1. The main results of the project and plans for the future - surviving from the tax reform and rationalization of the editing process
Ms. Suvi Kiema and Mr. Mika Sirviö,
Statistics Finland

2. Main actions and expected results of the project - Outline of the presentation
1) To investigate the quality effects of the new tax legislation
Do we have to enlarge the samples of direct data collection?
Do we have to develop a method for using quarterly data in monthly production of short-term indicators?
Goal: to survive the tax reform without increasing the response burden and compromising the quality
2) To develop methods for quality analysis and automatic recognizion of errors in VAT and PAYE data
Goal: more effective production process
3) To improve the accuracy of the first estimate
Goal: to take into account the influence of the companies left outside the inquiry methodologically
Eurostat workshop 2

3. Compilation of turnover and wages and salaries
Turnover and volume indices:
Trade: 30 (European sample), 45 (preliminary) and 75 (final) days delay
Services and construction: 45 (European sample) and 75 days delay
Industry: 75 days delay
Wages and salaries indices:
Delay is always 45 days.
Total wagebill index for the whole economy is published, but for turnover there is no corresponding indicator.
Eurostat workshop

4. Main data sources for STS
The main administrative data sources for STS are monthly VAT (T+75) and PAYE (T+45) data.
Monthly turnover data from businesses with >8500 euro yearly turnover and wages from all regular employers
Transmitted from the Tax Administration each month
Revised five times after initial transmission
Supplemented by a direct sales inquiry from 2,000 enterprises
largest enterprises in each branch of industry
Background information for the enterprises is acquired from Business Register
Branch (NACE code), institutional sector
Eurostat workshop 4

5. 1) Changes in monthly tax return data in 2010
Companies with yearly turnover less than euros will no longer be in monthly data.
If the yearly turnover is less than euros, but more than euros, companies will report quarterly.
the quality effect of the tax reform is rather small: even though VAT data covers monthly information of only 48,8% enterprises after the tax reform in year 2010, the share of the total turnover of enterprises reporting monthly basis is still high, 99,4%
no need to enlarge sample size of sales inquiry
method for reallocating the quarterly turnover will be developed later, because the tax reform has crucial impact only on chargeable activities
Eurostat workshop 5

6. Share of turnover and enterprises with annual turnover over 50 000 € in year 2007 in one-digit level
at the beginning of year 2010 monthly turnover data only from businesses with annual turnover > €
The coverage of the data was examined by one and two-digit levels
The changes in coverage are minimal in branches like manufacturing, where large enterprises account for a relative high share of turnover. Instead, the effects of tax reform are significant in small labour-intensive branches like service sector and caring industry.
Naturally the share of enterprises with annual turnover over 50 000 € varies considerably by industry
Eurostat workshop

7. 2) Effect of productivity
The compilation of statistics directly related to the VAT data requires almost 20 man years per year, including chargeable activities
This is because the editing is focused on micro level
Approximately 60% of the time used for STS turnover and wage sum is used for editing.
30% goes for direct data collection,
10% on data analysis and development
Eurostat workshop 7

8. The production process
Raw
Data
(VAT, BR)
Index
calculation
Unit
data
Adjustment of
observations
Indices
Publication
Sybase database
- SAS -batch programs (calculation)
- Power Buider Application
(determination of classifier parameters)
- Power Buider Application (adjustments)
- SAS -batch programs (re-calculation)
Pre-
processed
Macro-editing
Eurostat workshop

9. Effectiveness of micro-level editing
Currently it seems that micro-level editing has surprisingly little impact on the final figures
When comparing pre-calculated indices to the final (published) indices in one-digit level it was found that editing mostly only affects the latest month
over 80% of the revisions were explained by the manual editing made in the single most significant enterprise of the all edited enterprises
micro-level editing is inefficient
Eurostat workshop 9

10. Plans for the future
The project's main goal for the future is to significantly reduce the number of firms selected to the editing list (checked manually)
Working name for the new method is: cutting and filtering the data
1) Investigate the minimum number of enterprises that has to be checked manually in any case.
2) Develop automated processes for selecting enterprises with abnormal observations that may have to be checked manually.
Eurostat workshop 10

11. Automated processes for selecting enterprises with abnormal observations - The detection of outliers
The setup of the problem
Description of the method
Some results with Finnish turnover data
Eurostat workshop 11

