Full text of Economic Perspectives (Federal Reserve Bank of Chicago) : First Quarter 2006, Volume XXX, Issue 1

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

Federal Reserve Bank
of Chicago
RESEARCH LIBRARY
Federal Reserve Bank
of St. Louis

First Quarter 2006

MAR 2 0 2006

Economic

perspectives

2

The decline in teen labor force participation

19

Variations in consumer sentiment across demographic groups

39

Earnings announcements, private information, and liquidity

55

An alternative measure of inflation

11

perspectives

President
Michael H. Moskow
Senior Vice President and Director of Research
Charles Evans
Research Department
Financial Studies
Douglas Evanoff, Vice President

Macroeconomic Policy
David Marshall, Vice President
Microeconomic Policy
Daniel Sullivan, Vice President

Payment Studies
Richard Porter, Vice President
Regional Programs
William A. Testa, Vice President
Economics Editor
Craig Furfine, Economic Advisor

Editor
Helen O’D. Koshy

Associate Editors
Kathryn Moran
Han Y. Choi
Graphics and Layout
Rita Molloy
Production
Julia Baker
Economic Perspectives is published by the 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.

© 2006 Federal Reserve Bank of Chicago
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 con­
tact Helen Koshy, senior editor, at 312-322-5830 or email
Helen.Koshy@chi.frb.org.
Economic Perspectives and other Bank
publications are available on the World Wide Web
at http://www.chicagofed.org.

a Chicago fed, org
ISSN 0164-0682

Contents

First Quarter 2006, Volume XXX, Issue 1

2

The decline in teen labor force participation
Daniel Aaronson, Kyung-Hong Park, and Daniel Sullivan
The authors examine the recent decline in teen work activity, offering explanations for both the
long secular decline since the late 1970s and the recent acceleration in this decline since 2000.
They argue that much of this pattern is due to a significant increase in the rewards to formal
education. They also explore the importance of changes to labor demand, crowding out by
substitutable workers, the increased work activity of mothers, and increases in wealth.

19

Variations in consumer sentiment across demographic groups
Maude Toussaint-Comeau and Leslie McGranahan
Consumer sentiment is one of the many macroeconomic indicators tracked by policymakers and
the public. The aggregate numbers in consumer sentiment indexes, such as the University of
Michigan’s Index of Consumer Sentiment, conceal a wealth of demographic-specific information.
The authors’ findings suggest that index disaggregation by group matters because consumer
sentiment varies systematically by demographic group.

39

Earnings announcements, private information, and liquidity
Craig H. Furfine
In this article, the author examines how the price impact of a trade varies throughout the days
surrounding public earnings announcements. The results indicate that public news releases
correlate with a reduction in the price impact of a trade on the day of the announcement.

55

An alternative measure of inflation
Francois R. Velde
The author proposes an alternative measure of inflation that captures the intuition behind the use
of “core” measures. Inflation is modeled as an unobserved factor affecting the components of an
aggregate price index (including food and energy). The common component, estimated using
Kalman filtering, resembles usual measures of core inflation; its extrapolation can be used to
improve performance in forecasting core inflation.

66

Conference on Bank Structure and Competition announcement

The decline in teen labor force participation

Daniel Aaronson, Kyung-Hong Park, and Daniel Sullivan

Introduction and summary
By the middle of 2005, the U.S. civilian unemployment
rate had fallen to 5 percent, a level many analysts
consider consistent with essentially full employment.
However, individuals who have become discouraged
over their prospects of finding suitable employment
and, as a result, have given up looking are not counted
among the unemployed. Thus, analysts often look to
the labor force participation (LFP) rate, the fraction
of the population that is either employed or unemployed
as an additional indicator of labor market conditions.
In fact, the participation rate declined significantly
during and after the 2001 recession and remains well
below its 2000 level. This could imply more labor
market slack than the unemployment rate suggests.
The decline in LFP has been especially great
for teenagers. As figure 1 shows, teens’ participation
rates had been trending down since the late 1970s.
However, from 2000 to 2003, teen LFP fell a stun­
ning 7.5 percentage points, compared with a decline
in the overall rate of only 0.6 percentage points. Cur­
rently, the LFP for teenage boys is the lowest since at
least 1948 and for teenage girls is the lowest since
the early 1970s.
Figure 1 also shows that the decline since 2000
in the LFP rate for those 20 and older is considerably
less dramatic than the fall in the overall rate, which
includes those aged 16 to 19. Although those between
the ages of 16 and 19 represent only 4.2 percent of
employment (and 8.2 percent of population aged 16
to 69), they account for over half of the fall in aggre­
gate LFP since 2000. Strikingly, 16 year olds to 17
year olds, who account for only 1.6 percent of work­
ers and 4.3 percent of the population aged 16 to 69,
explain over one-third of the fall in aggregate partici­
pation since 2000. Thus, a better understanding of
the forces shaping the labor force participation of

2

teens may shed significant light on recent trends in
overall participation.
Another reason to look more closely at teen labor
force participation is to understand what this major
shift in the allocation of young people’s time may mean
for future productivity. The answer to this question like­
ly depends on what teens are doing instead of working
and whether those activities contribute to human cap­
ital development. On the one hand, if the reduction
in time spent working in the market has been accom­
panied by a concomitant increase in the time spent in
school or doing homework, one might reasonably ex­
pect an eventual increase in productivity consistent with
the well-documented returns to education.1 The impact
of the increase in schooling investments on the overall
economy might also include the positive externalities
associated with education, including spillover productivi­
ty effects on peers and other workers, lower crime, and
greater civil involvement in the public policy process.2
On the other hand, a shift in teens’ time allocation
from market work to leisure or other activities that do
not increase their human capital may negatively af­
fect their future productivity. In general, labor market
experience tends to raise subsequent earnings. More­
over, it is easy to imagine that moderate amounts of
time devoted to a part-time job during the summer
or while in school might inculcate good work habits
and allow young people to make more informed
educational and career choices.3

Daniel Aaronson is a senior economist and an economic
advisor, Kyung-Hong Park is an associate economist, and
Daniel Sullivan is a senior economist and vice president
in the Economic Research Department of the Federal
Reserve Bank of Chicago. The authors thank Merritt Lyon
for valuable research assistance and Craig Furfine and
Leslie McGranahan for helpful comments.

1Q/2006, Economic Perspectives

consistent with the existence of skill-bi­
ased technical change, the tendency for
recent technological innovations to raise
the productivity of highly educated work­
ers relative to those who are less educat­
ed, including teens.4 Both possibilities
may be true.
It is less clear what caused the more
recent acceleration in the decline of teen
LFP. Wage trends suggest that a softening
in teen labor demand may have played
some role. Other evidence, however, sug­
gests that the recent drop is unlikely to
represent a significant margin of addi­
tional labor market slack.

Trends in teen labor force activity

In this article, we examine the facts about teen
labor force participation in more detail. We show that,
although there is some variation in the magnitude,
the decline in teens’ labor force participation is ex­
tremely widespread. Virtually all groups of teens have
seen a decline in LFP. We then discuss a number of
possible explanations for this decline in teen labor
force participation over the past quarter century as well
as the sharper drop of the early 2000s. The possible
explanations that we consider can be grouped into
two categories: demand and supply. Those that would
tend to lower the wage associated with current work
can be thought of as reducing teen labor demand. Those
that increase the value of human capital investments
or tilt teens’ choices toward more leisure can be thought
of as reducing the per capita supply of teen labor.
In the end, it seems likely that the most important
factor behind the long-term decline in teen LFP over
the past 25 years is a supply-side development. The
significant increase in the rewards from formal edu­
cation (in the form of higher future earnings) began
to take hold shortly before teen participation peaked.
The fact that the average hourly wage rate of teens
relative to adult workers has changed relatively little
as teen labor supply has shifted in over the last quar­
ter century suggests either that the relative demand
for teen labor is relatively elastic or that it also has
been shifting in over time. The former possibility is
consistent with evidence we present on the impact
of increases in the number of competing workers on
teen participation. The latter possibility would be

Federal Reserve Bank of Chicago

We begin our analysis by reviewing
the history of LFP among 16 year olds to
19 year olds since 1948, the earliest year
for which we have data derived from the
Current Population Survey (CPS). The
CPS interviews a nationally representative sample,
which is currently approximately 60,000 households
per month. It collects information about the labor
market activities of all those at least 16 years of age.
The LFP rate shown in figure 1 is the share of civil­
ian noninstitutionalized 16 year olds to 19 year olds
who are either working or unemployed (available to
work and actively looking for work) in a given month.5
As the figure shows, there have been long periods
of expansions and contractions in teen participation
rates. Coming out of World War II, just over half of
teenagers were in the labor force. But, soon thereaf­
ter, LFP began to fall, reaching a low of just under
45 percent in the early 1960s. Over the next two de­
cades, teenagers slowly rejoined the labor market,
with their LFP rates peaking at 59 percent in the late
1970s. Since then, teen participation has pulled back
again, with LFP rates falling steadily, punctuated by
a particularly large decline starting around 2000.
Currently (as of December 2005), teen LFP stands
at 43.3 percent, over 15 percentage points below its
peak 25 years earlier, and at the lowest rate in our
50-plus-year sample.6
The broad swings in teen LFP may be partially
obscured by shorter-run fluctuations associated with
the business cycle. As one way to more clearly isolate
the longer-term movements from the business cycle,
figure 1 identifies periods, like the third quarter of 2005,
in which the aggregate unemployment rate was ap­
proximately equal to the Congressional Budget Office’s
(CBO) estimate of the non-accelerating inflation rate

3

of unemployment (NAIRU) after having been above
it for some time. Changes in teen LFP between such
quarters should be little affected by changes in busi­
ness cycle conditions.
As the figure displays, the rate of decline in teen
LFP over the latest full business cycle was much
more rapid than over the previous two cycles. The
average drop of about 1 percentage point per year
between the first quarter of 1997 and the third quar­
ter of 2005 was about three times faster than the pace
of decline going back to the third quarter of 1987. If
the slower rate of decline in place between 1987 and
1997 had been maintained, the current teen LFP rate
would be about 5.5 percentage points higher than it
is currently.
Teen LFP patterns differ by gender. Historically,
male teens were more likely to work than females.
However, teenage female LFP grew dramatically during
the late 1960s and 1970s, likely reflecting the same
economic and cultural forces underlying the increase
in adult female LFP. As a result, by the early 1980s,
there was virtually no gender difference among 16
year olds to 17 year olds. For 18 year olds to 19 year
olds, the gender gap, while narrowing, did not disap­
pear entirely until the mid-1990s. This likely reflects
the especially significant increase in female college
attendance that took place over this period.
As one way to isolate the trend in teen LFP sepa­
rately from developments related to gender, figure 2
shows the labor market activity of teenagers relative
to the gender-specific LFP rates of prime
age adults (25 years to 54 years of age).
Specifically, we display the percentage
difference between the teen LFP rate and
the same gender’s adult rate.7 The rela­
tive LFP of female 18 year olds to 19
year olds has fallen the most steadily. In
the late 1940s, 18-year-old to 19-yearold females were as much as 60 percent
more likely to work than adult women,
but now are about 25 percent less likely
to work than adult females. The steady
drop in the relative LFP of 18-year-old to
19-year-old females likely reflects their
equally impressive increases in college
attendance.8 For the other three age-gen­
der groups, the relative teen LFP rate fell
from the late 1940s until the mid-1960s,
when it began to rise. Between 1979 and
2000, these rates have fallen steadily, ac­
celerating again beginning around 2000.
For all four age-gender groups, the ratio
of teen LFP to the LFP of adults of the

4

same gender reached an all-time low during the cur­
rent cycle.
Generally, LFP is procyclical, rising during expan­
sions and falling during recessions. Figure 3 presents
teenage LFP rates since 1979 adjusted for normal
business cycle fluctuations in two alternative ways.9
The first version (the black dashed line), which we la­
bel the “time-series adjustment,” takes advantage of
the time-series relationships between LFP and aggre­
gate labor market conditions^ In particular, we run the re­
gression L, = a + 3[ (T7, - ) + P2t + P3t2 + P4t3 + e,,
where Lt is the LFP rate of group z at time /, Ut is
the overall unemployment rate at time /, Ut is the
CBO’s estimate of NAIRU, t is a time trend (1979 = 1,
1980 = 2, and so on), and e( is a white noise term.10
We define the cyclically adjusted LFP at time t as
Lt=Lt- Pj ([/, - U,). This assumes the business cycle
effect is proportional to the gap between the actual
unemployment rate and CBO’s NAIRU.11
The second version (the green dashed line),
which we label the “cross-sectional adjustment,”
also subtracts a constant multiple of the unemploy­
ment gap, but uses differences in state experiences
to estimate the parameter relating LFP to unemploy­
ment. Specifically, we regress state-level teen LFP
on state-level aggregate unemployment. To control
for long-term differences in LFP across states, we
also add state fixed effects. Thus, the identification
of p is based on within-state changes in teen LFP
and unemployment.12 As figure 3 shows, there are

1Q/2006, Economic Perspectives

three periods since 1979 when the cyclical adjustment
is important, although the degree depends somewhat
on which technique is used. In the early 1980s and
early 1990s, the economy slowed, unemployment
rates rose, and the teen labor market activity declined.
Had the unemployment rate remained at the natural
rate, the teenager labor market activity would have
risen by roughly 1 percentage point to 3.5 percentage
points in the early 1980s and 1 percentage point to
2 percentage points in the early 1990s. Given the
former adjustment, it might be the case that the un­
derlying trend in teen labor market activity peaked
in the early 1980s rather than the late 1970s. Likewise,
the booming economy of the late 1990s pushed up
teenage labor force participation by roughly 0.5 per­
centage points to 1.2 percentage points, thus exagger­
ating the decline since then.

TABLE 1

Percentage point change in teen LFP,
2000-05

Actual
Long-term trend
Cycle
Unexplained

Time-series
adjustment

Cross-section
adjustment

-8.4
-1.8
-1.1
-5.5

-8.4
-1.8
-0.8
-5.8

Source: Authors’ calculations based on data from the Current
Population Survey.

Federal Reserve Bank of Chicago

Table 1 shows that the unadjusted
series falls by 8.4 percentage points be­
tween 2000 and 2005. In rows 2 and 3,
we report how much of this decline is
due to previous secular trends and the cy­
cle, as computed using our two tech­
niques. We compute trend as the slope of
the line between 1987:Q3 and 1997:Q1,
two quarters when the unemployment
rate and the CBO’s natural rate were
roughly the same. Between those two
periods, teen LFP fell 0.3 percentage
points per year. This trend suggests that
teen LFP would have fallen by about
1.8 percentage points between 2000 and
2005. The cycle adds another 1 percent­
age point to the decline. So just over
5.5 percentage points of the 8.4 percent­
age point fall over this period remains
unexplained.
These cyclically adjusted figures are
derived from micro (that is, individuallevel) data from the CPS.13 This has the
advantage of allowing us to explore heterogeneity in
labor market activity across the teenage population.
For example, we can ask whether the labor market
activity of teens from high-income families looks
different than that of teens from low-income families.
For the rest of this section, all figures and tables use
cyclically adjusted (with the time-series adjustment)
rates in order to get a cleaner picture of secular trends.
Table 2 shows the change in teenage LFP from
1979 to 2005, as well as between 1987 and 1997 and
since 1997, by gender, race, and region. We also com­
pute changes by family income and school enrollment
but begin these calculations in 1984, when the vari­
ables become consistently available.14 Note that each
group’s series is cyclically adjusted separately, resulting
in some groups, such as enrolled students, having much
of the LFP decline explained by the business cycle.
The most striking aspect of table 2 is how wide­
spread the decline is. Although it is clearly not uniform,
the rate for every subgroup reported in the table has
fallen since the early 1980s, typically 2 percentage
points to 20 percentage points for 16 year olds to
17 year olds and 1 percentage point to 17 percentage
points for 18 year olds to 19 year olds. For nearly all
groups, the majority of the cyclically adjusted decline
in LFP has occurred just in the past five years; LFP
has fallen 5 percentage points to 9 percentage points
among younger teen groups and 2 percentage points
to 7 percentage points among older teens. While there
is substantial variation by age and school enrollment

5

■H
vdI

o 0
ocn =o

LO

•=3-

■sT
CO

0

■H
■si■H
I

Q. 'P
2 §

■c ■£
<*) «

TABLE 2

P.
53
01
□

cm

0
E
5

£
2
CQO

®
*
□a-

CO
LO

oo
co

00
cd

v-|

■si■H
I

°?

o

co
to

vd
LO

CO

■H

CM

tH

I

O
vdI '

&

OX)

co

xHI

CD
CO
CO

53

CO CM 00
LO
LO

7 77

00
■sf

53

Q-

CO O) d

777

53

QOu
o

CD

LO

•=3-

©

&
53

J

■sT
vd
I
00 to co
7°?7

00
Lfj

CM LO LO
I
I
I

o

■H
I

75 cn

(/>
2

o

0
co "co
0
LO
o
vH o
1 CM
CD
H

6

c
o
Q.
0
no
co
c
0
o
0
CL

LO
O
o
CM
I
CD
h<D
tH

LO
O
o
CM
I
00
CD

rCD
|
h00
CD

LO
O
o
CM
|
rCD
CD

LO
O
|
o
o
o
tH CM

co
■D
®
(3
0

0

CO
s“
LO
CD O
H O
1 CM
00
H

r> LO
a. o
o
CM
I
CD
hCD

■aO
CO
c
0
o
0
CL

LO
O
o
CM
|
•=3oo
CD

r<D
I
hoo
CD

tH

tH

LO
o
o
CM
|
hCD
CD

LO
O
|
o
o
o
CM

status, the patterns are fairly
similar within race and family
income groups.
Of course, many of these
measures are correlated. To iso­
late which of these groups expe­
rienced economically and
statistically significant drops,
conditional on other characteris­
tics, we ran multivariate regres­
sions of a teen’s decision to be
in the labor force (a dichotomous
0-1 variable for whether they
are in the labor force) on their
background characteristics, two
linear time trends—one that be­
gins in 1984 and the other in
1997—and each of their charac­
teristics interacted with the time
trends.15 Level shifts across back­
ground characteristics are picked
up by the covariates themselves
(for example, the female indica­
tor measures the average gender
gap for a person of the same race,
age, family income, and region).
The interaction terms measure
differences in average growth
rates across groups, after condi­
tioning on other characteristics
of the teen and her family. The
results are reported in table 3
separately by age (16 year olds
to 17 year olds versus 18 year
olds to 19 year olds). For exposi­
tion purposes, we only report the
coefficients of the time trends
and their interactions with the
background characteristics. How­
ever, all regressions include level
shifters for income, race, gender,
and region. The regression model
is parameterized so that the time
trend coefficients show the aver­
age time trend over all individuals
in the sample and the interaction
term coefficients show how the
time trend for a given group dif­
fers from the average trend.
On average, between 1984
and 1997, LFP fell by 0.22 per­
centage points and 0.28 percent­
age points per year among 16

1Q/2006, Economic Perspectives

TABLE 3

Teenage labor force participation time trends
Time trend 1 = 1984 to 1997
Time trend 2 = 1997 to 2005
16-17 year olds
Time trend 1

Intercept
Time
Male
Female
1st quartile income
2nd quartile income
3rd quartile income
4th quartile income
White
Black
Hispanic
Other race

-0.22**
-0.02
0.02
0.03
-0.06
0.08
-0.13
-0.05
0.13
0.19
-0.02

(0.045)
(0.042)
(0.045)
(0.057)
(0.108)
(0.085)
(0.084)
(0.012)
(0.121)
(0.148)
(0.205)

18-19 year olds

Time trend 2

-0.97**
-0.16**
0.17**
0.17*
-0.32*
-0.20
-0.05
-0.03*
-0.03
0.01
0.55*

(0.059)
(0.053)
(0.058)
(0.170)
(-0.320)
(0.108)
(0.107)
(-0.030)
(0.159)
(0.159)
(0.550)

Time trend 1

-0.28**
-0.05
0.06
-0.23**
0.41**
0.14
0.29**
-0.01
-0.08
0.23
-0.05

(0.052)
(0.046)
(0.054)
(0.069)
(0.124)
(0.097)
(0.093)
(0.013)
(0.143)
(0.175)
(0.243)

Time trend 2

-0.73** (0.069)
-0.04
(0.06)
0.05
(0.07)
0.34**
(0.09)
-0.61** (0.162)
-0.27*
(-0.27)
-0.17
(0.121)
-0.03
(0.015)
-0.28
(0.192)
0.00
(0.193)
0.51
(0.284)

*Significant at the 5 percent level.
**Significant at the 1 percent level.
Note: Standard errors are in parentheses.
Source: Authors’ calculations based on data from the Current Population Survey.

year olds to 17 year olds and 18 year olds to 19 year
olds, respectively. Since 1997, the decline has signif­
icantly accelerated: to almost 1 percentage point per
year among 16 year olds to 17 year olds and 0.7 per­
centage points per year among 18 year olds to 19 year
olds. The decline varies somewhat across groups, es­
pecially post-1997. Since then, teen LFP has fallen
fastest among 16-years-old to 17-years-old boys and
16 year olds to 19 year olds in the middle part of the
family income distribution (between the 25th and
75th percentiles). Racial gaps are negligible once
income is controlled.16
All calculations discussed thus far have been limit­
ed to the “extensive” margin of teens’ labor supply—
whether they are in or out of the labor force. Similar
developments have occurred on the “intensive” mar­
gin—the time spent working conditional on participa­
tion. For example, among those that work at all, the
average workweek length has declined almost 3.5 hours,
or 12 percent, since 1979. This is somewhat offset by
an increase in the number of weeks worked per year.17
Combining the two figures gives us an estimate of
annual hours worked, conditional on working at all.
Between 1979 and 2004, teens that work reduced their
market work activity by 70 hours per year or 9 per­
cent, and as with LFP, much of this decline has tran­
spired recently. Thus, a substantial decline in teen
work activity has occurred at both the extensive and
intensive margins over the past two and a half decades.

