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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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slide1

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

outline
Outline
  • 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)

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)

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
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)

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)
  • 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)

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)
  • 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)
  • 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 7 7
Complexity of Enterprise (7/7)
  • 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)
  • 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)
  • 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)
  • 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)

Best design:

  • Gave best coverage of employment so best for updating
  • Gave smallest MSEs for most outputs
conclusions
Conclusions
  • 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
  • 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
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