Communication and the Beliefsof Economic Agents
Bernardo Candia, Olivier Coibion and Yuriy Gorodnichenko

Abstract
New surveys provide a wealth of information on how economic
agents form their expectations and how those expectations shape
their decisions. We review recent evidence on how changes in macroeconomic expectations, particularly inflation expectations, affecthouseholds’ and firms’ actions. We show that the provision of information about inflation to households and firms can sometimes
backfire in terms of their subsequent decisions. Whether or not thisis the case hinges on how individuals interpret the news about inflation: supply-side interpretations (“inflation is bad for the economy”)
lead to negative income effects, which can depress economic activity. We show that households in advanced economies, unlike professional forecasters, typically have such a supply-side interpretation, as
do many firms. We propose new communication strategies to avoid
public misinterpretation of policy decisions.
I.

Introduction

On its own, a rise in inflation expectations should lead households
to spend more before the anticipated price increases materialize andfirms to invest in more capital and hire more workers. Central bankers view this as one of the mechanisms underlying the effectiveness of
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Bernardo Candia, Olivier Coibion and Yuriy Gorodnichenko

quantitative easing1 and forward guidance2, and more generally as adirect way to provide stimulus to the economy through communication. But does this mechanism work?
In this paper, we review recent evidence on the strength of this expectational mechanism and the scope for a more systematic use of
expectations management for stabilization purposes. While there is
now a growing body of evidence consistent with strong expectational
effects, we show that the provision of information to households andfirms can sometimes backfire in terms of households’ or firms’ actions
depending on their interpretation of that information. Consumers
who associate higher inflation with a worsening economy will tend
to reduce their spending when they anticipate higher inflation, whilefirms with the same view will reduce their employment and investment. As a result, central banks need to carefully craft their communication in order to have the desired effects.
We begin by discussing the increasing availability of survey information on the beliefs of households and firms, both in the UnitedStates and around the world. We have designed and fielded new
surveys that have helped provide more systematic and timely measures of both the macroeconomic and microeconomic expectationsof firms and households. These recently created surveys avoid manyof the pitfalls that have plagued historical surveys and are offering a
wealth of new data in real time to policymakers and researchers. For
example, they reveal widespread inattention to inflation and monetary policy on the part of U.S. households and firms as well as a lackof anchoring in inflation expectations. We also illustrate how these
new surveys provide timely guidance during the COVID-19 crisis.
A recent and rapidly growing body of work combines these new
surveys with randomized information treatments to characterize
how new information about policy or the economy affects the expectations and resulting decisions of households and firms. Thesestudies confirm that communication can shape macroeconomic beliefs and that exogenous changes in beliefs in turn affect economic
actions. However, the mechanism through which this occurs is notas simple as the typical “thought experiment” in which higher inflation expectations imply lower real interest rates, which stimulateCommunication and the Beliefs of Economic Agents303

household spending as well as firm hiring and investment. Recent
evidence shows that providing information that raises households’
inflation expectations sometimes leads them to reduce, not raise, theirspending. Similarly, while some evidence finds that an exogenous increase in inflation expectations leads firms to raise their employment
and investment, other evidence points in the opposite direction.
The adjustment of individuals’ broader economic expectations
largely determines whether inflation expectations affect decisions in
the desired direction. In particular, we find that many agents interpret inflation as having supply-side origins, such that higher inflation
is associated with worse economic outcomes. As a result, households
who expect higher inflation may lower their spending rather thanraise it, while firms with higher inflation expectations may reduce(rather than increase) their employment and investment. We provide new evidence for a range of household, firm and professional
forecasts across countries that characterizes the extent to which different economic actors associate higher inflation with better or worse
economic outcomes. Across all developed countries that we study,
professional forecasters seem to have a view consistent with demanddriven business cycles and a Phillips curve: high inflation is associated with higher forecasts for output growth. In sharp contrast,
households across all countries systematically have the opposite view:higher inflation is associated with worse growth forecasts. For firms,
the evidence is more mixed across countries: firms in New Zealand,for example, have a similar perspective to professional forecasters,
while firms in Italy have a more supply-side view.
Ultimately, these results suggest that the common thought experiment in which a policymaker raises inflation expectations throughcommunications is ill-defined: households, firms and other economic actors understand that inflation is an endogenous variable
and they make inferences about the source of the inflation when they
revise their inflation expectations. How this impacts their other macroeconomic expectations—and ultimately their actions—therefore
depends on this inference.3 Importantly, we show that households,
firms and professional forecasters do not necessarily make the same
inferences: their understanding of what drives inflation appears to be304

Bernardo Candia, Olivier Coibion and Yuriy Gorodnichenko

fundamentally different. As a result, providing the same information
to these agents can lead them to draw very different conclusions
about the state of the economy and its outlook.
Our results have several potential implications for monetary policy
communications. First, despite the widespread inattention to monetary policy displayed by households and firms alike, there is scope
for targeted communication that delivers simple and transparent
messages to the public. Simple messages can potentially lead to largechanges in beliefs and actions. Second, communication with the
public should target more than just inflation expectations or interest
rates. Instead, a more holistic message about the aggregate economy
should be emphasized to prevent households and firms from erroneously interpreting demand-side monetary policies as having supplyside effects. Third, it may be better to focus on a few desired outcomes(e.g., “we are putting in place policies to reduce unemployment andthereby raise inflation toward more desirable levels”) rather thandelve into the details of policy instruments, given the public’s lack ofunderstanding of what these entail and the dangers of information
effects associated with discussions of changes in policy. The idea ofemphasizing target outcomes is consistent with Angeletos and Sastry(2018), who argue that guidance about targets can be superior to
guidance about instruments. As more is learned about how to frame
policy communications in such a way as to move inflation-output
expectations jointly in the desired direction, the potential usefulnessof communications to the public will significantly increase.
This paper builds on two key and often overlapping literatures inmacroeconomics. The first studies the expectations formation process
for economic agents and the role this plays in macroeconomic dynamics. Much empirical work in this literature has focused on testing(and rejecting) the benchmark assumption of full-information rational expectations (FIRE) that has long been a central building blockof most macroeconomic models (e.g., Coibion and Gorodnichenko
2012, 2015a; Andrade and Le Bihan 2013). Theoretical work has
focused on developing theories that depart from FIRE, such as sticky
information (Mankiw and Reis 2002), noisy information (Woodford
2002), rational inattention (Sims 2003, Maćkowiak and Wiederholt

Communication and the Beliefs of Economic Agents3052009), sparsity (Gabaix 2014), imperfect common knowledge (Angeletos and Lian 2018), level-k thinking (Farhi and Werning 2019)and diagnostic expectations (Bordalo, Gennaioli and Shleifer 2018).
More recently, empirical work has tried to differentiate among thesecompeting models (e.g., Coibion, Gorodnichenko, Kumar and Ryngaert 2018; Bordalo, Gennaioli and Shleifer 2018; Angeletos, Huo
and Sastry 2020; Reis 2020) to provide a single and simple alternative to FIRE. Our results on differential perceived correlations between inflation and the real economy across agents contribute to thisliterature by documenting a novel feature of individual expectations
that can further help distinguish across models of expectations.
Our paper also relates to the literature on central bank communication (see Blinder et al. 2008 for a survey). While much of this literature has focused on central banks’ interactions with financial markets, how central bankers communicate with the public has also beena primary area of interest since Blinder (2009). One conclusion fromthis literature is that policy communications often fail to reach the
public or affect their beliefs (Lamla and Vinogradov 2019; Binder2017) but that more targeted communications with differential language can be more successful (Haldane and McMahon 2018; Haldane, Macaulay and McMahon 2020). Another conclusion is thatsuccessful monetary policy breeds inattention to monetary policy:
households and firms in countries with long histories of low and stable inflation have little incentive to track inflation and monetary policy decisions and tend to be systematically less well informed about
these than those living in countries with high or volatile inflation(Cavallo, Cruces and Perez-Truglia 2017; Coibion, Gorodnichenko,
Kumar and Pedemonte 2020). While this indifference to monetary
policy on the part of the general population in advanced economies
is a gratifying reflection of the past success of monetary policy, it canalso make policymaking more challenging in times of crisis, hence
the need for communication strategies that can break through theveil of inattention.
II.

