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Bank Loan-Loss Accounting:
A Review of Theoretical
and Empirical Evidence
L A R R Y D . W A L L A N D
T I M O T H Y W. K O C H
Wall is a research officer in the Atlanta Fed’s research
department. Koch holds the South Carolina Bankers
Association Chair of Banking in The Darla Moore
School of Business, University of South Carolina. The
authors thank Lucy Ackert, George Benston, Mark
Carey, Gerald Dwyer, Robert Eisenbeis, Frank King,
and Joseph Sinkey for helpful comments.











had a common-stock registration statement pending
before the SEC. As part of its agreement with the
SEC to obtain approval for the registration statement, SunTrust agreed to restate prior years’ financial statements to reduce its loan-loss provisions in
each of the three years 1994 through 1996, resulting
in a cumulative reduction in its allowance for loan
losses of $100 million.
The SEC’s move to force a bank to change its loanloss accounting was foreshadowed in a speech by the
SEC’s chief accountant, Michael H. Sutton (1997).
Sutton expressed concern about banks’ loan-loss
accounting, noting that the SEC had received “a
number of inquiries, both domestically and internationally, that suggest that allowances for loan losses
reported by U.S. banks may be overstated.”1 He then
proceeded to remind his audience of the basic rules
for loan-loss accounting.2
Some bank analysts criticized the SEC’s action in
the SunTrust case and argued that the bank was

merely following conservative financial practices.
Sean J. Ryan, an analyst at Bear, Stearns, and
Company, said, “In our view, SunTrust’s record of
earnings stability is a function of a conservative
credit culture and fast-growing markets” (quoted in
Brooks 1998).
Bank regulators may also have cause for concerns
about the issue of banks’ loan-loss accounting. If a
bank’s loan-loss allowance exceeds its expected
credit losses, the bank can absorb more unexpected
losses without failing and imposing losses on the
Federal Deposit Insurance Corporation (FDIC) if
all else is held constant. Conversely, loan-loss
allowances less than expected losses will ultimately
reduce the bank’s equity capital. Such a deficit in the
loan-loss allowance implies that a bank’s capital ratio
overstates its ability to absorb unexpected losses.
Finally, banking organizations are worried that they
might be caught in a conflict between bank regulators
and the SEC. Bank regulators may demand higher

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


loan-loss allowances to provide a larger cushion
should economic conditions weaken, and the SEC
may require reduced loan-loss allowances to lessen an
organization’s ability to manage reported earnings
during such a downturn. To provide banks with some
guidance about appropriate reserves, the SEC and
bank regulators issued joint interagency letters in
November 1998 and in March and July 1999.3 The
three letters stress that depository institutions should
have “prudent, conservative, but not excessive, loanloss allowances that fall within an acceptable range of
estimated losses.” The March 1999 letter goes on to
promise a number of steps that the agencies would
take as a group as well as measures they would try
to take in cooperation
with the Financial
Accounting Standards
If a bank’s loan-loss
Board (FASB) and the
allowance exceeds its
American Institute
of Certified Public
expected credit losses, the
Accountants. In the
bank can absorb more
July 1999 letter, the
unexpected losses without
SEC also committed to
“consult with the
failing and imposing losses
appropriate banking
on the Federal Deposit
regulators as a part of
Insurance Corporation if all
the SEC’s process in
determining whether
else is held constant.
to take a significant
action in its review of
the accounting for a
financial institution’s loan-loss allowance.”
This article reviews the issues and available evidence on bank loan-loss accounting. It begins with a
discussion of the differing philosophies that color
approaches to loan-loss accounting and then reviews
underlying accounting rules and their justification.
An important theme is that recent SEC actions on
bank loan-loss accounting are consistent with the
philosophy underlying generally accepted accounting principles (GAAP) applied to all U.S. firms,
including those principles that apply to loan-loss
accounting. Next, the discussion considers the role
of loan-loss accounting from a bank-supervisory perspective, focusing particularly on the importance of
building up the loan-loss allowance under current
regulatory capital-adequacy standards. A review of
the relevant theoretical and empirical research follows. The article concludes with an analysis of the
policy issues involved in bank loan-loss accounting.

Philosophies of Loan-Loss Accounting
he analysis identifies at least three different
philosophies on loan-loss accounting. First,
the economist’s view of loan-loss allowance is


that it is intended to capture expected future losses
that will occur if a borrower does not repay according to the loan contract. In contrast, the primary
concern of the FASB is the measurement of a firm’s
net income over a given period. Thus, the FASB
focuses on losses expected to result from events
during a given period and explicitly excludes the
expected effect of future events; economists, on
the other hand, are concerned with expected
future events. A third philosophy views loan losses
as a type of capital that should be built up during
good times to absorb losses during bad times. This
perspective differs from that of the economist or
the FASB in that it recommends maintaining loanloss allowances greater than expected losses during good times. This philosophy of loan-loss
accounting is implicit in existing capital regulations, which include part of the loan-loss allowance
as an element of capital. If loan-loss allowances are
determined only in relation to expected future
losses, then banks with higher loan-loss allowances
do not have the capital necessary to absorb unexpected losses. These banks merely have higher
expected losses.
Which philosophy is most advisable depends upon
one’s purpose.4 The economist’s view is most relevant in pricing pools of loans to be sold on the secondary market. This perspective is also implicit in
any attempt that relies on historic price data to
value loans or estimate their riskiness. Arguably,
reported values based on the FASB’s philosophy
allow investors to determine the riskiness of a company’s earnings more effectively. The philosophy
upon which capital regulations are based may be
superior at reducing bank failures if it does indeed
result in an increased capacity for banks to absorb
unexpected losses.
Although investors and regulators may prefer an
accounting philosophy tailored to their needs, ultimately a bank’s reported loan-loss allowance is largely
under its managers’ control, and managers are likely
to use any available discretion to attain their own
goals. Thus, the real key to evaluating the different
philosophies is the extent to which investors and
regulators can combine the reported loan-loss numbers and other information to obtain reasonable
estimates of the loan-loss measure that best meets
their needs. If such estimates are possible, then the
prevailing philosophy used to produce that number
may not be very important. If the information is
available, what is really critical is that investors
and regulators understand any differences between the philosophy underlying reported loan-loss
allowances and the approach most relevant to their
respective concerns.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000

The largest firms routinely provide investors with
a wide range of information about their loan portfolios. Sophisticated investors can use, currently do
use, and will almost surely continue to use this
information to evaluate the adequacy of banking
organizations’ loan-loss allowances, regardless of
how banks are required to account for loan losses on
their financial statements. Thus, requiring banks to
provide investors with loan-loss allowances that
more accurately track current financial reporting
requirements will, at best, provide marginal gains to
investors in the form of more accurate estimates or
lower costs of analysis. Similarly, bank regulators
combine public financial information with their own
analysis of each bank’s confidential records to evaluate the adequacy of the loan-loss allowance. If regulators determine that a bank’s cushion for
absorbing losses is inadequate given the risks in its
portfolio, they have ample authority to require a
bank to increase the cushion or take less risk.
Moreover, the interests of investors and bank regulators are not necessarily in opposition. Both
investors and regulators benefit if banks follow consistent procedures in setting loan-loss allowances
that facilitate comparability of earnings and
allowances across banks and through time.
Moreover, bank regulators have the ability to prevent banks from declaring dividends to shareholders
or paying interest on subordinated debt; they may
even close a bank if they judge it to have inadequate
capital. If bank regulators judge a bank to have an
inadequate loan-loss allowance, this assessment is
important information for investors even if a bank
has an adequate loan-loss allowance by accounting

The Perspective of the Standards for
Financial Accounting
he value of a business enterprise is ultimately
determined by the extent to which its cash
inflows exceed its cash outflows.5 Thus, the


information that investors ultimately need relates
primarily to the net cash flow of the firm. However,
merely reporting cash inflows and outflows may be
misleading because the period in which cash expenditures occur is often different from the period in
which cash revenues are received. For example, a
bank that takes in a money market deposit on which
it pays interest in one year may lend the funds to a
borrower who will not be required to make either
interest or principal payments until the following
year. Reporting income on a cash basis in this case
would be misleading. Firms that issue publicly traded
securities are therefore required to follow what is
called accrual accounting, in which revenue and the
expenses associated with generating that revenue
are recognized in the same period.
Accrual accounting requires that loan losses be
recognized in the period in which they occur, even if
an individual loan is not charged off until a subsequent period. For example, suppose that a textiles
plant, the primary employer in a rural town with no
other nearby sources of employment, closes on
December 1, 2000. Even though on December 31,
2000, none of the loans made by a bank in that town
have defaulted, the bank knows that because of the
plant closing, a major portion of the loans will not be
repaid. Under accrual accounting, the bank is obligated to recognize a reduction in the value of these
loans, in turn reducing the bank’s accounting earnings for the calendar year 2000.
The specific procedure used to account for loan
losses is a multistep process. First, a bank compares
the value of its loan-loss allowance (an adjustment
of the value of its loans, which constitutes a contraasset account) with the losses it expects to incur
based on current economic conditions. If, as is normally the case, the expected losses due to past
events exceed the amount in the allowance, the
bank increases its loan-loss allowance and reports
the increase on its income statement as its loan-loss
provision (a noncash expense).6 As loans go bad

1. A brief review of the SEC’s recent concern about bank loan-loss accounting is also provided in Baskin (1999) and
Levitt (1999).
2. Although no bank has been directly accused of earnings management, excessive or deficient loan-loss allowances may be used
to manage earnings. Earnings management is a practice that has attracted considerable SEC attention (see Levitt 1998). Also,
see Loomis (1999) for a discussion of the SEC’s overall efforts to reduce corporate earnings management.
3. The agencies issuing the joint interagency letters include the SEC, the Federal Deposit Insurance Corporation, the Federal
Reserve Board, the Office of the Comptroller of the Currency (OCC), and the Office of Thrift Supervision (OTS). For the text
of these interagency letters, visit the SEC’s Web site at
4. A framework for viewing the conflicting policy positions being taken by the SEC and bank regulators is provided by Wall and
Eisenbeis (forthcoming).
5. The timing of the cash flows and the probability distribution of the cash flows (risk) are also important in valuing firms.
6. The expected losses due to past events would normally exceed the loan-loss allowance for two reasons. The first reason would
be in recognition of the expected losses on loans made during the period. Second, the loan-loss allowance would have been
reduced over the course of the period as specific bad loans from prior periods were written off.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


during the course of the next period, the loans are
not charged off directly against net income but
instead reduce the balance in the loan-loss
allowance.7 At the end of the accounting period, the
cycle renews as the bank again compares the
expected losses on outstanding loans due to past
events with the balance in its loan-loss allowance.
Key in determining the accounting value of a bank’s
loan-loss provision is the assessment of the appropriate loan-loss allowance at the end of each period.
Which procedure banks should use to arrive at the
appropriate loan-loss allowance is at the heart of the
SEC’s discussions with SunTrust and other banks.
Accounting procedures are also a critical concern of
bank regulators. A
detailed discussion of
the financial accounting standards for loan
Loan-loss allowances less
losses (with referthan expected losses will
ences) is presented in
ultimately reduce the bank’s
the box on page 6.
This examination of
equity capital. Such a deficit
accounting methods
in the loan-loss allowance
reveals that the SEC is
implies that a bank’s capital
not imposing a new
standard on banks but
ratio overstates its ability to
rather appears to be
absorb unexpected losses.
trying to induce banks
to follow long-standing
accounting guidance.
The discussion that
follows presents the highlights of the more detailed
analysis provided in the box.
The SEC has the authority to set the accounting
procedures for financial reporting by publicly traded
firms, and sometimes it establishes procedures
directly. However, in most cases the SEC defers to the
FASB, which the SEC has designated as the privatesector organization responsible for setting the standards of financial accounting and reporting.
The FASB has established a set of broad principles for financial accounting by all publicly traded
firms in its Statements of Financial Accounting
Concepts. In its Statements of Financial Accounting
Standards, the FASB has applied these concepts to
a variety of specific accounting problems, including
that of loan-loss accounting. The specific guidance
provided by the FASB and the SEC with regard to
loan-loss accounting generally follows the principles
laid out in the concepts papers.
Some bank regulators would prefer more conservative, future-oriented loan-loss accounting procedures that better serve regulators’ goals of
maintaining bank safety and soundness. The principles laid out in the Statements of Financial Ac4

counting Concepts argue against setting financial
accounting standards to attain the bank regulators’
goals, contending that accounting standards should
be geared to the needs of general-purpose users,
such as equity investors, who cannot compel firms to
meet their specific need for information. Bank regulators can and do compel banks to divulge detailed
financial information and, hence, do not need financial accounting standards tailored to their needs.
The FASB principles also stipulate that financial
statements should fairly present the income produced within the current reporting period. Thus,
financial statements should not anticipate future
events. The SEC has taken this principle to its logical limit, telling lenders, for example, that their
loan-loss allowance at the end of 1999 should not
anticipate losses due to computer programming
errors that failed to properly handle the century
date change (commonly known as the Y2K problem). Further, conservative assessments of a firm’s
assets are not a virtue in financial reporting since
conservative estimates of asset values in one period
generally result in the overstatement of the net
income in future periods.

The Perspective of Bank Supervisors
everal key bank regulators, including Comptroller John D. Hawke, FDIC Chairman Donna
A. Tanoue, and Office of Thrift Supervision
Director Ellen Seidman, have expressed concern
that the effect of the SEC’s actions on loan-loss
accounting may be to reduce bank loan-loss
allowances. A reduction in the allowance “could
have a profound effect on the continued safety and
soundness of America’s banking system and would
not, in our judgment, be in the best interests of
American taxpayers.”8
High loan-loss allowances are thought to increase
banks’ ability to absorb losses without becoming
financially distressed or failing if all else is held constant. However, the direct consequence of an increase in a bank’s loan-loss allowance is merely that
an accounting entry is made increasing the allowance
and reducing reported net income (by increasing the
expense account called provision for loan losses).
The reduction in net income has the direct effect of
reducing a bank’s retained earnings and, thus, its
owners’ equity. Therefore, the ultimate effect of an
increase in the loan-loss allowance is merely to
increase the allowance on paper while decreasing
both reported net income and owners’ equity.
Any contribution of an increase in a bank’s loanloss allowance must occur indirectly through its
effect on either a bank’s risk exposure or by inducing a bank to increase its equity capital. One chan-


Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000

nel through which an increase in the loan-loss
allowance may eventually boost capital is by reducing a bank’s ability to pay dividends. The dividend
payments of all banks with national charters and
many state banks are limited to the current year’s
reported accounting earnings plus retained earnings
from recent years (two years in the case of national
banks). An increase in the loan-loss allowance
reduces both reported net income and the transfer
to retained earnings. This constraint is most likely to
be binding during periods of recession and, thus,
would generally not limit banks during periods of
sustained economic growth such as the late 1990s.
A second channel through which higher loan-loss
provisions could affect banks is through banks’ ability to comply with regulatory capital guidelines.
Current U.S. standards are a combination of an international agreement on risk-based capital (the Basle
Accord) and additional domestic leverage (or total
assets) limits. The narrower measure of capital is
called tier 1 capital and includes a variety of equity
capital accounts including common stock, perpetual
preferred stock, and retained earnings. Total capital
includes not only tier 1 capital but also other types of
accounts (tier 2 capital) that would absorb losses
before they were borne by depositors. Such accounts
include limited-life preferred stock, subordinated
debt, and the loan-loss allowance to the extent that
it is not allocated for losses on specific loans. The
risk-based standards set minimum ratios for both
tier 1 and total capital whereas the leverage constraint sets a minimum ratio only for tier 1 capital.9
Inclusion of the loan-loss allowance in total capital may seem undesirable if loan-loss accounting is
based on the philosophies of expected losses
espoused by economists and by the FASB, which
perceives the intent of capital regulations as being

to provide a cushion to absorb unexpected losses.
However, many banks have historically maintained a
loan-loss allowance in excess of expected losses.
Compliance with the regulatory capital requirements is an important issue for U.S. banks. Banks
with regulatory capital ratios judged insufficient by
regulators may be refused permission to acquire
other firms. Banks classified as undercapitalized
may not pay dividends to their shareholders, may
not pay management fees to their holding company,
and are required to provide an acceptable plan for
raising their capital ratios. Banks classified as critically undercapitalized (with equity capital less than
2 percent of assets) may be closed by the regulators
even though their accounting capital could be positive and they are otherwise able to repay creditors
in a timely manner.
If a bank increases its loan-loss allowance, the
effect is to increase tier 2 capital while reducing tier
1 capital. If the transfer causes the tier 1 constraint
to become binding, the bank would be required to
issue more capital or reduce its measured risk. At
present, tier 1 ratios are generally not binding for
large U.S. banking organizations that must issue
public financial statements complying with GAAP.
Most of these banking organizations have capital
ratios well in excess of the regulatory requirements
and thus appear to have their own target tier 1
ratios.10 Increases in the loan-loss allowance at
these banks may cause them to issue more capital or
reduce their risk as measured under the risk-based
capital standards of the Basle Accord.
Bank regulators stress the importance of building
up the allowance during good times to reduce the
financial stress on banks during periods of high loan
losses. If a bank’s capital ratio falls below regulatory
requirements during a recession, the bank has two

7. If the bank unexpectedly recovers part of a loan that it had previously charged off, the recovery is added back to the loanloss allowance. See Sinkey (1998) and Walter (1991) for a further explanation of the process for setting loan-loss reserves
as well as a discussion of various ways the reserve has been historically determined.
8. The quote is taken from Barancik (1999). A review of the history of FASB, SEC, and bank regulatory guidance from the perspective of the bank regulators is provided by Tanoue (1999).
9. The capital requirements for state-chartered banks that are members of the Federal Reserve are provided in Regulation H.
Appendixes A, B, and E discuss the measures of capital adequacy, and Subpart D of the regulation implements the provisions of prompt corrective action for state member banks. Prompt correction action defines five categories of capital adequacy (well capitalized, adequately capitalized, undercapitalized, significantly undercapitalized, and critically
undercapitalized) and provides a series of discretionary and mandatory actions as a bank’s capital falls through the categories. State nonmember banks, national banks, and thrifts are subject to similar regulations promulgated by the FDIC, the
OCC, and the OTS, respectively. Bank holding companies are also subject to minimum capital adequacy standards. The measures of bank holding company capital adequacy are defined in Appendixes A–E of Regulation Y. Bank holding companies
are not directly subject to prompt corrective action. However, the primary assets of most bank holding companies are their
banking subsidiaries. Thus, the continued viability of most bank holding companies would be in doubt if a relatively large
banking subsidiary were closed due to inadequate capital.
10. The higher target ratios may reflect management’s desire to maintain a buffer in case an unexpected growth opportunity
arises or unexpected losses occur.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


B O X

Financial Accounting Standards for Loan Losses
ll banks must file financial statements with bank
regulators that conform to regulatory accounting
principles. The SEC further requires that all publicly
traded bank holding companies report to investors
using generally accepted accounting principles
(GAAP).1 The accounting rules under regulatory
accounting principles are not necessarily the same as
those of GAAP; in fact significant differences have
existed in the past, particularly the regulatory accounting principles rules issued by the Federal Home Loan
Bank Board for savings and loans. However, most of the
differences between regulatory accounting principles
and GAAP have been eliminated, in part because bank
regulators were instructed by Congress to require that
regulatory reports “shall be uniform and consistent
with generally accepted accounting principles” by
Section 121 of the Federal Deposit Insurance Corporation Improvement Act (FDICIA). Nonetheless,
bank regulators need not always follow GAAP. If GAAP
is “inconsistent” with minimizing the cost of resolving
failed banks, the banking agencies may “prescribe an
accounting principle which is no less stringent than
generally accepted accounting principles.”
Although bank regulators could prescribe more stringent standards, in practice they have chosen to require
banks’ loan-loss accounting to follow GAAP. Thus, to
understand banks’ loan-loss accounting, one needs to
understand the relevant pronouncements of the FASB.
Two types of FASB pronouncements help explain GAAP
accounting for loan losses. One is a series of Statements
of Financial Accounting Concepts that explains the
broad principles underlying GAAP as applied to all publicly traded U.S. corporations. The other is a number of
Statements of Financial Accounting Standards that
apply the broad principles to specific accounting issues.


