Improvements in stratification in the UK's Office for National Statistics
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Improvements in stratification in the UK's Office for National Statistics Pete Brodie, Martina Portanti & Emily Carless UK Office for National Statistics. Outline. Context The B usiness R egister and E mployment S urvey Stratification Variables Employment Size Measure

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Outline

Improvements in stratification in the UK's Office for National Statistics Pete Brodie, Martina Portanti & Emily CarlessUK Office for National Statistics


Outline
Outline National Statistics

  • Context

    • The Business Register and Employment Survey

  • Stratification Variables

    • Employment Size Measure

    • Complexity of Enterprise

  • Sample Design and Estimation

  • Conclusion


Bres 1 2
BRES (1/2) National Statistics

Why design a new survey (BRES)?

  • The Allsopp Review of Statistics for Economic Policy Making

    • Utilise administrative data and improve regional statistics

  • The Integration of survey systems to improve efficiency.

    • Integrate ABI/1 (employment estimates)

    • and the BRS (register updating)


Bres 2 2
BRES (2/2) National Statistics

Annual

Business

Inquiry

(ABI)

Business Register Survey

(BRS)

Part 1

Part 2

Updates the business

register

Employment

Estimates

Financial

Estimates

Annual

Business

Inquiry

(ABI/2)

Financial Estimates

Current

Business

Register

and

Employment

Proposed

Survey

(BRES)

Employment Estimates


Stratification variables
Stratification Variables National Statistics

  • Register Employment (size banded) with Industrial Classification (SIC is the UK’s NACE)

  • For new survey

    • Considered the use of Full Time Equivalent (FTE) instead

    • Considered the use of a marker for complex businesses


Employment size measure 1 6
Employment Size Measure (1/6) National Statistics

  • Problems caused by using Headcount (HC) for stratification

    • Some businesses that employ many part time workers appear unduly large

    • For certain industries the correlation between this measure and returned employment is not particularly good


Employment size measure 2 6
Employment Size Measure (2/6) National Statistics

  • A more sensible measure may be the FTE but how to define it?

  • Tried two different definitions:

    • FTE1 = Full Time + 0.5 Part Time

    • FTE2 = Full Time + industry specific fraction for PT (using data from ASHE)

  • As well as HC = FT + PT


Employment size measure 3 6
Employment Size Measure (3/6) National Statistics

  • Firstly we examined the effect of these three measures on burden on business

  • Each FTE measure reduces business size compared to HC

  • Fewer businesses sampled (Osmotherly Rule)

  • Little difference between FTE1 and FTE2


Employment size measure 4 6
Employment Size Measure (4/6) National Statistics

  • Secondly we examined the effect of the measures on correlation with returned variables

  • The table below shows the correlation between returned values from the Annual Business Inquiry and the three employment measures for the whole economy.


Employment size measure 5 6
Employment Size Measure (5/6) National Statistics

  • Lastly we looked at the effect of the stratification variable on cv’s of estimates


Employment size measure 6 6
Employment Size Measure (6/6) National Statistics

  • FTE is a much better stratification variable

  • Reduces burden without unduly reducing quality

  • Markedly reduces cv’s for some variables without unduly reducing quality of others

  • No gain from using the complex definition so we will use simple FTE1 (=FT + 0.5 PT)


Complexity of enterprise 1 7
Complexity of Enterprise (1/7) National Statistics

EU Regulation:

“structure of units on the Register must be updated at least every four years”

ONS:

“structure of multiple Local Units (LUs) enterprises must be updated at least every four years”

With:

Number of LUs

LU variables (SIC, geography, employment)


Complexity of enterprise 2 7
Complexity of Enterprise (2/7) National Statistics

  • Would satisfy register updating requirements if there was good coverage of employment

    CURRENT REGISTER has:

    Single LU enterprises Multi LU enterprises

    2,138,000 LUs 63,000 enterprises

    547,000 LUs


Complexity of enterprise 3 7
Complexity of Enterprise (3/7) National Statistics

100%

90%

80%

70%

60%

50%

Multi

40%

Single

30%

20%

10%

0%

Enterprises

Employment


Complexity of enterprise 4 7
Complexity of Enterprise (4/7) National Statistics

  • But 40,000 of these multi LU enterprises have all LUs in the same region with the same SIC

  • Most employment is covered by defining complex enterprises:

    • LUs in more than one region

    • OR LUs classified to more than one SIC-2 industry

  • The smaller (less than 20 employment) businesses had very few LUs (all small also) so we did not consider these as complex


Complexity of enterprise 5 7
Complexity of Enterprise (5/7) National Statistics

  • The second main aim of BRES was to satisfy the Allsopp requirements:

    • to improve regional estimates

    • To retain fine industrial breakdowns

  • Use detailed LU data in estimation

  • Discrepancy between parent enterprise region and local unit region causes large differences in regional employment estimates


Complexity of enterprise 6 7
Complexity of Enterprise (6/7) National Statistics


Complexity of enterprise 7 7
Complexity of Enterprise (7/7) National Statistics

  • Complex enterprises are the ones with most likely discrepancy

  • Making complex enterprises take-all should improve regional estimation

  • Similarly improvements will be made in estimation at 2-digit SIC as Allsopp recommended


Sample design and estimation 1 4
Sample Design and Estimation (1/4) National Statistics

  • Tested three different designs with stratum cut-offs set to optimise register updating or estimation requirements

  • Tested whether stratification within size bands by geography or industry was best

  • Tested the use of a fully enumerated stratum for “unusual” enterprises.


Sample design and estimation 2 4
Sample Design and Estimation (2/4) National Statistics

  • Conventional Industry combined with Geographical stratification would spread sample too thinly

  • Tested a two partition solution

  • Calibrated to a geography partition and simultaneously to an Industry partition

  • Tested the auxiliary and variance model to be used in calibration


Sample design and estimation 3 4
Sample Design and Estimation (3/4) National Statistics

  • Created a LU level Pseudo Population from the current IDBR RU data

  • Returned values were created using a ratio model within strata to create residuals about the model

  • Imputed LU level variables for Industry and region (probabilistically)

  • Added outliers (0.1%)

  • Repeated sampling to test coverage and estimation properties of different options


Sample design and estimation 4 4
Sample Design and Estimation (4/4) National Statistics

Best design:

  • Gave best coverage of employment so best for updating

  • Gave smallest MSEs for most outputs


Conclusions
Conclusions National Statistics

  • We can improve both register updating and employment estimation by replacing two surveys with one more efficient survey

  • Uses the concept of a complex business to increase coverage of “important” businesses

  • Reduces burden on businesses by measuring size using FTEs

  • Increase efficiency of estimation by calibrating in two partitions


Register updating
Register Updating National Statistics

  • Talk by Daniel Lewis of the ONS

  • Evaluating the effect of business register updates on monthly survey estimates

  • Tomorrow afternoon (Wednesday) Session 39: Updating of Business Registers


Any questions
Any questions? National Statistics

Contact details:

Pete.Brodie@ons.gsi.gov.uk