OECD Workshop on Business and Consumer Tendency Surveys
Rome, 19 September 2006
Long-Run Biases in
Consumer Sentiment
Micro Evidence from European Surveys
Maurizio Bovi
ISAE, Italy
Plan

Fit

Goal and Contribution

Motivations

Data

Statistical Framework

Experiments and Results

Puzzles or well known Psycho-Biases?

Concluding Remarks
Fit



Data users often take consumer sentiment indexes
(CSI) as a given input and, by and large, they search
for links between CSI and Economic System “hard”
data.
Data producers think about CSI as a final output,
addressing issues such as data collection, response
rates, etc. Roughly speaking, they deal with mapping
Individuals into CSI.
My research examines “semi-worked” survey data
without reference to hard data and focuses on their
reliability and on how Individuals address the
Economic System.
Goal and Contribution
A long-run analysis of the consumer sentiment,
taking advantage of micro data (% of
respondents) and cognitive psychology
findings.
Keywords:
long-run analysis;
representative consumer;
micro data.
Motivations
Why micro data?
In many political and economic circles, CSI are
commonly diffused, commented and studied at their “face
value”. Looking at CSI components may be interesting
even from the data users point of view.

Analyzing micro data allows avoiding comparisons with
National Account data which, in turn, reduces problems
such as the vagueness/difficulty of the queries. E.g., how
does the respondent interpret queries about “general
economic conditions”?

Motivations (2)
Why a long-run analysis?

Inter alia, this Workshop is about consumer tendency.
I have enough data (assuming that twenty years are
enough for a long-run analysis).

To some extent, a long-run approach may sidestep
some data issues (changes in survey methods,
seasonality, etc.)

Data
Data are from the Business Surveys Unit of the European Commission.
I deal with the following queries and with the relative reply options:
Q1) How has the financial situation of your household changed over the last 12 months? It
has ...
Q2) How do you expect the financial position of your household to change over the next 12
months? It will ...
Q3) How do you think the general economic situation in the country has changed over the
past 12 months? It has ...
Q4) How do you expect the general economic situation in the country to develop over the
next 12 months? It will ...
PP)
P)
E)
M)
MM)
N)
get/got a lot better
get/got a little better
stay/stayed the same
get/got a little worse
get/got a lot worse
don't know.
Data (2)
•
are percentages of respondents: MM+M+E+P+PP+N=100
•
refer to fifteen European Union (EU) countries
•
start in January 1985 for nine out of fifteen countries
Exemptions are:
Austria (starting date 1995:10),
Finland (starting date 1987:11),
Luxembourg (starting date 2002:01),
Portugal (starting date 1986:06),
Spain (starting date 1986:06),
Sweden (starting date 1995:10).
•
stop in July 2005 for all countries.
Statistical Framework
I analyze:
i) stylized facts via
ii) full-sample descriptive statistics about
iii) representative consumers within
iv) the survey framework
All that should reduce some data issues (the lack of re-
interviews, changes in survey methods, seasonality,
vagueness of the queries), allowing robust findings.
Experiments and Results
Some simple experiments based on reply options allow verifying
the persistent presence of “logical” behaviors. They may be
thought of as somewhat supporting the reliability of the
answers. For instance:
Consumers should know their own situation better than
the system wide one.
Thus, e.g., the average share of individuals answering “don’t know”
to questions about the general economic environment should be
greater than the average share of individuals which do not know
how their own financial situation is going on.

Consumers should know past situations better than
future ones.
Thus, ex ante questions should show more “don’t know” than the
corresponding ex post ones.

