Transcript Joint EC-OECD Workshop on International Development of Business and Consumer Tendency Surveys Brussels 14-15 November 2005 Task Force on Harmonisation of Survey Operation and Technical Design Efficient.

Joint EC-OECD Workshop on International
Development of Business and Consumer Tendency
Surveys
Brussels
14-15 November 2005
Task Force on Harmonisation of Survey Operation and
Technical Design
Efficient Sample Design and Weighting
Methodologies
Analysis of Key Issues and Recommendations
Efficient Sample Design and Weighting Methodologies
The usefulness of BTS and CS strongly depends on
their statistical quality
•
Quality of survey data may be measured in terms of
(OECD, 2003):
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•
•
•
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reliability
timeliness of release
comparability over time
transparency and accessibility to the users
This task force has to do with sample design and
weighting methodologies
•
A sound definition of these aspects increases the
reliability – and therefore the overall quality – of the
data
•
Efficient Sample Design and Weighting Methodologies
Business Tendency Surveys (BTS) are conducted on
manufacturing, services, retail and construction
sectors
• Consumer
Surveys (CS) measure households’
opinion and expectations on personal and general
economic situation
• For
both BTS and CS, key issues in Efficient
Sampling Design and Weighting Methodologies will be
analyzed
• An overview of the current practices in these fields
will be presented, from which it will be derived a
number of draft recommendations aimed at
improving the overall quality of the surveys
•
Efficient Sampling Design for Business Tendency
Surveys
In the case of BTS, key Issues for an efficient
sample design are (see also Donzè, Etter, Sydow,
Zellweger, “Sample Design for Indystry Survey”,
ECFIN/2003/A3-03):
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•
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Identification of relevant Universe/reference population
Identification of the sample frame
Identification of the correct method for sample selection
Treatment of missing data
Efficient Sampling Design for Business Tendency
Surveys
The first step in setting up a BTS is the choice of the
Relevant Universe/reference population
• Typically, it is represented by all the firms operating
in a given sector, as resulting from some
official/statistical register
• Some firms may be excluded looking at their size
(i.e., below a certain size threshold) or location, or on
the basis of their structural characteristic (i.e.,
exclusion of government bodies)
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Efficient Sampling Design for Business Tendency
Surveys
The second step is the choice of the sample frame,
having the goal of maximize sample coverage and
minimize coverage errors
• BTS are usually based on a panel of responding
firms, that are re-interviewed each month
• Demographic and structural characteristic of the
respondents have to be known in order to build up
the sample, the construction of the frame implying
the following steps:
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•
•
•
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identification of the appropriate frame list
eventual adoption of a cut off strategy
identification of the sample, reporting and response unit
Updating of the frame list
Efficient Sampling Design for Business Tendency
Surveys
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The frame list may be derived from:
• official or statistical registers
• membership lists of business associations and chamber of
commerce
60%
50%
40%
30%
20%
10%
0%
non eu
eu-25
total
Manufacturing
Statistical registers
Business directory
Services
Retail
Construction
Governement registers and other
Efficient Sampling Design for Business Tendency
Surveys
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The adoption of a cut off strategy may respond to the need of:
• better focusing on the sector of interest (cut-off with respect to the
sector of activity)
• ensuring a certain stability of the panel, excluding firms below a
certain threshold (cut-off with respect to size)
The sample unit is the unit on which to perform the sample
selection procedure; the main choice is between having:
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• the whole firm
• establishments, local units, or kind of activity units (kau)
Even if the firm is chosen as the sample unit, it is possible to
have more reporting and response units within the firm, sending
the questionnaires to different establishments/local units/KAUs
within the firm
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Efficient Sampling Design for Business Tendency
Surveys
•90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
eu
non Eu
Total
Manufacturing
Establishemnt
Services
Enterprise
Retail
Construction
Mixed (establishment + kau)
Efficient Sampling Design for Business Tendency
Surveys
Finally, the list should be updated frequently in order to keep track of
the changes in the structure of the reference Universe and avoid
possible problems in terms of:
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Under coverage (new firms entering the market)
• ineligibility (old firms exiting the market)
• duplicate entries (errors)
50%
Manufacturing
Services
Retail
Construction
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
Continously
more than 1
time per year
yearly
between 1 and 6
years
other (ad hoc)
not available
Efficient Sampling Design for Business Tendency
Surveys
The third step implies the choice of an appropriate method of
sample selection
• The sample should be representative of the relevant Universe
• In order to build up a representative sample, choices have to
be made relatively to:
•
• The sampling method
• The sampling size
BTS are usually based on a fixed panel of reporting units
• A fixed sample structure may rise representitiveness problems,
because the panel may loose its initial representativeness if it is
not updated regularly
• For this reason, Institutes often use a rotating panel method,
in which a a fixed percentage of units are replaced at regular
intervals
• More precisely, the largest and most important firms should
ideally always be included, with smaller firms being rotated out
on a regular basis
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Efficient Sampling Design for Business Tendency
Surveys
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Possible methods of sample selection include:
• non-statistically founded methods, such as:
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comprehensive surveys with cut-off
