelatedtocontemporaneous GNP as the variables that were finally included in
the model. It is worth pointing out that this strategy is part
of the reason that the monthly indicators model does
relatively badly when no information on the indicator variabies is available (see Table 2).
3. In a vector autoregression each variable is regressed
on past values of itself and the other variables included in
the vector.
4..A series Yt is said to bea random walk with drift if its
behavior over time can be described as

v.> a

+ et ,

wheree t is a serially uncorrelated error term. In this case,
our best guess of the value of y tomorrow is its value today
plus the constant term a (the drift).
5. Employment data for a given month are generally
released on the first Friday of the following month. Data on
industrial production, retail sales, and the producer price
index become available around the 15th.
6. Since there is no straightforward conceptual relationship between the variables included in the BVAR, it is not
clear what interpretation can be placed upon the estimated coefficients. Even if this were possible, it would be
difficult to analyze the 60-odd coefficients contained in
each of the equations.

7. This is to avoid using coefficient estimates based on
information obtained after the forecast was made. This
exercise still exaggerates the degree of precision we
vvould obtain in real time becausewe use revised data.
Thisissue is discussed in the next section.
8. Asecondary reason for the large difference in the forecast errors of the different variables is that I tended to favor
priors that improved the forecast accuracy of the employment number at the expense of the others. This is because
the employment number has a much greater weight in the
equation for predicting real GNP than the other variables.
9. See Judge, et. al [1984] for a description of this criterion.
10. Braun (1987) provides an estimate of the effect of data
revisions in a study that uses labor market data to predict
contemporaneous output. He reports that using preliminary instead of currently available data raises the Root
Mean Square Error (RMSE) of the real GNP forecasts by
between 0.2 to 0.4 percentage points.
11. It is also worth pointing out that a Blue Chip forecast
made in the second month following the end of the quarter
being forecast is not available because no forecasts are
compiled once the advance estimate of real GNP is
released.

REFERENCES
Braun, Steven N. "Estimation of Current-Quarter GNP by
Pooling Preliminary Labor-Market Data," Board of
Governors of the Federal Reserve System, Working
Paper, no. 75, June 1987.
Granger, Clive w.J. and Ramu Ramanathan. "Improved
Methods of Combining Forecasts," Journal of Fore-

casting, 1984.
Judge, George G., W.E. Griffiths, R. Carter Hill, Helmut
Lutkepohl and Tsoung-Chao Lee. The Theory and
Practice of Econometrics. New York: John Wiley &
Sons, 1985.
Litterman, Robert B. "Forecasting with Bayesian Vector
Autoregressions--Five Years of Experience," Journal of Economic and Business Statistics, January
1986.

McNees, Stephen K. "The Accuracy of Two Forecasting
Techniques: Some Evidence and An Interpretation,"
New England Economic Review, Federal Reserve
Bank of Boston, March/Apri11986.
Pagan, Adrian R. "Model Evaluation by Variable Addition," in David F. Hendry and Kenneth F. Wallis, eds.,
Econometrics and Quantitative Economics. New York:
Basil Blackwell, Inc., 1984.
Roberds, William. "A Quarterly Bayesian VAR Model of the
U.S. Economy," Federal Reserve Bank of Atlanta,
Working Paper, 88-2, May 1988.
Todd, Richard M. "Improving Economic Forecasting with
Bayesian Vector Autoregressions," Quarterly Review,
Federal Reserve Bank of Minneapolis, Fall 1984.

EconomicReview / Winter 1989

Undocumented Workers and Regional Differences in
Apparel Labor Markets

Carolyn Sherwood-Call
Economist, Federal Reserve Bank of San Francisco.
Scott Gilbert provided capable research assistance. Editorial committee members were Brian Motley and Jonathan Neuberger.

The Immigration Reform and Control Act of 1986
(IRCA), which requires employers to verify that the workers they hire can work legally in the United States, would
be expected to reduce the supply of undocumented workers. California's apparel industry appears to be particularly vulnerable to these changes, since it relies heavily on
undocumented workers, but employment growth in California's apparel industry has continued to outpace that of
the nation by a wide margin since employer sanctions went
into effect. Empirical examination reveals little relationship between undocumented workers and employment in
the apparel industry, suggesting that other factors are
more important causes of growth in California's apparel
industry.