Expectations of Economic Agents

At the heart of the real interest rate mechanism emphasized by central bankers are the inflation expectations of households and firms, i.e.,306

Bernardo Candia, Olivier Coibion and Yuriy Gorodnichenko

those agents who engage in borrowing, saving, pricing, employment
and investment decisions, all of which are significant drivers of economic activity. The first step to assessing the strength of this mechanism is therefore being able to measure the economic expectations ofthese agents. While an extensive literature exists studying surveys ofthe expectations of professional forecasters (see Croushore 1998 for asurvey), more recent work has exploited the increasing availability ofsurveys of households’ and firms’ expectations (see Coibion, Gorodnichenko, Kumar and Pedemonte 2020 and Coibion, Gorodnichenko
and Kamdar 2018 for surveys). This work has extensively documented
that expectations of households and firms can materially diverge fromprofessional projections in low-inflation environments and that thesedifferences can matter for macroeconomic dynamics and estimates ofstructural parameters. As a result, having surveys of each type of agent
is important for understanding how policy communications are shaping expectations and aggregate outcomes.
In this section, we briefly review available surveys of household andfirm macroeconomic expectations in the U.S., focusing primarily on
newly created surveys that help fill important gaps relative to earlier
sources. For households, the long-running Michigan Survey of Consumers (MSC) has provided a wealth of information on household
expectations. This has been complemented with a more recent surveyof households: the New York Fed’s Survey of Consumer Expectations(SCE), which provides longer panels and more quantitative questions.4 Even more recently, we have been running a much larger-scalequarterly survey of households participating in the Nielsen Homescan Panel in order to have an even richer basis for understanding andutilizing households’ expectations.5 In addition, in March 2020, theFederal Reserve Bank of Cleveland launched a daily survey of households, which provides unprecedented high-frequency information on
household views.
In contrast, information on firms’ macroeconomic expectationsin the Unites States is rather scarce and available surveys (e.g., theAtlanta Fed’s Business Inflation Expectations survey) are limitedin coverage or elicit information that is useful (e.g., future pathof a firm’s unit cost) but not directly comparable to other surveys

Communication and the Beliefs of Economic Agents307

measuring expectations for aggregate inflation. Indeed, Bernanke(2007) observed, “Information on the price expectations of businesses–who are, after all, the price setters in the first instance–… is particularly scarce.” To address this challenge, we have been fielding a large
quarterly survey of U.S. chief executive officers (CEOs) to gather theirinflation expectations. We will use these novel data to shed new lighton firms’ expectations in general and on the recent dynamics during
the COVID-19 pandemic specifically. In addition, we show how thelarge scale of our quarterly survey of consumers implies that we canalso use it to measure the expectations of business managers.
II.i. U.S. Households
The MSC, going back to the 1960s, has long been the primary resource for those interested in studying the expectations of U.S. households. This monthly survey of approximately 500 households with alimited panel dimension includes a wide range of questions on theireconomic expectations. While most of these questions are qualitativein nature, a few, including for inflation expectations, are quantitative.
The resulting historical time series for household inflation expectations has been the basis of an extensive line of research (Curtin 2019).
In Table 1, we report recent values of U.S. households’ inflation expectations from the MSC, along with comparable year-ahead inflation
forecasts from the Federal Reserve and the Survey of Professional Forecasters (SPF). The latter have been forecasting inflation rates very close
to 2%, with a slight drop occurring in the second quarter of 2020 as
the economy went into recession following the pervasive imposition of
economic lockdowns. There is little disagreement among professional
forecasters, as can be seen by the low cross-sectional standard deviation
in forecasts. They are also very confident in their forecasts, and place
little weight on very high or low inflation outcomes (panel C of Table1). In contrast, household inflation expectations have consistently
been higher than those of professionals (around 3-3.5%) and display
tremendous cross-sectional dispersion. In addition, households havevery low levels of confidence in their forecasts. These well-known andsystematic differences between household and professional forecasts ofinflation have been the subject of an extensive literature (e.g., Mankiw,

2.4

2.9

2.5

1.5

2.0

1.8

1.8

2.5

0.0

-

1.6

1.6

2018Q3

2018Q4

2019Q1

2019Q2

2019Q3

2019Q4

2020Q1

2020Q2

2020Q3

2018Q4

2019Q4(1)

Recentdata

2018Q2

Survey
Date

2.0

2.0

-

1.2

-

1.9

1.8

1.7

1.9

1.9

2.0

2.1

(2)

Mean

0.0

0.0

-

0.35

-

0.10

0.18

0.10

0.10

0.30

0.10

0.15

(3)

SD

Central Bank

2.1

2.3

-

1.8

2.1

2.1

2.1

2.1

2.3

2.3

2.3

2.4

(4)

Mean
(6)

Mean

4.1†

3.2

2.9

2.9

3.1

2.9

3.0

3.4

3.4

3.1

0.2

0.2

2.6

2.8

B: 5-year inflation

-

0.7

0.4

0.3

0.3

0.3

0.3

0.3

0.4

0.4

2.2

2.2

3.4†

3.4

2.6

2.6

2.7

2.6

2.5

2.8

3.0

2.7

(7)

SD

Households

A: 1-year inflation

(5)

SD

Professional forecasters

Forecasts

2.4

3.6

2.0†

2.6

2.3

2.1

2.4

2.2

3.0

3.3

3.3

3.4

(8)

Mean

Firms

1.1

1.9

1.2†

2.2

0.9

1.2

1.0

1.1

1.4

1.7

2.0

1.7

(9)

SD

Table 1
Inflation Forecasts of Firm Managers and Other Agents for the United States

-

-

3.5†

3.8

3.2

-

-

-

2.5

-

-

-

(10)

Mean

-

-

2.4†

3.0

1.8

-

-

-

2.0

-

-

-

(11)

SD

Households withmanagerial duties(Nielsen)

308
Bernardo Candia, Olivier Coibion and Yuriy Gorodnichenko

1.6

-

-

-

-

-

-

-

-

-

-

2020Q2

2018Q2

2018Q3

2018Q4

2019Q1

2019Q2

2019Q3

2019Q4

2020Q1

2020Q2

2020Q3

-

-

-

-

-

-

-

-

-

-

2.0

-

-

-

-

-

-

-

-

-

-

-

0.0

-

-

0.7

0.9

0.6

0.8

0.9

1.1

0.3

0.9

1.3

1.9

2.2
2.9

2.8

-

3.4

2.8

1.9

2.1

2.4

2.9

0.6

2.4

3.7

42.5†

36.7

29.0

-

31.4

31.7

34.9

35.7

34.8

34.2

C: Uncertainty

0.4

0.2

36.0†

34.2

31.1

-

34.6

33.9

34.2

33.9

32.9

33.3

2.2

2.1

-

-

6.9

-

-

-

26.0

-

-

-

-

-

-

-

12.8

-

-

-

27.5

-

-

-

-

-

41.8†

34.0

29.1

-

23.8

25.7

25.9

-

-

-

-

-

37.1†

34.3

31.1

-

28.2

29.8

31.2

-

-

-

-

-

Notes: The table reports mean and standard deviation (SD) for 12-month-ahead inflation expectations (point predictions) as well as actual inflation rate in Panels A and B. Panel C reports mean andstandard deviation for the probability that inflation in the next 12 months will be greater than four %. Data are for the first month of each quarter. Recent data (column 1) shows CPI inflation rateover the last 12 months. Central Bank forecasts (columns 2 and 3) are from Economic Projections of Federal Reserve Board Members and Federal Reserve Bank Presidents. Inflation expectations arereported for PCE inflation. SD in column (3) is the upper end of the central tendency (excludes the three highest projections) minus the lower end of the central tendency (excludes the three lowest
projections). 12-month-ahead forecast is constructed as a weighted average of current and next-year projects where weights are the number of quarters from each year one needs to take to cover the
next 12 months. Professional forecasters’ expectations (columns 4 and 5) are from the Survey of Professional Forecasters. Households’ expectations (columns 6 and 7) are from the Michigan Survey ofConsumers (MSC), Survey of Consumer Expectations (SCE), and Nielsen Homescan Panel (NHP). Data in Panels A and B are from MSC (2020Q3 results are from NHP). We exclude responsesof consumers that are greater than 15% or less than -2%. Data in Panel C are from the SCE for 2018Q2-2019Q3 and NHP for 2020Q1-2020Q3. Firms’ inflation expectations (columns 8 and 9)
are from the survey described in Candia, Coibion, and Gorodnichenko (2020). We exclude responses of firms’ managers that are greater than 15% or less than -2%. Uncertainty for firms’ managers
is measured as the probability that inflation in the next 12 months will be greater than five %. All moments are computed with equal weights. Data for households with managerial responsibilities(columns 10 and 11) are from NHP. The sample includes households who report any managerial responsibility (supervise people, set prices, etc.). More details are in the notes for Table 2. Moments incolumns (10)-(11) of Panel A are computed using Huber-robust methods. † indicates preliminary results.

2.0

2020Q1

Communication and the Beliefs of Economic Agents
309

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Bernardo Candia, Olivier Coibion and Yuriy Gorodnichenko

Reis and Wolfers 2003) and are consistent with poorly anchored inflation expectations on the part of households.
Another striking difference that can be seen in Table 1 is that household inflation forecasts seem to have risen in the midst of the COVID-19
pandemic, rather than fallen like those of professional forecasters.
One might think that this is a statistical aberration: data on household inflation expectations are notoriously volatile, sensitive to extreme observations, etc. However, it is not. We show this in several
ways. First, the higher rate of inflation can be seen in each individual
month since the onset of the COVID-19 crisis, as shown in Chart
1, so the repeated nature of the higher observations suggests it isunlikely to be a statistical anomaly. With each passing month since
February 2020, inflation expectations have been rising. Second, as
we discuss below, the same pattern can be seen in other surveys of
U.S. households during this time period, as well as other countries
experiencing COVID-19 outbreaks. Third, the pattern does not appear to be inconsistent with the historical experience. To illustrate
this point, Chart 1 plots average beliefs of professional forecasters(Panel A) and households (Panel B) for both expected inflation andperceived business conditions, during both the early stages of theGreat Recession and the 2020 pandemic. For professional forecasters, we can see the pattern one would expect to see in a world driven
by demand shocks and a Phillips curve: as forecasters see a worsening
economy, they revise their inflation forecasts downward. This was
the case in the Great Recession and has been the case in 2020 as well.
For households, we see the opposite pattern: as their perceived stateof the economy worsened in each episode, they raised their inflation
forecasts. In other words, it’s as if the “Phillips curve” perceived byhouseholds is upward-sloping.
Evidence from other surveys broadly confirms this finding. Inflation expectations from the SCE also point toward a spike startingin the second quarter of 2020: the median one-year-ahead inflation
expectation was 2.5% in January 2020 but had risen to 3.0% by
May 2020. As described in Armantier et al. (2020), there was also
a surge in uncertainty about inflation that took place among households starting in March 2020. In a new (since 2018) quarterly survey

