View original document

The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.

1
Hello, I’m Ben Chabot – Financial Economist at the Federal Reserve Bank of Chicago.
Today I’m going to talk about the overnight money market. Understanding overnight money market
dynamics and participants is crucial to the effective implementation of monetary policy. Even more so
during the policy normalization process.
I will start by introducing various safe short-term investments available to investors with cash management
needs. Then I will briefly describe the traditional banking system and elaborate on what we call the shadow
banking system. We will talk about how the Fed interacts with the traditional banking system to target the
overnight federal funds rate and how the Fed can use its new tools, that is, the interest on excess reserves
and reverse repurchase agreements, to influence overnight interest rates in both the traditional and the
shadow banking systems.
Many investors want to hold cash or cash-like overnight investments. To this end, we refer to them as cash
pools. This demand is primarily from global corporations, large institutional investors, large asset managers,
security lenders, government-sponsored enterprises, foreign central banks, and state and local
governments—all of which need cash for payment and liquidity purposes.
When we say “cash-like” investments we mean investments that match the safety and utility of holding
cash. Cash is risk-free. The nominal value of cash is invariant to changes in interest rates, so a cash
investment is worth the same amount tomorrow as it is today. Not surprisingly, many investors wish to hold
cash-like investments to satisfy their payment and liquidity needs or just to have a risk-free pile of assets to
draw upon in times of financial stress.
So where do investors find cash-like investments? For starters, investors can hold one of the safest shortterm cash-like instruments, which is a very short-maturity Treasury bill. This instrument is guaranteed by the
U.S. government and at maturity it’s going to pay a fixed amount. Because of its very short maturity the
price of a Treasury bill is hardly exposed to interest rate fluctuations. Therefore, it can be liquidated early
with little interest rate risk. So Treasury bills satisfy investors’ need for safe overnight investments. However,
the supply of Treasury bills is insufficient to meet the large demand for safe overnight investments.
Therefore, cash pools must look for alternative cash-like investments.
For example, investors can hold bank deposits. Bank deposits have some appealing characteristics. Like cash,
the value of a bank deposit doesn’t fluctuate with interest rates and they can be used to meet payment
obligations. However, bank deposits above a certain amount are uninsured, which exposes large investors to
counterparty risk. If the bank fails, these investors are likely to lose money. Therefore, as opposed to safe
Treasury bills, bank deposits are considered an unsecured overnight investment.
Banks—also known as depository institutions—exchange funds overnight in the market for reserve balances
at the federal funds rate, that is, the overnight unsecured interest rate that banks borrowing reserves have
to pay to banks lending those reserves.
Here it is worthwhile to further discuss the federal funds market and how it has changed since the 2008
financial crisis.
The Federal Reserve (hereafter the Fed) interacts with the traditional banking sector by setting a target for
the federal funds rate. For decades prior to the 2008 crisis, the target was achieved through temporary or
permanent purchases or sales of government-guaranteed securities from and to banks. These transactions,

2
known as open market operations, aimed to adjust the supply of reserve balances in the banking system to
create conditions that encourage the fed funds rate to trade at the target level.
The Fed funds market consists of unsecured loans among banks and certain other eligible entities, 1 such as
GSEs like the Federal Home Loan Banks, which have deposits at the Fed, but unlike private banks, GSEs do
not get credited with any interest if they hold these balances overnight.
Before the 2008 financial crises there were few excess reserves in the system and the Fed could change the
equilibrium fed funds rate by conducting small open market operations, as this rate was very sensitive to
small changes in the total level of excess reserves.
During the financial crisis the Fed cut the federal funds target rate to a 0-basis-point through 25-basis-point
range, and with its target rate at its effective zero lower bound, the Fed decided to expand the objective for
open market operations to adjust the size and composition of its portfolio in order to stimulate the
economy.
These open market purchases of long-term government-guaranteed securities—known as Large Scale Asset
Purchases—dramatically increased the amount of reserves held at the Fed by banks and other entities
eligible to trade in the fed funds market. As a result, small changes in the amount of reserves outstanding no
longer resulted in noticeable changes in the equilibrium federal funds rate.
To better implement monetary policy in this new environment the Fed began paying interest on excess
reserves (or IOER) to depository institutions in October 2008, but as previously mentioned, some institutions
(such as the GSEs) are not eligible to earn IOER.
Paying IOER influences the equilibrium federal funds rate by setting a floor on the rate at which depository
institutions are willing to lend in the federal funds market. Banks that can earn a risk-free rate by holding
reserves at the Fed should have no incentive to lend excess reserves in the fed funds market at rates below
the IOER.
But IOER has not served as a firm floor on the rate at which all institutions are willing to lend federal funds.
One of the main reasons is that institutions such as GSEs do not earn IOER and therefore have an incentive
to lend extra reserves at a rate below the IOER, because a small positive rate is better than nothing.
As we mentioned earlier, there aren’t enough short-term Treasury bills to meet demand for cash-like riskfree investments. Therefore, cash pools are forced to look for alternative, private, cash-like safe
investments—things that have the characteristics of cash but are not guaranteed by the full faith and credit
of the United States government. Private agents produce these assets to meet the demand for cash-like
investments.
One of the more prominent examples of private money-like claims is shares issued by government-only
money market mutual funds. Money market funds offer shares that have cash-like characteristics. The funds
promise to redeem the shares for cash on demand at a fixed price. How does the money market fund do
this? One way is the fund invests its pool of money in slightly longer-term Treasury securities. These
Treasury securities have no default risk but are nonetheless riskier because their promised payments are
further in the future, so changes in interest rates can change the present value (or market price) of these
1

See 204.2 (a)(1)(vii)(A)

3
bonds.
Another option for cash pools looking for safe overnight cash-like investments is the overnight Treasury
repurchase agreement (or the repo market). A repo is a sale of securities coupled with an agreement to
repurchase the same securities at a higher price the next day. From the perspective of the cash pools
investing money, a repo closely resembles a collateralized loan.
For now, let’s focus on repos where Treasury securities are used as collateral. Broker dealers hold many
longer-maturity Treasuries and often wish to use these bonds to obtain cheap funding in the repo market.
Cash pools are willing to lend broker-dealers money at low rates because overnight repo loans are secured
by Treasury collateral, and therefore very safe.
However, repo lending does expose a cash pool to some counterparty risk. If the other side of the repo
defaults, the cash pool will have to sell the securities to get its money back. However, the security is a safe
government bond that can be sold easily. The lenders in a repo transaction further protect themselves by
marking down the value of the collateral purchased relative to its true market value. This adjustment is
known as a haircut. For example, the lender may agree to pay $100 today for securities that have $103 of
market value. If the haircut is big enough, the lender will be able to resell the securities for more than the
initial purchase price and suffer no loss.
Similarly, money market funds wishing to lend money safely overnight can enter into repo agreements with
broker-dealers.
In practice, cash pools and money market funds lends money today by purchasing the government security
and agreeing to sell it back to the original owner at a higher price tomorrow. The difference between the
sale prices is the interest rate earned on the money lent overnight.
The Repo market is an example of private agents creating an investment vehicle which transforms longerterm bonds into overnight debt instruments with safe cash-like characteristics. This is effectively private
issuance of money-like assets that substitutes for public issuance of safe assets such as Treasury Bills.
The repo market constitutes a very important part of what we call the “shadow banking market.”
Before moving on, let’s take a moment to discuss an important institutional feature of the repo market—the
tri-party system. For convenience reasons, many repo transactions take place through third party custodial
banks, in what is known as the tri-party system. Custodial banks offer a series of services, including trade
settlement, collateral valuation, and collateral eligibility screening.
Many repo market participants prefer the tri-party system because the custodial banks create a platform
that allows repo counterparties to come together and trade cheaply. The counterparties sign agreements
ahead of time, specifying the terms of the repo transactions—such as what kind of collateral will be
accepted and what haircuts will apply. The custodian then matches lenders to borrowers and holds the lent
money and the securities pledged for the repo.
For some time the Fed has used the tri-party system to conduct repurchase agreements with broker-dealers
when conducting open market operations.
Even after transforming government-guaranteed longer-term bonds into overnight instruments via repo,

