1. PRODCOM SURVEY IN THE UNITED KINGDOM
Neil Hedges

2. Areas to be covered: Why we conduct the PRODCOM survey and who uses it
How we collect and validate our data
Statistical processes
Publication processes
Challenges
Thoughts for the future

3. Purpose of the PRODCOM survey
Uses
EU regulation
Compilation of National Accounts
To aid Trade Associations in research and helping manufacturers identify market opportunities
Environmental issues (e.g. tree planting cycle)
A sampling frame for PPI
Chain linking of PPI

4. Collecting Data – Sample Design (1)
The UK uses a sample survey and grosses to provide estimates of the UK manufacturing population
Annual sample size of approximately 21,500 using Permanent Random Numbers sampling, post stratification
Stratification is the grouping together of similarly homogenous units
The UK PRODCOM Survey stratifies by NACE class and employment size to give a range of 5 strata;

5. Collecting Data – Sample Design (2)
Stratum
Employment 1
0 – 9 2
10 – 19 3
20 – 49 4
50 – 99 5
100+

6. Collecting Data – Sample Design (3)
Stratum
Sample
Universe 1
1,700
120,300 2
4,500
15,500 3
6,600
11,700 4
3,700
4,600 5
5,000
Total
21,500
157,100

7. Collecting Data – Sample Design (4)
Businesses within stratum 1 can only be selected once for any survey, and then have a rest period of three years. Known as the osmotherly guarantee
Due to UK regulation, only 1,700 osmotherly businesses can be selected. Therefore, except in unusual circumstances, 5 business per NACE are selected
Each NACE has an employment ‘cut-off’ which is either, 20, 50 or 100. All businesses with employment above cut-off are selected within their industry
Between 0-9 and cut-off, there is a 10% forced rotation, meaning that businesses are selected 10 times before having a break
Due to reclassifications and restructuring of businesses, it is likely that rotation would be 20%

8. Collecting Data – The Questionnaire (1)
(Please refer to handout)
Where a business is newly sampled, they are advised to go straight to Section B ‘Additional Products’ of the questionnaire
In Section B, the business will describe their products, provide a description and CN and the value of their production

9. Collecting Data – The Questionnaire (2)
Analysts ‘code out’ product descriptions to appropriate PRODCOM codes
The coding of the product is confirmed with the business by telephone and a volume for each product sought
In the absence of volume information, where there are other returns for the same product, a ‘batch construction’ can be inserted
A batch construction for a businesses absent volume is worked out by the addition of all returned values, divided by the all returned volumes. This figure is then divided by the businesses value

10. Collecting Data – The Questionnaire (3)
Business ‘A’ makes PCC – felt hats
It provides a value of €200,000 for this product
Therefore it is batch constructed by looking at returns of businesses ‘X, Y and Z’

11. Collecting Data – The Questionnaire (4)
PCC
Business X
Business Y
Business Z
Total
Value (€’s)
Volume (Items)
Unit Value (€’s per item)
352,000
457,000
249,000
1,058,000
12,139
19,041
9,960
41,140 24 25 29 26

12. Collecting Data – The Questionnaire (5)
Therefore for business ‘A’, its volume for PCC is €200,000/26 = 7692 items
It is important this is recorded for the estimation process later on
There is a complex method of outlier trimming on the observations we use in this process. In simplistic terms, extreme values are trimmed from the bottom and top end of unit values

13. Collecting Data – The Questionnaire (6)
Once a business has confirmed their production items, these are then added to their questionnaire in future surveys
The questionnaire asks for ‘other income’ in Section C and the addition of values in Sections, A, B and C should equal ‘Total Turnover’ in Section D

14. Collecting the Data – Imputation (1)
Imputation: An automated process that assumes those not responding this year but who responded last year have similar characteristics to those who have responded in both years

15. Collecting the Data – Imputation (2)
For each business that has said they have sold a product both this year and last, calculate the growth in sales
Provided there are enough businesses, eliminate the largest and smallest growths
Calculate the average of the remainder
Apply that growth as the imputation for business ‘A’

16. Collecting the Data – Imputation (3)
Business ‘A’ said it made (sold) €100,000 widgets last year. We have no figure for this year
There are four other manufacturers of widgets from which we have figures for both years. Their figures (€000’s) are:
Business
Sales Last Year
Sales This Year
Ratio W
100
125
125/100 = 1.25 X 80
100/80 = 1.25 Y
150
175
175/150 = 1.17 Z
200
200/175 = 1.14

17. Collecting the Data – Imputation (4)
Not enough observations to discard any
Take average of these 4 (= 1.20)
Apply that to all non-responders to widgets question that made a return last year
So our estimate of widget sales by business ‘A’ in the current year is €120,000

18. Collecting the Data – Validation (1)
On receipt of the questionnaire, these are scanned in to the system
The data is then tested against numerous credibility criteria
Validation is performed at product level and credibility gates are set individually for each product

19. Collecting the Data – Validation (2)
Examples of credibility gates;
Product value is +/- 20% and +/-£20k compared to previous return
Unit value is +/- 15% compared to previous return
Unit value is +/- 15% away from aggregate unit value

20. Statistical Processes - Estimation (1)
As PRODCOM UK is a sample survey, the returned information is used as a basis for estimation for the entire UK manufacturing population
Total UK manufacturer sales = Total returned sales of companies (including imputed & constructed) + estimated sales of companies who have never returned their form or were not in the sample