Federal Reserve Bank of Chicago

Has demand for teen labor been weak
recently?
As we noted earlier, a drop in LFP could, under
some circumstances, be a sign of some additional
labor market slack. At least in the case of teenagers,
we think that such an interpretation of current devel­
opments is hard to square with several facts.
First, the CPS asks whether those out of the
labor force want a job, and in recent years there has
not been a notable increase in the number of such
teens. As can be seen in figure 4, the fraction of the
teen population that is out of the labor force but
wants a job increased in the wake of the 1980-82
and 1990 recessions. But the most recent downturn
saw much less of an increase. The long-term trend,
moreover, is toward a lower fraction of teens being
classified as wanting a job, but not employed.
A second difficulty with the weak demand expla­
nation is apparent in the relative employment growth
of the industries most likely to hire teens. If the sharp
absolute and relative decline in their participation was
primarily due to weak demand, we would expect to
see that the industries that have traditionally hired
teenagers had fallen on hard times, disproportionately
impacting teenage work activity. However, we know
of no evidence that traditional employers of young
people have performed poorly recently. If anything,
the top five industry employers of teenagers (in order:

7

eating and drinking places, grocery
stores, miscellaneous entertainment and
services, construction, and department
stores), accounting for almost half of all
16 year olds to 19 year olds employed
in 1999, have together experienced em­
ployment growth well above the national
average. Since 2000, payroll employment
in these five industries combined rose
3.6 percent, while employment in the
remaining industries fell by 2.0 percent.
Trends in teens’ wage rates provide
another piece of evidence on the reasons
for the decline in their LFP. If the decline
in teen LFP was primarily due to weak
demand, one would expect their relative
wages to have fallen. Over the ten-year
period prior to 2002, that was clearly not
the case, as can be seen in figures 5 and
6. Figure 5 plots teenager real wages
(in 2000 dollars), as computed in the
CPS and deflated by the Personal Con­
sumption Expenditures (PCE) Price Index, along with
the real value of the federal minimum wage (green
line) and the real value of the minimum wage after
accounting for state laws (dashed line). Actual real
wages of teens were flat during the latter half of the
1980s and early 1990s but grew by 21 percent, or
roughly 2 percent per year, between 1993 and 2002,
which more than kept pace with the wages of less-ed­
ucated adults. The latter point can be seen in figure 6,
which plots the ratio of teen wages to adult wages and
teen wages to less-educated (high school diploma or
less) adult wages. In the 20 years prior to 2002, the
average hourly wage rate of teens rose roughly 5 per­
centage points relative to prime-age workers without
any college education, although it fell 2 percentage
points relative to all prime-age workers.
However, since 2002, the real wage rates of teen
workers, though still well above their levels in the
late 1980s and 1990s, have fallen modestly. This is
undoubtedly partly the consequence of a declining
real minimum wage.18 Although a number of states
have increased their minimum wages recently, the
average real minimum wage remains roughly 8 per­
cent below 1998 levels. Declining real wages could
also be consistent with some softening in the demand
for teen labor in the last few years. However, given
the lack of an increase in the rates at which teens re­
port they want a job, it is unlikely to be the major
factor in the decline in teen LFP.19

8

Crowding out by adult low-skilled workers
One possible demand-side explanation for lower
teen work activity is that teens are facing stiffer com­
petition for jobs from other workers. Card (1990)
provided a classic analysis of a similar question—
the effect of increased numbers of immigrants on
native workers’ labor market outcomes—by studying
the case of the large and likely exogenous increase in
the number of workers in the Miami labor market af­
ter the Mariel boatlift of 1980, when a mass exodus
of Cuban refugees landed on Florida’s (particularly
Miami’s) shore. He finds that this influx of roughly
7,000 low-skilled Cubans had a positive impact on
the employment of native Miamians, particularly rel­
ative to the employment of similar workers in four
comparable cities. Lewis (2004) shows that the boatlift
caused industries in Miami to adapt to less skill-intensive technologies, allowing the economy to pain­
lessly absorb new workers.
When we extend Card’s analysis to teenagers,
comparing how the teenage labor force participation
rate in Miami looked pre- and post-Mariel and relative
to Card’s four comparable cities (Atlanta, Houston,
Los Angeles, and Tampa-St. Petersburg),20 our re­
sults are quite similar to his. Table 4 shows that teen­
age labor force participation rates rose absolutely
(by 4.1 percent) and relative to the comparison cities
(by 8.2 percent) in the year after Mariel. Likewise,
teenage unemployment rates fell by over 6 percent in
Miami and almost 8 percent in the comparison cities.
Furthermore, when we extend the analysis past 1981,

1Q/2006, Economic Perspectives

it is apparent that the 1981 (non)effect remains stur­
dy years after the boatlift, suggesting that there is no
evidence of a delayed reaction to the influx of workers.
The boatlift is a valuable experiment because the
influx of workers into Miami likely had little to do
with the area’s pre-boatlift labor market conditions
but more to do with its geographic proximity to Cuba
and the decisions of the Cuban government. But, of
course, it is possible that Miami’s experience in the
wake of Mariel is not representative of other cases in

Federal Reserve Bank of Chicago

which the number of low-skilled workers
increased. Therefore, we explore two al­
ternative analyses.
The first is the sizable influx of loweducation single mothers with children
after the 1996 Welfare Reform Act. Since
1995, the LFP of such women rose 30
percent, while it increased only 5 percent
for low-education single women with no
children and fell for the population at
large. We concentrate on single mothers
with two or more children and a high
school diploma or less, given Meyer and
Sullivan’s (2004) evidence that the law
primarily impacted such women.21 We
break the data into individual states and
regress state teenage LFP on year and
state fixed effects, the share of low-edu­
cation single moms with two or more
children, and that group interacted with
an indicator of whether the year is 1996
or later. This interaction tells us whether
the influx of such women post-reform
had an impact on teenage work activity.
In fact, we find no evidence that increas­
es in low-education single women with
children crowd teens out of the work
force. Consistent with the Mariel evidence,
an F-test fails to reject the hypothesis
that the post-1996 year dummies inter­
acted with share of such women in the
state differ from zero.22
Our second analysis is not tied to
specific exogenous events. We created
a state panel from 1979 to 2004 of teen­
age labor force participation rates, along
with the share of the state population that
1) has less formal education (high school
diploma or less) or 2) has less formal ed­
ucation and is unemployed or out of the
labor force. We then regressed teen LFP
on each of these, including state and year
fixed effects in order to identify the asso­
ciation between within-state changes in teen work
activity and within-state changes in the share of poten­
tially substitutable workers. We also allowed each
state to have its own time trend. Here, we do find re­
sults consistent with crowding out. However, the size
of the effect is often economically small and statisti­
cally insignificant. More importantly, the size of the
unskilled adult work force has been shrinking over
time. In 1979, about 49 percent of the 25-year-old to

9

Increased time devoted to school
A massive literature has documented
The effect of immigration on teen LFP
that
the
financial return to obtaining
and unemployment rates: The case of the
more
education
has increased significant­
Mariel boatlift of 1980
ly in recent decades.23 This can be seen
Year
in figure 7, which is based on a standard
Group
1979
1981
1981 - 1979
methodology to value the effects of in­
creasing educational levels on hourly
A. Teenage LFP
wage
rates.24 As the figure shows, the re­
Miami
39.6
43.7
4.1
turn
to
having a college education began
(4.2)
(4.6)
(6.2)
to
rise
substantially
in the late 1970s,
Card (1990) comparison cities
56.8
52.7
-4.1
shortly
before
teen
LFP
began to decline.
(1.3)
(1.3)
(1.8)
Figure
8
shows
the
substantial
rise in
Miami-comparison difference
-17.2
-9.0
8.2
the
fraction
of
16
year
olds
to
19
year
olds
(4.4)
(4.7)
(6.5)
enrolled in school, particularly in the
B. Teenage unemployment rates
1980s. For each age group, it displays
Miami
27.3
21.2
-6.2
two measures of the fraction of the popu­
(5.6)
(5.5)
(7.8)
lation enrolled in school. The lines labeled
Card (1990) comparison cities
17.7
19.5
1.8
“October” are estimates of the enrollment
(2.0)
(1.4)
(1.5)
rate for the month of October that are de­
Miami-comparison difference
9.6
1.7
-7.9
rived from a special supplement to the
(5.6)
(5.6)
(8.1)
CPS that has been done every October
Notes: The comparison cities are Atlanta, Houston, Los Angeles, and Tampa/
since the late 1960s. The lines labeled
St. Petersburg. Standard errors are in parentheses
“
all months” are estimates of the average
Source: Authors' calculations based on data from the Current Population Survey.
enrollment rate over the entire year. They
are derived from a question on enrollment
status that was added to the basic CPS in
65-year-old population had a high school diploma or
1985. The all months lines are substantially lower
less. In 2004, only 35 percent did. Thus, in the aggre­
than the October lines because they include the summer
gate, this cannot explain the large secular decline in
months of June through August when most students
teen participation over this period.
have traditionally been on vacation from school. Both
the October lines and the all months lines show in­
Crowding out by peers
creases in enrollment over time, but the slope of the
The LFP of teens could also be affected by the
all months lines have been steeper recently. This is
sheer size of their peer group. Like the crowding out
because enrollment increases have been especially
story described previously, increases in the size of
great in the summer months. For example, summer
teen cohorts could cause their wages and LFP to
enrollments were only 20.5 percent in 1992, when
decline. However, the share of the working-age pop­
the increases began, but 44.3 percent in 2005.
ulation accounted for by teens fell substantially from
Table 5 reports a simple decomposition of the
roughly 12.5 percent in the 1970s to 8.5 percent in
change in teen LFP into components due to 1) the
the mid- 1990s and has been relatively flat since then.
increase in enrollments given constant within-enrollThus, if anything, the trend in teen cohort sizes should
ment-status-group LFP rates, and 2) the fall in LFP
have pushed their wages and LFP up through the
within-enrollment-status group given a constant en­
mid-1990s and been neutral since then.
rollment rate. The calculations are based on the more
comprehensive all months measure of enrollment
Supply explanations
mentioned earlier.
We suspect that teen LFP declines, particularly
Panel A of table 5 shows the decomposition for
over the long run, are driven primarily by labor sup­
the drop in LFP between 1987 and 1997. As we noted
ply choices. This section describes three possibilities:
above, aggregate labor market conditions were simi­
the increased time devoted to school, the increased
lar in the two years, so the changes reported should
time spent helping out at home as mothers return to
be largely free of business cycle effects. As the table
the labor force, and increases in wealth.
shows, the enrollment rate increased by 0.65 percent­
age points per year over this period.
TABLE 4

io

1Q/2006, Economic Perspectives

The contribution of this enrollment change to the over­
all decline in teen LFP was 0.18 percentage points
per year. This is the change in overall teen LFP that
would have occurred if LFP had remained constant
for both enrollees and non-enrollees. The increase in
enrollments at constant within-enrollment-status-group
LFP accounts for 60 percent of the decline in teen
LFP over the period.
The table also shows the contributions of withinenrollment-status-group LFP to the overall decline.

Federal Reserve Bank of Chicago

Among teens who were enrolled in
school, LFP declined by 0.12 percentage
points per year. This was a little over one
third of the 0.31 points per year rate at
which LFP declined overall. Given that
68 percent of teens were enrolled in
school, the contribution of their LFP de­
cline to the overall LFP decline was 0.08
percentage points per year, or 26 percent
of the total. The rate of LFP decline for
non-enrollees was slightly faster at 0.14
points per year. But because they are a
smaller fraction of the teen population
than enrollees, the non-enrollees’ decline
in LFP only accounted for 14 percent of
the total drop in LFP.
Panel B of table 5 shows the same
decomposition for the change in teen
LFP between 1997 and 2005. Again, these
were two years in which, by standard
measures, aggregate labor market condi­
tions were similar. As we discussed
earlier, the rate of decline in teen LFP
increased over this period to about 1 per­
centage point per year. Table 5 shows
that most of this acceleration was due to
faster declines in LFP within-enrollmentstatus groups. The rate at which enroll­
ments rose did increase somewhat
relative to the earlier period, resulting in
about a 10 percent increase in the annual
contribution of enrollment increase to
teen LFP decline. But, the biggest factor
in the acceleration was the significant in­
crease in the rate at which LFP declined
for those enrolled in school. The contri­
bution of that factor to the decline in teen
LFP increased by over 0.5 percentage
points per year and its share of the entire
decline increased to 62 percent. A faster
rate of decline in LFP for those not en­
rolled also contributed to the faster rate
of overall teen LFP decline.
The calculations just described only capture the
effects of increased schooling at the extensive mar­
gin.25 However, similar effects may be at work on the
intensive margin—conditional on being in school,
students may be devoting more time to their studies
and less to part-time or full-time jobs. However, the
evidence on this point is quite a bit sketchier. AU.S.
Department of Education (2005) publication reports
time spent in school increased 30 hours to 40 hours
per year (or about one hour per week) between 1987

11

TABLE 5

Decomposition of teen LFP decline into enrollment change and within-enrollment-status effects
1987

1997

61.07
43.71
71.69
54.69

67.60
42.55
70.56
51.62

1997

2005

67.60
42.55
70.56
51.62

73.16
35.80
65.17
43.68

Annual change9

Contribution to
LFP decline

Percent of total
LFP decline

-0.184b
-0.079°
-0.044d
-0.307

60.0
25.6
14.3
100.0

Contribution to
LFP decline

Percent of total
LFP decline

-0.195b
-0.617°
-0.18T
-0.993

19.6
62.2
18.2
100.0

A. 1987-97

Percentage enrolled
LFP of enrolled
LFP of not enrolled
Overall LFP

0.652
-0.116
-0.136
-0.307

Annual change9

B. 1997-2005

Percentage enrolled
LFP of enrolled
LFP of not enrolled
Overall LFP

0.696
-0.843
-0.674
-0.993

aPercentage points per year.
bAnnual change multiplied by initial period difference in LFP between enrolled and not enrolled.
cAnnual change multiplied by end period percentage enrolled.
dAnnual change multiplied by end period percentage not enrolled.
Note: Final column may not total due to rounding.
Source: Authors’ calculations based on data from the Current Population Survey.

and 1999. Another U.S. Department of Education
(2001) report found that for ages 13 to 17, the amount
of homework time increased between 1984 and 1999.
Juster, Ono, and Stafford (2004) find large increases
in schooling and studying time between 1981 and 2002,
although as we discuss later, there are reasons to think
that the time-use data on which that study is based
may be subject to substantial measurement error.
It is possible, albeit a bit speculative, that the in­
creasing recognition of the value of more education
in recent years has played a role in the sharp recent
decline in teen LFP. For example, after falling fairly
steadily by 3.3 percentage points between the 1983-84
and 1999-2000 school years, the high school gradua­
tion rate, defined as the number of diplomas issued
as a fraction of the population of 17 year olds, rose
5.1 percentage points to 74.9 percent in the 2003-04
school year. Perhaps recognition that schooling is in­
creasingly valuable is causing teens who are enrolled
in school to study harder and graduate more frequently.
As a side effect, it may be lowering their rate of labor
force participation.
Substituting house workfor market work
Among the biggest developments in labor markets
over the past several decades has been the increased
participation of women, particularly those with chil­
dren. There is substantial evidence that technological
innovations, such as the washing machine, dishwasher,

12

and the like have aided in this transition. Furthermore,
there has likely been an important reallocation of
home production from wives to husbands.26 But how
has the increase in female labor force participation
affected teenage children? Specifically, has it led
teenagers to substitute house work for market work?
As part of a pilot study on 322 children aged six
to 17 in the early 1980s, the Institute for Social Re­
search (ISR) at the University of Michigan conducted
a time-use survey, where parents filled out time diaries
in five minute increments. Juster, Ono, and Stafford
(2004) compared this survey to a similar one conducted
in 2001-02 using families from the Panel Study of
Income Dynamics (PSID). For 15 year olds to 17 year
olds, they show that market work fell by over one
hour per week over the two decades, while home
production work increased by two hours per week.27
However, there are some serious problems with this
survey, particularly in the earlier years. The authors
warn that the definition of home and market work may
have been altered between surveys. Furthermore, many
hours in the early 1980s survey are simply unclassi­
fied. But if we assume that these unaccounted hours
are not work hours, and even if we combine the two
work activities, we can infer that teen home produc­
tion must have increased given the sizable fall in
teenager market work hours documented in the CPS.
Nevertheless, because these results are based on
small samples with highly imperfect data, we turn to

1Q/2006, Economic Perspectives

more recent time-use data from the U.S. Bureau of
Labor Statistics to uncover within-household homemarket work distinctions.28 Because this survey only
began in 2003, we must rely on cross-sectional evi­
dence, in this case, differences in the number of earn­
ers in the family. We compare the work activity of
teenagers with two parents working to the work activity
of teenagers with one parent working and one parent
at home. Our informal test asks whether teenagers
with a mother working out of the home spend more
of their day doing housework and, consequently, less
of their day in market activity. In fact, we find no ev­
idence of this trade-off. Among teenagers living in a
home with both parents working, 11.3 percent of
their typical nonsummer day is spent working away
from home and 8.9 percent working within the home.
Among teenagers living in a two-parent home with
only their father working, market activity is lower
(9.8 percent) and home production work is higher
(9.7 percent), a result inconsistent with mothers shifting
more home production work to their older children.
Moreover, we find that number of siblings has little
impact on market activity and, if anything, leads to
more home production work, not less.
Finally, we find similar patterns when looking
directly at labor force participation by number of
parental earners in the family. If both parents work,
teenagers are more likely to work as well. This is
true even if we stratify the sample into family income
quartiles and look at teen work activity within income
quartiles or, in a regression context, if we control for
family income (and number of siblings). Consequent­
ly, we cannot conclude from these data that the rise
in adult female labor force participation has led to
the decrease in teenager labor force participation.

Wealth effects
A final explanation of the decline in teen work
activity that we explore is the role of increases in
wealth among families with teenage children. Basic
economic theory predicts that when wealth increases,
and the wage available to a worker in the market­
place, as well as preferences for leisure, remain the
same, people will want to work less and consume
more leisure. In practice, pure wealth effects are hard
to uncover because they require situations where these
assumptions (especially a constant market wage) hold.
Nevertheless, researchers have exploited a number of
clever examples where increased sources of wealth
are likely exogenous to the person supplying the la­
bor, including bequests, war reparations, and lottery
winnings.

Federal Reserve Bank of Chicago

At first glance, we find little support for such a
possibility. In particular, parental income and teen
LFP are, if anything, positively related in 2004 (as
well as all other years). However, this could be be­
cause parental income is correlated with many other
factors that might influence teen work. Moreover, in­
flation-adjusted median net worth has barely budged
for families with heads aged 35 to 54, the families
where the vast majority of teens reside. However, for
such families real mean net worth increased 1.5 per­
cent to 2 percent per year between 1983 and 2001
(the latest year of publicly available data) and aggre­
gate real median and mean net worth increased 2.2
percent and 3.7 percent, respectively, per year over
the same period. The vast majority of the aggregate
increase is due to older households.
Here, we provide several pieces of evidence to
quantify the role wealth may play in explaining the
recent acceleration in the decline in teenage LFP.
All three revolve around college pricing. A fall in the
price of college can have two implications for work
activity. First, cutting prices causes demand for that
product to rise. Since time is constrained, an increase
in enrollment pushes people out of work and other
activities. Second, as the cost of college falls, students,
particularly those at the margin of the enrollment de­
cision, need to work less to afford it. Keane and Wolpin
(2001) offer an example of this result within a dynamic
model of the school-work decision for young men.
Among the exercises they present is a simulation of a
$3,000 per semester tuition subsidy. Their results sug­
gest that the average full-time student earns over $450
less (and consumes over $1,200 more) per school year
than a baseline group that does not receive this subsi­
dy. Using the outgoing rotation files of the CPS to
compute hourly wages allows us to infer that some­
one in their sample (white male full-time student)
will work 89 fewer hours per school year after such
a subsidy. In other words, a transfer of wealth to stu­
dents and their families can significantly reduce
work time.
We attempted two simple exercises to test the
predictions of their simulations. First, we compared
the work participation rates of teenagers in states that
have introduced state-wide merit scholarships, often
called Hope Scholarships, with rates for states that
have not. The Hope Scholarship program, initiated in
Georgia in 1993 and adopted in some form by 15
other states since, offers students a free or highly re­
duced tuition to in-state universities so long as they
meet minimum entrance requirements, minimum
college performance criterion, and attend an in-state

13

college.29 In Georgia, for example, qualified in-state
students receive up to $4,500 ($3,000 for private
school) per academic year for tuition, fees, and book
expenses, regardless of family income.
Cornwell, Mustard, and Sridhar (2005) find that
the program is working as intended—in-state college
enrollment has increased. But their research describes
several other important results as well. First, Cornwell,
Lee, and Mustard (2005) document a number of “grade­
enhancing” strategies—including enrolling in fewer
classes and withdrawing from those where performance
is subpar—used by students to ensure qualification
for the scholarship. Second, roughly two-thirds of the
increase in in-state enrollment is due to students switch­
ing from out-of-state colleges to in-state colleges,
particularly four-year institutions. Finally, in line with
the Keane and Wolpin (2001) results on consump­
tion, Cornwell and Mustard (2005) show a positive
association between county-level car purchases and
Hope Scholarship grantees. Together, these results
are consistent with the notion that these programs are
transferring wealth to college-attending children and
their families with relatively little direct impact on
skill accumulation and current market wage rates.
Consequently, the Hope program can be thought
of as a useful experiment to analyze labor supply
wealth effects—what happens if we increase wealth
leaving all else unchanged, including a worker’s po­
tential market wage. As shown in table 6, among 16
year olds to 17 year olds in Hope states, LFP fell by
10.4 percentage points between 2000 and 2004. By
comparison, in states without a Hope program, the
decline was 8.7 percentage points. That is, young
teen LFP fell 1.7 percentage points more in Hope
states after 2000. Since 24 percent of all teens in the
country reside in states with merit scholarship pro­
grams like Hope, we can estimate the impact these
scholarships had on aggregate teen LFP. We find that
roughly 5 percent (0.24 times 1.7 divided by 8.8) of
the decline in young (16 to 17) teen LFP could be
traced to differences in Hope and non-Hope states.
The actual impact is likely bigger once we account
for timing and generosity differences across states,
which we plan to do in follow-up research.
Columns 2 and 3 of table 6 repeat this exercise
for 18 year olds to 19 year olds by school enroll­
ment. We find a small negative impact among those
in school (about 2 percent of the total decline among
18 year olds to 19 year olds between 2000 and 2004)
but no effect, at least on the extensive margin, among
those not currently enrolled.30

While these effects are relatively small, they also
represent just one of many financial aid programs of­
fered in the U.S. (see Wirtz, 2005). A natural way to
corroborate and generalize these findings is to see
how changes in tuition, more broadly defined, influ­
ence work decisions. Typically, such studies examine
the impact of tuition on college enrollment deci­
sions.31 Instead, we analyze the teenager labor force
participation rates of tuition using real annual tuition
and fees data from the College Board (2005). The
data are available back to 1975 for four-year private,
four-year public, and two-year public institutions.32
In general, the tuition results seem consistent
with those for Hope Scholarships. Overall, we find
that tuition changes are positively correlated with
teen work activity. From a statistical perspective,
the strongest results are those for two-year college
tuition rates. This is what we would expect since
these are the rates that likely affect students whose
enrollment decision are most price sensitive. Further­
more, we find that families from the upper middle of
the income distribution are more likely to respond to
college price changes. This strikes us as plausibly the
part of the income distribution for which enrollment
decisions are particularly sensitive to tuition.33 Final­
ly, while tuition at four year colleges has risen in re­
cent years, the cost of attending community college
is now substantially lower that during the second half
of the 1990s. For instance, the College Board reports
that community college tuition, net of grants and ed­
ucation tax benefits, fell from $1,000 for the 1997-98
school year to $200 for 2001-02. Our results suggest
this decline could have lowered LFP for some teens.34
Overall, we view the evidence as consistent with
the hypothesis that increased wealth, via lower educa­
tion prices, can reduce teen labor supply. The impor­
tance of this effect for recent trends depends critically
on the real net price of schooling over time, which
we believe has fallen at the margin. While the Keane
TABLE 6

Percentage point change in teen LFP,
by Hope Scholarship status, 2000-04

Hope states
(24% of total pop.)

16-17

18-19
in school

18-19
not in school

-10.4

-5.9

-3.6

Other states

-8.7

-5.4

-3.6

Difference

-1.7

-0.5

0.0

Source: Authors’ calculations based on data from the Current
Population Survey.

14

1Q/2006, Economic Perspectives

and Wolpin (2001) results are based on a larger tu­
ition reduction program than recent experience, the
flavor of their structural model matches our empiri­
cal findings.

Conclusion
Teens can be thought of as allocating their time
between current market work, current leisure, and
human capital investment. Since the late 1970s and
especially since 2000, they have devoted less of their
time to current market work. To a significant extent,
they have also been increasing the time they devote
to human capital investment. The increased value of
education for their future earnings has apparently
caused teens to increase their school enrollments and
likely also the intensity with which they pursue their
studies when enrolled. We know less about any pos­
sible changes in their leisure time. However, we have
found some preliminary evidence that wealth effects
from increased financial aid may have reduced their
work effort as well.

It is possible that a sudden drop in demand for
teen labor has played a role in the recent, sharp de­
cline in teen participation rates. The modest decline
in relative teen wages would be consistent with some
role for weakened labor demand. We doubt, however,
that this is the main explanation. The latest recession
ended more than four years ago. In an unusual devel­
opment, teens who are out of the labor force are not
likely to report that they want a job, and the indus­
tries that typically employ them have been reporting
stronger than usual overall employment growth. Of
course, only time will tell whether the recent drop in
teen participation is a manifestation of a weak labor
market or a new equilibrium. The increases that we
have noted in teen’s human capital investments,
however, do suggest some reason for optimism for
future levels of productivity.

NOTES
’See, for example, Aaronson and Sullivan (2001) for a discussion
of the impact of greater educational attainment on aggregate
productivity.
2See Moretti (2004) for a review of this evidence.
3See Ruhm (1997) and Stinebrickner and Stinebrickner (2003)
for interesting discussions of these issues.

4For example, see Katz and Autor (1999). See Card and DiNardo
(2002) for a skeptical view of the skill-biased hypothesis.
5This definition ignores several interesting groups. First, the data
does not include those who are under 16. Second, by concentrating
on the noninstitutionalized population, we are ignoring the sizable
increase in incarceration over the last three decades. The adult prison
population has grown from 0.2 percent of the adult population in
the early to mid 1970s to almost 1 percent by the late 1990s. See
Katz and Krueger (1999). Their study assumes that 35 percent of
the incarcerated would be employed if not in jail. A similar assump­
tion for incarcerated teenagers would lead to an even stronger
trend down in the teenager LFP over time. Finally, the civilian
population ignores the military. This might be of particular con­
cern during the 1960s.
6Recent declines in teenager work participation have occurred
throughout much, but not all, of the developed world, according
to data from the Organization for Economic Cooperation and
Development.
7For example, a “-20” reveals that that teenage group’s LFP is
20 percent lower than the same gender’s adult population.

Federal Reserve Bank of Chicago

8For example, in the early to mid-1970s, female school enrollment
was 3 percentage points to 6 percentage points lower than males
among 18 year olds to 19 year olds (calculated from the October
files of the CPS). By the early 1990s, this gap disappears. Several
years later, school enrollment among the same aged females was
1 percentage points to 4 percentage points higher than their male peers.
9We focus on the period since 1979 because the CPS outgoing ro­
tation files begin in that year. As a third alternative, we have also
used the Hodrick-Prescott Trend, a standard statistical tool to iso­
late a long-term trend from short-term fluctuations in time-series.
Those results provide a similar story to the two cyclically adjusted
series presented in figure 3.