4
there still may not be enough safe investments to meet the demand of cash pools. Some investors turn to
other short-term securities that may not be as safe as overnight investments collateralized by public
securities.
One prominent example of a riskier short-term instrument is commercial paper. Commercial paper is a
money-market security issued by corporations to obtain funds to meet short-term obligations. Commercial
paper offers a higher return because it is backed by only the corporation’s promise to pay the face amount
at maturity.
Prime money market funds, like government only money market funds, promise overnight liquidity by
redeeming their shares on demand at a fixed price.
But, unlike government-only money market funds, prime funds invest in unsecured private assets that have
some default risk. For example, they invest in commercial papers with high credit ratings and maturities of
90 days or less.
Cash pools can invest directly in commercial paper or invest indirectly via prime money market funds, which
offer better liquidity and easy access to a diversified portfolio of commercial paper. The combination of
diversification and liquidity makes the indirect investments through prime money market funds an option
with cash-like characteristics.
Direct and indirect investment in commercial paper constitutes an important link between the shadow
banking system and the real economy. Demand for commercial paper strongly affects the ability of
corporations to raise funds to meet short-term debt obligations such as payrolls.
In addition to private short-term securities, there are large amounts of private longer-term bonds and
equities held by risk portfolio managers, hedge funds, real estate investment trusts and exchange-traded
funds. These asset owners often wish to use their longer-maturity risky assets as collateral to obtain funding
at a cheap rate. To this end, they enter into repo transactions with broker-dealers.
To obtain short-term funding, these broker-dealers then re-pledge the acquired riskier collateral to prime
money market funds that want money-like claims collateralized by private securities.
Despite the riskier collateral, these private repos are close substitutes to money-like claims because they are
overnight investments and have larger haircuts than Treasury repos.
With a big enough haircut, securities such as government-agency mortgage-backed securities (MBS), private
MBS, asset-backed securities, corporate bonds, or even equities can be transformed into assets that are
viewed by market participants as safe overnight investments.
Collectively, private repos make up a large portion of private money-like claims and are a prominent
example of how money flows throughout the shadow banking system.
As in the case of Treasury repos, many private repo transactions take place in the tri-party system where
custodial banks offer the same services previously discussed.
It is important to point out that even though the Fed has been operating in the tri-party system for some
time, its actions have only indirectly influenced interest rates in the shadow banking system.

5

More specifically, by altering the fed funds rate, or more recently, the interest on excess reserves, the Fed
changed the marginal cost of lending in the formal banking sector. Because most banks interact in both the
formal and shadow banking sectors, raising the marginal cost of funds in one sector could affect the rate at
which banks are willing to lend in the other sector. The extent to which a change in the federal funds rate is
transmitted to interest rates in the shadow banking markets mainly depends upon banks’ perceived degree
of substitution across different lending opportunities.
You may have noticed that in what we have described so far, the Fed implemented monetary policy solely
through interactions with bank counterparties.
But, with the recent introduction of an overnight reverse repo facility, the Fed can now interact directly with
a broader range of money market participants. These include both the Fed’s traditional counterparties, such
as banks and broker dealers, and non-bank counterparties that play a major role in money markets.
Importantly, this allows the Fed to provide a safe overnight investment to participants that cannot earn
interest on excess reserves, effectively expanding the set of investors unwilling to lend at rates below those
offered by the Fed.
Let me explain in more detail. Due to Large Scale Asset Purchases, the Fed owns a large amount of safe
government securities. In a reverse repo, the Fed sells government securities and borrows reserves from the
eligible counterparties overnight at a fixed rate. This rate, known as the overnight reverse-repo rate is set by
the Fed.
Included among the non-bank counterparties eligible to participate in reverse repos with the Fed are the
GSEs.
Recall that the Fed funds rate often trades below IOER because institutions such as the GSEs are ineligible to
earn IOER and are therefore willing to lend their excess reserves into the federal funds market at rates
below IOER.
By making GSEs eligible counterparties for overnight reverse repo transactions, the Fed is better equipped
to influence rates in both the repo and the fed funds market. GSEs can now lend to the Fed at the fixed rate
offered at the overnight reverse repo facility. Because these loans have the Fed as a counterparty and are
collateralized by government securities, they are much safer than unsecured loans in the Fed funds market.
Therefore, a GSE should be unwilling to lend in the fed funds market at a rate below the overnight reverse
repo rate.
Government-only and prime money market funds are also included among the non-bank counterparties
eligible to participate in the overnight reverse repo facility. Here the same logic applies: These money
market funds should be unwilling to lend to counterparties riskier than the Fed at a rate below that offered
by the Fed at the overnight reverse repo facility.
Overall, this implies that the rate set by the Fed at the overnight reverse repo facility should provide a floor
on the level of short-term interest rates for cash-like investments in both the traditional and the shadow
banking systems. Because, as we already discussed in the case of the GSEs and money market funds, active
participants in overnight money markets should be unwilling to lend at a rate below the overnight reverse
repo rate.

6
To sum up, these new tools should allow the Fed to better control short-term rates in both the traditional
banking sector, by adjusting the interest on excess reserves, and the shadow banking sector, by adjusting
the rate on reverse repos.
As the FOMC indicated in the April 2015 Policy Normalization Principles and Plans, the Committee intends to
target a 25-basis-point range for the federal funds rate. By altering the rates on IOER and overnight reverse
repo, the Fed can adjust the upper and lower bounds of the targeted range. This should guide overnight
money market rates to levels consistent with the Fed’s goals of maximum employment and stable prices.
References
Baklanova, Viktoria, Copeland, Adam, and McCaughrin, Rebecca (2015). “Reference Guide to U.S. repo and
Securities Lending Markets,” Office of Financial Research Working Paper Series. Washington: Office of
Financial Research, U.S. Department of the Treasury, http://financialresearch.gov/workingpapers/files/OFRwp-2015-17_Reference-Guide-to-U.S.-Repo-and-Securities-Lending-Markets.pdf.
Bech, Morten, Elizabeth Klee, and Viktors Stebunovs (2014). "The Repo and Federal Funds Markets before,
during, and Emerging from the Financial Crisis," in Chadha, Jagjit S., Alain C. J. Durre, Michael A. S. Joyce and
Lucio Sarno eds., Developments in Macro-Finance Yield Curve Modelling. Cambridge, Mass: Cambridge
University Press, pp. 293-325.
Carlson, Mark, Burcu Duygan-Bump, Natalucci, Fabio, Nelson, William R., Ochoa, Marcelo, Stein, Jeremy, and
Van den Heuvel, Skander (2014). "The Demand for Short-Term, Safe Assets and Financial Stability: Some
Evidence and Implications for Central Bank Policies," Finance and Economics Discussion Series 2014-102.
Washington: Board of Governors of the Federal Reserve System,
http://www.federalreserve.gov/econresdata/feds/2014/files/2014102pap.pdf.
Dudley, William C. (2014). “The Economic Outlook and Implications for Monetary Policy,” Remarks before
the New York Association for Business Economics, New York City. New York: Federal Reserve Bank of New
https://www.newyorkfed.org/newsevents/speeches/2014/dud140520.
Dudley, William C. (2015). “The Economic Outlook and Implications for Monetary Policy,” Remarks before
the New York Association for Business Economics, New York City. New York: Federal Reserve Bank of New
York, https://www.newyorkfed.org/newsevents/speeches/2014/dud140520.
Frost, Josh, Lorie Logan, Antoine Martin, Patrick McCabe, Fabio Natalucci, and Julie Remache (2015).
“Overnight RRP Operations as a Monetary Policy Tool: Some Design Considerations,” Finance and Economics
Discussion Series 2015-010. Washington: Board of Governors of the Federal Reserve System,
http://dx.doi.org/10.17016/FEDS.2015.010.
Ihrig, Jane E., Meade , Ellen E., and Weinbach, Gretchen C. (2015). “Monetary Policy 101: A Primer on the
Fed’s Changing Approach to Policy Implementation,” Finance and Economics Discussion Series 2015-047.
Washington: Board of Governors of the Federal Reserve System, http://dx.doi.org/10.17016/FEDS.2015.047.
Potter, Simon (2014). “Implementation of Open Market Operations in a Time of Transition,” Remarks before
the Japan Center for Economic Research, Tokyo, Japan. Tokyo: Federal Reserve Bank of New York,
https://www.newyorkfed.org/newsevents/speeches/2014/pot140924.