Broad Principles
The FASB’s concept statements address several issues
that are central to the debate on bank loan-loss accounting. In particular, arguments frequently advanced by
bank regulators suggest that regulators’ goals of maintaining safety and soundness would be best served by
requiring loan losses that are (1) forward-looking and
(2) conservative. The concept statements argue against
the regulators’ position for each of the issues raised by
the regulators.


Statement of Financial Accounting Concepts No. 1:
Objectives of Financial Reporting by Business
Enterprises, issued by the FASB in November 1978,
notes in paragraph 24 that there are many users of corporate financial statements and that the various users
may have different needs. In paragraph 26, the paper
argues that some users of the financial statements may
have specialized needs but these users also have the
power to obtain the information. One example is government authorities that set taxes or the rates charged
by firms. Paragraph 28 indicates that the objectives of
the statement “stem primarily from the informational
needs of external users who lack the authority to prescribe the financial information they want from an enterprise.”2 The focus on investors in the concept statement
matches the SEC’s focus on investor protection. This
focus also suggests that when a conflict arises between
investors’ needs and bank regulators’ needs, GAAP
should focus on investors because bank regulators
can compel banks to provide them with any required
A second important issue raised by the regulators is
the extent to which expected future economic conditions
should be considered in setting an appropriate loan-loss
allowance. For example, economic conditions in 1999
were in many ways extremely favorable for the financial
condition of borrowers. A combination of technological
developments, domestic macroeconomic polices, and the
slowdown in some Asian countries in 1999 combined to
produce a year of rapid economic growth with low inflation. While such strong economic conditions are possible
and perhaps even likely in 2000, historical experience
would suggest that any change in economic conditions
would be likely to have adverse implications for banks’
loan losses. Thus, banks would probably report higher
loan-loss allowances if they took weighted averages of the
possible scenarios for 2000. However, the Statements of
Financial Accounting Concepts emphasize that accrual
accounting focuses on the events that occur within
a reporting period. For example, paragraph 44 of
Statement of Financial Accounting Concepts No. 1 states,
“Accrual accounting attempts to record the financial
effects on an enterprise of transactions and other events
and circumstances that have cash consequences for an
enterprise in the periods in which those transactions,
events, and circumstances occur.”

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000

The third issue raised by the regulators is whether
financial statements should place higher priority on
conservative estimates of the value of the firm’s assets
or on providing accurate measures of their income. At
one time, accountants emphasized conservative estimates of earnings and assets. In May 1980 the FASB
issued its Statement of Financial Accounting Concepts
No. 2: Qualitative Characteristics of Accounting
Information, which states in paragraph 93, “The convention of conservatism, which was once commonly
expressed in the admonition to ‘anticipate no profits
but anticipate all losses,’ developed during a time when
balance sheets were considered the primary (and often
only) financial statement and details of profits or other
operating results were rarely provided outside business enterprises. To the bankers or other lenders who
were the principal external users of financial statements, understatement for its own sake became widely
considered to be desirable, since the greater the understatement of assets the greater the margin of safety the
assets provided as security for loans or other debts.”
However, firms have since been required to provide
additional financial information, and the users of financial statements now often include investors in equity
and debt obligations who are primarily concerned with
the cash flow of the firm. Such investors may find earnings to be a more helpful indicator. Paragraph 43 of
Statement of Financial Accounting Concepts No. 1
states, “The primary focus of financial reporting is information about an enterprise’s performance provided by
measures of earnings and its components.” Paragraph
94 of Statement of Financial Accounting Concepts No. 2
continues, “Once the practice of providing information
about periodic income as well as balance sheets
became common, however, it also became evident that
understated assets frequently led to overstated income
in later periods.” Paragraph 96 adds, “The Board
emphasizes that any attempt to understate results consistently is likely to raise questions about the reliability
and integrity of information about those results and
will probably be self-defeating in the long run. . . . As a

result, unjustified excesses in either direction (conservative or unconservative) may mislead one group of
investors to the possible benefit or detriment of others.” Consistent application of these Statements of
Financial Concepts to loan-loss accounting would
require banks to place priority on accurate estimates
rather than conservative estimates of expected losses
due to past events when establishing their loan-loss

Accounting for Loan Losses
The general standard for setting loan losses is established by Statement of Financial Accounting Standards
No. 5: Accounting for Contingencies (1990a).
Paragraph 8 of Statement 5 sets two standards for
accruing an estimated loss from a loss contingency
(including losses on loans): “(a) Information available
prior to issuance of the financial statements indicates
that it is probable that an asset had been impaired or a
liability had been incurred at the date of the financial
statement. It is implicit in this condition that it must be
probable that one or more future events will occur confirming the fact of loss. (b) The amount of the loss can
be reasonably estimated.” Further guidance for measuring and disclosing losses on loans that “are individually deemed to be impaired” is offered in
Statement of Financial Accounting Standards No. 114:
Accounting by Creditors for Impairment of a Loan
(1993) according to Leonard, Lucas, and Seidman
(1999; emphasis in original).3
The conditions for recognizing a loan loss in
Statement of Financial Accounting Standards No. 5 are
consistent with the subsequent Statement of Financial
Accounting Concepts in several ways. Statement 5
focuses on the requirements of general purpose users
of financial statements who need “accurate” reported
net income rather than those of creditors, who might
benefit from conservative valuations. The statement
requires not merely that losses must stand a significant
chance of occurring but additionally stipulates that
losses must be “probable.” Moreover, Paragraph 59 of

1. Banks are specifically exempt from SEC disclosure requirements. However, nonbank firms that control banks—bank holding companies (BHCs)—are not exempt from SEC requirements. This structure gives the SEC direct power over banks’ accounting since virtually all major banking organizations are organized as BHCs and issue publicly traded securities.
2. While creditors and equity investors cannot compel the production of information, this restraint does not imply that they have no
influence over the information they receive about the firm. Potential creditors demand a higher rate of return and potential equity
investors pay a lower price for the stock of firms that they believe are providing inadequate information.
3. Statement 114 was subsequently amended by Statement of Financial Accounting Standards No. 118: Accounting by Creditors for
Impairment of a Loan—Income Recognition and Disclosures (1994).

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


Statement 5 is clear in stating that loan-loss provisions
should reflect events occurring within the reporting
period and not anticipate future events: “Further, even
losses that are reasonably estimable should not be
accrued if it is not probable that an asset has been
impaired or a liability has been incurred at the date of an
enterprise’s financial statements because those losses
relate to a future period rather than the current or a
prior period. Attribution of a loss to events of the current or prior periods is an element of asset impairment
or liability incurrence.”
The SEC has interpreted this statement to mean
that banks should not even take account of known
events that will affect loan losses if these events occur
outside the period. For example, in accounting for
increases in loan losses due to disruptions arising from
the Year 2000 computer-programming problems, the
SEC has told companies that their loan losses for 1999
should be based on events in 1999 only. Since the
“event” that causes the losses happens in the year
2000, the SEC pronouncement implies that the associated loan losses should be recognized in the year 2000
(see SEC 1998).
In terms of identifying loan losses, Statements 5 and
114 provide some further guidance. Statement 5
defines probable as “the event or events are likely to
occur.” Statement 5 does not require that all of the
loan-loss allowance be associated with specific loans.
Paragraph 22 permits loan losses “even though the particular receivables that are uncollectible may not be
identified.” Statement 114 provides some guidance on
how to measure the amount of the loss.
The guidance provided by Statements 5, 114, and
118 is supplemented by specific suggestions to banks
for estimating loan losses in the publication Audit and
Accounting Guide for Banks and Savings Institutions


by the American Institute of Certified Public Accountants
(1999). This publication suggests a number of factors
that banks may consider in setting loan-loss allowances,
but it does not provide an exact formula for calculating
the loan-loss allowance. Management must use its
judgment in selecting the right factors to consider for
individual loans or groups of loans. Further, management’s analysis may point to a range of losses rather
than indicate a single figure, and management must
use its judgment to select a figure from the range.
The SEC provided further guidance to banks with
Financial Reporting Release 28 (FRR-28), Accounting
for Loan Losses by Registrants Engaged in Lending
Activities. FRR-28 requires that banks have a systematic process for establishing a range of loan losses.
Further, the loan-loss allowance reported by a bank
should fall within the range of loan losses estimated by
the procedure. Chief Accountant of the SEC Lynn E.
Turner stated in a February 10, 1999, speech, “While the
staff understands that the determination of the
allowance is a process that involves judgment, we
believe that there should be documentation . . . which
clearly supports the estimated range of credit losses
inherent in the portfolio. We would question instances
where the recorded allowance is outside (higher or
lower) of this estimated range of probable credit losses”
(emphasis in original).
FRR-28 also calls for disclosure of the process used
to estimate loan losses and stipulates that the reported
allowance be consistent with the discussion in the
Management Discussion and Analysis section.
Speaking to the AICPA Bank and Savings Institutions
Annual Conference, Turner (1998) states, “You cannot
have it both ways: significant issues requiring higher
levels of allowances require full and fair disclosure of
those issues to investors.”

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000

options for significantly raising its tier 1 capital ratio
in the short run: (1) issue new equity capital or
(2) reduce its risk exposure as measured by the
Basle Accord. From the regulators’ viewpoint both
of these responses are problematic. Banks experiencing losses and falling capital are likely to have
relatively low stock prices, and their managers may
be unable or, more likely, unwilling to issue new
stock at what they perceive as a distressed price. Yet
the alternative—reducing measured risk exposure
solely to comply with the capital requirements—
may lead to decreased lending solely to lessen measured risk exposure. Such a reduction in lending may
cut loans to otherwise creditworthy borrowers, potentially creating a credit crunch that might worsen an
economic downturn.11
Further, building up the loan-loss allowance
account during good times and using part of the
increase to absorb losses during economic downturns is not necessarily manipulating income from a
regulatory perspective. Regulators believe that most
bad loans are made during good economic times.12 A
strong economy that helps banks increase earnings
also encourages banks to relax their underwriting
standards and take greater risks. During economic
downturns some of these bad loans are revealed.
Thus, requiring banks to bolster their allowances
during good times is merely forcing them to
acknowledge that they are probably making problem loans that will not be revealed until the next
recession. Allowing banks to operate with low
allowances that do not reflect the buildup of weak
loans exacerbates the problem of less strenuous
underwriting standards by increasing the reported
profitability associated with risky lending.

Theory and Evidence on Bank Loan-Loss
Accounting Practices
umerous studies have examined the recent
use of accounting policies to manage earnings. Some studies address the broad issue
of management of reported earnings. Others focus
specifically on the use of loan-loss accounting as a
tool for managing reported earnings and capital.
This literature addresses several important questions: (1) Do banks use loan-loss accounting to manage reported capital and earnings? (2) Have bank
regulators been consistent in requiring banks to follow conservative accounting policies on loan losses?


(3) If banks manage earnings, does doing so generate costs for investors or firms? (4) If banks manage
earnings, does doing so generate additional costs for
the bank regulatory system? In brief, the literature
provides conflicting evidence on the nature of bank
earnings management and suggests that bank regulators had not consistently required banks to follow
conservative policies before FDICIA; that investors
can see through loan-loss accounting but that earnings management may nevertheless generate costs;
and that even though bank regulators may see
through loan-loss accounting, individuals overseeing
the regulators may sometimes be deceived.
Is Loan-Loss Accounting Used to Manage
Earnings and Capital? If financial and managerial
labor markets were strong-form efficient, in the
sense that prices fully reflected all information, and
if regulators relied on market-value information,
banks would have no incentive to manage their
reported financial statements. Financial statements
would not affect how markets or regulators evaluate
banks or their managers. Thus, a good starting point
for assessing the management of loan-loss accounting is with a consideration of what incentives firms
might have to manage earnings and capital. If it is
established that firms have incentives to manage
earnings, then the next step is to look for evidence
that loan-loss accounting is managed to obtain earnings or capital targets.
Incentives to Manage Earnings and Capital.
One requirement for financial and labor markets to
be strong-form efficient is that the marginal cost of
obtaining and analyzing information must be zero. If
either is costly, decisionmakers (investors and
boards of directors) must weigh the costs against
gains. Further, incentives arise to find ways of structuring information production to obtain the most
efficient disclosure and analyses. For example, Dye
(1988) provides a model in which earnings management may maximize shareholder wealth for two reasons: (1) the cost-minimizing contract that spurs
managers to maximize firm value may also encourage earnings management, and (2) the firm may be
able to improve the terms of its contracts with outsiders by managing earnings.
Degeorge, Patel, and Zechhauser (1999) discuss
psychological evidence that all humans use thresholds in evaluating information. For example, people
may use rules of thumb to reduce transaction costs,

11. See Hancock and Wilcox (1998) and the sources cited therein for a discussion of the literature on the credit crunch. Also
see Eisenbeis (1998) for a critique of Hancock and Wilcox (1998).
12. See, for example, comments by Federal Reserve Bank of New York President William J. McDonough as quoted by
Cope (1999).

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


in this case the costs of obtaining and processing
information.13 The authors note that three thresholds may be relevant for reported earnings: zero
earnings, prior year’s earnings per share, and stock
analysts’ earnings expectations.
These thresholds may be important to investors.
For example, Barth, Elliott, and Finn (1999) demonstrate that firms able to sustain increases in reported
earnings per share over several years have higher
price-earnings ratios than other firms. These firms
also experience larger stock price declines relative
to earnings multiples at other firms when they
report an earnings decrease after a previous pattern
of increases. Holthausen, Larcker, and Sloan (1995)
and Guidry, Leone,
and Rock (1999) furThe key to evaluating the
ther show that managers respond to the
different accounting philoearnings target in
sophies is the extent to
their compensation
which investors and regucontracts.
Firms may have an
lators can combine the
incentive not only to
reported loan-loss numbers
manage accounting
and other information to
earnings but also to
manage accounting
obtain reasonable estimates
capital. Whether loanof the loan-loss measure
loss accounting influthat best meets their needs.
ences the market’s
evaluation of a bank’s
capital adequacy is
unclear. However, reported accounting capital plays
an important role in the regulator’s evaluation of a
bank’s capital adequacy as noted above. Wall and
Peterson (1987; 1995) find further evidence that in
most cases regulatory capital adequacy constraints
are binding on a bank’s management of its capital
adequacy ratios.
Theory of Earnings and Capital Management.
If firms could costlessly attain their targets for
reported income and capital, they would always
attain these targets. However, even a casual review
of the business press suggests that firms sometimes
miss their targets, which in turn suggests that firms
are not able to manage earnings costlessly. Given
that earnings management is costly over some
range, a question arises as to exactly how firms
should use their discretion over reported earnings.
Degeorge, Patel, and Zechhauser (1999) provide
a two-period model in which managers manage
reported earnings to maximize their own compensation, which is a function of reported earnings.14
Their model does not distinguish between managing
via the timing of real actions (investment, sales,
expenditures, or financing) and managing via con10

trol over the reporting of discretionary elements of
accounting. The starting point of their analysis is
with “latent earnings,” which are the earnings the
firm would report if its loan-loss provision were set
to its correct value. In their model, the firm’s latent
earnings may reflect one of three situations: (1) The
firm may be so far below the threshold that trying to
reach it via managing earnings would be too costly.
In this case, the firm seeks to report earnings less
than its latent earnings, an approach they call “saving for a better tomorrow.” (2) If the firm is below its
target but reaching the target is not too costly, managers use their influence to boost reported earnings
and achieve the target, a process they describe as
“borrowing for a better today.” (3) Firms above the
target reduce their current reported earnings (up to
some point) to be able to report higher earnings in
the next period, a process they call “reining in.”
Both the amount of saving for tomorrow and the
amount by which earnings are reined in are capped
in their model with certainty.
Koch and Wall (1999) focus more specifically on
the use of accounting expense accruals to help manage reported earnings. They develop a two-period
model of the use of accounting accruals in which
managers seek to maximize their own expected discounted earnings subject to constraints imposed by
auditors. Four different outcomes are possible in
their model, depending upon the parameters of the
managerial compensation function.15 One outcome,
which is identical to the results of Degeorge, Patel,
and Zechhauser, is called the Occasional Big Bath. A
second outcome is that firms always move toward
their reported earnings target in the first period, a
result they call Income Smoother. A third possible
outcome is that the firm always minimizes its loan
losses to report the highest possible income, an outcome that they call Live for Today.16 The fourth possibility, called Maximize Variability, may result in
firms moving away from their current earnings target.
Evidence of Earnings and Capital Management. While this analysis focuses on how banking organizations manage earnings via their
loan-loss provisions, the SEC is concerned with the
use of accounting discretion to manage earnings in
general and not just in the use of such discretion by
banks. Two studies of the distributions of firms’
reported earnings provide substantial evidence that
firms in general are managing their earnings.
Burgstahler and Dichev (1997) look at firms’ earnings in relation to two thresholds: zero earnings and
last year’s earnings per share. Their sample consists
of all firms on the annual industrial and research
Compustat databases for the years 1976 to 1994,
excluding only banks, financial institutions, and