10We have also estimated this equation with a lag in U to allow for
delays in responding to aggregate conditions. This has no appre­
ciable difference on the results. This equation seems adequate for
picking up the time trend since 1979 but would not work as well
over a longer period since there appears to be trend breaks in this
series in late 1970s and early 1980s and perhaps in the early 1960s
as well. In that case, we would simply estimate the time trends
separately for different periods. We chose to focus our analysis
on the post-1979 period.
’’However, Staiger, Stock, and Watson (1997) show how impre­
cisely estimated the natural rate is.

12These regressions also use time dummies rather than linear time
trends. Time dummies are unidentified in the time-series version.
13We use the outgoing rotation files of the CPS. Participating
households are surveyed for four months, left out of the sample
for eight months, and finally surveyed again for four additional
months. Those households in the fourth and eighth months of
their participation are known as the outgoing rotation groups.

15

14Our technique for matching teenagers with their parents exploits
the family relationship variable in the outgoing rotation files. This
variable begins in 1984. We subtract the teenager’s own income
from the family income measure.
15We specified the time trends so that there is a kink, rather than
a discontinuous jump, at 1997.
16We have also run these regressions with controls for school en­
rollment status and its interaction with the time trends. Adding
these additional regressors does not impact the gender, race, income,
or (unreported) regional time trends in a significant way. The im­
pact of enrollment on LFP is discussed later.
17The weeks worked calculation is based on the March CPS. We
are able to compute family income back to 1979 because the March
files contain an explicit measure of family income (that is, there
is no reason to have to match teenagers with other family members).
Again, we use family income less the teenager’s own income.
18Over the period shown, between 66 percent and 80 percent of
teenagers (and 80 percent to 90 percent of 16 year olds to 17 year
olds) had wage rates within 50 percent of the minimum wage.
19Of course, one possibility is that teens report not wanting a job
because they know wages are not above their reservation price
(that is, the lowest wage at which they are willing to work). This
story has particular resonance if we believe that teens look at the
minimum wage, which has declined steadily since last raised at
the federal level in 1997, rather than actual market wages when
deciding whether to work.
20Card (1990) selected these cities because of their similarity to
Miami in terms of racial composition and economic growth dur­
ing the late 1970s and early 1980s.
21For example, the LFP rate of single mothers with two or more
children has grown by 30 percent since 1996, while the rate for
single women with no children has been relatively flat.

"These results, as well as others referenced in the text without
tables and figures, are available upon request from the authors.
23See, for example, Katz and Autor (1999).

24The estimates are based on a regression of the natural logarithm
of wage rates on standard variables such as potential experience,
gender, and race and indicator variables for different levels of
schooling. The data are from the March CPS. See Aaronson and
Sullivan (2001).
25An alternative way that time in school may have increased is
through changes in legally mandated years in school. Acemoglu
and Angrist (2001) find that the number of years required in school
has not changed much since the middle of the 1900s. See also
Lochner and Moretti (2004).

16

26See Blau (1998) and Greenwood and Vandenbroucke (2005).

27In this article, the term “home production work” includes all
work performed within a household for which no compensation
is received from outside parties.
28The data are available at www.bls.gov/tus/home.htm. We use
both the 2003 and 2004 surveys. Hammermesh, Frazis, and
Stewart (2005) provide background.
29In some states (like Georgia), there are two components to the
Hope program—a merit scholarship that requires minimum grades
and is applied to degree programs and a grant that can be applied
to two-year and less than two-year programs but has no grade re­
quirements.
30However, Cornwell, Mustard, and Sridhar (2005) and Cornwell,
Lee, and Mustard (2005) find that a sizable fraction of the college
enrollment effect happens among freshmen who delayed college en­
rollment by more than 12 months past their high school graduation.
31See Mazumder (2003) for a nice review.

32To allow for information delays, we include two years of lags
on tuition. LFP is computed from September to August to corre­
spond with school year tuition data.
33When we estimate teen LFP regressions separately by family in­
come quartile, data limitations only allow the series to start in 1985.
We find that the only income quartile where there is a statistically
significant response to price changes is the second highest (in­
come between the median and 75th percentile), although all quartiles
have a positive, albeit imprecisely estimated, point estimate.
Despite the small sample sizes, we found that none of these
results are sensitive to outliers. We also tried using a separate dataset
on two-year college tuition rates provided by the Washington State
Education Group. The advantage of their data is that it is disag­
gregated by state. When we aggregate their data to the national
level, we find correlations that are very similar to the College Board
data. However, our attempts to use panel methods to take advan­
tage of state differences in tuition and teen LFP growth are unre­
liable. We suspect measurement error is severe at the state level,
which attenuates estimates of the betas. Mazumder (2003) finds
little correlation between this tuition measure and enrollment, which
is generally contrary to the literature. Obviously, there are many
refinements that could be made to each of these analyses that would
limit the damage from measurement error, including looking at
teenagers who are at the margin of deciding whether to go to col­
lege and improving our understanding of what the relevant tuition
measure is. The latter, for example, would entail better informa­
tion on financial aid.
34See College Board (2005). Since 2002, two-year college costs
have begun to rise and aid has stagnated, but net costs remain his­
torically low.

1Q/2006, Economic Perspectives

REFERENCES

Aaronson, Daniel, and Daniel Sullivan, 2001,
“Growth in worker quality,” Economic Perspectives,
Federal Reserve Bank of Chicago, Vol. 25, No. 4,
Fourth Quarter, pp. 53-74.

Hammermesh, Daniel S., Harley Frazis, and Jay
Stewart, 2005, “Data watch: The American Time Use
Survey,” Journal ofEconomic Perspectives, Vol. 19,
No. l,pp. 221-232.

Acemoglu, Daron, and Joshua Angrist, 2001, “How
large are human-capital externalities? Evidence from
compulsory schooling laws,” in NBER Macroeco­
nomics Annual 2000, Ben S. Bernanke and Kenneth
Rogoff (eds.), Cambridge, MA: MIT Press, pp. 9-59.

Juster, F. Thomas, Hiromi Ono, and Frank Stafford,
2004, “Changing times ofAmerican youth, 1981-2003,”
University of Michigan, working paper, November.

Blau, Francine, 1998, “Trends in the well-being of
American women, 1910-95,” Journal ofEconomic
Literature,No\. 36, No. l,pp. 112-165.

Card, David, 1990, “The impact of the Mariel boatlift
on the Miami labor market,” Industrial and Labor
Relations Review, Vol. 43, No. 2, pp. 245-257.

Card, David, and John DiNardo, 2002, “Skill-biased
technological change and rising wage inequality: Some
problems and puzzles,” Journal ofLabor Economics,
Vol. 20, No. 4, pp. 733-783.

College Board, 2005, Trends in College Pricing, re­
port, New York, available at www.collegeboard.com/
prod_downloads/press/cost05/trends_college_
pricing_05.pdf.
Cornwell, Christopher, Kyung Hee Lee, and
David Mustard, 2005, “Student responses to merit
scholarship retention rules,” Journal ofHuman Re­
sources, Vol. 40, No. 4, pp. 895-917.
Cornwell, Christopher, and David Mustard, 2005,
“Merit-based college scholarships and car sales,”
University of Georgia, working paper.
Cornwell, Christopher, David Mustard, and Deepa
Sridhar, 2005, “The enrollment effects of meritbased financial aid: Evidence from Georgia’s Hope
Scholarship,” University of Georgia, working paper.
Greenwood, Jeremy, and Guillaume Vandenbroucke,
2005, “Hours worked: Long-run trends,” University
of Rochester, working paper.

Federal Reserve Bank of Chicago

Katz, Lawrence, and David Autor, 1999, “Changes
in the wage structure and earnings inequality,” in
Handbook ofLabor Economics, Vol. 3 A, O. Ashenfelter
and D. Card (eds.), Amsterdam: Elsevier Science,
pp. 1463-1555.
Katz, Lawrence, and Alan Krueger, 1999, “The
high-pressure U.S. labor market of the 1990s,” in
Brookings Papers on Economic Activity 1999: 1,
Macroeconomics, William C. Brainard and George
L. Perry (eds.), pp. 1-65.
Keane, Michael, and Kenneth Wolpin, 2001, “The
effect of parental transfers and borrowing constraints
on educational attainment,” International Economic
Review, Vol. 42, No. 4, pp. 1051-1103.

Lewis, Ethan, 2004, “How did the Miami labor market
absorb the Mariel immigrants?,” Federal Reserve Bank
of Philadelphia, working paper, No. 04-3, January 12.

Lochner, Lance, and Enrico Moretti, 2004, “The
effect of education on crime: Evidence from prison
inmates, arrests, and self-reports,” American Economic
Review, Vol. 94, No. l,pp. 155-189.
Mazumder, Bhashkar, 2003, “Family resources and
college enrollment,” Economic Perspectives, Federal
Reserve Bank of Chicago, Vol. 27, No. 4, Fourth Quarter,
pp. 30-41.
Meyer, Bruce, and James Sullivan, 2004, “The effects
of welfare and tax reform: The material well-being of
single mothers in the 1980s and 1990s,” Journal of
Public Economics, Vol. 88, No. 7-8, pp. 1387-1420.

Moretti, Enrico, 2004, “Workers’ education, spillovers,
and productivity: Evidence from plant-level production
functions,” American Economic Review, Vol. 94,
No. 3, pp. 656-690.

17

Ruhm, Christopher, 1997, “Is high school employ­
ment consumption or investment?,” Journal ofLabor
Economics, Vol. 15, No. 4, pp. 735-776.
Staiger, Douglas, James Stock, and Mark Watson,
1997, “How precise are estimates of the natural rate
of unemployment?,” in Reducing inflation: Motivation
and Strategy’, Christina Romer and David Romer (eds.),
Chicago: University of Chicago Press, pp. 195-242.
Stinebrickner, Ralph, and Todd Stinebrickner,
2003, “Working during school and academic perfor­
mance,” Journal ofLabor Economics, Vol. 21,
No. 2, pp. 473-491.

U.S. Department of Education, National Center
for Education Statistics, 2005, The Condition of
Education 2005, report, Washington, DC, available
at http://nces.ed.gov/pubs2005/2005094.pdf.
__________ , 2001, The Condition ofEducation
2001, report, Washington, DC, available at http://
nces.ed.gov/pubs2001/2001072.pdf.
Wirtz, Ronald, 2005, “Is college unaffordable?,”
The Region, Federal Reserve Bank of Minneapolis,
December.

U.S. Bureau of Labor Statistics, 2005, American
Time Use Survey, report, Washington, DC, available
at www.bls.gov/tus/home.htm.

18

1Q/2006, Economic Perspectives

Variations in consumer sentiment across demographic groups

Maude Toussaint-Comeau and Leslie McGranahan

Introduction and summary
Consumer sentiment is one of the many macroeconomic
indicators tracked by policymakers. Consumer senti­
ment—as measured by indexes such as the Index of
Consumer Sentiment (ICS) and the Consumer Confi­
dence Index (CCI)—is seen as a barometer of economic
activity, one that is a reliable indicator of the way peo­
ple plan to spend their money. Consumer sentiment
is important because it affects household spending.
Nationally, household spending on final goods and
services (retail sales) represents about 65 percent of
all expenditures for final goods and services, the na­
tion’s gross domestic product (GDP). Since private
consumption expenditure accounts for such a large
proportion of GDP, consumer sentiment can signal
changes in the direction of the economy. Numerous
studies have assessed the extent to which consumer
sentiment is related to fluctuations in GDP, the stock
market, and other outcomes.
While the overall index scores, so closely watched
by the public, are important, these aggregate numbers
conceal a wealth of demographic-specific information
contained in the survey data. Analyzing the survey data
at disaggregated levels enhances the indexes’ informa­
tive power (Dominitz and Manski, 2004). Consumers’
expectations about specific sectors of the economy,
such as expectations of inflation, income, employment,
and home values, usually differ by demographic group
and often move in opposite directions by group. These
disparities in expectations translate into distinct spending
patterns by different groups. Additionally, personal
spending patterns vary across demographic groups.
For example, older consumers tend to spend more on
health care; also, poor consumers spend a higher pro­
portion of their income on food and shelter. Because
of these and other differences, examining disaggregated
consumer sentiment survey data can provide us a
more detailed picture of future expenditure.

Federal Reserve Bank of Chicago

Beyond predicting expenditure, household-level
sentiment data tell us something about the current
welfare of vulnerable populations. There is increasing
evidence that consumer expectations vary systemati­
cally across demographic and socioeconomic groups.
As policymakers seek to better understand the eco­
nomic experiences of various societal groups over
the business cycle, disaggregated consumer sentiment
data can be a useful tool. For example, if a certain
subpopulation expresses pessimism about general
business conditions during an economic recovery or
growth period, there is good reason to think that the
benefits of economic expansion may not be reaching
that group. These insights can inform policy initiatives
aimed at assisting these populations.
In this article, we use household micro-level
data to investigate the determinants of consumer sen­
timent. We use data from the University of Michigan’s
Surveys of Consumers, grouping respondents by char­
acteristics such as race, ethnicity, gender, and income,
among others.1 We examine responses to the questions
that go into calculating the University of Michigan’s
Index of Consumer Sentiment, as well as responses
to other questions in the survey. One important finding
is that sentiment differences across groups persist re­
gardless of whether the question asks about personal
situations or general situations—that is, groups have
different views not only of their own outlook, but of the
outlook for the country as a whole. We look into
consumers’ explanations of their sentiment to investi­
gate why this is, considering group-level subjective

Maude Toussaint-Comeau and Leslie McGranahan are
economists in the Consumer and Community Affairs
Division of the Federal Reserve Bank of Chicago. The
authors thank Dorothy Kronick and Lori Timmins for
providing valuable research assistance.

19

experiences and differences in information sets across
individuals as possible explanations for this gap in
sentiment.
We proceed with a brief literature review that out­
lines the basic theoretical framework for understand­
ing the relationship between consumer sentiment and
consumer behavior. Then we provide a description of
how consumer sentiment is measured. After this, we
continue with an analysis of the variations in sentiment
across groups, while exploring explanations for the
differences. Finally, we discuss the implications of
our findings and comment on areas for future research.

Literature review
There is a large amount of literature that deals
with the role of consumer sentiment in explaining con­
sumption.2 The point of departure for much of this
literature is the permanent income hypothesis (PIH)
(Friedman, 1957). The PIH maintains that consump­
tion is determined solely by individuals’ incomes over
their lifetimes—that is, expenditure depends only on
permanent income (wealth). Consistently, Hall (1978)
concluded that under conditions of perfect capital
markets, the PIH can be approximated by a random
walk, meaning that no past information (aside from
that needed to measure lifetime income) is required
to predict current consumption.
Research has found that consumption is partly
determined by current income, a notion that is referred
to as “excess sensitivity” of consumption relative to
income. For example, Campbell and Mankiw (1990)
find that only half of consumers tend to be “life-cyclers,”
following the PIH assumptions, while the others tend
to be “rule-of-thumbers,” or those who consume from
their current income rather than just from their lifetime
income. Studies have attributed excess sensitivity of
consumption relative to current income to liquidity
constraints and precautionary savings motives (Shea,
1995; Flavin, 1991; Alessie andLusardi, 1997). Li­
quidity constraints mean that individuals may not be
able to borrow as they desire. That is, even if consumers
anticipate more income (and consumers’ confidence
increases), with binding liquidity constraints, they
will not be able to immediately act on the improve­
ment in permanent income; the consumers will increase
consumption only when the rise in income materializes.
Some consumers accumulate precautionary savings
when there is uncertainty relative to future income,
which will cause them to have higher expected utility,
since they reduce current consumption in case of a
drop in income. In other words, even if consumers’
financial positions remain unchanged, greater uncer­
tainty about their future positions (hence a decrease

20

in confidence) might cause consumers to engage in
precautionary savings, which would affect their mar­
ginal propensity to consume. If lower consumer con­
fidence reflects higher uncertainty about the future
and enhances the precautionary motive for savings,
then lower consumer confidence today causes con­
sumption to decrease today relative to tomorrow. In
contrast, higher consumer confidence is associated
with lower savings and more consumption in the present,
as well as lower consumption growth in the future. In
the PIH framework, the ability of consumer sentiment
to explain consumption arises from the fact that con­
sumer sentiment serves as a proxy for liquidity con­
straints and precautionary savings motives
(Acemoglu and Scott, 1994).3
Empirical research using micro-level sentiment
data has focused on inflation expectations of house­
holds. For example, Bryan and Venkatu (2001b) find
that predictions of inflation significantly differ by so­
cioeconomic and demographic characteristics of con­
sumers. Palmqvist and Stromberg (2004), Lombardelli
and Saleheen (2003), and Ranchhod (2003) find sim­
ilar results in Sweden, the United Kingdom, and New
Zealand. Souleles (2004) provides some explanations
for people’s differences in sentiment as he suggests
that differences in people’s expectations may be due
to time-varying group-specific shocks (for example,
during a recession the less educated may be dispropor­
tionately adversely affected). Another set of research
examines the nature of the information to which con­
sumers have access. This includes information that might
help form their expectations, such as private local infor­
mation, information they have gathered from their in­
dustries, or news media reports. For example, Dunn
and Mirzaie (2004) calculate manufacturing employ­
ment concentration as a proxy to measure agents’
private information to explain regional variations in
consumer confidence.4 Sims (2003) presents a theo­
retical framework for evaluating the way people pro­
cess information, accounting for the fact that people
might have capacity constraints in processing infor­
mation and extracting signals from the information
that is transmitted to them. (That is, two people may
be exposed to the same information, but they may
not assimilate or use the information the same way.
Therefore, their expectations of the same event may
be different.) Doms and Morin (2004) analyze the
role of the news media. They suggest that even if me­
dia coverage affects consumer sentiment, the effects
are very short-lived. These findings underscore the
difficulty in assessing the role of information in con­
sumer sentiment and, ultimately, consumer behavior.

1Q/2006, Economic Perspectives

Friedman (1957) and Hall (1978), under the PIH
framework, assume specific types of preferences—
exogenous and stochastic income, no borrowing con­
straints, and rational expectations. However, the field
of behavioral economics has extended our understand­
ing of preferences to account for “psychological fac­
tors,” such as addiction and lack of self-control (Gul and
Pesendorfer, 2002) and discrimination (Becker, 1976).
There is in fact a tradition of portraying the connec­
tion between sentiment and behavior in psychological
terms: John Maynard Keynes (1936) wrote that house­
hold consumption is influenced by “spontaneous op­
timism” and “animal spirit.” Similarly, George Katona
(1975)—the founder of the Survey Research Center
(SRC) at the University of Michigan, which generates
the ICS—explained that in addition to factors that af­
fect a consumer’s ability to pay, consumption is based
on a consumer’s “willingness to pay.” These suggest
that households form their expectations about the fu­
ture based on preferences, technology, market frictions
or borrowing constraints, and subjective experiences;
indexes like the ICS and CCI are summaries of their
views. This article builds upon this literature with its
exploration of the possible links between consumer
sentiment, personal characteristics of individuals,
their subjective experiences, and exposure to news
information.

Measuring consumer sentiment
While sentiment surveys are well known, their
methods of construction are more obscure. Here, we
describe how the Index of Consumer Sentiment from
the University of Michigan’s Survey Research Center
is designed, and examine business cycle components
of its trends.5 This aggregate index, the ICS, is con­
structed using a formula based on responses to the
following five survey questions. (The names of the
variables, as identified by the survey, are in parenthe­
ses after the questions.)
1) We are interested in how people are getting along
financially these days. Would you say that you
(and your family living there) are better off or
worse off financially than you were a year ago?
(PAGO)

2)

Now looking ahead—do you think that a year
from now you (and your family living there) will
be better off financially, or worse off, or just
about the same as now? (PEXP)

3)

Now turning to business conditions in the coun­
try as a whole—do you think that during the «exZ
12 months we’ll have good times financially, or
bad times, or what? (BUS12)

Federal Reserve Bank of Chicago

4)

Looking ahead, which would you say is more
likely—that in the country as a whole we’ll have
continuous good times during the next five years
or so, or that we will have periods of widespread
wwemployment or depression, or what? (BUS5)

5)

About the big things people buy for their homes—
such as furniture, a refrigerator, stove, television,
and things like that. Generally speaking, do you
think now is a good or bad time for people to
buy major household items? (DUR)

To compute the ICS, first an index for each of
the five questions is constructed as the “net balance,”
where the proportion of negative responses is subtract­
ed from the proportion of positive responses. The over­
all ICS is then calculated as an average of the net
balance for these questions. There are two other in­
dexes derived from these questions. The Index of
Current Economic Conditions (ICC) is based on the
two questions that ask about present personal and
economic situations, PAGO and DUR. The Index of
Consumer Expectations (ICE) is based on the three
questions that ask about consumer-expected changes
in business conditions and respondents’ income, PEXP,
BUS12, m&BUS5.6
Figure 1 plots the three indexes—the ICS, the
ICE, and the ICC—using quarterly data for the peri­
od 1978 to 2004.7 (By design, the ICS lies between
the ICE and the ICC. The correlation between the ICE
and the ICC is 0.82). Looking at the figure, one can
note the relationship between the indexes and business
cycles. From 1978 to 2004, there were four recessions.
These are shown as shaded regions in the figure. The
most recent was from March 2001 to November 2001
(2001:Ql-2001:Q4). The ICS always takes a dip dur­
ing a recession, although there are some brief intervals
outside of the recession periods when this index also
takes a dip. The three indexes begin decreasing one
to four quarters ahead of three of the four recessions.
(The one exception is the 1980 recession, at which
time the indexes fell as the recession began.) The in­
dexes rise prior to all upturns. These observations sug­
gest the potential predictive power of the indexes. The
indexes climbed to historically high levels through­
out the expansionary years of the 1990s, before a re­
versal of the trend prior to the 2001 recession. Various
researchers have found that the Index of Consumer
Expectations has some predictive power for GDP, con­
sumption, and the stock market, among other outcomes.8

Sentiment and demographic characteristics
Next, we examine the ICS by group on a quar­
terly basis (due to space constraints, we do not report

21

the results for the ICE and the ICC in this section).9
We calculate the ICS for each group in a similar way
to the calculations for the entire sample.10 The results
are consistent with previous studies that noted that
consumer confidence varies by demographic charac­
teristics. In our study, we define the following set of
populations to be “vulnerable”: the poor, elderly, fe­
males, blacks, Hispanics, or those without a high school
diploma. By vulnerable, we mean that each of these
populations has lower income relative its complement,
for example, the poor relative to the nonpoor. The set
of plots in figure 2 shows that the populations that
we classify as vulnerable populations have, on aver­
age, lower confidence than their counterparts.11
Figure 2 reveals a number of patterns: For the poor
and nonpoor, we observe a large gap in the ICS in the
1980s, a sharp contrast to what later occurred with
the expansion of the 1990s.12 In both periods, the trend
in the ICS for the poor is more variable than that of
the nonpoor. The confidence pattern for the elderly
(age 65 or older) and non-elderly is similar to that
which we observe for the poor and nonpoor—that is,
it is lower and more variable for the elderly. Compar­
ing the ICS of blacks and whites, we find that during
the 1980 and the 1981-82 recessions there was an in­
crease in the gap between the two groups—consistent
with findings in other research that blacks may have
been disproportionately affected by these recessions

22

(Wall, 2003). Starting after the 1990-91
recession, there was a tendency for the
gap in the ICS between blacks and whites
to narrow. The gap appears to have wid­
ened recently. Differences in the ICS based
on education attainment and gender per­
sist and remain constant over time.
Because the ICS is made up of dis­
parate questions, the changes over time
in the ICS by group as indicated by fig­
ure 2 are not easy to interpret. The ICS is
made up of five component questions
concerning personal financial situations
as well as general business conditions.
These responses can move in opposite di­
rections, even within a demographic
group. If more blacks since 1991 are re­
sponding that their personal financial sit­
uations have improved relative to the
proportion of blacks who are projecting
that the economy will do well, then the
improvement in their ICS may be inter­
preted as reflecting an improvement in
their personal financial situations. The
opposite would indicate that blacks sim­
ply think that the economy will do well, although
they do not think that their own situations have
changed. We take a closer look at the components of
the index to ascertain the factors that might explain
changes over time in the ICS by group.
Figures 3 through 6 show the results of disaggre­
gating the ICS into its component questions for se­
lected groups. (The DUR variable is not reported in
our figures because the differences in responses by
group are minor by comparison to those of the other
variables.) First, consider the results for blacks and
whites (figure 3). Before 1991, the fact that consum­
er sentiment among blacks was lower can be attribut­
ed to their lower confidence in both the overall
economy and their personal financial situation. This
is indicated by the gap in the BUSS, BUS12, and
PAGO series over that period. In contrast, after 1991
the relatively higher consumer confidence of blacks
can be attributed to the fact that they had relatively
more confidence regarding their financial situation,
as indicated by the small gap in the PAGO series be­
tween blacks and whites and as evidenced by higher
PEXP among blacks in the post-1991 period. In
short, the convergence of black and white sentiment
measures since 1991 can be largely attributed to im­
provement in black consumers’ reports of their own
financial situation.

