Improvements in stratification in the UK's Office for National Statistics

Pete Brodie, Martina Portanti & Emily CarlessUK Office for National Statistics

Outline

• Context– The Business Register and Employment Survey

• Stratification Variables– Employment Size Measure– Complexity of Enterprise

• Sample Design and Estimation• Conclusion

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)

Business Register and Employment Survey (BRES)

Employment Estimates

Business Register Survey (BRS)

Updates the business register

Annual Business Inquiry (ABI)

Part 1

EmploymentEstimates

Part 2

FinancialEstimates

Annual Business Inquiry(ABI/2)

Financial Estimates

Current

Proposed

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)

• 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)

• 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)

• 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)

• 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.

Variable HC FTE1 FTE2

Employment 0.914 0.895 0.887

Employment Costs 0.778 0.824 0.834

Turnover 0.611 0.605 0.601

Purchases 0.523 0.504 0.499

Employment Size Measure (5/6)

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

Coefficients of variationTotals, ABI 2004 data

0%

1%

2%

3%

4%

5%

6%

Employment Turnover Employment costs Purchases

CV HC strata

FTE strata

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)

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 LUsLU variables (SIC, geography, employment)

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)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Enterprises Employment

Multi

Single

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)

• 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 (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)

• 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)

• 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)

• 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)

Size (FTE) Sampling rate

Complex 1 in 1

Simple 100+ 1 in 1

Simple 20 – 99 1 in 2

Simple 0 – 19 1 in 500

“Unusual” enterprises 1 in 1

• Gave best coverage of employment so best for updating

• Gave smallest MSEs for most outputs

Best design:

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

• 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?

Contact details:

Pete.Brodie@ons.gsi.gov.uk