21. Statistical Processes - Estimation (2)
The total return for each product is attributed to each stratum and NACE class
Estimation is performed in each stratum
The total of all strata are added together and then divided by the total return to give a grossing factor
This grossing factor is also applied to the total volume return (which is why it is important batch construction of volume takes place so that a return is created for each business)

22. Statistical Processes - Estimation (3)
The algorithm for estimation in each stratum is;
Strata grossed value = (Non-sampled Employment (ENS) + Never Responders Employment (ENR))X Product Propensity (PP) X Sales Per Head (SPH) + Returned Value
OR;
SGV = SPH x (ENS+ENR) x PP + Return

23. Statistical Processes - Estimation (4)
Example
SGV = SPH x (ENS+ENR) x PP + Return
Strata
Returned Value
Product Propensity
Non-sampled Employment
Never-responders employment
Industry SPH
SGV 1
589
0.2
857 12
3.94
1274 2
648
0.04
421 68
725 5
1,284
0.64
1284
Total
2,521
3283
Sum of SGV/sum of returned value = Grossing Factor of 1.3

24. Statistical Processes - Estimation (5)
The product propensity is the likelihood other businesses would have made the product if they’d have been sampled. It is calculated for each product in each stratum and NACE class. The calculation is number of businesses who tell us that make that product, divided by the number of businesses sampled in the same NACE class and stratum. In our example, stratum 1 had one business making the product out of the five sampled, therefore 1/5 or; 0.2

25. Statistical Processes - Estimation (6)
Sales Per Head is calculated for each business for each product by dividing the value of their product return by their employment
Outliers are automatically made to the top and bottom 5% of SPH values where there are at least ten observations, though can also be applied manually
Once outliers have been applied, the remaining values are added together and divided by the total of the employment to create an industry SPH

26. Statistical Processes - Estimation (7)
Sales of products classified to Industry B made by businesses classified to industry A are said to be carried out from industry A
Sales of products classified to industry A made by businesses not classified to industry A are said to be carried in to industry A
This can cause volatility in estimation, especially where there are diverse businesses carrying in to a product
As there needs to be at least ten observations from carry in industries for outliering to occur, these need to be manually marked as outliers if appropriate to do so
The grossing factor can also be manually amended if there are multiple entries in a product NACE class and outliering does not ‘zero’ SPH

27. Statistical Processes – Validation (8)
Grossed Estimates Results Checking
Each question is put the through the estimation procedure in SAS.
SAS compares data and looks for:
Large year on year changes
Consistency of unit values
Revisions of previous years data
Each of the above has failure gates. If these are breached then the product/industry are investigated by the analyst.

28. Statistical Processes – Validation (9)
Possible reasons for failures:
Entrance and departure of businesses from a sample
New business causing high grossing
Business misreporting sales
Figures given in £ rather than £thousands
Genuine increases
Businesses classified to the wrong industry

29. Statistical Processes – Disclosure (10)
All estimates are tested for their contribution to a product and are tested using a dominance threshold disclosure model
Where businesses dominate production of a product, a letter is sent to them asking for permission to publish
If permission is denied, then the estimate is suppressed

30. Are there 3 or more Enterprise Groups? No Yes
(2 Enterprises & Non-Sampled Element = 3) No
Yes
Yes
Is the dominant figure greater than 91% of the overall total figure?
If the dominant figure is less than 47% of the total figure then it is publishable
If there are more than 20 reporting units, then it is still publishable
If there are less than 20, then the 2 largest contributors are taken out of the grossed total, and the remainder are divided into the largest reporting unit.
If this is more than 10%, then it is publishable, if not – then the dominant figure must be suppressed.
NOT DISCLOSIVE
DISCLOSIVE

31. Publications (1)
The UK publishes its estimates on 30 June for the for the previous reference year, marking them as ‘Provisional Results’. These results are transmitted to Eurostat on same day
An ‘Intermediate Final’ set of results are then published in December, whereby at the same time, the previous years ‘Intermediate Final’ become ‘Final Revised’. After each publication, results are transmitted to Eurostat

32. Publications (2)
Up to and including 2007, the UK published a PDF report per NACE class
Following a quinquennial review, users expressed a preference towards Excel so that data may be manipulated
The UK began to publish 2008 estimates and onwards using Excel
For each product, the corresponding CN number is published, along with Intra and Extra EU imports/exports

33. Publications (3)

34. Challenges (1)
The UK was non-compliant with the PRODCOM Regulation in respect of timeliness for some years
The estimation procedure would not be run until an acceptable level of response in each NACE class was achieved
To resolve this, imputation is now run for all non-responders and large discrepancies in product estimates investigated
Analysis shows that 85% of ‘provisional estimates’ are within a 10% range of final estimates

35. Challenges (2)
NACE Revision 2 in 2007 was a large undertaking as a comprehensive revision of classification
In general terms, the UK converted its 2007 estimates on the previous NACE to the new code where products were a one to one conversion. This allowed comparisons to be drawn and year on year validation to be carried out
The one to many product codes were more problematic

36. Challenges (3)
Some coding anomalies
Gravy boats

37. Airline Meals

38. Tanks

39. The Future
The UK are developing methods to possibly use a median vale or inter-quartile range in respect of calculating missing volumes
Where there is estimation from a disparate NACE class (perhaps first two digits must match), then outliering automatically occurs
More user friendly outputs on the web, where users specify their requirements (Product code, year, trade etc)

40. Thank you for listening
QUESTIONS??