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

5.00

10.00

15.00

20.00

-3 2 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

5.00

10.00

15.00

20.00

-4 1 6 11 16 21

LFS

AES

Comparison LFS (primary jobs) and AES, ratio AES/LFS, average 1996-01 – 41 industries

0

0.5

1

1.5

2

2.5

0 5 10 15 20 25 30 35 40 45

Comparison LFS (primary jobs) and AES, ratio AES/LFS, average 1996-01,– 20 industries

0

0.5

1

1.5

2

2.5

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 2.5Mining, quarrying 0.1Manufacturing 5.1Electricity gas & water supply 0.1Construction 2.4Wholesale, retail & motor trade 11.3Hotels & restaurants 10.7Transport, storage & communication 3.2Financial intermediation 1.1Real estate, renting & business activ. 11.8Public administration & defence 6.4Education 13.7Health & social work 14.9Other community, social & personal 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

X

Old SIC New SIC

Y Z

X = Y + Z, so weights are Y/ (Y+Z) and Z/(Y+Z)

Industry concordances

Consistent – Often more complicated

X

Old SIC New SIC

Y Z

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

TW

Illustration of fudge methodTime series for industry x

break year

t-1 t

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