Transcript WP2 workshop, NIESR, November 24-25, 2005

WP2 workshop, NIESR,
November 24-25, 2005
Volume measures of labour input
Reconciling data from different sources
Which source to use: establishment surveys, labour
force surveys, other (social security statistics)
•Issues for discussion
•Options were to impose the same type of source on
all partners or allow each to decide on the best source
for their own country?
•In the latter can we adjust data to ensure definitions
are comparable across countries?
Reconciling data from different sources
UK example
Large number of sources – Employment Census [AES]
(establishment), Annual Business Inquiry [ABI]
(establishment), Labour Force Survey [LFS] (individual),
Social security data [SS] (individual)
Data availability
•AES – from 1978; LFS – from 1984; ABI – from 1998;
SS – 1970-1978
•All series at least 2 digit SIC – some 3 digit
•Sufficient detail to generate full EUKLEMS series
Comparison LFS (primary jobs) and AES,
annual growth 1996-01 – 41 industries
20.00
15.00
10.00
5.00
0.00
-3
2
-5.00
-10.00
-15.00
-20.00
7
12
17
22
27
32
37
42
LFS
AES
Comparison LFS (primary jobs) and AES,
annual growth 1996-01 – 20 industries
20.00
15.00
10.00
5.00
0.00
-4
1
-5.00
-10.00
-15.00
-20.00
6
11
16
21
LFS
AES
Comparison LFS (primary jobs) and AES,
ratio AES/LFS, average 1996-01 – 41 industries
2.5
2
1.5
1
0.5
0
0
5
10
15
20
25
30
35
40
45
Comparison LFS (primary jobs) and AES,
ratio AES/LFS, average 1996-01,– 20 industries
2.5
2
1.5
1
0.5
0
0
5
10
15
20
Reconciling data from different sources: UK
Attempt to redefine in terms of common definitions
LFS allocate second jobs to industry where labour is
employed - Mostly in services
Agriculture, hunting & forestry
Mining, quarrying
Manufacturing
Electricity gas & water supply
Construction
Wholesale, retail & motor trade
Hotels & restaurants
Transport, storage & communication
Financial intermediation
Real estate, renting & business activ.
Public administration & defence
Education
Health & social work
Other community, social & personal
2.5
0.1
5.1
0.1
2.4
11.3
10.7
3.2
1.1
11.8
6.4
13.7
14.9
16.6
100
Reconciling data from different sources: questions
To what extent have consortium members found similar
discrepancies between sources?
Which source should be used?
As control totals – NA if available, but what is this?
To divide by industry – small sample sizes implies
more variation
•LFS coefficient of variation significantly negatively
correlated with sample size
Should we combine data sources – one as control
total for broad sectors and use shares of sub-sectors
in broad sectors from another source to disaggregate
•How do we decide what is a small sample
Industry concordances
Options for concording
•Optimal – get NSI to do it
•Consistent – construct weights based on data for an
overlapping year
•Fudge – When data are not available for an
overlapping year. Use whatever information is
available to get an approximate concordance between
industry, then use growth rates in another series to
construct an overlapping year, to ensure no jumps
Industry concordances
Consistent – construct weights based on data for an
overlapping year
•Simplest case
Old SIC
X
New SIC
Y
Z
X = Y + Z, so weights are Y/ (Y+Z) and Z/(Y+Z)
Industry concordances
Consistent – Often more complicated
Old SIC
New SIC
X
Y
Z
T
W
Set of simultaneous equations but may need interative procedures if
sufficiently complicated
As long as overlapping year data exist there should not be jumps in
the data
Illustration of fudge method
Time series for industry x
t-1
t
break
year
Industry concordances
UK example – three SICs, 1968, 1980, 1992
•LFS – no overlapping year
•AES some overlapping years , e.g. 1990-93 on both
SIC80 and SIC92, but for detailed (3 digit industries)
data only available for GB.
•Fudge – for LFS could use growth in AES for
overlapping year to infer an overlapping year in LFS.
(note levels in AES and LFS differ so cannot use AES
weights applied to LFS)
Industry concordances
•Issues for discussion
•To what extent are industry concordances an issue?
•What methods have colleagues used to overcome
problems?
•Can prodsys help?
Historical data – how to fill gaps
Look for additional data – censuses, surveys
If not available what are the options
•If earlier data are more aggregated then can assume
growth in sub-industries equal growth in aggregate
•If no historical data available then what do we do?
•Assume growth rates the same as for aggregate
economy?
•Assume growth rates the same as other variable
in EUKLEMS dataset?
•Assume growth rate same as similar industry in
similar country?
Data delivery
•Deadline for revised data January 15
•Require prodsys readable form
•Important source of information
•Crucial for productivity calculations
•DOCUMENTATION
•Sources
•Assumptions