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000

firms in regulated industries (utilities). They plot
histograms of the distribution of earnings scaled by
market value and find a statistically significant dip in
the plots immediately below both of their thresholds. This dip is consistent with the hypothesis that
firms manage their earnings. Specifically, should
their underlying or latent accounting earnings prove
slightly less than a firm’s threshold, the firm undertakes measures to boost reported income.
Degeorge, Patel, and Zechhauser (1999) look at
the distribution of earnings in relation to the earnings expectations of stock analysts as well as the
two thresholds examined in Burgstahler and Dichev
(1997). Degeorge, Patel, and Zechhauser’s sample
consists of 5,387 firms providing partial or complete
data over the period from 1974 to 1996. They plot
earnings per share around some critical thresholds
but fail to find evidence of a dip prior to the threshold, a result that they argue represents methodological differences with Burgstahler and Dichev.
However, they do find statistically significant evidence of a pileup of observations at exactly the
threshold, which is also consistent with earnings
The results presented by these analysts do not
prove that the firms in the sample were using discretionary accounting policies to hit their earnings
targets. Indeed, Burgstahler and Dichev (1997)
found evidence that firms were managing cash flow
from operations and changes in working capital but
not through accruals merely to attain their earnings
thresholds. Although the concern about earnings
management is not limited to banks, these results
do not necessarily apply to banks, which were
excluded from their sample.
A number of studies have focused specifically on
the issue of firms’ use of accruals to manage reported

earnings. These studies, which are summarized in
Table 1, typically model banks’ loan-loss allowances
as a linear function of some fundamental explanatory variables, such as total loans and nonperforming loans, and of an earnings and capital target.
These empirical models, which generally predate
the theoretical models, implicitly assume that banks
are always moving toward their earnings target and
thus implicitly are based on a model similar to Koch
and Wall’s (1999) “Income Smoother.”
Although these analyses reach somewhat similar
conclusions, they also reveal important differences,
as shown in Table 1. These studies estimate the
amount of the loan-loss provision required to cover
expected losses and treat the remaining provision as
what is called discretionary loan-loss provision.
Each of the studies that has examined capital has
concluded that banks use their loan-loss accounting
to manage capital. However, they do not reach a
consensus about the direction of this effect. Collins,
Shackelford, and Wahlen (1995), Beaver and Engel
(1996), and Ahmed, Takeda, and Thomas (1999)
conclude that the discretionary loan-loss provision
is negatively related to capital; Beatty, Chamberlain,
and Magliolo (1995) conclude that discretionary
loan-loss provision is positively related to capital.
Disagreement also exists about whether banks use
discretionary loan-loss provision to manage earnings: Collins, Shackelford, and Wahlen and Beaver
and Engel find that they do; Beatty, Chamberlain,
and Magliolo and Ahmed, Takeda, and Thomas find
that they do not. One possible explanation for the
differences is that the studies used different sample
periods. If incomplete managerial discretion over
the loan-loss provision is a factor or if the asymmetry in costs is important, then the results may
depend on the sample period. For example, if the

13. Another interpretation of the use of thresholds may be loosely based on Persons’s (1997) argument that managerial lying may
sometimes be efficient. In his model, managers sometimes provide false reports to reduce inefficient monitoring and contract renegotiation. In the context of financial reporting, one possibility is that a firm’s board of directors and investors expect
management to use its accounting discretion to attain at least a particular threshold. Truthful accounting could lead to
excessive monitoring given that the threshold is somewhat arbitrary and that accounting data contain some noise. Instead,
the manager may be allowed limited discretion in managing accounting earnings given the common knowledge that failure
to attain the threshold would induce additional monitoring by the directors and a drop in stock price in financial markets. In
such a case, failure to attain the threshold would be a bad signal, suggesting that the firm’s underlying performance fell so
far below the threshold (in that period or cumulatively over several periods) that the firm lacked sufficient discretion to
attain its threshold. If everyone shared this set of beliefs, then additional monitoring by the board of directors (possibly leading to reduced managerial compensation or even termination) and a substantial drop in the firm’s stock price would be
14. Healy (1985) provides an earlier model that generates similar results with a variable bonus for exceeding the target. The key
to the similarity of results is that Healy imposes a cap on the magnitude of the variable bonus.
15. The model also permits the existence of interior solutions that depend on neither discontinuities in the managerial compensation function nor outside constraints on managerial discretion. However, nothing in the model guarantees the existence
of such interior solutions.
16. This outcome is possible if the manager places a very high discount rate on next period’s income.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


T A B L E 1 Summary of Loan-Loss Studies



Beatty, Chamberlain, and Magliolo (1995)




Collins, Shackelford, and Wahlen (1995)




Beaver and Engel (1996)




Ahmed, Takeda, and Thomas (1999)


Negative pre-1991



difference between book loan-loss allowance and
economic loan-loss allowance is near the auditors’
maximum allowable discrepancy, firms may not be
able to reduce their reported loan-loss provision to
obtain earnings targets. The auditor may insist on
minimum levels of loan-loss provision to maintain
the loan-loss allowance at acceptable levels.
Similarly, if the expected costs of missing the capital target during certain periods are large relative to
the costs of missing the earnings target (such as
when banks generally have low capital ratios), managers may choose to forgo attaining earnings targets
if necessary to achieve their capital targets.
Evidence that firms manage reported earnings to
help attain regulatory capital standards is provided by
Moyer (1990) and Scholes, Wilson, and Wolfson
(1990). Moyer finds that reported loan charge-offs, the
loan-loss provision, and the securities gains or losses
are all managed to help attain regulatory capital
adequacy guidelines. Scholes, Wilson, and Wolfson find
evidence consistent with the hypothesis that banks
choose to realize gains and defer losses to increase
their regulatory capital.
Summary of Evidence on Management via
Loan-Loss Accounting. More work is needed to
fully understand both the theory and practice of
banks’ loan-loss accounting. However, the available
evidence clearly suggests that banks have an incentive to use loan-loss accounting to help manage
reported earnings and capital. Further, the evidence
suggests that banks in general are using loan-loss
accounting to help manage the earnings and capital
they provide in their financial statements.
Have Bank Regulators Consistently
Required Conservative Loan-Loss Accounting?
One defense of requiring banks to follow GAAP
accounting is that past deviations from GAAP have
been very costly. Harvey J. Goldschmid, General
Counsel of the SEC, made this argument in testimony before the U.S. House of Representatives
Subcommittee on Financial Institutions and
Consumer Credit of the Banking Committee and
Financial Services Committee (1999). In particular,
he made numerous references to the problems that
arose for the Federal Savings and Loan Insurance

Corporation (FSLIC) when thrifts were allowed to
follow regulatory accounting principles that
allowed them to report higher capital than would
have been permitted under GAAP. Goldschmid was
correct in arguing that thrifts’ regulatory accounting principles in the 1980s were less stringent than
GAAP and that providing funds to honor the
FSLIC’s commitments imposed a substantial cost
on the U.S. Treasury. However, failure to adhere to
GAAP for loan-loss accounting was not the principal problem, and adherence to GAAP may not have
eliminated the losses to the Treasury. A substantial
part of the problem was that thrifts were allowed by
(indeed, required by) GAAP to carry long-term,
fixed-rate mortgages on their books at historic cost
even after the market value of these loans had
dropped substantially due to a large jump in market
interest rates.
A better example of how deviations from GAAP
accounting for loan losses can create a misleading
picture of banks’ financial condition over an extended period is provided by the way in which large
banks accounted for loans to less developed countries (LDCs) in the 1980s. Indeed, former FDIC
Chairman L. William Seidman stated that “U.S. bank
regulators, given a choice between creating panic in
the banking system or going easy on requiring banks
to set aside reserves for Latin American debt, had
chosen the latter course. It would appear the regulators made the right decision” (1993, 127). After
the fact, it appears that regulators made the “right
decision” because the affected banks were able to
generate enough earnings to allow them to report
adequate capital levels when they finally did
acknowledge the losses. This example of regulatory
forbearance in loan-loss accounting did not result in
the Treasury actually bearing losses. However, if
banks had been unable to generate sufficient earnings and had taken increased risk to boost their capital ratios, the cost to the Treasury from this
forbearance might have been substantial.
Today the Federal Deposit Insurance Corporation
Improvement Act gives regulators explicit directions to follow GAAP unless more stringent accounting would better protect the deposit insurance

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000

fund. Thus, one could argue that even if the regulators had exercised forbearance in the past, they
would not do so after FDICIA. Fortunately the
banking environment has been relatively benign
since the passage of FDICIA, so this requirement is,
as yet, untested.17
Does the Management of Loan-Loss Accounting Increase Costs to Investors or Managers?
Evidence that banks’ management of loan-loss
accounting deceives investors would provide important support for the SEC’s concern about the management of reported loan-loss allowances. The
available evidence, however, suggests that investors
understand that banks may use their loan-loss
accounting to manage their financial reports.
Anecdotal evidence that investors see through
banks’ loan-loss accounting is provided by R. Harold
Schroeder, a senior equity analyst in the research
group at Keefe, Bruyette & Woods, in his testimony before the U.S. House of Representatives
Subcommittee on Financial Institutions and
Consumer Credit of the Banking Committee and
Financial Services Committee. He argues that
“investors generally are able to separate ‘true core
earnings’ from ‘earnings management.’ ” He states
that “over time banks develop a reputation based on
past loss experiences that the market can readily
assess and take into consideration in evaluating the
quality of a specific bank’s earnings” (1999, 327).
Empirical studies generally support the claim that
investors see through the use of loan-loss accounting to manage earnings and capital. One set of tests
looks at investors’ responses to announcements that
imply higher loan losses between financial reports.
If investors are able to reasonably accurately anticipate losses before banks announce their loan-loss
provisions, then the provisions may not add much to
investors’ information set. Tests focusing on banks’
stock market returns around the time of announcements related to loans to less-developed countries
(principally Latin American countries) in the 1980s
are consistent with this conjecture. Musumeci and
Sinkey (1990b) analyze the case of the Brazilian
debt moratorium in 1987. They note that in 1987
banks provided information about their exposure in
Brazil to investors and reported that an active secondary market existed in LDC debt. They find that
investors acted rationally in responding to Brazil’s
actions and thus reduced banks’ stock prices in proportion to their individual exposure in Brazil—prior

to the release of the banks’ financial statements.
Thus, the market formed estimates of banks’ losses
on these loans prior to their recognition on the
banks’ financial statements.
The announcement of a debt moratorium by
Mexico in 1982 provided another test of financial
market response to news of loan quality problems.
While investors knew that many publicly traded
banks were making loans to Mexico and other LDCs,
banks were not disclosing their exposure to individual countries nor did they acknowledge a secondary
market in LDC debt. Nevertheless, studies by both
Bruner and Simms (1987) and Smirlock and Kaufold
(1987) find that the risk-adjusted returns of bank
stocks accurately reflected their exposure
to Mexico within, at
most, a few days of
Both investors and regulathe announcement of
tors benefit if banks follow
the debt moratorium.
consistent procedures
Addressing the question of banks’ loanin setting loan-loss
loss accounting more
allowances that facilitate
specifically, Musumeci
comparability of earnings
and Sinkey (1990a)
examine bank stock reand allowances across
turns around the time
banks and through time.
of Citicorp’s announcement (and subsequently by other large
banks) to increase
loan-loss allowances to provide for losses associated
with loans to LDCs. They find significant positive,
abnormal stock returns after the Citicorp announcement. Such a market response would be almost
impossible to rationalize if one believed that the
large banks’ accounting for the LDC loans had
deceived investors. However, if the market had
already adjusted Citicorp’s earnings and capital for
the unrecognized loan losses, the positive reaction
may be rationalized in a variety of ways. For example, the increased allowance may have been taken as
a signal that the bank would restructure to boost
earnings or take a more aggressive approach to collecting the loans to LDCs.
Empirical analysis of security prices and returns
in relation to banks’ loan-loss accounting also provides evidence that investors see through banks’
management of their loan-loss accounting. Beaver
and Engel (1996) estimate the nondiscretionary

17. Kane (1997) argues that the incentive structure facing regulators is an important determinant of their behavior. He suggests
that prior to FDICIA many of the incentives provided to regulators encouraged them to exercise forbearance on large, financially weak banks. He argues that FDICIA only partially corrects the problem.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


part of banks’ loan-loss allowance by regressing the
allowance on a variety of fundamental variables
including charge-offs and nonperforming loans. The
difference between the actual and estimated
allowance is assumed to be discretionary (plus a
random, mean-zero error). Beaver and Engel then
regress the market value of the bank on several variables, including net income, loan-loss allowance,
and discretionary loan-loss allowance. They find
that the coefficient on the total loan-loss allowance
is significantly negative (higher allowance implies
lower market value) but that the coefficient on
the discretionary part of the loan-loss allowance
is insignificant. This finding suggests that market participants see
through the reported
loan-loss allowance
Empirical evidence suggests
and place different
that banks’ loan-loss
values on the discretionary
and nondisaccounting policies reflect
cretionary portions.
more than just anticipated
Even if investors
future losses. Thus,
see through earnings
management without
investors would generally
incurring costs, it is
benefit from forming their
not necessarily costown estimates of each
less to firms and their
investors. Stein (1989)
bank’s expected loan losses.
develops a model in
which managers may
borrow from future
earnings to boost the current period’s reported net
income and managers’ compensation. This process
of borrowing from future earnings reduces the net
present value of the firm. Investors know that managers can and do borrow from future earnings, but
they cannot determine the extent of borrowing if it
is constrained within some limits. Even though borrowing from future earnings is irrational in the sense
that it reduces the expected returns to both managers and investors, shareholders anticipate earnings management such that a firm reporting low
earnings is assumed to have low permanent earnings. Therefore, if earnings management is anticipated and managers care about their firm’s share
price in the short run, individual managers will predictably attempt to borrow, and the market’s expectations are correct.
A firm’s management of loan-loss accounting is
not equivalent to borrowing from future earnings in
Stein’s (1989) model. Nevertheless, such management could impose costs on firms and investors. For
example, earnings management could be costly to
banks if their loan collection efforts were influenced
by accounting policies. Musumeci and Sinkey

(1990a) interpret Citicorp’s LDC loan provisioning
in 1987 as a measure that allowed it to take a
tougher negotiating stance with LDC borrowers.
MacDonald (1999) provides evidence that earnings
management may be costly to analysts and
investors. MacDonald quotes Michael Mayo, a bank
analyst at First Boston, as saying, “The reported
profits number is now considered an accounting fiction.” She reports that a number of stock analysts
are reducing the importance of reported earnings
and substituting an estimate of cash flow. While
finance theory suggests that investors should be
concerned about cash flows, the increased emphasis
on cash flows creates problems. Because the cash
account is subject to substantial manipulation, as
noted in the section on financial accounting standards, stock analysts do not literally look at changes
in it. Instead, they take pretax net income and
adjust it for items that do not have direct cash flow
implications, such as depreciation, to obtain a cashearnings estimate. There is nevertheless no common way of calculating cash flow. The resulting
confusion may increase costs to investors attempting to evaluate the stock recommendations of different analysts.
Does the Management of Loan-Loss Accounting Increase Costs to Bank Regulators or
Those Overseeing the Regulators? If regulators
were deceived by banks’ loan-loss accounting, it
could have serious implications for their ability to
reduce losses to the FDIC from bank failure.
However, such deception appears unlikely given
that regulators regularly send examiners to review
individual banks’ loan portfolios. Supporting evidence that examiners are not deceived is provided
by Dahl, O’Keefe, and Hanweck (1998), who find
that bank examiners use their independent analysis
of bank loan portfolios to influence the timing of a
bank’s loan-loss recognition.
A bigger concern is that misleading loan-loss
accounting may confuse taxpayers and their representatives in Congress. Kane (1997) suggests that
government regulators should be viewed as selfinterested agents serving both the regulatees and
the taxpayers. He observes that regulatees will pressure regulators in a variety of ways not to impose
discipline on the industry, including job offers to
“good regulators” and public criticism of “bad regulators,” and that regulatees will also circulate misinformation to discourage proactive regulatory
intervention. Kane argues that senior regulators
often have incentives to cooperate with the industry
in putting out disinformation to discourage intervention by taxpayers and their representatives. In
particular, he points to the savings and loan debacle:

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000

the problem developed over many years, but its
existence was first denied, and then its magnitude
was underestimated.
A partial solution to the issues discussed by Kane
(1997) is to provide the public with information that
does not underestimate taxpayers’ exposure to loss.
One important aspect of such information is banks’
loan-loss allowances. If taxpayers are to assess their
exposure accurately, they need reasonable estimates of expected loan losses. Kane’s analysis provides some support for a policy of preventing banks
from using their loan-loss accounting to manage
earnings and capital. However, his analysis does not
provide unqualified support for the position of the
financial accounting authorities. Taxpayers underestimate their exposure to loss in Kane’s analysis
when banks’ allowances understate expected losses
arising in response to events in any period.
Allowances that somewhat exceed expected losses
may not cause them to underestimate their exposure. Further, taxpayers’ interest in loan losses is
not limited to those precipitated only by events
prior to the end of a reporting period—the approach
currently required by GAAP. Taxpayers risk exposure from all sorts of losses, including those that
occur because of expected future events. Thus,
while banks’ adherence to GAAP for loan-loss
accounting may be an improvement over their loanloss accounting practices in some prior periods, taxpayers’ interest may be even better served if banks’
loan-loss accounting were more conservative and
forward-looking than GAAP permits. Moreover,
GAAP accounting prohibits the recognition of losses
due to changing interest rates for a large part of
banks’ portfolios.

Public Policy Issues
f banks follow the loan-loss accounting procedures designed to meet regulators’ needs most
effectively, the outcome would be substantial
income smoothing when compared with the FASB’s
approach, which measures income as the result of
events happening during the period ending on the
date of the financial statements.18 Conversely, if
loan-loss accounting follows the approach advocated
by the FASB, banks may have a smaller cushion for
absorbing expected losses arising because of
expected future events. Taking the comments of the
respective sides at face value provides a potential
public policy dilemma. Two unsatisfactory options
present themselves: (1) to provide investors with


the information they need to value firms and force
banks to be underreserved and, hence, possibly
undercapitalized or (2) to allow banks to “build a
cushion” during good times at the cost of misleading
investors about the true variability of earnings. An
important question in evaluating the two policy
alternatives is determining whether this dilemma is
unavoidable or whether it is a consequence of the
two sides’ positions on the importance of reported
net income and the process of measuring capital
Net Income. Empirical evidence suggests that
banks’ loan-loss accounting policies reflect more than
just anticipated future losses. Thus, investors would
generally benefit from
forming their own estimates of each bank’s
The question of whether
expected loan losses.
changes in banks’ loanAvailable evidence suggests that investors
loss accounting would
form such an estimate
threaten their safety and
for each bank using
soundness tends to overpublicly available information about nonlook regulators’ ability to
performing loans, the
require banks to substitute
growth rate and comhigher capital for lower
position of the loan
portfolio, the “credit
culture” as evidenced
by a bank’s historic
loan losses, and economic conditions. The issue is not whether the SEC’s
actions are required for investors to be able to form
reasonably accurate estimates of banks’ expected loan
losses. Banks already provide substantial information
for investors to form their own estimate of expected
provisions. Rather, the issue is whether investors
would form more accurate estimates of loan-loss provisions, allowances, and net income at lower cost as a
result of recent SEC actions.
Whether investors would significantly reduce their
efforts to calculate a true loan-loss allowance if
banks strictly followed the SEC’s guidelines is
unclear. The SEC’s guidelines, which direct banks to
adopt a consistent methodology, would lessen, but
not totally eliminate, banks’ ability to manage their
loan-loss provisions. While the SEC calls upon banks
to set loan-loss provisions that are within the range
of reasonable provisions indicated by the guidelines,
it has not required banks to adopt a formula that
would result in a single number to be used as a

18. For a general review of the literature on earnings management and the implications of the findings for public policy, see
Healy and Wahlen (1999).