Federal Reserve Bank of San Francisco

In November 1986, Congress passed the Immigration
Reform and Control Act of 1986, requiring employers to
verify that the workers they hire can work legally in the
United States. Stringent enforcement of this law should
reduce the supply of undocumented workers, causing
employment to fall and wages to rise in sectors and regions
where undocumented workers have comprised a significant proportion of the labor force.
California's apparel industry appears to be particularly
vulnerable to these changes. A number of analysts attribute its rapid growth, particularly when compared with
the decline of the apparel industry nationally, to the ready
supply of low-wage undocumented workers available in the
state (Maram, 1980; UCLA Forecast, 1987). Industry
participants note that in Southern California, which dominates apparel production in California, I most workers are
Mexican, and by most accounts a large proportion of these
are undocumented. Maram's study suggests that in 1980
about 60 percent of all garment workers in Los Angeles
were undocumented Hispanics.? Moreover, because the
apparel industry is highly competitive, with many small
producers in a large number of countries, easy entry and
exit, and relatively low profit margins, it is especially
vulnerable to any change that increases production costs.
Thus, a law that limits the supply of undocumented workers might be expected to retard growth in California's
apparel industry.
In fact, however, the growth in California's apparel
industry has continued to outpace that of the national
industry by a wide margin since the ban on employing
undocumented workers went into effect on June 1, 1987.
Between July 1987 and July 1988, employment in California's apparel industry posted healthy growth of 3.1 percent, compared with a 2.4 percent decline in U.S. apparel
employment during the same period.
Thus, despite its apparent vulnerability to the new law,
California's apparel industry does not yet appear to have
been affected by it. Why has the law not had the anticipated
effects? This paper examines the provisions of the law and
the characteristics of the apparel industry to evaluate the
impact of the law and to determine whether the law is likely
to affect the apparel industry in the future.

The paper is organized as follows. Section I discusses
the implementation of the law. Section II describes the
structure of the apparel industry. Section III sets out an
economic theory of the effects of undocumented workers

on regional labor markets. Section IV tests and interprets
the hypotheses generated in Section III. Section V summarizes and draws conclusions.

I. The Immigration Reform and Control Act of 1986
The Immigration Reform and Control Act of 1986
(IRCA) became law in November 1986, but its key provision regarding undocumented workers (UWs) did not go
into effect until June 1, 1987, when it became illegal for
employers to hire UWs, and employers were required to
verify the work status of all new employees. Even then,
these provisions initially were not enforced with the full
sanctions available under IRCA. Instead, on June 1, 1987,
the Immigration and Naturalization Service (INS) began
issuing citations to employers who violated these provisions of the law. Only the most egregious and repeated
violations resulted in fines, and these fines were heavily
publicized to discourage other employers from ignoring
the law. After a twelve-month "first citation" period, the
employer penalties became much more severe, with employers subject to fines of as much as $2,000 per violation
for a first instance of knowingly hiring UWs. Under the
law, even larger civil fines can be imposed for subsequent
violations, and criminal penalties, including jail terms,
can be imposed on employers who establish a "pattern or
practice" of illegal hiring.
As a result of this phase-in period for employer sanctions, the full force of the law did not take effect until June
1988. Thus, it is not surprising that the law appears to have

had no effect through July 1988. However, one cannot
necessarily infer from this that IRCA will have no effect
over the long term. The employer sanctions now in effect
ultimately may deter the hiring of UW s, causing the inflow
of migrants to slow substantially, and forcing significant
adjustments in affected labor markets.
But there also is reason to believe that the employer
sanctions may not deter employers from hiring UWs. The
law requires employers to check documents that indicate
workers' citizenship and residency status, but does not
require employers to ver(fy the authenticity of those documents. Moreover, the law explicitly bans employment discrimination on the basis of national origin or citizenship
status. As a consequence, UWs who obtain false documents still would be able to find work and so would not be
deterred from crossing the border." In fact, in June 1988,
the New York Times reported that illegal entries into the
U.S. continued to rise, despite IRCA's sanctions. Moreover, if enforcement at the borders increases, the stock of
UWs in the U.S. could rise, since Mexican workers who
otherwise might return to Mexico for part of the year may
stay in this country in order to minimize the number of
border crossings.