purposive or quota sampling
• statistically funded methods, such as:
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simple stratified sampling
stratified sampling (PPS, OAS)
Once selecting the sample,
appropriate sample sizes
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Institutes
have
to
choose
Generally speaking, sample size will be chosen accordingly to a
predetermined desired maximum measurement error
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Efficient Sampling Design for Business Tendency
Surveys
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Manufacturing Services Retail Construction
40%
35%
30%
25%
20%
15%
10%
5%
0%
Representative
panel
Quota
Simple stratified Stratified
Stratified
Cluster and
sampling sampling w ith sampling w ith
random
PPS
Neyman
sampling
allocation
Purposive Comprehensive
choice
or mixed
Efficient Sampling Design for Business Tendency
Surveys
The fourth step in building up BTS implies the treatment of
missing data
• It is possible to distinguish between
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• unit non response (entire interview is missing)
• item non response (some answer is missing)
If missing data occur with large, dominant firms the problem is
severe
• If there is missing data among the smallest firms, the problem
is less critical and missing data may easily be imputed on the
basis of the answer of similar firms
• Most commonly used method for dealing with missing data
problem is the use of follow-up techniques (re-interviewing with
telephone, fax or web based techniques)
• If missing data problems persist, re-weighting or imputation
methods should be used
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Weighting Methods for Business Tendency Surveys
Weighting is used to transform data for the realized sample into
estimates for the reference population
• Weights may be based on:
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• information coming from the survey itself (size, output of the firm)
• auxiliary sources (official/statistical data on the size of the reference
sector/region)
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Weighting methods are usually based on either:
• one stage weight scheme
• two stage weight scheme
In the one-stage scheme, a weight is associated with each
reporting unit, in order to take into account its relative importance
inside the sample
• In the second case, a unit-specific weight is used to calculate
strata results, further aggregating the strata with some external
sources in order to obtain industry aggregates.
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Weighting Methods for Business Tendency Surveys
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Manufacturing Services Retail Construction
60%
50%
40%
30%
20%
10%
0%
One-stage weighting
procedure
Two-stage weighting procedureNo weights/missing information
Efficient Sampling Design for Consumers Surveys
In the case of CS, in order to build up a representative sample choices
have to be made relatively to:
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• The sampling frame
• The sampling methods
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The construction of the sampling frame implies the following steps:
•
•
•
•
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identification of the appropriate frame list
eventual adoption of a cut off strategy
identification of the sample unit, reporting unit and response unit
updating of the frame list
In the identification of the appropriate frame list, it is crucial that
• the right population is being sampled,
• all the members of the population have the same chance of being sampled
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In OECD countries, frame lists are usually based on:
• official population register (including every adult member of the population)
• telephone register (arising possible bias problems, to be solved adopting
random extraction of phone numbers or using other sample techniques for
those excluded from the directories)
Efficient Sampling Design for Consumers Surveys
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A cut off strategy is often adopted
• on the basis of age (cut off age varying often across countries in EU);
• in some countries, geographical cut offs are also applied
Response unit may differ from the sample unit; typically, sample are
devised to be representative of all households, with the selected
respondent reporting on the household as a whole
• The list should be updated frequently in order to monitor as close as
possible the evolution of the relevant population
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35%
30%
25%
20%
15%
10%
5%
0%
Continously
4 times a year
yearly
betw een 1
and 6 years
more than 6
years
not available
Efficient Sampling Design for Consumers Surveys
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Key issues in sampling extraction include
•the choice of the appropriate sampling method
• the choice of the optimal size of the sample
In CS usually a independent cross-section of household is extracted
each month:
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• In EU a general strategy of simple random sampling is used
• In the US a rotating sampling design is usually applied, in which the
respondent chosen in each drawing is re-interviewed six months later, in
order to provide a regular assessment of change in consumers’ attitudes
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Most widely used methods of sampling extractions include:
• simple stratified sampling
• multiple stage stratified sampling
• Random Digit Dialing methods
There is no consensus in the literature on the appropriate sample size
• In practice, sample size currently converges to about 2000, a size
supposed to provide acceptable confidence intervals for this type of
survey
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Weighting Methodologies for Consumers Surveys
Information gathered from survey’ respondents may be
appropriately weighted to derive aggregate information on
household’ opinion and expectations
• Weights may be based on
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• auxiliary information (demographic or socio-economic weights)
• inverse selection probabilities (sample weights)
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Most commonly used weight variables are:
• demographic characteristics of the household:
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gender and age of the respondent
region of residence and size of the township
• socio economic characteristics of the household:
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economic occupation
level of education
housing condition, type of area/municipality
A number of Institutes do not use weights: this is appropriate
only when:
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•every household has an equal chance of selection
• there is no differential no response
Weighting Methodologies for Consumers Surveys
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80%
70%
60%
50%
40%
30%
20%
10%
0%
Sample weights
Socio Demographic
characteristic
No weights
Minimum requirements and recommendation for BTS:
sample design – the sample frame
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The frame lists
• Frame lists should include an as exhaustive as
possible account of active firms for the survey of
interest
• As a consequence, the use of official or
statistical registers of active firms is recommended
over that of – more partial – business or
membership registers
•
Cut off strategies
• Institutes are advised to use cut-off strategies in
order to stabilize the panel (size cut off) and for a
precise identification of the survey objectives
(branch cut off)
Minimum requirements and recommendation for BTS:
sample design – the sample frame
•
Sample units and reporting units
• Establishments may be considered the ideal choice for the sample
unit; however, it may be difficult to gather information at this level
• Use of KAU is advisable if we are particularly interested in the
industrial structure
• Use of local units is advisable if we are particularly interested in
the regional structure Sample frame: reporting units
• Even if the firms is identified as the sample unit, it is advisable –
if possible – to have different reporting units within the firm
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Response units
• In any case it is strongly recommended that the Institutes ensure
that the same response unit answer the questionnaire every month
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Updating of the lists
• As a minimum requirement, frame lists should be updated as
soon as a new census of active firms is available
Minimum requirements and recommendation for
BTS: sample design – sampling methods
As a recommendation, a fixed panel should be used…
• … established on a statistically founded basis…
• … using a rotating pattern of updating …
• … with a fixed percentage of participants being replaced at
regular intervals
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As a minimum requirement, sampling extraction should be
based on sound probabilistic considerations
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The use of exhaustive sampling is possible for small countries
or for a sub-set of the sample
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Avoiding of purposive or ad hoc sampling methods is strongly
recommended
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Different probabilistic methods of sample selection may be
used; as a general consideration, the more heterogeneous is the
population, the more is advisable the use of stratification based
sampling methods
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Minimum requirements and recommendation for
BTS: sample design – treatment of missing data
Institutes should define what procedures are used
for the treatment of item and unit non response
(missing data)
• As a minimum requirement, institutes are advised:
•
• to closely monitor the impact of missing data (especially
for large firms)
• to use follow up techniques in order to reduce their impact
(fax, telephone, web remainder)
The use of imputation methods to deal with
remaining missing data should be considered with
care, in order to avoid possible distortions
• The use of re-weighting techniques, taking into
account different composition of the panel in adjacent
surveys, may be advisable to reduce the bias
•
Minimum requirements and recommendation for
BTS: weighting methods
The use of weights is strongly
recommended in order to improve the
precision of the estimates
• As a minimum requirement the use of
a simple – one stage – system of
weights is suggested
• Two
stage
(or
multiple
stage)
weighting procedures are advisable for
heterogeneous population, especially in
large countries
•
Minimum requirements and recommendation for
CS: sample design – the sample frame
Frame list should include an as exhaustive
as possible account of the adult population
• As
a consequences, official census or
statistical registers are to be preferred to
telephone registers
• If telephone registers are used, appropriate
methods to correct for possible bias is
recommended
• Cut off strategies with respect to age are
advisable – this may call for further
harmonization in the EU
• As a recommendation, frame lists should be
updated yearly
•
Minimum requirements and recommendation for
CS: sample design – sampling methods
As a minimum requirement, random sampling
techniques have to be used in order to ensure survey
representitiveness
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In case of heterogeneous population, the use of
stratified sampling methods should be preferred to
simple random sampling
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Finally, a major difference emerges between EU
(using independent drawing of the sample each
month) and the US (using a rotating sample design)
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The adoption of the US method may possibly
enhance research option available to analyst even in
the EU
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Minimum requirements and recommendation for
CS: weighting
Weighting is recommended in order to
ensure better representitiveness
• Demographic
characteristic
of
the
households may be used as weights,
considering among them:
•
• age and gender
• region of residence and size of the township
Alternatively, socio-economic characteristics
may be used as weights:
•
• type of occupation
• Level of education
• type of area municipality
THANK YOU FOR YOUR ATTENTION!
Task Force Members are: Richard Curtin (University
of Michigan, United States), Isabelle De Greef
(National Bank of Belgium), Richard Etter (KOF / ETZ,
Switzerland),
Christian
Gayer
(European
Commission), Marie Hormannova (CZSO, Czech
Republic), Marco Malgarini (Institute for Studies and
Economic Analysis – ISAE, Italy), Rony Nilsson
(OECD), Raymund Petz (GKI Research, Hungary),
Takashi Sakuma (ESRI, Japan), Philippe Scherrer
(INSEE, France), Anna Stangl (Ifo Institute for
Economic Research, Germany), Andres Vertes (GKI
Research,
Humgary),
Peter
Weiss
(European
Commission), Jonathan Wood (Confederation of
British Industry – CBI, United Kingdom).