1Q/2006, Economic Perspectives

FIGURE 2

Index of Consumer Sentiment by demographic group
A. Poverty status

B. Age

index

index

— Poor

— Elderly

— Non poor

C. Gender

D. Race

index

index

— Non-elderly

E. Education attainment

index

— Without high school
diploma

— College graduate

Source: Authors’ calculations based on data from the University of Michigan, Survey Research Center, Surveys of Consumers.

Federal Reserve Bank of Chicago

23

FIGURE 3

Components of Index of Consumer Sentiment by race
A. PAGO

B. BUS5

index

index

— Black

— White

C. PEXP

D. BUS12

index

index

— Black

— White

— Black

— White

— Black

— White

Note: See the text for definitions of the variables.
Source: Authors’ calculations based on data from the University of Michigan, Survey Research Center, Surveys of Consumers.

Turning to figure 4, we note that the elderly have
a similar level of confidence in the overall economy
compared with the non-elderly. This is indicated by
the fact that the BUS12 and BUS5 series of the elderly
and non-elderly virtually coincide in both level and
pattern. The triggering factor for the lower overall
consumer sentiment shown by the elderly is their lower
assessment of their financial situation, at present and
as predicted for the future. This can be seen from the
gap between the PAGO and the PEXP series of the
elderly and the non-elderly. Similarly, for those with­
out a high school diploma (figure 5) and the poor
(figure 6), a lower confidence in their personal finan­
cial situation {PAGO) seems to be a contributing source
of lower consumer sentiment overall. (The pattern

24

for the PEXP series of the poor and nonpoor is less
clear than that for PAGO.) In addition, those without
a high school diploma and the poor are also less con­
fident about the economy as a whole {BUS12 and BUSS).
It is not surprising to find that respondents have
different expectations concerning their personal expe­
riences. However, it is puzzling that they should also
have different expectations of the same economic
events (business conditions). One possibility is that
they form their expectations of the economy based
on their own subjective experiences. We investigate
this possibility by looking at respondents’ expecta­
tions of unemployment and their actual (group-level)
experiences of unemployment. Besides the five ques­
tions mentioned previously, respondents are asked in

1Q/2006, Economic Perspectives

FIGURE 4

Components of Index of Consumer Sentiment by age
A. PAGO

B. BUS5

index

index

— Elderly

— Non-elderly

C. PEXP

D. BUS12

index

index

190

'

170

'

— Elderly

— Non-elderly

— Elderly

— Non-elderly

150
130
110
90
70
50 '

30

...............................................................................................

1978

'83

'88

— Elderly

'93

'98

2003

— Non-elderly

Note: See the text for definitions of the variables.
Source: Authors’ calculations based on data from the University of Michigan, Survey Research Center, Surveys of Consumers.

the SRC survey: “How about people out of work dur­
ing the coming 12 months—do you think that there
will be more ////employment than now, about the same,
or lessl” We calculate the coefficient of correlation
between the response to this question and actual unem­
ployment to ascertain whether a relationship exists
between the two series.13 The correlation coefficient
is a measure of the degree of linear association between
two variables, with -1 indicating perfect negative as­
sociation, +1 indicating perfect positive association,
and 0 indicating no association. The results, which
are summarized in table 1 (p. 28), indicate that a re­
spondent’s expectation of unemployment corresponds to
the experience of her own group (in the second column)
more closely than it corresponds to the experience of

Federal Reserve Bank of Chicago

the population as a whole (in the first column), even
though the question asks about the general situation.
This suggests that group-based aggregate experiences
tend to inform individuals’ expectations of the economy.
We also consider people’s expectations of price
changes and the actual changes in the Consumer Price
Index. We find evidence consistent with previous stud­
ies that expectations of inflation vary systematically
by demographic and socioeconomic characteristics.
In particular, female unmarried heads of households,
the poor, the less educated, and blacks have higher
expectations of inflation. Several studies have offered
potential explanations of the sources of differences in
inflation forecasts. These include differences in infor­
mation sets across agents and substantial variation in

25

FIGURE 5

Components of Index of Consumer Sentiment by education attainment
A. PAGO

B. BUS5

index

index

190

'

170

'

50 '
30

...............................................................................................

1978

'83

'88

— Without high school
diploma

'93

'98

'03
— Without high school
diploma

— College graduate

C. PEXP

D. BUS12

index

index

— Without high school
diploma

— College graduate

— Without high school
diploma

— College graduate

— College graduate

Note: See the text for definitions of the variables.
Source: Authors’ calculations based on data from the University of Michigan, Survey Research Center, Surveys of Consumers.

the cost of consumption baskets across individual
households (Carlson and Valev, 1999; Michael, 1979).
However, McGranahan and Paulson (2005) derive
inflation rates for specific population groups by re­
weighting price index components (price indexes for
individual items) based on the market basket consumed
by members of the population group of interest. They
find that from 1983 to 2004, the series are similar for
all the groups. Given this, variation among people’s
perceptions of inflation is difficult to explain in the
context of people’s own subjective experiences with
inflation (since inflation does not seem to vary by group).
Next, we review a two-part question about news
in the SRC survey to explore the role of information in

26

consumer sentiment. This survey question asks whether
the respondent has “heard of any favorable or unfavor­
able changes in business conditions.” If the respondent
answers “yes,” it further asks, “What did you hear?”
Respondents can provide up to two responses to this
two-part question. From the responses, we can exam­
ine whether different groups have different levels of ex­
posure to the news. Table 2 presents the percentage of
people that have heard any news—those who responded
“yes” to the first part of the question above—by demo­
graphic group. In table 2, we see that only 58 percent
of the sample reports hearing any news concerning the
economy. The differences across groups are quite sub­
stantial. While 72 percent of college graduates report

1Q/2006, Economic Perspectives

FIGURE 6

Components of Index of Consumer Sentiment by poverty status
A. PAGO

B. BUS5

index

index

— Poor

— Nonpoor

C. PEXP

— Poor

— Nonpoor

— Poor

— Nonpoor

D. BUS12

— Poor

— Nonpoor

Note: See the text for definitions of the variables.
Source: Authors’ calculations based on data from the University of Michigan, Survey Research Center, Surveys of Consumers.

having heard about business conditions, only 38 percent
of individuals without a high school diploma report
having heard about any such news. Similarly, 60 per­
cent of the nonpoor report having heard news about
the economy as opposed to 41 percent of the poor.
To further investigate the role of news, we divid­
ed the news into five areas—namely, employment,
prices, government programs or decisions, output/GDP,
and conditions in a respondent’s own industry. Tabula­
tions of news sources by group are presented in table 3.
Among those who have heard news, the most common
type of news pertains to the employment situation. A
large number of people also have heard news about GDP,
news about their own industry, and (a disproportionate

Federal Reserve Bank of Chicago

amount of bad) news about prices. The prevalence of
news about the respondent’s own line of work suggests
that news is not necessarily objective in nature, but is
filtered through a subjective lens. Furthermore, con­
sidering demographic characteristics, we found that
most groups get a consistent fraction of their news on
the same topics. However, there is one exception to
this pattern: Information on GDP is more common
among more educated and wealthier households than
among those that are less educated and have lower in­
comes. Less than half of the individuals in the vulner­
able groups we investigate report having heard any
news about business conditions. This fraction becomes
even smaller if we exclude individuals who have

27

TABLE 1

Correlation of predicted with actual
unemployment
Correlation
with overall
unemployment

Group

All
Without high
school diploma
College graduate
White
Black
Hispanic

Correlation
with own group’s
unemployment

0.814

0.490
0.804
0.680
0.676
0.360

0.541
0.826
0.790
0.808
0.607

Sources: Authors' calculations based on data from the University
of Michigan, Survey Research Center, Surveys of Consumers and
U.S. Bureau of Labor Statistics.

only heard news about conditions concerning their
own industries. If individuals have no exposure to
news, they must be forming their assessments of the
macroeconomy based on other information. This other
information would likely come from their personal
experiences, such as noticing prices in local stores
and conversing with peers. If this is the case, the dif­
ferences in expectations among the different groups
are easier to explain. Their disparate experiences, as
evidenced in the PAGO responses, translate into dif­
ferent expectations for the economy, especially given
the relative absence of objective information from
news sources within certain groups.

Empirical analysis
Previously, we have shown descriptively that
the Index of Consumer Sentiment differs across

demographic groups. In particular, we have shown that
respondents’ perceptions are less positive for those
groups that we label as vulnerable based on relative
income. Here, we take a closer look at the responses
of the individuals who make up the groups. Specifi­
cally, we look at the microdata to gain a better mea­
sure of the contributions of each demographic attribute
to each index. We base the analysis on measures of
the ICS, ICC, and ICE provided in the data for each
individual in the sample.14
We can ask how the different demographic at­
tributes of individuals contribute to their measures in
these indexes. We do this via regression analysis. We
ask what the contribution of each demographic char­
acteristic is to the different indexes, while holding the
other characteristics constant. The results from a se­
ries of regressions are presented in table 4 for the ICS.
The table contains three separate regressions. In the
regression presented in the first column, we predict
the index based only on demographic attributes and
region of residence. In the second column, we add in
four measures of the conditions of the macroecono­
my during the month of the survey—the unemploy­
ment rate, the percent change in real personal income
from one year ago, the year-over-year inflation rate,
and the percent change in the real value of the Dow
Jones Industrial Average from one year ago. Higher in­
come is likely to trigger higher consumption, with
accompanying stronger consumer confidence. Therefore,
we expect a positive relationship between past income
and confidence. An increase in the unemployment rate
is likely to generate an increase in uncertainty among
consumers, even though they may not themselves be
unemployed. This is likely to increase precautionary

TABLE 2

Have you heard of any favorable or unfavorable changes in business conditions?
Group

All
Elderly
Non-elderly
Poor
Nonpoor
Top income quartile
Bottom income quartile
Without high school diploma
College graduate
White
Black
Hispanic

Heard news within
own industry
(........................................ ................................... percent-----------

Heard any news

57.63
47.80
59.55
40.77
59.78
68.00
43.58
38.46
72.19
59.23
47.55
49.63

21.17
20.62
21.30
18.13
21.51
20.67
19.26
20.23
19.22
21.83
15.98
18.25

Heard only news within own
industry and no other news
)

10.11
10.04
10.13
9.47
10.18
8.62
11.22
10.81
7.80
10.35
7.80
10.20

Note: See the text for further details.
Source: Authors' calculations based on data from the University of Michigan, Survey Research Center, Surveys of Consumers.

28

1Q/2006, Economic Perspectives

TABLE 3

What types of news have people heard?
Group

Employment

Prices

Government
.......... percent-------------

GDP

Own industry

22.89
15.64
24.23
16.94
23.42
21.59
10.80
20.47
26.92
23.34
20.09
18.38

12.14
12.35
12.11
14.10
12.03
13.69
14.06
11.93
13.29
11.78
14.78
13.38

25.56
20.47
26.75
15.61
26.78
35.55
17.10
14.73
36.21
26.63
17.23
21.76

22.07
21.52
22.20
18.81
22.43
21.82
20.16
20.72
20.17
22.77
16.56
18.95

(...............................

All
Elderly
Non-elderly
Poor
Nonpoor
Top income quartile
Bottom income quartile
Without high school diploma
College graduate
White
Black
Hispanic

38.01
44.42
36.81
45.80
37.50
36.60
47.47
42.41
35.13
37.18
46.90
40.61

Note: See the text for further details.
Source: Authors’ calculations based on data from the University of Michigan, Survey Research Center, Surveys of Consumers.

savings and lower consumption and confidence. We
therefore expect a negative relationship between con­
sumer confidence and unemployment. Increased in­
flation decreases the purchasing power of the consumer.
Rising inflation can create an erosion of purchasing
power that could lower consumer confidence. Great­
er price volatility or inflation would create more un­
certainty surrounding real wage changes. Because of
this, changes in inflation are expected to be negatively
related to consumer sentiment. Stock market prices
may affect consumer confidence in two ways: An in­
crease in stock market prices may increase wealth and
directly boost confidence, or rising stock markets may
act as an indicator of higher expected labor income,
which would also increase confidence.
In the third column, we replace the macroeconomic
variables with a series of month-year dummies. These
dummies control for any changes in the economy or
overall national situation that affect all respondents
in a given month.
We see a number of patterns in table 4. First, each
of our attributes indicating vulnerability, in terms of
relative income, has an independent, statistically sig­
nificant negative effect on the index measure. The
poor, females, the less educated, the elderly, blacks,
and Hispanics are less optimistic about the economy.15
Second, the condition of the macroeconomy has a
strong effect on consumer sentiment. We can see this
in two ways—first, through the statistically signifi­
cant effect of the macroeconomic variables on the in­
dex measure and, second, through the increase in the
explanatory power of the regression as a whole (as
measured by the adjusted R-squared presented in the
final row of the table) once these independent variables
are added. At the same time, the coefficients on the

Federal Reserve Bank of Chicago

attributes change only slightly with the addition of
the macroeconomic measures or the time dummies.
The one exception to this is the Hispanic indicator,
which goes from being positive to negative once the
macroeconomic measures are included. Further in­
vestigation suggests that this is the result of the larger
Hispanic population in the later years of the sample
when the economy is also doing well. As a result, the
Hispanic measure in the initial regressions is partly
picking up the positive association between the con­
dition of the economy and the size of the Hispanic
population. We also find that most of the contribution
from the time dummies is captured by the four mea­
sures of the macroeconomic situation. Although the
adjusted R-squared increases when the dummies are
introduced, the jump is not dramatic. If we look at
the individual macroeconomic measures, we find that
a respondent’s sentiment is positively correlated with
the increase in stock market prices and changes in
disposable income and negatively correlated with the
unemployment and inflation rates. All of these signs
are in the direction we would anticipate because in­
creasing income and stock market prices are indica­
tors of economic strength and a rising unemployment
rate is a sign of economic weakness. While high in­
flation can be a sign of rapid economic activity, it
negatively affects consumer well-being. In the remain­
der of this article, we include our macroeconomic
measures rather than the series of time dummies be­
cause the macroeconomic variables lead to more
straightforward interpretations.
We ran a similar regression analysis for the ICE
and the ICC.16 The results are broadly similar to those
for the ICS. Groups with relatively lower income are
significantly less optimistic and have lower assessments

29

TABLE 4

Determinants of the Index of Consumer Sentiment
Demographic
characteristics

Add macroeconomic
measures

Add month-year
dummies

Poor

-6.447***
(0.346)

-7.340***
(0.337)

-7.986***
(0.336)

Resides in Northeast

-1.859***
(0.263)

-1.307***
(0.248)

-1.304***
(0.245)

Resides in South

2.024***
(0.230)

1.954***
(0.218)

1.956***
(0.216)

Resides in West

-0.172
(0.264)

0.286
(0.251)

0.314
(0.248)

Female

-8.694***
(0.180)

-8.652***
(0.171)

-8.737***
(0.169)

-17.516***
(0.307)

-12.918***
(0.298)

-12.868***
(0.297)

High school graduate

-8.380***
(0.225)

-6.341***
(0.214)

-6.363***
(0.212)

Some college

-3.296***
(0.247)

-1.959***
(0.233)

-1.983***
(0.230)

Black

-5.457***
(0.326)

-6.141***
(0.307)

-6.121***
(0.305)

Hispanic

1.822***
(0.452)

-1.898***
(0.436)

-1.818***
(0.434)

Other race (nonwhite)

-0.608
(0.597)

-2.786***
(0.574)

-2.773***
(0.563)

Elderly

-7.731***
(0.263)

-9.072***
(0.254)

-9.043***
(0.251)

0.192***
(0.072)

0.434***
(0.068)

0.469***
(0.067)

-1.080***
(0.204)

-1.152***
(0.193)

-1.134***
(0.191)

Without high school diploma

Family size

Married

Unemployment rate

-3.439***
(0.060)

Percent change in real disposable income
(year over year)

2.594***
(0.046)

Inflation rate
(year over year)

-1.280***
(0.034)

Percent change in real Dow Jones Industrial
Average (year over year)

0.205***
(0.006)

Constant

100.782***
(0.305)

119.720***
(0.493)

105.505***
(1.869)

Observations

167,507

167,507

167,507

Adjusted R-squared

0.06

0.16

0.18

♦Significant at the 10 percent level.
**Significant at the 5 percent level.
♦♦♦Significant at the 1 percent level.
Notes: Robust standard errors are in parentheses See the text for further details.
Sources: Authors' calculations based on data from the University of Michigan, Survey Research Center, Surveys of Consumers;
U.S. Bureau of Economic Analysis; U.S. Bureau of Labor Statistics; and Yahoo! Finance.

30

1Q/2006, Economic Perspectives

of the current state of the economy than their comple­
ments. For these other measures we also observe the
same pattern for Hispanic respondents—positive co­
efficients that reverse sign once the macroeconomic
measures or dummies are added in.
If we compare the results across the three index­
es, we find that being poor lowers the ICC by 12,
ICE by 5, and ICS by 7 index points relative to being
nonpoor. It is not surprising that the poor differ most
from the nonpoor in their assessments of current eco­
nomic conditions because being poor is partly the re­
sult of current financial distress. For individuals without
a high school diploma, we find that their ICC, ICE,
and ICS are all lower by a similar amount—between
12 and 13 index points—relative to those of college
graduates (the omitted category). Because low edu­
cation affects both current and future employment
prospects, this similarity across results is also not sur­
prising. For females, we find that relative to males
their ICC is lower by 6, their ICE by 10, and their ICS
by 9 index points—a pattern that suggests greater
pessimism on the part of women, despite their compa­
rable assessment of the current economic conditions.
This is consistent with other research that has found
women to have lower consumer confidence (Bryan
and Venkatu, 2001 a).
The effects of macroeconomic variables remain
similar to the effects found for the ICS as discussed
previously. We find that the coefficient on the unem­
ployment rate is larger in absolute value in the ICC
regression than in the ICE regression. In contrast, the
coefficients on the three other macroeconomic vari­
ables—disposable income, inflation, and stock mar­
kets—are larger in absolute value in the ICE regression
than in the ICC regression. By design, the ICS coef­
ficients he between those of the other two indexes.
To gain a better understanding of the pattern of these
responses and the rationale for them, we turn to an in­
vestigation of the five questions from which the three
indexes are calculated. As argued previously, in addi­
tion to giving us further insight into the rationale be­
hind the differences across the indexes, investigating
the responses themselves allows us to move away from
the arbitrary nature of the index calculations. In the re­
gressions presented in table 4 and the results for ICC and
ICE, we are explaining continuous index calculations,
but those calculations are based on discrete answers to
a series of five questions. We now look at the discrete
answers to the questions with respect to the ICS.
Table 5 presents the results from a series of ordered
logit regressions where the dependent variable indi­
cates whether the respondent is positive, neutral, or
negative about the question being asked. For instance,

Federal Reserve Bank of Chicago

for the PAGO question, the respondents are separated
into individuals who are better off than a year ago,
the same as a year ago, and worse off than a year
ago. For BUS12 and BUS5, respondents can say that
they expect good times, good times with qualification,
mixed experiences, bad times with qualifications,
and bad times. For these regressions, we group the
two positive and two negative responses together, in
order to be more consistent with the other questions.
These responses are also grouped in the calculation
of the ICS. Using the ordered logit framework, we
are able to include individuals who are neutral or the
same. These individuals are omitted from the calcu­
lation of the published indexes.17
For ease of interpretation, we present odds ratios
and z-statistics rather than coefficients and standard
errors in table 5. The odds ratios indicate how being
in the underlying group contributes to the probability
of responding more positively to the question relative
to the probability of responding more negatively. A co­
efficient less than one indicates that belonging to the
group leads to more negative responses relative to being
in the omitted category. The asterisks indicate whether
the odds ratio is significantly different from one,
which is equivalent to asking whether the estimated
coefficient is different from zero.
If we look at the first column of table 5 (the pre­
dictors of the responses to the PAGO question), we
see that the poor, females, the less educated, blacks,
and the elderly are all more likely to be negative than
positive about their previous year relative to individ­
uals in the omitted categories (nonpoor, males, college
graduates, whites, and the non-elderly). The coeffi­
cients on the education categories are monotonically
increasing with education level. The smallest coeffi­
cients (those furthest from one) are on the groups we
know from other sources to have poor earnings poten­
tial—the poor, those without a high school diploma,
and the elderly. The effects of the macroeconomic
variables remain very similar to the effects found in
the continuous regressions presented earlier. The PAGO
question is subjective—that is, it asks individuals
about their own economic experiences. Given that
members of groups have different economic experi­
ences, it is not surprising that we find differences
across groups.
The other subjective question is PEXP, which
asks individuals to anticipate whether they will be
financially better off in a year. These results are quite
different from those found with PAGO. The poor do
not have responses significantly different from the
nonpoor. Hispanics and blacks are both more optimis­
tic than whites. However, the less educated, females,

31

TABLE 5

Determinants of component questions of the Index of Consumer Sentiment

Poor

1
PAGO

2
DUR

3
PEXP

4
BUS12

0.649***
(23.56)

0.742***
(13.72)

0.998
(0.13)

0.805***
(9.96)

0.98
(1.39)

0.937***
(4.16)

1.002
(0.12)

5
BUS5

0.756***
(13.25)

Resides in Northeast

0.904***
(7.46)

0.961**
(2.32)

Resides in South

1.079***
(6.36)

0.982
(1.21)

1.163***
(12.14)

1.088***
(6.09)

1.049***
(3.68)

Resides in West

0.983
(1.24)

0.970*
(1.76)

1.177***
(11.35)

0.994
(0.37)

0.978
(1.50)

Female

0.848***
(17.68)

0.771***
(22.20)

0.835***
(18.56)

0.680***
(35.67)

0.620***
(47.24)

Without high school diploma

0.587***
(33.42)

0.739***
(15.58)

0.571***
(32.83)

0.766***
(13.95)

0.557***
(31.96)

High school graduate

0.722***
(27.66)

0.98
(1.33)

0.742***
(24.51)

0.907***
(7.25)

0.719***
(26.18)

Some college

0.825***
(14.80)

1.039**
(2.37)

0.946***
(4.18)

1.015
(0.98)

0.908***
(7.05)

Black

0.854***
(9.28)

0.841***
(8.45)

1.221***
(11.16)

Hispanic

0.978
(0.93)

0.823***
(6.62)

1.170***
(6.25)

0.958
(1.55)

0.846***
(6.39)

Other race (nonwhite)

0.908***
(3.05)

0.819***
(5.16)

0.978
(0.68)

0.905***
(2.75)

0.945*
(1.66)

0.600***
(38.29)

0.814***
(11.93)

0.365***
(67.86)

0.953***
(2.83)

1.075***
(4.59)

1.029***
(7.42)

1.015***
(3.50)

0.997
(0.81)

0.993
(0.57)

1.014
(1.21)

Elderly

0.709***
(17.60)

0.588***
(27.57)

Family size

1.031***
(7.96)

1.005
(1.10)

Married

0.975**
(2.35)

0.962***
(2.94)

0.812***
(18.78)

0.904***
(30.66)

0.804***
(54.67)

0.976***
(6.94)

0.874***
(35.40)

0.906***
(27.63)

Percent change in real
disposable income
(year over year)

1.067***
(25.61)

1.080***
(24.69)

1.028***
(10.69)

1.195***
(61.15)

1.082***
(28.85)

Inflation rate
(year over year)

0.981***
(10.20)

0.963***
(17.31)

0.941***
(31.25)

0.943***
(27.15)

0.940***
(30.04)

Percent change in real
Dow Jones Industrial
Average (year over year)

1.004***
(14.11)

1.009***
(23.56)

1.001***
(4.46)

1.016***
(43.57)

1.004***
(11.48)

Unemployment rate

Observations

167,012

158,277

163,085

152,227

155,809

♦Significant at the 10 percent level.
** Significant at the 5 percent level.
♦♦♦Significant at the 1 percent level.
Note: The absolute value of z-statistics are in parentheses. See the text for definitions of the variables and for further details.
Sources: Authors' calculations based on data from the University of Michigan, Survey Research Center, Surveys of Consumers;
U.S. Bureau of Economic Analysis; U.S. Bureau of Labor Statistics; and Yahoo! Finance.