II. The Structure of the Apparel Industry
Unlike most manufacturing industries, the apparel industry approximates a textbook case of perfect competition. It consists of a large number of relatively small firms.
Four-firm concentration ratios" in eight 4-digit SIC categories of apparel- range from eight to 25 (Parsons, 1988). A
ninth 4-digit category has a four-firm ratio of 49, which
also is low by the norms for most other industries. Moreover, firms in the apparel industry tend to be small. In
1985, California apparel firms averaged 26 employees per
establishment, compared with 44 for all manufacturing.
Finally, ease of entry and exit characterize the industry
because of its low capital-to-labor ratio. The value of
capital averages only $4000 per employee, compared with
$31,100 for all manufacturing industries (ILGWU 1985).

The Role of Labor
This low capital-to-labor ratio suggests that wages comprise a significant share of the cost of producing garments.
In fact, labor compensation accounts for 53 percent of the
value added by apparel manufacturers, and 27 percent of
the value of finished apparel products," according to the
Annual Survey of Manufacturers. As a result, wage levels
are an important determinant of the profitability of apparel
manufacturing.
As important as the cost of labor is its productivity.
Employers look for workers who are willing to work at the
low level of wages offered by apparel manufacturers.

Economic Review I Winter 1989

While employers prefer workers who are skilled and experienced garment makers, most garment workers have little
formal education, often know little English, and tend to
have few employment options outside the apparel industry.
Cities with large immigrant populations frequently provide such workers.

trims, and monitor the quality of production. Consequently, the cost of production labor is less important for
these more fashion-oriented producers than it is for more
standard garments, and they are more likely to locate in
fashion design centers such as New York or Los Angeles.

Technology

Trends in International Trade

Although garment manufacturing continues to be a
labor-intensive process, some technological improvements
have been made in recent years. Most of these improvements have been in the areas of fabric cutting and pattern
making, where laser cutters and computerized sizing and
pattern layouts are now in use. In addition, some products
are particularly suited to the development of specialized
machinery. For example, specialized machines are available for sewing pockets, zippers, or belt loops on blue jeans.
This more sophisticated machinery is widely available, but
only the larger plants can afford the substantial investment
it represents. As a result, its use is somewhat limited, and
many smaller shops continue to produce garments using
less specialized technology.

For all types of producers, pressures on profit margins
have grown in recent years, leading to increased use of
overseas production facilities. Estimates of import penetration indicate that imports have become significantly
more important during the past twenty years. 7 As a result,
patterns of international trade in garments play an increasingly important role in explaining the condition of the
industry.

Heterogeneity of Apparel Products
It is important to recognize that the apparel industry is
far from homogeneous. Some of the differences are obvious. For example, some producers specialize in women's
sportswear while others produce men's suits. These differences have important implications for the production
processes and the plant's location relative to factor and
product markets.
For one thing, production of some items can be automated more easily than can production of others. For
example, as mentioned above, production of standard blue
jeans can be automated or subcontracted to other locations.
In contrast, tailored clothing requires considerably more
hand work and closer supervision.
Production of high-fashion apparel also is difficult to
standardize. Most garments for which demand can be
predicted many months in advance, and for which designs
are well established, can be produced almost anywhere.
The manufacturer can subcontract the production to plants
in other states or other countries. However, garments for
which demand is less predictable need to be produced in a
shorter time frame. For these more fashion-oriented items,
the short lead time means that designers need to be close to
the production facility in order to be able to check samples
as they are made, make last-minute decisions regarding

Federal Reserve Bank of San Francisco

Overseas production offers the major advantage of lower
labor costs. However, longer lead times and higher transportation costs make it inappropriate for some types of
garments, particularly high-fashion garments. Although
some foreign producers seem to be more responsive than
their American counterparts are (Lardner, 1988), others
have lax production standards and quality control procedures that make relying on them risky (Jacobs, 1988).
Frequent changes in quota and tariff restrictions further
complicate life for overseas "sourcers." The Multi-Fiber
Agreement (MFA) establishes a series of bilateral quotas
for particular apparel items. Thus, most countries have
limits on the number of items (skirts, jackets, etc.) that
they can export to the US. These quotas are based on the
country's past exports of each item. Thus, US. distributors
cannot buy unlimited quantities of apparel items from the
lowest-cost or highest-quality producers. Indeed, there is a
strong incentive for countries to start producing apparel
items they never have produced before, in order to supply as
much as possible to the US. before quotas for that item
from that country are imposed.
Another legal arrangement that affects international
trade patterns is "Item 807," which permits US. firms to
export cut fabric to Caribbean and Latin American countries (including Mexico) for assembly. The finished product, when returned to the US., is subject to tariff only on
the value added in the foreign country-which, given
prevailing wage rates in Item 807 countries, usually is a
relatively small fraction of the finished price of the garment. Because materials and garments can be trucked to
and from Mexico at low transportation costs, producers in
California and Texas tend to be heavy users of Item 807.

