1. Use of monthly tax return data

2. Monthly tax return data
Data used in the production of Short Term Business Statistics
Value added tax
indices for turnover, exports, domestic turnover
Employer’s payments
wage and salary indices
Data is also used in National Accounts and Business Register
Monthly tax return data is very comprehensive. It includes all enterprises that are liable to pay taxes or those who are regular wage payers

3. Finnish tax system; Value added taxation and employer payments
Tax liability
Commercial selling of goods and services, imports, farms
All employers paying wages and salaries are obliged to withhold earned income tax from wages and salaries and to pay social security contributions
Exemptions
Small scale activity: annual turnover < EUR 8,500
Health care, social welfare, education, financial and insurance services, real property, lotteries and money games, performance fees
Non-regular wage and salary payer

4. Periodicity VAT return
Primary producers and visual artists: annual reporting and payment
Others: monthly tax return and payment
Return for employer payments
Regular wage and salary payers: monthly tax return and payment
Non-regular wage and salary payers: annual reporting and payment

5. Monthly tax return data
Arrives at Statistics Finland once a month from the National Board of Taxes
Every file contains data for 6 months: the reference month and 5 previous months. Therefore the indicators for turnover and wages and salaries may be revised 5 months after the first publication
The first delivery contains about 90% of the total sum of turnover/wages and salaries.

6. Data processing Checking the general validity of data
tax bases (22%, 17%, 8%), overall tax base
overall turnover = overall tax base + tax-exempt turnover + sales of goods to other EU member states
Calculation of variables
Checking of double data records
Correcting tenfold and hundredfold numbers

7. Data checking - “mechanical” and manual
All systematic and logical errors are corrected automatically in SAS programs = “mechanical” checking
VAT: ‘tax-exempt turnover’ misunderstood as tax base -> estimation of correct values by combining variables
Employer payments: Double wages
Manual checking
Comparing the actual amount of taxes paid by bank transfer to other variables => Special variables to indicate the inaccuracy in the data record
After corrections the data is uploaded to the Sybase database

8. Problems with monthly tax return data
The data is vulnerable to changes in legislation and accounting practices, which affect the figures and may become a source of bias
Other errors in administrative data: changes in the business structure
Some enterprises are not tax liable or are not required to report monthly (small scale activity, health care, etc.)
Human errors in filling the forms, errors in the optical reading

9. Changes in monthy 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.
If the yearly turnover is less than euros, companies will report yearly. But if such company is also an employer, the reporting will be quarterly.
Payments will no longer be divided into VAT payments and employer’s payments.

10. The purpose of the project
To develop new methods for quality analysis and automatic correction of the monthly VAT and employer payments data
automatic recognition and/or correction of 1) mergers and split-offs 2) changes in accounting practices and 3) outliers
Due to reformation of legislation in 2010 it is needed to investigate and develop a method for using quarterly data in monthly production of short term indicators

11. Prospective methods

12. Industrial production index (IPI) and tax return data
Significant part of small and medium sized enterprises in certain industries are left outside the IPI
Recent indications that the output of enterprises left out is developing differently
Turnover of small and medium sized enterprises from tax return data
Tax return data increases the coverage of the IPI
No increase in response burden

13. Problems - Current month T
Data from previous month T-1
Incomplete
Possibly incorrect
Tight schedule
Limited resources
Lengths of comparable time series are limited

14. Solutions Use of NACE group level data Automated checking
Data dependent acceptance regions for turnover or for statistics depending on turnover
Estimation of the real level of turnover for Month T-1 and Month T
SAS-implementation

15. Data processing

16. Automated checking - Mean absolute deviation

17. Automated checking - T2 chart

18. Filtering - Kalman filter - Previous month

19. Filtering - Kalman filter - Filtering error

20. Estimation - Kalman filter - Predicting current month

21. Estimation - Kalman filter - Prediction error

22. Firm level use of these methods
Mean absolute deviation
Problems with large variation of turnover
T2 chart
Problems with the length of turnover timeseries needed
CPU time requirements can be substantial
Further tests are needed