32

1Q/2006, Economic Perspectives

and the elderly remain more negative than the rele­
vant omitted categories. The odds ratio for the elder­
ly is especially low, 0.365. This indicates that the
elderly are nearly three times as likely to anticipate
being worse off as being better off in the coming
year. This probably results from the limited scope for
financial improvement among the elderly, many of
whom are on fixed incomes and out of the labor
force. The results for blacks and Hispanics are more
difficult to explain. Looking at the raw data, we ob­
serve that the relative optimism of both groups arises
from a higher likelihood of being positive and a low­
er likelihood of being neutral, not from a difference
in pessimism. Given that blacks and Hispanics, on
average, have lower incomes than whites, this find­
ing may demonstrate that they anticipate a forthcom­
ing improvement to their current relative income
status. In other words, they expect their future finan­
cial experience to conform more closely to the over­
all population average. However, the response to the
PAGO question indicates that this anticipation is mis­
placed. One year later a sample representing the
same population is more likely than whites to report
being worse off financially.
While the PAGO and PEXP questions are subjec­
tive and directed toward individual experiences, the
remainder of the questions are more objective and
ask respondents about their perceptions of the overall
macroeconomic climate. The effect of demographic
attributes on these perceptions is very consistent across
the three responses. For all three outcomes, the poor,
females, the less educated, and the nonwhite are all
more pessimistic or negative. The magnitude of the
odds ratios on these attributes is also consistent across
the three outcomes. The results for the elderly are
less consistent. Elderly respondents are more pessi­
mistic about purchasing durable goods, slightly more
pessimistic about the coming year, and slightly more
optimistic about the next five years.
It is challenging to explain why different groups
would have such different impressions about the pros­
pects for the macroeconomy. For these objective ques­
tions all the different individuals are being asked about
the same phenomenon—namely, their perception of
the prospects for, or condition of, the general econo­
my. In fact, the coefficients of the DUR, BUS12, and
BUS5 regressions are not all that different from the
coefficients of the PAGO regressions.
This led us to ask from where individuals are get­
ting their expectations for the macroeconomy. If re­
spondents are basing their expectations on their own
experiences, then we would expect their different
economic realities to translate into different expecta­

Federal Reserve Bank of Chicago

tions, as we find. On the other hand, if respondents
are getting their information from a common source,
such as the national news media, it is more difficult
to explain this pattern in which demographic attributes
affect expectations of the macroeconomy. Here, we
look at this issue econometrically and ask how PAGO
responses and news exposure translate into BUS12.
In doing so, we are inquiring about the source of the
BUS12 response. The results for the determinants of
BUS12 are presented in table 6.
As before, we estimate an ordered logit model.
We group the two positive and two negative responses
together, and present odds ratios in the tables. The
first column of table 6 investigates how good and
bad changes in personal economic experiences over
the past year translate into expectations for the national
economy in the coming year, controlling for the mac­
roeconomic climate. We find that individuals who are
better off than a year ago are more optimistic about
the coming year than those who are the same (the omit­
ted category), and we find that those who are worse
off are more pessimistic. This tells us that national
expectations are partly driven by recent individual
experiences. The macroeconomic variables have the
expected magnitudes. In the second column, we add
indicators of whether individuals have heard any good
or bad news about business conditions. Individuals
who have heard good news are more than twice as
likely to report being optimistic as being pessimistic
about economic prospects relative to those who have
heard no news (the omitted category), while individ­
uals who have heard bad news are only half as likely
to be optimistic as pessimistic. The addition of these
variables only changes the odds ratios on other vari­
ables by a small amount. In the third column of the
table, we add interactions between hearing no news
and recent past experience. We are inquiring whether
individuals with no exposure to news place more
weight on their own recent experience. We find that
hearing no news and having a good past year render
respondents more optimistic, while hearing no news
and having a bad year render respondents more pes­
simistic. In other words, in the absence of external
sources of information, individuals place more im­
portance on their own experiences.
In the fourth column of the table, we add in the
demographic characteristics. For the sake of compar­
ison, we include the coefficients from the regression
estimating BUS12 from table 5 in the final column
of table 6. We find that even when controlling for
past experiences and news exposure, the demograph­
ic characteristics matter. In fact, comparing the
fourth and fifth columns of table 6, we find that the

33

TABLE 6

Determinants of BUS12
1

2

3

PAGO
response

Add
news

Add news
x PAGO

4
Add
demographic
characteristics

5
Original
demographic
characteristics

Better off than a year ago

1.488***
(32.38)

1.430***
(28.00)

1.371***
(19.62)

1.338***
(16.81)

Worse off than a year ago

0.575***
(41.28)

0.585***
(38.49)

0.635***
(25.51)

0.651***
(22.71)

Unemployment rate

0.889***
(32.53)

0.869***
(36.80)

0.869***
(36.77)

0.873***
(33.57)

0.874***
(35.40)

1.179***
(58.97)

1.144***
(45.77)

1.144***
(45.88)

1.150***
(44.98)

1.195***
(61.15)

0.946***
(27.12)

0.938***
(29.70)

0.938***
(29.56)

0.937***
(27.89)

0.943***
(27.15)

1.015***
(41.97)

1.011***
(29.72)

1.011***
(29.72)

1.012***
(29.57)

1.016***
(43.57)

Heard good economic news

2.201***
(60.00)

2.187***
(48.90)

2.070***
(42.65)

Heard bad economic news

0.454***
(72.09)

0.450***
(51.75)

0.431***
(50.91)

1.108***
(4.58)

1.124***
(4.93)

0.829***
(7.52)

0.855***
(5.92)

Percent change in real
disposable income
(year over year)

Inflation rate (year over year)
Percent change in real
Dow Jones Industrial
Average (year over year)

Heard no news x better off
than a year ago
Heard no news x worse off
than a year ago

Poor

0.862***
(6.46)

0.805***
(9.96)

Female

0.713***
(29.66)

0.680***
(35.67)

Without high school diploma

0.791***
(11.46)

0.766***
(13.95)

High school graduate

0.917***
(5.98)

0.907***
(7.25)

Some college

1.029*
(1.82)

1.015
(0.98)

Black

0.716***
(16.12)

0.709***
(17.60)

Hispanic

0.932**
(2.39)

0.958
(1.55)

Elderly

1.065***
(3.48)

0.953***
(2.83)

Family size

0.999
(0.18)

1.015***
(3.50)

Observations

167,622

164,501

164,501

149,142

152,227

♦Significant at the 10 percent level.
** Significant at the 5 percent level.
♦♦♦Significant at the 1 percent level.
Notes: The absolute value of z-statistics are in parentheses. Region of residence, marital status, and other races are controlled for,
but not reported. See the text for definitions of the variables and for further details.
Sources: Authors’ calculations based on data from the University of Michigan, Survey Research Center, Surveys of Consumers;
U.S. Bureau of Economic Analysis; U.S. Bureau of Labor Statistics; and Yahoo! Finance.

34

1Q/2006, Economic Perspectives

magnitudes of the odds ratios are little changed from
the earlier regressions. Most of the odds ratios are
closer to one (indicating that the underlying coeffi­
cients are closer to zero), but the differences are not
large. This indicates that the PAGO and news vari­
ables can explain a small part of the contribution of
demographics to expectations. Individual experiences
may play a larger role in influencing expectations,
but these experiences are not fully captured in the
PAGO variable.18

Conclusion
Policy decisions that are made using aggregate
data are often ultimately aimed at particular income
and demographic groups. Therefore, it might be use­
ful to have an alternative measure of macroeconomic
situations from the perspective of lower-income pop­
ulations. We investigate this possibility with an anal­
ysis of group differences in consumer sentiment. Our
findings suggest that index disaggregation by group
matters because sentiment varies systematically by
group attributes. In addition, demographic character­
istics are found to influence responses to all five of
the component questions that contribute to the index
measure of the ICS. That is, the importance of demo­
graphic characteristics holds for both subjective and
objective questions. Individuals’ attributes not only
influence perceptions of their own experiences, but

Federal Reserve Bank of Chicago

also their expectations of the economy more generally.
Further investigation into this result shows that indi­
viduals form their expectations based on both their
individual experiences and their exposure to news.
However, many individuals in the sample report that
they have heard no news, leaving them dependent on
their idiosyncratic experiences and perceptions.
Future research might test whether consumer
sentiment forecasts the behavior of households actu­
ally surveyed (as opposed to merely capturing broad
aggregate economic trends). It might also be interest­
ing to determine whether differences in consumer sen­
timent might explain or predict groups’ differences
not only in consumption but also in savings and in­
vestment behavior. Future research using household
microdata might test whether accounting for the dis­
tribution of sentiment across different groups might
provide additional information to forecasts of macro­
level models. For instance, the current aggregate con­
sumer sentiment index, the ICS, is an equally weighted
average of the sentiment of the survey respondents,
which ignores the scale of the differences in consump­
tion across respondents. Future research might con­
struct a new sentiment series by taking into account
the distribution in sentiment across groups. For in­
stance, a new index could use group-level consumption-to-weight sentiment. Such a series could
potentially assist in the forecasting of consumption.

35

NOTES
!We are grateful to Richard Curtin, director of the University of
Michigan’s Surveys of Consumers, for providing us with the data.
2A short review of papers that use micro-level data is provided in
Souleles (2004). The papers include Leeper (1992); Matsusaka
and Sbordone (1995); Berg and Bergstrom (1996); Batchelor and
Dua (1998); Bram and Ludvigson (1998); Acemoglu and Scott
(1994); and Carroll, Fuhrer, and Wilcox (1994).

3Carroll, Fuhrer, and Wilcox (1994) and Bram and Ludvigson
(1998) find no correlation between sentiment and future spending,
which is inconsistent with the precautionary savings motive as­
sumption. However, Souleles (2004) analyzes micro-level data of
consumer sentiment and finds that higher confidence is correlated
with lower consumption growth or more savings, which are con­
sistent with precautionary motives. Aggregate indexes sum up re­
sponses of individuals that have different sentiments. These authors
attribute the discrepancy in the results with previous studies to
potential “aggregation bias” in the macro-level analysis approach
of other studies. The aggregation bias stems from the fact that it
is possible that the differences may not aggregate up.
4The idea is that the kinds of information that come from the manu­
facturing sector may be better known to the population that is geo­
graphically closer to its source. For example, layoffs may be more
visible and may have a bigger impact on the population’s region.
These consumers may have an earlier signal of change on which
to base their assessments of future economic trends.

5The University of Michigan, Survey Research Center’s ICS was
first introduced in 1952. In 1976 the index’s baseline was set at
the 1966 level of 100, which is a level generally considered to
represent a high level of optimism.
The survey population now consists of 500 nationally repre­
sentative individuals in the coterminous United States (prior to
1976, in the earlier years of the survey, two to three times as many
individuals were interviewed). This cross-sectional sample is con­
structed using a stratified system that assures proportional repre­
sentation of different states, geographic regions, and metropolitan
areas of varying sizes. (The survey also employs a rotating-panel
design in which respondents are reinterviewed six months after
the initial questioning, resulting in a monthly sample that is, on
average, 40 percent first-time respondents and 60 percent second­
time respondents.)
6More specifically, to generate the index number, a “score” for
each question is created. The score is equal to the difference be­
tween the percent of respondents giving unfavorable (pessimistic)
responses to each question and the percent of respondents giving
favorable (optimistic) responses to each question, plus 100. For
example, if 55 percent of interviewees expect to be better off next
year, 30 percent expect to be worse off, and 15 percent expect no
change, the score for question two is 125 (= 55-30 + 100). The
SRC adds the scores of the five questions together and divides
that sum by 6.7558, a constant which makes the index relative to
the 1966 base score of 100. Finally, the number 2.0 is added to
the index in order to “correct for sample design changes from the
1950s” (prior to December 1981, n = 2.7). This process is repre­
sented in the formula:

ICS = (PAGO + PEXP + BUS12 + BUS5 + DUR + n) / 6.7558.
The Index of Consumer Expectations (ICE) and the Index of Cur­
rent Economic Conditions (ICC) are calculated as follows:

7The sampling error is an important consideration in correctly in­
terpreting the ICS. With a monthly sample size of 500 and a quar­
terly sample size of 1,500, small shifts in the index may not be
significant. Specifically, the 95 percent confidence interval for
the monthly ICS is +/- 3.29 points (Curtin, 2002). The 95 percent
confidence interval for the quarterly data is +/-1.91 points.
8See, for example, Carroll, Fuhrer, and Wilcox (1994) and Bram
and Ludvingson (1998).

9The Survey Research Center at the University of Michigan weights
responses in order to generate a representative sample of all U.S.
households (or all individual adults, depending on which set of
weights is used). The weights correct for undersampling of cer­
tain populations, such as the poor. After weights are applied, most
subpopulations seem to be well represented in the 2000 Surveys
ofConsumers, compared with population data from the 2000 Census.
However, it does appear that undersampling of the Hispanic
population is not corrected when weights are applied. Additionally,
the population without a high school diploma is underrepresented
in the year 2000, although over all years of the SRC survey, 15.96
percent of respondents have less than a high school education
(after weighting).
10In particular, the number of negative responses given to the un­
derlying questions is subtracted from the positive responses. This
number is divided by the number of questions asked. Then 100 is
added to this number which is then multiplied by two and divided
by a scaling factor that depends on which index is being calculated.
Two is then added to this number after 1982 and 2.7 before 1982.

11 We calculated the index of individuals based on their regions of
residence. There is no noticeably strong difference in the consumer
sentiment across respondents living in different regions. We there­
fore do not report these results, but they are available from the
authors upon request.
12We use the annual poverty thresholds calculated by the U.S. Census
Bureau. The thresholds differ based on family composition and
the ages of household members. A household is considered poor
if household income falls below the threshold.

^Respondents’ expectations of the unemployment rate over the
12 months following the survey are based in part on what has
happened to unemployment over the previous six to 12 months.
They also predict, to some degree, unemployment over the subse­
quent six to 12 months. We measure change in actual unemployment
as a four-quarter moving average of the one-year change in the
unemployment rate. For example, the change in actual unemploy­
ment in 199O:Q1 is recorded as the average of the difference be­
tween the unemployment rates between: 1) 199O:Q1 and 1989:Q1;
2) 1989:Q4 and 1988:Q4; 3) 1989:Q3 and 1988:Q3; and 4) 1989:Q2
and 1988:Q2. In other words, “change in actual unemployment”
actually reflects the way the unemployment rate changed over the
previous year relative to the year before it. Therefore we can in­
terpret the two-quarter lagged correlation between expectation
and actual unemployment as the relationship between expectations
of unemployment over the coming year and actual changes in un­
employment during the six-month periods immediately preceding
and following the survey.
14Please refer to note 10 for more details.

ICC = (PAGO + DUR + w)/2.6424 and ICE = (PEXP + BUS12
+ BUS5 + n)/ 4.1134.

36

1Q/2006, Economic Perspectives

15Some studies have found that, at the local level, demographic
characteristics explain variation in consumer confidence. We run
these regressions separately by region and find that the effects of
demographic characteristics are still significant in explaining dif­
ferences in the indexes.
16Due to space constraints, the results for the ICE and the ICC are
not given in tables. They are available upon request.

17We ran these regressions using different specifications of the depen­
dent variables, including omitting neutral individuals, separating
the two positive and two negative BUS5 and BUS12 responses,
and measuring the net number of positive reasons given for the
PAGO question. These other specifications provided substantively

similar results. The only difference was for the elderly—when we
dropped individuals who were the same, the odds ratio moved farther
from one. Because the individuals who are the same are better off
than those who are worse off, and because more elderly report
that they are worse off than that they are better off, including in­
dividuals who are the same, this leads to an increase in the odds
ratio among the elderly.

18We perform a parallel set of analyses with the BUS5 variable. The
results are substantively similar to the BUS12 results except for
the odds ratio for the interaction between a good past year and
hearing no news. It is less than one and insignificant for BUS5,
while this odds ratio was greater than one and statistically signifi­
cant for BUS12.

REFERENCES

Acemoglu, D., and A. Scott, 1994, “Consumer con­
fidence and rational expectations: Are agents’ belief
consistent with the theory?,” Economic Journal,
Vol. 104, No. 422, pp. 1-19.

Campbell, J. Y., and N. G. Mankiw, 1990, “Perma­
nent income, current income, and consumption,”
Journal ofBusiness and Economic Statistics, Vol. 8,
No. 3, pp. 265-279.

Alessie, R., and A. Lusardi, 1997, “Saving and in­
come smoothing: Evidence from panel data,” Euro­
pean Economic Review, Vol. 41, No. 7, pp. 1251-1279.

Carlson J. A., and N. T. Valev, 1999, “Sources of
dispersion in inflation forecasts,” Purdue University,
Center for International Business Education and
Research, working paper, No. 99-002.

Batchelor, R., and P. Dua, 1998, “Improving mac­
ro-economic forecasts: The role of consumer confi­
dence,” International Journal ofForecasting, Vol. 14,
No. l,pp. 71-81.

Becker, G S., 1976, The Economic Approach to Hu­
man Behavior, Chicago and London: University of
Chicago Press.
Berg, L., and R. Bergstrom, 1996, “Consumer con­
fidence and consumption in Sweden,” University of
Uppsala, Department of Economics, working paper,
No. 1996:7.

Bram, J., and S. Ludvigson, 1998, “Does consumer
confidence forecast household expenditure? A senti­
ment index horse race,” Economic Policy Review,
Federal Reserve Bank of New York, Vol. 4, No. 2,
June, pp. 59-78.

Bryan M. F., and G Venkatu, 2001a, “The curiously
different inflation perspectives of men and women,”
Economic Commentary’, Federal Reserve Bank of
Cleveland, November 1.
__________ , 2001b, “The demographics of inflation
opinion surveys,” Economic Commentary, Federal
Reserve Bank of Cleveland, October 14.

Federal Reserve Bank of Chicago

Carroll, C. D., J. C. Fuhrer, and D. W. Wilcox,
1994, “Does consumer sentiment forecast household
spending? If so, why?,” American Economic Review,
Vol. 84, No. 5, pp. 1397-1408.

Curtin, R. T. (director), 2002, Sun’eys of Consumers,
University of Michigan, Survey Research Center.
Dominitz J., and C. F. Manski, 2004, “How should
we measure consumer confidence?,” Journal ofEco­
nomic Perspectives, Vol. 18, No. 2, Spring, pp. 51-66.

Doms, M., and N. Morin, 2004, “Consumer sentiment,
the economy, and the news media,” Federal Reserve
Bank of San Francisco, working paper, No. 2004-09.

Dunn, L. F., and I. A. Mirzaie, 2004, “Turns in con­
sumer confidence: An information advantage linked
to manufacturing,” Ohio State University, working
paper, No. 04-03, August.
Flavin, M., 1991, “The joint consumption/assets
demand decision: A case study in robust estimation,”
National Bureau of Economic Research, working
paper, No. 3802, August.

Friedman, M., 1957, A Theory’ of the Consumption
Function, Princeton, N.J.: Princeton University
Press.

37

Gul, F., and W. Pesendorfer, 2002, “Self control,
revealed preference, and consumption choice,” Prin­
ceton University, working paper, November.

Hall, R., 1978, “The stochastic implications of the
life cycle-permanent income hypothesis: Theory and
evidence,” Journal ofPolitical Economy, Vol. 86,
December, pp. 971-987.

Katona, G, 1975, Psychological Economics, Am­
sterdam: Elsevier Science.

Otoo, M. W., 1999, “Consumer sentiment and the
stock market,” Board of Governors of the Federal
Reserve System, Finance and Economics Discussion
Series, working paper, No. 1999-60, November.
Palmqvist, S., and L. Stromberg, 2004, “House­
holds’ inflation opinions—A tale of two surveys,”
Economic Review, Sveriges Riksbank (Bank of Sweden),
No. 4, pp. 23-41.

Keynes, J. M., 1936, 77?e General Theory ofEmploy­
ment, Interest, and Money, London: McMillan Books.

Ranchhod, S., 2003, “The relationship between in­
flation expectations survey data and inflation,” Bulle­
tin, Reserve Bank of New Zealand, Vol. 66, No. 4,
December, pp. 50-65.

Leeper, E. M., 1992, “Consumer attitudes: King for
a day,” Economic Review, Federal Reserve Bank of
Atlanta, July, pp. 1-15.

Shea, J., 1995, “Union contracts and the life-cycle/
permanent-income hypothesis,” American EconomicReview, Vol. 85, No. 1, pp. 186-200.

Lombardelli, C. and J. Saleheen, 2003, “Public ex­
pectations of UK inflation,” Quarterly Bulletin, Bank
of England, Autumn.

Sims, C., 2003, “Implications of rational inattention,”
Journal ofMonetary Economics, Vol. 50, No. 3,
pp. 665-690.

Matsusaka, J. G., and A. M. Sbordone, 1995,
“Consumer confidence and economic fluctuations,”
Economic Inquiry, Vol. 33, No. 2, April, pp. 296-318.

Souleles, N. S., 2004, “Expectations, heterogeneous
forecast errors, and consumption: Micro evidence
from the Michigan Consumer Sentiment Surveys,”
Journal ofMoney, Credit, and Banking, Vol. 36,
No. 1, February, pp. 39-72.

McGranahan, L., and A. L. Paulson, 2005, “The
incidence of inflation: Inflation experiences by de­
mographic group: 1981-2004,” Federal Reserve
Bank of Chicago, working paper, No. WP-2005-20.

Michael, R., 1979, “Variation across households in
the rate of inflation,” Journal ofMoney, Credit, and
Banking, Vol. 11, No. 1, February, pp. 32-46.

38

Wall, H. J., 2003, “Recessions, expansions, and
black employment,” Regional Economist, Federal
Reserve Bank of St. Louis, October.

1Q/2006, Economic Perspectives

Earnings announcements, private information, and liquidity

Craig H. Furfine

Introduction and summary
Efficient financial markets facilitate the smooth trans­
fer of money from those who save to those with prof­
itable investment opportunities. Such markets generally
exhibit high levels of trading volume and widespread
market participation. Investors are willing to partici­
pate because they are convinced that the prices at which
securities can be bought and sold are reasonably effi­
cient. For example, a market participant should be able
to buy or sell a share of stock in XYZ company at a
price very close to the present discounted value of
the market’s best estimate of XYZ’s future dividend
payments.1
So where do these market estimates come from?
There are two main types of information underlying
these estimates—one, information that is common to
all market participants (I call this public information),
and two, information that is specific to individual in­
vestors (I call this private information). An example
of public information in this context would be a news
release about company XYZ that might be expected
to move the company’s share price. A company mak­
ing a surprisingly good earnings announcement typi­
cally sees an immediate rise in its stock price. Bad
news generally has the reverse effect. Clearly, this
type of information impacts market prices.
What is less clear is whether and to what extent
private information impacts stock prices. On most
trading days, there is no obvious “news” (that is, public
information) regarding the value of a particular stock,
yet stocks still trade and often show noticeable price
changes. Some widely held stocks trade every few
seconds on all trading days. Although some of this
activity can be linked to public news, much of the trad­
ing and related price changes occur when there is no
easily observable event or publicly conveyed infor­
mation believed to be relevant to a given company’s

Federal Reserve Bank of Chicago

stock price. Suppose that an investor places a large
order to purchase shares of XYZ on a day when no
public news about XYZ is released. Depending on
the characteristics of this trade, the price of XYZ
may change. For example, if market participants be­
lieve this trade was made by an investor who believes
the stock is undervalued, others may revise their own
expectations and afford shares of XYZ a higher price.
Alternatively, participants observing a large purchase
order may attribute the purchase to the fact that the
given purchaser of XYZ is a manager of an index hind
that is well known to have been receiving large inflows
of investment capital. Thus, the purchase of XYZ con­
tains no information about the value of XYZ shares.
In this case, one might expect the purchase to have
a more limited impact on the stock’s price. In reality,
the underlying purpose of individual trades is not gen­
erally known, and therefore one can characterize trades
as containing some degree ofprivate information.
Understanding how both public and private types
of information influence security prices is one of the
main goals of financial market microstructure analysis.
Earnings announcements are perhaps the most visible
form of public information. At the most extreme, in­
sider trading by an executive knowing the contents
of a forthcoming news release is an example of private
information. However, private information can simply
be thought of as all information about a given securi­
ty price that is not known by all who trade it. For ex­
ample, a mutual fund manager’s decision to reduce

Craig H. Furfine is a senior economist and an economic
advisor in the Research Department at the Federal
Reserve Bank of Chicago. The author would like to thank
colleagues at the Federal Reserve Bank of Chicago for
their helpful comments and Lauren Gaudino for excellent
research assistance.

39

the holding of a given stock would be considered pri­
vate information capable of affecting security prices.
Private information, however, can be even less
tangible. Differing opinions as to the implications of
an earnings announcement may generate important
private information, since some may believe the opti­
mal response to the news is to buy a security, whereas
others may wish to sell. It is the collective trades of
market participants that move prices. Market micro­
structure analysis presumes that trading is necessary
to determine prices because it conveys private infor­
mation regarding the value of the underlying asset.
The intuition is relatively simple: As a sequence of sell
orders arrives, prices will be adjusted lower as poten­
tial buyers incorporate a higher probability that better
informed traders believe the previous price was too high.
This article attempts to shed light on the relative
importance of private versus public information in
moving security prices. I examine closely the intraday
trading activity of ten large companies trading on the
New York Stock Exchange and estimate an empirical
model that relates trading activity to price changes. My
focus is on how this trading-price change relationship
changes on days when there is a major release of pub­
lic information regarding the company in question—
in particular, quarterly earnings announcements. In
this way, my goal is to quantify by how much, if any,
the trading-price change relationship changes with a
large increase in public information.
My hypothesis is that the strength of the tradingprice change relationship is a measure of the impor­
tance of private information in security price formation.
An empirical implication of this is that a major release
of news should be accompanied by a reduction in the
strength of the relationship between trading and price
changes. I conduct a series of empirical exercises that
provide evidence consistent with this hypothesis. How­
ever, my results further indicate that even after an
earnings announcement, private information plays a
significant role in security price determination. Across
the firms in my sample, I find that the strength of the
trading-price change relationship, my proxy for the
importance of private information, declines by no more
than one-third on trading days immediately follow­
ing a company’s quarterly earnings announcement.
Thus, private information appears to be a significant
factor in the relationship between trading and prices.
The remainder of this article is organized as fol­
lows. First, I briefly review some related work. Then,
I describe the data and the empirical framework of
my analysis. Finally, I present my findings and dis­
cuss their implications.