III. Regional Labor Market Theory and Undocumented Workers
The role that the presence of UWs plays in California's
apparel industry can be examined by considering labor
markets in different regions, where a region is defined as a
state. To take the simplest possible case, assume that each
region produces an identical, homogeneous apparel product, and that the cost of living and the productivity of
workers are identical across regions.
Two such regions, A and B, are illustrated in Chart 1.
Initially, the supply of and demand for labor are So and DO
(with appropriate subscripts). Wages in the two regions are
equal, at WO, so neither workers nor firms have an incentive to move from one region to another. Employment
initially stands at L/in region A and at LbO in region B.
Now assume that region A experiences a sudden influx
of Uws." This shifts the labor supply curve in Chart la to
the right, to Sal, initially reducing wages in region A to
Wal, and increasing employment to L,'.
At this point, the system is in disequilibrium. The wage
in region A, W aI, is lower than the wage in region B,
which remains WO. Consequently, firms seeking lower
wages have an incentive to shift production from region B
to region A. At the same time, workers seeking higher
wages have an incentive to move from region A to region
B. Migration of labor and firms would continue until the
wage rates in the two regions are equalized.
In terms of Chart I, migration of firms from region B to
region A causes the labor demand curve to shift to the right
in region A and to the left in region B. Migration of
workers from region A to region B causes the labor supply
curve to shift to the left (from Sal) in region A and to the

right in region B. Migration stops, and the curves stop
shifting, when wages have risen in region A and fallen in
region B, to the point where they are equal in the two
regions, at W*. This equilibrium occurs when labor demand and supply reach D2 and S2 (with appropriate
subscripts) in the graphs. At this point, wages are lower
than they were initially (WO), but they also are higher than
they were in region A immediately after it received the
influx of UWs (Wal). Employment in region A settles at
L a*, higher than its initial level of LO, and either higher or
lower than the employment level after the initial influx of
immigrants, L,'. Likewise, in region B, the direction of
change in employment between LO and Lb* is indeterminate, and depends on the relative magnitudes of the shifts
in supply and demand curves.
If workers' productivity levels differ from one region to
another, or if the cost of living differs, then nominal wages
would not be expected to be equal in the two regions.
Nevertheless, if workers and firms migrate freely from one
region to another, the cost of labor, adjusted for differences
in productivity and the cost of living, still should be
equalized across regions.
However, this scenario assumes that both workers and
firms can move freely among regions, an assumption that
is not likely to be realized in practice. Workers as a group
tend to move slowly in response to changing economic
conditions. Apparel industry workers, who tend to have
little formal education, tend to be particularly closely tied
to their regions by strong cultural bonds. Thus, perhaps
paradoxically, apparel workers may be more mobile be-

Chart 1A

Chart 18
Region 8

Region A
Wage

Wage

•

L~l:
Employment

L~ L~
Employment

Economic Review / Winter 1989

tween Mexico and such centers of the Mexican community
in the U.S. as Los Angeles than they are between Los
Angeles and New York. Similarly, apparel workers in the
Southeast may be unwilling to move to the Northeast for
cultural or family reasons, despite higher pay in the Northeast.
Likewise, there may be reasons why firms do not respond to real wage differentials. For example, as mentioned earlier, proximity to designers can be important for
products that are new on the market or for which demand is
uncertain. These limits on firms' mobility could result in
real wage differences among regions.
Thus, disequilibrium in real, quality-adjusted wages
could persist because some workers and firms may be
unwilling or unable to move in response to wage differentials among regions. In this case, an increase in a
particular region's population of UWs would cause wages
to fall more than they would in other regions that do not
experience a similar influx of UWs. In such a disequilib-

rium world, wages (appropriately measured) could be
persistently lower in regions that have large UW populations.
These observations lead to two empirically testable
conjectures:
(1) Regions that receive undocumented workers from
other countries should have a higher proportion of their
employment in labor-intensive industries such as apparel
than they would have if their populations included no
UWs. This assumes that the initial influx of UWs is
localized, but does not depend on whether migration of
individuals and firms leads the system to approach equilibrium.
(2) If workers and firms are not perfectly mobile, wage
differentials can persist, and wages will be lower in regions
that receive UWs. However, iffactors are perfectly mobile,
there should be no significant regional differences in wage
rates, and a regression that attempted to explain those
differences might perform poorly.