12. Setup
A large number of time series are collected to produce turnover indicies.
At the moment data validation requires a large amount of resources, -both labour and time.
Good data quality is vital to produce good indicies, therefore data validation is an important step in index production.
The goal is to make the validation less time consuming, yet accurate.
Eurostat workshop 12

13. About the method
A long history of observations is available for each company (or industry) that reports its turnover to Statistics Finland.
Hence, we can model each time series in order to forecast the future values.
If a company reports a figure which differs significantly from the forecast, we may believe that the observation is an outlier.
Eurostat workshop

14. The first step
Create a time series model for each time series and forecats the next value.
Compare the observations and the forecast you have computed by using a t-test.
Now one can rank the observations according to the p-values of their t-tests.
Compare the observations and the forecast you have computed by using a t-test.
Now one can rank the observations according to the p-values of their t-tests.
1 minus the p-value tells how likely it is that the observation is an outlier.
Eurostat workshop

15. Then what?
If an observation is suspicious then one may wish to contact the company that has reported the figure.
However there are a huge number of companies. Therefore it would be desireable to know, which observations are (potentially) more harmfull to the index.
Hence the t-test itself is not enough.
Eurostat workshop 15

16. The potential harm
We multiply the forecast error by 1-(p-value) and by the company’s share in the aggregate index.
By multiplying the forecast error by 1-(p-value) we get the potential error of one obervation.
By multiplying this with the companys share in the whole turnout we get the observations potential harm to Finland’s turnover index.
Now one can rank different observations by their potential harm.
We multiply the forecast error by 1-(p-value)t-test and by the company’s share in the aggregate index.
By multiplying the forecast error by 1-(p-value)t-test we get the potential error of one obervation.
By multiplying this with the companys share in the whole turnout we get the observations potential harm to Finland’s turnover index.
Now one can rank different observations by their potential harm.
In mathematical terms what we want to compute is:
(forec. error)*(1 - p-valuet-test)*(share in the aggregate turnout) where:
(forecast error) requires the use of a time series model
(1 - p-valuet-test) is obtained from a t-test
(share in the aggregate turnout) is straightforward to calculate.
Eurostat workshop 16

17. Results: Manucacture of food products and beveridges, Potential error (%).
Potential harm in this industry around 0,2-0.7%
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18. Results: Manufacture of fabricated metal products, except machinery and equipuipment, Potential error (%).
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19. Benefits
The method can rank different observations according to their suspiciousness. This tells the statistician which observations to check if time is limited.
The method is computationally quite simple and can be integrated to the production system of indicies.
The time series approach can take into account the seasonal nature of the time series.
Eurostat workshop 19

20. Challenges
There must be special expertise, time and resources to create time series models and to run their diagnostic tests.
The time series models don’t quite function if the future doesn’t behave like the past.
The method only tells which observations are potentially harmfull but doesn’t reveal the outliers.
The statisticial must still use his/her insight to tell wheter an obervation is suspicious or not.
There should be enough time and resources to model the time series. Model update must take place with regular basis, at least once a year.
If one wants to go through all the companies then the time series must be modelled automatically, for example with the help of a BIC-criteria.
Eurostat workshop 20

21. 3) The computation of first estimates
The first estimates are based on the sample only
The VAT data is not yet available
Different development by size classes together with the slow accumulation and changes of the data causes revisions in the indices of turnover
The problems become more crucial at the turning points of the economy
Turnover has grown at a faster annual rate in small and medium sized manufacturing enterprises than in large enterprises, 5.0% vs. 2.7%
In construction sector the difference was even greater, 10.7% vs. 4.5%
Other reasons for revisions:
changes in classification category (e.g. change of industry)
changes in value or source data
company reorganisations
enterprise openings and closures
Growth and recession usually affect companies of different size at different points in time
The business cycle is reflected first in the large enterprises engaging in foreign trade
Small enterprises are less likely to survive from economic fluctuation
Or they tend to reach the growth path slower than large enterprises
Eurostat workshop

22. Goals
Improvement of the quality of data acquired from the direct data collection
Aim to take into account the influence of the companies left outside the inquiry methodologically
Use of regression models to estimate the rest of the population as a whole
Eurostat workshop