7
Potter, Simon (2014). “Interest Rate Control during Normalization,” Remarks at the SIFMA Conference on
Securities Financing Transactions, New York City. New York: Federal Reserve Bank of New York,
https://www.newyorkfed.org/newsevents/speeches/2014/pot141007.
Potter, Simon (2015). “Money Markets and Monetary Policy Normalization,” Remarks at the Money
Marketeers of New York University, New York City. New York: Federal Reserve Bank of New York,
https://www.newyorkfed.org/newsevents/speeches/2015/pot150415.html.
Pozsar, Zoltan (2014). “Shadow Banking: The Money View,” Office of Financial Research Working Paper
Series 14-04. Washington: Office of Financial Research, U.S. Department of the Treasury,
http://financialresearch.gov/workingpapers/files/OFRwp201404_Pozsar_ShadowBankingTheMoneyView.pdf.

The Chicago Fed Survey of Business Conditions:
Quantifying the Seventh District’s Beige Book report
Scott A. Brave, Thomas Walstrum, and Jacob Berman

Introduction and summary
The Beige Book is a Federal Reserve System report
describing current business conditions in each of the
Fed’s 12 Districts that is released to the public two
weeks prior to each meeting of the Federal Open Market
Committee (FOMC), the monetary policymaking arm
of the Fed.1 Each Federal Reserve Bank prepares a
report for its own District by surveying business
contacts on topics such as demand for their products or
services, capital spending, hiring, prices, and wages.
Information collection methods for the Beige Book
vary across the Reserve Banks, ranging from formal
surveys to face-to-face interactions.
By design, the Beige Book is an anecdotal, or
qualitative, account that is meant to provide context
for understanding trends in existing quantitative data.
The timeliness of the report plays a key role in this
function, as the gap between information collection
and the public release of the report can be as short as
one to two weeks. This feature of the Beige Book has
led some researchers to try to quantify the information
contained in the report so that it can be incorporated
in real time into quantitative economic models. But
because prose is open to different interpretations, it is
far from obvious how to quantify the information. Thus,
researchers have attempted a variety of techniques to
quantify the language of the Beige Book, ranging
from simple numerical scoring and word counts to
more sophisticated analyses of linguistic patterns.2
In this article, we describe a new survey methodology used by the Federal Reserve Bank of Chicago
in constructing its District’s Beige Book report called the
Chicago Fed Survey of Business Conditions (CFSBC).
The design of the survey allows us to create a new set
of quantitative indexes that track economic activity in
real time. The survey contains both quantitative and
qualitative questions. We use answers to the quantitative questions to construct diffusion indexes that cover

Federal Reserve Bank of Chicago

a variety of aspects of economic activity, and we ask
qualitative follow-up questions that provide context for
the indexes and the Beige Book. The survey is timed
to match the Beige Book schedule and has been operating since the Beige Book cycle for the March 6, 2013,
report, although only a limited portion of the survey’s
quantitative results have been published prior to this
article.3 Survey respondents represent a wide range of
industries in the Seventh Federal Reserve District,4
including construction, finance, manufacturing, real
estate, retail, and a variety of other service industries.
Scott A. Brave is a policy economist, Thomas Walstrum is a business
economist, and Jacob Berman is a former associate economist in
the Economic Research Department at the Federal Reserve Bank
of Chicago. The authors thank Spencer Krane, Lisa Barrow, Daniel
Sullivan, William Testa, William Strauss, and Gadi Barlevy for
their helpful comments and suggestions.
© 2015 Federal Reserve Bank of Chicago
Economic Perspectives is published by the Economic Research
Department of the Federal Reserve Bank of Chicago. The views
expressed are the authors’ and do not necessarily reflect the views
of the Federal Reserve Bank of Chicago or the Federal Reserve
System.
Charles L. Evans, President; Daniel G. Sullivan, Executive Vice
President and Director of Research; David Marshall, Senior Vice
President and Associate Director of Research; Spencer Krane,
Senior Vice President and Senior Research Advisor; Daniel Aaronson,
Vice President, microeconomic policy research; Jonas D. M.
Fisher, Vice President, macroeconomic policy research; Robert
Cox, Vice President, markets team; Anna L. Paulson, Vice President,
finance team; William A. Testa, Vice President, regional programs;
Lisa Barrow, Senior Economist and Economics Editor; Helen
Koshy and Han Y. Choi, Editors; Julia Baker, Production Editor;
Sheila A. Mangler, Editorial Assistant.
Economic Perspectives articles may be reproduced in whole or in
part, provided the articles are not reproduced or distributed for
commercial gain and provided the source is appropriately credited.
Prior written permission must be obtained for any other reproduction, distribution, republication, or creation of derivative works
of Economic Perspectives articles. To request permission, please
contact Helen Koshy, senior editor, at 312-322-5830 or email
Helen.Koshy@chi.frb.org.
ISSN 0164-0682

77

TABLE 1

CFSBC respondent composition
	
	

	percent
Banking and finance	
17
Construction and real estate	
18
Manufacturing	32
Nonfinancial services	
34

	

Note: The average number of respondents to the Chicago Fed
Survey of Business Conditions (CFSBC) is 75.
Source: Authors’ calculations.

A number of organizations conduct similar surveys,
such as the Institute for Supply Management (ISM)
and other Federal Reserve Banks. However, the design
of the Chicago Fed survey and the method used to
construct the diffusion indexes differ from what is done
for other business surveys in two important respects.
First, the CFSBC asks respondents about both current
and expected economic activity. And second, the resulting diffusion indexes are adjusted for inherent biases
in measurement and interpretation. We do this by calculating the diffusion indexes based on whether survey
participants’ answers are above or below their respective
average answers. Expressing the indexes in this way is
novel and is possible because most CFSBC respondents
regularly participate in the survey. While the indexes
remain works in progress, we believe that in their current
form they offer useful insight into current conditions
in the Seventh District and U.S. economies. Beginning
with the release of the January 13, 2016, Beige Book,
the Chicago Fed will make the CFSBC indexes publicly
available at https://www.chicagofed.org/cfsbc.
In what follows, we provide details on the CFSBC
and the diffusion indexes we calculate from it. We first
describe the survey’s quantitative questions and then
explain how we turn them into diffusion indexes. Next,
we explore how the unique adjustments we make to the
formula for a traditional diffusion index affect its properties. We then assess how well our headline index, the
CFSBC Activity Index, aligns with other indicators of
economic activity. Finally, we discuss the additional
information collected in the course of the survey that
covers hiring, capital expenditures, and cost pressures.
The survey and diffusion indexes
Beginning with the cycle for the March 6, 2013,
Beige Book, the Chicago Fed has been gathering information from its business contacts using an online survey
system. The Bank’s regional analysis staff oversees the
operation of the survey and invites individuals to join
based on interactions at business roundtables, advisory
councils, board meetings, conferences, speeches, and