Communication and the Beliefs of Economic Agents

311

Chart 1
Comovement of Inflation Expectations and Economic
Conditions During Recent Recessions in the U.S.
A. Survey of Professional Forecasters
Current GDP growth rate

Current GDP growth rate
20M02
20M03

09Q4

2

0
08Q3
08Q1

0

−2

−10
08Q2

09Q2

−20

08Q4
−30

−4

−6

09Q1

20M05

−40

20M06
.5

1

1.5

2

2.5

3

Expected Inflation
Great Recession

COVID (right axis)

B. Michigan Survey of Consumers
Current business conditions

Current business conditions

.5

.5

20M01
20M02
0

0

20M03
09M12
09M10

−.5

09M08

−.5
08M01
08M03

09M05
20M04

20M05

09M01

−1
3

4

08M07
08M05

08M10
5

−1
6

7

Expected Inflation
Great Recession
COVID (right axis)
Notes: Each point shows average inflation expectations and perceptions of current business conditions at the time ofa survey. Arrows indicate the time flow to show the dynamics. Expected inflation is for the one-year-ahead horizon.
Panel A: current GDP growth rate measures the annualized growth rate of GDP in the quarter when forecasts arereported. Observations for 2020:M3 and 2020:M6 are from Consensus Economics, other observations are fromthe Survey of Professional Forecasters. Panel B: current business conditions are measured as the average response
to “Would you say that at the present time business conditions are better or worse than they were a year ago?”;responses are coded as +1 (“better now”), 0 (“about the same”), -1 (“worse now”). The sample is restricted to include
responses with expected inflation between -2% and 25%.

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Bernardo Candia, Olivier Coibion and Yuriy Gorodnichenko

of U.S. households participating in the Nielsen Homescan Panel,
we elicit quantitative expectations from tens of thousands of households. As shown in Table 2 (column 11), average inflation expectations of all respondents rose from 2.1% in January 2020 to 3.4% in
July 2020, with some increase happening as early as April 2020, immediately after the imposition of lockdowns in March. This survey
also reveals a dramatic and persistent increase in uncertainty about
future inflation (Panel C of Table 1).
Rising inflation expectations in the midst of a COVID-19 outbreak do not appear to be unique to the United States. For example,
Gautier, Ulgazi and Vertier (2020) document an upsurge in the inflation expectations of French households. The European Commission survey reports that this is true more generally in Europe, with
inflation expectations rising from 5.5% in the first quarter of 2020
to 6.8% in the second quarter. In Australia, the Melbourne Institute
Survey of Consumer Inflationary and Wage Expectations documented an increase of 0.6% in average inflation expectations in April of2020 as consumer sentiment collapsed, despite a lot more respondents reporting that they expected prices to fall, indicative of a sharp
rise in inflation uncertainty. In the June 2020 Opinion Survey on the
General Public’s Views and Behavior run by the Bank of Japan, there
was a dramatic increase in the fraction of respondents saying that
economic conditions had worsened in the last year compared with
the March 2020 survey wave (72% vs. 40%, respectively). Inflation
expectations in the June wave increased to 4.3% from 3.4% in the
March wave, with the variance of answers also increasing sharply.
Evidence from developing countries is similar: inflation expectations
in South Africa shot up to 6.2% in the second quarter of 2020 from
4.8% in the first quarter of 2020, while median inflation expectations from the Reserve Bank of India’s Inflation Expectations Surveyof Households went from 9% in March 2020 to 10.2% in May 2020
as broader measures of consumer confidence collapsed.
This rise in household inflation expectations associated with thecoronavirus can be seen at an even higher frequency using the newly created daily Consumers and COVID-19 Survey Project sponsored by the Federal Reserve Bank of Cleveland, as described in

1.3

4.4

25.5

29.4

mean

st.dev.

uncertainty

st.dev. (uncert.)

30.9

st.dev. (uncert.)

10.0

25.5

uncertainty

Share of respondents

4.8

st.dev.

2019Q2wave 506/2019

1.3

mean

(2)

30.6

25.1

5.0

1.0

2.8

32.7

27.6

5.3

1.2

2.7

(1)

10.2

Share of respondents

2019Q1wave 403/2019

Supervise 1 to 10
other people

Statistics

Supervise 11 to 50
other people

Wave
(3)

Supervise more than
50 other people
29.8

28.3

4.8

1.1

0.9

32.6

29.1

5.8

0.9

1.0

30.9

25.2

4.3

1.6

4.2

30.6

24.8

4.5

1.5

4.4

(4)

Make decisions abouthiring/firing workers(5)Make decisions about
what prices to set
31.4

27.2

4.7

1.7

3.0

32.0

27.0

5.1

1.5

3.1

(6)

Make decisions about
capital expenditures
30.0

23.6

4.0

1.8

2.6

29.9

23.1

4.6

1.4

2.6

29.3

22.8

4.1

1.6

2.4

29.8

23.2

4.7

1.4

2.5

(7)

Make decisions about
wages/salaries

managerial responsibility

Employed

Table 2
Implied Mean, One-Year-Ahead Inflation Forecast(8)

Make decisions aboutmarketing or sales
30.6

25.3

4.5

1.6

3.6

30.4

24.3

4.9

1.3

3.7

(9)

Any
29.8

25.7

4.6

1.3

16.6

31.2

25.9

4.9

1.2

16.7

(10)

No Managerial
responsibility
30.9

27.8

4.8

1.4

32.4

33.0

29.6

5.1

1.7

36.4

(11)

Full sample
31.7

28.6

4.8

1.5

100.0

33.5

30.0

5.2

1.8

100.0

Communication and the Beliefs of Economic Agents
313

10.7

2.2

4.0

29.8

30.4

st.dev.

uncertainty

st.dev. (uncert.)

28.0

st.dev. (uncert.)

mean

23.2

uncertainty

Share of respondents

4.2

st.dev.

2020Q1wave 701/2020

1.0

mean

31.6

28.3

4.5

1.7

2.9

27.8

25.2

4.4

0.9

(2)
3.0

(1)
10.7

Share of respondents

2019Q3wave 6
09/2019

Supervise 1 to 10
other people

Statistics

Supervise 11 to 50
other people

Wave

Supervise more than
50 other people
29.2

28.8

4.1

2.2

0.9

32.8

26.1

5.6

0.8

(3)
1.0

33.0

29.8

4.2

2.5

4.3

29.4

23.7

4.1

1.3

(4)
4.8

Make decisions abouthiring/firing workers

Employed

Make decisions about
what prices to set
31.2

27.6

3.8

2.3

3.1

30.5

24.6

4.1

1.7

(5)
3.3

32.1

27.4

3.7

2.3

2.7

28.2

21.7

4.0

1.4

(6)
3.0

Make decisions about
capital expenditures

managerial responsibility

Table 2 continued

32.3

30.0

3.9

2.4

2.4

28.9

22.2

4.2

1.2

(7)
2.8

Make decisions about
wages/salaries

Make decisions aboutmarketing or sales
32.3

28.6

4.0

2.3

3.9

29.3

24.7

4.0

1.6

(8)
4.2

Any
30.7

29.1

4.1

2.0

17.8

28.2

23.8

4.4

1.0

(9)
17.8

No Managerial
responsibility
30.8

30.1

4.2

2.1

34.4

30.8

28.9

4.7

1.5

(10)
35.3

Full sample
31.2

30.3

4.3

2.1

100.0

30.8

28.9

4.7

1.5

(11)
100.0

314
Bernardo Candia, Olivier Coibion and Yuriy Gorodnichenko

2.4

5.3

34.2

34.3

10.1

3.8

5.3

42.3

38.3

mean

st.dev.

uncertainty

st.dev. (uncert.)

Share of respondents

meanst.dev.

uncertainty

st.dev. (uncert.)

(1)

35.7

40.2

5.6

2.9

2.9

35.6

36.1

6.0

2.3

3.1

(2)

Supervise 1 to 10
other people

10.0Supervise 11 to 50
other people

Share of respondents

Statistics

37.5

47.1

5.6

3.3

1.2

31.7

36.5

5.1

2.0

1.1

(3)

Supervise more than
50 other people

37.8

44.3

5.2

4.1

4.2

34.1

34.0

5.1

2.7

4.2

(4)

Make decisions about
what prices to set
37.2

44.8

5.2

3.8

3.3

31.8

27.1

5.3

1.9

3.0

(5)

Make decisions about
capital expenditures
36.4

42.3

4.4

3.8

2.9

35.6

34.0

4.7

3.4

2.6

(6)

37.3

45.8

5.5

4.0

2.8

33.9

30.5

5.3

2.2

2.5

(7)

Make decisions about
wages/salaries

managerial responsibility

Employed

Make decisions aboutmarketing or sales
37.5

46.0

5.2

4.0

4.1

35.5

32.7

5.8

2.4

3.8

(8)

Any
37.1

41.8

5.3

3.5

17.8

34.3

34.0

5.4

2.3

17.3

(9)

No Managerial
responsibility
35.5

41.8

5.3

3.5

34.1

35.0

38.0

5.6

2.5

34.5

(10)

36.0

42.5

5.3

3.4

100.0

35.0

37.1

5.7

2.4

100.0

(11)

Full sample

Notes: All results are based on subjective probability distribution for expected inflation (the format of the question follows the SCE).
†
indicates preliminary results. For each wave, MM/YYYY shows the start month when the survey was fielded. The number of respondents used for this table is 24,885 for wave 4, 28,579 for wave 5,15,915 for wave 6, 12,092 for wave 7, 6,445 for wave 8, 5,055 for wave 9 (preliminary).