Consumers’ Uncertainty on Personal vs General and on Past vs Future Economic Conditions
Personal
General
Table 1
(Q1) Past
(Q2) Future
(Q3) Past
(Q4) Future
AUSTRIA
0.98
2.97
2.43
4.04
BELGIUM
2.96
6.69
6.05
10.8
GERMANY
1.36
4.73
2.32
5.58
DENMARK
0.66
3.7
7.11
8.85
GREECE
0.16
4.46
1.95
7.68
SPAIN
1.09
9.68
5.37
14.8
FINLAND
0.6
3.59
3.34
4.89
FRANCE
0.48
4.23
1.66
8.76
IRELAND
0.94
4.88
1.97
6.97
ITALY
0.5
4.68
1.95
6.32
LUXEMB.
1.16
3.46
4.67
5.59
NETHER.
1.08
4.5
7
11
SWEDEN
0.77
2.21
5.49
4.77
PORTUG.
1.33
11.2
5.47
15.7
UK
1.57
5.93
4.62
9.84
EU_11
1.02
5.08
3.93
8.58
EU_11=Belgium, Germany, Denmark, Greece, Spain, Finland, France, Ireland, Italy, Netherlands, UK
(sample 87:11-05:07). Full sample average of responses “don’t’ know” (in % of total) to the questions Q1-Q4.
Experiments and Results (2)
Let us now turn the attention to the “E” answer. There
are reasons to believe that it should show the largest
scores:
1.
Since the queries are about “dynamics”, individuals
should respond, on average, “the same” the most
part of times, because it is hard to think of ever
improving/worsening economic conditions over many
years. It is important to note that it should hold
whatever “economic conditions” means for common
people.
2.
The preference of being “E” may be partly due to
short-cut heuristics - this “neutral” option may be
chosen
by
uninformed
and/or
uninterested
respondents.
Fig. 1. Distribution of Responses On Economic Conditions
EU11
70
E
E
60
50
E
40
30
20
M
P
P
MM
10 PP
P
M
E
M
P
MM
MM N PP
N PP
M
N PP
MM N
0
Q1
Q2
Q3
Q4
Histograms report full sample (87:11-05:07) means of each reply item.
EU11=Belgium, Germany, Denmark, Greece, Spain, Finland, France, Ireland,
Italy, Netherlands, UK.
Experiments and Results (3)
Given their “logically expected” outcomes, all the tests
performed so far on N and E lead to think that data
give a faithful representation of people’s opinions. In
passing, neither N nor E enter into CSI.
Data tell more than this. Figure 1 shows that the
number of E-agents is structurally much higher when
the question is about personal (Q1, Q2) as opposed
to general (Q3, Q4) economic developments (more
than 55% vs less than 40%).
This calls for ad hoc tests to contrast general vs
personal response options. One way to proceed is
computing mean values of [(Q1+Q2)-(Q3+Q4)]
for each single option. When referring to “MM” and
“M” (“PP” and “P”) percentages, negative (positive)
values imply that the personal condition is perceived
to be systematically better than the general one.
Personal vs General Sentiment in European Countries
Table 2
PP
P
M
MM
AUSTRIA
1
-19
-25
-8.1
BELGIUM
1
-8.9
-26
-19
GERMANY
0.6
-5.6
-23
-12
DENMARK
10
-1
-17
-3.4
GREECE
-0.7
-6.2
-5.3
-5.5
SPAIN
-0.3
-9.6
-16
-9.3
FINLAND
3.8
-19
-14
-3.9
FRANCE
2.2
1.5
-31
-23
IRELAND
-3.8
-16
-10
-13
ITALY
-1.8
-15
-22
-26
LUXEMB.
2.8
1.7
-50
-8
NETHERL.
5.1
-12
-15
-9.9
SWEDEN
-0.4
-4.6
-22
-8
PORTUGAL
6.6
-11
-21
-2.9
UK
5.3
-2.7
-14
-14
EU_11
2.1
-8.1
-18.6
-12.7
EU_11=Belgium, Germany, Denmark, Greece, Spain, Finland, France, Ireland, Italy, Netherlands, UK
(sample 87:11-05:07).
Black values indicate that the personal condition is perceived to be better than the general one.
Experiments and Results (4)
According to one of the basic axiom of standard neoclassical models,
agents should not persist in repeating the same mistake.
In the present framework, it may be addressed by looking at the gap
between “contemporaneous” ex ante (Q2, Q4) and ex post (Q1,
Q3) responses, to which I refer as the “forecast error” (i=PP, P, E,
M, MM):
forecast error = 100*[Q1i-Q2i-12)]/[Q1i+Q2i-12]
Likewise for general conditions (Q3-Q4).
For MM and M, positive errors indicate over-optimistic expectations
and/or over-pessimistic judgments. E.g., today 30% judges the
last a “worse” year, 12 months ago 10% foresaw it as a “worse”
year. The reverse holds for PP and P.
It is noteworthy that, in this setting, there is no need for agents to
correctly address what an “economic situation” really is. In fact, I
just compare answers given to the same question.
Europeans’ Forecast Errors (MM)
AUSTRIA_PERSONAL
AUSTRIA_GENERAL
BELGIUM_PERSONAL
BELGIUM_GENERAL
100
100
100
100
50
50
50
50
0
0
0
0
-50
-50
-50
-50
-100
-100
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
-100
1986
1988
1990
GERMANY_PERSONAL
1992
1994
1996
1998
2000
2002
2004
-100
1986
1988
GERMANY_GENERAL
1990
1992
1994
1996
1998
2000
2002
2004
1986
100
100
50
50
50
50
0
0
0
0
-50
-50
-50
-50
-100
1988
1990
1992
1994
1996
1998
2000
2002
2004
-100
1986
1988
1990
GREECE_PERSONAL
1992
1994
1996
1998
2000
2002
2004
1988
1990
GREECE_GENERAL
1992
1994
1996
1998
2000
2002
2004
1986
100
50
50
50
50
0
0
0
0
-50
-50
-50
-50
-100
1990
1992
1994
1996
1998
2000
2002
2004
-100
1986
1988
1990
FINLAND_PERSONAL
1992
1994
1996
1998
2000
2002
2004
1988
1990
FINLAND_GENERAL
1992
1994
1996
1998
2000
2002
2004
1986
100
50
50
50
50
0
0
0
0
-50
-50
-50
-50
-100
1990
1992
1994
1996
1998
2000
2002
2004
-100
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2000
2002
2004
1992
1994
1996
1998
2000
2002
2004
1988
1990
1992
1994
1996
1998
2000
2002
2004
2002
2004
FRANCE_GENERAL
100
1988
1990
FRANCE_PERSONAL
100
1986
1998
-100
1986
100
-100
1996
SPAIN_GENERAL
100
1988
1988
SPAIN_PERSONAL
100
1986
1994
-100
1986
100
-100
1992
DENMARK_GENERAL
100
1986
1990
DENMARK_PERSONAL
100
-100
1988
-100
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
1986
1988
1990
1992
1994
1996
1998
2000
Europeans’ Forecast Errors (MM)
IRELAND_PERSONAL
IRELAND_GENERAL
ITALY_PERSONAL
ITALY_GENERAL
100
100
100
100
50
50
50
50
0
0
0
0
-50
-50
-50
-50
-100
-100
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
-100
1986
1988
LUXEMBOURG_PERSONAL
1990
1992
1994
1996
1998
2000
2002
2004
-100
1986
1988
LUXEMBOURG_GENERAL
1990
1992
1994
1996
1998
2000
2002
2004
1986
1988
NETHERLANDS_PERSONAL
1990
1992
1994
1996
1998
2000
2002
2004
2002
2004
2002
2004
NETHERLANDS_GENERAL
100
100
100
100
50
50
50
50
0
0
0
0
-50
-50
-50
-50