40

Related literature
As mentioned previously, market microstructure
theory argues that order flow (that is, the sequence of
buy and sell orders) affects prices because it conveys
private information regarding the value of the underly­
ing asset. In Glosten and Milgrom (1985), for example,
the authors formally model why private information
leads immediately to the presence of a bid-ask spread
(the difference between the proposed purchase price
and proposed sale price for the same security) as well
as a relationship between trading and price changes.
In their model, a marketmaker2 for a given security
stands ready to buy or sell. The marketmaker believes,
however, that some of the potential buyers have pri­
vate information that indicates that the marketmaker’s
current price of the security is too low. Alternatively,
or perhaps in addition, the marketmaker believes that
some potential sellers of the security have private in­
formation indicating the marketmaker’s price is too
high. The result of this asymmetry, along with the mar­
ketmaker’s continued willingness to trade, is a positive
bid-ask spread. That is, the price at which a market­
maker is willing to buy is lower than the price at which
he is willing to sell. This spread serves as compensa­
tion for trades made with those counterparties with
superior information.3 As a sequence of sell orders
arrive, marketmakers lower bid prices, incorporating
the probability that the order flow implies that better
informed investors believe the previous price was too
high. This adjustment of posted spreads implies an
analogous change in observed transaction prices.
Over the past two decades, the microstructure
literature has explored how, when, and how much
order flow affects stock prices. Here, I focus on the
work most related to my current analysis, specifically
work on the relationship between trading and price
changes.4 This relationship ultimately provides a(n
inverse) measure of a security’s liquidity because a
stock whose price changes a lot in response to in­
coming trades would be deemed relatively illiquid.
The literature shows that liquidity itself and the rela­
tionship between public news releases and liquidity
can be measured in a number of ways.
Seppi (1992) conducts empirical tests to determine
the informativeness of block trades (typically, 10,000
shares or more) and how this informativeness corre­
lates with public news releases. In particular, he docu­
ments that the prices at which such trades are filled
are positively correlated with the earnings surprises.
That is, block trades occur at higher prices before
positive earnings surprises and at lower prices before
negative earnings surprises. This is consistent with the
belief that investors making such trades, on average,

1Q/2006, Economic Perspectives

have some knowledge about the information that will
be made public in a subsequent earnings announcement
and are therefore anticipating the change in stock price.
Lee, Mucklow, and Ready (1993) explicitly describe
various alternative measures of market liquidity. They
focus not only on the size of the bid-ask spread, but
also on a stock’s posted depth, which measures the
quantity of shares a marketmaker is willing to transact
at the posted bid and ask prices. Their study documents
that both spreads and depth adjust to the perceived
amount of private information in the market. In partic­
ular, spreads generally widen and depth generally falls
preceding earnings announcements.
Koski and Michaely (2000) extend these findings
by examining the relationship between measures of
liquidity (for example, spreads and depth) across key
information periods, which include both earnings and
dividend periods. They find that these liquidity mea­
sures do relate to the perceived information content
of the trade. In particular, large trades before dividend
announcement periods tend to reduce depth and in­
crease spreads most strongly. Similar results, though
smaller in magnitude, are found during announcement
periods. This is consistent with the notion that private
information is at its highest level just prior to a news
release. However, since these authors combined in­
formation from before and after an announcement pe­
riod, they could not distinguish precisely how news
affects liquidity over the period immediately preced­
ing and following announcements, nor did they for­
mally analyze the trading-price change relationship.
Green (2004) conducts a study of the relationship
between announcements and the information content
of trading in U.S. Treasury bonds. For Treasury bonds,
news announcements are not about corporate earnings,
but rather about the latest release of economic data.
Green finds that when macroeconomic news is released,
the information component of trading increases. Thus,
unlike Koski and Michaely (2000), Green associates
public news release with an increase in private infor­
mation. Perhaps macroeconomic news releases gen­
erate more information on which individual traders
can disagree, generating a higher share of private in­
formation that in turn affects security prices.
Thus, the previous empirical work provides evi­
dence that the more informative a given trade, the
greater its influence on security prices. However, the
evidence is somewhat mixed with regard to how the
overall liquidity of a security is influenced by news.
In particular, there is not yet a consensus as to whether
public news arrival reduces or generates private
information. Rather, it appears from previous work
that public news releases have the potential either to

Federal Reserve Bank of Chicago

generate or eliminate private information. Thus, it re­
mains an empirical question to decide whether public
news arrival will strengthen the trading-price change
relationship (consistent with private information gen­
eration) or weaken it (consistent with private infor­
mation elimination).

Data and empirical framework
My analysis relies on data from three sources.
I begin with the universe of firms whose earnings
history was available on Briefing.com. To select my
sample, I required that Briefing.com reported the
date, time, value, and market expectation of every
earnings announcement that a firm reported between
January 29, 2001, and December 31, 2004. For my
purposes, the Briefing.com data provide an important
piece of information unavailable in the more tradi­
tionally used sources of announcement histories, such
as Thomson Financial’s FirstCall and the Institutional
Brokers’ Estimate System: whether a firm’s earnings
announcement was made prior to the stock market
opening, during the trading day, or after the market
close. Thus, it is possible to know precisely which
day of stock market trading is associated with the re­
action to the earnings announcement. This distinction
will be crucial to my analysis. In what follows, I refer
to the first trading day following the announcement
as a company’s “announcement day.” For example,
if a company announces its earnings on a Tuesday
before or during trading hours, its announcement day
is that Tuesday. If the announcement is made on a
Tuesday after the market closes, its announcement
day will be that Wednesday. Furthermore, my analysis
carefully considered the role of weekends and public
holidays in order to correctly pair a given announce­
ment with the next possible trading day.
I then compared this sample of firms to the data­
base provided by the Center for Research in Security
Prices. I considered only those firms that were listed
on the New York Stock Exchange (NYSE) to avoid
the well-known differences in the liquidity (and by
extension, the strength of the trading-price change
relationship) of stocks trading on different exchang­
es. I then calculated each firm’s market capitalization
based on stock price data as of December 31, 2001,
and selected the ten largest remaining firms to be the
focus of my study.
Having identified the ten stocks in my sample,
I then combined the earnings announcement informa­
tion from Briefing.com with high frequency data on
the trading of the stocks of these ten firms from the
NYSE Trade and Quote (TAQ) database. Although
Briefing.com provides earnings information since

41

TABLE 1

Summary statistics
Average trade
size, shares

Average number
of trades

Average bid-ask
spread, dollars

Average depth,
round lots

Bristol-Myers Squibb Co. (BMY)

1,537.9490
(527.6960)

4,175.6380
(1,326.6590)

0.0334
(0.0238)

31.6022
(14.9068)

EMC Corp. (EMC)

2,343.7580
(655.9954)

7,070.8470
(2,606.1300)

0.0331
(0.0375)

79.0106
(36.9236)

General Electric Co. (GE)

2,040.1440
(703.1345)

11,271.3300
(3,437.3040)

0.0271
(0.0156)

68.7537
(40.4598)

Home Depot Inc. (HD)

1,339.4020
(339.1715)

6,446.9410
(2,160.3100)

0.0304
(0.0178)

36.5258
(20.9643)

1,100.4560
(384.0330)

6,776.2480
(1,680.0090)

0.0523
(0.0302)

16.3487
(8.0540)

Coca-Cola Co. (KO)

1,415.3070
(473.8881)

3,827.4650
(1,321.2770)

0.0290
(0.0149)

23.3063
(10.2120)

Merck & Co. Inc. (MRK)

1,403.8030
(448.6887)

5,421.9910
(3,776.4130)

0.0382
(0.0258)

26.9202
(22.3486)

Nortel Networks Corp. (NT)

4,906.4550
(3099.6320)

5,451.2300
(3,899.5310)

0.0182
(0.0170)

720.4567
(730.3201)

Pfizer Inc. (PFE)

1,982.2700
(465.7550)

8,839.0240
(3,908.9970)

0.0270
(0.0152)

55.0667
(38.1269)

SBC Communications Inc. (SBC)

1,784.0950
(516.5796)

4,608.6720
(1,219.6850)

0.0282
(0.0162)

40.3322
(19.2419)

Name/Ticker

International Business Machines
Corp. (IBM)

Notes: This table reports the mean (and standard deviation in parentheses) of various measures of trading activity for each
stock in the sample. Averages are taken across all 959 days that are not within one day of an earnings announcement.
Sources: Author’s calculations based on data from Briefing.com and the New York Stock Exchange Trade and Quote database.

1997,1 restrict my sample period to January 29, 2001,
through December 31, 2004. The starting date of my
sample corresponds to the first day on which all stocks
listed on the NYSE began trading with a minimum
price increment (tick) of one cent (that is, decimal­
ization). This eliminates the need to consider how
minimum tick size changes might influence the rela­
tionship between earnings announcements and liquidity,
since a vast literature has documented the importance
of minimum tick size to liquidity in general.
I then adjusted the data according to procedures
common in the microstructure literature. I dropped
quotes with obviously erroneous data (for example,
quotes with bid or ask prices equal to zero or quotes
with bid-ask spreads dramatically different from the
previous or subsequent quote). Following Hasbrouck
(1991), I kept only quotes originating from the NYSE
and considered multiple trades on a regional exchange
for the same stock at the same price and time as one
trade. Then, I sorted the trade data (for each compa­
ny and day) by time, with the prevailing quote at

42

transaction t defined as the last quote that was posted
at least five seconds before the transaction (Lee and
Ready, 1991). I provide a complete listing of the
stocks in my sample, along with summary statistics
on their trading, in table 1.
The summary statistics show many facts about
stock market trading. First, these ten stocks are very
heavily traded. The least actively traded stock in my
sample is Coca-Cola (KO), yet shares of this stock
traded over 3,800 times each day, a trading intensity
of roughly once every six seconds. The most actively
traded stock in my sample is General Electric (GE),
whose shares traded over 11,000 times each day on
average (approximately once every two seconds).
Bid-ask spreads on all of the sample stocks are typi­
cally quite narrow. On average, spreads range from a
low of 1.82 cents for Nortel (NT) to a high of 5.23
cents for IBM.
I am interested in changes in trading characteris­
tics that occur on or around earnings announcement
days. To present some preliminary evidence on this

1Q/2006, Economic Perspectives

subject, I regress the daily values of various measures
of trading activity on a set of dummy variables that
indicate the day before, the day of, and the day after
an earnings announcement. Coefficients from this re­
gression, which represent differences relative to all
other days, are presented in table 2. This table indicates
that there are noticeable changes in common proxies
for stock market liquidity on earnings announcement
days. Most strikingly, trading volume increases. Trade
size and average depth, which represents the number
of shares available at the posted spread, also tend to
rise on announcement days, although these results
appear to be statistically significant for only a subset
of my sample firms. For KO shares, for example, aver­
age trade size increases by 587 shares, and typical depth
rises by 7.4 round lots (that is, 740 shares) on announce­
ment days. Not all statistical indicators of liquidity,
however, indicate greater liquidity on announcement
days. Although not statistically significant in most cases,
bid-ask spreads tend to rise on announcement days. For
instance, IBM’s bid-ask spread typically increases by
1.4 cents on an announcement day. Data across these
ten stocks tell a similarly inconsistent story with re­
gard to the relationship between liquidity and an­
nouncement days—namely, that announcement days
witness an increase in trading volume and depth, but
either little change or a widening of bid-ask spreads.
Because announcement days are correlated with
heavier trading volume and higher depth but wider
spreads, it would be useful to focus on a measure of
stock market liquidity that may account for these
changes. Here, I use the price impact of a trade as a
measure of a stock’s liquidity that embeds the impact
of volume, spreads, and depth. That is, I take the po­
sition that price impact is the quantity that ultimately
relates to the strength of the trading-price change re­
lationship and that volume, spreads, and depth (among
other observable characteristics) are noisy indicators
of such a relationship.
I adopt the general empirical framework of
Hasbrouck (1991), who estimates a vector autoregres­
sion (VAR) model of two equations. The first equa­
tion models trade-to-trade stock returns as a function
of past returns as well as current and past trades, ex­
plicitly considering whether the trade was to purchase
or to sell shares. The second equation models the de­
cision to buy or sell as a function of both past trading
and past stock returns. In such a framework, Hasbrouck
delivered some benchmark results upon which I build
in my analysis. In particular, Hasbrouck documents the
positive relationship between order flow and price changes
using a sample of 80 NYSE and American Stock and
Options Exchange (AMEX) stocks. That is, buy orders

Federal Reserve Bank of Chicago

lead to price increases, and sell orders lead to price de­
clines. Hasbrouck further extended his analysis to in­
dicate that larger trades tend to move prices more, a
finding that I incorporate into my framework.
My empirical results are based upon VAR models
of increasing complexity. The first merely replicates
a version of the Hasbrouck (1991) analysis. I specify
this by equations 1 and 2, which I estimate separately
for each of the ten firms in my sample.
L

d

L

r, =yLairt-,+yLyT
ix>-i +v«,
7=1

7=0

L

L

2) v = E a<r>-i+E ? * x>-i+ v« •
7=1

7=1

The unit of observation is the trade, which is in­
dexed by the subscript t. The variable r is defined as
the change in the natural logarithm of the midquote
(average of the current bid and ask price) of a given
stock that follows the trade at time 1.1 use midquotes
as my price variable to eliminate the well-known
problems with using actual transaction prices in em­
pirical analysis, notably the tendency of transaction
prices to bounce between the bid and ask prices with­
out indicating any true movement in the underlying
security value. Also following Hasbrouck (1991),
I define xf as the log of the number of shares of trade
7, signed to indicate whether or not trade t was initiated
by a buy order or a sell order. That is, a positive value
of x indicates a buyer-initiated trade, and a negative
value indicates a seller-initiated trade. As the TAQ
data do not indicate which party initiated each trade,
I follow the literature’s convention and assume that
trades at a transaction price greater than the midquote
were buyer-initiated and trades below the midquote
were seller-initiated. For trades at the midquote, I de­
termine the side of trade origination according to the
tick rule (see Lee and Ready, 1991).
I truncate the VAR model by setting L equal to
eight for all stocks and for all time periods. Though
longer than the five lags adopted by Hasbrouck (1991),
this reflects the higher level of trading in more recent
periods. Finally, I estimate equations 1 and 2 by ordi­
nary least squares and correct standard errors using
White’s (1980) methodology.
I then expand the model in several ways to ex­
plore how the relationship between trading and price
might change in ways related to earnings announce­
ments. My first additional model can be expressed by
equations 3 and 4.

43

TABLE 2

Deviations around announcement dates
Name/Ticker

Bristol-Myers Squibb Co. (BMY)
Day before announcement

Average number
of trades

Average bid-ask
spread, dollars

Average depth,
round lots

72.634
(105.172)

28.562
(257.771)

-0.005
(0.004)

-3.274
(2.716)

1,484.962
(527.086)**

0.003
(0.004)

4.404
(3.835)

Day of announcement

565.982
(144.990)**

Day after announcement

266.810
(155.257)

615.362
(296.985)*

-0.000
(0.005)

-0.466
(2.896)

243.296
(158.294)

668.353
(840.003)

-0.002
(0.010)

9.878
(10.834)

2,981.953
(947.399)**

0.003
(0.009)

40.342
(11.384)**

476.953
(614.181)

-0.003
(0.009)

15.625
(8.527)

97.373
(133.625)

1,056.405
(1,076.020)

0.000
(0.003)

-0.966
(4.692)

Day of announcement

129.556
(100.314)

4,079.672
(1,441.108)**

0.001
(0.003)

12.941
(10.381)

Day after announcement

-192.732
(102.162)

1,288.405
(1,190.934)

-0.001
(0.003)

-0.772
(7.713)

48.028
(70.942)

1,261.246
(720.627)

-0.001
(0.003)

0.163
(3.166)

EMC Corp. (EMC)
Day before announcement

Day of announcement

735.740
(176.647)**

Day after announcement

353.466
(158.172)*

General Electric Co. (GE)
Day before announcement

Home Depot Inc. (HD)
Day before announcement

Day of announcement

375.450
(86.186)**

Day after announcement

219.703
(96.178)*

International Business Machines
Corp. (IBM)
Day before announcement

Day of announcement

Day after announcement

Coca-Cola Co. (KO)
Day before announcement

44

Average trade
size, shares

4,809.309
(1,315.088)**

0.013
(0.005)**

18.399
(6.817)**

2,343.559
(913.464)*

-0.000
(0.003)

6.231
(3.282)

170.724
(104.526)

1,727.752
(380.877)**

0.004
(0.008)

1.907
(1.826)

626.777
(117.181)**

2,918.418
(509.751)**

0.014
(0.006)*

3.711
(1.689)*

213.212
(98.206)*

596.018
(349.537)

-0.004
(0.005)

1.969
(1.967)

78.408
(74.701)

-48.028
(274.959)

-0.000
(0.003)

1.610
(1.895)

Day of announcement

587.421
(104.822)**

1,125.597
(584.509)

0.008
(0.004)

7.404
(3.442)*

Day after announcement

431.017
(157.521)**

202.347
(365.853)

0.003
(0.003)

1.503
(1.675)

1Q/2006, Economic Perspectives

TABLE 2 (CONT.)

Deviations around announcement dates
Average trade
size, shares

Average number
of trades

Average bid-ask
spread, dollars

175.166
(103.951)

-312.124
(566.237)

-0.003
(0.005)

2.818
(6.171)

Day of announcement

368.051
(179.600)*

769.009
(743.123)

0.009
(0.010)

3.516
(4.235)

Day after announcement

141.529
(112.102)

521.743
(610.989)

-0.003
(0.004)

0.014
(3.631)

820.685
(767.880)

518.699
(943.700)

-0.002
(0.002)

339.403
(347.604)

4,894.836
(3,370.319)

2,287.913
(1,918.236)

-0.001
(0.002)

377.644
(314.681)

514.354
(698.632)

570.556
(1,366.929)

-0.003
(0.002)

80.856
(214.662)

-143.558
(58.216)*

154.976
(531.356)

-0.003
(0.002)

-6.791
(5.924)

Day of announcement

357.292
(219.192)

2,769.176
(1,017.787)**

0.001
(0.003)

21.312
(16.608)

Day after announcement

198.882
(156.403)

1,007.643
(581.881)

-0.003
(0.002)

9.601
(13.909)

358.595
(350.205)

-0.001
(0.003)

5.006
(7.154)

Name/Ticker

Merck & Co. Inc. (MRK)
Day before announcement

Nortel Networks Corp. (NT)
Day before announcement

Day of announcement

Day after announcement

Pfizer Inc. (PFE)
Day before announcement

SBC Communications Inc. (SBC)
Day before announcement

315.454
(141.799)*

Average depth,
round lots

Day of announcement

751.766
(109.573)**

1,028.662
(432.073)*

0.002
(0.003)

9.746
(6.838)

Day after announcement

533.909
(146.305)**

682.195
(350.751)

-0.000
(0.003)

6.627
(4.980)

♦Significant at the 5 percent level.
♦♦Significant at the 1 percent level.
Note: This table reports the coefficient estimates (and the Newey-West standard errors in parentheses) of a regression of average daily
values of each stock on the days surrounding an announcement date.
Sources: Author’s calculations based on data from Briefing.com and the New York Stock Exchange Trade and Quote database.

3)

7=1

4)

+0'a,_J.Y,_y + v»>

r, =^a;r,_y

x, =Y,air<-<
7=1

7=0

+Z(y*

+ v„.

7=1

In this empirical specification, I add terms to the
model that interact the trade size variable x( with a
dummy variable a(, which is set equal to one if trade t
occurs on an announcement date. This allows the rela­
tionship between trading and price changes to be dif­
ferent on announcement days. For example, positive

Federal Reserve Bank of Chicago

estimated values for 0 would indicate that the rela­
tionship between trading and stock returns becomes
stronger on announcement days.
Because I have identified a positive relationship
between trading volume and announcement days, it
is important to confirm that any relationship I find be­
tween announcement days and price impact when I
estimate equations 3 and 4 is due to the announcement
and not simply an artifact of higher trading volume.
To this end, I next estimate an expanded version of
equations 3 and 4, where I interact the trade indicator
variable with a variable /(, measuring trading volume
on the day on which trade t occurs. This expanded
specification is shown in equations 5 and 6.

45

5)

r, = Z a'ir'-i + Z (^ + Qia,-i + KX) x,-i
7=1

+'

z=0

6) *, =Z°X +Z(vr +6Xy+<ty)^-y +v.
7=1

7=1

My next empirical specification extends the model
described by equations 5 and 6 to explore whether days
immediately surrounding announcement days may be
noticeably different than other days. As described in
equations 7 and 8,1 do this by interacting variables bf,
defined to equal one if trade t occurs on the day be­
fore an announcement date and zero otherwise, and
ft, defined analogously for the day after an announce­
ment date, with the trade size variable x(, as follows:

between trading and returns varies according to the
proximity in calendar time from the announcement
date, I instead explore whether the importance of the
announcement date varies according to the realized con­
tent of the given announcement. That is, I wish to dis­
tinguish between announcements that contain surprising
information and those that do not. In particular, I es­
timate the model described by equations 9 and 10,

9) x = Z«Xy + Z(y; + eX + PXy^y
7=1I
7=0
+kc'7,
'l t-lK
.
) t- + V

10) X, =Yair,-i + Z(X0X +PX-A-;
7=1

7=1

+K7,-y)T-y

7=1

7=0

V7y-y)Vy +vrt>

+k'.,

8) T = S^-y +Z(yy XX
7=1

+v,y

XX +6'«,-y XX

7) r, = S^-y

XX

7=1

X)5

Here, I define st as an indicator variable that is equal
to one when the actual earnings announced differed
from expected earnings as reported by Briefmg.com
by more than $0.01 per share.5 One hypothesis is that
surprising earnings releases reveal more private infor­
mation than those that are unsurprising. If this were
true, one would expect the coefficients [A to be negative.

Empirical results
I estimate the empirical model described by equa­
tions 7 and 8 to explore whether the private information
content of a trade varies according to the proximity
to a public announcement rather than only depending
on whether the announcement was just made. For ex­
ample, one might believe that if announcement days
reduce the private information content of stock trading,
then the day before such an announcement might be
expected to contain a higher than average amount of
private information. That is, the likelihood of a trader’s
having private information regarding a future earnings
announcement might be expected to be the greatest
immediately before the announcement. If this were
the case, one might expect that the X. coefficients
would be greater than zero. As for the day following
the announcement, allowing the relationship between
trading and stock returns to differ facilitates an explora­
tion as to whether any changes detected on the announce­
ment day persist until the following day. To the extent
that there is persistence, one might expect to estimate
values for 0. very close to the values estimated for <(>..
Much like equations 7 and 8, my final empirical
specification extends the model described by 5 and 6.
However, rather than exploring whether the relationship

46

I form my estimate of the price impact of a trade
by calculating the cumulative impulse response of a
shock to xt on stock returns r. As a point of departure,
figure 1 graphs these responses for each firm, when
the size of the shock xt is set equal to each stock’s
median trade size and also to the stock’s 90th percen­
tile trade size. This allows one to judge the overall
liquidity of a stock on average across the roughly
four years of data and to measure by how much more
a large trade moves prices than a more typical trade.
For example, panel A of figure 1 depicts the impulse
response functions for Bristol-Myers Squibb Co. (BMY).
The graph shows that a median-sized buy order is es­
timated to eventually raise the price of BMY shares
by approximately 1.4 basis points. A large trade that
was unexpected is estimated to have a long-run im­
pact of increasing BMY share prices by a little over
1.8 basis points. The main findings illustrated by
figure 1 are that even across a sample of large firms,
market liquidity varies across firms and across trades
of a given firm. For instance, across these ten stocks,
a median-sized trade is estimated to raise prices by
between 0.7 and 1.9 basis points, depending on the

1Q/2006, Economic Perspectives

FIGURE 1

The long-run price impact of median- and large-sized trades
A. Bristol-Myers Squibb Co. (BMY)

B. EMC Corp. (EMC)

basis points

basis points

trades since shock

C.