†

2020Q3wave 907/2020

2020Q2wave 804/2020

Wave

Make decisions abouthiring/firing workers

Table 2 continued
Communication and the Beliefs of Economic Agents
315

316

Bernardo Candia, Olivier Coibion and Yuriy Gorodnichenko

Dietrich, Kuester, Müller and Schoenle (2020) and Knotek et al.
(2020). While smaller in cross-section (between 50 and 200 different individuals are surveyed each day) than other surveys describedabove, the daily frequency provides an unprecedented view of theevolution of expectations in response to the coronavirus. Knotek etal. (2020) document that the coronavirus crisis induced a rise in inflation expectations occurring at the same time as a decline in the
expected growth rate of GDP among respondents since March 10,
the start of the survey. In Chart 2, we present additional evidence
that inflation expectations due to COVID-19 are associated with bad
outcomes by households. For example, there is strong positive comovement between the median inflation expectation and the shareof respondents that are worried about losing their jobs, the share of
respondents who are delaying large purchases, the fraction who arestoring extra food supplies and the fraction who are engaged in extra
personal saving. The results from the Cleveland Fed survey therefore
confirm that inflation expectations are closely tied to a more general
sense of bad outcomes on the part of households.
II.ii. U.S. Firms
Measuring the macroeconomic expectations of U.S. firms has long
been a challenge. Indeed, conducting a high-quality survey of CEOsor other business executives is an exercise fraught with many difficulties ranging from establishing contacts with CEOs (e.g., one has to go
through multiple layers of various filters, secretaries, assistants, etc.)
to the very limited time that CEOs can spare on answering questions(one can hope to have responses to only a few simple questions). Constructing a representative panel of CEOs willing to repeatedly participate in a survey takes many years and a great deal of trust-building; nosuch survey has existed in the United States until recently.
However, as with households, there has been a pronounced effort in recent years to fill this gap and develop much more systematic, representative and quantitative surveys of firms’ expectations.
One such attempt is the Survey of Business Uncertainty. Begun in2013, this is a monthly survey of approximately 1,750 firms acrossindustries and firm sizes (see Altig et al. 2019 for an overview of thisCommunication and the Beliefs of Economic Agents

317

Chart 2
Comovement of Inflation Expectations and Consumer Choices
During the COVID-19 Crisis in the U.S.

10

E(inflation), percent

Fear of job loss,
share, percent

8

50

10

45

8

70

6

60

4

50

40

6

E(inflation), percent

Refrain frompurchases,
share, percent

80

35
4

30

2

25

10

E(inflation), percent

Store more
food supplies,
share, percent

8

10/3
07/4
05/5
02/6
30/6
24/3
21/4
19/5
16/6
14/7

65

10

60

8

55

6

40

2

10/3
07/4
05/5
02/6
30/6
24/3
21/4
19/5
16/6
14/7

Increased personalE(inflation), percent savings, share, percent

45

6

50
4

45

2
10/3
07/4
05/5
02/6
30/6
24/3
21/4
19/5
16/6
14/7

40

50

40
4
35

2
10/3
07/4
05/5
02/6
30/6
24/3
21/4
19/5
16/6
14/7

Notes: The chart uses data from the Cleveland Fed’s daily survey of households. All time series are seven-day moving averages. In each panel, the black, solid line shows the time-series median for one-year-ahead expectations ofinflation. In each panel, the dashed line shows the time series of the share of respondents (in percent) reporting a
consumer choice indicated on the right scale.

survey). However, the survey focuses on firm-specific conditions anddoes not measure expectations about the aggregate economy.
More recently, we have teamed up with a prominent survey firmthat has been collecting CEOs’ and top executives’ perceptions andexpectations for various firm-specific outcomes. The results of thesesurveys are widely used in business, policy and academic circles. In
2017, this survey firm agreed to add two inflation-related questions
to its quarterly survey. The survey covers firms in manufacturing andservices. Each wave includes responses from 300 to 600 firms, a relatively large cross-section for a firm survey. Since 2017, 1,123 firms
have participated in the survey and firms, on average, participate
in 3.8 waves. The first of the two new questions to respondents is318

Bernardo Candia, Olivier Coibion and Yuriy Gorodnichenko

always about their 12-month-ahead inflation expectations. The second question rotates quarterly across one of four topics: long-term
inflation expectations, perceptions of recent inflation rates, inflation
uncertainty and belief about the Federal Reserve’s inflation target.
Jointly, these questions provide a comprehensive assessment of firms’
inflation expectations.6
Columns (8) and (9) in Table 1 report results of the survey. Consistent with Kumar, Afrouzi, Coibion and Gorodnichenko (2015) documenting evidence for firms in New Zealand, American CEOs haveinflation expectations higher and more dispersed than those of professional forecasters. This pattern is observed for short-run expectations(Panel A) and longer-run expectations (Panel B). Similar to Kumar etal. (2015), we find high correlation between short- and long-run inflation expectations, which contrasts with the weak (if any) correlationin professionals’ forecasts. Again, in line with Kumar et al. (2015), we
observe that, in normal conditions (first quarter of 2019), CEOs arerather uncertain about inflation and assign, on average, a 26% probability that inflation will exceed 5% in the next 12 months. Hence,along these dimensions, firms’ inflation forecasts are much more similar to households’ than they are to professionals’.
Perhaps most strikingly, another similarity between households
and firms is the behavior of their inflation forecasts during the COVID-19 pandemic: the average inflation expectations of CEOs went
up by 0.3 percentage point between the first and second quarters of2020 even as the economy went into a tailspin and as professional
forecasters predicted a decline in inflation. The cross-sectional dispersion of inflation forecasts also rose dramatically.7
Of course, as with households, one must be wary of placing too
much weight on the results from one survey wave, given the noiseand sensitivity that a single wave displays. Unfortunately, unlikewith households, there are no other directly comparable surveys offirms to verify the statistics. As a result, we consider an alternative approach that exploits the large-scale Nielsen Homescan Panel. Due tothe large number of respondents, the survey includes many individuals who have managerial responsibilities and also includes a question

Communication and the Beliefs of Economic Agents319

specifically designed to identify these individuals in the survey. As aresult, we can create a survey of managers from the survey of households. While it is not directly comparable to a survey of CEOs andother top business executives, many decisions are made by middleand low-ranking managers and, as a practical matter, their choices
may be just as, if not more, important as the “top brass” decisions.
Specifically, we ask respondents participating in the Nielsen Homescan Panel to indicate their responsibilities at their current employment from a set of options including whether he/she sets prices or
wages, fires/hires personnel, supervises personnel (1-10 people, 1150 people, more than 50 people), makes decisions about capital expenditures or makes decisions about marketing/sales. If a respondent
selects at least one of these responsibilities, we identify this respondent as a manager. To the best of our knowledge, no other household
survey with inflation expectations elicits this information, and thus,
we provide a new perspective. Approximately a third of employed
respondents report some managerial responsibility.8 Columns (10)(11) of Table 1 report the average inflation expectations for managers
identified in the household survey, measured using point forecasts forcomparison to the other surveys in Table 1. Between the first andsecond quarters of 2020, we find that managers reported an increasein their inflation expectations from 3.2% to 3.8%, an increase of asimilar order of magnitude as the one found for households. In short,
this alternative survey of managers leads to a similar conclusion as thesurvey of CEOs.
In Table 2, we provide more details on the inflation expectations ofrespondents based on which managerial characteristics respondents
identified with. Inflation expectations here are based on distributional questions in which respondents assign probabilities to various
inflation ranges, which were included in the survey more systematically than point forecasts. We do not find any large differences acrossgroups. Managers who supervise 10 or more individuals tend to havesomewhat lower inflation expectations while those who set prices ormake capital expenditures have slightly higher expectations, but thedifferences are relatively small.

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Bernardo Candia, Olivier Coibion and Yuriy Gorodnichenko

II.iii. Summary
There has been great interest in developing new surveys of households and firms in recent years, both in the United States and abroad.
These surveys are providing real-time insight into the expectations ofagents, insights which can be invaluable during crises. We documentthat since the onset of the COVID-19 pandemic, there has been asignificant divergence in the evolution of inflation forecasts of professional forecasters (which have declined in line with a standard Phillipscurve view of the world) versus those of households and firms (whichhave risen). Rising inflation expectations on the part of households
and firms, at least while at the zero lower bound, should be stimulative
to the economy since they imply lower real interest rates and therefore
higher consumption, hiring and investment. Are they?
III.

Inflation Expectations and Decisions

The development of surveys of households and firms has led not
only to better and more systematic measurement of the expectationsof these agents but also to more research on how these expectations
relate to individual economic decisions. A key question that thisresearch has focused on is precisely whether higher inflation
expectations of households and firms do indeed lead to higher levelsof spending, employment and investment.
III.i. The State of the Literature
Bachmann, Berg and Sims (2015) was the first study to exploit theavailability of survey microdata to relate the inflation expectations ofhouseholds to their spending decisions or perceptions of whether now
is a good time to purchase durable goods. Using data from the MSC,
they found little systematic relationship between individuals’ inflation
expectations and their spending, except for a small subset of more educated and higher-income individuals. Subsequent work for the United States and other countries (Crump, Eusepi, Tambalotti and Topa
2015; Burke and Ozdagli 2013; Ichiue and Nishiguchi 2015; Dräger
and Nghiem (forthcoming)) has been more supportive of a positive
relationship between inflation expectations and spending.