78

other Bank events. Table 1 shows that survey respondents come from a variety of industries, with the
largest representation coming from the nonfinancial
services sector and the manufacturing sector. Over
600 invitations to participate in the survey are sent
out each Beige Book reporting period, with a typical
response rate of about 17 percent per survey. About
70 percent of survey participants are repeat responders.
The survey asks respondents to quantify how
aspects of their businesses have changed over the past
four to six weeks (or are expected to change in the next
six to 12 months) using a seven-point scale: increased
substantially (+3), increased moderately (+2), increased
slightly (+1), no change (0), decreased slightly (–1),
decreased moderately (–2), and decreased substantially (–3). The survey covers a variety of topics, including product demand, prices, and productivity. We
also ask sector-specific questions. For example, we ask
our real estate contacts about changes in home prices.
As a follow-up to each quantitative question, we ask
respondents to provide anecdotes that explain why they
answered the way they did. We then use this combination of quantitative and qualitative data as background
material for the Seventh District’s Beige Book report.
Using the responses to the quantitative questions
in the survey, we construct diffusion indexes in order to
track growth in economic activity in real time. The appendix lists the set of survey questions used to construct
the indexes. The indexes cover the following topics:
1) overall activity (based on demand for products and
services), 2) manufacturing activity, 3) nonmanufacturing activity, 4) the outlook for the U.S. economy,
5) hiring, 6) hiring plans, 7) capital spending, 8) capital
spending plans, 9) wage costs, and 10) nonwage costs.
Diffusion indexes are intended to be leading indicators, capturing changes in the prevailing direction
of economic activity. The formula for CFSBC diffusion
indexes is
#Above-Average (Positive) Responses
– #Below-Average (Negative) Responses
.
100 *
#Responses (smoothed)
In many ways, this is a very traditional formula for a
diffusion index, but we make a couple of adjustments
that we would argue improve it. The first and most novel
adjustment is to measure individuals’ responses relative
to their respective average responses.5 To calculate a
respondent’s average response, we assign numerical
values ranging from +3 to –3 along a seven-point scale,
and take the average across all responses, including
the current one. We then count a response as positive
if it is above a respondent’s average response and negative if it is below a respondent’s average response.

3Q/2015, Economic Perspectives

FIGURE 1

CFSBC Activity Index
index
80
60
40
20
0
–20
–40
–60
2013

’14

’15

As published (detrended)
Not detrended
Notes: CFSBC means Chicago Fed Survey of Business
Conditions. The detrended version will be published in the
official Chicago Fed release of the survey’s results. See the
text for details on the detrending methodology.
Source: Authors’ calculations.

For example, if a respondent’s average response is
+1.5, substantial and moderate increases are counted
as positive responses and all other answers are counted
as negative responses. Given our formula, the index
ranges from +100 to –100 and will be +100 if every
respondent in a given survey has an above-average
response to a question and –100 if every respondent
has a below-average response.6
We do not include respondents’ answers in the
index until they have answered the survey twice. This
is because those who respond only once would have
neutral answers by definition, as a single response is
necessarily the average response. Note, too, that over
time, the index could change because respondents’
averages will change with each response. Of course, the
longer the history for a respondent, the more stable
his average will be, so that this source of variation will
become less important over time. And, as discussed
later, in practice, changes in average responses do not
appear to be an important concern in interpreting the
CFSBC indexes.
Calculating the indexes using a survey participant’s
average response as a baseline—also known as detrending—allows us to correct for two types of potential
biases. First, individuals may interpret phrases such as
“substantially increased” differently, so that our numerical
scores have different meanings for different people. A
common criticism of the Beige Book is that the wording
used to describe changes in economic activity seldom
varies and can often be so broad as to make it difficult
to interpret differences across regions and industries.
So we rely upon individuals’ own assessments of “average” growth in order to assess how far from “normal”

Federal Reserve Bank of Chicago

business conditions currently are. This process amounts
to a rescaling of the responses to the survey questions
that is unique to each respondent prior to constructing
what would otherwise be a traditional diffusion index.
The second type of potential bias is that the industries and firms represented in our data may have
different growth trends than the overall economy, which
could bias the indexes because we do not have a random
sample of respondents. For example, because manufacturers represent a significant share of our respondents,
our index could overrepresent trends in the manufacturing sector. The share of manufacturing output and
employment in the U.S. economy has been declining
for decades, so that in general, trends in the manufacturing sector are becoming less and less representative
of trends in the overall economy. For this reason, other
similar surveys often report separate manufacturing
and nonmanufacturing indexes and either refrain from
combining them into a single index or weight them
based on their representativeness. However, because
calculating a CFSBC index relative to respondents’
average responses removes respondent-, firm-, and
industry-specific trends, we can safely combine manufacturers and nonmanufacturers into the same respondent pool and mitigate some of the bias from having a
nonrandom sample of respondents. Like the researchers
conducting similar surveys, we calculate manufacturing
and nonmanufacturing indexes separately, and these
indexes also benefit from detrending because it removes
respondent- and firm-specific trends.
Another adjustment we make to the standard diffusion index formula is to smooth the denominator, which
is the total number of repeat responses in a given period.
We do this by calculating the denominator as a threeperiod moving average of the lag period, the current
period, and the lead period.7 We do this because there
are a few periods over the history of the index where
there was a notable increase in the number of repeat
respondents.8 A large increase in repeat respondents
from one period to the next could cause a change in
the value of an index that is unrelated to a change in the
distribution of responses. To better understand why,
consider the following scenario. Suppose that there are
two adjacent periods where, initially, all respondents
are present in both periods and their responses are identical in both periods. In this case, the diffusion index
will indicate that activity did not change from the first
period to the second. Now suppose that we add 20 new
repeat respondents in the second period and that ten
give above-average reports and ten give below-average
reports. In this scenario, the numerator of the index is
unaffected, but the denominator is larger, so that the
diffusion index is now lower in the second period, even

79

FIGURE 2

Distribution of responses, by type, for the CFSBC Activity Index
A. Detrended

B. Not detrended

frequency

frequency

800

800

600

600

400

400

200

200

0

negative

neutral

positive

0

negative

neutral

positive

Notes: CFSBC means Chicago Fed Survey of Business Conditions. Panel A shows the distribution of responses, by type, for the detrended
index—which is the CFSBC Activity Index that will be published in the official Chicago Fed release of the survey’s results (also shown as the
blue line in figure 1). See the text for details on the detrending methodology.
Source: Authors’ calculations.

though the difference between above-average and belowaverage responses is unchanged from the first period.
We would view such a decline in the index as misleading.
Smoothing the denominator helps to mitigate the distortion, though not perfectly. In practice, we find little
difference between our preferred versions of the indexes
(which will be published in the official Chicago Fed
release of the survey’s results) and versions without a
smoothed denominator.
How does detrending influence the CFSBC
diffusion indexes?
To explain the implications of our detrending
methodology, we examine how two of our indexes,
the CFSBC Activity Index and CFSBC Outlook Index,
would differ if we instead calculated them using the
traditional diffusion index formula.
Figure 1 (p. 79) shows the CFSBC Activity Index
and a version calculated from the traditional formula. The
activity index is based on respondents’ answers to questions
about demand for their products or services. Respondents typically report that demand increased slightly,
so detrending shifts the index down. In general, the
shapes of the paths of both indexes are quite similar:
Both indexes capture the slowdowns in the first quarters
of 2014 and 2015 and the strength of growth in the

80

second half of 2014. That said, compared with the version
calculated from the traditional formula, the official
CFSBC Activity Index (which has been detrended)
indicates that the downturns were sharper. This is not
surprising, since detrending will increase the variance
of respondents’ answers if their answers always fall
in a limited range (in this case mostly positive). To
clarify why the variance of the index increases when
the index is detrended, we show in figure 2 the distribution of positive, neutral, and negative responses to
the demand questions across all survey rounds before
and after detrending. Because the average response for
many respondents is that demand increased slightly,
detrending converts many of the neutral responses into
negative responses. There are also some respondents
who typically report a moderate increase in demand,
so that detrending converts responses indicating a slight
increase in demand into negative responses. The new
distribution created by detrending is more spread out,
increasing its variance, and, not surprisingly, increasing the variance of the diffusion index as well.
As noted in the previous section, detrending is also
useful because it adjusts for differences in the respondents’ interpretations of the quantitative scale that we
use. For example, some respondents may be more
optimistic or pessimistic in general, and detrending

3Q/2015, Economic Perspectives

FIGURE 3

FIGURE 4

CFSBC Outlook Index

Respondents eligible for the CFSBC Activity
Index, by minimum response requirement

index

number of respondents

80

100

60
80

40

60

20

40

0

20

–20
2013

’14

0
2013

’15

’14

As published (detrended)
Not detrended
Notes: CFSBC means Chicago Fed Survey of Business
Conditions. The detrended version will be published in the
official Chicago Fed release of the survey’s results. See the
text for details on the detrending methodology.
Source: Authors’ calculations.