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


requirement. A “cookie cutter” approach that applies
a single formula is unlikely to be optimal because
difficult-to-quantify factors may cause variations in
the best estimate of appropriate provisions across
both banks and time for individual banks. Yet, any
discretion allowed to banks in setting provisions may
be used to manage reported earnings and capital.
Further, investors not only care about changes in
loan values due to events that occurred within a
reporting period but also about changes in loan values due to events that are expected after a period
ends. For example, in valuing a bank’s securities
investors might be as interested in changes in loan
losses resulting from, say, changes in bankruptcy
laws taking effect
next year or from anticipated changes in
Bank regulators use their
macroeconomic conmeasures of capital adeditions as they would
be in losses resulting
quacy to determine whether
from the failure of a
one banking organization
firm in the current
may acquire another,
year. Existing accounting
guidelines neverwhether a bank may pay
theless require banks
dividends, or even whether
to ignore the implia bank will be allowed to
cations of expected
future events on the
continue in operation.
current period’s reported provisions and
allowance even if the
future event is virtually certain to occur. Thus,
investors could not rely on the reported loan-loss
allowance to incorporate all information on a bank’s
expected losses based on past and future events
under existing accounting guidelines.
Capital Adequacy. The question of whether
changes in banks’ loan-loss accounting would
threaten their safety and soundness tends to overlook regulators’ ability to require banks to substitute higher capital for lower provisions. Implicit in
current U.S. capital regulations is the assumption
that banks’ allowances generally incorporate total
expected losses, including those arising from
future events.
If the assumption that part of the loan-loss
allowance represents “extra” provisioning for unexpected losses is incorrect, the treatment of the
allowance for capital adequacy purposes is inappropriate. U.S. regulators could revise the definition of
tier 2 capital to exclude the loan-loss allowance.
If the assumption that the loan-loss allowance
fully captures expected future losses is faulty, regulators should consider measures to require banks to
increase their cushion to absorb losses. Although

this assumption appears to be inaccurate under
existing accounting standards, the FASB’s trend
toward requiring fair value accounting for financial
instruments may address regulatory concerns about
expected future losses. The FASB has already
required the use of fair values for financial reporting
in two capital statements.19 Further, in testimony
before the U.S. House of Representatives Subcommittee on Financial Institutions and Consumer
Credit of the Banking Committee and Financial
Services Committee on June 16, 1999, Timothy S.
Lucas, director of research and technical activities
at the FASB, stated that the FASB is currently working on a project that would require all financial
instruments, including loans, to be carried at full
value. He stated that the FASB believes in fair-value
accounting for all financial instruments once certain
issues are addressed, adding that the board plans to
issue a “preliminary views document” in the fourth
quarter of 1999.20 If banks were required to recognize the fair value of loans in their financial statements, losses due to expected future events would
be incorporated into financial statements because
investors do not draw a distinction between likely
losses due to past events and likely losses due to
future events.
If fair-value accounting for loans is not required,
bank regulators need to adjust their accounting or
capital guidelines to ensure that banks are maintaining an allowance that incorporates expected
future events. One way of doing so would be to
change regulatory accounting principles, which currently follow the GAAP definition, so that loan-loss
allowance is based on all expected credit losses on a
bank’s portfolio. Alternatively, regulators might
change the capital adequacy guidelines to require
additional capital equal to the difference between
expected losses due to past and future events and
expected losses due solely to past events.

he current debate over banks’ loan-loss accounting is sometimes portrayed as a choice
between providing investors with vital information and maintaining banks’ capital adequacy.
The previous analysis suggests that both positions
are overstated. Using currently available data,
investors can and do form estimates of the “economically true” amount of banks’ loan-loss
allowances, provisions, net income, and equity capital. Strict adherence to SEC guidelines may
improve the quality of the data, at least in some
periods, but the guidelines may not eliminate the
benefit or reduce the cost for investors making
their own estimates. Conversely, the effectiveness


Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000

of the current capital adequacy regulations may be
reduced by strict adherence to SEC guidelines.
However, bank regulators have ample power to
adjust their regulations to reflect whatever definition of loan-loss allowance the SEC requires for
reporting to investors.
Given that bank regulators may achieve their
desired policy outcome regardless of the way in
which the loss allowance is calculated, a better
question is which definition of the loan-loss
allowance best serves the interests of investors. On
the one hand, adherence to the SEC’s position may
aid investors seeking to compare reported accounting results across industries. If accounting figures
meant the same thing in each industry, such reporting could reduce the costs to investors of analyzing
firms in different industries. However, like banks,
other industries’ reported net incomes are virtually

always subject to differences between reported net
income and economic net income, especially in
accounting for intangible assets and depreciation.
Thus, the ability to compare across industries is
less valuable than it may first appear.
On the other hand, bank regulators use their
measures of capital adequacy to determine whether
one banking organization may acquire another,
whether a bank may pay dividends, or even
whether a bank will be allowed to continue in operation. Thus, investors have a strong interest in regulators’ judgments about the adequacy of a bank’s
loan-loss allowance and capital adequacy. If regulatory judgments regarding the adequacy of a bank’s
loan-loss allowance differ from those obtained
under GAAP, investors would also need to know or
estimate the regulators’ evaluation.

19. Statement No. 107 (1991) requires firms to disclose, but not recognize in their financial statements, the fair value of those
financial instruments for which it is practicable to provide an estimate. Statement of Financial Accounting Standards No.
105: Accounting for Derivative Instruments and Hedging Activities (1990b) requires firms to recognize the fair value of
their derivatives contracts in their financial statements.
20. The Financial Accounting Standards Board issued Preliminary Views on Major Issues Related to Reporting Financial
Instruments and Certain Related Assets and Liabilities at Fair Value on December 14, 1999.

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BROOKS, RICK. 1998. “SunTrust Banks Restates Earnings
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BEAVER, WILLIAM H., AND ELLEN E. ENGEL. 1996. “Discretionary Behavior with Respect to Allowances for Loan

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ZECHHAUSER. 1999. “Earnings Management to Exceed
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DYE, RONALD A. 1988. “Earnings Management in an Overlapping Generations Model.” Journal of Accounting
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SLOAN. 1995. “Annual Bonus Schemes and the Manipulation of Earnings.” Journal of Accounting and
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EISENBEIS, ROBERT A. 1998. “Comment on Hancock and
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KOCH, TIMOTHY W., AND LARRY D. WALL. 1999. “The Use of
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———. 1980. Qualitative Characteristics of Accounting Information. Statement of Financial Accounting
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———. 1990a. Accounting for Contingencies.
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LEVITT, ARTHUR. 1998. “The Numbers Game.” Speech
presented at the New York University Center for
Law and Business, New York, N.Y., September 28.

———. 1990b. Disclosure of Information about
Financial Instruments with Off-Balance-Sheet Risk
and Financial Instruments with Concentrations of
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of a Loan: An Amendment of FASB Statements No. 5
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of a Loan—Income Recognition and Disclosures: An
Amendment of FASB Statement No. 114. Statement of
Financial Accounting Standards No. 118 (October).
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Loss Allowances. Hearing before the Subcommittee on
Financial Institutions and Consumer Credit of the Committee on Banking and Financial Services, U.S. House of
Representatives. 106th Cong., 1st sess. June 16. Serial
No. 106-27.
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by Business-Unit Managers.” Journal of Accounting
and Economics 26 (January): 113–42.
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———. 1999. Testimony at the Hearing on Accounting
for Loan Loss Reserves before the Subcommittee on
Securities of the U.S. Senate Banking Committee.
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LOOMIS, CAROL J. 1999. “The Crackdown Is Here: Quit
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the Subcommittee on Financial Institutions and
Consumer Credit of the Committee on Banking and
Financial Services, U.S. House of Representatives. 106th
Cong., 1st sess. June 16. Serial No. 106-27.
MACDONALD, ELIZABETH. 1999. “Analysts Increasingly
Favor Using Cash Flow Over Reported Earnings in Stock
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Contagion, and Bank Security Returns in 1987: The
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Banking 22 (May): 209–20.

HEALY, PAUL M. 1985. “The Effect of Bonus Schemes on
Accounting Decisions.” Journal of Accounting and
Economics 7 (April): 85–107.

PERSONS, JOHN C. 1997. “Liars Never Prosper? How
Management Misrepresentation Reduces Monitoring
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(October): 269–306.

HEALY, PAUL M., AND JAMES M. WAHLEN. 1999. “A Review of
the Earnings Management Literature and Its Implications

WOLFSON. 1990. “Tax Planning, Regulatory Capital


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Planning, and Financial Reporting Strategy for
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Keefe, Bruyette, & Woods, Inc. Hearing before the
Subcommittee on Financial Institutions and Consumer
Credit of the Committee on Banking and Financial
Services, U.S. House of Representatives. 106th Cong.,
1st sess. June 16. Serial No. 106-27.
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The Great S&L Debacle and Other Washington Sagas.”
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Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


Central Bank Forecasting:
An International

C .


The author is a senior economist and policy adviser
in the macropolicy section of the Atlanta Fed’s research department. This article is based in part on
the chapter “Forecasting for Practical Policy,” by
Adrian Pagan and John Robertson, in Companion
to Economic Forecasting, edited by M.P. Clements and
D.F. Hendry (Oxford: Blackwell, 2000). He thanks
Ellis Tallman for valuable comments on earlier drafts.







discussion not only because they are fairly well documented but also because it could be argued that
their forecast procedures are representative of
those of other central banks.
An obvious initial question that arises when considering central bank forecasting is that of whose
forecasts are being discussed. This article concentrates mostly upon the forecasting process of policy
advisers rather than that of policymakers, even if
there may be considerable overlap. In the United
States staff forecasts are presented to policymakers
as a basis for policy discussions, but these forecasts
need not represent the forecasts of an individual
policymaker (see Reifschneider, Stockton, and
Wilcox 1997). The influence of these staff forecasts
on policy decisions is largely unknown. It is clearly
not zero, but a reading of some of the Federal Open
Market Committee (FOMC) discussions shows that
individual U.S. policymakers’ responses to the projections of policy advisers can vary a great deal (see


Edison and Marquez 1998).1 In other cases, such as
at the Bank of England, there is an official published
forecast that is the outcome of an explicitly defined
interaction between the bank staff and the policy
committee (George 1997). These forecasts therefore come much closer to representing those of the
policymakers. A similar, but less formal, interaction
takes place at the Reserve Bank of New Zealand,
which publishes forecasts on a regular basis under
the name of the governor, although the projections
themselves are based on staff models (Drew and
Frith 1998).
The next section of the article sets out a number
of common elements in the forecasting processes of
central banks. The discussion then summarizes the
forecasting procedures at the Reserve Bank of New
Zealand, the Bank of England, and the Board of
Governors of the U.S. Federal Reserve System, with
particular attention given to the differences and
similarities among the core models used by staff at

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


these institutions. The conclusion suggests that
there is considerable similarity across central banks
in the basic mechanics in producing forecasts.
However, there are differences in the emphasis
given to model-based forecasts relative to judgmental forecasts and those based on expert opinion.
Banks with mandated inflation objectives have tended
to favor model-based approaches as part of a strategy
of ensuring that policy decisions are consistent with
their inflation objectives and are as transparent to
the public as possible.

Some Forecasting Issues
hat Needs to Be Forecast and for How
Long? For monetary policy decisions,
forecasts of inflation and aggregate output
growth are the obvious candidates, but policymakers
may also want to examine forecasts of variables such
as investment, consumer spending, and wages as
well as projections for the global outlook. Even so,
this list is rarely exhaustive, simply because the construction of a forecast for, say, inflation may require
a forecast of other variables such as productivity.
These intermediate forecasts may or may not be presented to the policymakers, but they are often
implicitly provided when discussing the environment
surrounding a forecast. A second reason why a relatively large number of variables may need to be forecast is that a policy adviser needs to provide an
explanation of the forecasts. For example, a forecast
of aggregate demand may need to be separated into
consumption and investment components since the
current economic environment may suggest that
these are likely to evolve in different directions. As
Dawes (1999) observes, it is easier to be convincing
about a forecast’s validity if one can provide a plausible economic interpretation, and some degree of disaggregation may aid this process. Policymakers
would need to weigh the plausibility of the aggregate
forecasts against their own beliefs about the constituent parts. Disaggregation creates problems,
however; some components are inherently more difficult to predict compared with either the aggregate
or other components. One example is the relative
ease in forecasting the output of the manufacturing
sector, for which data are relatively accessible, compared with forecasting the quantitatively more
important service sector, for which reliable data are
sparse. Also, the degree of disaggregation cannot be
too extreme as it can easily become hard to present
a consistent story about the whole picture.
The length of the forecast horizon will depend in
large part on how long it is believed to take for
changes in policy instruments to affect the economy.
Generally, households and firms seem to respond



with sufficient inertia to require forecasts with a
one- to three-year horizon.
The Use of Models. Perhaps the main factor in
favor of a central bank using models relates to the
issue of what is called transparency. Even if a model
and its forecasts are only one element in the thinking behind a policy action, examining its structure
can be very useful in educating and informing markets about the reasoning behind changes in policy
instruments. In order to focus on key issues and to
avoid being distracted by excessive detail, most central banks appear to have adopted relatively smallscale econometric models as the main vehicles for
their medium-term forecasting exercises.2
There also appears to be a growing tendency in
central banks to use information from more than
one type of model. For example, although a detailed
model may give a relatively precise short-term forecast of inflation, a simpler and more stylized model
may be of greater use for understanding the longerterm relation between the instruments of policy and
targets such as output growth and inflation. In part
this characteristic reflects the fact that models are
used in the policy process for other purposes, such
as estimating the likely effects of alternative policy
prescriptions or changes in the way inflation expectations are formed. These types of simulations may
be difficult to implement in the primary forecasting
model. Even if it is feasible, there is often a desire to
provide consistency checks on the simulations using
smaller theoretically based models. Moreover, it is
doubtful that policymakers place great emphasis
upon the point forecasts presented to them, frequently seeing the forecasting process instead more
as an aid to bolstering their understanding of the
available options.
There are, of course, many private forecasting
agencies that produce forecasts, and these forecasts
are relatively easily accessible to central banks. All
this information might be collated and used; one
motivation for doing so would be that one would
thereby acquire information from a wide variety of
“models,” something that policymakers might find
attractive. The main disadvantage of relying strictly
on pooled information is the lack of a consistent
story that can be associated with the resulting forecasts. This lack of a comprehensive picture is an
obvious impediment to use of this kind of information by policymakers, but it is also of limited value
for policy advisers, as the latter generally need to
address policy meetings and so must have formed
some view on the economic rationale for a particular forecast outcome.
What then are the types of models central banks
use? The Bank of England (1999) details five types

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000

of models that contribute to decisions made by its
Monetary Policy Committee, and their categories
are useful for commenting briefly on the nature of
models used in forecasting in a monetary policy
The “Core” Model. Most policy-setting institutions have a “core” model that summarizes the main
relationships within the macroeconomy and is the
reference point for forecasting and policy evaluation
in the medium term. These models usually contain
about 30–50 stochastic equations and determine
another 100–200 variables through identities. The
modeling philosophy often involves selecting a set of
long-run relations such as a constant labor share
and debt-to-output ratio, a production function, and
a constant long-run real exchange rate. A variety of
mechanisms is then invoked to relate the short run
and the long run, with departures from the long-run
equilibrium values being an important factor in the
adjustment process. Of course, this philosophy is
now a very standard one in macroeconomic modeling, finding its most precise expression in error correction models.3 Even though there is a shared
vision in these models, there are also significant differences, particularly in regard to the relative roles
played by expectations in determining nonfinancial
variables, such as the rate of inflation or levels of
Core models often do not generate the most accurate forecasts, particularly at a fairly short-term horizon. Nonetheless, when the forecast horizon
lengthens and one wishes to look at the sensitivity of
outcomes to a policy change, it is hard to find a better alternative. The importance of a core model
depends largely on the relative mix of scenario analysis and forecasts in the making of policy decisions.
Well-designed core models can have some specific
features that may be of assistance in formulating policy. One of these is a steady-state solution that can be
consulted to view the long-run consequences of a policy action. Another, once values for variables not
determined within the model are incorporated, is the

generation of medium-term equilibrium paths, that is,
the core model’s prediction of where the economy is
heading in the medium term.
Small, Forward-Looking Models. These models
embody what has sometimes been referred to as
the central bank model (see McCallum 1999 and
Clarida, Gali, and Gertler 1999). They contain a socalled IS curve that relates growth in gross domestic product (GDP) to factors such as interest rates,
expected inflation, and past and expected output
growth. They also contain a Phillips curve that connects inflation to past and expected future inflation
as well as the deviation of output from
“capacity” levels. The
Some institutions favor
small, forward-looking
models also usually
a forecasting approach
contain some mechathat is structured explicitly
nism for setting polwithin a model framework.
icy. If money supply is
the instrument, then
Others place much greater
a money demand
emphasis upon the judgfunction needs to
ments of sector experts
be appended to the
system, but in most
and the experience of
cases the system is
policy advisers.
closed with a simple
interest rate rule.
These models differ
from the core models in terms of the degree of
aggregation. However, they also often tend to place
greater emphasis upon forward-looking behavior in
the IS curve and the wage-price sector than do the
larger-scale models. It is probably true that these
models are used more for simulating policy actions
than for forecasting per se, but the distinction is a
fine one. The Batini-Haldane model discussed in
Bank of England (1999) is a good example of an
open-economy version of this framework that augments the fundamental elements above with an
uncovered interest-parity condition. The Reserve
Bank of New Zealand has developed a similar model

1. It should be noted that Federal Reserve disclosure policies permit public examination of official forecast documents only with
a five-year delay. Hence, some of the discussion in the article may not accurately describe current practice by the staff of the
Board of Governors. The Bank of England, the Reserve Bank of New Zealand, and other central banks release current forecast documents on a quarterly basis.
2. The role of judgment in forecasting is one important aspect of the forecasting process that will not be discussed systematically in what follows basically because it is hard to get specific information on how it is used. One thing that is clear, however, is that monetary policy institutions rarely, if ever, rely solely on mechanical model-based forecasts. If the science of
forecasting is the model, then the art of forecasting is the judgment that is applied by the individuals involved.
3. Error correction equations relate current growth rates to past deviations from equilibrium and lagged growth rates. However,
some models describe the out-of-equilibrium behavior of nonfinancial variables either in terms of so-called polynomial adjustment cost (Brayton and others 1997) or target-seeking behavior (Coletti and others 1996). The resulting equations differ
from standard error correction equations by also including discounted expected future equilibrium values. This forwardlooking aspect is a key feature of the core models of all the central banks discussed in this article.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


(Hargreaves 1999). Because of the relatively small
size of this type of model, it is relatively easy to
experiment with alternative assumptions and
altered parameter settings. Hence, they can provide a useful cross-check on the policy simulations from the core model. However, the high
degree of aggregation and the tendency to have a
simplified dynamic structure means that they may
not be very useful for either short-term forecasting or for explaining sources of business cycle
Vector Autoregressions. Vector autoregression
(VAR) models are used primarily to explore specific
questions such as
the role of monetary
aggregates in predicting inflation and output growth.4 As such,
Most central banks appear
VAR models are rarely
used as the core
to have adopted relatively
model. One difficulty
small-scale econometric
in using them for polmodels as the main vehiicy analysis is that
they treat policy as
cles for their medium-term
partly unexpected
forecasting exercises.
(exogenous) events
and partly as determined by the history
of the variables appearing in a VAR. It is
true that an exogenous policy shock may be identified through a VAR with some loose economic reasoning, but such shocks are rarely easy to relate to
actual policy events (see Rudebusch 1998).
Moreover, in practice VARs ascribe most of the variation in policy instruments to systematic behavior. A
user of VAR forecasts therefore has to accept that the
policy instrument will vary continuously over the
forecast horizon, something that is not easy to
explain to policymakers who are considering whether
to make a change in a policy instrument that they feel
will be sustained over the forecast horizon.5 Such
reservations mean that VARs tend to be used simply
as forecasting devices and not for policy analysis. In
the former role the emphasis can be placed upon
their statistical characteristics, and this characteristic
perhaps accounts for why the most popular versions
have been Bayesian VARs. The latter involve approximate prior restrictions upon the coefficients that
might be regarded as plausible given the nature of
many economic times series (see, for example,
Robertson and Tallman 1999). Even in that role
they have the disadvantage that it is hard to isolate the story that underlies any predictions made
with them (see Meyer’s 1999 comment in this vein).