IV. Testing the Undocumented Worker Hypothesis
The model presented in Section III can be formalized.
To do so, consider the factors that determine the supply of
and demand for labor in the apparel industry. The model of
Section III and the information about the industry presented in Section II suggest that the number of workers
available to apparel manufacturers in a particular region
should rise if apparel wages rise, if the number of UWs is
greater, and if a large proportion of the region's population
has few alternatives to apparel industry employment. Education is used to proxy the general job skills that would
allow workers a wide range of employment alternatives.
Moreover, the demand for labor among apparel manufacturers would be greater if wages are lower, and if the state's
production activity is more closely tied to design activity.
These factors suggest the following structural model:
SL = f(UW, UNED, WAGE)

(1)

DL = f(DESIGN, WAGE)

(2)

WAGE = apparel industry wage
UW = undocumented workers as a proportion of population
UNED = proportion of population without a high school
education
DESIGN
importance of design to the state's apparel
industry
If labor demand and supply curves are linear, demand
and supply take the following form:

Federal Reserve Bank of San Francisco

= t + u UW + v UNED + w WAGE
DL = X + Y DESIGN + z WAGE

(1')

(2')

The theory suggests that u, v, w, and y should be
positive, and z should be negative. The region's labor
market clears when the wage is such that labor supply
equals labor demand." Using these conditions along with
equations (1') and (2'), one can solve for equilibrium
employment, EMP, and wages:
EMP

[(zt-xw) + uz UW + vz UNED
- ywDESIGNHlI(z-w)]

WAGE = [(x-t) - u UW - v UNED
+ yDESIGN][lI(w - z)]

(3)

(4)

To simplify the expression of the reduced form, define
the following variables:
zt - xw
x
e - --a =
z - w
w - z
uz
z-w
vz

c=---->o
z

z - w

w
g

---<0
w
z
y

w-z

Thus, the model is estimated in the following form:
EMP = a + b UW + c UNED + d DESIGN
WAGE = e + f UW + g UNED + h DESIGN

(3')
(4')

In the employment equation (3'), the coefficients b, c,
and d are expected to be positive. That is, apparel employment should be more important in states where undocumented workers, less educated workers, and the design
function, all are more prevalent.
In the wage equation (4'), f and g should be negative,
since wages should be lower in regions that have greater
supplies of potential apparel workers, as measured by the
population's education level and undocumented workers.
The coefficient h should be positive, since a more important design function would increase the demand for workers, and hence raise wages, ceteris paribus.
Based on the theoretical discussion in section III, the
coefficient on UWs in equation (3'), b, should be positive,
since an influx of UWs in a particular region should lead to
a higher level of apparel employment than would exist
otherwise. If factors are not perfectly mobile, there also
may be systematic differences in wages, and so a higher
UW population would be associated with lower wages.
Thus, the coefficient f in equation (4') might be expected
to be negative. However, if factors are quite mobile, there
may be little interregional wage variation, and so equation
(4') may have little predictive power.

The Data
The empirical work focuses on the states that have
apparel industries of significant size, where "significant"
is defined as having more than ten thousand workers in
either 1975 or 1985. Table 1 lists total employment for each
of the fifty states plus the District of Columbia for these
two years. The states that meet this criterion comprise the
nineteen most important apparel-producing states for both
years, and account for about 95 percent of total U.S.
apparel employment in both 1975 and 1985.
Table 2 lists each state's measure of each variable used in
the regressions, along with the variables' means and standard deviations. The data sources and precise definitions of
the variables are explained below.

