1. ANALYSIS OF POSSIBILITY TO USE TAX AUTHORITY DATA IN STS. RESULTS
ANDRIUS
ČIGINAS
Chief specialist
Sampling methods sub-division, Methodology and Quality division
Luxembourg, June 2009, Workshop II
Analysis of possibility to use Tax authority data in STS. Results

2. STS surveys: 1) quarterly survey of service enterprises;
2) quarterly survey of construction enterprises;
3) monthly survey of industrial enterprises;
4) monthly survey of domestic trade enterprises.
Luxembourg, June 2009, Workshop II
Analysis of possibility to use Tax authority data in STS. Results

3. Basic variable and indicator
Variable of interest in each survey population – income from actual for specific survey economical activity.
Indicator of interest in each population – sum of income.
Luxembourg, June 2009, Workshop II
Analysis of possibility to use Tax authority data in STS. Results

4. Auxiliary variable
The VAT declaration data can be obtained from the State Tax Inspectorate database.
Very important derivative variable is selling, which is calculated using data of taxes paid by enterprises and is close to income of enterprises.
Selling exists for considerable part of sample and non-sample enterprises, which are VAT payers. It can include income from all economical actions of enterprise.
Luxembourg, June 2009, Workshop II
Analysis of possibility to use Tax authority data in STS. Results

5. Availability and use of VAT data
The monthly VAT declarations data is available in 45 days since the end of accounting period.
For quarterly statistical surveys the VAT data of all three months of the quarter is available at the moment of estimation of sums of income.
For monthly statistical surveys only the VAT data of the last month is available. We shall use it, because compared with other auxiliary information it is more “hot” information about potential sizes of income of enterprises.
Luxembourg, June 2009, Workshop II
Analysis of possibility to use Tax authority data in STS. Results

6. Editing of VAT data I
The sampling designs and estimation domains of each survey are connected with economical activity groups and sizes groups of enterprises. Assuming the data of survey have been checked, for every “natural” group of enterprises we examine properties of empirical distributions of variable
in order to detect not typical observations of selling of such group of enterprises. We choose the lower and upper bounds for values of separate distribution and use they to edit outlying observations.
Main conclusion: primary VAT data can not be used for straightforward imputation.
Luxembourg, June 2009, Workshop II
Analysis of possibility to use Tax authority data in STS. Results

7. Editing of VAT data II
The main purpose is to prepare VAT data for estimators, which use auxiliary information. So, we need to edit pairs (income, selling) which income is not known (ussually it is non-sample enterprises).
Assuming that such pairs in separate group of enterprises have the same distribution as the pairs (income, selling) where both components are known, we apply the method of nearest neighbour to edit selling for non-sample enterprises, like we do it in the first step of editing.
Result - the sums of VAT data were calibrated.
Luxembourg, June 2009, Workshop II
Analysis of possibility to use Tax authority data in STS. Results

8. Two ways to proceed
To apply combined estimator right now. That is, we can use direct (which do not use auxiliary information) estimator of sum of income for that part of the estimation domain for which selling is not available and use regression or ratio estimator for the remaining part of estimation domain. So, we try it.
To stop and complete the set of selling values. That is, to estimate “non-existing” selling.
Luxembourg, June 2009, Workshop II
Analysis of possibility to use Tax authority data in STS. Results

9. Completion of selling
We shall use the model of simple regression to complete the set of values of selling. Using variable “number of employees of the last year respective period” we apply model
where we assume that parameters of such model can be different for separate economical activity group of enterprises.
More, we do not include into the model of prediction that outlying (influential) pairs of model data, for which we observed considerable change of they “number of employees” during one year.
Let the predicted values for all population are denoted sellingX.
Luxembourg, June 2009, Workshop II
Analysis of possibility to use Tax authority data in STS. Results

10. Editing of VAT data III
After completion of selling data we shall return to the situation after second step of VAT data editing and further examine pairs (income, selling), where both components are known, by using simple regresion model
which is closely related to application of ratio or regression estimators of sum of income. Model is different for each group of enterprises.
For influential or outlying pairs, where selling still considerably differs from income, using the results of selling completion model we shall input
if this value differs from income less.
Luxembourg, June 2009, Workshop II
Analysis of possibility to use Tax authority data in STS. Results

11. Estimation
We shall use combination of direct and regression (or ratio) estimators to estimate the sums of income. That is the direct estimate can be always used for strata of sample design or for parts of estimation domains, where we have no selling data or we can not detect relations between income and selling.
Were compared:
direct estimator HT, which does not use selling;
combination of direct and ratio estimators HTS1, when selling is only edited;
combination of direct and regression estimators HTR1, when selling is only edited;
combination of direct and ratio estimators HTS2, with estimated selling;
combination of direct and regression estimators HTR2, with estimated selling.
The estimates of sums and the estimates of coefficients of variation for years data were compared.
Luxembourg, June 2009, Workshop II
Analysis of possibility to use Tax authority data in STS. Results

12. Improvement of accuracy of estimates
SURVEY
YEAR
PERIOD HT
HTS1
HTR1
HTS2
HTR2
service 7 3
1,94
0,22
0,21
0,20 8 2
3,65
0,26
0,28
0,29
0,31
construction 5 4
3,81
0,74
0,77
0,73
0,75 1
2,80
0,72
0,70
0,69
Industry 6
1,12
0,39
0,37
0,40
industry
0,32
0,33
trade 10
2,82
1,01
0,94
0,91 9
0,71
0,42
0,45
0,46
0,44
Here are typical examples of estimates of coefficients of variation (in percents) for actual surveys:
Luxembourg, June 2009, Workshop II
Analysis of possibility to use Tax authority data in STS. Results

13. Basic results
Estimated possible reduction of surveys samples sizes, if accuracy remains the same as for direct estimate or remains acceptable:
1) survey of service enterprises: 24-29%,
2) survey of construction enterprises: %,
3) survey of industrial enterprises: 23-28%,
4) survey of domestic trade enterprises: 13-30%.
Luxembourg, June 2009, Workshop II
Analysis of possibility to use Tax authority data in STS. Results