Fundamentally, the case for a VAR in prediction relies
on the fact that prediction can be based on recognition of regularities in data without requiring explanation of these regularities.
Single-Equation Regression Models. Examples
of single-equation regression models are Phillips
curve models and relations summarizing the connection between the exchange rate and the terms of
trade (or commodity prices) in open economies.
The main advantages of such models are their simplicity and that they can be readily used to calculate
forecasts conditional on a range of alternative paths
for the explanatory variables. In some cases the conditional forecasts might be used as cross-checks on
the forecasts from the core model, and sometimes
the purpose is to give policymakers some feel for
longer-term relationships in the economy.
Dynamic Optimizing Models. Often it is necessary
to form a view about the likely economic consequences
of a particular structural change or an atypical shock.
One general problem with using regression-based
models for this task is that their coefficients are functions of underlying preferences and technology as well
as government policy, and it is usually difficult to predict the effect that a change in these parameters would
have for the estimated coefficients. Largely because of
their stronger structural basis, dynamic optimizing
models tend to be the mainstay of the academic literature. They rarely produce forecasts directly but can be
an ingredient in a forecast and are sometimes important in producing an understanding of forecasts.
Models in this class range from dynamic stochastic
general equilibrium models and asset pricing models to
more deterministic versions such as McKibbin and
Wilcoxen’s (1995) G3 model.

The Reserve Bank of New Zealand
onetary policy at the Reserve Bank of New
Zealand is conducted in the context of an
explicit inflation target (currently 0–3 percent in a consumer price index that excludes interest payments) and is implemented via the Bank’s
influence on overnight interbank cash rates. There
are eight interest rate reviews each year. The governor of the Bank makes policy decisions after advice
from an internal monetary policy committee. The
forecasts published in the quarterly Monetary
Policy Statement are actually issued under the governor’s name. These institutional arrangements are
similar to those of the Bank of Canada. Indeed, the
forecasting and policy system implemented at
the Reserve Bank of New Zealand was inspired by
the Bank of Canada’s so-called quarterly projection
system and was built under contract by some of the
Canadian system’s developers.


Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000

Basic Structure of the Reserve Bank of New Zealand’s Forecasting and Policy System

Indicator Models and
Sector Analysts’ Judgment

Core Model

Satellite Models

Policy Advice

Source: Breece and Cassino (1998, figure 1)

Forecasts for a wide range of variables in the New
Zealand economy, not just output and inflation, are
published each quarter. In particular a forecast is
given for the ninety-day bill rate. By doing so, the
central bank is effectively also providing a statement
about the anticipated future course of policy. It
seems that the Reserve Bank of New Zealand is
unique among central banks in providing such a
statement on a regular basis.
As described in Drew and Frith (1998) and Drew
and Hunt (1998b), the forecasting round begins
with previous baseline and updated forecasts of
exogenous variables taken from a number of outside
sources. Indicator models are then used to produce
forecasts over the monitoring quarters, and these
become the starting points for producing forecasts
from the core model for the longer horizons.
Modifications are then made through intercept
adjustments or “add” factors in each of the equations of the model until a central scenario emerges,
which then forms the basis of the published forecasts.
The Core and Related Models. In terms of
dividing the forecasting process into four elements,
the forecasting and policy system explicitly deals

with three of them: (1) indicator models to handle
short-run predictions (up to two quarters); (2) a
core model used to produce medium-term (one- to
two-year) forecasts and to perform policy analysis;
and (3) satellite models that disaggregate the forecasts from the core model. The basic structure of
the forecasting and policy system can be summarized in the schematic in Chart 1.6
The indicator models used within the bank are not
publicly documented but are designed to capture
the short-term time series characteristics of
detailed macroeconomic data and to utilize the sector analyst’s judgment. For example, data for tons of
cement produced are found to have a close relationship to data for commercial construction (Drew and
Frith 1998, 318).
The core model contains the key features that
were present in the Bank of Canada Canadian Policy
Analysis Model (Black and Rose 1997); that is, it
contains a well-defined steady state, explicit stockflow accounting and budget constraints, endogenous
monetary policy with an inflation target as the nominal anchor, and the separation of dynamic adjustments in nonfinancial sectors into “expectational”

4. VAR models used for forecasting in a policy environment are described in Zha (1998). Basically, a VAR model attempts to
describe the mathematical expectation of future values for a set of variables as a linear function of current and recent past
values of these variables. The adequacy of the description is usually measured in terms of forecast accuracy.
5. Of course, nothing precludes one from doing forecasts by constructing shocks that keep the monetary policy instrument on
some given path (Leeper and Zha 1999). However, in order to be consistent with the notion of rational expectations, the
required shock sequence would have to be not too persistent and not too large.
6. It seems reasonable to suppose that a similar schematic summarizes the forecasting system at the Bank of Canada.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


and “intrinsic” components. Monetary policy is
accounted for with a policy reaction function based
on forward-looking inflation control targets with a
six- to seven-quarter horizon. Numerical values of
parameters in the model are set to produce “reasonable” responses rather than being estimated
directly from time series data. Because the shortterm forecasts are not generated directly from the
core model, the model’s short-term fit to historical
data is not used as a criterion for adequacy.
Most of the nonfinancial sector of the forecasting
and policy system core model is specified using a
framework that describes out-of-equilibrium behavior in terms of adjustment costs. For example,
growth in consumption
by forward-looking
consumers converges
Monetary policy at the
to its equilibrium
Reserve Bank of New
value subject to an
adjustment structure
Zealand is conducted in
as well as to the influthe context of an explicit
ence of certain special
inflation target and is
disequilibrium effects.
The specification of
implemented via the
the price adjustment
Bank’s influence on
mechanism in the
overnight interbank
forecasting and policy
system model departs
cash rates.
from this approach,
however, and instead
resembles more the
type of Phillips curve equation common to the small
forward-looking models described earlier. In particular, price inflation for domestically produced
and consumed output is driven primarily by current and past deviations between the demand for
goods and services and the productive capacity of
the economy (the so-called output gap).7 Inflation
expectations also play an important role, with expectations assumed to have both a backward-looking and
a forward-looking, model-consistent component and
with most weight on recent inflation and near-term
expected inflation. Changes in the costs of production inputs also influence inflation even if there is no
output gap. These costs include wage growth and
changes in indirect taxes. The direct effects from
exchange rates and changes in the foreign dollar
price of imported consumption goods are added to
domestic prices to derive consumer price inflation.
The forecasting and policy system core model is
simulated to produce paths for the main macroeconomic variables, including policy variables, that are
consistent with the Bank’s inflation targets. Three
satellite models are then used to translate this projection into implications for more disaggregated

variables. The method used for disaggregation is to
first prescribe an equilibrium share on the basis of
some idea about where particular components are
headed. The dynamics around the equilibrium path
for variables in the satellite models are then derived
from estimated “autoregressive” functions. The
adjustment rate is not constant but is modified
according to variables such as relative prices and
disequilibrium in stocks and flows. In practice the
equilibrium paths are derived using a detrending
procedure that converges to a fixed steady-state
share (Breece and Cassino 1998). The main advantages of this process are that it allows the dynamics
of the core model to be kept relatively simple and
the satellite models are quite transparent and
amenable to modification by sector specialists. In
addition to its role in providing a framework for the
preparation of economic forecasts, the forecasting
and policy system core model has been used as a
policy analysis tool. The basic technique of analysis
is stochastic simulation (see Drew and Hunt 1998a
and Ha 2000).
Auxiliary Models. A smaller forward-looking,
demand-side model has been developed by
Hargreaves (1999) and denoted as the SDS-FPS
model. Designed to produce simulations even more
cheaply than the larger forecasting and policy system core model, it is nonetheless calibrated to replicate the dynamic properties of the core model for
key aggregates. The heart of the SDS-FPS model is
an IS curve, a Phillips curve, an exchange rate equation, and a monetary policy reaction function. It also
contains additional equations to determine the relative prices of consumption goods, inflation and
exchange rate expectations, interest rates, and the
prices of exports and imports. A VAR model was constructed in Drew and Hunt (1998a) that is mainly
used to produce shocks that could be fed into the
forecasting and policy system and SDS-FPS models
for stochastic simulation purposes.

The Bank of England
n the United Kingdom monetary policy is set by
the Monetary Policy Committee, which is composed of three Bank of England representatives
and six non-Bank members. This committee meets
monthly. It has a stated inflation target of 2.5 percent in a retail price index (with a reporting range of
plus or minus 1 percent). Forecasts of inflation and
output have been presented quarterly in the Bank’s
Inflation Report since February 1993. These forecasts are meant to summarize the views of the
Monetary Policy Committee members and, as such,
are intended to explain any policy actions. The current forecasting process at the Bank of England and


Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000

The Bank of England’s Forecasting Process

Core Model

Other Models

Assumptions and Judgment

Other Issues and
Policy Judgment


Source: Vickers (1999)

the resulting inflation and output “fan charts” represent an explicit attempt to map the policymaker’s
uncertainty about alternative economic assumptions onto a distribution of future outcomes via a
combination of models and judgment. Chart 2, taken
from Vickers (1999), provides a schematic summary of the forecasting process and how it relates to
policy decisions.
As described in Britton, Fischer, and Whitley
(1998) and in Vickers (1999), a series of meetings
takes place between the Monetary Policy Committee
and the Bank of England’s forecasting staff, beginning about one month before the Inflation Report is
published. At the first meeting, current issues, key
assumptions, and an initial assessment of the relative
likelihood of various future paths for the economic
variables are discussed. Following these discussions
the forecasting staff prepares central (most likely)
forecast paths together with forecast distributions
constructed to reflect as accurately as possible the
Monetary Policy Committee’s assessment of relative
risks (skewness) and the overall uncertainty (variability). These forecast distributions might be
revised following subsequent meetings between the
committee and Bank staff. If the Monetary Policy
Committee judges that the distribution is inconsistent with its assessment of the issues, then the staff
will be asked to make changes. For example, the
type of assumptions, their probability, or perhaps

the core model itself might be changed. Notably, two
sets of forecasts are published in the Inflation
Report. The first is based on the assumption of
unchanged U.K. short-term interest rates during the
forecast period while the second allows interest
rates to follow the Monetary Policy Committee’s
assessment of market expectations.
The Core Model. The Bank of England maintains a suite of models and has made descriptions of
the various models publicly available (Bank of
England 1999). However, exactly what relative
weights are ultimately given to these models in
the committee’s published forecast is unknown.
Speeches of Monetary Policy Committee members
have not shed a great deal of light upon this question. The core model, termed MM (Bank of England
1999), involves about 20 behavioral relations and
130 variables in total. In some respects the MM
model can be categorized as having been constructed
from a “bottom-up” (equation-by-equation) perspective rather than the “top-down” philosophy that
is a feature of the Reserve Bank of New Zealand
core model. Also, unlike the New Zealand model,
the parameters of the MM are estimated econometrically from time series data.
The underlying structure of the MM involves the
specification of (1) a long-run equilibrium in real
variables that is independent of the price level
and exhibits no long-run inflation trade-off; (2) a

7. The inflation dynamics also depend on an asymmetric output gap term from which the positive effect of excess demand on
inflation is stronger than the negative effect of the equivalent degree of excess supply.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


nominal variable equilibrium determined via an
inflation target and a feedback rule for short-term
nominal interest rates; and (3) a sluggish adjustment to shocks due to both real and nominal rigidities. The explicitly forward-looking expectation
aspects of the MM are limited to the foreign
exchange market, and the dynamics of nonfinancial
sectors are generally determined by conventional
error correction mechanisms. Thus, for example, a
forward-looking Phillips curve cannot be derived
analytically from the
wage-price system
within MM. In equiThe current forecasting
librium, retail prices
process at the Bank of
are set as a markup
over marginal costs,
England attempts to map
with marginal costs a
the policymaker’s uncerweighted average of
tainty about alternative
unit labor costs and
import prices. Retail
economic assumptions onto
price inflation adjusts
a distribution of future outslowly, and it responds
comes via a combination
to past deviations
from equilibrium as
of models and judgment.
well as to changes in
wages, import prices,
and the level of capacity utilization. As part of preparations for the forecast in which alternative risks are assessed, the
MM is used extensively to estimate the effects of
various exogenous shocks such as a shift in the
inflation target or a temporary change in shortterm interest rates.
Auxiliary Models. The Bank also maintains
some small, forward-looking models, of which the
leading example is that based on Batini and Haldane
(1999). This model is a less-detailed but more theoretically consistent version of MM, and its estimated
parameters are chosen to satisfy numerous theoretically motivated constraints. The smaller size makes
it more tractable, and the results are often easier to
interpret in economic terms. Also, because there are
fewer equations and parameters it is easier to
experiment with alternative behavioral assumptions, such as the degree of forward-looking behavior in agents’ decision making. Against these
advantages is that the higher level of aggregation
means that the smaller model is not necessarily as
accurate or reliable a forecasting tool as the largerscale version, particularly at short horizons.
Bank of England staff have also used various singleequation Phillips curve models to investigate the
relationship between inflation and summary measures of disequilibrium in the real economy and to
simulate the implications for inflation of alternative

unemployment rate paths. Along with VAR models,
these are used as a cross-check on the inflation forecasts produced by the core model.

The U.S. Federal Reserve
onetary policy in the United States is set by
the Federal Open Market Committee
(FOMC) and consists of twelve voting
members: seven members of the Board of Governors
of the Federal Reserve System and the presidents of
five of the twelve Reserve Banks (“regional Feds”).8
The staff of the Board of Governors prepares forecasts of U.S. and international economic activity
prior to each of the eight FOMC meetings held each
year. Independently, the staff of each of the regional
Feds may also produce forecasts as part of briefing
their Bank’s president prior to an FOMC meeting.
The various Board and regional Fed forecasts are
not made publicly available until several years after
an FOMC meeting. However, a summary of the outlook of the policymakers is contained in the forecasts of GDP, inflation, and unemployment
documented in the Humphrey-Hawkins testimony
on monetary policy submitted to Congress twice
each year. The focus here is on the forecasting system implemented at the Board of Governors.
Information available to the FOMC policymakers
comes from a number of sources. First, each Federal
Reserve Bank gathers anecdotal information on
current economic conditions in its district through
reports from directors of the bank and its branches
and interviews with key business contacts, economists, market experts, and other sources. The
so-called Beige Book summarizes this information. In
addition the Board receives information directly from
various advisory councils that can provide an assessment of recent economic developments. Second, staff
at the Board of Governors produce several documents for FOMC meetings. One is titled “Current
Economic and Financial Conditions” and is commonly
referred to as the Greenbook because of its green
cover. The Greenbook lays out the staff’s assessment
of recent developments in the domestic macroeconomy
together with an analysis of financial and international
developments. The Greenbook also presents quarterly point forecasts for key aggregates in the domestic economy such as the broad components of GDP,
unemployment, and prices and wages. The forecast
horizon in the Greenbook is as much as two years
ahead although it is sometimes as short as six quarters. Another document, the Bluebook, contains
model simulations to examine alternative strategies
for monetary policy over a longer period, often up to
five years. These simulations are presented formally
at least twice each year.


Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000

Published accounts of the forecasting system suggest that, despite its role in the overall policy-making
process, a core macroeconometric model is not the
tool used for producing the Greenbook forecasts
(Reifschneider, Stockton, and Wilcox 1997). In fact,
the forecasts are primarily judgmental in nature,
relying heavily on the expertise of sector specialists
and senior advisers. The process of generating a
Greenbook forecast begins with a forecast coordinator who provides the conditioning assumptions and
initial forecasts for several key aggregates such as
inflation and output.9 Staff experts on various sectors of the economy then quantify how their sectorspecific forecasts are affected by the aggregate
baseline forecast as well as data that has become
available since the last FOMC meeting. Each sector
specialist would potentially use a range of econometric models relevant to their sector for guidance
in preparing their forecasts. The sector forecasts are
then blended by the coordinator into revised aggregate forecasts, which are returned to the sector specialists who may again adjust their sector forecasts
in view of the new aggregate baseline. After some
iteration, the “consensus” forecast is reported in the
High-frequency time series data (monthly, weekly)
are used to tune the short-range forecasts by providing better estimates of initial conditions. For
example, a newly available monthly labor market
report or retail sales report might affect the assessment of current-quarter GDP growth. Several statistical models are used to filter the high-frequency
data. Anecdotal evidence also plays a potentially
important role in identifying trends that may not yet
have shown up in official statistics.
The Core Model. The Board maintains a core
domestic model, known as the FRB/US model. This
model contains around forty behavioral equations
(see Brayton and Tinsley 1996 and Brayton and others 1997 for overviews). There is no money supply
and demand relationship with short-term interest
rates determined by policy rules that can be toggled
on or off. The FRB/US model is the successor to the
larger so-called MPS model that was used up until
the early 1990s and is distinguished from its predecessor mainly by its explicit separation of the
macrodynamics of the nonfinancial sector into
adjustment costs and expectations-formation components. In particular, most nonfinancial sector variables are assumed to move gradually to eliminate

past disequilibria (deviations of actual from desired
levels) and also respond to the path that the equilibrium is expected to follow in the future. This
forward-looking “target-seeking” feature is also
common to the nonfinancial sectors of the models
used at the Bank of Canada and the Reserve Bank of
New Zealand, except for the wage/price block as
described below. The financial sector of FRB/US is
based on various instantaneous arbitrage equilibria.
For example, long-term interest rates are determined
via the expected path
of short rates plus a
time-varying term
premium while the
While the Federal Reserve
real value of the stock
Board of Governors primarmarket is determined
ily uses an expert-based
via the discounted
expected future flow
system for producing baseof dividend payments.
line forecasts, they rely
Like the Bank of
on a detailed core macroEngland’s core model,
the parameters of the
econometric model for
FRB/US model are
policy analysis.
estimated econometrically from time
series data.
In the FRB/US
model the price-wage system contains an equilibrium
condition in which firms set the profit-maximizing
price of their output as a markup over marginal
costs, with marginal costs a weighted average of unit
labor and energy costs. The equilibrium price level
of domestic production is also assumed to vary
inversely with the degree of slack in the economy as
measured by the gap between the actual unemployment rate and the rate of unemployment that is
believed to be consistent with nonaccelerating inflation. The dynamic process for inflation depends on
the distance between the actual and targeted price
level, the intrinsic rate at which inflation adjusts
over time, and expectations. The specification gives
a little more weight to past price inflation than to
unit costs expected to prevail in the future. An
increase in the current or expected future unemployment gap has a negative impact on inflation
rates because it foreshadows increasing labor market tightness. Finally, consumption prices depend
on direct effects due to changes in relative nonoil
import prices and energy prices. Thus, apart from
the special disequilibrium factors, the price (and

8. The President of the Federal Reserve Bank of New York is a permanent member of the FOMC; the other presidential members rotate on a prespecified annual basis.
9. The key conditioning assumptions, such as the path for the federal funds rate and fiscal policy as well as stock and energy
prices, are discussed in the text of the Greenbook although generally not in detail.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


wage) block of the FRB/US model is modeled in the
same manner as the rest of the nonfinancial system.
The FRB/US model is used as input to the
Greenbook forecasting system, primarily as a check
on the plausibility of the forecasts, and for identifying the sources of any discrepancy. Also, if a major
change in underlying assumptions occurs between
meetings, then the FRB/US model may be used to
provide a new benchmark baseline. In practice the
core model is add-factored in order to replicate the
final Greenbook forecasts over the forecast horizon.
This adjusted version of the core model is then used
for various exercises,
such as generating
intervals around the
Greenbook forecasts
There is a greater reliance
based on stochastic
on models in producing
simulations and occasionally for producing
forecasts at central banks
forecasts at horizons
that have explicit inflation
beyond two years.
objectives, such as the
The core model also
appears to play an
Bank of England and the
important role in simReserve Bank of New
ulation experiments
such as predicting the
effects of alternative
policy paths or alternative assumptions
about exogenous variables. The simulation results
are typically presented in the Bluebook.

his article summarizes some of the basic
issues that arise when forecasting is being
conducted in the context of a monetary policy
decision and describes some of the responses that
three central banks have made to these issues.
Broadly speaking, the basic mechanism of the forecasting process might be summarized as comprising
four elements:
• a series of models or methods that are used to produce short-run (current quarter and one–two
quarters ahead) forecasts;
• a relatively small core model that produces forecasts of major aggregates of interest over a one- to
three-year horizon;
• a method for disaggregating the aggregated forecasts from the core model to incorporate the
insights of sector specialists;
• a collection of auxiliary models that are designed
to provide information about policy actions (such
as policy simulations) or yield information relating
to forecasts that are hard to analyze with the core
model (such as the effects of unusual events).



These elements are part of most of the forecasting
systems studied in this article although the emphasis given to each differs across institutions.
Moreover, the way each component is implemented
varies a great deal—for example, the degree to
which the core model is closely linked to data versus
how much theoretical structure is imposed.
Additional theoretical structure might reduce the
model’s forecast accuracy but will generally aid its
economic interpretability.
Some institutions appear to favor an approach
that is structured explicitly within a model framework. Of the central banks studied, the Reserve
Bank of New Zealand appears to be representative
of this approach. Others, such as the Board of
Governors, place much greater emphasis upon the
judgments of sector experts and the experience of
policy advisers in generating forecasts and evaluating policy choices. In some ways the distinction is
really between those favoring relatively formal
methods of forecasting and those who find the use
of expert systems appealing. Of course, the distinction is not a sharp one. For example, while the
Board of Governors primarily uses an expert-based
system for producing baseline forecasts, they rely on
a detailed core macroeconometric model for policy
analysis as well as a cross-check on the economic
plausibility of the baseline forecasts. At the Bank of
England a core model is used to produce forecasts,
but the policymakers assign subjective weights to
various alternative assumptions in producing a forecast distribution. Ultimately, even if no institution
relies entirely on econometric models to produce
forecasts, they do use economic models of some
variety to provide the rationale for the forecast
numbers. It is also perhaps not surprising that there
is a greater reliance on models at central banks that
have explicit inflation objectives, such as the Bank
of England and the Reserve Bank of New Zealand.
In those cases it is particularly important that the
policymakers ensure that policy decisions are consistent with the inflation objectives and are as transparent to the public as possible.
Although this article has focused on only three
central banks it appears that, in general, banks that
have moved toward inflation-targeting objectives
have also tended to put greater emphasis on producing timely and publicly available model-based
forecasts. In doing so these banks have made the
monetary policy-making process increasingly transparent. However, even then there can be differences
in the nature of the published information. For
example, as the discussion shows, the Bank of
England publishes point forecasts that are conditional on no change to the policy instrument over

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000

the forecast horizon. But because this course might
not be considered the most likely one for future policy, an associated forecast distribution is used to
convey the relative risks to the conditional projection. In contrast, the point forecasts published by
the Reserve Bank of New Zealand appear to directly

condition on a policy instrument path that is consistent with its inflation objectives. In this case an
objective-consistent instrument path is conveyed
directly to the public rather than being implicit in
the shape of the forecast distribution.

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———. 1998b. “The Forecasting and Policy System:
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Policy Process.” Carnegie-Rochester Conference Series
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Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


Autoregressions: Forecasting and Reality.” Federal
Reserve Bank of Atlanta Economic Review 84 (First
Quarter): 4–18.

VICKERS, JOHN. 1999. “Economic Models and Monetary
Policy.” Speech to the Governors of the National Institute
of Economic and Social Research, March 18. <http://>.

RUDEBUSCH, GLENN D. 1998. “Do Measures of Monetary
Policy in a VAR Make Sense?” International Economic
Review 39 (November): 907–31.

ZHA, TAO. 1998. “A Dynamic Multivariate Model for Use in
Formulating Policy.” Federal Reserve Bank of Atlanta
Economic Review 83 (First Quarter): 16–29.


Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000

Are Displaced Workers
Now Finished
at Age Forty?
Rodriguez is an assistant professor of organization
and management at Goizueta Business School,
Emory University. Zavodny is a senior economist in
the regional section of the Atlanta Fed’s research
department. They thank Donna Ginther and Tom
Cunningham for helpful comments.






workers over age forty unable to find jobs that pay
as much as their former positions (Munk 1999).
Similarly, the business press characterized the
downturn in the early 1990s as “much tougher than
past ones on older workers” (Labich 1993). A series
on corporate downsizing in the New York Times
(1996) reported that the share of laid-off workers
aged 30–50 rose from 44 percent in 1981–83 to 56
percent in 1991–93.
Workers who are permanently involuntarily dismissed from their jobs are called displaced (or
downsized) workers.1 These workers have been the
focus of considerable attention from economists as
well as the media. Economists have focused particular attention on whether displacement has
increased over time in the United States, and
whether job security has concomitantly declined.
The fraction of workers who are displaced tends to
move with the business cycle; however, the displacement rate (the fraction of workers displaced
during a given interval) did not fall as much as usual

during the earlier phases of the current expansion,
leading to concerns that job security had permanently declined (Valletta 1997b; Aaronson and
Sullivan 1998).
The magnitude of displacement is sizable. During
1995–96, about 2.2 million workers were displaced
from jobs they had held for three or more years, or
about 3 percent of workers with at least three years
of tenure (Hipple 1999).
Many displaced workers incur significant costs,
including wage losses. Among workers displaced
during 1981–95 who found other jobs, real (inflationadjusted) weekly postdisplacement earnings were
13 percent less than predisplacement earnings
(Farber 1997). Several factors underlie these earnings losses. Other employers are unlikely to value
job- or employer-specific skills gained on the lost
job, so displaced workers are no longer compensated
for those skills. In addition, displaced workers lose
any seniority-related benefits that accrued with tenure at their previous employer.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


Displaced workers also incur the costs of searching for a new job, including a period of nonemployment for many. Of the 2.2 million workers displaced
during 1995–96 from jobs they had held for at least
three years, only 83 percent were reemployed in
February 1998 (Hipple 1999). These economic costs
make the displacement rate and the effects of displacement on workers of concern to policymakers.
The probability and costs of displacement are traditionally believed to vary with age because older
workers tend to have more firm-specific human capital than do younger workers. The theory of specific
human capital posits that a firm and a worker share
the cost of a worker acquiring job- or firm-specific
skills, skills that raise
a worker’s productivity at that particular
firm but not at other
The conventional wisdom
firms (Becker 1975).
The firm recoups its
that middle-aged workers
investment because
face an increased risk
the worker is more
of being displaced and
productive, and over
time the worker gains
increased difficulties after
the benefits of his or
displacement is partially
her investment in
borne out by this analysis.
specific human capital.2 Wages then are
observed to rise with
tenure at the firm.
The acquisition of
specific human capital lowers the likelihood that a
worker will be displaced because the firm would no
longer be able to recoup its investment if the worker
is laid off. In addition, the firm would have to pay
part of the cost of a new employee gaining the specific skills held by the displaced worker. Firms
should therefore be less likely to lay off experienced, older workers who have more specific human
capital than less experienced, younger workers
(Topel 1991).
Although specific human capital may help protect
older workers from displacement, a worker’s investment in firm-specific skills raises his or her costs of
displacement. Because workers’ earnings on the lost
job incorporated the value of job- or firm-specific
skills that other employers will not value, older and
more experienced workers tend to incur higher
earnings losses after displacement than do younger
and less experienced workers (Valletta 1991). Older
displaced workers also tend to experience a longer
period of nonemployment before finding another job
than do younger displaced workers (Valletta 1991).
An increase in the number of older workers who are
displaced would raise the social and private costs of

displacement, making it important to assess
whether the likelihood of displacement has
increased over time for older workers.
This article examines whether the likelihood of
displacement has risen for older workers relative to
younger workers over the 1980s and 1990s. It also
examines whether the likelihood that older displaced workers will find another job has declined
relative to reemployment trends among younger
displaced workers and whether earnings losses
among older displaced workers who find other jobs
have increased over time relative to losses experienced by their younger counterparts. Data from the
1984–98 Displaced Worker Surveys indicate that
displacement rates tend to decline with age.3
However, relative displacement rates appear to have
risen over time for workers in their 40s and 50s.
After job loss, older workers tend to have lower
reemployment rates and larger earnings losses than
do younger workers. The results do not indicate that
the costs incurred by older displaced workers have
risen significantly over time, except for relative
earnings losses for middle-aged managerial and professional workers.

Why Might the Age Profile of Displacement
Have Changed?
here are several potential reasons the age
distribution of displaced workers and the
effects of displacement on workers of different ages may have changed over time. Adoption of
new technologies, changes in the age distribution of
the labor force, and increased cost-cutting pressures may have led to differential changes across
age groups in the likelihood of displacement and in
the costs of displacement.
Advances in technology may have shifted the age
distribution of displaced workers. As computer use
has increased, the specific human capital that traditionally shielded older, more experienced workers
from displacement may have become less valuable to
employers.4 As Aaronson and Housinger (1999) discuss, firms may replace older workers with younger
workers because older workers may be more expensive to train in new technologies than younger workers; in addition, firms will have a longer time to recoup
the costs of training younger workers than older
workers. Similarly, postdisplacement outcomes may
have worsened over time for older workers if their
skills have not kept pace with increases in employers’
demand for computer and other technical skills.
Demographics may also have contributed to any
changes in the age structure of displacement. The
aging of the baby boomers may account for the
much-hyped increase in the number of middle-aged


Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000

displaced workers even if the likelihood that a
middle-aged individual is displaced has not risen
over time. Indeed, Rodriguez and Zavodny (2000)
find that almost two-thirds of the shift in the age distribution of displaced workers is due to the aging of
the labor force.
The gradual shift from a manufacturing-based to a
service-based economy may also be a factor in shifts
in the age distribution of displaced workers. Older
workers may be more concentrated in declining
manufacturing industries than are younger workers.
Rodriguez and Zavodny (2000) report that industry
shifts have played a small role in changes in the age
structure of displacement.
Anecdotal evidence also suggests that costcutting pressures have prompted firms to replace
older, higher-paid workers with younger workers
who earn lower salaries. Labich (1993) declared
that “companies have shut their doors to older
workers” and quoted a displaced 54-year-old manager who asked why a company would hire him
when “they can get someone in their 20s for half the
price.” Similarly, Munk (1999) quoted a 41-year-old
as saying, “For my salary the company could hire
two twenty-somethings.” The cost savings from
hiring a younger worker may outweigh the value of
an older worker’s years of experience.
Previous research indicates that older workers
are less likely to be displaced than are younger
workers. Data on involuntary job loss from 1968 to
1992 indicates that the likelihood of involuntary
job loss is higher among younger men than among
older men with the same educational attainment
(Boisjoly, Duncan, and Smeeding 1998). The probability of displacement also declines with age when
data on both sexes are used (Farber 1993, 1997).

Although displacement rates decline with age,
older workers tend to experience more difficulties
after displacement than do younger workers. Data
from 1981 to 1995 indicate that displaced workers
aged 45–64 are less likely to find another job within
a few years after they are displaced than are displaced workers aged 20–24; workers aged 35–44,
however, are more likely to be reemployed than are
workers aged 20–24 (Farber 1997). In addition, the
difference between pre- and postdisplacement earnings among workers who find other jobs increases
monotonically with age, indicating that older displaced workers experience larger wage losses than
do younger workers (Farber 1997).
Although older workers have always been less
likely to be laid off, their probability of displacement
may have increased over time. The evidence is
mixed.5 Farber (1993) reports that displacement
rates for workers aged 40–59 were significantly
higher in 1990–91 than in 1982–83 relative to workers aged 20–24. Using data from the Panel Survey of
Income Dynamics, Polsky (1999) finds a sizable
increase from 1976–81 to 1986–91 in the probability
that a separation was involuntary for men aged
45–54 relative to men aged 25–34, but the effect is
significant only at the 10 percent level. Gottschalk
and Moffitt (1999) find that the proportion of exits
that were involuntary increased significantly over
the 1980s and 1990s for older workers; however, the
probability of involuntary termination did not rise
over time.
Previous studies have found little support for
anecdotal claims that the effects of displacement
have worsened over time for older workers. Farber
(1993) reports that reemployment rates among
older displaced workers were unchanged between

1. Workers who are temporarily laid off, quit, or fired for cause are not viewed as displaced workers. Temporarily laid-off workers expect to be recalled to their jobs whereas workers who quit voluntarily leave their jobs. Workers who are fired for cause
are not viewed as displaced because the dismissal is due to the workers’ poor performance; displaced workers are those who
permanently lose their jobs for reasons unrelated to their own performance, such as their firms closing.
2. The costs are shared instead of having either the firm or the worker bear all of the cost. If the firm bears all of the cost, it has
no assurance that the worker will not quit before the firm has recouped its investment; the employee has an incentive to stay
if the firm raises his or her wage over time. If the worker bears all of the cost, he or she has no assurance that employment
with the firm will continue long enough for the worker to recoup the gains from specific skills; the firm has an incentive to
keep the worker if the firm has partially paid for specific training.
3. Displaced Worker Surveys are supplements to the Current Population Surveys, which are conducted monthly by the Bureau
of Labor Statistics.
4. Krueger (1993) reports that the fraction of workers who use a computer at work declines across the 25–39, 40–54, and 55–65
age groups. Addison, Fox, and Ruhm (1996) and Aaronson and Housinger (1999) find that the likelihood of displacement
increases with investment in computer technology. However, Aaronson and Housinger (1999) find little evidence that the
relationship between computer investment and displacement varies across age groups.
5. Studies of retention rates have also reached mixed conclusions. Diebold, Neumark, and Polsky (1997) and Neumark, Polsky,
and Hansen (1999) report that four-year job retention rates for younger workers relative to older workers declined from
1983–87 to 1987–91 and rose from 1987–91 to 1991–95. (Job retention rates encompass quits as well as involuntary job loss.)
Gottschalk and Moffitt (1999) similarly find that the likelihood that a job will end declined over 1981–92, with the largest
declines occurring for older workers.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


1982–83 and 1990–91 relative to trends among
younger displaced workers. Polsky (1999) reports
that the probability of reemployment fell for displaced workers aged 35–44 relative to workers
aged 25–34 between 1976–81 and 1986–91, but
the decline is not statistically significant. Polsky’s
results also do not indicate that the relative probability of reemployment has changed over time for
displaced workers aged 45–54. Neither study
examined changes over time in the difference
between pre- and postdisplacement earnings across
age groups.