Communication and the Beliefs of Economic Agents321

One limitation of this approach is the absence of clear causality:
a positive relationship can reflect that higher inflation expectations
induce households to spend more, but causality can also go in the
opposite direction. For example, households who are spending more
today may think other households are doing the same and therefore
that prices will have to rise in the future. Alternatively, if the priceof goods purchased by households rises today and they do not alter their spending patterns much, their spending will be higher andthey will likely raise their inflation expectations, since households
generally form expectations based on the prices of goods they typically purchase (D’Acunto, Malmendier, Ospina and Weber 2019).
A positive correlation between spending and inflation expectations
therefore need not imply a causal relationship from expectations tospending decisions.
One approach to breaking this endogeneity is to identify a large
policy change that alters inflation expectations. D’Acunto, Hoang
and Weber (2018) study one such instance: the 2005 announcementof an increase in the German value-added tax for 2007. Household
inflation expectations spiked in the intermittent period, correctly anticipating the rise in prices, and households simultaneously perceived
this same period to be a good time to purchase durable goods. As no
comparable change in household expected income occurred during
this time period, the change in the perceived desirability of purchasing durable goods can then clearly be assigned to an inflation expectation effect.
Another approach to breaking the endogeneity of expectations is
to use randomized controlled trials (RCTs). Coibion, Georgarakos,
Gorodnichenko and van Rooij (2019) do so using a survey of households in the Netherlands. A random subset of households is provided with (publicly available) information about inflation, whereas
households in the control group are not provided with any additional
information. The provision of information to some households induces an exogenous change in their inflation expectations, which
can be used to assess the causal effects of expectations on spending
decisions. Coibion et al. (2019) find that when Dutch households
have exogenously higher inflation expectations, they sharply reduce322

Bernardo Candia, Olivier Coibion and Yuriy Gorodnichenko

their spending on durable goods but not non-durables and services,
the opposite of the expected effect. Unlike what was observed by
D’Acunto, Hoang and Weber (2018) for German households, theprovision of information about inflation to Dutch households ledthem to significantly revise not just their views about aggregate inflation but also about the broader economic outlook. Dutch households who raised their inflation expectations tended to become much
more pessimistic about the state of the economy and their future
income growth, thereby potentially explaining why their spendingon durable goods went down.
This RCT approach has also been applied to the question of how
firms change their decisions when they revise their inflation expectations. Coibion, Gorodnichenko and Kumar (2018) run a survey offirms in New Zealand with an RCT in which a randomly selected
subset of firms is provided with (publicly available) information about
inflation. Six months later, those firms are surveyed again to determine
how their prices, employment, investment and wages have evolved.
Coibion et al. (2018) find that those firms that were initially uninformed about inflation but were treated with information significantly
revised their inflation expectations downward relative to the control
group but did not change their other macroeconomic expectations.
Over the course of the next few months, those same treated firms significantly lowered their employment and investment relative to thecontrol group, suggesting a positive link between both employment
and investment with respect to firms’ inflation expectations.
Coibion, Gorodnichenko and Ropele (2020) provide another example of an RCT approach to firms. In a long-running survey of Italianfirms, a randomly selected subset of firms was given information about
recent inflation while other firms in the survey were not, from 2012
to 2018, providing exogenous variation in the inflation expectations offirms across the two groups as well as over time. They find that exogenously higher inflation expectations lead firms to significantly reduce
their employment and investment, increase their leverage, and seekout new sources of credit. As with Dutch households, however, a risein inflation expectations on the part of firms comes with increasing
pessimism about the outlook for both the broader economy and forCommunication and the Beliefs of Economic Agents323

the firm itself. But for the sub-sample during which the interest rate
was constrained by the effective lower bound (ELB), this pessimistic
effect dissipated: firms associated higher inflation expectations with astronger economic outlook, leading them to raise their prices sharply
and no longer cut back on employment or investment.
In short, the evidence on how inflation expectations relate to theeconomic decisions of households and firms is mixed. Clearly exogenous increases in inflation expectations have been found to leadto either higher or lower desired household spending, as well as either higher or lower employment and investment by firms. In eachcase, the differential response seems to reflect the variation in the way
households and firms interpreted the source of the news about inflation and what it meant for the broader economic outlook.
III.ii. The Perceived Relationship between Inflationand the Real Economy
The nature of the relationship between inflation and the real side ofthe economy is, of course, not an easy one to decipher. Macroeconomists themselves have long argued about the importance of the Phillips curve and whether supply or demand shocks are more important.
What do households and firms think about this relationship?
As a benchmark, it’s useful to first look at the properties of beliefs
of professional forecasters. We do so using the panel of Consensus
Economics forecasters reporting predictions of output growth andinflation for not only the United States, but also Germany, France,
Italy, Spain, the Netherlands and New Zealand. For each country, we
compare the cross-section of forecasts of output growth and inflationfrom 2001 to 2020 after removing time and country fixed effects. We
present the resulting correlations in binscatter form for each country
in Chart 3. The results are uniform across countries: forecasters who
predict higher inflation in a given period also tend to predict higher
output growth. This positive comovement is in agreement with professional forecasters using models which include a Phillips curve anddemand shocks as primary sources of fluctuations.
We can then study whether this pattern applies to households aswell. For the United States, we use the microdata from the MSC from

1

−1

0

.5

Expected inflation−.5

Netherlands

1

−.2

−.1

0

.1

.2

−1

−1

0

.5
Expected inflation−.5

New Zealand

−.5
0
.5
Expected inflation

Expected output growth

Italy

1

1

−.2

−.1

0

.1

.2

−1

−.5
0
.5
Expected inflation

Expected output growth

Spain

1

Notes: Each panel plots a binscatter for the joint distribution of expectations of output growth rate and inflation in the next calendar year. For each variable, we take out the time×country fixed effectso that all variables are mean zero. The sample period is 2001-20.

Expected inflation

−.4

−.2

.5

−.2

−.1

0

0

0

−.5

.2

.1

−1

.4

France

.2

−.5
0
.5
Expected inflation

−.3
−1

−.2

−.2

−.5 0
.5
1
Expected inflation

−.2

−.1

−.1

−1

−.1

0

0

0

.1

.1

.1

.2

Expected output growth

Germany.2

Expected output growth

United States

Chart 3
Joint Distribution of Inflation and Output Growth Expectations, Professional Forecasters(Consensus Economics)
324
Bernardo Candia, Olivier Coibion and Yuriy Gorodnichenko

Communication and the Beliefs of Economic Agents

325

1978 to 2020. While households are not asked to provide a quantitative forecast for the real economy, they are asked about whether they
expect business conditions in the next year to improve, stay the sameor deteriorate. We assign point values to each answer ranging from 1(improve) to -1 (deteriorate). We then compare the households’ responses to this question and their quantitative inflation forecasts, after
taking out time fixed effects. As documented in the first panel of Chart4, we find a strong negative relationship in the binscatter summarizingthese forecasts: U.S. households that predict higher inflation in any
given period also tend to predict worse economic outcomes in the
future. This is the opposite correlation of what we observe for professional forecasters, but it is very much consistent with the reaction of
Dutch households to news about inflation in Coibion, Georgarakos,
Gorodnichenko and van Rooij (2019).
Other work has also documented results in this spirit for households. Kamdar (2018), for example, documents that U.S. households in the MSC and SCE associate high inflation with high unemployment, i.e., they have a stagflationary view of inflation.9 She
furthermore finds this positive correlation between inflation andunemployment in households’ expectations even during the Great
Moderation, when inflation was largely demand-driven. Similarly,
Dräger, Lamla and Pfajfar (2016) argue that only a small fractionof households within the MSC have expectations that are consistent
with a Phillips curve.
We extend this analysis to European countries using the newly created Consumer Expectations Survey (CES) of households from theEuropean Central Bank.10 This survey was launched in January 2020
and it currently includes six countries: Belgium, France, Germany,
Italy, the Netherlands and Spain. The monthly survey includes apanel dimension and includes approximately 1,000 households in
Belgium and the Netherlands and 2,000 households in other countries, thus providing a unique cross-country perspective. Participants
are asked to answer a number of questions covering their perceptions and expectations of inflation, the general economic outlook,housing markets, labor market conditions and their personal income
and consumption decisions. Specifically, households are asked about

5

Expected inflation

0

10

−10

0
10 20 30
Expected inflation

FranceExpected output growth

−5

−10

−5

0

5

10

−10

−5

0

10

20

Expected inflation

0

−10

30

0
10 20 30
Expected inflation

Belgium
Expected output growth

−10

−10

−5

0

5

−10

−5

0

5

10

20

Expected inflation

0

−10

30

0
10 20 30
Expected inflation

Netherlands
Expected output growth−10

Expected output growth

10

Expected output growth5

Expected output growth

−10

−5

0

5

−10

10

20

Expected inflation

0

Expected output growth

Spain

30

Notes: Each panel plots a binscatter for the joint distribution of expectations for output growth rate and inflation in the next calendar year. For each variable, we take out the time×country fixed effectso that all variables are mean zero. The sample period for the United States (Michigan Survey of Consumers) is 1978-2020. The sample period for other countries (ECB’s Consumer Expectation
Survey) is January-June 2020.