(or, more appropriately, demeaning) allows us to put
them on equal footing. This particularly applies to
when the survey asks for respondents’ outlooks for
the U.S. economy over the next six to 12 months.
Detrending makes it so that our moderately optimistic
respondents are counted as having a positive outlook
only when they are feeling very optimistic and our
moderately pessimistic respondents are counted as
having a positive outlook even when they are feeling
slightly pessimistic.
Figure 3 shows the CFSBC Outlook Index and a
version constructed in the traditional way (that is, without
detrending). Here, as with the CFSBC Activity Index,
respondents typically report that they have a positive
outlook, so the line for the official CFSBC Outlook
Index is below the line for the index without detrending.
Moreover, the shapes of the paths of both indexes are
quite similar, though the detrended index has a greater
variance than the index constructed using the traditional
formula. In other words, we see that the two outlook
indexes in figure 3 behave similarly to the two activity
indexes in figure 1 (p. 79).
As noted previously, detrending also leads us to
exclude survey participants who have responded only
once to the survey from the respondent pool because
all of their responses are neutral by definition. In
addition, we expect that survey participants’ average
responses will change as their response histories grow.
The longer the history is, the more stable the average
response is—and the more reliably we can identify
deviations from a long-run trend. Thus, because some
survey participants respond more consistently than
others do, we face a trade-off between maximizing

Federal Reserve Bank of Chicago

’15

No minimum
At least two responses
At least three responses
At least four responses
Note: CFSBC means Chicago Fed Survey of Business Conditions.
Source: Authors’ calculations.

FIGURE 5

Distribution of the number of times responded, by
survey respondent, for the CFSBC Activity Index
percent
25
20
15
10
5
0

0

5

10

15

20

number of times responded
Notes: CFSBC means Chicago Fed Survey of Business Conditions.
As of November 2015, there are 264 unique respondents and
23 unique survey periods.
Source: Authors’ calculations.

the quantity of respondents and the quality of responses
in calculating the indexes.
Figure 4 shows how the number of respondents
used to calculate the CFSBC Activity Index declines
as we increase the minimum number of responses per
respondent required. The vast majority of survey participants who contributed to the index prior to 2015 have
responded at least four times. Throughout 2015, we have
added a number of new survey participants, and most
of them have not yet responded four times. Figure 5
provides another perspective on the CFSBC Activity
Index’s respondent pool. It shows the distribution of
the number of survey responses by respondent as of

81

FIGURE 6

FIGURE 8

CFSBC Activity Index,
by minimum response requirement

CFSBC Activity Index and the MEI

index
60
40

index

index
50

0.87

25

0.57

0

0.27

20
0
–20

–25

–0.03

–40
–60
2013

’14

’15

At least two responses
At least three responses
At least four responses
Note: CFSBC means Chicago Fed Survey of Business Conditions.
Source: Authors’ calculations.

FIGURE 7

Revisions to the CFSBC Activity Index
index
60
40
20
0
–20
–40
–60
2013

’14

’15

Range of previous values
Current values
Note: CFSBC means Chicago Fed Survey of Business Conditions.
Source: Authors’ calculations.

November 2015. Just under 75 percent of the respondent pool (made up of 264 unique respondents) has
responded at least two times, allowing that share to
be included in the least restrictive version of the index,
which is our preferred version (displayed as the blue
line in figure 1 on p. 79).
Figure 6 shows how the CFSBC Activity Index
would change if we altered the minimum number of
responses required for a survey participant’s response
to be counted in the calculation of the index. Increasing
the requirement from two to four has little effect on the
index except at the very beginning when the respondent pool was small in comparison to its current size.
There are some differences in 2015 related to the large
number of new respondents without a long history,

82

–50
2013

’14

’15

–0.33

CFSBC Activity Index (left-hand scale)
MEI (right-hand scale)
Notes: CFSBC means Chicago Fed Survey of Business
Conditions. MEI means the Midwest Economy Index.
We align the MEI’s average over the period March 2013–
September 2015 with a value of zero from the CFSBC
Activity Index to facilitate the comparison. The average of
the MEI over the reference period is 0.27. The correlation
coefficient between the indexes is 0.51.
Source: Authors’ calculations.

but they are small and do not substantially change how
we would interpret the index.
A bigger concern is the fact that as we add to the
histories of our respondents, their average responses
evolve, sometimes changing whether we treat earlier
responses as positive, neutral, or negative, which in
turn changes earlier values of the CFSBC indexes.
Figure 7 shows the current CFSBC Activity Index as
well as the range of values it has had in the past. The
range of values provides some indication of how much
more-recent values may change as future survey rounds
are completed. In our view, the ranges are small enough
that they have little effect on how one would interpret
the index’s value. Moreover, we expect the size of
such revisions to decline in the coming years as we
continue to add to our respondents’ histories and their
respective average responses become more stable.9
Comparing the CFSBC Activity Index with
other measures of activity
In this section, we explore how well the CFSBC
Activity Index aligns with other indicators of economic
activity at the District and national level, such as the
Chicago Fed’s Midwest Economy Index (MEI), the
Chicago Fed National Activity Index (CFNAI), and the
national surveys of manufacturing and nonmanufacturing activity conducted by the Institute for Supply
Management. The CFSBC Activity Index (as well as its
manufacturing and nonmanufacturing subindexes) largely
traces a path similar to the paths of these indicators, but

3Q/2015, Economic Perspectives

FIGURE 9

FIGURE 10

CFSBC Activity Index and the CFNAI

CFSBC Manufacturing Activity Index
and the ISM manufacturing PMI

index

index

50

0.52

25

0.28

0

0.04

index

index

50

60

25

57

0

54

–25

51

–0.20

–25
–50
2013

–0.44
’14

’15

CFSBC Activity Index (left-hand scale)
CFNAI (right-hand scale)
Notes: CFSBC means Chicago Fed Survey of Business Conditions.
CFNAI means the Chicago Fed National Activity Index. We
align the CFNAI’s average over the period March 2013–
September 2015 with a value of zero from the CFSBC Activity
Index to facilitate the comparison. The average of the CFNAI
over the reference period is 0.04. The correlation coefficient
between the indexes is 0.49.
Source: Authors’ calculations.

the timeliness of the information it provides gives it
an advantage over some of these indicators. We also
show that the CFSBC Activity Index correlates quite
well with the Seventh District’s real gross state product
(GSP) growth and U.S. real gross domestic product
(GDP) growth, suggesting that the index may have
some value to researchers interested in forecasting
these measures in real time.
Figure 8 presents the CFSBC Activity Index and
the Midwest Economy Index together. The MEI is a
weighted average of 129 Seventh District state and
regional indicators measuring growth in nonfarm business activity from four broad sectors of the Midwest
economy: manufacturing, construction and mining,
services, and consumer spending.10 Thus, it summarizes
a wide range of information about economic activity
in the Seventh District. Because the MEI has been above
its long-run trend for most of the time since March 2013,
we equate the MEI’s average over this reference period11
to a value of zero from the CFSBC Activity Index to
facilitate the comparison.
The CFSBC Activity Index and MEI demonstrate
considerable co-movement, such as the ramp up in
activity in the middle of 2014 and the more recent slowdown. However, there are also times when they move
in different directions. One possible explanation for
this is that the MEI tends to be subject to large revisions,
as much of its underlying data series are subject to
benchmark revisions. In previous work, we showed
that an early version of the CFSBC Activity Index was

