An editing strategy for annual VAT-turnover

Montreal

June 18-21, 2007

*

Jeffrey Hoogland

Grietje van Haren

– Register-based business statistics– Micro-editing of VAT-data for fiscal units– Conversion of fiscal units to enterprises– Macro-editing of VAT-turnover for enterprises– Objectives

Outline

- Pressure from Dutch government and business community

- Use available register information instead of questionnaires

- Decrease response burden for small and medium sized companies

Register-based business statistics

1. micro-editing of VAT data for fiscal units

2. conversion of VAT-turnover for fiscal units to VAT-turnover for enterprises

3. macro-editing of VAT-turnover for enterprises

An editing strategy for annual VAT-turnover

– Fiscal units submit a VAT-declaration per month, quarter, or year

– Those with large turnover tax or those that try to mislead the IRS have to report per month

– For a fiscal unit i compute VAT-turnover for each time period t in year r and year r-1

– Detect outliers within a fiscal unit that (usually) report a substantial VAT-turnover

Micro-editing of VAT-data for fiscal units

VAT-turnover of a fiscal unit substantial and possibly too large for a time period in year r if:

> d million euro and

> c × Median value of VAT-turnover across time periods of fiscal unit in year r and year r-1

Micro-editing of VAT-data for fiscal units

Example: d=1; c=9; year 2006

2.516 out of 3.150.000 monthly declarations 535 out of 2.740.000 quarterly declarations

VAT-turnover of a fiscal unit substantial and possibly too small for a time period in year r if:

Median value of VAT-turnover > d million euro and

VAT-turnover < Median value / c

Example: d=1; c=9; year 2006

1.549 out of 3.150.000 monthly declarations 272 out of 2.740.000 quarterly declarations

Conversion of fiscal units to enterprises

fiscal unit fiscal person CBS person enterprise

fiscal unit

fiscal person

fiscal person

CBS person

CBS person

enterprise

enterprise

Selectivity in available VAT-turnover

Table 1. Average edited SBS turnover (thousands of euros), for groups of enterprises

with, or without converted VAT data.

Average SBS

turnover

# SBS

records

Average SBS

turnover

# SBS

records

Number of

employees

No VAT VAT

Significance

T-test

1 411 48 238 269 0,041

2-4 665 52 518 190 0,269

5-9 1170 92 1053 219 0,348

10-19 2251 271 1849 460 0,019

20-49 5003 423 4212 653 0,017

50-99 11745 227 10587 188 0,139

Figure 1. Box plots for raw and edited SBS-turnover (thousands of euros) for enterprises in building industry with 2-4 employees. There are two groups: SBS-records with VAT-turnover and SBS-records without VAT-turnover.

VAT availableNo VAT available

5.000

4.000

3.000

2.000

1.000

0

Edited SBS-turnoverRaw SBS-turnover

Table 2. Average raw SBS turnover (thousands of euros), for groups of enterprises

with, or without converted VAT data.

Average SBS

turnover

# SBS

records

Average SBS

turnover

# SBS

records

Number of

employees

No VAT VAT

Significance

T-test

1 674 47 673 267 0,996

2-4 894 51 971 190 0,750

5-9 1192 91 1348 219 0,393

10-19 2074 271 1881 460 0,255

20-49 3534 422 3471 652 0,631

50-99 4547 227 4705 188 0,629

Macro-editing of VAT-turnover

– VAT-data incomplete – Weight VAT-turnover– Direct weighting if available VAT-data not selective– Reference aggregates

– in design phase: VAT-, SBS-, and CT-turnover

– in production phase: VAT-turnover of last year

– Detect influential inconsistencies

- For a specific publication cell

- Stratify by enterprise size

- Detect outliers per stratum

- Using box plot criterium for i = log |yi-ymed|

-

- Direct weighting of outliers per stratum

- Direct weighting of non-outliers per stratum

Direct weighting of VAT-turnover

s Oiis

Piis

sss

ss

ss

yyun

uA

5,1 with ),( 133 cmmcmBoundary

Table 3. Net turnover for 17 publication cells for builders with 1-99 employees.

Publication cell

SBS-turnover

VAT-turnover

Percentual Difference

1 583 522 -10,3 2 1.026 1.078 5,0 3 15.355 13.972 -9,0 4 587 502 -14,5 5 884 794 -10,1 6 2.611 2.081 -20,3 7 247 229 -7,3 8 2.710 2.308 -14,8 9 2.901 2.691 -7,2

10 2.545 2.543 -0,1 11 1.694 1.660 -2,0 12 287 248 -13,8 13 609 501 -17,8 14 1.917 1.570 -18,1 15 1.436 1.198 -16,6 16 2.469 2.468 0,0 17 808 642 -20,6

Detecting influential inconsistencies

When comparing registers and surveys three groups are distinguished:

,BA ,\ BA and AB \ , where A and B are two different sources.

n

ii

Bi

Biii

n

ii

Ai

Aiii

BAyw

wyx

xw

wyxSF

11

,3 ,max

Example of score function for A B, e.g. PS VAT

Score functions are used to detect influential inconsistencies

Objectives

– Improve cooperation with tax authorities – Reduce differences in definitions and filling-in behaviour– Improve weighting and outlier detection for VAT-turnover– Use tax assessments and surveys to develop a treatment strategy for VAT-declarations– Reduce use of business surveys