−10

−5

0

5

−.6

−.4

−.2

0

.2

.4

Italy

Germany

United States

Chart 4
Joint Distribution of Inflation and Output Growth Expectations, Households
326
Bernardo Candia, Olivier Coibion and Yuriy Gorodnichenko

Communication and the Beliefs of Economic Agents

327

expected inflation over the next 12 months (using the same distribution question as used in the SCE) and about the growth rate of theircountry’s economy over the next 12 months. We use these two questions to assess how households’ beliefs about the two evolve jointly. Following the approach for the MSC, we find (Chart 4) results
remarkably consistent with those observed for the United States:
households who expect higher inflation also tend to expect worse economic conditions in all euro area countries included in the survey.11
In short, we find uniform evidence across countries that households’
beliefs about inflation are consistent with a supply-side narrative inwhich high inflation is associated with a bad economy, not the good
economy that would induce demand-driven price inflation.
Do firms hold similar beliefs? Once again, the absence of large-scale
historical surveys of firms limits our ability to study the properties offirms’ expectations. Our new survey of U.S. CEOs and top executives, for example, only includes questions about aggregate inflationand not the broader economic outlook. Instead, we use the Livingston survey, a long-running survey of large financial and non-financial
companies in the U.S. This survey is not representative in its coverage
but extends back to the 1950s on a semiannual frequency. Using theindividual forecasts for inflation and output growth of nonfinancial
corporations, we plot (in a binscatter) the correlation between these
individual forecasts over time after removing time fixed effects in thefirst panel of Chart 5. We find neither a strong positive relationship(as we did with U.S. professional forecasters) nor a strong negative
relationship (as we did with U.S. households). Instead, the correlation is at best weakly positive but economically small. In short, U.S.
firms (at least those large firms represented in the Livingston survey)
do not display a clear correlation between inflation and output intheir forecasts.
A similar result holds for firms in New Zealand. Using the surveyof firms in Coibion, Gorodnichenko and Kumar (2018), in whicha large and broadly representative cross-section of firms were asked
about both their output growth and inflation expectations, we canconsider the cross-sectional correlation in these forecasts. As reported
in Chart 5, we again find a weak relationship between the two. The

328

Bernardo Candia, Olivier Coibion and Yuriy Gorodnichenko

Chart 5
Joint Distribution of Inflation and Output Growth
Expectations, Firms
United States
1

Expected output growth25

Italy (not at the ELB)

Italy (at the ELB)

Expected business conditions

Expected business conditions

10

20
.5

5

15
10

0

0

5
0

−.5

−5

−5
−1

−10
−2

−1

0

1

2

−10
−2

Expected inflation

.4

−1

0

1

2

3

Expected inflation

New Zealand
Expected output growth

−1

0

1

Expected inflation

Ukraine.4 Expected output growth

.2

.2

0

0

−.2
−.2

−.4

−.4

−.6
−5

0

5

10

Expected inflation

15

−20

−10

0

10

20

Expected inflation

Notes: Each panel plots a binscatter for the joint distribution of expectations for output growth rate and inflationin the next calendar year. For each variable, we take out the time×country fixed effect so that all variables are mean
zero. Inflation expectations are for the one-year-ahead horizon. United States: output expectations are one-yearahead predictions for real GDP growth rate. The Livingston survey is the source of the data. The sample is restricted
to non-financial corporations. The sample period is 1992-2019. New Zealand: output expectations measure
one-year-ahead projections for GDP growth. Coibion, Gorodnichenko and Kumar (2018) is the source of the data.
The sample period is 2014-17. Italy: output expectations are responses to a multiple-choice question (“What do you
think is the probability of an improvement in Italy’s general economic situation in the next three months?”) with size
options: zero, 1-25%, 26-50%, 51-75%, 76-99%%, 100%. For each option, we code responses as mid-points of thechosen ranges. Coibion, Gorodnichenko and Ropele (2020) is the source of the data. The sample period is 2012-19.
Ukraine: inflation expectations are reported as answers to multiple-choice questions (typically seven to nine options;e.g., the bins could be “less than 5%,” “5 to 10%,” “10 to 15%,” …, “more than 40%”). Output expectations are
responses to a multiple-choice question (“What changes do you expect in the dynamics for output of goods andservices in Ukraine over the next 12 months?”) with three options: “increase” (coded as “+1”), “same” (coded as “0”),“decrease” (coded as “-1”). The sample period is 2007-20.
Source: The National Bank of Ukraine.

2

Communication and the Beliefs of Economic Agents329

absence of a strong correlation between output and inflation expectations for firms in New Zealand is consistent with the experimental
evidence in Coibion et al. (2018): when a random subset of firms wasprovided with information about the Reserve Bank of New Zealand’s
inflation target, their expectations of inflation moved strongly toward that target but their views about the broader economic outlook
were effectively unchanged.
This pattern is visible not only in the cross-section of firms but also
within a firm’s own joint distribution over nominal and real variables. In
the survey of New Zealand firms, we asked respondents to assign probabilities to a range of outcomes defined over both future unemployment and future aggregate wage growth. This novel survey question
comprehensively describes the joint distribution of each firm’s belief
about future unemployment and wages. As illustrated in Chart 6 (taken from Coibion, Gorodnichenko, Kumar and Ryngaert 2020), there
is a weak negative relationship between the expected wage growth andexpected unemployment: firms perceive a weak Phillips-curve relationship between nominal and real variables. The consistency between
the within-firm correlation (Chart 6) and the across-firm correlation(Chart 5) suggests that the latter is indeed informative about firms’
perceived relationship between inflation and the economic outlook.
Another available survey of firms is in Italy (Bank of Italy’s Survey of Inflation and Growth Expectations, SIGE), which has beenrunning since 1999. This survey consists of a representative panelof about 2,000 firms (with employment greater than 50 employees)
and includes quantitative questions about inflation over the next 12months and qualitative questions about the expected state of theeconomy in three months. After taking out time fixed effects, weplot in Chart 5 the cross-sectional correlations between expectationsof inflation and business conditions both before the effective lower bound (2012-14) and at the effective lower bound (2014-19).12
Consistent with Coibion, Gorodnichenko and Ropele (2020), there
is a significant change in how firms seem to view inflation before andduring the lower-bound period. Prior to the lower bound, firms havea somewhat supply-side view of inflation: firms with higher inflation expectations tend to be, if anything, more pessimistic about the

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Bernardo Candia, Olivier Coibion and Yuriy Gorodnichenko

Chart 6
Joint Expectations of Wage Growth and Unemployment Rate,Firms in New Zealand4

Deviation of expected wage growth

Deviation of expected wage growth

4

2

2

0

0

−2

−2

−4

−4
−2

−1

0
Deviation of expected unemployment rate12

Notes: The chart shows negative correlation in the within-firm joint distribution of subjective expectations for
future wage growth and unemployment rate in a survey of firm managers in New Zealand. The chart shows contour
maps for the average joint distribution (kernel density) of expected wage growth and unemployment rate. The
joint distribution is elicited by asking managers to assign probabilities to each cell in a table of wage-growth andunemployment-rate outcomes (each dimension of the table has a series of bins with a range of possible outcomes).
To aggregate distributions across managers, we demean each manager’s distribution using his/her implied meanfor future wage growth and unemployment rate and then we take an average across managers. The horizontal axis
measures deviation of manager i’s expectation from his/her mean forecast for unemployment rate over the next 12months. The vertical axis measures deviation of manager i’s expectation from his/her mean forecast for wage growthover the next 12 months. More details are available in Coibion, Gorodnichenko, Kumar and Ryngaert (2020).

economic outlook. During the lower-bound period, however, firmsthat have higher inflation expectations tend to be more optimistic
about the economic outlook. This alternative interpretation of theinflation process during the lower-bound period is consistent with
New Keynesian models, which predict very different dynamics whenmonetary policy is constrained, but firms’ supply-side view of inflation prior to the lower bound is quite different from the view taken
by professional forecasters and more akin to the process perceived byhouseholds seen in Chart 4.
The final country for which there is a suitable survey of firm expectations is Ukraine, a country with a recent history of volatile, highinflation. As described in Coibion and Gorodnichenko (2015b), theNational Bank of Ukraine runs a quarterly, nationally representative
survey of around 1,000 firms. This survey includes a wide range ofCommunication and the Beliefs of Economic Agents

331

qualitative and quantitative questions about firms’ macroeconomic
expectations. In Chart 5, we plot a binscatter of firms’ joint expectations over inflation and output growth in the next 12 months, after
taking out time fixed effects. The results point toward a very strong
negative correlation between the two: firms that expect higher inflation in Ukraine also tend to expect lower output growth.13
III.iii. Summary
The growing literature on the effects of expectations on decisions
is almost unanimous in one respect: agents’ macroeconomic expectations do ultimately affect their decisions. But the way in which
agents’ actions actually change when they revise their inflation expectations seems to depend crucially on whether/how they revise theirbroader economic outlook along with their inflation expectations.
Households seem to consistently have a supply-side view of inflation,such that they become more pessimistic about the economic outlook when their inflation expectations rise. Higher inflation expectations can therefore induce not just an intertemporal substitution
effect (which would raise current consumption) but also an income
effect (which would lower current consumption). Firms in many, but
not all, of the countries for which we have data also seem to have a
supply-side view of inflation and the strength of this effect can help
explain why higher inflation expectations seem to induce higher employment and investment in some places and lower employment andinvestment in others.
IV.