Federal Reserve Bank of Chicago

–50
2013

48
’14

’15

CFSBC Manufacturing Activity Index
(left-hand scale)
ISM manufacturing PMI (right-hand scale)
Notes: CFSBC means Chicago Fed Survey of Business Conditions.
ISM means the Institute for Supply Management; PMI means
purchasing managers’ index. We align the ISM manufacturing PMI’s
average over the period March 2013–October 2015 with a value
of zero from the CFSBC Manufacturing Activity Index to facilitate
the comparison. The average of the ISM manufacturing PMI over
the reference period is 54. The correlation coefficient between the
indexes is 0.52.
Sources: Authors’ calculations and Institute for Supply Management
data from Haver Analytics.

predictive of the direction of these revisions in recent
years.12 Another possible explanation is that the CFSBC
Activity Index is capturing a combination of regional
and national economic activity because many of the
firms in the survey sample have a national footprint.
Figure 9, therefore, compares the plots of the
CFSBC Activity Index and the Chicago Fed National
Activity Index, which is a weighted average of 85 national
indicators measuring growth in production and income;
employment, unemployment, and hours; personal consumption and housing; and sales, orders, and inventories.13
As with the MEI comparison, we equate the CFNAI’s
average over the reference period to a value of zero
from the CFSBC Activity Index to facilitate the comparison. Comparing figures 8 and 9, one can see that
at least some of the periods of divergence between the
CFSBC Activity Index and the MEI align with periods
where the CFSBC Activity Index instead more closely
follows the CFNAI. Overall, the CFSBC Activity Index
appears to capture similar information on growth in
economic activity as the MEI and CFNAI, which is
confirmed by its correlation coefficients of 0.51 with
the MEI and 0.49 with the CFNAI.
It is important to keep in mind, though, that the
CFSBC Activity Index is timelier than the MEI and
CFNAI. New releases of the CFSBC indexes will typically
come out one to two weeks after the survey is completed,
while both the MEI and CFNAI are released with a

83

FIGURE 11

FIGURE 12

CFSBC Nonmanufacturing Activity Index
and the ISM nonmanufacturing PMI

CFSBC Activity Index and real GSP growth
for the Seventh Federal Reserve District

index

index

60

61.0

30

58.5

0

56.0

–30

53.5

–60
2013

51.0
’14

’15

CFSBC Nonmanufacturing Activity Index
(left-hand scale)
ISM nonmanufacturing PMI (right-hand scale)
Notes: CFSBC means Chicago Fed Survey of Business
Conditions. ISM means the Institute for Supply Management;
PMI means purchasing managers’ index. We align the
ISM nonmanufacturing PMI’s average over the period
March 2013–October 2015 with a value of zero from the
CFSBC Nonmanufacturing Activity Index to facilitate the
comparison. The average of the ISM nonmanufacturing PMI
over the reference period is 56. The correlation coefficient
between the indexes is 0.58.
Sources: Authors’ calculations and Institute for Supply Management
data from Haver Analytics.

one-month lag. Thus, while the CFSBC Activity Index
contains similar information as the MEI and CFNAI, it
can signal movements in Seventh District and national
economic activity sooner.
We next compare the CFSBC Manufacturing and
Nonmanufacturing Activity Indexes with the Institute
for Supply Management’s manufacturing and
nonmanufacturing purchasing managers’ indexes
(PMIs).14 The ISM’s PMIs are quite similar to the CFSBC
indexes in that they are also diffusion indexes constructed
from a survey that asks purchasing and supply executives whether their production, employment, orders, and
inventories are higher or lower than (or the same as)
in the previous reporting period. While ISM PMIs
exist for some cities and regions in the Seventh District,
the ISM only calculates manufacturing and nonmanufacturing indexes separately at the national level.
Figure 10 (p. 83) shows the CFSBC Manufacturing
Activity Index and ISM manufacturing PMI together.
In this figure, we equate the average value of the ISM
manufacturing PMI over the reference period to the
baseline of the CFSBC Manufacturing Activity Index to
facilitate the comparison. The indexes largely move
together, with the exception of the first half of 2013.
In addition, the ISM manufacturing PMI declined
faster than the CFSBC Manufacturing Activity Index
did in the first half of 2015, likely because of strong

84

index

percent

60

8.8

40

6.6

20

4.4

0

2.2

–20

0.0

–40

–2.2

–60
2013

–4.4
’14

’15

CFSBC Activity Index (left-hand scale)
Real GSP growth (right-hand scale)
Notes: CFSBC means Chicago Fed Survey of Business Conditions.
GSP means gross state product. Seventh District GSP is the sum
of the GSP of the Seventh District states (Illinois, Indiana, Iowa,
Michigan, and Wisconsin). We align the Seventh District’s average
real GSP growth over the period 2013:Q1–2014:Q4 with a value
of zero from the CFSBC Activity Index to facilitate the comparison.
The average quarterly (annualized) real GSP growth rate over the
reference period is 2.2 percent. The correlation coefficient between
the index and the growth rate is 0.54.
Sources: Authors’ calculations and U.S. Bureau of Economic Analysis
data from Haver Analytics.

growth in the Seventh District’s auto industry. Figure 11
shows the CFSBC Nonmanufacturing Activity Index
and the ISM nonmanufacturing PMI together; similar
to previous comparisons, we equate the average value
of the ISM nonmanufacturing PMI over the reference
period to the baseline of the CFSBC Nonmanufacturing
Index. They, too, generally move together. Similar to
what we saw for the CFSBC Activity Index vis-à-vis the
MEI and the CFNAI, the co-movement of the CFSBC
activity indexes and the ISM PMIs is notable: The
correlation coefficient between the manufacturing indexes is 0.52, and the correlation coefficient between
the nonmanufacturing indexes is 0.58.
Figures 10 and 11 suggest that the CFSBC Activity
Index also aligns quite well with other survey measures
of economic activity. This finding highlights one advantage that the overall CFSBC Activity Index has
over the individual ISM indexes: It efficiently combines the information from both manufacturers and
nonmanufacturers into a single index that accounts
for the differences in trend growth in each sector.15
Another advantage of the CFSBC Activity Index
over the ISM’s PMIs is that it shares much conceptually
with traditional measures of gross output because of
its focus on the demand for firms’ products and services. Figures 12 and 13 explore this relationship by
comparing the CFSBC Activity Index with the real

3Q/2015, Economic Perspectives

FIGURE 13

CFSBC Activity Index and U.S. real GDP growth
index

percent

70

7.2

35

4.8

0

2.4

–35

0.0

–70
2013

–2.4
’14

’15

CFSBC Activity Index (left-hand scale)
Real GDP growth (right-hand scale)
Notes: CFSBC means Chicago Fed Survey of Business
Conditions. GDP means gross domestic product. We align
average real GDP growth over the period 2013:Q1–2015:Q3
with a value of zero from the CFSBC Activity Index to facilitate
the comparison. The average quarterly (annualized) real
GDP growth rate over the reference period is 2.4 percent. The
correlation coefficient between the index and the growth rate
is 0.54.
Sources: Authors’ calculations and U.S. Bureau of Economic
Analysis data from Haver Analytics.