EMP
EM? is defined as apparel industry employment, divided by the state's total payroll employment, to control for
state size. These figures are computed using data for SIC
2310 (Apparel and Other Textile Products) from the Employment and Earnings data base for 1980. 11 These data
are compiled from a survey of all employers who file

Economic Review / Winter 1989

reports with the Treasury Department. Cornelius' survey
(l988b) suggests that very few employers of UWs operate
completely "underground," so the vast majority would be
included in the survey. Employers who do not comply with
labor laws, including the minimum wage and overtime
provisions, may report wage and employment levels inaccurately in order to avoid detection.F For example, employers may report their total wage bills correctly, but
under-report the number of workers if they are violating
minimum wage laws or violating overtime provisions.
This would lead EMP to be underestimated in states where
UWs are important, which would bias the results toward
finding no significant effect of UWs on apparel employment.
WAGE

The variable WAGENOM is defined as nominal average
hourly earnings for production workers in the apparel
industry (SIC 23). The wage data also are subject to
potential biases from misreporting by employers who are
violating minimum wage and overtime laws. In addition,
nominal wages may not be strictly comparable across
states because costs of living differ and workers' productivity may differ systematically by state.
An adjusted measure, WAGEADJ, can be constructed
by dividing the average hourly wage in apparel by the
average hourly wage in all manufacturing. Since states
with the highest costs of living are likely to have the
highest manufacturing wages, a high ratio of apparel to
manufacturing wages would imply that "real" apparel
wages in that state are higher than are real wages in a state
with a lower ratio of apparel to manufacturing wages.
Normalizing by manufacturing wages also may adjust
for productivity differences, if interstate differences in
apparel workers' productivity are highly correlated with
interstate differences in manufacturing workers' productivity. Of course, the skills required for apparel production
are quite different from those required for other types of
manufacturing, and the populations of workers also are
quite different. Consequently, apparel workers' skill levels
may not be highly correlated with the skill levels of
workers in other manufacturing industries. However, the
available data do not permit a better approximation of
regional differences in apparel workers' skill levels.

Federal Reserve Bank of San Francisco

UW
In principle, constructing the UW variable is straightforward. To measure the importance of UWs in the labor
force, one can divide the number of UWs by the working
population. Here, the working population of a given state
is defined as the number of respondents to the 1980 census
who listed that state as their place of work.
However, reliable data on the presence of UWs is, for
obvious reasons, both scarce and based on incomplete
information. For example, the 1980 Census included detailed questions about nationality, birthplace, and language
use. It did not include questions specifically about residency status, although several researchers (for example,
Hill and Pearce, 1987; McCarthy and Valdez, 1986; Pearce
and Gunther, 1985) have argued that the number of UWs in
a given locality is highly correlated with the number of
aliens who speak a language other than English at home.
Defining UWs in this way has obvious problems, since
many legal immigrants speak their native language at
home.

More sophisticated estimates of the number of undocumented aliens residing in each state were calculated by two
staff members at the Census Department, Passel and
Woodrow (1984). They used 1980 Census data on the total
alien population and INS data on the legally resident alien
population, and estimated the number of undocumented
aliens residing in each state by calculating the residual and
making adjustments to account for known biases in the
data. UWis the number of undocumented residents in each
state, as estimated by Passel and Woodrow, divided by the
state's.total working population.
Even this measure has clear limitations. For one thing, it
provides estimates of the stock of UWs in the U.S. during
1980, but does not permit analysis of the changes in that
stock over time. 13 A more fundamental problem is that it
relies on official data regarding a segment of the population with a strong incentive to hide its existence. Nevertheless, these estimates do represent a serious attempt to
construct consistent data across states, using all available information regarding the presence of undocumented
aliens.
UNED
The apparel industry, which depends heavily on workers
with few employment alternatives, would be expected to be
more important in states with relatively uneducated populations. UNED is defined as the proportion of the state's
population without a high school education. Presumably, a
higher value of UNED indicates that a relatively large
proportion of the state's workers have few employment
options.
DESIGN
The design variable is an attempt to account for differences in the fashion content of apparel production in
various states by measuring the importance of the design
community to the state's apparel industry. There are two
alternative specifications of the design dummy. In one,
separate dummies represent New York (DES/GNNY) and
California (DES/GNCA). In the alternative specification,
DES/GND is a dummy variable which equals 1 for New
York and California, and 0 for all other states. 14