Implications for Monetary Policymakers

What does all of this mean for policymakers? We emphasize twogeneral points. First, an important lesson from the RCT approach isthat simple pieces of information, when they get through to the public, can have very large effects on inflation expectations. This suggeststhat there is significant potential in using communication strategies
to affect the expectations of the general public as a tool to help stabilize the economy. But most current policy communications do notreach the public. Hence, more work needs to be done in designingcommunications strategies that successfully reach the public. Second, it may not be enough just to communicate information about

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Bernardo Candia, Olivier Coibion and Yuriy Gorodnichenko

inflation or interest rates. Households and firms understand that, forexample, inflation is endogenous and will therefore make an inference about what is driving the new information about inflation thatthey receive. How they do so will depend on their understanding of
the inflation process, which for many seems to reflect a supply-side
view of inflation. The ultimate effect on economic decisions may
therefore be different from what is desired by policymakers. Instead,
it may be preferable to offer more holistic messages that describe thebroader outcomes that policymakers are trying to achieve.
IV.i. Reaching the Public
Economic expectations depend on a myriad of factors (e.g., ability
and incentives of economic agents to collect, process and interpret
information) and policy actions are but one of these many factors. As
a result, economic agents may be largely unaware of policy actions
in normal times. For example, Binder (2017) documents that thegeneral public does not know basic facts about central banks suchas leadership, objectives and even the very existence of these institutions.14 This lack of knowledge apparently extends to firm managers,
too. Table 3 reports the distribution for the perceived inflation target of the Federal Reserve for U.S. CEOs (using our survey of U.S.
firms), households (using the Nielsen Homescan Panel) and professional forecasters (using the Survey of Professional Forecasters). We
find that CEOs have perceptions similar to those of households participating in the Nielsen survey: there is a wide distribution with asignificant mass above 2.5% and many respondents choosing “donot know.” The fraction of CEOs who choose “do not know” varies
across waves but is generally above 20%.15 Households and firms do
not appear to track changes in policies, either. For example, Lamla
and Vinogradov (2019) use high-frequency measurement of households’ expectations around FOMC decisions and document little
knowledge about these decisions on the part of households, as well as
weak revision of their expectations in response to the decisions.
The fact that policymakers have to penetrate through a veil of
inattention to reach households and firms in advanced economies
might seem an insurmountable roadblock for policy communicationand hence the management of expectations—but we have a more

1.83

22.15

7.08

2.51

3.42

0.46

0.00

0.46

0.00

0.68

55.02

6.16

(1.5, 2.5]

(2.5, 3.5]

(3.5, 4.5]

(4.5, 5.5]

(5.5, 6.5]

(6.5, 7.5]

(7.5, 8.5]

(8.5, 9.5]

(9.5, +∞)

DNK

NA

18.20

19.52

0.44

0.00

0.00

0.00

0.22

1.54

3.51

12.72

35.75

7.46

0.66

30.26

30.59

0.00

0.00

0.00

0.33

0.00

0.66

0.33

6.58

19.08

7.57

4.62

(3)

2020Q2

-

40.37

15.28

0.19

0.47

0.67

0.70

6.48

3.39

11.30

16.33

3.35

1.45

(4)

2018Q2

-

-

38.33

0.21

1.86

0.88

1.95

9.01

5.64

11.05

15.29

3.65

12.14

(5)

2018Q3

-

-

43.26

0.16

1.69

1.04

1.35

9.92

4.97

11.24

15.00

3.12

8.25

(6)

2018Q4

Households(Nielsen Homescan Panel)

Share of responses

-

-

0.00

0.00

0.00

0.00

0.00

0.00

0.00

14.81

85.19

0.00

0.00

(7)

2019Q4

Survey ofProfessional
Forecasters

-

-

-

-

-

-

-

6.00

2.00

23.00

59.00

4.00

5.00

(8)

2017Q2Atlanta Fed
BIE

Notes: Results in columns (1)-(3) are from the U.S. surveys of CEOs, see Candia, Coibion and Gorodnichenko (2020). The survey question is “What annual inflation rate do you think the U.S.
Federal Reserve is trying to achieve on average?” Results in columns (4)-(6) are for households participating in the Nielsen Homescan Panel. The survey question is “What is your best guess about theannual inflation rate that the Federal Reserve tries to achieve on average over long periods of time? Please use a percent between -100 and 100)”. In the 2018Q2 wave (column 4), households were allowed to choose “do not know” option. In other waves, this option was not available. Column (7) reports the distribution of forecasts for the 5-Year Forward 5-Year Annual-Average CPI Inflation Rate
(CPIF5) in the Survey of Professional Forecasters. For the Business Inflation Expectations (BIE) Survey of the Federal Reserve Bank of Atlanta (column 8), the (occasional) survey question is: “Whatannual rate of inflation (inflation target) do you think the Federal Reserve is aiming for over the long run? Please provide a number in percentage terms.” DNK stands for “do not know” or “prefer not
to answer.” NA stands for “not available” or “unusable response,” i.e., a respondent did not provide an estimate or a usable numeric estimate for the inflation target.

0.23

(2)

(1)

(0.5, 1.5]

2019Q2

Firms (this paper)

2018Q2(-∞, 0.5]

Perceived Inflation targetof the Fed(a)Table 3
Perceived Fed Inflation Target and Managers’ Inflation Forecasts
Communication and the Beliefs of Economic Agents
333

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Bernardo Candia, Olivier Coibion and Yuriy Gorodnichenko

sanguine take on this matter. First, experimental evidence unambiguously indicates that providing economic agents with relevant
information can shape their expectations. For example, Coibion,
Gorodnichenko and Ropele (2020) and Coibion, Gorodnichenko
and Kumar (2018) document that informing firms about current
inflation—publicly available information!—significantly moves their
expectations. In a similar vein, Coibion, Gorodnichenko and Weber(2019), Binder and Rodrigue (2018) and others find that providing
households with various inflation statistics (e.g., current inflation,
inflation forecast, central bank target, FOMC statement)—again,
publicly available information—leads to considerable revisions of inflation expectations as well as reduced disagreement and uncertainty
about future inflation. One conclusion from this line of work is thateven simple messages—as simple as one sentence informing agents
about an inflation target or a recent inflation rate—can be much
more effective in influencing beliefs than complex statements, a recommendation strongly supported by multiple RCTs (e.g., Haldane
and McMahon 2018; Bholat, Broughton, Ter Meer and Walczak
2019; Kryvtsov and Petersen 2020). Furthermore, to the extent that
there is variation in awareness about policy (e.g., people with lower
education or IQ scores tend to have worse expectations; D’Acunto,
Hoang, Paloviita and Weber 2019) and in credibility of sources (e.g.,
people appear to think that social media is more trustworthy thangovernment sources, which in turn are more trustworthy than newspapers and other conventional media; Coibion, Gorodnichenko andWeber 2019), one can utilize targeted messages and channels to maximize the power of communication. At the same time, these “verbal”
interventions tend to move expectations only for up to six months,
thus underscoring the importance of sustained campaigns rather
than one-off policy statements. In short, while financial markets andprofessional forecasters quickly absorb new information from central
banks, other agents are less informed, but they do respond when they
are provided with relevant information.
Second, there are examples of successful communications campaigns that have moved expectations and outcomes. In addition to
famous policy statements (e.g., Mario Draghi’s “whatever it takes”)
that moved financial markets in dramatic ways, we have direct

Communication and the Beliefs of Economic Agents

335

evidence for the ability of policymakers to influence the beliefs of thegeneral public. For instance, D’Acunto, Hoang and Weber (2018)
document that widely publicized policy announcements about future VAT increases in Germany moved inflation expectations andstimulated consumers to buy durable goods. Pedemonte (2020) findsthat President Franklin D. Roosevelt was highly successful in using
radio—a new technology at the time that allowed direct communication with the public, much like Twitter today—to communicate
policy in his fireside chats. Specifically, areas with larger penetrationof radio had, ceteris paribus, a stronger consumer response to Roosevelt’s talks aimed to introduce new policies and to boost optimism.
While there is ample evidence (see Zhuravskaya, Petrova and Enikolopov (2020) for a survey) that media can have a strong effect on the
beliefs (especially political) of the general public, recent evidence suggests that other sources can be even more powerful. First, conventional media have a weaker effect on economic expectations than othersources even if conventional media contain the same information. For
example, Coibion, Gorodnichenko and Weber (2019) document thatproviding households with an actual FOMC statement moves inflation expectations twice as much as providing households with USA
Today (a popular newspaper in the United States) coverage of the samestatement. Second, as discussed in Coibion, Gorodnichenko, Kumar
and Pedemonte (2020), salient prices such as the price of gasoline,
food or domestic currency—easily visible, homogeneous goods—havea disproportionate effect on households’ inflation expectations. Pricesof recent purchases and the composition of consumption baskets also
influence inflation expectations (Cavallo, Cruces and Perez-Truglia
2017; D’Acunto, Malmendier, Ospina and Weber 2019), thus suggesting that “local” prices are used to form expectations about aggregate variables.16 This pattern may provide the basis for targeted communication. Indeed, households and firms can rationally choose tofocus on “local” conditions (Maćkowiak and Wiederholt 2009; Afrouzi 2016), but policymakers can tailor their messages to specific audiences and circumstances to utilize this reliance on “local” conditions
and influence aggregate inflation expectations.