growth rates of Seventh District GSP16 and U.S. GDP.
In these figures, we have aligned the average of the
gross output measures over the reference period with
the baseline of the CFSBC Activity Index. One can
see from the figures that the CFSBC Activity Index
and growth in both gross output measures often move
together, with periods where the activity index is above
zero tending to correspond with quarters characterized
by above-average gross output growth and vice versa.
While the time series of the CFSBC Activity Index is
not quite long enough to allow for a formal test of these
relationships, the strong correlations in these figures
do suggest that the index might be a valuable input
into nowcasting models of both gross output measures.17
Additional current and forward-looking indexes
We also use the CFSBC to construct current and
forward-looking diffusion indexes for a number of additional topics typically covered in the Beige Book: current
and expected hiring and capital spending, as well as
wage and nonwage cost pressures. These indexes are
also constructed using detrended responses, so that they
share the same interpretation as that for the activity and
outlook indexes—where a positive value reflects aboveaverage growth and a negative value reflects belowaverage growth. Figure 14 presents all of these indexes,
and also includes the latest results from follow-up
questions that we ask about the types of workers firms
are looking to hire, the types of capital spending firms

Federal Reserve Bank of Chicago

are undertaking, and the types of wage and nonwage
cost pressures firms are facing.
These additional indexes, particularly those related
to hiring and capital spending, provide further context
for the responses underlying the CFSBC Activity and
Outlook Indexes shown in the top left panel of figure 14.
Both the indexes for current hiring and capital spending
tend to rise and fall with the CFSBC Activity Index,
while the indexes for hiring and capital spending plans
(for the next six to 12 months) tend to more closely
follow the CFSBC Outlook Index. The current hiring
and capital spending indexes are also highly reflective
of the mild nature of the expansion in activity over the
past few years, with both measures generally exhibiting
below-average growth for most of this time. Similarly,
the muted inflation that has persisted during this period
is consistent with the below-average index values for
both wage and nonwage cost pressures.
The bar charts in figure 14 provide further information about the forces driving the values of the indexes.
When tracked over time, they, too, provide valuable information for the Seventh District’s Beige Book report.
For instance, when asked to identify the types of occupations that their firms are hiring for, respondents have
long indicated stronger demand for professional and
technical occupations than for other categories. Consistent with this, respondents tend to report greater wage
pressure for such occupations. Respondents have also
tended to describe capital spending as being confined
mostly to replacement of existing information technology and industrial equipment. As both hiring and
capital spending trended up in 2014, respondents noted
a broadening in the types of occupations in demand,
sources of wage pressures, and types of capital spending.
More-recent values of these indexes reflect a weakening
of business conditions in 2015.
Conclusion
The Chicago Fed Survey of Business Conditions
offers a new set of diffusion indexes for tracking economic
activity in real time. The design of the survey and the
method used to construct the diffusion indexes are
unique in that the survey asks respondents about both
current and expected economic activity and the resulting
diffusion indexes are adjusted for inherent biases in
measurement and interpretation. By calculating our
diffusion indexes based on whether participants’ answers
are above or below their respective average answers,
we address potential biases from respondent-, firm-,
and industry-specific trends that arise because our
respondent pool is not a random sample. The correlation of the resulting diffusion indexes with other regional
and national indexes of economic activity, as well as

85

FIGURE 14

CFSBC diffusion indexes and related data
Activity

Hiring

index
60

Occupations (percent)

index
60

Current
Outlook

Current
Plans

Management

40

40

Professional
& technical

20

20

Sales

0

44
35

Administration

0

0

30

19

Maintenance
–20

–16 –20

–40
–60
2013

–21
–33

–40

’14

’15

12

Production

Transportation

19
4

Other

–60
2013

’14

16

’15

Capital spending
Spending for (percent)

index
60

Current
Plans

Spending on (percent)

Replacement

40

Expansion

20

Research &
development
Mergers &
acquisitions

0

65

Industrial
equipment

34

IT equipment

41

49

Transportation
equipment

29

8

Structures

6

35

Intellectual
property

–20

–25
–26

28

Other

12

–40
–60
2013

’14

’15

Cost pressures
Increasing wage costs (percent)

index
60
40

Wage
Nonwage

Management

Sales

0

Administration

–20

–18 Maintenance
–19

58
16

Transportation
Other
2013

’14

Energy

6

Equipment

6
12

Benefits

28
35

Shipping

7

Taxes &
regulations

7

Other

–60

16

Property

23

Production

–40

Materials

47

Professional
& technical

20

Increasing nonwage costs (percent)

69
4
35
6

’15

Notes: CFSBC means Chicago Fed Survey of Business Conditions. This multipaneled figure will be featured on the back page of the official
Chicago Fed release of the survey’s results. For details on how to interpret the various line graphs, see the text. The bar graphs report the
percentage of responses for types of occupations currently in demand; reasons for capital spending and its allocation; and sources of
wage and nonwage cost pressures. The numbers may add up to more than 100 percent because more than one option can be chosen.
Source: Authors’ calculations.

86

3Q/2015, Economic Perspectives

gross output growth, is solid. Additionally, the CFSBC
diffusion indexes offer insight into the hiring and capital
spending decisions of business contacts surveyed for
the Seventh District’s Beige Book report.
As we have demonstrated, the indexes in their
current form offer useful insights into current conditions
in the Seventh District and U.S. economies. However,
they remain works in progress. For example, as the

time range of observation grows, we will be able to
adjust for potential seasonality in the indexes. We also
plan to calculate standard errors for the indexes based
on work by Pinto, Sarte, and Sharp (2015). Finally,
we may be able to release indexes based on other
questions we ask our respondents, such as those regarding prices, productivity, and credit conditions.

NOTES
More specifically, the Beige Book is always released on a Wednesday
two weeks ahead of the final day of an FOMC meeting. For the
official description of the Beige Book and to view the latest report
as well as the archives, visit https://www.federalreserve.gov/
monetarypolicy/beigebook/. Information about the 12 Federal Reserve
Districts and Banks is available at https://www.federalreserve.gov/
otherfrb.htm.
1

Balke and Petersen (2002) go through 14 years of Beige Book
reports and assign numerical scores (ranging from –2 to 2) to the
level of growth described in the text. Both authors read passages in
a random order and use the average of their two scores. They then
use their measure to predict current and next quarter real gross domestic product (GDP) growth and are able to outperform the consensus forecasts in the Blue Chip Economic Indicators for some
specifications. Armesto et al. (2009) use linguistics software to measure
the degree to which language in the Beige Book is either optimistic
or pessimistic. They employ the resulting index in an econometric
model that explicitly accounts for the irregular schedule of the Beige
Book. They find that the national Beige Book summary can predict
both GDP and employment growth. They also find that the District
Beige Book reports can predict regional employment growth. Balke,
Fulmer, and Zhang (2015) use textual analysis techniques developed
in Fulmer (2014) to quantify Beige Book language. They find that
their measure provides unique information about current economic
conditions, particularly during recessions, in the context of a dynamic
factor model of U.S. business conditions.
2

In a related article, Brave and Walstrum (2014) introduce a subset
of these indexes and show how they are useful for understanding
turning points in economic activity.
3

The Seventh Federal Reserve District (which is served by the
Chicago Fed) comprises all of Iowa and most of Illinois, Indiana,
Michigan, and Wisconsin; for more details, see note 1 and
https://www.chicagofed.org/utilities/about-us/seventh-district-economy.
4

This adjustment is akin to controlling for respondent fixed effects
in terms of a traditional linear regression model.
5

While not a concern in practice, it is possible for the diffusion indexes
to breach these bounds (of +100 and –100) given the smoothing
procedure that we employ for the denominator in the formula.
We explain the smoothing procedure a little later in this section
(describing the survey and the construction of the diffusion indexes).
6

The denominator for the latest period is a two-period moving average
of the current and lag period, since the lead period is unknown.
7

Federal Reserve Bank of Chicago

Notable jumps in the number of repeat survey respondents (visible
in figure 4 on p. 81) coincide with initiatives undertaken to increase
the sample size of the survey.
8

It is worth noting, however, that the timing of respondents’ entrances
into the survey relative to the business cycle will have an important
effect on our estimate of their long-run trends. For example, we will
underestimate the long-run growth trends of respondents who enter
during a recession until we have sufficient observations of their responses during an expansion. If we add a large number of respondents
to the survey when the economy is not growing near its long-term
trend, this could bias the indexes. Because data for the indexes cover
a period of growth that is near trend, we do not believe this concern
is currently a major source of bias.
9

For more information on the MEI, see https://www.chicagofed.org/mei.