Displaced Worker Data
he Displaced Worker Survey supplements to
the Current Population Survey are the primary source of data on displaced workers
in the United States.
The Current Population Survey is a large,
Workers in their 40s
nationally representative survey of labor
are relatively more likely
market status and
to be displaced in the
related variables that
1990s than in the 1980s.
is conducted monthly.
January of evenHowever, their reemploynumbered years from
ment and earnings losses
1984 to 1992 and
have not changed signifiFebruary of evennumbered years from
cantly over time relative
1994 to 1998, the
to younger workers.
Current Population
Survey included a
special supplement
that asked individuals about displacement.
Individuals are included in the Displaced Worker
Survey if they answered that they lost or left a job
for one of six reasons:
• their plant or company shut down or moved;
• their company had slack or insufficient work;
• their position or shift was abolished;
• a seasonal job was completed;
• a self-operated business failed;
• some other similar reason.
This study focuses on the first three displacement
categories, which more closely correspond to most
people’s idea of displacement.6 Displacements due
to company closure or insufficient work are
demand-driven phenomena, reflecting the business
cycle, while corporate restructuring is likely to
result in positions being abolished.
The Displaced Worker Survey asks workers about
characteristics of the lost job, including weekly
earnings. The survey also includes questions about
individuals’ current employment status and weekly



earnings in the current job, allowing an examination of postdisplacement outcomes at the time of
the survey.
The Displaced Worker Survey has several limitations, including that it records only one job loss per
worker. Workers who were displaced more than
once during the displacement window are instructed
to answer questions for the predisplacement job
with the longest tenure. The Displaced Worker
Survey therefore leads to an underestimate of the
number of displacement incidents during a given
period. The data are better regarded as yielding
estimates of the fraction of individuals displaced at
least once during a given period and of their characteristics relative to workers who report not being
displaced than as a count of the total number of displaced workers.
Another limitation of the surveys is a change in
the displacement interval. The 1984–92 surveys
asked whether individuals were displaced during
the previous five years, but the 1994–98 surveys
asked about displacement during the previous
three years. In the 1984–92 Displaced Worker
Surveys, workers who were displaced during the
first or second year of the five-year displacement
window and then were displaced again during the
next three years would report the first displacement episode if they had longer tenure on the first
lost job than on the subsequent lost job. As noted
above, the result is potential undercounting, in this
case for the three years prior to the survey. In the
1994–98 Displaced Worker Surveys, such workers
would always report the more recent lost job and be
counted as displaced during the previous three
years. This study includes only workers who report
being displaced within the three years prior to the
survey. In the analysis of displacement rates, the
change in the Displaced Worker Survey displacement interval is corrected for using the method
developed by Farber (1997).7
An additional shortcoming of the surveys is a
change in the way data were collected for workers
with different reasons for displacement. The
1994–98 Displaced Worker Surveys did not follow
up with questions about jobs reported lost because
of a seasonal job ending, self-employment failing, or
other reasons. Workers in those categories were not
asked what year they were displaced or their earnings on the lost job, for example. Because of the
incomplete information in the surveys, this analysis
does not include workers displaced for those three
categories of reasons. As Kletzer (1998) and Farber
(1997) discuss, the fraction of workers displaced
from seasonal jobs or self-employment has remained
fairly constant over time; however, workers dis-

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000

placed for other reasons account for an increasing
share of displaced workers over time.8
This study uses data on individuals aged 20–64 at
the time of the survey (not at the time of displacement).9 Workers over age 64 are not included
because retirement decisions may influence their
postdisplacement behavior differently than for
workers under age 64 and because retirement
behavior may have changed over time.10

Descriptive Statistics on Displacement
his study evaluates displacement rates across
age groups and examines whether older
workers are more likely to be displaced than
are younger workers. It also examines whether two
postdisplacement outcomes, the probability of
reemployment and the difference between pre- and
postdisplacement earnings, vary across age groups.
The analysis focuses on whether the incidence and
costs of displacement have changed over time
across age groups.
There has been a dramatic shift in the age composition of displacement, as shown in Chart 1. Chart 1
shows kernel density estimates of age-displacement
profiles for the 1984, 1990, and 1998 Displaced
Worker Surveys. Kernel density estimates are
essentially smoothed histograms and provide a useful means of summarizing changes in a distribution
over time.11 These figures display the distribution of
displaced workers by age at the time of the surveys;
the plot for 1984 in Chart 1, for example, shows the
age distribution of workers displaced during the survey period of 1981 to 1983. The fraction of displaced


workers who are middle-aged has clearly risen over
time. In the 1984 Displaced Worker Survey, the age
of displaced workers is concentrated in the late 20s,
whereas the distribution is considerably more evenly spread across ages in the 1998 survey. The chart
suggests that much of the “flattening of the hump”
occurred in the 1990s.
There are several potential explanations for the
shift in the age-displacement profile shown in Chart 1.
As discussed above, changes in the age distribution of
the labor force may account for the increase in the
fraction of displaced workers who are middle-aged.
Increased emphasis on computer and other technical
skills may have contributed to the shift if older workers’ skills are poorer or outdated relative to those of
younger workers. The concentration of older workers
in declining industries or an increase in cost-cutting
pressures also may have contributed to the shift in
the age distribution of displaced workers.
Table 1 reports three-year displacement rates for
five-year age groups for each Displaced Worker
Survey.12 About 13.6 percent of workers aged 20–24
were displaced during 1981–83, for example, compared with 7.6 percent of workers aged 40–44.
Although displacement rates are countercyclical for
all age groups, displacement rates are more variable
across the business cycle for younger workers than
for older workers.
The displacement rates provide some support for
the hypothesis that the likelihood of displacement
may have risen over time for middle-aged workers
relative to younger workers. The recession in the
early 1990s appears to have been tougher on

6. Aaronson and Housinger (1999) discuss the limitations of the Displaced Worker Survey categories of reasons for displacement.
7. Using data from the Panel Survey of Income Dynamics, Farber (1997) calculated that workers displaced in a given year have
a 0.3017 percent probability, on average, of being displaced again over the next three years. For workers displaced a year
ago, the average probability of being displaced again during the next three years is 0.2705. Workers displaced four and five
years ago are assigned these probabilities of being displaced in the three years prior to the survey.
8. Abraham (1997) notes that only 24 percent to 31 percent of workers who said in the 1996 Displaced Worker Survey that
they were displaced for “other” reasons should be categorized as displaced, based on follow-up interviews.
9. Approximate age at the time of displacement could be backed out using the year of displacement and the age at the survey,
but the age at displacement can only be bracketed within a three-year window.
10. The mean age at withdrawal from the labor force in the United States declined from 62.9 in 1980–85 to 62.2 in 1990–95 for
men and from 62.9 to 62.7 for women (Gendell 1998).
11. See Valletta (1997a) for a nontechnical discussion of kernel density estimation or Silverman (1986) for a more technical discussion. The estimates presented here used an Epanechnikov kernel and a bandwidth of 1.4795799, which is the optimal
bandwidth for the combined 1984, 1990, and 1998 Displaced Worker Survey samples.
12. The numerator is the number of workers who report being displaced because of plant closure, position abolished, or slack
work in the last three years of each survey. As mentioned earlier, Farber’s 1997 method is used to correct for the change in
the displacement window. The denominator is the number of persons who were employed during that three-year period,
based on the Current Population Survey outgoing rotations group data. The Current Population Survey final weights were
used to calculate the number of displaced workers and employed persons. Because the Displaced Worker Survey reports age
at survey, not age at displacement, the workers in the denominator are “aged”; the denominator for 20–24 year-olds in the
1984 Displaced Worker Survey calculation is the average of the number of 18–22 year-olds employed in 1981, 19–23 yearolds in 1982, and 20–24 year-olds in 1983.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


Age Distribution of Displaced Workers, 1984, 1990, and 1998





Age at time of survey

Source: Author’s calculations from Displaced Worker Survey data

middle-aged workers than was the recession in the
early 1980s. Among workers aged 40–59, displacement rates were higher during 1989–91 than in
1981–83. Among workers aged 20–39, however, displacement rates were lower in 1989–91 than in
1981–83. The displacement rates reported in Table 1
also suggest that the 1990s recovery may have initially been more sluggish for workers in their 40s
than younger workers.
Although displacement rates appear to have been
relatively high among middle-aged workers during
the early and mid-1990s, it is not clear whether
middle-aged workers were more likely to be laid off
during the most recent period than were younger
workers. As Table 1 reports, displacement rates
among workers aged 40–54 during 1995–97 were
similar to their levels in 1985–87. Workers aged
20–24 were more likely to be displaced in 1995–97
than in 1985–87, while displacement rates were
lower in 1995–97 than in 1985–87 for workers aged
25–34. Whether displacement rates remained relatively high for middle-aged workers during the most
recent period depends on which comparison group
is used.
Among workers who were displaced, reemployment rates differ across age groups. Table 2 reports
the fraction of displaced workers who are employed
at the time of the survey by five-year age intervals.
The fraction of displaced workers who find new jobs
is cyclical, with reemployment rates higher during
expansions than during recessions. Reemployment
rates generally appear to rise with age until age

40–44 and then decline. The decline in reemployment rates among older displaced workers may
reflect voluntary withdrawal from the labor force
(retirement) or may indicate that displaced workers
in their 50s and early 60s have more difficulty finding new jobs than do workers in their 30s and 40s.
Reemployment rates do not appear to have worsened over time for older workers. During the downturn in the early 1990s, reemployment rates for
displaced workers in their 40s were higher than during the 1980s recession; about 64 percent of displaced workers aged 40–44 were reemployed at the
time of the 1992 Displaced Worker Survey, for example, compared with 59 percent in the 1984 survey.
Reemployment rates for workers in their 20s, in contrast, were lower in 1989–91 than in 1981–83, and
they were about even for workers in their 30s.
Displacement rates during the economic recovery in
the 1990s are higher than in the 1980s for all age
groups. The rates reported in Table 2 thus do not
indicate that reemployment rates have declined
among displaced workers in their 40s and 50s relative to displaced workers in their 20s and 30s, as suggested by the media.
The descriptive statistics on reemployment also
are not consistent with the popular perception that
older downsized workers have been increasingly
forced into involuntary retirement over time. If
older displaced workers want to find new jobs but
are not able to do so, they may withdraw from the
labor force before their preferred retirement age.
However, the reemployment rates in Table 2 do not

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000

T A B L E 1 Three-Year Displacement Rates, by Age
















Note: Shown is the ratio of workers displaced during the three years prior to the Displaced Worker Survey because of plant closure, position
abolished, or slack work to the average number of workers in that age group employed during that three-year period.

T A B L E 2 Reemployment Rates of Displaced Workers, by Age


















Note: Data include only workers displaced during the three years prior to the Displaced Worker Survey because of plant closure, position
abolished, or slack work. Observations are weighted using the Current Population Survey final weights. Each column is estimated from a
separate Displaced Worker Survey.

suggest an increased trend toward involuntary
retirement among older displaced workers.13
Table 3 shows the average percentage difference
between pre- and postdisplacement real weekly
earnings for displaced workers who were employed
at the time of the survey.14 Almost all age groups
incurred wage losses after displacement in each survey. Wage losses show a cyclical pattern, with the
difference between pre- and postdisplacement earnings rising during recessions and shrinking during
expansions. The average weekly earnings loss after
displacement, given reemployment, tends to
increase with age, perhaps indicating that older dis-

placed workers have more difficulty earning as
much per hour in their new jobs than do younger
workers or reflecting a relative decline in hours
worked per week among older displaced workers.
The sample means reported in Table 3 provide little evidence that wage losses increased over time
for older displaced workers relative to younger
workers. The youngest displaced workers, those
aged 20–24, experienced earnings losses during
recessions and the early stages of economic recoveries but actually experienced earnings increases
after displacement in the later phases of the 1980s
and 1990s expansions. Workers aged 25–29 who

13. The Displaced Worker Survey data are not ideal for examining whether workers have been forced or pressured into retiring
from their jobs before their preferred age of retirement. If such workers view themselves as involuntarily displaced, they
would presumably be included in the survey. However, such workers may not view themselves as displaced if they received
an early retirement compensation or incentive package from their employers.
14. The wages are deflated using the consumer price index (CPI) for urban workers. The wage at the time of the survey is deflated
using the CPI for the survey month, and the predisplacement wage is deflated using the CPI annual average for the year of

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


T A B L E 3 Average Percentage Change in Real Weekly Earnings, by Age











Note: Shown are the mean values of the natural log of real postdisplacement earnings minus the natural log of real predisplacement earnings for each age group. Data include only workers displaced during the three years prior to the Displaced Worker Sur vey because of plant
closure, position abolished, or slack work who are reemployed at the time of the survey. Observations are weighted using the Current
Population Survey final weights. Each column is estimated from a separate Displaced Worker Survey.

were displaced in 1993–95 or 1995–97 also do not
appear to experience earnings losses. Although displaced workers aged 30 and older continue to experience a decline in earnings after displacement, the
magnitude of the average loss is smaller for age 30plus workers displaced in the mid-1990s than for
those displaced in the mid-1980s.
The descriptive statistics suggest that the likelihood of displacement may have risen over time for
middle-aged workers, relative to younger workers,
particularly during the 1990s recession. The sample
means do not suggest that postdisplacement outcomes have worsened for older workers relative to
younger workers. However, other factors that affect
postdisplacement outcomes, such as tenure or education, may have changed differently across age
groups and may mask a change in postdisplacement
outcomes across age groups. The next section uses
a multivariate framework to examine differences
across age groups in the probability of displacement
and in postdisplacement outcomes.

Regression Analysis Methods
he likelihood that a worker is displaced
because of plant closure, position abolished,
or slack work is estimated using probit
regressions.15 The dependent variable is one if a
worker reports being displaced during the three
years prior to the survey and zero otherwise, and a
separate regression is estimated for each Displaced
Worker Survey.16 The probability that a displaced
worker is reemployed at the time of the survey is
similarly estimated using a separate probit regression for each survey year, where the dependent variable is one if a displaced worker has found a new job
and zero otherwise. The ordinary least squares
(OLS) regression method is used to estimate the



determinants of the percentage difference between
pre- and postdisplacement earnings among workers
who are reemployed.
This study focuses on the effect of age, which is
measured using five-year age intervals. If the probability of displacement has risen over time for older
workers, the estimated relationship between the
likelihood of displacement and the age indicator
variables should increase over time for older workers relative to younger workers. Similarly, the estimated relationship between the probability of
reemployment or earnings losses and the age indicator variables should decline over time for older
workers relative to younger workers if the consequences of displacement have worsened over time
for older workers. The 20–24 age group is omitted in
the regressions for identification, so the estimated
coefficients on the other age variables are relative to
workers aged 20–24.
The regressions include other variables that are
likely to affect postdisplacement outcomes and may
vary over time within age groups. For example, the
likelihood of displacement may decrease with education, and the probability of reemployment may
increase with education. If the educational composition of workers within five-year age groups has
changed over time, the coefficients on the age variables might reflect changes in education if the
regressions do not control for education. The
regressions include indicator variables for three of
four educational categories as well as female, nonwhite, and married indicator variables.17
The postdisplacement outcomes regressions also
control for reason for displacement and years since
displacement. Reason for displacement may affect
the likelihood of reemployment because plant closure or slack work may indicate an industry down-

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000

turn, which affects the likelihood of finding another
job in the same industry. Earnings losses tend to be
larger if a displaced worker switches industries
(Jacobson, Lalonde, and Sullivan 1993), which may
be more likely if the reason for displacement is
plant closure or slack work. The longer the period
since displacement, the more time workers have
had to find another job and to receive raises on a
new job.
The postdisplacement outcomes regressions also
control for years worked at the predisplacement job
(tenure) because job security may have changed
over time. The earnings losses experienced by displaced workers tend to increase with the level of
tenure on the predisplacement job (Topel 1991;
Farber 1993). If tenure levels have fallen over time
among older workers who are displaced, earnings
losses among older displaced workers may fall over
time because of changes in tenure, not because of
changes in earnings losses directly due to age.18 In
addition, the relationship between tenure and
wages may have weakened over time. Older workers
tend to have higher tenure levels than younger
workers, a tendency that partially underlies the ageearnings gap. If the relationship between tenure and
wages has weakened over time, older workers may
not be earning as much in predisplacement jobs relative to younger workers. The observed age gap in
earnings losses would then narrow over time if
tenure is not controlled for in the regressions.
Tenure at the lost job is measured using a linear
For the displacement and reemployment probit
regressions, the marginal coefficients, evaluated at
the sample means, are presented for ease of interpretation. For the age-group indicator variables, the
coefficients indicate the estimated change in the
probability of reemployment if the indicator variable
changes from zero to one. Observations are weighted
using the Current Population Survey final weights.

he likelihood that a worker is displaced generally declined with age in the 1980s. As Table 4
shows, the probability that a worker aged
40–44 was displaced in 1981–83 was 3.5 percent lower
than the probability for a 20–24 year-old. Among
workers aged 45–54, the relative probability of displacement was 4.4 percent lower than for workers
aged 20–24. The negative relationship between displacement and age holds through the period 1989–91;
however, the coefficients generally become less negative
The data presented in this
through the 1980s,
article suggest that much
suggesting that the relative probability of disof the concern about displacement increased
placement may soon begin
for middle-aged workto abate. Displacement
ers during the 1980s.
The negative relarates during 1995–97
tionship between the
returned to levels similar
probability of displaceto those during the 1980s
ment and age is less
evident in the 1990s.
During the 1991–93
period, workers aged
35–44 were more likely
to be displaced than workers aged 20–24, in sharp contrast to the previous period. In another change from
previous trends, workers above age 44 were as likely to
be displaced as workers aged 20–24 during 1991–93.
During 1993–97, middle-aged workers did not regain
the relatively protected status they enjoyed during
the 1980s; workers aged 35–49 remained as likely to
be laid off as workers aged 20–24. These results indicate that the likelihood of displacement has increased
for older workers relative to workers aged 20–24.
In results not reported in Table 4, women are significantly less likely to be displaced than are men,
but the relationship weakens over time. There is no


15. A regression gives the mathematical relationship between a dependent variable and a set of independent variables. A probit
regression has a dependent variable that equals zero or one.
16. The displacement sample includes individuals who were displaced in the three years prior to the Displaced Worker Survey
because of plant closure, position abolished, or slack work and nondisplaced individuals who are employed at the time of the
survey. As in Farber (1997), the weights of individuals who were displaced four or five years ago are adjusted to reflect the
probability of being displaced in the three years prior to the survey.
17. There are four educational categories: less than high school diploma, high school diploma only, some college, and college
degree or higher.
18. The evidence on changes in tenure over time is mixed, with some studies suggesting a small decline in tenure (Aaronson and
Sullivan 1998; Marcotte 1999; Neumark, Polsky, and Hansen 1999).
19. The tenure question changes slightly across the Displaced Worker Surveys. In 1984–92, the survey asks, “How many years
had [the displaced worker] worked continuously there when that job ended?” The 1994 survey asks, “How many years had
you worked for that employer when you lost that job?” The 1996–98 surveys ask, “How long had you worked for [that employer] when that job ended?” and displaced workers were asked to specify the periodicity (days/weeks/months/years) of their
answer. The 1996–98 answers were converted into years for this analysis.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


TA B L E 4
Regression Estimates of Probability of Displacement, by Age







Age 25–29




Age 30–34



Age 35–39


Age 40–44



























Age 45–49









Age 50–54









Age 55–59









Age 60–64




















* significant at the .05 level
** significant at the .01 level
Note: Shown are the marginal probit coefficients evaluated at the sample means. The dependent variable is one if a worker reports being
displaced in the three years prior to the survey because of plant closure, position abolished, or slack work and zero otherwise. Other variables in the regressions are indicator variables for female, nonwhite, married, and three of four educational categories (less than high
school, some college, college graduate). The omitted age category is 20–24, so the other age groups are relative to workers aged 20–24.
Observations are weighted using the Current Population Survey final weights. Each column is from a separate regression.