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Bernardo Candia, Olivier Coibion and Yuriy Gorodnichenko

In short, through the transmission of simple messages, especiallyabout variables that are more transparent or relatable to individuals
than aggregate statistics, and the use of social media, which reaches
households more directly than traditional media, there is scope forcommunication strategies to more successfully reach and inform thebroader public.
IV.ii. Promoting Holistic Policy Messages
A negative association between inflation and the state of the economy in households’ (and many firms’) expectations presents a potential challenge for policymakers. Even if they are successfully able toraise the inflation expectations of, say, households through communications, they may also unintentionally trigger unemployment fears,
which can suppress (rather than stimulate) consumer spending via income effects. As a result, a “verbal” intervention aimed to stimulate
the economy by raising inflation expectations and thus lowering realinterest rates can backfire if unintentional income effects overwhelm
the desired intertemporal substitution effects. As illustrated in Section
III, research has found cases where this was the outcome as well asothers where it was not. Ultimately, because inflation is endogenous,
when agents learn new information about it, they will make an inference about the broader implications of that information for othereconomic conditions, which can lead to negative income effects if they
believe inflation is driven by supply-side factors.17
From a practical point of view, this is similar to “information effects”
emphasized in the literature on forward guidance. Campbell, Evans,
Fisher and Justiniano (2012), for example, observed that policy communication can not only reveal a future path of actions (Odyssean effect) but also the current state of the economy (Delphic effect). As aresult, policies aimed to stimulate the economy by signaling current
or future policy stimulus may be weakened because economic agents
may interpret such actions as signaling a poor economy instead. Bothinformation effects and joint inference about inflation and broader
macroeconomic conditions mean that policies can have unintended
consequences because of how agents interpret them. Since monetary
policy is already confusing for the general public, trying to clarify thetransmission mechanism is unlikely to be successful in simple messages.

Communication and the Beliefs of Economic Agents

337

Rare crises present additional challenges. For example, Maćkowiak
and Wiederholt (2018) argue that economic agents may have a hardtime thinking through the implications of policy actions during rare
events (such as, e.g., COVID-19) because they do not have the incentive to invest their attention into preparing for such events innormal times. Consistent with this prediction, as well as strong information effects, Coibion, Gorodnichenko and Weber (2020a) findin a randomized controlled trial that informing households about
recent policy actions of the Fed and other government agencies (as ofApril 2020), as well as basic health facts about COVID-19, has minimal (if any) effect on their expectations for inflation, unemployment,
mortgage rates or income. Furthermore, even weak reactions to information treatments about policy actions are effectively nullified
when information treatments also include data about COVID-19
infection/mortality rate. Relatedly, Binder (2020) documents that,shortly after the massive cuts in policy rates by the Fed in early March
2020, only approximately a third of households were aware of thispolicy action. Furthermore, after being informed about the policy,
few households (approximately 20-30%) revised their beliefs and,conditional on revising beliefs, roughly half of respondents became
more pessimistic about unemployment.18
We conjecture that a more transparent approach for policymakers
may be to transmit simple messages about the broader outcomes they
are trying to achieve, what we refer to as “holistic” messaging. For
example, when Federal Reserve officials want to raise inflation expectations, they could state that they are “putting in place new policies
designed to increase employment and thereby help raise inflation toward more desirable levels.” Such a message not only makes it clearthat inflation will rise, which should help raise inflation expectations
immediately (the desired intertemporal substitution effect), but alsothat this will happen with higher employment, not lower employment as might otherwise be inferred by households and firms (positive income effect).19
A focus on the desired outcomes—rather than on single variables
or policies, which leave much to the interpretation of the individual—could help address both information effects and the joint

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Bernardo Candia, Olivier Coibion and Yuriy Gorodnichenko

determination of economic beliefs as well as provide economic agentswith a guiding narrative to ensure the correct interpretation of policy actions. This is conceptually similar to the recommendation ofAngeletos and Sastry (2018) that guidance about targets is likely to
be more successful than guidance about instruments when generalequilibrium forces are strong and common knowledge among the
public is not guaranteed. Such messages can therefore remain transparent, which is necessary in order to be successfully transmitted
to the general public, and achieve the desired outcomes in terms ofmoving expectations in the right direction. Future work should establish empirically—via RCTs or other research designs—whether
this approach is preferable in practice.
V.

Concluding Remarks

The development of new surveys of firms and households both in
the United States and abroad is leading to an explosion of researchon the nature of the expectations formation process and the role that
expectations play in economic decision-making. One result that unambiguously comes out of this research agenda is that expectations
matter. They matter for the decisions that agents make, and theymatter for policymaking. But the exact nature of how agents form
their expectations remains ambiguous, and as a result, so does thebest way to communicate with households and firms.
Recent evidence nonetheless makes clear that there is a role forpolicy communications with the public. While reaching the public
may not be easy, it can have significant effects on macroeconomic
outcomes and therefore provide an additional tool for policymakers
in times of crisis. How to wield that tool effectively must be a central
area of future research.
In particular, we propose that central bankers focus on simple,
transparent and “holistic” messages to the broader public. Simplicity
and transparency are needed to break through the veil of inattention.
A holistic approach is needed to ensure that desired effects are notconfounded with unintended income or information effects. This isespecially true when it comes to inflation and monetary policy, topics

Communication and the Beliefs of Economic Agents

339

that the public is notoriously uninformed about. Emphasizing thedesired outcomes, rather than the instruments or the mechanisms,
provides a way to inform in an effective manner.
Communicating with the public remains more of an art than a science. But the science is gradually overtaking the art.

Authors’ Notes: We are grateful to Jake Lyons for research assistance and Dimitris
Georgarakos, Edward Knotek, Tiziano Ropele and Raphael Schoenle for sharing
figures for this project. We thank Esdward Knotek and Claudia Sahm for feedbackon an earlier version of the paper. This research was funded in part by National
Science Foundation grant 1530467.

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Bernardo Candia, Olivier Coibion and Yuriy Gorodnichenko

Endnotes
Mario Draghi (2015) summarized, “When inflation expectations go up withzero nominal rates, real rates go down. When real rates go down, investments andthe economic activity improves. That’s the reasoning [of QE].”
1

2
Janet Yellen (2018) observed, “The strategy [of forward guidance] also potentially supports aggregate demand by raising inflation expectations, thereby lowering real long-term rates relative to a Taylor Rule type baseline.”

A closely related interpretation is the simple heuristics view emphasized in Andre, Pizzinelli, Roth and Wohlfart (2019). They provide evidence that consumers
use simple heuristics (e.g., good vs. bad) to relate different macroeconomic variables. Inflation is a “bad” and therefore moves together with other “bads” like unemployment. Another interpretation is that households view their nominal income
as fixed, so higher prices imply a reduced real income.
3

The Bank of Canada launched a similar survey (Canadian Survey of Consumer
Expectations) in 2014. The European Central Bank has recently created a comparable survey of households in a subset of the euro area countries. We discuss thissurvey in Section III.
4

More details on this new survey can be found in Coibion, Georgarakos, Gorodnichenko and Weber (2020) and Coibion, Gorodnichenko and Weber (2019).
5

Candia, Coibion and Gorodnichenko (2020) and http://firm-expectations.org/
provide more details on this survey.
6

7
Preliminary data for July 2020 suggest that, relative to April 2020, managers
have lower and less dispersed inflation expectations.

More details on this survey can be found in Coibion, Gorodnichenko and Weber (2020b).
8

Consistent with Kamdar’s (2018) interpretation, people strongly associate inflation with reduced standards of living (Shiller 1997). Andre, Pizzinelli, Roth andWohlfart (2019) present evidence based on households’ responses to hypotheticalscenarios and, like Kamdar (2018), argue that households appear to exhibit “goodbad heuristics” for inflation and treat inflation as a bad state.
9More detail about the survey is provided in Coibion, Georgarakos, Gorodnichenko and Kenny (2020), and at https://www.ecb.europa.eu/stats/ecb_surveys/
consumer_exp_survey/html/index.en.html
10

We thank Dimitris Georgarakos for producing these figures.

11

We thank Tiziano Ropele for producing these figures.

12

The interpretation of a negative correlation could be more nuanced for
Ukraine and similar countries with histories of high, volatile inflation. Because
13Communication and the Beliefs of Economic Agents

341

hyperinflation (or high inflation) is so destructive for the economy, economic
agents can interpret elevated inflation as an increased probability of a hyperinflation and consequently become more pessimistic about output and employment.
Given that hyperinflation undermines the productive capacity of the economy, one
may still interpret the resulting correlation as reflecting supply-side factors.
Detmeister, Jorento, Massaro and Peneva (2015) document that households’
inflation expectations did not react to the Fed announcing its 2% inflation target
in 2012: median one-year-ahead inflation expectations stayed constant at approximately three percent before and after the announcement.
14

We also find large revisions in the perceived inflation target from one wave toanother (the standard deviation of the revision is 2.8%), which is similar in magnitude to revisions in short-term inflation forecasts. These results could also signal
weak credibility of the central bank, but this is an unlikely interpretation because
inflation expectations are highly correlated with the perceived inflation target (ρ=0.5). That is, CEOs believe that the Fed can hit its inflation target.
15

These “local” effects are also present in firms’ expectations. Andrade, Coibion,
Gautier and Gorodnichenko (2020) use a large panel of French firms to documentthat industry-specific inflation shocks—which by construction have no aggregate
implications—are strong predictors of firms’ expectations for aggregate inflation.
Furthermore, firms exhibit gradual learning about the aggregate conditions fromfirms’ idiosyncratic circumstances. These findings suggest that the “island” modelof expectations developed in Lucas (1972) and subsequent work (e.g., Lorenzoni
2009; Angeletos and La’O 2013; Nimark 2014) has empirical support.
16

The joint-inference issue is not limited to information about inflation. Coibion, Georgarakos, Gorodnichenko and Weber (2020), for example, show thatproviding information to households about interest rates can also lead to large
movements in beliefs about inflation.
17

Andre, Pizzinelli, Roth and Wohlfart (2019) provide an alternative interpretation: many households think that expansionary monetary policy actually reduces
output while raising inflation.
18

A related, albeit weaker, strategy could be to focus communications only on
the real economy, if income effects are stronger than intertemporal effects. We view
our strategy as more powerful, as it aims to utilize both effects, but messages relying
on income effects could be simpler to implement.
19

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Bernardo Candia, Olivier Coibion and Yuriy Gorodnichenko

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