10

Reference periods will vary depending on the availability and
frequency of the index or gross output measure to which a CFSBC
index is compared in figures 8–13 (the precise periods appear in
the figures’ notes).
11

12

See Brave and Walstrum (2014).

For more information on the CFNAI, see https://www.chicagofed.org/
cfnai.
13

For more information on the ISM’s PMIs, see https://www.
instituteforsupplymanagement.org/news/content.cfm?ItemNumber
=28965&&navItemNumber=28882.
14

There are, however, other ways in which to combine the information
from manufacturers and nonmanufacturers. For instance, some analysts have combined the ISM manufacturing and nonmanufacturing
PMIs into a composite index by weighting them by the relative contribution of manufacturing and nonmanufacturing industries to GDP.
15

Seventh District GSP is the sum of the GSP of the Seventh District
states (Illinois, Indiana, Iowa, Michigan, and Wisconsin).
16

The term nowcasting is derived from combining the words now and
forecasting. Nowcasting techniques are commonly used in economics
nowadays because they permit economists to predict today the present
(and recent past) of standard measures of the economy (such as
real GDP), which are often determined after a long delay. For more
information on nowcasting real GDP growth with indexes of economic
activity, see Brave and Butters (2014). For more information on
forecasting real GSP growth, see Brave and Wang (2011).
17

87

APPENDIX: CFSBC DIFFUSION INDEX QUESTIONS

1.	 In the past four to six weeks, demand for my firm’s
products or services has
○○ increased substantially.
○○ increased moderately.
○○ increased slightly.
○○ not changed.
○○ decreased slightly.
○○ decreased moderately.
○○ decreased substantially.
2.	 My outlook for the U.S. economy in the next six
to 12 months is
○○ very positive.
○○ moderately positive.
○○ slightly positive.
○○ neutral.
○○ slightly negative.
○○ moderately negative.
○○ very negative.
3.	 In the past four to six weeks, my firm’s work
force has
○○ increased substantially.
○○ increased moderately.
○○ increased slightly.
○○ not changed.
○○ decreased slightly.
○○ decreased moderately.
○○ decreased substantially.
4.	 In the next six to 12 months, I expect my firm’s
work force to
○○ increase substantially.
○○ increase moderately.
○○ increase slightly.
○○ not change.
○○ decrease slightly.
○○ decrease moderately.
○○ decrease substantially.

88

5.	 My firm is hiring or looking to hire for
these occupations:
□□ managerial (executive, accountant, HR manager,
marketing, etc.)
□□ professional and technical (engineer, IT support,
lawyer, etc.)
□□ sales
□□ administrative support
□□ maintenance (mechanic, custodian, etc.)
□□ production (operator, assembler, quality assurance,
laborer, etc.)
□□ transportation (driver, material handling, etc.)
□□ other
6.	 In the past four to six weeks, my firm’s capital
spending has
○○ increased substantially.
○○ increased moderately.
○○ increased slightly.
○○ not changed.
○○ decreased slightly.
○○ decreased moderately.
○○ decreased substantially.
	
A. My firm’s capital spending has been for:
□□ replacing equipment or remodeling
structures
□□ capacity expansion
□□ research and development
□□ mergers and acquisitions
	
B.	My firm’s capital spending has been on:
□□ industrial equipment
□□ IT equipment
□□ transportation equipment
□□ structures
□□ intellectual property
□□ other
7.	 In the next six to 12 months, I expect my firm’s
capital spending to
○○ increase substantially.
○○ increase moderately.
○○ increase slightly.
○○ not change.
○○ decrease slightly.
○○ decrease moderately.
○○ decrease substantially.

3Q/2015, Economic Perspectives

8.	 In the past four to six weeks, my firm’s overall
wage costs have
○○ increased substantially.
○○ increased moderately.
○○ increased slightly.
○○ not changed.
○○ decreased slightly.
○○ decreased moderately.
○○ decreased substantially.
	 A.	Please select the occupations for which
		 wage costs have increased:
□□ managerial (executive, accountant,
HR manager, marketing, etc.)
□□ professional and technical (engineer,
IT support, lawyer, etc.)
□□ sales
□□ administrative support
□□ maintenance (mechanic, custodian, etc.)
□□ production (operator, assembler, quality
assurance, laborer, etc.)
□□ transportation (driver, material
handling, etc.)
□□ other

9.	 In the past four to six weeks, my firm’s overall
nonwage costs have
○○ increased substantially.
○○ increased moderately.
○○ increased slightly.
○○ not changed.
○○ decreased slightly.
○○ decreased moderately.
○○ decreased substantially.
	
A.	Please select the areas where nonwage 		
		 costs have increased:
□□ raw materials or wholesale goods
□□ energy
□□ equipment
□□ property
□□ benefits (health insurance,
retirement, etc.)
□□ shipping costs
□□ taxes and regulations
□□ other

REFERENCES

Armesto, Michelle T., Rubén Hernández-Murillo,
Michael T. Owyang, and Jeremy Piger, 2009,
“Measuring the information content of the Beige
Book: A mixed data sampling approach,” Journal of
Money, Credit and Banking, Vol. 41, No. 1, February,
pp. 35–55.

Brave, Scott A., and Thomas Walstrum, 2014, “Using
the Federal Reserve’s Beige Book to track economic
activity,” Chicago Fed Letter, Federal Reserve Bank
of Chicago, No. 328, November, available at
https://www.chicagofed.org/publications/chicago-fedletter/2014/november-328.

Balke, Nathan S., Michael Fulmer, and Ren Zhang,
2015, “Incorporating the Beige Book into a quantitative
index of economic activity,” Southern Methodist
University and Federal Reserve Bank of Dallas, mimeo,
August 26, available at http://www.ssc.upenn.edu/
~fdiebold/NoHesitations/Balkeetal2015.pdf.

Brave, Scott A., and Norman Wang, 2011, “Predicting
gross state product growth with the Chicago Fed’s
Midwest Economy Index,” Chicago Fed Letter, Federal
Reserve Bank of Chicago, No. 293, December,
available at https://www.chicagofed.org/publications/
chicago-fed-letter/2011/december-293.

Balke, Nathan S., and D’Ann Petersen, 2002, “How
well does the Beige Book reflect economic activity?
Evaluating qualitative information quantitatively,”
Journal of Money, Credit and Banking, Vol. 34, No. 1,
February, pp. 114–136.

Fulmer, Michael, 2014, “A text analytics analysis of
the Federal Reserve Beige Book corpus,” Southern
Methodist University, doctoral dissertation.

Brave, Scott A., and R. Andrew Butters, 2014,
“Nowcasting using the Chicago Fed National Activity
Index,” Economic Perspectives, Federal Reserve Bank
of Chicago, Vol. 38, First Quarter, pp. 19–37, available
at https://www.chicagofed.org/publications/
economic-perspectives/2014/1q-brave-butters.

Federal Reserve Bank of Chicago

Pinto, Santiago, Pierre-Daniel Sarte, and Robert Sharp,
2015, “Learning about consumer uncertainty from
qualitative surveys: As uncertain as ever,” Federal
Reserve Bank of Richmond, working paper, No. 15-09,
August, available at https://www.richmondfed.org/
publications/research/working_papers/2015/wp_15-09.

89