Employment Regressions
Results of employment regressions using various combinations of explanatory variables are listed in the top
panel of Table 3. The UW variable is expected to have a
positive coefficient in all of the employment regressions,
but the coefficients are negative when the regressions

include the design variable. Moreover, the statistical significance is higher in the regressions that have negative
coefficients. Thus, the presence of undocumented workers
does not explain the variation in the ratio of apparel to
manufacturing employment.
Failure to confirm the hypothesis could be because the
presence of UWs does not have a significant effect on the
supply of labor to apparel manufacturers, or because the
UW.variable is mismeasured. Alternatively, it could be
because the employment variable does not capture the
importance of apparel employment very well. As discussed earlier, if firms that do not comply with minimum
wage and overtime laws under-report employment to hide
theit activities, estimated coefficients would be less likely
to show a positive relationship between UWs and apparel
employment. However, the fact that the education and
design variables do have the expected signs suggests that
EMP provides some information about the importance of
the apparel industry.
One way to get around these problems would be to run
regressions using rates of change in apparel employment

Economic Review / Winter 1989

andUWs rather than proportionsof the total populations at
a singlepoint in time. However, the data on UWs existonly
for the census year 1980. In principle, data on other
demographic variables, such as the Hispanic population,
could be used to proxy for UWS.15 However, only four of
the states included in the empirical work had data on both
wages and Hispanic population for 1975, so regressions
using rates of change for the UW proxy include little
information. Nevertheless, it is worth noting that among
those four states, between 1975 and 1985 Florida and
California had faster rates of growth in Hispanic population (79 and 63 percent, respectively) and growing apparel
employment (16 and 17 percent). In contrast, Illinois and
New York had slower rates of growth in Hispanic population (58 and 46 percent) and shrinking apparel employment (at rates of 38 and 25 percent, respectively).
Although these figures are inadequate to substantiate the
claim that the presence of UWs affects the apparel industry's health, they do support the possibility that the failure
to confirm that hypothesis may be due to measurement
problems rather than an inadequate theory.
Wage Regressions
The results of regressions using nominal apparel wages
and the ratio of apparel to manufacturing wages are listed
in the lowertwo panels of Table3. Thecoefficients on UW
consistently are negative in all six regressions, as the
theory predicts, although the statistical significance of the
coefficients varies among the regressions. The design
dummiesalso havethe expected signs but varying levels of
statistical significance.

The performance of the education variable depends
crucially on the specification. In the nominal wage equations,it is negative, as expected, and highly significant.
However, in the adjusted wage equations it is positive but
insignificant.
Overall, the wage equations suggest that there are
significant differences in real, quality-adjusted wages
among regions which are related systematically to the
presence of UWs. Thus, immobilities of firms and/or
workers appear to be significant in preventing labor markets from reaching interregional equilibrium.

Summary of Empirical Work
The effect of UWs on the apparel industry is unclear.
Although the data provide little support for the contention
that the presence of UWs stimulates employment (and,
presumably, production) in the apparel industry, the evidence also is not strong enough to dismiss the possibility.
Nevertheless, other factors, such as the employment alternatives of the legal population (including legal aliens) and
the nature of the region's apparel industry, seemto be more
important factors.
The regressions do suggest that the presence of UWs
maybe associatedwith lowerwages, althoughUWs do not
appear to be the most important factor affecting apparel
wages. In all of the wage regressions, the coefficient on
UWs is of the expected sign and is at least marginally
significant. Thus, factor immobilities appear to preventan
interregional labor market equilibrium in which wages
(however measured) are equalized across states.

V. Conclusions and Implications
This paper started by asking whetherthe new immigration law, IRCA, would stifle growth in California's apparel
industry. The analysis presented here suggests that the
impact of IRCA on the industry shouldbe modest, for two
reasons.
First, it is unlikely that the sanctionsthe law imposes on
employers of undocumented aliens will effectively reduce
employment of undocumented workers. Employers can
comply with the law simply by requiring workers to
providedocumentationof their workstatus. Employers are
notrequiredto verifythose documents, and are specifically
forbidden from discriminating on the basis of national
origin or citizenship status. As a result, employers are
likely to continue to provide jobs to UWs. As long as jobs
exist on this side of the border, there is an incentive for