General Electric Co. (GE)

D.

basis points

Home Depot Inc. (HD)

basis points

trades since shock

E. International Business Machines Corp. (IBM)

F. Coca-Cola Co. (KO)

basis points

basis points

trades since shock

trades since shock

G. Merck & Co. Inc. (MRK)

H. Nortel Networks Corp. (NT)

basis points

basis points

trades since shock

J. SBC Communications Inc. (SBC)

I. Pfizer Inc. (PFE)

basis points

basis points

trades since shock
---------- Median trade
Sources: Author’s calculations based on data from Briefing.com

Federal Reserve Bank of Chicago

trades since shock
.......... Large trade
the New York Stock Exchange Trade and Quote database.

47

stock. Furthermore, for every stock in the sample, larger
trades appear to have a greater price impact.
As illustrated in figure 1, the cumulative impulse
response of a trade shock on stock returns generally
begins with a rapid increase immediately following
the shock and then levels off at a point higher than its
initial value. As I extend the initial model to account
for announcement days, this shape remains. Because
of this, I choose to present the remaining results by
reporting values based on the “long-run” impulse re­
sponse, which is derived from the cumulative im­
pulse response function by reading off the final value
calculated—in this case, the value of the cumulative
shock to returns seen 16 trades after the initial shock.
Note that this long run typically takes less than a
minute. To illustrate, figure 2 reports the cumulative
long-run impulse responses on returns of a trade
shock for each of the ten stocks in my sample after
I estimate equations 3 and 4, which extend the previ­
ous model by allowing the price impact of a trade to
vary according to whether the given trade occurs on
a day with an earnings announcement. Each panel of
figure 2 reports two values. The first bar reports the
long-run price impact of a median-sized trade on a
nonannouncement day (that is, normal day). The sec­
ond bar reports the long-run cumulative value of the
same sized trade on an announcement date.
Qualitatively, the results are similar across all ten
firms in the sample. In particular, the price impact of
a median-sized trade is uniformly lower on an announce­
ment day than on other days during the sample period.
This result is consistent with the notion that price im­
pact is partially explained by marketmakers defend­
ing themselves against asymmetric information. In other
words, prices move in response to trades because
marketmakers believe some traders have private in­
formation. Furthermore, this private information is
reduced when a public earnings announcement is re­
leased. The magnitude of the reduction in price im­
pact varies across the ten firms. In the case of BMY,
the reduction in price impact is rather small. My model
estimates that the long-run price impact of a trade de­
clines from approximately 1.42 basis points to 1.39
basis points, a reduction of only 2.1 percent. For other
companies, the reduction in price impact on announce­
ment days is far more pronounced. The impact of a
median-sized trade of Home Depot (HD) stock is rough­
ly 1.2 basis points on nonannouncement days, but
only 0.8 basis points on announcement days. This
represents a reduction in price impact of 33 percent.
The results of figure 2 show that announcement
days witness a decline in the price impact of trading,
suggesting that the release of public information does

48

reduce the private information embedded in a trade.
I reached this conclusion by estimating a model that
allowed the relationship between trading and returns
to vary according to whether a given trade occurred
on an announcement day. As mentioned previously,
it is important to attribute the lower price impact of
a trade on announcement days to the announcement
and not to the typically higher trading volume wit­
nessed on announcement days. Figure 3 reports the
analogous results to those of figure 2, only with the
long-run price impact measures being derived from
an estimation of equations 5 and 6, which control for
daily trading volume.6 As shown in figure 3, for nine
out of ten stocks, announcement days remain corre­
lated with a reduction in the long-run price impact of
a trade. Moreover, the one stock for which this result
is not found is BMY, which had a negligible decline
in price impact when I did not control for trading
volume. The magnitude of the decline is also largely
comparable to what was reported in figure 2. The im­
pact of a median-sized trade of Home Depot (HD) stock
on a day with typical trading volume, for example, is
estimated to be 0.93 basis points on nonannouncement
days, but only 0.62 basis points on announcement
days. This represents the same 33 percent reduction
in price impact for HD that was reported in figure 2.
Next, I analyze my extensions to this basic
framework. One extension is to explore whether the
private information that does get released in an earn­
ings announcement may partially “leak” to the public
before the official release or, alternatively, whether
the private information is at a maximum before the
release. A related question is how the private informa­
tion component of price impact varies after the an­
nouncement date. For example, does the relationship
between trading and returns immediately revert to a
more normal level or does price impact remain at a low­
er level for some time following the earnings release?
Figure 4 (p. 51) addresses these questions by
reporting the cumulative long-run price impact for a
median-sized trade, calculated from an analysis using
equations 7 and 8. Recall that in this model specifica­
tion, the relationship between trading and returns is
allowed to vary not only on an announcement day,
but also on the day before and the day after an an­
nouncement. To illustrate the information contained
in figure 4,1 highlight the results for shares of Nortel
Networks Corp. (NT). The first bar in panel H reports
that the cumulative long-run impact of a median-sized
trade of NT stock is 1.6 basis points on a day not in
proximity to an earnings announcement. The second
through fourth bars calculate the same quantity only
on the day of, the day before, and the day after an

1Q/2006, Economic Perspectives

FIGURE 2

The long-run price impact of a trade on normal days and announcement days
A. Bristol-Myers Squibb Co. (BMY)

B. EMC Corp. (EMC)

basis points

basis points

C. General Electric Co. (GE)

D. Home Depot Inc. (HD)

basis points

basis points

E. International Business Machines Corp. (IBM)

F. Coca-Cola Co. (KO)

basis points

basis points

Normal day

G. Merck & Co. Inc. (MRK)

H. Nortel Networks Corp. (NT)

basis points

basis points

Federal Reserve Bank of Chicago

Announcement day

49

FIGURE 3

The long-run price impact of a trade on normal days and announcement days,
controlling for changes in trading volume
A. Bristol-Myers Squibb Co. (BMY)

B. EMC Corp. (EMC)

basis points

basis points

C. General Electric Co. (GE)

D. Home Depot Inc. (HD)

basis points

basis points

E. International Business Machines Corp. (IBM)

F. Coca-Cola Co. (KO)

G. Merck & Co. Inc. (MRK)

H. Nortel Networks Corp. (NT)

basis points

I. Pfizer Inc. (PFE)

J. SBC Communications Inc. (SBC)

basis points

basis points

0.80

1.50 r

0.60
0.40

0.20
0
Normal day

Announcement day

Normal day

Announcement day

Sources: Author’s calculations based on data from Briefing.com and the New York Stock Exchange Trade and Quote database.

50

1Q/2006, Economic Perspectives

FIGURE 4

The long-run price impact of a trade before, during, and after announcements,
controlling for changes in trading volume

Sources: Author’s calculations based on data from Briefing.com and the New York Stock Exchange Trade and Quote database.

Federal Reserve Bank of Chicago

51

earnings announcement. As reported in the second
bar, the price impact of a trade of NT falls to around
1.3 basis points on an announcement day. However,
the figure also indicates that the price impact remains
near this lower level on the following day. On the
day before the announcement, however, the price im­
pact of an NT trade is higher than is typical, measur­
ing approximately 1.8 basis points.
This pattern is consistent with the following sto­
ry of the private information content of price impact.
Suppose that every day new information about the value
of NT shares is generated, but initially, this new informa­
tion is private. Suppose further that none of this private
information is released until the day of the announce­
ment. In this scenario, one expects that the amount of
private information is greatest just before the announce­
ment. According to microstructure theory, this would
then cause the price impact of a trade to be greatest just
before an announcement and to fall after an announce­
ment. This story is therefore consistent with the esti­
mated price impact of NT trading around announcement
dates. Four of the ten sample stocks, however, are es­
timated to have a greater price impact on the day be­
fore an earnings announcement, and so this story is
potentially an explanation for only some firms.
Consider a different case such as the one illustrated
by the results for trading of IBM stock. The long-run
price impact of a trade of IBM is lower on days im­
mediately before and immediately after an earnings
announcement than it is on other days. Five of the ten
stocks match this pattern. If private information was
the source of the change in price impact, then these re­
sults suggest that private information is reduced before
the announcement date. This would be consistent with
a potential information leak or perhaps with informa­
tion being intentionally released by the company prior
to its formal quarter earnings release.
One final issue I explore is whether the reduction
in price impact observed on announcement days is
related to what news is actually released in the announce­
ment. For example, an earnings release that is in line
with market expectations may not reduce private in­
formation very much, since even in the absence of a
formal announcement, market participants seemed
quite knowledgeable about the announcement’s contents.
A surprising announcement, however, may reveal a
greater amount of private information. An alternative
hypothesis is that a surprising announcement may gen­
erate more private information because there may be
more differences in opinion as to the implication of an
earnings surprise on the fundamental value of the stock.
I explore the relationship between price impact and
announcement content by estimating equations 9 and

52

10, which allow the trading and return relationship to
vary according to whether a trade occurs on an an­
nouncement date and whether the given announcement
is surprising. I define a surprising announcement as
one in which the market’s expected earnings were
more than $0.01 per share away from the actual re­
ported value. For nine of the ten firms in the sample,
this identified roughly half of all announcements as
surprises. The tenth firm, General Electric Co. (GE),
did not have a surprising announcement over the en­
tire sample period, with earnings never being more
than a penny away from the market’s expectation.
For this reason, I do not include GE in this final
empirical estimation.
Figure 5 presents the long-run price impact of a
median-sized trade of each of the remaining nine com­
panies. As is illustrated in the figure, there does not
appear to be a general relationship between private
information content and announcement surprise con­
tent. In particular, a trade of six of the nine stocks is
associated with a lower price impact when the announce­
ment is more surprising relative to when it is not. For
instance, a median-sized trade of Pfizer Inc. (PFE)
stock typically moves the price by 0.66 basis points.
On a day when an unsurprising announcement is made,
price impact falls to 0.62 basis points. On a day when
the earnings announcement is also more than a penny
away from the market’s expectation, price impact falls
by even more, to 0.51 basis points. The evidence from
the remaining three stocks indicates the opposite re­
lationship between announcement content and price
impact reduction. For instance, a trade in the stock of
SBC Communications Inc. (SBC) typically moves
the share price by 1.23 basis points. This falls to 1.14
basis points on an announcement day without an earn­
ings surprise, but falls by less than 0.01 basis points
on announcement days when earnings miss expecta­
tions by more than $0.01.

Conclusion
In this article, I examine how the price impact of
a trade varies throughout the days surrounding public
earnings announcements. My results indicate that pub­
lic news releases correlate with a reduction in the price
impact of a trade. This finding is consistent with
earnings releases generally reducing the asymmetric
information component of stock trading. Moreover,
this result is robust to the typical increase in trading
volume generally observed on such days. Extending
the sample beyond a focus on the announcement day
alone, however, fails to uncover systematic relation­
ships on either the day before or the day after earnings
announcements. In particular, the reduction in price

1Q/2006, Economic Perspectives

FIGURE 5

The long-run price impact of a trade for surprising and unsurprising announcements,
controlling for changes in trading volume

C. General Electric Co. (GE) (results not applicable)
basis points

1.20 ■

0.80 ■
0.40 ■

Normal day

Announcement Announcement
day (no surprise) day (surprise)

Notes: Results for General Electric Co. (GE) are not applicable. See the text for further details.
Sources: Author’s calculations based on data from Briefing.com and the New York Stock Exchange Trade and Quote database.

Federal Reserve Bank of Chicago

53

impact on announcement days does not typically per­
sist beyond one trading day, nor do markets seem to
contain higher than average levels of asymmetric in­
formation on the day prior to anticipated announcements.
Perhaps most surprisingly, I do not find a predictable
relationship between the change in price impact and

the information content of the announcement. For some
firms, surprising announcements tend to increase asym­
metric information and price impact relative to unsur­
prising announcements, whereas for other firms the
reverse is true.

NOTES
JMore generally, the price of a share of stock should equal the present
value of future dividends discounted at a rate commensurate with
the risk of the given payment stream, where risk is measured by an
asset pricing model. Thus, expectations about both future dividends
and future risk are relevant in determining current market prices.

2A marketmaker is an individual or firm authorized by the stock
exchange to buy and sell a particular security with an objective to
provide trading liquidity for the security. Generally, a marketmaker
is obliged to announce buying and selling prices for a particular
security at a particular time.
3An implication of this model is that traders lacking private infor­
mation face higher trading costs in that they must compensate the
marketmaker for being willing to transact at posted prices in the
presence of those with more information.

"Readers who are more interested may wish to begin a more indepth review of market microstructure analysis by reading Biais,
Glosten, and Spatt (2005).

5Briefing.com reports earnings expectations from Zacks Invest­
ment Research and from Reuters. As it is more complete, I choose
the expectation reported by Zacks, but use Reuters data when
Zacks data are missing.
Tor these calculations, trading volume is set to a stock’s median
(across days in the sample) daily trading volume. Regression re­
sults indicate a strong negative relationship between trading vol­
ume and price impact. That is, days with higher trading volume
are associated with lower price impact of a single trade.

REFERENCES

Biais, Bruno, Larry Glosten, and Chester Spatt,
2005, “Market microstructure: A survey of microfoun­
dations, empirical results, and policy implications,”
Journal ofFinancial Markets, Vol. 8, No. 2, May,
pp. 217-264.

Glosten, Lawrence R., and Paul R. Milgrom,
1985, “Bid, ask, and transaction prices in a specialist
market with heterogeneously informed traders,”
Journal ofFinancial Economics, Vol. 14, pp. 71-100.
Green, T. Clifton, 2004, “Economic news and the
impact of trading on bond prices,” Journal ofFinance,
Vol. 59, No. 3, pp. 1201-1234.
Hasbrouck, Joel, 1991, “Measuring the information
content of stock trades,” Journal ofFinance, Vol. 46,
No. l,pp. 179-207.

Lee, Charles M. C., Belinda Mucklow, and Mark
J. Ready, 1993, “Spreads, depths, and the impact of
earnings information: An intraday analysis,” Review
ofFinancial Studies, Vol. 6, No. 2, pp. 345-374.
Lee, Charles M. C., and Mark J. Ready, 1991,
“Inferring trade direction from intraday data,” Jour­
nal ofFinance, Vol. 46, No. 2, pp. 733-746.

Seppi, Duane J., 1992, “Block trading and informa­
tion revelation around quarterly earnings announce­
ments,” Review ofFinancial Studies, Vol. 5, No. 2,
pp. 281-305.
White, Halbert, 1980, “A heteroskedasticity consis­
tent covariance matrix estimator and a direct test for
heteroskedasticity,” Econometrica, Vol. 48, No. 4,
pp. 817-838.

Koski, Jennifer Lynch, and Roni Michaely, 2000,
“Prices, liquidity, and the information content of
trades,” Review ofFinancial Studies, Vol. 13, No. 3,
pp. 659-696.

54

1Q/2006, Economic Perspectives

An alternative measure of inflation

Francois R. Velde

Introduction and summary
Controlling inflation is a primary goal of monetary
policy. In order to control inflation, central bankers
need to be able to measure and forecast inflation as
best they can. Forecasting is particularly important,
given the fact that monetary policy operates with “long
and variable lags,” and therefore policymakers need
to act well in advance of actual developments in in­
flation, on the basis of their forecasts.
Both the measurement and the forecasting of in­
flation have been subjects of ongoing debate and re­
search in recent years. This article reports on my
research on both aspects. Specifically, I develop a new
measure of inflation, which can also be used to gen­
erate a forecast. The research is still very preliminary,
but the first results are encouraging. In particular, it
appears to provide some gains in forecasting compared
with what remains the best and simplest forecasting
model, namely, the random walk model of inflation.1
Motivation
For the U.S. Federal Reserve (and for many other
central banks), price stability is a primary goal, man­
dated by law. This stability is usually interpreted to mean
a low level of inflation (how low will not be debated
here). So what is inflation and how do we measure it?
Inflation is generally defined as the rate of change
of some price index: Well-known examples are the
Consumer Price Index (CPI) and the Personal Consump­
tion Expenditures (PCE) Price Index. Price indexes,
generally speaking, result from the attempt to mea­
sure with a single number a change in a collection of
pricesp. (for /= 1, ...,«).
The simplest conceivable index is to take a straight
average of the prices in each period, ignoring the quan­
tities. But it seems more reasonable for many purposes
to weight the prices. Movements in the price of an

Federal Reserve Bank of Chicago

item that is of little importance relative to the others
should not be given much weight. An item of little
importance is one that does not represent a large share
of expenditures, which naturally leads one to use ex­
penditure shares in creating the weights (see box 1).
More generally, suppose we have observations
on prices at which a given range of goods and services
are bought and sold and also observations on the quan­
tities bought and sold at those prices. We thus have a
collection {/?.} and a collection {q.}. Suppose further
that we have observations in two periods, 0 and 1. One
period (either 0 or 1) is chosen as the reference peri­
od. The problem of constructing an index (for either
prices or quantities) is that of devising a formula that
takes the prices and quantities in both periods and yields
a single number. The formula must be such that, if
the prices and quantities are the same in the reference
period and the other period, the number is 1. Note that,
even if prices are unchanged between the two periods,
a change in quantities will generally result in the index
being different from 1. Even though prices are un­
changed, the weighting of the prices, which is based
in part on the quantities, will change the overall index.
From this brief overview, one can draw some
general observations about price indexes. Most price
indexes require information on quantities in order to
weight the prices. For certain applications, the fact
that quantities are measured less precisely, less easily,
and less quickly than prices can be a problem. At a
deeper level, there is an important connection between
Francois R. Velde is a senior economist at the Federal
Reserve Bank of Chicago. The author thanks Michael
Kouparitsasfor all his advice, David Kangfor outstanding
research assistance, and his colleagues at the Chicago
Fed, particularly Craig Furfine and Marco Bassetto, as
well as Lawrence Christiano and Lars Hansen, for their
comments.

55

BOX 1

Different kinds of indexes
A straightforward weighting scheme is to use the
expenditure shares to weight the items. And, since
the choice of the units to measure the quantities of
goods, and therefore the prices per unit of goods,
is arbitrary, the absolute level of an index is mean­
ingless, and an index can only measure changes
relative to a reference period. One thus arrives at
the classic price indexes to measure changes be­
tween period 0 and period 1—the Laspeyres and
Paasche indexes, depending on whether one chooses
period 0 or period 1 as the reference period, and
the Fisher index, which takes the geometric aver­
age of the two indexes and thus achieves a pleas­
ing symmetry between the two periods.
The CPI is essentially a Paasche index, with
weights based on a reference period that is changed
from time to time. The current PCE index is a type
of Fisher index, with no fixed reference period:
The changes computed period by period are chained
together to form an index series. The Fisher in­
dex has another nice feature—a quantity index
that can be computed in exactly the same way
(weighting quantities by expenditure shares); in
any period the quantity index times the price in­
dex equals the total expenditure. Thus, the price
index can be seen as a deflator of the nominal ex­
penditures that yields an index of real quantities.

the prices and quantities. In much of index theory, the
two collections are intimately related, and the index
makes sense with respect to a particular set of quanti­
ties. For example, the CPI is based on weights repre­
senting the typical basket of goods and services
consumed by an average urban consumer. The PCE
deflator is based on the quantities of goods and services
consumed in the economy as a whole. These indexes
yield different inflation rates, partly because they
are tied to different collections of goods and services,
and the computation of the index depends on the col­
lections themselves, as well as on the quantities.
More generally, price indexes are designed for a
certain purpose and have optimal properties for that
purpose, but they may not be well suited for others.
Monetary policy needs measures of inflation, but it
may well be that indexes designed to measure the
value of a basket of consumer goods or to convert
nominal consumption expenditures into real con­
sumption expenditures are not perfectly suited for the
goals of monetary policy.
In fact, policymakers have come to use variants
of both the CPI and PCE index, the so-called core

56

measures. The intuition behind these measures is that
monetary policy is interested in broad and persistent
movements in inflation and that certain price series,
being too volatile, introduce noise and confusion in
the measurement of these broad movements. There­
fore, the troublesome series (typically food and ener­
gy-related items) are removed altogether from the
price index.
The alternative approach I propose here extends
the intuition behind the core measures of inflation.
My premise is that inflation is a general movement in
the price level or, put differently, a movement that is
common to all individual price series. Once we posit
the object to be measured (inflation) as a statistical
series of its own, then the measurement problem can be
seen in a different light, as a signal extraction problem.
Constructing (weighted) averages is a way of mea­
suring inflation that makes particular assumptions
about the movements that are specific to each series:
essentially, that they are a sort of observation noise
that can be removed by taking averages and counting
on the law of large numbers. But these movements
specific to each price series can have a more complex
structure than being just noise. As it happens, statisti­
cal tools are available to measure inflation and allow
for more complex structures. The result of this ap­
proach is still, in a way, a weighting scheme, but it is
a dynamic weighting scheme, and it is one that weights
series not by their importance in a basket, but accord­
ing to the information that they contain.

Method
The Kalman filter
The Kalman filter relies on a distinction made
between what is observed and what is not. This is for­
malized by writing two equations, known as the state
equation and the observation equation. The first equa­
tion posits the evolution over time of the hidden vari­
ables, gathered in a vector called the state vector. The
specification is typically dynamic, meaning that cur­
rent value taken by the state depends on past values.
One of these hidden variables will be our general move­
ment in the price level. The second equation describes
the relation between the state and the observables.
I call the vector of observable variables That
is, at every point in time /, (y1(, y2(, y3(,...) represent
the values of the series 1, 2, 3 at time t. There is an­
other vector, made up of variables that are not observed:
It is called the state vector, x(. The state equation de­
scribes how this (unobserved) state changes over
time. The general form is

1Q/2006, Economic Perspectives

where A is a matrix and u is a noise or error term.
The observation equation relates the observables
and the state in the following way:

y, = b xt + vt,
with v, another noise or error term, uncorrelated with
u. Having specified this model of the relationships
between the state and observables, I need to supply
initial guesses about two things: the initial value taken
by the state and the uncertainty surrounding that initial
value. Typically, the initial value is assumed (in the
absence of any other information) to be the long-run
average of the state, and the uncertainty surrounding
this value can be derived from the state equation.
I am now ready to apply the Kalman filter. It may
seem a little magical to estimate the value of a variable
(the state) that is never observed. The way it works is
as follows: The method is recursive, meaning that at
any point in time it takes the most recent guess and
updates it in a systematic manner based on the newly
available information. Given a guess as to the value
of the state yesterday, and the uncertainty around it,
the mechanical application of the state equation pro­
vides a best guess as to its value today (before I intro­
duce any of today’s information).
How do I represent today’s information? The basic
rule here is learning from one’s mistakes. Since I have
a guess of today’s state, I can make a guess of today’s
observables, using the observation equation. Then
I compare this guess with what actually happened:
The difference between the two is the new information
that is relevant to my model.
How do I incorporate today’s information? I project
the (unknown) value of today’s state onto all of the
information, which can be decomposed into the infor­
mation available yesterday and the new information
that became available today. A classic result of regres­
sion analysis tells us that this projection is the sum of
two terms. The first is simply the best guess of today’s
state using the information up to yesterday. The second
corrects this guess with the new information, but weights
it according to two expectations: how correlated it is
likely to be with today’s state and how noisy it is. The
more correlated this new information is with the state,
the more weight I place on it; on the other hand, the
noisier it is, the more I discount it. How these weights
are determined depends on the particular values I have
assigned to the coefficients of the state and observa­
tion equations.
This leads to a recursive formula: Today’s guess
is yesterday’s guess updated with the (appropriately
weighted) new information. Tomorrow, I will take

Federal Reserve Bank of Chicago

today’s guess of today’s state, derive a guess of to­
morrow’s state, and repeat the procedure.
Of course, when tomorrow rolls around, I will
correct my guess of tomorrow’s state that was based
on today’s information. But I could also correct my
guess of today’s state based on today’s information,
or even yesterday’s guess of yesterday’s state. More
generally, having proceeded recursively from the be­
ginning to the end of the available sample, it is possible
to go back and correct the guesses made for the value
of the state in earlier periods based on the information
of the whole sample. This procedure, which is also re­
cursive but backwards (as it updates yesterday’s guess
based on today’s error), is called the Kalman smoother.
This approach to measuring inflation, like any
other, has costs and benefits. Some of the benefits are
apparent if we think back to the initial motivation.
Modeling inflation as a hidden variable allows me to
bypass a number of the issues that arise for standard
indexes. For example, the basic intuition behind core
measures of inflation is frilly extended. Price series
are not ignored or deleted when they are volatile; rather,
optimal use is made of the information that they con­
tain. The approach helps me deal with the choice of
optimal weights to apply to the price series because
the Kalman filter algorithm itself chooses the weights
that it applies recursively. But it doesn’t choose them
arbitrarily; rather, it tries to extract the information
contained in the price series.2 The choice of the series
themselves is not eliminated, of course, but it is of less
importance. There is no conceptual problem in mixing
series of different origins (say, the PCE index and CPI)
or choosing a subset of either collection of series. There
is no “adding up” constraint; there is no need to frilly
represent a given basket or bundle of goods and ser­
vices. The main consideration in adding another series
to the collection we use should be: Is that additional
series likely to provide information about inflation
that was not contained in our collection already?
Finally, one major benefit of the approach is that
it yields a forecasting tool at no additional cost, so to
speak. My best guess of the value of the state at time
t+1 based on information available at time t is simply
my best guess of the state at t, projected forward one
period using the state equation. The Kalman filter ap­
proach thus folds into one operation measurement
and forecast.
There are disadvantages, however. One cost is of
a technical nature, and another is more of a conceptual
problem.
The technical difficulty becomes more apparent
in the next section. Although index theory may rely
on some assumptions about the economic process that

57

generated the prices and quantities, the considerations
that lead to the choice of an index are quite general
and make few or no assumptions about the prices them­
selves. The Kalman filter approach requires that a
modeling choice be made about the statistical pro­
cesses that best represent the price series and the un­
derlying inflation as well. That is, I have to take a
stand on the structure of prices, their interdependence,
the correlations of a series with its past values, as
well as those between the series themselves, and so
on. Fortunately, some statistical tests guide the choice
of that structure, as I discuss in the next section.
The conceptual difficulty is the following: One
might fairly argue that I am not so much measuring
inflation as inventing a concept of inflation that I can
measure. The underlying inflation, or “latent inflation,”
may be just a statistical artifact. My response is that,
although it is indeed an invented concept, it is one
that captures the intuition we have about inflation.
But it would be better to think of the series I uncover
as an index, perhaps not of inflation itself, but of the
forces that affect inflation dynamics, at least in the
short run. For lack of a simpler term, I choose to call
this index latent inflation, but I need to show that, in
practice, it can be closely related to more standard
measures of inflation. I do this in the second part of
the section that follows.

