Examining the use of administrative data for annual business statisticsJoanna Woods, Ria Sanderson, Tracy Jones, Daniel Lewis

Overview

• Background - Motivation- Admin data- Variables of interest

• Methods tested- Discontinuing the survey- Cut-off sampling

• Results• Conclusions

Motivation

• Drive to increase the use of admin data for business statistics

- reduce survey costs

- decrease burden on survey respondents

• One possibility - replace survey data with admin data

- Some variables have admin data directly available

- Other variables do not have a direct source of admin data available

Annual Business Survey

• The Annual Business Survey (ABS) collects financial variables

• Target population = UK economy• Stratified simple random sample by industry,

region & employment • Samples approximately 60,000 businesses• Businesses with employment > 249 are

completely enumerated• Ratio estimation

Available administrative data

• Two main sources available:- VAT turnover data

- Company accounts data (balance sheet variables)

• These overlap with, but do not fully cover, the target population

• Properties of these data sources are different

Survey population and admin data

Survey population

Survey population and admin data

Survey population

Administrative data

Survey population and admin data

Survey population

Administrative data

MATCHED PART

Administrative data sources

VAT turnover Company Accounts (balance sheets)

Created annual data sets for 2003-2008

Annual data from April 2003 to March 2009

Matched to units in the survey population

Complex matches to units in survey population

Match rate 73-75%, few missing values

Low match rate and many missing values

ABS variables

Acquisitions & Disposals

Methods Tested

• Aim: to see if admin data sources can be helpful as auxiliary variables in estimating these totals to reduce the sample size.

• Discontinuing the survey- Predict values for investment variables based on

models derived from past survey data.

• Cut-off sampling- Stop sampling some businesses- Use admin data to estimate for these units- Consider simple ratio adjustment

Methods Tested: Considerations

Discontinuing the survey

Cut-off sampling

Advantages No survey is required (provided admin data is available for all)

Reduces the burden placed on small businesses

Reduces survey costs

Disadvantages Model parameters fixed, cannot respond to changes in economy, may introduce bias

Different models required for different survey variables

Still requires a survey component

May introduce bias

Methods tested: Discontinuing the survey

• Produce models using past survey & admin data to produce estimates

• Linear model – predict values for positive returns• Logistic model – predict probability of positive return

• Build a model using data from last survey

• Model covariates can be admin data variables

• Apply model to future years & evaluate results.

Methods tested: Discontinuing the survey - Linear model

• Aim - predict values for acquisitions/disposals

• Have skewed data, use log transformation

• Use positive returns from year t to create a model

• Apply model to year t+1, t+2 ... to get predicted

value for each business

• Back transform prediction to get back to original linear scale

Methods tested: Discontinuing the survey - Logistic model

• Aim – predict probability of company returning a positive value

• Use all returned data from year t to model the probability of a business returning a positive value

• Apply model to predicted values in year t+1

• Multiply linear model prediction & logistic model probability to produce predicted value for every unit

Results: Discontinuing the survey

• Acquisitions• Best linear model for predicting log(total acquisitions) – Intercept,

– Standard Industrial Classification(SIC) at three digit level,

– Region,

– Employment band,

– log turnover,

– log turnover *SIC section

• R-squared = 0.66

Results: Discontinuing the survey

• Acquisitions• Best logistic model for predicting probability of a

positive return– Intercept,

– SIC division level,

– Region,

– Employment band,

– log turnover,

• Produced one of the lowest AIC

Results: Discontinuing the survey

Methods tested: Cut-off sampling

• Reduces burden but introduces bias• Create a cut-off, based on employment• Stop sampling below the cut-off• Use sample information above the cut-off to

estimate for units below the cut-off in an effort to reduce bias

• Missing data and match rates are the main difficulty => can’t be applied to full survey population, still need a sample

Simple ratio adjustment

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• Estimate for units below the cut-off:

Total of auxiliary variable below cut-off

Estimate of variable of interest above cut-off

Estimate of auxiliary variable above cut-off

Results: Simple ratio adjustment

Conclusions

• Discontinuing survey

- not an option for this variable• Under predicts• Growth rates differ

• Cut-off sampling with simple ratio adjustment

- can give reasonable results in some divisions but not all

- sample size savings can be made where method works well but is dependent on match rate

- multiple auxiliary variables are required

Any questions?

Joanna.Woods@ons.gsi.gov.uk