-100
-100
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
-100
1986
1988
PORTUGAL_PERSONAL
1990
1992
1994
1996
1998
2000
2002
2004
-100
1986
1988
1990
PORTUGAL_GENERAL
1992
1994
1996
1998
2000
2002
2004
1986
100
100
50
50
50
50
0
0
0
0
-50
-50
-50
-50
-100
1988
1990
1992
1994
1996
1998
2000
2002
2004
-100
1986
1988
1990
UK_PERSONAL
1992
1994
1996
1998
2000
2002
2004
2000
2002
2004
UK_GENERAL
100
100
50
50
0
0
-50
-50
-100
-100
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
1986
1988
1990
1992
1994
1996
1998
1992
1994
1996
1998
2000
SWEDEN_GENERAL
100
1986
1990
SWEDEN_PERSONAL
100
-100
1988
-100
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
1986
1988
1990
1992
1994
1996
1998
2000
Europeans’ Forecast Errors Statistics
(mean; % in  5% band)
(PP)
-3.1
-15.1
4.5
-10.1
17.0
22.1
6.1
4.2
-12.3
-25.2
6.2
17.4
6.4
11.9
-9.8
-19.3
14.2
4.0
25.1
16.6
14.0
6.8
17.9
21.3
7.7
6.4
15.1
12.8
17.9
4.5
20.0
8.9
23.6
28.3
-1.7
-32.2
2.9
-27.5
16.8
4.0
2.1
9.2
12.8
-1.4
9.0
-7.3
11.1
5.5
13.6
7.7
19.4
9.7
8.5
6.4
18.3
17.9
17.9
5.7
23.4
14.9
(P)
-28.4
-17.9
-10
-16.4
3.8
-10.4
-4.6
-4.4
-24.7
-24.6
-17.3
-10.9
-9.7
-16.7
-23
-39.9
0.9
12.3
23.8
8.9
26.8
10.6
44.7
17.0
10.6
8.1
11.0
28.0
44.3
20.9
2.1
6.8
-5.5
-5.3
-25.5
-35.6
-16.5
-34.0
-3.2
-13.7
-16.9
-18.2
-8.1
-10.7
-10.8
-15.5
35.7
17.4
14.0
6.8
9.7
0.0
37.0
12.8
9.6
9.2
41.5
14.2
24.3
16.2
(E)
1.7
-15.7
-2.1
-8.5
-7.2
-12.0
-2.3
2.8
-5.4
-8.4
3.3
-4.4
-0.9
-4.0
0.6
-5.5
50.9
13.2
67.2
26.0
34.5
23.0
75.3
50.6
34.9
31.5
66.5
47.7
90.0
26.4
89.8
40.4
-8.3
-18.6
-4.5
-15.1
4.5
-13.4
-3.5
-4.0
7.2
-1.7
0.4
-1.7
-3.9
-8.6
26.4
12.3
64.7
23.0
58.1
25.8
64.3
24.3
36.2
23.9
80.2
46.2
47.7
29.8
Europeans’ Forecast Errors Statistics
(mean; % in  5% band)
(M)
4.6
16.6
21.5
12.7
20.2
7.1
15.9
-5.3
21.8
25.3
22.9
18.7
16.7
0.6
16.4
12.6
25.5
26.4
12.8
21.7
11.1
15.7
15.7
13.6
8.9
3.8
6.9
11.0
10.4
9.0
12.3
15.7
21.7
4.6
32.8
20.3
7.6
26.1
4.4
-6.3
12.1
14.1
7.1
6.2
13.8
12.7
5.1
29.8
1.7
13.6
25.8
3.2
21.7
16.6
25.2
18.3
29.2
17.0
16.2
17.4
(MM)
34.1
31.8
36.9
25.4
35.6
29.3
39.5
-0.7
13.3
9.6
35.6
27.1
42.3
21.3
33.5
31.2
3.8
7.5
8.1
9.8
1.3
5.1
4.3
18.7
11.5
11.5
1.8
5.0
3.0
10.4
1.7
7.2
36.5
25.6
46.7
38.2
33.0
24.4
29.5
13.7
21.8
18.4
40.5
22.0
32.8
24.4
3.8
5.5
0.9
5.1
3.2
6.5
3.0
14.0
8.3
6.0
1.9
6.6
1.3
8.5
Puzzling Results
People’s tendency to judge over-pessimistically and/or to forecast
over-optimistically.
The ambiguity arises because of the lack of a “hard” benchmark (e.g.,
GDP, Consumption, etc.). However, it implies that:



people’s forecasts show a long run bias.
people’s tendency to think that their own economic situation is
better than the general one - the representative consumer think to
become “richer” than himself.
To sum up, there seems to be a structural mantra echoing across Europe:
As Usual, it Has Got Worse Than I Expected.
Especially for the Others.
Nevertheless, I Still Think That it Will Get Better.
Especially for Me.
Puzzles or well known Psycho-Biases?

Over-pessimism in judgments
Availability bias. Mere repetition of certain information in the media,
regardless of its accuracy, makes it more easily available and
therefore falsely perceived as more accurate. Since the media tend
to overweight bad economic news (Doms and Morin, 2004), there
are reasons inducing individuals toward dispositional pessimism.
Moreover, the information flow may also run from people to media
(Curtin, 2003), creating a perverse spiral.

Over-optimism in forecasts
Irrational exuberance. In uncertain situations people tend to make
forecasts by assuming, often without sufficient reasoning, that
future favorable patterns will resemble past ones.
Law of small numbers. People tend to over-inference from too short
sequences of observations.
Hindsight/Confirmation bias. Individuals tend to concoct ex post
“logical” explanations for ex ante totally unexpected events.
All that prevents agents from adequately learning from the past and
from being aware of their errors, leading to long-run biases.
Puzzles or well known Psycho-Biases? (2)

Over-pessimism in judgments and
over-optimism in forecasts
Mental Accounting. People “allocate” current and future
income in different “accounts”.

Over-self-confidence
Illusion of Control. People have an expectancy of a personal
success probability inappropriately higher than the
objective probability would warrant.
Depressive realism. One interpretation of it is that nondepressed people possess a positive bias, which allows
them to feel in control of their environment. Since,
hopefully, the representative European is non-depressed,
evidence supports the agents’ egocentric bias.
Concluding remarks
When elicited about economic conditions, people tend
to reply both as expected and irrationally.
Empirical evidence highlights paradoxical (rectius,
emotionally-driven) responses, even when dealing
with familiar conditions.
Thus, it is not only a problem of the amount/quality of
available information and/or the difficulty of the
exercise - there is something else preventing
“Muthian” results.
My research suggests that psychology may be of some
help. To the extent it is true:
 not necessarily the detected puzzling outcomes
reduce the reliability of survey data,
 it may be useful adding psychological considerations
to CSI – biases are structural => manageable
After all, everybody should agree that
the sentiment is a mix of rationality
and feelings…