TA B L E 5
Regression Estimates of Probability of Reemployment after Displacement, by Age



Age 25–29



Age 30–34


Age 35–39




























Age 40–44









Age 45–49









Age 50–54









Age 55–59









Age 60–64


















* significant at the .05 level
** significant at the .01 level
Note: Shown are the marginal probit coefficients evaluated at the sample means. The dependent variable is one if a displaced worker is
reemployed at the time of the survey and zero otherwise. Other variables in the regressions are indicator variables for female, nonwhite,
married, three of four educational categories (less than high school, some college, college graduate), years since displacement (two or three),
reason for displacement (plant closed or slack work), and a linear variable for tenure on the predisplacement job. The omitted age category is
age 20–24. Observations are weighted using the Current Population Survey final weights. Each column is from a separate regression.


Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000

TA B L E 6
Regression Estimates of Percentage Change in Real Weekly Earnings, by Age




Age 25–29




Age 30–34



Age 35–39


Age 40–44
































Age 45–49









Age 50–54









Age 55–59









Age 60–64


















* significant at the .05 level
** significant at the .01 level
Note: The dependent variable is the natural log of real postdisplacement earnings minus the natural log of real predisplacement earnings.
Other variables in the regressions are indicator variables for female, nonwhite, married, three of four educational categories (less than high
school, some college, college graduate), years since displacement (two or three), reason for displacement (plant closed or slack work), and a
linear variable for tenure on the predisplacement job. The omitted age category is age 20–24. Observations are weighted using the Current
Population Survey final weights. Each column is from a separate regression.

clear relationship between race and the probability
of displacement, and married individuals are less
likely to be displaced than are unmarried workers.
The likelihood of displacement declines monotonically with educational attainment.
Table 5 presents the reemployment probit regression results. Displaced workers aged 50 and older
are significantly less likely to find new jobs than are
workers aged 20–24 in most of the Displaced Worker
Surveys. Workers aged 50–54 who were displaced in
1981–83, for example, are 10.6 percent less likely to
have been reemployed at the time of the survey
than workers aged 20–24. In general, workers in
their 30s and 40s are as likely to find other jobs as
are workers aged 20–24. In results not shown in
Table 5, women are less likely to find new jobs than
are men, and nonwhites are less likely to be reemployed than are whites. Reemployment probabilities
increase with education and with time elapsed since
displacement. In most survey years, workers who
were displaced because of slack work are significantly less likely to find new jobs than workers displaced because their jobs were abolished.
The results do not indicate that the relative probability of finding other jobs has deteriorated over time
for older displaced workers. The estimated coefficients do not become significantly more negative over

time for any age group; indeed, relative reemployment probabilities generally appear higher for workers displaced in 1995–97 than for workers of the
same age displaced in the 1980s. These results provide little evidence for the hypothesis that displaced
workers over age 40 face increased difficulties finding new jobs. Instead, they suggest that the likelihood
of reemployment among older workers may be more
sensitive to the business cycle than it is among
younger workers. As the economy boomed during the
mid-1990s, older displaced workers appear to have
had relatively little difficulty finding new jobs.
Table 6 shows the results of the OLS regressions
for the percentage change in real weekly earnings
among displaced workers who are reemployed at
the time of the survey. The first entry in column 1,
for example, indicates that earnings losses of workers aged 25–29 who were displaced in 1981–83 are
9.4 percent larger than the earnings losses among
workers aged 20–24. Earnings losses generally
increase with age, although the relationship is not
monotonic. In results not reported in the table,
earnings losses decrease with educational attainment, and earnings losses increase by 1.1 to 1.9 percent for each year of tenure on the lost job.
Older displaced workers who find new jobs do
not appear to experience larger earnings losses in

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


the 1990s than in the 1980s relative to the youngest
displaced workers. Earnings losses for middle-aged
workers displaced in 1993–95 appear higher than in
the surrounding periods, but the differences are not
significant. These results do not indicate that relative
earnings losses have risen over time for middleaged workers.

Middle-Aged Managers
iddle-aged managers and professionals
have been the focus of many media reports
about corporate downsizing. The New York
Times noted in 1996, for example, “Increasingly the
jobs that are disappearing are those of higher-paid,
white-collar workers, many at large corporations,
women as well as men, many at the peak of their
careers.” Farber (1997) reports that rates of job loss
among managers rose substantially over 1987–89 to
1991–93 but then fell during 1993–95.
Chart 2 displays displacement rates for privatesector middle-aged managerial and professional
workers, blue collar workers, and all workers aged
40–54. Displacement rates of managerial and professional workers are considerably lower than displacement rates for blue-collar workers, but the
difference is not constant over time. As Farber
(1997) notes, the 1990s recession was more evenly
spread across occupations than the 1980s recession,
which was concentrated among blue-collar workers.


During the 1990s, displacement rates among bluecollar workers have declined more than have displacement rates among managerial workers.
Reemployment rates do not appear to have worsened over time for managerial and professional
workers who are displaced relative to other workers. As Chart 3 indicates, reemployment rates have
risen during the 1990s for all workers aged 40–54,
and the trends are similar for blue-collar and managerial and professional workers.
Earnings losses appear to have worsened over time
for managerial and professional workers who are displaced and find other jobs. As Chart 4 shows, until
1989–91, middle-aged managerial and professional
workers experienced smaller-than-average wage
losses. Beginning with the 1992 Displaced Worker
Survey, managerial and professional workers experienced larger wage losses than the average middleaged worker and than the average blue-collar
middle-aged worker. Earnings losses among managerial and professional workers appear to have rebounded particularly slowly during the early phases
of the 1990s recovery in comparison with bluecollar workers.

isplacement and corporate downsizing have
received considerable attention from the
media in recent years. The conventional


Displacement Rates, Workers Aged 40–54, by Occupation




Blue Collar



Managerial and










D i s p l a c e d Wo r k e r S u r v e y
Note: Shown is the ratio of workers aged 40–54 at the time of the survey who were displaced during the three years prior to the Displaced
Worker Survey because of plant closure, position abolished, or slack work to the average number of workers in that age and occupation group
employed during the three-year period.


Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000

Reemployment Rates, Displaced Workers Aged 40–54, by Occupation
Managerial and




Blue Collar









D i s p l a c e d Wo r k e r S u r v e y
Note: Shown is the percent of workers aged 40–54 at the time of the survey who were displaced during the three years prior to the Displaced
Worker Survey because of plant closure, position abolished, or slack work and were reemployed at the time of the survey.

Average Percentage Change in Real Earnings, Workers Aged 40–54, by Occupation



Managerial and

Blue Collar









D i s p l a c e d Wo r k e r S u r v e y
Note: Shown is the average percentage difference between real weekly earnings at the predisplacement job and the postdisplacement job for
workers aged 40–54 at the time of the survey who were displaced during the three years prior to the Displaced Worker Survey because of
plant closure, position abolished, or slack work and were reemployed at the time of the survey.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


wisdom that middle-aged workers face an increased
risk of being displaced and increased difficulties after
displacement is partially borne out by this analysis.
Displacement rates among middle-aged workers rose
relative to younger workers during the 1990s recession, and the relative likelihood of displacement for
middle-aged workers has not returned to the levels
of the 1980s. Thus, workers in their 40s are relatively more likely to be displaced in the 1990s than they
were in the 1980s. However, the two postdisplacement outcomes examined here, reemployment and
earnings losses, have not changed significantly over
time for older workers relative to younger workers.
Middle-aged managerial and professional workers do
not appear to face increased risks of displacement
relative to middle-aged blue-collar workers, but their
relative earnings losses following displacement and
reemployment appear to have worsened over time.
Future research should examine why the relative
likelihood of displacement has increased over time

for older workers and why relative earnings losses
and the probability of reemployment have worsened
over time for middle-aged managers. One potential
explanation is increased use of technology in the
workplace, which might create a relative disadvantage for older workers if technological change has
rendered their human capital obsolete.
The data presented in this article also suggest
that much of the concern about displacement may
soon begin to abate. Displacement rates during
1995–97, the most recent period for which data are
available, returned to levels similar to those during
the 1980s expansion. Reemployment rates for workers displaced during 1995–97 were at their highest
levels for all age groups since the Displaced Worker
Survey began in 1984, and the gap between pre- and
postdisplacement earnings has shrunk during the
most recent period.

Impact of Technology on Displacement and Reemployment.” Federal Reserve Bank of Chicago Economic
Perspectives 23 (Second Quarter): 14–30.
Decline of Job Security in the 1990s: Displacement,
Anxiety, and Their Effect on Wage Growth.” Federal
Reserve Bank of Chicago Economic Perspectives 22
(First Quarter): 17–43.
ABRAHAM, KATHERINE. 1997. “Comment on ‘The Changing
Face of Job Loss in the United States, 1981–1995.’ ”
Brookings Papers on Economic Activity: Microeconomics, 135–42.
RUHM. 1996. “Trade Sensitivity, Technology, and Labor
Displacement.” National Bureau of Economic Research
Working Paper W5621, June.
BECKER, GARY. 1975. Human Capital. New York:
Columbia University Press.
SMEEDING. 1998. “The Shifting Incidence of Involuntary
Job Losses from 1968 to 1992.” Industrial Relations 37
(April): 207–31.
1997. “Job Stability in the United States.” Journal of
Labor Economics 15 (April): 206–33.
FARBER, HENRY S. 1993. “The Incidence and Costs of Job
Loss: 1982–91.” Brookings Papers on Economic
Activity: Microeconomics, no. 1:73–119.


———. 1997. “The Changing Face of Job Loss in the
United States, 1981–1995.” Brookings Papers on
Economic Activity: Microeconomics, 55–128.
GENDELL, MURRAY. 1998. “Trends in Retirement Age in
Four Countries, 1965–95.” Monthly Labor Review 121
(August): 20–30.
in Job Instability and Insecurity Using Monthly Survey
Data.” Journal of Labor Economics 17 (October, pt. 2):
HIPPLE, STEVEN. 1999. “Worker Displacement in the Mid1990s.” Monthly Labor Review 122 (July): 15–32.
SULLIVAN. 1993. “Earnings Losses of Displaced Workers.”
American Economic Review 83 (September): 685–709.
KLETZER, LORI G. 1998. “Job Displacement.” Journal of
Economic Perspectives 12 (Winter): 115–36.
KRUEGER, ALAN B. 1993. “How Computers Have Changed
the Wage Structure: Evidence from Microdata,
1984–1989.” Quarterly Journal of Economics 108
(February): 33–60.
LABICH, KENNETH. 1993. “The New Unemployed.”
Fortune, March 8, 40–49.
MARCOTTE, DAVE E. 1999. “Has Job Stability Declined?
Evidence from the Panel Study of Income Dynamics.”
American Journal of Economics and Sociology 58
(April): 197–216.

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MUNK, NINA. 1999. “Finished at Forty.” Fortune,
February 1, 50–66.

SILVERMAN, B.W. 1986. Density Estimation for Statistics
and Data Analysis. London: Chapman and Hall.

1999. “Has Job Stability Declined Yet? New Evidence for
the 1990s.” Journal of Labor Economics 17 (October,
pt. 2): S29–64.

TOPEL, ROBERT. 1991. “Specific Capital, Mobility, and
Wages: Wages Rise with Job Seniority.” Journal of
Political Economy 99 (February): 145–76.

NEW YORK TIMES. 1996. “The Downsizing of America,”
March 3–9.
POLSKY, DANIEL. 1999. “Changing Consequences of Job
Separation in the United States.” Industrial and Labor
Relations Review 52 (July): 565–80.
“Explaining Changes in the Age Distribution of Displaced
Workers.” Federal Reserve Bank of Atlanta Working
Paper 2000-1, February.

VALLETTA, ROBERT G. 1991. “Job Tenure and Joblessness
of Displaced Workers.” Journal of Human Resources 26
(Fall): 726–41.
———. 1997a. “The Effects of Industry Employment
Shifts on the U.S. Wage Structure, 1979–1995.” Federal
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no. 1:16–32.
———. 1997b. “Job Loss during the 1990s.” Federal
Reserve Bank of San Francisco Economic Letter
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Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


Atlanta Fed Conference
on Fiscal Policy in
Latin America


Eisenbeis is senior vice president and director of the
Atlanta Fed’s research department.






generation reforms like those for fiscal policy—is
increasingly apparent.
Because sound fiscal policy is key to viable monetary policy and sustainable economic growth, few
issues are as critical to the region’s economic future.
This encompassing relevance is the reason the
Federal Reserve Bank of Atlanta chose to sponsor a
conference on sustainable public sector finance in
Latin America, which took place in Atlanta on November 1 and 2, 1999. While the Federal Reserve’s job is
the supervision of the U.S. financial sector and the
formulation of domestic monetary policy, the reality
is that it is no longer possible to think of economic
policy—be it monetary policy or supervisory policy—in purely domestic terms. The Federal
Reserve’s mandate is indeed domestic, but the setting in which that mandate is carried out is increasingly global.
In order to address these broad policy implications, participants in the conference “Sustainable
Public Sector Finance in Latin America” were asked
to explore the issue from various angles, employing
different disciplinary approaches. The multidisciplinary approach allowed participants to examine the
wide-ranging nature of fiscal policy. The following

overview of the papers and presentations from the
conference provides insight into some of these perspectives on several important issues.
The basic policy elements of sustainable public
sector finance were explored in a paper by
Elizabeth McQuerry, Michael Chriszt, and Stephen
Kay of the Atlanta Fed’s Research Department.
Economic fundamentals and the notion of policy
credibility were presented through a review of existing research on fiscal policy and an overview of how
Latin American governments have performed in
achieving their fiscal policy objectives. The review
highlighted a newfound appreciation of the role that
institutions play in producing policy outcomes
alongside the long-standing consensus about the
deleterious impact of prolonged fiscal imbalances.
The authors stressed that sustainable public sector
finance is neither a purely financial nor institutional/political problem. Rather, the two dimensions
of the problem are inextricably intertwined.
The paper by Larry Graham (University of Texas
at Austin) presented a framework for thinking
about fiscal-reform policies through a comprehensive, historical survey of the different developmental paths found in Argentina, Brazil, Mexico, and

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000


Venezuela. Dr. Graham’s analysis argued that an
examination of these four distinct political trends,
which to varying degrees have been manifested
throughout the region, provides critical knowledge
not only about the past but about how historical
politics and economics in the region influence the
path of reform today.
Ben Ross Schneider (Northwestern University)
explored why administrative reform has been so
problematic in the region. Varying types of administrative reform tend to stimulate different dynamics in terms of the policy process as well as the
particular collection
of reformers and allies
behind the reform.
While price stabilization
Drawing insight from
and liberalizing reforms
institutional economhave placed regional
ics to examine the
specific political dyeconomies in a much more
namics of the life cycompetitive position over
cle of reform—from
the past two decades, the
e l e c t i o n s t o f i n al
need for so-called secondSchneider’s analysis
generation reforms like
has important polthose for fiscal policy is
icy implications for
efforts to reform
increasingly apparent.
bureaucracies and
public services as well
as for other types of economic and political reforms.
Juan Carlos Echeverry and Verónica Navas
(Colombian National Planning Department) evaluated fiscal policy in Colombia, analyzing public
sector net worth using both flow and stock
approaches. The authors argued that the feasibility of a particular fiscal package depends on not
only a sound economic approach but also the
establishment of a new political and judicial
approach to the decision-making process that
would avoid the type of institutional conflicts that
have occurred in some countries. Echeverry and
Navas also argued that policy should be directed
toward the pursuit of a dynamic equilibrium related
to public sector net worth as opposed to explicit
debt and deficit targets.
Deficit finance was the subject of a panel featuring remarks by two experienced practitioners of
debt management in Latin America. Carlos Boloña,
who served as Minister of Economy and Finance in
Peru from 1991 to 1993, shared insight from his
experiences reining in government expenditure
during a very difficult period in the country’s economic and political history. His tenure was also the
period when Peru effectively came to terms with
many public finance concerns.

The discussion by Fábio de Oliveira Barbosa, the
Secretary of the Treasury in Brazil, mapped out the
debt strategy being pursued by his government. His
presentation demonstrated many of the choices facing countries seeking to establish and maintain
access to international credit lines as they build
domestic credit markets. The Brazilian experience
also illustrates how fiscal policy and global economic
conditions sometimes work at cross-purposes, presenting policymakers with even larger budgetary
The final panel featured a discussion of international lending and capital flows by Francisco GilDíaz, formerly the vice governor of the Banco de
México, and Graham Stock, a vice president with
Chase Securities. In his review of capital flows to
developing countries, Gil-Díaz asserted that all foreign debt in emerging market economies—whether
held by the government or in private hands—is,
in essence, a sovereign liability, providing a sobering reminder that the rapid increase in capital
flows and international markets has wide-ranging
Graham Stock traced the evolution of international
lending in the 1990s from the perspective of both
the borrower and the lender, providing insight into
capital flows, credit fundamentals, and commercial
bank lending. Stock noted that beyond the many
changes in capital flows and markets lies a fundamental certainty: credit analysis of emerging market
economies is essentially the same as other credit
analysis, and countries that meet these criteria will
find ready access to international capital markets
while those that fail to meet them must resort to
much more onerous terms.
The conference also featured two individual
speakers. Ann Helwege (Tufts University) shared
her research on how Latin American governments
have fared with poverty alleviation efforts and outlined the prospects for future efforts in an environment of resource constraints. Helwege noted
several improvements in social policies in the region
but also warned that privatization and decentralization have thus far done little to improve the region’s
inequalities. Cláudia Costin provided the keynote
address from her first-hand experience with state
reform as Brazil’s secretary of state for administration and government property. The Brazilian example illustrates many of the multifaceted challenges
facing reformers.
The conference also benefited from a knowledgeable and engaged group of participants—encompassing viewpoints from academia, banking,
government, and the private sector—who shared
their perspectives and first-hand experiences in

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000

designing fiscal policy. This depth allowed participants to discuss public sector finance along a broad
spectrum during the two-day conference.
At the end of the proceedings, participants were
asked to identify the primary areas on which further
research on fiscal policy would most fruitfully be
focused. Three areas figured prominently in this discussion: (1) the need for greater understanding of
the role of institutions in fiscal policy reform, espe-

cially in regard to constitutional reform, Congress,
and transparency of the policy process; (2) the need
for further study and specification of several issues,
such as tax reform, income distribution, foreign
direct investment, hidden public debt, and pension
liabilities, to determine the significance of their role
in fiscal policy reform; and (3) the need for a greater
understanding of the relationship between fiscal
policy and dollarization.

Federal Reserve Bank of Atlanta E C O N O M I C R E V I E W Second Quarter 2000