The model
Generalform
The general form of the model I use is relatively
straightforward. Let T.( denote the individual inflation
series, with z = 1,..., N. Let Pt be the latent inflation.
I assume that the relation between them takes the form
Y=
fP
+P.„
it
it
ir

where X. is called the “loading factor.” The term P.t
represents the component specific to the individual
inflation series z. I call it the “relative inflation” for
the good or service z.
One would expect the loading factors to be close
to 1. (As I explain later, one of them is normalized to
be 1.) Indeed, it may be hard to think of a theory in
which they would not be 1, since one would expect
inflation to have the same impact on all series. To the
extent that I do not find them to be 1, this can be re­
interpreted as capturing any immediate dependence
on the relative inflation from the general inflation,
for example, the product of distortions generated by
inflation on the pricing decisions in one sector. Formally,
the equation can be rewritten as 7?, = 7^ + 7?^ with
Pjt = (fj-\)Pl + Pjl. An alternative explanation is that

58

loading factors different from 1 are picking up some
model misspecification, such as nonlinear time trends.
Within this general framework, I consider a vari­
ety of statistical models for the relative inflation rates
P, and the latent inflation P.

Specificform
As I explained previously, part of the cost of the
approach is that it involves many choices: not only a
choice of series, but also a choice of the statistical model
to apply to the series. Partly to avoid deciding, but
mostly to explore the properties of the general model,
I have experimented with a number of variations.
The PCE index or the CPI can be thought of as
the apex of a pyramid. The general price index corre­
sponds to the most aggregated level of observation.
Immediately below, there is a first level of disaggre­
gation. In the case of the PCE index, it contains three
series: an index of durables prices, an index of non­
durables prices, and an index of services prices. Fur­
ther down is a second level of disaggregation, which
contains 13 series, and a third level. At this stage of
the research, I have experimented with a collection
of three series of the PCE index (the first level of
disaggregation), 13 series (the second level), and
52 series (selected from the third level).
The next decision is the choice of a statistical model
for the individual series. For the sake of simplicity, I
have imposed the same model on the latent and the
relative inflation series, but I have varied the model.
All models belong to the ARIMA (autoregressive in­
tegrated moving average) families of models, which
I now explain.
The simplest statistical model one can think of is
that a series is white noise; that is, it consists of real­
izations from uncorrelated, identically distributed statis­
tical processes— each observation (at time /) is drawn
from, say, a normal distribution with constant mean
and variance. Obviously, this is not a good model for
inflation, which is highly persistent, but it serves as a
building block for other models. The next step is to
allow for serial correlation, and imagine that inflation at
time t can be decomposed into the sum of last period’s
realization multiplied by a factor p, and white noise e;.
P,= PPM+C-

This introduces some persistence in the process.
More generally, one can suppose that the process de­
pends on more than one lag. The general form is then
that of an AR(p), autoregressive process withp lags:

^=P1^_1 + p2P,-2 + ---+PZ^ + er

1Q/2006, Economic Perspectives

Another step is to allow the innovation ef to have
effects that extend beyond the period when it occurs,
without having as much persistence as the autore­
gressive part. That is, the innovation e affects not
just P but also P(+1:
P, = P, P,^ +^P,_2+-.. + P,,P

+ et + 0 e,_v

This is a mixture of an autoregressive (AR) process
with a moving average (MA) component: It is called
an ARMA process. The moving average part can
have q terms:

P, = P, P,^ + P2

+ • • • + Pp P,-p + ^, + 6,

highly persistent that it can look like a random walk.
One solution is to take the difference of inflation and
to model that difference as an ARMA process; the origi­
nal process is said to be integrated of order 1 if the
first difference is stationary, and the process is called
an ARIMA(/?. l.<y) process, where 1 denotes the fact
that inflation needs to be differenced once. (I do not
consider higher orders of integration.)
A final variant that I consider is to allow for feed­
back from the relative inflation to the latent inflation.
This takes the following form: The relative inflation
series are modeled as ARMA(p,(/), and the latent in­
flation is modeled as

*,_!
P, = P, P,^ + P2 ^_2 + • • • + Pp P,^p + ^, + 0,

+ ...+ 0q et-q’,

+ ...+ 0q <?t-q +ZtirP.
(.
i u
t

in which case the process is called ARMA(p,(/).
Estimation is much simpler if the process is sta­
tionary; that is, its properties do not vary over time,
and it tends to revert to its mean rather than drift away.
This will be true if the sum of the autoregressive co­
efficients is less than 1 in absolute value. But this may
not be a good assumption for inflation, which is so

I denote the model as ARIMA(/?.zv/.i(/) if I allow
for such feedback and ARIMAfp,otherwise. The
same model is imposed on all series. (Further refine­
ment of the analysis will involve imposing different
models on the different series, eliminating terms that
appear to be insignificant in the estimation.) Constants

Other methods and related literature
In addition to presenting my results, I want to say
a few words about other methods.
The approach taken here is related to other
work. The model I use can be seen as a special
case of what are called “dynamic factor models.”
These models represent a given collection of vari­
ables {Aj f, X1, ...} as being detennined by a set
of unobserved common factors {F t,F , ...} and
their lags, to which observation noise is added.
The general fonn of the equation modeling each
variable would be
A7, = «,ioFi, + «,nFi,-i +

+ «,-20F2.,

where the u.l,t tenns are not correlated over time
and with each other (Sargent and Sims, 1977).
Typically, the number of factors is kept small rel­
ative to the number of variables being modeled.
More recently, researchers have found that the
principal components of the collection A can be
used to approximate the common factors F, an
approximation that becomes valid as the number
of variables Abecomes large relative to the number
of factors (Stock and Watson, 1998; Fomi et al.,
2000). These techniques are used by the Chicago

Federal Reserve Bank of Chicago

Fed’s National Activity Index (Evans, Liu, and PhamKanter, 2002). Cristadoro et al. (2002) use these
methods to compute a measure of core inflation for
the euro area using large numbers of economic se­
ries and extracting the slow-moving component of
the common factor associated with inflation.
My approach is a particular fonn of a dynamic fac­
tor model, where the number of factors is the number
of series plus one and estimation proceeds along the
more traditional (and computer-intensive) line of max­
imum likelihood. Bryan and Cecchetti (1983) use this
method with a small-scale model to estimate the de­
gree of bias in the CPI (the bias being the difference
between actual CPI and the estimated latent variable).
They do not assess the properties of their estimated
variable or its forecasting ability. Jain (1992, 2001)
uses the state space approach with only price series
to remove seasonal fluctuations from price series, but
the focus is not on estimating latent inflation. Other
uses of the state space model approach to estimate or
predict inflation include Bomhoff (1982), who uses
a small economic model to relate inflation to money
and output; Hamilton (1985) and Bunneister, Wall, and
Hamilton (1986), who estimate current expectations
of inflation using variables such as interest rates;
and Laubach and Williams (2003).

59

are included in all the ARMA models, allowing for
potentially different trends in relative inflation.

Estimation method
To estimate each model, I use the so-called esti­
mation-maximization (EM) algorithm detailed in
Watson and Engle (1983). The problem is to find the
values of the parameters of the model: the loading
factors; the p, 0, i|i coefficients; and the variances of
the innovations et for each relative inflation and for
the latent inflation. The difficulty is that the Kalman
filter and smoother formulas can compute estimates
of the latent variable assuming that these parameters
are known; however, they are not known, and they
must themselves be estimated.
The EM algorithm uses the classic approach of
assuming we know what we don’t know. Specifical­
ly, one starts with a guess for the parameters, applies
the Kalman smoother, and computes estimated series
(the estimation step); then, pretending that these esti­
mated series are observed data, one finds new esti­
mates of the parameters, essentially by regressing the
observed price series on the relative and latent infla­
tion to compute the loading factors, as well as the
hidden variables on their lags to compute the param­
eters of the ARMA model? The main drawback of
this algorithm is that it converges very slowly and
makes computation time-intensive.
In box 2 (p. 59), I present a brief overview of
other methods. In the following section, I discuss my
results.

on the basis of fit—the ARIMA(2,0,0, ~) and the
ARIMA(3,0,l, i|r)—and a model that will turn out
to have good forecasting ability in the next section,
the ARIMA(2,0,2, ~). The figures also show the
forecasted path of the latent variable over the next
12 quarters. Note that this is not a forecast of core
inflation, but only a forecast of the latent variable.
I use this forecast of the latent inflation in the next
section to forecast core inflation. Another important
point is that neither the level nor the amplitude of
the latent variable can be determined. The estimation
procedure normalizes to 1 the first loading factor (in
effect, I scale the latent variable so that its amplitude
is comparable to that of the first price series), and in
figures 1-3 I add the value of core inflation in 1959:Q2
to the level of the latent variable. Thus, the scale of
the figure only applies to core inflation, and if the
figures allow us to compare visually the two series,
they should not be taken to mean that core inflation
is higher or lower than the latent variable at any par­
ticular point in time.
Overall, the behavior of the latent variable is sim­
ilar to that of core inflation. It’s worth recalling that
I did not remove food or energy from the series I used
to estimate the latent variable.
Forecasting with the latent inflation measure
As I mentioned previously, one difficulty with
the latent inflation approach is that the variable I am
measuring is a construct. How can I be sure that it is

Results
The estimated latent inflation
As I have explained, the approach
to modeling relative prices as well as
latent inflation is somewhat agnostic:
A variety of models have been estimated.
Which does one choose? One criterion is
how well the model fits the existing data
(I use the sample period from 1959:Q1 to
2005: Q1). The estimation procedure tries
to maximize the likelihood that the ob­
served data were generated by the esti­
mated model, and one can simply compare
the resulting likelihood across models.
Of course, models with more parameters
will tend to do better, simply because they
have more parameters, and ways have
been devised to take this into account.4
Figures 1-3 show the estimated val­
ues of the latent variable over the sample,
compared with the quarterly core inflation
rate, for three models: two models chosen

60

1Q/2006, Economic Perspectives

out an out-of-sample forecasting exercise
similar to Fisher, Liu, and Zhou (2002)
and Brave and Fisher (2004). As this re­
search has emphasized, the naive model
of inflation, which predicts that inflation
in the future will be what its most recent
value was, is “the man to beat.”
I proceed as follows. For each quar­
ter T between 1984:Q2 and 2002:Q2,1
take the sample ranging from the begin­
ning of the series (1959:Q 1) to the cho­
sen quarter T. Using only the data in this
sample, I estimate a family of ARIMA
models. Then, I run various regressions
of core inflation over various horizons
(that is, core inflation from quarter t - H
to quarter /, where //ranges from 1 to 8)
on the estimated measure of the latent in­
flation and current and lagged core infla­
tion within the sample. Then, 1 construct
a forecast of latent inflation over the ho­
rizon T to T+H and use those forecasts
as well as the values for current and
lagged core inflation to project core infla­
tion over the horizon T to T + H. Having
done this for all quarters T between
1985:Q1 and 2002:Q2,1 compute the
root mean squared error (RMSE) of these
forecasts. 1 compare this RMSE to the
RMSE of the naive model, which simply
predicts that core inflation over T to
T + //will be what core inflation was
from T - 4 to T.
Using the same notation as Brave and
Fisher (2004), core inflation from t - H
to t is

Jif = ln/?,-ln/?,_H,
while core inflation from t - 1 to t is sim­
ply denoted nt = In p- In pt r
Note that
77
‘'■l

measuring what 1 think it might be measuring? Is it
of any use or, more precisely, does it capture the latent
inflationary pressures that are in play, at least in the
short term?
One way to evaluate the latent inflation measure
is to find out if it holds any predictive power for in­
flation as it is commonly measured. To find out, I carry

Federal Reserve Bank of Chicago

= 71

4- 77
4i-H+1 T ‘'■l-H+2 T '

Latent inflation xt is calculated as the latent vari­
able in the statistical model, and the latent variable
over the T to T + //horizon is simply
H _
.
.
Xt ~ Xt-H+1
Xt-H+2 T-Xr

61

The regression I run is
1)

+ y.

jif = cuf +

The statistical model allows me to project x"/f
and then construct an estimate
^7+77 — ^xt+h

+ Pi^r-i + 7-

Note that, in equation 1, latent inflation is in­
cluded in the regression in addition to lagged inflation.
Such an inclusion usually hurts the predictive power
of the forecasting equation (out of sample). By con­
trast, if latent inflation helps significantly, this is a
success. Note also that, although a lot of work goes
into coming up with the series vf and the forecast
ax?+H, the regression itself is simple and has only
three regressors.

The results in terms of relative RMSE are shown
in table 1. For each model and each horizon (one to
eight quarters ahead), the table shows the model’s
RMSE relative to the naive model (a number lower
than 1 indicates that the model performs better). The
models are sorted by order of increasing likelihood.
The pattern of performance varies considerably
across models. One group of models does substantially
worse than the others: As it turns out, these are the mod­
els that allow feedback from lagged relative inflation
to latent inflation. The models without feedback do
markedly better than regressing core inflation on two
quarters of inflation, the performance of which is given
in the last row of table 1. In other words, the addition
of the latent inflation to the regression substantially im­
proves the forecasting performance. Which model per­
forms best depends on the horizon: At the short
horizon (one to three quarters), the ARIMA(2,l,0,~)

TABLE 1

Root mean squared error relative to naive model
Forecast horizon one to eight quarters ahead

ARIMA(2,l,0) without feedback
ARIMA(2,l,0) with feedback
ARIMA(3,l,0) with feedback
ARIMA(2,0,l) without feedback
ARIMA(2,0,2) without feedback
ARIMA(3,0,l) without feedback
ARIMA(2,0,0) without feedback
ARIMA(3,0,0) without feedback
ARIMA(2,0,0) with feedback
ARIMA(3,0,0) with feedback
ARIMA(3,0,l) with feedback
1 lag of inflation alone

1.09
1.34
1.30
1.11
1.12
1.10
1.17
1.19
1.38
1.36
1.41
1.08

1.13
1.70
1.81
1.16
1.15
1.15
1.24
1.25
1.74
1.74
1.83
1.17

1.12
1.71
1.84
1.15
1.11
1.11
1.25
1.22
1.91
1.91
2.04
1.24

1.16
1.82
1.96
1.17
1.10
1.11
1.27
1.21
2.02
2.00
2.17
1.31

1.20
1.89
2.05
1.20
1.10
1.12
1.29
1.22
2.07
2.07
2.25
1.37

1.23
1.91
2.12
1.23
1.10
1.11
1.29
1.22
2.08
2.09
2.30
1.44

1.24
1.93
2.16
1.24
1.09
1.11
1.28
1.21
2.06
2.08
2.30
1.50

1.25
1.94
2.18
1.25
1.08
1.10
1.28
1.20
2.03
2.05
2.29
1.54

TABLE 2

Performance of the moving average versions of the models’ forecasts
Two-quarter moving average

ARIMA(2,l,0)
ARIMA(2,0,l)
ARIMA(2,0,2)
ARIMA(3,0,l)
ARIMA(2,0,0)
ARIMA(3,0,0)

without
without
without
without
without
without

feedback
feedback
feedback
feedback
feedback
feedback

1.02
1.04
1.05
1.03
1.08
1.08

1.02
1.01
1.01
1.00
1.04
1.03

1.03
0.97
0.96
0.95
1.01
0.98

1.05
0.97
0.95
0.94
1.00
0.96

1.08
0.99
0.95
0.95
1.00
0.96

1.09
1.01
0.97
0.97
1.00
0.97

1.11
1.05
1.00
1.01
1.02
1.00

1.15
1.08
1.03
1.04
1.05
1.03

1.12
1.08
1.03
1.04
1.04
1.02

1.16
1.12
1.07
1.08
1.09
1.06

Three-quarter moving average

ARIMA(2,l,0)
ARIMA(2,0,l)
ARIMA(2,0,2)
ARIMA(3,0,l)
ARIMA(2,0,0)
ARIMA(3,0,0)

62

without
without
without
without
without
without

feedback
feedback
feedback
feedback
feedback
feedback

0.98
0.98
0.99
0.97
1.01
0.99

0.96
0.95
0.95
0.94
0.98
0.96

1.00
0.94
0.93
0.92
0.97
0.93

1.02
0.96
0.93
0.93
0.97
0.93

1.06
0.99
0.95
0.96
0.98
0.95

1.08
1.03
0.99
1.00
1.00
0.98

1Q/2006, Economic Perspectives

does slightly better; for all other horizons, the winner
is the ARIMA(2,0,2,~), with the ARIMA(3,0,1,~) not
far behind. Neither one, however, manages to do any
better than the naive model, though they come rea­
sonably close, within 10 percent of the RMSE of the
naive model.

Federal Reserve Bank of Chicago

Figure 4 compares the forecasts of
core inflation produced by the naive model
(gray line) and the latent inflation model
ARIMA(2,0,2,~) (green line) with actual
core inflation (black line) at the two-year
horizon. The date on the horizontal axis
is the date at which the forecast is made.
The gray line is the black line shifted by
two years, since the naive model predicts
that inflation two years hence will be the
same as today. The predictions of the la­
tent model are not substantially different
from those of the naive model, and hence
the latent model does not perform any
better. But what is striking is how variable
the green line is, relative to the gray line.
The reason is as follows. The gray line
averages actual inflation over the previ­
ous eight quarters and therefore smoothes
out a lot of the quarter-to-quarter vari­
ability in inflation. The latent inflation
model incorporates the new information
that arrives in each quarter, and even
though it weights it appropriately, the
new information shifts the estimate of
where latent inflation currently stands;
this in turn shifts the whole projected
path of latent inflation, and hence the
forecast of core inflation. There is no
smoothing mechanism here.
It is possible, of course, to add a
smoothing mechanism.5 For example,
I have tried replacing the latent model’s
forecast with a two-quarter or three-quar­
ter moving average of itself. This ad hoc
procedure produces a smoother forecast.
Its performance is shown in table 2, only
for selected models.
The performance of both the ARIMA
(2,0,2,~) and the ARIMA(3,0,l,~) models
is improved markedly. Just taking a twoquarter moving average reduces the rela­
tive RMSE for the ARIMA(2,0,2,~) from
1.10 to 0.95. It becomes possible to beat
the naive model, although not by a great
amount. Figure 5 compares the predic­
tions of this moving average: The green line is clearly
smoother, and in some instances, it seems to do better
in terms of predicting changes in inflation (for exam­
ple, the downturn in the mid-1980s and the pick up
in the early 1990s).

63

Conclusion
This article has presented recent research on
measuring and forecasting inflation. The approach
taken, that of state space modeling, consists of repre­
senting latent inflation as an unobserved variable af­
fecting simultaneously a collection of individual price
series, for example, the main components of an ag­
gregate price index like the PCE deflator. The approach
extends the intuition that lies behind the use of core
measures of inflation in that it takes the individual

price series to be noisy observations on true, underly­
ing inflation, and filters out the noise in the individu­
al price series. The resulting estimated latent inflation
validates the use of core inflation, since the two se­
ries look very much alike. The latent inflation approach
has the additional benefit of yielding a forecast of fu­
ture inflation, and preliminary results indicate that some
progress can be made in reducing out-of-sample fore­
casting error.

NOTES

2Note that I am not fully escaping the use of weighted indexes,
since the individual price series will, in practice, be indexes of
their own.

4Two such criteria are commonly used, the Bayesian information
criterion (BIC) and the Akaike information criterion (AIC), the
former tending to be stricter than the latter. In my family of models,
the BIC chooses the most parsimonious model, the ARIMA(2,0,0)
with no feedback, while the AIC ranks almost equally the ARIMA
(3,0,0) with feedback and the ARIMA(3,0,l) with feedback.

3If the model has moving average components, the et series are
treated as yet another unobserved variable.

5I thank former Federal Reserve Board Chairman Alan Greenspan
for this suggestion.

xFor an explanation of the random walk model, please refer to
Brave and Fisher (2004) and Fisher, Liu, and Zhou (2002).

REFERENCES

Bomhoff, Eduard J., 1982, “Predicting the price
level in a world that changes all the time,” CarnegieRochester Conference Series on Public Policy,
Vol. 17, pp. 7-56.
Brave, Scott, and Jonas D. M. Fisher, 2004, “In
search of a robust inflation forecast,” Economic Per­
spectives, Federal Reserve Bank of Chicago, Vol. 28,
No. 4, Fourth Quarter, pp. 12-31.

Bryan, Michael F., and Stephen G Cecchetti, 1983,
“The Consumer Price Index as a measure of inflation,”
National Bureau of Economic Research, working pa­
per, No. 4505, October.

Burmeister, Edwin, Kent D. Wall, and James D.
Hamilton, 1986, “Estimation of unobserved expected
monthly inflation using Kalman filtering,” Journal of
Business & Economic Statistics, Vol. 4, No. 2, April,
pp. 147-160.

Cristadoro, Riccardo, Mario Forni, Lucrezia
Reichlin, and Giovanni Veronese, 2002, “A core in­
flation index for the euro area,” Center for Economic
Policy Research, discussion paper, No. DP3097.

64

Evans, Charles L., Chin Te Liu, and Genevieve
Pham-Kanter, 2002, “The 2001 recession and the
Chicago Fed National Activity Index: Identifying
business cycle turning points,” Economic Perspectives,
Federal Reserve Bank of Chicago, Vol. 26, No. 3,
Third Quarter, pp. 26-43.
Fisher, Jonas D. M., Chin Te Liu, and Ruilin
Zhou, 2002, “When can we forecast inflation?,”
Economic Perspectives, Federal Reserve Bank of
Chicago, Vol. 26, No. 1, First Quarter, pp. 30-42.

Forni, Mario, Marc Hallin, Marco Lippi, and
Lucrezia Reichlin, 2000, “The generalized dynamic
factor model: Identification and estimation,” Review
ofEconomics and Statistics, Vol. 82, No. 4, Novem­
ber, pp. 540-554.

Hamilton, James D., 1985, “Uncovering financial
market expectations of inflation,” Journal ofPoliti­
cal Economy, December, Vol. 93, No. 6, pp. 1224—1241.

Jain, Raj K., 2001, “A state space model-based
method of seasonal adjustment,” Monthly Labor
Review, Vol. 124, No. 7, July, pp. 37-45.

1Q/2006, Economic Perspectives

__________ , 1992, “A state space modelling approach
to the seasonal adjustment of the Consumer Price and
other BLS Indexes: Some empirical results,” U.S. Bu­
reau of Labor Statistics, working paper, No. WP-229.
Laubach, Thomas, and John C. Williams, 2003,
“Measuring the natural rate of interest,” Review of
Economies and Statistics, Vol. 85, No. 4, November,
pp. 1063-1070.

Stock, James H. and Mark W. Watson, 1998,
“Diffusion indexes,” National Bureau of Economic
Research, working paper, No. 6702, August.

Watson, Mark W., and Robert F. Engle, 1983,
“Alternative algorithms for the estimation of dynamic
factor, MIMIC, and varying coefficient regression
models,” Journal ofEconometrics, Vol. 23, No. 3,
December, pp. 385-400.

Sargent, Thomas J., and Christopher A. Sims,
1977, “Business cycle modeling without pretending
to have too much a priori economic theory,” in New
Methods ofBusiness Cycle Research, Christopher A.
Sims (ed.), Federal Reserve Bank of Minneapolis,
pp. 45-109.

Federal Reserve Bank of Chicago

65