Federal Reserve Bank of San Francisco

illegal immigration, and UWs likely will continue to
comprise an important share of the U.S. labor supply.
Second, evenifIRCA doesreduce the supplyof undocumented workers in the United States, such a reduction
probablywouldnot have a major effect on labor markets in
the apparel industry. The empirical relationship between
undocumented workers and employment is inconclusive.
Data problems may be partially responsible, but the contrast between the inconclusiveness of the undocumented
workerresults and the conclusivenessof the resultsregarding the education and design variables suggests that the
presence of undocumented workers probably was not the
most important factor determining regional employment
patterns within the apparel industry. The empirical work
does not address the possibility that the presence of immi-

grants (including documented workers) is an important
determinant of apparel industry health, but previous studies (such as Waldinger 1986) suggest that this may be the
case.
Since undocumented workers apparently have not been
the most important cause of the observed rapid growth in
California's apparel industry during recent years, even if
IRCA does effectively reduce the supply of undocumented
workers to California's apparel industry, California should

continue to be an attractive location for U.S. apparel
manufacturers. Some firms may encounter problems finding sufficient labor at prevailing wages, and some marginallyprofitable firms may be driven out of business.
Nevertheless, California's growing role as a design center,
and its large populations of Hispanic and Asian immigrants as well, suggest that California's apparel industry
could survive a reduction in the number of undocumented
workers available to it.

ENDNOTES
1. In 1985, 74 percent of California's apparel workers
were in Los Angeles County alone.
2. Although it is commonly believed that agriculture is
the most important employer of undocumented workers,
Cornelius (1988b) estimates that less than 15 percent
of undocumented workers currently work in agriculture.
Nonagricultural industries that account for large shares of
undocumented workers include food processing, hotels,
and manufacturing (including apparel).
3. UWs who are found to be carrying false documents are
subject to deportation, but as the experience of the past
several years indicates, the threat of deportation does not
deter most would-be UWs.
4. The four-firm concentration ratio, defined as the percentage of the market covered by the industry's four
largest firms, is a standard measure of the concentration
and, by implication, the competitiveness of an industry.
5. In 1985, these eight categories accounted for 48 percent of U.S. apparel employment.
6. By way of comparison, among all manufacturing industries, labor compensation accounts for only 41 percent of
value added and 17 percent of the value of shipments.
7. Specific estimates differ, however. Whereas Cline
(1987) calculated that the import penetration ratio for
apparel rose from 4 percent during the 1961-65 period to
31 percent in 1986, the ILGWU (1988) calculates that it
rose from 9 percent in 1967 to 58 percent in 1987.
8. If agents were motivated only by economic incentives,
the initial influx of immigrants would be expected to be
spread evenly among the regions. Nevertheless, available evidence overwhelmingly supports the contention that
immigrants arrive in only a few regions, due to cultural,
language, social, and geographic factors.
9. This market clearing simply implies that a region's
wages are determined by that region's labor demand and
labor supply schedules, and should not be confused with
the interregional labor market equilibrium which implies
equal real wages across regions.

10. These data include the three-digit SIC category 239,
which includes nonapparel textile items such as carpets,
drapes, and automobile upholstery.
11. More recent employment data are available, but 1980
data are used because 1980 is the only year forwhich data
are available on UWs.
12. Researchers disagree about whether these problems
are important. According to Maram's 1980 study of Los
Angeles apparel workers, 39 percent of the UWs reported
making less than the minimum wage, and 82 percent
reported violations of overtime regulations. In sharp contrast, Cornelius' broader 1984 worker survey (reported in
(1988b)) reveals that only 2 of 177 firms paid their workers
the minimum wage, and "virtually all workers who worked
overtime were compensated for it."
13. The 1980 Census was the first that was designed
with the problem of undercounting minority and undocumented residents in mind. However, most observers
agree that the stock of UWs has been growing more or
less continuously at least for the past fifteen years.
14. A third alternative would be to construct a variable
that reflects the proportion of apparel employment in
nonproduction jobs. However, because there are nonproduction jobs other than design, and because the ratio
of nonproduction to production jobs varies with the type
of apparel produced, this variable does not reflect accurately the relative importance of the design function
across states.
15. The Hispanic population obviously is a very crude
proxy for the population of UWs, both because many
Hispanics are in the U.S. legally and because many UWs
are not Hispanic.

Economic Review I Winter 1989

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Federal Reserve Bank of San Francisco

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