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USES OF BUSINESS AND
CONSUMER OPINION SURVEY
DATA, IMPLICATIONS FOR DATA
PRODUCERS
Giuseppe Parigi
Bank of Italy, Economic Research Department
The art and science of short-term analysis of high
frequency economic data is extremely important to
economic policy decision makers, such as central bankers.
“Good diagnosis helps in making
predictions” Katona (1957)
Among short-term indicators,
survey data play a prominent role
Nowadays there is an increasing
demand of high quality survey data
SURVEY DATA might be represented as containing
three types of information (see Fuhrer, 1988):

Information on current developments
 Forward-looking information
 “Animal spirits” information
Information on current developments
Katona (1957): “Expectations – intentions as well as other notions
about the future – are current data which help to understand what is
going on at the time when expectations are held.”
TIMELINESS
Survey data are available soon after the end of the reference
period (generally, the month) and are not revised
Bridge models
Coincident indicators
Early estimate of data released with delay
NBER and Factor models
Nowcasting
Help establish initial conditions
Bridge Models
Publication of
quarterly national
accounts within 70
days after the end of
reference period
Flash estimates
in 45 days
Survey data and
other short term
(composite)
indicators
Need timelier information about National accounts
BRIDGE MODELS
High frequency data
National account data
Bridge Models: matching variables and indicators
Univariate model
Retail sales, CSI, UR
Business surveys (expected
demand), construction comp.
Trade variables, real exch.
rates, IP, Surveys data
Collective consumption
Private consumption
Gross fixed capital formation
Exports of goods and services
Forecasts
Imports of goods and services
IP, Business surveys
GDP
SUPPLY SIDE
DEMAND SIDE
GDP, Surveys data
Changes in stocks
________________________________________________________________
(GDP+Imports)
GDP= CON + COC + INV + EXP - IMP + VSP
Coincident indicator – Eurocoin
Industrial Production
Trade variables
150 series
40 series
Total
Money
Prices
800 variables
160 series
130 series
Miscellanea
80 series
Labour market
40 series
Survey data
200 series
25%
Forward-looking and “animal spirit”
information
Events which are difficult
to quantify (tax changes)
Expectations with
self-fulfilling properties
Survey data
Forecasting power
Leading Indicators
Turning points detection
Anticipate the evolution of the cycle
Estimates of the probability of being
in a recession/expansion
Theoretical and Empirical Models
Interpretations of survey data: what is this thing called confidence?
The problem of sometime too vague verbal questions
Survey data and Economic analysis
Although some consensus emerged in the literature that SD
could play a role, this appears to be ad hoc. A convincing
representation of SD is needed…

SD as an alias of macroeconomic variables?

SD as a proxy of non-linearities (shocks)?

SD as a proxy of unobserved variables?
… but their informative content is still a mystery
Survey data and Economic analysis
EXPECTATIONS
Scepticism of economists to the use of survey data:
one should believe only what people do and not what people say.
Revealed preference analysis
Economists attempt to infer expectations by combining data
on realized experience (choice data) with assumptions about
the process of expectation formation.
Survey data and Economic analysis
The prevailing practice has been to assume that agents have
expectations that are objectively correct (i.e. rational).
But lack of empirical evidence on the validity of the
expectations assumptions has led to a crisis of credibility.
Survey data is a possible solution…
“The data I have in mind are self-reports of expectations
elicited in the form called for by modern economic theory;
that is subjective probabilities” (Mansky, 2004)
Survey data
Probabilistic questions
 Juster (1966) showed that elicited purchase probabilities
are better predictors of subsequent behaviour
 Vague concepts like “future economic conditions” may be
avoided with questions about personal facts
 Harmonization of survey across countries is more likely to
be complete when based on numeric response scales
 Numeric probability scales allow the comparability of
responses among different people, across situations and
over time
Survey data
Probabilistic questions: Examples
 The Health and Retirement Study in the USA (subj. prob. of
living 75/85, job loss etc.)
 The Bank of Italy Survey on Household Income and
Wealth and The Dutch VSB-Panel Survey (subj. prob. of
one year-ahead growth rates in income)
 The Bank of Italy Survey on Business Investment (one of
the few examples of probabilistic questions to firms)
 The Michigan Survey of Consumers
Survey data: Further improvements
Survey should follow the evolution of
people behaviour

Developments of financial markets

Aging populations

Reforms of the welfare
… Imply new forms of uncertainty
Survey data : Further improvements
Survey data should be released in a more
detailed way…

on a geographical, sectoral, dimensional basis (but
also new classifications as technologically advanced
v. traditional sectors)

by income, age, employment classes (better match
with macroeconomic variables)